From 68f90b91f3c699ffa5bc82b1785e29bc1ce7e716 Mon Sep 17 00:00:00 2001 From: dajiang0085 <417103740@qq.com> Date: Wed, 3 Apr 2024 07:26:47 +0000 Subject: [PATCH 1/7] fix docs bugs --- docs/api/paddle/onnx/Overview_cn.rst | 19 +++++++++++++++++++ docs/api/paddle/optimizer/Overview_cn.rst | 2 ++ docs/api/paddle/regularizer/Overview_cn.rst | 20 ++++++++++++++++++++ docs/api/paddle/sparse/Overview_cn.rst | 1 + docs/api/paddle/sysconfig/Overview_cn.rst | 21 +++++++++++++++++++++ 5 files changed, 63 insertions(+) create mode 100644 docs/api/paddle/onnx/Overview_cn.rst create mode 100644 docs/api/paddle/regularizer/Overview_cn.rst create mode 100644 docs/api/paddle/sysconfig/Overview_cn.rst diff --git a/docs/api/paddle/onnx/Overview_cn.rst b/docs/api/paddle/onnx/Overview_cn.rst new file mode 100644 index 00000000000..1fdbd5eb4ff --- /dev/null +++ b/docs/api/paddle/onnx/Overview_cn.rst @@ -0,0 +1,19 @@ +.. _cn_overview_onnx: + +paddle.onnx +--------------------- + +paddle.onnx 目录下包含飞桨框架开放神经网络交换相关的 API。具体如下： + +-  :ref:`模型导出相关 API ` + +.. _about_export: + +模型导出相关 API +:::::::::::::::::::: + +.. csv-table:: +    :header: "API 名称", "API 功能" +    :widths: 10, 30 + +    " :ref:`export ` ", "将 PaddlePaddle 模型导出为 ONNX 格式" diff --git a/docs/api/paddle/optimizer/Overview_cn.rst b/docs/api/paddle/optimizer/Overview_cn.rst index ef663a19386..fa805385fc7 100644 --- a/docs/api/paddle/optimizer/Overview_cn.rst +++ b/docs/api/paddle/optimizer/Overview_cn.rst @@ -43,10 +43,12 @@ paddle.optimizer 目录下包含飞桨框架支持的优化器算法相关的 AP :widths: 10, 30 " :ref:`CosineAnnealingDecay ` ", "Cosine Annealing 学习率衰减" + " :ref:`CosineAnnealingWarmRestarts ` ", "CosineAnnealingWarmRestarts 学习率衰减" " :ref:`ExponentialDecay ` ", "Exponential 学习率衰减" " :ref:`InverseTimeDecay ` ", "Inverse Time 学习率衰减" " :ref:`LRScheduler ` ", "学习率衰减的基类" " :ref:`LambdaDecay ` ", "Lambda 学习率衰减" + " :ref:`LinearLR ` ", "LinearLR 学习率衰减" " :ref:`LinearWarmup ` ", "Linear Warmup 学习率衰减" " :ref:`MultiStepDecay ` ", "MultiStep 学习率衰减" " :ref:`NaturalExpDecay ` ", "NatualExp 学习率衰减" diff --git a/docs/api/paddle/regularizer/Overview_cn.rst b/docs/api/paddle/regularizer/Overview_cn.rst new file mode 100644 index 00000000000..0e6af2c381e --- /dev/null +++ b/docs/api/paddle/regularizer/Overview_cn.rst @@ -0,0 +1,20 @@ +.. _cn_overview_regularizer: + +paddle.regularizer +--------------------- + +paddle.regularizer 目录下包含飞桨框架正则化相关的 API。具体如下： + +-  :ref:`正则化相关 API ` + +.. _about_regularizer: + +正则化相关 API +:::::::::::::::::::: + +.. csv-table:: +    :header: "API 名称", "API 功能" +    :widths: 10, 30 + +    " :ref:`L1Decay ` ", "L1 正则化" +    " :ref:`L2Decay ` ", "L2 正则化" diff --git a/docs/api/paddle/sparse/Overview_cn.rst b/docs/api/paddle/sparse/Overview_cn.rst index 8641a452775..457be7f7970 100644 --- a/docs/api/paddle/sparse/Overview_cn.rst +++ b/docs/api/paddle/sparse/Overview_cn.rst @@ -55,6 +55,7 @@ paddle.sparse 目录包含飞桨框架支持稀疏数据存储和计算相关的 " :ref:`paddle.sparse.subtract ` ", "对稀疏 Tensor 逐元素相减" " :ref:`paddle.sparse.multiply ` ", "对稀疏 Tensor 逐元素相乘" " :ref:`paddle.sparse.divide ` ", "对稀疏 Tensor 逐元素相除" + " :ref:`paddle.sparse.sum ` ", "给定维度 axis 上稀疏张量 x 元素的和" " :ref:`paddle.sparse.is_same_shape ` ", "判断两个稀疏 Tensor 或稠密 Tensor 的 shape 是否一致" " :ref:`paddle.sparse.reshape ` ", "改变一个 SparseTensor 的形状" " :ref:`paddle.sparse.coalesce` ", "对 SparseCooTensor 进行排序并合并" diff --git a/docs/api/paddle/sysconfig/Overview_cn.rst b/docs/api/paddle/sysconfig/Overview_cn.rst new file mode 100644 index 00000000000..e27bbd81246 --- /dev/null +++ b/docs/api/paddle/sysconfig/Overview_cn.rst @@ -0,0 +1,21 @@ + +.. _cn_overview_sysconfig: + +paddle.sysconfig +--------------------- + +paddle.sysconfig 目录下包含飞桨框架系统配置信息的接口相关的 API。具体如下： + +-  :ref:`系统配置信息的接口和函数相关 API ` + +.. _about_sysconfig: + +系统配置信息的接口相关 API +:::::::::::::::::::: + +.. csv-table:: +    :header: "API 名称", "API 功能" +    :widths: 10, 30 + +    " :ref:`get_include ` ", "获取包含飞桨 C++头文件的目录" +    " :ref:`get_lib ` ", "获取包含飞桨 lib 框架的目录" From 1fea0615165ac09166a54f270adb636f87614a6c Mon Sep 17 00:00:00 2001 From: ooooo <3164076421@qq.com> Date: Fri, 12 Jul 2024 10:47:58 +0800 Subject: [PATCH 2/7] update --- docs/api/paddle/onnx/Overview_cn.rst | 19 ------ docs/api/paddle/regularizer/Overview_cn.rst | 10 ++-- docs/api/paddle/sysconfig/Overview_cn.rst | 14 ++--- .../api_difference/ops/torch.tensor.md | 60 ++++--------------- 4 files changed, 25 insertions(+), 78 deletions(-) delete mode 100644 docs/api/paddle/onnx/Overview_cn.rst diff --git a/docs/api/paddle/onnx/Overview_cn.rst b/docs/api/paddle/onnx/Overview_cn.rst deleted file mode 100644 index 1fdbd5eb4ff..00000000000 --- a/docs/api/paddle/onnx/Overview_cn.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. _cn_overview_onnx: - -paddle.onnx ---------------------- - -paddle.onnx 目录下包含飞桨框架开放神经网络交换相关的 API。具体如下： - --  :ref:`模型导出相关 API ` - -.. _about_export: - -模型导出相关 API -:::::::::::::::::::: - -.. csv-table:: -    :header: "API 名称", "API 功能" -    :widths: 10, 30 - -    " :ref:`export ` ", "将 PaddlePaddle 模型导出为 ONNX 格式" diff --git a/docs/api/paddle/regularizer/Overview_cn.rst b/docs/api/paddle/regularizer/Overview_cn.rst index 0e6af2c381e..0131c4f139e 100644 --- a/docs/api/paddle/regularizer/Overview_cn.rst +++ b/docs/api/paddle/regularizer/Overview_cn.rst @@ -5,7 +5,7 @@ paddle.regularizer paddle.regularizer 目录下包含飞桨框架正则化相关的 API。具体如下： --  :ref:`正则化相关 API ` +- :ref:`正则化相关 API ` .. _about_regularizer: @@ -13,8 +13,8 @@ paddle.regularizer 目录下包含飞桨框架正则化相关的 API。具体如 :::::::::::::::::::: .. csv-table:: -    :header: "API 名称", "API 功能" -    :widths: 10, 30 + :header: "API 名称", "API 功能" + :widths: 10, 30 -    " :ref:`L1Decay ` ", "L1 正则化" -    " :ref:`L2Decay ` ", "L2 正则化" + " :ref:`L1Decay ` ", "L1 正则化" + " :ref:`L2Decay ` ", "L2 正则化" diff --git a/docs/api/paddle/sysconfig/Overview_cn.rst b/docs/api/paddle/sysconfig/Overview_cn.rst index e27bbd81246..04e2d541373 100644 --- a/docs/api/paddle/sysconfig/Overview_cn.rst +++ b/docs/api/paddle/sysconfig/Overview_cn.rst @@ -4,18 +4,18 @@ paddle.sysconfig --------------------- -paddle.sysconfig 目录下包含飞桨框架系统配置信息的接口相关的 API。具体如下： +paddle.sysconfig 目录下包含飞桨框架系统配置信息的相关 API。具体如下： --  :ref:`系统配置信息的接口和函数相关 API ` +- :ref:`系统配置信息的相关 API ` .. _about_sysconfig: -系统配置信息的接口相关 API +系统配置信息的相关 API :::::::::::::::::::: .. csv-table:: -    :header: "API 名称", "API 功能" -    :widths: 10, 30 + :header: "API 名称", "API 功能" + :widths: 10, 30 -    " :ref:`get_include ` ", "获取包含飞桨 C++头文件的目录" -    " :ref:`get_lib ` ", "获取包含飞桨 lib 框架的目录" + " :ref:`get_include ` ", "获取包含飞桨 C++头文件的目录" + " :ref:`get_lib ` ", "获取包含飞桨 lib 框架的目录" diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md index dffc762fadb..ff946db584c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md @@ -1,58 +1,24 @@ -## [torch 参数更多 ]torch.tensor -### [torch.tensor](https://pytorch.org/docs/stable/generated/torch.tensor.html?highlight=tensor#torch.tensor) +## [ 仅 paddle 参数更多 ] torch.Tensor -```python -torch.tensor(data, - dtype=None, - device=None, - requires_grad=False, - pin_memory=False) -``` - -### [paddle.to_tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/to_tensor_cn.html#to-tensor) +### [torch.Tensor](https://pytorch.org/docs/stable/tensors.html) ```python -paddle.to_tensor(data, - dtype=None, - place=None, - stop_gradient=True) +torch.Tensor(data) ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数映射 -| PyTorch | PaddlePaddle | 备注 | -| ------------- | ------------ | ------------------------------------------------------ | -| data | data | 表示输入的数据。 | -| dtype | dtype | 表示数据的类型。 | -| device | place | 表示 Tensor 存放设备位置，两者参数类型不相同，需要转写。 | -| requires_grad | stop_gradient | 表示是否计算梯度，两者参数功能相反，需要转写。 | -| pin_memeory | - | 表示是否使用锁页内存， Paddle 无此参数，需要转写。 | - -### 转写示例 +### [paddle.to_tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/to_tensor_cn.html#to-tensor) -#### device: Tensor 的设备 ```python -# PyTorch 写法 -torch.tensor(3, device=torch.device('cpu')) - -# Paddle 写法 -y = paddle.to_tensor(3, place=paddle.CPUPlace()) +paddle.to_tensor(data, dtype=None, place=None, stop_gradient=True) ``` -#### requires_grad：是否不阻断梯度传导 -```python -# PyTorch 写法 -x = torch.tensor(3, requires_grad=True) - -# Paddle 写法 -x = paddle.to_tensor(3, stop_gradient=False) -``` +Paddle 比 PyTorch 支持更多参数，具体如下： -#### pin_memory：是否分配到固定内存上 -```python -# PyTorch 写法 -x = torch.tensor(3, pin_memory=True) +### 参数映射 -# Paddle 写法 -x = paddle.to_tensor(3).pin_memory() -``` +| PyTorch | PaddlePaddle | 备注 | +| ------- | ------------ | ----------------------------------------------------------- | +| data | data | 要转换的数据。 | +| - | dtype | Tensor 的数据类型，PyTorch 无此参数，Paddle 保持默认即可。 | +| - | place | Tensor 的设备，PyTorch 无此参数，Paddle 需设置为 'cpu' 。 | +| - | stop_gradient | 是否阻断 Autograd 的梯度传导。PyTorch 无此参数，Paddle 保持默认即可。 | From 41e22fd9db1b5d129c81694ddb3bbd6a4306ec6c Mon Sep 17 00:00:00 2001 From: ooooo <3164076421@qq.com> Date: Fri, 12 Jul 2024 10:51:03 +0800 Subject: [PATCH 3/7] update --- .../api_difference/ops/torch.tensor.md | 24 ------------------- 1 file changed, 24 deletions(-) delete mode 100644 docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md deleted file mode 100644 index ff946db584c..00000000000 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md +++ /dev/null @@ -1,24 +0,0 @@ -## [ 仅 paddle 参数更多 ] torch.Tensor - -### [torch.Tensor](https://pytorch.org/docs/stable/tensors.html) - -```python -torch.Tensor(data) -``` - -### [paddle.to_tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/to_tensor_cn.html#to-tensor) - -```python -paddle.to_tensor(data, dtype=None, place=None, stop_gradient=True) -``` - -Paddle 比 PyTorch 支持更多参数，具体如下： - -### 参数映射 - -| PyTorch | PaddlePaddle | 备注 | -| ------- | ------------ | ----------------------------------------------------------- | -| data | data | 要转换的数据。 | -| - | dtype | Tensor 的数据类型，PyTorch 无此参数，Paddle 保持默认即可。 | -| - | place | Tensor 的设备，PyTorch 无此参数，Paddle 需设置为 'cpu' 。 | -| - | stop_gradient | 是否阻断 Autograd 的梯度传导。PyTorch 无此参数，Paddle 保持默认即可。 | From 7d51f21243ceca3c70ec4c5c674a30d0d4d3670c Mon Sep 17 00:00:00 2001 From: ooooo <3164076421@qq.com> Date: Fri, 12 Jul 2024 10:58:20 +0800 Subject: [PATCH 4/7] update --- .pre-commit-config.yaml | 2 +- ci_scripts/doc-build-config/en/conf.py | 22 +- ci_scripts/doc-build-config/zh/conf.py | 8 +- ci_scripts/gendoc.sh | 2 +- ci_scripts/utils.sh | 2 +- docs/advanced_guide/index_cn.rst | 14 - 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| 12 + docs/api/paddle/log_normal_cn.rst | 40 + docs/api/paddle/median_cn.rst | 4 +- docs/api/paddle/meshgrid_cn.rst | 2 +- docs/api/paddle/nn/AdaptiveAvgPool2D_cn.rst | 2 +- docs/api/paddle/nn/AdaptiveAvgPool3D_cn.rst | 2 +- .../nn/AdaptiveLogSoftmaxWithLoss_cn.rst | 43 + docs/api/paddle/nn/AdaptiveMaxPool1D_cn.rst | 2 +- docs/api/paddle/nn/AdaptiveMaxPool3D_cn.rst | 2 +- docs/api/paddle/nn/AlphaDropout_cn.rst | 2 +- docs/api/paddle/nn/AvgPool1D_cn.rst | 4 +- docs/api/paddle/nn/AvgPool2D_cn.rst | 4 +- docs/api/paddle/nn/AvgPool3D_cn.rst | 2 +- docs/api/paddle/nn/Bilinear_cn.rst | 2 +- docs/api/paddle/nn/ChannelShuffle_cn.rst | 2 +- docs/api/paddle/nn/Conv2DTranspose_cn.rst | 2 +- docs/api/paddle/nn/CosineEmbeddingLoss_cn.rst | 2 +- docs/api/paddle/nn/CrossEntropyLoss_cn.rst | 2 +- docs/api/paddle/nn/Dropout2D_cn.rst | 2 +- docs/api/paddle/nn/Dropout3D_cn.rst | 2 +- docs/api/paddle/nn/Dropout_cn.rst | 2 +- docs/api/paddle/nn/FeatureAlphaDropout_cn.rst | 27 + 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docs/images/api_legend/reshape.png delete mode 100644 docs/install/FAQ.md delete mode 100644 docs/install/FAQ_en.md delete mode 100644 docs/install/install_script.md diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d7f33f1b6b9..413c62b98cf 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,7 +26,7 @@ repos: files: \.md$|\.rst$ # For Python files - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.3.3 + rev: v0.5.0 hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix, --no-cache] diff --git a/ci_scripts/doc-build-config/en/conf.py b/ci_scripts/doc-build-config/en/conf.py index 8ddb9b3c196..9e106dbea05 100644 --- a/ci_scripts/doc-build-config/en/conf.py +++ b/ci_scripts/doc-build-config/en/conf.py @@ -26,7 +26,7 @@ # General information about the project. project = "PaddlePaddle" -author = "%s developers" % project +author = f"{project} developers" copyright = "%d, %s" % (time.localtime(time.time()).tm_year, author) github_doc_root = "https://github.com/PaddlePaddle/docs/docs" @@ -104,6 +104,10 @@ version = "" templates_path = ["/templates"] +# Show type hints in the description +autodoc_typehints = "description" +autodoc_typehints_description_target = "documented" + # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' @@ -227,7 +231,7 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, "%s.tex" % project, project, author, "manual"), + (master_doc, f"{project}.tex", project, author, "manual"), ] numfig = True highlight_language = "python" @@ -352,14 +356,20 @@ def handle_api_aliases(): exec(f'{oname}.__name__ = "{tn}"') -def remove_doctest_directives(app, what, name, obj, options, lines): +def remove_directives(app, what, name, obj, options, lines): """ - Remove `doctest` directives from docstring + Remove `doctest` and `mypy` `type: ignore: xxx` directives from docstring """ # Modify the lines inplace # remove doctest directive pattern_doctest = re.compile(r"\s*>>>\s*#\s*x?doctest:\s*.*") - lines[:] = [line for line in lines if not pattern_doctest.match(line)] + # remove `# type: ignore ...` + pattern_typing_ignore = re.compile(r"# type:[^#]*") + lines[:] = [ + pattern_typing_ignore.sub("", line) + for line in lines + if not pattern_doctest.match(line) + ] # remove blank ps(`>>>`) lines[:] = [line for line in lines if not line.strip() == ">>>"] @@ -394,4 +404,4 @@ def setup(app): app.add_transform(AutoStructify) # remove doctest directives and blank ps - app.connect("autodoc-process-docstring", remove_doctest_directives) + app.connect("autodoc-process-docstring", remove_directives) diff --git a/ci_scripts/doc-build-config/zh/conf.py b/ci_scripts/doc-build-config/zh/conf.py index 900003fdd69..42c1ffa6f1e 100644 --- a/ci_scripts/doc-build-config/zh/conf.py +++ b/ci_scripts/doc-build-config/zh/conf.py @@ -30,7 +30,7 @@ templates_path = ["/templates"] project = "PaddlePaddle" -author = "%s developers" % project +author = f"{project} developers" copyright = "%d, %s" % (time.localtime(time.time()).tm_year, author) github_doc_root = "https://github.com/PaddlePaddle/docs/docs" @@ -139,6 +139,10 @@ # html_permalinks_icon='P' +# Show type hints in the description +autodoc_typehints = "description" +autodoc_typehints_description_target = "documented" + # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [ @@ -266,7 +270,7 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, "%s.tex" % project, project, author, "manual"), + (master_doc, f"{project}.tex", project, author, "manual"), ] # Use the .. admonition:: directive for Notes sections. diff --git a/ci_scripts/gendoc.sh b/ci_scripts/gendoc.sh index 9baea37fe66..384187b610f 100755 --- a/ci_scripts/gendoc.sh +++ b/ci_scripts/gendoc.sh @@ -17,7 +17,7 @@ export DOCROOT # PADDLE_WHL is defined in ci_start.sh pip3 list --disable-pip-version-check | grep paddlepaddle > /dev/null if [ $? -ne 0 ] ; then - pip3 install --no-cache-dir -i https://mirror.baidu.com/pypi/simple ${PADDLE_WHL} + pip3 install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple ${PADDLE_WHL} fi diff --git a/ci_scripts/utils.sh b/ci_scripts/utils.sh index a71cbf05259..cd7540220a0 100644 --- a/ci_scripts/utils.sh +++ b/ci_scripts/utils.sh @@ -79,7 +79,7 @@ function get_paddle_pr_num_from_docs_pr_info(){ function install_paddle() { # try to download paddle, and install # PADDLE_WHL is defined in ci_start.sh - pip install --no-cache-dir -i https://mirror.baidu.com/pypi/simple ${PADDLE_WHL} 1>nul + pip install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple ${PADDLE_WHL} 1>nul # if failed, build paddle if [ $? -ne 0 ];then build_paddle diff --git a/docs/advanced_guide/index_cn.rst b/docs/advanced_guide/index_cn.rst deleted file mode 100644 index d866963d281..00000000000 --- a/docs/advanced_guide/index_cn.rst +++ /dev/null @@ -1,14 +0,0 @@ -######## -进阶指南 -######## - -如果您已经学会使用飞桨来完成常规任务，期望了解更多飞桨在工业部署方面的能力，请阅读： - - - - `预测与部署 <../advanced_guide/inference_deployment/index_cn.html>`_ ：介绍如何应用训练好的模型进行预测 - -.. toctree:: - :hidden: - - inference_deployment/index_cn.rst - flags/flags_cn.rst diff --git a/docs/advanced_guide/index_en.rst b/docs/advanced_guide/index_en.rst deleted file mode 100644 index f44212dadfb..00000000000 --- a/docs/advanced_guide/index_en.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. _user_guide_en_: - -#################### -Advanced User Guides -#################### - -.. todo:: - -So far you have already been familiar with PaddlePaddle. And the next expectation, read more on: - - - - `Deploy Inference Model `_ ：How to deploy the trained network to perform practical inference - - -.. toctree:: - :hidden: - - inference_deployment/index_en.rst - flags/flags_en.rst diff --git a/docs/advanced_guide/performance_improving/amp/amp.md b/docs/advanced_guide/performance_improving/amp/amp.md deleted file mode 100644 index db053907dd2..00000000000 --- a/docs/advanced_guide/performance_improving/amp/amp.md +++ /dev/null @@ -1,171 +0,0 @@ -# 混合精度训练最佳实践 - -Automatic Mixed Precision (AMP) 是一种自动混合使用半精度（FP16）和单精度（FP32）来加速模型训练的技术。AMP 技术可方便用户快速将使用 FP32 训练的模型修改为使用混合精度训练，并通过黑白名单和动态`loss scaling`来保证训练时的数值稳定性进而避免梯度 Infinite 或者 NaN(Not a Number)。借力于新一代 NVIDIA GPU 中 Tensor Cores 的计算性能，PaddlePaddle AMP 技术在 ResNet50、Transformer 等模型上训练速度相对于 FP32 训练加速比可达 1.5～2.9。 - -### 半精度浮点类型 FP16 - -如图 1 所示，半精度（Float Precision16，FP16）是一种相对较新的浮点类型，在计算机中使用 2 字节（16 位）存储。在 IEEE 754-2008 标准中，它亦被称作 binary16。与计算中常用的单精度（FP32）和双精度（FP64）类型相比，FP16 更适于在精度要求不高的场景中使用。 - -
- missing -
图 1. 半精度和单精度数据示意图
-
- -### 英伟达 GPU 的 FP16 算力 - -在使用相同的超参数下，混合精度训练使用半精度浮点（FP16）和单精度（FP32）浮点即可达到与使用纯单精度训练相同的准确率，并可加速模型的训练速度。这主要得益于英伟达推出的 Volta 及 Turing 架构 GPU 在使用 FP16 计算时具有如下特点： - -* FP16 可降低一半的内存带宽和存储需求，这使得在相同的硬件条件下研究人员可使用更大更复杂的模型以及更大的 batch size 大小。 -* FP16 可以充分利用英伟达 Volta 及 Turing 架构 GPU 提供的 Tensor Cores 技术。在相同的 GPU 硬件上，Tensor Cores 的 FP16 计算吞吐量是 FP32 的 8 倍。 - -### PaddlePaddle AMP 功能——牛刀小试 - -如前文所述，使用 FP16 数据类型可能会造成计算精度上的损失，但对深度学习领域而言，并不是所有计算都要求很高的精度，一些局部的精度损失对最终训练效果影响很微弱，却能使吞吐和训练速度带来大幅提升。因此，混合精度计算的需求应运而生。具体而言，训练过程中将一些对精度损失不敏感且能利用 Tensor Cores 进行加速的运算使用半精度处理，而对精度损失敏感部分依然保持 FP32 计算精度，用以最大限度提升访存和计算效率。 - -为了避免对每个具体模型人工地去设计和尝试精度混合的方法，PaddlePaddle 框架提供自动混合精度训练（AMP）功能，解放"炼丹师"的双手。在 PaddlePaddle 中使用 AMP 训练是一件十分容易的事情，用户只需要增加一行代码即可将原有的 FP32 训练转变为 AMP 训练。下面以`MNIST`为例介绍 PaddlePaddle AMP 功能的使用示例。 - -**MNIST 网络定义** - -```python -import paddle.fluid as fluid - -def MNIST(data, class_dim): - conv1 = fluid.layers.conv2d(data, 16, 5, 1, act=None, data_format='NHWC') - bn1 = fluid.layers.batch_norm(conv1, act='relu', data_layout='NHWC') - pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2, data_format='NHWC') - conv2 = fluid.layers.conv2d(pool1, 64, 5, 1, act=None, data_format='NHWC') - bn2 = fluid.layers.batch_norm(conv2, act='relu', data_layout='NHWC') - pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2, data_format='NHWC') - fc1 = fluid.layers.fc(pool2, size=64, act='relu') - fc2 = fluid.layers.fc(fc1, size=class_dim, act='softmax') - return fc2 -``` - -针对 CV(Computer Vision)类模型组网，为获得更高的训练性能需要注意如下三点： - -* `conv2d`、`batch_norm`以及`pool2d`等需要将数据布局设置为`NHWC`，这样有助于使用 TensorCore 技术加速计算过程1。 -* Tensor Cores 要求在使用 FP16 加速卷积运算时 conv2d 的输入/输出通道数为 8 的倍数2，因此设计网络时推荐将 conv2d 层的输入/输出通道数设置为 8 的倍数。 -* Tensor Cores 要求在使用 FP16 加速矩阵乘运算时矩阵行数和列数均为 8 的倍数3，因此设计网络时推荐将 fc 层的 size 参数设置为 8 的倍数。 - - -**FP32 训练** - -为了训练 MNIST 网络，还需要定义损失函数来更新权重参数，此处使用的优化器是 SGDOptimizer。为了简化说明，这里省略了迭代训练的相关代码，仅体现损失函数及优化器定义相关的内容。 - -```python -import paddle -import numpy as np - -data = fluid.layers.data( - name='image', shape=[None, 28, 28, 1], dtype='float32') -label = fluid.layers.data(name='label', shape=[None, 1], dtype='int64') - -out = MNIST(data, class_dim=10) -loss = fluid.layers.cross_entropy(input=out, label=label) -avg_loss = fluid.layers.mean(loss) - -sgd = fluid.optimizer.SGDOptimizer(learning_rate=1e-3) -sgd.minimize(avg_loss) -``` - -**AMP 训练** - -与 FP32 训练相比，用户仅需使用 PaddlePaddle 提供的`fluid.contrib.mixed_precision.decorate` 函数将原来的优化器 SGDOptimizer 进行封装，然后使用封装后的优化器（mp_sgd）更新参数梯度即可完成向 AMP 训练的转换，代码如下所示： - -```python -sgd = SGDOptimizer(learning_rate=1e-3) -# 此处只需要使用 fluid.contrib.mixed_precision.decorate 将 sgd 封装成 AMP 训练所需的 -# 优化器 mp_sgd，并使用 mp_sgd.minimize(avg_loss)代替原来的 sgd.minimize(avg_loss)语句即可。 -mp_sgd = fluid.contrib.mixed_precision.decorator.decorate(sgd) -mp_sgd.minimize(avg_loss) -``` - -运行上述混合精度训练 python 脚本时为得到更好的执行性能可配置如下环境参数，并保证 cudnn 版本在 7.4.1 及以上。 - -```shell -export FLAGS_conv_workspace_size_limit=1024 # MB，根据所使用的 GPU 显存容量及模型特点设置数值，值越大越有可能选择到更快的卷积算法 -export FLAGS_cudnn_exhaustive_search=1 # 使用穷举搜索方法来选择快速卷积算法 -export FLAGS_cudnn_batchnorm_spatial_persistent=1 # 用于触发 batch_norm 和 relu 的融合 -``` - -上述即为最简单的 PaddlePaddle AMP 功能使用方法。ResNet50 模型的 AMP 训练示例可[点击此处](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/README.md#%E6%B7%B7%E5%90%88%E7%B2%BE%E5%BA%A6%E8%AE%AD%E7%BB%83)查看，其他模型使用 PaddlePaddle AMP 的方法也与此类似。若 AMP 训练过程中出现连续的 loss nan 等不收敛现象，可尝试使用[check nan inf 工具](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/flags/check_nan_inf_cn.html#span-id-speed-span)进行调试。 - - -### PaddlePaddle AMP 功能——进阶使用 - -上一小节所述均为默认 AMP 训练行为，用户当然也可以改变一些默认的参数设置来满足特定的模型训练场景需求。接下来的章节将介绍 PaddlePaddle AMP 功能使用中用户可配置的参数行为，即进阶使用技巧。 - -#### 自定义黑白名单 - -PaddlePaddle AMP 功能实现中根据 FP16 数据类型计算稳定性和加速效果在框架内部定义了算子（Op）的黑白名单。具体来说，将对 FP16 计算友好且能利用 Tensor Cores 的 Op 归类于白名单，将使用 FP16 计算会导致数值不稳定的 Op 归类于黑名单，将对 FP16 计算没有多少影响的 Op 归类于灰名单。然而，框架开发人员不可能考虑到所有的网络模型情况，尤其是那些特殊场景中使用到的模型。用户可以在使用`fluid.contrib.mixed_precision.decorate` 函数时通过指定自定义的黑白名单列表来改变默认的 FP16 计算行为。 - -```python -sgd = SGDOptimizer(learning_rate=1e-3) -# list1 是白名单 op 列表，list2 是黑名单 op 列表，list3 是黑名单 var_name 列表（凡是以这些黑名单 var_name 为输入或输出的 op 均会被视为黑名单 op） -amp_list = AutoMixedPrecisionLists(custom_white_list=list1, custom_black_list=list2, custom_black_varnames=list3) -mp_sgd = fluid.contrib.mixed_precision.decorator.decorate(sgd, amp_list) -mp_sgd.minimize(avg_loss) -``` - -#### 自动 loss scaling - -为了避免梯度 Infinite 或者 NAN，PaddlePaddle AMP 功能支持根据训练过程中梯度的数值自动调整 loss scale 值。用户在使用`fluid.contrib.mixed_precision.decorate` 函数时也可以改变与 loss scaling 相关的参数设置，示例如下： - -```python -sgd = SGDOptimizer(learning_rate=1e-3) -mp_sgd = fluid.contrib.mixed_precision.decorator.decorate(sgd, - amp_lists=None, - init_loss_scaling=2**8, - incr_every_n_steps=500, - decr_every_n_nan_or_inf=4, - incr_ratio=2.0, - decr_ratio=0.5, - use_dynamic_loss_scaling=True) -mp_sgd.minimize(avg_loss) -``` - -`init_loss_scaling `、`incr_every_n_steps` 以及`decr_every_n_nan_or_inf`等参数控制着自动 loss scaling 的行为。它们仅当 `use_dynamic_loss_scaling`设置为 True 时有效。下面详述这些参数的意义： - -* init_loss_scaling(float)：初始 loss scaling 值。 -* incr_every_n_steps(int)：每经过 incr_every_n_steps 个连续的正常梯度值才会增大 loss scaling 值。 -* decr_every_n_nan_or_inf(int)：每经过 decr_every_n_nan_or_inf 个连续的无效梯度值(nan 或者 inf)才会减小 loss scaling 值。 -* incr_ratio(float)：每次增大 loss scaling 值的扩增倍数，其为大于 1 的浮点数。 -* decr_ratio(float)：每次减小 loss scaling 值的比例系数，其为小于 1 的浮点数。 - -### 多卡 GPU 训练的优化 - -PaddlePaddle AMP 功能对多卡 GPU 训练进行了深度优化。如图 2 所示，优化之前的参数梯度更新特点：梯度计算时虽然使用的是 FP16 数据类型，但是不同 GPU 卡之间的梯度传输数据类型仍为 FP32。 - -
- missing -
图 2. 不同 GPU 卡之间传输梯度使用 FP32 数据类型（优化前）
-
- -为了降低 GPU 多卡之间的梯度传输带宽，我们将梯度传输提前至`Cast`操作之前，而每个 GPU 卡在得到对应的 FP16 梯度后再执行`Cast`操作将其转变为 FP32 类型，具体操作详见图 2。这一优化在训练大模型时对减少带宽占用尤其有效，如多卡训练 BERT-Large 模型。 - -
- missing -
图 3. 不同 GPU 卡之间传输梯度使用 FP16 数据类型（优化后）
-
- -### 训练性能对比（AMP VS FP32） - -PaddlePaddle AMP 技术在 ResNet50、Transformer 等模型上训练速度相对于 FP32 训练上均有可观的加速比，下面是 ResNet50 和 ERNIE Large 模型的 AMP 训练相对于 FP32 训练的加速效果。 - - - - - - - -
图 4. Paddle AMP 训练加速效果（横坐标为卡数，如 8*8 代表 8 机 8 卡）
missing missing
- -从图 4 所示的图表可以看出，ResNet50 的 AMP 训练相对与 FP32 训练加速比可达$2.8 \times$以上，而 ERNIE Large 的 AMP 训练相对与 FP32 训练加速比亦可达 $1.7 \times -- 2.1 \times$ 。 - -### 参考文献 - -*

Mixed Precision Training

-*

使用自动混合精度加速 PaddlePaddle 训练

-*

Tensor Layouts In Memory: NCHW vs NHWC ↩

-*

Channels In And Out Requirements ↩

-*

Matrix-Matrix Multiplication Requirements ↩

diff --git a/docs/advanced_guide/performance_improving/index_cn.rst b/docs/advanced_guide/performance_improving/index_cn.rst deleted file mode 100644 index b50f091f8c7..00000000000 --- a/docs/advanced_guide/performance_improving/index_cn.rst +++ /dev/null @@ -1,16 +0,0 @@ -######## -性能调优 -######## - -.. toctree:: - :maxdepth: 1 - - singlenode_training_improving/training_best_practice.rst - singlenode_training_improving/memory_optimize.rst - device_switching/device_switching.md - amp/amp.md - multinode_training_improving/cpu_train_best_practice.rst - multinode_training_improving/dist_training_gpu.rst - multinode_training_improving/gpu_training_with_recompute.rst - inference_improving/paddle_tensorrt_infer.md - analysis_tools/index_cn.rst diff --git a/docs/advanced_guide/performance_improving/index_en.rst b/docs/advanced_guide/performance_improving/index_en.rst deleted file mode 100644 index f57e2a3d060..00000000000 --- a/docs/advanced_guide/performance_improving/index_en.rst +++ /dev/null @@ -1,12 +0,0 @@ -############### -Practice Improving -############### - -.. toctree:: - :maxdepth: 1 - - singlenode_training_improving/memory_optimize_en.rst - multinode_training_improving/cpu_train_best_practice_en.rst - multinode_training_improving/gpu_training_with_recompute_en.rst - inference_improving/paddle_tensorrt_infer_en.md - analysis_tools/index_en.rst diff --git a/docs/advanced_guide/performance_improving/inference_improving/paddle_xpu_infer_cn.md b/docs/advanced_guide/performance_improving/inference_improving/paddle_xpu_infer_cn.md deleted file mode 100644 index 8818e259155..00000000000 --- a/docs/advanced_guide/performance_improving/inference_improving/paddle_xpu_infer_cn.md +++ /dev/null @@ -1,120 +0,0 @@ -# 使用昆仑预测 - -百度的昆仑芯⽚是一款⾼性能的 AI SoC 芯⽚，⽀持推理和训练。昆仑芯⽚采⽤百度的先进 AI 架构，⾮常适合常⽤的深度学习和机器学习算法的云端计算需求，并能适配诸如⾃然语⾔处理、⼤规模语⾳识别、⾃动驾驶、⼤规模推荐等多种终端场景的计算需求。 - -Paddle Inference 集成了[Paddle-Lite 预测引擎](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/kunlunxin_xpu.html)在昆仑 xpu 上进行预测部署。 - -## 编译注意事项 - -请确保编译的时候设置了 WITH_LITE=ON，且 XPU_SDK_ROOT 设置了正确的路径。 - -## 使用介绍 - -在使用 Predictor 时，我们通过配置 Config 中的接口，在 XPU 上运行。 - -```c++ -config->EnableLiteEngine( - precision_mode=PrecisionType::kFloat32, - zero_copy=false, - passes_filter={}, - ops_filter={}, -) -``` - -- **`precision_mode`**，类型：`enum class PrecisionType {kFloat32 = 0, kHalf, kInt8,};`, 默认值为`PrecisionType::kFloat32`。指定 lite 子图的运行精度。 -- **`zero_copy`**，类型：bool，lite 子图与 Paddle 之间的数据传递是否是零拷贝模式。 -- **`passes_filter`**，类型：`std::vector`，默认为空，扩展借口，暂不使用。 -- **`ops_filer`**，类型：`std::vector`，默认为空，显示指定哪些 op 不使用 lite 子图运行。 - -Python 接口如下： - -```python -config.enable_lite_engine( - precision_mode=PrecisionType.Float32, - zero_copy=False, - passes_filter=[], - ops_filter=[] -) -``` - -### Python demo - -因目前 Paddle-Inference 目前未将 xpu sdk 打包到 whl 包内，所以需要用户下载 xpu sdk，并加入到环境变量中，之后会考虑解决该问题。 - -下载[xpu_tool_chain](https://paddle-inference-dist.bj.bcebos.com/inference_demo/xpu_tool_chain.tgz)，解压后将 shlib 加入到 LD_LIBRARY_PATH - -``` -tar xzf xpu_tool_chain.tgz -``` -``` -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$PWD/output/XTDK/shlib/:$PWD/output/XTDK/runtime/shlib/ -``` - -下载[resnet50](https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50.tar.gz)模型，并解压，运行如下命令将会调用预测引擎 - -```bash -python resnet50_subgraph.py --model_file ./ResNet50/model --params_file ./ResNet50/params -``` - -resnet50_subgraph.py 的内容是： - -``` -import argparse -import time -import numpy as np -from paddle.inference import Config, PrecisionType -from paddle.inference import create_predictor - -def main(): - args = parse_args() - - config = set_config(args) - - predictor = create_predictor(config) - - input_names = predictor.get_input_names() - input_handle = predictor.get_input_handle(input_names[0]) - - fake_input = np.ones((args.batch_size, 3, 224, 224)).astype("float32") - input_handle.reshape([args.batch_size, 3, 224, 224]) - input_handle.copy_from_cpu(fake_input) - - for i in range(args.warmup): - predictor.run() - - start_time = time.time() - for i in range(args.repeats): - predictor.run() - - output_names = predictor.get_output_names() - output_handle = predictor.get_output_handle(output_names[0]) - output_data = output_handle.copy_to_cpu() - end_time = time.time() - print(output_data[0, :10]) - print('time is: {}'.format((end_time-start_time)/args.repeats * 1000)) - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", type=str, help="model dir") - parser.add_argument("--model_file", type=str, help="model filename") - parser.add_argument("--params_file", type=str, help="parameter filename") - parser.add_argument("--batch_size", type=int, default=1, help="batch size") - parser.add_argument("--warmup", type=int, default=0, help="warmup") - parser.add_argument("--repeats", type=int, default=1, help="repeats") - parser.add_argument("--math_thread_num", type=int, default=1, help="math_thread_num") - - return parser.parse_args() - -def set_config(args): - config = Config(args.model_file, args.params_file) - config.enable_lite_engine(PrecisionType.Float32, True) - # use lite xpu subgraph - config.enable_xpu(10 * 1024 * 1024) - # use lite cuda subgraph - # config.enable_use_gpu(100, 0) - config.set_cpu_math_library_num_threads(args.math_thread_num) - return config - -if __name__ == "__main__": - main() -``` diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice.rst deleted file mode 100644 index 6df05163486..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice.rst +++ /dev/null @@ -1,161 +0,0 @@ -.. _api_guide_cpu_training_best_practice: - -#################### -分布式 CPU 训练优秀实践 -#################### - -提高 CPU 分布式训练的训练速度，主要要从四个方面来考虑： -1）提高训练速度，主要是提高 CPU 的使用率；2）提高通信速度，主要是减少通信传输的数据量；3）提高数据 IO 速度；4）更换分布式训练策略，提高分布式训练速度。 - -提高 CPU 的使用率 -============= - -提高 CPU 使用率主要依赖 :code:`ParallelExecutor`，可以充分利用多个 CPU 的计算能力来加速计算。 - -简单实例用法： - -.. code-block:: python - - # 配置执行策略，主要是设置线程数 - exec_strategy = fluid.ExecutionStrategy() - exec_strategy.num_threads = 8 - - # 配置构图策略，对于 CPU 训练而言，应该使用 Reduce 模式进行训练 - build_strategy = fluid.BuildStrategy() - if int(os.getenv("CPU_NUM")) > 1: - build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce - - pe = fluid.ParallelExecutor( - use_cuda=False, - loss_name=avg_cost.name, - main_program=main_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy) - -以上参数中： - -- :code:`num_threads` ： 模型训练使用的线程数，最好和训练所在机器的物理 CPU 核数接近 -- :code:`reduce_strategy` ： 对于 CPU 训练而言，应该选择 fluid.BuildStrategy.ReduceStrategy.Reduce - - -通用环境变量配置： - -- :code:`CPU_NUM` ：模型副本 replica 的个数，最好和 num_threads 一致 - - -提高通信速度 -========== - -要减少通信数据量，提高通信速度，主要是使用稀疏更新 ，目前支持 :ref:`api_guide_sparse_update` 的主要是 :ref:`cn_api_fluid_layers_embedding` 。 - -.. code-block:: python - - data = fluid.layers.data(name='ids', shape=[1], dtype='int64') - fc = fluid.layers.embedding(input=data, size=[dict_size, 16], is_sparse=True) - -以上参数中： - -- :code:`is_sparse` ： 配置 embedding 使用稀疏更新，如果 embedding 的 dict_size 很大，而每次数据 data 很少，建议使用 sparse 更新方式。 - - -提高数据 IO 速度 -========== - -要提高 CPU 分布式的数据 IO 速度，可以首先考虑使用 dataset API 进行数据读取。 dataset 是一种多生产者多消费者模式的数据读取方法，默认情况下耦合数据读取线程与训练线程，在多线程的训练中，dataset 表现出极高的性能优势。 - -API 接口介绍可以参考： :ref:`cn_api_distributed_QueueDataset` - -结合实际的网络，比如 CTR-DNN 模型，引入的方法可以参考：https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/ctr/dnn - -最后使用 :code:`train_from_dataset` 接口来进行网络的训练： - -.. code-block:: python - - dataset = fluid.DatasetFactory().create_dataset() - exe = fluid.Executor(fluid.CPUPlace()) - exe.run(fluid.default_startup_program()) - exe.train_from_dataset(program=fluid.default_main_program(),dataset=dataset) - - -更换分布式训练策略 -========== - -CPU 分布式训练速度进一步提高的核心在于选择合适的分布式训练策略，比如定义通信策略、编译策略、执行策略等等。paddlepaddle 于 v1.7 版本发布了 :code:`DistributedStrategy` 功能，可以十分灵活且方便的指定分布式运行策略。 - -首先需要在代码中引入相关库： - -.. code-block:: python - - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet - import paddle.fluid.incubate.fleet.base.role_maker as role_maker - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy_factory import DistributedStrategyFactory - -然后指定 CPU 分布式运行的训练策略，目前可选配置有四种：同步训练（Sync）、异步训练（Async）、半异步训练（Half-Async）以及 GEO 训练。 - - -通过如下代码引入上述策略的默认配置，并进行 CPU 分布式训练： - -.. code-block:: python - - # step1: 引入 CPU 分布式训练策略 - # 同步训练策略 - strategy = DistributedStrategyFactory.create_sync_strategy() - # 半异步训练策略 - strategy = DistributedStrategyFactory.create_half_async_strategy() - # 异步训练策略 - strategy = DistributedStrategyFactory.create_async_strategy() - # GEO 训练策略 - strategy = DistributedStrategyFactory.create_geo_strategy(update_frequency=400) - - # step2: 定义节点角色 - role = role_maker.PaddleCloudRoleMaker() - fleet.init(role) - - # step3: 分布式训练 program 构建 - optimizer = fluid.optimizer.SGD(learning_rate) # 以 SGD 优化器为例 - optimizer = fleet.distributed_optimizer(optimizer, strategy) - optimizer.minimize(loss) - - # step4.1: 启动参数服务器节点（Server） - if fleet.is_server(): - fleet.init_server() - fleet.run_server() - - # step4.2: 启动训练节点（Trainer） - elif fleet.is_worker(): - fleet.init_worker() - exe.run(fleet.startup_program) - # Do training - exe.run(fleet.main_program) - fleet.stop_worker() - - -paddlepaddle 支持对训练策略中的细节进行调整： - -- 创建 compiled_program 所需的 build_strategy 及 exec_strategy 可以直接基于 strategy 获得 - -.. code-block:: python - - compiled_program = fluid.compiler.CompiledProgram(fleet.main_program).with_data_parallel( - loss_name=loss.name, - build_strategy=strategy.get_build_strategy(), - exec_strategy=strategy.get_execute_strategy()) - - -- 自定义训练策略细节，支持对 DistributeTranspilerConfig、TrainerRuntimeConfig、ServerRuntimeConfig、fluid.ExecutionStrategy、fluid.BuildStrategy 进行自定义配置。以 DistributeTranspilerConfig 为例，修改方式如下所示： - -.. code-block:: python - - strategy = DistributedStrategyFactory.create_sync_strategy() - - # 方式一（推荐）： - config = strategy.get_program_config() - config.min_block_size = 81920 - - - # 方式二：调用 set_program_config 修改组网相关配置，支持 DistributeTranspilerConfig 和 dict 两种数据类型 - config = DistributeTranspilerConfig() - config.min_block_size = 81920 - # config = dict() - # config['min_block_size'] = 81920 - strategy.set_program_config(config) diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice_en.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice_en.rst deleted file mode 100644 index 350606f34ea..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice_en.rst +++ /dev/null @@ -1,164 +0,0 @@ -.. _api_guide_cpu_training_best_practice_en: - -###################################################### -Best practices of distributed training on CPU -###################################################### - -To improve the training speed of CPU distributed training, we must consider two aspects: - -1. Improve the training speed mainly by improving utilization rate of CPU; -2. Improve the communication speed mainly by reducing the amount of data transmitted in the communication; -3. Improve the data IO speed by dataset API; -4. Improve the distributed training speed by changing distributed training strategy. - -Improve CPU utilization -============================= - -The CPU utilization mainly depends on :code:`ParallelExecutor`, which can make full use of the computing power of multiple CPUs to speed up the calculation. - -For detailed API usage, please refer to :ref:`api_fluid_ParallelExecutor` . A simple example: - -.. code-block:: python - - # Configure the execution strategy, mainly to set the number of threads - exec_strategy = fluid.ExecutionStrategy() - exec_strategy.num_threads = 8 - - # Configure the composition strategy, for CPU training, you should use the Reduce mode for training. - build_strategy = fluid.BuildStrategy() - if int(os.getenv("CPU_NUM")) > 1: - build_strategy.reduce_strategy=fluid.BuildStrategy.ReduceStrategy.Reduce - - pe = fluid.ParallelExecutor( - use_cuda=False, - loss_name=avg_cost.name, - main_program=main_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy) - -Among the parameters above: - -- :code:`num_threads` : the number of threads used by the model training. It is preferably close to the number of the physical CPU cores of the machine where the training is performed. -- :code:`reduce_strategy` : For CPU training, you should choose fluid.BuildStrategy.ReduceStrategy.Reduce - - -Configuration of general environment variables: - -- :code:`CPU_NUM`: The number of replicas of the model, preferably the same as num_threads - - -Improve communication speed -============================== - -To reduce the amount of communication data and improve communication speed is achieved mainly by using sparse updates, the current support for `sparse update <../layers/sparse_update_en.html>`_ is mainly :ref:`api_fluid_layers_embedding`. - -.. code-block:: python - - data = fluid.layers.data(name='ids', shape=[1], dtype='int64') - fc = fluid.layers.embedding(input=data, size=[dict_size, 16], is_sparse=True) - -Among the parameters above: - -- :code:`is_sparse`: Use sparse updates to configure embedding. If the dict_size of embedding is large but the number of data are very small each time, it is recommended to use the sparse update method. - - -Improve data IO speed -============================== - -To improve the CPU's distributed training speed, you can first consider using the dataset API as data reader. Dataset is a multi producer and multi consumer data reading method. By default, data reading thread and training thread are coupled. In multi-threaded training, dataset shows a high performance advantage. - -Refer to this page for API introduction: https://www.paddlepaddle.org.cn/documentation/docs/en/api/dataset/QueueDataset.html - -Combined with the actual model CTR-DNN, you can learn more about how to use dataset: https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/ctr/dnn - -Using :code:`train_from_dataset` for network training. - -.. code-block:: python - - dataset = fluid.DatasetFactory().create_dataset() - exe = fluid.Executor(fluid.CPUPlace()) - exe.run(fluid.default_startup_program()) - exe.train_from_dataset(program=fluid.default_main_program(),dataset=dataset) - - -Change distributed training strategy -============================== - -The core of improving CPU distributed training speed is to choose appropriate distributed training strategy, such as defining communication strategy, compiling strategy, executing strategy and so on. PaddlePaddle released :code:`DistributedStrategy` API in V1.7 version , which can be very flexible and convenient to specify distributed operation strategy. - -First, we need to introduce relevant libraries into the code: - -.. code-block:: python - - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet - import paddle.fluid.incubate.fleet.base.role_maker as role_maker - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy_factory import DistributedStrategyFactory - -At present, there are four kinds of training strategies: synchronous training, asynchronous, half asynchronous training and GEO training. - - -The default configuration of the above policy is introduced by the following code: - -.. code-block:: python - - # step1: get distributed strategy - # Sync - strategy = DistributedStrategyFactory.create_sync_strategy() - # Half-Async - strategy = DistributedStrategyFactory.create_half_async_strategy() - # Async - strategy = DistributedStrategyFactory.create_async_strategy() - # GEO - strategy = DistributedStrategyFactory.create_geo_strategy(update_frequency=400) - - # step2: define role of node - role = role_maker.PaddleCloudRoleMaker() - fleet.init(role) - - # step3: get distributed training program - optimizer = fluid.optimizer.SGD(learning_rate) # 以 SGD 优化器为例 - optimizer = fleet.distributed_optimizer(optimizer, strategy) - optimizer.minimize(loss) - - # step4.1: run parameter server node - if fleet.is_server(): - fleet.init_server() - fleet.run_server() - - # step4.2: run worker node - elif fleet.is_worker(): - fleet.init_worker() - exe.run(fleet.startup_program) - # Do training - exe.run(fleet.main_program) - fleet.stop_worker() - -PaddlePaddle supports adjusting the details of the training strategy: - -- The build_strategy and exec_strategy which used to create compiled_program can generate from strategy: - -.. code-block:: python - - compiled_program = fluid.compiler.CompiledProgram(fleet.main_program).with_data_parallel( - loss_name=loss.name, - build_strategy=strategy.get_build_strategy(), - exec_strategy=strategy.get_execute_strategy()) - - -- Training strategy details can be customized, Paddlepaddle supports customized configuration of distributetranspierconfig, trainerruntimeconfig, serverruntimeconfig, fluid.executionstrategy and fluid.buildstrategy. Take distributetranspillerconfig as an example. The modification method is as follows: - -.. code-block:: python - - strategy = DistributedStrategyFactory.create_sync_strategy() - - # Mode 1 (recommended)： - config = strategy.get_program_config() - config.min_block_size = 81920 - - - # Mode 2 - config = DistributeTranspilerConfig() - config.min_block_size = 81920 - # config = dict() - # config['min_block_size'] = 81920 - strategy.set_program_config(config) diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/dist_training_gpu.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/dist_training_gpu.rst deleted file mode 100644 index 32188831ca9..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/dist_training_gpu.rst +++ /dev/null @@ -1,133 +0,0 @@ -.. _best_practice_dist_training_gpu: - -##################### -分布式 GPU 训练优秀实践 -##################### - -开始优化您的 GPU 分布式训练任务 ---------------------------- - -PaddlePaddle Fluid 支持在现代 GPU [#]_ 服务器集群上完成高性能分布式训练。通常可以通过以下方法优化在多机多卡环境训练性能，建议在进行性能优化时，检查每项优化点并验证对应提升，从而提升最终的性能。 - -一个简单的验证当前的训练程序是否需要进一步优化性能的方法，是查看 GPU 的计算利用率 [#]_ ，通常用 :code:`nvidia-smi` 命令查看。如果 GPU 利用率较低，则可能存在较大的优化空间。下面主要从数据准备、训练策略设置和训练方式三个方面介绍 GPU 分布式训练中常用的优化方法。 - -1、数据准备 -=========== - -数据读取的优化在 GPU 训练中至关重要，尤其在不断增加 batch_size 提升吞吐时，计算对 reader 性能会有更高对要求，优化 reader 性能需要考虑的点包括： - - - 使用 :code:`DataLoader` 。参考 `这里 `_ 使用 DataLoader，并建议开启 :code:`use_double_buffer` 。 - - reader 返回 uint8 类型数据。图片在解码后一般会以 uint8 类型存储，如果在 reader 中转换成 float 类型数据，会将数据体积扩大 4 倍。直接返回 uint8 数据，然后在 GPU 上转化成 float 类型进行训练可以提升数据读取效率。 - - 减少 reader 初始化时间 (infinite read)。在训练任务开始执行第一轮训练时，reader 开始不断异步地从磁盘或其他存储中读取数据并执行预处理，然后将处理好的数据填充到队列中供计算使用。从 0 开始填充这个队列直到数据可以源源不断供给计算，需要一定时间的预热。所以，如果每轮训练都重新填充队列，会产生一些时间的开销。所以，在使用 DataLoader 时，可以让 reader 函数不断地产生数据，直到训练循环结束： - - .. code-block:: python - :linenos: - - def infinite_reader(file_path): - while True: - with open(file_path) as fn: - for line in fn: - yield process(line) - - def train(): - ... - for pass_id in xrange(NUM_PASSES): - if pass_id == 0: - data_loader.start() - for batch_id in (iters_per_pass): - exe.run() - data_loader.reset() - - -另外，可以使用 DALI 库提升数据处理性能。DALI 是 NVIDIA 开发的数据加载库，更多内容请参考 `官网文档 `_ 。飞桨中如何结合使用 DALI 库请参考 `使用示例 `_ 。 - -2、训练策略设置 -=========== - -训练参数设置表 - -.. csv-table:: - :header: "选项", "类型", "默认值", "说明" - :widths: 3, 3, 3, 5 - - ":code:`num_threads`", "int", "1", "CPU 线程数" - ":code:`nccl_comm_num`", "int", "1", "nccl 通信器数量" - ":code:`fuse_all_reduce_ops`", "bool", "False", "多卡训练时，将 AllReduce 操纵进行融合" - ":code:`use_hierarchical_allreduce` ", "bool", "False", "分级式 reduce" - ":code:`num_iteration_per_drop_scope`", "int", "1", "scope drop 频率，设置每隔几个 batch 的迭代之后执行一次清理 scope" - ":code:`fetch_frequency`", "int", "1", "fetch 的刷新频率" - ":code:`fuse_bn_act_ops`", "bool", "False", "是否开启 batch normalization 和激活函数的融合" - ":code:`fuse_elewise_add_act_ops`", "bool", "False", "是否开启 elementwise add 函数和激活函数的融合" - -说明： - -- 关于设置合适的 CPU 线程数 :code:`num_threads` 和 nccl 通信器数量 :code:`nccl_comm_num` 。PaddlePaddle Fluid 使用“线程池” [#]_ 模型调度并执行 Op，Op 在启动 GPU 计算之前，通常需要 CPU 的协助，然而如果 Op 本身占用时间很小，“线程池”模型下又会带来额外的调度开销。使用多进程模式时，如果神经网络的计算图 [#]_ 节点间有较高的并发度，即使每个进程只在一个 GPU 上运行，使用多个线程可以更大限度的提升 GPU 利用率。nccl 通信器数量 :code:`nccl_comm_num` 可以加快 GPU 之间的通信效率，建议单机设置为 1，多机设置为 2。针对 CPU 线程数 :code:`num_threads` ，建议单机设置为 1，多机设置为 :code:`nccl_comm_num` +1。 -- 关于 AllReduce 融合 :code:`fuse_all_reduce_ops` ，默认情况下会将同一 layer 中参数的梯度的 AllReduce 操作合并成一个，比如对于 :code:`fluid.layers.fc` 中有 Weight 和 Bias 两个参数，打开该选项之后，原本需要两次 AllReduce 操作，现在只用一次 AllReduce 操作。此外，为支持更大粒度的参数梯度融合，Paddle 提供了 :code:`FLAGS_fuse_parameter_memory_size` 和 :code:`FLAGS_fuse_parameter_groups_size` 两个环境变量选项。用户可以指定融合 AllReduce 操作之后，每个 AllReduce 操作的梯度字节数，比如希望每次 AllReduce 调用传输 16MB 的梯度，:code:`export FLAGS_fuse_parameter_memory_size=16` ，经验值为总通信量的十分之一。可以指定每次 AllReduce 操作的最大层数，即到达该层数就进行 AllReduce，如指定 50 层 :code:`export FLAGS_fuse_parameter_groups_size=50` 。注意：目前不支持 sparse 参数梯度。 -- 关于使用分级式 reduce :code:`use_hierarchical_allreduce` 。对于多机模式，针对小数据量的通信，Ring AllReduce 通信效率低，采用 Hierarchical AllReduce 可以解决该问题。 -- 关于降低 scope drop 频率 :code:`num_iteration_per_drop_scope` 和 fetch 频率 :code:`fetch_frequency` 。减少 scope drop 和 fetch 频率，可以减少频繁的变量内存申请、释放和拷贝，从而提升性能。 -- 关于操作融合：通过参数融合可以提升训练性能。 - -设置这些参数可以参考： - -.. code-block:: python - :linenos: - - dist_strategy = DistributedStrategy() - dist_strategy.nccl_comm_num = 2 #建议多机设置为 2，单机设置为 1 - exec_strategy = fluid.ExecutionStrategy() - exe_st.num_threads = 3 #建议多机设置为 nccl_comm_num+1，单机设置为 1 - exec_strategy.num_iteration_per_drop_scope = 30 #scope drop 频率 - dist_strategy.exec_strategy = exec_strategy - dist_strategy.fuse_all_reduce_ops = True #AllReduce 是否融合 - ... - with fluid.program_guard(main_prog, startup_prog): #组网 - params = model.params - optimizer = optimizer_setting(params) - dist_optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) - dist_optimizer.minimize(avg_cost) - ... - for pass_id in range(PASS_NUM): - batch_id = 0 - while True: - if batch_id % fetch_frequency == 0: #fetch 频率 - fetched = exe.run(main_prog, fetch_list) - else: - exe.run([]) - - -3、训练方式 -=========== - -1、Local SGD - -GPU 多机多卡同步训练过程中存在慢 trainer 现象，即每步中训练快的 trainer 的同步通信需要等待训练慢的 trainer。由于每步中慢 trainer 的 rank 具有随机性，因此我们使用局部异步训练的方式——LocalSGD，通过多步异步训练（无通信阻塞）实现慢 trainer 时间均摊，从而提升同步训练性能。Local SGD 训练方式主要有三个参数，分别是： - -.. csv-table:: - :header: "选项", "类型", "可选值", "说明" - :widths: 3, 3, 3, 5 - - ":code:`use_local_sgd`", "bool", "False/True", "是否开启 Local SGD，默认不开启" - ":code:`local_sgd_is_warm_steps`", "int", "大于 0", "训练多少轮之后才使用 Local SGD 方式训练" - ":code:`local_sgd_steps`", "int", "大于 0", "Local SGD 的步长" - -说明： - -- Local SGD 的 warmup 步长 :code:`local_sgd_is_warm_steps` 影响最终模型的泛化能力，一般需要等到模型参数稳定之后在进行 Local SGD 训练，经验值可以将学习率第一次下降时的 epoch 作为 warmup 步长，之后再进行 Local SGD 训练。 -- Local SGD 步长 :code:`local_sgd_steps` ，一般该值越大，通信次数越少，训练速度越快，但随之而来的时模型精度下降。经验值设置为 2 或者 4。 - -具体的 Local SGD 的训练代码可以参考：https://github.com/PaddlePaddle/PaddleFleetX/tree/old_develop/deprecated/examples/local_sgd/resnet - - -2、使用混合精度训练 - -V100 GPU 提供了 `Tensor Core `_ 可以在混合精度计算场景极大的提升性能。使用混合精度计算的例子可以参考：https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#using-mixed-precision-training - -目前 Paddle 只提供在两个模型（ResNet, BERT）的混合精度计算实现并支持 static loss scaling，其他模型使用混合精度也可以参考以上的实现完成验证。 - -附录 ----- - -.. [#] 现代 GPU：指至少支持运行 `CUDA `_ 版本 7.5 以上的 GPU -.. [#] GPU 利用率：这里指 GPU 计算能力被使用部分所占的百分比 -.. [#] https://en.wikipedia.org/wiki/Thread_pool -.. [#] https://en.wikipedia.org/wiki/Data-flow_diagram diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_low_bandwidth_dgc.md b/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_low_bandwidth_dgc.md deleted file mode 100644 index ef1017c61c4..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_low_bandwidth_dgc.md +++ /dev/null @@ -1,123 +0,0 @@ -# 低配网络的分布式 GPU 训练 - -## 1. 背景 - 大规模分布式训练需要较高的网络带宽以便进行梯度的聚合更新，这限制了多节点训练时的可扩展性同时也需要昂贵的高带宽设备。在低带宽云网络等环境下进行分布式训练会变得更加糟糕。现有[Deep Gradient Compression](https://arxiv.org/abs/1712.01887)研究表明，分布式 SGD 中有 99.9%的梯度交换都是冗余的，可以使用深度梯度压缩选择重要梯度进行通信来减少通信量，降低对通信带宽的依赖。Paddle 目前实现了 DGC 的稀疏通信方式，可有效在低配网络下进行 GPU 分布式训练。下面将介绍 DGC 稀疏通信方式的使用方法、适用场景及基本原理。 - -## 2. 使用方法 -`注意：使用 DGC 请使用 1.6.2 及其之后版本，之前版本存在有若干 bug。` -DGC 稀疏通信算法以 DGCMomentumOptimizer 接口的形式提供，目前只支持 GPU 多卡及 GPU 多机分布式，由于现有 fuse 策略会造成 DGC 失效，所以使用 DGC 时需设置`strategy.fuse_all_reduce_ops=False`关闭 fuse。DGC 只支持 Momentum 优化器，使用时把当前代码中的 Momentum 优化器替换为 DGCMomentumOptimizer，并添加 DGC 所需参数即可。如下代码所示，其中 rampup_begin_step 表示从第几步开始使用 DGC，更详细参数可见[api 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/optimizer_cn/DGCMomentumOptimizer_cn.html#dgcmomentumoptimizer)。 -``` python -import paddle.fluid as fluid -# optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9) -# 替换 Momentum 优化器，添加 DGC 所需参数 -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=0) -optimizer.minimize(cost) -``` -在 fleet 中我们提供了[DGC 的示例](https://github.com/PaddlePaddle/PaddleFleetX/tree/old_develop/deprecated/examples/dgc_example)。示例中以数字手写体识别为例，将程序移植为分布式版本（注：DGC 亦支持单机多卡），再加上 DGC 优化器。可参照此示例将单机单卡程序迁移到 DGC。在单机单卡迁移到 DGC 过程中，一般需要先对齐多机 Momentum 的精度，再对齐 DGC 的精度。 - -## 3. 调参&适用场景 -### 3.1 预热调参 -对于正常的训练，使用 DGC 一般需进行预热训练，否则可能会有精度损失。如下图是 ResNet50 模型 Imagenet 数据集的训练结果，未进行预热训练的 DGC 最终损失了约 0.3%的精度。 -
-![DGC Resnet50 acc1](images/dgc_resnet50_acc1.png) -
- -预热训练调参可参照论文的设置。对图像分类，论文在 Cifar10 和 ImageNet 数据集上共 164 和 90 个 epochs 的训练中都采用了 4 个 epochs 的预热训练。在语言模型 PTB 数据集上，在共 40 个 epochs 的训练中选择了 1 个 epoch 进行预热训练。在语音识别 AN4 数据集上，80 个 epochs 中选择 1 个 epoch 进行预热训练。 -论文中使用了 75%, 93.75%, 98.4375%, 99.6%, 99.9%稀疏度逐渐提升的策略。由于 paddle 稀疏梯度聚合通信使用了 AllGather，通信量会随卡数增加而增长，所以在卡数较多时不推荐较低稀疏度的预热训练。如 75%稀疏度时每张卡会选择 25%的梯度进行通信，卡数为 32 时通信量是正常 dense 通信的 32\*(1-0.75)=8 倍，所以前几个 epoch 使用正常的 dense 通信为佳。可参照如下写法 -``` python -# 1. 以 1252 个 step 为一个 epoch，前 2 个 epochs 使用正常 dense 通信，后 3 个 epochs 逐步提升稀疏度为 99.9% -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=1252*2, - rampup_step=1252*3, sparsity=[0.984375, 0.996, 0.999]) -# 2. 前面 4 个 epochs 都使用 dense 通信，之后默认 0.999 稀疏度运行 -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=1252*4) -``` -对于 Fine-tuning 训练，现测试可无需预热训练，从第 0 个 epoch 直接使用 DGC 即可。 -``` python -# 从第 0 步开始 DGC 稀疏通信 -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=0) -``` -### 3.2 适用场景 -DGC 稀疏通信在低带宽通信瓶颈时会有较大的性能提升，但在单机多卡及 RDMA 网络通信并非瓶颈情况下，并不会带来性能上的提升。同时由于 AllGather 的通信量会随卡数的增多而增大，所以 DGC 的多机训练规模也不宜过大。故 DGC 适用于低配网络，同时节点规模不宜过大，如>128 张卡。在云网络或高带宽网络设备昂贵时，DGC 可有效降低训练成本。 - -## 4. 原理 -本节原理部分基本来自[Deep Gradient Compression](https://arxiv.org/abs/1712.01887)论文，本文进行了部分理解翻译，英文较好者建议直接阅读论文。 -### 4.1 梯度稀疏 -DGC 的基本思路是通过只传送重要梯度，即只发送大于给定阈值的梯度来减少通信带宽的使用。为避免信息的丢失，DGC 会将剩余梯度在局部累加起来，最终这些梯度会累加大到足以传输。 -换个角度，从理论依据上来看，局部梯度累加等同于随时间推移增加 batch size，（DGC 相当于每一个梯度有自己的 batch size）。设定 $F(w)$ 为需要优化的 loss 函数，则有着 N 个训练节点的同步分布式 SGD 更新公式如下 -$$ -F(w)=\\frac{1}{\|\\chi\|}\\sum\_{x\\in\\chi}f(x, w), \\qquad w\_{t+1}=w\_{t}-\\eta\\frac{1}{N b}\\sum\_{k=0}^{N}\\sum\_{x\\in\\mathcal{B}\_{k,t}}\\nabla f\\left(x, w\_{t}\\right) \\tag{1} -$$ -其中$\chi$是训练集，$w$是网络权值，$f(x, w)$是每个样本$x \in \chi$的 loss，$\eta$是学习率，N 是训练节点个数，$\mathcal{B}_{k, t}$代表第$k$个节点在第$t$个迭代时的 minibatch，大小为 b。 -考虑权重的第 i 个值，在 T 次迭代后，可获得 -$$ -w\_{t+T}^{(i)}=w\_{t}^{(i)}-\\eta T \\cdot \\frac{1}{N b T} \\sum\_{k=1}^{N}\\left(\\sum\_{\\tau=0}^{T-1} \\sum\_{x \\in \\mathcal{B}\_{k, t+\\tau}} \\nabla^{(i)} f\\left(x, w\_{t+\\tau}\\right)\\right) \\tag{2} -$$ -等式 2 表明局部梯度累加可以被认为 batch size 从$Nb$增大为$NbT$，其中 T 是$w^{(i)}$两次更新的稀疏通信间隔。 -### 4.2 局部梯度累加改进 -正常情况，稀疏更新会严重影响收敛性。DGC 中采用动量修正(Momentum Correction)和局部梯度裁减(local gradient clipping)来解决这个问题。 -#### 4.2.1 动量修正 -有着 N 个节点分布式训练中 vanilla momentum SGD 公式， -$$ -u\_{t}=m u\_{t-1}+\\sum\_{k=1}^{N}\\left(\\nabla\_{k, t}\\right), \\quad w\_{t+1}=w\_{t}-\\eta u\_{t} \\tag{3} -$$ -其中$m$是动量因子，$N$是节点数，$\nabla_{k, t}=\frac{1}{N b} \sum_{x \in \mathcal{B}_{k, t}} \nabla f\left(x, w_{t}\right)$。 -考虑第 i 个权重$w^{(i)}$，在 T 次迭代后，权重更新公式如下， -$$ -w\_{t+T}^{(i)}=w\_{t}^{(i)}-\\eta\\left[\\cdots+\\left(\\sum\_{\\tau=0}^{T-2} m^{\\tau}\\right) \\nabla\_{k, t+1}^{(i)}+\\left(\\sum\_{\\tau=0}^{T-1} m^{\\tau}\\right) \\nabla\_{k, t}^{(i)}\\right] \\tag{4} -$$ -如果直接应用动量 SGD 到稀疏梯度更新中，则有公式， -$$ -v_{k, t}=v_{k, t-1}+\\nabla_{k, t}, \\quad u_{t}=m u_{t-1}+\\sum_{k=1}^{N} \\operatorname{sparse}\\left(v_{k, t}\\right), \\quad w_{t+1}=w_{t}-\\eta u_{t} \\tag{5} -$$ -其中$v_k$是训练节点 k 上的局部梯度累加项，一旦$v_k$大于某一阈值，则会在第二项中压缩梯度进行动量更新，并使用 sparse()函数获得 mask 清空大于阈值的梯度。 -$w^{(i)}$在 T 次稀疏更新后的权重为, -$$ -w_{t+T}^{(i)}=w_{t}^{(i)}-\\eta\\left(\\cdots+\\nabla_{k, t+1}^{(i)}+\\nabla_{k, t}^{(i)}\\right) \\tag{6} -$$ -相比传统动量 SGD，方程 6 缺失了累积衰减因子$\sum_{\tau=0}^{T-1} m^{\tau}$，会导致收敛精度的损失。如下图 A，正常梯度更新从 A 点到 B 点，但是方程 6 则从 A 点到 C 点。当稀疏度很高时，会显著降低模型性能，所以需要在方程 5 基础上对梯度进行修正。 -
- - -
-若将方程 3 中速度项$u_t$当作“梯度”，则方程 3 第二项可认为是在”梯度“$u_t$上应用传统 SGD，前面已经证明了局部梯度累加在传统 SGD 上是有效的。因此，可以使用方程 3 局部累加速度项$u_t$而非累加真实的梯度$\nabla_{k, t}$来修正方程 5， -$$ -u_{k, t}=m u_{k, t-1}+\\nabla_{k, t}, \\quad v_{k, t}=v_{k, t-1}+u_{k, t}, \\quad w_{t+1}=w_{t}-\\eta \\sum_{k=1}^{N} \\operatorname{sparse}\\left(v_{k, t}\\right) \\tag{7} -$$ -修正后，如上图(b)，方程可正常从 A 点到 B 点。除了传统动量方程修正，论文还给出了 Nesterov 动量 SGD 的修正方程。 -#### 4.2.2 局部梯度修剪 -梯度修剪是防止梯度爆炸的常用方法。这方法由 Pascanu 等人在 2013 年提出，当梯度的 l2-norms 和大于给定阈值时，就对梯度 rescale。正常梯度修剪在梯度聚合后使用，而 DGC 因为每个节点独立的进行局部梯度累加，所以 DGC 在使用$G_t$累加前对其进行局部梯度修剪。阈值缩放为原来的$N^{-1/2}$ -$$ -thr_{G^{k}}=N^{-1 / 2} \\cdot thr_{G} \\tag{8} -$$ -### 4.3 克服迟滞效应 -因为推迟了较小梯度更新权重的时间，所以会有权重陈旧性问题。稀疏度为 99.9%时大部分参数需 600 到 1000 步更新一次。迟滞效应会减缓收敛并降低模型精度。DGC 中采用动量因子掩藏和预热训练来解决这问题。 -#### 4.3.1 动量因子掩藏 -DGC 中使用下面方程来掩藏动量因子减缓陈旧性问题。 -$$ -Mask \\leftarrow\\left|v_{k, t}\\right|>t h r, \\quad v_{k, t} \\leftarrow v_{k, t} \\odot \\neg Mask, \\quad u_{k, t} \\leftarrow u_{k, t} \\odot \\neg Mask \\tag{9} -$$ -此掩码可以停止延迟梯度产生的动量，防止陈旧梯度把权重引入错误的方向。 - -#### 4.3.2 预热训练 -在训练初期，梯度变动剧烈，需要及时更新权重，此时迟滞效应影响会很大。为此 DGC 采用预热训练的方法，在预热期间使用更小的学习率来减缓网络的变化速度，并使用较小的稀疏度来减少需推迟更新的梯度数量。预热期间会线性增大学习率，指数型增加稀疏度到最终值。 - -### 4.4 正则化(Weight Decay)项修正 -Paddle 框架以 Weight Decay 的形式实现正则化。以 L2Decay 为例，公式(3)中传统 momentum 添加 weight decay 后公式为 -$$ -G_{t}=\\sum_{k=1}^{N}\\left(\\nabla_{k, t}\\right)+\\lambda w_{t}, \\quad u_{t}=m u_{t-1}+G_{t}, \\quad w_{t+1}=w_{t}-\\eta u_{t} \\tag{10} -$$ -其中$\lambda$为 Weight Decay 系数，$G_{t}$为添加 L2Decay 项之后的聚合梯度。由于在公式 7 中进行了局部动量修正，所以按照相同思路在局部梯度上运用修正的 Weight Decay 项。如下公式在局部梯度上添加局部 Weight Decay 项即可。 -$$ -\\nabla_{k, t}=\\nabla_{k, t}+\\frac{\\lambda}{N} w_{t} \\tag{11} -$$ -在模型实际训练中，通常会设置 weight decay 的系数$\lambda=10^{-4}$，在卡数较多如 4 机 32 卡的情况下局部 weight decay 系数为$\frac{\lambda}{N}=\frac{10^{-4}}{32}=3.125*10^{-6}$，在数值精度上偏低，测试训练时会损失一定精度。为此还需对局部 weight decay 项进行数值修正。如下公式， -$$ -\\nabla_{k, t}^{'}=N \\nabla_{k, t}+\\lambda w_{t}, \\quad -G_{t}^{'}=\\sum_{k=1}^{N}\\left(\\nabla_{k, t}^{'}\\right)=N\\sum_{k=1}^{N}\\left(\\nabla_{k, t}\\right)+N\\lambda w_{t}, \\quad -G_{t}=\\frac{G_{t}^{'}}{N}=\\sum_{k=1}^{N}\\left(\\nabla_{k, t}\\right)+\\lambda w_{t} \\tag{12} -$$ -具体做法为对局部梯度乘以卡数求得$\nabla_{k, t}^{'}$，此时$\lambda$项则无需除以卡数，聚合梯度求得$G_{t}^{'}$再对聚合梯度除以卡数得到$G_{t}$即可。 diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute.rst deleted file mode 100644 index ad16707dd87..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute.rst +++ /dev/null @@ -1,160 +0,0 @@ - -重计算：大 Batch 训练特性 -============= - -背景 ---------- - -随着训练数据规模的逐渐增加，训练更大、更深的深度学习模型成为一个主流趋势。目前的深度学习模型训练，通常要求保留前向计算的隐层结果，并且需要保存结果的数量会随着模型层数的增加线性增加，这对于目前能够使用的 AI 芯片的内存大小是个挑战。Forward Recomputation Backpropagation（FRB）可以在额外增加少量计算的情况下，显著增加模型的层数和宽度，同时也可以显著提升模型训练的 batch 大小。 - -原理 ---------- - -我们知道，深度学习网络的一次训练迭代包含三个步骤： - -- **前向计算**：运行前向算子(Operator) 来计算中间隐层(Variable)的值 -- **反向计算**：运行反向算子来计算参数(Parameter)的梯度 -- **优化**：应用优化算法以更新参数值 - -在前向计算过程中，前向算子会输出大量的中间计算结果，在 Paddle 中，使用 -Variable 来存储这些隐层的中间结果。当模型层数加深时，其数量可达成千上万个， -占据大量的内存。Paddle 的 `显存回收机制 `_ -会及时清除无用的中间结果，以节省存储。 -然而，有些中间结果是反向算子的输入，这些 Variable 必须存储在内存中，直到相应的反向算子计算完毕。 - -举个简单的例子, 我们定义一个由 mul 算子构成的网络，其前向计算为： - -.. math:: - - y = W_1 * x - - z = W_2 * y - -其中 :math:`x, y, z` 为向量， :math:`W_1, W_2` 为矩阵。容易知道，求 :math:`W_2` 梯度的反向计算为： - -.. math:: - W_{2}^{'} = z^{'} / y - -可以看到反向计算中用到了前向计算生成的变量 :math:`y` ，因此变量 :math:`y` 必须存储在内存中，直到这个反向算子计算完毕。当模型加深时，我们会有大量的“ :math:`y` ”，占据了大量的内存。 - -Forward Recomputation Backpropagation（FRB）的思想是将深度学习网络切分为 k 个部分（segments）。对每个 segment 而言：前向计算时，除了小部分必须存储在内存中的 Variable 外(我们后续会讨论这些特殊 Variable)，其他中间结果都将被删除；在反向计算中，首先重新计算一遍前向算子，以获得中间结果，再运行反向算子。简而言之，FRB 和普通的网络迭代相比，多计算了一遍前向算子。 - -我们把切分网络的变量叫做 checkpoints。 -那么问题来了，如何选择 checkpoints 呢？自从 FRB 方法提出以来 \ :sup:`[1], [2]`，大量学者在研究这一关键问题。 -我们知道深度学习网络通常是由一个个模块串联得到的，比如 ResNet-50 由 16 个 block 串联而成， -Bert-Large 由 24 个 transformer 串联而成，以两个子模块中间的变量作为切分点就是一个很好的选择。 -对于非串联的网络（比如含有大量 shortcut 结构的网络），FRB 也支持对其做切分， -只是可能多耗费一点内存（用于存储 shortcut 的 Variable）。 -Mitsuru Kusumoto \ :sup:`[3]` 等提出了一种基于动态规划的算法， -可以根据指定的内存自动搜索合适的 checkpoints，支持各种各样的网络结构。 - -下图是由 4 个 fc Layer、3 个 relu Layer、1 个 sigmoid Layer 和 1 个 log-loss Layer 串联而成的一个网络：最左侧为其前向计算流程、中间是普通的前向计算和反向计算流程、最右侧为添加 FRB 后的前向计算和反向计算流程。其中方框代表算子(Operator)，红点代表前向计算的中间结果、蓝点代表 checkpoints。 - -.. image:: images/recompute.png - -注：该例子完整代码位于 `source `_ - -添加 FRB 后，前向计算中需要存储的中间 Variable 从 4 个(红点)变为 2 个(蓝点)， -从而节省了这部分内存。当然了，重计算的部分也产生了新的中间变量， -这就需要根据实际情况来做权衡了。这个例子里的网络比较浅，通常来讲， -对层数较深的网络，FRB 节省的内存要远多于新增加的内存。 - -使用方法 ---------- - -我们实现了基于 Paddle 的 FRB 算法，叫做 RecomputeOptimizer， -您可以根据其 `源码 `_ -与 -`文档 `_ -更深入地了解这一算法。我们为用户提供了两个使用 RecomputeOptimizer 的方法: -直接调用与 Fleet API 中使用。在单机单卡或者 CPU 训练中建议您直接调用 RecomputeOptimizer， -在多卡训练或者多机训练任务上建议您在 Fleet API 中使用 Recompute。 - -**1. 直接调用** - -直接调用 RecomputeOptimizer 非常简单，首先要定义一个经典的 Optimizer，比如 Adam； -然后在外面包一层 RecomputeOptimizer；最后设置 checkpoints 即可。 - -.. code-block:: python - - import paddle.fluid as fluid - # 定义网络 - def mlp(input_x, input_y, hid_dim=128, label_dim=2): - print(input_x) - fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) - prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') - cost = fluid.layers.cross_entropy(input=prediction, label=input_y) - sum_cost = fluid.layers.reduce_mean(cost) - return sum_cost, fc_1, prediction - input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') - input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') - cost, fc_1, pred = mlp(input_x, input_y) - # 定义 RecomputeOptimizer - sgd = fluid.optimizer.Adam(learning_rate=0.01) - sgd = fluid.optimizer.RecomputeOptimizer(sgd) - # 设置 checkpoints - sgd._set_checkpoints([fc_1, pred]) - # 运行优化算法 - sgd.minimize(cost) - -Recompute 原则上适用于所有 Optimizer。 - -**2. 在 Fleet API 中使用 Recompute** - -`Fleet API `_ -是基于 Fluid 的分布式计算高层 API。在 Fleet API 中添加 RecomputeOptimizer -仅需要 2 步： - -- 设置 dist_strategy.forward_recompute 为 True； - -- 设置 dist_strategy.recompute_checkpoints。 - -.. code-block:: python - - from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy - dist_strategy = DistributedStrategy() - dist_strategy.forward_recompute = True - dist_strategy.recompute_checkpoints=checkpoints - optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) - optimizer.minimize(loss) - -为了帮助您快速地用 Fleet API 使用 Recompute 任务，我们提供了一些例子， -并且给出了这些例子的计算速度、效果和显存节省情况： - -- 用 Recompute 做 Bert Fine-tuning: `source `_ - -- 用 Recompute 做目标检测：开发中. - -Q&A -------- - -- **是否支持带有随机性的 Op？** - - 目前 Paddle 中带随机性的 Op 有：dropout，Recompute 支持 - dropout Operator，可以保证重计算与初次计算结果保持一致。 - -- **有没有更多 Recompute 的官方例子？** - - 更多 Recompute 的例子将更新在 `examples `_ - 和 `Fleet `_ 库下，欢迎关注。 - -- **有没有添加 checkpoints 的建议？** - - 我们建议将子网络连接部分的变量添加为 checkpoints，即： - 如果一个变量能将网络完全分为前后两部分，那么建议将其加入 checkpoints。 - checkpoints 的数目会影响内存的消耗：如果 checkpoints 很少， - 那么 Recompute 起的作用有限；如果 checkpoints 数量过多， - 那么 checkpoints 本身占用的内存量就较大，内存消耗可能不降反升。 - - 我们后续会添加一个估算内存用量的工具， - 可以对每个 Operator 运算前后的显存用量做可视化， - 帮助用户定位问题。 - -[1] Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin . Training deep nets with sublinear memory cost. -arXiv preprint, arXiv:1604.06174, 2016. - -[2] Audrunas Gruslys , Rémi Munos , Ivo Danihelka , Marc Lanctot , and Alex Graves. Memory efficient -backpropagation through time. In Advances in Neural Information Processing Systems (NIPS), pages 4125 4133, -2016. - -[3] Kusumoto, Mitsuru, et al. "A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation." arXiv preprint arXiv:1905.11722 (2019). diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute_en.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute_en.rst deleted file mode 100644 index d9a556bb74c..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute_en.rst +++ /dev/null @@ -1,196 +0,0 @@ - -Recompute: Training with bigger batch size -============= - -Context ---------- - -As the amount of training data increases, training deeper neural network models becomes more and more popular. Current deep-learning training usually keeps the hidden layer outputs in memory during the forward propagation, -and the number of outputs increases linearly with -the increase of the number of model layers, -which becomes a challenge of the memory size -for common devices. - - -Theory ---------- - -As we know, a training process of a deep-learning network contains 3 steps: - -- **Forward Propagation**：Running forward operators and generate temporary variables as output -- **Backward Propagation**：Running backward operators to compute gradients of parameters -- **Optimization**：Applying optimization algorithm to update parameters - -When the model becomes deeper, the number of temporary variables -generated in the forward propagation process can reach tens -of thousands, occupying a large amount of memory. -The `Garbage Collection mechanism `_ -in Paddle can delete useless variables for the sake of saving memory. -However, some variables serve as inputs of backward operators, -they must be kept in memory until particular operator finish. - -Take a simple example, define a network contains two `mul` operators, -the forward propagation works as follows: - -.. math:: - - y = W_1 * x - - z = W_2 * y - -where :math:`x, y, z` are vectors， :math:`W_1, W_2` are matrix。It is easy to conduct that the gradient of :math:`W_2` is: - -.. math:: - W_{2}^{'} = z^{'} / y - -We can see that :math:`y` is used in the backward propagation process, -thus it must be kept in the memory during the whole forward propagation. -When network grows deeper, more 'y's need to be stored, -adding more requirements to the memory. - -Forward Recomputation Backpropagation(FRB) splits a deep network to k segments. -For each segment, in forward propagation, -most of the temporary variables are erased in time, -except for some special variables (we will talk about that later); -in backward propagation, the forward operators will be recomputed -to get these temporary variables before running backward operators. -In short, FBR runs forward operators twice. - -But how to split the network? A deep learning network usually consists -of connecting modules in series: -ResNet-50 contains 16 blocks and Bert-Large contains 24 transformers. -It is a good choice to treat such modules as segments. -The variables among segments are -called as checkpoints. - -The following picture is a network with 4 fc layers, 3 relu layers, -1 sigmoid layer and 1 log-loss layer in series. -The left column is the forward propagation, -the middle column is the normal backward propagation, -and the right column is the FRB. -Rectangular boxes represent the operators, red dots represent -the intermediate variables in forward computation, blue dots -represent checkpoints and arrows represent the dependencies between operators. - -.. image:: images/recompute.png - -Note: the complete source code of this example: `source `_ - -After applying FBR, the forward computation only needs to store -2 variables (the blue dots) instead of 4 variables (the red -dots), saving the corresponding memories. It is notable that -recomputing operators generate new intermediate variables at the same time, -a trade-off needs to be considered in this situation. -While according to our experiments, -FBR usually saves rather than increase the memory load. - -Usage ---------- - -We have implemented the FRB algorithm named "RecomputeOptimizer" -based on Paddle. More information about this algorithm can -be learned by the `source code `_ -and the -`document `_ -of RecomputeOptimizer. - -There are 2 methods to apply RecomputeOptimizer in your Paddle -program: call RecomputeOptimizer directly or use it with Fleet -API. For single-GPU card training or CPU training, we recommend -directly calling; For multi-GPU training, we -recommend using with Fleet API. - -**1. Directly calling** - -Calling RecomputeOptimizer is very easy: first, define a classic -optimizer, such as Adam; second, wrap it with RecomputeOptimizer; -third, set the checkpoints. - -.. code-block:: python - - import paddle.fluid as fluid - # Define the network - def mlp(input_x, input_y, hid_dim=128, label_dim=2): - print(input_x) - fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) - prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') - cost = fluid.layers.cross_entropy(input=prediction, label=input_y) - sum_cost = fluid.layers.reduce_mean(cost) - return sum_cost, fc_1, prediction - input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') - input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') - cost, fc_1, pred = mlp(input_x, input_y) - # define RecomputeOptimizer - sgd = fluid.optimizer.Adam(learning_rate=0.01) - sgd = fluid.optimizer.RecomputeOptimizer(sgd) - # set checkpoints - sgd._set_checkpoints([fc_1, pred]) - # apply optimization - sgd.minimize(cost) - -In principle, recompute is for all kinds of optimizers in Paddle. - -**2. Using Recompute in Fleet API** - -`Fleet API `_ -is a high-level API for distributed training in Fluid. Adding -RecomputeOptimizer to Fluid takes two steps: - -- set dist_strategy.forward_recompute to True - -- set dist_strategy.recompute_checkpoints - -.. code-block:: python - - from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy - dist_strategy = DistributedStrategy() - dist_strategy.forward_recompute = True - dist_strategy.recompute_checkpoints=checkpoints - optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) - optimizer.minimize(loss) - -We supply some examples of using recompute in Fleet API for users. -We also post corresponding training speed, -test results and memory usages of these examples for reference. - - -- Fine-tuning Bert Large model with recomputing: `source `_ - -- Training object detection models with recomputing：developing. - -Q&A -------- - -- **Does RecomputeOptimizer support operators with random outputs?** - -We currently found that the dropout operator has random results -and RecomputeOptimizer is able to keep the outputs of -first-computation and recomputation consistent. - - -- **Are there more official examples of Recompute?** - - More examples will be updated at `examples `_ -and `Fleet `_ . Feel free to -raise issues if you get any problem with these examples. - -- **How should I set checkpoints?** - -The position of checkpoints is important: -we suggest setting the variable between the sub-model as checkpoints, -that is, set a variable as a checkpoint if it -can separate the network into two parts without short-cut connections. -The number of checkpoints is also important: -too few checkpoints will reduce the memory saved by recomputing while -too many checkpoints will occupy a lot of memory themselves. -We will add a tool to estimate the memory usage with specific checkpoints, -helping users to choose checkpointing variables. - -[1] Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin . Training deep nets with sublinear memory cost. -arXiv preprint, arXiv:1604.06174, 2016. - -[2] Audrunas Gruslys , Rémi Munos , Ivo Danihelka , Marc Lanctot , and Alex Graves. Memory efficient -backpropagation through time. In Advances in Neural Information Processing Systems (NIPS), pages 4125 4133, -2016. - -[3] Kusumoto, Mitsuru, et al. 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ztz>fH3k*sC4%MoPZvtOt2E3dXxCB17cHvo@`MWQK$x1@I3CMm3(sSrPJ7sbaB5aUl zk(}wV6}VI>q`Sz5K!^!=>;x(t^d>qup6Wlrh@OnW$!*1g12YVhwRWgOZ(N-q&9wMu z^fYNvM?EFbRdya>+N~HV6nLw*YCuGU#5$ho%G=ih?`ArFEvE~V>jc6W|4nP%`s_sc b43>ZLdbWM0`Z=>47=Xaj)z4*}Q$iB}?8Sbc diff --git a/docs/api/copy_codes_from_en_doc.py b/docs/api/copy_codes_from_en_doc.py index 39e4ed107ac..f484b6528ba 100644 --- a/docs/api/copy_codes_from_en_doc.py +++ b/docs/api/copy_codes_from_en_doc.py @@ -244,6 +244,7 @@ def instert_codes_into_cn_rst_if_need(cnrstfilename): rst_lines, copy_from_info = read_rst_lines_and_copy_info(cnrstfilename) update_needed = False pattern_doctest = re.compile(r"\s*>>>\s*#\s*doctest:\s*.*") + pattern_typing_ignore = re.compile(r"# type:[^#]*") if copy_from_info: logger.info( @@ -266,6 +267,11 @@ def instert_codes_into_cn_rst_if_need(cnrstfilename): cb_new.append("") indent += 4 for line in cb_need["codes"].splitlines(): + # remove `# type: ignore ...` + line = pattern_typing_ignore.sub("", line) + if line.strip() == ">>>": + continue + if not pattern_doctest.match(line): cb_new.append(" " * indent + line) diff --git a/docs/api/gen_alias_api.py b/docs/api/gen_alias_api.py index ce245308e6a..171417f493e 100644 --- a/docs/api/gen_alias_api.py +++ b/docs/api/gen_alias_api.py @@ -87,9 +87,9 @@ def _find_real_api_by_grep_file(self, api_list): obj = eval(api) if inspect.isclass(obj): - reg = "class %s(" % api.split(".")[-1] + reg = "class {}(".format(api.split(".")[-1]) elif inspect.isfunction(obj): - reg = "def %s(" % api.split(".")[-1] + reg = "def {}(".format(api.split(".")[-1]) shell_cmd = f"find {self.paddle_root_path} -name '*.py' | xargs grep \"{reg}\" " diff --git a/docs/api/paddle/DataParallel_cn.rst b/docs/api/paddle/DataParallel_cn.rst index 884870c0cc3..dea7f30d2c3 100644 --- a/docs/api/paddle/DataParallel_cn.rst +++ b/docs/api/paddle/DataParallel_cn.rst @@ -46,17 +46,17 @@ COPY-FROM: paddle.DataParallel:dp-example :::::::::::: COPY-FROM: paddle.DataParallel:dp-pylayer-example -.. py:function:: no_sync() +方法 +:::::::::::: +no_sync() +''''''''' 用于暂停梯度同步的上下文管理器。在 no_sync()中参数梯度只会在模型上累加；直到 with 之外的第一个 forward-backward，梯度才会被同步。 -代码示例 -:::::::::::: +**代码示例** COPY-FROM: paddle.DataParallel.no_sync -方法 -:::::::::::: state_dict(destination=None, include_sublayers=True) ''''''''' diff --git a/docs/api/paddle/Overview_cn.rst b/docs/api/paddle/Overview_cn.rst index 8667aaa066a..0705f61847e 100755 --- a/docs/api/paddle/Overview_cn.rst +++ b/docs/api/paddle/Overview_cn.rst @@ -21,6 +21,7 @@ paddle 目录下包含 tensor、device、framework 相关 API 以及某些高层 - :ref:`device 相关 ` - :ref:`高层 API 相关 ` - :ref:`稀疏 API 相关 ` +- :ref:`环境变量 FLAGS 相关 ` @@ -67,6 +68,8 @@ tensor 数学操作 " :ref:`paddle.expm1 ` ", "逐元素进行 exp(x)-1 运算" " :ref:`paddle.floor ` ", "向下取整函数" " :ref:`paddle.floor_divide ` ", "逐元素整除算子，输入 x 与输入 y 逐元素整除，并将各个位置的输出元素保存到返回结果中" + " :ref:`paddle.fmax ` ", "返回 x 与 y 逐元素的最大值构成的新 Tensor，若遇任一 NaN，取另一值；两 NaN 则取首 NaN" + " :ref:`paddle.fmin ` ", "返回 x 与 y 逐元素的最小值构成的新 Tensor，若遇任一 NaN，取另一值；两 NaN 则取首 NaN" " :ref:`paddle.gammaincc ` ", "计算正则化上不完全伽玛函数" " :ref:`paddle.gammainc ` ", "计算正则化下不完全伽玛函数" " :ref:`paddle.gammaln ` ", "逐元素计算输入 x 的伽马函数的绝对值的自然对数" @@ -81,12 +84,14 @@ tensor 数学操作 " :ref:`paddle.log ` ", "Log 激活函数（计算自然对数）" " :ref:`paddle.log10 ` ", "Log10 激活函数（计算底为 10 的对数）" " :ref:`paddle.log2 ` ", "计算 Log1p（加一的自然对数）结果" + " :ref:`paddle.logaddexp ` ", "计算 ``x`` 和 ``y`` 的以 e 为底的指数的和的自然对数" " :ref:`paddle.logcumsumexp ` ", "计算 x 的指数的前缀和的对数" " :ref:`paddle.logical_and ` ", "逐元素的对 x 和 y 进行逻辑与运算" " :ref:`paddle.logical_not ` ", "逐元素的对 X Tensor 进行逻辑非运算" " :ref:`paddle.logical_or ` ", "逐元素的对 X 和 Y 进行逻辑或运算" " :ref:`paddle.logical_xor ` ", "逐元素的对 X 和 Y 进行逻辑异或运算" " :ref:`paddle.logit ` ", "计算 logit 结果" + " :ref:`paddle.logspace ` ", "返回区间 :math:`[base^{start}, base^{stop}]` 内固定数量的对数均匀分布的值" " :ref:`paddle.bitwise_and ` ", "逐元素的对 x 和 y 进行按位与运算" " :ref:`paddle.bitwise_not ` ", "逐元素的对 X Tensor 进行按位取反运算" " :ref:`paddle.bitwise_or ` ", "逐元素的对 X 和 Y 进行按位或运算" @@ -111,6 +116,10 @@ tensor 数学操作 " :ref:`paddle.ldexp ` ", "计算 x 乘以 2 的 y 次幂" " :ref:`paddle.multigammaln ` ", "计算多元伽马函数的对数" " :ref:`paddle.nan_to_num ` ", "替换 x 中的 NaN、+inf、-inf 为指定值" + " :ref:`paddle.quantile ` ", "沿给定的轴 ``axis`` 计算 ``x`` 中元素的分位数" + " :ref:`paddle.nanmean ` ", "沿 ``axis`` 计算 ``x`` 的平均值，且忽略掉 ``NaNs`` 值" + " :ref:`paddle.nanquantile ` ", "沿给定的轴 ``axis`` 计算 ``x`` 中元素的分位数, 忽略元素中的 ``NaN``" + " :ref:`paddle.nansum ` ", "计算给定轴上的元素之和，并将非数字元素（NaNs）视为 0" " :ref:`paddle.neg ` ", "计算输入 x 的相反数并返回" " :ref:`paddle.nextafter ` ", "逐元素将 x 之后的下一个浮点值返回" " :ref:`paddle.not_equal ` ", "逐元素地返回 x!=y 的逻辑值" @@ -124,6 +133,7 @@ tensor 数学操作 " :ref:`paddle.signbit ` ", "对输入 x 的每个元素符号位进行判断" " :ref:`paddle.sgn ` ", "对输入 x 中每个元素进行正负判断，对于复数则输出单位向量" " :ref:`paddle.sin ` ", "计算输入的正弦值" + " :ref:`paddle.sinc ` ", "计算输入的归一化 sinc 值" " :ref:`paddle.sinh ` ", "双曲正弦函数" " :ref:`paddle.sqrt ` ", "计算输入的算数平方根" " :ref:`paddle.square ` ", "逐元素取平方运算" @@ -164,6 +174,7 @@ tensor 数学操作 " :ref:`paddle.hypot ` ", "对输入 直角三角形的直角边 Tensor x, y， 计算其斜边" " :ref:`paddle.combinations ` ", "对输入 Tensor 计算长度为 r 的情况下的所有组合" " :ref:`paddle.select_scatter ` ", "根据 axis 和 index（整数） 填充 value 值至输入 Tensor" + " :ref:`paddle.reduce_as ` ", "对 x 在某些维度上求和，使其结果与 target 的 shape 一致" .. _tensor_math_inplace: tensor 数学操作原位（inplace）版本 @@ -175,6 +186,7 @@ tensor 数学操作原位（inplace）版本 " :ref:`paddle.remainder_ ` ", "Inplace 版本的 remainder API，对输入 x 采用 Inplace 策略" " :ref:`paddle.abs_ ` ", "Inplace 版本的 abs API，对输入 x 采用 Inplace 策略" + " :ref:`paddle.bernoulli_ ` ", "Inplace 版本的 bernoulli API，对输入 x 采用 Inplace 策略" " :ref:`paddle.tanh_ ` ", "Inplace 版本的 tanh API，对输入 x 采用 Inplace 策略" " :ref:`paddle.erf_ ` ", "Inplace 版本的 erf API，对输入 x 采用 Inplace 策略" " :ref:`paddle.erfinv_ ` ", "Inplace 版本的 erfinv API，对输入 x 采用 Inplace 策略" @@ -197,6 +209,7 @@ tensor 数学操作原位（inplace）版本 " :ref:`paddle.sqrt_ ` ", "Inplace 版本的 sqrt API，对输入 x 采用 Inplace 策略" " :ref:`paddle.square_ ` ", "Inplace 版本的 square API，对输入 x 采用 Inplace 策略" " :ref:`paddle.sin_ ` ", "Inplace 版本的 sin API，对输入 x 采用 Inplace 策略" + " :ref:`paddle.sinc_ ` ", "Inplace 版本的 sinc API，对输入 x 采用 Inplace 策略" " :ref:`paddle.sinh_ ` ", "Inplace 版本的 sinh API，对输入 x 采用 Inplace 策略" " :ref:`paddle.subtract_ ` ", "Inplace 版本的 subtract API，对输入 x 采用 Inplace 策略" " :ref:`paddle.tan_ ` ", "Inplace 版本的 tan API，对输入 x 采用 Inplace 策略" @@ -212,6 +225,7 @@ tensor 数学操作原位（inplace）版本 " :ref:`paddle.index_fill_ ` ", "Inplace 版本的 index_fill API，对输入 x 采用 Inplace 策略" " :ref:`paddle.bitwise_left_shift_ ` ", "Inplace 版本的 bitwise_left_shift API，对输入 x 采用 Inplace 策略" " :ref:`paddle.bitwise_right_shift_ ` ", "Inplace 版本的 bitwise_right_shift API，对输入 x 采用 Inplace 策略" + " :ref:`paddle.log_normal_ ` ", "Inplace 版本的 log_normal API，对输入 x 采用 Inplace 策略" .. _tensor_logic: @@ -226,8 +240,12 @@ tensor 逻辑操作 " :ref:`paddle.is_empty ` ", "测试变量是否为空" " :ref:`paddle.is_tensor ` ", "用来测试输入对象是否是 paddle.Tensor" " :ref:`paddle.isfinite ` ", "返回输入 tensor 的每一个值是否为 Finite（既非 +/-INF 也非 +/-NaN ）" + " :ref:`paddle.isin ` ", "测试输入 x 的每一个值是否在 test_x 中" " :ref:`paddle.isinf ` ", "返回输入 tensor 的每一个值是否为 +/-INF" " :ref:`paddle.isnan ` ", "返回输入 tensor 的每一个值是否为 +/-NaN" + " :ref:`paddle.isposinf ` ", "返回输入 tensor 的每一个值是否为 +INF" + " :ref:`paddle.isneginf ` ", "返回输入 tensor 的每一个值是否为 -INF" + " :ref:`paddle.isreal ` ", "返回输入 tensor 的每一个值是否为实数类型" .. _tensor_attribute: @@ -259,6 +277,7 @@ tensor 创建相关 " :ref:`paddle.arange ` ", "返回以步长 step 均匀分隔给定数值区间[start, end)的 1-D Tensor，数据类型为 dtype" " :ref:`paddle.diag ` ", "如果 x 是向量（1-D Tensor），则返回带有 x 元素作为对角线的 2-D 方阵；如果 x 是矩阵（2-D Tensor），则提取 x 的对角线元素，以 1-D Tensor 返回。" + " :ref:`paddle.diag_embed ` ", "创建一个 Tensor，其在指定的 2D 平面（由 ``dim1`` 和 ``dim2`` 指定）上的对角线由输入 ``input`` 填充" " :ref:`paddle.diagflat ` ", "如果 x 是一维 Tensor，则返回带有 x 元素作为对角线的二维方阵；如果 x 是大于等于二维的 Tensor，则返回一个二维 Tensor，其对角线元素为 x 在连续维度展开得到的一维 Tensor 的元素。" " :ref:`paddle.empty ` ", "创建形状大小为 shape 并且数据类型为 dtype 的 Tensor" " :ref:`paddle.empty_like ` ", "根据 x 的 shape 和数据类型 dtype 创建未初始化的 Tensor" @@ -296,6 +315,7 @@ tensor 元素查找相关 " :ref:`paddle.argsort ` ", "对输入变量沿给定轴进行排序，输出排序好的数据的相应索引，其维度和输入相同" " :ref:`paddle.index_sample ` ", "对输入 x 中的元素进行批量抽样" " :ref:`paddle.index_select ` ", "沿着指定轴 axis 对输入 x 进行索引" + " :ref:`paddle.kthvalue ` ", "在指定的轴上查找第 k 小的元素和其对应所在的索引信息" " :ref:`paddle.masked_select ` ", "返回一个 1-D 的 Tensor, Tensor 的值是根据 mask 对输入 x 进行选择的" " :ref:`paddle.nonzero ` ", "返回输入 x 中非零元素的坐标" " :ref:`paddle.sort ` ", "对输入变量沿给定轴进行排序，输出排序好的数据，其维度和输入相同" @@ -326,6 +346,7 @@ tensor random 相关 " :ref:`paddle.bernoulli ` ", "以输入 x 为概率，生成一个伯努利分布（0-1 分布）的 Tensor，输出 Tensor 的形状和数据类型与输入 x 相同" " :ref:`paddle.binomial ` ", "以输入 count 为总实验次数， prob 为实验成功的概率，生成一个二项分布的 Tensor" + " :ref:`paddle.log_normal ` ", "返回符合对数正态分布（对应正态分布的均值为 mean ，标准差为 std）的随机 Tensor" " :ref:`paddle.multinomial ` ", "以输入 x 为概率，生成一个多项分布的 Tensor" " :ref:`paddle.normal ` ", "返回符合正态分布（均值为 mean ，标准差为 std 的正态随机分布）的随机 Tensor" " :ref:`paddle.rand ` ", "返回符合均匀分布的，范围在[0, 1)的 Tensor" @@ -359,10 +380,13 @@ tensor 线性代数相关 " :ref:`paddle.rank ` ", "计算输入 Tensor 的维度（秩）" " :ref:`paddle.t ` ", "对小于等于 2 维的 Tensor 进行数据转置" " :ref:`paddle.tril ` ", "返回输入矩阵 input 的下三角部分，其余部分被设为 0" + " :ref:`paddle.tril_indices ` ", "返回行数和列数已知的二维矩阵中下三角矩阵元素的行列坐标" " :ref:`paddle.triu ` ", "返回输入矩阵 input 的上三角部分，其余部分被设为 0" " :ref:`paddle.triu_indices ` ", "返回输入矩阵在给定对角线右上三角部分元素坐标" " :ref:`paddle.cdist ` ", "计算两组输入集合 x, y 中每对之间的 p 范数" " :ref:`paddle.pdist ` ", "计算输入形状为 N x M 的 Tensor 中 N 个向量两两组合(pairwise)的 p 范数" + " :ref:`paddle.renorm ` ", "计算沿轴的 p 范数" + .. _tensor_manipulation: @@ -375,6 +399,7 @@ tensor 元素操作相关（如：转置，reshape 等） " :ref:`paddle.broadcast_to ` ", "根据 shape 指定的形状广播 x ，广播后， x 的形状和 shape 指定的形状一致" " :ref:`paddle.broadcast_tensors ` ", "对一组输入 Tensor 进行广播操作, 输入应符合广播规范" + " :ref:`paddle.cartesian_prod ` ", "对输入 Tensor 序列进行笛卡尔积操作" " :ref:`paddle.cast ` ", "将输入的 x 的数据类型转换为 dtype 并输出" " :ref:`paddle.chunk ` ", "将输入 Tensor 分割成多个子 Tensor" " :ref:`paddle.concat ` ", "对输入沿 axis 轴进行联结，返回一个新的 Tensor" @@ -443,6 +468,8 @@ tensor 元素操作相关原位（inplace）版本 :header: "API 名称", "API 功能" :widths: 10, 30 + " :ref:`paddle.index_add_ ` ", "Inplace 版本的 :ref:`cn_api_paddle_index_add` API，对输入 `x` 采用 Inplace 策略" + " :ref:`paddle.index_put_ ` ", "依据索引 ``indices`` ，将指定位置的 ``x`` 重新赋值为 ``value`` " " :ref:`paddle.reshape_ ` ", "Inplace 版本的 reshape API，对输入 x 采用 Inplace 策略" " :ref:`paddle.scatter_ ` ", "Inplace 版本的 scatter API，对输入 x 采用 Inplace 策略 " " :ref:`paddle.squeeze_ ` ", "Inplace 版本的 squeeze API，对输入 x 采用 Inplace 策略" @@ -475,11 +502,13 @@ framework 相关 " :ref:`paddle.NPUPlace ` ", "一个设备描述符，指 NCPUPlace 则 Tensor 将被自动分配在该设备上，并且模型将会运行在该设备上" " :ref:`paddle.disable_signal_handler ` ", "关闭 Paddle 系统信号处理方法" " :ref:`paddle.disable_static ` ", "关闭静态图模式" + " :ref:`paddle.enable_grad ` ", "创建一个上下文来启用动态图梯度计算" " :ref:`paddle.enable_static ` ", "开启静态图模式" " :ref:`paddle.get_default_dtype ` ", "得到当前全局的 dtype" " :ref:`paddle.get_rng_state ` ", "获取指定设备的随机数生成器的所有随机状态。" " :ref:`paddle.grad ` ", "对于每个 inputs ，计算所有 outputs 相对于其的梯度和" " :ref:`paddle.in_dynamic_mode ` ", "查看 paddle 当前是否在动态图模式中运行" + " :ref:`paddle.LazyGuard ` ", "用于设置模型（继承自 ``paddle.nn.Layer`` ） 中参数延迟初始化的上下文管理器" " :ref:`paddle.load ` ", "从指定路径载入可以在 paddle 中使用的对象实例" " :ref:`paddle.no_grad ` ", "创建一个上下文来禁用动态图梯度计算" " :ref:`paddle.ParamAttr ` ", "创建一个参数属性对象" @@ -513,3 +542,15 @@ device 相关 " :ref:`paddle.Model ` ", "一个具备训练、测试、推理的神经网络" " :ref:`paddle.summary ` ", "打印网络的基础结构和参数信息" " :ref:`paddle.flops ` ", "打印网络的基础结构和参数信息" + +.. _about_flags_api: + +环境变量 FLAGS 相关 +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`paddle.get_flags ` ", "获取指定的 Paddle 环境变量 FLAGS 状态。详情请查看 :ref:`cn_guides_flags_flags`" + " :ref:`paddle.set_flags ` ", "设置 Paddle 环境变量 FLAGS，详情请查看 :ref:`cn_guides_flags_flags`" diff --git a/docs/api/paddle/Tensor/Overview_en.rst b/docs/api/paddle/Tensor/Overview_en.rst index 2a7b526909a..9326c1de430 100644 --- a/docs/api/paddle/Tensor/Overview_en.rst +++ b/docs/api/paddle/Tensor/Overview_en.rst @@ -79,6 +79,7 @@ Methods astype atan backward + bernoulli_ bitwise_and bitwise_not bitwise_or @@ -152,8 +153,12 @@ Methods is_empty is_tensor isfinite + isin isinf isnan + isneginf + isposinf + isreal item kron less_equal @@ -168,6 +173,7 @@ Methods logical_not logical_or logical_xor + log_normal_ logsumexp masked_select matmul @@ -229,6 +235,8 @@ Methods sign sgn sin + sinc + sinc_ sinh slice solve diff --git a/docs/api/paddle/Tensor_cn.rst b/docs/api/paddle/Tensor_cn.rst index b42252f841d..cf46634ec2a 100755 --- a/docs/api/paddle/Tensor_cn.rst +++ b/docs/api/paddle/Tensor_cn.rst @@ -654,6 +654,11 @@ bincount(weights=None, minlength=0) 请参考 :ref:`cn_api_paddle_bincount` +bernoulli_(p=0.5, name=None) +::::::::: + +Inplace 版本的 :ref:`cn_api_paddle_bernoulli` API，对输入 `x` 采用 Inplace 策略。 + bitwise_and(y, out=None, name=None) ::::::::: @@ -1474,6 +1479,15 @@ isfinite(name=None) 请参考 :ref:`cn_api_paddle_isfinite` +isin(test_x, assume_unique=False, invert=False, name=None) +::::::::: + +返回：计算后的 Tensor + +返回类型：Tensor + +请参考 :ref:`cn_api_paddle_isin` + isinf(name=None) ::::::::: @@ -1492,6 +1506,33 @@ isnan(name=None) 请参考 :ref:`cn_api_paddle_isnan` +isposinf(name=None) +::::::::: + +返回：计算后的 Tensor + +返回类型：Tensor + +请参考 :ref:`cn_api_paddle_isposinf` + +isneginf(name=None) +::::::::: + +返回：计算后的 Tensor + +返回类型：Tensor + +请参考 :ref:`cn_api_paddle_isneginf` + +isreal(name=None) +::::::::: + +返回：计算后的 Tensor + +返回类型：Tensor + +请参考 :ref:`cn_api_paddle_isreal` + kthvalue(k, axis=None, keepdim=False, name=None) ::::::::: @@ -1634,6 +1675,11 @@ logsumexp(axis=None, keepdim=False, name=None) 返回类型：Tensor +log_normal_(mean=0.0, std=1.0, name=None) +::::::::: + +Inplace 版本的 :ref:`cn_api_paddle_log_normal` API，对输入 `x` 采用 Inplace 策略。 + 请参考 :ref:`cn_api_paddle_logsumexp` masked_select(mask, name=None) @@ -3206,3 +3252,26 @@ signbit(x, name=None) 返回类型：Tensor 请参考 :ref:`cn_api_paddle_signbit` + +block_diag(inputs, name=None) +::::::::: + +根据 `inputs` 创建对角矩阵。 + +返回：对角矩阵 Tensor。 + +返回类型：Tensor + +请参考 :ref:`cn_api_paddle_block_diag` + +ormqr(x, tau, other, left=True, transpose=False) +::::::::: + +计算维度为(m, n)的矩阵 C（由 :attr:`other` 给出）和一个矩阵 Q 的乘积， +其中 Q 由 Householder 反射系数 (:attr:`x`, :attr:`tau`) 表示。 + +返回：乘积 Tensor。 + +返回类型：Tensor + +请参考 :ref:`cn_api_paddle_linalg_ormqr` diff --git a/docs/api/paddle/amp/Overview_cn.rst b/docs/api/paddle/amp/Overview_cn.rst index 3f0835c0129..5e169e47f94 100644 --- a/docs/api/paddle/amp/Overview_cn.rst +++ b/docs/api/paddle/amp/Overview_cn.rst @@ -8,6 +8,7 @@ paddle.amp 目录下包含飞桨框架支持的动态图自动混合精度(AMP) - :ref:`AMP 相关 API ` - :ref:`开启 AMP 后默认转化为 float16 计算的相关 OP ` - :ref:`开启 AMP 后默认使用 float32 计算的相关 OP ` +- :ref:`AMP 场景下判断设备是否支持特定数据类型 ` paddle.amp 目录下包含 debugging 目录， debugging 目录中存放用于算子模型精度问题定位的 API。具体如下： @@ -63,6 +64,18 @@ AMP 相关 API "cross_entropy", "交叉熵" "cross_entropy2", "交叉熵" +.. _about_supported: + +AMP 场景下判断设备是否支持特定数据类型 +::::::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`is_bfloat16_supported ` ", "判断设备是否支持 bfloat16" + " :ref:`is_float16_supported ` ", "判断设备是否支持 float16" + .. _about_debugging: Debug 相关辅助类 @@ -84,6 +97,7 @@ Debug 相关辅助类 :header: "API 名称", "API 功能" :widths: 10, 30 + " :ref:`collect_operator_stats ` ", "收集不同数据类型的算子调用次数" " :ref:`enable_operator_stats_collection ` ", "启用以收集不同数据类型的算子调用次数" " :ref:`disable_operator_stats_collection ` ", "禁用收集不同数据类型的算子调用次数" @@ -99,3 +113,15 @@ Debug 相关辅助类 " :ref:`enable_tensor_checker ` ", "开启模块级别的精度检查" " :ref:`disable_tensor_checker ` ", "关闭模块级别的精度检查" " :ref:`compare_accuracy ` ", "精度比对接口" + +.. _about_amp_numerics_check_api: + +数值检查相关 API +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`check_layer_numerics ` ", "Layer 输入、输出数据的数值检查" + " :ref:`check_numerics ` ", "调试 Tensor 数值，检查其异常值(NaN、Inf) 和零元素" diff --git a/docs/api/paddle/argsort_cn.rst b/docs/api/paddle/argsort_cn.rst index b72a6b431e2..195058413d5 100644 --- a/docs/api/paddle/argsort_cn.rst +++ b/docs/api/paddle/argsort_cn.rst @@ -3,7 +3,7 @@ argsort ------------------------------- -.. py:function:: paddle.argsort(x, axis=-1, descending=False, name=None) +.. py:function:: paddle.argsort(x, axis=-1, descending=False, stable=False, name=None) 对输入变量沿给定轴进行排序，输出排序好的数据的相应索引，其维度和输入相同。默认升序排列，如果需要降序排列设置 ``descending=True`` 。 @@ -15,6 +15,7 @@ argsort - **x** (Tensor) - 输入的多维 ``Tensor``，支持的数据类型：float16、bfloat16、float32、float64、int16、int32、int64、uint8。 - **axis** (int，可选) - 指定对输入 Tensor 进行运算的轴，``axis`` 的有效范围是 [-R, R)，R 是输入 ``x`` 的 Rank， ``axis`` 为负时与 ``axis`` + R 等价。默认值为 -1。 - **descending** (bool，可选) - 指定算法排序的方向。如果设置为 True，算法按照降序排序。如果设置为 False 或者不设置，按照升序排序。默认值为 False。 + - **stable** (bool，可选) - 是否使用稳定排序算法。若设置为 True，则使用稳定排序算法，即相同元素的顺序在排序结果中将会被保留。默认值为 False，此时的算法不一定是稳定排序算法。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/audio/save_cn.rst b/docs/api/paddle/audio/save_cn.rst index 6fca39e246e..88ec8fc8bad 100644 --- a/docs/api/paddle/audio/save_cn.rst +++ b/docs/api/paddle/audio/save_cn.rst @@ -3,19 +3,19 @@ save ------------------------------- -.. py:function:: paddle.audio.save(filepath: str, src: paddle.Tensor, sample_rate: int, channels_first: bool = True, encoding: Optional[str] = None, bits_per_sample: Optional[int] = 16) +.. py:function:: paddle.audio.save(filepath, src, sample_rate, channels_first=True, encoding=None, bits_per_sample=16) 保存音频数据。 参数 :::::::::::: - - **filepath** (str 或者 Path) - 保存音频路径。 - - **src** (paddle.Tensor) - 音频数据。 + - **filepath** (str) - 保存音频路径。 + - **src** (Tensor) - 音频数据。 - **sample_rate** (int) - 采样率。 - - **channels_first** (bool，可选) - 如果是 True，那么 src 的 Tensor 形状是[channel，time]，如果是 False，则是[time，channel]。 - - **encoding** (Optional[str]，可选) - 默认是 None，编码信息。 - - **bits_per_sample** (Optional[int]，可选) - 默认是 16，编码位长。 + - **channels_first** (bool，可选) - 如果是 True，那么 src 的 Tensor 形状是[channel，time]，如果是 False，则是[time，channel]。默认是 True。 + - **encoding** (str|None，可选) - 默认是 None，编码信息。 + - **bits_per_sample** (int|None，可选) - 默认是 16，编码位长。 返回 ::::::::: 无 diff --git a/docs/api/paddle/autograd/Overview_cn.rst b/docs/api/paddle/autograd/Overview_cn.rst new file mode 100644 index 00000000000..64b63255d13 --- /dev/null +++ b/docs/api/paddle/autograd/Overview_cn.rst @@ -0,0 +1,36 @@ +.. _cn_overview_autograd: + +paddle.autograd +--------------------- + +paddle.autograd 目录下包含飞桨框架支持的自动微分相关的 API 和类。具体如下： + +- :ref:`自动微分相关 API ` +- :ref:`自动微分相关辅助类 ` + +.. _about_autograd: + +自动微分相关 API +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`backward ` ", "计算给定的 Tensors 的反向梯度" + " :ref:`hessian ` ", "计算因变量 ``ys`` 对 自变量 ``xs`` 的海森矩阵" + " :ref:`jacobian ` ", "计算因变量 ``ys`` 对 自变量 ``xs`` 的雅可比矩阵" + " :ref:`saved_tensors_hooks ` ", "用于动态图中为保存的 Tensor 注册一对 pack / unpack hook" + + +.. _about_autograd_class: + +自动微分相关辅助类 +:::::::::::::::::::: + +.. csv-table:: + :header: "类名称", "辅助类功能" + :widths: 10, 30 + + " :ref:`PyLayer ` ", "通过创建 ``PyLayer`` 子类的方式实现 Python 端自定义算子" + " :ref:`PyLayerContext ` ", "``PyLayerContext`` 对象能够辅助 :ref:`cn_api_paddle_autograd_PyLayer` 实现某些功能" diff --git a/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst b/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst index d184a17bd8f..ffb2c2310c3 100644 --- a/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst +++ b/docs/api/paddle/autograd/saved_tensors_hooks_cn.rst @@ -18,7 +18,12 @@ saved_tensors_hooks 用于动态图，注册一对 pack / unpack hook，用于 无 -代码示例 +代码示例 1 :::::::::::: -COPY-FROM: paddle.autograd.saved_tensors_hooks +COPY-FROM: paddle.autograd.saved_tensors_hooks:code-example1 + +代码示例 2 +:::::::::::: + +COPY-FROM: paddle.autograd.saved_tensors_hooks:code-example2 diff --git a/docs/api/paddle/bernoulli__cn.rst b/docs/api/paddle/bernoulli__cn.rst new file mode 100644 index 00000000000..99370f9f26b --- /dev/null +++ b/docs/api/paddle/bernoulli__cn.rst @@ -0,0 +1,12 @@ +.. _cn_api_paddle_bernoulli_: + +bernoulli\_ +------------------------------- + +.. py:function:: paddle.bernoulli_(x, p=0.5, name=None) + +Inplace 版本的 :ref:`cn_api_paddle_bernoulli` API，对输入 x 采用 Inplace 策略。 + +更多关于 inplace 操作的介绍请参考 `3.1.3 原位（Inplace）操作和非原位操作的区别`_ 了解详情。 + +.. _3.1.3 原位（Inplace）操作和非原位操作的区别: https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/tensor_cn.html#id3 diff --git a/docs/api/paddle/block_diag_cn.rst b/docs/api/paddle/block_diag_cn.rst new file mode 100644 index 00000000000..28852dbf908 --- /dev/null +++ b/docs/api/paddle/block_diag_cn.rst @@ -0,0 +1,23 @@ +.. _cn_api_paddle_block_diag: + +block_diag +------------------------------- + +.. py:function:: paddle.block_diag(inputs, name=None) + +根据 `inputs` 创建对角矩阵。 + +参数 +::::::::: + - **inputs** (list|tuple) - 是一个 Tensor 列表或 Tensor 元组，其子项为 0、1、2 维的 Tensor 。数据类型为： `bool`、 `float16`、 `float32`、 `float64`、 `uint8`、 `int8`、 `int16`、 `int32`、 `int64`、 `bfloat16`、 `complex64`、 `complex128`。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 `None`。 + +返回 +::::::::: + +Tensor， 与 ``inputs`` 数据类型相同。 + +代码示例 +::::::::: + +COPY-FROM: paddle.block_diag diff --git a/docs/api/paddle/cartesian_prod_cn.rst b/docs/api/paddle/cartesian_prod_cn.rst new file mode 100644 index 00000000000..a8af9bd3c13 --- /dev/null +++ b/docs/api/paddle/cartesian_prod_cn.rst @@ -0,0 +1,28 @@ +.. _cn_api_paddle_cartesian_prod: + +cartesian_prod +------------------------------- + +.. py:function:: paddle.cartesian_prod(x, name=None) + + +对指定的 tensor 序列进行笛卡尔积操作。该行为类似于 python 标准库中的 itertools.product 方法。 +相当于将所有的输入 tensors 转换为列表后，对其使用 itertools.product 方法，最终将返回的列表转换为 tensor。 + + +参数 +::::::::: + + - **x** (list[Tensor]|tuple[Tensor]) – 任意数量的 1-D Tensor 序列，支持的数据类型：bfloat16、float16、float32、float64、int32、int64、complex64、complex128。 + + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: + +笛卡尔积运算后的 Tensor，数据类型与输入 Tensor 相同。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.cartesian_prod diff --git a/docs/api/paddle/cross_cn.rst b/docs/api/paddle/cross_cn.rst index aba805db1b0..570cd67ffa9 100644 --- a/docs/api/paddle/cross_cn.rst +++ b/docs/api/paddle/cross_cn.rst @@ -12,8 +12,8 @@ cross 参数 ::::::::: - - **x** (Tensor) - 第一个输入 Tensor，数据类型为：float16、float32、float64、int32、int64。 - - **y** (Tensor) - 第二个输入 Tensor，数据类型为：float16、float32、float64、int32、int64。 + - **x** (Tensor) - 第一个输入 Tensor，数据类型为：float16、float32、float64、int32、int64、complex64、complex128。 + - **y** (Tensor) - 第二个输入 Tensor，数据类型为：float16、float32、float64、int32、int64、complex64、complex128。 - **axis** (int，可选) - 沿着此维进行向量积操作。默认值是 9，意思是选取第一个长度为 3 的 ``axis`` 。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/device/Overview_cn.rst b/docs/api/paddle/device/Overview_cn.rst new file mode 100644 index 00000000000..e6817718e8c --- /dev/null +++ b/docs/api/paddle/device/Overview_cn.rst @@ -0,0 +1,116 @@ +.. _cn_overview_device: + +paddle.device +============================ + +`paddle.device` 模块提供了一系列与设备相关的 API，用于管理和配置计算设备。具体如下： + +- :ref:`设备设置与属性获取 ` +- :ref:`编译环境检测 ` +- :ref:`设备描述符 ` +- :ref:`Stream 与 Event 辅助类 ` +- :ref:`Stream 与 Event 相关 API ` + +paddle.device 目录下包含 cuda 目录， cuda 目录中存放 CUDA 相关的 API。具体如下： + +- :ref:`CUDA 相关 ` + +.. _cn_device_setting: + +设备设置与属性获取 +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`get_all_custom_device_type ` ", "获得所有可用的自定义设备类型" + " :ref:`get_all_device_type ` ", "获得所有可用的设备类型" + " :ref:`get_available_custom_device ` ", "获得所有可用的自定义设备" + " :ref:`get_available_device ` ", "获得所有可用的设备" + " :ref:`get_cudnn_version ` ", "获得 cudnn 的版本" + " :ref:`set_device ` ", "指定 OP 运行的全局设备" + " :ref:`get_device ` ", "获得 OP 运行的全局设备" + +.. _cn_device_compile: + +编译环境检测 +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`is_compiled_with_cinn ` ", "检查 ``whl`` 包是否可以被用来在 CINN 上运行模型" + " :ref:`is_compiled_with_cuda ` ", "检查 ``whl`` 包是否可以被用来在 GPU 上运行模型" + " :ref:`is_compiled_with_custom_device ` ", "检查 ``whl`` 包是否可以被用来在指定类型的自定义新硬件上运行模型" + " :ref:`is_compiled_with_ipu ` ", "检查 ``whl`` 包是否可以被用来在 Graphcore IPU 上运行模型" + " :ref:`is_compiled_with_mlu ` ", "检查 ``whl`` 包是否可以被用来在 Cambricon MLU 上运行模型" + " :ref:`is_compiled_with_npu ` ", "检查 ``whl`` 包是否可以被用来在 NPU 上运行模型" + " :ref:`is_compiled_with_rocm ` ", "检查 ``whl`` 包是否可以被用来在 AMD 或海光 GPU(ROCm) 上运行模型" + " :ref:`is_compiled_with_xpu ` ", "检查 ``whl`` 包是否可以被用来在 Baidu Kunlun XPU 上运行模型" + +.. _cn_device_descriptor: + +设备描述符 +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`IPUPlace ` ", "``IPUPlace`` 是一个设备描述符，指定 ``IPUPlace`` 则模型将会运行在该设备上" + " :ref:`MLUPlace ` ", "``MLUPlace`` 是一个设备描述符，指定 ``MLUPlace`` 则模型将会运行在该设备上" + " :ref:`XPUPlace ` ", "``XPUPlace`` 是一个设备描述符，表示一个分配或将要分配 ``Tensor`` 的 Baidu Kunlun XPU 设备" + +.. _cn_device_stream_event: + +Stream 与 Event 辅助类 +:::::::::::::::::::: + +.. csv-table:: + :header: "类名称", "辅助类功能" + :widths: 10, 30 + + " :ref:`Stream ` ", "``StreamBase`` 的设备流包装器" + " :ref:`Event ` ", "``StreamBase`` 的设备事件包装器" + + +.. _cn_device_stream_event_api: + +Stream 与 Event 相关 API +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`current_stream ` ", "通过 device 返回当前的 stream" + " :ref:`set_stream ` ", "设置当前的 stream" + " :ref:`stream_guard ` ", "切换当前的 stream 为输入指定的 stream，该 API 目前仅支持动态图模式" + " :ref:`synchronize ` ", "等待给定的设备上的计算完成" + + +.. _cn_device_cuda: + +CUDA 相关 +:::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 10, 30 + + " :ref:`Stream ` ", "CUDA ``StreamBase`` 的设备流包装器，该 API 未来计划废弃，不推荐使用" + " :ref:`Event ` ", "CUDA ``StreamBase`` 的设备事件包装器，该 API 未来计划废弃，不推荐使用" + " :ref:`current_stream ` ", "通过 device 返回当前的 CUDA stream" + " :ref:`device_count ` ", "返回值是 int，表示当前程序可用的 GPU 数量" + " :ref:`empty_cache ` ", "用于释放显存分配器中空闲的显存" + " :ref:`get_device_capability ` ", "获取 CUDA 设备计算能力的主要和次要修订号" + " :ref:`get_device_name ` ", "获取 CUDA 设备名称" + " :ref:`get_device_properties ` ", "获取 CUDA 设备属性" + " :ref:`max_memory_allocated ` ", "返回给定设备上分配给 Tensor 的显存峰值" + " :ref:`max_memory_reserved ` ", "返回给定设备上由 Allocator 管理的显存峰值" + " :ref:`memory_allocated ` ", "返回给定设备上当前分配给 Tensor 的显存大小" + " :ref:`memory_reserved ` ", "返回给定设备上当前由 Allocator 管理的显存大小" + " :ref:`stream_guard ` ", "切换当前的 CUDA stream 为输入指定的 stream，该 API 目前仅支持动态图模式" + " :ref:`synchronize ` ", "等待给定的 CUDA 设备上的计算完成" diff --git a/docs/api/paddle/distributed/Overview_cn.rst b/docs/api/paddle/distributed/Overview_cn.rst index c4ec8c53b8e..6c41ddcf0ec 100644 --- a/docs/api/paddle/distributed/Overview_cn.rst +++ b/docs/api/paddle/distributed/Overview_cn.rst @@ -12,6 +12,7 @@ paddle.distributed 目录包含的 API 支撑飞桨框架大规模分布式训 - :ref:`Stream 集合通信高级 API <05>` - :ref:`RPC API <06>` - :ref:`自动并行 API <07>` +- :ref:`Sharding API <08>` .. _01: @@ -39,6 +40,7 @@ Fleet 分布式高层 API " :ref:`UtilBase ` ", "分布式训练工具的基类，用户集合通信、文件系统操作" " :ref:`utils.HDFSClient ` ", "Hadoop 文件系统查看和管理" " :ref:`utils.LocalFS ` ", "本地文件系统查看和管理" + " :ref:`utils.recompute ` ", "重新计算中间激活函数值来节省显存" .. _02: @@ -49,6 +51,7 @@ Fleet 分布式高层 API :header: "API 名称", "API 功能" :widths: 20, 50 + " :ref:`is_initialized ` ", "检查分布式环境是否已经被初始化" " :ref:`is_available ` ", "检查分布式环境是否可用" " :ref:`init_parallel_env ` ", "初始化并行训练环境，支持动态图模式" " :ref:`launch ` ", "启动分布式训练进程，支持集合通信及参数服务器架构" @@ -56,8 +59,12 @@ Fleet 分布式高层 API " :ref:`get_rank ` ", "获取当前进程的 rank 值" " :ref:`get_world_size ` ", "获取当前进程数" " :ref:`new_group ` ", "创建分布式通信组" + " :ref:`get_group ` ", "通过通信组 id 获取通信组实例" " :ref:`destroy_process_group ` ", "销毁分布式通信组" " :ref:`get_backend ` ", "获取指定分布式通信组后端的名称" + " :ref:`gloo_init_parallel_env ` ", "初始化 ``GLOO`` 上下文用于 CPU 间的通信" + " :ref:`gloo_release ` ", "释放当前并行环境的 gloo 上下文" + " :ref:`ParallelEnv ` ", "这个类用于获取动态图模型并行执行所需的环境变量值" .. _03: @@ -100,6 +107,8 @@ Fleet 分布式高层 API " :ref:`send ` ", "发送一个 tensor 到指定进程" " :ref:`recv ` ", "接收一个来自指定进程的 tensor" " :ref:`barrier ` ", "同步路障，阻塞操作以实现组内进程同步" + " :ref:`gloo_barrier ` ", "使用初始化的 gloo 上下文直接调用基于 gloo 封装的 barrier 函数" + " :ref:`wait ` ", "同步通信组，在指定的通信组中同步特定的 tensor 对象" .. _05: @@ -142,7 +151,6 @@ RPC API " :ref:`rpc.get_all_worker_infos ` ", "获取所有 worker 的信息" " :ref:`rpc.get_current_worker_info ` ", "获取当前 worker 的信息" - .. _07: 自动并行 API @@ -155,7 +163,23 @@ RPC API :widths: 20, 50 " :ref:`shard_tensor ` ", "创建带有分布式切分信息的分布式 Tensor" + " :ref:`dtensor_from_fn ` ", "通过一个 paddle API 结合分布式属性 placements 创建一个带分布式属性的 Tensor" " :ref:`shard_layer ` ", "按照指定方式将 Layer 中的参数转换为分布式 Tensor" " :ref:`reshard `", "对一个带有分布式信息的 Tensor 重新进行分布/切片" " :ref:`to_static `", "将带有分布式切分信息的动态图模型转换为静态图分布式模型" " :ref:`Strategy `", "配置静态图分布式训练时所使用的并行策略和优化策略" + " :ref:`DistAttr ` ", "指定 Tensor 在 ProcessMesh 上的分布或切片方式" + " :ref:`shard_optimizer ` ", "将单卡视角的优化器转变为分布式视角" + " :ref:`split ` ", "切分指定操作的参数到多个设备，并且并行计算得到结果" + +.. _08: + +Sharding API +:::::::::::::::::::::::::: + +.. csv-table:: + :header: "API 名称", "API 功能" + :widths: 20, 50 + + " :ref:`sharding.group_sharded_parallel `", "对模型、优化器和 GradScaler 做 group sharded 配置" + " :ref:`sharding.save_group_sharded_model `", "对 group_sharded_parallel 配置后的模型和优化器状态进行保存" diff --git a/docs/api/paddle/distributed/destroy_process_group_cn.rst b/docs/api/paddle/distributed/destroy_process_group_cn.rst index bfc3e17ae13..17140d84d78 100644 --- a/docs/api/paddle/distributed/destroy_process_group_cn.rst +++ b/docs/api/paddle/distributed/destroy_process_group_cn.rst @@ -4,7 +4,7 @@ destroy_process_group ------------------------------- -.. py:function:: destroy_process_group(group=None) +.. py:function:: paddle.distributed.destroy_process_group(group=None) 销毁一个指定的通信组。 diff --git a/docs/api/paddle/distributed/fleet/DistributedStrategy_cn.rst b/docs/api/paddle/distributed/fleet/DistributedStrategy_cn.rst index 949ce91d54e..6f0ddde3dd7 100755 --- a/docs/api/paddle/distributed/fleet/DistributedStrategy_cn.rst +++ b/docs/api/paddle/distributed/fleet/DistributedStrategy_cn.rst @@ -28,21 +28,10 @@ load_from_prototxt COPY-FROM: paddle.distributed.fleet.DistributedStrategy.load_from_prototxt -execution_strategy -''''''''' - -`Post Local SGD `__ - -配置 DistributedStrategy 中的 `ExecutionStrategy `_ - -**代码示例** - -COPY-FROM: paddle.distributed.fleet.DistributedStrategy.execution_strategy - build_strategy ''''''''' -配置 DistributedStrategy 中的 `BuildStrategy `_ +配置 DistributedStrategy 中的 `BuildStrategy `_ **代码示例** diff --git a/docs/api/paddle/distributed/fleet/Fleet_cn.rst b/docs/api/paddle/distributed/fleet/Fleet_cn.rst index 595aabd6e0d..a2db6cd6473 100644 --- a/docs/api/paddle/distributed/fleet/Fleet_cn.rst +++ b/docs/api/paddle/distributed/fleet/Fleet_cn.rst @@ -9,6 +9,18 @@ Fleet Fleet 是飞桨分布式训练统一 API，只需要 import fleet 并简单初始化后即可快速开始使用飞桨大规模分布式训练 +返回 +::::::::: +返回一个类型为 Fleet 的实例 + +**代码示例 1** + +COPY-FROM: paddle.distributed.fleet.Fleet:code-example1 + +**代码示例 2** + +COPY-FROM: paddle.distributed.fleet.Fleet:code-example2 + 方法 :::::::::::: init(role_maker=None, is_collective=False, strategy=None) diff --git a/docs/api/paddle/distributed/get_group_cn.rst b/docs/api/paddle/distributed/get_group_cn.rst index 668b86e312f..7ddaf3c33dd 100644 --- a/docs/api/paddle/distributed/get_group_cn.rst +++ b/docs/api/paddle/distributed/get_group_cn.rst @@ -3,7 +3,7 @@ get_group ------------------------------- -.. py:function:: get_group(id=0) +.. py:function:: paddle.distributed.get_group(id=0) 通过通信组 id 获取通信组实例 diff --git a/docs/api/paddle/distributed/get_rank_cn.rst b/docs/api/paddle/distributed/get_rank_cn.rst index 5a37b888dd9..5bb5f2cb177 100644 --- a/docs/api/paddle/distributed/get_rank_cn.rst +++ b/docs/api/paddle/distributed/get_rank_cn.rst @@ -3,7 +3,7 @@ get_rank ---------- -.. py:function:: paddle.distributed.get_rank(group=None) +.. py:function:: paddle.distributed.get_rank(group=None) 返回当前进程在给定通信组下的 rank，rank 是在 [0, world_size) 范围内的连续整数。如果没有指定通信组，则默认使用全局通信组。 diff --git a/docs/api/paddle/distributed/new_group_cn.rst b/docs/api/paddle/distributed/new_group_cn.rst index a93b92079ac..5004d9aaab6 100644 --- a/docs/api/paddle/distributed/new_group_cn.rst +++ b/docs/api/paddle/distributed/new_group_cn.rst @@ -4,7 +4,7 @@ new_group ------------------------------- -.. py:function:: new_group(ranks=None, backend=None) +.. py:function:: paddle.distributed.new_group(ranks=None, backend=None) 创建分布式通信组。 diff --git a/docs/api/paddle/distributed/rpc/rpc_async_cn.rst b/docs/api/paddle/distributed/rpc/rpc_async_cn.rst index cecd5e68fea..cbcdf032c6a 100644 --- a/docs/api/paddle/distributed/rpc/rpc_async_cn.rst +++ b/docs/api/paddle/distributed/rpc/rpc_async_cn.rst @@ -6,7 +6,7 @@ rpc_async .. py:function:: paddle.distributed.rpc.rpc_async(to, fn, args=None, kwargs=None, timeout=-1) -发起一个非阻塞的 RPC 调用，在 to 上运行函数 fn +发起一个非阻塞的 RPC 调用，在 to 上运行函数 fn。注意：请用户务必在安全的网络环境下使用本功能。 参数 ::::::::: diff --git a/docs/api/paddle/distributed/rpc/rpc_sync_cn.rst b/docs/api/paddle/distributed/rpc/rpc_sync_cn.rst index fc96f8e9bb8..a3dd044d564 100644 --- a/docs/api/paddle/distributed/rpc/rpc_sync_cn.rst +++ b/docs/api/paddle/distributed/rpc/rpc_sync_cn.rst @@ -5,7 +5,7 @@ rpc_sync .. py:function:: paddle.distributed.rpc.rpc_sync(to, fn, args=None, kwargs=None, timeout=-1) -发起一个阻塞的 RPC 调用，在 to 上运行函数 fn +发起一个阻塞的 RPC 调用，在 to 上运行函数 fn。注意：请用户务必在安全的网络环境下使用本功能。 参数 ::::::::: diff --git a/docs/api/paddle/distributed/to_static_cn.rst b/docs/api/paddle/distributed/to_static_cn.rst index 2f4b1d965cf..091359cde18 100644 --- a/docs/api/paddle/distributed/to_static_cn.rst +++ b/docs/api/paddle/distributed/to_static_cn.rst @@ -3,7 +3,7 @@ to_static ------------------------------- -.. py:function:: to_static(layer, loader, loss=None, optimizer=None, strategy=None) +.. py:function:: paddle.distributed.to_static(layer, loader, loss=None, optimizer=None, strategy=None) 将带有分布式切分信息的动态图 ``layer`` 转换为静态图分布式模型, 可在静态图模式下进行分布式训练；同时将动态图下所使用的数据迭代器 ``loader`` 转换为静态图分布式训练所使用的数据迭代器。 diff --git a/docs/api/paddle/distributed/unshard_dtensor_cn.rst b/docs/api/paddle/distributed/unshard_dtensor_cn.rst index 66efcc7dd23..55f71aef0e4 100644 --- a/docs/api/paddle/distributed/unshard_dtensor_cn.rst +++ b/docs/api/paddle/distributed/unshard_dtensor_cn.rst @@ -3,7 +3,7 @@ unshard_dtensor ------------------------------- -.. py:function:: unshard_dtensor(dist_tensor) +.. py:function:: paddle.distributed.unshard_dtensor(dist_tensor) 将带有分布式信息的分布式 Tensor 转换为普通 Tensor。 diff --git a/docs/api/paddle/distributed/wait_cn.rst b/docs/api/paddle/distributed/wait_cn.rst index 0d2373e97ec..49193a0abcb 100644 --- a/docs/api/paddle/distributed/wait_cn.rst +++ b/docs/api/paddle/distributed/wait_cn.rst @@ -4,7 +4,7 @@ wait ------------------------------- -.. py:function:: wait(tensor, group=None, use_calc_stream=True) +.. py:function:: paddle.distributed.wait(tensor, group=None, use_calc_stream=True) 同步通信组 diff --git a/docs/api/paddle/distribution/Chi2_cn.rst b/docs/api/paddle/distribution/Chi2_cn.rst new file mode 100644 index 00000000000..8398e76d608 --- /dev/null +++ b/docs/api/paddle/distribution/Chi2_cn.rst @@ -0,0 +1,116 @@ +.. _cn_api_paddle_distribution_Chi2: + +Chi2 +------------------------------- +.. py:class:: paddle.distribution.Chi2(df) + + +卡方分布是基于标准正态分布随机变量的平方和定义的一种连续概率分布。如果有 *k* 个独立的标准正态分布随机变量 +:math:`Z_1, Z_2, \ldots, Z_k`, 那么这些随机变量的平方和 +:math:`X = Z_1^2 + Z_2^2 + \cdots + Z_k^2` + +服从自由度为 *k* 的卡方分布，记作 +:math:`X \sim \chi^2(k)` + + + +参数 +:::::::::::: + + - **df** (float|Tensor) - 参数表示自由度，该值必须大于零。支持 Broadcast 语义。当参数类型为 Tensor 时，表示批量创建多个不同参数的分布，``batch_shape`` (参考 :ref:`cn_api_paddle_distribution_Distribution` 基类) 为参数。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.distribution.Chi2 + + +属性 +::::::::: + +mean +''''''''' +卡方分布的均值。 + + +variance +''''''''' +卡方分布的方差。 + + +方法 +::::::::: + +prob(value) +''''''''' +卡方分布的概率密度函数。 + +**参数** + + - **value** (float|Tensor) - 输入值。 + + +**返回** + + - **Tensor** - value 对应的概率密度。 + + +log_prob(value) +''''''''' +卡方分布的对数概率密度函数。 + +**参数** + + - **value** (float|Tensor) - 输入值。 + +**返回** + + - **Tensor** - value 对应的对数概率密度。 + + +entropy() +''''''''' +卡方分布的信息熵。 + +**返回** + + - Tensor: 信息熵。 + + +kl_divergence(other) +''''''''' +两个卡方分布之间的 KL 散度。 + +**参数** + + - **other** (Geometric) - Chi2 的实例。 + +**返回** + + - Tensor: 两个卡方分布之间的 KL 散度。 + + +sample(shape) +''''''''' +随机采样，生成指定维度的样本。 + +**参数** + + - **shape** (Sequence[int], optional) - 采样的样本维度。 + +**返回** + + - **Tensor** - 指定维度的样本数据。数据类型为 float32。 + + +rsample(shape) +''''''''' +重参数化采样，生成指定维度的样本。 + +**参数** + + - **shape** (Sequence[int], optional) - 重参数化采样的样本维度。 + +**返回** + + - **Tensor** - 指定维度的样本数据。数据类型为 float32。 diff --git a/docs/api/paddle/distribution/LKJCholesky_cn.rst b/docs/api/paddle/distribution/LKJCholesky_cn.rst new file mode 100644 index 00000000000..6a10821ae64 --- /dev/null +++ b/docs/api/paddle/distribution/LKJCholesky_cn.rst @@ -0,0 +1,62 @@ +.. _cn_api_paddle_distribution_LKJCholesky: + +LKJCholesky +------------------------------- +.. py:class:: paddle.distribution.LKJCholesky(dim, concentration=1.0, sample_method = 'onion') + + + +LKJ 分布是一种用于生成随机相关矩阵的概率分布，广泛应用于贝叶斯统计中，特别是作为协方差矩阵的先验分布。它能够调节相关矩阵的集中度，从而控制变量间的相关性。 + +LKJ 分布通常定义为对相关矩阵 :math:`\Omega` 的分布，其密度函数为： + +.. math:: + + p(\Omega \mid \eta) \propto |\Omega|^{\eta - 1} + +其中，:math:`\Omega` 是一个 :math:`n \times n` 的相关矩阵，:math:`\eta` 是分布的形状参数，:math:`|\Omega|` 是矩阵的行列式。参数 :math:`\eta` 调节矩阵元素的分布集中度。 + + +相关矩阵的下三角 Choleskey 因子的 LJK 分布支持两种 sample 方法:`onion` 和 `cvine` + +参数 +:::::::::::: + + - **dim** (int) - 目标相关矩阵的维度。 + - **concentration** (float|Tensor) - 集中参数，这个参数控制了生成的相关矩阵的分布，值必须大于 0。concentration 越大，生成的矩阵越接近单位矩阵。 + - **sample_method** (str) - 不同采样策略，可选项有：`onion` 和 `cvine`. 这两种 sample 方法都在 `Generating random correlation matrices based on vines and extended onion method `_ 中提出，并且在相关矩阵上提供相同的分布。但是它们在如何生成样本方面是不同的。默认为“onion”。 +代码示例 +:::::::::::: + +COPY-FROM: paddle.distribution.LKJCholesky + + +方法 +::::::::: + + +log_prob(value) +''''''''' +卡方分布的对数概率密度函数。 + +**参数** + + - **value** (float|Tensor) - 输入值。 + +**返回** + + - **Tensor** - value 对应的对数概率密度。 + + + +sample(shape) +''''''''' +随机采样，生成指定维度的样本。 + +**参数** + + - **shape** (Sequence[int], optional) - 采样的样本维度。 + +**返回** + + - **Tensor** - 指定维度的样本数据。数据类型为 float32。 diff --git a/docs/api/paddle/distribution/Normal_cn.rst b/docs/api/paddle/distribution/Normal_cn.rst index 8ffb4003ed6..0e2c041bd85 100644 --- a/docs/api/paddle/distribution/Normal_cn.rst +++ b/docs/api/paddle/distribution/Normal_cn.rst @@ -8,7 +8,7 @@ Normal 正态分布 -数学公式： +若 `loc` 是实数，概率密度函数为： .. math:: @@ -16,6 +16,14 @@ Normal Z = (2 \pi \sigma^2)^{0.5} +若 `loc` 是复数，概率密度函数为： + +.. math:: + + pdf(x; \mu, \sigma) = \frac{1}{Z}e^{\frac {-(x - \mu)^2} {\sigma^2} } + + Z = \pi \sigma^2 + 上面的数学公式中： - :math:`loc = \mu`：平均值； @@ -25,7 +33,7 @@ Normal 参数 :::::::::::: - - **loc** (int|float|list|tuple|numpy.ndarray|Tensor) - 正态分布平均值。数据类型为 float32 或 float64。 + - **loc** (int|float|complex|list|tuple|numpy.ndarray|Tensor) - 正态分布平均值。数据类型为 float32、float64、complex64 或 complex128。 - **scale** (int|float|list|tuple|numpy.ndarray|Tensor) - 正态分布标准差。数据类型为 float32 或 float64。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 @@ -85,12 +93,18 @@ entropy() 信息熵 -数学公式： +实高斯分布信息熵的数学公式： .. math:: entropy(\sigma) = 0.5 \log (2 \pi e \sigma^2) +复高斯分布信息熵的数学公式： + +.. math:: + + entropy(\sigma) = \log (\pi e \sigma^2) + 1 + 上面的数学公式中： :math:`scale = \sigma`：标准差。 @@ -130,7 +144,7 @@ kl_divergence(other) 两个正态分布之间的 KL 散度。 -数学公式： +实高斯分布 KL 散度的数学公式： .. math:: @@ -140,6 +154,16 @@ kl_divergence(other) diff = \mu_1 - \mu_0 +复高斯分布 KL 散度的数学公式： + +.. math:: + + KL\_divergence(\mu_0, \sigma_0; \mu_1, \sigma_1) = ratio^2 + (\frac{diff}{\sigma_1})^2 - 1 - 2 \ln {ratio} + + ratio = \frac{\sigma_0}{\sigma_1} + + diff = \mu_1 - \mu_0 + 上面的数学公式中： - :math:`loc = \mu_0`：当前正态分布的平均值； diff --git a/docs/api/paddle/distribution/Overview_cn.rst b/docs/api/paddle/distribution/Overview_cn.rst index 8526a5d7ada..d336043476d 100644 --- a/docs/api/paddle/distribution/Overview_cn.rst +++ b/docs/api/paddle/distribution/Overview_cn.rst @@ -27,6 +27,7 @@ paddle.distribution 目录下包含飞桨框架支持的随机变量的概率分 " :ref:`ContinuousBernoulli ` ", "ContinuousBernoulli 概率分布类" " :ref:`Categorical ` ", "Categorical 概率分布类" " :ref:`Cauchy ` ", "Cauchy 概率分布类" + " :ref:`Chi2 ` ", "Chi2 概率分布类" " :ref:`Normal ` ", "Normal 概率分布类" " :ref:`Uniform ` ", "Uniform 概率分布类" " :ref:`Beta ` ", "Beta 概率分布类" @@ -36,12 +37,14 @@ paddle.distribution 目录下包含飞桨框架支持的随机变量的概率分 " :ref:`Independent ` ", "Independent 概率分布类" " :ref:`TransfomedDistribution ` ", "TransformedDistribution 概率分布类" " :ref:`Laplace `", "Laplace 概率分布类" + " :ref:`LKJCholesky `", "LKJCholesky 概率分布类" " :ref:`LogNormal ` ", "LogNormal 概率分布类" " :ref:`Poisson ` ", "Poisson 概率分布类" " :ref:`Gumbel ` ", "Gumbel 概率分布类" " :ref:`Geometric ` ", "Geometric 概率分布类" " :ref:`Exponential ` ", "Exponential 概率分布类" " :ref:`Gamma ` ", "Gamma 概率分布类" + " :ref:`StudentT ` ", "StudentT 概率分布类" .. _about_distribution_transform: diff --git a/docs/api/paddle/distribution/StudentT_cn.rst b/docs/api/paddle/distribution/StudentT_cn.rst new file mode 100644 index 00000000000..05ccf1f62f1 --- /dev/null +++ b/docs/api/paddle/distribution/StudentT_cn.rst @@ -0,0 +1,122 @@ +.. _cn_api_paddle_distribution_StudentT: + +StudentT +------------------------------- + +.. py:class:: paddle.distribution.StudentT(df, loc, scale, name=None) + + +学生 t 分布 + +数学公式： + +.. math:: + + pdf(x; \nu, \mu, \sigma) = \frac{\Gamma[(\nu+1)/2]}{\sigma\sqrt{\nu\pi}\Gamma(\nu/2)[1+(\frac{x-\mu}{\sigma})^2/\nu]^{(1+\nu)/2}} + + +上面的数学公式中： + +- :math:`df = \nu`：自由度； +- :math:`loc = \mu`：平移变换参数； +- :math:`scale = \sigma`：缩放变换参数； +- :math:`\Gamma(\cdot)`：gamma 函数； + +参数 +:::::::::::: + + - **df** (float|Tensor) - 学生 t 分布的自由度，需大于 0。若输入类型是 float， :attr:`df` 会被转换成数据类型为 paddle 全局默认数据类型的 1-D tensor。若输入类型是 tensor，则支持的数据类型有 float32 或 float64。 + - **loc** (float|Tensor) - 学生 t 分布的平移变换参数。若输入类型是 float， :attr:`loc` 会被转换成数据类型为 paddle 全局默认数据类型的 1-D tensor。若输入类型是 tensor，则支持的数据类型有 float32 或 float64。 + - **scale** (float|Tensor) - 学生 t 分布的缩放变换参数，需大于 0。若输入类型是 float， :attr:`scale` 会被转换成数据类型为 paddle 全局默认数据类型的 1-D tensor。若输入类型是 tensor，则支持的数据类型有 float32 或 float64。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +代码示例 +:::::::::::: + + +COPY-FROM: paddle.distribution.StudentT + + +属性 +::::::::: + +mean +''''''''' + +学生 t 分布的均值 + +**返回** + +Tensor，均值 + +variance +''''''''' + +学生 t 分布的方差 + +**返回** + +Tensor，方差 + +方法 +::::::::: + +prob(value) +''''''''' + +计算 value 的概率。 + +**参数** + + - **value** (Tensor) - 待计算值。 + +**返回** + +Tensor，value 的概率。数据类型与 :attr:`df` 相同。 + + +log_prob(value) +''''''''' + +计算 value 的对数概率。 + +**参数** + + - **value** (Tensor) - 待计算值。 + +**返回** + +Tensor，value 的对数概率。数据类型与 :attr:`df` 相同。 + + +sample() +''''''''' + +从学生 t 分布中生成满足特定形状的样本数据。最终生成样本形状为 ``shape+batch_shape`` 。 + +**参数** + + - **shape** (Sequence[int]，可选)：采样次数。 + +**返回** + +Tensor：样本数据。其维度为 :math:`\text{sample shape} + \text{batch shape}` 。 + +entropy() +''''''''' + +计算学生 t 分布的信息熵。 + +.. math:: + + H = \log(\frac{\Gamma(\nu/2)\Gamma(1/2) \sigma \sqrt{\nu}}{\Gamma[(1+\nu)/2]}) + \frac{(1+\nu)}{2} \cdot \{\psi[(1+\nu)/2] - \psi(\nu/2)\} + +上面的数学公式中： + +- :math:`\nu`：自由度； +- :math:`\Gamma(\cdot)`：gamma 函数； +- :math:`\psi(\cdot)`：digamma 函数； + +**返回** + +学生 t 分布的信息熵，数据类型与 :attr:`df` 相同。 diff --git a/docs/api/paddle/fluid/Overview_cn.rst b/docs/api/paddle/fluid/Overview_cn.rst index 44fe15b4332..618a71b9566 100644 --- a/docs/api/paddle/fluid/Overview_cn.rst +++ b/docs/api/paddle/fluid/Overview_cn.rst @@ -3,4 +3,4 @@ paddle.fluid --------------------- .. warning:: - 从飞桨框架 2.5 版本开始，我们已经废弃了 `paddle.fluid` namespace 下的 API，请使用其他的替代 API。 + 从飞桨框架 2.5 版本开始，我们已经废弃了 `paddle.fluid` namespace 下的 API，请使用其他的替代 API，请参考 :ref:`cn_guides_api_mapping` 获取更多信息。如有疑问，请于 `Issue 区 `_ 按规范提交 Issue。 diff --git a/docs/api/paddle/incubate/LookAhead_cn.rst b/docs/api/paddle/incubate/LookAhead_cn.rst index 6348332b212..06cfc95573d 100644 --- a/docs/api/paddle/incubate/LookAhead_cn.rst +++ b/docs/api/paddle/incubate/LookAhead_cn.rst @@ -3,7 +3,7 @@ LookAhead ------------------------------- -.. py:function:: class paddle.incubate.LookAhead(inner_optimizer, alpha=0.5, k=5, name=None) +.. py:class:: paddle.incubate.LookAhead(inner_optimizer, alpha=0.5, k=5, name=None) 此 API 为论文 `Lookahead Optimizer: k steps forward, 1 step back `_ 中 Lookahead 优化器的实现。 Lookahead 保留两组参数：fast_params 和 slow_params。每次训练迭代中 inner_optimizer 更新 fast_params。 Lookahead 每 k 次训练迭代更新 slow_params 和 fast_params，如下所示： diff --git a/docs/api/paddle/incubate/xpu/resnet_block/ResNetBasicBlock_cn.rst b/docs/api/paddle/incubate/xpu/resnet_block/ResNetBasicBlock_cn.rst index 0f585178ef4..a4cc5c78748 100644 --- a/docs/api/paddle/incubate/xpu/resnet_block/ResNetBasicBlock_cn.rst +++ b/docs/api/paddle/incubate/xpu/resnet_block/ResNetBasicBlock_cn.rst @@ -2,7 +2,7 @@ ResNetBasicBlock ------------------------------- -.. py:class:: paddle.incubate.xpu.ResNetBasicBlock(num_channels1, num_filter1, filter1_size, num_channels2, num_filter2, filter2_size, num_channels3, num_filter3, filter3_size, stride1=1, stride2=1, stride3=1, act='relu', momentum=0.9, eps=1e-5, data_format='NCHW', has_shortcut=False, use_global_stats=False, is_test=False, filter1_attr=None, scale1_attr=None, bias1_attr=None, moving_mean1_name=None, moving_var1_name=None, filter2_attr=None, scale2_attr=None, bias2_attr=None, moving_mean2_name=None, moving_var2_name=None, ilter3_attr=None, scale3_attr=None, bias3_attr=None, moving_mean3_name=None, moving_var3_name=None, padding1=0, padding2=0, padding3=0, dilation1=1, dilation2=1, dilation3=1, trainable_statistics=False, find_conv_max=True) +.. py:class:: paddle.incubate.xpu.resnet_block.ResNetBasicBlock(num_channels1, num_filter1, filter1_size, num_channels2, num_filter2, filter2_size, num_channels3, num_filter3, filter3_size, stride1=1, stride2=1, stride3=1, act='relu', momentum=0.9, eps=1e-5, data_format='NCHW', has_shortcut=False, use_global_stats=False, is_test=False, filter1_attr=None, scale1_attr=None, bias1_attr=None, moving_mean1_name=None, moving_var1_name=None, filter2_attr=None, scale2_attr=None, bias2_attr=None, moving_mean2_name=None, moving_var2_name=None, ilter3_attr=None, scale3_attr=None, bias3_attr=None, moving_mean3_name=None, moving_var3_name=None, padding1=0, padding2=0, padding3=0, dilation1=1, dilation2=1, dilation3=1, trainable_statistics=False, find_conv_max=True) 该接口用于构建 ``ResNetBasicBlock`` 类的一个可调用对象，实现一次性计算多个 ``Conv2D``、 ``BatchNorm`` 和 ``ReLU`` 的功能，排列顺序参见源码链接。 diff --git a/docs/api/paddle/io/IterableDataset_cn.rst b/docs/api/paddle/io/IterableDataset_cn.rst index 88a14d73f9c..36799624911 100644 --- a/docs/api/paddle/io/IterableDataset_cn.rst +++ b/docs/api/paddle/io/IterableDataset_cn.rst @@ -9,7 +9,7 @@ IterableDataset 迭代式(iterable style)数据集需要继承这个基类，迭代式数据集为只能依次迭代式获取样本的数据集，类似 Python 中的迭代器，所有迭代式数据集须实现以下方法： -``__iter__``：依次返回数据赝本。 +``__iter__``：依次返回数据样本。 .. note:: 迭代式数据集不需要实现 ``__getitem__`` 和 ``__len__``，也不可以调用迭代式数据集的这两个方法。 diff --git a/docs/api/paddle/io/RandomSampler_cn.rst b/docs/api/paddle/io/RandomSampler_cn.rst index 6b1df9b6918..da91031c18d 100644 --- a/docs/api/paddle/io/RandomSampler_cn.rst +++ b/docs/api/paddle/io/RandomSampler_cn.rst @@ -11,7 +11,7 @@ RandomSampler ::::::::: - **data_source** (Dataset) - 此参数必须是 :ref:`cn_api_paddle_io_Dataset` 或 :ref:`cn_api_paddle_io_IterableDataset` 的一个子类实例或实现了 ``__len__`` 的 Python 对象，用于生成样本下标。默认值为 None。 - **replacement** (bool，可选) - 如果为 ``False`` 则会采样整个数据集，如果为 ``True`` 则会按 ``num_samples`` 指定的样本数采集。默认值为 ``False`` 。 - - **num_samples** (int，可选) - 如果 ``replacement`` 设置为 ``True`` 则按此参数采集对应的样本数。默认值为 None,不启用。 + - **num_samples** (int，可选) - 按此参数采集对应的样本数。默认值为 None，此时设为 ``data_source`` 的长度。 - **generator** (Generator，可选) - 指定采样 ``data_source`` 的采样器。默认值为 None，不启用。 返回 diff --git a/docs/api/paddle/io/random_split_cn.rst b/docs/api/paddle/io/random_split_cn.rst index f8896a11da6..853ca17fab4 100644 --- a/docs/api/paddle/io/random_split_cn.rst +++ b/docs/api/paddle/io/random_split_cn.rst @@ -11,7 +11,7 @@ random_split :::::::::::: - **dataset** (Dataset) - 此参数必须是 ``paddle.io.Dataset`` 或 ``paddle.io.IterableDataset`` 的一个子类实例或实现了 ``__len__`` 的 Python 对象，用于生成样本下标。默认值为 None。 - - **lengths** (list) - 总和为原数组长度的，子集合长度数组。 + - **lengths** (list) - 总和为原数组长度，表示子集合长度数组；或总和为 1.0，表示子集合长度占比的数组。 - **generator** (Generator，可选) - 指定采样 ``data_source`` 的采样器。默认值为 None。 返回 diff --git a/docs/api/paddle/isin_cn.rst b/docs/api/paddle/isin_cn.rst new file mode 100644 index 00000000000..adaef448445 --- /dev/null +++ b/docs/api/paddle/isin_cn.rst @@ -0,0 +1,25 @@ +.. _cn_api_paddle_isin: + +isin +----------------------------- + +.. py:function:: paddle.isin(x, test_x, assume_unique=False, invert=False, name=None) + +检验 ``x`` 中的每一个元素是否在 ``test_x`` 中。 + +参数 +::::::::: + - **x** (Tensor) - 输入的 tensor，数据类型为：'bfloat16', 'float16', 'float32', 'float64', 'int32', 'int64'。 + - **test_x** (Tensor) - 用于检验的 tensor，数据类型为：'bfloat16', 'float16', 'float32', 'float64', 'int32', 'int64'。 + - **assume_unique** (bool，可选) - 如果设置为 True，表示 ``x`` 与 ``test_x`` 的元素均是唯一的，这种情况可以提升计算的速度。默认值为 False。 + - **invert** (bool，可选) - 是否输出反转的结果。如果为 True，表示将结果反转。默认值为 False。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +``Tensor``，输出的 tensor 与输入 ``x`` 形状相同。 + +代码示例 +::::::::: + +COPY-FROM: paddle.isin diff --git a/docs/api/paddle/isinf_cn.rst b/docs/api/paddle/isinf_cn.rst index 53c547bc84c..84ad7518905 100644 --- a/docs/api/paddle/isinf_cn.rst +++ b/docs/api/paddle/isinf_cn.rst @@ -9,7 +9,7 @@ isinf 参数 ::::::::: - - **x** (Tensor)：输入的 `Tensor`，数据类型为：float16、float32、float64、int32、int64。 + - **x** (Tensor)：输入的 `Tensor`，数据类型为：float16、float32、float64、int8、int16、int32、int64、uint8。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/isneginf_cn.rst b/docs/api/paddle/isneginf_cn.rst new file mode 100644 index 00000000000..92cc528840d --- /dev/null +++ b/docs/api/paddle/isneginf_cn.rst @@ -0,0 +1,22 @@ +.. _cn_api_paddle_isneginf: + +isneginf +----------------------------- + +.. py:function:: paddle.isneginf(x, name=None) + +返回输入 tensor 的每一个值是否为 `-INF` 。 + +参数 +::::::::: + - **x** (Tensor)：输入的 `Tensor`，数据类型为：float16、float32、float64、int8、int16、int32、int64、uint8。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +``Tensor``，每个元素是一个 bool 值，表示输入 `x` 的每个元素是否为 `-INF` 。 + +代码示例 +::::::::: + +COPY-FROM: paddle.isneginf diff --git a/docs/api/paddle/isposinf_cn.rst b/docs/api/paddle/isposinf_cn.rst new file mode 100644 index 00000000000..b30cf9a8895 --- /dev/null +++ b/docs/api/paddle/isposinf_cn.rst @@ -0,0 +1,22 @@ +.. _cn_api_paddle_isposinf: + +isposinf +----------------------------- + +.. py:function:: paddle.isposinf(x, name=None) + +返回输入 tensor 的每一个值是否为 `+INF` 。 + +参数 +::::::::: + - **x** (Tensor)：输入的 `Tensor`，数据类型为：float16、float32、float64、int8、int16、int32、int64、uint8。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +``Tensor``，每个元素是一个 bool 值，表示输入 `x` 的每个元素是否为 `+INF` 。 + +代码示例 +::::::::: + +COPY-FROM: paddle.isposinf diff --git a/docs/api/paddle/isreal_cn.rst b/docs/api/paddle/isreal_cn.rst new file mode 100644 index 00000000000..77b0128c501 --- /dev/null +++ b/docs/api/paddle/isreal_cn.rst @@ -0,0 +1,24 @@ +.. _cn_api_paddle_isreal: + +isreal +------------------------------- + +.. py:function:: paddle.isreal(x, name=None) + + +判断输入 tensor 的每一个值是否为实数类型(非 complex64 或者 complex128)。 + +参数 +::::::::: + - **x** (Tensor) - 输入 Tensor。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +``Tensor``，每个元素是一个 bool 值，表示输入 `x` 的每个元素是否为实数类型。 + + +代码示例 +::::::::: + +COPY-FROM: paddle.isreal diff --git a/docs/api/paddle/linalg/Overview_cn.rst b/docs/api/paddle/linalg/Overview_cn.rst index 2858955fd2c..4f9f7a521f3 100644 --- a/docs/api/paddle/linalg/Overview_cn.rst +++ b/docs/api/paddle/linalg/Overview_cn.rst @@ -58,12 +58,15 @@ paddle.linalg 目录下包含飞桨框架支持的线性代数相关 API。具 " :ref:`paddle.linalg.eigh ` ", "计算厄米特矩阵或者实数对称矩阵的特征值和特征向量" " :ref:`paddle.linalg.eigvalsh ` ", "计算厄米特矩阵或者实数对称矩阵的特征值" " :ref:`paddle.linalg.cholesky ` ", "计算一个实数对称正定矩阵的 Cholesky 分解" + " :ref:`paddle.linalg.cholesky_inverse ` ", "使用 Cholesky 因子 `U` 计算对称正定矩阵的逆矩阵" " :ref:`paddle.linalg.svd ` ", "计算矩阵的奇异值分解" + " :ref:`paddle.linalg.svd_lowrank ` ", "对低秩矩阵进行奇异值分解" " :ref:`paddle.linalg.pca_lowrank ` ", "对矩阵进行线性主成分分析" " :ref:`paddle.linalg.qr ` ", "计算矩阵的正交三角分解（也称 QR 分解）" " :ref:`paddle.linalg.lu ` ", "计算矩阵的 LU 分解" " :ref:`paddle.linalg.lu_unpack ` ", "对矩阵的 LU 分解结果进行展开得到各单独矩阵" " :ref:`paddle.linalg.householder_product ` ", "计算 Householder 矩阵乘积的前 n 列(输入矩阵为 `[*,m,n]` )" + " :ref:`paddle.linalg.ormqr ` ", "计算一个普通矩阵与 Householder 矩阵的乘积" .. _about_solvers: diff --git a/docs/api/paddle/linalg/cholesky_inverse_cn.rst b/docs/api/paddle/linalg/cholesky_inverse_cn.rst new file mode 100644 index 00000000000..1ab78f917d6 --- /dev/null +++ b/docs/api/paddle/linalg/cholesky_inverse_cn.rst @@ -0,0 +1,36 @@ +.. _cn_api_paddle_linalg_cholesky_inverse: + +cholesky_inverse +------------------------------- + +.. py:function:: paddle.linalg.cholesky_inverse(x, upper=False, name=None) + +使用 Cholesky 因子 `U` 计算对称正定矩阵的逆矩阵，返回矩阵 `inv`。 +如果 `upper` 是 `False`，则 `U` 为下三角矩阵: + + .. math:: + + inv = (UU^{T})^{-1} + +如果 `upper` 是 `True`，则 `U` 为上三角矩阵: + + .. math:: + + inv = (U^{T}U)^{-1} + + +参数 +:::::::::::: + + - **x** （Tensor）- 输入变量是形状为 `[N, N]` 的对称正定矩阵的下三角或上三角 Cholesky 分解张量。支持数据类型为 float32、float64。 + - **upper** （bool，可选）- 如果 `upper` 是 `False`，则输入为下三角矩阵，否则为上三角矩阵。默认值为 False。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +:::::::::::: +Tensor，求解得到的逆矩阵。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.linalg.cholesky_inverse diff --git a/docs/api/paddle/linalg/ormqr_cn.rst b/docs/api/paddle/linalg/ormqr_cn.rst new file mode 100644 index 00000000000..81b2615b01d --- /dev/null +++ b/docs/api/paddle/linalg/ormqr_cn.rst @@ -0,0 +1,27 @@ +.. _cn_api_paddle_linalg_ormqr: + +ormqr +------------------------------- + +.. py:function:: paddle.linalg.ormqr(x, tau, other, left=True, transpose=False) + +计算维度为(m, n)的矩阵 C（由 :attr:`other` 给出）和一个矩阵 Q 的乘积， +其中 Q 由 Householder 反射系数 (:attr:`x`, :attr:`tau`) 表示。 + +参数 +:::::::::::: + + - **x** (Tensor) - 输入一个或一批矩阵，类型为 Tensor。 ``x`` 的形状应为 ``[*, MN, K]``，其中 ``*`` 为零或更大的批次维度，数据类型支持 float32， float64。 + - **tau** (Tensor) - 输入一个或一批 Householder 反射系数，类型为 Tensor。 ``tau`` 的形状应为 ``[*, min(MN, K)]``，其中 ``*`` 为零或更大的批次维度，数据类型支持 float32， float64。 + - **other** (Tensor) - 输入一个或一批矩阵，类型为 Tensor。 ``other`` 的形状应为 ``[*, M, N]``，其中 ``*`` 为零或更大的批次维度，数据类型支持 float32， float64。 + - **left** (bool， 可选) - 决定了矩阵乘积运算的顺序。如果 left 为 ``True`` ，计算顺序为 op(Q) * other ，否则，计算顺序为 other * op(Q)。默认值： ``True`` 。 + - **transpose** (bool， 可选) - 如果为 ``True`` ，对矩阵 Q 进行共轭转置变换，否则，不对矩阵 Q 进行共轭转置变换。默认值： ``False`` 。 +返回 +:::::::::::: + + ``Tensor``，维度和数据类型都与 :attr:`other` 一致。 + +代码示例 +:::::::::: + +COPY-FROM: paddle.linalg.ormqr diff --git a/docs/api/paddle/linalg/pca_lowrank_cn.rst b/docs/api/paddle/linalg/pca_lowrank_cn.rst index 2eac305af1a..f045d479237 100644 --- a/docs/api/paddle/linalg/pca_lowrank_cn.rst +++ b/docs/api/paddle/linalg/pca_lowrank_cn.rst @@ -20,6 +20,7 @@ pca_lowrank - **x** (Tensor) - 输入的需要进行线性主成分分析的一个或一批方阵，类型为 Tensor。 ``x`` 的形状应为 ``[*, M, N]``，其中 ``*`` 为零或更大的批次维度，数据类型支持 float32， float64。 - **q** (int，可选) - 对输入 :math:`X` 的秩稍微高估的预估值，默认值是 :math:`q=min(6,N,M)`。 - **center** (bool，可选) - 是否对输入矩阵进行中心化操作，类型为 bool ，默认为 True。 + - **niter** (int) - 需要进行的子空间迭代次数。默认值为 2。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/linalg/svd_lowrank_cn.rst b/docs/api/paddle/linalg/svd_lowrank_cn.rst new file mode 100644 index 00000000000..8e7d65fd8a4 --- /dev/null +++ b/docs/api/paddle/linalg/svd_lowrank_cn.rst @@ -0,0 +1,42 @@ +.. _cn_api_paddle_linalg_svd_lowrank: + +svd_lowrank +------------------------------- + +.. py:function:: paddle.linalg.svd_lowrank(x, q=None, niter=2, M=None, name=None) + + +计算在低秩矩阵或者批次的矩阵上进行奇异值分解（SVD）。 + +记 :math:`X` 为一个矩阵或者批次矩阵，输出结果满足： + +.. math:: + X \approx U * diag(S) * V^{T} + +若提供了 :math:`M` ，输出结果满足： + +.. math:: + X - M \approx U * diag(S) * V^{T} + +参数 +:::::::::::: + + - **x** (Tensor) - 输入的需要进行奇异值分解的一个或一批方阵，类型为 Tensor。 ``x`` 的形状应为 ``[*, M, N]``，其中 ``*`` 为零或更大的批次维度，数据类型支持 float32， float64。 + - **q** (int，可选) - 对输入 :math:`X` 的秩稍微高估的预估值，默认值为 None，代表预估值取 6。 + - **niter** (int) - 需要进行的子空间迭代次数。默认值为 2。 + - **M** (Tensor) - 输入矩阵在 `axis=-2` 维上的均值，形状应为 ``[*, 1, N]``，默认为 None。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +:::::::::::: + + - Tensor U，形状为 N x q 的矩阵。 + - Tensor S，长度为 q 的向量。 + - Tensor V，形状为 M x q 的矩阵。 + + tuple (U, S, V): 对输入 :math:`X` 或 :math:`X-M` 的奇异值分解的近似最优解。 + +代码示例 +:::::::::: + +COPY-FROM: paddle.linalg.svd_lowrank diff --git a/docs/api/paddle/log_normal__cn.rst b/docs/api/paddle/log_normal__cn.rst new file mode 100644 index 00000000000..4b3e6f1ba1c --- /dev/null +++ b/docs/api/paddle/log_normal__cn.rst @@ -0,0 +1,12 @@ +.. _cn_api_paddle_log_normal_: + +log\_normal\_ +------------------------------- + +.. py:function:: paddle.log_normal_(x, mean=1.0, std=2.0, name=None) + +Inplace 版本的 :ref:`cn_api_paddle_log_normal` API，对输入 `x` 采用 Inplace 策略。 + +更多关于 inplace 操作的介绍请参考 `3.1.3 原位（Inplace）操作和非原位操作的区别`_ 了解详情。 + +.. _3.1.3 原位（Inplace）操作和非原位操作的区别: https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/tensor_cn.html#id3 diff --git a/docs/api/paddle/log_normal_cn.rst b/docs/api/paddle/log_normal_cn.rst new file mode 100644 index 00000000000..4bea2115753 --- /dev/null +++ b/docs/api/paddle/log_normal_cn.rst @@ -0,0 +1,40 @@ +.. _cn_api_paddle_log_normal: + +log\_normal +------------------------------- + +.. py:function:: paddle.log_normal(mean=1.0, std=2.0, shape=None, name=None) + + +返回符合对数正态分布（对应正态分布的均值为 ``mean``，标准差为 ``std``）的随机 Tensor，对数正态分布的分布函数如下 + +.. math:: + + f(x) = \frac{1}{x\sigma\sqrt{2\pi}}e^{-\frac{(\ln{x}-\mu)^2}{2\sigma^2}} + +其中： + +- :math:`mu` 表示对应正态分布的平均值。 +- :math:`sigma` 表示对应正态分布的标准差。 + +如果 ``mean`` 是 Tensor，则输出 Tensor 和 ``mean`` 具有相同的形状和数据类型。 +如果 ``mean`` 不是 Tensor，且 ``std`` 是 Tensor，则输出 Tensor 和 ``std`` 具有相同的形状和数据类型。 +如果 ``mean`` 和 ``std`` 都不是 Tensor，则输出 Tensor 的形状为 ``shape``，数据类型为 float32。 +如果 ``mean`` 和 ``std`` 都是 Tensor，则 ``mean`` 和 ``std`` 的元素个数应该相同。 + +参数 +:::::::::: + + - **mean** (float|Tensor，可选) - 输出 Tensor 对应正态分布的平均值。如果 ``mean`` 是 float，则表示输出 Tensor 中所有元素的正态分布的平均值。如果 ``mean`` 是 Tensor (支持的数据类型为 float32、float64)，则表示输出 Tensor 中每个元素对应正态分布的平均值。默认值为 1.0。 + - **std** (float|Tensor，可选) - 输出 Tensor 对应正态分布的标准差。如果 ``std`` 是 float，则表示输出 Tensor 中所有元素的正态分布的标准差。如果 ``std`` 是 Tensor (支持的数据类型为 float32、float64)，则表示输出 Tensor 中每个元素对应正态分布的标准差。默认值为 2.0。 + - **shape** (list|tuple|Tensor，可选) - 生成的随机 Tensor 的形状。如果 ``shape`` 是 list、tuple，则其中的元素可以是 int，或者是形状为[]且数据类型为 int32、int64 的 0-D Tensor。如果 ``shape`` 是 Tensor，则是数据类型为 int32、int64 的 1D Tensor。如果 ``mean`` 或者 ``std`` 是 Tensor，输出 Tensor 的形状和 ``mean`` 或者 ``std`` 相同(此时 ``shape`` 无效)。默认值为 None。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +:::::::::: + Tensor，符合对数正态分布（对应正态分布的均值为 ``mean``，标准差为 ``std``）的随机 Tensor。 + +示例代码 +:::::::::: + +COPY-FROM: paddle.log_normal diff --git a/docs/api/paddle/median_cn.rst b/docs/api/paddle/median_cn.rst index f34bf545255..64468a1d3a5 100644 --- a/docs/api/paddle/median_cn.rst +++ b/docs/api/paddle/median_cn.rst @@ -9,7 +9,7 @@ median 参数 :::::::::: - - **x** (Tensor) - 输入的 Tensor，数据类型为：bool、float16、float32、float64、int32、int64。 + - **x** (Tensor) - 输入的 Tensor，数据类型为：float16、float32、float64、int32、int64。 - **axis** (int，可选) - 指定对 ``x`` 进行计算的轴。``axis`` 可以是 int。``axis`` 值应该在范围 [-D, D) 内，D 是 ``x`` 的维度。如果 ``axis`` 或者其中的元素值小于 0，则等价于 :math:`axis + D`。如果 ``axis`` 是 None，则对 ``x`` 的全部元素计算中位数。默认值为 None。 - **keepdim** (bool，可选) - 是否在输出 Tensor 中保留输入的维度。除非 keepdim 为 True，否则输出 Tensor 的维度将比输入 Tensor 小一维，默认值为 False。 - **mode** (str，可选) - 当输入 Tensor ``x`` 在 ``axis`` 轴上有偶数个元素时，可选择按照中间两个数的平均值或最小值确定中位数。可选的值是 'avg' 或 'min'。默认为 'avg'。 @@ -17,7 +17,7 @@ median 返回 :::::::::: - Tensor 或 (Tensor, Tensor)。 + Tensor 或 (Tensor, Tensor)。 若 ``mode == 'avg'``，返回值是一个中位数 Tensor； 若 ``mode == 'min'`` 且 ``axis`` 是 None，返回值是一个中位数 Tensor； 若 ``mode == 'min'`` 且 ``axis`` 不是 None，返回值是两个 Tensor，第一个是中位数，第二个是中位数对应的下标； diff --git a/docs/api/paddle/meshgrid_cn.rst b/docs/api/paddle/meshgrid_cn.rst index 7505f248fc1..46ada40c95a 100644 --- a/docs/api/paddle/meshgrid_cn.rst +++ b/docs/api/paddle/meshgrid_cn.rst @@ -13,7 +13,7 @@ meshgrid 参数 :::::::::::: - - **args** (Tensor|Tensor 数组) - 输入变量为 k 个一维 Tensor，形状分别为(N1,), (N2,), ..., (Nk, )。支持数据类型为 float32、float64、int32 和 int64。 + - **args** (Tensor|Tensor 数组) - 输入变量为 k 个一维 Tensor，形状分别为(N1,), (N2,), ..., (Nk, )。支持数据类型为 bfloat16、float16、float32、float64、int32、int64、complex64 和 complex128。 - **kargs** (可选) - 目前只接受 name 参数（str），具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/nn/AdaptiveAvgPool2D_cn.rst b/docs/api/paddle/nn/AdaptiveAvgPool2D_cn.rst index 6d28a9f1039..2725c5715ec 100755 --- a/docs/api/paddle/nn/AdaptiveAvgPool2D_cn.rst +++ b/docs/api/paddle/nn/AdaptiveAvgPool2D_cn.rst @@ -3,7 +3,7 @@ AdaptiveAvgPool2D ------------------------------- -.. py:function:: paddle.nn.AdaptiveAvgPool2D(output_size, data_format="NCHW", name=None) +.. py:class:: paddle.nn.AdaptiveAvgPool2D(output_size, data_format="NCHW", name=None) 根据输入 `x` , `output_size` 等参数对一个输入 Tensor 计算 2D 的自适应平均池化。输入和输出都是 4-D Tensor， 默认是以 `NCHW` 格式表示的，其中 `N` 是 batch size, `C` 是通道数，`H` 是输入特征的高度，`W` 是输入特征的宽度。 diff --git a/docs/api/paddle/nn/AdaptiveAvgPool3D_cn.rst b/docs/api/paddle/nn/AdaptiveAvgPool3D_cn.rst index 2225a086437..4f91c6ac08c 100755 --- a/docs/api/paddle/nn/AdaptiveAvgPool3D_cn.rst +++ b/docs/api/paddle/nn/AdaptiveAvgPool3D_cn.rst @@ -3,7 +3,7 @@ AdaptiveAvgPool3D ------------------------------- -.. py:function:: paddle.nn.AdaptiveAvgPool3D(output_size, data_format="NCDHW", name=None) +.. py:class:: paddle.nn.AdaptiveAvgPool3D(output_size, data_format="NCDHW", name=None) 根据输入 `x` , `output_size` 等参数对一个输入 Tensor 计算 3D 的自适应平均池化。输入和输出都是 5-D Tensor， 默认是以 `NCDHW` 格式表示的，其中 `N` 是 batch size, `C` 是通道数，`D` 是特征图长度，`H` 是输入特征的高度，`W` 是输入特征的宽度。 diff --git a/docs/api/paddle/nn/AdaptiveLogSoftmaxWithLoss_cn.rst b/docs/api/paddle/nn/AdaptiveLogSoftmaxWithLoss_cn.rst new file mode 100644 index 00000000000..f6f9bf8f5bd --- /dev/null +++ b/docs/api/paddle/nn/AdaptiveLogSoftmaxWithLoss_cn.rst @@ -0,0 +1,43 @@ +.. _cn_api_paddle_nn_AdaptiveLogSoftmaxWithLoss: + +AdaptiveLogSoftmaxWithLoss +------------------------------- + +.. py:class:: paddle.nn.AdaptiveLogSoftmaxWithLoss(in_features, n_classes, cutoffs, div_value=4.0, head_bias=False, name=None) +AdaptiveLogSoftmaxWithLoss 是一种高效的策略，通常用于自然语言处理任务中的语言模型训练，尤其是在处理具有大量词汇且标签分布显著不平衡的语料库时。 + +AdaptiveLogSoftmaxWithLoss 将标签按照频率划分为多个组，每个组包含的目标数量不同，且在频率较低的标签所在的组中会采用较低维度的嵌入，从而显著减少计算量。 + +在每个训练的小批量中，只有当至少有一个目标标签出现时，相应的组才会被计算。这种方法的设计理念是，频繁访问的组（如包含最常见标签的初始组）应该具有较低的计算成本。 + +对于参数 ``cutoffs``，按升序排序的整数序列。它控制组的数量和目标分配到组的方式。例如，设置 ``cutoffs = [10, 100, 1000]`` 意味着前 10 个目标将分配到 AdaptiveLogSoftmaxWithLoss 的 ``head``，目标 11, 12, ..., 100 将分配到第一个组，而目标 101, 102, ..., 1000 将分配到第二个组，而目标 1001, 1002, ..., n_classes - 1 将分配到第三个组。 + +对于参数 ``div_value``，用于计算每个附加组的大小，其值为 :math:`\left\lfloor \frac{\text{in\_features}}{\text{div\_value}^{\text{idx}}} \right\rfloor`，其中 ``idx`` 是组索引（对于较不频繁的单词，组索引较大，索引从 :math:`1` 开始）。 + +对于参数 ``head_bias``，如果设置为 True，将在 AdaptiveLogSoftmaxWithLoss 的 ``head`` 上添加偏置项。详细信息请参阅论文：https://arxiv.org/abs/1609.04309 。 + + + +参数 +::::::::: + - **in_features** (int): 输入 Tensor 的特征数量。 + - **n_classes** (int): 数据集中类型的个数。 + - **cutoffs** (Sequence): 用于将 label 分配到不同存储组的截断值。 + - **div_value** (float, 可选): 用于计算组大小的指数值。默认值：4.0。 + - **head_bias** (bool, 可选): 如果为 ``True``，AdaptiveLogSoftmaxWithLoss 的 ``head`` 添加偏置项。默认值： ``False``. + - **name** (str, 可选): 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +形状 +::::::::: + - **input** (Tensor): - 输入 Tensor，形状为 ``[N, in_features]``， ``N`` 是批尺寸。 + - **label** (Tensor): - 目标值，形状为 ``[N]``。 + - **output1** (Tensor): - 形状为 ``[N]``。 + - **output2** (Scalar): - 标量，无形状 + +返回 +::::::::: +用于计算自适应 softmax 的可调用对象。 + +代码示例 +::::::::: +COPY-FROM: paddle.nn.AdaptiveLogSoftmaxWithLoss diff --git a/docs/api/paddle/nn/AdaptiveMaxPool1D_cn.rst b/docs/api/paddle/nn/AdaptiveMaxPool1D_cn.rst index 8b60b1f38e8..60a02836c31 100755 --- a/docs/api/paddle/nn/AdaptiveMaxPool1D_cn.rst +++ b/docs/api/paddle/nn/AdaptiveMaxPool1D_cn.rst @@ -3,7 +3,7 @@ AdaptiveMaxPool1D ------------------------------- -.. py:function:: paddle.nn.AdaptiveMaxPool1D(output_size, return_mask=False, name=None) +.. py:class:: paddle.nn.AdaptiveMaxPool1D(output_size, return_mask=False, name=None) 根据输入 `x` , `output_size` 等参数对一个输入 Tensor 计算 1D 的自适应最大池化。输入和输出都是 3-D Tensor， 默认是以 `NCL` 格式表示的，其中 `N` 是 batch size, `C` 是通道数，`L` 是输入特征的长度。 diff --git a/docs/api/paddle/nn/AdaptiveMaxPool3D_cn.rst b/docs/api/paddle/nn/AdaptiveMaxPool3D_cn.rst index bcce9a1e3b9..25df013b17a 100644 --- a/docs/api/paddle/nn/AdaptiveMaxPool3D_cn.rst +++ b/docs/api/paddle/nn/AdaptiveMaxPool3D_cn.rst @@ -3,7 +3,7 @@ AdaptiveMaxPool3D ------------------------------- -.. py:function:: paddle.nn.AdaptiveMaxPool3D(output_size, return_mask=False, name=None) +.. py:class:: paddle.nn.AdaptiveMaxPool3D(output_size, return_mask=False, name=None) 根据输入 `x` , `output_size` 等参数对一个输入 Tensor 计算 3D 的自适应最大池化。输入和输出都是 5-D Tensor， 默认是以 `NCDHW` 格式表示的，其中 `N` 是 batch size， `C` 是通道数，`D` ， `H` ， `W` 分别是输入特征的深度，高度，宽度。 diff --git a/docs/api/paddle/nn/AlphaDropout_cn.rst b/docs/api/paddle/nn/AlphaDropout_cn.rst index 8a4bbcbde8f..d28802ebc4a 100644 --- a/docs/api/paddle/nn/AlphaDropout_cn.rst +++ b/docs/api/paddle/nn/AlphaDropout_cn.rst @@ -3,7 +3,7 @@ AlphaDropout ------------------------------- -.. py:function:: paddle.nn.AlphaDropout(p=0.5, name=None) +.. py:class:: paddle.nn.AlphaDropout(p=0.5, name=None) AlphaDropout 是一种具有自归一化性质的 dropout。均值为 0，方差为 1 的输入，经过 AlphaDropout 计算之后，输出的均值和方差与输入保持一致。AlphaDropout 通常与 SELU 激活函数组合使用。论文请参考：`Self-Normalizing Neural Networks `_ diff --git a/docs/api/paddle/nn/AvgPool1D_cn.rst b/docs/api/paddle/nn/AvgPool1D_cn.rst index c3144635fea..01a9342e929 100755 --- a/docs/api/paddle/nn/AvgPool1D_cn.rst +++ b/docs/api/paddle/nn/AvgPool1D_cn.rst @@ -3,7 +3,7 @@ AvgPool1D ------------------------------- -.. py:function:: paddle.nn.AvgPool1D(kernel_size, stride=None, padding=0, exclusive=True, ceil_mode=False, name=None) +.. py:class:: paddle.nn.AvgPool1D(kernel_size, stride=None, padding=0, exclusive=True, ceil_mode=False, name=None) 根据输入 `x` , `kernel_size` 等参数对一个输入 Tensor 计算 1D 的平均池化。输入和输出都是 3-D Tensor， 默认是以 `NCL` 格式表示的，其中 `N` 是 batch size, `C` 是通道数，`L` 是输入特征的长度。 @@ -18,7 +18,7 @@ AvgPool1D ::::::::: - **kernel_size** (int|list|tuple) - 池化核的尺寸大小。如果 kernel_size 为 list 或 tuple 类型，其必须包含一个整数，最终池化核的大小为该数值。 - **stride** (int|list|tuple，可选) - 池化操作步长。如果 stride 为 list 或 tuple 类型，其必须包含一个整数，最终池化操作的步长为该数值。默认值为 None，这时会使用 kernel_size 作为 stride。 - - **padding** (str|int|list|tuple，可选) - 池化补零的方式。如果 padding 是一个字符串，则必须为 `SAME` 或者 `VALID`。如果是 turple 或者 list 类型，则应是 `[pad_left, pad_right]` 形式。如果 padding 是一个非 0 值，那么表示会在输入的两端都 padding 上同样长度的 0。默认值为 0。 + - **padding** (str|int|list|tuple，可选) - 池化补零的方式。如果 padding 是一个字符串，则必须为 `SAME` 或者 `VALID`。如果是 tuple 或者 list 类型，若其长度为 1，则会在输入的两端都填充上长度为 padding[0] 的 0，若其长度为 2，则应是 `[pad_before, pad_after]` 形式。如果 padding 是一个非 0 整数，那么表示会在输入的两端都填充上同样长度的 0。如果 padding 是长度为 2 的序列组成的 list 或 tuple，则其中的每个元素表示在相应维度上的填充长度。注意：在 batch_size 维度和通道维度应该设置为 [0, 0] 或 (0, 0)。默认值为 0。 - **exclusive** (bool，可选) - 是否用额外 padding 的值计算平均池化结果，默认为 True。 - **ceil_mode** (bool，可选) - 是否用 ceil 函数计算输出的 height 和 width，如果设置为 False，则使用 floor 函数来计算，默认为 False。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/nn/AvgPool2D_cn.rst b/docs/api/paddle/nn/AvgPool2D_cn.rst index ea63ab21b89..e64d2b617db 100644 --- a/docs/api/paddle/nn/AvgPool2D_cn.rst +++ b/docs/api/paddle/nn/AvgPool2D_cn.rst @@ -3,7 +3,7 @@ AvgPool2D ------------------------------- -.. py:function:: paddle.nn.AvgPool2D(kernel_size, stride=None, padding=0, ceil_mode=False, exclusive=True, divisor_override=None, data_format="NCHW", name=None) +.. py:class:: paddle.nn.AvgPool2D(kernel_size, stride=None, padding=0, ceil_mode=False, exclusive=True, divisor_override=None, data_format="NCHW", name=None) 构建 `AvgPool2D` 类的一个可调用对象，其将构建一个二维平均池化层，根据输入参数 `kernel_size`, `stride`, `padding` 等参数对输入做平均池化操作。 @@ -31,7 +31,7 @@ AvgPool2D ::::::::: - **kernel_size** (int|list|tuple)：池化核大小。如果它是一个元组或列表，它必须包含两个整数值，(pool_size_Height, pool_size_Width)。若为一个整数，则它的平方值将作为池化核大小，比如若 pool_size=2，则池化核大小为 2x2。 - **stride** (int|list|tuple，可选)：池化层的步长。如果它是一个元组或列表，它将包含两个整数，(pool_stride_Height, pool_stride_Width)。若为一个整数，则表示 H 和 W 维度上 stride 均为该值。默认值为 None，这时会使用 kernel_size 作为 stride。 - - **padding** (str|int|list|tuple，可选) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法，计算细节可参考上述 pool_padding = "SAME"或 pool_padding = "VALID" 时的计算公式。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 + - **padding** (str|int|list|tuple，可选) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 - **ceil_mode** (bool，可选)：是否用 ceil 函数计算输出高度和宽度。如果是 True，则使用 `ceil` 计算输出形状的大小。默认为 False。 - **exclusive** (bool，可选)：是否在平均池化模式忽略填充值，默认是 `True`。 - **divisor_override** (int|float，可选)：如果指定，它将用作除数，否则根据`kernel_size`计算除数。默认`None`。 diff --git a/docs/api/paddle/nn/AvgPool3D_cn.rst b/docs/api/paddle/nn/AvgPool3D_cn.rst index cbc1c435320..f90f1d10b58 100644 --- a/docs/api/paddle/nn/AvgPool3D_cn.rst +++ b/docs/api/paddle/nn/AvgPool3D_cn.rst @@ -3,7 +3,7 @@ AvgPool3D ------------------------------- -.. py:function:: paddle.nn.AvgPool3D(kernel_size, stride=None, padding=0, ceil_mode=False, exclusive=True, divisor_override=None, data_format="NCDHW", name=None) +.. py:class:: paddle.nn.AvgPool3D(kernel_size, stride=None, padding=0, ceil_mode=False, exclusive=True, divisor_override=None, data_format="NCDHW", name=None) 构建 `AvgPool3D` 类的一个可调用对象，其将构建一个二维平均池化层，根据输入参数 `kernel_size`, `stride`, `padding` 等参数对输入做平均池化操作。 diff --git a/docs/api/paddle/nn/Bilinear_cn.rst b/docs/api/paddle/nn/Bilinear_cn.rst index 5552a7e4322..92b84008f12 100644 --- a/docs/api/paddle/nn/Bilinear_cn.rst +++ b/docs/api/paddle/nn/Bilinear_cn.rst @@ -3,7 +3,7 @@ Bilinear ------------------------------- -.. py:function:: paddle.nn.Bilinear(in1_features, in2_features, out_features, weight_attr=None, bias_attr=None, name=None) +.. py:class:: paddle.nn.Bilinear(in1_features, in2_features, out_features, weight_attr=None, bias_attr=None, name=None) 该层对两个输入执行双线性 Tensor 积。 diff --git a/docs/api/paddle/nn/ChannelShuffle_cn.rst b/docs/api/paddle/nn/ChannelShuffle_cn.rst index 9132080579a..f25abc0a84a 100644 --- a/docs/api/paddle/nn/ChannelShuffle_cn.rst +++ b/docs/api/paddle/nn/ChannelShuffle_cn.rst @@ -3,7 +3,7 @@ ChannelShuffle ------------------------------- -.. py:function:: paddle.nn.ChannelShuffle(groups, data_format="NCHW", name=None) +.. py:class:: paddle.nn.ChannelShuffle(groups, data_format="NCHW", name=None) 将一个形为 [N, C, H, W] 或是 [N, H, W, C] 的 Tensor 按通道分成 g 组，得到形为 [N, g, C/g, H, W] 或 [N, H, W, g, C/g] 的 Tensor，然后转置为 [N, C/g, g, H, W] 或 [N, H, W, C/g, g] 的形状，最后重塑为原来的形状。这样做可以增加通道间的信息流动，提高特征的重用率。详见张祥雨等人在 2017 年发表的论文 `ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices `_ 。 diff --git a/docs/api/paddle/nn/Conv2DTranspose_cn.rst b/docs/api/paddle/nn/Conv2DTranspose_cn.rst index 2cc1dc7c123..47042f27a66 100644 --- a/docs/api/paddle/nn/Conv2DTranspose_cn.rst +++ b/docs/api/paddle/nn/Conv2DTranspose_cn.rst @@ -8,7 +8,7 @@ Conv2DTranspose 二维转置卷积层（Convlution2d transpose layer） -该层根据输入（input）、卷积核（kernel）和空洞大小（dilations）、步长（stride）、填充（padding）来计算输出特征层大小或者通过 output_size 指定输出特征层大小。输入(Input)和输出(Output)为 NCHW 或 NHWC 格式，其中 N 为批尺寸（batch size），C 为通道数（channel），H 为特征层高度，W 为特征层宽度。卷积核是 MCHW 格式，M 是输出图像通道数，C 是输入图像通道数，H 是卷积核高度，W 是卷积核宽度。如果组数大于 1，C 等于输入图像通道数除以组数的结果。转置卷积的计算过程相当于卷积的反向计算。转置卷积又被称为反卷积（但其实并不是真正的反卷积）。欲了解转置卷积层细节，请参考下面的说明和 `参考文献 `_。如果参数 bias_attr 不为 False，转置卷积计算会添加偏置项。 +该层根据输入（input）、卷积核（kernel）和空洞大小（dilations）、步长（stride）、填充（padding）来计算输出特征层大小或者通过 output_size 指定输出特征层大小。输入(Input)和输出(Output)为 NCHW 或 NHWC 格式，其中 N 为批尺寸（batch size），C 为通道数（channel），H 为特征层高度，W 为特征层宽度。卷积核是 MCHW 格式，M 是输出图像通道数，C 是输入图像通道数，H 是卷积核高度，W 是卷积核宽度。如果组数大于 1，C 等于输入图像通道数除以组数的结果。转置卷积的计算过程相当于卷积的反向计算。转置卷积又被称为反卷积（但其实并不是真正的反卷积）。欲了解转置卷积层细节，请参考下面的说明和 `参考文献 `_。如果参数 bias_attr 不为 False，转置卷积计算会添加偏置项。 输入 :math:`X` 和输出 :math:`Out` 函数关系如下： diff --git a/docs/api/paddle/nn/CosineEmbeddingLoss_cn.rst b/docs/api/paddle/nn/CosineEmbeddingLoss_cn.rst index cad97861cff..e9d3eb4e063 100644 --- a/docs/api/paddle/nn/CosineEmbeddingLoss_cn.rst +++ b/docs/api/paddle/nn/CosineEmbeddingLoss_cn.rst @@ -3,7 +3,7 @@ CosineEmbeddingLoss ------------------------------- -.. py:function:: paddle.nn.CosineEmbeddingLoss(margin=0, reduction='mean', name=None) +.. py:class:: paddle.nn.CosineEmbeddingLoss(margin=0, reduction='mean', name=None) 该函数计算给定的输入 input1, input2 和 label 之间的 `CosineEmbedding` 损失，通常用于学习非线性嵌入或半监督学习 diff --git a/docs/api/paddle/nn/CrossEntropyLoss_cn.rst b/docs/api/paddle/nn/CrossEntropyLoss_cn.rst index 3e5de0e4666..15696a50798 100644 --- a/docs/api/paddle/nn/CrossEntropyLoss_cn.rst +++ b/docs/api/paddle/nn/CrossEntropyLoss_cn.rst @@ -3,7 +3,7 @@ CrossEntropyLoss ------------------------------- -.. py:function:: paddle.nn.CrossEntropyLoss(weight=None, ignore_index=-100, reduction='mean', soft_label=False, axis=-1, use_softmax=True, name=None) +.. py:class:: paddle.nn.CrossEntropyLoss(weight=None, ignore_index=-100, reduction='mean', soft_label=False, label_smoothing=0.0, axis=-1, use_softmax=True, name=None) 默认情况下， CrossEntropyLoss 使用 softmax 实现（即 use_softmax=True ）。该函数结合了 softmax 操作的计算和交叉熵损失函数，以提供更稳定的数值计算。 diff --git a/docs/api/paddle/nn/Dropout2D_cn.rst b/docs/api/paddle/nn/Dropout2D_cn.rst index 97236897770..649b995f90d 100644 --- a/docs/api/paddle/nn/Dropout2D_cn.rst +++ b/docs/api/paddle/nn/Dropout2D_cn.rst @@ -3,7 +3,7 @@ Dropout2D ------------------------------- -.. py:function:: paddle.nn.Dropout2D(p=0.5, data_format='NCHW', name=None) +.. py:class:: paddle.nn.Dropout2D(p=0.5, data_format='NCHW', name=None) 根据丢弃概率 `p`，在训练过程中随机将某些通道特征图置 0 (对一个形状为 `NCHW` 的 4 维 Tensor，通道特征图指的是其中的形状为 `HW` 的 2 维特征图)。Dropout2D 可以提高通道特征图之间的独立性。论文请参考：`Efficient Object Localization Using Convolutional Networks `_ diff --git a/docs/api/paddle/nn/Dropout3D_cn.rst b/docs/api/paddle/nn/Dropout3D_cn.rst index 779be4b2b2e..815c8ddeb0b 100644 --- a/docs/api/paddle/nn/Dropout3D_cn.rst +++ b/docs/api/paddle/nn/Dropout3D_cn.rst @@ -3,7 +3,7 @@ Dropout3D ------------------------------- -.. py:function:: paddle.nn.Dropout3D(p=0.5, data_format='NCDHW', name=None) +.. py:class:: paddle.nn.Dropout3D(p=0.5, data_format='NCDHW', name=None) 根据丢弃概率 `p`，在训练过程中随机将某些通道特征图置 0 (对一个形状为 `NCDHW` 的 5 维 Tensor，通道特征图指的是其中的形状为 `DHW` 的 3 维特征图)。Dropout3D 可以提高通道特征图之间的独立性。论文请参考：`Efficient Object Localization Using Convolutional Networks `_ diff --git a/docs/api/paddle/nn/Dropout_cn.rst b/docs/api/paddle/nn/Dropout_cn.rst index e1859dc9baf..d745868c47a 100644 --- a/docs/api/paddle/nn/Dropout_cn.rst +++ b/docs/api/paddle/nn/Dropout_cn.rst @@ -3,7 +3,7 @@ Dropout ------------------------------- -.. py:function:: paddle.nn.Dropout(p=0.5, axis=None, mode="upscale_in_train", name=None) +.. py:class:: paddle.nn.Dropout(p=0.5, axis=None, mode="upscale_in_train", name=None) Dropout 是一种正则化手段，根据给定的丢弃概率 `p`，在训练过程中随机将一些神经元输出设置为 0，通过阻止神经元节点间的相关性来减少过拟合。论文请参考：`Improving neural networks by preventing co-adaptation of feature detectors `_ diff --git a/docs/api/paddle/nn/FeatureAlphaDropout_cn.rst b/docs/api/paddle/nn/FeatureAlphaDropout_cn.rst new file mode 100644 index 00000000000..32affdb85d9 --- /dev/null +++ b/docs/api/paddle/nn/FeatureAlphaDropout_cn.rst @@ -0,0 +1,27 @@ +.. _cn_api_paddle_nn_FeatureAlphaDropout: + +FeatureAlphaDropout +------------------------------- + +.. py:function:: paddle.nn.FeatureAlphaDropout(p=0.5, name=None) + +一个通道是一个特征图， `FeatureAlphaDropout` 会随机屏蔽掉整个通道。 `AlphaDropout` 是一种具有自归一化性质的 `dropout` 。均值为 0 ，方差为 1 的输入，经过 `AlphaDropout` 计算之后，输出的均值和方差与输入保持一致。 `AlphaDropout` 通常与 SELU 激活函数组合使用。论文请参考：`Self-Normalizing Neural Networks `_ + +在动态图模式下，请使用模型的 `eval()` 方法切换至测试阶段。 + +.. note:: + 对应的 `functional 方法` 请参考：:ref:`cn_api_paddle_nn_functional_feature_alpha_dropout` 。 + +参数 +::::::::: + - **p** (float)：将输入节点置 0 的概率，即丢弃概率。默认：0.5。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +`Tensor` ，经过 FeatureAlphaDropout 之后的结果，与输入 x 形状相同的 `Tensor` 。 + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.FeatureAlphaDropout diff --git a/docs/api/paddle/nn/Flatten_cn.rst b/docs/api/paddle/nn/Flatten_cn.rst index bdb6a278ffa..af961c40c65 100644 --- a/docs/api/paddle/nn/Flatten_cn.rst +++ b/docs/api/paddle/nn/Flatten_cn.rst @@ -3,7 +3,7 @@ Flatten ------------------------------- -.. py:function:: paddle.nn.Flatten(start_axis=1, stop_axis=-1) +.. py:class:: paddle.nn.Flatten(start_axis=1, stop_axis=-1) diff --git a/docs/api/paddle/nn/Fold_cn.rst b/docs/api/paddle/nn/Fold_cn.rst index bc0affe8bd3..b52ad8a09f8 100644 --- a/docs/api/paddle/nn/Fold_cn.rst +++ b/docs/api/paddle/nn/Fold_cn.rst @@ -3,7 +3,7 @@ Fold ------------------------------- -.. py:function:: paddle.nn.Fold(output_sizes, kernel_sizes, dilations=1, paddings=0, strides=1, name=None) +.. py:class:: paddle.nn.Fold(output_sizes, kernel_sizes, dilations=1, paddings=0, strides=1, name=None) 将一个滑动局部块组合成一个大的 Tensor。通常也被称为 col2im，用于批处理二维图像 Tensor。Fold 通过对所有包含块的值求和来计算结果中的每个大 Tensor 的组合值。 diff --git a/docs/api/paddle/nn/FractionalMaxPool3D_cn.rst b/docs/api/paddle/nn/FractionalMaxPool3D_cn.rst index 6611496699e..4e7d9e32b5a 100644 --- a/docs/api/paddle/nn/FractionalMaxPool3D_cn.rst +++ b/docs/api/paddle/nn/FractionalMaxPool3D_cn.rst @@ -4,7 +4,7 @@ FractionalMaxPool3D ------------------------------- -.. py:function:: paddle.nn.FractionalMaxPool3D(output_size, kernel_size=None, random_u=None, return_mask=False, name=None) +.. py:class:: paddle.nn.FractionalMaxPool3D(output_size, kernel_size=None, random_u=None, return_mask=False, name=None) 对输入的 Tensor `x` 采取 `2` 维分数阶最大值池化操作，具体可以参考论文： diff --git a/docs/api/paddle/nn/GroupNorm_cn.rst b/docs/api/paddle/nn/GroupNorm_cn.rst index 2f9bf4214d8..f7ba9217427 100644 --- a/docs/api/paddle/nn/GroupNorm_cn.rst +++ b/docs/api/paddle/nn/GroupNorm_cn.rst @@ -17,13 +17,13 @@ GroupNorm - **epsilon** (float，可选) - 为防止方差除零，增加一个很小的值。默认值：1e-05。 - **weight_attr** (ParamAttr|bool，可选) - 指定权重参数属性的对象。如果为 False，表示参数不学习。默认值为 None，表示使用默认的权重参数属性。具体用法请参见 :ref:`cn_api_paddle_ParamAttr` 。 - **bias_attr** (ParamAttr|bool，可选) - 指定偏置参数属性的对象。如果为 False，表示参数不学习。默认值为 None，表示使用默认的偏置参数属性。具体用法请参见 :ref:`cn_api_paddle_ParamAttr` 。 - - **data_format** (string，可选) - 只支持“NCHW”(num_batches，channels，height，width)格式。默认值：“NCHW”。 + - **data_format** (string，可选) - 支持 “NCL”，“NCHW”，“NCDHW”，“NLC”，“NHWC”，“NDHWC” 格式。默认值：“NCHW”。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 形状 :::::::::::: - - input：形状为(批大小，通道数，\*) 的 Tensor。 + - input：形状为 (批大小，通道数，\*) 或 (批大小，\*，通道数) 的 Tensor。 - output：和输入形状一样。 代码示例 diff --git a/docs/api/paddle/nn/Hardsigmoid_cn.rst b/docs/api/paddle/nn/Hardsigmoid_cn.rst index bf11afa3248..e6f703f9176 100644 --- a/docs/api/paddle/nn/Hardsigmoid_cn.rst +++ b/docs/api/paddle/nn/Hardsigmoid_cn.rst @@ -3,7 +3,7 @@ Hardsigmoid ------------------------------- -.. py:function:: paddle.nn.Hardsigmoid(name=None) +.. py:class:: paddle.nn.Hardsigmoid(name=None) Hardsigmoid 激活层，用于创建一个 `Hardsigmoid` 类的可调用对象。sigmoid 的分段线性逼近激活函数，速度比 sigmoid 快，详细解释参见 `Noisy Activation Functions `_ 。 diff --git a/docs/api/paddle/nn/Hardswish_cn.rst b/docs/api/paddle/nn/Hardswish_cn.rst index 43497931f60..600303c8e80 100644 --- a/docs/api/paddle/nn/Hardswish_cn.rst +++ b/docs/api/paddle/nn/Hardswish_cn.rst @@ -3,7 +3,7 @@ Hardswish ------------------------------- -.. py:function:: paddle.nn.Hardswish(name=None) +.. py:class:: paddle.nn.Hardswish(name=None) Hardswish 激活函数。创建一个 `Hardswish` 类的可调用对象。在 MobileNetV3 架构中被提出，相较于 :ref:`cn_api_paddle_nn_Swish` 函数，具有数值稳定性好，计算速度快等优点，具体原理请参考：`Searching for MobileNetV3 `_ 。 diff --git a/docs/api/paddle/nn/KLDivLoss_cn.rst b/docs/api/paddle/nn/KLDivLoss_cn.rst index fdefb06b710..388c5000a9b 100644 --- a/docs/api/paddle/nn/KLDivLoss_cn.rst +++ b/docs/api/paddle/nn/KLDivLoss_cn.rst @@ -3,16 +3,22 @@ KLDivLoss ------------------------------- -.. py:class:: paddle.nn.KLDivLoss(reduction='mean') +.. py:class:: paddle.nn.KLDivLoss(reduction='mean', log_target=False) 创建一个 `KLDivLoss` 类的可调用对象，以计算输入(Input)和输入(Label)之间的 Kullback-Leibler 散度损失。注意其中输入(Input)应为对数概率值，输入(Label)应为概率值。 -kL 发散损失计算如下： +若 ``log_target`` 为 ``False``: .. math:: l(input, label) = label * (log(label) - input) +若 ``log_target`` 为 ``True``: + +.. math:: + + l(input, label) = exp(label) * (label - input) + 当 ``reduction`` 为 ``none`` 时，输出损失与输入（input）形状相同，各点的损失单独计算，不会对结果做 reduction 。 @@ -26,6 +32,7 @@ kL 发散损失计算如下： :::::::::::: - **reduction** (str，可选) - 要应用于输出的 reduction 类型，可用类型为‘none’ | ‘batchmean’ | ‘mean’ | ‘sum’，‘none’表示无 reduction，‘batchmean’ 表示输出的总和除以批大小，‘mean’ 表示所有输出的平均值，‘sum’表示输出的总和。 + - **log_target** (bool，可选) - 表示输入的 ``label`` 变量是否属于 log 空间。默认值为 False，表示不属于。 形状 :::::::::::: diff --git a/docs/api/paddle/nn/LPPool1D_cn.rst b/docs/api/paddle/nn/LPPool1D_cn.rst new file mode 100644 index 00000000000..69a96f963fe --- /dev/null +++ b/docs/api/paddle/nn/LPPool1D_cn.rst @@ -0,0 +1,41 @@ +.. _cn_api_paddle_nn_LPPool1D: + +LPPool1D +------------------------------- + +.. py:function:: paddle.nn.LPPool1D(norm_type, kernel_size, stride=None, padding=0, ceil_mode=False, data_format="NCL", name=None) + +根据输入 `x` , `kernel_size` 等参数对一个输入 Tensor 计算 1D 的幂平均池化。输入和输出都是 3-D Tensor， +默认是以 `NCL` 格式表示的，其中 `N` 是 batch size, `C` 是通道数，`L` 是输入特征的长度。 + +假设输入形状是(N, C, L)，输出形状是 (N, C, L_{out})，卷积核尺寸是 k, 1d 平均池化计算公式如下： + +.. math:: + + Output(N_i, C_i, l) = sum(Input[N_i, C_i, stride \times l:stride \times l+k]^{norm\_type})^{1/norm\_type} + +参数 +::::::::: + - **norm_type** (int|float)：幂平均池化的指数，不可以为 0。 + - **kernel_size** (int|list|tuple) - 池化核的尺寸大小。如果 kernel_size 为 list 或 tuple 类型，其必须包含一个整数，最终池化核的大小为该数值。 + - **stride** (int|list|tuple，可选) - 池化操作步长。如果 stride 为 list 或 tuple 类型，其必须包含一个整数，最终池化操作的步长为该数值。默认值为 None，这时会使用 kernel_size 作为 stride。 + - **padding** (string|int|list|tuple) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 + - **ceil_mode** (bool，可选) - 是否用 ceil 函数计算输出的 height 和 width，如果设置为 False，则使用 floor 函数来计算，默认为 False。 + - **data_format** (str，可选)：输入和输出的数据格式，可以是"NCL"和"NLC"。N 是批尺寸，C 是通道数，L 是特征长度。默认值："NCL" + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + + +形状 +::::::::: + - **x** (Tensor)：默认形状为（批大小，通道数，长度），即 NCL 格式的 3-D Tensor。其数据类型为 float32 或 float64。 + - **output** (Tensor)：默认形状为（批大小，通道数，输出特征长度），即 NCL 格式的 3-D Tensor。其数据类型与输入 x 相同。 + +返回 +::::::::: +计算 LPPool1D 的可调用对象 + + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.LPPool1D diff --git a/docs/api/paddle/nn/LPPool2D_cn.rst b/docs/api/paddle/nn/LPPool2D_cn.rst new file mode 100644 index 00000000000..a8c45309699 --- /dev/null +++ b/docs/api/paddle/nn/LPPool2D_cn.rst @@ -0,0 +1,57 @@ +.. _cn_api_paddle_nn_LPPool2D: + +LPPool2D +------------------------------- + +.. py:function:: paddle.nn.LPPool2D(norm_type, kernel_size, stride=None, padding=0, ceil_mode=False, data_format="NCHW", name=None) +构建 `LPPool2D` 类的一个可调用对象，其将构建一个二维幂平均池化层，根据输入参数 `kernel_size`, `stride`, +`padding` 等参数对输入做平均池化操作。 + +例如： + +输入： + + X 形状：:math:`\left ( N,C,H_{in},W_{in} \right )` + +属性： + + - kernel_size: :math:`ksize` + - stride: :math:`stride` + - norm_type: :math:`norm\_type` + +输出： + + Out 形状：:math:`\left ( N,C,H_{out},W_{out} \right )` + +.. math:: + out(N_i, C_j, h, w) = (\sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1} + input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)^{norm\_type})^{1 / norm\_type} + + +参数 +::::::::: + - **norm_type** (int|float)：幂平均池化的指数，不可以为 0。 + - **kernel_size** (int|list|tuple)：池化核大小。如果它是一个元组或列表，它必须包含两个整数值，(pool_size_Height, pool_size_Width)。若为一个整数，则它的平方值将作为池化核大小，比如若 pool_size=2，则池化核大小为 2x2。 + - **stride** (int|list|tuple，可选)：池化层的步长。如果它是一个元组或列表，它将包含两个整数，(pool_stride_Height, pool_stride_Width)。若为一个整数，则表示 H 和 W 维度上 stride 均为该值。默认值为 None，这时会使用 kernel_size 作为 stride。 + - **padding** (str|int|list|tuple，可选) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 + - **ceil_mode** (bool，可选)：是否用 ceil 函数计算输出高度和宽度。如果是 True，则使用 `ceil` 计算输出形状的大小。默认为 False。 + - **data_format** (str，可选)：输入和输出的数据格式，可以是"NCHW"和"NHWC"。N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度。默认值："NCHW" + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +形状 +::::::::: + - **x** (Tensor)：默认形状为（批大小，通道数，高度，宽度），即 NCHW 格式的 4-D Tensor。其数据类型为 float16, float32, float64, int32 或 int64。 + - **output** (Tensor)：默认形状为（批大小，通道数，输出特征高度，输出特征宽度），即 NCHW 格式的 4-D Tensor。其数据类型与输入相同。 + + + +返回 +::::::::: +计算 LPPool2D 的可调用对象 + + + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.LPPool2D diff --git a/docs/api/paddle/nn/LSTMCell_cn.rst b/docs/api/paddle/nn/LSTMCell_cn.rst index 06a79bce161..53f7b70b1d3 100644 --- a/docs/api/paddle/nn/LSTMCell_cn.rst +++ b/docs/api/paddle/nn/LSTMCell_cn.rst @@ -3,7 +3,7 @@ LSTMCell ------------------------------- -.. py:class:: paddle.nn.LSTMCell(input_size, hidden_size, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, bias_hh_attr=None, name=None) +.. py:class:: paddle.nn.LSTMCell(input_size, hidden_size, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, bias_hh_attr=None, proj_size=0, name=None) @@ -23,6 +23,10 @@ LSTMCell h_{t} &= o_{t} * \tanh (c_{t})\\ y_{t} &= h_{t} +若设置了 `proj_size`，隐状态 :math:`h_{t}` 将会被映射到指定维度： + +.. math:: + h_{t} = h_{t} * W_{proj\_size} 其中： @@ -41,6 +45,7 @@ LSTMCell - **weight_hh_attr** (ParamAttr，可选) - weight_hh 的参数。默认为 None。 - **bias_ih_attr** (ParamAttr，可选) - bias_ih 的参数。默认为 None。 - **bias_hh_attr** (ParamAttr，可选) - bias_hh 的参数。默认为 None。 + - **proj_size** (int，可选) - 若大于 0，则会使用投影层将隐状态隐射到指定大小，其值必须小于 `hidden_size` 。默认为 0。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 变量 @@ -60,8 +65,8 @@ LSTMCell 输出 :::::::::::: - - **outputs** (Tensor) - 输出。形状为[batch_size, hidden_size]，对应公式中的 :math:`h_{t}`。 - - **new_states** (tuple) - 一个包含两个 Tensor 的元组，每个 Tensor 的形状都为[batch_size, hidden_size]，新一轮的隐藏状态。形状为[batch_size, hidden_size]，对应公式中的 :math:`h_{t}，c_{t}`。 + - **outputs** (Tensor) - 输出。形状为[batch_size, hidden_size]，对应公式中的 :math:`h_{t}`, 当设置了 `proj_size` 时，输出形状会被映射为[batch_size, proj_size]。 + - **new_states** (tuple) - 一个包含两个 Tensor 的元组，每个 Tensor 的形状都为[batch_size, hidden_size]，新一轮的隐藏状态。形状为[batch_size, hidden_size]，对应公式中的 :math:`h_{t}，c_{t}`。当设置了 `proj_size` 时，:math:`h_{t}` 会被映射为[batch_size, proj_size]。 .. note:: 所有的变换矩阵的权重和偏置都默认初始化为 Uniform(-std, std)，其中 std = :math:`\frac{1}{\sqrt{hidden\_size}}`。对于参数初始化，详情请参考 :ref:`cn_api_paddle_ParamAttr`。 diff --git a/docs/api/paddle/nn/LSTM_cn.rst b/docs/api/paddle/nn/LSTM_cn.rst index 6e39a0793de..063735377f1 100644 --- a/docs/api/paddle/nn/LSTM_cn.rst +++ b/docs/api/paddle/nn/LSTM_cn.rst @@ -3,7 +3,7 @@ LSTM ------------------------------- -.. py:class:: paddle.nn.LSTM(input_size, hidden_size, num_layers=1, direction="forward", dropout=0., time_major=False, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, bias_hh_attr=None, name=None) +.. py:class:: paddle.nn.LSTM(input_size, hidden_size, num_layers=1, direction="forward", dropout=0., time_major=False, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, bias_hh_attr=None, proj_size=0, name=None) @@ -29,6 +29,10 @@ LSTM y_{t} & = h_{t} +若设置了 `proj_size`，隐状态 :math:`h_{t}` 将会被映射到指定维度： + +.. math:: + h_{t} = h_{t} * W_{proj\_size} 其中： - :math:`\sigma` ：sigmoid 激活函数。 @@ -40,12 +44,13 @@ LSTM - **hidden_size** (int) - 隐藏状态 :math:`h` 大小。 - **num_layers** (int，可选) - 循环网络的层数。例如，将层数设为 2，会将两层 GRU 网络堆叠在一起，第二层的输入来自第一层的输出。默认为 1。 - **direction** (str，可选) - 网络迭代方向，可设置为 forward 或 bidirect（或 bidirectional）。foward 指从序列开始到序列结束的单向 GRU 网络方向，bidirectional 指从序列开始到序列结束，又从序列结束到开始的双向 GRU 网络方向。默认为 forward。 - - **time_major** (bool，可选) - 指定 input 的第一个维度是否是 time steps。如果 time_major 为 True，则 Tensor 的形状为[time_steps,batch_size,input_size]，否则为[batch_size,time_steps,input_size]。`time_steps` 指输入序列的长度。默认为 False。 + - **time_major** (bool，可选) - 指定 input 的第一个维度是否是 time steps。如果 time_major 为 True，则 Tensor 的形状为[time_steps, batch_size, input_size]，否则为[batch_size, time_steps, input_size]。`time_steps` 指输入序列的长度。默认为 False。 - **dropout** (float，可选) - dropout 概率，指的是除第一层外每层输入时的 dropout 概率。范围为[0, 1]。默认为 0。 - **weight_ih_attr** (ParamAttr，可选) - weight_ih 的参数。默认为 None。 - **weight_hh_attr** (ParamAttr，可选) - weight_hh 的参数。默认为 None。 - **bias_ih_attr** (ParamAttr，可选) - bias_ih 的参数。默认为 None。 - **bias_hh_attr** (ParamAttr，可选) - bias_hh 的参数。默认为 None。 + - **proj_size** (int，可选) - 若大于 0，则会使用投影层将隐状态隐射到指定大小，其值必须小于 `hidden_size` 。默认为 0。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 输入 @@ -58,8 +63,9 @@ LSTM 输出 :::::::::::: - - **outputs** (Tensor) - 输出，由前向和后向 cell 的输出拼接得到。如果 time_major 为 True，则 Tensor 的形状为[time_steps,batch_size,num_directions * hidden_size]，如果 time_major 为 False，则 Tensor 的形状为[batch_size,time_steps,num_directions * hidden_size]，当 direction 设置为 bidirectional 时，num_directions 等于 2，否则等于 1。`time_steps` 指输出序列的长度。 - - **final_states** (tuple) - 最终状态，一个包含 h 和 c 的元组。形状为[num_layers * num_directions, batch_size, hidden_size]，当 direction 设置为 bidirectional 时，num_directions 等于 2，返回值的前向和后向的状态的索引是 0，2，4，6..。和 1，3，5，7...，否则等于 1。 + - **outputs** (Tensor) - 输出，由前向和后向 cell 的输出拼接得到。如果 time_major 为 True，则 Tensor 的形状为[time_steps, batch_size, num_directions * hidden_size]，当设置了 `proj_size` 时，输出形状会被映射为[time_steps, batch_size, num_directions * proj_size]，如果 time_major 为 False，则 Tensor 的形状为[batch_size,time_steps,num_directions * hidden_size]，当 direction 设置为 bidirectional 时，num_directions 等于 2，否则等于 1。`time_steps` 指输出序列的长度。 + - **final_states** (tuple) - 最终状态，一个包含 h 和 c 的元组。形状为[num_layers * num_directions, batch_size, hidden_size]，当设置了 `proj_size` 时， h 的形状会被映射为[num_layers * num_directions, batch_size, proj_size]。 当 direction 设置为 bidirectional 时，num_directions 等于 2，返回值的前向和后向的状态的索引是 0，2，4，6..。和 1，3，5，7...，否则等于 1。 + 代码示例 :::::::::::: diff --git a/docs/api/paddle/nn/Layer_cn.rst b/docs/api/paddle/nn/Layer_cn.rst index 12d1c7655ab..f7a2e8a9203 100644 --- a/docs/api/paddle/nn/Layer_cn.rst +++ b/docs/api/paddle/nn/Layer_cn.rst @@ -228,11 +228,15 @@ sublayers(include_self=False) COPY-FROM: paddle.nn.Layer.sublayers -clear_gradients() +clear_gradients(set_to_zero=True) ''''''''' 清除该层所有参数的梯度。 +**参数** + + - **set_to_zero** (bool，可选) - 是否将可训练参数的梯度设置为 0 ，若为 False 则设为 None。默认值：True。 + **返回** 无 @@ -377,7 +381,7 @@ Parameter，传入的参数实例 COPY-FROM: paddle.nn.Layer.add_parameter -state_dict(destination=None, include_sublayers=True, use_hook=True) +state_dict(destination=None, include_sublayers=True, structured_name_prefix='', use_hook=True, keep_vars=True) ''''''''' 获取当前层及其子层的所有参数和可持久性 buffers。并将所有参数和 buffers 存放在 dict 结构中。 @@ -386,7 +390,9 @@ state_dict(destination=None, include_sublayers=True, use_hook=True) - **destination** (dict，可选) - 如果提供 ``destination``，则所有参数和可持久性 buffers 都将存放在 ``destination`` 中。默认值：None。 - **include_sublayers** (bool，可选) - 如果设置为 True，则包括子层的参数和 buffers。默认值：True。 + - **structured_name_prefix** (str，可选) - 添加到参数和缓冲区名称的前缀。默认值：''。 - **use_hook** (bool，可选) - 如果设置为 True，将_state_dict_hooks 中注册的函数应用于 destination。默认值：True。 + - **keep_vars** (bool，可选) - 如果设置为 False，状态字典中返回的 tensors 将脱离计算图。默认值：True。 **返回** dict，包含所有参数和可持久行 buffers 的 dict diff --git a/docs/api/paddle/nn/LocalResponseNorm_cn.rst b/docs/api/paddle/nn/LocalResponseNorm_cn.rst index 7f6c7da749a..a8bbb426df7 100644 --- a/docs/api/paddle/nn/LocalResponseNorm_cn.rst +++ b/docs/api/paddle/nn/LocalResponseNorm_cn.rst @@ -3,7 +3,7 @@ LocalResponseNorm ------------------------------- -.. py:function:: paddle.nn.LocalResponseNorm(size, alpha=0.0001, beta=0.75, k=1.0, data_format="NCHW", name=None) +.. py:class:: paddle.nn.LocalResponseNorm(size, alpha=0.0001, beta=0.75, k=1.0, data_format="NCHW", name=None) 局部响应正则化（Local Response Normalization）用于对局部输入区域进行正则化，执行一种侧向抑制（lateral inhibition）。更多详情参考：`ImageNet Classification with Deep Convolutional Neural Networks `_ diff --git a/docs/api/paddle/nn/MSELoss_cn.rst b/docs/api/paddle/nn/MSELoss_cn.rst index feab8758a4a..c762fe43238 100644 --- a/docs/api/paddle/nn/MSELoss_cn.rst +++ b/docs/api/paddle/nn/MSELoss_cn.rst @@ -3,7 +3,7 @@ MSELoss ------------------------------- -.. py:function:: paddle.nn.MSELoss(reduction='mean') +.. py:class:: paddle.nn.MSELoss(reduction='mean') 计算预测值和目标值的均方差误差。 diff --git a/docs/api/paddle/nn/MaxPool1D_cn.rst b/docs/api/paddle/nn/MaxPool1D_cn.rst index bea33184834..f1d498e66bf 100755 --- a/docs/api/paddle/nn/MaxPool1D_cn.rst +++ b/docs/api/paddle/nn/MaxPool1D_cn.rst @@ -3,7 +3,7 @@ MaxPool1D ------------------------------- -.. py:function:: paddle.nn.MaxPool1D(kernel_size, stride=None, padding=0, return_mask=False, ceil_mode=False, name=None) +.. py:class:: paddle.nn.MaxPool1D(kernel_size, stride=None, padding=0, return_mask=False, ceil_mode=False, name=None) 根据输入 `x` ， `kernel_size` 等参数对一个输入 Tensor 计算 1D 的最大值池化。输入和输出都是 3-D Tensor，默认是以 `NCL` 格式表示的，其中 `N` 是 batch size，`C` 是通道数，`L` 是输入特征的长度。 @@ -17,7 +17,7 @@ MaxPool1D ::::::::: - **kernel_size** (int|list|tuple) - 池化核的尺寸大小。如果 kernel_size 为 list 或 tuple 类型，其必须包含一个整数，最终池化核的大小为该数值。 - **stride** (int|list|tuple，可选) - 池化操作步长。如果 stride 为 list 或 tuple 类型，其必须包含一个整数，最终池化操作的步长为该数值。默认值为 None，这时会使用 kernel_size 作为 stride。 - - **padding** (str|int|list|tuple，可选) - 池化补零的方式。如果 padding 是一个字符串，则必须为 `SAME` 或者 `VALID`。如果是 turple 或者 list 类型，则应是 `[pad_left, pad_right]` 形式。如果 padding 是一个非 0 值，那么表示会在输入的两端都 padding 上同样长度的 0。 + - **padding** (str|int|list|tuple，可选) - 池化补零的方式。如果 padding 是一个字符串，则必须为 `SAME` 或者 `VALID`。如果是 tuple 或者 list 类型，若其长度为 1，则会在输入的两端都填充上长度为 padding[0] 的 0，若其长度为 2，则应是 `[pad_before, pad_after]` 形式。如果 padding 是一个非 0 整数，那么表示会在输入的两端都填充上同样长度的 0。如果 padding 是长度为 2 的序列组成的 list 或 tuple，则其中的每个元素表示在相应维度上的填充长度。注意：在 batch_size 维度和通道维度应该设置为 [0, 0] 或 (0, 0)。默认值为 0。 - **return_mask** (bool，可选) - 是否返回最大值的索引，默认为 False。 - **ceil_mode** (bool，可选) - 是否用 ceil 函数计算输出的 height 和 width，如果设置为 False，则使用 floor 函数来计算，默认为 False。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/nn/MaxPool2D_cn.rst b/docs/api/paddle/nn/MaxPool2D_cn.rst index a010cb6d6b8..61d57d8b87f 100644 --- a/docs/api/paddle/nn/MaxPool2D_cn.rst +++ b/docs/api/paddle/nn/MaxPool2D_cn.rst @@ -3,7 +3,7 @@ MaxPool2D ------------------------------- -.. py:function:: paddle.nn.MaxPool2D(kernel_size, stride=None, padding=0, ceil_mode=False, return_mask=False, data_format="NCHW", name=None) +.. py:class:: paddle.nn.MaxPool2D(kernel_size, stride=None, padding=0, ceil_mode=False, return_mask=False, data_format="NCHW", name=None) 构建 `MaxPool2D` 类的一个可调用对象，其将构建一个二维最大池化层，根据输入参数 `kernel_size`, `stride`, `padding` 等参数对输入做最大池化操作。 diff --git a/docs/api/paddle/nn/MaxPool3D_cn.rst b/docs/api/paddle/nn/MaxPool3D_cn.rst index 71af423b16a..ee9fae76af4 100644 --- a/docs/api/paddle/nn/MaxPool3D_cn.rst +++ b/docs/api/paddle/nn/MaxPool3D_cn.rst @@ -3,7 +3,7 @@ MaxPool3D ------------------------------- -.. py:function:: paddle.nn.MaxPool3D(kernel_size, stride=None, padding=0, ceil_mode=False, return_mask=False, data_format="NCDHW", name=None) +.. py:class:: paddle.nn.MaxPool3D(kernel_size, stride=None, padding=0, ceil_mode=False, return_mask=False, data_format="NCDHW", name=None) 构建 `MaxPool3D` 类的一个可调用对象，其将构建一个二维最大池化层，根据输入参数 `kernel_size`, `stride`, `padding` 等参数对输入做最大池化操作。 diff --git a/docs/api/paddle/nn/MaxUnPool1D_cn.rst b/docs/api/paddle/nn/MaxUnPool1D_cn.rst index 5e142f5c43e..8eb5abbd6c4 100644 --- a/docs/api/paddle/nn/MaxUnPool1D_cn.rst +++ b/docs/api/paddle/nn/MaxUnPool1D_cn.rst @@ -32,7 +32,7 @@ MaxUnPool1D 形状 ::::::::: - - **x** (Tensor)：默认形状为（批大小，通道数，长度），即 NCL 格式的 3-D Tensor。其数据类型为 float32 或 float64。 + - **x** (Tensor)：默认形状为（批大小，通道数，长度），即 NCL 格式的 3-D Tensor。其数据类型为 float32， float64 或 int64。 - **indices** (Tensor)：默认形状为（批大小，通道数，输出特征长度），即 NCL 格式的 3-D Tensor。其数据类型为 int32。 - **output** (Tensor)：默认形状为（批大小，通道数，输出特征长度），即 NCL 格式的 3-D Tensor。其数据类型与输入一致。 diff --git a/docs/api/paddle/nn/MaxUnPool2D_cn.rst b/docs/api/paddle/nn/MaxUnPool2D_cn.rst index da97d69f62b..e53057677ba 100644 --- a/docs/api/paddle/nn/MaxUnPool2D_cn.rst +++ b/docs/api/paddle/nn/MaxUnPool2D_cn.rst @@ -35,7 +35,7 @@ MaxUnPool2D 形状 ::::::::: - - **x** (Tensor)：默认形状为（批大小，通道数，高度，宽度），即 NCHW 格式的 4-D Tensor。其数据类型为 float32 或 float64。 + - **x** (Tensor)：默认形状为（批大小，通道数，高度，宽度），即 NCHW 格式的 4-D Tensor。其数据类型为 float32， float64 或 int64。 - **indices** (Tensor)：默认形状为（批大小，通道数，输出特征高度，输出特征宽度），即 NCHW 格式的 4-D Tensor。其数据类型为 int32。 - **output** (Tensor)：默认形状为（批大小，通道数，输出特征高度，输出特征宽度），即 NCHW 格式的 4-D Tensor。其数据类型与输入一致。 diff --git a/docs/api/paddle/nn/MaxUnPool3D_cn.rst b/docs/api/paddle/nn/MaxUnPool3D_cn.rst index d390cf9fee2..71d20d51756 100644 --- a/docs/api/paddle/nn/MaxUnPool3D_cn.rst +++ b/docs/api/paddle/nn/MaxUnPool3D_cn.rst @@ -38,7 +38,7 @@ MaxUnPool3D 形状 ::::::::: - - **x** (Tensor)：默认形状为（批大小，通道数，深度，高度，宽度），即 NCDHW 格式的 5-D Tensor。其数据类型为 float32 或 float64。 + - **x** (Tensor)：默认形状为（批大小，通道数，深度，高度，宽度），即 NCDHW 格式的 5-D Tensor。其数据类型为 float32， float64 或 int64。 - **indices** (Tensor)：默认形状为（批大小，通道数，输出特征深度，输出特征高度，输出特征宽度），即 NCDHW 格式的 5-D Tensor。其数据类型为 int32。 - **output** (Tensor)：默认形状为（批大小，通道数，输出特征深度，输出特征高度，输出特征宽度），即 NCDHW 格式的 5-D Tensor。其数据类型与输入一致。 diff --git a/docs/api/paddle/nn/Overview_cn.rst b/docs/api/paddle/nn/Overview_cn.rst index 0bb5c3724ac..ee40272f162 100644 --- a/docs/api/paddle/nn/Overview_cn.rst +++ b/docs/api/paddle/nn/Overview_cn.rst @@ -95,6 +95,8 @@ pooling 层 " :ref:`paddle.nn.MaxUnPool3D ` ", "三维最大反池化层" " :ref:`paddle.nn.FractionalMaxPool2D ` ", "二维分数阶最大值池化层" " :ref:`paddle.nn.FractionalMaxPool3D ` ", "三维分数阶最大值池化层" + " :ref:`paddle.nn.LPPool1D ` ", "一维幂平均池化层" + " :ref:`paddle.nn.LPPool2D ` ", "二维幂平均池化层" .. _padding_layers: @@ -108,7 +110,9 @@ Padding 层 " :ref:`paddle.nn.Pad1D ` ", "一维填充层" " :ref:`paddle.nn.Pad2D ` ", "二维填充层" " :ref:`paddle.nn.Pad3D ` ", "三维填充层" + " :ref:`paddle.nn.ZeroPad1D ` ", "一维零填充层" " :ref:`paddle.nn.ZeroPad2D ` ", "二维零填充层" + " :ref:`paddle.nn.ZeroPad3D ` ", "三维零填充层" .. _activation_layers: @@ -201,6 +205,7 @@ Transformer 相关 " :ref:`paddle.nn.MultiHeadAttention ` ", "多头注意力机制" " :ref:`paddle.nn.functional.scaled_dot_product_attention ` ", "点乘注意力机制，并在此基础上加入了对注意力权重的缩放" + " :ref:`paddle.nn.functional.sparse_attention ` ", "稀疏版本的 Attention API，对 Transformer 模块中的 Attention 矩阵进行了稀疏化，从而减少内存消耗和计算量" " :ref:`paddle.nn.Transformer ` ", "Transformer 模型" " :ref:`paddle.nn.TransformerDecoder ` ", "Transformer 解码器" " :ref:`paddle.nn.TransformerDecoderLayer ` ", "Transformer 解码器层" @@ -229,6 +234,7 @@ Dropout 层 " :ref:`paddle.nn.AlphaDropout ` ", "具有自归一化性质的 dropout" + " :ref:`paddle.nn.FeatureAlphaDropout ` ", "具有自归一化性质的 dropout ，屏蔽整个通道" " :ref:`paddle.nn.Dropout ` ", "Dropout" " :ref:`paddle.nn.Dropout2D ` ", "一维 Dropout" " :ref:`paddle.nn.Dropout3D ` ", "二维 Dropout" @@ -259,6 +265,7 @@ Loss 层 " :ref:`paddle.nn.CrossEntropyLoss ` ", "交叉熵损失层" " :ref:`paddle.nn.CTCLoss ` ", "CTCLoss 层" " :ref:`paddle.nn.HSigmoidLoss ` ", "层次 sigmoid 损失层" + " :ref:`paddle.nn.HingeEmbeddingLoss ` ", "HingeEmbeddingLoss 损失层" " :ref:`paddle.nn.KLDivLoss ` ", "Kullback-Leibler 散度损失层" " :ref:`paddle.nn.L1Loss ` ", "L1 损失层" " :ref:`paddle.nn.MarginRankingLoss ` ", "MarginRankingLoss 层" @@ -266,12 +273,14 @@ Loss 层 " :ref:`paddle.nn.NLLLoss ` ", "NLLLoss 层" " :ref:`paddle.nn.GaussianNLLLoss ` ", "GaussianNLLLoss 层" " :ref:`paddle.nn.PoissonNLLLoss `", "PoissonNLLLoss 层" + " :ref:`paddle.nn.RNNTLoss `", "RNNTLoss 层" " :ref:`paddle.nn.SmoothL1Loss ` ", "平滑 L1 损失层" " :ref:`paddle.nn.SoftMarginLoss ` ", "SoftMarginLoss 层" " :ref:`paddle.nn.TripletMarginLoss ` ", "TripletMarginLoss 层" " :ref:`paddle.nn.TripletMarginWithDistanceLoss ` ", "TripletMarginWithDistanceLoss 层" " :ref:`paddle.nn.MultiLabelSoftMarginLoss ` ", "多标签 Hinge 损失层" " :ref:`paddle.nn.MultiMarginLoss ` ", "MultiMarginLoss 层" + " :ref:`paddle.nn.AdaptiveLogSoftmaxWithLoss ` ", "自适应 logsoftmax 损失类" .. _vision_layers: @@ -365,6 +374,8 @@ Pooling 相关函数 " :ref:`paddle.nn.functional.max_unpool3d ` ", "三维最大反池化层" " :ref:`paddle.nn.functional.fractional_max_pool2d ` ", "二维分数阶最大值池化" " :ref:`paddle.nn.functional.fractional_max_pool3d ` ", "三维分数阶最大值池化" + " :ref:`paddle.nn.functional.lp_pool1d ` ", "一维平均幂池化" + " :ref:`paddle.nn.functional.lp_pool2d ` ", "二维平均幂池化" .. _padding_functional: @@ -459,6 +470,7 @@ Dropout 方法 " :ref:`paddle.nn.functional.alpha_dropout ` ", "一种具有自归一化性质的 dropout" + " :ref:`paddle.nn.functional.feature_alpha_dropout ` ", "一种具有自归一化性质的 dropout ，屏蔽整个通道" " :ref:`paddle.nn.functional.dropout ` ", "Dropout" " :ref:`paddle.nn.functional.dropout2d ` ", "一维 Dropout" " :ref:`paddle.nn.functional.dropout3d ` ", "二维 Dropout" @@ -507,7 +519,10 @@ Embedding 相关函数 " :ref:`paddle.nn.functional.triplet_margin_loss ` ", "用于计算 TripletMarginLoss" " :ref:`paddle.nn.functional.triplet_margin_with_distance_loss ` ", "用户自定义距离函数用于计算 triplet margin loss 损失" " :ref:`paddle.nn.functional.multi_label_soft_margin_loss ` ", "用于计算多分类的 hinge loss 损失函数" + " :ref:`paddle.nn.functional.hinge_embedding_loss ` ", "计算输入 input 和标签 label（包含 1 和 -1） 间的 `hinge embedding loss` 损失" + " :ref:`paddle.nn.functional.rnnt_loss ` ", "计算 RNNT loss，也可以叫做 softmax with RNNT" " :ref:`paddle.nn.functional.multi_margin_loss ` ", "用于计算 multi margin loss 损失函数" + " :ref:`paddle.nn.functional.adaptive_log_softmax_with_loss ` ", "自适应 logsoftmax 损失函数" .. _common_functional: @@ -538,6 +553,7 @@ Embedding 相关函数 " :ref:`paddle.nn.functional.temporal_shift ` ", "用于对输入 X 做时序通道 T 上的位移操作，为 TSM 中使用的操作" " :ref:`paddle.nn.functional.upsample ` ", "用于调整一个 batch 中图片的大小" " :ref:`paddle.nn.functional.class_center_sample ` ", "用于 PartialFC 类别中心采样" + " :ref:`paddle.nn.functional.channel_shuffle ` ", "将一个形为[N, C, H, W]或是[N, H, W, C]的 Tensor 按通道分成 g 组，得到形为[N, g, C/g, H, W]或[N, H, W, g, C/g]的 Tensor，然后转置为[N, C/g, g, H, W]或[N, H, W, C/g, g]的形状，最后重新排列为原来的形状" .. _about_initializer: diff --git a/docs/api/paddle/nn/PixelShuffle_cn.rst b/docs/api/paddle/nn/PixelShuffle_cn.rst index c49e5a65811..4d91ea78485 100644 --- a/docs/api/paddle/nn/PixelShuffle_cn.rst +++ b/docs/api/paddle/nn/PixelShuffle_cn.rst @@ -3,7 +3,7 @@ PixelShuffle ------------------------------- -.. py:function:: paddle.nn.PixelShuffle(upscale_factor, data_format="NCHW", name=None) +.. py:class:: paddle.nn.PixelShuffle(upscale_factor, data_format="NCHW", name=None) 将一个形为 :math:`[N, C, H, W]` 或是 :math:`[N, H, W, C]` 的 Tensor 重新排列成形为 :math:`[N, C/r^2, H \times r, W \times r]` 或 :math:`[N, H \times r, W \times r, C/r^2]` 的 Tensor。这样做有利于实现步长（stride）为 :math:`1/r` 的高效 sub-pixel（亚像素）卷积。详见 Shi 等人在 2016 年发表的论文 `Real Time Single Image and Video Super Resolution Using an Efficient Sub Pixel Convolutional Neural Network `_ 。 diff --git a/docs/api/paddle/nn/PixelUnshuffle_cn.rst b/docs/api/paddle/nn/PixelUnshuffle_cn.rst index 8a84a7cd8b5..30bfac2d8c3 100644 --- a/docs/api/paddle/nn/PixelUnshuffle_cn.rst +++ b/docs/api/paddle/nn/PixelUnshuffle_cn.rst @@ -3,7 +3,7 @@ PixelUnshuffle ------------------------------- -.. py:function:: paddle.nn.PixelUnshuffle(downscale_factor, data_format="NCHW", name=None) +.. py:class:: paddle.nn.PixelUnshuffle(downscale_factor, data_format="NCHW", name=None) 将一个形为 :math:`[N, C, H, W]` 或是 :math:`[N, H, W, C]` 的 Tensor 重新排列成形为 :math:`[N, r^2C, H/r, W/r]` 或 :math:`[N, H/r, W/r, r^2C]` 的 Tensor，这里 :math:`r` 是减小空间分辨率的减小因子。这个算子是 PixelShuffle 算子（请参考：:ref:`cn_api_paddle_nn_PixelShuffle`）的逆算子。详见施闻哲等人在 2016 年发表的论文 `Real Time Single Image and Video Super Resolution Using an Efficient Sub Pixel Convolutional Neural Network `_ 。 .. code-block:: text diff --git a/docs/api/paddle/nn/RNNCellBase_cn.rst b/docs/api/paddle/nn/RNNCellBase_cn.rst index 677e322121b..db785a26339 100644 --- a/docs/api/paddle/nn/RNNCellBase_cn.rst +++ b/docs/api/paddle/nn/RNNCellBase_cn.rst @@ -11,12 +11,14 @@ RNNCellBase 该 OP（RNNCellBase）是一个抽象表示根据输入和隐藏状态来计算输出和新状态的基本类，最适合也最常用于循环神经网络。 -.. py:function:: get_initial_states(batch_ref,shape=None,dtype=None,init_value=0.,batch_dim_idx=0): +方法 +:::::::::::: +get_initial_states(batch_ref,shape=None,dtype=None,init_value=0.,batch_dim_idx=0): +''''''''' 根据输入的形状，数据类型和值生成初始状态。 -参数 -:::::::::::: +**参数** - **batch_ref** (Tensor) - 一个 Tensor，其形状决定了生成初始状态使用的 batch_size。当 batch_ref 形状为 d 时，d[batch_dim_idx]为 batch_size。 - **shape** (list|tuple，可选) - 隐藏层的形状（可以是多层嵌套的），列表或元组的第一位为 batch_size，默认为-1。shape 为 None 时，使用 state_shape(property)。默认为 None。 @@ -24,7 +26,6 @@ RNNCellBase - **init_value** (float，可选) -用于初始状态的浮点数值。默认为 0。 - **batch_dim_idx** (int，可选) - 用于指定 batch_size 在 batch_ref 的索引位置的整数值。默认为 0。 -返回 -:::::::::::: +**返回** - **init_state** (Tensor|tuple|list) - 根据输出的数据类型，形状和嵌套层级返回的初始状态 Tensor。 diff --git a/docs/api/paddle/nn/Unflatten_cn.rst b/docs/api/paddle/nn/Unflatten_cn.rst index 6e5b411b59c..eed84aee977 100644 --- a/docs/api/paddle/nn/Unflatten_cn.rst +++ b/docs/api/paddle/nn/Unflatten_cn.rst @@ -3,7 +3,7 @@ Unflatten ------------------------------- -.. py:function:: paddle.nn.Unflatten(axis, shape, name=None) +.. py:class:: paddle.nn.Unflatten(axis, shape, name=None) diff --git a/docs/api/paddle/nn/UpsamplingBilinear2D_cn.rst b/docs/api/paddle/nn/UpsamplingBilinear2D_cn.rst index 62922c5dbd2..28f43f75a09 100644 --- a/docs/api/paddle/nn/UpsamplingBilinear2D_cn.rst +++ b/docs/api/paddle/nn/UpsamplingBilinear2D_cn.rst @@ -3,7 +3,7 @@ UpsamplingBilinear2D ------------------------------- -.. py:function:: paddle.nn.UpsamplingBilinear2D(size=None,scale_factor=None, data_format='NCHW',name=None) +.. py:class:: paddle.nn.UpsamplingBilinear2D(size=None,scale_factor=None, data_format='NCHW',name=None) diff --git a/docs/api/paddle/nn/UpsamplingNearest2D_cn.rst b/docs/api/paddle/nn/UpsamplingNearest2D_cn.rst index aef9a229ebf..db2b4f03511 100644 --- a/docs/api/paddle/nn/UpsamplingNearest2D_cn.rst +++ b/docs/api/paddle/nn/UpsamplingNearest2D_cn.rst @@ -3,7 +3,7 @@ UpsamplingNearest2D ------------------------------- -.. py:function:: paddle.nn.UpsamplingNearest2D(size=None,scale_factor=None, data_format='NCHW',name=None) +.. py:class:: paddle.nn.UpsamplingNearest2D(size=None,scale_factor=None, data_format='NCHW',name=None) diff --git a/docs/api/paddle/nn/ZeroPad1D_cn.rst b/docs/api/paddle/nn/ZeroPad1D_cn.rst new file mode 100644 index 00000000000..6773a902436 --- /dev/null +++ b/docs/api/paddle/nn/ZeroPad1D_cn.rst @@ -0,0 +1,32 @@ +.. _cn_api_paddle_nn_ZeroPad1D: + +ZeroPad1D +------------------------------- +.. py:class:: paddle.nn.ZeroPad1D(padding, data_format="NCL", name=None) + +**ZeroPad1D** + +按照 padding 属性对输入进行零填充。 + +参数 +::::::::: + + - **padding** (Tensor | List[int] | int) - 如果输入数据类型为 int，则在所有待填充边界使用相同的填充， + 否则填充的格式为[pad_left, pad_right]。 + - **data_format** (str) - 指定输入的 format，可为 ``'NCL'`` 或者 ``'NLC'``，默认值为 ``'NCL'``，其中 `N` 是 batch size， `C` 是通道数， `L` 输入特征的长度。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +:::::::::::: +无 + +形状 +::::::::: + + - x(Tensor): ZeroPadD 层的输入，要求形状为 3-D，dtype 为 ``'float32'`` 或 ``'float64'`` + - output(Tensor)：输出，形状为 3-D，dtype 与 ``'input'`` 相同 + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.ZeroPad1D diff --git a/docs/api/paddle/nn/ZeroPad3D_cn.rst b/docs/api/paddle/nn/ZeroPad3D_cn.rst new file mode 100644 index 00000000000..9e40c1de5dd --- /dev/null +++ b/docs/api/paddle/nn/ZeroPad3D_cn.rst @@ -0,0 +1,32 @@ +.. _cn_api_paddle_nn_ZeroPad3D: + +ZeroPad3D +------------------------------- +.. py:class:: paddle.nn.ZeroPad3D(padding, data_format="NCDHW", name=None) + +**ZeroPad3D** + +按照 padding 属性对输入进行零填充。 + +参数 +::::::::: + + - **padding** (Tensor | List[int] | int) - 如果输入数据类型为 int，则在所有待填充边界使用相同的填充， + 否则填充的格式为[pad_left, pad_right, pad_top, pad_bottom, pad_front, pad_back]。 + - **data_format** (str，可选) - 指定输入的 format，可为 ``'NCDHW'`` 或者 ``'NDHWC'``，默认值为 ``'NCDHW'``，其中 `N` 是批尺寸， `C` 是通道数， `D` 是特征层深度， `H` 是特征层高度， `W` 是特征层宽度。。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +:::::::::::: +无 + +形状 +::::::::: + + - x(Tensor): ZeroPadD 层的输入，要求形状为 5-D，dtype 为 ``'float32'`` 或 ``'float64'`` + - output(Tensor)：输出，形状为 5-D，dtype 与 ``'input'`` 相同 + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.ZeroPad3D diff --git a/docs/api/paddle/nn/functional/adaptive_log_softmax_with_loss_cn.rst b/docs/api/paddle/nn/functional/adaptive_log_softmax_with_loss_cn.rst new file mode 100644 index 00000000000..0832dffec91 --- /dev/null +++ b/docs/api/paddle/nn/functional/adaptive_log_softmax_with_loss_cn.rst @@ -0,0 +1,29 @@ +.. _cn_api_paddle_nn_functional_adaptive_log_softmax_with_loss: + +adaptive_log_softmax_with_loss +------------------------------- + +.. py:function:: paddle.nn.functional.adaptive_log_softmax_with_loss(input, label, head_weight, tail_weights, cutoffs, head_bias=None, name=None) + +计算自适应 logsoftmax 结果以及 input 和 label 之间的负对数似然。参数 `head_weight`、`tail_weights`、`cutoffs`和 `head_bias` 是 `AdaptiveLogSoftmaxWithLoss` 的内部成员。 +请参考：:ref:`cn_api_paddle_nn_AdaptiveLogSoftmaxWithLoss` + + +参数 +::::::::: + - **input** (Tensor): 输入张量，数据类型为 float32 或 float64。 + - **label** (Tensor): 标签张量，数据类型为 float32 或 float64。 + - **head_weight** (Tensor): 用于线性计算的权重矩阵，数据类型为 float32 或 float64。 + - **tail_weights** (Tensor): 用于线性计算的权重矩阵，数据类型为 float32 或 float64。 + - **cutoffs** (Sequence): 用于将 label 分配到不同存储桶的截断值。 + - **head_bias** (Tensor, 可选): 用于线性计算的偏置矩阵，数据类型为 float32 或 float64。 + - **name** (str, 可选): 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: + - **output** (Tensor): - 自适应 logsoftmax 计算结果，形状为[N]。 + - **loss** (Tensor): - input 和 label 之间的 logsoftmax 损失值。 + +代码示例 +::::::::: +COPY-FROM: paddle.nn.functional.adaptive_log_softmax_with_loss diff --git a/docs/api/paddle/nn/functional/avg_pool2d_cn.rst b/docs/api/paddle/nn/functional/avg_pool2d_cn.rst index 8c4cd324b9d..8c565b2d5f2 100644 --- a/docs/api/paddle/nn/functional/avg_pool2d_cn.rst +++ b/docs/api/paddle/nn/functional/avg_pool2d_cn.rst @@ -26,7 +26,7 @@ avg_pool2d - **x** (Tensor)：形状为 `[N,C,H,W]` 或 `[N,H,W,C]` 的 4-D Tensor，N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度，数据类型为 float32 或 float64。 - **kernel_size** (int|list|tuple)：池化核大小。如果它是一个元组或列表，它必须包含两个整数值，(pool_size_Height, pool_size_Width)。若为一个整数，则它的平方值将作为池化核大小，比如若 pool_size=2，则池化核大小为 2x2。 - **stride** (int|list|tuple)：池化层的步长。如果它是一个元组或列表，它将包含两个整数，(pool_stride_Height, pool_stride_Width)。若为一个整数，则表示 H 和 W 维度上 stride 均为该值。默认值为 kernel_size。 - - **padding** (string|int|list|tuple) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法，计算细节可参考上述 pool_padding = "SAME"或 pool_padding = "VALID" 时的计算公式。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 + - **padding** (str|int|list|tuple，可选) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 - **ceil_mode** (bool)：是否用 ceil 函数计算输出高度和宽度。如果是 True，则使用 `ceil` 计算输出形状的大小。默认为 None - **exclusive** (bool)：是否在平均池化模式忽略填充值，默认是 `True`。 - **divisor_override** (int|float)：如果指定，它将用作除数，否则根据`kernel_size`计算除数。默认`None`。 diff --git a/docs/api/paddle/nn/functional/cross_entropy_cn.rst b/docs/api/paddle/nn/functional/cross_entropy_cn.rst index 88aff0576fc..5000f16e3e3 100644 --- a/docs/api/paddle/nn/functional/cross_entropy_cn.rst +++ b/docs/api/paddle/nn/functional/cross_entropy_cn.rst @@ -3,7 +3,7 @@ cross_entropy ------------------------------- -.. py:function:: paddle.nn.functional.cross_entropy(input, label, weight=None, ignore_index=-100, reduction="mean", soft_label=False, axis=-1, name=None) +.. py:function:: paddle.nn.functional.cross_entropy(input, label, weight=None, ignore_index=-100, reduction="mean", soft_label=False, label_smoothing=0.0, axis=-1, name=None) 实现了 softmax 交叉熵损失函数。该函数会将 softmax 操作、交叉熵损失函数的计算过程进行合并，从而提供了数值上更稳定的计算。 diff --git a/docs/api/paddle/nn/functional/feature_alpha_dropout_cn.rst b/docs/api/paddle/nn/functional/feature_alpha_dropout_cn.rst new file mode 100644 index 00000000000..f3aa7264971 --- /dev/null +++ b/docs/api/paddle/nn/functional/feature_alpha_dropout_cn.rst @@ -0,0 +1,24 @@ +.. _cn_api_paddle_nn_functional_feature_alpha_dropout: + +feature_alpha_dropout +------------------------------- + +.. py:function:: paddle.nn.functional.feature_alpha_dropout(x, p=0.5, training=True, name=None) + +一个通道是一个特征图， `feature alpha dropout` 会随机屏蔽掉整个通道。 `alpha dropout` 是一种具有自归一化性质的 `dropout` 。均值为 0 ，方差为 1 的输入，经过 `alpha dropout` 计算之后，输出的均值和方差与输入保持一致。 `alpha dropout` 通常与 SELU 激活函数组合使用。 + +参数 +::::::::: + - **x** (Tensor)：输入的多维 `Tensor`，数据类型为：bfloat16、float16、float32 或 float64。 + - **p** (float)：将输入节点置 0 的概率，即丢弃概率。默认：0.5。 + - **training** (bool)：标记是否为训练阶段。默认：True。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +`Tensor` ，经过 feature_alpha_dropout 之后的结果，与输入 x 形状相同的 `Tensor` 。 + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.functional.feature_alpha_dropout diff --git a/docs/api/paddle/nn/functional/group_norm_cn.rst b/docs/api/paddle/nn/functional/group_norm_cn.rst index 590d2e8dda5..021bdcb7075 100644 --- a/docs/api/paddle/nn/functional/group_norm_cn.rst +++ b/docs/api/paddle/nn/functional/group_norm_cn.rst @@ -10,7 +10,7 @@ group_norm .. math:: y = \frac{x - E(x)}{\sqrt(Var(x)+ epsilon )} \ast weight + bias -- :math:`x`: 形状为 [批大小，通道数，\*]，其中通道数必须是 ``num_groups`` 的整数倍 +- :math:`x`: 形状为 [批大小，通道数，\*] 或 [批大小，\*，通道数]，其中通道数必须是 ``num_groups`` 的整数倍 - :math:`E(x)`, :math:`Var(x)`: 每一组中 ``x`` 的均值和方差 更多详情请参考：`Group Normalization `_ 。 @@ -23,7 +23,7 @@ group_norm - **epsilon** (float，可选) - 为防止方差除零，增加一个很小的值。默认值：1e-05。 - **weight** (Tensor，可选) - 权重的 Tensor，形状为 [通道数]，默认为 None。 - **bias** (Tensor，可选) - 偏置的 Tensor，形状为 [通道数]，默认为 None。 - - **data_format** (string，可选) - 只支持 “NCHW” [num_batches，channels，height，width] 格式。默认值：“NCHW”。 + - **data_format** (string，可选) - 支持 “NCL”，“NCHW”，“NCDHW”，“NLC”，“NHWC”，“NDHWC” 格式。默认值：“NCHW”。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/nn/functional/kl_div_cn.rst b/docs/api/paddle/nn/functional/kl_div_cn.rst index a08f9f58ef1..5ac2bc19425 100644 --- a/docs/api/paddle/nn/functional/kl_div_cn.rst +++ b/docs/api/paddle/nn/functional/kl_div_cn.rst @@ -3,16 +3,24 @@ kl_div ------------------------------- -.. py:function:: paddle.nn.functional.kl_div(input, label, reduction='mean', name=None) +.. py:function:: paddle.nn.functional.kl_div(input, label, reduction='mean', log_target=False, name=None) 计算输入(Input)和输入(Label)之间的 Kullback-Leibler 散度损失。注意其中输入(Input)应为对数概率值，输入(Label)应为概率值。 kL 发散损失计算如下： +若 ``log_target`` 为 ``False``: + .. math:: l(input, label) = label * (log(label) - input) +若 ``log_target`` 为 ``True``: + +.. math:: + + l(input, label) = exp(label) * (label - input) + 当 ``reduction`` 为 ``none`` 时，输出损失与输入（x）形状相同，各点的损失单独计算，不会对结果做 reduction 。 @@ -27,6 +35,7 @@ kL 发散损失计算如下： - **input** (Tensor) - KL 散度损失算子的输入 Tensor。维度为[N, \*]的多维 Tensor，其中 N 是批大小，\*表示任何数量的附加维度，数据类型为 float32 或 float64。 - **label** (Tensor) - KL 散度损失算子的 Tensor。与输入 ``input`` 的维度和数据类型一致的多维 Tensor。 - **reduction** (str，可选) - 要应用于输出的 reduction 类型，可用类型为‘none’ | ‘batchmean’ | ‘mean’ | ‘sum’，‘none’表示无 reduction，‘batchmean’ 表示输出的总和除以批大小，‘mean’ 表示所有输出的平均值，‘sum’表示输出的总和。 + - **log_target** (bool，可选) - 表示输入的 ``label`` 变量是否属于 log 空间。默认值为 False，表示不属于。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/nn/functional/lp_pool1d_cn.rst b/docs/api/paddle/nn/functional/lp_pool1d_cn.rst new file mode 100644 index 00000000000..0ef48a89fff --- /dev/null +++ b/docs/api/paddle/nn/functional/lp_pool1d_cn.rst @@ -0,0 +1,37 @@ +.. _cn_api_paddle_nn_functional_lp_pool1d: + +lp_pool1d +------------------------------- + +.. py:function:: paddle.nn.functional.lp_pool1d(x, norm_type, kernel_size, stride=None, padding=0, ceil_mode=False, data_format="NCL", name=None) + +根据输入 `x` , `kernel_size` 等参数对一个输入 Tensor 计算 1D 的幂平均池化。输入和输出都是 3-D Tensor， +默认是以 `NCL` 格式表示的，其中 `N` 是 batch size, `C` 是通道数，`L` 是输入特征的长度。 + +.. note:: + 详细请参考对应的 `Class` 请参考：:ref:`cn_api_paddle_nn_LPPool1D` 。 + + +参数 +::::::::: + - **x** (Tensor)：当前算子的输入，其是一个形状为 `[N, C, L]` 的 3-D Tensor。其中 `N` 是 batch size, `C` 是通道数，`L` 是输入特征的长度。其数据类型为 float16、float32 和 float64。 + - **norm_type** (int|float)：幂平均池化的指数，不可以为 0。 + - **kernel_size** (int|list|tuple)：池化核的尺寸大小。如果 kernel_size 为 list 或 tuple 类型，其必须包含一个整数。 + - **stride** (int|list|tuple)：池化操作步长。如果 stride 为 list 或 tuple 类型，其必须包含一个整数。 + - **padding** (string|int|list|tuple)：池化补零的方式。如果 padding 是一个字符串，则必须为 `SAME` 或者 `VALID`。如果是 turple 或者 list 类型，则应是 `[pad_left, pad_right]` 形式。如果 padding 是一个非 0 值，那么表示会在输入的两端都 padding 上同样长度的 0。 + - **ceil_mode** (bool)：是否用 ceil 函数计算输出的 height 和 width，如果设置为 False，则使用 floor 函数来计算，默认为 False。 + - **data_format** (str)：输入和输出的数据格式，可以是"NCL"和"NLC"。N 是批尺寸，C 是通道数，L 是特征长度。默认值："NCL" + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + + + +返回 +::::::::: +``Tensor``，输入 `x` 经过幂平均池化计算得到的目标 3-D Tensor，其数据类型与输入相同。 + + + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.functional.lp_pool1d diff --git a/docs/api/paddle/nn/functional/lp_pool2d_cn.rst b/docs/api/paddle/nn/functional/lp_pool2d_cn.rst new file mode 100644 index 00000000000..97a53cad8d6 --- /dev/null +++ b/docs/api/paddle/nn/functional/lp_pool2d_cn.rst @@ -0,0 +1,53 @@ +.. _cn_api_paddle_nn_functional_lp_pool2d: + +lp_pool2d +------------------------------- + +.. py:function:: paddle.nn.functional.lp_pool2d(x, norm_type, kernel_size, stride=None, padding=0, ceil_mode=False, data_format="NCHW", name=None) +该函数是一个二维幂平均池化函数，其将构建一个二维幂平均池化层，根据输入参数 `kernel_size`, `stride`, +`padding` 等参数对输入做平均池化操作。 + +例如： + +输入： + + X 形状：:math:`\left ( N,C,H_{in},W_{in} \right )` + +属性： + + - kernel_size: :math:`ksize` + - stride: :math:`stride` + - norm_type: :math:`norm\_type` + +输出： + + Out 形状：:math:`\left ( N,C,H_{out},W_{out} \right )` + +.. math:: + out(N_i, C_j, h, w) = (\sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1} + input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)^{norm\_type})^{1 / norm\_type} + + +参数 +::::::::: + - **x** (Tensor)：形状为 `[N,C,H,W]` 或 `[N,H,W,C]` 的 4-D Tensor，N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度，数据类型为 float32 或 float64。 + - **norm_type** (int|float)：幂平均池化的指数，不可以为 0。 + - **kernel_size** (int|list|tuple)：池化核大小。如果它是一个元组或列表，它必须包含两个整数值，(pool_size_Height, pool_size_Width)。若为一个整数，则它的平方值将作为池化核大小，比如若 pool_size=2，则池化核大小为 2x2。 + - **stride** (int|list|tuple)：池化层的步长。如果它是一个元组或列表，它将包含两个整数，(pool_stride_Height, pool_stride_Width)。若为一个整数，则表示 H 和 W 维度上 stride 均为该值。默认值为 kernel_size。 + - **padding** (string|int|list|tuple) 池化填充。如果它是一个字符串，可以是"VALID"或者"SAME"，表示填充算法。如果它是一个元组或列表，它可以有 3 种格式：(1)包含 2 个整数值：[pad_height, pad_width]；(2)包含 4 个整数值：[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]；(3)包含 4 个二元组：当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]，当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数，则表示 H 和 W 维度上均为该值。默认值：0。 + - **ceil_mode** (bool)：是否用 ceil 函数计算输出高度和宽度。如果是 True，则使用 `ceil` 计算输出形状的大小。默认为 None + - **data_format** (str)：输入和输出的数据格式，可以是"NCHW"和"NHWC"。N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度。默认值："NCHW" + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + + + + +返回 +::::::::: +4-D Tensor，数据类型与输入 x 一致。 + + +代码示例 +::::::::: + +COPY-FROM: paddle.nn.functional.lp_pool2d diff --git a/docs/api/paddle/nn/functional/max_unpool1d_cn.rst b/docs/api/paddle/nn/functional/max_unpool1d_cn.rst index 0ee57eff1f7..ebec9dd68cf 100644 --- a/docs/api/paddle/nn/functional/max_unpool1d_cn.rst +++ b/docs/api/paddle/nn/functional/max_unpool1d_cn.rst @@ -24,7 +24,7 @@ max_unpool1d 参数 ::::::::: - - **x** (Tensor) - 形状为 `[N,C,L]` 的 3-D Tensor，N 是批尺寸，C 是通道数，L 是特征长度，数据类型为 float32 或 float64。 + - **x** (Tensor) - 形状为 `[N,C,L]` 的 3-D Tensor，N 是批尺寸，C 是通道数，L 是特征长度，数据类型为 float32， float64 或 int64。 - **indices** (Tensor) - 形状为 `[N,C,L]` 的 3-D Tensor，N 是批尺寸，C 是通道数，L 是特征长度，数据类型为 int32。 - **kernel_size** (int|list|tuple) - 反池化的滑动窗口大小。 - **stride** (int|list|tuple，可选) - 池化层的步长。如果它是一个元组或列表，它必须包含一个整数，(pool_stride_Length)，默认值：None。 diff --git a/docs/api/paddle/nn/functional/max_unpool2d_cn.rst b/docs/api/paddle/nn/functional/max_unpool2d_cn.rst index fabdc14fb18..17bbc1fa4fa 100644 --- a/docs/api/paddle/nn/functional/max_unpool2d_cn.rst +++ b/docs/api/paddle/nn/functional/max_unpool2d_cn.rst @@ -27,7 +27,7 @@ max_unpool2d 参数 ::::::::: - - **x** (Tensor) - 形状为 `[N,C,H,W]` 或 `[N,H,W,C]` 的 4-D Tensor，N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度，数据类型为 float32 或 float64。 + - **x** (Tensor) - 形状为 `[N,C,H,W]` 或 `[N,H,W,C]` 的 4-D Tensor，N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度，数据类型为 float32， float64 或 int64。 - **indices** (Tensor) - 形状为 `[N,C,H,W]` 的 4-D Tensor，N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度，数据类型为 int32。 - **kernel_size** (int|list|tuple) - 反池化的滑动窗口大小。 - **stride** (int|list|tuple，可选) - 池化层的步长。如果它是一个元组或列表，它必须是两个相等的整数，(pool_stride_Height, pool_stride_Width)，默认值：None。 diff --git a/docs/api/paddle/nn/functional/max_unpool3d_cn.rst b/docs/api/paddle/nn/functional/max_unpool3d_cn.rst index 50fdf055cfa..404a6cc42ba 100644 --- a/docs/api/paddle/nn/functional/max_unpool3d_cn.rst +++ b/docs/api/paddle/nn/functional/max_unpool3d_cn.rst @@ -30,7 +30,7 @@ max_unpool3d 参数 ::::::::: - - **x** (Tensor) - 形状为 `[N,C,D,H,W]` 的 5-D Tensor，N 是批尺寸，C 是通道数，D 是特征深度，H 是特征高度，W 是特征宽度，数据类型为 float32 或 float64。 + - **x** (Tensor) - 形状为 `[N,C,D,H,W]` 的 5-D Tensor，N 是批尺寸，C 是通道数，D 是特征深度，H 是特征高度，W 是特征宽度，数据类型为 float32， float64 或 int64。 - **indices** (Tensor) - 形状为 `[N,C,D,H,W]` 的 5-D Tensor，N 是批尺寸，C 是通道数，D 是特征深度，H 是特征高度，W 是特征宽度，数据类型为 int32。 - **kernel_size** (int|list|tuple) - 反池化的滑动窗口大小。 - **stride** (int|list|tuple，可选) - 池化层的步长。如果它是一个元组或列表，它必须是三个相等的整数，(pool_stride_Depth, pool_stride_Height, pool_stride_Width)，默认值：None。 diff --git a/docs/api/paddle/nn/initializer/Normal_cn.rst b/docs/api/paddle/nn/initializer/Normal_cn.rst index 310182a44f4..0cd6fd01600 100644 --- a/docs/api/paddle/nn/initializer/Normal_cn.rst +++ b/docs/api/paddle/nn/initializer/Normal_cn.rst @@ -11,7 +11,7 @@ Normal 参数 :::::::::::: - - **mean** (float，可选) - 正态分布的平均值。默认值为 0。 + - **mean** (float|complex，可选) - 正态分布的平均值。默认值为 0。 - **std** (float，可选) - 正态分布的标准差。默认值为 1.0。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/nn/initializer/TruncatedNormal_cn.rst b/docs/api/paddle/nn/initializer/TruncatedNormal_cn.rst index 0220362136c..824428f4c7e 100644 --- a/docs/api/paddle/nn/initializer/TruncatedNormal_cn.rst +++ b/docs/api/paddle/nn/initializer/TruncatedNormal_cn.rst @@ -3,14 +3,19 @@ TruncatedNormal ------------------------------- -.. py:class:: paddle.nn.initializer.TruncatedNormal(mean=0.0, std=1.0, name=None) - +.. py:class:: paddle.nn.initializer.TruncatedNormal(mean=0.0, std=1.0, a=-2.0, b=2.0, name=None) 截断正态分布（高斯分布）初始化方法。 +.. note:: + 在参数设置时建议将 `mean` 设为 :math:`a \le mean \le b`。 + 若 :math:`mean < a - 2 \cdot std` 或 :math:`mean > b + 2 \cdot std`，采样值的分布可能是有误的。 + 参数 - **mean** (float，可选) - 正态分布的均值，默认值为 :math:`0.0`。 - **std** (float，可选) - 正态分布的标准差，默认值为 :math:`1.0`。 + - **a** (float，可选) - 截断正态分布的下界，默认值为 :math:`-2.0`。 + - **b** (float，可选) - 截断正态分布的上界，默认值为 :math:`2.0`。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/nn/initializer/XavierNormal_cn.rst b/docs/api/paddle/nn/initializer/XavierNormal_cn.rst index 6c7711d4c96..8373e1c682f 100644 --- a/docs/api/paddle/nn/initializer/XavierNormal_cn.rst +++ b/docs/api/paddle/nn/initializer/XavierNormal_cn.rst @@ -3,7 +3,7 @@ XavierNormal ------------------------------- -.. py:class:: paddle.nn.initializer.XavierNormal(fan_in=None, fan_out=None, name=None) +.. py:class:: paddle.nn.initializer.XavierNormal(fan_in=None, fan_out=None, gain=1.0, name=None) 使用正态分布的泽维尔权重初始化方法。泽维尔权重初始化方法出自泽维尔·格洛特和约书亚·本吉奥的论文 `Understanding the difficulty of training deep feedforward neural networks `_ 。 @@ -12,13 +12,14 @@ XavierNormal .. math:: - x = \sqrt{\frac{2.0}{fan\_in+fan\_out}}. + x = gain \times \sqrt{\frac{2.0}{fan\_in+fan\_out}}. 参数 :::::::::::: - **fan_in** (float，可选) - 用于泽维尔初始化的 fan_in，从 Tensor 中推断，默认值为 None。 - **fan_out** (float，可选) - 用于泽维尔初始化的 fan_out，从 Tensor 中推断，默认值为 None。 + - **gain** (float，可选) - 缩放因子。默认值为 1.0。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/nn/initializer/XavierUniform_cn.rst b/docs/api/paddle/nn/initializer/XavierUniform_cn.rst index f413c7e0a5f..b35422f8ed0 100644 --- a/docs/api/paddle/nn/initializer/XavierUniform_cn.rst +++ b/docs/api/paddle/nn/initializer/XavierUniform_cn.rst @@ -3,7 +3,7 @@ XavierUniform ------------------------------- -.. py:class:: paddle.nn.initializer.XavierUniform(fan_in=None, fan_out=None, name=None) +.. py:class:: paddle.nn.initializer.XavierUniform(fan_in=None, fan_out=None, gain=1.0, name=None) 使用均匀分布的泽维尔权重初始化方法。泽维尔权重初始化方法出自泽维尔·格洛特和约书亚·本吉奥的论文 `Understanding the difficulty of training deep feedforward neural networks `_ 。 @@ -12,13 +12,14 @@ XavierUniform .. math:: - x = \sqrt{\frac{6.0}{fan\_in+fan\_out}}. + x = gain \times \sqrt{\frac{6.0}{fan\_in+fan\_out}}. 参数 :::::::::::: - **fan_in** (float，可选) - 用于泽维尔初始化的 fan_in，从 Tensor 中推断，默认值为 None。 - **fan_out** (float，可选) - 用于泽维尔初始化的 fan_out，从 Tensor 中推断，默认值为 None。 + - **gain** (float，可选) - 缩放因子。默认值为 1.0。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/normal_cn.rst b/docs/api/paddle/normal_cn.rst index cb7fc6435b1..0a3b0420f43 100644 --- a/docs/api/paddle/normal_cn.rst +++ b/docs/api/paddle/normal_cn.rst @@ -14,9 +14,12 @@ normal 如果 ``mean`` 和 ``std`` 都是 Tensor，则 ``mean`` 和 ``std`` 的元素个数应该相同。 +如果 ``mean`` 是一个复数，则输出 Tensor 服从复正态分布，其数据类型为 complex64。 +如果 ``mean`` 是复数类型的 Tensor，则输出 Tensor 的数据类型与 ``mean`` 相同。 + 参数 :::::::::: - - **mean** (float|Tensor，可选) - 输出 Tensor 的正态分布的平均值。如果 ``mean`` 是 float，则表示输出 Tensor 中所有元素的正态分布的平均值。如果 ``mean`` 是 Tensor (支持的数据类型为 float32、float64)，则表示输出 Tensor 中每个元素的正态分布的平均值。默认值为 0.0。 + - **mean** (float|complex|Tensor，可选) - 输出 Tensor 的正态分布的平均值。如果 ``mean`` 是 float，则表示输出 Tensor 中所有元素的正态分布的平均值。如果 ``mean`` 是 Tensor (支持的数据类型为 float32、float64、complex64、complex128)，则表示输出 Tensor 中每个元素的正态分布的平均值。默认值为 0.0。 - **std** (float|Tensor，可选) - 输出 Tensor 的正态分布的标准差。如果 ``std`` 是 float，则表示输出 Tensor 中所有元素的正态分布的标准差。如果 ``std`` 是 Tensor (支持的数据类型为 float32、float64)，则表示输出 Tensor 中每个元素的正态分布的标准差。默认值为 0.0。 - **shape** (list|tuple|Tensor，可选) - 生成的随机 Tensor 的形状。如果 ``shape`` 是 list、tuple，则其中的元素可以是 int，或者是形状为[]且数据类型为 int32、int64 的 0-D Tensor。如果 ``shape`` 是 Tensor，则是数据类型为 int32、int64 的 1D Tensor。如果 ``mean`` 或者 ``std`` 是 Tensor，输出 Tensor 的形状和 ``mean`` 或者 ``std`` 相同(此时 ``shape`` 无效)。默认值为 None。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/optimizer/NAdam_cn.rst b/docs/api/paddle/optimizer/NAdam_cn.rst new file mode 100644 index 00000000000..85a5c0b095d --- /dev/null +++ b/docs/api/paddle/optimizer/NAdam_cn.rst @@ -0,0 +1,166 @@ +.. _cn_api_paddle_optimizer_NAdam: + +NAdam +------------------------------- + +.. py:class:: paddle.optimizer.NAdam(learning_rate=0.002, beta1=0.9, beta2=0.999, epsilon=1.0e-8, momentum_decay=0.004, parameters=None, weight_decay=None, grad_clip=None, name=None) + + +在论文 `Incorporating Nesterov Momentum into Adam `_ 中， NAdam 优化器的实现是基于 :ref:`Adam ` 优化算法实现的。主要的改进是结合了 Nesterov 动量和 Adam 自适应学习率的优点。 + +其参数更新的计算公式如下： + +.. math:: + + \begin{aligned} + &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ + &\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2} \rho ^{t \psi} \big) \\ + &\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96 ^{(t+1)\psi}\big)\\ + &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ + &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ + &\hspace{5mm}\widehat{m_t} \leftarrow \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i) + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i}) \\ + &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ + &\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ + \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ + &\hspace{0mm} \text{ with: } \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ + &\hspace{0mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)} \\ + \end{aligned} + + +参数 +:::::::::::: + + - **learning_rate** (float|LRScheduler，可选) - 学习率，用于参数更新的计算。可以是一个浮点型值或者一个 `LRScheduler` 类。默认值为 0.002。 + - **parameters** (list，可选) - 指定优化器需要优化的参数。在动态图模式下必须提供该参数；在静态图模式下默认值为 None，这时所有的参数都将被优化。 + - **beta1** (float，可选) - 一阶矩估计的指数衰减率，默认值为 0.9。 + - **beta2** (float，可选) - 二阶矩估计的指数衰减率，默认值为 0.999。 + - **epsilon** (float，可选) - 保持数值稳定性的短浮点类型值，默认值为 1e-08。 + - **weight_decay** (float|Tensor，可选) - 正则化方法。可以是 float 类型或者 Tensor。默认值为 None，表示没有正则化。 + - **momentum_decay** (float，可选): 动量衰减率。默认值为 0.004. + - **grad_clip** (GradientClipBase，可选) – 梯度裁剪的策略，支持三种裁剪策略：:ref:`paddle.nn.ClipGradByGlobalNorm ` 、 :ref:`paddle.nn.ClipGradByNorm ` 、 :ref:`paddle.nn.ClipGradByValue ` 。默认值为 None，此时将不进行梯度裁剪。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +.. note:: + + 目前 ``NAdam`` 不支持 Sparse Parameter Optimization（稀疏参数优化）。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.optimizer.NAdam + +方法 +:::::::::::: + +step() +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +执行一次优化器并进行参数更新。 + +**返回** + +无。 + + +**代码示例** + +COPY-FROM: paddle.optimizer.NAdam.step + +minimize(loss, startup_program=None, parameters=None, no_grad_set=None) +''''''''' + +为网络添加反向计算过程，并根据反向计算所得的梯度，更新 parameters 中的 Parameters，最小化网络损失值 loss。 + +**参数** + + - **loss** (Tensor) - 需要最小化的损失值变量。 + - **startup_program** (Program，可选) - 用于初始化 parameters 中参数的 :ref:`cn_api_paddle_static_Program`，默认值为 None，此时将使用 :ref:`cn_api_paddle_static_default_startup_program`。 + - **parameters** (list，可选) - 待更新的 Parameter 或者 Parameter.name 组成的列表，默认值为 None，此时将更新所有的 Parameter。 + - **no_grad_set** (set，可选) - 不需要更新的 Parameter 或者 Parameter.name 组成集合，默认值为 None。 + +**返回** + + tuple(optimize_ops, params_grads)，其中 optimize_ops 为参数优化 OP 列表；param_grads 为由(param, param_grad)组成的列表，其中 param 和 param_grad 分别为参数和参数的梯度。在静态图模式下，该返回值可以加入到 ``Executor.run()`` 接口的 ``fetch_list`` 参数中，若加入，则会重写 ``use_prune`` 参数为 True，并根据 ``feed`` 和 ``fetch_list`` 进行剪枝，详见 ``Executor`` 的文档。 + +**代码示例** + +COPY-FROM: paddle.optimizer.NAdam.minimize + + +clear_grad() +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + + +清除需要优化的参数的梯度。 + +**代码示例** + +COPY-FROM: paddle.optimizer.NAdam.clear_grad + +set_lr(value) +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +手动设置当前 ``optimizer`` 的学习率。当使用_LRScheduler 时，无法使用该 API 手动设置学习率，因为这将导致冲突。 + +**参数** + + value (float) - 需要设置的学习率的值。 + +**返回** + +无。 + +**代码示例** + +COPY-FROM: paddle.optimizer.NAdam.set_lr + +set_lr_scheduler(scheduler) +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +手动设置当前 ``optimizer`` 的学习率为 LRScheduler 类。 + +**参数** + + scheduler (LRScheduler) - 需要设置的学习率的 LRScheduler 类。 + +**返回** + +无。 + +**代码示例** + +COPY-FROM: paddle.optimizer.NAdam.set_lr_scheduler + +get_lr() +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +获取当前步骤的学习率。当不使用_LRScheduler 时，每次调用的返回值都相同，否则返回当前步骤的学习率。 + +**返回** + +float，当前步骤的学习率。 + + +**代码示例** + +COPY-FROM: paddle.optimizer.NAdam.get_lr diff --git a/docs/api/paddle/optimizer/Optimizer_cn.rst b/docs/api/paddle/optimizer/Optimizer_cn.rst index 3cc83e5191e..172e659f96b 100755 --- a/docs/api/paddle/optimizer/Optimizer_cn.rst +++ b/docs/api/paddle/optimizer/Optimizer_cn.rst @@ -3,7 +3,7 @@ Optimizer ------------------------------- -.. py:class::class paddle.optimizer.Optimizer(learning_rate, parameters=None, weight_decay=None, grad_clip=None, name=None) +.. py:class:: paddle.optimizer.Optimizer(learning_rate, parameters=None, weight_decay=None, grad_clip=None, name=None) diff --git a/docs/api/paddle/optimizer/Overview_cn.rst b/docs/api/paddle/optimizer/Overview_cn.rst index fa805385fc7..e786934e0c8 100644 --- a/docs/api/paddle/optimizer/Overview_cn.rst +++ b/docs/api/paddle/optimizer/Overview_cn.rst @@ -28,7 +28,9 @@ paddle.optimizer 目录下包含飞桨框架支持的优化器算法相关的 AP " :ref:`Lamb ` ", "Lamb 优化器" " :ref:`LBFGS ` ", "LBFGS 优化器" " :ref:`Momentum ` ", "Momentum 优化器" + " :ref:`NAdam ` ", "NAdam 优化器" " :ref:`Optimizer ` ", "飞桨框架优化器基类" + " :ref:`RAdam ` ", "RAdam 优化器" " :ref:`RMSProp ` ", "RMSProp 优化器" " :ref:`Rprop ` ", "Rprop 优化器" " :ref:`SGD ` ", "SGD 优化器" diff --git a/docs/api/paddle/optimizer/RAdam_cn.rst b/docs/api/paddle/optimizer/RAdam_cn.rst new file mode 100644 index 00000000000..096b92228e7 --- /dev/null +++ b/docs/api/paddle/optimizer/RAdam_cn.rst @@ -0,0 +1,169 @@ +.. _cn_api_paddle_optimizer_RAdam: + +RAdam +------------------------------- + +.. py:class:: paddle.optimizer.RAdam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1.0e-8, parameters=None, weight_decay=None, grad_clip=None, name=None) + + +在论文 `On the Variance of the Adaptive Learning Rate and Beyond `_ 中， RAdam 优化器的实现是基于 :ref:`Adam ` 优化算法实现的。RAdam 通过修改 Adam 的动量项，提高了训练的初始稳定性。 + +其参数更新的计算公式如下： + +.. math:: + + \begin{aligned} + &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ + &\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ + &\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ + &\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ + &\hspace{6mm}\rho_t \leftarrow \rho_{\infty} - + 2 t \beta^t_2 /\big(1-\beta_2^t \big) \\ + &\hspace{6mm}\textbf{if} \: \rho_t > 5 \\ + &\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\ + &\hspace{12mm} r_t \leftarrow + \sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\ + &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} r_t l_t \\ + &\hspace{6mm}\textbf{else} \\ + &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} \\ + &\hspace{0mm} \text{ with: } \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)}, \: \theta_t \text{ (params)} \\ + &\hspace{0mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1 + \end{aligned} + + +参数 +:::::::::::: + + - **learning_rate** (float|LRScheduler，可选) - 学习率，用于参数更新的计算。可以是一个浮点型值或者一个 `LRScheduler` 类。默认值为 0.001。 + - **parameters** (list，可选) - 指定优化器需要优化的参数。在动态图模式下必须提供该参数；在静态图模式下默认值为 None，这时所有的参数都将被优化。 + - **beta1** (float，可选) - 一阶矩估计的指数衰减率，默认值为 0.9。 + - **beta2** (float，可选) - 二阶矩估计的指数衰减率，默认值为 0.999。 + - **epsilon** (float，可选) - 保持数值稳定性的短浮点类型值，默认值为 1e-08。 + - **weight_decay** (float|Tensor，可选) - 正则化方法。可以是 float 类型或者 Tensor。默认值为 None，表示没有正则化。 + - **grad_clip** (GradientClipBase，可选) – 梯度裁剪的策略，支持三种裁剪策略：:ref:`paddle.nn.ClipGradByGlobalNorm ` 、 :ref:`paddle.nn.ClipGradByNorm ` 、 :ref:`paddle.nn.ClipGradByValue ` 。默认值为 None，此时将不进行梯度裁剪。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +.. note:: + + 目前 ``RAdam`` 不支持 Sparse Parameter Optimization（稀疏参数优化）。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.optimizer.RAdam + +方法 +:::::::::::: + +step() +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +执行一次优化器并进行参数更新。 + +**返回** + +无。 + + +**代码示例** + +COPY-FROM: paddle.optimizer.RAdam.step + +minimize(loss, startup_program=None, parameters=None, no_grad_set=None) +''''''''' + +为网络添加反向计算过程，并根据反向计算所得的梯度，更新 parameters 中的 Parameters，最小化网络损失值 loss。 + +**参数** + + - **loss** (Tensor) - 需要最小化的损失值变量。 + - **startup_program** (Program，可选) - 用于初始化 parameters 中参数的 :ref:`cn_api_paddle_static_Program`，默认值为 None，此时将使用 :ref:`cn_api_paddle_static_default_startup_program`。 + - **parameters** (list，可选) - 待更新的 Parameter 或者 Parameter.name 组成的列表，默认值为 None，此时将更新所有的 Parameter。 + - **no_grad_set** (set，可选) - 不需要更新的 Parameter 或者 Parameter.name 组成集合，默认值为 None。 + +**返回** + + tuple(optimize_ops, params_grads)，其中 optimize_ops 为参数优化 OP 列表；param_grads 为由(param, param_grad)组成的列表，其中 param 和 param_grad 分别为参数和参数的梯度。在静态图模式下，该返回值可以加入到 ``Executor.run()`` 接口的 ``fetch_list`` 参数中，若加入，则会重写 ``use_prune`` 参数为 True，并根据 ``feed`` 和 ``fetch_list`` 进行剪枝，详见 ``Executor`` 的文档。 + +**代码示例** + +COPY-FROM: paddle.optimizer.RAdam.minimize + + +clear_grad() +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + + +清除需要优化的参数的梯度。 + +**代码示例** + +COPY-FROM: paddle.optimizer.RAdam.clear_grad + +set_lr(value) +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +手动设置当前 ``optimizer`` 的学习率。当使用_LRScheduler 时，无法使用该 API 手动设置学习率，因为这将导致冲突。 + +**参数** + + value (float) - 需要设置的学习率的值。 + +**返回** + +无。 + +**代码示例** + +COPY-FROM: paddle.optimizer.RAdam.set_lr + +set_lr_scheduler(scheduler) +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +手动设置当前 ``optimizer`` 的学习率为 LRScheduler 类。 + +**参数** + + scheduler (LRScheduler) - 需要设置的学习率的 LRScheduler 类。 + +**返回** + +无。 + +**代码示例** + +COPY-FROM: paddle.optimizer.RAdam.set_lr_scheduler + +get_lr() +''''''''' + +.. note:: + +该 API 只在 `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ 模式下生效。 + +获取当前步骤的学习率。当不使用_LRScheduler 时，每次调用的返回值都相同，否则返回当前步骤的学习率。 + +**返回** + +float，当前步骤的学习率。 + + +**代码示例** + +COPY-FROM: paddle.optimizer.RAdam.get_lr diff --git a/docs/api/paddle/randn_cn.rst b/docs/api/paddle/randn_cn.rst index d49257a9d6c..76fc07c2e89 100644 --- a/docs/api/paddle/randn_cn.rst +++ b/docs/api/paddle/randn_cn.rst @@ -10,7 +10,7 @@ randn 参数 :::::::::: - **shape** (list|tuple|Tensor) - 生成的随机 Tensor 的形状。如果 ``shape`` 是 list、tuple，则其中的元素可以是 int，或者是形状为[]且数据类型为 int32、int64 的 0-D Tensor。如果 ``shape`` 是 Tensor，则是数据类型为 int32、int64 的 1-D Tensor。 - - **dtype** (str|np.dtype，可选) - 输出 Tensor 的数据类型，支持 float32、float64。当该参数值为 None 时，输出 Tensor 的数据类型为 float32。默认值为 None。 + - **dtype** (str|np.dtype，可选) - 输出 Tensor 的数据类型，支持 float32、float64、complex64、complex128。当该参数值为 None 时，输出 Tensor 的数据类型为 float32。默认值为 None。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/reduce_as_cn.rst b/docs/api/paddle/reduce_as_cn.rst new file mode 100644 index 00000000000..8041530e1fa --- /dev/null +++ b/docs/api/paddle/reduce_as_cn.rst @@ -0,0 +1,25 @@ +.. _cn_api_paddle_reduce_as: + +reduce_as +------------------------------- + +.. py:function:: paddle.reduce_as(x, target, name=None): + +对 x 在某些维度上求和，使其结果与 target 的 shape 一致。 + + +参数 +:::::::::::: + + - **x** (Tensor) - 输入变量为多维 Tensor，支持数据类型为 bool、float16、 float32、float64、int8、uint8、int16、uint16、int32、int64、complex64 以及 complex128。 + - **target** (Tensor) - 输入变量为多维 Tensor，x 的 shape 长度必须大于或等于 target 的 shape 长度。支持数据类型为 bool、float16、 float32、float64、int8、uint8、int16、uint16、int32、int64、complex64 以及 complex128。 + - **name** (str, 可选) - 具体用法请参见 :ref:`api_guide_Name` ，一般无需设置，默认值为 None。 +返回 +:::::::::::: + +Tensor，输入 x 沿某些轴进行求和，使其结果与 target 的 shape 相同，如果 x 的 type 为 bool 或者为 int32，返回值的 type 将变为 int64，否则返回值的 type 与 x 相同。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.reduce_as diff --git a/docs/api/paddle/regularizer/L1Decay_cn.rst b/docs/api/paddle/regularizer/L1Decay_cn.rst index 2d814a8f46d..0eb12e5fc76 100644 --- a/docs/api/paddle/regularizer/L1Decay_cn.rst +++ b/docs/api/paddle/regularizer/L1Decay_cn.rst @@ -3,7 +3,7 @@ L1Decay ------------------------------- -.. py:attribute:: paddle.regularizer.L1Decay(coeff=0.0) +.. py:class:: paddle.regularizer.L1Decay(coeff=0.0) L1Decay 实现 L1 权重衰减正则化，用于模型训练，使得权重矩阵稀疏。 diff --git a/docs/api/paddle/regularizer/L2Decay_cn.rst b/docs/api/paddle/regularizer/L2Decay_cn.rst index ea67ac573af..a65af545fae 100644 --- a/docs/api/paddle/regularizer/L2Decay_cn.rst +++ b/docs/api/paddle/regularizer/L2Decay_cn.rst @@ -3,7 +3,7 @@ L2Decay ------------------------------- -.. py:attribute:: paddle.regularizer.L2Decay(coeff=0.0) +.. py:class:: paddle.regularizer.L2Decay(coeff=0.0) L2Decay 实现 L2 权重衰减正则化，用于模型训练，有助于防止模型对训练数据过拟合。 diff --git a/docs/api/paddle/renorm_cn.rst b/docs/api/paddle/renorm_cn.rst new file mode 100644 index 00000000000..a9eb180a885 --- /dev/null +++ b/docs/api/paddle/renorm_cn.rst @@ -0,0 +1,25 @@ +.. _cn_api_paddle_renorm: + +renorm +------------------------ + +.. py:function:: paddle.renorm(x, p, axis, max_norm) + +此操作用于沿指定轴计算 p 范数（p-norm），假设指定轴维度的形状值为 T，则将张量分为 T 个部分，每个部分都计算 p 范数。如果第 i 部分的 p-norm 大于 max-norm，则第 i 部分中的每个元素应以相同的比例重新归一化，以使第 i 部分的 p-norm 完全等于 max-norm；否则，第 i 部分保持不变。 + + +参数 +:::::::::::: +- **x** (Tensor) - 输入张量 +- **p** (float) - 范数运算的幂。 +- **axis** (int) - 对张量进行切片的维度。 +- **max-norm** (float) - 最大范数限制。 + +返回 +::::::::: +``Tensor``，经过 renorm 处理的张量。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.renorm diff --git a/docs/api/paddle/reshape_cn.rst b/docs/api/paddle/reshape_cn.rst index 40dc0fc3436..b95a475a69b 100644 --- a/docs/api/paddle/reshape_cn.rst +++ b/docs/api/paddle/reshape_cn.rst @@ -23,6 +23,11 @@ reshape - 2。给定一个形状为[2,4,6]的三维 Tensor x，目标形状为[2,3,-1,2]，则将 x 变换为形状为[2,3,4,2]的 4-D Tensor，且 x 的数据保持不变。在这种情况下，目标形状的一个维度被设置为-1，这个维度的值是从 x 的元素总数和剩余维度推断出来的。 - 3。给定一个形状为[2,4,6]的三维 Tensor x，目标形状为[-1,0,3,2]，则将 x 变换为形状为[2,4,3,2]的 4-D Tensor，且 x 的数据保持不变。在这种情况下，0 对应位置的维度值将从 x 的对应维数中复制，-1 对应位置的维度值由 x 的元素总数和剩余维度推断出来。 +下图展示了第一个例子中的情形——一个形状为[2,4,6]的三维张量通过 reshape 操作转变为形状为[6,8]的二维张量，同时保持了张量中元素的顺序和值不变。通过比较，可以清晰地看到张量形状变化前后各元素的对应关系。 + +.. image:: ../../images/api_legend/reshape.png + :alt: 图例 + 参数 :::::::::::: diff --git a/docs/api/paddle/round_cn.rst b/docs/api/paddle/round_cn.rst index 4aa61d40d79..b63625b7986 100644 --- a/docs/api/paddle/round_cn.rst +++ b/docs/api/paddle/round_cn.rst @@ -25,6 +25,7 @@ round - **x** (Tensor) - 支持任意维度的 Tensor。数据类型为 float32，float64 或 float16。 + - **decimals** (int，可选) - 要舍入到的小数点位数。如果 decimals 为负数，则指定小数点左边的位数。默认为 0。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/searchsorted_cn.rst b/docs/api/paddle/searchsorted_cn.rst index f387429cf51..318be10795b 100644 --- a/docs/api/paddle/searchsorted_cn.rst +++ b/docs/api/paddle/searchsorted_cn.rst @@ -9,8 +9,8 @@ searchsorted 参数 :::::::: - - **sorted_sequence** (Tensor) - 输入的 N 维或一维 Tensor，支持的数据类型：float32、float64、int32、int64。该 Tensor 的数值在其最后一个维度递增。 - - **values** (Tensor) - 输入的 N 维 Tensor，支持的数据类型：float32、float64、int32、int64。 + - **sorted_sequence** (Tensor) - 输入的 N 维或一维 Tensor，支持的数据类型：bfloat16、float16、float32、float64、int32、int64。该 Tensor 的数值在其最后一个维度递增。 + - **values** (Tensor) - 输入的 N 维 Tensor，支持的数据类型：bfloat16、float16、float32、float64、int32、int64。 - **out_int32** (bool，可选) - 输出的数据类型支持 int32、int64。默认值为 False，表示默认的输出数据类型为 int64。 - **right** (bool，可选) - 根据给定 ``values`` 在 ``sorted_sequence`` 查找对应的上边界或下边界。如果 ``sorted_sequence``的值为 nan 或 inf，则返回最内层维度的大小。默认值为 False，表示在 ``sorted_sequence`` 的查找给定 ``values`` 的下边界。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 diff --git a/docs/api/paddle/sinc__cn.rst b/docs/api/paddle/sinc__cn.rst new file mode 100644 index 00000000000..47ea5ead796 --- /dev/null +++ b/docs/api/paddle/sinc__cn.rst @@ -0,0 +1,12 @@ +.. _cn_api_paddle_sinc_: + +sinc\_ +------------------------------- + +.. py:function:: paddle.sinc_(x, name=None) + +Inplace 版本的 :ref:`cn_api_paddle_sinc` API，对输入 `x` 采用 Inplace 策略。 + +更多关于 inplace 操作的介绍请参考 `3.1.3 原位（Inplace）操作和非原位操作的区别`_ 了解详情。 + +.. _3.1.3 原位（Inplace）操作和非原位操作的区别: https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/tensor_cn.html#id3 diff --git a/docs/api/paddle/sinc_cn.rst b/docs/api/paddle/sinc_cn.rst new file mode 100644 index 00000000000..0d87f5eed25 --- /dev/null +++ b/docs/api/paddle/sinc_cn.rst @@ -0,0 +1,35 @@ +.. _cn_api_paddle_sinc: + +sinc +------------------------------- + +.. py:function:: paddle.sinc(x, name=None) + +计算输入的归一化 sinc 值。 + +计算公式为： + +.. math:: + + out_i = + \left\{ + \begin{aligned} + &1 & \text{ if $x_i = 0$} \\ + &\frac{\sin(\pi x_i)}{\pi x_i} & \text{ otherwise} + \end{aligned} + \right. + +参数 +:::::::::::: + + - **x** (Tensor) - 输入 Tensor。数据类型为 bfloat16，float16，float32，float64。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +:::::::::::: +返回类型为 Tensor，数据类型同输入一致。 + +代码示例 +:::::::::::: + +COPY-FROM: paddle.sinc diff --git a/docs/api/paddle/sort_cn.rst b/docs/api/paddle/sort_cn.rst index 6356b00f426..5778ddc5f8b 100644 --- a/docs/api/paddle/sort_cn.rst +++ b/docs/api/paddle/sort_cn.rst @@ -3,7 +3,7 @@ sort ------------------------------- -.. py:function:: paddle.sort(x, axis=-1, descending=False, name=None) +.. py:function:: paddle.sort(x, axis=-1, descending=False, stable=False, name=None) @@ -16,6 +16,7 @@ sort - **x** (Tensor) - 输入的多维 ``Tensor``，支持的数据类型：float32、float64、int16、int32、int64、uint8。 - **axis** (int，可选) - 指定对输入 Tensor 进行运算的轴，``axis`` 的有效范围是[-R, R)，R 是输入 ``x`` 的 Rank， ``axis`` 为负时与 ``axis`` +R 等价。默认值为-1。 - **descending** (bool，可选) - 指定算法排序的方向。如果设置为 True，算法按照降序排序。如果设置为 False 或者不设置，按照升序排序。默认值为 False。 + - **stable** (bool，可选) - 是否使用稳定排序算法。若设置为 True，则使用稳定排序算法，即相同元素的顺序在排序结果中将会被保留。默认值为 False，此时的算法不一定是稳定排序算法。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/sparse/Overview_cn.rst b/docs/api/paddle/sparse/Overview_cn.rst index 457be7f7970..b43c79564d6 100644 --- a/docs/api/paddle/sparse/Overview_cn.rst +++ b/docs/api/paddle/sparse/Overview_cn.rst @@ -60,6 +60,7 @@ paddle.sparse 目录包含飞桨框架支持稀疏数据存储和计算相关的 " :ref:`paddle.sparse.reshape ` ", "改变一个 SparseTensor 的形状" " :ref:`paddle.sparse.coalesce` ", "对 SparseCooTensor 进行排序并合并" " :ref:`paddle.sparse.transpose ` ", "在不改变数据的情况下改变 ``x`` 的维度顺序, 支持 COO 格式的多维 SparseTensor 以及 COO 格式的 2 维和 3 维 SparseTensor" + " :ref:`paddle.sparse.mask_as` ", "稀疏张量的掩码逻辑，使用稀疏张量 `mask` 的索引过滤输入的稠密张量 `x`" .. _about_sparse_nn: diff --git a/docs/api/paddle/sparse/abs_cn.rst b/docs/api/paddle/sparse/abs_cn.rst index f7fda037455..e328a3c4d51 100644 --- a/docs/api/paddle/sparse/abs_cn.rst +++ b/docs/api/paddle/sparse/abs_cn.rst @@ -15,7 +15,7 @@ abs 参数 ::::::::: - - **x** (SparseTensor) - 输入的稀疏 Tensor，可以为 Coo 或 Csr 格式，数据类型为 float32、float64。 + - **x** (SparseTensor) - 输入的稀疏 Tensor，可以为 Coo 或 Csr 格式，数据类型为 float32、float64、complex64、complex128。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/sparse/mask_as_cn.rst b/docs/api/paddle/sparse/mask_as_cn.rst new file mode 100644 index 00000000000..ed350913989 --- /dev/null +++ b/docs/api/paddle/sparse/mask_as_cn.rst @@ -0,0 +1,24 @@ +.. _cn_api_paddle_sparse_mask_as: + +mask_as +------------------------------- + +.. py:function:: paddle.sparse.mask_as(x, mask, name=None) + +使用稀疏张量 `mask` 的索引过滤输入的稠密张量 `x`，并生成相应格式的稀疏张量。输入的 `x` 和 `mask` 必须具有相同的形状，且返回的稀疏张量具有与 `mask` 相同的索引，即使对应的索引中存在 `零` 值。 + +参数 +::::::::: + - **x** (DenseTensor) - 输入的 DenseTensor。数据类型为 float32，float64，int32，int64，complex64，complex128，int8，int16，float16。 + - **mask** (SparseTensor) - 输入的稀疏张量，可以为 SparseCooTensor、SparseCsrTensor。当其为 SparseCsrTensor 时，应该是 2D 或 3D 的形式。 + - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 + +返回 +::::::::: +SparseTensor: 其稀疏格式、dtype、shape 均与 `mask` 相同。 + + +代码示例 +::::::::: + +COPY-FROM: paddle.sparse.mask_as diff --git a/docs/api/paddle/sparse/nn/Conv2d_cn.rst b/docs/api/paddle/sparse/nn/Conv2D_cn.rst similarity index 99% rename from docs/api/paddle/sparse/nn/Conv2d_cn.rst rename to docs/api/paddle/sparse/nn/Conv2D_cn.rst index 6b61d82cc4c..4e7dfac3aff 100644 --- a/docs/api/paddle/sparse/nn/Conv2d_cn.rst +++ b/docs/api/paddle/sparse/nn/Conv2D_cn.rst @@ -35,13 +35,13 @@ Conv2D - **kernel_size** (int|list|tuple) - 卷积核大小。 - **stride** (int|list|tuple，可选) - 步长大小。可以为单个整数或包含三个整数的元组或列表，分别表示卷积沿着深度，高和宽的步长。如果为单个整数，表示沿着高和宽的步长都等于该整数。默认值：1。 - **padding** (int|str|tuple|list，可选) - 填充大小。填充可以是以下形式之一： - + 1. 字符串 ['valid', 'same']。 2. 一个整数，表示每个空间维度（高度、宽度）的零填充大小。 3. 长度为空间维度数量的列表[int]或元组[int]，包含每个空间维度两边的填充量。形式为 [pad_d1, pad_d2, ...]。 4. 长度为空间维度数量的两倍的列表[int]或元组[int]。形式为 [pad_before, pad_after, pad_before, pad_after, ...]。 5. 成对整数的列表或元组。形式为 [[pad_before, pad_after], [pad_before, pad_after], ...]。 - + 注意，批量维度和通道维度也包括在内。每对整数对应输入的一个维度的填充量。批量维度和通道维度的填充应为 [0, 0] 或 (0, 0)。默认值为 0。 - **dilation** (int|list|tuple，可选) - 空洞大小。可以为单个整数或包含三个整数的元组或列表，分别表示卷积核中的元素沿着深度，高和宽的空洞。如果为单个整数，表示深度，高和宽的空洞都等于该整数。默认值：1。 - **groups** (int，可选) - 二维卷积层的组数。根据 Alex Krizhevsky 的深度卷积神经网络（CNN）论文中的成组卷积：当 group=n，输入和卷积核分别根据通道数量平均分为 n 组，第一组卷积核和第一组输入进行卷积计算，第二组卷积核和第二组输入进行卷积计算，……，第 n 组卷积核和第 n 组输入进行卷积计算。默认值：1。 diff --git a/docs/api/paddle/sparse/nn/MaxPool3D_cn.rst b/docs/api/paddle/sparse/nn/MaxPool3D_cn.rst index e966be62fb9..e51dec69ce8 100644 --- a/docs/api/paddle/sparse/nn/MaxPool3D_cn.rst +++ b/docs/api/paddle/sparse/nn/MaxPool3D_cn.rst @@ -3,7 +3,7 @@ MaxPool3D ------------------------------- -.. py:function:: paddle.sparse.nn.MaxPool3D(kernel_size, stride=None, padding=0, ceil_mode=False, return_mask=False, data_format="NDHWC", name=None) +.. py:class:: paddle.sparse.nn.MaxPool3D(kernel_size, stride=None, padding=0, ceil_mode=False, return_mask=False, data_format="NDHWC", name=None) 构建 `MaxPool3D` 类的一个可调用对象，其将构建一个三维最大池化层，根据输入参数 `kernel_size`, `stride`, `padding` 等参数对稀疏输入特征做最大池化操作。 输入输出都是 "NDHWC" 格式，其中 N 是批大小， C 是特征的通道数， H 是特征的高， W 是特征的宽。 diff --git a/docs/api/paddle/standard_normal_cn.rst b/docs/api/paddle/standard_normal_cn.rst index 83a2149edbe..97e1dcbc32a 100644 --- a/docs/api/paddle/standard_normal_cn.rst +++ b/docs/api/paddle/standard_normal_cn.rst @@ -10,7 +10,7 @@ standard_normal 参数 :::::::::: - **shape** (list|tuple|Tensor) - 生成的随机 Tensor 的形状。如果 ``shape`` 是 list、tuple，则其中的元素可以是 int，或者是形状为[]且数据类型为 int32、int64 的 0-D Tensor。如果 ``shape`` 是 Tensor，则是数据类型为 int32、int64 的 1-D Tensor。 - - **dtype** (str|np.dtype，可选) - 输出 Tensor 的数据类型，支持 float32、float64。当该参数值为 None 时，输出 Tensor 的数据类型为 float32。默认值为 None。 + - **dtype** (str|np.dtype，可选) - 输出 Tensor 的数据类型，支持 float32、float64、complex64、complex128。当该参数值为 None 时，输出 Tensor 的数据类型为 float32。默认值为 None。 - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name`，一般无需设置，默认值为 None。 返回 diff --git a/docs/api/paddle/summary_cn.rst b/docs/api/paddle/summary_cn.rst index 94fb2c3c5b8..c28d4987bc4 100644 --- a/docs/api/paddle/summary_cn.rst +++ b/docs/api/paddle/summary_cn.rst @@ -19,7 +19,17 @@ summary ::::::::: 字典，包含了总的参数量和总的可训练的参数量。 -代码示例 +代码示例 1 ::::::::: -COPY-FROM: paddle.summary +COPY-FROM: paddle.summary:code-example-1 + +代码示例 2 +::::::::: + +COPY-FROM: paddle.summary:code-example-2 + +代码示例 3 +::::::::: + +COPY-FROM: paddle.summary:code-example-3 diff --git a/docs/api/paddle/vision/get_image_backend_cn.rst b/docs/api/paddle/vision/get_image_backend_cn.rst index 43f30820fc2..60cba4709cb 100644 --- a/docs/api/paddle/vision/get_image_backend_cn.rst +++ b/docs/api/paddle/vision/get_image_backend_cn.rst @@ -3,7 +3,7 @@ get_image_backend ------------------------------- -.. py:function:: paddle.vision.image.get_image_backend() +.. py:function:: paddle.vision.get_image_backend() 获取用于加载图像的模块名称。 @@ -16,4 +16,4 @@ get_image_backend 代码示例 ::::::::: -COPY-FROM: paddle.vision.image.get_image_backend +COPY-FROM: paddle.vision.get_image_backend diff --git a/docs/api/paddle/vision/image_load_cn.rst b/docs/api/paddle/vision/image_load_cn.rst index 70b889fd285..d6c7f844321 100644 --- a/docs/api/paddle/vision/image_load_cn.rst +++ b/docs/api/paddle/vision/image_load_cn.rst @@ -3,7 +3,7 @@ image_load ------------------------------- -.. py:function:: paddle.vision.image.image_load(path, backend=None) +.. py:function:: paddle.vision.image_load(path, backend=None) 读取一个图像。 @@ -21,4 +21,4 @@ image_load 代码示例 ::::::::: -COPY-FROM: paddle.vision.image.image_load +COPY-FROM: paddle.vision.image_load diff --git a/docs/api/paddle/vision/models/AlexNet__upper_cn.rst b/docs/api/paddle/vision/models/AlexNet__upper_cn.rst index cac727b04ba..614278ef263 100644 --- a/docs/api/paddle/vision/models/AlexNet__upper_cn.rst +++ b/docs/api/paddle/vision/models/AlexNet__upper_cn.rst @@ -3,7 +3,7 @@ AlexNet ------------------------------- -.. py:function:: paddle.vision.models.AlexNet(num_classes=1000) +.. py:class:: paddle.vision.models.AlexNet(num_classes=1000) AlexNet 模型，来自论文 `"ImageNet Classification with Deep Convolutional Neural Networks" `_ 。 diff --git a/docs/api/paddle/vision/models/GoogLeNet__upper_cn.rst b/docs/api/paddle/vision/models/GoogLeNet__upper_cn.rst index 49826c58ac7..77b0da0eb8a 100644 --- a/docs/api/paddle/vision/models/GoogLeNet__upper_cn.rst +++ b/docs/api/paddle/vision/models/GoogLeNet__upper_cn.rst @@ -3,7 +3,7 @@ GoogLeNet ------------------------------- -.. py:function:: paddle.vision.models.googlenet(num_classes=1000, with_pool=True) +.. py:class:: paddle.vision.models.GoogLeNet(num_classes=1000, with_pool=True) GoogLeNet（Inception v1）模型，来自论文 `"Going Deeper with Convolutions" `_ 。 diff --git a/docs/api/paddle/vision/models/SqueezeNet_cn.rst b/docs/api/paddle/vision/models/SqueezeNet_cn.rst index 7c270fd2276..15f1547dc1d 100644 --- a/docs/api/paddle/vision/models/SqueezeNet_cn.rst +++ b/docs/api/paddle/vision/models/SqueezeNet_cn.rst @@ -3,7 +3,7 @@ SqueezeNet ------------------------------- -.. py:function:: paddle.vision.models.SqueezeNet(version, num_classes=1000, with_pool=True) +.. py:class:: paddle.vision.models.SqueezeNet(version, num_classes=1000, with_pool=True) SqueezeNet 模型，来自论文 `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" `_ 。 diff --git a/docs/api/paddle/vision/set_image_backend_cn.rst b/docs/api/paddle/vision/set_image_backend_cn.rst index 18fa54aa826..f68ec618739 100644 --- a/docs/api/paddle/vision/set_image_backend_cn.rst +++ b/docs/api/paddle/vision/set_image_backend_cn.rst @@ -3,7 +3,7 @@ set_image_backend ------------------------------- -.. py:function:: paddle.vision.image.set_image_backend(backend) +.. py:function:: paddle.vision.set_image_backend(backend) 在 :ref:`cn_api_paddle_vision_datasets_ImageFolder` 和 :ref:`cn_api_paddle_vision_datasets_DatasetFolder` 类中指定用于加载图像的后端。现在支持后端是 ``pillow`` 和 ``opencv``。如果未设置后端，则默认使用 ``pil`` 。 @@ -16,4 +16,4 @@ set_image_backend 代码示例 ::::::::: -COPY-FROM: paddle.vision.image.set_image_backend +COPY-FROM: paddle.vision.set_image_backend diff --git a/docs/api_guides/index_cn.rst b/docs/api_guides/index_cn.rst index ba504f3a84f..532612a1d63 100755 --- a/docs/api_guides/index_cn.rst +++ b/docs/api_guides/index_cn.rst @@ -17,7 +17,6 @@ API 功能分类 low_level/inference.rst low_level/memory_optimize.rst low_level/executor.rst - low_level/parallel_executor.rst low_level/compiled_program.rst low_level/parameter.rst low_level/distributed/index.rst diff --git a/docs/api_guides/index_en.rst b/docs/api_guides/index_en.rst index 012a3a401c7..0a7891f43d4 100755 --- a/docs/api_guides/index_en.rst +++ b/docs/api_guides/index_en.rst @@ -17,7 +17,6 @@ This section introduces the Fluid API structure and usage, to help you quickly g low_level/inference_en.rst low_level/memory_optimize_en.rst low_level/executor_en.rst - low_level/parallel_executor_en.rst low_level/compiled_program_en.rst low_level/parameter_en.rst low_level/distributed/index_en.rst diff --git a/docs/api_guides/low_level/executor.rst b/docs/api_guides/low_level/executor.rst index 8745ff019a1..8b5a3f92486 100644 --- a/docs/api_guides/low_level/executor.rst +++ b/docs/api_guides/low_level/executor.rst @@ -4,7 +4,7 @@ 执行引擎 ########## -:code:`Executor` 实现了一个简易的执行器，所有的操作在其中顺序执行。你可以在 Python 脚本中运行 :code:`Executor` 。PaddlePaddle Fluid 中有两种执行器。一种是 :code:`Executor` 默认的单线程执行器，另一种是并行计算执行器，在 :ref:`api_guide_parallel_executor` 中进行了解释。``Executor`` 和 :ref:`api_guide_parallel_executor` 的配置不同，这可能会给部分用户带来困惑。为使执行器更加灵活，我们引入了 :ref:`api_guide_compiled_program` ， :ref:`api_guide_compiled_program` 用于把一个程序转换为不同的优化组合，可以通过 :code:`Executor` 运行。 +:code:`Executor` 实现了一个简易的执行器，所有的操作在其中顺序执行。你可以在 Python 脚本中运行 :code:`Executor` 。 :code:`Executor` 的逻辑非常简单。建议在调试阶段用 :code:`Executor` 在一台计算机上完整地运行模型，然后转向多设备或多台计算机计算。 diff --git a/docs/api_guides/low_level/executor_en.rst b/docs/api_guides/low_level/executor_en.rst index 232016b35a3..aff077f1f07 100755 --- a/docs/api_guides/low_level/executor_en.rst +++ b/docs/api_guides/low_level/executor_en.rst @@ -4,7 +4,7 @@ Executor ################ -:code:`Executor` realizes a simple executor in which all operators will be executed in order. You can run :code:`Executor` in a Python script. There are two kinds of executors in PaddlePaddle Fluid. One is single-thread executor which is the default option for :code:`Executor` and the other is the parallel executor which is illustrated in :ref:`api_guide_parallel_executor_en` . The config of `Executor` and :ref:`api_guide_parallel_executor_en` is different, it may be a bit confusing for some users. To make the executor more facility, we introduce :ref:`api_guide_compiled_program_en` , :ref:`api_guide_compiled_program_en` is used to transform a program for various optimizations, and it can be run by :code:`Executor`. +:code:`Executor` realizes a simple executor in which all operators will be executed in order. You can run :code:`Executor` in a Python script. The logic of :code:`Executor` is very simple. It is suggested to thoroughly run the model with :code:`Executor` in debugging phase on one computer and then switch to mode of multiple devices or multiple computers to compute. diff --git a/docs/api_guides/low_level/layers/data_in_out_en.rst b/docs/api_guides/low_level/layers/data_in_out_en.rst index 2be989f5121..e79c95ee96a 100755 --- a/docs/api_guides/low_level/layers/data_in_out_en.rst +++ b/docs/api_guides/low_level/layers/data_in_out_en.rst @@ -25,6 +25,3 @@ Fluid supports obtaining data for the current batch in the training/inference ph The user can fetch expected variables from :code:`executor.run(fetch_list=[...], return_numpy=...)` . User can determine whether to convert the output data to numpy array by setting the :code:`return_numpy` parameter. If :code:`return_numpy` is :code:`False` , data of type :code:`LoDTensor` will be returned. - -For specific usage, please refer to the relevant API documentation :ref:`api_fluid_executor_Executor` and -:ref:`api_fluid_ParallelExecutor`. diff --git a/docs/api_guides/low_level/parallel_executor.rst b/docs/api_guides/low_level/parallel_executor.rst deleted file mode 100644 index 4ac66bc5061..00000000000 --- a/docs/api_guides/low_level/parallel_executor.rst +++ /dev/null @@ -1,66 +0,0 @@ -.. _api_guide_parallel_executor: - -##### -数据并行执行引擎 -##### - - -:code:`ParallelExecutor` 是以数据并行的方式在多个节点上分别执行 :code:`Program` 的执行器。用户可以通过 Python 脚本驱动 :code:`ParallelExecutor` 执行， :code:`ParallelExecutor` 的执行过程： - -- 首先根据 :code:`Program` 、 :code:`GPU` 卡的数目（或者 :code:`CPU` 的核数）以及 :ref:`cn_api_fluid_BuildStrategy` 构建 :code:`SSA Graph` 和一个线程池; -- 执行过程中，根据 Op 的输入是否 Ready 决定是否执行该 Op，这样可以使没有相互依赖的多个 Op 可在线程池中并行执行； - -:code:`ParallelExecutor` 在构造时需要指定当前 :code:`Program` 的设备类型， :code:`GPU` 或者 :code:`CPU` ： - -* 使用 :code:`GPU` 执行： :code:`ParallelExecutor` 会自动检测当前机器可以使用 :code:`GPU` 的个数，并在每个 :code:`GPU` 上分别执行 :code:`Program` ，用户也可以通过设置 :code:`CUDA_VISIBLE_DEVICES` 环境变量来指定执行器可使用的 :code:`GPU` ； -* 使用 :code:`CPU` 多线程执行：:code:`ParallelExecutor` 会自动检测当前机器可利用的 :code:`CPU` 核数，并将 :code:`CPU` 核数作为执行器中线程的个数，每个线程分别执行 :code:`Program` ，用户也可以通过设置 :code:`CPU_NUM` 环境变量来指定当前训练使用的线程个数。 - -:code:`ParallelExecutor` 支持模型训练和模型预测： - -* 模型训练： :code:`ParallelExecutor` 在执行过程中对多个节点上的参数梯度进行聚合，然后进行参数的更新； -* 模型预测： :code:`ParallelExecutor` 在执行过程中各个节点独立运行当前的 :code:`Program` ； - -:code:`ParallelExecutor` 在模型训练时支持两种模式的梯度聚合, :code:`AllReduce` 和 :code:`Reduce` ： - -* :code:`AllReduce` 模式下， :code:`ParallelExecutor` 调用 AllReduce 操作使多个节点上参数梯度完全相等，然后各个节点独立进行参数的更新； -* :code:`Reduce` 模式下， :code:`ParallelExecutor` 会预先将所有参数的更新分派到不同的节点上，在执行过程中 :code:`ParallelExecutor` 调用 Reduce 操作将参数梯度在预先指定的节点上进行聚合，并进行参数更新，最后调用 Broadcast 操作将更新后的参数发送到其他节点。 - -这两种模式通过 :code:`build_strategy` 来指定，使用方法，请参考 :ref:`cn_api_fluid_BuildStrategy` 。 - -**注意** ：如果在 Reduce 模式下使用 :code:`CPU` 多线程执行 :code:`Program` ， :code:`Program` 的参数在多个线程间是共享的，在某些模型上，Reduce 模式可以大幅节省内存。 - -鉴于模型的执行速率和模型结构及执行器的执行策略有关，:code:`ParallelExecutor` 允许你修改执行器的相关参数，例如线程池的规模( :code:`num_threads` )、为清除临时变量 :code:`num_iteration_per_drop_scope` 需要进行的循环次数。 - - -.. code-block:: python - - # 注释： - # - 如果你想在 ParallelExecutor 中指定用于运行的 GPU 卡，需要在环境中定义 - # CUDA_VISIBLE_DEVICES - # - 如果你想在 ParallelExecutor 中使用多 CPU 来运行程序，需要在环境中定义 - # CPU_NUM - # 首先创建 Executor。 - place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - exe = fluid.Executor(place) - # 运行启动程序仅一次。 - exe.run(fluid.default_startup_program()) - # 定义 train_exe 和 test_exe - exec_strategy = fluid.ExecutionStrategy() - exec_strategy.num_threads = dev_count * 4 # the size of thread pool. - build_strategy = fluid.BuildStrategy() - build_strategy.memory_optimize = True if memory_opt else False - train_exe = fluid.ParallelExecutor(use_cuda=use_cuda, - main_program=train_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy, - loss_name=loss.name) - # 注释：对于 test_exe，loss_name 是不必要的。 - test_exe = fluid.ParallelExecutor(use_cuda=True, - main_program=test_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy, - share_vars_from=train_exe) - train_loss, = train_exe.run(fetch_list=[loss.name], feed=feed_dict) - test_loss, = test_exe.run(fetch_list=[loss.name], feed=feed_dict) -- 相关 API : - - :ref:`cn_api_paddle_static_BuildStrategy` diff --git a/docs/api_guides/low_level/parallel_executor_en.rst b/docs/api_guides/low_level/parallel_executor_en.rst deleted file mode 100644 index 64abf3a2ccf..00000000000 --- a/docs/api_guides/low_level/parallel_executor_en.rst +++ /dev/null @@ -1,76 +0,0 @@ -.. _api_guide_parallel_executor_en: - -############################## -Parallel Executor -############################## - -:code:`ParallelExecutor` is an upgraded version of Executor, in addition, it supports model training of :code:`Program` in parallel with data. Users can use the Python script to run :code:`ParallelExecutor`. The execution process of :code:`ParallelExecutor` is as follows: - -- First it builds :code:`SSA Graph` and a thread pool based on :code:`Program`, the number of :code:`GPU` cards (or :code:`CPU` cores) and :ref:`api_fluid_BuildStrategy` ; -- During execution, it executes the Op depending on whether the input of Op is ready, so that multiple Ops that do not depend on each other can be executed in parallel in the thread pool; - -When constructing :code:`ParallelExecutor`, you need to specify the device type of the current :code:`Program`, namely :code:`GPU` or :code:`CPU` : - -* execution on :code:`GPU` : :code:`ParallelExecutor` will automatically detect the number of currently available :code:`GPU` s, and execute :code:`Program` on each :code:`GPU` . The user can also specify the :code:`GPU` that the executor can use by setting the :code:`CUDA_VISIBLE_DEVICES` environment variable; -* execution on multi-threaded :code:`CPU` : :code:`ParallelExecutor` will automatically detect the number of currently available :code:`CPU` s, and take it as the number of threads in the executor . Each thread executes :code:`Program` separately. The user can also specify the number of threads currently used for training by setting the :code:`CPU_NUM` environment variable. - -:code:`ParallelExecutor` supports model training and model prediction: - -* Model training: :code:`ParallelExecutor` aggregates the parameter gradients on multiple nodes during the execution process, and then updates the parameters; -* Model prediction: during the execution of :code:`ParallelExecutor`, each node runs the current :code:`Program` independently; - -:code:`ParallelExecutor` supports two modes of gradient aggregation during model training, :code:`AllReduce` and :code:`Reduce` : - -* In :code:`AllReduce` mode, :code:`ParallelExecutor` calls AllReduce operation to make the parameter gradients on multiple nodes completely equal, and then each node independently updates the parameters; -* In :code:`Reduce` mode, :code:`ParallelExecutor` will pre-allocate updates of all parameters to different nodes. During the execution :code:`ParallelExecutor` calls Reduce operation to aggregate parameter gradients on the pre-specified node, and the parameters are updated. Finally, the Broadcast operation is called to send the updated parameters to other nodes. - -These two modes are specified by :code:`build_strategy`. For how to use them, please refer to :ref:`api_fluid_BuildStrategy` . - -**Note**: If you use :code:`CPU` to execute :code:`Program` in multi-thread in Reduce mode, the parameters of :code:`Program` will be shared among multiple threads. On some models , Reduce mode can save a lot of memory. - -Since the execution speed of the model is related to the model structure and the execution strategy of the executor, :code:`ParallelExecutor` allows you to modify the relevant parameters of the executor, such as the size of thread pool ( :code:`num_threads` ), how many iterations should be done to clean up temporary variables :code:`num_iteration_per_drop_scope` . For more information, please refer to :ref:`api_fluid_ExecutionStrategy`. - - -.. code-block:: python - - # Note: - # - If you want to specify the GPU cards which are used to run - # in ParallelExecutor, you should define the CUDA_VISIBLE_DEVICES - # in environment. - # - If you want to use multi CPU to run the program in ParallelExecutor, - # you should define the CPU_NUM in the environment. - - # First create the Executor. - place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - exe = fluid.Executor(place) - - # Run the startup program once and only once. - exe.run(fluid.default_startup_program()) - - # Define train_exe and test_exe - exec_strategy = fluid.ExecutionStrategy() - exec_strategy.num_threads = dev_count * 4 # the size of thread pool. - build_strategy = fluid.BuildStrategy() - build_strategy.memory_optimize = True if memory_opt else False - - train_exe = fluid.ParallelExecutor(use_cuda=use_cuda, - main_program=train_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy, - loss_name=loss.name) - # NOTE: loss_name is unnecessary for test_exe. - test_exe = fluid.ParallelExecutor(use_cuda=True, - main_program=test_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy, - share_vars_from=train_exe) - - train_loss, = train_exe.run(fetch_list=[loss.name], feed=feed_dict) - test_loss, = test_exe.run(fetch_list=[loss.name], feed=feed_dict) - -**Note**: :code:`fluid.Executor` and :code:`fluid.ParallelExecutor` are two completely different executors. First of all, their execution objects are different. The execution object of :code:`fluid.Executor` is :code:`fluid.Program` and the execution object of :code:`fluid.ParallelExecutor` is Graph. Secondly, their execution schedules are different. :code:`fluid.Executor` runs one by one according to the order of operators in :code:`fluid.Program`. :code:`fluid.ParallelExecutor` executes concurrently according to the dependencies between nodes in Graph. - -- Related API : - - :ref:`api_fluid_ParallelExecutor` - - :ref:`api_fluid_BuildStrategy` - - :ref:`api_fluid_ExecutionStrategy` diff --git a/docs/design/concepts/images/parallel_executor_overview.dot b/docs/design/concepts/images/parallel_executor_overview.dot deleted file mode 100644 index f013bb45602..00000000000 --- a/docs/design/concepts/images/parallel_executor_overview.dot +++ /dev/null @@ -1,83 +0,0 @@ -digraph G { - subgraph cluster_init { - label="Initialization" - startup_program [label="startup", shape=box] - node_w_g0 [label="W\nGPU0"] - startup_program -> node_w_g0 [label="Initialize"] - node_w_g1 [label="W\nGPU1"] - node_w_g0 -> node_w_g1 [label="broadcast"] - } - - subgraph cluster_train { - label="forward_backward" - - subgraph cluster_gpu0 { - label="GPU0" - fc_0 [label="fc\nGPU0", shape=box] - hidden_0 [label="hidden\nGPU0"] - node_w_g0 -> fc_0 - fc_0 -> hidden_0 - loss0 [label="loss\nGPU0"] - hidden_0 -> loss0 [label="many ops omitted"] - scale_loss_0 [label="scale_loss_gradient\nGPU0", shape=box] - loss_g0 [label="loss_grad\nGPU0"] - scale_loss_0->loss_g0 - - fc_g_0 [label="w_grad\nGPU0", shape=box] - loss0 -> fc_g_0 - loss_g0 -> fc_g_0 - hidden_0 -> fc_g_0 - } - - subgraph cluster_gpu1 { - label="GPU1" - fc_1 [label="fc\nGPU1", shape=box] - hidden_1 [label="hidden\nGPU1"] - node_w_g1 -> fc_1 - fc_1 -> hidden_1 - loss1 [label="loss\nGPU1"] - hidden_1 -> loss1 [label="many ops omitted"] - scale_loss_1 [label="scale_loss_gradient\nGPU1", shape=box] - loss_g1 [label="loss_grad\nGPU1"] - scale_loss_1->loss_g1 - - fc_g_1 [label="w_grad\nGPU1", shape=box] - loss1 -> fc_g_1 - loss_g1 -> fc_g_1 - hidden_1 -> fc_g_1 - } - } - - all_reduce_w [label="Merge Gradients(AllReduce)", shape=box] - fc_g_0 -> all_reduce_w - fc_g_1 -> all_reduce_w - - fc_g_0_merged [label="w_grad\nMerged\nGPU0"] - fc_g_1_merged [label="w_grad\nMerged\nGPU1"] - all_reduce_w -> fc_g_0_merged - all_reduce_w -> fc_g_1_merged - - subgraph cluster_optimization { - label="Optimization" - subgraph cluster_opt_gpu0 { - label="GPU0" - sgd_0 [label="SGD Op\nGPU0", shape=box] - - fc_g_0_merged -> sgd_0 - node_w_g0 -> sgd_0 - optimized_w_0 [label="Optimized W\nGPU0"] - sgd_0 -> optimized_w_0 - } - subgraph cluster_opt_gpu1 { - label="GPU1" - sgd_1 [label="SGD Op\nGPU1", shape=box] - - fc_g_1_merged -> sgd_1 - node_w_g1 -> sgd_1 - optimized_w_1 [label="Optimized W\nGPU0"] - sgd_1 -> optimized_w_1 - } - } - - -} diff --git a/docs/design/concepts/images/parallel_executor_overview.png b/docs/design/concepts/images/parallel_executor_overview.png deleted file mode 100644 index d890c0ffee3b38dc7cb74a2b56c2ab4831532211..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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The operator handles are the wrapper of `OperatorBase`. Some `OpHandle` is not an `OperatorBase`, such as `NCCLAllReduceOpHandle`, because `AllReduceOpHandle` will use new device contexts. - -When the `ProgramDesc` converted into an `SSA` Graph, the [data hazard](https://en.wikipedia.org/wiki/Hazard_(computer_architecture)) problem is also need to be taken care. The dummy variables, which represent the dependency between operators, will be manually inserted into SSA graph to resolve the [data hazard](https://en.wikipedia.org/wiki/Hazard_(computer_architecture)) problem. - -## Execute SSA Graph - -The SSA graph can be out-of-order executed by an approximate [topological sorting](https://en.wikipedia.org/wiki/Topological_sorting) algorithm. The algorithm is - -1. Maintaining a map of an operator and its needed input number. -2. If a variable is not generated by an operator, i.e., `var.generated_op == nullptr`, decrease the needed input number of its pending operators. -3. If there is an operator which needed input number is decreased to zero, just run this operator. -4. After run this operator, just mark the variables are generated and repeat step 2 until all variables are generated. - -Running an operator can be asynchronized. There is a thread pool to execute an `SSA` graph. - -## Synchronize GPU Kernels - -The GPU is a non-blocking device. The different streams need be synchronized when switching streams. In current implementation, the synchronization based on the following algorithm: - -1. `OpHandle` will record `DeviceContext` that it is used. -2. In `OpHandle::Run`, if the `DeviceContext` of current operator is different from `DeviceContext` of any input variable, just wait the generate operator of this input variable. - -The `wait` are implemented by two strategies: - -1. Invoke `DeviceContext->Wait()`, It will wait all operators on this device contexts complete. -2. Uses `cudaStreamWaitEvent` to sending a event to the stream. It is a non-blocking call. The wait operators will be executed in GPU. - -Generally, the `cudaStreamWaitEvent` will have a better performance. However, `DeviceContext->Wait()` strategy is easier to debug. The strategy can be changed in runtime. - -## What's next? - -* Merging gradient of dense parameters has been done. However, the merging of sparse parameters has not been done. -* The CPU version of Parallel Executor has not been implemented. The out-of-order logic will make CPU computation faster, too. -* A better strategy to merge gradients can be introduced. We can shrink the gradients from `float32` to `int8` or `int4` while merging. It will significantly speed up multi-GPUs training without much loss of precision. -* Combine multi-Nodes implementation. 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    +
    图 1 Python API 代码样例
    @@ -75,7 +75,7 @@ API 作为用户使用飞桨框架的接口，承接着实现用户模型开发 ```python def zeros(shape, dtype=None, name=None): - # 为了突出重点，省略中间的文档和示例部分 + # 为了突出重点，省略类型标注与中间的文档和示例部分 if dtype is None: dtype = 'float32' return fill_constant(value=0.0, shape=shape, dtype=dtype, name=name) @@ -168,10 +168,10 @@ def trace(x, offset=0, axis1=0, axis2=1, name=None): - 输入参数的检查一般仅在静态图分支中使用。主要原因是静态图下该函数仅在模型组网时执行一次，运行期不会再执行；而动态图下该函数会被多次执行，Python 端过多的输入检查会影响执行效率。并且由于动态图即时执行的优势，如果发生错误也可以通过分析 C++ 端的报错信息定位问题。 - 示例中输入参数检查的代码逻辑比较复杂但仅用于 `trace` 函数，因此在该函数内定义一个检查输入参数的函数 `__check_input`，代码见下文。 - **创建输出 Tensor ，添加 OP：** - - 先创建 LayerHelper 对象，再使用 LayerHelper 对象创建输出 Tensor（[LayerHelper](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/layer_helper.py) 是一个用于创建 OP 输出变量、向 静态图 Program 中添加 OP 的辅助工具类）。 + - 先创建 LayerHelper 对象，再使用 LayerHelper 对象创建输出 Tensor（[LayerHelper](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/base/layer_helper.py) 是一个用于创建 OP 输出变量、向 静态图 Program 中添加 OP 的辅助工具类）。 - 在 `append_op` 添加 `inputs` 和 `outputs` 项，其中的 key 值（静态图中变量名）一般与 Python 接口中定义的输入输出 Tensor 变量名的命名相同。（注意：这里 `trace` 中的 `Input` 没有与 Python 接口中 `x` 命名直接对应是由于为了兼容旧算子体系下 `trace` 算子的定义实现而做了额外的映射，新增算子时无需考虑这种情况。） -输入参数检查的 `__check_input` 函数代码如下所示，其中检测 Tensor 的数据类型可以用 [check_variable_and_dtype](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/data_feeder.py#L80) 或 [check_type](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/data_feeder.py#L128) 函数进行检测。 +输入参数检查的 `__check_input` 函数代码如下所示，其中检测 Tensor 的数据类型可以用 [check_variable_and_dtype](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/base/data_feeder.py#L164) 或 [check_type](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/base/data_feeder.py#L176) 函数进行检测。 ```python def __check_input(x, offset, axis1, axis2): diff --git a/docs/dev_guides/code_contributing_path_cn.md b/docs/dev_guides/code_contributing_path_cn.md index 56a71666cba..01220d2e0b5 100644 --- a/docs/dev_guides/code_contributing_path_cn.md +++ b/docs/dev_guides/code_contributing_path_cn.md @@ -164,7 +164,6 @@ Tabs remover (C++)...................................(no files to check)Skipped Tabs remover (Python)....................................................Passed copyright_checker........................................................Passed black....................................................................Passed -flake8...................................................................Passed ruff.....................................................................Passed clang-format.........................................(no files to check)Skipped cpplint..............................................(no files to check)Skipped @@ -250,7 +249,7 @@ upstream https://github.com/PaddlePaddle/Paddle.git (push) （6）确保通过 CI 测试 -提交 Pull Request 后会触发 CI（Continuous Integration，持续集成）测试，并且之后每提交一次代码合入（`git push`）都会触发一次 CI 测试。CI 测试可尽可能保障代码质量，详细测试内容可参见 [Paddle CI 测试详解](../git_guides/paddle_ci_manual_cn.html)。 +提交 Pull Request 后会触发 CI（Continuous Integration，持续集成）测试，并且之后每提交一次代码合入（`git push`）都会触发一次 CI 测试。CI 测试可尽可能保障代码质量，详细测试内容可参见 [Paddle CI 测试详解](./git_guides/paddle_ci_manual_cn.html)。 提交 Pull Request 后，请关注 CI 测试进程，一般会在几个小时内完成。 diff --git a/docs/dev_guides/docs_contributing_guides_cn.md b/docs/dev_guides/docs_contributing_guides_cn.md index e267e6486e4..39101c1581c 100644 --- a/docs/dev_guides/docs_contributing_guides_cn.md +++ b/docs/dev_guides/docs_contributing_guides_cn.md @@ -120,11 +120,9 @@ fix end of files.........................................................Passed trim trailing whitespace.................................................Passed CRLF end-lines remover...................................................Passed Tabs remover.............................................................Passed -CN-[whitespace]-EN fixer.................................................Passed -convert jinja2 into html.............................(no files to check)Skipped -convert-markdown-into-html...........................(no files to check)Skipped -black................................................(no files to check)Skipped -ruff.................................................(no files to check)Skipped +CN-[whitespace]-EN fixer.............................(no files to check)Skipped +ruff.....................................................................Passed +ruff-format..............................................................Passed [my-cool-stuff ee8f4441f] fix docs bugs 1 file changed, 19 insertions(+), 34 deletions(-) ``` diff --git a/docs/dev_guides/git_guides/codestyle_check_guide_cn.md b/docs/dev_guides/git_guides/codestyle_check_guide_cn.md index b6d6ec4f3a4..4aa37e4bcc9 100644 --- a/docs/dev_guides/git_guides/codestyle_check_guide_cn.md +++ b/docs/dev_guides/git_guides/codestyle_check_guide_cn.md @@ -43,7 +43,6 @@ Tabs remover (C++).......................................................Passed Tabs remover (Python)................................(no files to check)Skipped copyright_checker........................................................Passed black................................................(no files to check)Skipped -flake8...............................................(no files to check)Skipped ruff.................................................(no files to check)Skipped clang-format.............................................................Failed - hook id: clang-format @@ -85,7 +84,7 @@ Date: xxx | [Lucas-C/pre-commit-hooks](https://github.com/Lucas-C/pre-commit-hooks.git) | 社区维护的一些通用的 hook，含将 CRLF 改为 LF、移除 Tab 等 hook | 1.5.1 | | [copyright_checker](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/codestyle/copyright.hook) | Copyright 检查 | 本地脚本 | | [black](https://github.com/psf/black) | Python 代码格式化 | 23.3.0 | -| [ruff](https://github.com/astral-sh/ruff) | Python 代码风格检查 | 0.3.0 | +| [ruff](https://github.com/astral-sh/ruff) | Python 代码风格检查 | 0.5.0 | | [clang-format](https://github.com/llvm/llvm-project/tree/main/clang/tools/clang-format) | C++ 代码格式化 | 13.0.0 | | [cpplint](https://github.com/cpplint/cpplint) | C++ 代码风格检查 | 1.6.0 | | [clang-tidy](https://github.com/llvm/llvm-project/tree/main/clang-tools-extra/clang-tidy) | C++ 代码风格检查 | 15.0.2.1 | diff --git a/docs/dev_guides/git_guides/paddle_ci_manual_cn.md b/docs/dev_guides/git_guides/paddle_ci_manual_cn.md index de0cb71bdc5..0af5c637b38 100644 --- a/docs/dev_guides/git_guides/paddle_ci_manual_cn.md +++ b/docs/dev_guides/git_guides/paddle_ci_manual_cn.md @@ -131,6 +131,12 @@ CI 测试包含的具体测试任务和执行顺序如下图所示： - **【执行脚本】** `paddle/scripts/paddle_build.sh cicheck_py37` - **【触发条件】** `PR-CI-Clone`通过后自动触发。 +#### PR-CI-Py3-PIR + +- **【条目描述】** 检查 PR 在 PIR 模式下单测执行情况（test/deprecated 目录中的单测不进行测试) +- **【执行脚本】** `paddle/scripts/paddle_build.sh cicheck_py37_pir` +- **【触发条件】** `PR-CI-Py3`通过后自动触发。 + #### PR-CI-Coverage - **【条目描述】** 检测当前 PR 在 GPU、Python3 版本的编译与单测是否通过，同时增量代码需满足行覆盖率大于 90% 的要求。可在 PR 页面点击该 CI 后的 details 查看覆盖率，如下图所示： @@ -297,9 +303,15 @@ CI 测试包含的具体测试任务和执行顺序如下图所示： - **【执行脚本】** `paddle/scripts/paddle_build.sh check_xpu_coverage` - **【触发条件】** `PR-CI-Clone`通过后自动触发。 +#### PR-CI-Kunlun-bxcheck + +- **【条目描述】** 检测 PR 中的修改能否在昆仑芯 XPU 上单测是否通过。 +- **【执行脚本】** `paddle/scripts/paddle_build.sh test` +- **【触发条件】** `PR-CI-Kunlun-R200`编译成功后自动触发。 + ### 海光 DCU 测试项 -#### PR-CI-ROCM-Compile +#### PR-CI-Hygon-DCU - **【条目描述】** 检测 PR 中的修改能否在海光 DCU 芯片上编译通过。 - **【执行脚本】** `paddle/scripts/musl_build/build_paddle.sh build_only` diff --git a/docs/dev_guides/images/ci_exec_order.png b/docs/dev_guides/images/ci_exec_order.png index 2443440ea3f4e29ad337ca408d3e7f958434f198..659f0de5d2fac0530257b4af9dabe95c0fc77996 100644 GIT binary patch literal 123868 zcmdqJby!qg-#-e7ga}9@-AId+poG#b4bq@UinKI{bc1wvcMA+E-BJ>cfI}k;Auu#& zjSs%heZRl+yUzdTy5>@t&FsBq?G>N;?$Bo{vbeV>ZlR!{;L6KMsiUBvAy81zYq2oE zI}b#YeTun(^FWU_TFP`x8c7O2<7b|Y&Gkx)UD1&TM$`77!g0455R1zeN|B& z2yNk8J0FQ@cm+?nTedcuB^wZkF6Z};*QaG{T{UQ$*X<1y?3jPN?x7JQsIZtM(Ej@} 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z`nQ5qqE9q-flP;3rC_7Sj|p<*x7-=Yw`O(kZnMH?-$5ZnCk4Cp!(+}8t7#w_8rMZy z-V12|?t(Y81mSPsSq*{9n6YM=e0+OeuzC?re4Va_2N= z>|_kVdD>+l5RXwg_9q8e9T$J?(*PPEpJ^Bx@AC_`AwQ$kC7SbcRO?Y3ZFb%R1H3aX zmZ}{;f5$qdlXu{3<>@tIh?uM7VX@jc;9zVzgMLj&!a4ziq-LyD&1OsHf44?*DMnDkcHUdi zB&O$>PNsu-mqh!hKAu$?_vYySyz$FAT;V6=7-$ zT(Yf>hZ;izmqd39BRg3NyFgvXs!-XD?eRV9;k16?8<|eZwFnehE)kg?^@KXThbWm_y4iL&ywcLgh}ZkOp<&2cn^AmasnG=!ii9= z3(huw7@MLMXSo0EDpQZOWCQ|k4qYUvX7)h!T&qSV#ym`mC(l79>g-CZyB}_E7rXX= z2c(F*{9}tjl6KG*=KUdq|3~r4gowBn%r9rX^2}x>PS=C69LxFM;Q1eJJ0;`d`_ : Introduce the block-level CUDA functions provided by PaddlePaddle to speed up operator development. +- `Kernel Primitives API <./op_optimization/kernel_primitive_api/index_en.html>`_ : Introduce the block-level CUDA functions provided by PaddlePaddle to speed up operator development. - `custom_device_docs <./custom_device_docs/index_en.html>`_ : Contribution guidelines overview. @@ -14,5 +14,5 @@ Similarly, if you feel that this document is missing, or that the description is :hidden: - kernel_primitive_api/index_en.rst + op_optimization/kernel_primitive_api/index_en.rst custom_device_docs/index_en.rst diff --git a/docs/dev_guides/op_optimization/op_optimization_method_introduction_cn.md b/docs/dev_guides/op_optimization/op_optimization_method_introduction_cn.md index 8465daffe0a..cc2bcf9d329 100644 --- a/docs/dev_guides/op_optimization/op_optimization_method_introduction_cn.md +++ b/docs/dev_guides/op_optimization/op_optimization_method_introduction_cn.md @@ -45,7 +45,7 @@ GPU Kernel 直接影响了算子性能, 我们推荐采用以下等通用优化 -### 2.3 [索引计算优化](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/platform/fast_divmod.h): +### 2.3 [索引计算优化](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/phi/kernels/funcs/fast_divmod.h): 当 GPU Kernel 的索引计算中存在除法或取模操作, 将在导致汇编层面计算开销变大, 我们建议采用快速除法优化这部分的计算开销。飞桨内[Pooling OP](https://github.com/PaddlePaddle/Paddle/blob/890c73158f663b327be7664ed6c4d08fb2c236a9/paddle/phi/kernels/funcs/pooling.cu#L41-L101) 采用索引优化计算后, 性能提升 1 倍. diff --git a/docs/dev_guides/style_guide_and_references/code_typing_writing_specification_cn.md b/docs/dev_guides/style_guide_and_references/code_typing_writing_specification_cn.md new file mode 100644 index 00000000000..014796b637b --- /dev/null +++ b/docs/dev_guides/style_guide_and_references/code_typing_writing_specification_cn.md @@ -0,0 +1,250 @@ +# Paddle 中的类型提示与 Q&A + +Python 在 3.5 版本通过 [PEP 484 – Type Hints](https://peps.python.org/pep-0484) 正式规范了 `类型提示` 功能，以帮助开发者提高代码质量。Paddle 推荐开发者使用此特性，并且将静态类型检查工具 (如 `mypy` ) 集成在 CI 流水线中，以保证基础的类型标注的准确性。但是，由于 Paddle 中存在较多非公开 API 与 c++ 接口，目前版本 (2.6.0) 不声明 Paddle 具有类型标注的完备性 + +## Paddle 中的类型提示 + +Paddle 中的 `类型提示` 主要关注以下几个部分： + +- 函数输入参数的类型 +- 函数输出参数的类型 + +以如下函数为例： + +``` python +def greeting( + name: str # (1) +) -> str: # (2) + """ + Say hello to your friend! + + Args: + name (str): The name of your friend. # (3) + + Returns: + str, The greeting. # (4) + """ + return 'Hello ' + name +``` + +其中： + +- (1) 函数输入参数的类型 +- (2) 函数输出参数的类型 +- (3) 函数文档中输入参数的类型 +- (4) 函数文档中输出参数的类型 + +其中 (1) 和 (3) ，以及 (2) 和 (4) 需要一一对应。 + +## Paddle 中类型提示的实现方案 + +Python 的类型标注可以通过不同的方式实现，参考 [PEP 561 – Distributing and Packaging Type Information](https://peps.python.org/pep-0561/#implementation) ： + +> - The package maintainer would like to add type information inline. +> - The package maintainer would like to add type information via stubs. +> - A third party or package maintainer would like to share stub files for a package, but the maintainer does not want to include them in the source of the package. + +Paddle 采用 `Inline type annotation + Stub files in package` 的方案，即： + +- Python 接口，使用 `inline` 方式标注，如： + + ``` python + def greeting(name): + return 'Hello ' + name + ``` + + 需要标注为： + + ``` python + def greeting(name: str) -> str: + return 'Hello ' + name + ``` + +- 非 Python 接口，提供 `stub` 标注文件，如： + + 存在一个 c++ 实现的模块 + + ``` shell + foo + └── bar.py + ``` + + 则应在同一个文件夹下添加 `stub` 文件 `bar.pyi` + + ``` shell + foo + ├── bar.py + └── bar.pyi + ``` + + `stub` 文件不需要实现具体的代码逻辑，只需要保留函数定义，具体可以参考 [PEP 561 – Distributing and Packaging Type Information](https://peps.python.org/pep-0561/#implementation) + +Python 目前 (`3.12` 版本) 已经完成的相关 `PEP` 有 `21` 个，具体的实现方案可参考 [Typing PEPs](https://peps.python.org/topic/typing/) 。 + +## Q&A + +### **问:** 我该如何下手？ + +答：Python 的类型标注特性一直在完善，目前已经是个相对庞大的体系了。 + +可以先学习一下 Python 官方的文档：[Static Typing with Python](https://typing.readthedocs.io/en/latest/)，熟悉一下相关的 PEP 。 + +以 `通过 CI 检查` 作为最基础的实现目标。 + +另外，目前 Paddle 添加了 `_typing` 模块，对于一些常用的公用类型做了统一整理，如： + +``` pyton +# python/paddle/_typing/layout.py +DataLayout2D: TypeAlias = Literal["NCHW", "NHCW"] +DataLayout3D: TypeAlias = Literal["NCDHW", "NDHWC"] +``` + +标注时应尽量使用 `_typing` 模块中的类型，以方便后续维护。 + +### **问:** docstring 中的 Args 与 type annotation 有什么区别？ + +答：Paddle 之前的版本 (2.6.0 及以前) 未统一进行类型标注，而在 docstring 中描述了参数类型。 +docstring 中 Args 的参数类型以方便用户理解为目的，在与 type annotation 不冲突的前提下，可以保持简洁。如： + +``` python +def test(a: int | list[int] | tuple[int, ...]) -> None: + """ + ... + + Args: + a (int|list|tuple): xxx + + Returns: + None, xxx + + ... + """ +``` + +### **问:** docstring 中的 Args 与 type annotation 不一致怎么办？ + +答：首先需要保证 type annotation 的正确性，如果 docstring 原有 Args 中的类型不正确，需要进行修改，并且，同时检查此接口的 `中文文档` (即 `docs`)是否正确，如发现错误，需要对 `docs` 单独提 PR 进行修改。 + +### **问:** 该使用 `Union` 还是 `|` 以及 `from __future__ import annotations` ？ + +答：尽可能的使用 `|` ，通常需要 `from __future__ import annotations` 。 + +由于目前 Paddle (2.6.0) 支持的 Python 最低版本为 `3.8` ，因此，`|` 只能在类型标注的情况下使用，而不能在表达式中使用，并且，同时需要 `from __future__ import annotations`，如： + +``` python +from __future__ import annotations +def test(a: int | str): ... +``` + +而在表达式中仍使用 `Union` ： + +``` python +from typing import Union +t = Union[int, str] +``` + +可参考 [PEP 563 – Postponed Evaluation of Annotations](https://peps.python.org/pep-0563/) 。 + +### **问:** 如果测试无法通过怎么办？ + +答：可以使用 `# type: ignore` 进行规避。 + +Paddle 通过工具 (如 `mypy`) 对接口的示例代码进行检查，进而保证类型标注的正确性。 + +类型标注的过程中，难免产生接口依赖问题，如果依赖的是 `私有接口` 或 `外部接口` ，则可以使用 `# type: ignore` 规避相应的类型检查，如： + +``` python +>>> import abcde # type: ignore +>>> print('ok') +``` + +或者规避整个代码检查： + +``` python +>>> # type: ignore +>>> import abcde +>>> print('ok') +``` + +### **问:** 能否使用 `Any` 类型？ + +答：可以，但应尽量避免。 + +### **问:** 如果出现 `circular import` 错误怎么办？ + +答：出现此情况可以参考以下处理方法： + +- 添加 `from __future__ import annotations` +- 将类型单独通过 `typing.TYPE_CHECKING` 引入，如： + + ``` python + from typing import TYPE_CHECKING + if TYPE_CHECKING: + import paddle.xxx as xxx + + def tmp() -> xxx: ... + ``` + + 另外，如果标注的类型仅用作 type hints，也尽可能的使用 `TYPE_CHECKING` ，以减少不必要的模块导入。 + +### **问:** 使用 `Tensor` 还是 `Variable`？ + +答：尽量使用 `Tensor` ，不将静态图的 `Variable/Value` 概念暴露给用户。 + +更详细的讨论可以参考 https://github.com/PaddlePaddle/community/pull/858#discussion_r1564552690 + +### **问:** 如果遇到需要根据不同输入类型有不同输出类型的函数怎么办？ + +答：出现此情况可以参考以下处理方法： + +- 添加 `from typing import overload` +- 标注多个同名函数，并用装饰器装饰，如： + + ``` python + from typing import overload + + @overload + def array_length(array: list[Any]) -> int:... + + @overload + def array_length(array: paddle.Tensor) -> paddle.Tensor:... + + def array_length(array): ... # 具体实现的代码，不再进行标注 + ``` + +### **问:** 什么时候用 `Sequence` ，什么时候用 `list` 和 `tuple`？ + +答：Python 的 PEP 中有提示： + +> Note: Dict, DefaultDict, List, Set and FrozenSet are mainly useful for annotating return values. For arguments, prefer the abstract collection types defined below, e.g. Mapping, Sequence or AbstractSet. + +也就是说，输入中用 `Sequence` ，返回值用 `list` 。 + +但是，如果代码中使用到了 `list` 的方法，如 `append` ，或者明确表示此输入只能是 `list` ，则不应再使用 `Sequence` 。 + +### **问:** 标注的时候用 `Tensor` 还是 `paddle.Tensor`？ + +答：两者皆可。 + +若文件中出现较多 `paddle.Tensor` ，出于简洁的考虑，可以使用 `Tensor` 代替，但是需要在导入包时注意： + +``` python +if TYPE_CHECKING: + from paddle import Tensor +``` + +可参考讨论：https://github.com/PaddlePaddle/Paddle/pull/65073#discussion_r1636116450 + +### **问:** 该用 `paddle.framework.Block` 还是 `paddle.pir.Block`？ + +答：统一使用 `paddle.pir.Block`。 + +可参考讨论：https://github.com/PaddlePaddle/Paddle/pull/65095#discussion_r1637570850 + +## 参考资料 + +- [PEP 484 – Type Hints](https://peps.python.org/pep-0484/) +- [PEP 563 – Postponed Evaluation of Annotations](https://peps.python.org/pep-0563/) +- [PEP 561 – Distributing and Packaging Type Information](https://peps.python.org/pep-0561/#implementation) +- [Typing PEPs](https://peps.python.org/topic/typing/) +- [Static Typing with Python](https://typing.readthedocs.io/en/latest/index.html#) diff --git a/docs/dev_guides/style_guide_and_references/index_cn.rst b/docs/dev_guides/style_guide_and_references/index_cn.rst index a03165d7f6e..89bf55c91fe 100644 --- a/docs/dev_guides/style_guide_and_references/index_cn.rst +++ b/docs/dev_guides/style_guide_and_references/index_cn.rst @@ -9,7 +9,7 @@ - `代码风格检查指南 <../git_guides/codestyle_check_guide_cn.html>`_ : Paddle 进行代码风格检查相关工具和说明。 - `Paddle CI 测试详解 <../git_guides/paddle_ci_manual_cn.html>`_ : Paddle CI 测试流水线中的测试项的详细介绍，以及 CI 失败的处理方法。 - `Python 文档示例代码书写规范 <./code_example_writing_specification_cn.html>`_ : Python 文档示例代码书写规范。 - +- `Paddle 中的类型提示与 Q&A <./code_typing_writing_specification_cn.html>`_ : Python 文档示例代码书写规范。 .. toctree:: :hidden: @@ -19,3 +19,4 @@ ../git_guides/codestyle_check_guide_cn.md ../git_guides/paddle_ci_manual_cn.md code_example_writing_specification_cn.md + code_typing_writing_specification_cn.md diff --git a/docs/eval/evaluation_of_docs_system.md b/docs/eval/evaluation_of_docs_system.md index f446855348c..7a909a32597 100644 --- a/docs/eval/evaluation_of_docs_system.md +++ b/docs/eval/evaluation_of_docs_system.md @@ -480,7 +480,7 @@ MindSpore 的有自己独立的文档分类标准和风格，所以硬套本文 - PyTorch-PaddlePaddle API 映射表 - 硬件支持 - 飞桨产品硬件支持表 - - 昆仑 XPU 芯片运行飞桨 + - 昆仑芯 XPU 芯片运行飞桨 - 海光 DCU 芯片运行飞桨 - 昇腾 NPU 芯片运行飞桨 - 参与开发 @@ -564,7 +564,7 @@ MindSpore 的有自己独立的文档分类标准和风格，所以硬套本文 | 自定义算子 | Tensors and operations Custom layers Custom traning: walkthrough Create an op Extension types | 5 | Double Backward with Custom Functions Fusing Convolution and Batch Norm using Custom Function Custom C++ and CUDA Extensions Extending TorchScript with Custom C++ Operators Extending TorchScript with Custom C++ Classes Registering a Dispatched Operator in C++ Extending dispatcher for a new backend in C++ | 7 | 算子分类 运算重载 自定义算子（CPU） 自定义算子（GPU） 自定义算子（Ascend） 自定义算子（基于 Custom 表达） | 6 | 自定义原生算子 原生算子开发注意事项 自定义外部算子 自定义 Python 算子 API 介绍 API 示例 本地开发指南 提交 PR 注意事项 FAQ | 9 | | 分布式训练 | Distributed training with Kereas Distributed training with DTensors Using DTensors with Keras Custom training loops Multi-worker training with Keras Multi-worker training with CTL Parameter Server Training Distributed input Distributed training | 9 | PyTorch Distributed Overview Single-Machine Model Parallel Best PracticesGetting Started with Distributed Data Parallel Writing Distributed Applications with PyTorch Getting Started with Fully Sharded Data Prallel Customize Process Group Backends Using Cpp Extension Getting Started with Distributed RPC Framework Implementing a Parameter Server Using Distributed RPC Framework Distributed Pipeline Parallelsim using RPC Implementing Batch RPC Processing Using Asynchronous Executions Combining Distributed DataPrallel with Distributed RPC Framework Training Transformer models using Pipeline Parallelism Training Transformer models using Distributed Data Parallel and Pipeline Parallelism Distributed Training with Uneven Inputs Using the Join Context Manager | 16 | 分布式并行总览 分布式集合通信原语 分布式并行训练基础样例（Ascend） 分布式并行训练基础样例（GPU） 分布式推理 保存和加载模型（HyBrid Parallel 模式） 分布式并行训练 Transformer 模型 鹏程·盘古模型网络多维度混合并行解析 分布式故障恢复 | 9 | 单机多卡训练 分布式训练开始 使用 FleetAPI 进行分布式训练 | 3 | | 框架设计文档 | Random number generation | 1 | 分散在 API 文档、源码中，其实比较丰富。30+ | 30+ | 设计白皮书 全场景统一 函数式微分编程 动静态图结合 异构并行训练 分布式并行 中间表达 MindIR 高性能数据处理引擎 图算融合加速引擎 二阶优化 可视化调试调优 安全可信 术语 | 13 | | 0 | -| 其它 | Integrated gradients Uncertainty quantification with SNGP Probabilistic regression Keras 一级标题下的 13 篇文章 Thinking in TensorFlow 2 Data input pipelines 一级标题下的 3 篇 GPU TPU | 20 | Learn the Basics Quickstart Deep Learning with PyTorch: A 60 Minute Blitz Building a Convolution/Batch Norm fuser in FX Building a Simple CPU Performance Profiler with FX Channels Last Memory Format in PyTorch Forward-mode Automatic Differentiation Using the PyTorch C++ Frontend Dynamic Parallelism in TorchScript Autograd in C++ Frontend Static Quantization with Eager Model in PyTorch | 11 | 基本介绍 快速入门 进阶案例：线性拟合 混合精度 梯度累积算法 自适应梯度求和算法 降维训练算法 | 7 | 10 分钟快速上手飞桨 使用线性回归预测波士顿房价 模型导出 ONNX 协议 飞桨产品硬件支持表 昆仑 XPU 芯片运行飞桨 海光 DCU 芯片运行飞桨 昇腾 NPU 芯片运行飞桨 环境变量 FLAGS 下 9 篇 hello paddle：从普通程序走向机器学习程序 通过 AutoEncoder 实现时序数据异常检测 广播介绍 自动混合精度训练 梯度裁剪 升级指南 | 20+ | +| 其它 | Integrated gradients Uncertainty quantification with SNGP Probabilistic regression Keras 一级标题下的 13 篇文章 Thinking in TensorFlow 2 Data input pipelines 一级标题下的 3 篇 GPU TPU | 20 | Learn the Basics Quickstart Deep Learning with PyTorch: A 60 Minute Blitz Building a Convolution/Batch Norm fuser in FX Building a Simple CPU Performance Profiler with FX Channels Last Memory Format in PyTorch Forward-mode Automatic Differentiation Using the PyTorch C++ Frontend Dynamic Parallelism in TorchScript Autograd in C++ Frontend Static Quantization with Eager Model in PyTorch | 11 | 基本介绍 快速入门 进阶案例：线性拟合 混合精度 梯度累积算法 自适应梯度求和算法 降维训练算法 | 7 | 10 分钟快速上手飞桨 使用线性回归预测波士顿房价 模型导出 ONNX 协议 飞桨产品硬件支持表 昆仑芯 XPU 芯片运行飞桨 海光 DCU 芯片运行飞桨 昇腾 NPU 芯片运行飞桨 环境变量 FLAGS 下 9 篇 hello paddle：从普通程序走向机器学习程序 通过 AutoEncoder 实现时序数据异常检测 广播介绍 自动混合精度训练 梯度裁剪 升级指南 | 20+ | 可以看除，PaddlePaddle 在文档上是比较完备的，在本文划分的 19 个具体领域中的 17 个领域中都已有文档，包括： diff --git a/docs/faq/distributed_cn.md b/docs/faq/distributed_cn.md index 3876749941d..9ddfc042c7e 100644 --- a/docs/faq/distributed_cn.md +++ b/docs/faq/distributed_cn.md @@ -80,8 +80,6 @@ * `FLAGS_fuse_parameter_memory_size` → `strategy.fuse_grad_size_in_MB` * `FLAGS_fuse_parameter_groups_size` → `strategy.fuse_grad_size_in_TFLOPS` - DistributedStrategy 中`exec_strategy`配置项不兼容升级为`execution_strategy`。 - DistributedStrategy 中`forward_recompute`配置项不兼容升级为`recompute`。 DistributedStrategy 中`recompute_checkpoints`配置项不兼容升级为`recompute_configs`字典下的字段，如下： diff --git a/docs/faq/install_cn.md b/docs/faq/install_cn.md index eabd50dae60..efea7de0b70 100644 --- a/docs/faq/install_cn.md +++ b/docs/faq/install_cn.md @@ -1,5 +1,19 @@ # 安装常见问题 +#### 问题：conda 环境下安装 paddlepaddle 3.0.0b0 版本，运行`import paddle`时报错,报错信息为找不到 libpython.so 文件 + ++ 问题描述： +> ImportError: libpython3.12.so.1.0: cannot open shared object file: No such file or directory + ++ 问题分析： +遇到该问题是因为 3.0.0b0 版本中增加了对于 libpython 的依赖，但是使用 conda 安装的 python 环境时，未把 libpython.so 文件所在路径加入到环境变量中，导致找不到该文件。 + ++ 解决办法： +例如：执行`find / -name libpython3.12.so.1.0`, 发现 libpython 的路径如`/opt/conda/lib/`，使用如下命令安装即可; + +```bash +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/opt/conda/lib/ +``` ##### 问题：使用过程中报找不到 tensorrt 库的日志 diff --git a/docs/faq/params_cn.md b/docs/faq/params_cn.md index f6befef785b..cfef5e32852 100644 --- a/docs/faq/params_cn.md +++ b/docs/faq/params_cn.md @@ -97,5 +97,5 @@ sdg.step() # 更新参数前，会先对参 1. 要定义全新优化器，自定义优化器中参数的更新规则，可以通过继承 fluid.Optimizer，重写_append_optimize_op 方法实现。不同优化器实现原理各不相同，一般流程是先获取 learning_rate，gradients 参数，可训练参数，以及该优化器自身特别需要的参数，然后实现更新参数的代码，最后返回更新后的参数。 在实现更新参数代码时，可以选择直接调用[paddle 的 API](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html)或者使用[自定义原生算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/index_cn.html)。在使用自定义原生算子时，要注意动态图与静态图调用方式有所区别： 需要首先使用`framework.in_dygraph_mode()`判断是否为动态图模式，如果是动态图模式，则需要调用`paddle._C_ops`中相应的优化器算子；如果不是动态图模式，则需要调用`block.append_op` 来添加优化器算子。 - 代码样例可参考[paddle 源码](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/optimizer.py)中 AdamOptimizer 等优化器的实现。 + 代码样例可参考[paddle 源码](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/optimizer/optimizer.py)中 AdamOptimizer 等优化器的实现。 2. 使用现有的常用优化器，可以在创建`Param`的时候，可以通过设置[ParamAttr](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/ParamAttr_cn.html#paramattr)的属性来控制参数的属性，可以通过设置`regularizer`，`learning_rate`等参数简单设置参数的更新规则。 diff --git a/docs/faq/save_cn.md b/docs/faq/save_cn.md index 12ea1b6d00e..d5ac9addd3f 100644 --- a/docs/faq/save_cn.md +++ b/docs/faq/save_cn.md @@ -13,7 +13,7 @@ 该接口用于保存推理模型和参数，2.0 的`paddle.static.save_inference_model`保存结果为`*.pdmodel`和`*.pdiparams`两个文件，其中`*.pdmodel`为推理使用的模型 program 描述，`*.pdiparams`为推理用的参数，这里存储格式与`*.pdparams`不同（注意两者后缀差个`i`），`*.pdiparams`为二进制 Tensor 存储格式，不含变量名。1.8 的`fluid.io.save_inference_model`默认保存结果为`__model__`文件，和以参数名为文件名的多个分散参数文件，格式与 2.0 一致。 - 3. 关于更多 2.0 动态图模型保存和加载的介绍可以参考教程：[模型存储与载入](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/08_model_save_load_cn.html) + 3. 关于更多 2.0 动态图模型保存和加载的介绍可以参考教程：[模型保存与加载](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/model_save_load_cn.html) ---------- @@ -88,10 +88,10 @@ adam.set_state_dict(opti_state_dict) + 答复： 1. 在 paddle2.x 可使用``paddle.jit.save``接口以及``paddle.static.save_inference_model``,通过指定``path``来保存成为``path.pdmodel``和``path.pdiparams``,可对应 paddle1.x 中使用``save_inference_model``指定 dirname 和 params_filename 生成``dirname/__model__``和``dirname/params 文件``。paddle2.x 保存模型文件详情可参考: - - [paddle.jit.save/load](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/08_model_save_load_cn.html#dongtaitumoxing-canshubaocunzairu-xunliantuili) - - [paddle.static.save/load_inference_model](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/08_model_save_load_cn.html#jingtaitumoxing-canshubaocunzairu-tuilibushu) + - [paddle.jit.save/load](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/model_save_load_cn.html#sanyongyutuilibushuchangjing) + - [paddle.static.save/load_inference_model](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/model_save_load_cn.html#jingtaitumoxingdebaocunyujiazai) 2. 如果想要在 paddle2.x 中读取 paddle 1.x 中的 model 文件，可参考: - - [兼容载入旧格式模型](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.2rc/guides/01_paddle2.0_introduction/load_old_format_model.html#cn-guides-load-old-format-model) + - [兼容载入旧格式模型](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/model_convert/load_old_format_model_cn.html) ##### 问题：paddle 如何单独 load 存下来所有模型变量中某一个变量，然后修改变量中的值？ diff --git a/docs/faq/train_cn.md b/docs/faq/train_cn.md index 153b9eabe65..b8bc46bf421 100644 --- a/docs/faq/train_cn.md +++ b/docs/faq/train_cn.md @@ -110,7 +110,7 @@ export FLAGS_fast_eager_deletion_mode=1 export FLAGS_fraction_of_gpu_memory_to_use=0 ``` -详细请参考官方文档[存储分配与优化](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/performance_improving/singlenode_training_improving/memory_optimize.html) 调整相关配置。 +详细请参考官方文档[存储分配与优化](https://www.paddlepaddle.org.cn/documentation/docs/zh/dev_guides/api_contributing_guides/new_cpp_op_cn.html#xiancunyouhua) 调整相关配置。 此外，建议您使用[AI Studio 学习与 实训社区训练](https://aistudio.baidu.com/aistudio/index)，获取免费 GPU 算力，提升您的训练效率。 @@ -130,7 +130,7 @@ export FLAGS_fraction_of_gpu_memory_to_use=0 ##### 问题：如何处理变长 ID 导致程序内存占用过大的问题？ -+ 答复：请先参考[显存分配与优化文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/performance_improving/singlenode_training_improving/memory_optimize.html) 开启存储优化开关，包括显存垃圾及时回收和 Op 内部的输出复用输入等。若存储空间仍然不够，建议： ++ 答复：请先参考[显存分配与优化文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/dev_guides/api_contributing_guides/new_cpp_op_cn.html#xiancunyouhua) 开启存储优化开关，包括显存垃圾及时回收和 Op 内部的输出复用输入等。若存储空间仍然不够，建议： 1. 降低 `batch_size` ； 2. 对 index 进行排序，减少 padding 的数量。 diff --git a/docs/guides/06_distributed_training/cluster_quick_start_cn.rst b/docs/guides/06_distributed_training/cluster_quick_start_cn.rst deleted file mode 100644 index d95471fb4dd..00000000000 --- a/docs/guides/06_distributed_training/cluster_quick_start_cn.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _cluster_quick_start: - -分布式训练快速开始 -================== - -一、快速开始-数据并行 -------------------------------- - - - -二、ParameterServer 训练快速开始 -------------------------------- diff --git a/docs/guides/06_distributed_training/deployment_cn.rst b/docs/guides/06_distributed_training/deployment_cn.rst index 9daa0be52ed..fc948ba2f85 100644 --- a/docs/guides/06_distributed_training/deployment_cn.rst +++ b/docs/guides/06_distributed_training/deployment_cn.rst @@ -12,7 +12,7 @@ PaddlePaddle 分布式对不同系统和硬件的支持情况如下表所示， * - - CPU - GPU - - 昆仑 XPU + - 昆仑芯 XPU - 海光 DCU - 昇腾 NPU * - Linux diff --git a/docs/guides/06_distributed_training/fleet_api_howto_cn.rst b/docs/guides/06_distributed_training/fleet_api_howto_cn.rst deleted file mode 100644 index b3d5be7a094..00000000000 --- a/docs/guides/06_distributed_training/fleet_api_howto_cn.rst +++ /dev/null @@ -1,275 +0,0 @@ - -使用 FleetAPI 进行分布式训练 -========================== - -FleetAPI 设计说明 ------------------ - -Fleet 是 PaddlePaddle 分布式训练的高级 API。Fleet 的命名出自于 PaddlePaddle，象征一个舰队中的多只双桨船协同工作。Fleet 的设计在易用性和算法可扩展性方面做出了权衡。用户可以很容易从单机版的训练程序，通过添加几行代码切换到分布式训练程序。此外，分布式训练的算法也可以通过 Fleet -API 接口灵活定义。 - -Fleet API 快速上手示例 ---------------------- - -下面会针对 Fleet -API 最常见的两种使用场景，用一个模型做示例，目的是让用户有快速上手体验的模板。 - - -* - 假设我们定义 MLP 网络如下： - - .. code-block:: python - - import paddle - - def mlp(input_x, input_y, hid_dim=128, label_dim=2): - fc_1 = paddle.static.nn.fc(input=input_x, size=hid_dim, act='tanh') - fc_2 = paddle.static.nn.fc(input=fc_1, size=hid_dim, act='tanh') - prediction = paddle.static.nn.fc(input=[fc_2], size=label_dim, act='softmax') - cost = paddle.static.nn.cross_entropy(input=prediction, label=input_y) - avg_cost = paddle.static.nn.mean(x=cost) - return avg_cost - -* - 定义一个在内存生成数据的 Reader 如下： - - .. code-block:: python - - import numpy as np - - def gen_data(): - return {"x": np.random.random(size=(128, 32)).astype('float32'), - "y": np.random.randint(2, size=(128, 1)).astype('int64')} - -* - 单机 Trainer 定义 - - .. code-block:: python - - import paddle - from nets import mlp - from utils import gen_data - - input_x = paddle.static.data(name="x", shape=[None, 32], dtype='float32') - input_y = paddle.static.data(name="y", shape=[None, 1], dtype='int64') - - cost = mlp(input_x, input_y) - optimizer = paddle.optimizer.SGD(learning_rate=0.01) - optimizer.minimize(cost) - place = paddle.CUDAPlace(0) - - exe = paddle.static.Executor(place) - exe.run(paddle.static.default_startup_program()) - step = 1001 - for i in range(step): - cost_val = exe.run(feed=gen_data(), fetch_list=[cost.name]) - print("step%d cost=%f" % (i, cost_val[0])) - -* - Parameter Server 训练方法 - - 参数服务器方法对于大规模数据，简单模型的并行训练非常适用，我们基于单机模型的定义给出使用 Parameter Server 进行训练的示例如下： - - .. code-block:: python - - import paddle - paddle.enable_static() - - import paddle.distributed.fleet.base.role_maker as role_maker - import paddle.distributed.fleet as fleet - - from nets import mlp - from utils import gen_data - - input_x = paddle.static.data(name="x", shape=[None, 32], dtype='float32') - input_y = paddle.static.data(name="y", shape=[None, 1], dtype='int64') - - cost = mlp(input_x, input_y) - optimizer = paddle.optimizer.SGD(learning_rate=0.01) - - role = role_maker.PaddleCloudRoleMaker() - fleet.init(role) - - strategy = paddle.distributed.fleet.DistributedStrategy() - strategy.a_sync = True - - optimizer = fleet.distributed_optimizer(optimizer, strategy) - optimizer.minimize(cost) - - if fleet.is_server(): - fleet.init_server() - fleet.run_server() - - elif fleet.is_worker(): - place = paddle.CPUPlace() - exe = paddle.static.Executor(place) - exe.run(paddle.static.default_startup_program()) - - step = 1001 - for i in range(step): - cost_val = exe.run( - program=paddle.static.default_main_program(), - feed=gen_data(), - fetch_list=[cost.name]) - print("worker_index: %d, step%d cost = %f" % - (fleet.worker_index(), i, cost_val[0])) - -* - Collective 训练方法 - - Collective Training 通常在 GPU 多机多卡训练中使用，一般在复杂模型的训练中比较常见，我们基于上面的单机模型定义给出使用 Collective 方法进行分布式训练的示例如下： - - .. code-block:: python - - import paddle - paddle.enable_static() - - import paddle.distributed.fleet.base.role_maker as role_maker - import paddle.distributed.fleet as fleet - - from nets import mlp - from utils import gen_data - - input_x = paddle.static.data(name="x", shape=[None, 32], dtype='float32') - input_y = paddle.static.data(name="y", shape=[None, 1], dtype='int64') - - cost = mlp(input_x, input_y) - optimizer = paddle.optimizer.SGD(learning_rate=0.01) - role = role_maker.PaddleCloudRoleMaker(is_collective=True) - fleet.init(role) - - optimizer = fleet.distributed_optimizer(optimizer) - optimizer.minimize(cost) - place = paddle.CUDAPlace(0) - - exe = paddle.static.Executor(place) - exe.run(paddle.static.default_startup_program()) - - step = 1001 - for i in range(step): - cost_val = exe.run( - program=paddle.static.default_main_program(), - feed=gen_data(), - fetch_list=[cost.name]) - print("worker_index: %d, step%d cost = %f" % - (fleet.worker_index(), i, cost_val[0])) - - -Fleet API 相关的接口说明 ------------------------ - -Fleet API 接口 -^^^^^^^^^^^^^ - - -* init(role_maker=None) - - * fleet 初始化，需要在使用 fleet 其他接口前先调用，用于定义多机的环境配置 - -* is_worker() - - * Parameter Server 训练中使用，判断当前节点是否是 Worker 节点，是则返回 True，否则返回 False - -* is_server(model_dir=None) - - * Parameter Server 训练中使用，判断当前节点是否是 Server 节点，是则返回 True，否则返回 False - -* init_server() - - * Parameter Server 训练中，fleet 加载 model_dir 中保存的模型相关参数进行 parameter - server 的初始化 - -* run_server() - - * Parameter Server 训练中使用，用来启动 server 端服务 - -* init_worker() - - * Parameter Server 训练中使用，用来启动 worker 端服务 - -* stop_worker() - - * 训练结束后，停止 worker - -* distributed_optimizer(optimizer, strategy=None) - - * 分布式优化算法装饰器，用户可带入单机 optimizer，并配置分布式训练策略，返回一个分布式的 optimizer - -RoleMaker -^^^^^^^^^ - - -* - PaddleCloudRoleMaker - - - * - 描述：PaddleCloudRoleMaker 是一个高级封装，支持使用 paddle.distributed.launch 或者 paddle.distributed.launch_ps 启动脚本 - - * - Parameter Server 训练示例： - - .. code-block:: python - - import paddle - paddle.enable_static() - - import paddle.distributed.fleet.base.role_maker as role_maker - import paddle.distributed.fleet as fleet - - role = role_maker.PaddleCloudRoleMaker() - fleet.init(role) - - * - 启动方法： - - .. code-block:: python - - python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 trainer.py - - * - Collective 训练示例： - - .. code-block:: python - - import paddle - paddle.enable_static() - - import paddle.distributed.fleet.base.role_maker as role_maker - import paddle.distributed.fleet as fleet - - role = role_maker.PaddleCloudRoleMaker(is_collective=True) - fleet.init(role) - - * - 启动方法： - - .. code-block:: python - - python -m paddle.distributed.launch trainer.py - -* - UserDefinedRoleMaker - - - * - 描述：用户自定义节点的角色信息，IP 和端口信息 - - * - 示例： - - .. code-block:: python - - import paddle - paddle.enable_static() - - import paddle.distributed.fleet.base.role_maker as role_maker - import paddle.distributed.fleet as fleet - - role = role_maker.UserDefinedRoleMaker( - current_id=0, - role=role_maker.Role.SERVER, - worker_num=2, - server_endpoints=["127.0.0.1:36011", "127.0.0.1:36012"]) - - fleet.init(role) diff --git a/docs/guides/06_distributed_training/images/placements.png 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auto_parallel_cn.md visual_pipeline_parallel_static_mode_cn.rst diff --git a/docs/guides/06_distributed_training/pipeline_parallel_cn.rst b/docs/guides/06_distributed_training/pipeline_parallel_cn.rst index c2bc350bd3c..b889ee3c9e0 100644 --- a/docs/guides/06_distributed_training/pipeline_parallel_cn.rst +++ b/docs/guides/06_distributed_training/pipeline_parallel_cn.rst @@ -57,9 +57,8 @@ import os import paddle from paddle.distributed import fleet - from paddle.fluid.dygraph.container import Sequential + from paddle.nn import Sequential, Layer import paddle.nn as nn - from paddle.fluid.dygraph.layers import Layer from paddle.distributed.fleet.meta_parallel import LayerDesc, PipelineLayer import paddle.nn.functional as F import paddle.distributed as dist diff --git a/docs/guides/06_distributed_training/ps/cluster_overview_ps_cn.rst b/docs/guides/06_distributed_training/ps/cluster_overview_ps_cn.rst index 532239a5e07..6bbf3c36013 100644 --- a/docs/guides/06_distributed_training/ps/cluster_overview_ps_cn.rst +++ b/docs/guides/06_distributed_training/ps/cluster_overview_ps_cn.rst @@ -86,7 +86,7 @@ RPC&NCCL 混合通信可以将部分稀疏参数采用 RPC 协议跨节点通信 1. 定制化 GPU 机型，调整机器内 CPU 与 GPU 的硬件配比。 2. 混布 CPU 和 GPU 机器节点，来调整机器间的硬件配比。 -基于这两种解决方案，飞桨框架 2.0 版本创新性地推出了通用异构参数服务器功能(HeterPS)。一举解除了传统参数服务器模式下，Worker 节点必须严格使用同一种硬件型号的枷锁，使训练任务对硬件型号不敏感，即可以同时使用不同的硬件混合异构训练，如 CPU、AI 专用芯片（如百度昆仑 XPU）以及不同型号的 GPU 如 v100、P40、K40 等。同时还可以解决大规模稀疏特征模型训练场景下 IO 占比过高导致的芯片资源利用率过低的问题。通过异构参数服务器训练模式，用户可以在硬件异构集群中部署分布式训练任务，例如云服务器集群，高效利用不同算力芯片，为用户提供更高吞吐、更低资源消耗的训练能力。 +基于这两种解决方案，飞桨框架 2.0 版本创新性地推出了通用异构参数服务器功能(HeterPS)。一举解除了传统参数服务器模式下，Worker 节点必须严格使用同一种硬件型号的枷锁，使训练任务对硬件型号不敏感，即可以同时使用不同的硬件混合异构训练，如 CPU、AI 专用芯片（如百度昆仑芯 XPU）以及不同型号的 GPU 如 v100、P40、K40 等。同时还可以解决大规模稀疏特征模型训练场景下 IO 占比过高导致的芯片资源利用率过低的问题。通过异构参数服务器训练模式，用户可以在硬件异构集群中部署分布式训练任务，例如云服务器集群，高效利用不同算力芯片，为用户提供更高吞吐、更低资源消耗的训练能力。 .. image:: ../images/heterps.jpeg :width: 600 diff --git a/docs/guides/10_contribution/hackathon_cn.md b/docs/guides/10_contribution/hackathon_cn.md index a5e0e3316dd..9555eb0cdc7 100644 --- a/docs/guides/10_contribution/hackathon_cn.md +++ b/docs/guides/10_contribution/hackathon_cn.md @@ -59,9 +59,9 @@ | **时间** | **日程** | | :------------------------: | :------------------------: | | **2024/03/20 19:00 GMT+8** | 活动正式上线，发布贡献任务 | -| **2024/05/29 19:00 GMT+8** | RFC 提交截止 | -| **2024/06/12 19:00 GMT+8** | PR 提交截止 | -| **2024/06/17 19:00 GMT+8** | 结果公示 | +| **2024/06/07 19:00 GMT+8** | RFC 提交截止 | +| **2024/06/14 19:00 GMT+8** | PR 提交截止 | +| **2024/06/20 19:00 GMT+8** | 结果公示 | ### 2. 飞桨护航计划集训营 @@ -169,7 +169,7 @@ RFC 通过并合入后形成 Tracking Issue。原则上完成 RFC 的开发者 > [!NOTE] > -> 1. 优秀 RFC 案例：[将 xdoctest 引入到飞桨框架工作流中](https://github.com/PaddlePaddle/community/blob/master/rfcs/Docs/将%20xdoctest%20引入到飞桨框架工作流中.md) +> 1. 优秀 RFC 案例：[将 xdoctest 引入到飞桨框架工作流中](https://github.com/PaddlePaddle/community/blob/master/rfcs/Docs/将%20xdoctest%20 引入到飞桨框架工作流中.md) > 2. 优秀 Tracking Issue 案例：[将 xdoctest 引入到飞桨框架工作流中 Tracking Issue](https://github.com/PaddlePaddle/Paddle/issues/54705) **(6) 任务开发、提交作品** @@ -182,7 +182,7 @@ RFC 通过并合入后形成 Tracking Issue。原则上完成 RFC 的开发者 在通过技术评审后，你会获得技术 approve，PR 被 merge 表示你完成了一次开源贡献，可继续选择其他未完成的子任务进行开发。 -在 **06/13-06/14** 期间，各团队需要准备材料参与答辩，飞桨研发将根据答辩情况和作品完成度进行最终评审。 +在 **06/17-06/20** 期间，各团队需要准备材料参与答辩，飞桨研发将根据答辩情况和作品完成度进行最终评审。 **(8) 奖金发放** @@ -206,8 +206,8 @@ RFC 通过并合入后形成 Tracking Issue。原则上完成 RFC 的开发者 | **2024/03/20 19:00 GMT+8** | 活动正式上线，发布贡献任务 | | **2024/05/29 19:00 GMT+8** | RFC 提交截止 | | **2024/06/12 19:00 GMT+8** | PR 提交截止 | -| **2023/06/13-2023/06/14** | 集中答辩 | -| **2024/06/17 19:00 GMT+8** | 结果公示 | +| **2023/06/17-2023/06/20** | 集中答辩 | +| **2024/06/20 19:00 GMT+8** | 结果公示 | ### 4. 优秀稿件征集与传播 diff --git a/docs/guides/advanced/auto_type_promotion_cn.md b/docs/guides/advanced/auto_type_promotion_cn.md new file mode 100644 index 00000000000..427cdd1526b --- /dev/null +++ b/docs/guides/advanced/auto_type_promotion_cn.md @@ -0,0 +1,260 @@ +隐式数据类型提升介绍 +================ + +本篇文章主要为你介绍飞桨的隐式类型提升机制，帮助你更好的使用飞桨。 + +一、背景 +-------- + +类型提升是当两个不同数据类型进行运算(+/-/*//)时，自动计算结果应得的数据类型，这个操作在不同类型数据之间进行数据计算时，是必不可少的一个功能。 + +类型提升计算时，根据调用分别分为运算符重载、二元 API： + +- 运算符重载方法指通过运算符直接进行计算，如 a+b, a-b 等调用方法； + +- 二元 API 则是通过 API 的形式对函数进行调用，如 paddle.add(a,b)等。 + +隐式数据类型提升即为在不需要用户进行额外操作的情况下，自动的帮用户进行类型提升的行为。 + +二、飞桨的类型提升规则 +------------------------------ + +飞桨的类型提升的支持范围和规则在 Tensor 之间，Tensor 和 Scalar 之间会有所不同，以下将分别展开介绍。 + + ```python + + import paddle + + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + print (a + b) # 当 a 和 b 均为 Tensor 时，视为 Tensor 之间的计算 + + a = paddle.rand([3,3], dtype = 'float16') + b = 1.0 + print (a + b) # 当 a 和 b 其中任意一个为 Scalar 时，视为 Tensor 和 Scalar 之间的计算 + + ``` + +1、 Tensor 之间的隐式类型提升规则介绍 + +- 由于模型训练中通常不会出现浮点型以外的不同类型之间的计算，为了更方便用户能够快速排查由于类型导致的问题，Tensor 之间的自动类型提升将仅支持浮点型之间的计算，以及复数和实数之间的计算，计算原则为返回两个 Tensor 中更大的数据类型，详情见下表： + ++/-/* | bf16 | f16 | f32 | f64 | bool | u8 | i8 | i16 | i32 | i64 | c64 | c128 | +:-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | +bf16 | bf16 | f32 | f32 | f64 | - | - | - | - | - | - | c64 | c128 | +f16 | f32 | f16 | f32 | f64 | - | - | - | - | - | - | c64 | c128 | +f32 | f32 | f32 | f32 | f64 | - | - | - | - | - | - | c64 | c128 | +f64 | f64 | f64 | f64 | f64 | - | - | - | - | - | - | c128 | c128 | +bool | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +u8 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i8 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i16 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i32 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i64 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +c64 | c64 | c64 | c64 | c64 | c64 | c64 | c64 | c64 | c64 | c128 | c64 | c128 | +c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | + + +- 以 paddle.add(a, b) 为例，上表中行为 a，列为 b。 + +以下是 Tensor 之间类型提升的样例： + + ```python + + import paddle + + # 浮点数间的计算 + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + c = a + b # 此时将自动进行类型提升，将 a 的数据类型 cast 为 float32，不需要用户进行额外操作 + print (c.dtype) # 输出类型为 float32 + + a = paddle.rand([3,3], dtype = 'bfloat16') + b = paddle.rand([3,3], dtype = 'float64') + c = a + b # 此时将自动进行类型提升，将 a 的数据类型 cast 为 float64，不需要用户进行额外操作 + print (c.dtype) # 输出类型为 float64 + + # 复数与实数之间的计算 + a = paddle.ones([3,3], dtype = 'complex64') + b = paddle.rand([3,3], dtype = 'float64') + c = a + b # 此时将自动进行类型提升，将 a 和 b 的数据类型 cast 为 complex128，不需要用户进行额外操作 + print (c.dtype) # 输出类型为 complex128 + + # 复数之间的计算 + a = paddle.ones([3,3], dtype = 'complex128') + b = paddle.ones([3,3], dtype = 'complex64') + c = a + b # 此时将自动进行类型提升，将 b 的数据类型 cast 为 complex128，不需要用户进行额外操作 + print (c.dtype) # 输出类型为 complex128 + + ``` + +2、 Tensor 与 Scalar 之间的隐式类型提升规则介绍 + +- Tensor 与 Scalar 之间支持全类型自动类型提升，计算原则为在 Scalar 大类型一致（同为整型、浮点型等）时，向 Tensor 类型靠拢，否则与 Tensor 之间的计算结果一致。 + +- 其中视 int -> int64，float -> float32, bool -> bool, complex -> complex64。具体规则如下： + ++/-/* | bool | int | float | complex | +:-----: | :-----: | :-----: | :-----: | :-----: | +bool | bool | i64 | f32 | c64 | +u8 | u8 | u8 | f32 | c64 | +i8 | i8 | i8 | f32 | c64 | +i16 | i16 | i16 | f32 | c64 | +i32 | i32 | i32 | f32 | c64 | +i64 | i64 | i64 | f32 | c64 | +bf16 | bf16 | bf16 | bf16 | c64 | +f16 | f16 | f16 | f16 | c64 | +f32 | f32 | f32 | f32 | c64 | +f64 | f64 | f64 | f64 | c128 | +c64 | c64 | c64 | c64 | c64 | +c128 | c128 | c128 | c128 | c128 | + +以下是 Tensor 与 Scalar 之间类型提升的样例： + + ```python + + import paddle + + # 当 Scalar 在大类型上与 Tensor 类型一致时，结果返回 Tensor 的类型 + a = paddle.rand([3,3], dtype = 'float16') + b = 1.0 + c = a + b # a 与 b 大类型一致，都为 float 类型，因此将 b 自动 cast 为 float16，不需要用户进行额外操作 + print (c.dtype) # 输出类型为 float16 + + # 当 Scalar 在大类型上与 Tensor 类型不一致时，遵循 Tensor 之间的计算规则 + a = 1.0 + b = paddle.ones([3,3], dtype = 'int64') + c = a + b # a 与 b 大类型不一致，将 b 自动 cast 为 float32，不需要用户进行额外操作 + print (c.dtype) # 输出类型为 float32 + + ``` + +三、飞桨的隐式类型提升使用方法说明 +------------------------------ + +1、对于支持隐式类型提升的情况 + + ```python + + import paddle + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + c = a + b # 此时将自动进行类型提升，将 a 的数据类型 cast 为 float32，不需要用户进行额外操作 + print (c.dtype) # float32 + + # 符合交换律 + d = b + a + print (d.dtype) # float32 + print (paddle.allclose(c, d)) # Tensor(shape=[], dtype=bool, place=Place(gpu:0), stop_gradient=True, True) + + # 与二元 API 计算结果一致 + e = paddle.add(a, b) + print (e.dtype) # float32 + print (paddle.allclose(c, e)) # Tensor(shape=[], dtype=bool, place=Place(gpu:0), stop_gradient=True, True) + + # 与静态图计算结果一致 + paddle.enable_static() + exe = paddle.static.Executor() + train_program = paddle.static.Program() + startup_program = paddle.static.Program() + with paddle.static.program_guard(train_program, startup_program): + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + f = paddle.add(a, b) + res = exe.run(train_program, fetch_list=[f]) + print (res[0].dtype) # float32 + paddle.disable_static() + print (paddle.allclose(c, paddle.to_tensor(res[0]))) # Tensor(shape=[], dtype=bool, place=Place(gpu:0), stop_gradient=True, True) + + ``` + +2、对于不支持隐式类型提升的情况 + + ```python + + import paddle + a = paddle.ones([3,3], dtype = 'int64') + b = paddle.rand([3,3], dtype = 'float32') + c = a + b # 此时由于不再支持 int 和 float 类型之间的自动类型提升，会报 Type Error + + # 对于不支持自动类型提升的情况，建议用户进行手动进行类型提升 + # 方法一：使用 astype + a = a.astype("float32") + a = a.astype(b.dtype) + + # 方法二：使用 cast + a = paddle.cast(a, "float32") + a = paddle.cast(a, b.dtype) + + ``` + +四、飞桨隐式类型提升的适用范围 +------------------------------ + +截止到 Paddle 2.6 版本，具体支持的二元 API 及规则如下： + +序号 | API | Tensor 之间的计算 | Tensor 和 Scalar 之间的计算 | +:-----: | :-----: | :-----: | :-----: | +1 | add | 通用规则 | 通用规则 | +2 | subtract | 通用规则 | 通用规则 | +3 | multiply | 通用规则 | 通用规则 | +4 | divide | 通用规则 | 除法规则 | +5 | floor_divide | 通用规则 | 通用规则 | +6 | pow | 通用规则 | 通用规则 | +7 | equal | 逻辑规则 | 逻辑规则 | +8 | not_equal | 逻辑规则 | 逻辑规则 | +9 | less_than | 逻辑规则 | 逻辑规则 | +10 | less_equal | 逻辑规则 | 逻辑规则 | +11 | greater_than | 逻辑规则 | 逻辑规则 | +12 | greater_equal | 逻辑规则 | 逻辑规则 | +13 | logical_and | 逻辑规则 | 逻辑规则 | +14 | logical_or | 逻辑规则 | 逻辑规则 | +15 | logical_xor | 逻辑规则 | 逻辑规则 | +16 | bitwise_and | - | 通用规则 | +17 | bitwise_or | - | 通用规则 | +18 | bitwise_xor | - | 通用规则 | +19 | where | 通用规则 | 通用规则 | +20 | fmax | 通用规则 | - | +21 | fmin | 通用规则 | - | +22 | logaddexp | 通用规则 | - | +23 | maximum | 通用规则 | - | +24 | minimum | 通用规则 | - | +25 | remainder(mod) | 通用规则 | 通用规则 | +26 | huber_loss | 通用规则 | - | +27 | nextafter | 通用规则 | - | +28 | atan2 | 通用规则 | - | +29 | poisson_nll_loss | 通用规则 | - | +30 | l1_loss | 通用规则 | - | +31 | huber_loss | 通用规则 | - | +32 | mse_loss | 通用规则 | - | + +上表中存在两种特殊规则： + +- 特殊规则-除法规则： 对于特殊 API divide 来说，其不会返回比 float 更小的类型。如 int32 / Scalar 返回 float32 。 + + ```python + + import paddle + a = paddle.ones([3,3], dtype = 'int32') + b = 1 + c = a / b + print (c.dtype) # float32 + + ``` + +- 特殊规则-逻辑规则： 对于逻辑性 API 来说，由于复数类型无法直接进行逻辑运算，因此复数相关计算不在类型提升的支持范围内，在支持范围内其结果均返回 bool 值。 + + ```python + + import paddle + a = paddle.rand([3,3], dtype = 'float32') + b = paddle.rand([3,3], dtype = 'float16') + c = a == b + print (c.dtype) # bool + + ``` + +五、总结 +------------------------ + +Paddle 在支持隐式类型提升的基础上保证了计算符合交换律，运算符重载与二元 API 结果统一，动态图与静态图结果统一。本篇文章中明确了隐式类型提升的规则和支持范围，在介绍使用方法的同时根据当前版本总结了支持类型提升的二元 API 种类，希望能够借此增加用户在使用 Paddle 时的便利性。 diff --git a/docs/guides/advanced/auto_type_promotion_en.md b/docs/guides/advanced/auto_type_promotion_en.md new file mode 100644 index 00000000000..40dda224649 --- /dev/null +++ b/docs/guides/advanced/auto_type_promotion_en.md @@ -0,0 +1,260 @@ +Introduction to Data Type Promotion +================ + +This article introduces PaddlePaddle's data type promotion mechanism, aiding better usage of PaddlePaddle. + +Background +-------- + +Type promotion automatically determines the resulting data type when performing operations (+/-/*//) on different data types. This essential function facilitates data calculations between varied types. + +Type promotion calculations are categorized based on the call method: + +- Operator Overloading: Uses operators directly for computations, such as a + b or a - b. + +- Binary API: Uses API functions for computations, such as paddle.add(a, b). + +Data type promotion automatically handles type promotion without user intervention. + +Type Promotion Rules +------------------------------ + +The scope and rules of type promotion differ between Tensor-to-Tensor and Tensor-to-Scalar operations. The following will be introduced separately. + + ```python + + import paddle + + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + print (a + b) # when both a and b is tensor, treats as Tensor-to-Tensor + + a = paddle.rand([3,3], dtype = 'float16') + b = 1.0 + print (a + b) # when either a or b is Scalar, treats as Tensor-to-Scalar + + ``` + +1. Type Promotion Rules in Tensor-to-Tensor + +- In model training, computations between different data types are usually limited to floating-point types. To help users quickly troubleshoot type-related issues, automatic type promotion between Tensors will only support calculations between floating-point types, as well as between complex and real numbers. The principle is to return the larger data type of the two Tensors. More details are shown in the table below: + ++/-/* | bf16 | f16 | f32 | f64 | bool | u8 | i8 | i16 | i32 | i64 | c64 | c128 | +:-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | +bf16 | bf16 | f32 | f32 | f64 | - | - | - | - | - | - | c64 | c128 | +f16 | f32 | f16 | f32 | f64 | - | - | - | - | - | - | c64 | c128 | +f32 | f32 | f32 | f32 | f64 | - | - | - | - | - | - | c64 | c128 | +f64 | f64 | f64 | f64 | f64 | - | - | - | - | - | - | c128 | c128 | +bool | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +u8 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i8 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i16 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i32 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +i64 | - | - | - | - | - | - | - | - | - | - | c64 | c128 | +c64 | c64 | c64 | c64 | c64 | c64 | c64 | c64 | c64 | c64 | c128 | c64 | c128 | +c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | c128 | + + +- Taking Paddle add (a, b) as an example, in the table above, the row represents 'a' and the column represents 'b'. + +Sample Code: + + ```python + + import paddle + + # Calculation between floating points + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + c = a + b # type promotion will automatically occur, casting 'a' to float32, and no additional user actions required + print (c.dtype) # the dtype of 'c' is float32 + + a = paddle.rand([3,3], dtype = 'bfloat16') + b = paddle.rand([3,3], dtype = 'float64') + c = a + b # type promotion will automatically occur, casting 'a' to float64, and no additional user actions required + print (c.dtype) # the dtype of 'c' is float64 + + # Calculation between complex and real number + a = paddle.ones([3,3], dtype = 'complex64') + b = paddle.rand([3,3], dtype = 'float64') + c = a + b # type promotion will automatically occur, casting both 'a' and 'b' to complex128, and no additional user actions required + print (c.dtype) # the dtype of 'c' is complex128 + + # Calculation between complex numbers + a = paddle.ones([3,3], dtype = 'complex128') + b = paddle.ones([3,3], dtype = 'complex64') + c = a + b # type promotion will automatically occur, casting 'b' to complex128, and no additional user actions required + print (c.dtype) # the dtype of 'c' is complex128 + + ``` + +2. Type Promotion Rules in Tensor-to-Scalar + +- Type promotion between Tensor and Scalar supports all types. The principle is to promote towards the Tensor's type when the Scalar's broad type (both are integers or both are floating-point, etc.) matches. Otherwise, the result follows the Tensor-to-Tensor rules. + +- The scalar operand has default dtype: int -> int64，float -> float32, bool -> bool, complex -> complex64. More Details show in this table: + ++/-/* | bool | int | float | complex | +:-----: | :-----: | :-----: | :-----: | :-----: | +bool | bool | i64 | f32 | c64 | +u8 | u8 | u8 | f32 | c64 | +i8 | i8 | i8 | f32 | c64 | +i16 | i16 | i16 | f32 | c64 | +i32 | i32 | i32 | f32 | c64 | +i64 | i64 | i64 | f32 | c64 | +bf16 | bf16 | bf16 | bf16 | c64 | +f16 | f16 | f16 | f16 | c64 | +f32 | f32 | f32 | f32 | c64 | +f64 | f64 | f64 | f64 | c128 | +c64 | c64 | c64 | c64 | c64 | +c128 | c128 | c128 | c128 | c128 | + +Sample Code: + + ```python + + import paddle + + # Both Scalar and Tensor are floating-point, then return Tensor's type + a = paddle.rand([3,3], dtype = 'float16') + b = 1.0 + c = a + b # type promotion will automatically occur, casting 'b' to float16, and no additional user actions required + print (c.dtype) # the dtype of 'c' is float16 + + # Scalar and Tensor unmatch in broad type, the result follows the Tensor-to-Tensor rules + a = 1.0 + b = paddle.ones([3,3], dtype = 'int64') + c = a + b # type promotion will automatically occur, casting 'b' to float32, and no additional user actions required + print (c.dtype) # the dtype of 'c' is float16 + + ``` + +How to Use Type Promotion +------------------------------ + +1. For Supported Case + + ```python + + import paddle + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + c = a + b # type promotion will automatically occur, casting 'a' to float32, and no additional user actions required + print (c.dtype) # float32 + + # Coincidence computative law + d = b + a + print (d.dtype) # float32 + print (paddle.allclose(c, d)) # Tensor(shape=[], dtype=bool, place=Place(gpu:0), stop_gradient=True, True) + + # Same with binary API + e = paddle.add(a, b) + print (e.dtype) # float32 + print (paddle.allclose(c, e)) # Tensor(shape=[], dtype=bool, place=Place(gpu:0), stop_gradient=True, True) + + # Same with static graph + paddle.enable_static() + exe = paddle.static.Executor() + train_program = paddle.static.Program() + startup_program = paddle.static.Program() + with paddle.static.program_guard(train_program, startup_program): + a = paddle.rand([3,3], dtype = 'float16') + b = paddle.rand([3,3], dtype = 'float32') + f = paddle.add(a, b) + res = exe.run(train_program, fetch_list=[f]) + print (res[0].dtype) # float32 + paddle.disable_static() + print (paddle.allclose(c, paddle.to_tensor(res[0]))) # Tensor(shape=[], dtype=bool, place=Place(gpu:0), stop_gradient=True, True) + + ``` + +2、For Unsupported Case + + ```python + + import paddle + a = paddle.ones([3,3], dtype = 'int64') + b = paddle.rand([3,3], dtype = 'float32') + c = a + b # due to the unsupported of automatic type promotion between int and float, TypeError will be raised + + # For those unsupported cases, we suggest that users manually perform type promotion + # method 1: use astype API + a = a.astype("float32") + a = a.astype(b.dtype) + + # method 2：use cast API + a = paddle.cast(a, "float32") + a = paddle.cast(a, b.dtype) + + ``` + +The Scope of Type Promotion +------------------------------ + +As of Paddle version 2.6, the supported binary APIs and their rules are as follows: + +Number | API | Tensor-to-Tensor | Tensor-to-Scalar | +:-----: | :-----: | :-----: | :-----: | +1 | add | Common | Common | +2 | subtract | Common Rule | Common Rule | +3 | multiply | Common Rule | Common Rule | +4 | divide | Common Rule | Divide Rule | +5 | floor_divide | Common Rule | Common Rule | +6 | pow | Common Rule | Common Rule | +7 | equal | Logic Rule | Logic Rule | +8 | not_equal | Logic Rule | Logic Rule | +9 | less_than | Logic Rule | Logic Rule | +10 | less_equal | Logic Rule | Logic Rule | +11 | greater_than | Logic Rule | Logic Rule | +12 | greater_equal | Logic Rule | Logic Rule | +13 | logical_and | Logic Rule | Logic Rule | +14 | logical_or | Logic Rule | Logic Rule | +15 | logical_xor | Logic Rule | Logic Rule | +16 | bitwise_and | - | Common Rule | +17 | bitwise_or | - | Common Rule | +18 | bitwise_xor | - | Common Rule | +19 | where | Common Rule | Common Rule | +20 | fmax | Common Rule | - | +21 | fmin | Common Rule | - | +22 | logaddexp | Common Rule | - | +23 | maximum | Common Rule | - | +24 | minimum | Common Rule | - | +25 | remainder(mod) | Common Rule | Common Rule | +26 | huber_loss | Common Rule | - | +27 | nextafter | Common Rule | - | +28 | atan2 | Common Rule | - | +29 | poisson_nll_loss | Common Rule | - | +30 | l1_loss | Common Rule | - | +31 | huber_loss | Common Rule | - | +32 | mse_loss | Common Rule | - | + +There are two specail rules in this table above: + +- Divide Rule: For divide API, it will not return dtype smaller than float. Such as int32 / Scalar returns float32. + + ```python + + import paddle + a = paddle.ones([3,3], dtype = 'int32') + b = 1 + c = a / b + print (c.dtype) # float32 + + ``` + +- Logic Rule: For logical API, since complex types cannot be directly used for logical operations, calculations involving complex types are not within the scope of type promotion support. Within the supported scope, all results return bool type. + + ```python + + import paddle + a = paddle.rand([3,3], dtype = 'float32') + b = paddle.rand([3,3], dtype = 'float16') + c = a == b + print (c.dtype) # bool + + ``` + +Summary +------------------------ + +Paddle ensures that calculations comply with the commutative property while supporting data type promotion, with consistent results for operator overloading and binary APIs, as well as for dynamic and static graphs. This article clarifies the rules and scope of data type promotion, summarizes the types of binary APIs that support data type promotion in the current version, and aims to enhance user convenience when using PaddlePaddle by introducing usage methods. diff --git a/docs/guides/advanced/index_cn.rst b/docs/guides/advanced/index_cn.rst index b802fdab725..604c4d3c281 100755 --- a/docs/guides/advanced/index_cn.rst +++ b/docs/guides/advanced/index_cn.rst @@ -9,6 +9,7 @@ - `自定义 Loss、Metric 及 Callback <./customize_cn.html>`_ - `梯度裁剪 <./gradient_clip_cn.html>`_ - `模型导出 ONNX 协议 <./model_to_onnx_cn.html>`_ +- `隐式数据类型提升 <./auto_type_promotion_cn.html>`_ .. toctree:: :hidden: @@ -19,3 +20,4 @@ customize_cn.ipynb gradient_clip_cn.rst model_to_onnx_cn.md + auto_type_promotion_cn.md diff --git a/docs/guides/advanced/index_en.rst b/docs/guides/advanced/index_en.rst index 5157c6e1fd5..710d9e03018 100644 --- a/docs/guides/advanced/index_en.rst +++ b/docs/guides/advanced/index_en.rst @@ -5,6 +5,7 @@ More Uses for Model Development - `Model Visualization <./visualdl_usage_en.html>`_ - `Model and Layer <./layer_and_model_en.html>`_ - `Gradient Clip <./gradient_clip_en.html>`_ +- `Data Type Promotion <./auto_type_promotion_en.html>`_ .. toctree:: :hidden: @@ -12,3 +13,4 @@ More Uses for Model Development visualdl_usage_en.md layer_and_model_en.md gradient_clip_en.rst + auto_type_promotion_en.md diff --git a/docs/guides/advanced/visualdl_cn.md b/docs/guides/advanced/visualdl_cn.md deleted file mode 100644 index 3de1f8ded86..00000000000 --- a/docs/guides/advanced/visualdl_cn.md +++ /dev/null @@ -1,300 +0,0 @@ -# VisualDL 工具简介 - - -

    - -

    - - - -VisualDL 是飞桨可视化分析工具，以丰富的图表呈现训练参数变化趋势、模型结构、数据样本、直方图、PR 曲线及高维数据分布。可帮助用户更清晰直观地理解深度学习模型训练过程及模型结构，进而实现高效的模型优化。 - -具体功能使用方式请参见**VisualDL 使用指南**。项目正处于高速迭代中，敬请期待新组件的加入。 - -VisualDL 支持浏览器种类：Chrome（81 和 83）、Safari 13、Firefox（77 和 78）、Edge（Chromium 版）。 - -VisualDL 原生支持 python 的使用， 通过在模型的 Python 配置中添加几行代码，便可为训练过程提供丰富的可视化支持。 - - - -## 目录 - -* [核心亮点](#核心亮点) -* [安装方式](#安装方式) -* [使用方式](#使用方式) -* [可视化功能概览](#可视化功能概览) -* [开源贡献](#开源贡献) -* [更多细节](#更多细节) -* [技术交流](#技术交流) - - - -## 核心亮点 - -### 简单易用 - -API 设计简洁易懂，使用简单。模型结构一键实现可视化。 - -### 功能丰富 - -功能覆盖标量、数据样本、图结构、直方图、PR 曲线及数据降维可视化。 - -### 高兼容性 - -全面支持 Paddle、ONNX、Caffe 等市面主流模型结构可视化，广泛支持各类用户进行可视化分析。 - -### 全面支持 - -与飞桨服务平台及工具组件全面打通，为您在飞桨生态系统中提供最佳使用体验。 - - - -## 安装方式 - -### 使用 pip 安装 - -```shell -pip install --upgrade --pre visualdl -``` - -### 使用代码安装 - -``` -git clone https://github.com/PaddlePaddle/VisualDL.git -cd VisualDL - -python setup.py bdist_wheel -pip install --upgrade dist/visualdl-*.whl -``` - -需要注意，官方自 2020 年 1 月 1 日起不再维护 Python2，为了保障代码可用性，VisualDL 现仅支持 Python3 - -## 使用方式 - -VisualDL 将训练过程中的数据、参数等信息储存至日志文件中后，启动面板即可查看可视化结果。 - -### 1. 记录日志 - -VisualDL 的后端提供了 Python SDK，可通过 LogWriter 定制一个日志记录器，接口如下： - -```python -class LogWriter(logdir=None, - comment='', - max_queue=10, - flush_secs=120, - filename_suffix='', - write_to_disk=True, - **kwargs) -``` - -#### 接口参数 - -| 参数 | 格式 | 含义 | -| --------------- | ------- | ------------------------------------------------------------ | -| logdir | string | 日志文件所在的路径，VisualDL 将在此路径下建立日志文件并进行记录，如果不填则默认为`runs/${CURRENT_TIME}` | -| comment | string | 为日志文件夹名添加后缀，如果制定了 logdir 则此项无效 | -| max_queue | int | 日志记录消息队列的最大容量，达到此容量则立即写入到日志文件 | -| flush_secs | int | 日志记录消息队列的最大缓存时间，达到此时间则立即写入到日志文件 | -| filename_suffix | string | 为默认的日志文件名添加后缀 | -| write_to_disk | boolean | 是否写入到磁盘 | - -#### 示例 - -设置日志文件并记录标量数据： - -```python -from visualdl import LogWriter - -# 在`./log/scalar_test/train`路径下建立日志文件 -with LogWriter(logdir="./log/scalar_test/train") as writer: - # 使用 scalar 组件记录一个标量数据 - writer.add_scalar(tag="acc", step=1, value=0.5678) - writer.add_scalar(tag="acc", step=2, value=0.6878) - writer.add_scalar(tag="acc", step=3, value=0.9878) -``` - -### 2. 启动面板 - -在上述示例中，日志已记录三组标量数据，现可启动 VisualDL 面板查看日志的可视化结果，共有两种启动方式： - -#### 在命令行启动 - -使用命令行启动 VisualDL 面板，命令格式如下： - -```python -visualdl --logdir --host --port --cache-timeout --language --public-path --api-only -``` - -参数详情： - -| 参数 | 意义 | -| --------------- | ------------------------------------------------------------ | -| --logdir | 设定日志所在目录，可以指定多个目录，VisualDL 将遍历并且迭代寻找指定目录的子目录，将所有实验结果进行可视化 | -| --model | 设定模型文件路径(非文件夹路径)，VisualDL 将在此路径指定的模型文件进行可视化，目前可支持 PaddlePaddle、ONNX、Keras、Core ML、Caffe 等多种模型结构，详情可查看[graph 支持模型种类]([https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/README.md#Graph--%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84%E7%BB%84%E4%BB%B6](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/README.md#Graph--网络结构组件)) | -| --host | 设定 IP，默认为`127.0.0.1` | -| --port | 设定端口，默认为`8040` | -| --cache-timeout | 后端缓存时间，在缓存时间内前端多次请求同一 url，返回的数据从缓存中获取，默认为 20 秒 | -| --language | VisualDL 面板语言，可指定为'EN'或'ZH'，默认为浏览器使用语言 | -| --public-path | VisualDL 面板 URL 路径，默认是'/app'，即访问地址为'http://<host>:<port>/app' | -| --api-only | 是否只提供 API，如果设置此参数，则 VisualDL 不提供页面展示，只提供 API 服务，此时 API 地址为'http://<host>:<port>/<public_path>/api'；若没有设置 public_path 参数，则默认为'http://<host>:<port>/api' | - -针对上一步生成的日志，启动命令为： - -``` -visualdl --logdir ./log -``` - -#### 在 Python 脚本中启动 - -支持在 Python 脚本中启动 VisualDL 面板，接口如下： - -```python -visualdl.server.app.run(logdir, - host="127.0.0.1", - port=8080, - cache_timeout=20, - language=None, - public_path=None, - api_only=False, - open_browser=False) -``` - -请注意：除`logdir`外，其他参数均为不定参数，传递时请指明参数名。 - -接口参数具体如下： - -| 参数 | 格式 | 含义 | -| ------------- | ------------------------------------------------ | ------------------------------------------------------------ | -| logdir | string 或 list[string_1, string_2, ... , string_n] | 日志文件所在的路径，VisualDL 将在此路径下递归搜索日志文件并进行可视化，可指定单个或多个路径 | -| model | string | 模型文件路径(非文件夹路径)，VisualDL 将在此路径指定的模型文件进行可视化 | -| host | string | 指定启动服务的 ip，默认为`127.0.0.1` | -| port | int | 启动服务端口，默认为`8040` | -| cache_timeout | int | 后端缓存时间，在缓存时间内前端多次请求同一 url，返回的数据从缓存中获取，默认为 20 秒 | -| language | string | VisualDL 面板语言，可指定为'en'或'zh'，默认为浏览器使用语言 | -| public_path | string | VisualDL 面板 URL 路径，默认是'/app'，即访问地址为'http://:/app' | -| api_only | boolean | 是否只提供 API，如果设置此参数，则 VisualDL 不提供页面展示，只提供 API 服务，此时 API 地址为'http://://api'；若没有设置 public_path 参数，则默认为 http://:/api' | -| open_browser | boolean | 是否打开浏览器，设置为 True 则在启动后自动打开浏览器并访问 VisualDL 面板，若设置 api_only，则忽略此参数 | - -针对上一步生成的日志，我们的启动脚本为： - -```python -from visualdl.server import app - -app.run(logdir="./log") -``` - -在使用任意一种方式启动 VisualDL 面板后，打开浏览器访问 VisualDL 面板，即可查看日志的可视化结果，如图： - -

    - -

    - - - -## 可视化功能概览 - -### Scalar - -以图表形式实时展示训练过程参数，如 loss、accuracy。让用户通过观察单组或多组训练参数变化，了解训练过程，加速模型调优。具有两大特点： - -#### 动态展示 - -在启动 VisualDL 后，LogReader 将不断增量的读取日志中数据并供前端调用展示，因此能够在训练中同步观测指标变化，如下图： - -

    - -

    - - - -#### 多实验对比 - -只需在启动 VisualDL 时将每个实验日志所在路径同时传入即可，每个实验中相同 tag 的指标将绘制在一张图中同步呈现，如下图： - -

    - -

    - - - -### Image - -实时展示训练过程中的图像数据，用于观察不同训练阶段的图像变化，进而深入了解训练过程及效果。 - -

    - -

    - - - -### Audio - -实时查看训练过程中的音频数据，监控语音识别与合成等任务的训练过程。 - -

    - -

    - - - -### Graph - -一键可视化模型的网络结构。可查看模型属性、节点信息、节点输入输出等，并支持节点搜索，辅助用户快速分析模型结构与了解数据流向。 - -

    - -

    - - - -### Histogram - -以直方图形式展示 Tensor（weight、bias、gradient 等）数据在训练过程中的变化趋势。深入了解模型各层效果，帮助开发者精准调整模型结构。 - -- Offset 模式 - -

    - -

    - - - -- Overlay 模式 - -

    - -

    - - - -### PR Curve - -精度-召回率曲线，帮助开发者权衡模型精度和召回率之间的平衡，设定最佳阈值。 - -

    - -

    - - -### High Dimensional - -将高维数据进行降维展示，目前支持 T-SNE、PCA 两种降维方式，用于深入分析高维数据间的关系，方便用户根据数据特征进行算法优化。 - -

    - -

    - -## 开源贡献 - -VisualDL 是由 [PaddlePaddle](https://www.paddlepaddle.org/) 和 [ECharts](https://echarts.apache.org/) 合作推出的开源项目。 -Graph 相关功能由 [Netron](https://github.com/lutzroeder/netron) 提供技术支持。 -欢迎所有人使用，提意见以及贡献代码。 - - -## 更多细节 - -想了解更多关于 VisualDL 可视化功能的使用详情介绍，请查看**VisualDL 使用指南**。 - -## 技术交流 - -欢迎您加入 VisualDL 官方 QQ 群：1045783368 与飞桨团队以及其他用户共同针对 VisualDL 进行讨论与交流。 diff --git a/docs/guides/advanced/visualdl_en.md b/docs/guides/advanced/visualdl_en.md deleted file mode 100755 index 6aa671f8a3b..00000000000 --- a/docs/guides/advanced/visualdl_en.md +++ /dev/null @@ -1,263 +0,0 @@ -# Introduction to VisualDL Toolset - -

    - -

    - -## Introduction -VisualDL is a deep learning visualization tool that can help design deep learning jobs. -It includes features such as scalar, parameter distribution, model structure and image visualization. -Currently it is being developed at a high pace. -New features will be continuously added. - -At present, most DNN frameworks use Python as their primary language. VisualDL supports Python by nature. -Users can get plentiful visualization results by simply add a few lines of Python code into their model before training. - -Besides Python SDK, VisualDL was writen in C++ on the low level. It also provides C++ SDK that -can be integrated into other platforms. - - -## Component -VisualDL provides following components: - -- scalar -- histogram -- image -- audio -- graph -- high dimensional - -### Scalar -Scalar can be used to show the trends of error during training. - -

    - -

    - -### Histogram -Histogram can be used to visualize parameter distribution and trends for any tensor. - -

    - -

    - -### Image -Image can be used to visualize any tensor or intermediate generated image. - -

    - -

    - -### Audio -Audio can be used to play input audio samples or generated audio samples. - -### Graph - -VisualDL graph supports displaying paddle model, furthermore is compatible with ONNX ([Open Neural Network Exchange](https://github.com/onnx/onnx)), -Cooperated with Python SDK, VisualDL can be compatible with most major DNN frameworks, including -PaddlePaddle, PyTorch and MXNet. - -

    - -

    - -To display the paddle model, all you have to do is: - -1. call the `fluid.io.save_inference_model()`interface to save paddle model -2. use `visualdl --model_pb [paddle_model_dir]` to load paddle model in command line - - -### High Dimensional -High Dimensional can be used to visualize data embeddings by projecting high-dimensional data into 2D / 3D. - -

    - -

    - -## Quick Start -To give the VisualDL a quick test, please use the following commands. - -``` -# Install the VisualDL. Preferably under a virtual environment or anaconda. -pip install --upgrade visualdl - -# run a demo, vdl_create_scratch_log will create logs for testing. -vdl_create_scratch_log -visualdl --logdir=scratch_log --port=8080 - -# visit http://127.0.0.1:8080 -``` - -If you encounter the error `TypeError: __init__() got an unexpected keyword argument 'file'`, that is due to protobuf version is not 3.5+，simply run `pip install --upgrade protobuf` will fix the issue. - -If you run into any other issues in above steps, it could be error caused by environmental issues by different python or pip versions. -Following installation methods might fix the issues. - -## Install with Virtualenv - -[Virtualenv](https://virtualenv.pypa.io/en/stable/) creates isolated Python environment that prevents interfering -by other Python programs on the same machine and make sure Python and pip are located properly. - -On macOS, install pip and virtualenv by: -``` -sudo easy_install pip -pip install --upgrade virtualenv -``` - -On Linux, install pip and virtualenv by: -``` -sudo apt-get install python3-pip python3-dev python-virtualenv -``` - -Then create a Virtualenv environment by one of following command: -``` -virtualenv -p python3 ~/vdl for Python 3.x -``` - -```~/vdl``` will be your Virtualenv directory, you may choose to install anywhere. - -Activate your Virtualenv environment by: -``` -source ~/vdl/bin/activate -``` - -Now you should be able to install VisualDL and run our demo: - -``` -pip install --upgrade visualdl - -# run a demo, vdl_create_scratch_log will create logs for testing. -vdl_create_scratch_log -visualdl --logdir=scratch_log --port=8080 - -# visit http://127.0.0.1:8080 -``` - -If you still have issues installing VisualDL from Virtualenv, try following installation method. - - -## Install with Anaconda - -Anaconda is a python distribution, with installation and package management tools. Also it is an environment manager, -which provides the facility to create different python environments, each with their own settings. - -Follow the instructions on the [Anaconda download site](https://www.anaconda.com/download) to download and install Anaconda. -Download Python 3.6 version command-Line installer. - -Create a conda environment named ```vdl``` or anything you want by: -``` -conda create -n vdl pip python=2.7 # or python=3.3, etc. -``` - -Activate the conda environment by: -``` -source activate vdl -``` - -Now you should be able to install VisualDL and run our demo: - -``` -pip install --upgrade visualdl - -# run a demo, vdl_create_scratch_log will create logs for testing. -vdl_create_scratch_log -visualdl --logdir=scratch_log --port=8080 - -# visit http://127.0.0.1:8080 -``` - -If you still have issues installing VisualDL, try installing from sources as in following section. - - -### Install from source -``` -#Preferably under a virtualenv or anaconda. -git clone https://github.com/PaddlePaddle/VisualDL.git -cd VisualDL - -python setup.py bdist_wheel -pip install --upgrade dist/visualdl-*.whl -``` - -If there are still issues regarding the ```pip install```, you can still start Visual DL by starting the dev server -[here](https://github.com/PaddlePaddle/VisualDL/tree/develop/docs#%E5%AE%89%E8%A3%85%E6%96%B9%E5%BC%8F) - - -## SDK -VisualDL provides both Python SDK and C++ SDK in order to fit more use cases. - - -### Python SDK -VisualDL now supports both Python 2 and Python 3. -Below is an example of creating a simple Scalar component and inserting data from different timestamps: - -```python -import random -from visualdl import LogWriter - -logdir = "./tmp" -logger = LogWriter(logdir, sync_cycle=10000) - -# mark the components with 'train' label. -with logger.mode("train"): - # create a scalar component called 'scalars/scalar0' - scalar0 = logger.scalar("scalars/scalar0") - -# add some records during DL model running. -for step in range(100): - scalar0.add_record(step, random.random()) -``` - -### C++ SDK -Here is the C++ SDK identical to the Python SDK example above: - -```c++ -#include -#include -#include "visualdl/logic/sdk.h" - -namespace vs = visualdl; -namespace cp = visualdl::components; - -int main() { - const std::string dir = "./tmp"; - vs::LogWriter logger(dir, 10000); - - logger.SetMode("train"); - auto tablet = logger.AddTablet("scalars/scalar0"); - - cp::Scalar scalar0(tablet); - - for (int step = 0; step < 1000; step++) { - float v = (float)std::rand() / RAND_MAX; - scalar0.AddRecord(step, v); - } - - return 0; -} -``` - -## Launch Visual DL -After some logs have been generated during training, users can launch Visual DL application to see real-time data visualization by: - - -``` -visualdl --logdir -``` - -visualDL also supports following optional parameters: - -- `--host` set IP -- `--port` set port -- `-m / --model_pb` specify ONNX format for model file to view graph - - -### Contribute - -VisualDL is initially created by [PaddlePaddle](http://www.paddlepaddle.org/) and -[ECharts](http://echarts.baidu.com/). -We welcome everyone to use, comment and contribute to VisualDL :) - -## More details - -For more details about how to use VisualDL, please take a look at [documents](https://github.com/PaddlePaddle/VisualDL/tree/develop/demo) diff --git a/docs/guides/beginner/model_save_load_cn.ipynb b/docs/guides/beginner/model_save_load_cn.ipynb index 39b77e07a1e..1b276424076 100644 --- a/docs/guides/beginner/model_save_load_cn.ipynb +++ b/docs/guides/beginner/model_save_load_cn.ipynb @@ -196,7 +196,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "> 注：`paddle.save` 的文件名称是自定义的，以输入参数 `path` （如 \"linear_net.pdparams\"）直接作为存储结果的文件名。为了便于辩识，我们推荐使用统一的标椎文件后缀： \n", + "> 注：`paddle.save` 的文件名称是自定义的，以输入参数 `path` （如 \"linear_net.pdparams\"）直接作为存储结果的文件名。为了便于辩识，我们推荐使用统一的标准文件后缀： \n", "> 1. 对于 `Layer.state_dict()` （模型参数），推荐使用后缀 `.pdparams` ； \n", "> 2. 对于 `Optimizer.state_dict()` （优化器参数），推荐使用后缀 `.pdopt` 。\n", "#### 2.2.2 加载动态图模型\n", diff --git a/docs/guides/beginner/quick_start_cn.ipynb b/docs/guides/beginner/quick_start_cn.ipynb index ed456894a31..c979560dd60 100644 --- a/docs/guides/beginner/quick_start_cn.ipynb +++ b/docs/guides/beginner/quick_start_cn.ipynb @@ -422,7 +422,7 @@ "3. **使用 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。** 配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等，执行后将自动完成模型的训练循环。\n", "\n", "\n", - "因为是分类任务，这里损失函数使用常见的 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) （交叉熵损失函数），优化器使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam)，评价指标使用 [Accuracy](../../api/paddle/metric/Accuracy_cn.html#accuracy) 来计算模型在训练集上的精度。" + "因为是分类任务，这里损失函数使用常见的 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) （交叉熵损失函数），优化器使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam)，评价指标使用 [Accuracy](../../api/paddle/metric/accuracy_cn.html#accuracy) 来计算模型在训练集上的精度。" ] }, { diff --git a/docs/guides/beginner/train_eval_predict_cn.ipynb b/docs/guides/beginner/train_eval_predict_cn.ipynb index 92da127c525..30fca137bde 100644 --- a/docs/guides/beginner/train_eval_predict_cn.ipynb +++ b/docs/guides/beginner/train_eval_predict_cn.ipynb @@ -111,7 +111,7 @@ "\n", "> 注：\n", "> * 本文仅以单机单卡场景为例，介绍模型训练的方法，如果需要使用单机多卡、多机多卡训练，请参考如下章节：[分布式训练](../06_distributed_training/index_cn.html)。\n", - "> * 飞桨框架除了支持在 CPU、GPU 上训练，还支持在百度昆仑 XPU、华为昇腾 NPU 等 AI 计算处理器上训练，对应的训练指导请参考 [硬件支持](../hardware_support/index_cn.html) 章节。\n" + "> * 飞桨框架除了支持在 CPU、GPU 上训练，还支持在百度昆仑芯 XPU、华为昇腾 NPU 等 AI 计算处理器上训练，对应的训练指导请参考 [硬件支持](../hardware_support/index_cn.html) 章节。\n" ] }, { diff --git a/docs/guides/flags/executor_cn.rst b/docs/guides/flags/executor_cn.rst index bcc3cf75758..d5bfd420576 100644 --- a/docs/guides/flags/executor_cn.rst +++ b/docs/guides/flags/executor_cn.rst @@ -3,36 +3,6 @@ ================== -FLAGS_enable_parallel_graph -******************************************* -(始于 1.2.0) - -该 flag 用于 ParallelExecutor 以禁用并行图执行模式。 - -取值范围 ---------------- -Bool 型，缺省值为 False。 - -示例 -------- -FLAGS_enable_parallel_graph=False - 通过 ParallelExecutor 强制禁用并行图执行模式。 - - -FLAGS_pe_profile_fname -******************************************* -(始于 1.3.0) - -该 flag 用于 ParallelExecutor 的调试。ParallelExecutor 会通过 gpertools 生成配置文件结果，并将结果存储在 FLAGS_pe_profile_fname 指定的文件中。仅在编译选项选择 `WITH_PRIFILER=ON` 时有效。如果禁用则设为 empty。 - -取值范围 ---------------- -String 型，缺省值为 empty ("")。 - -示例 -------- -FLAGS_pe_profile_fname="./parallel_executor.perf" - 将配置文件结果存储在 parallel_executor.perf 中。 - - FLAGS_print_sub_graph_dir ******************************************* (始于 1.2.0) diff --git a/docs/guides/flags/executor_en.rst b/docs/guides/flags/executor_en.rst index 689b5fa4d54..94911702409 100644 --- a/docs/guides/flags/executor_en.rst +++ b/docs/guides/flags/executor_en.rst @@ -3,36 +3,6 @@ executor ================== -FLAGS_enable_parallel_graph -******************************************* -(since 1.2.0) - -This Flag is used for ParallelExecutor to disable parallel graph execution mode. - -Values accepted ---------------- -Bool. The default value is False. - -Example -------- -FLAGS_enable_parallel_graph=False will force disable parallel graph execution mode by ParallelExecutor. - - -FLAGS_pe_profile_fname -******************************************* -(since 1.3.0) - -This Flag is used for debugging for ParallelExecutor. The ParallelExecutor will generate the profile result by gperftools, and the profile result will be stored in the file which is specified by FLAGS_pe_profile_fname. Only valid when compiled `WITH_PRIFILER=ON`. Empty if disable. - -Values accepted ---------------- -String. The default value is empty (""). - -Example -------- -FLAGS_pe_profile_fname="./parallel_executor.perf" will store the profile result to parallel_executor.perf. - - FLAGS_print_sub_graph_dir ******************************************* (since 1.2.0) diff --git a/docs/guides/hardware_support/dcu/install_cn.md b/docs/guides/hardware_support/dcu/install_cn.md index b6a9f0898d6..e577865a57f 100644 --- a/docs/guides/hardware_support/dcu/install_cn.md +++ b/docs/guides/hardware_support/dcu/install_cn.md @@ -21,9 +21,8 @@ docker pull registry.baidubce.com/device/paddle-dcu:dtk23.10.1-kylinv10-gcc73-py310 # 参考如下命令，启动容器 -docker run -it --name paddle-dcu-dev -v $(pwd):/work \ - --workdir=/work --shm-size=128G --network=host \ - --device=/dev/kfd --device=/dev/dri --group-add video \ +docker run -it --name paddle-dcu-dev -v `pwd`:/work \ + -w=/work --shm-size=128G --network=host --privileged \ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ registry.baidubce.com/device/paddle-dcu:dtk23.10.1-kylinv10-gcc73-py310 /bin/bash @@ -51,11 +50,8 @@ DCU Temp AvgPwr Fan Perf PwrCap VRAM% DCU% 在启动的 docker 容器中，下载并安装飞桨官网发布的 wheel 包。 ```bash -# 下载 wheel 包 -wget https://paddle-device.bj.bcebos.com/0.0.0/dcu/paddlepaddle_rocm-0.0.0-cp310-cp310-linux_x86_64.whl - -# 安装 wheel 包 -pip install -U paddlepaddle_rocm-0.0.0-cp310-cp310-linux_x86_64.whl +# 下载并安装 wheel 包 +pip install paddlepaddle-rocm -i https://www.paddlepaddle.org.cn/packages/nightly/dcu ``` ### 安装方式二：源代码编译安装 diff --git a/docs/guides/hardware_support/hardware_info_cn.md b/docs/guides/hardware_support/hardware_info_cn.md index 70640374a3e..1acb111f7d1 100644 --- a/docs/guides/hardware_support/hardware_info_cn.md +++ b/docs/guides/hardware_support/hardware_info_cn.md @@ -10,7 +10,7 @@ | 服务端 GPU | | NVIDIA | Ada Lovelace、Hopper、 Ampere、Turing、 Volta 架构 | [安装](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) | [源码编译](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/compile/linux-compile.html) | ✔️ | | | AI 加速芯片 | 达芬奇 | 华为 | 昇腾 910 系列 | [安装](./npu/install_cn.html#wheel) | [源码编译](./npu/install_cn.html) | | ✔️ | | AI 加速芯片 | GPGPU | 海光 | 海光 Z100 系列 | [安装](./dcu/install_cn.html#wheel) | [源码编译](./dcu/install_cn.html) | | [支持模型](./dcu/support_cn.html) | -| AI 加速芯片 | XPU | 百度 | 昆仑 R200、R300 等 | [安装](./xpu/install_cn.html#wheel) | [源码编译](./xpu/install_cn.html#xpu) | | [支持模型](./xpu/support_cn.html) | +| AI 加速芯片 | XPU | 百度 | 昆仑芯 R200、R300 等 | [安装](./xpu/install_cn.html#wheel) | [源码编译](./xpu/install_cn.html#xpu) | | [支持模型](./xpu/support_cn.html) | | AI 加速芯片 | IPU | Graphcore | GC200 | | | | ✔️ | | AI 加速芯片 | MLU | 寒武纪 | MLU370 系列 | [安装](./mlu/install_cn.html#wheel) | [源码编译](./mlu/install_cn.html) | | [支持模型](./mlu/support_cn.html) | | AI 加速芯片 | | 天数智芯 | 天垓 100 | [安装](https://gitee.com/deep-spark/deepsparkhub/blob/master/docker/Iluvatar/README.md) || | | ✔️ | @@ -29,7 +29,7 @@ | AI 加速芯片 | MLU | 寒武纪 | MLU370 系列 | | [源码编译](./mlu/install_cn.html) | | ✔️ | | AI 加速芯片 | MUSA | 摩尔线程 | MTT S 系列 GPU | | | | | | AI 加速芯片 | GPGPU | 海光 | 海光 Z100 系列 | | [源码编译](https://www.paddlepaddle.org.cn/inference/master/guides/hardware_support/dcu_hygon_cn.html) | | [支持模型](./dcu/support_cn.html) | -| AI 加速芯片 | XPU | 百度 | 昆仑 R200、R300 等 | | [源码编译](https://www.paddlepaddle.org.cn/inference/master/guides/hardware_support/xpu_kunlun_cn.html) | | [支持模型](./xpu/support_cn.html) | +| AI 加速芯片 | XPU | 百度 | 昆仑芯 R200、R300 等 | | [源码编译](https://www.paddlepaddle.org.cn/inference/master/guides/hardware_support/xpu_kunlun_cn.html) | | [支持模型](./xpu/support_cn.html) | | 服务端 CPU | ARM | 飞腾 | FT-2000+/64、S2500 | |[源码编译](../../install/compile/arm-compile.html#anchor-1) | | | | 服务端 CPU | ARM | 华为 | 鲲鹏 920 2426SK | |[源码编译](../../install/compile/arm-compile.html) | | | | 服务端 CPU | MIPS | 龙芯 | 龙芯 3A4000、3A5000、3C5000L | |[源码编译](../../install/compile/mips-compile.html#anchor-0) | | | @@ -49,7 +49,7 @@ | AI 加速芯片 | | 瑞芯微 | RK18xx 系列 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/rockchip_npu.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/rockchip_npu.html#id1) | | AI 加速芯片 | | 联发科 | NeuroPilot APU | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/mediatek_apu.html#id1) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/mediatek_apu.html#id1) | | AI 加速芯片 | | Imagination | PowerVR 2NX | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/huawei_kirin_npu.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/huawei_kirin_npu.html#id1) | -| AI 加速芯片 | | 百度 | 昆仑 R200、R300 等 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id4) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id1) | +| AI 加速芯片 | | 百度 | 昆仑芯 R200、R300 等 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id4) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id1) | | AI 加速芯片 | | 寒武纪 | 思元系列芯片 | | [源码编译](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/cambricon_mlu.html#cankaoshiliyanshi) | | [支持模型](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/cambricon_mlu.html#paddle) | | AI 加速芯片 | | 比特大陆 | 算丰 BM16 系列芯片 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/bitmain.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/bitmain.html#id1) | | AI 加速芯片 | | 紫光展锐 | T820 | | [源码编译](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/android_nnapi.html#android-nnapi-paddle-lite) | | [支持模型](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/android_nnapi.html#paddle) | @@ -58,20 +58,22 @@ | FPGA | | 百度 | 百度 Edgeboard 开发板 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id4) | | [支持模型](https://ai.baidu.com/ai-doc/HWCE/Qkda68drw) | ## Paddle2ONNX -| 分类 | 架构 | 公司 | 型号 | 支持部分模型 | 模型库链接 | -| ---- | ---- | ---- | ---- | ---- | ---- | -| AI 加速芯片 | | 登临 | Goldwasser 系列加速卡 | ✔️ | [模型库](https://github.com/denglin-github/DLPaddleModelZoo) | -| AI 加速芯片 | | 墨芯 | Moffett S4 | ✔️ | [模型库](https://github.com/MoffettSystem/moffett-modelzoo-paddle) | -| AI 加速芯片 | | 海飞科 | Compass C10 | ✔️ | [模型库](https://github.com/hexaflakeai/model_zoo) | -| AI 加速芯片 | | 清微智能 | TX5368 | ✔️ | [模型库](https://github.com/tsingmicro-toolchain/ts.knight-modelzoo) | -| AI 加速芯片 | | 爱芯元智 | AX620A | ✔️ | [模型库](https://github.com/AXERA-TECH/ax-samples/tree/main) | -| AI 加速芯片 | | 沐曦 | N100 | ✔️ | [模型库](https://github.com/denglin-github/DLPaddleModelZoo) | + +| 分类 | 公司 | 型号 | 支持部分模型 | 模型库链接 | +| ---- | ---- | ---- | ---- | ---- | +| AI 加速芯片 | 登临 | Goldwasser 系列加速卡 | ✔️ | [模型库](https://github.com/denglin-github/DLPaddleModelZoo) | +| AI 加速芯片 | 墨芯 | Moffett S4 | ✔️ | [模型库](https://github.com/MoffettSystem/moffett-modelzoo-paddle) | +| AI 加速芯片 | 海飞科 | Compass C10 | ✔️ | [模型库](https://github.com/hexaflakeai/model_zoo) | +| AI 加速芯片 | 清微智能 | TX5368 | ✔️ | [模型库](https://github.com/tsingmicro-toolchain/ts.knight-modelzoo) | +| AI 加速芯片 | 爱芯元智 | AX620A | ✔️ | [模型库](https://github.com/AXERA-TECH/ax-samples/tree/main) | +| AI 加速芯片 | 沐曦 | N100 | ✔️ | [模型库](https://github.com/denglin-github/DLPaddleModelZoo) | ## TVM -| 分类 | 架构 | 公司 | 型号 | 支持部分模型 | 模型库链接 | -| ---- | ---- | ---- | ---- | ---- | ---- | -| 嵌入式芯片 | | Arm | Cortex-M 系列 | ✔️ | [模型库](https://github.com/ArmDeveloperEcosystem/Paddle-examples-for-AVH) | -| AI 加速芯片 | | 安谋科技 | 周易 NPU | ✔️ | [模型库](https://github.com/Arm-China/PaddlePaddle_example_for_Zhouyi_NPU) | -| AI 加速芯片 | | Imagination | PowerVR 2NX | ✔️ | [模型库](https://github.com/imaginationtech/PaddlePaddle_Model_zoo) | + +| 分类 | 公司 | 型号 | 支持部分模型 | 模型库链接 | +| ---- | ---- | ---- | ---- | ---- | +| 嵌入式芯片 | Arm | Cortex-M 系列 | ✔️ | [模型库](https://github.com/ArmDeveloperEcosystem/Paddle-examples-for-AVH) | +| AI 加速芯片 | 安谋科技 | 周易 NPU | ✔️ | [模型库](https://github.com/Arm-China/PaddlePaddle_example_for_Zhouyi_NPU) | +| AI 加速芯片 | Imagination | PowerVR 2NX | ✔️ | [模型库](https://github.com/imaginationtech/PaddlePaddle_Model_zoo) | **注意:** 如果你想了解更多芯片支持的信息，请联系我们，邮箱为 Paddle-better@baidu.com。 diff --git a/docs/guides/hardware_support/index_cn.rst b/docs/guides/hardware_support/index_cn.rst index cee30ce1216..1ab20ff6446 100644 --- a/docs/guides/hardware_support/index_cn.rst +++ b/docs/guides/hardware_support/index_cn.rst @@ -7,7 +7,7 @@ 你可以通过以下内容，了解飞桨框架硬件支持相关的内容: - `飞桨硬件支持 <./hardware_info_cn.html>`_ : 说明飞桨产品支持的硬件。 -- `昆仑 XPU 芯片运行飞桨 <./xpu/index_cn.html>`_ : 介绍如何在昆仑 XPU 芯片环境上安装和使用飞桨。 +- `昆仑芯 XPU 芯片运行飞桨 <./xpu/index_cn.html>`_ : 介绍如何在昆仑芯 XPU 芯片环境上安装和使用飞桨。 - `海光 DCU 芯片运行飞桨 <./dcu/index_cn.html>`_ : 介绍如何在海光 DCU 芯片环境上安装和使用飞桨。 - `昇腾 NPU 芯片运行飞桨 <./npu/index_cn.html>`_ : 介绍如何在昇腾 NPU 芯片环境上安装和使用飞桨。 - `寒武纪 MLU 芯片运行飞桨 <./mlu/index_cn.html>`_ : 介绍如何在寒武纪 MLU 芯片环境上安装和使用飞桨。 diff --git a/docs/guides/hardware_support/mlu/install_cn.md b/docs/guides/hardware_support/mlu/install_cn.md index 4e691864798..0d186715db2 100644 --- a/docs/guides/hardware_support/mlu/install_cn.md +++ b/docs/guides/hardware_support/mlu/install_cn.md @@ -62,11 +62,10 @@ cnmon ```bash # 先安装飞桨 CPU 安装包 -pip install paddlepaddle==0.0.0 -f https://www.paddlepaddle.org.cn/whl/linux/cpu-mkl/develop.html +pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu # 再安装飞桨 MLU 插件包 -wget https://paddle-device.bj.bcebos.com/0.0.0/mlu/paddle_custom_mlu-0.0.0-cp310-cp310-linux_x86_64.whl -pip install -U paddle_custom_mlu-0.0.0-cp310-cp310-linux_x86_64.whl +pip install paddle-custom-mlu -i https://www.paddlepaddle.org.cn/packages/nightly/mlu ``` ### 安装方式二：源代码编译安装 @@ -81,13 +80,13 @@ git clone https://github.com/PaddlePaddle/PaddleCustomDevice cd PaddleCustomDevice/backends/mlu # 先安装飞桨 CPU 安装包 -pip install paddlepaddle==0.0.0 -f https://www.paddlepaddle.org.cn/whl/linux/cpu-mkl/develop.html +pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu # 执行编译脚本 - submodule 在编译时会按需下载 bash tools/compile.sh # 飞桨 MLU 插件包在 build/dist 路径下，使用 pip 安装即可 -pip install -U paddle_custom_mlu-0.0.0-cp310-cp310-linux_x86_64.whl +pip install build/dist/paddle_custom_mlu*.whl ``` ## 基础功能检查 @@ -99,9 +98,10 @@ pip install -U paddle_custom_mlu-0.0.0-cp310-cp310-linux_x86_64.whl python -c "import paddle_custom_device; paddle_custom_device.mlu.version()" # 预期得到如下输出结果 version: 0.0.0 -commit: 9797ec4269d8d84812eb9c6d0eb67216506a5495 +commit: 147d506b2baa1971ab47b4550f0571e1f6b201fc cntoolkit: 3.8.2 cnnl: 1.23.2 +cnnl_extra: 1.6.1 cncl: 1.14.0 mluops: 0.11.0 @@ -119,5 +119,5 @@ PaddlePaddle is installed successfully! Let's start deep learning with PaddlePad 请使用以下命令卸载： ```bash -pip uninstall paddlepaddle-mlu +pip uninstall paddlepaddle paddle-custom-mlu ``` diff --git a/docs/guides/hardware_support/npu/install_cn.md b/docs/guides/hardware_support/npu/install_cn.md index c498fc7398c..9e6516cfbd6 100644 --- a/docs/guides/hardware_support/npu/install_cn.md +++ b/docs/guides/hardware_support/npu/install_cn.md @@ -9,20 +9,27 @@ | 要求类型 | 要求内容 | | --------- | -------- | -| 芯片型号 | 昇腾 910 系列，包括 910A、910B | -| 操作系统 | Linux 操作系统，包括 Ubuntu、KylinV10 | +| 芯片型号 | 昇腾 910B | +| 操作系统 | Linux 操作系统，包括 Ubuntu、KylinV10 等 | + +**注意**：[develop](https://github.com/PaddlePaddle/PaddleCustomDevice/blob/develop/backends/npu/README_cn.md) 分支仅支持『昇腾 910B』芯片，如需『昇腾 910』芯片的支持请切换到 [release/2.6](https://github.com/PaddlePaddle/PaddleCustomDevice/blob/release/2.6/backends/npu/README_cn.md) 分支。查看芯片类型请参考如下命令： + +```bash +# 系统环境下运行如下命令，如果有设备列表输出，则表示当前为『昇腾 910』芯片 +lspci | grep d801 + +# 系统环境下运行如下命令，如果有设备列表输出，则表示当前为『昇腾 910B』芯片 +lspci | grep d802 +``` ## 运行环境准备 推荐使用飞桨官方发布的昇腾 NPU 开发镜像，该镜像预装有[昇腾基础软件开发平台（CANN）](https://www.hiascend.com/software/cann)。 ```bash -# 昇腾 910A 芯片 - 系统环境下查看 lspci | grep d801 是否有输出 -docker pull registry.baidubce.com/device/paddle-npu:cann80T2-910A-ubuntu18-x86_64 # X86 架构 -docker pull registry.baidubce.com/device/paddle-npu:cann80T2-910A-ubuntu18-aarch64 # ARM 架构 -# 昇腾 910B 芯片 - 系统环境下查看 lspci | grep d802 是否有输出 -docker pull registry.baidubce.com/device/paddle-npu:cann80T2-910B-ubuntu18-x86_64 # X86 架构 -docker pull registry.baidubce.com/device/paddle-npu:cann80T2-910B-ubuntu18-aarch64 # ARM 架构 +# 拉取镜像 +docker pull registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39 # X86 架构 +docker pull registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-aarch64-gcc84-py39 # ARM 架构 # 考如下命令启动容器，ASCEND_RT_VISIBLE_DEVICES 可指定可见的 NPU 卡号 docker run -it --name paddle-npu-dev -v $(pwd):/work \ @@ -31,23 +38,23 @@ docker run -it --name paddle-npu-dev -v $(pwd):/work \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/dcmi:/usr/local/dcmi \ -e ASCEND_RT_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" \ - registry.baidubce.com/device/paddle-npu:cann80T2-910B-ubuntu18-x86_64 /bin/bash + registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-$(uname -m)-gcc84-py39 /bin/bash # 检查容器内是否可以正常识别昇腾 NPU 设备 npu-smi info # 预期得到类似如下的结果 +------------------------------------------------------------------------------------------------+ -| npu-smi 23.0.rc3 Version: 23.0.rc3 | +| npu-smi 23.0.3 Version: 23.0.3 | +---------------------------+---------------+----------------------------------------------------+ | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)| | Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) | +===========================+===============+====================================================+ -| 0 910B2C | OK | 83.4 35 0 / 0 | -| 0 | 0000:5A:00.0 | 0 0 / 0 4302 / 65536 | +| 0 910B2C | OK | 89.9 53 0 / 0 | +| 0 | 0000:5A:00.0 | 0 0 / 0 3317 / 65536 | +===========================+===============+====================================================+ -| 1 910B2C | OK | 83.4 37 0 / 0 | -| 0 | 0000:29:00.0 | 0 0 / 0 4302 / 65536 | +| 1 910B2C | OK | 93.8 53 0 / 0 | +| 0 | 0000:29:00.0 | 0 0 / 0 3316 / 65536 | +===========================+===============+====================================================+ +---------------------------+---------------+----------------------------------------------------+ | NPU Chip | Process id | Process name | Process memory(MB) | @@ -68,11 +75,10 @@ npu-smi info ```bash # 先安装飞桨 CPU 安装包 -pip install paddlepaddle==0.0.0 -f https://www.paddlepaddle.org.cn/whl/linux/cpu-mkl/develop.html +pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu # 再安装飞桨 NPU 插件包 -wget https://paddle-device.bj.bcebos.com/0.0.0/npu/paddle_custom_npu-0.0.0-cp310-cp310-linux_x86_64.whl -pip install -U paddle_custom_npu-0.0.0-cp310-cp310-linux_x86_64.whl +pip install paddle-custom-npu -i https://www.paddlepaddle.org.cn/packages/nightly/npu ``` ### 安装方式二：源代码编译安装 @@ -87,13 +93,13 @@ git clone https://github.com/PaddlePaddle/PaddleCustomDevice cd PaddleCustomDevice/backends/npu # 先安装飞桨 CPU 安装包 -pip install paddlepaddle==0.0.0 -f https://www.paddlepaddle.org.cn/whl/linux/cpu-mkl/develop.html +pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu # 执行编译脚本 - submodule 在编译时会按需下载 bash tools/compile.sh # 飞桨 NPU 插件包在 build/dist 路径下，使用 pip 安装即可 -pip install -U paddle_custom_npu-0.0.0-cp39-cp39-linux_x86_64.whl +pip install build/dist/paddle_custom_npu*.whl ``` ## 基础功能检查 @@ -105,8 +111,8 @@ pip install -U paddle_custom_npu-0.0.0-cp39-cp39-linux_x86_64.whl python -c "import paddle_custom_device; paddle_custom_device.npu.version()" # 预期得到如下输出结果 version: 0.0.0 -commit: 7163a1e74066d86c2d4ef48e4affe1f9ad07aa95 -cann: 8.0.T2 +commit: 147d506b2baa1971ab47b4550f0571e1f6b201fc +cann: 8.0.RC1 .... # 飞桨基础健康检查 @@ -123,5 +129,5 @@ PaddlePaddle is installed successfully! Let's start deep learning with PaddlePad 请使用以下命令卸载 Paddle: ```bash -pip uninstall paddlepaddle-npu +pip uninstall paddlepaddle paddle-custom-npu ``` diff --git a/docs/guides/hardware_support/xpu/example_cn.md b/docs/guides/hardware_support/xpu/example_cn.md index ab26b4704f3..822b9b6dfa1 100644 --- a/docs/guides/hardware_support/xpu/example_cn.md +++ b/docs/guides/hardware_support/xpu/example_cn.md @@ -1,10 +1,10 @@ -# 昆仑 XPU 运行示例 +# 昆仑芯 XPU 运行示例 -**预先要求**：请先根据文档 [昆仑 XPU 安装说明](./install_cn.html) 准备昆仑 XPU 运行环境，建议以下步骤都在 docker 环境中运行。 +**预先要求**：请先根据文档 [昆仑芯 XPU 安装说明](./install_cn.html) 准备昆仑芯 XPU 运行环境，建议以下步骤都在 docker 环境中运行。 ## 训练示例 -以 [ResNet50_vd](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/quick_start/quick_start_classification_new_user.md) 模型为例，介绍如何使用昆仑 XPU 进行训练。 +以 [ResNet50_vd](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/quick_start/quick_start_classification_new_user.md) 模型为例，介绍如何使用昆仑芯 XPU 进行训练。 ### 一、下载套件代码 @@ -65,7 +65,7 @@ python tools/eval.py -c ./ppcls/configs/quick_start/ResNet50_vd.yaml \ ## 推理示例 -以 [ResNet50](https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz) 模型为例，介绍如何使用昆仑 XPU 进行推理。 +以 [ResNet50](https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz) 模型为例，介绍如何使用昆仑芯 XPU 进行推理。 ### 一、下载推理程序 diff --git a/docs/guides/hardware_support/xpu/index_cn.rst b/docs/guides/hardware_support/xpu/index_cn.rst index e0222bcaaed..27e25c56743 100644 --- a/docs/guides/hardware_support/xpu/index_cn.rst +++ b/docs/guides/hardware_support/xpu/index_cn.rst @@ -1,16 +1,20 @@ .. _cn_xpu_information: #################### -昆仑 XPU 芯片 +昆仑芯 XPU 芯片 #################### -百度昆仑 AI 计算处理器（Baidu KUNLUN AI Computing Processor）是百度集十年 AI 产业技术实践于 2019 年推出的全功能 AI 芯片。基于自主研发的先进 XPU 架构，为云端和边缘端的人工智能业务而设计。 百度昆仑与飞桨及其他国产软硬件强强组合，打造一个全面领先的国产化 AI 技术生态，部署和应用于诸多 “人工智能+“的行业领域，包括智能云和高性能计算，智慧制造、智慧城市和安防等。更多昆仑 XPU 芯片详情及技术指标请 `点击这里 `_ 。 +昆仑芯是百度集十年 AI 产业技术实践、自主研发的 AI 芯片。该产品拥有创新的 XPU 架构、领先的产品竞争力及丰富的场景落地实践等优势，目前已被广泛部署在百度搜索、小度、大模型等业务并在百度外有数百家客户，将 AI 算力赋能千行百业智能化升级。 -飞桨框架支持基于昆仑 XPU 芯片的训练和推理，请参考以下内容快速体验： +昆仑芯和飞桨的适配自 2018 年启动，当前已携手共同完成了一套端到端的 AI 计算系统解决方案，并致力于携手打造一个全栈式软硬一体的 AI 生态，夯实算力基础设施建设。“昆仑芯+飞桨”方案已在智慧金融、工业质检等领域成功部署落地。 -- `昆仑 XPU 安装说明 <./install_cn.html>`_ : 昆仑 XPU 安装说明 -- `昆仑 XPU 运行示例 <./example_cn.html>`_ : 昆仑 XPU 运行示例 -- `昆仑 XPU 支持模型 <./support_cn.html>`_ : 昆仑 XPU 支持模型 +更多昆仑芯 XPU 芯片详情及技术指标请 `点击这里 `_ 。 + +飞桨框架支持基于昆仑芯 XPU 芯片的训练和推理，请参考以下内容快速体验： + +- `昆仑芯 XPU 安装说明 <./install_cn.html>`_ : 昆仑芯 XPU 安装说明 +- `昆仑芯 XPU 运行示例 <./example_cn.html>`_ : 昆仑芯 XPU 运行示例 +- `昆仑芯 XPU 支持模型 <./support_cn.html>`_ : 昆仑芯 XPU 支持模型 .. toctree:: :hidden: diff --git a/docs/guides/hardware_support/xpu/install_cn.md b/docs/guides/hardware_support/xpu/install_cn.md index 12e6e2fed88..e5377983103 100644 --- a/docs/guides/hardware_support/xpu/install_cn.md +++ b/docs/guides/hardware_support/xpu/install_cn.md @@ -1,20 +1,20 @@ -# 昆仑 XPU 安装说明 +# 昆仑芯 XPU 安装说明 飞桨框架 XPU 版支持昆仑芯 XPU 的训练和推理，提供两种安装方式： 1. 通过飞桨官网发布的 wheel 包安装 2. 通过源代码编译安装得到 wheel 包 -## 昆仑 XPU 系统要求 +## 昆仑芯 XPU 系统要求 | 要求类型 | 要求内容 | | --------- | -------- | -| 芯片型号 | 昆仑芯 2 代，包括 R200、R300、R200-8F、R200-8FS、RG800 | +| 芯片型号 | 昆仑芯 2 代，包括 R200、R300、R200-8F、RG800 | | 操作系统 | Linux 操作系统，包括 Ubuntu、CentOS、KylinV10 | ## 运行环境准备 -推荐使用飞桨官方发布的昆仑 XPU 开发镜像，该镜像预装有昆仑基础运行环境库（XRE）。 +推荐使用飞桨官方发布的昆仑芯 XPU 开发镜像，该镜像预装有昆仑芯基础运行环境库（XRE）。 ```bash # 拉取镜像 @@ -26,7 +26,7 @@ docker run -it --name paddle-xpu-dev -v $(pwd):/work \ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ registry.baidubce.com/device/paddle-xpu:ubuntu20-x86_64-gcc84-py310 /bin/bash -# 检查容器内是否可以正常识别昆仑 XPU 设备 +# 检查容器内是否可以正常识别昆仑芯 XPU 设备 xpu_smi # 预期得到输出如下 @@ -55,18 +55,15 @@ Driver Version: 4.0 ## 安装飞桨框架 -**注意**：当前飞桨 develop 分支仅支持 X86 架构，如需昆仑 XPU 的 ARM 架构支持，请切换到 [release/2.6](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.6/guides/hardware_support/xpu/install_cn.html) 分支。 +**注意**：当前飞桨 develop 分支仅支持 X86 架构，如需昆仑芯 XPU 的 ARM 架构支持，请切换到 [release/2.6](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.6/guides/hardware_support/xpu/install_cn.html) 分支。 ### 安装方式一：wheel 包安装 在启动的 docker 容器中，下载并安装飞桨官网发布的 wheel 包。 ```bash -# 下载 wheel 包 -wget https://paddle-wheel.bj.bcebos.com/develop/xpu/paddlepaddle_xpu-0.0.0-cp310-cp310-linux_x86_64.whl - -# 安装 wheel 包 -pip install -U paddlepaddle_xpu-0.0.0-cp310-cp310-linux_x86_64.whl +# 下载并安装 wheel 包 +pip install paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu ``` ### 安装方式二：源代码编译安装 diff --git a/docs/guides/hardware_support/xpu/support_cn.md b/docs/guides/hardware_support/xpu/support_cn.md index 7df6e0f7f31..24eaf41876c 100644 --- a/docs/guides/hardware_support/xpu/support_cn.md +++ b/docs/guides/hardware_support/xpu/support_cn.md @@ -1,4 +1,4 @@ -# 昆仑 XPU 支持模型 +# 昆仑芯 XPU 支持模型 飞桨框架在昆仑芯 XPU 上经验证的模型的支持情况如下： diff --git a/docs/guides/index_cn.rst b/docs/guides/index_cn.rst index 1f5cba841c5..3a033a98f5c 100644 --- a/docs/guides/index_cn.rst +++ b/docs/guides/index_cn.rst @@ -8,6 +8,7 @@ 使用教程分为如下的模块： +- `飞桨 3.0 全新特性 <./paddle_v3_features/index_cn.html>`_ - `模型开发入门 <./beginner/index_cn.html>`_ - `模型开发更多用法 <./advanced/index_cn.html>`_ - `动态图转静态图 <./jit/index_cn.html>`_ @@ -22,6 +23,7 @@ .. toctree:: :hidden: + paddle_v3_features/index_cn.rst beginner/index_cn.rst advanced/index_cn.rst jit/index_cn.rst diff --git a/docs/guides/infer/inference/inference_cn.md b/docs/guides/infer/inference/inference_cn.md index d1966b100c3..c7da0e5f226 100644 --- a/docs/guides/infer/inference/inference_cn.md +++ b/docs/guides/infer/inference/inference_cn.md @@ -56,7 +56,7 @@ PaddleSlim 是飞桨深度学习模型压缩工具，Paddle Inference 可联动 ### 主流软硬件环境兼容适配 -支持服务器端 X86 CPU、NVIDIA GPU 芯片，兼容 Linux/Mac/Windows 系统，同时对飞腾、鲲鹏、曙光、昆仑等国产 CPU/NPU 进行适配。。支持所有飞桨训练产出的模型，完全做到即训即用。 +支持服务器端 X86 CPU、NVIDIA GPU 芯片，兼容 Linux/Mac/Windows 系统，同时对飞腾、鲲鹏、曙光、昆仑芯等国产 CPU/NPU 进行适配。。支持所有飞桨训练产出的模型，完全做到即训即用。 ### 主流、国产操作系统全适配 diff --git a/docs/guides/jit/basic_usage_cn.md b/docs/guides/jit/basic_usage_cn.md index 17defdbaa64..c08fb4281c6 100644 --- a/docs/guides/jit/basic_usage_cn.md +++ b/docs/guides/jit/basic_usage_cn.md @@ -866,7 +866,7 @@ class SimpleNet(Layer): super().__init__() self.linear = paddle.nn.Linear(10, 3) - @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), {'x': InputSpec(shape=[3], name='bias')}]) + @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), {'x': InputSpec(shape=[3], name='bias_info')}]) def forward(self, x, bias_info): x_bias = bias_info['x'] out = self.linear(x) diff --git a/docs/guides/jit/limitations_cn.md b/docs/guides/jit/limitations_cn.md index d5f03cf6c98..4cc29f444a7 100644 --- a/docs/guides/jit/limitations_cn.md +++ b/docs/guides/jit/limitations_cn.md @@ -557,7 +557,7 @@ class mylayer(paddle.nn.Layer): return self.linear(x) ``` -### 3. 检查 isinstanse 的使用 +### 3. 检查 isinstance 的使用 在动转静中，组网相关的变量有可能被转换为静态图 Tensor，因此使用 isinstance 对变量类型进行判断存在一定风险，请留意下列变量有可能转化为 Tensor： diff --git a/docs/guides/model_convert/convert_from_older_versions/paddle_api_mapping_cn.rst b/docs/guides/model_convert/convert_from_older_versions/paddle_api_mapping_cn.rst index 0153b42b25a..acc1cc6d202 100644 --- a/docs/guides/model_convert/convert_from_older_versions/paddle_api_mapping_cn.rst +++ b/docs/guides/model_convert/convert_from_older_versions/paddle_api_mapping_cn.rst @@ -146,7 +146,7 @@ Paddle 1.8 与 Paddle 2.0 API 映射表 "126", "`paddle.fluid.layers.clip_by_norm `_ ", "`paddle.nn.clip_by_norm `_", "False" "127", "`paddle.fluid.layers.concat `_ ", "`paddle.concat `_", "True" "128", "`paddle.fluid.layers.cond `_ ", "`paddle.static.nn.cond `_", "True" - "129", "`paddle.fluid.layers.conv2d `_ ", "`paddle.nn.functional.conv2d(动态图) `_, `paddle.static.nn.conv2d(静态图) `_, ", "False" + "129", "`paddle.fluid.layers.conv2d `_ ", "`paddle.nn.functional.conv2d(动态图) `_, `paddle.static.nn.conv2d(静态图) `_, ", "False" "130", "`paddle.fluid.layers.conv2d_transpose `_ ", "`paddle.nn.functional.conv2d_transpose(动态图) `_, `paddle.static.nn.conv2d_transpose(静态图) `_", "False" "131", "`paddle.fluid.layers.conv3d `_ ", "`paddle.nn.functional.conv3d(动态图) `_, `paddle.static.nn.conv3d(静态图) `_", "False" "132", "`paddle.fluid.layers.conv3d_transpose `_ ", "`paddle.nn.functional.conv3d_transpose(动态图) `_, `paddle.static.nn.conv3d_transpose(静态图) `_", "False" diff --git a/docs/guides/model_convert/convert_from_pytorch/.gitignore b/docs/guides/model_convert/convert_from_pytorch/.gitignore index a52918e42d0..cd1a36c7536 100644 --- a/docs/guides/model_convert/convert_from_pytorch/.gitignore +++ b/docs/guides/model_convert/convert_from_pytorch/.gitignore @@ -1 +1,8 @@ +# temp files used locally +pytorch_api_mapping_cn.tmp.md + +# files will be used in PaConvert docs_mappings.json + +# files copied from PaConvert +api_alias_mapping.json diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.addmm_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.addmm_.md index 2c9174b75aa..148320a7fa3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.addmm_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.addmm_.md @@ -17,7 +17,7 @@ paddle.Tensor.addmm_(x, y, alpha=1.0, beta=1.0) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | +| -------- | ------------ | -------------------------------- | | mat1 | x | 表示输入的 Tensor，仅参数名不一致。 | | mat2 | y | 表示输入的 Tensor，仅参数名不一致。 | | alpha | alpha | 乘以 x*y 的标量，数据类型支持 float32、float64，默认值为 1.0。| diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.all.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.all.md index e8aa80a3bc1..0561eec0e65 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.all.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.all.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.all +## [ 输入参数类型不一致 ]torch.Tensor.all ### [torch.Tensor.all](https://pytorch.org/docs/stable/generated/torch.Tensor.all.html?highlight=torch+tensor+all#torch.Tensor.all) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.allclose.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.allclose.md index 88155d7d6c1..1bed9931908 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.allclose.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.allclose.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.allclose +## [ 返回参数类型不一致 ]torch.Tensor.allclose ### [torch.Tensor.allclose](https://pytorch.org/docs/stable/generated/torch.Tensor.allclose.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.any.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.any.md index 23928793ee6..b267e319ad2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.any.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.any.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.any +## [ 输入参数类型不一致 ]torch.Tensor.any ### [torch.Tensor.any](https://pytorch.org/docs/stable/generated/torch.Tensor.any.html?highlight=torch+tensor+any#torch.Tensor.any) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.argsort.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.argsort.md index c7297688930..e25a76eb6be 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.argsort.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.argsort.md @@ -3,13 +3,13 @@ ### [torch.Tensor.argsort](https://pytorch.org/docs/stable/generated/torch.Tensor.argsort.html) ```python -torch.Tensor.argsort(dim=-1, descending=False) +torch.Tensor.argsort(dim=-1, descending=False, stable=False) ``` ### [paddle.Tensor.argsort](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#argsort-axis-1-descending-false-name-none) ```python -paddle.Tensor.argsort(axis=-1, descending=False, name=None) +paddle.Tensor.argsort(axis=-1, descending=False, stable=False, name=None) ``` 其中 PyTorch 和 Paddle 功能一致，仅参数名不一致，具体如下： @@ -20,3 +20,4 @@ paddle.Tensor.argsort(axis=-1, descending=False, name=None) | ---------- | ------------ | -- | | dim | axis | 指定对输入 Tensor 进行运算的轴，仅参数名不一致。 | | descending | descending | 指定算法排序的方向。如果设置为 True，算法按照降序排序。如果设置为 False 或者不设置，按照升序排序。默认值为 False。 | +| stable | stable | 是否使用稳定排序。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.copy_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.copy_.md index a07a4e059ce..5a34a40c43b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.copy_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.copy_.md @@ -13,7 +13,9 @@ paddle.assign(x, output=None) ``` 两者功能类似，torch 参数多，具体如下： -### 参数差异 + +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | src | x | 待复制的 tensor，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.eq_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.eq_.md index ca92cf9ad5b..e39735b417b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.eq_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.eq_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.eq_ +## [ 输入参数类型不一致 ]torch.Tensor.eq_ ### [torch.Tensor.eq_](https://pytorch.org/docs/stable/generated/torch.Tensor.eq_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.expand.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.expand.md index e38ae9345c0..ce20cb5983d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.expand.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.expand.md @@ -12,7 +12,7 @@ paddle.Tensor.expand(shape, name=None) ``` 两者功能一致，仅参数名不一致，具体差异如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | size | shape | 扩张后的维度，size 是可变参数，paddle 是 list/tuple | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide.md index 36baba2c8db..ba8ff51b7c5 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.floor_divide +## [ 输入参数类型不一致 ]torch.Tensor.floor_divide ### [torch.Tensor.floor_divide](https://pytorch.org/docs/stable/generated/torch.Tensor.floor_divide.html?highlight=floor_divide#torch.Tensor.floor_divide) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide_.md index 812bff6595b..594b9afff6d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.floor_divide_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.floor_divide_ +## [ 输入参数类型不一致 ]torch.Tensor.floor_divide_ ### [torch.Tensor.floor_divide_](https://pytorch.org/docs/stable/generated/torch.Tensor.floor_divide_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.fmod.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.fmod.md index 98772dead4c..e2e976205ca 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.fmod.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.fmod.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.fmod +## [ 输入参数类型不一致 ]torch.Tensor.fmod ### [torch.Tensor.fmod](https://pytorch.org/docs/stable/generated/torch.Tensor.fmod.html#torch.Tensor.fmod) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge.md index 4c429dd5f83..2777d08e31f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.ge +## [ 输入参数类型不一致 ]torch.Tensor.ge ### [torch.Tensor.ge](https://pytorch.org/docs/stable/generated/torch.Tensor.ge.html?highlight=torch+tensor+ge#torch.Tensor.ge) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge_.md index 82706618deb..f1978890f34 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ge_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.ge_ +## [ 输入参数类型不一致 ]torch.Tensor.ge_ ### [torch.Tensor.ge_](https://pytorch.org/docs/stable/ge_nerated/torch.Tensor.ge_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater.md index cce4bcc28b4..c8558fb2871 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.greater +## [ 输入参数类型不一致 ]torch.Tensor.greater ### [torch.Tensor.greater](https://pytorch.org/docs/stable/generated/torch.Tensor.greater.html?highlight=torch+tensor+greater#torch.Tensor.greater) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_.md index 2541946f0a8..38d72b33852 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.greater_ +## [ 输入参数类型不一致 ]torch.Tensor.greater_ ### [torch.Tensor.greater_](https://pytorch.org/docs/stable/generated/torch.Tensor.greater_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal.md index 13f6087f7bd..52c2a6f12ef 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.greater_equal +## [ 输入参数类型不一致 ]torch.Tensor.greater_equal ### [torch.Tensor.greater_equal](https://pytorch.org/docs/stable/generated/torch.Tensor.greater_equal.html?highlight=torch+tensor+greater_equal#torch.Tensor.greater_equal) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal_.md index 8a4da98ea9c..e1f954d424f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.greater_equal_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.greater_equal_ +## [ 输入参数类型不一致 ]torch.Tensor.greater_equal_ ### [torch.Tensor.greater_equal_](https://pytorch.org/docs/stable/generated/torch.Tensor.greater_equal_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt.md index ae207c82dd7..56c219ee6da 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.gt +## [ 输入参数类型不一致 ]torch.Tensor.gt ### [torch.Tensor.gt](https://pytorch.org/docs/stable/generated/torch.Tensor.gt.html?highlight=torch+tensor+gt#torch.Tensor.gt) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt_.md index b86ca1cc88d..0059c977dab 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.gt_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.gt_ +## [ 输入参数类型不一致 ]torch.Tensor.gt_ ### [torch.Tensor.gt_](https://pytorch.org/docs/stable/generated/torch.Tensor.gt_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.histc.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.histc.md index 18a31c82294..d2565be4c71 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.histc.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.histc.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.histc +## [ 返回参数类型不一致 ]torch.Tensor.histc ### [torch.Tensor.histc](https://pytorch.org/docs/stable/generated/torch.Tensor.histc.html?highlight=torch+tensor+histc#torch.Tensor.histc) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le.md index 620ba89137a..3f990545299 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.le +## [ 输入参数类型不一致 ]torch.Tensor.le ### [torch.Tensor.le](https://pytorch.org/docs/stable/generated/torch.Tensor.le.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le_.md index bd49ecaa4ea..3a27a1e5c38 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.le_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.le_ +## [ 输入参数类型不一致 ]torch.Tensor.le_ ### [torch.Tensor.le_](https://pytorch.org/docs/stable/generated/torch.Tensor.le_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less.md index 6b0c3e4b22f..9e433a04857 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.less +## [ 输入参数类型不一致 ]torch.Tensor.less ### [torch.Tensor.less](https://pytorch.org/docs/stable/generated/torch.Tensor.less.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_.md index 1f0a2d49fa9..d5bdf8edb5a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.less_ +## [ 输入参数类型不一致 ]torch.Tensor.less_ ### [torch.Tensor.less_](https://pytorch.org/docs/stable/generated/torch.Tensor.less_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal.md index 2e345ee6451..dfa1a74af14 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.less_equal +## [ 输入参数类型不一致 ]torch.Tensor.less_equal ### [torch.Tensor.less_equal](https://pytorch.org/docs/stable/generated/torch.Tensor.less_equal.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal_.md index 606a674454a..970d84002f7 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.less_equal_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.less_equal_ +## [ 输入参数类型不一致 ]torch.Tensor.less_equal_ ### [torch.Tensor.less_equal_](https://pytorch.org/docs/stable/generated/torch.Tensor.less_equal_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.log_normal_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.log_normal_.md new file mode 100644 index 00000000000..f4b5a0f34ec --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.log_normal_.md @@ -0,0 +1,33 @@ +## [ torch 参数更多 ]torch.Tensor.log_normal_ + +### [torch.Tensor.log_normal_](https://pytorch.org/docs/stable/generated/torch.Tensor.log_normal_.html#torch-tensor-log-normal) + +```python +torch.Tensor.log_normal_(mean=1, std=2, *, generator=None) +``` + +### [paddle.Tensor.log_normal_](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#log_normal-mean-1-0-std-2-0-shape-none-name-none) + +```python +paddle.Tensor.log_normal_(mean=1.0, std=2.0, name=None) +``` + +PyTorch 相比 Paddle 支持更多其他参数，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ----------------------------------------------------------------------------------- | +| generator | - | 用于采样的伪随机数生成器， Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | + +### 转写示例 + +```python +# torch 写法 +x = torch.randn(2, 3) +y = x.log_normal_() + +# paddle 写法 +x = paddle.randn([2, 3]) +y = x.log_normal_() +``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lstsq.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lstsq.md index b278928e98c..1c18602c863 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lstsq.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lstsq.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.lstsq +## [ 输入参数用法不一致 ]torch.Tensor.lstsq ### [torch.Tensor.lstsq](https://pytorch.org/docs/1.9.0/generated/torch.Tensor.lstsq.html?highlight=torch%20tensor%20lstsq#torch.Tensor.lstsq) @@ -12,7 +12,7 @@ torch.Tensor.lstsq(A) paddle.Tensor.lstsq(y, rcond=None, driver=None, name=None) ``` -两者功能一致，参数不一致，具体如下： +两者功能一致，输入参数用法不一致，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt.md index 6ba59ba8c22..71aedb190be 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.lt +## [ 输入参数类型不一致 ]torch.Tensor.lt ### [torch.Tensor.lt](https://pytorch.org/docs/stable/generated/torch.Tensor.lt.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt_.md index f28ef384c88..02ea43b54c2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.lt_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.lt_ +## [ 输入参数类型不一致 ]torch.Tensor.lt_ ### [torch.Tensor.lt_](https://pytorch.org/docs/stable/generated/torch.Tensor.lt_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.max.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.max.md new file mode 100644 index 00000000000..6acd6f608d2 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.max.md @@ -0,0 +1,60 @@ +## [ 返回参数类型不一致 ]torch.Tensor.max + +该 api 有两组参数列表重载，因此有两组差异分析。 + +----------------------------------------------- + +### [torch.Tensor.max](https://pytorch.org/docs/stable/generated/torch.Tensor.max.html) + +```python +torch.Tensor.max(dim=None, keepdim=False) +``` + +### [paddle.Tensor.max](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#max-axis-none-keepdim-false-name-none) + +```python +paddle.Tensor.max(axis=None, keepdim=False, name=None) +``` + +其中 PyTorch 与 Paddle 指定 `dim` 后返回值不一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------ | +| dim | axis | 求最大值运算的维度， 仅参数名不一致。 | +| keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度。 | +| 返回值 | 返回值 | 表示返回结果，当指定 dim 后，PyTorch 会返回比较结果和元素索引， Paddle 不会返回元素索引，需要转写。 | + +### 转写示例 + +#### 指定 dim 后的返回值 +```python +# PyTorch 写法 +result = x.max(dim=1) + +# Paddle 写法 +result = x.max(dim=1), x.argmax(dim=1) +``` + +-------------------------------------------------------------- + +### [torch.Tensor.max](https://pytorch.org/docs/stable/generated/torch.Tensor.max.html) + +```python +torch.Tensor.max(other) +``` + +### [paddle.Tensor.maximum](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#maximum-y-axis-1-name-none) + +```python +paddle.Tensor.maximum(y) +``` + +两者功能一致且参数用法一致，仅参数名不一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ----------------- | ---------------------- | ------------------------------------ | +| other | y | 输⼊ Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.maximum.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.maximum.md index 3c246b78f73..6e8ce912b07 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.maximum.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.maximum.md @@ -14,6 +14,8 @@ paddle.Tensor.maximum(y) 两者功能一致且参数用法一致，仅参数名不一致，具体如下： +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | |----------------------------------|---------------------------------|------------------------------------| | other | y | 输⼊第二个 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.min.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.min.md new file mode 100644 index 00000000000..03b2e2a4087 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.min.md @@ -0,0 +1,60 @@ +## [ 返回参数类型不一致 ]torch.Tensor.min + +该 api 有两组参数列表重载，因此有两组差异分析。 + +----------------------------------------------- + +### [torch.Tensor.min](https://pytorch.org/docs/stable/generated/torch.Tensor.min.html) + +```python +torch.Tensor.min(dim=None, keepdim=False) +``` + +### [paddle.Tensor.min](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#min-axis-none-keepdim-false-name-none) + +```python +paddle.Tensor.min(axis=None, keepdim=False, name=None) +``` + +其中 PyTorch 与 Paddle 指定 `dim` 后返回值不一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------ | +| dim | axis | 求最小值运算的维度， 仅参数名不一致。 | +| keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度。 | +| 返回值 | 返回值 | 表示返回结果，当指定 dim 后，PyTorch 会返回比较结果和元素索引， Paddle 不会返回元素索引，需要转写。 | + +### 转写示例 + +#### 指定 dim 后的返回值 +```python +# PyTorch 写法 +result = x.min(dim=1) + +# Paddle 写法 +result = x.min(dim=1), x.argmin(dim=1) +``` + +-------------------------------------------------------------- + +### [torch.Tensor.min](https://pytorch.org/docs/stable/generated/torch.Tensor.min.html) + +```python +torch.Tensor.min(other) +``` + +### [paddle.Tensor.minimum](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#minimum-y-axis-1-name-none) + +```python +paddle.Tensor.minimum(y) +``` + +两者功能一致且参数用法一致，仅参数名不一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ----------------- | ---------------------- | ------------------------------------ | +| other | y | 输⼊ Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.minimum.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.minimum.md index 360e6b77f33..d3d25f24bf0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.minimum.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.minimum.md @@ -14,6 +14,8 @@ paddle.Tensor.minimum(y) 两者功能一致且参数用法一致，仅参数名不一致，具体如下： +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | |----------------------------------|---------------------------------|------------------------------------| | other | y | 输⼊第二个 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.moveaxis.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.moveaxis.md index debeec5ea6d..daf6f545212 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.moveaxis.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.moveaxis.md @@ -14,6 +14,8 @@ paddle.Tensor.moveaxis(source, destination,name = None) 两者功能一致且参数用法一致，具体如下： +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | |------------------------------------|------------------------------------|----------------------------------| | source | source | 将被移动的轴的位置。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.movedim.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.movedim.md index a3d4a415356..62e6655c1fb 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.movedim.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.movedim.md @@ -14,6 +14,8 @@ paddle.Tensor.moveaxis(source, destination, name = None) 两者功能一致且参数用法一致，具体如下： +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | |------------------------------------|------------------------------------|----------------------------------| | source | source | 将被移动的轴的位置。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.mul.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.mul.md index 05654002a91..efdb7cc5b83 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.mul.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.mul.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.mul +## [ 输入参数类型不一致 ] torch.Tensor.mul ### [torch.Tensor.mul](https://pytorch.org/docs/stable/generated/torch.Tensor.mul.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply.md index a493dbaa2a2..afff2fea8d2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.multiply +## [ 输入参数类型不一致 ] torch.Tensor.multiply ### [torch.Tensor.multiply](https://pytorch.org/docs/stable/generated/torch.Tensor.multiply.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply_.md index ad5e8474b31..03f6310a8ba 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.multiply_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.multiply_ +## [ 输入参数类型不一致 ] torch.Tensor.multiply_ ### [torch.Tensor.multiply_](https://pytorch.org/docs/stable/generated/torch.Tensor.multiply_.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.nanquantile.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.nanquantile.md index 03e88b20919..a62c9ceb326 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.nanquantile.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.nanquantile.md @@ -1,4 +1,4 @@ -## [ torch 参数更多 ] torch.Tensor.nanquantile +## [ 仅参数名不一致 ] torch.Tensor.nanquantile ### [torch.Tensor.nanquantile](https://pytorch.org/docs/stable/generated/torch.nanquantile.html#torch.nanquantile) @@ -9,10 +9,10 @@ torch.Tensor.nanquantile(q, dim=None, keepdim=False, *, interpolation='linear') ### [paddle.Tensor.nanquantile](https://github.com/PaddlePaddle/Paddle/pull/41343) ```python -paddle.Tensor.nanquantile(q, axis=None, keepdim=False) +paddle.Tensor.nanquantile(q, axis=None, keepdim=False, interpolation='linear') ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： +两者功能一致且参数用法一致，仅参数名不一致，具体如下： ### 参数映射 @@ -21,4 +21,4 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： | q | q | 待计算的分位数。| | dim | axis | 求乘积运算的维度，仅参数名不一致。 | | keepdim | keepdim | 是否在输出 Tensor 中保留输入的维度。 | -| interpolation | interpolation | 指定当所需分位数位于两个数据点之间时使用的插值方法。| +| interpolation | interpolation | 指定当所需分位数位于两个数据点之间时使用的插值方法。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.narrow.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.narrow.md index c533b5765de..097fd7ed4b1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.narrow.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.narrow.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.narrow +## [ 输入参数用法不一致 ]torch.Tensor.narrow ### [torch.Tensor.narrow](https://pytorch.org/docs/stable/generated/torch.Tensor.narrow.html#torch.Tensor.narrow) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne.md index 82e50bbdb27..392c8237e76 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.ne +## [ 输入参数类型不一致 ] torch.Tensor.ne ### [torch.Tensor.ne](https://pytorch.org/docs/stable/generated/torch.Tensor.ne.html?highlight=ne) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne_.md index 89d6043dca4..7b63ba0e910 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.ne_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.ne_ +## [ 输入参数类型不一致 ] torch.Tensor.ne_ ### [torch.Tensor.ne_](https://pytorch.org/docs/stable/generated/torch.Tensor.ne_.html) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal.md index 0f30356ddba..8420f5955b6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.not_equal +## [ 输入参数类型不一致 ] torch.Tensor.not_equal ### [torch.Tensor.not_equal](https://pytorch.org/docs/stable/generated/torch.Tensor.not_equal.html) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal_.md index 72aa0ab9b86..24f4e7deaa7 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.not_equal_.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.not_equal_ +## [ 输入参数类型不一致 ] torch.Tensor.not_equal_ ### [torch.Tensor.not_equal_](https://pytorch.org/docs/stable/generated/torch.Tensor.not_equal_.html) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.numel.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.numel.md index 2e43571b21a..691f52e905e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.numel.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.numel.md @@ -6,10 +6,10 @@ torch.Tensor.numel() ``` -### [paddle.Tensor.size](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/fluid/layers/size_cn.html#cn-api-fluid-layers-size) +### [paddle.Tensor.size](https://www.paddlepaddle.org.cn/documentation/docs/guides/beginner/tensor_cn.html#tensor-shape) ```python -paddle.Tensor.size() +paddle.Tensor.size ``` 两者功能一致，均无参数，用于返回张量的单元数量，是一个 int 型的整数。 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.permute.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.permute.md index 2a2ce0fcf0c..c96140e0ef3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.permute.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.permute.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.permute +## [ 输入参数用法不一致 ]torch.Tensor.permute ### [torch.Tensor.permute](https://pytorch.org/docs/stable/generated/torch.Tensor.permute.html) @@ -13,6 +13,7 @@ paddle.Tensor.transpose(perm, name=None) ``` PyTorch 的 `*dims` 相比于 paddle 的 `perm` 额外支持可变参数的用法，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.qr.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.qr.md index e6123737b48..eda48973fbe 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.qr.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.qr.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.qr +## [ 输入参数类型不一致 ] torch.Tensor.qr ### [torch.Tensor.qr](https://pytorch.org/docs/stable/generated/torch.Tensor.qr.html?highlight=torch+tensor+qr#torch.Tensor.qr) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.quantile.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.quantile.md index bff524225fe..6757255ccf6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.quantile.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.quantile.md @@ -1,4 +1,4 @@ -## [torch 参数更多]torch.Tensor.quantile +## [ 仅参数名不一致 ]torch.Tensor.quantile ### [torch.Tensor.quantile](https://pytorch.org/docs/stable/generated/torch.Tensor.quantile.html#torch.Tensor.quantile) @@ -12,13 +12,13 @@ torch.Tensor.quantile(q, dim=None, keepdim=False, *, interpolation='linear') paddle.Tensor.quantile(q, axis=None, keepdim=False, interpolation='linear', name=None) ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： +两者功能一致且参数用法一致，仅参数名不一致，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------- | ------------ | ------------------------ | -| q | q |待计算的分位数。 | -| dim | axis |指定对 x 进行计算的轴，仅参数名不一致。| -| keepdim | keepdim |是否在输出 Tensor 中保留减小的维度。| -| interpolation | interpolation |两个数据点的插补取值方法| +| q | q | 待计算的分位数。 | +| dim | axis | 指定对 x 进行计算的轴，仅参数名不一致。| +| keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度。| +| interpolation | interpolation | 两个数据点的插补取值方法。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder.md index f334ffc5fd7..47cab98c911 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder.md @@ -1,4 +1,5 @@ -## [ 参数不一致 ]torch.Tensor.remainder +## [ 输入参数类型不一致 ]torch.Tensor.remainder + ### [torch.Tensor.remainder](https://pytorch.org/docs/stable/generated/torch.Tensor.remainder.html?highlight=torch+tensor+remainder#torch.Tensor.remainder) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder_.md index b7ee69c3fce..4d8764f976b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.remainder_.md @@ -1,4 +1,5 @@ -## [ 参数不一致 ]torch.Tensor.remainder_ +## [ 输入参数类型不一致 ]torch.Tensor.remainder_ + ### [torch.Tensor.remainder_](https://pytorch.org/docs/stable/generated/torch.Tensor.remainder_.html?highlight=torch+tensor+remainder_#torch.Tensor.remainder_) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.repeat.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.repeat.md index d083aac695b..1a790a8b7b1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.repeat.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.repeat.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.repeat +## [ 输入参数用法不一致 ]torch.Tensor.repeat ### [torch.Tensor.repeat](https://pytorch.org/docs/stable/generated/torch.Tensor.repeat.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.requires_grad_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.requires_grad_.md index f2990bc4584..282d0c2ff82 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.requires_grad_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.requires_grad_.md @@ -13,7 +13,7 @@ paddle.Tensor.stop_gradient = False ``` 两者功能一致，torch 为 funtion 调用方式，paddle 为 attribution 赋值方式，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | requires_grad | - | 是否计算梯度，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.reshape.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.reshape.md index 5a31cfb8a86..5d635f14c63 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.reshape.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.reshape.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.reshape +## [ 输入参数用法不一致 ]torch.Tensor.reshape ### [torch.Tensor.reshape](https://pytorch.org/docs/stable/generated/torch.Tensor.reshape.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.roll.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.roll.md index 10cdabeb9e1..d20e97309ae 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.roll.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.roll.md @@ -12,7 +12,7 @@ paddle.Tensor.roll(shifts, axis=None) ``` 两者功能一致且参数用法一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | shifts | shifts | 表示偏移量。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add.md index ad528d0b210..506848c0731 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add.md @@ -19,7 +19,7 @@ paddle.Tensor.put_along_axis(indices, 其中 Paddle 相比 PyTorch 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | dim | axis | 表示在哪一个维度 scatter ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add_.md index 410c1bf217f..a669e647559 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.scatter_add_.md @@ -20,7 +20,7 @@ paddle.Tensor.put_along_axis_(indices, 其中 Paddle 相比 PyTorch 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | dim | axis | 表示在哪一个维度 scatter ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.select_scatter.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.select_scatter.md new file mode 100644 index 00000000000..a85146162dc --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.select_scatter.md @@ -0,0 +1,23 @@ +## [ 仅参数名不一致 ] torch.Tensor.select_scatter + +### [torch.Tensor.select_scatter](https://pytorch.org/docs/stable/generated/torch.Tensor.select_scatter.html#torch-select-scatter) + +```python +torch.Tensor.select_scatter(src, dim, index) +``` + +### [paddle.Tensor.select_scatter](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#) + +```python +paddle.Tensor.select_scatter(values, axis, index, name=None) +``` + +两者功能一致且参数用法一致，仅参数名不一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------- | ------------ | ---------------------------------------- | +| src | values | 用于嵌入的张量，仅参数名不一致。 | +| dim | axis | 嵌入的维度，仅参数名不一致。 | +| index | index | 选择的索引。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.slice_scatter.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.slice_scatter.md index e61cb11e1ba..3a054ee20af 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.slice_scatter.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.slice_scatter.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.slice_scatter +## [ 输入参数类型不一致 ]torch.Tensor.slice_scatter ### [torch.Tensor.slice_scatter](https://pytorch.org/docs/stable/generated/torch.Tensor.slice_scatter.html#torch-tensor-slice-scatter) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.softmax.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.softmax.md index 65a6dc09d37..10fd4786a62 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.softmax.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.softmax.md @@ -12,7 +12,7 @@ paddle.nn.functional.softmax(x, axis=-1, dtype=None, name=None) ``` 功能一致，torch 是类成员方式，paddle 是 function 调用，具体差异如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | dim | axis | 计算 softmax 的轴，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sort.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sort.md index bd8fd70b3a9..340b91aa877 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sort.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sort.md @@ -3,13 +3,13 @@ ### [torch.Tensor.sort](https://pytorch.org/docs/stable/generated/torch.Tensor.sort.html#torch-tensor-sort) ```python -torch.Tensor.sort(dim=- 1, descending=False) +torch.Tensor.sort(dim=- 1, descending=False, stable=False) ``` ### [paddle.sort](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/sort_cn.html#sort) ```python -paddle.sort(x, axis=- 1, descending=False) +paddle.sort(x, axis=- 1, descending=False, stable=False) ``` 两者功能一致，仅参数名不一致，具体如下： @@ -20,3 +20,4 @@ paddle.sort(x, axis=- 1, descending=False) | ------------- | ------------ | ------------------------------------------------------ | | dim | axis | 指定对输入 Tensor 进行运算的轴。默认值为-1, 仅参数名不一致。 | | descending |descending | 指定算法排序的方向, 参数完全一致。 | +| stable | stable | 是否使用稳定排序。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sparse_mask.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sparse_mask.md new file mode 100644 index 00000000000..c3850feed09 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.sparse_mask.md @@ -0,0 +1,32 @@ +## [ 参数完全一致 ] torch.Tensor.sparse_mask + +### [torch.Tensor.sparse_mask](https://pytorch.org/docs/stable/generated/torch.Tensor.sparse_mask.html) + +```python +torch.Tensor.sparse_mask(mask) +``` + +### [paddle.sparse.mask_as](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/sparse/mask_as_cn.html) + +```python +paddle.sparse.mask_as(x, mask, name=None) +``` + +两者功能一致，但调用方式不同，torch 通过 Tensor 类方法调用，而 paddle 是直接调用函数，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ---------- | ------------ | ------------------------------------ | +| - | x | 输入的 DenseTensor。 | +| mask | mask | 掩码逻辑的 mask，参数完全一致。 | + +### 转写示例 + +```python +# torch 调用 Tensor 类方法 +x.sparse_mask(mask) + +# paddle 直接调用函数 +paddle.sparse.mask_as(x, mask) +``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.split.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.split.md index bd0fb33c85f..f66af21833b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.split.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.split.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.split +## [ 输入参数用法不一致 ]torch.Tensor.split ### [torch.Tensor.split](https://pytorch.org/docs/stable/generated/torch.Tensor.split.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.svd.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.svd.md index be4e2dfb440..4965566ef88 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.svd.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.svd.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.svd +## [ 输入参数用法不一致 ]torch.Tensor.svd ### [torch.Tensor.svd](https://pytorch.org/docs/stable/generated/torch.Tensor.svd.html#torch.Tensor.svd) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapaxes.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapaxes.md index c469eb5ef6d..73c622e3f69 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapaxes.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapaxes.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.swapaxes +## [ 输入参数用法不一致 ] torch.Tensor.swapaxes ### [torch.Tensor.swapaxes](https://pytorch.org/docs/stable/generated/torch.Tensor.swapaxes.html#torch.Tensor.swapaxes) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapdims.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapdims.md index 3d52eeb473a..b58a15ee7f5 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapdims.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.swapdims.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.swapdims +## [ 输入参数用法不一致 ] torch.Tensor.swapdims ### [torch.Tensor.swapdims](https://pytorch.org/docs/stable/generated/torch.Tensor.swapdims.html#torch.Tensor.swapdims) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tile.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tile.md index 94f44d94e11..e86c8d857e6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tile.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tile.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.Tensor.tile +## [ 输入参数用法不一致 ] torch.Tensor.tile ### [torch.Tensor.tile](https://pytorch.org/docs/stable/generated/torch.Tensor.tile.html#torch.Tensor.tile) @@ -17,7 +17,7 @@ paddle.Tensor.tile(repeat_times, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| ------- | ------------ | :----------------------------------------------------------- | +| ------- | ------------ | ------------------------------------------------------------ | | *reps | repeat_times | 维度复制次数， PyTorch 参数 reps 既可以是可变参数，也可以是 list/tuple/tensor 的形式， Paddle 参数 repeat_times 为 list/tuple/tensor 的形式。 | 转写示例 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.topk.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.topk.md index 93786434dab..e3608fb83a7 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.topk.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.topk.md @@ -17,7 +17,7 @@ paddle.Tensor.topk(k, axis=None, largest=True, sorted=True, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| ------- | ------------ | :------------------------------- | +| ------- | ------------ | -------------------------------- | | k | k | 表示前 k 个最大项。 | | dim | axis | 表示排序的维度，仅参数名不一致。 | | largest | largest | True: 最大值， False: 最小值。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.trace.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.trace.md index 9795e5b822b..7acab3b852f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.trace.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.trace.md @@ -17,7 +17,7 @@ Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :-----: | :----------: | :----------------------------------------------------------: | +| ------- | ------------ | ------------------------------------------------------------ | | - | offset | 表示指定的二维平面中获取对角线的位置，PyTorch 无此参数，Paddle 保持默认即可。 | | - | axis1 | 表示获取对角线的二维平面的第一维，PyTorch 无此参数，Paddle 保持默认即可。 | | - | axis2 | 表示获取对角线的二维平面的第二维，PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.transpose.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.transpose.md index bde793a0e97..6ef8e09da61 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.transpose.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.transpose.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.transpose +## [ 输入参数用法不一致 ]torch.Tensor.transpose ### [torch.Tensor.transpose](https://pytorch.org/docs/stable/generated/torch.Tensor.transpose.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triangular_solve.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triangular_solve.md index 166f4cde285..2b2341dac60 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triangular_solve.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triangular_solve.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.Tensor.triangular_solve +## [ 输入参数用法不一致]torch.Tensor.triangular_solve ### [torch.Tensor.triangular_solve](https://pytorch.org/docs/stable/generated/torch.Tensor.triangular_solve.html#torch.Tensor.triangular_solve) @@ -12,7 +12,7 @@ torch.Tensor.triangular_solve(A, upper=True, transpose=False, unitriangular=Fals paddle.Tensor.triangular_solve(b, upper=True, transpose=False, unitriangular=False, name=None) ``` -其中两者功能一致且参数用法一致，仅参数名不一致，具体如下： +其中两者功能一致，输入参数用法不一致，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril.md index 090842e455d..24faa39e517 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril.md @@ -17,5 +17,5 @@ paddle.Tensor.tril(diagonal=0, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | +| -------- | ------------ | -------------------------------- | | diagonal | diagonal | 表示运算的对角线，参数完全一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril_.md index 94433dddfbe..b1fb17346d9 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.tril_.md @@ -17,5 +17,5 @@ paddle.Tensor.tril_(diagonal=0) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | +| -------- | ------------ | -------------------------------- | | diagonal | diagonal | 表示运算的对角线，参数完全一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu.md index 9ca92ff159a..654c518cd27 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu.md @@ -17,5 +17,5 @@ paddle.Tensor.triu(diagonal=0, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | +| -------- | ------------ | -------------------------------- | | diagonal | diagonal | 表示运算的对角线，参数完全一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu_.md index 5740d5ea609..301e7f3835f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.triu_.md @@ -17,5 +17,5 @@ paddle.Tensor.triu_(diagonal=0) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | +| -------- | ------------ | -------------------------------- | | diagonal | diagonal | 表示运算的对角线，参数完全一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide.md index a19c0f99a32..1c83d79e420 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide.md @@ -17,5 +17,5 @@ paddle.Tensor.divide(y, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | +| -------- | ------------ | -------------------------------- | | value | y | 表示输入的 Tensor，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide_.md index f63ac163db8..d4aa4b715b0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.true_divide_.md @@ -17,5 +17,5 @@ paddle.Tensor.divide_(y) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------: | :----------: | :------------------------------: | -| other | y | 表示输入的 Tensor，仅参数名不一致。 | +| -------- | ------------ | -------------------------------- | +| other | y | 表示输入的 Tensor，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unbind.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unbind.md index 5993ae0df59..74c46914260 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unbind.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unbind.md @@ -17,5 +17,5 @@ paddle.Tensor.unbind(axis=0) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :-----: | :----------: | :--------------------------------: | +| ------- | ------------ | ---------------------------------- | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unfold.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unfold.md index fdd4ab2ae7b..c427aefcb1c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unfold.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unfold.md @@ -22,7 +22,7 @@ paddle.Tensor.unfold(axis, ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| ------- | :----------: | :----------------------------------------------------------: | +| ------- | ------------ | ------------------------------------------------------------ | | dimension | axis | 表示需要提取的维度，仅参数名不一致。 | | size | size | 表示需要提取的窗口长度。 | | step | step | 表示每次提取跳跃的步长。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.uniform_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.uniform_.md index 1854d545cab..b038d4c82ce 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.uniform_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.uniform_.md @@ -17,7 +17,7 @@ Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| ------- | :----------: | :----------------------------------------------------------: | +| ------- | ------------ | ------------------------------------------------------------ | | from | min | 表示生成元素的起始位置，仅参数名不一致。 | | to | max | 表示生成元素的结束位置，仅参数名不一致。 | | - | seed | 表示用于生成随机数的随机种子，PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique.md index 4ec7202c2c2..67d7cb98b8d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique.md @@ -12,7 +12,7 @@ paddle.Tensor.unique(return_index=False, return_inverse=False, return_counts=Fal ``` 两者功能一致，torch 参数更多，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | sorted | - | 是否返回前进行排序，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique_consecutive.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique_consecutive.md index 97bed5db852..4dc4cc08622 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique_consecutive.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unique_consecutive.md @@ -17,7 +17,7 @@ Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------: | :------------: | :----------------------------------------------------------: | +| -------------- | -------------- | ------------------------------------------------------------ | | return_inverse | return_inverse | 表示输入 Tensor 的元素对应在连续不重复元素中的索引。参数完全一致。 | | return_counts | return_counts | 表示每个连续不重复元素在输入 Tensor 中的个数。参数完全一致。 | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze.md index 34599af5637..98b9b2be484 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze.md @@ -17,5 +17,5 @@ paddle.Tensor.unsqueeze(axis, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :-----: | :----------: | :--------------------------------: | +| ------- | ------------ | ---------------------------------- | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze_.md index 81b433a3d59..eaee5065fa9 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.unsqueeze_.md @@ -17,5 +17,5 @@ paddle.Tensor.unsqueeze_(axis, name=None) ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :-----: | :----------: | :--------------------------------: | +| ------- | ------------ | ---------------------------------- | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.view.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.view.md index c6603d90c70..f127884b38d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.view.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.view.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.view +## [ 输入参数用法不一致 ]torch.Tensor.view ### [torch.Tensor.view](https://pytorch.org/docs/stable/generated/torch.Tensor.view.html?highlight=view#torch.Tensor.view) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.where.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.where.md index dc0291b07c9..991983b53e0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.where.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/Tensor/torch.Tensor.where.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.Tensor.where +## [ 输入参数用法不一致 ]torch.Tensor.where ### [torch.Tensor.where](https://pytorch.org/docs/stable/generated/torch.Tensor.where.html#torch.Tensor.where) @@ -13,7 +13,7 @@ paddle.Tensor.where(x, y, name=None) ``` 两者功能一致，参数名和参数用法不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | condition | - | condition 为判断条件。Paddle 无此参数，需要转写。| diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.Stream__upper.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.Stream__upper.md index 7f6da8f26f5..59f90d86273 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.Stream__upper.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.Stream__upper.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.cuda.Stream +## [ 输入参数用法不一致 ]torch.cuda.Stream ### [torch.cuda.Stream](https://pytorch.org/docs/stable/generated/torch.cuda.Stream.html#torch.cuda.Stream) @@ -12,7 +12,7 @@ torch.cuda.Stream(device=None, priority=0, **kwargs) paddle.device.cuda.Stream(device=None, priority=None) ``` -两者功能一致，参数不一致，具体如下： +两者功能一致，参数用法不一致，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.device.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.device.md index 2c3ce1a01fd..6a8818f687a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.device.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.device.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.cuda.device +## [ 输入参数类型不一致 ]torch.cuda.device ### [torch.cuda.device](https://pytorch.org/docs/stable/generated/torch.cuda.device.html#torch.cuda.device) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_rng_state_all.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_rng_state_all.md index 081137e9fd6..b08e2359c4e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_rng_state_all.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_rng_state_all.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.cuda.get_rng_state_all +## [ 返回参数类型不一致 ]torch.cuda.get_rng_state_all ### [torch.cuda.get_rng_state_all](https://pytorch.org/docs/stable/generated/torch.cuda.get_rng_state_all.html#torch.cuda.get_rng_state_all) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.manual_seed.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.manual_seed.md index 68632e62b1a..9ac35208089 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.manual_seed.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.manual_seed.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.cuda.manual_seed +## [ 返回参数类型不一致 ]torch.cuda.manual_seed ### [torch.cuda.manual_seed](https://pytorch.org/docs/stable/generated/torch.cuda.manual_seed.html#torch.cuda.manual_seed) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_device.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_device.md index a5debb826c8..bfb98afe3e3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_device.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_device.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.cuda.set_device +## [ 输入参数类型不一致 ]torch.cuda.set_device ### [torch.cuda.set_device](https://pytorch.org/docs/stable/generated/torch.cuda.set_device.html#torch.cuda.set_device) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_rng_state_all.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_rng_state_all.md index 3dd236ebf54..314257f7727 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_rng_state_all.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_rng_state_all.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.cuda.set_rng_state_all +## [ 输入参数类型不一致 ]torch.cuda.set_rng_state_all ### [torch.cuda.set_rng_state_all](https://pytorch.org/docs/stable/generated/torch.cuda.set_rng_state_all.html#torch.cuda.set_rng_state_all) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.ReduceOp.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.ReduceOp.md index 07c0af95f9a..975d7b63d6e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.ReduceOp.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.ReduceOp.md @@ -16,13 +16,15 @@ paddle.distributed.ReduceOp 其中，规约操作对应如下： -| torch | paddle | -| ---- | ---- | -| SUM | SUM | -| PRODUCT | PROD | -| MIN | MIN | -| MAX | MAX | -| BAND | - | -| BOR | - | -| BXOR | - | -| PREMUL_SUM | - | +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ---- | ---- | --- | +| SUM | SUM | | +| PRODUCT | PROD | | +| MIN | MIN | | +| MAX | MAX | | +| BAND | - | | +| BOR | - | | +| BXOR | - | | +| PREMUL_SUM | - | | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_gather.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_gather.md index 1d996856a17..f667f49f97a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_gather.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_gather.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.all_gather +## [ 输入参数用法不一致 ]torch.distributed.all_gather ### [torch.distributed.all_gather](https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_gather) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_reduce.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_reduce.md index 254719c11bf..44fef890700 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_reduce.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_reduce.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.all_reduce +## [ 输入参数用法不一致 ]torch.distributed.all_reduce ### [torch.distributed.all_reduce](https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_reduce) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_to_all.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_to_all.md index 7af27ab5dab..021f2cbf5a4 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_to_all.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_to_all.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.all_to_all +## [ 输入参数用法不一致 ]torch.distributed.all_to_all ### [torch.distributed.all_to_all](https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.broadcast.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.broadcast.md index 28f62730a64..3dca94fffda 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.broadcast.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.broadcast.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.broadcast +## [ 输入参数用法不一致 ]torch.distributed.broadcast ### [torch.distributed.broadcast](https://pytorch.org/docs/stable/distributed.html#torch.distributed.broadcast) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.gather.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.gather.md index 43b0c0e83b8..077d7476916 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.gather.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.gather.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.gather +## [ 输入参数用法不一致 ]torch.distributed.gather ### [torch.distributed.gather](https://pytorch.org/docs/stable/distributed.html#torch.distributed.gather) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce.md index 41f73b4c727..34213083f11 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.reduce +## [ 输入参数用法不一致 ]torch.distributed.reduce ### [torch.distributed.reduce](https://pytorch.org/docs/stable/distributed.html#torch.distributed.reduce) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce_scatter.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce_scatter.md index c935474d9f3..53fd8efbe7d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce_scatter.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce_scatter.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.reduce_scatter +## [ 输入参数用法不一致 ]torch.distributed.reduce_scatter ### [torch.distributed.reduce_scatter](https://pytorch.org/docs/stable/distributed.html#torch.distributed.reduce_scatter) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.scatter.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.scatter.md index 870f61527cd..20187bd69a6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.scatter.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.scatter.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.distributed.scatter +## [ 输入参数用法不一致 ]torch.distributed.scatter ### [torch.distributed.scatter](https://pytorch.org/docs/stable/distributed.html#torch.distributed.scatter) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.beta.Beta.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.beta.Beta.md index 52f7a43f91a..2bdfe7f7753 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.beta.Beta.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.beta.Beta.md @@ -17,7 +17,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| ------- | :----------- | -------------------------------------------------------- | +| ------- | ------------ | -------------------------------------------------------- | | concentration1 | alpha | 表示输入的参数 ，仅参数名不一致。 | | concentration0 | beta | 表示输入的参数，仅参数名不一致。 | | validate_args | - | 是否添加验证环节。Paddle 无此参数，一般对训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool1d.md index b8085bd1b53..e04ba1ea15e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.avg_pool1d +## [ 输入参数用法不一致 ]torch.nn.functional.avg_pool1d ### [torch.nn.functional.avg_pool1d](https://pytorch.org/docs/stable/generated/torch.nn.functional.avg_pool1d.html#torch.nn.functional.avg_pool1d) @@ -12,6 +12,7 @@ paddle.nn.functional.avg_pool1d(x, kernel_size, stride=None, padding=0, exclusiv ``` 其中 PyTorch 与 Paddle 参数不一致，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool2d.md index e210504bf88..e3022e3a418 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.avg_pool2d +## [ 输入参数用法不一致 ]torch.nn.functional.avg_pool2d ### [torch.nn.functional.avg_pool2d](https://pytorch.org/docs/stable/generated/torch.nn.functional.avg_pool2d.html#torch.nn.functional.avg_pool2d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool3d.md index d8a7151595a..112705e8400 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.avg_pool3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.avg_pool3d +## [ 输入参数用法不一致 ]torch.nn.functional.avg_pool3d ### [torch.nn.functional.avg_pool3d](https://pytorch.org/docs/stable/generated/torch.nn.functional.avg_pool3d.html#torch.nn.functional.avg_pool3d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.batch_norm.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.batch_norm.md index 8224032c8c1..29686476e8f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.batch_norm.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.batch_norm.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.batch_norm +## [ 输入参数用法不一致 ]torch.nn.functional.batch_norm ### [torch.nn.functional.batch_norm](https://pytorch.org/docs/stable/generated/torch.nn.functional.batch_norm.html#torch.nn.functional.batch_norm) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.bilinear.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.bilinear.md index c5c45bb037a..80d4b100792 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.bilinear.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.bilinear.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.bilinear +## [ 输入参数用法不一致 ]torch.nn.functional.bilinear ### [torch.nn.functional.bilinear](https://pytorch.org/docs/stable/generated/torch.nn.functional.bilinear.html?highlight=bilinear#torch.nn.functional.bilinear) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.cross_entropy.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.cross_entropy.md index 34285204d3c..363e5950b9c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.cross_entropy.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.cross_entropy.md @@ -7,7 +7,7 @@ torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, - ignore_index=- 100, + ignore_index=-100, reduce=None, reduction='mean', label_smoothing=0.0) @@ -19,25 +19,28 @@ torch.nn.functional.cross_entropy(input, paddle.nn.functional.cross_entropy(input, label, weight=None, - ignore_index=- 100, + ignore_index=-100, reduction='mean', soft_label=False, - axis=- 1, + label_smoothing=0.0, + axis=-1, use_softmax=True) ``` + 两者功能一致，torch 参数更多，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | input | 表示预测的 Tensor 。 | -| target | label | 表示真实的 Tensor 。 | +| target | label | 表示真实的 Tensor，仅参数名不同。 | | weight | weight | 表示权重。 | | size_average | - | 已弃用 。 | | ignore_index | ignore_index | 表示忽略的标签值 。 | | reduce | - | 已弃用 。 | | reduction | reduction | 表示应用于输出结果的计算方式 。 | -| label_smoothing | - | 指定计算损失时的平滑量， Paddle 无此参数，暂无转写方式。| +| label_smoothing | label_smoothing | 指定计算损失时的平滑量，参数完全一致。| | - | soft_label | 指明 label 是否为软标签， PyTorch 无此参数， Paddle 保持默认即可。| | - | axis | 进行 softmax 计算的维度索引， PyTorch 无此参数， Paddle 保持默认即可。| | - | use_softmax | 指定是否对 input 进行 softmax 归一化， PyTorch 无此参数， Paddle 保持默认即可。| diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.feature_alpha_dropout.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.feature_alpha_dropout.md new file mode 100644 index 00000000000..36f9ae1d46f --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.feature_alpha_dropout.md @@ -0,0 +1,24 @@ +## [ torch 参数更多 ]torch.nn.functional.feature_alpha_dropout + +### [torch.nn.functional.feature\_alpha\_dropout](https://pytorch.org/docs/stable/generated/torch.nn.functional.feature_alpha_dropout.html) + +```python +torch.nn.functional.feature_alpha_dropout(input, p=0.5, training=False, inplace=False) +``` + +### [paddle.nn.functional.feature\_alpha\_dropout](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/functional/feature_alpha_dropout_cn.html#feature_alpha-dropout) + +```python +paddle.nn.functional.feature_alpha_dropout(x, p=0.5, training=True, name=None) +``` + +其中 PyTorch 相比 Paddle 支持更多其他参数，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| -------- | ------------ | -- | +| input | x | 输入的多维 Tensor，仅参数名不一致。 | +| p | p | 将输入节点置 0 的概率。 | +| training | training | 标记是否为训练阶段，PyTorch 默认值为 False，paddle 默认值为 True。 | +| inplace | - | 表示在不更改变量的内存地址的情况下，直接修改变量的值，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.gelu.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.gelu.md index 1e5280961cf..746971e5485 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.gelu.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.gelu.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.functional.gelu +## [ 输入参数用法不一致]torch.nn.functional.gelu ### [torch.nn.functional.gelu](https://pytorch.org/docs/stable/generated/torch.nn.functional.gelu.html#torch.nn.functional.gelu) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.instance_norm.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.instance_norm.md index 52a56946aa8..d999ab26e43 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.instance_norm.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.instance_norm.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.instance_norm +## [ 输入参数用法不一致 ]torch.nn.functional.instance_norm ### [torch.nn.functional.instance_norm](https://pytorch.org/docs/stable/generated/torch.nn.functional.instance_norm.html#torch.nn.functional.instance_norm) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.kl_div.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.kl_div.md index c476ce903ce..239d2e29845 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.kl_div.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.kl_div.md @@ -16,17 +16,20 @@ torch.nn.functional.kl_div(input, ```python paddle.nn.functional.kl_div(input, label, - reduction='mean') + reduction='mean', + log_target=False) ``` 其中 PyTorch 相比 Paddle 支持更多的参数，具体如下： +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | | ------------ | ------------ | ------------------------------------------------------ | | size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | | reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | | reduction | reduction | 表示对输出结果的计算方式。 | -| log_target | - | 指定目标是否为 log 空间，Paddle 无此参数，暂无转写方式。 | +| log_target | log_target | 指定目标是否属于 log 空间。 | ### 转写示例 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.l1_loss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.l1_loss.md index e16f3f68c63..21bb3e4a38b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.l1_loss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.l1_loss.md @@ -18,7 +18,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | -| input | input | 表示输入的 Tensor。 +| input | input | 表示输入的 Tensor。 | | target | label | 标签，和 input 具有相同的维度，仅参数名不一致。 | | size_average | - | 已废弃，和 reduce 组合决定损失计算方式。Paddle 无此参数，需要转写。 | | reduce | - | 已废弃，和 size_average 组合决定损失计算方式。Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.linear.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.linear.md index 2a5bc04005b..f674090bf19 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.linear.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.linear.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.linear +## [ 输入参数用法不一致 ]torch.nn.functional.linear ### [torch.nn.functional.linear](https://pytorch.org/docs/stable/generated/torch.nn.functional.linear.html?highlight=linear#torch.nn.functional.linear) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool1d.md index 595207f82a6..2ec05b9439e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.max_unpool1d +## [ 输入参数用法不一致 ]torch.nn.functional.max_unpool1d ### [torch.nn.functional.max_unpool1d](https://pytorch.org/docs/stable/generated/torch.nn.functional.max_unpool1d.html?highlight=max_unpool1d#torch.nn.functional.max_unpool1d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool2d.md index dd0e11882c2..7d3695bd269 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.max_unpool2d +## [ 输入参数类型不一致 ]torch.nn.functional.max_unpool2d ### [torch.nn.functional.max_unpool2d](https://pytorch.org/docs/stable/generated/torch.nn.functional.max_unpool2d.html?highlight=max_unpool2d#torch.nn.functional.max_unpool2d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool3d.md index f4e7d086ac9..bba10908b1a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.max_unpool3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.max_unpool3d +## [ 输入参数类型不一致 ]torch.nn.functional.max_unpool3d ### [torch.nn.functional.max_unpool3d](https://pytorch.org/docs/stable/generated/torch.nn.functional.max_unpool3d.html?highlight=max_unpool3d#torch.nn.functional.max_unpool3d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.one_hot.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.one_hot.md index 1c014cd8025..21a1527969b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.one_hot.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.one_hot.md @@ -1,10 +1,10 @@ -## [ 参数不一致 ]torch.nn.functional.one_hot +## [ 返回参数类型不一致 ]torch.nn.functional.one_hot ### [torch.nn.functional.one_hot](https://pytorch.org/docs/stable/generated/torch.nn.functional.one_hot.html?highlight=one_hot#torch.nn.functional.one_hot) ```python torch.nn.functional.one_hot(tensor, - num_classes=- 1) + num_classes=-1) ``` ### [paddle.nn.functional.one_hot](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/functional/one_hot_cn.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.smooth_l1_loss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.smooth_l1_loss.md index 8a699aac659..f51f32d72b2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.smooth_l1_loss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.smooth_l1_loss.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.functional.smooth_l1_loss +## [ 输入参数用法不一致 ]torch.nn.functional.smooth_l1_loss ### [torch.nn.functional.smooth_l1_loss](https://pytorch.org/docs/stable/generated/torch.nn.functional.smooth_l1_loss.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.softmax.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.softmax.md index 3474cf8d9e4..26b26f5cb1a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.softmax.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.softmax.md @@ -1,27 +1,24 @@ -## [ 仅参数名不一致 ]torch.nn.functional.softmax +## [ torch 参数更多 ]torch.nn.functional.softmax -### [torch.nn.functional.softmax](https://pytorch.org/docs/stable/generated/torch.nn.functional.softmax.html?highlight=softmax#torch.nn.functional.softmax) +### [torch.nn.functional.softmax](https://pytorch.org/docs/stable/generated/torch.nn.functional.softmax.html#torch.nn.functional.softmax) ```python -torch.nn.functional.softmax(input, - dim=None, - dtype=None) +torch.nn.functional.softmax(input, dim=None, _stacklevel=3, dtype=None) ``` -### [paddle.nn.functional.softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/functional/softmax_cn.html) +### [paddle.nn.functional.softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/functional/softmax_cn.html#softmax) ```python -paddle.nn.functional.softmax(x, - axis=- 1, - dtype=None, - name=None) +paddle.nn.functional.softmax(x, axis=-1, dtype=None, name=None) ``` -两者功能一致，仅参数名不一致，具体如下： +PyTorch 相比 Paddle 支持更多其他参数，具体如下： + ### 参数映射 -| PyTorch | PaddlePaddle | 备注 | -| ------------- | ------------ | ------------------------------------------------------ | -| input | x | 表示输入 Tensor ，仅参数名不一致。 | -| dim | axis | 表示对输入 Tensor 运算的轴 ，仅参数名不一致。 | -| dtype | dtype | 表示输入 Tensor 的数据类型 。 | +| PyTorch | PaddlePaddle | 备注 | +| --------- | -------------- | ----------------------------------------------------- | +| input | x | 表示输入张量，仅参数名不一致。 | +| dim | axis | 表示对输入 Tensor 进行运算的轴，仅参数名不一致。 | +| dtype | dtype | 表示返回张量所需的数据类型。 | +| _stacklevel | - | Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.trunc_normal_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.trunc_normal_.md new file mode 100644 index 00000000000..89bf02c2f4b --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.trunc_normal_.md @@ -0,0 +1,45 @@ +## [ 组合替代实现 ]torch.nn.init.trunc_normal_ + +### [torch.nn.init.trunc_normal_](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.trunc_normal_) + +```python +torch.nn.init.trunc_normal_(tensor, + mean=0.0, + std=1.0, + a=-2.0, + b=2.0) +``` + +### [paddle.nn.initializer.TruncatedNormal](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/initializer/TruncatedNormal_cn.html) + +```python +paddle.nn.initializer.TruncatedNormal(mean=0.0, + std=1.0, + a=-2.0, + b=2.0, + name=None) +``` + +两者用法不同：torch 是 inplace 的用法，paddle 是类设置的，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------ | +| tensor | - | n 维 tensor。Paddle 无此参数，因为是通过调用类的 __call__ 函数来进行 tensor 的初始化。 | +| mean | mean | 正态分布的平均值。参数名和默认值一致。 | +| std | std | 正态分布的标准差。参数名和默认值一致。 | +| a | a | 截断正态分布的下界。参数名和默认值一致。 | +| b | b | 截断正态分布的上界。参数名和默认值一致。 | + +### 转写示例 +```python +# PyTorch 写法 +conv = torch.nn.Conv2d(4, 6, (3, 3)) +torch.nn.init.trunc_normal_(conv.weight) + +# Paddle 写法 +conv = nn.Conv2D(in_channels=4, out_channels=6, kernel_size=(3,3)) +init_trunc_normal = paddle.nn.initializer.TruncatedNormal() +init_trunc_normal(conv.weight) +``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_normal_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_normal_.md index f0da7128a31..c31a95dc74a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_normal_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_normal_.md @@ -12,6 +12,7 @@ torch.nn.init.xavier_normal_(tensor, ```python paddle.nn.initializer.XavierNormal(fan_in=None, fan_out=None, + gain=1.0, name=None) ``` @@ -22,7 +23,7 @@ paddle.nn.initializer.XavierNormal(fan_in=None, | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | tensor | - | n 维 tensor。Paddle 无此参数，因为是通过调用类的 __call__ 函数来进行 tensor 的初始化。 | -| gain | - | 缩放因子。Paddle 无此参数，暂无转写方式。 | +| gain | gain | 缩放因子。 | | - | fan_in | 用于泽维尔初始化的 fan_in。PyTorch 无此参数，Paddle 保持默认即可。 | | - | fan_out | 用于泽维尔初始化的 fan_out。PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_uniform_.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_uniform_.md index 787f65c0274..809c56a90ea 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_uniform_.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_uniform_.md @@ -12,6 +12,7 @@ torch.nn.init.xavier_uniform_(tensor, ```python paddle.nn.initializer.XavierUniform(fan_in=None, fan_out=None, + gain=1.0, name=None) ``` @@ -22,7 +23,7 @@ paddle.nn.initializer.XavierUniform(fan_in=None, | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | tensor | - | n 维 tensor。Paddle 无此参数，因为是通过调用类的 __call__ 函数来进行 tensor 的初始化。 | -| gain | - | 缩放因子。Paddle 无此参数，暂无转写方式。 | +| gain | gain | 缩放因子。 | | - | fan_in | 用于泽维尔初始化的 fan_in。PyTorch 无此参数，Paddle 保持默认即可。 | | - | fan_out | 用于泽维尔初始化的 fan_out。PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.slogdet.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.slogdet.md index 7c38073ba43..6d06f903e1b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.slogdet.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.slogdet.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.linalg.slogdet +## [ 返回参数类型不一致 ]torch.linalg.slogdet ### [torch.linalg.slogdet](https://pytorch.org/docs/stable/generated/torch.linalg.slogdet.html#torch.linalg.slogdet) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool1d.md index 0468943a3c8..1497c4b143e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool1d.md @@ -13,3 +13,9 @@ paddle.nn.AdaptiveAvgPool1D(output_size, name=None) ``` 两者功能一致，参数完全一致。 + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------ | +| output_size | output_size | 表示输出 Tensor 的 size 。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool1d.md index 3d36659f015..2194f4afc22 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.AvgPool1d +## [ 输入参数用法不一致 ]torch.nn.AvgPool1d ### [torch.nn.AvgPool1d](https://pytorch.org/docs/stable/generated/torch.nn.AvgPool1d.html?highlight=avgpool1d#torch.nn.AvgPool1d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool2d.md index b921da13117..396605018db 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.AvgPool2d +## [ 输入参数用法不一致 ]torch.nn.AvgPool2d ### [torch.nn.AvgPool2d](https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html?highlight=avgpool2d#torch.nn.AvgPool2d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool3d.md index 334035eb893..59e17ae0064 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.AvgPool3d +## [ 输入参数用法不一致 ]torch.nn.AvgPool3d ### [torch.nn.AvgPool3d](https://pytorch.org/docs/stable/generated/torch.nn.AvgPool3d.html?highlight=avgpool3d#torch.nn.AvgPool3d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm1d.md index 3b8bf8fe0f1..93846a8d914 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.BatchNorm1d +## [ 输入参数用法不一致 ]torch.nn.BatchNorm1d ### [torch.nn.BatchNorm1d](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm1d.html?highlight=torch%20nn%20batchnorm1d#torch.nn.BatchNorm1d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm2d.md index bffb8efdb2d..e7ade042be0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.BatchNorm2d +## [ 输入参数用法不一致 ]torch.nn.BatchNorm2d ### [torch.nn.BatchNorm2d](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html?highlight=batchnorm2d#torch.nn.BatchNorm2d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm3d.md index 6e30b4c9354..8621d80d990 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.BatchNorm3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.BatchNorm3d +## [ 输入参数用法不一致 ]torch.nn.BatchNorm3d ### [torch.nn.BatchNorm3d](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm3d.html?highlight=torch%20nn%20batchnorm3d#torch.nn.BatchNorm3d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CTCLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CTCLoss.md index a77cc48eb00..f3722ced51b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CTCLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CTCLoss.md @@ -19,8 +19,8 @@ paddle.nn.CTCLoss(blank=0, ### 参数映射 -| PyTorch | Paddle | 备注 | -| ------------- | --------- | ------------------------------------------------------------ | -| blank | blank | 空格标记的 ID 值。 | -| reduction | reduction | 表示应用于输出结果的计算方式。 | -| zero_infinity | - | 是否将无穷大损失及其梯度置 0，Paddle 无此参数，暂无转写方式。 | +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------------ | +| blank | blank | 空格标记的 ID 值。 | +| reduction | reduction | 表示应用于输出结果的计算方式。 | +| zero_infinity | - | 是否将无穷大损失及其梯度置 0，Paddle 无此参数，暂无转写方式。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv1d.md index 59a23497a62..003a5d7be6e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.Conv1d +## [ 输入参数用法不一致 ]torch.nn.Conv1d ### [torch.nn.Conv1d](https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html?highlight=conv1d#torch.nn.Conv1d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv2d.md index a02fda3aece..ceb8abe1855 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.Conv2d +## [ 输入参数用法不一致 ]torch.nn.Conv2d ### [torch.nn.Conv2d](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html?highlight=conv2d#torch.nn.Conv2d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv3d.md index 801cfaf2d38..b33d1af86de 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Conv3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.Conv3d +## [ 输入参数用法不一致 ]torch.nn.Conv3d ### [torch.nn.Conv3d](https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html?highlight=conv3d#torch.nn.Conv3d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose1d.md index 9c1fda904bb..17bdbf19a50 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ConvTranspose1d +## [ 输入参数用法不一致 ]torch.nn.ConvTranspose1d ### [torch.nn.ConvTranspose1d](https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose1d.html?highlight=convtranspose1d#torch.nn.ConvTranspose1d) ```python torch.nn.ConvTranspose1d(in_channels, diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose2d.md index c34c41dba40..c52086cb993 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ConvTranspose2d +## [ 输入参数用法不一致 ]torch.nn.ConvTranspose2d ### [torch.nn.ConvTranspose2d](https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html?highlight=convtranspose2d#torch.nn.ConvTranspose2d) ```python torch.nn.ConvTranspose2d(in_channels, diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose3d.md index a33c7b93b2b..88fc0619148 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ConvTranspose3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ConvTranspose3d +## [ 输入参数用法不一致 ]torch.nn.ConvTranspose3d ### [torch.nn.ConvTranspose3d](https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose3d.html?highlight=convtranspose3d#torch.nn.ConvTranspose3d) ```python torch.nn.ConvTranspose3d(in_channels, diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CrossEntropyLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CrossEntropyLoss.md index 9defe9873a0..1eef5b9aad1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CrossEntropyLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CrossEntropyLoss.md @@ -4,7 +4,7 @@ ```python torch.nn.CrossEntropyLoss(weight=None, size_average=None, - ignore_index=- 100, + ignore_index=-100, reduce=None, reduction='mean', label_smoothing=0.0) @@ -17,11 +17,13 @@ paddle.nn.CrossEntropyLoss(weight=None, ignore_index=-100, reduction='mean', soft_label=False, + label_smoothing=0.0, axis=-1, name=None) ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | @@ -31,7 +33,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： | ignore_index | ignore_index | 表示忽略的一个标签值。 | | reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | | reduction | reduction | 表示应用于输出结果的计算方式。 | -| label_smoothing | - | 指定计算损失时的平滑量，Paddle 无此参数，暂无转写方式。 | +| label_smoothing | label_smoothing | 指定计算损失时的平滑量，参数完全一致。 | | - | soft_label | 指明 label 是否为软标签，PyTorch 无此参数，Paddle 保持默认即可。 | | - | axis | 进行 softmax 计算的维度索引，PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.DataParallel.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.DataParallel.md index d99352dc8a7..349e50c0ca6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.DataParallel.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.DataParallel.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.DataParallel +## [ torch 参数更多 ]torch.nn.DataParallel ### [torch.nn.DataParallel](https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html?highlight=dataparallel#torch.nn.DataParallel) ```python @@ -18,7 +18,8 @@ paddle.DataParallel(layers, find_unused_parameters=False) ``` -两者功能一致但参数不一致，具体如下： +PyTorch 相比 Paddle 支持更多其他参数，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.FeatureAlphaDropout.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.FeatureAlphaDropout.md new file mode 100644 index 00000000000..5497ad6e250 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.FeatureAlphaDropout.md @@ -0,0 +1,22 @@ +## [torch 参数更多]torch.nn.FeatureAlphaDropout + +### [torch.nn.FeatureAlphaDropout](https://pytorch.org/docs/stable/generated/torch.nn.FeatureAlphaDropout.html#torch.nn.FeatureAlphaDropout) + +```python +torch.nn.FeatureAlphaDropout(p=0.5, inplace=False) +``` + +### [paddle.nn.FeatureAlphaDropout](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/FeatureAlphaDropout_cn.html) + +```python +paddle.nn.FeatureAlphaDropout(p=0.5, name=None) +``` + +PyTorch 相比 Paddle 支持更多其他参数，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------- | ------------ | --------------------------------------------------------------------------------------------------------------- | +| p | p | 将输入节点置 0 的概率，即丢弃概率。 | +| inplace | - | 表示在不更改变量的内存地址的情况下，直接修改变量的值，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GELU.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GELU.md index 95f0f5eb9c7..91ebfbfaac5 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GELU.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GELU.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.GELU +## [ 输入参数用法不一致 ]torch.nn.GELU ### [torch.nn.GELU](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html#torch.nn.GELU) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GLU.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GLU.md new file mode 100644 index 00000000000..4f5b440af70 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GLU.md @@ -0,0 +1,21 @@ +## [仅参数名不一致]torch.nn.GLU + +### [torch.nn.GLU](https://pytorch.org/docs/stable/generated/torch.nn.GLU.html#torch.nn.GLU) + +```python +torch.nn.GLU(dim=-1) +``` + +### [paddle.nn.GLU](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/GLU_cn.html) + +```python +paddle.nn.GLU(axis=-1, name=None) +``` + +两者功能一致且参数用法一致，仅参数名不一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------- | ------------ | --------------------------------------------------------------------------------------------------------------- | +| dim | axis | GLU 划分输入的轴，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRU.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRU.md index 0b709d36414..23f0fea94e9 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRU.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRU.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.GRU +## [ 输入参数用法不一致 ]torch.nn.GRU ### [torch.nn.GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html?highlight=torch%20nn%20gru#torch.nn.GRU) ```python torch.nn.GRU(input_size, diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRUCell.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRUCell.md index fce44973abe..d056c6a2d66 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRUCell.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRUCell.md @@ -1,4 +1,5 @@ -## [ 参数不一致 ]torch.nn.GRUCell +## [ 输入参数用法不一致 ]torch.nn.GRUCell + ### [torch.nn.GRUCell](https://pytorch.org/docs/stable/generated/torch.nn.GRUCell.html#torch.nn.GRUCell) ```python torch.nn.GRUCell(input_size, hidden_size, bias=True, device=None, dtype=None) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.HingeEmbeddingLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.HingeEmbeddingLoss.md index 45a0bc68acf..b57799bcc83 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.HingeEmbeddingLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.HingeEmbeddingLoss.md @@ -21,12 +21,12 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 -| PyTorch | Paddle | 备注 | -| ------------ | --------- | ------------------------------------------------------------ | -| margin | margin | 当 label 为-1 时，该值决定了小于 margin 的 input 才需要纳入 hinge embedding loss 的计算。 | -| size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | -| reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | -| reduction | reduction | 表示应用于输出结果的计算方式。 | +| PyTorch | PaddlePaddle | 备注 | +| ------------ | ------------ | ------------------------------------------------------------ | +| margin | margin | 当 label 为-1 时，该值决定了小于 margin 的 input 才需要纳入 hinge embedding loss 的计算。 | +| size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | +| reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | +| reduction | reduction | 表示应用于输出结果的计算方式。 | ### 转写示例 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm1d.md index 9c7b147e2fe..560df50438b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.InstanceNorm1d +## [ 输入参数用法不一致 ]torch.nn.InstanceNorm1d ### [torch.nn.InstanceNorm1d](https://pytorch.org/docs/stable/generated/torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm2d.md index c2018600613..78cff9d8dd3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.InstanceNorm2d +## [ 输入参数用法不一致 ]torch.nn.InstanceNorm2d ### [torch.nn.InstanceNorm2d](https://pytorch.org/docs/stable/generated/torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.KLDivLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.KLDivLoss.md index ddd231e6d6c..11b20ffacdf 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.KLDivLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.KLDivLoss.md @@ -11,17 +11,18 @@ torch.nn.KLDivLoss(size_average=None, ### [paddle.nn.KLDivLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/KLDivLoss_cn.html#kldivloss) ```python -paddle.nn.KLDivLoss(reduction='mean') +paddle.nn.KLDivLoss(reduction='mean', + log_target=False) ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | | reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | | reduction | reduction | 表示对输出结果的计算方式。 | -| log_target | - | 指定目标是否为日志空间，Paddle 无此参数，暂无转写方式。 | +| log_target | log_target | 指定目标是否属于 log 空间。 | ### 转写示例 #### size_average diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTM.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTM.md index 156e322e8b3..a139fc71f3c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTM.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTM.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.LSTM +## [ 输入参数用法不一致 ]torch.nn.LSTM ### [torch.nn.LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html?highlight=lstm#torch.nn.LSTM) ```python @@ -24,10 +24,12 @@ paddle.nn.LSTM(input_size, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, - bias_hh_attr=None) + bias_hh_attr=None, + proj_size=0) ``` 两者功能一致但参数不一致，部分参数名不同，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | @@ -39,7 +41,7 @@ paddle.nn.LSTM(input_size, | batch_first | time_major | PyTorch 表示 batch size 是否为第一维，PaddlePaddle 表示 time steps 是否为第一维，它们的意义相反。需要转写。 | | dropout | dropout | 表示 dropout 概率。 | | bidirectional | direction | PyTorch 表示是否进行双向，Paddle 使用字符串表示是双向 LSTM（`bidirectional`）还是单向 LSTM（`forward`）| -| proj_size | - | 表示 LSTM 后将映射到对应的大小，Paddle 无此参数，暂无转写方式。 | +| proj_size | proj_size | 表示 LSTM 后将 `hidden state` 映射到对应的大小。 | | - |weight_ih_attr| weight_ih 的参数，PyTorch 无此参数，Paddle 保持默认即可。 | | - |weight_hh_attr| weight_hh 的参数，PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTMCell.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTMCell.md index 787c5a06f83..c1a675c98a4 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTMCell.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTMCell.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.LSTMCell +## [ 输入参数用法不一致 ]torch.nn.LSTMCell ### [torch.nn.LSTMCell](https://pytorch.org/docs/stable/generated/torch.nn.LSTMCell.html#torch.nn.LSTMCell) ```python torch.nn.LSTMCell(input_size, hidden_size, bias=True, device=None, dtype=None) @@ -6,7 +6,7 @@ torch.nn.LSTMCell(input_size, hidden_size, bias=True, device=None, dtype=None) ### [paddle.nn.LSTMCell](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/LSTMCell_cn.html#lstmcell) ```python -paddle.nn.LSTMCell(input_size, hidden_size, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, bias_hh_attr=None, name=None) +paddle.nn.LSTMCell(input_size, hidden_size, weight_ih_attr=None, weight_hh_attr=None, bias_ih_attr=None, bias_hh_attr=None, proj_size=0, name=None) ``` 两者功能一致但参数不一，具体如下： @@ -21,21 +21,22 @@ paddle.nn.LSTMCell(input_size, hidden_size, weight_ih_attr=None, weight_hh_attr= | dtype | - | Tensor 的所需数据类型，一般对网络训练结果影响不大，可直接删除。 | | - |weight_ih_attr| weight_ih 的参数， PyTorch 无此参数， Paddle 保持默认即可。 | | - |weight_hh_attr| weight_hh 的参数， PyTorch 无此参数， Paddle 保持默认即可。 | +| - |proj_size|表示是否将 `hidden state` 映射到对应的大小， PyTorch 无此参数， Paddle 保持默认即可。 | ### 转写示例 #### bias：是否使用偏置 ```python # PyTorch 写法 -torch.nn.LSTM(16, 32, bias=True) +torch.nn.LSTMCell(16, 32, bias=True) # Paddle 写法 -paddle.nn.LSTM(16, 32) +paddle.nn.LSTMCell(16, 32) ``` ```python # PyTorch 写法 -torch.nn.LSTM(16, 32, bias=False) +torch.nn.LSTMCell(16, 32, bias=False) # Paddle 写法 -paddle.nn.LSTM(16, 32, bias_ih_attr=False, bias_hh_attr=False) +paddle.nn.LSTMCell(16, 32, bias_ih_attr=False, bias_hh_attr=False) ``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LayerNorm.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LayerNorm.md index 4a1ca96b243..60db3e85f2c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LayerNorm.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LayerNorm.md @@ -20,7 +20,7 @@ paddle.nn.LayerNorm(normalized_shape, ``` 两者功能一致但参数不一致，torch 参数更多，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | elementwise_affine | - | 是否进行仿射变换，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Linear.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Linear.md index 20c7d865ae9..42e47cad5a0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Linear.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Linear.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.Linear +## [ 输入参数用法不一致 ]torch.nn.Linear ### [torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MarginRankingLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MarginRankingLoss.md index d555ec87f92..09da4bf052a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MarginRankingLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MarginRankingLoss.md @@ -21,12 +21,12 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 -| PyTorch | Paddle | 备注 | -| ------------ | --------- | -------------------------------------------- | -| margin | margin | 用于加和的 margin 值。 | -| size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | -| reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | -| reduction | reduction | 表示应用于输出结果的计算方式。 | +| PyTorch | PaddlePaddle | 备注 | +| ------------ | ------------ | -------------------------------------------- | +| margin | margin | 用于加和的 margin 值。 | +| size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | +| reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | +| reduction | reduction | 表示应用于输出结果的计算方式。 | ### 转写示例 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MaxPool3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MaxPool3d.md index 1c5d552a9d1..b495170ac79 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MaxPool3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MaxPool3d.md @@ -23,7 +23,7 @@ paddle.nn.MaxPool3D(kernel_size, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | kernel_size | kernel_size | 表示池化核大小。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.bfloat16.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.bfloat16.md index e3f64a03746..6fc1cd3f15d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.bfloat16.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.bfloat16.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.bfloat16 +## [ 仅 paddle 参数更多 ]torch.nn.Module.bfloat16 ### [torch.nn.Module.bfloat16](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.bfloat16) @@ -12,7 +12,7 @@ torch.nn.Module.bfloat16() paddle.nn.Layer.to(dtype=paddle.bfloat16) ``` -两者参数用法不一致，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cpu.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cpu.md index c2faf88fa9f..6f4b44248a6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cpu.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cpu.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.cpu +## [ 仅 paddle 参数更多 ]torch.nn.Module.cpu ### [torch.nn.Module.cpu](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cpu) @@ -12,7 +12,7 @@ torch.nn.Module.cpu() paddle.nn.Layer.to("cpu") ``` -两者参数用法不一致，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cuda.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cuda.md index bc1377f48a9..2cab422ce32 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cuda.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cuda.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.cuda +## [ 仅 paddle 参数更多 ]torch.nn.Module.cuda ### [torch.nn.Module.cuda](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda) @@ -12,7 +12,7 @@ torch.nn.Module.cuda(device=None) paddle.nn.Layer.to(device="gpu") ``` -两者参数用法不一致，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.double.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.double.md index d76efadfd7e..c1f6fd31a69 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.double.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.double.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.double +## [ 仅 paddle 参数更多 ]torch.nn.Module.double ### [torch.nn.Module.double](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.double) @@ -12,7 +12,7 @@ torch.nn.Module.double() paddle.nn.Layer.to(dtype="float64") ``` -两者参数用法不一致，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.float.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.float.md index 67e7ef383c8..e6f15c9294a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.float.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.float.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.float +## [ 仅 paddle 参数更多 ]torch.nn.Module.float ### [torch.nn.Module.float](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.float) @@ -12,7 +12,7 @@ torch.nn.Module.float() paddle.nn.Layer.to(dtype="float32") ``` -两者参数用法不一致，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.half.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.half.md index be32473cf25..9c41bf5850a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.half.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.half.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.half +## [ 仅 paddle 参数更多 ]torch.nn.Module.half ### [torch.nn.Module.half](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.half) @@ -12,7 +12,7 @@ torch.nn.Module.half() paddle.nn.Layer.to(dtype="float16") ``` -两者参数用法不一致，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.parameters.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.parameters.md index 9bffda490b6..0b0c741a311 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.parameters.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.parameters.md @@ -12,7 +12,7 @@ paddle.nn.Layer.parameters(include_sublayers=True) ``` 两者功能一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | recurse | include_sublayers | 是否返回子层的参数，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_buffer.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_buffer.md index fd2f9ef6fe4..bc38fa93eeb 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_buffer.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_buffer.md @@ -12,7 +12,7 @@ paddle.nn.Layer.register_buffer(name, tensor, persistable=True) ``` 两者功能一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | name | name | 注册 buffer 的名字。可以通过此名字来访问已注册的 buffer。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_hook.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_hook.md index ee46d514a3b..da2ca03d98d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_hook.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_hook.md @@ -12,7 +12,7 @@ paddle.nn.Layer.register_forward_post_hook(hook) ``` PyTorch 参数更多，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | hook | hook | 被注册的 hook。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_pre_hook.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_pre_hook.md index a46b0efa57f..ab88e1d1aad 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_pre_hook.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_pre_hook.md @@ -12,7 +12,7 @@ paddle.nn.Layer.register_forward_pre_hook(hook) ``` PyTorch 参数更多，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | hook | hook | 被注册的 hook。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_module.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_module.md index c2ac4b9f0bc..b52be1a2294 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_module.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_module.md @@ -12,7 +12,7 @@ paddle.nn.Layer.add_sublayer(name, sublayer) ``` 两者功能一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | name | name | 注册 buffer 的名字。可以通过此名字来访问已注册的 buffer。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.state_dict.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.state_dict.md index 932e6370b83..6b69384f70a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.state_dict.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.state_dict.md @@ -1,24 +1,24 @@ -## [ torch 参数更多 ] torch.nn.Module.state_dict +## [ 仅 paddle 参数更多 ] torch.nn.Module.state_dict ### [torch.nn.Module.state_dict](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.state_dict) ```python -torch.nn.Module.state_dict(*, destination, prefix, keep_vars) +torch.nn.Module.state_dict(*, destination, prefix='', keep_vars=False) ``` ### [paddle.nn.Layer.state_dict](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/Layer_cn.html#state-dict-destination-none-include-sublayers-true-use-hook-true) ```python -paddle.nn.Layer.state_dict(destination=None, include_sublayers=True, use_hook=True) +paddle.nn.Layer.state_dict(destination=None, include_sublayers=True, structured_name_prefix='', use_hook=True, keep_vars=True) ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： +Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | destination | destination | 所有参数和可持久性 buffers 都将存放在 destination 中 。 | -| prefix | - | 添加到参数和缓冲区名称的前缀，Paddle 无此参数，暂无转写方式。 | -| keep_vars | - | 状态字典中返回的 Tensor 是否与 autograd 分离，Paddle 无此参数，暂无转写方式 | +| prefix | structured_name_prefix | 添加到参数和缓冲区名称的前缀。 | +| keep_vars | keep_vars | 状态字典中返回的 Tensor 是否与 autograd 分离，PyTorch 默认值为 False，Paddle 为 True，Paddle 需设置为与 PyTorch 一致。 | | - | include_sublayers | 包括子层的参数和 buffers, PyTorch 无此参数，Paddle 保持默认即可。 | | - | use_hook | 将_state_dict_hooks 中注册的函数应用于 destination, PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.to.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.to.md index 37b7ce3ada4..df930cfdd8b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.to.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.to.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.to +## [ 输入参数用法不一致 ]torch.nn.Module.to ### [torch.nn.Module.to](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.type.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.type.md index b235335a6e7..5920da3abdd 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.type.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.type.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.type +## [ 输入参数类型不一致 ]torch.nn.Module.type ### [torch.nn.Module.type](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.type) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.xpu.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.xpu.md index e241768da1d..1de3f2f55fd 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.xpu.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.xpu.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.nn.Module.xpu +## [ 输入参数用法不一致 ]torch.nn.Module.xpu ### [torch.nn.Module.xpu](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.xpu) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.zero_grad.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.zero_grad.md index 73c15410c27..c8bab45c525 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.zero_grad.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.zero_grad.md @@ -1,4 +1,4 @@ -## [torch 参数更多]torch.nn.Module.zero_grad +## [ 输入参数用法不一致 ]torch.nn.Module.zero_grad ### [torch.nn.Module.zero_grad](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.zero_grad) @@ -9,13 +9,23 @@ torch.nn.Module.zero_grad(set_to_none=True) ### [paddle.nn.Layer.clear_gradients](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/Layer_cn.html#clear-gradients) ```python -paddle.nn.Layer.clear_gradients() +paddle.nn.Layer.clear_gradients(set_to_zero=True) ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： +PyTorch 的 `Module.zero_grad` 参数与 Paddle 的 `Layer.clear_gradients` 参数用法刚好相反，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ----------- | ------------ | ------------------------------------------------ | -| set_to_none | - | 是否设置为 None，Paddle 无此参数，暂无转写方式。 | +| set_to_none | set_to_zero | 设置如何清空梯度，PyTorch 默认 set_to_none 为 True，Paddle 默认 set_to_zero 为 True，两者功能刚好相反，Paddle 需设置为 False。 | + +### 转写示例 + +```python +# PyTorch 写法 +torch.nn.Module.zero_grad(set_to_none=True) + +# Paddle 写法 +paddle.nn.Layer.clear_gradients(set_to_zero=False) +``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.NLLLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.NLLLoss.md index a47d772c848..2d7d6a8278a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.NLLLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.NLLLoss.md @@ -23,7 +23,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 -| PyTorch | Paddle | 备注 | +| PyTorch | PaddlePaddle | 备注 | | ------------ | ------------ | -------------------------------------------- | | weight | weight | 表示每个类别的权重。 | | size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.PReLU.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.PReLU.md index 9ac477b425f..a3378c3cf26 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.PReLU.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.PReLU.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.PReLU +## [ torch 参数更多 ]torch.nn.PReLU ### [torch.nn.PReLU](https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html?highlight=prelu#torch.nn.PReLU) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ParameterList.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ParameterList.md index 175f86f4634..63696e098cd 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ParameterList.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ParameterList.md @@ -12,7 +12,7 @@ paddle.nn.ParameterList(parameters=None) ``` 两者功能一致且参数用法一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | values | parameters | 可迭代的 Parameters，参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNN.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNN.md index a2e96f47c23..3e49cdb8af0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNN.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNN.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.RNN +## [ 输入参数用法不一致 ]torch.nn.RNN ### [torch.nn.RNN](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html#torch.nn.RNN) ```python torch.nn.RNN(input_size, diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNBase.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNBase.md index 51687f3e2f0..7b59964bec0 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNBase.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNBase.md @@ -12,7 +12,7 @@ torch.nn.RNNBase(mode: str, input_size: int, hidden_size: int, paddle.nn.layer.rnn.RNNBase(mode, input_size, hidden_size, num_layers=1, direction="forward", time_major=False, dropout=0.0, weight_ih_attr=None, weight_hh_attr=None, - bias_ih_attr=None, bias_hh_attr=None) + bias_ih_attr=None, bias_hh_attr=None, proj_size=0) ``` 两者功能一致但参数不一致，部分参数名不同，具体如下： @@ -20,7 +20,7 @@ paddle.nn.layer.rnn.RNNBase(mode, input_size, hidden_size, | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | -| mode | mode | 表示 `RNN` 模型的类型,torch 取值为 `'LSTM', 'GRU', 'RNN_TANH', 'RNN_RELU`，paddle 取值为 `'LSTM', 'GRU'`，需要转写。| +| mode | mode | 表示 `RNN` 模型的类型, 取值为 `'LSTM', 'GRU', 'RNN_TANH', 'RNN_RELU`，参数完全一致。| | input_size | input_size | 表示输入 x 的大小。 | | hidden_size | hidden_size | 表示隐藏状态 h 大小。 | | num_layers | num_layers | 表示循环网络的层数。 | @@ -28,36 +28,12 @@ paddle.nn.layer.rnn.RNNBase(mode, input_size, hidden_size, | batch_first | time_major | PyTorch 表示 batch size 是否为第一维，PaddlePaddle 表示 time steps 是否为第一维，它们的意义相反。需要转写。 | | dropout | dropout | 表示 dropout 概率。 | | bidirectional | direction | PyTorch 表示是否进行双向 RNN，Paddle 使用字符串表示是双向 RNN（`bidirectional`）还是单向 RNN（`forward`）。 | -| proj_size | - | PyTorch 表示投影层大小，默认为 0，表示不使用投影。Paddle 无此参数，一般对网络训练结果影响不大，现阶段直接删除。 | +| proj_size | proj_size | 表示投影层大小，默认为 0，表示不使用投影。| | - |weight_ih_attr| weight_ih 的参数， PyTorch 无此参数，Paddle 保持默认即可。 | | - |weight_hh_attr| weight_hh 的参数， PyTorch 无此参数，Paddle 保持默认即可。 | ### 转写示例 -#### mode：RNN 模型类别 -```python -# PyTorch 写法 -torch.nn.RNNBase('LSTM', 16, 32) - -# Paddle 写法 -paddle.nn.layer.rnn.RNNBase('LSTM', 16, 32) -``` - -```python -# PyTorch 写法 -torch.nn.RNNBase('GRU', 16, 32) - -# Paddle 写法 -paddle.nn.layer.rnn.RNNBase('GRU', 16, 32) -``` - -```python -# PyTorch 写法 -torch.nn.RNNBase('RNN_TANH', 16, 32) - -# Paddle 写法 -paddle.nn.layer.rnn.RNNBase('SimpleRNN', 16, 32) -``` #### bias：是否使用偏置 ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNCell.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNCell.md index 3c6534a4ec7..981a0f765fc 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNCell.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNCell.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.RNNCell +## [ 输入参数用法不一致 ]torch.nn.RNNCell ### [torch.nn.RNNCell](https://pytorch.org/docs/stable/generated/torch.nn.RNNCell.html#torch.nn.RNNCell) ```python torch.nn.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', device=None, dtype=None) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad1d.md index a016d7bbd8d..7e136850477 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ReflectionPad1d +## [ 输入参数类型不一致 ]torch.nn.ReflectionPad1d ### [torch.nn.ReflectionPad1d](https://pytorch.org/docs/stable/generated/torch.nn.ReflectionPad1d.html?highlight=pad#torch.nn.ReflectionPad1d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad2d.md index 0fbd97e31f8..94260bb684d 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ReflectionPad2d +## [ 输入参数类型不一致 ]torch.nn.ReflectionPad2d ### [torch.nn.ReflectionPad2d](https://pytorch.org/docs/stable/generated/torch.nn.ReflectionPad2d.html?highlight=pad#torch.nn.ReflectionPad2d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad3d.md index f6e50177ebd..6988f4dae3e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReflectionPad3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ReflectionPad3d +## [ 输入参数类型不一致 ]torch.nn.ReflectionPad3d ### [torch.nn.ReflectionPad3d](https://pytorch.org/docs/stable/generated/torch.nn.ReflectionPad3d.html?highlight=pad#torch.nn.ReflectionPad3d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad1d.md index b880d53d177..cd0707314c9 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ReplicationPad1d +## [ 输入参数类型不一致 ]torch.nn.ReplicationPad1d ### [torch.nn.ReplicationPad1d](https://pytorch.org/docs/stable/generated/torch.nn.ReplicationPad1d.html?highlight=pad#torch.nn.ReplicationPad1d) ```python @@ -16,7 +16,7 @@ paddle.nn.Pad1D(padding, ``` 其中 Paddle 与 PyTorch 的 padding 所支持的参数类型不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | padding | padding | 填充大小，PyTorch 和 Paddle 的 padding 参数的类型分别为 (int/tuple) 和 (int/Tensor/list)。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad2d.md index 92d7febf3ba..e329254466f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ReplicationPad2d +## [ 输入参数类型不一致 ]torch.nn.ReplicationPad2d ### [torch.nn.ReplicationPad2d](https://pytorch.org/docs/stable/generated/torch.nn.ReplicationPad2d.html?highlight=pad#torch.nn.ReplicationPad2d) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad3d.md index d5e5eed0bbc..6eb7421c8b2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReplicationPad3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.ReplicationPad3d +## [ 输入参数类型不一致 ]torch.nn.ReplicationPad3d ### [torch.nn.ReplicationPad3d](https://pytorch.org/docs/stable/generated/torch.nn.ReplicationPad3d.html?highlight=pad#torch.nn.ReplicationPad3d) ```python @@ -16,7 +16,7 @@ paddle.nn.Pad3D(padding, ``` 其中 Paddle 与 PyTorch 的 padding 所支持的参数类型不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | padding | padding | 填充大小，PyTorch 和 Paddle 的 padding 参数的类型分别为 (int/tuple) 和 (int/Tensor/list) ，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Sequential.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Sequential.md index e85dc5d5d72..6a2fd8b1fee 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Sequential.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Sequential.md @@ -1,4 +1,4 @@ -## [参数用法不一致]torch.nn.Sequential +## [ 输入参数用法不一致 ]torch.nn.Sequential ### [torch.nn.Sequential](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SoftMarginLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SoftMarginLoss.md index e75aefee643..b98c9b29afa 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SoftMarginLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SoftMarginLoss.md @@ -19,11 +19,11 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 -| PyTorch | Paddle | 备注 | -| ------------ | --------- | -------------------------------------------- | -| size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | -| reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | -| reduction | reduction | 表示应用于输出结果的计算方式。 | +| PyTorch | PaddlePaddle | 备注 | +| ------------ | ------------ | -------------------------------------------- | +| size_average | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | +| reduce | - | PyTorch 已弃用， Paddle 无此参数，需要转写。 | +| reduction | reduction | 表示应用于输出结果的计算方式。 | ### 转写示例 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softplus.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softplus.md index 487d63b3567..217e96558cb 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softplus.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softplus.md @@ -12,4 +12,11 @@ torch.nn.Softplus(beta=1, threshold=20) paddle.nn.Softplus(beta=1, threshold=20, name=None) ``` -两者功能一致，参数完全一致。 +两者功能一致，参数完全一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------ | +| beta | beta | Softplus 公式的 beta 值。 | +| threshold | threshold | 恢复线性公式的阈值。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SyncBatchNorm.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SyncBatchNorm.md index ea6db0200ca..9b6252e045b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SyncBatchNorm.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SyncBatchNorm.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.SyncBatchNorm +## [ 输入参数用法不一致 ]torch.nn.SyncBatchNorm ### [torch.nn.SyncBatchNorm](https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html#torch.nn.SyncBatchNorm) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TripletMarginWithDistanceLoss.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TripletMarginWithDistanceLoss.md index 8ba47014935..500386dd973 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TripletMarginWithDistanceLoss.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TripletMarginWithDistanceLoss.md @@ -12,4 +12,13 @@ torch.nn.TripletMarginWithDistanceLoss(*, distance_function=None, margin=1.0, sw paddle.nn.TripletMarginWithDistanceLoss(distance_function=None, margin: float = 1.0, swap: bool = False, reduction: str = 'mean', name: str = None) ``` -两者功能一致，参数完全一致。 +两者功能一致，参数完全一致，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| ----------------- | ------------ | ---------------------------------------------- | +| distance_function | distance_function | 手动指定范数，参数完全一致。 | +| margin | margin | 手动指定间距，参数完全一致。 | +| swap | swap | 默认为 False。 | +| reduction | reduction | 指定应用于输出结果的计算方式，参数完全一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Unfold.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Unfold.md index 5fbd073cf23..77713a9fe4f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Unfold.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Unfold.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.Unfold +## [ 仅参数名不一致 ]torch.nn.Unfold ### [torch.nn.Unfold](https://pytorch.org/docs/stable/generated/torch.nn.Unfold.html?highlight=nn+unfold#torch.nn.Unfold) ```python @@ -17,15 +17,17 @@ paddle.nn.Unfold(kernel_size=[3, 3], dilation=1, name=None) ``` -其中 Paddle 与 PyTorch 前四个参数所支持的参数类型不一致，具体如下： + +其中功能一致, 仅参数名不一致，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | -| kernel_size | kernel_sizes | 卷积核大小， PyTorch 参数类型为 int、tuple(int) 或者 list(int)， Paddle 参数类型为 int 或者 list(int)。 | -| dilation | dilations | 卷积膨胀，PyTorch 参数类型为 int、tuple(int) 或者 list(int)， Paddle 参数类型为 int 或者 list(int)。 | -| padding | paddings | 每个维度的扩展，PyTorch 参数类型为 int、tuple(int) 或者 list(int)， Paddle 参数类型为 int 或者 list(int)。 | -| stride | strides | 步长大小，PyTorch 参数类型为 int、tuple(int) 或者 list(int)， Paddle 参数类型为 int 或者 list(int)。| +| kernel_size | kernel_sizes | 卷积核的尺寸。 | +| dilation | dilations | 卷积膨胀。 | +| padding | paddings | 每个维度的扩展，仅参数名不一致。 | +| stride | strides | 卷积步长，仅参数名不一致。 | ### 转写示例 ``` python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.UpsamplingBilinear2D.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.UpsamplingBilinear2d.md similarity index 100% rename from docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.UpsamplingBilinear2D.md rename to docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.UpsamplingBilinear2d.md diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.functional.softmax.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.functional.softmax.md deleted file mode 100644 index 5411ae94848..00000000000 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.functional.softmax.md +++ /dev/null @@ -1,24 +0,0 @@ -## [ torch 参数更多 ]torch.nn.functional.softmax - -### [torch.nn.functional.softmax](https://pytorch.org/docs/stable/generated/torch.nn.functional.softmax.html#torch.nn.functional.softmax) - -```python -torch.nn.functional.softmax(input, dim=None, _stacklevel=3, dtype=None) -``` - -### [paddle.nn.functional.softmax](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nn/functional/softmax_cn.html#softmax) - -```python -paddle.nn.functional.softmax(x, axis=-1, dtype=None, name=None) -``` - -PyTorch 相比 Paddle 支持更多其他参数，具体如下： - -### 参数映射 - -| PyTorch | PaddlePaddle | 备注 | -|:-------:|:------------:| :---------------------------------------------------: | -| input | x | 表示输入张量，仅参数名不一致。 | -| dim | axis | 表示对输入 Tensor 进行运算的轴，仅参数名不一致。 | -| dtype | dtype | 表示返回张量所需的数据类型。 | -| _stacklevel | - | Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.Tensor.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.Tensor__upper.md similarity index 100% rename from docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.Tensor.md rename to docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.Tensor__upper.md diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.acos.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.acos.md index 32e723a4e0d..8f8b2e2ac2a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.acos.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.acos.md @@ -20,7 +20,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| ------- | :----------- | -------------------------------------------------------- | +| ------- | ------------ | -------------------------------------------------------- | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.all.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.all.md index 2a6eec47a01..db53132ab92 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.all.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.all.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.all +## [ 仅参数名不一致 ]torch.all ### [torch.all](https://pytorch.org/docs/stable/generated/torch.all.html?highlight=all#torch.all) @@ -15,21 +15,12 @@ paddle.all(x, name=None) ``` -其中 Paddle 与 PyTorch 的 `input` 参数所支持的类型不一致，具体如下： +其中 PyTorch 和 Paddle 功能一致，仅参数名不一致，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | -| input | x | 输入的多维 Tensor ，PyTorch 支持布尔和数值类型的输入，Paddle 仅支持布尔类型，需要转写。 | +| input | x | 输入的多维 Tensor ，参数完全一致。 | | dim | axis | 表示运算的维度，仅参数名不一致。 | | keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度，参数完全一致。 | - -### 转写示例 -```python -# PyTorch 写法 -y = torch.all(x) - -# Paddle 写法 -y = paddle.all(x.astype('bool')) -``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.any.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.any.md index dae4cdad475..212ca1a5f0a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.any.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.any.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.any +## [ 仅参数名不一致 ]torch.any ### [torch.any](https://pytorch.org/docs/stable/generated/torch.any.html?highlight=any#torch.any) @@ -15,21 +15,12 @@ paddle.any(x, name=None) ``` -其中 Paddle 与 PyTorch 的 `input` 参数所支持的类型不一致，具体如下： +其中 PyTorch 和 Paddle 功能一致，仅参数名不一致，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | -| input | x | 输入的多维 Tensor ，PyTorch 支持布尔和数值类型的输入，Paddle 仅支持布尔类型，需要转写。 | +| input | x | 输入的多维 Tensor ，仅参数名不一致。 | | dim | axis | 表示运算的维度，仅参数名不一致。 | | keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度，参数完全一致。 | - -### 转写示例 -```python -# PyTorch 写法 -y = torch.any(x) - -# Paddle 写法 -y = paddle.any(x.astype('bool')) -``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arange.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arange.md index 752ca799665..b31c802dbec 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arange.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arange.md @@ -29,7 +29,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | start | start | 表示区间起点（且区间包括此值）。 | | end | end | 表示区间终点（且通常区间不包括此值）。 | | step | step | 表示均匀分割的步长。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.argsort.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.argsort.md index 0936465c2ad..c04f2caf9b7 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.argsort.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.argsort.md @@ -1,17 +1,17 @@ -## [ torch 参数更多 ]torch.argsort +## [ 仅参数名不一致 ]torch.argsort ### [torch.argsort](https://pytorch.org/docs/stable/generated/torch.argsort.html#torch.argsort) ```python -torch.argsort(input, dim=- 1, descending=False, stable=False) +torch.argsort(input, dim=-1, descending=False, stable=False) ``` ### [paddle.argsort](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/argsort_cn.html#argsort) ```python -paddle.argsort(x, axis=- 1, descending=False, name=None) +paddle.argsort(x, axis=-1, descending=False, stable=False, name=None) ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： +两者功能一致且参数用法一致，仅参数名不一致，具体如下： ### 参数映射 @@ -20,4 +20,4 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： | input | x | 输入的多维 Tensor ，仅参数名不一致。 | | dim | axis | 指定进行运算的轴，仅参数名不一致。 | | descending | descending | 是否使用降序排列。 | -| stable | - | 是否使用稳定排序。Paddle 无此参数，暂无转写方式。 | +| stable | stable | 是否使用稳定排序。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_1d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_1d.md index 942d83985c7..bcbdfbd55a1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_1d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_1d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.atleast_1d +## [ 输入参数用法不一致 ]torch.atleast_1d ### [torch.atleast_1d](https://pytorch.org/docs/stable/generated/torch.atleast_1d.html#torch-atleast-1d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_2d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_2d.md index 16699cf896a..73a3cd02c0b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_2d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_2d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.atleast_2d +## [ 输入参数用法不一致 ]torch.atleast_2d ### [torch.atleast_2d](https://pytorch.org/docs/stable/generated/torch.atleast_2d.html#torch-atleast-2d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_3d.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_3d.md index 537dfddb743..09ec5953aac 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_3d.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atleast_3d.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.atleast_3d +## [ 输入参数用法不一致 ]torch.atleast_3d ### [torch.atleast_3d](https://pytorch.org/docs/stable/generated/torch.atleast_3d.html#torch-atleast-3d) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_shapes.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_shapes.md index a0d1bb249a3..4027e9c337f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_shapes.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_shapes.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.broadcast_shapes +## [ 输入参数用法不一致 ]torch.broadcast_shapes ### [torch.broadcast_shapes](https://pytorch.org/docs/stable/generated/torch.broadcast_shapes.html#torch.broadcast_shapes) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_tensors.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_tensors.md index 1ba96d447df..04d18ce398a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_tensors.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_tensors.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.broadcast_tensors +## [ 输入参数类型不一致 ]torch.broadcast_tensors ### [torch.broadcast_tensors](https://pytorch.org/docs/stable/generated/torch.broadcast_tensors.html?highlight=broadcast_tensors#torch.broadcast_tensors) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.clip.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.clip.md index 993ab3ed2e2..0878a03e36b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.clip.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.clip.md @@ -19,11 +19,12 @@ paddle.clip(x, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | -| input| | x | 表示输入的 Tensor，仅参数名不一致。 | +| input | x | 表示输入的 Tensor，仅参数名不一致。 | | min | min | 表示裁剪的最小值。 | | max | max | 表示裁剪的最大值。 | | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diff.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diff.md index 7fdbcfdbadf..ab9b1a64a74 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diff.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diff.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.diff +## [ 仅参数名不一致 ]torch.diff ### [torch.diff](https://pytorch.org/docs/stable/generated/torch.diff.html?highlight=diff#torch.diff) ```python @@ -20,14 +20,14 @@ paddle.diff(x, name=None) ``` -两者功能一致，但参数 `n` 的支持范围不同，具体如下： +其中 PyTorch 和 Paddle 功能一致，仅参数名不一致，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | -| n | n | 表示需要计算前向差值的次数。torch 支持 n 为任意值，paddle 仅支持 n=1，暂无转写方式。 | +| n | n | 表示需要计算前向差值的次数。 | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | | prepend | prepend | 表示在计算前向差值之前，沿着指定维度 axis 附加到输入 x 的前面。 | | append | append | 表示在计算前向差值之前，沿着指定维度 axis 附加到输入 x 的后面。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty.md index d433736d619..c169482b70a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty.md @@ -27,7 +27,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | *size | shape | 表示输出形状大小， PyTorch 是可变参数用法， Paddle 是列表或元组，需要转写。 | | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | | dtype | dtype | 表示输出 Tensor 类型。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty_like.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty_like.md index 7c94a080578..37a0c1285d2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty_like.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty_like.md @@ -25,7 +25,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | | dtype | dtype | 表示输出 Tensor 类型。 | | layout | - | 表示布局方式，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.eye.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.eye.md index b5f151669af..8941c2b9880 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.eye.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.eye.md @@ -27,7 +27,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | n | num_rows | 表示生成 2-D Tensor 的行数，仅参数名不一致。 | | m | num_columns | 表示生成 2-D Tensor 的列数， 仅参数名不一致。 | | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.feature_alpha_dropout.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.feature_alpha_dropout.md new file mode 100644 index 00000000000..c162e63a5b1 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.feature_alpha_dropout.md @@ -0,0 +1,23 @@ +## [ 仅参数名不一致 ]torch.feature_alpha_dropout + +### [torch.feature\_alpha\_dropout](https://pytorch.org/docs/stable/jit_builtin_functions.html) + +```python +torch.feature_alpha_dropout(input, p, training) +``` + +### [paddle.nn.functional.feature\_alpha\_dropout](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/functional/feature_alpha_dropout_cn.html#feature_alpha-dropout) + +```python +paddle.nn.functional.feature_alpha_dropout(x, p=0.5, training=True, name=None) +``` + +其中 PyTorch 相比 Paddle 支持更多其他参数，具体如下： + +### 参数映射 + +| PyTorch | PaddlePaddle | 备注 | +| -------- | ------------ | -- | +| input | x | 输入的多维 Tensor，仅参数名不一致。 | +| p | p | 将输入节点置 0 的概率。 | +| training | training | 标记是否为训练阶段。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flipud.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flipud.md index 79c8730303c..c5661414817 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flipud.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flipud.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.flipud +## [ 仅 paddle 参数更多 ]torch.flipud ### [torch.flipud](https://pytorch.org/docs/stable/generated/torch.flipud.html?highlight=flipud#torch.flipud) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.floor_divide.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.floor_divide.md index 428a22a0df7..a453efdeb5b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.floor_divide.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.floor_divide.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.floor_divide +## [ 输入参数类型不一致 ]torch.floor_divide ### [torch.floor_divide](https://pytorch.org/docs/stable/generated/torch.floor_divide.html?highlight=torch+floor_divide#torch.floor_divide) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.frac.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.frac.md index db643675d3d..71551e5e46c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.frac.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.frac.md @@ -15,7 +15,7 @@ paddle.frac(x, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.full.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.full.md index 503879ff8fe..a6943235952 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.full.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.full.md @@ -27,7 +27,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | size | shape | 表示创建 Tensor 的形状，仅参数名不一致。 | | fill_value | fill_value | 表示初始化输出 Tensor 的常量数据的值 | | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.get_rng_state.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.get_rng_state.md index e0e150db6a1..7a18a224902 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.get_rng_state.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.get_rng_state.md @@ -1,4 +1,4 @@ -## [参数不一致] torch.get_rng_state +## [ 返回参数类型不一致] torch.get_rng_state ### [torch.get_rng_state](https://pytorch.org/docs/stable/generated/torch.get_rng_state.html#torch.get_rng_state) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histc.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histc.md index d691fba5334..1dd7ad56c9e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histc.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histc.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.histc +## [ 返回参数类型不一致 ]torch.histc ### [torch.histc](https://pytorch.org/docs/stable/generated/torch.histc.html#torch-histc) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histogram.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histogram.md index 6fd84918442..117d4eadadf 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histogram.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.histogram.md @@ -1,4 +1,4 @@ -## [参数不一致]torch.histogram +## [ 返回参数类型不一致]torch.histogram ### [torch.histogram](https://pytorch.org/docs/stable/generated/torch.histogram.html#torch.histogram) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.le.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.le.md index 082626e04bf..30a242e92fe 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.le.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.le.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.le +## [ 输入参数类型不一致 ]torch.le ### [torch.le](https://pytorch.org/docs/stable/generated/torch.le.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lerp.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lerp.md index 1075ccbfc7e..6e99601a57b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lerp.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lerp.md @@ -19,7 +19,7 @@ paddle.lerp(x, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.less_equal.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.less_equal.md index c85700cb285..fef135ead73 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.less_equal.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.less_equal.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.less_equal +## [ 输入参数类型不一致 ]torch.less_equal ### [torch.less_equal](https://pytorch.org/docs/stable/generated/torch.less_equal.html#torch.less_equal) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lgamma.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lgamma.md index aea058f51de..6d9a7f0117e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lgamma.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.lgamma.md @@ -15,7 +15,7 @@ paddle.lgamma(x ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.linspace.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.linspace.md index 6cc9fc109cb..60e1d8aa822 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.linspace.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.linspace.md @@ -29,7 +29,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | start | start | 表示区间开始的变量。 | | end | stop | 表示区间结束的变量，仅参数名不一致。 | | steps | num | 表示给定区间内需要划分的区间数，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log10.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log10.md index b6634e9f0d0..438254d671e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log10.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log10.md @@ -15,7 +15,7 @@ paddle.log10(x, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.logspace.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.logspace.md index 2d29bd4417b..31aa48b252a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.logspace.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.logspace.md @@ -31,7 +31,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | start | start | 表示区间开始值以 base 为底的指数。 | | end | stop | 表示区间结束值以 base 为底的指数，仅参数名不一致。 | | steps | num | 表示给定区间内需要划分的区间数，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.max.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.max.md index aba99bdbabc..8624d87b5f9 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.max.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.max.md @@ -1,7 +1,8 @@ -## [ 参数不一致 ]torch.max +## [ 返回参数类型不一致 ]torch.max 输入一个 Tensor 对应 paddle.max，输入两个 Tensor 对应 paddle.maximum，因此有两组差异分析，分别如下： --------------------------------------------------------------------------------------------------- +------------------------------------------------------------------------------------------------- + ### [torch.max](https://pytorch.org/docs/stable/generated/torch.max.html?highlight=max#torch.max) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.median.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.median.md index 728bf4e88fc..ccbf206ca36 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.median.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.median.md @@ -1,9 +1,9 @@ -## [ 参数不一致 ]torch.median +## [ torch 参数更多 ]torch.median ### [torch.median](https://pytorch.org/docs/stable/generated/torch.median.html?highlight=median#torch.median) ```python torch.median(input, - dim=- 1, + dim=-1, keepdim=False, *, out=None) @@ -12,29 +12,29 @@ torch.median(input, ### [paddle.median](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/median_cn.html#median) ```python -paddle.median(x, - axis=None, - keepdim=False, - name=None) +paddle.median(x, axis=None, keepdim=False, mode='avg', name=None) ``` -其中 PyTorch 和 Paddle 在指定 `dim` 后返回 Tensor 的个数不一致，具体如下： -### 参数差异 +PyTorch 相比 Paddle 支持更多其他参数，具体如下： + +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | | keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度。 | +| - | mode | 当 x 在所需要计算的轴上有偶数个非 NaN 元素时，选择使用平均值或最小值确定非 NaN 中位数的值， PyTorch 无此参数，Paddle 需设置为 'min'。 | | out | - | 表示输出的 Tensor ， Paddle 无此参数，需要转写。 | -| 返回值 | 返回值 | 不指定 dim 时，两者返回值一致，均只返回中位数；指定 dim 时，两者返回 Tensor 的个数不同，PyTorch 会返回中位数和元素索引（两个 Tensor），Paddle 只返回中位数，暂无转写方式。 | ### 转写示例 + #### out：指定输出 + ```python # PyTorch 写法 torch.median([3, 5], out=y) # Paddle 写法 -paddle.assign(paddle.median([3, 5]), y) +paddle.assign(paddle.median([3, 5], mode='min'), y) ``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.meshgrid.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.meshgrid.md index 3ce99046925..3d91848ea79 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.meshgrid.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.meshgrid.md @@ -12,7 +12,7 @@ paddle.meshgrid(*args, **kargs) ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | tensors | args | 输入的 Tensor 列表，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.min.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.min.md index 76d0b0cf9aa..c58fac16139 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.min.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.min.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.min +## [ 返回参数类型不一致 ]torch.min 输入一个 Tensor 对应 paddle.min，输入两个 Tensor 对应 paddle.minimum，因此有两组差异分析，分别如下： -------------------------------------------------------------------------------------------------- diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.movedim.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.movedim.md index 3e8e34a8478..2fec1dc85f2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.movedim.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.movedim.md @@ -17,7 +17,7 @@ paddle.moveaxis(x, ``` 两者功能一致且参数用法一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.multiply.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.multiply.md index 6b4062b05d9..5058520adf6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.multiply.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.multiply.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.multiply +## [ 输入参数类型不一致 ]torch.multiply ### [torch.multiply](https://pytorch.org/docs/stable/generated/torch.multiply.html?highlight=torch+multiply#torch.multiply) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanmedian.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanmedian.md index c20e3104e90..5ddeccc6085 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanmedian.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanmedian.md @@ -12,28 +12,28 @@ torch.nanmedian(input, ### [paddle.nanmedian](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nanmedian_cn.html#nanmedian) ```python -paddle.nanmedian(x, - axis=None, - keepdim=True, - name=None) +paddle.nanmedian(x, axis=None, keepdim=False, mode='avg', name=None) ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 + +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | | dim | axis | 表示进行运算的轴，仅参数名不一致。 | -| keepdim | keepdim | 表示是否在输出 Tensor 中保留减小的维度。 | +| keepdim | keepdim | 表示是否在输出 Tensor 中保留减小的维度。 | +| - | mode | 当 x 在所需要计算的轴上有偶数个非 NaN 元素时，选择使用平均值或最小值确定非 NaN 中位数的值， PyTorch 无此参数，Paddle 需设置为 'min'。 | | out | - | 表示输出的 Tensor ， Paddle 无此参数，需要转写。 | ### 转写示例 + #### out：指定输出 ```python # PyTorch 写法 torch.nanmedian(a, -1, out=y) # Paddle 写法 -paddle.assign(paddle.nanmedian(a, -1), y) +paddle.assign(paddle.nanmedian(a, -1, mode='min'), y) ``` diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanquantile.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanquantile.md index db718fdabae..12b56a8d4cc 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanquantile.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nanquantile.md @@ -12,27 +12,28 @@ torch.nanquantile(input, out=None) ``` -### [paddle.nanquantile]() +### [paddle.nanquantile](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/nanquantile_cn.html#nanquantile) ```python paddle.nanquantile(x, - q, - axis=None, - keepdim=False, - interpolation='linear', - name=None) + q, + axis=None, + keepdim=False, + interpolation='linear', + name=None) ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor，仅参数名不一致。 | | q | q | 待计算的分位数。 | -| dim | axis| 指定对 x 进行计算的轴，仅参数名不一致。 | -|keepdim|keepdim| 是否在输出 Tensor 中保留减小的维度。| -|interpolation| interpolation | 当所需分位数位于两个数据点之间时使用的插值方法| +| dim | axis | 指定对 x 进行计算的轴，仅参数名不一致。 | +| keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度。| +| interpolation | interpolation | 当所需分位数位于两个数据点之间时使用的插值方法。 | | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.narrow.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.narrow.md index 19c27e8b933..546a1f897cd 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.narrow.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.narrow.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.narrow +## [ 输入参数用法不一致 ]torch.narrow ### [torch.narrow](https://pytorch.org/docs/stable/generated/torch.narrow.html?highlight=narrow#torch.narrow) ```python torch.narrow(input, diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nn.modules.batchnorm._BatchNorm.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nn.modules.batchnorm._BatchNorm.md index d7c1b8b1a47..9abaf5a10c3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nn.modules.batchnorm._BatchNorm.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.nn.modules.batchnorm._BatchNorm.md @@ -21,7 +21,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： | num_features | num_features | 特征数量。 | | eps | epsilon | 一个极小正数，仅参数名不一致。 | | momentum | momentum | 动量因子，参数默认值不一致。 | -| affine | - | 是否进行仿射变换，Paddle 无此参数，需要转写。 +| affine | - | 是否进行仿射变换，Paddle 无此参数，需要转写。 | | track_running_stats | - | 是否跟踪运行时的 mean 和 var， Paddle 无此参数。暂无转写方式。 | | device | - | 表示 Tensor 存放设备位置，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | | dtype | - | 参数类型，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.numel.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.numel.md index d1a3810e2c1..48870a16b1e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.numel.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.numel.md @@ -1,18 +1,17 @@ -## [torch 参数更多 ]torch.numel +## [ 无参数 ]torch.numel ### [torch.numel](https://pytorch.org/docs/stable/generated/torch.numel.html?highlight=numel#torch.numel) ```python torch.numel(input) ``` -### [paddle.Tensor.size](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/Tensor_cn.html#size) +### [paddle.Tensor.size](https://www.paddlepaddle.org.cn/documentation/docs/guides/beginner/tensor_cn.html#tensor-shape) ```python paddle.Tensor.size ``` -Paddle 使用类方法来实现，因此忽略第一个参数，无其他参数。 - +两者功能一致，均无参数。 ### 转写示例 ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones.md index 6ead163c84c..f6422378beb 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.ones +## [ 输入参数用法不一致 ]torch.ones ### [torch.ones](https://pytorch.org/docs/stable/generated/torch.ones.html?highlight=ones#torch.ones) ```python @@ -12,7 +12,6 @@ torch.ones(*size, ``` ### [paddle.ones](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/ones_cn.html#ones) - ```python paddle.ones(shape, dtype=None, @@ -20,7 +19,7 @@ paddle.ones(shape, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | *size | shape | 表示输出形状大小，PyTorch 以可变参数方式传入，Paddle 以 list 或 tuple 的方式传入。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones_like.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones_like.md index 42790793738..736aaa80812 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones_like.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ones_like.md @@ -25,7 +25,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | input | x | 表示输入 Tensor ，仅名称不同。 | | dtype | dtype | 表示输出 Tensor 类型。 | | layout | - | 表示布局方式，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.quantile.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.quantile.md index 9e00dfce92a..c719f1a03fa 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.quantile.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.quantile.md @@ -24,15 +24,16 @@ paddle.quantile(x, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： + ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入的 Tensor，仅参数名不一致。 | | q | q | 待计算的分位数。 | -| dim | axis| 指定对 x 进行计算的轴，仅参数名不一致。 | -|keepdim|keepdim| 是否在输出 Tensor 中保留减小的维度。| -|interpolation| interpolation | 当所需分位数位于两个数据点之间时使用的插值方。| +| dim | axis | 指定对 x 进行计算的轴，仅参数名不一致。 | +| keepdim | keepdim | 是否在输出 Tensor 中保留减小的维度。 | +| interpolation | interpolation | 当所需分位数位于两个数据点之间时使用的插值方法。| | out | - | 表示输出的 Tensor，Paddle 无此参数，需要转写。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.rand.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.rand.md index e827e1d4b8c..24863c38e57 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.rand.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.rand.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.rand +## [ 输入参数用法不一致 ]torch.rand ### [torch.rand](https://pytorch.org/docs/stable/generated/torch.rand.html?highlight=rand#torch.rand) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.randn.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.randn.md index e925c9dd6be..7dc90ea8dfb 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.randn.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.randn.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.randn +## [ 输入参数用法不一致 ]torch.randn ### [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html?highlight=randn#torch.randn) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.range.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.range.md index d9df9edc78c..c64a77933e5 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.range.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.range.md @@ -29,7 +29,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | start | start | 表示区间起点（且区间包括此值）。 | | end | end | 表示区间终点（且通常区间不包括此值）。 | | step | step | 表示均匀分割的步长。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.roll.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.roll.md index 7d57add8d8d..50c8a44cf7e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.roll.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.roll.md @@ -17,7 +17,7 @@ paddle.roll(x, ``` 两者功能一致且参数用法一致，仅参数名不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 输入的 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.scatter_add.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.scatter_add.md index 0cbf374643b..4886e0a667a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.scatter_add.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.scatter_add.md @@ -23,7 +23,7 @@ paddle.put_along_axis(arr, 其中 Paddle 相比 PyTorch 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | arr | 表示输入 Tensor ，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_default_tensor_type.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_default_tensor_type.md index 7e893ed2bd7..acf827505a3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_default_tensor_type.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_default_tensor_type.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.set_default_tensor_type +## [ 输入参数用法不一致 ]torch.set_default_tensor_type ### [torch.set\_default\_tensor\_type](https://pytorch.org/docs/stable/generated/torch.set_default_tensor_type.html) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_rng_state.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_rng_state.md index 3ddb6998f3e..a4de1010bb4 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_rng_state.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.set_rng_state.md @@ -1,4 +1,4 @@ -## [参数不一致] torch.set_rng_state +## [ 输入参数用法不一致 ] torch.set_rng_state ### [torch.set_rng_state](https://pytorch.org/docs/stable/generated/torch.set_rng_state.html#torch.set_rng_state) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sigmoid.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sigmoid.md index eebfae2da44..c7ab1385ae4 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sigmoid.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sigmoid.md @@ -12,7 +12,7 @@ paddle.nn.functional.sigmoid(x, name=None) ``` 功能一致，torch 参数更多，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 输入的 Tensor。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slice_scatter.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slice_scatter.md index 5ade4a9d99f..b4a3c26b1e1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slice_scatter.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slice_scatter.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.slice_scatter +## [ 输入参数用法不一致 ]torch.slice_scatter ### [torch.slice_scatter](https://pytorch.org/docs/stable/generated/torch.slice_scatter.html#torch.slice_scatter) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slogdet.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slogdet.md index 706345b5616..7a59083e313 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slogdet.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.slogdet.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.slogdet +## [ 返回参数类型不一致 ]torch.slogdet ### [torch.slogdet](https://pytorch.org/docs/stable/generated/torch.slogdet.html?highlight=slogdet#torch.slogdet) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sort.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sort.md index 0f9311303a5..bafb6d9bb7f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sort.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sort.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.sort +## [ 返回参数类型不一致 ]torch.sort ### [torch.sort](https://pytorch.org/docs/stable/generated/torch.sort.html?highlight=sort#torch.sort) @@ -17,6 +17,7 @@ torch.sort(input, paddle.sort(x, axis=-1, descending=False, + stable=False, name=None) ``` @@ -29,7 +30,7 @@ PyTorch 相比 Paddle 支持更多其他参数，同时两个 api 的返回参 | input | x | 输入的 Tensor ，仅参数名不一致。 | | dim | axis | 指定对输入 Tensor 进行运算的轴。默认值为-1, 仅参数名不一致。 | | descending |descending | 指定算法排序的方向。如果设置为 True，算法按照降序排序。如果设置为 False 或者不设置，按照升序排序。默认值为 False，参数名相同。 | -| stable | - | 使排序程序更稳定，保证等价元素的顺序得以保留。Paddle 无此参数，对于排序算法的稳定性来说不是很重要，直接删除即可。 | +| stable | stable | 使排序程序更稳定，保证等价元素的顺序得以保留。 | | out | - | 表示以(Tensor, LongTensor)输出的元组，含义是排序后的返回值和对应元素索引。Paddle 无此参数，若返回排序后的元素，需要转写；若需要返回元素和元素索引，需要结合 argsort 进行转写。 | 注：PyTorch 返回 (Tensor, LongTensor)，Paddle 返回 Tensor 。 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sparse_coo_tensor.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sparse_coo_tensor.md index 515bf9aae26..276cd056fb1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sparse_coo_tensor.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.sparse_coo_tensor.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.sparse_coo_tensor +## [ 输入参数用法不一致 ]torch.sparse_coo_tensor ### [torch.sparse_coo_tensor](https://pytorch.org/docs/stable/generated/torch.sparse_coo_tensor.html?highlight=torch+sparse_coo_tensor#torch.sparse_coo_tensor) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.split.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.split.md index fa5c4d041ce..48493256526 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.split.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.split.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.split +## [ 输入参数用法不一致 ]torch.split ### [torch.split](https://pytorch.org/docs/stable/generated/torch.split.html?highlight=torch%20split#torch.split) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.svd_lowrank.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.svd_lowrank.md index 541a43b14d6..8835cda7b3b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.svd_lowrank.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.svd_lowrank.md @@ -1,4 +1,4 @@ -## [ torch 参数更多 ] torch.svd_lowrank +## [ 仅参数名不一致 ] torch.svd_lowrank ### [torch.svd_lowrank](https://pytorch.org/docs/stable/generated/torch.svd_lowrank.html?highlight=torch+svd_lowrank#torch.svd_lowrank) @@ -6,20 +6,19 @@ torch.svd_lowrank(A, q=6, niter=2, M=None) ``` -### [paddle.linalg.svd](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/linalg/svd_cn.html#svd) +### [paddle.linalg.svd_lowrank](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/linalg/svd_lowrank_cn.html) ```python -paddle.linalg.svd(x, full_matrics=False, name=None) +paddle.linalg.svd_lowrank(x, q=None, niter=2, M=None, name=None) ``` -PyTorch 相比 Paddle 支持更多其他参数，具体如下： +两者功能一致，仅参数名不一致，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | A | x | 表示输入 Tensor，仅参数名不一致。 | -| q | - | 表示输入 Tensor 略高估计秩。 Paddle 无此参数，暂无转写方式。 | -| niter | - | 表示子空间进行迭代的数量。Paddle 无此参数，暂无转写方式。 | -| M | - | 表示输入 Tensor 的平均 size。 Paddle 无此参数，暂无转写方式。 | -| - | full_matrics | 表示是否计算完整的 U 和 V 矩阵。 PyTorch 无此参数，Paddle 保持默认即可。 | +| q | q | 表示输入 Tensor 略高估计秩。 | +| niter | niter | 表示子空间进行迭代的数量。 | +| M | M | 表示输入 Tensor 的平均值矩阵。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapaxes.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapaxes.md index 6a0161daab3..3471536960b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapaxes.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapaxes.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.swapaxes +## [ 输入参数用法不一致 ] torch.swapaxes ### [torch.swapaxes](https://pytorch.org/docs/stable/generated/torch.swapaxes.html#torch.swapaxes) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapdims.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapdims.md index afc9ec803f1..e0b9f83fb5c 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapdims.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.swapdims.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.swapdims +## [ 输入参数用法不一致 ] torch.swapdims ### [torch.swapdims](https://pytorch.org/docs/stable/generated/torch.swapdims.html#torch.swapdims) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md deleted file mode 100644 index ff946db584c..00000000000 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md +++ /dev/null @@ -1,24 +0,0 @@ -## [ 仅 paddle 参数更多 ] torch.Tensor - -### [torch.Tensor](https://pytorch.org/docs/stable/tensors.html) - -```python -torch.Tensor(data) -``` - -### [paddle.to_tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/to_tensor_cn.html#to-tensor) - -```python -paddle.to_tensor(data, dtype=None, place=None, stop_gradient=True) -``` - -Paddle 比 PyTorch 支持更多参数，具体如下： - -### 参数映射 - -| PyTorch | PaddlePaddle | 备注 | -| ------- | ------------ | ----------------------------------------------------------- | -| data | data | 要转换的数据。 | -| - | dtype | Tensor 的数据类型，PyTorch 无此参数，Paddle 保持默认即可。 | -| - | place | Tensor 的设备，PyTorch 无此参数，Paddle 需设置为 'cpu' 。 | -| - | stop_gradient | 是否阻断 Autograd 的梯度传导。PyTorch 无此参数，Paddle 保持默认即可。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.topk.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.topk.md index 5f133aa7b86..ded7e2e0375 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.topk.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.topk.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.topk +## [ 返回参数类型不一致 ]torch.topk ### [torch.topk](https://pytorch.org/docs/stable/generated/torch.topk.html?highlight=topk#torch.topk) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.transpose.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.transpose.md index b0574cde58e..e2666176be3 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.transpose.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.transpose.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.transpose +## [ 输入参数用法不一致 ] torch.transpose ### [torch.transpose](https://pytorch.org/docs/stable/generated/torch.transpose.html?highlight=transpose#torch.transpose) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.unique.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.unique.md index 9c6018a88f1..04143be975b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.unique.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.unique.md @@ -22,7 +22,7 @@ paddle.unique(x, ``` PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | input | x | 表示输入 Tensor。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros.md index 4ff5673cb9d..829b3432dbc 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.zeros +## [ 输入参数用法不一致 ]torch.zeros ### [torch.zeros](https://pytorch.org/docs/stable/generated/torch.zeros.html?highlight=zeros#torch.zeros) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros_like.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros_like.md index 6bf9adf5173..0a4d422052a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros_like.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.zeros_like.md @@ -25,7 +25,7 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 | PyTorch | PaddlePaddle | 备注 | -| :------------ | :----------- | :----------------------------------------------------------- | +| ------------- | ------------ | ------------------------------------------------------------ | | input | x | 表示输入 Tensor ，仅名称不同。 | | dtype | dtype | 表示输出 Tensor 类型。 | | layout | - | 表示布局方式，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.get_rng_state.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.get_rng_state.md index 342213ff97f..c29cbd653e6 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.get_rng_state.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.get_rng_state.md @@ -1,4 +1,4 @@ -## [参数不一致] torch.random.get_rng_state +## [ 返回参数类型不一致 ] torch.random.get_rng_state ### [torch.random.get_rng_state](https://pytorch.org/docs/stable/random.html#torch.random.get_rng_state) @@ -12,7 +12,7 @@ torch.random.get_rng_state() paddle.get_rng_state() ``` -其中 PyTorch 与 Paddle 的返回参数类型不一致 +其中 PyTorch 与 Paddle 的返回参数类型不一致。 ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.set_rng_state.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.set_rng_state.md index f80c3610e77..f3a413ee2eb 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.set_rng_state.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.set_rng_state.md @@ -1,4 +1,4 @@ -## [参数不一致] torch.random.set_rng_state +## [ 输入参数类型不一致 ] torch.random.set_rng_state ### [torch.random.set_rng_state](https://pytorch.org/docs/stable/random.html#torch.random.set_rng_state) @@ -12,7 +12,7 @@ torch.random.set_rng_state(new_state) paddle.set_rng_state(state_list) ``` -其中 PyTorch 与 Paddle 的输入参数类型不一致 +其中 PyTorch 与 Paddle 的输入参数类型不一致。 ### 参数映射 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.where.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.where.md index cbb97e65b47..337c78b5ca5 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.where.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.where.md @@ -14,7 +14,7 @@ paddle.where(condition, x, y, name=None) PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | condition | condition | 判断条件。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/pytorch_api_mapping_format_cn.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/pytorch_api_mapping_format_cn.md index 59d655e373f..bd6f94d3fd4 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/pytorch_api_mapping_format_cn.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/pytorch_api_mapping_format_cn.md @@ -10,21 +10,21 @@ > 注：第 1~3 类均为 API 层面一对一映射，根据参数层面的映射关系将其细分为三类。 * 第 1 类又分为五种情况：`无参数`、`参数完全一致`、`仅参数名不一致`、`仅 paddle 参数更多`、`仅参数默认值不一致`。 -> 注：分类优先级依次递增，即如果同时 `参数名不一致` + `paddle 参数更多`，则写成后者 `仅 paddle 参数更多` 。 +> 注：分类优先级依次递增，即如果同时 `参数名不一致` + `仅 paddle 参数更多`，则写成后者 `仅 paddle 参数更多` 。 * 第 2 类为 `torch 参数更多`。如果 torch 和 paddle 都支持更多参数，统一写成`torch 参数更多`。 -* 第 3 类为 `参数不一致`。包括不限于 输入参数支持类型不一致、输入参数用法不一致、返回参数类型不一致 等情况。 -> 注意：这里的一致都是从 torch 的角度来看，如果 paddle 可涵盖 torch，即 torch 是 paddle 的功能子集，即认为是一致。例如：torch 参数仅支持 list，paddle 参数支持 list/tuple，则认为两者一致。反之则不一致。 +* 第 3 类又分为三种情况：`输入参数类型不一致`、`输入参数用法不一致`、`返回参数类型不一致`。 +> 注意：这里的**不一致**都是从 torch 的角度来看，如果 paddle 可涵盖 torch，即 torch 是 paddle 的功能子集，则认定为一致（例如：torch 参数仅支持 list，paddle 参数支持 list/tuple），反之才认定为不一致。 * 第 4 类为 `组合替代实现` ，表示该 API 没有可直接对应的 API，需要通过多个 API 组合实现。 -* 第 5 类为 `用法不同：涉及上下文修改` ，表示涉及到上下文分析，需要修改其他位置的代码。 -> 举例：所有的 `torch.optim.lr_scheduler.*`、`torch.nn.init.*`、`torch.nn.utils.clip*` 都为该类。主要由于设计上与 Paddle 具有较大的差异，需要对上文例如 Layer 的`weight_attr`进行设置，涉及到上文代码联动修改。 +* 第 5 类为 `涉及上下文修改` ，表示涉及到上下文的分析，需要修改其他位置的代码。 +> 举例：所有的 `torch.optim.lr_scheduler.*`、`torch.nn.init.*`、`torch.nn.utils.clip*` 都为该类。此类 API 由于两者在设计上具有较大的差异，需要对上下文进行分析，涉及到上下文代码的联动修改。 -* 第 6 类为 `对应 API 不在主框架` 。例如 `torch.hamming_window` 对应 API 在 `paddlenlp` 中。 +* 第 6 类为 `可删除` 。表示可直接删除该 API，则无需写差异分析文档，仅标注即可 -* 第 7 类为 `功能缺失` ，表示当前无该功能，则无需写差异分析文档，仅标注到 pytorch_api_mapping_cn.md 文件中即可。 +* 第 7 类为 `功能缺失` ，表示 Paddle 当前无对应 API，则无需写差异分析文档，仅标注即可。 > 注意： > 1. 分类优先级依次递增，即如果同时 `第 2 类：torch 参数更多` 与 `第 3 类：参数不一致` ，则写成后者 `第 3 类：参数不一致` 。 @@ -130,6 +130,7 @@ paddle.Tensor.clip(min=None, max=None, name=None) 两者功能一致，参数完全一致，具体如下： ### 参数映射 + | PyTorch | PaddlePaddle | 备注 | |---------|--------------| -------------------------------------------------- | | min | min | 裁剪的最小值，输入中小于该值的元素将由该元素代替。 | @@ -195,6 +196,7 @@ paddle.nn.ZeroPad2D(padding, Paddle 相比 PyTorch 支持更多其他参数，具体如下： ### 参数映射 + | PyTorch | Paddle | 备注 | | ------- | ------------- | ------------------------------------------------------------ | | - | axis | 指定进行运算的轴， PyTorch 无此参数， Paddle 保持默认即可。 | @@ -253,6 +255,7 @@ paddle.abs(x, PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 + | PyTorch | Paddle | 备注 | | ------------- | ------ | ------------------------------------------------------------ | | input | x | 表示输入的 Tensor ，仅参数名不一致。 | @@ -335,7 +338,7 @@ else: ## 模板 7 -### [ 参数不一致 ] torch.transpose +### [ 输入参数用法不一致 ] torch.transpose ### [torch.transpose](https://pytorch.org/docs/stable/generated/torch.transpose.html?highlight=transpose#torch.transpose)(仅作为示例) @@ -356,6 +359,7 @@ paddle.transpose(x, PyTorch 的 `tensors` 参数与 Paddle 的 `inputs` 参数用法不同，具体如下： ### 参数映射 + | PyTorch | Paddle | 备注 | | ------- | ------------- | ------------------------------------------------------------ | |*tensors | inputs | 一组输入 Tensor，PyTorch 的 tensors 为可变参数，Paddle 的 inputs 为 list(Tensor) 或 tuple(Tensor) 用法，需要转写。 | @@ -422,7 +426,7 @@ paddle.add(input, value * tensor1 * tensor2) ## 模板 9 -### [ 用法不同：涉及上下文修改 ] torch.nn.utils.clip_grad_value_ +### [ 涉及上下文修改 ] torch.nn.utils.clip_grad_value_ ### [torch.nn.utils.clip_grad_value_](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_value_.html?highlight=clip_grad_value_#torch.nn.utils.clip_grad_value_)(仅作为示例) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.mm.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.mm.md index e7c20476c4e..ce485c9a6b8 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.mm.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.mm.md @@ -17,7 +17,7 @@ paddle.sparse.matmul(x, y, name=None) ### 参数映射 - |PyTorch | PaddlePaddle | 备注| - |--------| ------------- | ------| - |sparse | x| 输入的 Tensor，仅参数名不一致。| - |dense | y |输入的第二个 Tensor，仅参数名不一致。| +| PyTorch | PaddlePaddle | 备注 | +| ------- | ------------ | ------ | +| sparse | x | 输入的 Tensor，仅参数名不一致。 | +| dense | y | 输入的第二个 Tensor，仅参数名不一致。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.softmax.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.softmax.md index 343f325672e..6922c522442 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.softmax.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.softmax.md @@ -16,11 +16,11 @@ PyTorch 相比 Paddle 支持更多其他参数，具体如下： ### 参数映射 - |PyTorch | PaddlePaddle | 备注 | - |--------| -------------| --------------------------------------------------------------------------------------| - |input |x | 输入的稀疏 Tensor，仅参数名不一致。| - |dim | axis| 指定对输入 SparseTensor 计算 softmax 的轴，Paddle 的默认值：-1。仅参数名不一致。| - |dtype | - | 指定数据类型，可选项，PyTorch 默认值为 None，Paddle 无此参数，需要转写。| +| PyTorch | PaddlePaddle | 备注 | +| -------- | ----------- | ---------------------------------------------------- | +| input | x | 输入的稀疏 Tensor，仅参数名不一致。| +| dim | axis | 指定对输入 SparseTensor 计算 softmax 的轴，Paddle 的默认值：-1。仅参数名不一致。| +| dtype | - | 指定数据类型，可选项，PyTorch 默认值为 None，Paddle 无此参数，需要转写。| ### 转写示例 #### dytpe：指定数据类型 ```Python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.sum.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.sum.md index 5bca5e0c535..a6cfa36e958 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.sum.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.sum.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ] torch.sparse.sum +## [ 返回参数类型不一致 ] torch.sparse.sum ### [torch.sparse.sum](https://pytorch.org/docs/stable/generated/torch.sparse.sum.html?highlight=sparse+sum#torch.sparse.sum) @@ -16,13 +16,13 @@ paddle.sparse.sum(x, axis=None, dtype=None, keepdim=False, name=None) ### 参数映射 - |PyTorch | PaddlePaddle | 备注| - |--------| ------------- | ------| - |input | x| 输入的 Tensor，仅参数名不一致。| - |dim | axis |输入的第二个 Tensor，仅参数名不一致。| - |dtype | dtype |输出数据的类型。| - |- | keepdim |是否留减少的维度， PyTorch 无此参数， Paddle 保持默认即可。| - |返回值 | 返回值 |当不指定 dim 时，PyTorch 返回 0D Tensor， Paddle 返回 Sparse Tensor。| +| PyTorch | PaddlePaddle | 备注| +| -------- | ------------- | ------| +| input | x| 输入的 Tensor，仅参数名不一致。| +| dim | axis |输入的第二个 Tensor，仅参数名不一致。| +| dtype | dtype |输出数据的类型。| +| - | keepdim |是否留减少的维度， PyTorch 无此参数， Paddle 保持默认即可。| +| 返回值 | 返回值 |当不指定 dim 时，PyTorch 返回 0D Tensor， Paddle 返回 Sparse Tensor。| ### 转写示例 #### 返回类型：当不指定 dim 时，PyTorch 返回 0D Tensor， Paddle 返回 Sparse Tensor。 diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.parameter.Parameter.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.parameter.Parameter.md index 4b952c0e438..d2148e4aa0e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.parameter.Parameter.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.parameter.Parameter.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.nn.parameter.Parameter +## [ torch 参数更多 ]torch.nn.parameter.Parameter ### [torch.nn.parameter.Parameter](https://pytorch.org/docs/stable/generated/torch.nn.parameter.Parameter.html?highlight=torch%20nn%20parameter#torch.nn.parameter.Parameter) ```python @@ -18,7 +18,7 @@ paddle.create_parameter(shape, ``` 两者功能一致但参数不一致，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | data | - | 参数 Tensor，Paddle 无此参数。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DataLoader.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DataLoader.md index d0a714ce0f3..fc740065f41 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DataLoader.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DataLoader.md @@ -1,4 +1,5 @@ -## [ 参数不一致 ]torch.utils.data.DataLoader +## [ torch 参数更多 ]torch.utils.data.DataLoader + ### [torch.utils.data.DataLoader](https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader) ```python torch.utils.data.DataLoader(dataset, @@ -39,7 +40,7 @@ paddle.io.DataLoader(dataset, ``` -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ------------------------------------------------------ | | sampler | - | 表示数据集采集器，Paddle 无此参数。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DistributedSampler.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DistributedSampler.md index 94753f863cc..a24b4b54254 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DistributedSampler.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DistributedSampler.md @@ -22,7 +22,7 @@ paddle.io.DistributedBatchSampler(dataset=None, ``` PyTorch 参数更多，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ----- | ---------- | ---------- | | dataset | dataset | 被采样的数据集。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.RandomSampler.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.RandomSampler.md index 1d3fe88583d..36902daceb2 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.RandomSampler.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.RandomSampler.md @@ -20,5 +20,5 @@ paddle.io.RandomSampler(data_source, replacement=False, num_samples=None, genera | ----------- | ------------ | -------------------------------------------------------------------- | | data_source | data_source | Dataset 或 IterableDataset 的一个子类实例或实现了 `__len__` 的 Python 对象。 | | replacement | replacement | 如果为 False 则会采样整个数据集。 | -| num_samples | num_samples | 如果 replacement 设置为 True 则按此参数采集对应的样本数。 | +| num_samples | num_samples | 按此参数采集对应的样本数。 | | generator | generator | 指定采样 data_source 的采样器。 | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.TensorDataset.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.TensorDataset.md index d7ef2b93e7a..7c350d32363 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.TensorDataset.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.TensorDataset.md @@ -1,4 +1,4 @@ -## [参数用法不一致]torch.utils.data.TensorDataset +## [ 输入参数用法不一致 ]torch.utils.data.TensorDataset ### [torch.utils.data.TensorDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.TensorDataset) diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data._utils.collate.default_collate.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data._utils.collate.default_collate.md index 723afbf9ecd..007839e5bfc 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data._utils.collate.default_collate.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data._utils.collate.default_collate.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.utils.data._utils.collate.default_collate +## [ 返回参数类型不一致 ]torch.utils.data._utils.collate.default_collate ### [torch.utils.data._utils.collate.default_collate](https://pytorch.org/docs/stable/data.html?highlight=default_collate#torch.utils.data.default_collate) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.dataloader.default_collate.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.dataloader.default_collate.md index aa3ec3ddc31..a1888db40c1 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.dataloader.default_collate.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.dataloader.default_collate.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.utils.data.dataloader.default_collate +## [ 返回参数类型不一致 ]torch.utils.data.dataloader.default_collate ### [torch.utils.data.dataloader.default_collate](https://pytorch.org/docs/stable/data.html?highlight=default_collate#torch.utils.data.default_collate) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.default_collate.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.default_collate.md index 59849d92b35..d074c98e663 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.default_collate.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.default_collate.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.utils.data.default_collate +## [ 返回参数类型不一致 ]torch.utils.data.default_collate ### [torch.utils.data.default_collate](https://pytorch.org/docs/stable/data.html?highlight=default_collate#torch.utils.data.default_collate) ```python diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.distributed.DistributedSampler.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.distributed.DistributedSampler.md index c61e9c4418d..3de233664ae 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.distributed.DistributedSampler.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.distributed.DistributedSampler.md @@ -24,7 +24,7 @@ paddle.io.DistributedBatchSampler(dataset=None, PyTorch 相比 Paddle 支持更多其他参数，具体如下： -### 参数差异 +### 参数映射 | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ | ---------- | diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.random_split.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.random_split.md index a4046adc75d..9baa9243a7b 100644 --- a/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.random_split.md +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.random_split.md @@ -1,4 +1,4 @@ -## [ 参数不一致 ]torch.utils.data.random_split +## [ 参数完全一致 ]torch.utils.data.random_split ### [torch.utils.data.random_split](https://pytorch.org/docs/stable/data.html?highlight=torch+utils+data+random_split#torch.utils.data.random_split) ```python @@ -15,27 +15,12 @@ paddle.io.random_split(dataset, generator=None) ``` -两者参数除 lengths 外用法一致，具体如下： -### 参数差异 +两者功能一致，参数完全一致，具体如下： + +### 参数映射 + | PyTorch | PaddlePaddle | 备注 | | ------------- | ------------ |---------------------------------------------------------------------| | dataset | dataset | 表示可迭代数据集。 | -| lengths | lengths | PyTorch:可为子集合长度列表，列表总和为原数组长度。也可为子集合所占比例列表，列表总和为 1.0。PaddlePaddle: 子集合长度列表，列表总和为原数组长度 | +| lengths | lengths | 可为子集合长度列表，列表总和为原数组长度。也可为子集合所占比例列表，列表总和为 1.0。 | | generator | generator | 指定采样 data_source 的采样器。默认值为 None。 | - -### 转写示例 -#### lenghts: 子集合长度列表 -```python -# PyTorch 写法 -lengths = [0.3, 0.3, 0.4] -datasets = torch.utils.data.random_split(dataset, - lengths, - generator=torch.manual_seed(0)) - -# Paddle 写法 -lengths = [0.3, 0.3, 0.4] -lengths = [length * len(dataset) for length in lengths] -datasets = paddle.io.random_split(dataset, - lengths, - generator=paddle.seed(0)) -``` diff --git a/docs/guides/model_convert/convert_from_pytorch/apply_reference_from_api_difference.py b/docs/guides/model_convert/convert_from_pytorch/apply_reference_from_api_difference.py index d9c13766ed6..6c34eda10ef 100644 --- a/docs/guides/model_convert/convert_from_pytorch/apply_reference_from_api_difference.py +++ b/docs/guides/model_convert/convert_from_pytorch/apply_reference_from_api_difference.py @@ -8,6 +8,7 @@ print(script_dir) from validate_mapping_in_api_difference import ( + DiffMeta, get_meta_from_diff_file, process_mapping_index as reference_mapping_item, ) @@ -30,9 +31,10 @@ def mapping_type_to_description(mapping_type): return "功能一致，" + mapping_type, True mapping_type_3 = [ + # "参数不一致", "返回参数类型不一致", - "参数不一致", - "参数用法不一致", + "输入参数类型不一致", + "输入参数用法不一致", ] if mapping_type in mapping_type_3: return "功能一致，" + mapping_type, True @@ -41,7 +43,7 @@ def mapping_type_to_description(mapping_type): if mapping_type in mapping_type_4: return "组合替代实现", True - mapping_type_5 = ["用法不同：涉及上下文修改"] + mapping_type_5 = ["涉及上下文修改"] if mapping_type in mapping_type_5: return "功能一致，" + mapping_type, True @@ -63,72 +65,136 @@ def mapping_type_to_description(mapping_type): return "【未知类型】", False -REFERENCE_PATTERN = re.compile( +# 以后没有 REFERENCE-ITEM 需要维护了，全部从 api_difference/ 目录生成 +_REFERENCE_ITEM_PATTERN = re.compile( r"^\| *REFERENCE-MAPPING-ITEM\( *(?P[^,]+) *, *(?P.+) *\) *\|$" ) +REFERENCE_TABLE_PATTERN = re.compile( + r"^\| *REFERENCE-MAPPING-TABLE\( *(?P[^,]+) *(, *max_depth *= *(?P\d+) *)?\) *\|$" +) +ALIAS_PATTERN = re.compile( + r"^\| *ALIAS-REFERENCE-ITEM\( *(?P[^,]+) *, *(?P[^,]+) *\) *\|$" +) NOT_IMPLEMENTED_PATTERN = re.compile( r"^\| *NOT-IMPLEMENTED-ITEM\( *(?P[^,]+) *, *(?P.+) *\) *\|$" ) -def apply_reference_to_row(line, metadata_dict, table_row_idx, line_idx): - reference_match = REFERENCE_PATTERN.match(line) - not_implemented_match = NOT_IMPLEMENTED_PATTERN.match(line) +DOCS_REPO_BASEURL = "https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/" - if reference_match: - torch_api = reference_match["torch_api"].strip("`").replace(r"\_", "_") - diff_url = reference_match["diff_url"] - row_idx_s = str(table_row_idx) +def docs_url_to_relative_page(url): + """将映射文档的 PaddlePaddle/docs url 转换为网页路径""" + if not url.startswith(DOCS_REPO_BASEURL): + return url - if torch_api not in metadata_dict: - raise Exception( - f"Cannot find torch_api: {torch_api} in line {line_idx}" - ) + md_path = url[len(DOCS_REPO_BASEURL) :] + if md_path.endswith(".md"): + return md_path[:-3] + ".html" + return md_path - reference_item = metadata_dict.get(torch_api, None) - torch_api_url = reference_item["torch_api_url"] - torch_api_column = f"[`{torch_api}`]({torch_api_url})" - mapping_type = reference_item["mapping_type"] - mapping_type_column = mapping_type +def doc_path_to_relative_page(path): + """将映射文档的本地路径转换为网页相对路径""" + md_path = os.path.relpath(path, script_dir) + + assert md_path.endswith(".md"), f"Unexpected mapping doc path: {path}" + + return md_path[:-3] + ".html" + + +def reference_table_match_to_condition(m): + api_prefix = m["api_prefix"].strip("`") + max_depth = m["max_depth"] + if max_depth is None: + max_depth = 255 + else: + max_depth = int(max_depth) + return api_prefix, max_depth + + +def get_referenced_api_columns(torch_api, metadata_dict, alias=None): + assert ( + torch_api in metadata_dict + ), f'Error: cannot find mapping doc of api "{torch_api}"' + api_data: DiffMeta = metadata_dict[torch_api] + + diff_page_url = doc_path_to_relative_page(api_data["source_file"]) + + torch_api_url = api_data["torch_api_url"] + api_disp_name = torch_api if alias is None else alias + torch_api_column = f"[`{api_disp_name}`]({torch_api_url})" + + mapping_type = api_data["mapping_type"] + mapping_type_column = mapping_type - _mapping_type_desc, show_diff_url = mapping_type_to_description( - mapping_type + _mapping_type_desc, show_diff_url = mapping_type_to_description( + mapping_type + ) + desc_column = "" + if show_diff_url: + desc_column = f"[详细对比]({diff_page_url})" + if alias is not None: + desc_column = f"`{torch_api}` 别名，{desc_column}" + + if "paddle_api" not in api_data: + if mapping_type not in ["组合替代实现", "可删除", "功能缺失"]: + print(f"Error: cannot find paddle_api for torch_api: {torch_api}") + paddle_api_column = "" + else: + paddle_api = api_data["paddle_api"] + paddle_api_url = api_data["paddle_api_url"] + paddle_api_column = f"[`{paddle_api}`]({paddle_api_url})" + + return [ + torch_api_column, + paddle_api_column, + mapping_type_column, + desc_column, + ] + + +def apply_reference_to_row_ex(line, metadata_dict, context, line_idx): + reference_table_match = REFERENCE_TABLE_PATTERN.match(line) + alias_match = ALIAS_PATTERN.match(line) + not_implemented_match = NOT_IMPLEMENTED_PATTERN.match(line) + + row_idx_s = str(context["table_row_idx"]) + + if reference_table_match: + condition = reference_table_match_to_condition(reference_table_match) + api_list = context["c2a_dict"][ + condition + ] # 这个键一定存在，否则说明前面出错了 + output_lines = [] + cur_row_idx = context["table_row_idx"] + for api in api_list: + content = get_referenced_api_columns(api, metadata_dict) + content.insert(0, str(cur_row_idx)) + output = "| " + " | ".join(content) + " |\n" + output_lines.append(output) + cur_row_idx += 1 + # 因为外面会给 table_row_idx 自动加 1，所以这里减去 1 + context["table_row_idx"] = cur_row_idx - 1 + return output_lines + elif alias_match: + alias_name = alias_match["alias_name"].strip("`").replace(r"\_", "_") + torch_api = alias_match["torch_api"].strip("`").replace(r"\_", "_") + + content = get_referenced_api_columns( + torch_api, metadata_dict, alias=alias_name ) - mapping_url_column = "" - if show_diff_url: - mapping_url_column = f"[详细对比]({diff_url})" - - if "paddle_api" not in reference_item: - if mapping_type not in ["组合替代实现", "可删除", "功能缺失"]: - print( - f"Cannot find paddle_api for torch_api: {torch_api} in line {line_idx}" - ) - paddle_api_column = "" - else: - paddle_api = reference_item["paddle_api"] - paddle_api_url = reference_item["paddle_api_url"] - paddle_api_column = f"[`{paddle_api}`]({paddle_api_url})" - content = [ - row_idx_s, - torch_api_column, - paddle_api_column, - mapping_type_column, - mapping_url_column, - ] + content.insert(0, row_idx_s) output = "| " + " | ".join(content) + " |\n" - return output + return [output] elif not_implemented_match: torch_api = ( not_implemented_match["torch_api"].strip("`").replace(r"\_", "_") ) torch_api_url = not_implemented_match["torch_api_url"].strip() - row_idx_s = str(table_row_idx) - torch_api_column = f"[`{torch_api}`]({torch_api_url})" paddle_api_column = "" @@ -144,12 +210,12 @@ def apply_reference_to_row(line, metadata_dict, table_row_idx, line_idx): ] output = "| " + " | ".join(content) + " |\n" - return output + return [output] else: raise ValueError( f"found manual-maintaining row at line [{line_idx}]: {line}" ) - # return line + return [line] def reference_mapping_item_processer(line, line_idx, state, output, context): @@ -163,11 +229,7 @@ def reference_mapping_item_processer(line, line_idx, state, output, context): # check column names in common process output.append(line) return True - elif state == 1: - # check seperator of table to ignore in common process - output.append(line) - return True - elif state == 2: + elif state == 1 or state == 2: # check seperator of table to process in common process output.append(line) return True @@ -177,17 +239,56 @@ def reference_mapping_item_processer(line, line_idx, state, output, context): return True elif state == 6: # check content of table to process in common process - referenced_row = apply_reference_to_row( - line, metadata_dict, context["table_row_idx"], line_idx + 1 + output_lines = apply_reference_to_row_ex( + line, metadata_dict, context, line_idx + 1 ) - output.append(referenced_row) + output += output_lines return True print(state) return False +def reference_table_scanner(line, _line_idx, state, output, context): + if not line.startswith("|"): + return True + + if state >= 0 and state <= 2: + return True + elif state == 5: + return True + elif state == 6: + # check content of table to process in common process + rtm = REFERENCE_TABLE_PATTERN.match(line) + if rtm: + condition = reference_table_match_to_condition(rtm) + context["table_conditions"].append(condition) + return True + + return False + + +def get_c2a_dict(conditions, meta_dict): + c2a_dict = {c: [] for c in conditions} + conditions.sort( + key=lambda c: (-len(c[0]), c[1]) + ) # 先按照字符串长度降序，随后按照最大深度升序 + for api in meta_dict: + for api_prefix, max_depth in conditions: + if not api.startswith(api_prefix): + continue + depth = len(api.split(".")) - 1 + if depth > max_depth: + continue + c2a_dict[(api_prefix, max_depth)].append(api) + break + else: + print(f"Warning: cannot find a suitable condition for api {api}") + + return c2a_dict + + if __name__ == "__main__": # convert from pytorch basedir cfp_basedir = os.path.dirname(__file__) @@ -218,7 +319,21 @@ def reference_mapping_item_processer(line, line_idx, state, output, context): "metadata_dict": meta_dict, "ret_code": 0, "output": [], + "table_conditions": [], } + + # 第一遍预读，用来分析有哪些表格和匹配条件 + ret_code = reference_mapping_item( + mapping_index_file, reference_table_scanner, reference_context + ) + assert ret_code == 0 + reference_context["output"] = [] + + # 现在 c2a_dict 包含每个条件对应的 api 列表 + c2a_dict = get_c2a_dict(reference_context["table_conditions"], meta_dict) + reference_context["c2a_dict"] = c2a_dict + + # 第二遍正式读，读并处理 ret_code = reference_mapping_item( mapping_index_file, reference_mapping_item_processer, reference_context ) diff --git a/docs/guides/model_convert/convert_from_pytorch/index_cn.rst b/docs/guides/model_convert/convert_from_pytorch/index_cn.rst index c9d82028683..cc44f001d6f 100644 --- a/docs/guides/model_convert/convert_from_pytorch/index_cn.rst +++ b/docs/guides/model_convert/convert_from_pytorch/index_cn.rst @@ -4,24 +4,25 @@ 您可以通过下面的内容，如何将 PyTorch 训练代码迁移到飞桨: - +- `代码自动转换工具 <./paconvert_introduction_cn.html>`_ : 介绍 Pytorch 代码自动转 Paddle 工具使用方法。 +- `PyTorch API 映射表 <./pytorch_api_mapping_cn.html>`_ : 说明 PyTorch 最新 release 版本 与 Paddle develop 版本 API 对应关系。 - `CV - 快速上手 <./cv_quick_start_cn.html>`_ : 以 MobileNetV3 为例，介绍如何从 PyTorch 迁移到飞桨。 - `CV - 迁移经验总结 <./cv_experience_cn.html>`_ : 介绍 CV 各个方向从 PyTorch 迁移到飞桨的基本流程、常用工具、定位问题的思路及解决方法。 - `NLP - 快速上手 <./nlp_fast_explore_cn.html>`_ : 以 Bert 为例，介绍如何从 PyTorch 迁移到飞桨。 - `NLP - 迁移经验总结 <./nlp_migration_experiences_cn.html>`_ : 介绍 NLP 各个方向从 PyTorch 迁移到飞桨的基本流程、常用工具、定位问题的思路及解决方法。 - `解读网络结构转换 <./convert_net_structure_cn.html>`_ : 介绍网络结构转换的思路和方法。 - `解读 Bert 模型权重转换 <./convert_bert_weights_cn.html>`_ : 介绍如何进行不同框架下的模型权重转换。 -- `PyTorch API 映射表 <./pytorch_api_mapping_cn.html>`_ : 说明 PyTorch 2.1.0 版本与 Paddle develop 版本 API 对应关系。 - `PyTorch 自定义算子转写教程 <./pytorch_custom_op_convert_cn.html>`_ : 介绍 PyTorch 中自定义算子转写成 Paddle 自定义算子的思路和方法。 .. toctree:: :hidden: + paconvert_introduction_cn.md + pytorch_api_mapping_cn.md cv_quick_start_cn.md cv_experience_cn.md nlp_fast_explore_cn.md nlp_migration_experiences_cn.md convert_net_structure_cn.md convert_bert_weights_cn.md - pytorch_api_mapping_cn.md pytorch_custom_op_convert_cn.md diff --git a/docs/guides/model_convert/convert_from_pytorch/nlp_fast_explore_cn.md b/docs/guides/model_convert/convert_from_pytorch/nlp_fast_explore_cn.md index d03b086e8dc..cf83cf41c2e 100644 --- a/docs/guides/model_convert/convert_from_pytorch/nlp_fast_explore_cn.md +++ b/docs/guides/model_convert/convert_from_pytorch/nlp_fast_explore_cn.md @@ -393,7 +393,7 @@ def _init_weights(self, module): **【转换前】** -PyTorc 和 Paddle 都是用套件的形式来进行 Bert 模型组装，在 3.1 通过替换模型组件就可以完成模型网络迁移，而相应的模型权重只需要通过转换脚本就能得到,需要首先下载 Huggingface 的 BERT 预训练模型到该目录下，下载地址为：https://huggingface.co/bert-base-uncased/blob/main/pytorch_model.bin +PyTorch 和 Paddle 都是用套件的形式来进行 Bert 模型组装，在 3.1 通过替换模型组件就可以完成模型网络迁移，而相应的模型权重只需要通过转换脚本就能得到,需要首先下载 Huggingface 的 BERT 预训练模型到该目录下，下载地址为：https://huggingface.co/bert-base-uncased/blob/main/pytorch_model.bin **【转换后】** diff --git a/docs/guides/model_convert/convert_from_pytorch/paconvert_introduction_cn.md b/docs/guides/model_convert/convert_from_pytorch/paconvert_introduction_cn.md new file mode 100644 index 00000000000..74777dca163 --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/paconvert_introduction_cn.md @@ -0,0 +1,184 @@ +# 代码自动转换工具 + +![](https://img.shields.io/badge/version-v2.0-brightgreen) ![](https://img.shields.io/badge/docs-latest-brightgreen) ![](https://img.shields.io/badge/PRs-welcome-brightgreen) ![](https://img.shields.io/badge/pre--commit-Yes-brightgreen) + +**[PaConvert Github](https://github.com/PaddlePaddle/PaConvert)** + +**Pa**ddlePaddle Code **Convert** Toolkits + +## 🤗 公告 🤗 +- 本工具由 Paddle 官方团队维护与建设，所有转换代码均已经过测试，欢迎使用，高效迁移 Pytorch 代码到 PaddlePaddle + +- 当前共支持约 1300+个 Pytorch API 的一键转换，我们通过 300+个 Pytorch 模型测试，代码行数平均转换率约为 **90+%** + +- 本工具基于 [PyTorch 最新 release 与 Paddle develop API 映射表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/model_convert/convert_from_pytorch/pytorch_api_mapping_cn.html) 实现，表中 API 均经过详细对比分析，欢迎查阅 + +- 有使用问题和建议欢迎在 [PaConvert GitHub Issues](https://github.com/PaddlePaddle/PaConvert/issues) 中提出 + +## 概述 + +`代码自动转换工具` 能自动将其它深度学习框架训练或推理的**代码**，转换为 PaddlePaddle 的**代码**，方便快速自动地 **模型代码迁移**。 + +目前仅支持自动转换 Pytorch 代码，其它深度学习框架的支持后续新增中，转换时会尽量保持原代码的风格与结构，将其它深度学习框架的 API 接口 转换为 PaddlePaddle 的 API 接口。 + +转换过程中不会改动原文件，会将原项目中的文件一一转换到 `out_dir` 文件夹中（如不指定`out_dir`，则默认在当前目录下新建`paddle_project/`）。对不同类型的文件的处理逻辑分别为： + +- Python 代码文件：识别代码中调用其它深度学习框架的接口并转换为 PaddlePaddle 的接口 +- requirements.txt： 替换其中的安装依赖为 `paddlepaddle-gpu` +- 其他文件：原样拷贝 + +## 安装与使用 + +由于使用了一些较新的 Python 功能特性，你需要使用 `>=python3.8` 的解释器。 + +1. 使用 pip 安装 + +```bash +pip install -U paconvert +paconvert --in_dir torch_project [--out_dir paddle_project] [--exclude_dirs exclude_dirs] [--log_dir log_dir] [--log_level "DEBUG"] [--run_check 1] +``` + +2. 使用源码安装 + +```bash +git clone https://github.com/PaddlePaddle/PaConvert.git +python paconvert/main.py --in_dir torch_project [--out_dir paddle_project] [--exclude_dirs exclude_dirs] [--log_dir log_dir] [--log_level "DEBUG"] [--run_check 1] +``` + +**参数介绍** + +``` +--in_dir 输入 torch 项目文件，可以为单个文件或文件夹 +--out_dir 可选，输出 paddle 项目文件，可以为单个文件或文件夹，默认在当前目录下创建./paddle_project/ +--exclude_dirs 可选，排除转换的文件或文件夹，排除多项时请用逗号分隔，默认不排除 +--log_dir 可选，输出日志的路径，默认会在终端上打印日志 +--log_level 可选，打印 log 等级，仅支持"INFO" "DEBUG"，默认"INFO" +--run_check 可选，工具自检 +``` + + +## 转换示例 + +以下面 Pytorch 代码为例，转换前： +``` +import torch +import torch.nn as nn +import torch.optim as optim +import torch.nn.Linear as Linear +import torch.nn.functional as F + +class MyNet(nn.Module): + test = "str" + + def __init__(self): + self._fc1 = torch.nn.Linear(10, 10) + self._fc2 = nn.Linear(10, 10) + self._fc3 = Linear(10, 10) + + @torch.no_grad() + def forward(self, x): + x = self._fc1(x) + x = self._fc2(x) + x = self._fc3(x) + y = torch.add(x, x) + return F.relu(y) + +net = MyNet() + +sgd = optim.SGD(net.parameters(), lr=0.1, momentum=0.9) +lr = optim.lr_scheduler.MultiStepLR(sgd, milestones=[2, 4, 6], gamma=0.8) +``` + +转换后： +``` +import paddle + + +class MyNet(paddle.nn.Layer): + test = 'str' + + def __init__(self): + self._fc1 = paddle.nn.Linear(in_features=10, out_features=10) + self._fc2 = paddle.nn.Linear(in_features=10, out_features=10) + self._fc3 = paddle.nn.Linear(in_features=10, out_features=10) + + @paddle.no_grad() + def forward(self, x): + x = self._fc1(x) + x = self._fc2(x) + x = self._fc3(x) + y = paddle.add(x=x, y=paddle.to_tensor(x)) + return paddle.nn.functional.relu(x=y) + + +net = MyNet() +>>>>>>sgd = torch.optim.SGD(net.parameters(), lr=0.1, momentum=0.9) +tmp_lr = paddle.optimizer.lr.MultiStepDecay(milestones=[2, 4, 6], gamma=0.8, + learning_rate=sgd.get_lr()) +sgd.set_lr_scheduler(tmp_lr) +lr = tmp_lr +``` + +打印信息如下： + +```text +=========================================== +PyTorch to Paddle Convert Start ------>: +=========================================== +Start convert file: /workspace/PaConvert/test_code.py --> /workspace/PaConvert/paddle_project/test_code.py +[test_code.py:1] remove 'import torch' +[test_code.py:2] remove 'import torch.nn as nn' +[test_code.py:3] remove 'import torch.optim as optim' +[test_code.py:4] remove 'import torch.nn.Linear as Linear' +[test_code.py:5] remove 'import torch.nn.functional as F' +[test_code.py] add 'import paddle' in line 1 +[test_code.py:1] [Success] Convert torch.nn.Module to Paddle +[test_code.py:11] [Success] Convert torch.nn.Linear to Paddle +[test_code.py:12] [Success] Convert torch.nn.Linear to Paddle +[test_code.py:13] [Success] Convert torch.nn.Linear to Paddle +[test_code.py:20] [Success] Convert torch.add to Paddle +[test_code.py:21] [Success] Convert torch.nn.functional.relu to Paddle +[test_code.py:15] [Success] Convert torch.no_grad to Paddle +[test_code.py:25] [Success] Convert Class Method: torch.nn.Module.parameters to Paddle +[test_code.py:25] [Not Support] convert torch.optim.SGD to Paddle is not supported currently +[test_code.py:26] [Success] Convert torch.optim.lr_scheduler.MultiStepLR to Paddle +Finish convert /workspace/PaConvert/test_code.py --> /workspace/PaConvert/paddle_project/test_code.py + + +=========================================== +Convert Summary: +=========================================== +There are 10 Pytorch APIs in this Project: + 9 Pytorch APIs have been converted to Paddle successfully! + 1 Pytorch APIs are not supported to convert to Paddle currently! + Convert Rate is: 90.000% + +For these 1 Pytorch APIs that currently do not support to convert, which have been marked by >>> before the line, +please refer to [https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/model_convert/convert_from_pytorch/pytorch_api_mapping_cn.html] +and convert it by yourself manually. In addition, these APIs will be supported in future. + +Thank you to use Paddle Code Convert Tool. You can make any suggestions +to us by submitting issues to [https://github.com/PaddlePaddle/PaConvert]. + +**************************************************************** +______ _____ _ +| ___ \ / ____| | | +| |_/ /_ _| | ___ _ ____ _____ _ __| |_ +| __/ _ | | / _ \| \_ \ \ / / _ \ \__| __| +| | | (_| | |___| (_) | | | \ V / __/ | | |_ +\_| \__,_|\_____\___/|_| |_|\_/ \___|_| \__| + +*************************************************************** + +``` + +转换完成后，会打印 **转换总结** ，包含 **总 API 数、成功转换 API 数、不支持转换 API 数、转化率** 。例如，上述代码里一共有 10 个 Pytorch API，其中 9 个被成功转换，1 个不支持转换，因此转换率为 `90.00%` 。 + +**对于成功转换的 API**：代码风格会略有变化，会 **补全 API 全名、补全参数关键字、移除注释、移除多余空行** 。因为在代码识别的过程中，**注释、空行** 等无法识别。 + +**对于不支持转换的 API**：将 **补全为 Pytorch API 全名**，同时在行前通过 `>>>>>>` 的形式加以标记，用户需要对该 API 进行人工手动转换，然后删除 `>>>>>>` 标记，否则代码无法运行。 + + +## 贡献代码 + +代码自动转换工具（[PaConvert](https://github.com/PaddlePaddle/PaConvert)）为开源贡献形式，欢迎你向我们贡献代码，详细开发步骤请参考 [贡献代码教程](https://github.com/PaddlePaddle/PaConvert/blob/master/docs/CONTRIBUTING.md) diff --git a/docs/guides/model_convert/convert_from_pytorch/pytorch_api_mapping_cn.md b/docs/guides/model_convert/convert_from_pytorch/pytorch_api_mapping_cn.md index 1ca3344e04a..65b47bb117a 100644 --- a/docs/guides/model_convert/convert_from_pytorch/pytorch_api_mapping_cn.md +++ b/docs/guides/model_convert/convert_from_pytorch/pytorch_api_mapping_cn.md @@ -1,6 +1,6 @@ -# PyTorch 2.1.0 与 Paddle develop API 映射表 +# PyTorch 最新 release 与 Paddle develop API 映射表 -本文梳理了 PyTorch 最新发行版（当前 v2.1.0） API 与 PaddlePaddle develop 版本 API 对应关系与差异分析。通过本文档，帮助开发者快速迁移 PyTorch 使用经验，完成模型的开发与调优。 +本文梳理了 PyTorch 最新发行版（当前 v2.3.0） API 与 PaddlePaddle develop 版本 API 对应关系与差异分析。通过本文档，帮助开发者快速迁移 PyTorch 使用经验，完成模型的开发与调优。 ## 贡献代码 @@ -20,359 +20,23 @@ | [torch.autograd.XX](#id20) | 主要为`torch.autograd.XX`类 API | | [torch.cuda.XX](#id7) | 主要为`torch.cuda.XX`类 API | | [torch.distributed.XX](#id8) | 主要为`torch.distributed.XX`类 API | -| [torch.distributions.XX](#id9) | 主要为`torch.distributions.XX`类 API | -| [torch.fft.XX](#id10) | 主要为`torch.fft.XX`类 API | -| [torch.hub.XX](#id14) | 主要为`torch.hub.XX`类 API | -| [torch.linalg.XX](#id11) | 主要为`torch.linalg.XX`类 API | -| [torch.onnx.XX](#id15) | 主要为`torch.onnx.XX`类 API | -| [torch.profiler.XX](#id21) | 主要为`torch.profiler.XX`类 API | -| [torch.optim.XX](#id22) | 主要为`torch.optim.XX`类 API | -| [torch.sparse.XX](#id12) | 主要为`torch.sparse.XX`类 API | -| [其他](#id13) | 其他 API | +| [torch.distributions.XX](#id9) | 主要为`torch.distributions.XX`类 API | +| [torch.fft.XX](#id10) | 主要为`torch.fft.XX`类 API | +| [torch.hub.XX](#id14) | 主要为`torch.hub.XX`类 API | +| [torch.linalg.XX](#id11) | 主要为`torch.linalg.XX`类 API | +| [torch.onnx.XX](#id15) | 主要为`torch.onnx.XX`类 API | +| [torch.profiler.XX](#id21) | 主要为`torch.profiler.XX`类 API | +| [torch.optim.XX](#id22) | 主要为`torch.optim.XX`类 API | +| [torch.sparse.XX](#id12) | 主要为`torch.sparse.XX`类 API | +| [其他](#id13) | 其他 API | ## torch.XX API 映射列表 梳理了`torch.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.abs`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.abs.md) | -| REFERENCE-MAPPING-ITEM(`torch.absolute`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.absolute.md) | -| REFERENCE-MAPPING-ITEM(`torch.acos`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.acos.md) | -| REFERENCE-MAPPING-ITEM(`torch.add`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.add.md) | -| REFERENCE-MAPPING-ITEM(`torch.addmv`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.addmv.md) | -| REFERENCE-MAPPING-ITEM(`torch.addr`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.addr.md) | -| REFERENCE-MAPPING-ITEM(`torch.all`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.all.md) | -| REFERENCE-MAPPING-ITEM(`torch.allclose`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.allclose.md) | -| REFERENCE-MAPPING-ITEM(`torch.any`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.any.md) | -| REFERENCE-MAPPING-ITEM(`torch.arange`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arange.md) | -| REFERENCE-MAPPING-ITEM(`torch.arccos`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arccos.md) | -| REFERENCE-MAPPING-ITEM(`torch.arcsin`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arcsin.md) | -| REFERENCE-MAPPING-ITEM(`torch.arctan`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.arctan.md) | -| REFERENCE-MAPPING-ITEM(`torch.argmax`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.argmax.md) | -| REFERENCE-MAPPING-ITEM(`torch.argmin`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.argmin.md) | -| REFERENCE-MAPPING-ITEM(`torch.asin`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.asin.md) | -| REFERENCE-MAPPING-ITEM(`torch.as_tensor`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.as_tensor.md) | -| REFERENCE-MAPPING-ITEM(`torch.atan`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.atan.md) | -| REFERENCE-MAPPING-ITEM(`torch.baddbmm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.baddbmm.md) | -| REFERENCE-MAPPING-ITEM(`torch.bernoulli`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.bernoulli.md) | -| REFERENCE-MAPPING-ITEM(`torch.bincount`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.bincount.md) | -| REFERENCE-MAPPING-ITEM(`torch.broadcast_tensors`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_tensors.md) | -| REFERENCE-MAPPING-ITEM(`torch.broadcast_to`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.broadcast_to.md) | -| REFERENCE-MAPPING-ITEM(`torch.cat`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.cat.md) | -| REFERENCE-MAPPING-ITEM(`torch.ceil`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.ceil.md) | -| REFERENCE-MAPPING-ITEM(`torch.chain_matmul`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.chain_matmul.md) | -| REFERENCE-MAPPING-ITEM(`torch.cholesky_inverse`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.cholesky_inverse.md) | -| REFERENCE-MAPPING-ITEM(`torch.cholesky_solve`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.cholesky_solve.md) | -| REFERENCE-MAPPING-ITEM(`torch.chunk`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.chunk.md) | -| REFERENCE-MAPPING-ITEM(`torch.clamp`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.clamp.md) | -| REFERENCE-MAPPING-ITEM(`torch.conj`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.conj.md) | -| REFERENCE-MAPPING-ITEM(`torch.corrcoef`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.corrcoef.md) | -| REFERENCE-MAPPING-ITEM(`torch.cos`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.cos.md) | -| REFERENCE-MAPPING-ITEM(`torch.cosh`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.cosh.md) | -| REFERENCE-MAPPING-ITEM(`torch.count_nonzero`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.count_nonzero.md) | -| REFERENCE-MAPPING-ITEM(`torch.cov`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.cov.md) | -| REFERENCE-MAPPING-ITEM(`torch.det`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.det.md) | -| REFERENCE-MAPPING-ITEM(`torch.diag`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diag.md) | -| REFERENCE-MAPPING-ITEM(`torch.diagflat`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diagflat.md) | -| REFERENCE-MAPPING-ITEM(`torch.diagonal`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diagonal.md) | -| REFERENCE-MAPPING-ITEM(`torch.diff`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.diff.md) | -| REFERENCE-MAPPING-ITEM(`torch.dist`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.dist.md) | -| REFERENCE-MAPPING-ITEM(`torch.div`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.div.md) | -| REFERENCE-MAPPING-ITEM(`torch.divide`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.divide.md) | -| REFERENCE-MAPPING-ITEM(`torch.empty`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty.md) | -| REFERENCE-MAPPING-ITEM(`torch.empty_like`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.empty_like.md) | -| REFERENCE-MAPPING-ITEM(`torch.equal`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.equal.md) | -| REFERENCE-MAPPING-ITEM(`torch.erf`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.erf.md) | -| REFERENCE-MAPPING-ITEM(`torch.exp`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.exp.md) | -| REFERENCE-MAPPING-ITEM(`torch.eye`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.eye.md) | -| REFERENCE-MAPPING-ITEM(`torch.flatten`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flatten.md) | -| REFERENCE-MAPPING-ITEM(`torch.flip`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flip.md) | -| REFERENCE-MAPPING-ITEM(`torch.fliplr`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.fliplr.md) | -| REFERENCE-MAPPING-ITEM(`torch.flipud`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.flipud.md) | -| REFERENCE-MAPPING-ITEM(`torch.floor`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.floor.md) | -| REFERENCE-MAPPING-ITEM(`torch.floor_divide`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.floor_divide.md) | -| REFERENCE-MAPPING-ITEM(`torch.fmod`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.fmod.md) | -| REFERENCE-MAPPING-ITEM(`torch.from_numpy`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.from_numpy.md) | -| REFERENCE-MAPPING-ITEM(`torch.full`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.full.md) | -| REFERENCE-MAPPING-ITEM(`torch.full_like`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.full_like.md) | -| REFERENCE-MAPPING-ITEM(`torch.gather`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.gather.md) | -| REFERENCE-MAPPING-ITEM(`torch.imag`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.imag.md) | -| REFERENCE-MAPPING-ITEM(`torch.index_select`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.index_select.md) | -| REFERENCE-MAPPING-ITEM(`torch.isclose`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.isclose.md) | -| REFERENCE-MAPPING-ITEM(`torch.isfinite`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.isfinite.md) | -| REFERENCE-MAPPING-ITEM(`torch.isinf`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.isinf.md) | -| REFERENCE-MAPPING-ITEM(`torch.isnan`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.isnan.md) | -| REFERENCE-MAPPING-ITEM(`torch.istft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.istft.md) | -| REFERENCE-MAPPING-ITEM(`torch.is_complex`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.is_complex.md) | -| REFERENCE-MAPPING-ITEM(`torch.is_floating_point`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.is_floating_point.md) | -| REFERENCE-MAPPING-ITEM(`torch.is_tensor`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.is_tensor.md) | -| REFERENCE-MAPPING-ITEM(`torch.kthvalue`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.kthvalue.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.lstsq`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.lstsq.md) | -| REFERENCE-MAPPING-ITEM(`torch.linspace`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.linspace.md) | -| REFERENCE-MAPPING-ITEM(`torch.load`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.load.md) | -| REFERENCE-MAPPING-ITEM(`torch.log`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log.md) | -| REFERENCE-MAPPING-ITEM(`torch.log10`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log10.md) | -| REFERENCE-MAPPING-ITEM(`torch.log1p`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log1p.md) | -| REFERENCE-MAPPING-ITEM(`torch.log2`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.log2.md) | -| REFERENCE-MAPPING-ITEM(`torch.logical_and`, 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https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softshrink.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Unfold`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Unfold.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Upsample`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Upsample.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.UpsamplingBilinear2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.UpsamplingBilinear2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.UpsamplingNearest2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.UpsamplingNearest2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.ZeroPad2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ZeroPad2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.AdaptiveAvgPool1d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool1d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.AdaptiveAvgPool2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.AdaptiveAvgPool3d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AdaptiveAvgPool3d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.LogSigmoid`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LogSigmoid.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Sigmoid`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Sigmoid.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Softplus`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softplus.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Softsign`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softsign.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Tanh`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Tanh.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Tanhshrink`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Tanhshrink.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.TransformerDecoder`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TransformerDecoder.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Identity`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Identity.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.TripletMarginWithDistanceLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TripletMarginWithDistanceLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.GroupNorm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GroupNorm.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.LayerNorm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LayerNorm.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.CTCLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CTCLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.HingeEmbeddingLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.HingeEmbeddingLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.MarginRankingLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MarginRankingLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.NLLLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.NLLLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.SoftMarginLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SoftMarginLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Mish`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Mish.md) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.GLU`, https://pytorch.org/docs/stable/generated/torch.nn.GLU.html#torch.nn.GLU) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyBatchNorm1d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyBatchNorm1d.html#torch.nn.LazyBatchNorm1d) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyBatchNorm2d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyBatchNorm2d.html#torch.nn.LazyBatchNorm2d) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyBatchNorm3d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyBatchNorm3d.html#torch.nn.LazyBatchNorm3d) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyInstanceNorm1d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyInstanceNorm1d.html#torch.nn.LazyInstanceNorm1d) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyInstanceNorm2d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyInstanceNorm2d.html#torch.nn.LazyInstanceNorm2d) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyInstanceNorm3d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyInstanceNorm3d.html#torch.nn.LazyInstanceNorm3d) | -| REFERENCE-MAPPING-ITEM(`torch.nn.AlphaDropout`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AlphaDropout.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Bilinear`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Bilinear.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.CELU`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CELU.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.CosineEmbeddingLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.CosineEmbeddingLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.GaussianNLLLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GaussianNLLLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.GELU`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GELU.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.GRUCell`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.GRUCell.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Hardswish`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Hardswish.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Hardtanh`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Hardtanh.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.HuberLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.HuberLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.InstanceNorm1d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm1d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.InstanceNorm2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.InstanceNorm3d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.InstanceNorm3d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.LSTMCell`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.LSTMCell.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Unflatten`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Unflatten.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.MultiheadAttention`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MultiheadAttention.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.MultiLabelMarginLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MultiLabelMarginLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.MultiLabelSoftMarginLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MultiLabelSoftMarginLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.MultiMarginLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.MultiMarginLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.parallel.DistributedDataParallel`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.parallel.DistributedDataParallel.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Parameter`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Parameter.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.PoissonNLLLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.PoissonNLLLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.ReLU6`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.ReLU6.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.RNN`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNN.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.RNNBase`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNBase.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.RNNCell`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RNNCell.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.RReLU`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.RReLU.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.SELU`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SELU.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Sequential`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Sequential.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.SiLU`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SiLU.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Softmax2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Softmax2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.SyncBatchNorm.convert_sync_batchnorm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SyncBatchNorm.convert_sync_batchnorm.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.SyncBatchNorm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.SyncBatchNorm.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Threshold`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Threshold.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Transformer`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Transformer.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.TransformerDecoderLayer`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TransformerDecoderLayer.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.TransformerEncoder`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TransformerEncoder.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.TransformerEncoderLayer`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TransformerEncoderLayer.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.TripletMarginLoss`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.TripletMarginLoss.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.parameter.Parameter`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.parameter.Parameter.md) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.MultiLabelMarginLoss`, https://pytorch.org/docs/stable/generated/torch.nn.MultiLabelMarginLoss) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.CTCLoss`, https://pytorch.org/docs/stable/generated/torch.nn.CTCLoss.html#torch.nn.CTCLoss) | +| REFERENCE-MAPPING-TABLE(`torch.nn.`) | ***持续更新...*** ## torch.nn.functional.XX API 映射列表 梳理了`torch.nn.functional.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.adaptive_avg_pool1d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.adaptive_avg_pool1d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.adaptive_avg_pool2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.adaptive_avg_pool2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.adaptive_avg_pool3d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.adaptive_avg_pool3d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.adaptive_max_pool1d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.adaptive_max_pool1d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.adaptive_max_pool2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.adaptive_max_pool2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.adaptive_max_pool3d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.adaptive_max_pool3d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.fractional_max_pool2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.fractional_max_pool2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.fractional_max_pool3d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.fractional_max_pool3d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.affine_grid`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.affine_grid.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.bilinear`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.bilinear.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.conv1d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.conv1d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.conv2d`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/functional/torch.nn.functional.conv2d.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.functional.conv3d`, 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REFERENCE-MAPPING-ITEM(`torch.nn.init.kaiming_uniform_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.kaiming_uniform_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.normal_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.normal_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.ones_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.ones_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.orthogonal_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.orthogonal_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.uniform_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.uniform_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.xavier_normal_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_normal_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.xavier_uniform_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.xavier_uniform_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.init.zeros_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/init/torch.nn.init.zeros_.md) | +| REFERENCE-MAPPING-TABLE(`torch.nn.init.`) | ***持续更新...*** ## torch.nn.utils.XX API 映射列表 梳理了`torch.nn.utils.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.clip_grad_value_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.utils.clip_grad_value_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.remove_weight_norm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.utils.remove_weight_norm.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.spectral_norm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.utils.spectral_norm.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.weight_norm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.utils.weight_norm.md) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.utils.parametrize.is_parametrized`, https://pytorch.org/docs/stable/generated/torch.nn.utils.parametrize.is_parametrized.html#torch.nn.utils.parametrize.is_parametrized) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.parameters_to_vector`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.utils.parameters_to_vector.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.vector_to_parameters`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.utils.vector_to_parameters.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.clip_grad_norm_`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.utils.clip_grad_norm_.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.utils.parametrizations.spectral_norm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.nn.utils.parametrizations.spectral_norm.md) | - +| REFERENCE-MAPPING-TABLE(`torch.nn.utils.`) | ***持续更新...*** ## torch.nn.Module.XX API 映射列表 梳理了`torch.nn.Module.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| NOT-IMPLEMENTED-ITEM(`torch.nn.Module.to_empty`, https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to_empty) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.Module.register_full_backward_pre_hook`, https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_full_backward_pre_hook) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.Module.register_full_backward_hook`, https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_full_backward_hook) | -| NOT-IMPLEMENTED-ITEM(`torch.nn.Module.requires_grad_`, https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.requires_grad_) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.add_module`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.add_module.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.apply`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.apply.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.bfloat16`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.bfloat16.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.buffers`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.buffers.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.children`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.children.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.cpu`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cpu.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.cuda`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.cuda.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.double`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.double.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.eval`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.eval.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.float`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.float.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.get_buffer`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.get_buffer.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.get_parameter`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.get_parameter.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.get_submodule`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.get_submodule.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.half`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.half.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.load_state_dict`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.load_state_dict.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.named_buffers`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.named_buffers.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.named_children`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.named_children.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.named_modules`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.named_modules.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.named_parameters`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.named_parameters.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.parameters`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.parameters.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.register_buffer`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_buffer.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.register_forward_hook`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_hook.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.register_forward_pre_hook`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_forward_pre_hook.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.register_module`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_module.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.register_parameter`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.register_parameter.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.state_dict`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.state_dict.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.to`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.to.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.train`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.train.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.type`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.type.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.xpu`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.xpu.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module.zero_grad`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.zero_grad.md) | -| REFERENCE-MAPPING-ITEM(`torch.nn.Module`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.Module.md) | - +| REFERENCE-MAPPING-TABLE(`torch.nn.Module.`) | ***持续更新...*** ## torch.autograd.XX API 映射列表 梳理了`torch.autograd.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| NOT-IMPLEMENTED-ITEM(`torch.autograd.profiler.profile.self_cpu_time_total`, https://pytorch.org/docs/stable/generated/torch.autograd.profiler.profile.self_cpu_time_total.html#torch.autograd.profiler.profile.self_cpu_time_total) | -| NOT-IMPLEMENTED-ITEM(`torch.autograd.function.FunctionCtx.mark_dirty`, https://pytorch.org/docs/stable/generated/torch.autograd.function.FunctionCtx.mark_dirty.html#torch.autograd.function.FunctionCtx.mark_dirty) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.backward`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.backward.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.Function.backward`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.Function.backward.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.Function.forward`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.Function.forward.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.function.FunctionCtx.mark_non_differentiable`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.function.FunctionCtx.mark_non_differentiable.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.function.FunctionCtx.save_for_backward`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.function.FunctionCtx.save_for_backward.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.function.FunctionCtx.set_materialize_grads`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.function.FunctionCtx.set_materialize_grads.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.Function`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.Function.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.functional.hessian`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.functional.hessian.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.functional.jacobian`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.functional.jacobian.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.functional.jvp`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.functional.jvp.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.functional.vjp`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.functional.vjp.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.grad`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.grad.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.graph.saved_tensors_hooks`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.graph.saved_tensors_hooks.md) | -| REFERENCE-MAPPING-ITEM(`torch.autograd.profiler.profile.export_chrome_trace`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.autograd.profiler.profile.export_chrome_trace.md) | +| REFERENCE-MAPPING-TABLE(`torch.autograd.`) | ***持续更新...*** ## torch.cuda.XX API 映射列表 梳理了`torch.cuda.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.cuda.max_memory_allocated`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.max_memory_allocated.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.Event`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.Event.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.current_device`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.current_device.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.current_stream`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.current_stream.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.device_count`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.device_count.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.get_device_capability`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_device_capability.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.get_device_properties`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_device_properties.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.set_device`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.set_device.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.empty_cache`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.empty_cache.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.manual_seed`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.manual_seed.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.max_memory_reserved`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.max_memory_reserved.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.max_memory_allocated`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.max_memory_allocated.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.memory_reserved`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.memory_reserved.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.memory_allocated`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.memory_allocated.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.synchronize`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.synchronize.md) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.memory_usage`, https://pytorch.org/docs/stable/generated/torch.cuda.memory_usage.html#torch.cuda.memory_usage) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.mem_get_info`, https://pytorch.org/docs/stable/generated/torch.cuda.mem_get_info.html#torch.cuda.mem_get_info) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.set_rng_state`, https://pytorch.org/docs/stable/generated/torch.cuda.set_rng_state.html#torch.cuda.set_rng_state) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.default_stream`, https://pytorch.org/docs/stable/generated/torch.cuda.default_stream.html?highlight=torch+cuda+default_stream#torch.cuda.default_stream) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.get_arch_list`, https://pytorch.org/docs/stable/generated/torch.cuda.get_arch_list.html?highlight=torch+cuda+get_arch_list#torch.cuda.get_arch_list) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.is_initialized`, https://pytorch.org/docs/stable/generated/torch.cuda.is_initialized.html?highlight=torch+cuda+is_initialized#torch.cuda.is_initialized) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.StreamContext`, https://pytorch.org/docs/stable/generated/torch.cuda.StreamContext.html#torch.cuda.StreamContext) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.is_current_stream_capturing`, https://pytorch.org/docs/stable/generated/torch.cuda.is_current_stream_capturing.html#torch.cuda.is_current_stream_capturing) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.amp.autocast`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.amp.autocast.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.amp.GradScaler`, 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REFERENCE-MAPPING-ITEM(`torch.cuda.get_rng_state_all`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.get_rng_state_all.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.HalfTensor`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.HalfTensor.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.is_available`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.is_available.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.manual_seed_all`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.manual_seed_all.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.nvtx.range_pop`, 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REFERENCE-MAPPING-ITEM(`torch.cuda.stream`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.stream.md) | -| REFERENCE-MAPPING-ITEM(`torch.cuda.Stream`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/cuda/torch.cuda.Stream__upper.md) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.comm.scatter`, https://pytorch.org/docs/stable/generated/torch.cuda.comm.scatter.html#torch-cuda-comm-scatter) | -| NOT-IMPLEMENTED-ITEM(`torch.cuda.comm.gather`, https://pytorch.org/docs/stable/generated/torch.cuda.comm.gather.html#torch-cuda-comm-gather) | +| REFERENCE-MAPPING-TABLE(`torch.cuda.`) | ***持续更新...*** ## torch.distributed.XX API 映射列表 梳理了`torch.distributed.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.distributed.init_process_group`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.init_process_group.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.barrier`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.barrier.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.ReduceOp`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.barrier.md) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.rpc.WorkerInfo`, https://pytorch.org/docs/stable/rpc.html#torch.distributed.rpc.WorkerInfo) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.rpc.functions.async_execution`, https://pytorch.org/docs/stable/rpc.html#torch.distributed.rpc.functions.async_execution) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.broadcast_multigpu`, https://pytorch.org/docs/stable/distributed.html#torch.distributed.broadcast_multigpu) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.all_reduce_multigpu`, https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_reduce_multigpu) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.reduce_multigpu`, https://pytorch.org/docs/stable/distributed.html#torch.distributed.reduce_multigpu) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.all_gather_multigpu`, https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_gather_multigpu) | -| NOT-IMPLEMENTED-ITEM(`torch.distributed.reduce_scatter_multigpu`, https://pytorch.org/docs/stable/distributed.html#torch.distributed.reduce_scatter_multigpu) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.constraints.Constraint`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.constraints.Constraint) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.constraint_registry.ConstraintRegistry`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.constraint_registry.ConstraintRegistry) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.all_gather`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_gather.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.all_gather_object`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_gather_object.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.all_reduce`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_reduce.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.all_to_all`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.all_to_all.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.broadcast`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.broadcast.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.broadcast_object_list`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.broadcast_object_list.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.get_rank`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.get_rank.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.get_world_size`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.get_world_size.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.irecv`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.irecv.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.isend`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.isend.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.new_group`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.new_group.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.recv`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.recv.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.reduce`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.reduce_scatter`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.reduce_scatter.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.rpc.get_worker_info`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.rpc.get_worker_info.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.rpc.rpc_async`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.rpc.rpc_async.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.rpc.rpc_sync`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.rpc.rpc_sync.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.rpc.shutdown`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.rpc.shutdown.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.scatter`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.scatter.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.scatter_object_list`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.scatter_object_list.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributed.send`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributed/torch.distributed.send.md) | - +| REFERENCE-MAPPING-TABLE(`torch.distributed.`) | ***持续更新...*** ## torch.distributions.XX API 映射列表 梳理了`torch.distributions.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.distributions.kl.kl_divergence`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.kl.kl_divergence.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.kl.register_kl`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.kl.register_kl.md) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.binomial.Binomial`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.binomial.Binomial) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.continuous_bernoulli.ContinuousBernoulli`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.exponential.Exponential`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.exponential.Exponential) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.one_hot_categorical.OneHotCategorical`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.one_hot_categorical.OneHotCategorical) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.transforms.CumulativeDistributionTransform`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.transforms.CumulativeDistributionTransform) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.transforms.SoftplusTransform`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.transforms.SoftplusTransform) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.bernoulli.Bernoulli`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.bernoulli.Bernoulli.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.beta.Beta`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.beta.Beta.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.categorical.Categorical`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.categorical.Categorical.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.cauchy.Cauchy`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.cauchy.Cauchy.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.dirichlet.Dirichlet`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.dirichlet.Dirichlet.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.distribution.Distribution`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.distribution.Distribution.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.exp_family.ExponentialFamily`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.exp_family.ExponentialFamily.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.geometric.Geometric`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.geometric.Geometric.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.gumbel.Gumbel`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.gumbel.Gumbel.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.independent.Independent`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.independent.Independent.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.laplace.Laplace`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.laplace.Laplace.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.log_normal.LogNormal`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.log_normal.LogNormal.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.multinomial.Multinomial`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.multinomial.Multinomial.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.normal.Normal`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.normal.Normal.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transformed_distribution.TransformedDistribution`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transformed_distribution.TransformedDistribution.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.ComposeTransform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.ComposeTransform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.IndependentTransform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.IndependentTransform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.ReshapeTransform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.ReshapeTransform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.SoftmaxTransform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.SoftmaxTransform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.StackTransform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.StackTransform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.StickBreakingTransform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.StickBreakingTransform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.transforms.Transform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.transforms.Transform.md) | -| REFERENCE-MAPPING-ITEM(`torch.distributions.uniform.Uniform`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/distributions/torch.distributions.uniform.Uniform.md) | -| NOT-IMPLEMENTED-ITEM(`torch.distributions.transforms.CatTransform`, https://pytorch.org/docs/stable/distributions.html#torch.distributions.transforms.CatTransform) | - +| REFERENCE-MAPPING-TABLE(`torch.distributions.`) | ***持续更新...*** ## torch.fft.XX API 映射列表 梳理了`torch.fft.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.fft.fftshift`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.fftshift.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ifftshift`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ifftshift.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.hfft2`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.hfft2.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.hfftn`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.hfftn.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ihfft2`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ihfft2.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ihfftn`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ihfftn.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.fft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.fft.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.fft2`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.fft2.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.fftfreq`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.fftfreq.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.fftn`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.fftn.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.hfft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.hfft.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ifft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ifft.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ifft2`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ifft2.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ifftn`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ifftn.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.ihfft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.ihfft.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.irfft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.irfft.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.irfft2`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.irfft2.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.irfftn`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.irfftn.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.rfft`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.rfft.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.rfftfreq`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.rfftfreq.md) | -| REFERENCE-MAPPING-ITEM(`torch.fft.rfftn`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/fft/torch.fft.rfftn.md) | - +| REFERENCE-MAPPING-TABLE(`torch.fft.`) | ***持续更新...*** ## torch.hub.XX API 映射列表 + 梳理了`torch.hub.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| NOT-IMPLEMENTED-ITEM(`torch.hub.get_dir`, https://pytorch.org/docs/stable/hub.html?highlight=torch+hub+get_dir#torch.hub.get_dir) | -| NOT-IMPLEMENTED-ITEM(`torch.hub.set_dir`, https://pytorch.org/docs/stable/hub.html?highlight=torch+hub+get_dir#torch.hub.set_dir) | -| REFERENCE-MAPPING-ITEM(`torch.hub.download_url_to_file`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/hub/torch.hub.download_url_to_file.md) | -| REFERENCE-MAPPING-ITEM(`torch.hub.help`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/hub/torch.hub.help.md) | -| REFERENCE-MAPPING-ITEM(`torch.hub.list`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/hub/torch.hub.list.md) | -| REFERENCE-MAPPING-ITEM(`torch.hub.load`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/hub/torch.hub.load.md) | +| REFERENCE-MAPPING-TABLE(`torch.hub.`) | ***持续更新...*** ## torch.linalg.XX API 映射列表 + 梳理了`torch.linalg.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.linalg.svd`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.svd.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.matrix_power`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.matrix_power.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.matrix_exp`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.matrix_exp.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.cholesky`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.cholesky.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.matrix_rank`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.matrix_rank.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.cross`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.cross.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.lu`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.lu.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.solve_triangular`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.solve_triangular.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.eigvals`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.eigvals.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.eig`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.eig.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.multi_dot`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.multi_dot.md) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.matrix_exp`, https://pytorch.org/docs/stable/generated/torch.linalg.matrix_exp.html#torch.linalg.matrix_exp) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.matrix_norm`, https://pytorch.org/docs/stable/generated/torch.linalg.matrix_norm.html#torch.linalg.matrix_norm) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.vector_norm`, https://pytorch.org/docs/stable/generated/torch.linalg.vector_norm.html#torch.linalg.vector_norm) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.cholesky_ex`, https://pytorch.org/docs/stable/generated/torch.linalg.cholesky_ex.html#torch.linalg.cholesky_ex) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.inv_ex`, https://pytorch.org/docs/stable/generated/torch.linalg.inv_ex.html#torch.linalg.inv_ex) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.cond`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.cond.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.det`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.det.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.diagonal`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.diagonal.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.eigh`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.eigh.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.eigvalsh`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.eigvalsh.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.inv`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.inv.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.matmul`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.matmul.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.norm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.norm.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.pinv`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.pinv.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.qr`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.qr.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.slogdet`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.slogdet.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.solve`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.solve.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.svdvals`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.svdvals.md) | -| REFERENCE-MAPPING-ITEM(`torch.linalg.vander`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/linalg/torch.linalg.vander.md) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.cholesky_ex`, https://pytorch.org/docs/stable/generated/torch.linalg.cholesky_ex.html#torch.linalg.cholesky_ex) | -| NOT-IMPLEMENTED-ITEM(`torch.linalg.inv_ex`, https://pytorch.org/docs/stable/generated/torch.linalg.inv_ex.html#torch.linalg.inv_ex) | +| REFERENCE-MAPPING-TABLE(`torch.linalg.`) | ***持续更新...*** ## torch.onnx.XX API 映射列表 + 梳理了`torch.onnx.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| NOT-IMPLEMENTED-ITEM(`torch.onnx.enable_log`, https://pytorch.org/docs/stable/onnx.html#torch.onnx.enable_log) | -| NOT-IMPLEMENTED-ITEM(`torch.onnx.disable_log`, https://pytorch.org/docs/stable/onnx.html#torch.onnx.disable_log) | -| REFERENCE-MAPPING-ITEM(`torch.onnx.export`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.onnx.export.md) | +| REFERENCE-MAPPING-TABLE(`torch.onnx.`) | ***持续更新...*** ## torch.optim.XX API 映射列表 梳理了`torch.optim.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| NOT-IMPLEMENTED-ITEM(`torch.optim.lr_scheduler.LinearLR`, https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.LinearLR.html#torch.optim.lr_scheduler.LinearLR) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Adadelta`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Adadelta.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Adagrad`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Adagrad.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Adam`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Adam.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Adamax`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Adamax.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.AdamW`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.AdamW.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.LBFGS`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.LBFGS.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.ConstantLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.ConstantLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.CosineAnnealingLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.CosineAnnealingLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.CyclicLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.CyclicLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.ExponentialLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.ExponentialLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.LambdaLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.LambdaLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.MultiplicativeLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.MultiplicativeLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.MultiStepLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.MultiStepLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.OneCycleLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.OneCycleLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.ReduceLROnPlateau`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.ReduceLROnPlateau.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.lr_scheduler.StepLR`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.lr_scheduler.StepLR.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Optimizer.load_state_dict`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Optimizer.load_state_dict.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Optimizer`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Optimizer.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Optimizer.state_dict`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Optimizer.state_dict.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Optimizer.step`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.Optimizer.step.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.RMSprop`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.RMSprop.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.SGD`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/optimizer/torch.optim.SGD.md) | -| REFERENCE-MAPPING-ITEM(`torch.optim.Optimizer.add_param_group`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.optim.Optimizer.add_param_group.md) | +| REFERENCE-MAPPING-TABLE(`torch.optim.`) | ***持续更新...*** ## torch.profiler.XX API 映射列表 + 梳理了`torch.profiler.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| NOT-IMPLEMENTED-ITEM(`torch.profiler.ProfilerActivity`, https://pytorch.org/docs/stable/profiler.html#torch.profiler.ProfilerActivity) | -| NOT-IMPLEMENTED-ITEM(`torch.profiler.ProfilerAction`, https://pytorch.org/docs/stable/profiler.html#torch.profiler.ProfilerAction) | -| REFERENCE-MAPPING-ITEM(`torch.profiler.profile`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.profiler.profile.md) | -| REFERENCE-MAPPING-ITEM(`torch.profiler.schedule`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.profiler.schedule.md) | +| REFERENCE-MAPPING-TABLE(`torch.profiler.`) | ***持续更新...*** ## torch.sparse.XX API 映射列表 + 梳理了`torch.sparse.XX`类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.sparse.addmm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.addmm.md) | -| REFERENCE-MAPPING-ITEM(`torch.sparse.mm`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.mm.md) | -| REFERENCE-MAPPING-ITEM(`torch.sparse.softmax`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/sparse/torch.sparse.softmax.md) | -| NOT-IMPLEMENTED-ITEM(`torch.sparse.sum`, https://pytorch.org/docs/stable/generated/torch.sparse.sum.html?highlight=torch+sparse+sum#torch.sparse.sum) | -| NOT-IMPLEMENTED-ITEM(`torch.sparse.sampled_addmm`, https://pytorch.org/docs/stable/generated/torch.sparse.sampled_addmm.html#torch.sparse.sampled_addmm) | +| REFERENCE-MAPPING-TABLE(`torch.sparse.`) | ***持续更新...*** ## 其他类 API 映射列表 + 梳理了其他类 API 的 PyTorch-PaddlePaddle API 映射列表。 -| 序号 | PyTorch-2.1 | PaddlePaddle-dev | 映射关系分类 | 详细对比 | +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | +| ----- | ----------- | ----------------- | ----------- | ------- | +| REFERENCE-MAPPING-TABLE(`torch.`) | + +***持续更新...*** + +## API 别名映射列表 + +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | +| ---- | -------------------- | -------------- | ----------- | ---- | +| ALIAS-REFERENCE-ITEM(`torch.Tensor.absolute_`, `torch.Tensor.abs_`) | +| ALIAS-REFERENCE-ITEM(`torch.Tensor.acos`, `torch.Tensor.arccos`) | +| ALIAS-REFERENCE-ITEM(`torch.Tensor.asin`, `torch.Tensor.arcsin`) | +| ALIAS-REFERENCE-ITEM(`torch.Tensor.atan`, `torch.Tensor.arctan`) | +| ALIAS-REFERENCE-ITEM(`torch.Tensor.atan2`, `torch.Tensor.arctan2`) | +| ALIAS-REFERENCE-ITEM(`torch.absolute_`, `torch.abs_`) | +| ALIAS-REFERENCE-ITEM(`torch.adaptive_avg_pool1d`, `torch.nn.functional.adaptive_avg_pool1d`) | +| ALIAS-REFERENCE-ITEM(`torch.autograd.function.Function`, `torch.autograd.Function`) | +| ALIAS-REFERENCE-ITEM(`torch.avg_pool1d`, `torch.nn.functional.avg_pool1d`) | +| ALIAS-REFERENCE-ITEM(`torch.bilinear`, `torch.nn.functional.bilinear`) | +| ALIAS-REFERENCE-ITEM(`torch.conv1d`, `torch.nn.functional.conv1d`) | +| ALIAS-REFERENCE-ITEM(`torch.conv2d`, `torch.nn.functional.conv2d`) | +| ALIAS-REFERENCE-ITEM(`torch.conv3d`, `torch.nn.functional.conv3d`) | +| ALIAS-REFERENCE-ITEM(`torch.conv_transpose1d`, `torch.nn.functional.conv_transpose1d`) | +| ALIAS-REFERENCE-ITEM(`torch.conv_transpose2d`, `torch.nn.functional.conv_transpose2d`) | +| ALIAS-REFERENCE-ITEM(`torch.conv_transpose3d`, `torch.nn.functional.conv_transpose3d`) | +| ALIAS-REFERENCE-ITEM(`torch.cuda.amp.autocast_mode.autocast`, `torch.cuda.amp.autocast`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.AbsTransform`, `torch.distributions.transforms.AbsTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.AffineTransform`, `torch.distributions.transforms.AffineTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Bernoulli`, `torch.distributions.bernoulli.Bernoulli`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Beta`, `torch.distributions.beta.Beta`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Categorical`, `torch.distributions.categorical.Categorical`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Cauchy`, `torch.distributions.cauchy.Cauchy`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.ComposeTransform`, `torch.distributions.transforms.ComposeTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Dirichlet`, `torch.distributions.dirichlet.Dirichlet`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Distribution`, `torch.distributions.distribution.Distribution`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.ExpTransform`, `torch.distributions.transforms.ExpTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.ExponentialFamily`, `torch.distributions.exp_family.ExponentialFamily`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Geometric`, `torch.distributions.geometric.Geometric`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Gumbel`, `torch.distributions.gumbel.Gumbel`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Independent`, `torch.distributions.independent.Independent`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.IndependentTransform`, `torch.distributions.transforms.IndependentTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Laplace`, `torch.distributions.laplace.Laplace`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.LogNormal`, `torch.distributions.log_normal.LogNormal`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Multinomial`, `torch.distributions.multinomial.Multinomial`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Normal`, `torch.distributions.normal.Normal`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.PowerTransform`, `torch.distributions.transforms.PowerTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.ReshapeTransform`, `torch.distributions.transforms.ReshapeTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.SigmoidTransform`, `torch.distributions.transforms.SigmoidTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.SoftmaxTransform`, `torch.distributions.transforms.SoftmaxTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.StackTransform`, `torch.distributions.transforms.StackTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.StickBreakingTransform`, `torch.distributions.transforms.StickBreakingTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.TanhTransform`, `torch.distributions.transforms.TanhTransform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Transform`, `torch.distributions.transforms.Transform`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.TransformedDistribution`, `torch.distributions.transformed_distribution.TransformedDistribution`) | +| ALIAS-REFERENCE-ITEM(`torch.distributions.Uniform`, `torch.distributions.uniform.Uniform`) | +| ALIAS-REFERENCE-ITEM(`torch.greater_equal`, `torch.ge`) | +| ALIAS-REFERENCE-ITEM(`torch.group_norm`, `torch.nn.functional.group_norm`) | +| ALIAS-REFERENCE-ITEM(`torch.hardshrink`, `torch.nn.functional.hardshrink`) | +| ALIAS-REFERENCE-ITEM(`torch.layer_norm`, `torch.nn.functional.layer_norm`) | +| ALIAS-REFERENCE-ITEM(`torch.logsumexp`, `torch.special.logsumexp`) | +| ALIAS-REFERENCE-ITEM(`torch.matrix_exp`, `torch.linalg.matrix_exp`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.NLLLoss2d`, `torch.nn.NLLLoss`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.AvgPool1d`, `torch.nn.AvgPool1d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.AvgPool2d`, `torch.nn.AvgPool2d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.AvgPool3d`, `torch.nn.AvgPool3d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.BatchNorm1d`, `torch.nn.BatchNorm1d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.BatchNorm2d`, `torch.nn.BatchNorm2d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.BatchNorm3d`, `torch.nn.BatchNorm3d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.CosineSimilarity`, `torch.nn.CosineSimilarity`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.Dropout`, `torch.nn.Dropout`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.GroupNorm`, `torch.nn.GroupNorm`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.LSTM`, `torch.nn.LSTM`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.Module`, `torch.nn.Module`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.RNN`, `torch.nn.RNN`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.RNNBase`, `torch.nn.RNNBase`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.RNNCell`, `torch.nn.RNNCell`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.SyncBatchNorm`, `torch.nn.SyncBatchNorm`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.activation.ReLU`, `torch.nn.ReLU`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.batchnorm.BatchNorm1d`, `torch.nn.BatchNorm1d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.batchnorm.BatchNorm2d`, `torch.nn.BatchNorm2d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.batchnorm.BatchNorm3d`, `torch.nn.BatchNorm3d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.batchnorm.SyncBatchNorm`, `torch.nn.SyncBatchNorm`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.conv.Conv2d`, `torch.nn.Conv2d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.distance.CosineSimilarity`, `torch.nn.CosineSimilarity`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.module.Module`, `torch.nn.Module`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.pooling.AvgPool1d`, `torch.nn.AvgPool1d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.pooling.AvgPool2d`, `torch.nn.AvgPool2d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.pooling.AvgPool3d`, `torch.nn.AvgPool3d`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.rnn.LSTM`, `torch.nn.LSTM`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.rnn.RNN`, `torch.nn.RNN`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.rnn.RNNBase`, `torch.nn.RNNBase`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.rnn.RNNCell`, `torch.nn.RNNCell`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.modules.sparse.Embedding`, `torch.nn.Embedding`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.parallel.DataParallel`, `torch.nn.DataParallel`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.parallel.data_parallel.DataParallel`, `torch.nn.DataParallel`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.parallel.distributed.DistributedDataParallel`, `torch.nn.parallel.DistributedDataParallel`) | +| ALIAS-REFERENCE-ITEM(`torch.nn.utils.clip_grad_norm`, `torch.nn.utils.clip_grad_norm_`) | +| ALIAS-REFERENCE-ITEM(`torch.optim.sgd.SGD`, `torch.optim.SGD`) | +| ALIAS-REFERENCE-ITEM(`torch.orgqr`, `torch.linalg.householder_product`) | +| ALIAS-REFERENCE-ITEM(`torch.pairwise_distance`, `torch.nn.functional.pairwise_distance`) | +| ALIAS-REFERENCE-ITEM(`torch.pdist`, `torch.nn.functional.pdist`) | +| ALIAS-REFERENCE-ITEM(`torch.pixel_shuffle`, `torch.nn.functional.pixel_shuffle`) | +| ALIAS-REFERENCE-ITEM(`torch.pixel_unshuffle`, `torch.nn.functional.pixel_unshuffle`) | +| ALIAS-REFERENCE-ITEM(`torch.prelu`, `torch.nn.functional.prelu`) | +| ALIAS-REFERENCE-ITEM(`torch.relu_`, `torch.nn.functional.relu_`) | +| ALIAS-REFERENCE-ITEM(`torch.rrelu_`, `torch.nn.functional.rrelu_`) | +| ALIAS-REFERENCE-ITEM(`torch.tanh`, `torch.nn.functional.tanh`) | +| ALIAS-REFERENCE-ITEM(`torch.threshold`, `torch.nn.functional.threshold`) | +| ALIAS-REFERENCE-ITEM(`torch.torch.Tensor`, `torch.Tensor`) | +| ALIAS-REFERENCE-ITEM(`torch.torch.finfo`, `torch.finfo`) | +| ALIAS-REFERENCE-ITEM(`torch.trapz`, `torch.trapezoid`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.dataloader.DataLoader`, `torch.utils.data.DataLoader`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.dataset.ConcatDataset`, `torch.utils.data.ConcatDataset`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.dataset.Dataset`, `torch.utils.data.Dataset`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.sampler.BatchSampler`, `torch.utils.data.BatchSampler`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.sampler.RandomSampler`, `torch.utils.data.RandomSampler`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.sampler.Sampler`, `torch.utils.data.Sampler`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.sampler.SequentialSampler`, `torch.utils.data.SequentialSampler`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.sampler.SubsetRandomSampler`, `torch.utils.data.SubsetRandomSampler`) | +| ALIAS-REFERENCE-ITEM(`torch.utils.data.sampler.WeightedRandomSampler`, `torch.utils.data.WeightedRandomSampler`) | + + ## 尚未实现的 API 列表 + +| 序号 | Pytorch 最新 release | Paddle develop | 映射关系分类 | 备注 | | ----- | ----------- | ----------------- | ----------- | ------- | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.random_split`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.random_split.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.distributed.DistributedSampler`, https://github.com/PaddlePaddle/X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/utils/torch.utils.data.distributed.DistributedSampler.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.Dataset`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.Dataset.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.BatchSampler`, https://github.com/PaddlePaddle/X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/utils/torch.utils.data.BatchSampler.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.Sampler`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.Sampler.md) | -| REFERENCE-MAPPING-ITEM(`torch.random.manual_seed`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.random.manual_seed.md) | -| REFERENCE-MAPPING-ITEM(`torch.special.expm1`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.special.expm1.md) | -| NOT-IMPLEMENTED-ITEM(`torch.backends.cudnn.version`, https://pytorch.org/docs/stable/backends.html?highlight=torch+backends+cudnn+version#torch.backends.cudnn.version) | -| REFERENCE-MAPPING-ITEM(`torch.special.erf`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/others/torch.special.erf.md) | +| NOT-IMPLEMENTED-ITEM(`torch.bitwise_left_shift`, https://pytorch.org/docs/stable/generated/torch.bitwise_left_shift.html#torch.bitwise_left_shift) | +| NOT-IMPLEMENTED-ITEM(`torch.bitwise_right_shift`, https://pytorch.org/docs/stable/generated/torch.bitwise_right_shift.html#torch.bitwise_right_shift) | +| NOT-IMPLEMENTED-ITEM(`torch.can_cast`, https://pytorch.org/docs/stable/generated/torch.can_cast.html) | +| NOT-IMPLEMENTED-ITEM(`torch.geqrf`, https://pytorch.org/docs/stable/generated/torch.geqrf.html?highlight=geqrf#torch.geqrf) | +| NOT-IMPLEMENTED-ITEM(`torch.get_deterministic_debug_mode`, https://pytorch.org/docs/stable/generated/torch.get_deterministic_debug_mode.html#torch.get_deterministic_debug_mode) | +| NOT-IMPLEMENTED-ITEM(`torch.gradient`, https://pytorch.org/docs/stable/generated/torch.gradient.html#torch.gradient) | +| NOT-IMPLEMENTED-ITEM(`torch.is_conj`, https://pytorch.org/docs/stable/generated/torch.is_conj.html#torch.is_conj) | +| NOT-IMPLEMENTED-ITEM(`torch.isin`, https://pytorch.org/docs/stable/generated/torch.isin.html#torch.isin) | +| NOT-IMPLEMENTED-ITEM(`torch.layout`, https://pytorch.org/docs/stable/tensor_attributes.html#torch.layout) | +| NOT-IMPLEMENTED-ITEM(`torch.positive`, https://pytorch.org/docs/stable/generated/torch.positive.html#torch.positive) | +| NOT-IMPLEMENTED-ITEM(`torch.resolve_conj`, https://pytorch.org/docs/stable/generated/torch.resolve_conj.html#torch.resolve_conj) | +| NOT-IMPLEMENTED-ITEM(`torch.resolve_neg`, https://pytorch.org/docs/stable/generated/torch.resolve_neg.html#torch.resolve_neg) | +| NOT-IMPLEMENTED-ITEM(`torch.scatter_reduce`, https://pytorch.org/docs/stable/generated/torch.scatter_reduce.html#torch.scatter_reduce) | +| NOT-IMPLEMENTED-ITEM(`torch.set_deterministic_debug_mode`, https://pytorch.org/docs/stable/generated/torch.set_deterministic_debug_mode.html#torch.set_deterministic_debug_mode) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.GLU`, https://pytorch.org/docs/stable/generated/torch.nn.GLU.html#torch.nn.GLU) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyBatchNorm1d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyBatchNorm1d.html#torch.nn.LazyBatchNorm1d) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyBatchNorm2d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyBatchNorm2d.html#torch.nn.LazyBatchNorm2d) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyBatchNorm3d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyBatchNorm3d.html#torch.nn.LazyBatchNorm3d) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyInstanceNorm1d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyInstanceNorm1d.html#torch.nn.LazyInstanceNorm1d) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyInstanceNorm2d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyInstanceNorm2d.html#torch.nn.LazyInstanceNorm2d) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.LazyInstanceNorm3d`, https://pytorch.org/docs/stable/generated/torch.nn.LazyInstanceNorm3d.html#torch.nn.LazyInstanceNorm3d) | +| NOT-IMPLEMENTED-ITEM(`torch.nn.functional.multilabel_margin_loss`, https://pytorch.org/docs/stable/generated/torch.nn.functional.multilabel_margin_loss.html#torch.nn.functional.multilabel_margin_loss) | +| NOT-IMPLEMENTED-ITEM(`torch.Tensor.bitwise_left_shift`, https://pytorch.org/docs/stable/generated/torch.Tensor.bitwise_left_shift.html#torch.Tensor.bitwise_left_shift) | +| NOT-IMPLEMENTED-ITEM(`torch.Tensor.bitwise_right_shift`, https://pytorch.org/docs/stable/generated/torch.Tensor.bitwise_right_shift.html#torch.Tensor.bitwise_right_shift) | +| 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https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.cpp_extension.BuildExtension.with_options.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.cpp_extension.CUDA_HOME`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.cpp_extension.CUDA_HOME.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.cpp_extension.BuildExtension`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.cpp_extension.BuildExtension.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.ChainDataset`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.ChainDataset.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.dataloader.default_collate`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.dataloader.default_collate.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.default_collate`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.default_collate.md) | -| REFERENCE-MAPPING-ITEM(`torch.utils.data.DistributedSampler`, https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/utils/torch.utils.data.DistributedSampler.md) | +| NOT-IMPLEMENTED-ITEM(`torch.utils.cpp_extension.load_inline`, https://pytorch.org/docs/stable/cpp_extension.html#torch.utils.cpp_extension.load_inline) | ***持续更新...*** diff --git a/docs/guides/model_convert/convert_from_pytorch/validate_mapping_in_api_difference.py b/docs/guides/model_convert/convert_from_pytorch/validate_mapping_in_api_difference.py index b01cbc292f6..16bc1308308 100644 --- a/docs/guides/model_convert/convert_from_pytorch/validate_mapping_in_api_difference.py +++ b/docs/guides/model_convert/convert_from_pytorch/validate_mapping_in_api_difference.py @@ -1,10 +1,17 @@ +import collections import json import os import re +import sys +import traceback import typing +import urllib +import urllib.parse from enum import IntEnum from typing import TypedDict +PADDLE_DOCS_BASE_URL = "https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/" + mapping_type_set = { # type 1 "无参数", @@ -15,13 +22,14 @@ # type 2 "torch 参数更多", # type 3 + # "参数不一致", "返回参数类型不一致", - "参数不一致", - "参数用法不一致", + "输入参数类型不一致", + "输入参数用法不一致", # type 4 "组合替代实现", # type 5 - "用法不同：涉及上下文修改", + "涉及上下文修改", # type 6 "对应 API 不在主框架", # type 7 @@ -38,6 +46,7 @@ class DiffMeta(TypedDict): paddle_api: typing.Optional[str] paddle_api_url: typing.Optional[str] paddle_signature: typing.Optional[str] + args_mapping: typing.Optional[typing.List[typing.Dict[str, str]]] mapping_type: str source_file: str @@ -55,7 +64,12 @@ class ParserState(IntEnum): wf_paddle_code = 7 wf_paddle_code_end = 8 - end = 9 + wait_for_args = 9 + wf_args_table_title = 10 + wf_args_table_sep = 11 + wf_args_table_end = 12 + + end = 13 def unescape_api(api): @@ -63,6 +77,11 @@ def unescape_api(api): def reformat_signature(code): + """ + 从函数签名代码中解析出函数名和参数列表 + - code: 函数签名代码 + - 返回值: 函数名和参数列表 + """ lines = [l for l in code.split("\n") if len(l.strip()) > 0] assert len(lines) > 0, "code have no lines." buffer = "".join([l.strip() for l in lines]) @@ -103,6 +122,11 @@ def reformat_signature(code): def get_meta_from_diff_file(filepath): + """ + 该函数从指定的映射文件中解析出元数据信息 + - filepath: 映射文件路径 + - 返回值: DiffMeta 类型的元数据信息 + """ meta_data: DiffMeta = {"source_file": filepath} state = ParserState.wait_for_title title_pattern = re.compile(r"^## +\[(?P[^\]]+)\] *(?P.+)$") @@ -116,9 +140,13 @@ def get_meta_from_diff_file(filepath): code_pattern = re.compile(r"^(?P(paddle|torch)[^\( ]+)(.*?)$") code_end_pattern = re.compile(r"^```$") + args_pattern = re.compile(r"^### 参数映射$") + ARGS_EXPECT_HEADERS = ["PyTorch", "PaddlePaddle", "备注"] + + mapping_type = "" signature_cache = None - with open(filepath, "r") as f: + with open(filepath, "r", encoding="utf-8") as f: for line in f.readlines(): # 现在需要考虑内容信息了 # if not line.startswith("##"): @@ -132,6 +160,12 @@ def get_meta_from_diff_file(filepath): meta_data["torch_api"] = unescape_api(torch_api) meta_data["mapping_type"] = mapping_type + + if mapping_type not in mapping_type_set: + raise ValueError( + f"Unexpected mapping type: {mapping_type} in {filepath}" + ) + state = ParserState.wait_for_torch_api else: raise Exception(f"Cannot parse title: {line} in {filepath}") @@ -222,7 +256,7 @@ def get_meta_from_diff_file(filepath): state = ParserState.wait_for_paddle_api elif state == ParserState.wf_paddle_code_end: meta_data["paddle_signature"] = signature_info - state = ParserState.end + state = ParserState.wait_for_args else: raise ValueError( f"Unexpected state {state} when process {filepath} line: {line}" @@ -237,6 +271,57 @@ def get_meta_from_diff_file(filepath): raise ValueError( f"Unexpected state {state} when process {filepath} line: {line}" ) + elif state == ParserState.wait_for_args: + args_match = args_pattern.match(line) + if args_match: + state = ParserState.wf_args_table_title + elif state == ParserState.wf_args_table_title: + if line.startswith("|"): + args_table_headers = [ + c.strip() for c in line.split("|") if len(c.strip()) > 0 + ] + if args_table_headers != ARGS_EXPECT_HEADERS: + raise Exception( + f"Unexpected args table headers: {args_table_headers} in {filepath}" + ) + else: + state = ParserState.wf_args_table_sep + meta_data["args_mapping"] = [] + elif state == ParserState.wf_args_table_sep: + if line.startswith("|"): + args_table_seps = [ + c.strip() for c in line.split("|") if len(c.strip()) > 0 + ] + if len(args_table_seps) == len(ARGS_EXPECT_HEADERS): + state = ParserState.wf_args_table_end + else: + raise Exception( + f"Unexpected args table seps: {args_table_seps} in {filepath}" + ) + else: + raise Exception( + f"Unexpected args table sep: {line} in {filepath}" + ) + elif state == ParserState.wf_args_table_end: + if line.startswith("|"): + args_table_content = [c.strip() for c in line.split("|")][ + 1:-1 + ] + if len(args_table_content) == len(ARGS_EXPECT_HEADERS): + torch_arg, paddle_arg, note = args_table_content + meta_data["args_mapping"].append( + { + "torch_arg": torch_arg, + "paddle_arg": paddle_arg, + "note": note, + } + ) + else: + raise Exception( + f"Unexpected args table end: {args_table_content} in {filepath}" + ) + else: + state = ParserState.end elif state == ParserState.end: break else: @@ -246,6 +331,15 @@ def get_meta_from_diff_file(filepath): # print(state) + # 允许没有参数映射列表 + if mapping_type in ["无参数", "组合替代实现"]: + if state == ParserState.wait_for_args: + state = ParserState.end + # 必须有参数映射列表，但是可以随时停止 + else: + if state == ParserState.wf_args_table_end: + state = ParserState.end + # 允许的终止状态，解析完了 paddle_api 或者只有 torch_api # 这些映射类型必须要有对应的 paddle_api if mapping_type in [ @@ -292,85 +386,89 @@ def get_meta_from_diff_file(filepath): r"^https://github.com/PaddlePaddle/((docs/tree/develop/docs/guides/model_convert/convert_from_pytorch/api_difference/)|(X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/))" ) +TABLE_MACRO_ROW_PATTERN = re.compile( + r"^(?P[\w-]+)\( *(?P[^,]+) *(, *(?P.+) *)? *\)$" +) -def validate_mapping_table_row(columns, row_idx, line_idx): - idx_s, torch_api_s, paddle_api_s, mapping_s = columns - idx = int(idx_s) - if row_idx != idx: - raise Exception( - f"Table row index [{row_idx}] != {idx} at line {line_idx}." - ) +INDEX_ALL_APIS = {} +INDEX_TABLES_APIS = [] - torch_api_match = TABLE_COLUMN_TORCH_API_PATTERN.match(torch_api_s) - if torch_api_match: - torch_api = torch_api_match["torch_api"] - torch_api_url = torch_api_match["url"][1:-1] # remove '(' and ')' - else: - raise Exception( - f"Table row torch api not match: {torch_api_s} at line {line_idx}." - ) - paddle_api_match = TABLE_COLUMN_PADDLE_API_PATTERN.match(paddle_api_s) - if len(paddle_api_s) > 0: - if paddle_api_match: - paddle_api = paddle_api_match["paddle_api"] - paddle_api_url = paddle_api_match["url"][1:-1] # remove '(' and ')' +def validate_mapping_table_macro_row(columns, row_idx, line_idx): + assert ( + len(columns) == 1 + ), f"Table macro row must have 1 column at line {line_idx}." + macro_match = TABLE_MACRO_ROW_PATTERN.match(columns[0]) + + if macro_match: + macro_type = macro_match["macro_type"] + if macro_type not in [ + "REFERENCE-MAPPING-ITEM", + "NOT-IMPLEMENTED-ITEM", + "REFERENCE-MAPPING-TABLE", + ]: + print(f"Unknown macro type: {macro_type} at line {line_idx}.") + return False + + if macro_type == "REFERENCE-MAPPING-TABLE": + pass else: - raise Exception( - f"Table row paddle api not match: {paddle_api_s} at line {line_idx}." - ) - else: - paddle_api = None - paddle_api_url = None - - mapping_type_match = TABLE_COLUMN_MAPPING_PATTERN.match(mapping_s) - if mapping_type_match: - mapping_type = mapping_type_match["type"].strip() - mapping_diff_name = mapping_type_match["diff_name"] - diff_url = mapping_type_match["diff_url"] - - if mapping_diff_name != "差异对比" and mapping_diff_name is not None: - print( - f"Table row mapping diff name not match: {mapping_diff_name} at line {line_idx}." - ) + torch_api = macro_match["torch_api"].strip("`") + diff_url = macro_match["diff_url"] - if diff_url is not None and not MAPPING_DIFF_SOURCE_PATTERN.match( - diff_url - ): - raise Exception( - f"Table row mapping diff url invalid: {diff_url} at line {line_idx}." - ) - mapping_diff_url = diff_url - else: - raise Exception( - f"Table row mapping type not match: {mapping_s} at line {line_idx}." - ) + if torch_api in INDEX_ALL_APIS: + raise Exception( + f"Duplicate torch api: {torch_api} at line {line_idx}." + ) + INDEX_ALL_APIS[torch_api] = columns[0] + + return torch_api - return { - "torch_api": torch_api, - "torch_api_url": torch_api_url, - "paddle_api": paddle_api, - "paddle_api_url": paddle_api_url, - "mapping_type": mapping_type, - "mapping_diff_name": mapping_diff_name, - "mapping_diff_url": mapping_diff_url, - "line_idx": line_idx, - } + return False -def collect_mapping_item_processor(item, line_idx, state, output, context): +def collect_mapping_item_processor(_line, line_idx, state, output, context): + if state == 0 or state == 1 or state == 5: + return True + + if state == 2: + INDEX_TABLES_APIS.append({}) + return True if state == 6: table_row_idx = context["table_row_idx"] columns = context["columns"] - item = validate_mapping_table_row(columns, table_row_idx, line_idx + 1) - output.append(item) - return True + assert ( + len(columns) == 1 + ), f"table row must have 1 column at line {line_idx}." + api_name = validate_mapping_table_macro_row( + columns, table_row_idx, line_idx + 1 + ) + INDEX_TABLES_APIS[-1][api_name] = columns[0] + output.append(api_name) + return bool(api_name) return False def process_mapping_index(index_path, item_processer, context={}): + """ + 线性处理 `pytorch_api_mapping_cn.md` 文件 + - index_path: 该 md 文件路径 + - item_processer: 对文件每行的处理方式，输入参数 (line, line_idx, state, output, context)。 + 如果处理出错则返回 False，否则返回 True。 + - context: 用于存储处理过程中的上下文信息 + - output: 使用 context["output"] 初始化，如果不调用 item_processer，直接加入原文件对应行，否则 item_processer 处理 output 逻辑。 + - 返回值：是否成功处理，成功返回 0。 + + 其中 state 信息如下： + - 0: 等待表头（如果未进入表格则始终为 0） + - 1: 无需处理的表格，分隔行（表头和内容的分割线） + - 2: 需要处理的表格，分隔行 + - 5: 无需处理的表格，表格内容 + - 6: **需要处理的表格，表格内容** + + """ if not os.path.exists(index_path): raise Exception(f"Cannot find pytorch_api_mapping_cn.md: {index_path}") @@ -393,10 +491,10 @@ def process_mapping_index(index_path, item_processer, context={}): expect_column_names = [ "序号", - "PyTorch-2.1", - "PaddlePaddle-dev", + "Pytorch 最新 release", + "Paddle develop", "映射关系分类", - "详细对比", + "备注", ] context["table_row_idx"] = context.get("table_row_idx", -1) @@ -413,11 +511,12 @@ def process_mapping_index(index_path, item_processer, context={}): continue columns = [c.strip() for c in content.split("|")] - if len(columns) <= 2: - raise Exception( - f"Table column count must > 0, but found {len(columns) - 2} at line {i+1}: {line}" - ) - columns = columns[1:-1] + columns = [c for c in columns if len(c) > 0] + # if len(columns) <= 2: + # raise Exception( + # f"Table column count must > 0, but found {len(columns) - 2} at line {i+1}: {line}" + # ) + # continue if state == 0: column_names.clear() @@ -471,13 +570,16 @@ def process_mapping_index(index_path, item_processer, context={}): # f"Table content not match at line {i+1}: {line}" # ) try: + context["columns"] = columns if not item_processer(line, i, state, output, context): break context["table_row_idx"] += 1 except Exception as e: print(e) print(f"Error at line {i+1}: {line}") + traceback.print_exc() ret_code = 1 + sys.exit(-5) # state = 6 else: @@ -503,6 +605,170 @@ def process_mapping_index(index_path, item_processer, context={}): return 0 +_TABLE_PREFIXES = sorted( + [ + "torch.nn.", + "torch.nn.functional.", + "torch.Tensor.", + "torch.nn.init.", + "torch.nn.utils.", + "torch.nn.Module.", + "torch.autograd.", + "torch.cuda.", + "torch.distributed.", + "torch.distributions.", + "torch.fft.", + "torch.hub.", + "torch.linalg.", + "torch.onnx.", + "torch.optim.", + "torch.profiler.", + "torch.sparse.", + # others + ], + key=lambda x: -len(x), +) + + +def get_api_type_from_torch_api(torch_api: str) -> str: + for prefix in _TABLE_PREFIXES: + if torch_api.startswith(prefix): + return prefix + "XX" + else: + if len(torch_api.split(".")) > 2: + return "others" + else: + return "torch.XX" + return "untyped" + + +def get_doc_url_from_meta(basedir, meta): + relpath = os.path.relpath(meta["source_file"], basedir).replace("\\", "/") + diffurl = urllib.parse.urljoin(PADDLE_DOCS_BASE_URL, relpath) + return diffurl + + +def auto_fill_index_from_api_diff(basedir, meta_dict) -> None: + target = {k + "XX": {} for k in _TABLE_PREFIXES} + target["torch.XX"] = {} + target["others"] = {} + + for torch_api, column in INDEX_ALL_APIS.items(): + api_type = get_api_type_from_torch_api(torch_api) + target[api_type][torch_api] = column + + filled_count = 0 + + for torch_api, meta in meta_dict.items(): + api_type = get_api_type_from_torch_api(torch_api) + + diffurl = get_doc_url_from_meta(basedir, meta) + + column = f"REFERENCE-MAPPING-ITEM(`{torch_api}`, {diffurl})" + if torch_api in target[api_type]: + # if conflict, + # pass means replace, continue means skip + if target[api_type][torch_api] != column: + # if before is NOT-IMPLEMENTED-ITEM, replace + if target[api_type][torch_api].startswith( + "NOT-IMPLEMENTED-ITEM" + ): + pass + # if before is X2Paddle, skip + elif ( + "https://github.com/PaddlePaddle/X2Paddle" + in target[api_type][torch_api] + ): + continue + else: + print(f"Conflict: {torch_api} in {api_type}") + print(f" {target[api_type][torch_api]}") + print(f" {column}") + continue + else: + continue + + filled_count += 1 + target[api_type][torch_api] = column + + alias_filename = "api_alias_mapping.json" + alias_filepath = os.path.join(basedir, alias_filename) + if os.path.exists(alias_filepath) and os.path.isfile(alias_filepath): + target["alias"] = {} + with open(alias_filepath, "r", encoding="utf-8") as f: + api_alias = json.load(f) + for alias_name, api_name in api_alias.items(): + if api_name in meta_dict: + pass + elif alias_name in meta_dict: + # 如果反着有，就交换 + api_name, alias_name = alias_name, api_name + else: + # 都没有就抛出警告 + print( + f"Alias Warning: api `{api_name}` have no mapping doc, failed to reference from alias `{alias_name}`" + ) + continue + + if alias_name in INDEX_ALL_APIS: + # 如果别名和本名都在前面表里，就跳过 + continue + + meta_data = meta_dict[api_name] + + paddle_api = meta_data.get("paddle_api", "-") + mapping_type = meta_data["mapping_type"] + url = get_doc_url_from_meta(basedir, meta_data) + + alias_col = f"`{alias_name}`" + paddle_col = f"`{paddle_api}`" + if "torch_api_url" in meta_data: + alias_col = f'[{alias_col}]({meta_data["torch_api_url"]})' + if "paddle_api_url" in meta_data: + paddle_col = ( + f'[{paddle_col}]({meta_data["paddle_api_url"]})' + ) + + # line = " | ".join( + # [ + # alias_col, + # paddle_col, + # mapping_type, + # f"`{api_name}`别名，[详细对比]({url})", + # ] + # ) + # target["alias"][alias_name] = line + macro_line = ( + f"ALIAS-REFERENCE-ITEM(`{alias_name}`, `{api_name}`)" + ) + target["alias"][alias_name] = macro_line + filled_count += 1 + + if filled_count > 0: + print(f"filled {filled_count} torch apis for ./api_difference") + + output_path = os.path.join(basedir, "pytorch_api_mapping_cn.tmp.md") + with open(output_path, "w", encoding="utf-8") as f: + for prefix, apis in target.items(): + f.write(f"## {prefix}\n\n") + od_apis = collections.OrderedDict(sorted(apis.items())) + for api, ref in od_apis.items(): + if ref.startswith("REFERENCE-MAPPING-ITEM"): + f.write(f"| {ref} |\n") + for api, ref in od_apis.items(): + if ref.startswith("NOT-IMPLEMENTED-ITEM"): + f.write(f"| {ref} |\n") + for api, ref in od_apis.items(): + if not ( + ref.startswith("REFERENCE-MAPPING-ITEM") + or ref.startswith("NOT-IMPLEMENTED-ITEM") + ): + f.write(f"| {ref} |\n") + f.write("\n") + + print(f'generated temp file: "{output_path}"') + + if __name__ == "__main__": # convert from pytorch basedir cfp_basedir = os.path.dirname(__file__) @@ -555,3 +821,5 @@ def process_mapping_index(index_path, item_processer, context={}): with open(api_diff_output_path, "w", encoding="utf-8") as f: json.dump(metas, f, ensure_ascii=False, indent=4) + + auto_fill_index_from_api_diff(cfp_basedir, meta_dict) diff --git a/docs/guides/model_convert/index_cn.rst b/docs/guides/model_convert/index_cn.rst index b440f95ea35..fdb6e037212 100644 --- a/docs/guides/model_convert/index_cn.rst +++ b/docs/guides/model_convert/index_cn.rst @@ -7,13 +7,14 @@ - `迁移指南 <./convert_guide_cn.html>`_ : 介绍模型迁移场景及概览。 - `从 PyTorch 迁移到飞桨 <./convert_from_pytorch/index_cn.html>`_ : 介绍如何将 PyTorch 训练代码迁移到飞桨。 + - `代码自动转换工具 <./paconvert_introduction_cn.html>`_ : 介绍 Pytorch 代码自动转 Paddle 工具使用方法。 + - `PyTorch API 映射表 <./pytorch_api_mapping_cn.html>`_ : 说明 PyTorch 最新 release 版本 与 Paddle develop 版本 API 对应关系。 - `CV - 快速上手 <./convert_from_pytorch/cv_quick_start_cn.html>`_ : 以 MobileNetV3 为例，介绍如何从 PyTorch 迁移到飞桨。 - `CV - 迁移经验总结 <./convert_from_pytorch/cv_experience_cn.html>`_ : 介绍 CV 各个方向从 PyTorch 迁移到飞桨的基本流程、常用工具、定位问题的思路及解决方法。 - `NLP - 快速上手 <./convert_from_pytorch/nlp_fast_explore_cn.html>`_ : 以 Bert 为例，介绍如何从 PyTorch 迁移到飞桨。 - `NLP - 迁移经验总结 <./convert_from_pytorch/nlp_migration_experiences_cn.html>`_ : 介绍 NLP 各个方向从 PyTorch 迁移到飞桨的基本流程、常用工具、定位问题的思路及解决方法。 - `解读网络结构转换 <./convert_from_pytorch/convert_net_structure_cn.html>`_ : 介绍网络结构转换的思路和方法。 - `解读 Bert 模型权重转换 <./convert_from_pytorch/convert_bert_weights_cn.html>`_ : 介绍如何进行不同框架下的模型权重转换。 - - `PyTorch API 映射表 <./convert_from_pytorch/pytorch_api_mapping_cn.html>`_ : 说明 PyTorch 1.8 版本与 Paddle 2.0 API 对应关系。 - `PyTorch 自定义算子转写教程 <./pytorch_custom_op_convert_cn.html>`_ : 介绍 PyTorch 中自定义算子转写成 Paddle 自定义算子的思路和方法。 - `使用 X2Paddle 迁移推理模型 <./convert_with_x2paddle_cn.html>`_ : 介绍如何使用 X2Paddle 工具将 PyTorch、ONNX、TensorFlow、Caffe 推理模型迁移到飞桨。 - `迁移飞桨旧版本 <./convert_from_older_versions/index_cn.html>`_ : 介绍如何将飞桨 1.X 版本的训练代码与模型迁移到飞桨最新版。 diff --git a/docs/guides/model_convert/load_old_format_model_cn.rst b/docs/guides/model_convert/load_old_format_model_cn.rst deleted file mode 100644 index 4320e04c4f6..00000000000 --- a/docs/guides/model_convert/load_old_format_model_cn.rst +++ /dev/null @@ -1,271 +0,0 @@ -.. _cn_guides_load_old_format_model: - - -兼容载入旧格式模型 -==================== - -如果你是从飞桨框架 1.x 切换到 2.1，曾经使用飞桨框架 1.x 的 fluid 相关接口保存模型或者参数，飞桨框架 2.1 也对这种情况进行了兼容性支持，包括以下几种情况。 - -飞桨 1.x 模型准备及训练示例，该示例为后续所有示例的前序逻辑： - -.. code-block:: python - - import numpy as np - import paddle - import paddle.fluid as fluid - import paddle.nn as nn - import paddle.optimizer as opt - - BATCH_SIZE = 16 - BATCH_NUM = 4 - EPOCH_NUM = 4 - - IMAGE_SIZE = 784 - CLASS_NUM = 10 - - # enable static graph mode - paddle.enable_static() - - # define a random dataset - class RandomDataset(paddle.io.Dataset): - def __init__(self, num_samples): - self.num_samples = num_samples - - def __getitem__(self, idx): - image = np.random.random([IMAGE_SIZE]).astype('float32') - label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') - return image, label - - def __len__(self): - return self.num_samples - - image = fluid.data(name='image', shape=[None, 784], dtype='float32') - label = fluid.data(name='label', shape=[None, 1], dtype='int64') - pred = fluid.layers.fc(input=image, size=10, act='softmax') - loss = fluid.layers.cross_entropy(input=pred, label=label) - avg_loss = fluid.layers.mean(loss) - - optimizer = fluid.optimizer.SGD(learning_rate=0.001) - optimizer.minimize(avg_loss) - - place = fluid.CPUPlace() - exe = fluid.Executor(place) - exe.run(fluid.default_startup_program()) - - # create data loader - dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) - loader = paddle.io.DataLoader(dataset, - feed_list=[image, label], - places=place, - batch_size=BATCH_SIZE, - shuffle=True, - drop_last=True, - num_workers=2) - - # train model - for data in loader(): - exe.run( - fluid.default_main_program(), - feed=data, - fetch_list=[avg_loss]) - - -1 从 ``paddle.fluid.io.save_inference_model`` 保存结果中载入模型&参数 ----------------------------------------------------------------------------- - -1.1 同时载入模型和参数 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -使用 ``paddle.jit.load`` 配合 ``**configs`` 载入模型和参数。 - -如果你是按照 ``paddle.fluid.io.save_inference_model`` 的默认格式存储的，可以按照如下方式载入（接前述示例）： - -.. code-block:: python - - # save default - model_path = "fc.example.model" - fluid.io.save_inference_model( - model_path, ["image"], [pred], exe) - - # enable dynamic mode - paddle.disable_static(place) - - # load - fc = paddle.jit.load(model_path) - - # inference - fc.eval() - x = paddle.randn([1, IMAGE_SIZE], 'float32') - pred = fc(x) - -如果你指定了存储的模型文件名，可以按照以下方式载入（接前述示例）： - -.. code-block:: python - - # save with model_filename - model_path = "fc.example.model.with_model_filename" - fluid.io.save_inference_model( - model_path, ["image"], [pred], exe, model_filename="__simplenet__") - - # enable dynamic mode - paddle.disable_static(place) - - # load - fc = paddle.jit.load(model_path, model_filename="__simplenet__") - - # inference - fc.eval() - x = paddle.randn([1, IMAGE_SIZE], 'float32') - pred = fc(x) - -如果你指定了存储的参数文件名，可以按照以下方式载入（接前述示例）： - -.. code-block:: python - - # save with params_filename - model_path = "fc.example.model.with_params_filename" - fluid.io.save_inference_model( - model_path, ["image"], [pred], exe, params_filename="__params__") - - # enable dynamic mode - paddle.disable_static(place) - - # load - fc = paddle.jit.load(model_path, params_filename="__params__") - - # inference - fc.eval() - x = paddle.randn([1, IMAGE_SIZE], 'float32') - pred = fc(x) - -1.2 仅载入参数 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -如果你仅需要从 ``paddle.fluid.io.save_inference_model`` 的存储结果中载入参数，以 state_dict 的形式配置到已有代码的模型中，可以使用 ``paddle.load`` 配合 ``**configs`` 载入。 - -如果你是按照 ``paddle.fluid.io.save_inference_model`` 的默认格式存储的，可以按照如下方式载入（接前述示例）： - -.. code-block:: python - - model_path = "fc.example.model" - - load_param_dict = paddle.load(model_path) - -如果你指定了存储的模型文件名，可以按照以下方式载入（接前述示例）： - -.. code-block:: python - - model_path = "fc.example.model.with_model_filename" - - load_param_dict = paddle.load(model_path, model_filename="__simplenet__") - -如果你指定了存储的参数文件名，可以按照以下方式载入（接前述示例）： - -.. code-block:: python - - model_path = "fc.example.model.with_params_filename" - - load_param_dict = paddle.load(model_path, params_filename="__params__") - -.. note:: - 一般预测模型不会存储优化器 Optimizer 的参数，因此此处载入的仅包括模型本身的参数。 - -.. note:: - 由于 ``structured_name`` 是动态图下独有的变量命名方式，因此从静态图存储结果载入的 state_dict 在配置到动态图的 Layer 中时，需要配置 ``Layer.set_state_dict(use_structured_name=False)`` 。 - - -2 从 ``paddle.fluid.save`` 存储结果中载入参数 ----------------------------------------------------------------------------- - - ``paddle.fluid.save`` 的存储格式与 2.x 动态图接口 ``paddle.save`` 存储格式是类似的，同样存储了 dict 格式的参数，因此可以直接使用 ``paddle.load`` 载入 state_dict，但需要注意不能仅传入保存的路径，而要传入保存参数的文件名，示例如下（接前述示例）： - -.. code-block:: python - - # save by fluid.save - model_path = "fc.example.model.save" - program = fluid.default_main_program() - fluid.save(program, model_path) - - # enable dynamic mode - paddle.disable_static(place) - - load_param_dict = paddle.load("fc.example.model.save.pdparams") - - -.. note:: - 由于 ``paddle.fluid.save`` 接口原先在静态图模式下的定位是存储训练时参数，或者说存储 Checkpoint，故尽管其同时存储了模型结构，目前也暂不支持从 ``paddle.fluid.save`` 的存储结果中同时载入模型和参数，后续如有需求再考虑支持。 - - -3 从 ``paddle.fluid.io.save_params/save_persistables`` 保存结果中载入参数 ----------------------------------------------------------------------------- - -这两个接口在飞桨 1.x 版本时，已经不再推荐作为存储模型参数的接口使用，故并未继承至飞桨 2.x，之后也不会再推荐使用这两个接口存储参数。 - -对于使用这两个接口存储参数兼容载入的支持，分为两种情况，下面以 ``paddle.fluid.io.save_params`` 接口为例介绍相关使用方法： - - -3.1 使用默认方式存储，各参数分散存储为单独的文件，文件名为参数名 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -这种存储方式仍然可以使用 ``paddle.load`` 接口兼容载入，使用示例如下（接前述示例）： - -.. code-block:: python - - # save by fluid.io.save_params - model_path = "fc.example.model.save_params" - fluid.io.save_params(exe, model_path) - - # load - state_dict = paddle.load(model_path) - print(state_dict) - -3.2 指定了参数存储的文件，将所有参数存储至单个文件中 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -将所有参数存储至单个文件中会导致存储结果中丢失 Tensor 名和 Tensor 数据之间的映射关系，因此这部分丢失的信息需要用户传入进行补足。为了确保正确性，这里不仅要传入 Tensor 的 name 列表，同时要传入 Tensor 的 shape 和 dtype 等描述信息，通过检查和存储数据的匹配性确保严格的正确性，这导致载入数据的恢复过程变得比较复杂，仍然需要一些飞桨 1.x 的概念支持。后续如果此项需求较为普遍，飞桨将会考虑将该项功能兼容支持到 ``paddle.load`` 中，但由于信息丢失而导致的使用复杂性仍然是存在的，因此建议你避免仅使用这两个接口存储参数。 - -目前暂时推荐你使用 ``paddle.static.load_program_state`` 接口解决此处的载入问题，需要获取原 Program 中的参数列表传入该方法，使用示例如下（接前述示例）： - -.. code-block:: python - - # save by fluid.io.save_params - model_path = "fc.example.model.save_params_with_filename" - fluid.io.save_params(exe, model_path, filename="__params__") - - # load - import os - params_file_path = os.path.join(model_path, "__params__") - var_list = fluid.default_main_program().all_parameters() - state_dict = paddle.io.load_program_state(params_file_path, var_list) - - -4 从 ``paddle.static.save`` 保存结果中载入参数 ----------------------------------------------------------------------------- -``paddle.static.save`` 接口生成三个文件： ``*.pdparams`` 、 ``*.pdopt`` 、 ``*.pdmodel`` ，分别保存了组网的参数、优化器的参数、静态图的 Program。推荐您使用 ``paddle.load`` 分别加载这三个文件，然后使用 ``set_state_dict`` 接口将参数设置到 ``Program`` 中 。如果您已经在代码中定义了 ``Program`` ，您可以不加载 ``*.pdmodel`` 文件；如果您不需要恢复优化器中的参数，您可以不加载 ``*.pdopt`` 文件。使用示例如下： - - -.. code-block:: python - - import os - import paddle - - paddle.enable_static() - x = paddle.static.data( - name="static_x", shape=[None, 224], dtype='float32') - z = paddle.static.nn.fc(x, 10) - z = paddle.static.nn.fc(z, 10, bias_attr=False) - - place = paddle.CPUPlace() - exe = paddle.static.Executor(place) - exe.run(paddle.static.default_startup_program()) - prog = paddle.static.default_main_program() - - path = os.path.join("test_static_save_load", "model") - paddle.static.save(prog, path) - - # load program - program=paddle.load(path + '.pdmodel') - - state_dict_param = paddle.load(path + '.pdparams') - program.set_state_dict(state_dict_param) - - state_dict_opt = paddle.load(path + '.pdopt') - program.set_state_dict(state_dict_opt) diff --git a/docs/guides/model_convert/migration_cn.rst b/docs/guides/model_convert/migration_cn.rst deleted file mode 100644 index 18310749967..00000000000 --- a/docs/guides/model_convert/migration_cn.rst +++ /dev/null @@ -1,215 +0,0 @@ -.. _cn_guides_migration: - -版本迁移工具 -==================== - -在飞桨框架 2.0 中，Paddle API 的位置、命名、参数、行为，进行了系统性的调整和规范, 将 API 体系从 1.X 版本的 ``paddle.fluid.*`` 迁移到了 ``paddle.*`` 下。``paddle.fluid`` 目录下暂时保留了 1.8 版本 API，主要是兼容性考虑，未来会被删除。 - -使用版本迁移工具自动迁移 Paddle 1.X 的代码到 Paddle 2.0 ------------------------------------- - -飞桨提供了版本迁移工具，该工具按 Paddle 2.0 对于 Paddle 1.X 的变化，能够自动实现以下功能： - -- 按照 :ref:`API 映射表 ` ，将转换工具能否转换这列为 True 的 API 由 Paddle 1.X 转为 Paddle 2.0，为 False 的 API 打印 WARNING，提示手动升级。 -- 因为 Paddle 2.0.0 默认开启动态图，所以删除用于开启动态图上下文的 ``with paddle.fluid.dygraph.guard(place)`` ，并修改该上下文的代码缩进； -- 删除组网 API 中的 ``act`` 参数，并自动添加相关的激活函数； - -目前，版本迁移工具能够处理的 API 数量为 X 个，如果你有代码迁移的需求，使用转换工具能够节省你部分时间，帮助你快速完成代码迁移。 - -.. warning:: - - 版本迁移工具并不能处理所有的情况，对于 API 的处理只能按照 :ref:`API 映射表 ` 中的关系完成 API 的变化。如代码中包含有转换工具能否转换这列为 False 的 API 或不在此表中的 API，在使用本工具后，仍然需要手工来进行检查并做相应的调整。 - -安装 -~~~~ - -版本迁移工具可以通过 pip 的方式安装，方式如下: - -.. code:: ipython3 - - $ pip install paddle_upgrade_tool - -基本用法 -~~~~~~~~ - -paddle_upgrade_tool 可以使用下面的方式，快速使用: - -.. code:: ipython3 - - $ paddle_upgrade_tool --inpath /path/to/model.py - -这将在命令行中，以\ ``diff``\ 的形式，展示 model.py 从 Paddle 1.x 转换为 Paddle 2.0 的变化。如果你确认上述变化没有问题，只需要再执行： - -.. code:: ipython3 - - $ paddle_upgrade_tool --inpath /path/to/model.py --write - -就会原地改写 model.py，将上述变化改写到你的源文件中。 -注意：版本转换工具会默认备份源文件，到~/.paddle_upgrade_tool/下。 - -参数说明如下： - -- –inpath 输入文件路径，可以为单个文件或文件夹。 -- –write 是否原地修改输入的文件，默认值 False，表示不修改。如果为 True，表示对文件进行原地修改。添加此参数也表示对文件进行原地修改。 -- –backup 可选，是否备份源文件，默认值为\ ``~/.paddle_upgrade_tool/``\ ，在此路径下备份源文件。 -- –no-log-file 可选，是否需要输出日志文件，默认值为 False，即输出日志文件。 -- –log-filepath 可选，输出日志的路径，默认值为\ ``report.log``\ ，输出日志文件的路径。 -- –no-confirm 可选，输入文件夹时，是否逐文件确认原地写入，只在\ ``--write``\ 为 True 时有效，默认值为 False，表示需要逐文件确认。 -- –parallel 可选，控制转换文件的并发数，当 \ ``no-confirm`` 为 True 时不生效，默认值:\ ``None``\ 。 -- –log-level 可选，log 级别，可为[‘DEBUG’,‘INFO’,‘WARNING’,‘ERROR’] 默认值：\ ``INFO``\ 。 -- –refactor 可选，debug 时使用。 -- –print-match 可选，debug 时使用。 - -使用教程 -~~~~~~~~ - -开始 -^^^^ - -在使用 paddle_upgrade_tool 前，需要确保已经安装了 Paddle 2.0.0+版本。 - -.. code:: ipython3 - - import paddle - print (paddle.__version__) - -.. parsed-literal:: - - 2.0.0 - - -克隆\ `paddlePaddle/models `__\ 来作为工具的测试。 - -.. code:: ipython3 - - $ git clone https://github.com/PaddlePaddle/models - -.. parsed-literal:: - - Cloning into 'models'... - remote: Enumerating objects: 8, done. - remote: Counting objects: 100% (8/8), done. - remote: Compressing objects: 100% (8/8), done. - remote: Total 35011 (delta 1), reused 0 (delta 0), pack-reused 35003 - Receiving objects: 100% (35011/35011), 356.97 MiB | 1.53 MiB/s, done. - Resolving deltas: 100% (23291/23291), done. - - -查看帮助文档 -^^^^^^^^^^^^ - -你可以直接通过下面的方式，查看帮助文档。 - -.. code:: ipython3 - - $ paddle_upgrade_tool -h - - -.. parsed-literal:: - - usage: paddle_upgrade_tool [-h] [--log-level {DEBUG,INFO,WARNING,ERROR}] - [--no-log-file] [--log-filepath LOG_FILEPATH] -i - INPATH [-b [BACKUP]] [-w] [--no-confirm] - [-p PARALLEL] - [-r {refactor_import,norm_api_alias,args_to_kwargs,refactor_kwargs,api_rename,refactor_with,post_refactor}] - [--print-match] - - optional arguments: - -h, --help show this help message and exit - --log-level {DEBUG,INFO,WARNING,ERROR} - set log level, default is INFO - --no-log-file don't log to file - --log-filepath LOG_FILEPATH - set log file path, default is "report.log" - -i INPATH, --inpath INPATH - the file or directory path you want to upgrade. - -b [BACKUP], --backup [BACKUP] - backup directory, default is the - "~/.paddle_upgrade_tool/". - -w, --write modify files in-place. - --no-confirm write files in-place without confirm, ignored without - --write. - -p PARALLEL, --parallel PARALLEL - specify the maximum number of concurrent processes to - use when refactoring, ignored with --no-confirm. - -r {refactor_import,norm_api_alias,args_to_kwargs,refactor_kwargs,api_rename,refactor_with,post_refactor}, --refactor {refactor_import,norm_api_alias,args_to_kwargs,refactor_kwargs,api_rename,refactor_with,post_refactor} - this is a debug option. Specify refactor you want to - run. If none, all refactors will be run. - --print-match this is a debug option. Print matched code and node - for each file. - -Paddle 1.x 的例子 -^^^^^^^^^^^^^^ - -这里是一个基于 Paddle 1.x 实现的一个 mnist 分类，部分内容如下： - -.. code:: ipython3 - - $ head -n 198 models/dygraph/mnist/train.py | tail -n 20 - - -.. code:: ipython3 - - with fluid.dygraph.guard(place): - if args.ce: - print("ce mode") - seed = 33 - np.random.seed(seed) - fluid.default_startup_program().random_seed = seed - fluid.default_main_program().random_seed = seed - - if args.use_data_parallel: - strategy = fluid.dygraph.parallel.prepare_context() - mnist = MNIST() - adam = AdamOptimizer(learning_rate=0.001, parameter_list=mnist.parameters()) - if args.use_data_parallel: - mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy) - - train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True) - if args.use_data_parallel: - train_reader = fluid.contrib.reader.distributed_batch_reader( - train_reader) - - -使用 paddle_upgrade_tool 进行转化 -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -paddle_upgrade_tool 支持单文件的转化，你可以通过下方的命令直接转化单独的文件。 - -.. code:: ipython3 - - $ paddle_upgrade_tool --inpath models/dygraph/mnist/train.py - -注意，对于参数的删除及一些特殊情况，迁移工具都会打印 WARNING 信息，需要你仔细核对相关内容。 -如果你觉得上述信息没有问题，可以直接对文件进行原地修改，方式如下： - -.. code:: ipython3 - - $ paddle_upgrade_tool --inpath models/dygraph/mnist/train.py --write - -此时，命令行会弹出下方的提示： - -.. code:: ipython3 - - "models/dygraph/mnist/train.py" will be modified in-place, and it has been backed up to "~/.paddle_upgrade_tool/train.py_backup_2020_09_09_20_35_15_037821". Do you want to continue? [Y/n]: - -输入\ ``y`` -后即开始执行代码迁移。为了高效完成迁移，工具这里采用了原地写入的方式。此外，为了防止特殊情况，工具会备份转换前的代码到 -``~/.paddle_upgrade_tool`` 目录下，如果需要，你可以在备份目录下找到转换前的代码。 - -代码迁移完成后，会生成一个 report.log 文件，记录了迁移的详情。内容如下： - -.. code:: ipython3 - - $ cat report.log - -注意事项 -~~~~~~~~ - -- 本迁移工具不能完成所有 API 的迁移，有少量的 API 需要你手动完成迁移，具体信息可见 WARNING。 - -使用 Paddle 2.0 -~~~~~~~~~~~~~~~~ - -完成迁移后，代码就从 Paddle 1.x 迁移到了 Paddle 2.0，你就可以在 Paddle 2.0 下进行相关的开发。 diff --git a/docs/guides/model_convert/paddle_api_mapping_cn.rst b/docs/guides/model_convert/paddle_api_mapping_cn.rst deleted file mode 100644 index 0153b42b25a..00000000000 --- a/docs/guides/model_convert/paddle_api_mapping_cn.rst +++ /dev/null @@ -1,382 +0,0 @@ -.. _cn_guides_api_mapping: - -Paddle 1.8 与 Paddle 2.0 API 映射表 -===================== - -本文档基于 Paddle 1.8 梳理了常用 API 与 Paddle 2.0 对应关系。你可以根据对应关系，快速熟悉 Paddle 2.0 的接口使用。 - -.. note:: - - - 2.0 版本将会是一个长期维护的版本，我们将会发布新增二位版本号版本进行功能增强、以及性能优化，通过发布新增三位版本号版本进行 bugfix。 - - 我们还会继续维护 1.8 版本，但仅限于严重的 bugfix。 - -.. note:: - - 其中，迁移工具能否转换，是指使用迁移工具能否直接对 PaddlePaddle 1.8 的 API 进行迁移，了解更多关于迁移工具的内容，请参考 :ref:`版本迁移工具 ` - -.. csv-table:: - :header: "序号", "PaddlePaddle 1.8 API", "PaddlePaddle 2.0 API", "迁移工具能否转换" - :widths: 1, 8, 8, 8 - - "0", "`paddle.fluid.BuildStrategy `_ ", "`paddle.static.BuildStrategy `_", "True" - "1", "`paddle.fluid.CompiledProgram `_ ", "`paddle.static.CompiledProgram `_", "True" - "2", "`paddle.fluid.cpu_places `_ ", "`paddle.static.cpu_places `_", "True" - "3", "`paddle.fluid.CPUPlace `_ ", "`paddle.CPUPlace `_", "True" - "4", "`paddle.fluid.cuda_places `_ ", "`paddle.static.cuda_places `_", "True" - "5", "`paddle.fluid.CUDAPinnedPlace `_ ", "`paddle.CUDAPinnedPlace `_", "True" - "6", "`paddle.fluid.CUDAPlace `_ ", "`paddle.CUDAPlace `_", "True" - "7", "`paddle.fluid.default_main_program `_ ", "`paddle.static.default_main_program `_", "True" - "8", "`paddle.fluid.default_startup_program `_ ", "`paddle.static.default_startup_program `_", "True" - "9", "`paddle.fluid.disable_dygraph `_ ", "`paddle.enable_static `_", "True" - "10", "`paddle.fluid.embedding `_ ", "`paddle.nn.functional.embedding(动态图) `_, `paddle.static.nn.embedding(静态图) `_", "False" - "11", "`paddle.fluid.enable_dygraph `_ ", "`paddle.disable_static `_", "True" - "12", "`paddle.fluid.enable_imperative `_ ", "`paddle.disable_static `_", "True" - "13", "`paddle.fluid.ExecutionStrategy `_ ", "`paddle.static.ExecutionStrategy `_", "True" - "14", "`paddle.fluid.Executor `_ ", "`paddle.static.Executor `_", "True" - "15", "`paddle.fluid.global_scope `_ ", "`paddle.static.global_scope `_", "True" - "16", "`paddle.fluid.gradients `_ ", "`paddle.static.gradients `_", "True" - "17", "`paddle.fluid.in_dygraph_mode `_ ", "`paddle.in_dynamic_mode `_", "True" - "18", "`paddle.fluid.is_compiled_with_cuda `_ ", "`paddle.is_compiled_with_cuda `_", "True" - "19", "`paddle.fluid.load `_ ", "`paddle.static.load `_", "True" - "21", "`paddle.fluid.name_scope `_ ", "`paddle.static.name_scope `_", "True" - "22", "`paddle.fluid.one_hot `_ ", "`paddle.nn.functional.one_hot `_", "False" - "23", "`paddle.fluid.ParallelExecutor `_ ", "`paddle.static.ParallelExecutor `_", "True" - "24", "`paddle.fluid.ParamAttr `_ ", "`paddle.ParamAttr `_", "True" - "25", "`paddle.fluid.Program `_ ", "`paddle.static.Program `_", "True" - "26", "`paddle.fluid.program_guard `_ ", "`paddle.static.program_guard `_", "True" - "27", "`paddle.fluid.require_version `_ ", "`paddle.utils.require_version `_", "True" - "28", "`paddle.fluid.save `_ ", "`paddle.save `_ ", "False" - "29", "`paddle.fluid.scope_guard `_ ", "`paddle.static.scope_guard `_", "True" - "30", "`paddle.fluid.Variable `_ ", "`paddle.static.Variable `_", "True" - "31", "`paddle.fluid.WeightNormParamAttr `_ ", "`paddle.static.WeightNormParamAttr `_", "True" - "32", "`paddle.fluid.backward.append_backward `_ ", "`paddle.static.append_backward `_", "True" - "33", "`paddle.fluid.backward.gradients `_ ", "`paddle.static.gradients `_", "True" - "34", "`paddle.fluid.clip.GradientClipByGlobalNorm `_ ", "`paddle.nn.ClipGradByGlobalNorm `_", "False" - "35", "`paddle.fluid.clip.GradientClipByNorm `_ ", "`paddle.nn.ClipGradByNorm `_", "False" - "36", "`paddle.fluid.clip.GradientClipByValue `_ ", "`paddle.nn.ClipGradByValue `_", "False" - "37", "`paddle.fluid.dataset.InMemoryDataset `_ ", "`paddle.distributed.InMemoryDataset `_", "True" - "38", "`paddle.fluid.dataset.QueueDataset `_ ", "`paddle.distributed.QueueDataset `_", "True" - "39", "`paddle.fluid.dygraph.BatchNorm `_ ", "`paddle.nn.BatchNorm1D `_, `paddle.nn.BatchNorm2D `_, `paddle.nn.BatchNorm3D `_", "True" - "40", "`paddle.fluid.dygraph.BCELoss `_ ", "`paddle.nn.BCELoss `_", "True" - "41", "`paddle.fluid.dygraph.BilinearTensorProduct `_ ", "`paddle.nn.Bilinear `_", "True" - "42", "`paddle.fluid.dygraph.Conv2D `_ ", "`paddle.nn.Conv2D `_", "True" - "43", "`paddle.fluid.dygraph.Conv2DTranspose `_ ", "`paddle.nn.Conv2DTranspose `_", "False" - "44", "`paddle.fluid.dygraph.Conv3D `_ ", "`paddle.nn.Conv3D `_", "True" - "45", "`paddle.fluid.dygraph.Conv3DTranspose `_ ", "`paddle.nn.Conv3DTranspose `_", "True" - "46", "`paddle.fluid.dygraph.CosineDecay `_ ", "`paddle.optimizer.lr.CosineAnnealingDecay `_", "False" - "47", "`paddle.fluid.dygraph.DataParallel `_ ", "`paddle.DataParallel `_", "True" - "48", "`paddle.fluid.dygraph.disable_dygraph `_ ", "`paddle.enable_static `_", "True" - "49", "`paddle.fluid.dygraph.Dropout `_ ", "`paddle.nn.Dropout `_, `paddle.nn.Dropout2D `_, `paddle.nn.Dropout3D `_", "False" - "50", "`paddle.fluid.dygraph.Embedding `_ ", "`paddle.nn.Embedding `_", "False" - "51", "`paddle.fluid.dygraph.enable_dygraph `_ ", "`paddle.disable_static `_", "True" - "52", "`paddle.fluid.dygraph.enable_imperative `_ ", "`paddle.disable_static `_", "True" - "53", "`paddle.fluid.dygraph.grad `_ ", "`paddle.grad `_", "True" - "54", "`paddle.fluid.dygraph.GroupNorm `_ ", "`paddle.nn.GroupNorm `_", "True" - "55", "`paddle.fluid.dygraph.InstanceNorm `_ ", "`paddle.nn.InstanceNorm1D `_, `paddle.nn.InstanceNorm2D `_, `paddle.nn.InstanceNorm3D `_", "False" - "56", "`paddle.fluid.dygraph.L1Loss `_ ", "`paddle.nn.L1Loss `_", "True" - "57", "`paddle.fluid.dygraph.Layer `_ ", "`paddle.nn.Layer `_", "True" - "58", "`paddle.fluid.dygraph.LayerList `_ ", "`paddle.nn.LayerList `_", "True" - "59", "`paddle.fluid.dygraph.LayerNorm `_ ", "`paddle.nn.LayerNorm `_", "False" - "60", "`paddle.fluid.dygraph.Linear `_ ", "`paddle.nn.Linear `_", "True" - "61", "`paddle.fluid.dygraph.load_dygraph `_ ", "`paddle.load `_", "True" - "62", "`paddle.fluid.dygraph.MSELoss `_ ", "`paddle.nn.MSELoss `_", "True" - "63", "`paddle.fluid.dygraph.NaturalExpDecay `_ ", "`paddle.optimizer.lr.NaturalExpDecay `_", "False" - "64", "`paddle.fluid.dygraph.NLLLoss `_ ", "`paddle.nn.NLLLoss `_", "True" - "65", "`paddle.fluid.dygraph.NoamDecay `_ ", "`paddle.optimizer.lr.NoamDecay `_", "False" - "66", "`paddle.fluid.dygraph.ParameterList `_ ", "`paddle.nn.ParameterList `_", "True" - "67", "`paddle.fluid.dygraph.no_grad `_ ", "`paddle.no_grad `_", "True" - "68", "`paddle.fluid.dygraph.PolynomialDecay `_ ", "`paddle.optimizer.lr.PolynomialDecay `_", "False" - "69", "`paddle.fluid.dygraph.Pool2D `_ ", "`paddle.nn.MaxPool2D `_, `paddle.nn.AvgPool2D `_", "False" - "70", "`paddle.fluid.dygraph.PRelu `_ ", "`paddle.nn.PReLU `_", "False" - "71", "`paddle.fluid.dygraph.ProgramTranslator `_ ", "`paddle.jit.ProgramTranslator `_", "True" - "72", "`paddle.fluid.dygraph.Sequential `_ ", "`paddle.nn.Sequential `_", "True" - "73", "`paddle.fluid.dygraph.SpectralNorm `_ ", "`paddle.nn.SpectralNorm `_", "True" - "74", "`paddle.fluid.dygraph.to_variable `_ ", "`paddle.to_tensor `_", "True" - "75", "`paddle.fluid.dygraph.TracedLayer `_ ", "`paddle.jit.TracedLayer `_", "True" - "76", "`paddle.fluid.executor.Executor `_ ", "`paddle.static.Executor `_", "True" - "77", "`paddle.fluid.executor.global_scope `_ ", "`paddle.static.global_scope `_", "True" - "78", "`paddle.fluid.executor.scope_guard `_ ", "`paddle.static.scope_guard `_", "True" - "79", "`paddle.fluid.initializer.Bilinear `_ ", "`paddle.nn.initializer.Bilinear `_", "True" - "80", "`paddle.fluid.initializer.BilinearInitializer `_ ", "`paddle.nn.initializer.Bilinear `_", "True" - "81", "`paddle.fluid.initializer.Constant `_ ", "`paddle.nn.initializer.Constant `_", "True" - "82", "`paddle.fluid.initializer.ConstantInitializer `_ ", "`paddle.nn.initializer.Constant `_", "True" - "83", "`paddle.fluid.initializer.MSRA `_ ", "`paddle.nn.initializer.KaimingNormal `_, `paddle.nn.initializer.KaimingUniform `_", "False" - "84", "`paddle.fluid.initializer.MSRAInitializer `_ ", "`paddle.nn.initializer.KaimingNormal `_, `paddle.nn.initializer.KaimingUniform `_", "False" - "85", "`paddle.fluid.initializer.Normal `_ ", "`paddle.nn.initializer.Normal `_", "True" - "86", "`paddle.fluid.initializer.NormalInitializer `_ ", "`paddle.nn.initializer.Normal `_", "True" - "87", "`paddle.fluid.initializer.NumpyArrayInitializer `_ ", "`paddle.nn.initializer.Assign `_", "True" - "88", "`paddle.fluid.initializer.TruncatedNormal `_ ", "`paddle.nn.initializer.TruncatedNormal `_", "False" - "89", "`paddle.fluid.initializer.TruncatedNormalInitializer `_ ", "`paddle.nn.initializer.TruncatedNormal `_", "False" - "90", "`paddle.fluid.initializer.Uniform `_ ", "`paddle.nn.initializer.Uniform `_", "True" - "91", "`paddle.fluid.initializer.UniformInitializer `_ ", "`paddle.nn.initializer.Uniform `_", "True" - 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"103", "`paddle.fluid.layers.acos `_ ", "`paddle.acos `_", "True" - "104", "`paddle.fluid.layers.adaptive_pool2d `_ ", "`paddle.nn.functional.adaptive_avg_pool2d `_, `paddle.nn.functional.adaptive_max_pool2d `_", "False" - "105", "`paddle.fluid.layers.adaptive_pool3d `_ ", "`paddle.nn.functional.adaptive_max_pool3d `_, `paddle.nn.functional.adaptive_avg_pool3d `_", "False" - "106", "`paddle.fluid.layers.embedding `_ ", "`paddle.nn.functional.embedding(动态图) `_, `paddle.static.nn.embedding(静态图) `_", "False" - "107", "`paddle.fluid.layers.addmm `_ ", "`paddle.addmm `_", "True" - "108", "`paddle.fluid.layers.affine_grid `_ ", "`paddle.nn.functional.affine_grid `_", "True" - "109", "`paddle.fluid.layers.allclose `_ ", "`paddle.allclose `_", "True" - "110", "`paddle.fluid.layers.arange `_ ", "`paddle.arange `_", "True" - "111", "`paddle.fluid.layers.argmax `_ ", "`paddle.argmax `_", "True" - "112", "`paddle.fluid.layers.argmin `_ ", "`paddle.argmin `_", "True" - "113", "`paddle.fluid.layers.argsort `_ ", "`paddle.argsort `_", "True" - "114", "`paddle.fluid.layers.asin `_ ", "`paddle.asin `_", "True" - "115", "`paddle.fluid.layers.atan `_ ", "`paddle.atan `_", "True" - "116", "`paddle.fluid.layers.auc `_ ", "`paddle.metric.Auc `_", "True" - "117", "`paddle.fluid.layers.batch_norm `_ ", "`paddle.static.nn.batch_norm `_", "False" - "118", "`paddle.fluid.layers.bilinear_tensor_product `_ ", "`paddle.nn.functional.bilinear `_", "True" - "119", "`paddle.fluid.layers.bmm `_ ", "`paddle.bmm `_", "True" - "120", "`paddle.fluid.layers.case `_ ", "`paddle.static.nn.case `_", "True" - "121", "`paddle.fluid.layers.cast `_ ", "`paddle.cast `_", "True" - "122", "`paddle.fluid.layers.Categorical `_ ", "`paddle.distribution.Categorical `_", "True" - "123", "`paddle.fluid.layers.ceil `_ ", "`paddle.ceil `_", "True" - "124", "`paddle.fluid.layers.chunk_eval `_ ", "`paddle.metric.chunk_eval `_", "True" - "125", "`paddle.fluid.layers.clamp `_ ", "`paddle.clip `_", "False" - "126", "`paddle.fluid.layers.clip_by_norm `_ ", "`paddle.nn.clip_by_norm `_", "False" - "127", "`paddle.fluid.layers.concat `_ ", "`paddle.concat `_", "True" - "128", "`paddle.fluid.layers.cond `_ ", "`paddle.static.nn.cond `_", "True" - "129", "`paddle.fluid.layers.conv2d `_ ", "`paddle.nn.functional.conv2d(动态图) `_, `paddle.static.nn.conv2d(静态图) `_, ", "False" - "130", "`paddle.fluid.layers.conv2d_transpose `_ ", "`paddle.nn.functional.conv2d_transpose(动态图) `_, `paddle.static.nn.conv2d_transpose(静态图) `_", "False" - "131", "`paddle.fluid.layers.conv3d `_ ", "`paddle.nn.functional.conv3d(动态图) `_, `paddle.static.nn.conv3d(静态图) `_", "False" - "132", "`paddle.fluid.layers.conv3d_transpose `_ ", "`paddle.nn.functional.conv3d_transpose(动态图) `_, `paddle.static.nn.conv3d_transpose(静态图) `_", "False" - "133", "`paddle.fluid.layers.cos `_ ", "`paddle.cos `_", "True" - "134", "`paddle.fluid.layers.cos_sim `_ ", "`paddle.nn.functional.cosine_similarity `_", "True" - "135", "`paddle.fluid.layers.create_parameter `_ ", "`paddle.create_parameter `_", "True" - "136", "`paddle.fluid.layers.crf_decoding `_ ", "`paddle.static.nn.crf_decoding `_", "True" - "137", "`paddle.fluid.layers.crop `_ ", "`paddle.crop `_", "True" - "138", "`paddle.fluid.layers.cross `_ ", "`paddle.cross `_", "True" - "139", "`paddle.fluid.layers.cumsum `_ ", "`paddle.cumsum `_", "False" - "140", "`paddle.fluid.layers.data `_ ", "`paddle.static.data `_", "True" - "141", "`paddle.fluid.layers.data_norm `_ ", "`paddle.static.nn.data_norm `_", "True" - "142", "`paddle.fluid.layers.deformable_conv `_ ", "`paddle.static.nn.deform_conv2d `_", "False" - "143", "`paddle.fluid.layers.diag `_ ", "`paddle.diag `_", "False" - "144", "`paddle.fluid.layers.diag_embed `_ ", "`paddle.nn.functional.diag_embed `_", "True" - "145", "`paddle.fluid.layers.dice_loss `_ ", "`paddle.nn.functional.dice_loss `_", "True" - "146", "`paddle.fluid.layers.dist `_ ", "`paddle.dist `_", "True" - "147", "`paddle.fluid.layers.dot `_ ", "`paddle.dot `_", "True" - "148", "`paddle.fluid.layers.dropout `_ ", "`paddle.nn.functional.dropout `_, `paddle.nn.functional.dropout2d `_, `paddle.nn.functional.dropout3d `_", "False" - "149", "`paddle.fluid.layers.dynamic_gru `_ ", "`paddle.nn.GRU `_", "False" - "150", "`paddle.fluid.layers.dynamic_decode `_ ", "`paddle.nn.dynamic_decode `_", "True" - "151", "`paddle.fluid.layers.elementwise_add `_ ", "`paddle.add `_", "True" - "152", "`paddle.fluid.layers.elementwise_div `_ ", "`paddle.divide `_", "True" - "153", "`paddle.fluid.layers.elementwise_equal `_ ", "`paddle.equal `_", "True" - "154", "`paddle.fluid.layers.elementwise_floordiv `_ ", "`paddle.floor_divide `_", "True" - "155", "`paddle.fluid.layers.elementwise_max `_ ", "`paddle.maximum `_", "True" - "156", "`paddle.fluid.layers.elementwise_min `_ ", "`paddle.minimum `_", "True" - "157", "`paddle.fluid.layers.elementwise_mod `_ ", "`paddle.mod `_", "True" - "158", "`paddle.fluid.layers.elementwise_mul `_ ", "`paddle.multiply `_", "True" - "159", "`paddle.fluid.layers.elu `_ ", "`paddle.nn.functional.elu `_", "True" - "160", "`paddle.fluid.layers.embedding `_ ", "`paddle.nn.functional.embedding(动态图) `_, `paddle.static.nn.embedding(静态图) `_", "True" - 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"350", "`paddle.fluid.layers.row_conv `_ ", "`paddle.static.nn.row_conv `_ ", "True" - "351", "`paddle.fluid.layers.pad `_ ", "`paddle.nn.functional.pad `_ ", "False" - "352", "`paddle.fluid.layers.cross_entropy `_ ", "`paddle.nn.functional.cross_entropy `_ ", "True" - "353", "`paddle.fluid.layers.range `_ ", "`paddle.arange `_ ", "True" - "354", "`paddle.fluid.layers.DynamicRNN `_ ", "`paddle.nn.RNN `_ ", "False" - "355", "`paddle.fluid.layers.BeamSearchDecoder `_ ", "`paddle.nn.BeamSearchDecoder `_ ", "True" - "356", "`paddle.fluid.layers.elementwise_pow `_ ", "`paddle.pow `_ ", "True" - "357", "`paddle.fluid.layers.assign `_ ", "`paddle.assign `_ ", "True" - "358", "`paddle.fluid.layers.dynamic_lstmp `_ ", "`paddle.nn.LSTM `_ ", "False" - "359", "`paddle.fluid.layers.lstm_unit `_ ", "`paddle.nn.LSTMCell `_ ", "False" - "360", "`paddle.fluid.layers.LSTMCell `_ ", "`paddle.nn.LSTMCell `_ ", "False" - "361", "`paddle.fluid.optimizer.RMSProp `_ ", "`paddle.optimizer.RMSProp `_ ", "True" - "362", "`paddle.fluid.layers.gru_unit `_ ", "`paddle.nn.GRUCell `_ ", "False" diff --git a/docs/guides/model_convert/update_cn.md b/docs/guides/model_convert/update_cn.md deleted file mode 100644 index 5e0717d75e0..00000000000 --- a/docs/guides/model_convert/update_cn.md +++ /dev/null @@ -1,560 +0,0 @@ -# 升级指南 - -## 升级概要 -飞桨 2.0 版本，相对 1.8 版本有重大升级，涉及开发方面的重要变化如下： - - - 动态图功能完善，动态图模式下数据表示概念为`Tensor`，推荐使用动态图模式； - - API 目录体系调整，API 的命名和别名进行了统一规范化，虽然兼容老版 API，但请使用新 API 体系开发； - - 数据处理、组网方式、模型训练、多卡启动、模型保存和推理等开发流程都有了对应优化，请对应查看说明； - -以上变化请仔细阅读本指南。对于已有模型的升级，飞桨还提供了 2.0 转换工具（见附录）提供更自动化的辅助。 -其他一些功能增加方面诸如动态图对量化训练、混合精度的支持、动静转换等方面不在本指南列出，具体可查看[Release Note](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/release_note_cn.html)或对应文档。 - -## 一、动态图 - -### 推荐优先使用动态图模式 -飞桨 2.0 版本将会把动态图作为默认模式（如果还想使用静态图，可通过调用`paddle.enable_static`切换）。 - -```python -import paddle -``` - -### 使用 Tensor 概念表示数据 -静态图模式下，由于组网时使用的数据不能实时访问，Paddle 用`Variable`来表示数据。 -动态图下，从直观性等角度考虑，将数据表示概念统一为`Tensor`。动态图下`Tensor`的创建主要有两种方法： - -1. 通过调用`paddle.to_tensor`函数，将`python scalar/list`，或者`numpy.ndarray`数据转换为 Paddle 的`Tensor`。具体使用方法，请查看官网的 API 文档。 - -```python -import paddle -import numpy as np - -paddle.to_tensor(1) -paddle.to_tensor((1.1, 2.2)) -paddle.to_tensor(np.random.randn(3, 4)) -``` - -2. 通过调用 `paddle.zeros, paddle.ones, paddle.full, paddle.arange, paddle.rand, paddle.randn, paddle.randint, paddle.normal, paddle.uniform` 等函数，创建并返回 Tensor。 - -## 二、API -### API 目录结构 - -为了 API 组织更加简洁和清晰，将原来 padddle.fluid.xxx 的目录体系全新升级为 paddle.xxx，并对子目录的组织进行了系统的条理化优化。同时还增加了高层 API，可以高低搭配使用。paddle.fluid 目录下暂时保留了 1.8 版本 API，主要是兼容性考虑，未来会被删除。 -**基于 2.0 的开发任务，请使用 paddle 目录下的 API，不要再使用 paddle.fluid 目录下的 API。** 如果发现 Paddle 目录下有 API 缺失的情况，推荐使用基础 API 进行组合实现；你也可以通过在 [github](https://github.com/paddlepaddle/paddle) 上提 issue 的方式反馈。 - -**2.0 版本的 API 整体目录结构如下**： - -| 目录 | 功能和包含的 API | -| :--- | --------------- | -| paddle.* | paddle 根目录下保留了常用 API 的别名，当前包括：paddle.tensor、paddle.framework 和 paddle.device 目录下的所有 API | -| paddle.tensor | tensor 操作相关的 API，例如创建 zeros 、矩阵运算 matmul 、变换 concat 、计算 add 、查找 argmax 等。| -| paddle.framework | 框架通用 API 和动态图模式的 API，例如 no_grad 、 save 、 load 等。| -| paddle.device | 设备管理相关 API，比如：set_device， get_device 等 | -| paddle.amp | paddle 自动混合精度策略，包括 auto_cast 、 GradScaler 等。| -| paddle.callbacks | paddle 日志回调类，包括 ModelCheckpoint 、 ProgBarLogger 等。| -| paddle.nn | 组网相关的 API，例如 Linear 、卷积 Conv2D 、循环神经网络 LSTM 、损失函数 CrossEntropyLoss 、激活函数 ReLU 等。 | -| paddle.static | 静态图下基础框架相关 API，比如：Variable, Program, Executor 等 | -| paddle.static.nn | 静态图下组网专用 API，例如全连接层 fc 、控制流 while_loop/cond 。| -| paddle.optimizer | 优化算法相关 API，比如：SGD、Adagrad、Adam 等。| -| paddle.optimizer.lr | 学习率衰减相关 API，例如 NoamDecay 、 StepDecay 、 PiecewiseDecay 等。| -| paddle.metric | 评估指标计算相关的 API，比如：Accuracy, Auc 等。 | -| paddle.io | 数据输入输出相关 API，比如：Dataset, DataLoader 等 | -| paddle.distributed | 分布式相关基础 API | -| paddle.distributed.fleet | 分布式相关高层 API | -| paddle.vision | 视觉领域 API，例如数据集 Cifar10 、数据处理 ColorJitter 、常用基础网络结构 ResNet 等。| -| paddle.text | 目前包括 NLP 领域相关的数据集，如 Imdb 、 Movielens 。| - -### API 别名规则 - -- 为了方便使用，API 会在不同的路径下建立别名： - - 所有 device, framework, tensor 目录下的 API，均在 paddle 根目录建立别名；除少数特殊 API 外，其他 API 在 paddle 根目录下均没有别名。 - - paddle.nn 目录下除 functional 目录以外的所有 API，在 paddle.nn 目录下均有别名；functional 目录中的 API，在 paddle.nn 目录下均没有别名。 -- **推荐优先使用较短的路径的别名**，比如`paddle.add -> paddle.tensor.add`，推荐优先使用`paddle.add` -- 以下为一些特殊的别名关系，推荐使用左边的 API 名称： - - paddle.tanh -> paddle.tensor.tanh -> paddle.nn.functional.tanh - - paddle.remainder -> paddle.mod -> paddle.floor_mod - - paddle.rand -> paddle.uniform - - paddle.randn -> paddle.standard_normal - - Layer.set_state_dict -> Layer.set_dict - -### 常用 API 名称变化 - -- 加、减、乘、除使用全称，不使用简称 -- 对于当前逐元素操作，不加 elementwise 前缀 -- 对于按照某一轴操作，不加 reduce 前缀 -- Conv, Pool, Dropout, BatchNorm, Pad 组网类 API 根据输入数据类型增加 1D, 2D, 3D 后缀 - - | Paddle 1.8 API 名称 | Paddle 2.0 对应的名称| - | --------------- | ------------------------ | - | paddle.fluid.layers.elementwise_add | paddle.add | - | paddle.fluid.layers.elementwise_sub | paddle.subtract | - | paddle.fluid.layers.elementwise_mul | paddle.multiply | - | paddle.fluid.layers.elementwise_div | paddle.divide | - | paddle.fluid.layers.elementwise_max | paddle.maximum | - | paddle.fluid.layers.elementwise_min | paddle.minimum | - | paddle.fluid.layers.reduce_sum | paddle.sum | - | paddle.fluid.layers.reduce_prod | paddle.prod | - | paddle.fluid.layers.reduce_max | paddle.max | - | paddle.fluid.layers.reduce_min | paddle.min | - | paddle.fluid.layers.reduce_all | paddle.all | - | paddle.fluid.layers.reduce_any | paddle.any | - | paddle.fluid.dygraph.Conv2D | paddle.nn.Conv2D | - | paddle.fluid.dygraph.Conv2DTranspose | paddle.nn.Conv2DTranspose | - | paddle.fluid.dygraph.Pool2D | paddle.nn.MaxPool2D, paddle.nn.AvgPool2D | - -## 三、开发流程 -### 数据处理 -数据处理推荐使用**paddle.io 目录下的 Dataset，Sampler, BatchSampler, DataLoader 接口**，不推荐 reader 类接口。一些常用的数据集已经在 paddle.vision.datasets 和 paddle.text.datasets 目录实现，具体参考 API 文档。 - -```python -from paddle.io import Dataset - -class MyDataset(Dataset): - """ - 步骤一：继承 paddle.io.Dataset 类 - """ - def __init__(self, mode='train'): - """ - 步骤二：实现构造函数，定义数据读取方式，划分训练和测试数据集 - """ - super().__init__() - - if mode == 'train': - self.data = [ - ['traindata1', 'label1'], - ['traindata2', 'label2'], - ['traindata3', 'label3'], - ['traindata4', 'label4'], - ] - else: - self.data = [ - ['testdata1', 'label1'], - ['testdata2', 'label2'], - ['testdata3', 'label3'], - ['testdata4', 'label4'], - ] - - def __getitem__(self, index): - """ - 步骤三：实现__getitem__方法，定义指定 index 时如何获取数据，并返回单条数据（训练数据，对应的标签） - """ - data = self.data[index][0] - label = self.data[index][1] - - return data, label - - def __len__(self): - """ - 步骤四：实现__len__方法，返回数据集总数目 - """ - return len(self.data) - -# 测试定义的数据集 -train_dataset = MyDataset(mode='train') -val_dataset = MyDataset(mode='test') - -print('=============train dataset=============') -for data, label in train_dataset: - print(data, label) - -print('=============evaluation dataset=============') -for data, label in val_dataset: - print(data, label) -``` - -### 组网方式 -#### Sequential 组网 - -针对顺序的线性网络结构可以直接使用 Sequential 来快速完成组网，可以减少类的定义等代码编写。 - -```python -import paddle - -# Sequential 形式组网 -mnist = paddle.nn.Sequential( - paddle.nn.Flatten(), - paddle.nn.Linear(784, 512), - paddle.nn.ReLU(), - paddle.nn.Dropout(0.2), - paddle.nn.Linear(512, 10) -) -``` - -#### SubClass 组网 - - 针对一些比较复杂的网络结构，就可以使用 Layer 子类定义的方式来进行模型代码编写，在`__init__`构造函数中进行组网 Layer 的声明，在`forward`中使用声明的 Layer 变量进行前向计算。子类组网方式也可以实现 sublayer 的复用，针对相同的 layer 可以在构造函数中一次性定义，在`forward 中多次调用。 - -```python -import paddle - -# Layer 类继承方式组网 -class Mnist(paddle.nn.Layer): - def __init__(self): - super().__init__() - - self.flatten = paddle.nn.Flatten() - self.linear_1 = paddle.nn.Linear(784, 512) - self.linear_2 = paddle.nn.Linear(512, 10) - self.relu = paddle.nn.ReLU() - self.dropout = paddle.nn.Dropout(0.2) - - def forward(self, inputs): - y = self.flatten(inputs) - y = self.linear_1(y) - y = self.relu(y) - y = self.dropout(y) - y = self.linear_2(y) - - return y - -mnist = Mnist() -``` - -### 模型训练 - -#### 使用高层 API - -增加了`paddle.Model`高层 API，大部分任务可以使用此 API 用于简化训练、评估、预测类代码开发。注意区别 Model 和 Net 概念，Net 是指继承 paddle.nn.Layer 的网络结构；而 Model 是指持有一个 Net 对象，同时指定损失函数、优化算法、评估指标的可训练、评估、预测的实例。具体参考高层 API 的代码示例。 - -```python -import paddle -from paddle.vision.transforms import ToTensor - -train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor()) -test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor()) -lenet = paddle.vision.models.LeNet() - -# Mnist 继承 paddle.nn.Layer 属于 Net，model 包含了训练功能 -model = paddle.Model(lenet) - -# 设置训练模型所需的 optimizer, loss, metric -model.prepare( - paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()), - paddle.nn.CrossEntropyLoss(), - paddle.metric.Accuracy() - ) - -# 启动训练 -model.fit(train_dataset, epochs=2, batch_size=64, log_freq=200) - -# 启动评估 -model.evaluate(test_dataset, log_freq=20, batch_size=64) -``` - -#### 使用基础 API - -```python -import paddle -from paddle.vision.transforms import ToTensor - -train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor()) -test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor()) -lenet = paddle.vision.models.LeNet() -loss_fn = paddle.nn.CrossEntropyLoss() - -# 加载训练集 batch_size 设为 64 -train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True) - -def train(): - epochs = 2 - adam = paddle.optimizer.Adam(learning_rate=0.001, parameters=lenet.parameters()) - # 用 Adam 作为优化函数 - for epoch in range(epochs): - for batch_id, data in enumerate(train_loader()): - x_data = data[0] - y_data = data[1] - predicts = lenet(x_data) - acc = paddle.metric.accuracy(predicts, y_data) - loss = loss_fn(predicts, y_data) - loss.backward() - if batch_id % 100 == 0: - print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy())) - adam.step() - adam.clear_grad() - -# 启动训练 -train() -``` - -### 单机多卡启动 -2.0 增加`paddle.distributed.spawn`函数来启动单机多卡训练，同时原有的`paddle.distributed.launch`的方式依然保留。 - -#### 方式 1、launch 启动 - -##### 高层 API 场景 - -当调用`paddle.Model`高层来实现训练时，想要启动单机多卡训练非常简单，代码不需要做任何修改，只需要在启动时增加一下参数`-m paddle.distributed.launch`。 - -```bash -# 单机单卡启动，默认使用第 0 号卡 -$ python train.py - -# 单机多卡启动，默认使用当前可见的所有卡 -$ python -m paddle.distributed.launch train.py - -# 单机多卡启动，设置当前使用的第 0 号和第 1 号卡 -$ python -m paddle.distributed.launch --selected_gpus='0,1' train.py - -# 单机多卡启动，设置当前使用第 0 号和第 1 号卡 -$ export CUDA_VISIBLE_DEVICES=0,1 -$ python -m paddle.distributed.launch train.py -``` - -##### 基础 API 场景 - -如果使用基础 API 实现训练，想要启动单机多卡训练，需要对单机单卡的代码进行 3 处修改，具体如下： - -```python -import paddle -from paddle.vision.transforms import ToTensor - -# 第 1 处改动，导入分布式训练所需要的包 -import paddle.distributed as dist - -train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor()) -test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor()) -lenet = paddle.vision.models.LeNet() -loss_fn = paddle.nn.CrossEntropyLoss() - -# 加载训练集 batch_size 设为 64 -train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True) - -def train(model): - # 第 2 处改动，初始化并行环境 - dist.init_parallel_env() - - # 第 3 处改动，增加 paddle.DataParallel 封装 - lenet = paddle.DataParallel(model) - epochs = 2 - adam = paddle.optimizer.Adam(learning_rate=0.001, parameters=lenet.parameters()) - # 用 Adam 作为优化函数 - for epoch in range(epochs): - for batch_id, data in enumerate(train_loader()): - x_data = data[0] - y_data = data[1] - predicts = lenet(x_data) - acc = paddle.metric.accuracy(predicts, y_data) - loss = loss_fn(predicts, y_data) - loss.backward() - if batch_id % 100 == 0: - print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy())) - adam.step() - adam.clear_grad() - -# 启动训练 -train(lenet) -``` - -修改完后保存文件，然后使用跟高层 API 相同的启动方式即可 - -**注意：** 单卡训练不支持调用 ``init_parallel_env``，请使用以下几种方式进行分布式训练。 - -```bash - -# 单机多卡启动，默认使用当前可见的所有卡 -$ python -m paddle.distributed.launch train.py - -# 单机多卡启动，设置当前使用的第 0 号和第 1 号卡 -$ python -m paddle.distributed.launch --selected_gpus '0,1' train.py - -# 单机多卡启动，设置当前使用第 0 号和第 1 号卡 -$ export CUDA_VISIBLE_DEVICES=0,1 -$ python -m paddle.distributed.launch train.py -``` - -#### 方式 2、spawn 启动 - -launch 方式启动训练，以文件为单位启动多进程，需要在启动时调用 ``paddle.distributed.launch`` ，对于进程的管理要求较高。飞桨框架 2.0 版本增加了 ``spawn`` 启动方式，可以更好地控制进程，在日志打印、训练退出时更友好。使用示例如下： - -```python -import paddle -import paddle.nn as nn -import paddle.optimizer as opt -import paddle.distributed as dist - -class LinearNet(nn.Layer): - def __init__(self): - super().__init__() - self._linear1 = nn.Linear(10, 10) - self._linear2 = nn.Linear(10, 1) - - def forward(self, x): - return self._linear2(self._linear1(x)) - -def train(print_result=False): - - # 1. 初始化并行训练环境 - dist.init_parallel_env() - - # 2. 创建并行训练 Layer 和 Optimizer - layer = LinearNet() - dp_layer = paddle.DataParallel(layer) - - loss_fn = nn.MSELoss() - adam = opt.Adam( - learning_rate=0.001, parameters=dp_layer.parameters()) - - # 3. 运行网络 - inputs = paddle.randn([10, 10], 'float32') - outputs = dp_layer(inputs) - labels = paddle.randn([10, 1], 'float32') - loss = loss_fn(outputs, labels) - - if print_result is True: - print("loss:", loss.numpy()) - - loss.backward() - - adam.step() - adam.clear_grad() - -# 使用方式 1：仅传入训练函数 -# 适用场景：训练函数不需要任何参数，并且需要使用所有当前可见的 GPU 设备并行训练 -if __name__ == '__main__': - dist.spawn(train) - -# 使用方式 2：传入训练函数和参数 -# 适用场景：训练函数需要一些参数，并且需要使用所有当前可见的 GPU 设备并行训练 -if __name__ == '__main__': - dist.spawn(train, args=(True,)) - -# 使用方式 3：传入训练函数、参数并指定并行进程数 -# 适用场景：训练函数需要一些参数，并且仅需要使用部分可见的 GPU 设备并行训练，例如： -# 当前机器有 8 张 GPU 卡 {0,1,2,3,4,5,6,7}，此时会使用前两张卡 {0,1}； -# 或者当前机器通过配置环境变量 CUDA_VISIBLE_DEVICES=4,5,6,7，仅使 4 张 -# GPU 卡可见，此时会使用可见的前两张卡 {4,5} -if __name__ == '__main__': - dist.spawn(train, args=(True,), nprocs=2) - -# 使用方式 4：传入训练函数、参数、指定进程数并指定当前使用的卡号 -# 使用场景：训练函数需要一些参数，并且仅需要使用部分可见的 GPU 设备并行训练，但是 -# 可能由于权限问题，无权配置当前机器的环境变量，例如：当前机器有 8 张 GPU 卡 -# {0,1,2,3,4,5,6,7}，但你无权配置 CUDA_VISIBLE_DEVICES，此时可以通过 -# 指定参数 selected_gpus 选择希望使用的卡，例如 selected_gpus='4,5'， -# 可以指定使用第 4 号卡和第 5 号卡 -if __name__ == '__main__': - dist.spawn(train, nprocs=2, selected_gpus='4,5') - -# 使用方式 5：指定多卡通信的起始端口 -# 使用场景：端口建立通信时提示需要重试或者通信建立失败 -# Paddle 默认会通过在当前机器上寻找空闲的端口用于多卡通信，但当机器使用环境 -# 较为复杂时，程序找到的端口可能不够稳定，此时可以自行指定稳定的空闲起始 -# 端口以获得更稳定的训练体验 -if __name__ == '__main__': - dist.spawn(train, nprocs=2, started_port=12345) -``` - -### 模型保存 -Paddle 保存的模型有两种格式，一种是训练格式，保存模型参数和优化器相关的状态，可用于恢复训练；一种是预测格式，保存预测的静态图网络结构以及参数，用于预测部署。 -#### 高层 API 场景 - -高层 API 下用于预测部署的模型保存方法为： - -```python -model = paddle.Model(Mnist()) -# 预测格式，保存的模型可用于预测部署 -model.save('mnist', training=False) -# 保存后可以得到预测部署所需要的模型 -``` - -#### 基础 API 场景 - -动态图训练的模型，可以通过动静转换功能，转换为可部署的静态图模型，具体做法如下： - -```python -import paddle -from paddle.jit import to_static -from paddle.static import InputSpec - -class SimpleNet(paddle.nn.Layer): - def __init__(self): - super().__init__() - self.linear = paddle.nn.Linear(10, 3) - - # 第 1 处改动 - # 通过 InputSpec 指定输入数据的形状，None 表示可变长 - # 通过 to_static 装饰器将动态图转换为静态图 Program - @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]) - def forward(self, x, y): - out = self.linear(x) - out = out + y - return out - - -net = SimpleNet() - -# 第 2 处改动 -# 保存静态图模型，可用于预测部署 -paddle.jit.save(net, './simple_net') -``` -### 推理 -推理库 Paddle Inference 的 API 做了升级，简化了写法，以及去掉了历史上冗余的概念。API 的变化为纯增，原有 API 保持不变，但推荐新的 API 体系，旧 API 在后续版本会逐步删除。 - -#### C++ API - -重要变化： - -- 命名空间从 `paddle` 变更为 `paddle_infer` -- `PaddleTensor`, `PaddleBuf` 等被废弃，`ZeroCopyTensor` 变为默认 Tensor 类型，并更名为 `Tensor` -- 新增 `PredictorPool` 工具类简化多线程 predictor 的创建，后续也会增加更多周边工具 -- `CreatePredictor` (原 `CreatePaddlePredictor`) 的返回值由 `unique_ptr` 变为 `shared_ptr` 以避免 Clone 后析构顺序出错的问题 - -API 变更 - -| 原有命名 | 现有命名 | 行为变化 | -| ---------------------------- | ---------------------------- | ----------------------------- | -| 头文件 `paddle_infer.h` | 无变化 | 包含旧接口，保持向后兼容 | -| 无 | `paddle_inference_api.h` | 新 API，可以与旧接口并存 | -| `CreatePaddlePredictor` | `CreatePredictor` | 返回值变为 shared_ptr | -| `ZeroCopyTensor` | `Tensor` | 无 | -| `AnalysisConfig` | `Config` | 无 | -| `TensorRTConfig` | 废弃 | | -| `PaddleTensor` + `PaddleBuf` | 废弃 | | -| `Predictor::GetInputTensor` | `Predictor::GetInputHandle` | 无 | -| `Predictor::GetOutputTensor` | `Predictor::GetOutputHandle` | 无 | -| | `PredictorPool` | 简化创建多个 predictor 的支持 | - -使用新 C++ API 的流程与之前完全一致，只有命名变化 - -```c++ -#include "paddle_infernce_api.h" -using namespace paddle_infer; - -Config config; -config.SetModel("xxx_model_dir"); - -auto predictor = CreatePredictor(config); - -// Get the handles for the inputs and outputs of the model -auto input0 = predictor->GetInputHandle("X"); -auto output0 = predictor->GetOutputHandle("Out"); - -for (...) { - // Assign data to input0 - MyServiceSetData(input0); - - predictor->Run(); - - // get data from the output0 handle - MyServiceGetData(output0); -} -``` - -#### Python API - -Python API 的变更与 C++ 基本对应，会在 2.0 版发布。 - - -## 附录 -### 2.0 转换工具 -为了降级代码升级的成本，飞桨提供了转换工具，可以帮助将 Paddle 1.8 版本开发的代码，升级为 2.0 的 API。由于相比于 Paddle 1.8 版本，2.0 版本的 API 进行了大量的升级，包括 API 名称，参数名称，行为等。转换工具当前还不能覆盖所有的 API 升级；对于无法转换的 API，转换工具会报错，提示手动升级。 - -https://github.com/PaddlePaddle/paddle_upgrade_tool - -对于转换工具没有覆盖的 API，请查看官网的 API 文档，手动升级代码的 API。 - -### 2.0 文档教程 -以下提供了 2.0 版本的一些示例教程： - -你可以在官网[应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/practices/index_cn.html)栏目内进行在线浏览，也可以下载在这里提供的源代码: -https://github.com/PaddlePaddle/docs/tree/develop/docs/practices diff --git a/docs/guides/paddle_v3_features/auto_parallel_cn.md b/docs/guides/paddle_v3_features/auto_parallel_cn.md new file mode 100644 index 00000000000..4a127e86048 --- /dev/null +++ b/docs/guides/paddle_v3_features/auto_parallel_cn.md @@ -0,0 +1,358 @@ +自动并行训练 +======================= + +# 一、背景动机 + +超大模型已经成为 AI 最重要的核心竞争力之一，随着模型规模持续快速增长，各种并行策略和关键技术相继提出，可以看出底层平台技术已呈收敛趋势，超大模型分布式训练逐渐地从『增量』过渡到『存量』竞争。如何更灵活支持各类应用场景对复杂并行策略的需求，如何帮助用户更简单进行分布式训练，如何兼容动态图灵活调试和静态图性能优势的优点等都是亟待解决挑战。 + +飞桨当前支持分布式训练当前有几种方式：动态图手动并行（动手）、静态图手动并行（静手）、动静统一自动并行（自动并行）几种方式。 + +手动并行（包括动态图手动并行和静态图手动并行）需要用户在组网时直接感知到分布式实现的细节，例如通信原语``Allreduce``、``Allgather``，通信组 ``process_group``，以及在组网中添加各类并行策略相关的 API，例如张量并行的 ``ColumnParallelLinear`` 和 ``RowParallelLinear`` 等，对于不同的分布式并行策略，都需要调用不同的接口，相对来说比较使用起来复杂。 + +自动并行为了降低用户开发分布式程序的门槛，提供了对不同分布式并行策略的统一抽象，让用户可以通过 `张量切分` 的语法标记即可实现不同并行策略。用户仅需使用少量的张量切分标注，框架便能自动推导出所有张量和算子的分布式切分状态，并添加合适的通信算子。同时自动并行还支持一键动转静分布式训练，开发者可以快速实现任意混合并行策略，大幅简化了混合并行训练代码的开发过程。 + +# 二、基本概念 + +## 2.1 自动并行 API + +根据功能，我们将自动并行支持的 API 分为标记信息、动转静、Save&Load 三类。 + +1.1 标记信息 +* paddle.distributed.shard_tensor：标记创建分布式张量 +* paddle.distributed.reshard：重切分，可以从一种分布式状态转换到另一种分布式状态 +* paddle.distributed.shard_layer：根据用户传入的条件，将组网中的参数转换为分布式张量 +* paddle.distributed.shard_dataloader：根据用户传入的条件，将 dataloader 中的张量转换为分布式张量 +* paddle.distributed.shard_optimizer：将优化器转变为分布式视角，定制化优化器状态的切分方式 + +1.2 动转静 +* paddle.distributed.to_static：将带有分布式切分信息的动态图 layer 动转静为静态图模型 + + +1.3 存储和加载模型 +* paddle.distributed.save_state_dict：保存模型参数结构到指定路径 +* paddle.distributed.load_state_dict：从指定路径加载模型 + +## 2.2 分布式张量 + +目前已有的分布式策略，数据并行、模型并行等，都是通过（1）切分输入/输出（2）切分模型参数 （3）切分计算 这三种方式，满足在多计算设备上加速训练大模型的需求。为了提供更易用的分布式接口，我们引入分布式张量这一概念，描述由多个计算设备上的局部物理张量通过既定计算共同组成的逻辑张量，用户可以通过 paddle.distributed.shard_tensor 来创建分布式张量。 + +为了描述分布式张量和计算设备之前的映射关系，我们引入 ``Placements`` 和 ``ProcessMesh`` 两个分布式概念。``ProcessMesh`` 可以理解为是用一个高维矩阵对分布集群中计算设备的抽象，比如一个 4 机 32 卡的集群可以用一个 shape=[4,8] 的 mesh 矩阵进行描述；``Placements`` 是由 ``Replicate``、``Shard``、``Partial`` 三种分布式标记组成的列表，长度和 ``ProcessMesh`` 的维度个数一致，用于表示分布式张量在对应计算设备的维度上，按照什么方式做切分，这三种分布式标记的详细描述如下： + +* Replicate，指张量在所有计算设备上保持全量状态。 +* Shard(axis)，指将张量沿 axis 维度做切分后，放到不同的计算设备上。 +* Partial，指每个计算设备只拥有部分值，需要通过指定的规约操作才能恢复成全量数据。 + +

    + +

    + +在如下的示例中，我们希望在 6 个计算设备上，创建一个形状为(4, 3)的分布式张量，其中沿着计算设备的 x 维，切分张量的 0 维；沿着计算设备的 y 维上，切分张量的 1 维。最终，每个计算设备实际拥有大小为(2, 1)的实际张量，如图所示。 + +```python +import paddle +import paddle.distributed as dist + +mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=['x', 'y']) + +dense_tensor = paddle.to_tensor([[1,2,3], + [4,5,6], + [7,8,9], + [10,11,12]]) + +placements = [dist.Shard(0), dist.Shard(1)] +dist_tensor = dist.shard_tensor(dense_tensor, mesh, placements) +``` +

    + +

    + +## 2.3 张量重切分 + +如果我们希望改变一个分布式张量在集群中的分布式状态，需要使用``重切分`` 功能， 框架中通过``paddle.distributed.reshard``接口提供。 +通过重切分我们可以支持跨 ``ProcessMesh`` 的分布式张量转换，比如，我们可以把在[0, 1] 两个设备上状态为 ``Replicate`` 的分布式张量，转换到 [2, 3] 这两个设备上，并变成状态为 ``Shard`` 的分布式张量。 + +```python +import paddle +import paddle.distributed as dist + +mesh0 = dist.ProcessMesh([0, 1], dim_names=['x']) +mesh1 = dist.ProcessMesh([2, 3], dim_names=['x']) + +dense_tensor = paddle.to_tensor([[1,2,3], + [4,5,6]]) + +placements0 = [dist.Replicate()] +placements1 = [dist.Shard(0)] + +dist_tensor = dist.shard_tensor(dense_tensor, mesh0, placements0) +dist_tensor_after_reshard = dist.reshard(dist_tensor, mesh1, placements1) +``` +

    + +

    + +# 三、原理简介 + +下面我们用一个简单的列子介绍自动并行框架底层的执行流程和原理。 + +在单卡逻辑视角下我们希望完成计算 C = Matmul(A, B)，D = Relu(C)。 +假设用户将 TensorB 标记成按列切分，表示在实际分布式集群中 TensorB 被按行切分到不同的 Devices 上。将 TensorA 标记成复制，表示所有 Devices 上都有完整 TensorA 副本。 + +```python +import paddle +import paddle.distributed as dist + +mesh = dist.ProcessMesh([0, 1], dim_names=['x']) +dense_tensorA = paddle.to_tensor([[1,2,], [3,4]]) +dense_tensorB = paddle.to_tensor([[5,6], [7,8]]) +placementsA = [dist.Replicate()] +placementsB = [dist.Shard(0)] + +dist_tensorA = dist.shard_tensor(dense_tensorA, mesh, placementsA) +dist_tensorB = dist.shard_tensor(dense_tensorB, mesh, placementsB) +dist_tensorC = Matmul(dist_tensorA, dist_tensorB) +dist_tensorD = relu(dist_tensorC) +``` + +

    + +

    + +接下来就会进入自动并行的第一个核心逻辑 **切分推导**。 +当前用户标记的输入切分状态是无法被 Matmul 算子实际计算的(TensorA 的第 0 维和 TensorB 的第 1 维不匹配)。 +这时候自动并行框架会使用当前算子的切分推导规则(e.g. MatmulSPMD Rule)，根据输入 tensors 的切分状态，推导出一套合法且性能较优的 输入-输出 张量的切分状态。 +在上述输入的切分状态下，框架会推导出会将 TensorA 的切分状态推导成按列切分，TensorB 保持切分状态不变，Matmul 的计算结果 TensorC 的切分状态是 Partial。 +因为后续的 Relu 算子是非线性的，输入不能是 Partial 状态，所以框架会根据 ReluSPMD Rule 将 TensorC 输入 Relu 前的的分布式状态推导成 Replicated。 +

    + +

    + +接下来就会进入自动并行的第二个核心逻辑 **切分转换**。 +框架会根据 tensor 当前的切分状态(src_placement)，和切分推导规则推导出的算子计算需要的切分状态(dst_placement),添加对应的通信/张量维度变换算子。 +根据上图的切分推导，在计算 Matmul 添加 split 算子，在计算 Relue 添加 Allreduce，将输入 tensor 转换成需要的切分状态进行实际计算。 + +

    + +

    + + + +# 四、使用示例 + +## 4.1 数据并行 + +数据并行是深度学习领域最常用的并行方法，在此策略下将数据沿 batch 维切分成多份，每个计算资源上保存完整的模型参数并独立处理一份子数据集。用自动并行的语义，用户只需要将输入标记为沿着 batch 维做切分，不需要进行其他额外的操作。 + +在下面的例子中，我们在 4 个计算设备上做数据并行，每一轮输入数据的形状为（4, 128, 1024），沿 0 维做切分后，每个计算设备上有大小为（1，128，1024）的数据做计算。 + +```python +# 启动脚本： +# python3 -m paddle.distributed.launch --device=0,1,2,3 train.py +import paddle +import paddle.distributed as dist +from paddle.io import BatchSampler, DataLoader, Dataset +import numpy as np + +mesh = dist.ProcessMesh([0, 1, 2, 3], dim_names=['x']) + +class RandomDataset(Dataset): + def __init__(self, seq_len, hidden, num_samples=100): + super().__init__() + self.seq_len = seq_len + self.hidden = hidden + self.num_samples = num_samples + + def __getitem__(self, index): + input = np.random.uniform(size=[self.seq_len, self.hidden]).astype("float32") + return input + + def __len__(self): + return self.num_samples + +class MlpModel(paddle.nn.Layer): + def __init__(self): + super(MlpModel, self).__init__() + self.w0 = self.create_parameter(shape=[1024, 4096]) + self.w1 = self.create_parameter(shape=[4096, 1024]) + + def forward(self, x): + # 标记数据为切分状态 + dist.shard_tensor(x, mesh, [dist.Shard(0)]) + y = paddle.matmul(x, self.w0) + z = paddle.matmul(y, self.w1) + return z + +model = MlpModel() +dataset = RandomDataset(128, 1024) +sampler = BatchSampler( + dataset, + batch_size=4, +) +dataloader = DataLoader( + dataset, + batch_sampler=sampler, +) +opt = paddle.optimizer.AdamW(learning_rate=0.001, parameters=model.parameters()) +opt = dist.shard_optimizer(opt) + +for step, inputs in enumerate(dataloader): + data = inputs + logits = model(data) + loss = paddle.mean(logits) + loss.backward() + opt.step() + opt.clear_grad() +``` + +## 4.2 张量并行 + +张量并行是在保证数学上正确的前提下，将组网中的参数切分到不同的计算设备，达到降低单个计算设备上的显存消耗的目的。用户需要显式在组网里标记切分参数的方式。 + +在下述例子中，我们将第一层 ``Linear`` 的 ``weight`` 参数按列切分，第二层 ``Linear`` 的 ``weight`` 参数按行切分，最终得到的结果为``Partial`` 状态，每个计算设备有全量的数据，但需要经过 ``reduce`` 相关计算得到正确的值。 + +```python +# 启动脚本： +# python3 -m paddle.distributed.launch --device=0,1,2,3 train.py + +mesh = dist.ProcessMesh([0, 1, 2, 3], dim_names=['x']) + +class MlpModel(paddle.nn.Layer): + def __init__(self): + super(MlpModel, self).__init__() + # 标记参数为切分状态，w0 沿 1 维切分 + self.w0 = dist.shard_tensor( + self.create_parameter(shape=[1024, 4096]), + mesh, [dist.Shard(1)]) + # w1 沿 0 维切分 + self.w1 = dist.shard_tensor( + self.create_parameter(shape=[4096, 1024]), + mesh, [dist.Shard(0)]) + + def forward(self, x): + y = paddle.matmul(x, self.w0) + z = paddle.matmul(y, self.w1) + return z +``` + +## 4.3 流水并行 + +流水并行将模型的不同层放到不同的计算设备上，达到降低单个计算设备的显存消耗的目的。流水并行需要用户显式调用 ``paddle.distributed.reshard``，将前一个流水并行层的计算结果，显式传输到当前流水并行层作为输入。 + +```python +# 启动脚本： +# python3 -m paddle.distributed.launch --device=0,1,2,3,4,5,6,7 train.py + +mesh0 = dist.ProcessMesh([0, 1, 2, 3], dim_names=['x']) +mesh1 = dist.ProcessMesh([4, 5, 6, 7], dim_names=['x']) + +class MlpModel(paddle.nn.Layer): + def __init__(self): + super(MlpModel, self).__init__() + self.w0 = dist.shard_tensor( + self.create_parameter(shape=[1024, 4096]), + mesh0, [dist.Replicate()]) + self.w1 = dist.shard_tensor( + self.create_parameter(shape=[4096, 1024]), + mesh1, [dist.Replicate()]) + + def forward(self, x): + y = paddle.matmul(x, self.w0) + # 重切分，将 stage0 上的中间计算结果传输给 stage1 + y = dist.reshard(y, mesh1, [dist.Replicate()]) + z = paddle.matmul(y, self.w1) + return z +``` + +## 4.4 3D 混合并行策略 + +下面是一个完整的包含数据并行、张量并行、流水并行三种策略的示例，在 ``ProcessMesh`` 的 0 维上做数据并行，1 维上做张量并行，跨 ``mesh``上做流水并行。 + +```python +# 启动脚本： +# python3 -m paddle.distributed.launch --device=0,1,2,3,4,5,6,7 train.py + +import paddle +import paddle.distributed as dist +from paddle.io import BatchSampler, DataLoader, Dataset +import numpy as np + +mesh0 = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=['x', 'y']) # 创建进程网格 +mesh1 = dist.ProcessMesh([[4, 5], [6, 7]], dim_names=['x', 'y']) # 创建进程网格 + +class RandomDataset(Dataset): + def __init__(self, seq_len, hidden, num_samples=100): + super().__init__() + self.seq_len = seq_len + self.hidden = hidden + self.num_samples = num_samples + + def __getitem__(self, index): + input = np.random.uniform(size=[self.seq_len, self.hidden]).astype("float32") + return input + + def __len__(self): + return self.num_samples + +class MlpModel(paddle.nn.Layer): + def __init__(self): + super(MlpModel, self).__init__() + self.w0 = dist.shard_tensor( + self.create_parameter(shape=[1024, 4096]), + mesh0, [dist.Replicate(), dist.Shard(1)]) # 模型并行，列切 + self.w1 = dist.shard_tensor( + self.create_parameter(shape=[4096, 1024]), + mesh1, [dist.Replicate(), dist.Shard(0)]) # 模型并行，行切 + + def forward(self, x): + dist.shard_tensor(x, mesh0, [dist.Shard(0), dist.Replicate()]) + y = paddle.matmul(x, self.w0) + y = dist.reshard(y, mesh1, [dist.Shard(0), dist.Shard(2)]) #流水线并行 + z = paddle.matmul(y, self.w1) + return z + +model = MlpModel() +dataset = RandomDataset(128, 1024) +sampler = BatchSampler( + dataset, + batch_size=2, +) +dataloader = DataLoader( + dataset, + batch_sampler=sampler, +) +dataloader = dist.shard_dataloader(dataloader, meshes=[mesh0, mesh1], shard_dims='x') + +opt = paddle.optimizer.AdamW(learning_rate=0.001, parameters=model.parameters()) +opt = dist.shard_optimizer(opt) + +for step, inputs in enumerate(dataloader): + data = inputs + logits = model(data) + loss = paddle.mean(logits) + loss.backward() + opt.step() + opt.clear_grad() +``` + +## 4.5 动转静训练 + +动态图和静态图是框架的两种执行模式，动态图方便用户调试和开发，可以即时得到执行结果，静态图会做性能优化和调度编排，将硬件资源用到极致，为了兼备两者的优点，我们提供动转静机制，支持用户在动态图上开发调试后，转成静态图执行。 + +自动并行的 API 在设计之初，就以实现统一的用户标记接口和逻辑为目标，保证动静半框架保证在相同的用户标记下，动静态图分布式执行逻辑一致。这样用户在全流程过程中只需要标记一套动态图组网，即可以实现动态图下的分布式训练 Debug 和 静态图下的分布式推理等逻辑。整个动转静训练的逻辑如下： + +

    + +

    + +```python +... +dist_model = dist.to_static( + model, dataloader, paddle.mean, opt +) + +dist_model.train() +for step, inputs in enumerate(dataloader()): + data = inputs + loss = dist_model(data) + print(step, loss) +``` diff --git a/docs/guides/paddle_v3_features/cinn_cn.md b/docs/guides/paddle_v3_features/cinn_cn.md new file mode 100644 index 00000000000..1e0eb35f5a4 --- /dev/null +++ b/docs/guides/paddle_v3_features/cinn_cn.md @@ -0,0 +1,360 @@ +# CINN 神经网络编译器 + +## 一、概念简介 +深度学习编译器是一种专门为深度学习模型优化和部署而设计的工具，用于提高模型的计算效率、降低内存占用、加速训练推理过程。其功能是将高层次的深度学习模型转换为低层次的、高效的、底层硬件可执行的代码。简单来说，深度学习编译器在深度学习框架和底层硬件之间充当了“翻译”的角色，能够将用户定义的神经网络模型描述转化为底层硬件能够理解和执行的指令。编译器在实现这种转换的过程中，应用了一系列优化技术，以提高模型在各种硬件平台上（如 CPU、GPU）的执行效率。 +深度学习编译器的主要功能包括： +- **模型转换**：将高层次的深度学习模型转换为适合目标硬件的中间表示（IR）。 +- **优化**：应用各种编译优化技术，如图优化、内存优化、算子融合等，以提高执行效率。 +- **代码生成**：生成适合目标硬件的可执行代码。 + +## 二、背景与动机 +深度学习模型的训练和推理过程涉及大量的计算，对硬件性能要求很高。飞桨框架虽然提供了高级的编程接口和丰富的算子库，但在执行效率和模型部署方面还有很大的优化空间。使用深度学习编译器的主要动机包括： +#### 1. 优化性能与资源利用率 +深度学习模型往往需要处理大量的数据和复杂的计算，直接在高层次框架上执行可能无法充分利用底层硬件的能力。深度学习编译器能够深入硬件特性，应用多种优化技术，提高计算效率，降低延迟。并且通过优化模型的计算图和内存使用，深度学习编译器也能够明显降低模型的内存和 IO 资源的消耗，进而提高计算性能。 +#### 2. 硬件多样性支持 +不同的硬件平台有不同的特性和优化需求。在现有机制下，新的异构硬件设备接入深度学习框架需要手工实现几百个算子对应的硬件 Kernel 代码，开发的工作量非常大。如果使用深度学习编译器，理论上仅需实现新硬件 IR 层面的对接，以及相应的硬件 IR 优化策略就能完成与深度学习框架的对接，相比于实现几百个硬件 Kernel，开发的工作量会大幅减少。 +#### 3. 提升开发效率 +深度学习编译器可以自动化许多优化过程，减少手动调优的工作量。开发者只需关注模型的设计和训练，而不必深入了解底层硬件优化细节，从而提高开发效率。 + +## 三、使用示例： +飞桨框架编译器（CINN, Compiler Infrastructure for Neural Networks）使用时仅需在原先的模型动转静或推理流程下打开编译器相关 FLAGS 即可，无需对模型代码做任何改动。以下是一个使用样例： + +示例代码文件：`run_net.py` +```python +import paddle +from paddle import nn +from paddle.static import InputSpec + +# 定义神经网络 +class RMSNorm(nn.Layer): + def __init__(self): + super().__init__() + paddle.seed(2024) + self.hidden_size = 768 + self.weight = paddle.randn([self.hidden_size], dtype="float32") + self.variance_epsilon = 1e-6 + + def forward(self, hidden_states): + variance = (hidden_states * hidden_states).sum(-1, keepdim=True) / 768 + hidden_states = ( + paddle.rsqrt(variance + self.variance_epsilon) * hidden_states + ) + return hidden_states * self.weight + + +def run_net(input_data): + net = RMSNorm() + + # 指定输入变量的维度、数据类型等信息，具体接口可参考： + # https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/jit/basic_usage_cn.html#inputspec + input_spec = [ + InputSpec(shape=[1, None, 768], dtype='float32'), + ] + net = paddle.jit.to_static( + net, + input_spec=input_spec, + full_graph=True, + ) + # 使用 eval 模式 + net.eval() + # 执行计算图 + out = net(input_data) + return out + +# 创建输入数据 +input_data = paddle.randn([1, 2048, 768], dtype="float32") +# 运行神经网络 +out = run_net(input_data) +print(out) +``` + +脚本执行：`run.sh` +``` +# 打开组合算子 +export FLAGS_prim_enable_dynamic=true && export FLAGS_prim_all=true + +# 打开 CINN 编译器相关 FLAG +export FLAGS_use_cinn=true +export FLAGS_cinn_new_group_scheduler=true +export FLAGS_group_schedule_tiling_first=true +export FLAGS_cinn_bucket_compile=true + +# 打开 PIR 模式 +export FLAGS_enable_pir_api=true + +# 是否打印 Program IR 信息 +export FLAGS_print_ir=false + +python run_net.py +``` + +上述代码示例中我们创建了一个简单的`rms_norm`计算子图，使用飞桨的动转静流程将子图转为静态图并调用编译器 CINN 进行优化和执行。经过性能对比测试，在 A100 GPU 环境中上述子图使用 CINN 可以取得 3 倍左右的性能提升（该性能数据仅供学习参考，在实际应用模型中能够取得的性能提升效果一般会低于该数据）。 + +注：由于飞桨的编译器仍然处在快速迭代开发阶段，我们设置了较多 FLAGS 进行分支的选择和调试，因此现阶段在使用 CINN 时需要对如下 FLAGS（`FLAGS_prim_enable_dynamic`、 `FLAGS_cinn_new_group_scheduler`、 `FLAGS_group_schedule_tiling_first`、 `FLAGS_cinn_bucket_compile`、 `FLAGS_enable_pir_api`） 进行手动设置，待后续相关功能完备后这些 FLAGS 会默认开启，无需再手动设置。 + +## 四、设计架构 +
    +
    图 1 CINN 整体架构

    + +飞桨框架编译器（CINN, Compiler Infrastructure for Neural Networks）整体架构如上图所示，大体可以分为三个模块，分别是编译器前端、编译器后端和执行器部分。 + +### 1. 编译器前端 +一般来说编译器前端需要将不同框架和格式的深度学习模型转换为编译器的内部 IR 并进行图级别的优化，CINN 作为飞桨框架原生编译器，可以直接使用飞桨框架提供的模型加载和中间表示（Paddle IR，简称 PIR）组件，因此 CINN 前端的主要功能是基于 PIR 进行图层级别的优化，并对子图进行划分为后端高性能 Kernel 代码生成提供支持。CINN 前端关键的流程可分为三部分： + +#### a. 组合算子拆分 +飞桨框架中将算子划分为基础算子（也称作原子算子，语义上该算子无法更进一步拆分成其他算子。基础算子语义上可以通过重组等价实现组合算子的逻辑）和非基础算子两类大，由于非基础算子数量较多，并且在编译器中较难识别和处理，因此我们使用组合算子拆分的方式将非基础算子拆分为等价的基础算子组合，原始计算图经过组合算子拆分后可以大幅提升性能的可优化空间。 + +#### b. 图优化 Pass +在计算图层级进行 PIR 的 Pass 优化，常见的图优化 Pass 包括：常量折叠、死代码消除（DCE）、公共子表达式消除（CSE）、冗余算子消除、算子计算合并等。 + +#### c. 算子融合 +算子融合是编译器前端非常重要的一个功能，主要是将多个算子打包到一个子图中（对应为一个 FusionOp），交给编译器后端生成一个高效的硬件相关计算 Kernel。 +算子融合的本质是通过 IO 优化加速访存密集算子，如果我们将两个连续 Kernel 合并为一个 Kernel 调用，我们会减少中间变量的读写开销，因此在访存密集型的 2 个 Op 上，融合可以获取更高的性能。举个例子，如下图： +
    +
    图 2 算子融合示例

    + +我们有两个算子 Relu 和 Scale，因为两个算子都是 IO 密集型算子（计算复杂度不高）。正常情况下我们需要读取 A 和 B 一次，写 B 和 C 一次。但是对于融合之后的 Kernel（右图）而言，我们只需要读取 A 和写 C 一次，这样我们通过算子融合可以取得更少的访存次数，在 IO 密集算子而言，可以极大提高性能。 +具体的算子融合策略实现非常复杂，这里不做展开介绍，感兴趣的读者可以阅读相关源码 [#cinn_group_cluster_pass](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/cinn/hlir/dialect/operator/transforms/cinn_group_cluster_pass.cc)。 + +### 2. 编译器后端 +编译器后端主要负责将前端处理后的 IR 转换为目标硬件可执行的代码或硬件描述。主要功能包括基于硬件特性的 IR 优化、高效内存管理和代码生成等。 + +#### 2.1. CINN AST IR +AST IR 打印示例： +``` +ScheduleBlock(root) +{ + serial for (i, 0, 32) + { + serial for (j_0, 0, 64) + { + serial for (j_1, 0, 128) + { + ScheduleBlock(A) + { + vi, vj = axis.bind(i, j_0 * 64 + j_1) // tensor 下标与循环变量的仿射变换 + A[vi, vj] = X[vi, vj] * 2 + } + } + } + } +} +``` +CINN AST IR 中包含了以下信息，但集合和映射并不显示使用某种数据结构进行存储。 + +  **集合**：语句实例 & 内存单元 **
    ** +  **映射**：**
    ** +   访存关系：语句实例 <---> 内存单元 **
    ** +   依赖关系：语句实例 <---> 语句实例 **
    ** +   执行顺序：语句实例 -----> 语句实例 **
    ** + +  执行顺序 = 语句实例的先后关系 **
    ** +  语句实例集合范围 = 循环边界 + 循环步长 ------ 循环构成一个带约束的整数空间，即迭代空间，迭代空间决定了语句实例，语句实例充满了迭代空间。 + +#### 2.2. 基于 AST IR 的 Schedule +Schedule 为定义在 CINN AST IR 上的优化策略，常见的 Schedule 包括：LoopAlignment, Tile, Inline, Vectorize, Unroll 等。**
    ** +以一个组合算子为例模拟可能的 AST 变换过程：**
    ** + [S1, S2, 1024] ==E=> [S1, S2, 1024] ==R=> [S1, S2] ==E=> [S1, S2] ==B=> [S1, S2, 1024] ==E=> [S1, S2, 1024] + +**(1) LowerToAst 得到的结果** +``` +// Elemenwise-1 +serial for (i, 0, S1) + serial for (j, 0, S2) + serial for (k, 0, 1024) + ScheduleBlock(A) + vi, vj, vk = axis.bind(i, j, k) + A[vi, vj, vk] = X[vi, vj, vk] * 2 +// Elemenwise-2 +serial for (i, 0, S1) + serial for (j, 0, S2) + serial for (k, 0, 1024) + ScheduleBlock(B) + vi, vj, vk = axis.bind(i, j, k) + B[vi, vj, vk] = A[vi, vj, vk] + 1 +// Reduce-1 +serial for (i, 0, S1) + serial for (j, 0, S2) + ScheduleBlock(C__reduce_init) + vi, vj = axis.bind(i, j) + C_init[vi, vj] = 0 +serial for (i, 0, S1) + serial for (j, 0, S2) + serial for (k, 0, 1024) // Reduce + ScheduleBlock(C) + vi, vj, vk = axis.bind(i, j, k) + C[vi, vj] = C[vi, vj] + B[vi, vj, vk] +// Elemenwise-3 +serial for (i, 0, S1) + serial for (j, 0, S2) + ScheduleBlock(D) + vi, vj = axis.bind(i, j) + D[vi, vj] = C[vi, vj] * 2 +// Broadcast-1 +serial for (i, 0, S1) + serial for (j, 0, S2) + serial for (k, 0, 1024) // Broadcast + ScheduleBlock(E) + vi, vj, vk = axis.bind(i, j, k) + E[vi, vj, vk] = D[vi, vj] +// Elemenwise-4 +serial for (i, 0, S1) + serial for (j, 0, S2) + serial for (k, 0, 1024) + ScheduleBlock(F) + vi, vj, vk = axis.bind(i, j, k) + F[vi, vj, vk] = E[vi, vj, vk] + 1 +``` +**(2) 迭代空间对齐** +``` +// 所有 ScheduleBlock 的 loop nest 都变为以下 2 种格式中的一种 +// 1 +serial for (sp, 0, S1 * S2) // pure_spatial_iter + serial for (rb, 0, 1024) // impure_spatial_iter + ScheduleBlock(XXX) + vsp1, vsp2, vrb = axis.bind(sp / S2, sp % S2, rb) + XXX = XXXXXX +// 2 +serial for (sp, 0, S1 * S2) // pure_spatial_iter + ScheduleBlock(XXX) + vsp1, vsp2 = axis.bind(sp / S2, sp % S2) + XXX = XXXXXX +``` +**(3) Tile: 对所有 ScheduleBlock 的 loop nest 做相同的 Tile** +``` +// pure_spatial 轴 Tile 为：-1 * 16 * 64 Tile size 可为参数传入 +serial for (sp1, 0, S1 * S2 / 1024) + serial for (sp2, 0, 16) + serial for (sp3, 0, 64) // S1 * S2 / 16 / 64, predicate: sp1 * 1024 + sp2 * 16 + sp3 < S1 * S2 + XXXXXX +// impure_spatial_iter 轴 Tile 为 32 +serial for (sp1, 0, S1 * S2 / 1024) + serial for (sp2, 0, 16) + serial for (sp3, 0, 64) + serial for (rb1, 0, 32) + serial for (rb2, 0, 32) + ScheduleBlock(XXX) + predicate = sp1 * 1024 + sp2 * 16 + sp3 < S1 * S2 + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + vrb = axis.bind(rb1 * 32 + rb2) + XXX = XXXXX +``` +**(4) ComputeInline** +``` +// 例如 ScheduleBlock(A) inline 到 ScheduleBlock(B) +serial for (sp1, 0, S1 * S2 / 1024) + serial for (sp2, 0, 16) + serial for (sp3, 0, 64) + serial for (rb1, 0, 32) + serial for (rb2, 0, 32) + ScheduleBlock(A) + predicate = sp1 * 1024 + sp2 * 16 + sp3 < S1 * S2 + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + vrb = axis.bind(rb1 * 32 + rb2) + B[vsp1, vsp2, vrb] = (X[vsp1, vsp2, vrb] * 2) + 1 +``` +**(5) Reduce 优化: two step reduce & 绑定部分 reduce 轴到 cuda** +``` +// 为了简洁，此处省略 reduce_init Block 和 predicate +serial for (sp1, 0, S1 * S2 / 1024) + serial for (sp2, 0, 16) + serial for (sp3, 0, 64) + CudaBind[ThreadIdx.x] for (rb1, 0, 32) + serial for (rb2, 0, 32) + ScheduleBlock(C_rf) + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + vrb1 = axis.bind(rb1) + vrb2 = axis.bind(rb2) + C_rf[vsp1, vsp2, vrb1] = C_rf[vsp1, vsp2, vrb1] + B[vsp1, vsp2, vrb1 * 32 + vrb2] + ScheduleBlock(C) + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + vrb1 = axis.bind(rb1) + C[vsp1, vsp2] = C[vsp1, vsp2] + C_rf[vsp1, vsp2, vrb1] +``` +**(6) 循环融合: ComputeAt && SimpleComputeAt，融合外层循环乘积相同的循环，并且保证不破坏图级别依赖（规则负责）和元素级别依赖（原语负责）** +``` +serial for (sp1, 0, S1 * S2 / 1024) + serial for (sp2, 0, 16) + serial for (sp3, 0, 64) + ScheduleBlock(D) + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + D[vsp1, vsp2] = C[vsp1, vsp2] * 2 + serial for (rb1, 0, 32) + serial for (rb2, 0, 32) + ScheduleBlock(E) + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + vrb = axis.bind(rb1 * 32 + rb2) + E[vsp1, vsp2, vrb] = D[vsp1, vsp2] + ScheduleBlock(F) + vsp1 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) / S2) + vsp2 = axis.bind((sp1 * 1024 + sp2 * 16 + sp3) % S2) + vrb = axis.bind(rb1 * 32 + rb2) + F[vsp1, vsp2, vrb] = E[vsp1, vsp2, vrb] + 1 +``` +**(7) Bind Cuda 轴：在第二步中，所有 ScheduleBlock 对应的循环要 bind 到同一 Cuda 轴** +``` +serial for (sp1, 0, S1 * S2 / 1024) + CudaBind[BlockIdx.x] for (sp2, 0, 16) + CudaBind[ThreadIdx.y] for (sp3, 0, 64) + CudaBind[ThreadIdx.x] for (rb1, 0, 32) + serial for (rb2, 0, 32) + ScheduleBlock(XXX) +``` + +#### 2.3. Kernel 代码生成与编译 + +Codegen 在 CINN IR AST 上做前序遍历，打印出对应硬件的指令，并通过硬件相对应的编译器（如 llvm、nvcc 等）进行编译得到可运行的函数指针，该指针会被封装到 `JitKernelOp`` 中用于后续执行器的解析执行。 + +a. 以函数定义为例子，cuda kernel func 和 x86 kernel func 的不同的是，cuda kernel func 会在函数名前增加 `__global__` + +针对 x86 硬件，转义 `ir::_LoweredFunc_` 的代码如下： +``` +void CodeGenC::Visit(const ir::_LoweredFunc_ *op) { + PrintFunctionDeclaration(op); // 前序遍历继续转义函数名、函数参数等 + str_ += "\n"; + ... + ... +} +``` +在 NV GPU 上的转义代码如下： +``` +void CodeGenCUDA_Dev::Visit(const ir::_LoweredFunc_ *op) { + str_ += "__global__\n"; // 和 x86 的不同，增加 __global__ + PrintFunctionDeclaration(op); // 前序遍历继续转义函数名、函数参数等 + str_ += "\n"; + ... + ... +} +``` +b. 在动态形状场景下，还会 codegen 出 infer shape function, infer shape function 的 CINN IR 会在 Bucket Lowering 中得到，转义过程复用的 x86 硬件的 codegen。infer shape kernel 如下： +``` +// infer shape 函数名字的组成：kernel_name + "infer_shape" +// 函数参数： +// kernel_args: 指针数组，和 kernel func args 一致 +// kernel_args_num: kernel_args 的长度 +// tensor_shape_args: 指针数组，存储输出 tensor 的 shape +function fn_exp_0_subtract_0_infer_shape (kernel_args, kernel_args_num, tensor_shape_args) +{ + int64 S0 = cinn_get_value_in_cuda_kernel_args(kernel_args, 2) + { + // CINN IR 暂时不支持数据索引的语法，暂时用函数调用实现，下面 2 条语句等价于 + // tensor_shape_args[0] = {S0, 256ll}; + // 即第 0 个出 tensor 的 shape 为{S0, 256ll}; + infer_shape_set_value(0, 0, S0, tensor_shape_args) + infer_shape_set_value(0, 1, 256ll, tensor_shape_args) + } +} +``` + +### 3. 执行器 + +编译器生成的 Kernel 代码需要与深度学习框架执行器完成交互和集成才能最终运行起来，因此需要基于执行器的运行调度接口对编译器生成的 Kernel 进行封装。 + +接入执行器后在运行时对于经过编译器处理的子图将执行 CINN 生成的 Kernel, 否则将执行常规的 PHI 算子 Kernel。 diff --git a/docs/guides/paddle_v3_features/higher_order_ad_cn.md b/docs/guides/paddle_v3_features/higher_order_ad_cn.md new file mode 100644 index 00000000000..ddf6f160ece --- /dev/null +++ b/docs/guides/paddle_v3_features/higher_order_ad_cn.md @@ -0,0 +1,209 @@ +高阶自动微分功能支持科学计算 +================ + +本篇文章主要为你介绍飞桨的高阶微分机制，帮助你更好的使用飞桨。 + +一、背景与动机 +-------- + +深度学习模型的训练过程涉及使用随机梯度下降（SGD）等优化算法来更新模型参数。在这一过程中，深度学习框架的自动微分功能发挥着核心作用，它利用链式法则自动计算出损失函数相对于模型参数的梯度。尽管大多数深度学习任务只需计算一阶导数，但在某些 AI for Science 场景中，却需要计算高阶导数，这无疑增加了自动微分的复杂性。以 2D 矩形平板分布受载问题为例，该问题的内在机理需要使用 4 阶微分方程来描述。为了求解这类问题，深度学习框架必须支持高阶自动微分功能。 + +
    + +
    + +
    + +
    + +二、设计思想 +------------------------------ + +高阶自动微分的实现面临诸多挑战。具体而言，框架需要为每个算子编写高阶微分规则。随着阶数的增加，微分规则的复杂性也随之上升。当阶数达到三阶或更高时，编写这些规则变得极其困难，同时正确性难以保证。为了解决这一问题，飞桨提出了基于基础算子组合的高阶自动微分技术。该技术的关键在于将复杂算子（如 log_softmax）拆解为多个基础算子的组合。然后，我们对这些基础算子进行一阶自动微分变换。重要的是，基础算子经过一阶自动微分变换后，其得到的计算图仍然是由基础算子所构成。通过反复应用一阶自动微分规则，我们可以轻松地获得高阶自动微分结果。 + +**log_softmax 拆解与微分示例** + +根据 log_softmax 表达式拆解为 exp、max、log 等细粒度基础算子组成，基础算子是指由简单运算逻辑组成的有限集合，数量较少。基于飞桨的自动微分体系，使用基础算子的微分规则自动推导 log_softmax 一阶微分，注意基础算子微分规则仍由基础算子实现，因此 log_softmax 的一阶微分仍由基础算子组成。重复上述微分过程实现 log_softmax 高阶微分求解。 + +
    + +
    + +三、框架架构 +------------------------------ + +为了支持高阶自动微分，飞桨框架精心设计与实现了组合算子机制。这一机制不仅兼容动态图模式和静态图模式，而且在动态图模式下支持 N+1 阶微分的拆分，同时在静态图模式下能够进行编译器融合优化。创新性地设计并实现了动静一体的算子组合规则，这意味着同一套组合规则在动态图和静态图两种模式下均可复用，从而避免了重复开发。在构建基础算子体系时，我们以 Tensor 作为核心操作对象，确保了算子的原子性、实用性和完备性。此外，我们还支持自定义反向操作和自动重计算功能，这些特性不仅提升了模型的精度，还有效地减少了显存占用，为用户提供了更高效、更灵活的深度学习体验。 + +
    + +
    + + **基础算子集合设计** + +基础算子集合的设计需要兼顾通用性、计算效率、易用性和兼容性，此外，还需要具备可扩展性，以便可以方便地添加新的数据处理操作和模型，并可以组合支撑更加复杂的计算工作。飞桨制定了基础算子集合设计原则，1）原子性，即基础算子的操作不能拆分为更基础的操作，如不能把大于等于拆分为不小于；2）实用性，基础算子有实际应用场景；3）面向张量，基础算子的操作粒度为张量，如果一个算子需要在张量的元素粒度上进行复杂操作，则这个算子本身应为基础算子；4）完备性，可以支持复杂算子拆分需求。基于上述原则设计和实现基础算子集合，最终预期基础算子规模约控制到 200 左右，当前还在持续演进中。 + +**动静一体组合规则** + +组合规则是指使用基础算子接口组合实现的复杂算子集合，为了能够在动态图和静态图体系下复用同一套组合规则，减少编码工作量，在基础算子层，设计一套抽象接口，屏蔽动态图基础算子和静态图基础算子实现细节，组合规则的实现调用抽象接口实现，并设计一套分发机制，根据动态图和静态图数据类型的不同进行分发到具体基础算子执行，从而实现动态图和静态图不同模式下组合规则的复用。 + +**从机制上保障性能** + +随着算子细粒度拆分，算子数量会急剧膨胀，算子调度开销也会加大。动态图模式算子动态执行，无法提前优化，为了减少算子拆分造成的动态图性能损耗，飞桨采取了拆解 N+1 阶算子方法，即如果现有算子已经实现了 N 阶反向大算子，那么为了保证现有模型性能不降，实现 N+1 拆解逻辑，从而调度上优先运行 1-N 阶大算子逻辑，N+1 拆解成基础算子，保证性能同时支持高阶微分。静态图模式下，由于可以提前整图优化，基于飞桨编译器技术进行图层调度优化和算子融合优化，并且由于算子粒度更细，存在优化空间更大，部分模型上基于组合算子体系和编译器优化的模型性能已经超越了原有大算子体系下模型性能。 + +**从机制上保障显存和精度** + +模型执行过程通常是先执行前向计算，并保存反向计算依赖的中间变量，反向计算复用这些中间变量进行计算。算子细粒度拆分，使需要保存的中间变量急剧增大，模型运行需要的显存大幅增加。飞桨使用自定义反向技术解决该问题，对于一个复杂大算子，支持自定义其反向微分规则，该微分规则实现只依赖其前向大算子的输入输出，并在框架调度上优先保障走该算子的自定义反向微分，而非自动推导的微分规则，从而减少中间变量，降低显存。 + + + +四、开始使用 +------------------------------ + +飞桨提供了完善高阶自动微分求解 API，包括通用反向微分求解 paddle.grad，多元函数雅可比矩阵计算 `paddle.autograd.jacobian` ，多元函数海森矩阵计算 `paddle.autograd.hessian`. 功能与链接具体参考 4.1. + +下面通过一个简单示例演示飞桨高阶自动微分用法。 + +**第一步：导入依赖** + +```python +import paddle +``` + +**第二步：编写组网代码** + +以单层的全联接网络为例，MyNet 继承自 paddle.nn.Layer，在__init__方法中初始化网络参数，在 forward 方法中实现前向运行逻辑。注意，当前高阶自动微分支持大部分飞桨常用 API，覆盖主流的科学计算模型，如果您在写新的模型遇到飞桨高阶微分问题，可通过飞桨 ISSUE 反馈。 + +```python +class MyNet(paddle.nn.Layer): + def __init__(self): + super(MyNet, self).__init__() + self.weight = self.create_parameter(shape=(2,2), dtype=paddle.float32, is_bias=False) + self.bias = self.create_parameter(shape=(2,2), dtype=paddle.float32, is_bias=True) + self.add_parameter("weight", self.weight) + self.add_parameter("bias", self.bias) + + def forward(self, x): + y = paddle.matmul(x, self.weight) + self.bias + return paddle.tanh(y) +``` + +**第三步：创建网络及声明输入数据，执行前向计算过程** + +```python +x = paddle.randn(shape=(2,2), dtype=paddle.float32) +net = MyNet() +y = net(x) +``` + +**第四步：计算 Loss** + +为了演示高阶微分用法，此处 Loss 定义中使用了`paddle.grad` API 计算`y`对`x`二阶微分，使用`L2 norm` 归一化。 + +```python +grad1 = paddle.grad(y, x) +grad2 = paddle.grad(grad1, x) +loss = paddle.norm(grad2, p=2) + +opt = paddle.optimizer.Adam(0.01) +opt.update(loss) +``` + +**第五步：执行反向计算过程，使用用 Adam 优化器更新参数** + +```python +opt = paddle.optimizer.Adam(parameters=net.parameters()) +loss.backward() +opt.step() +``` + + + + +### 4.1 自动微分相关 API 列表 + + +API 名称 | API 功能 | +:-----: | :-----: | +[paddle.grad](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/grad_cn.html#grad) | 反向模式自动微分 | +[paddle.auto.jacobian](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/autograd/jacobian_cn.html#jacobian) | 雅可比矩阵计算 | +[paddle.autograd.hessian](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/autograd/hessian_cn.html#hessian) | 海森矩阵计算 | + + + +**使用反向微分 API paddle.grad 计算 tanh 高阶导数** + + +```python +import paddle + +# 组网代码 +x = paddle.rand((2,)) +y = paddle.tanh(x) +grad1 = paddle.grad(y, x, create_graph=True) # 一阶微分 +grad2 = paddle.grad(grad1, x, create_graph=True) # 二阶微分 +grad3 = paddle.grad(grad2, x) # 三阶微分 + +print(grad1, grad2, grad3) +# [0.41997433] [-0.6397] [0.6216267] +``` + +**使用 paddle.autograd.jacobian 计算 Jacobian 矩阵** + +```python +import paddle + +x1 = paddle.randn([3, ]) +x2 = paddle.randn([3, ]) +x1.stop_gradient = False +x2.stop_gradient = False + +y = x1 + x2 + +J = paddle.autograd.jacobian(y, (x1, x2)) +J_y_x1 = J[0][:] # evaluate result of dy/dx1 +J_y_x2 = J[1][:] # evaluate result of dy/dx2 + +print(J_y_x1.shape) +# [3, 3] +print(J_y_x2.shape) +# [3, 3] +``` + +**使用 paddle.autograd.hessian 计算 Hessian 矩阵** + +```python +import paddle + +x1 = paddle.randn([3, ]) +x2 = paddle.randn([4, ]) +x1.stop_gradient = False +x2.stop_gradient = False + +y = x1.sum() + x2.sum() + +H = paddle.autograd.hessian(y, (x1, x2)) +H_y_x1_x1 = H[0][0][:] # evaluate result of ddy/dx1x1 +H_y_x1_x2 = H[0][1][:] # evaluate result of ddy/dx1x2 +H_y_x2_x1 = H[1][0][:] # evaluate result of ddy/dx2x1 +H_y_x2_x2 = H[1][1][:] # evaluate result of ddy/dx2x2 + +print(H_y_x1_x1.shape) +# [3, 3] +print(H_y_x1_x2.shape) +# [3, 4] +print(H_y_x2_x1.shape) +# [4, 3] +print(H_y_x2_x2.shape) +# [4, 4] +``` + + + +五、飞桨支撑科学计算 AI For Science +------------------------------ + +基于飞桨框架 3.0 为科学计算提供了高阶自动微分、编译优化、分布式训练能力支撑，提供了面向通用数理问题求解的赛桨 PaddleScience 以及专注于生物计算的螺旋桨 PaddleHelix 工具组件。为了更好地支撑 AI for Science 生态，飞桨对国内外主流开源科学计算工具进行了适配，并被国际主流的科学计算深度学习库 DeepXDE 唯一推荐。在与 NVIDIA 合作适配其 AI Physics 工具 Modulus 的过程中，飞桨利用其高阶自动微分与编译优化技术，成功完成了全量模型适配，实现了方程求解类模型性能的大幅优化，相比 Modulus 现有后端求解速度平均提升 71%。 + +
    + +
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LRAj-@CV~G05|6sT literal 0 HcmV?d00001 diff --git a/docs/guides/paddle_v3_features/index_cn.rst b/docs/guides/paddle_v3_features/index_cn.rst new file mode 100644 index 00000000000..3ea3431c8a4 --- /dev/null +++ b/docs/guides/paddle_v3_features/index_cn.rst @@ -0,0 +1,30 @@ +############### +飞桨 3.0 全新特性 +############### + + +**以下将详细地介绍飞桨 3.0 版本下发布的全新 Feature 内容：** + +- `3.0 新特性总览 `_ ：概述了飞桨框架 3.0 版本下的架构设计、新特性、性能提升等 + +- `动静统一自动并行 `_ ：介绍了飞桨动静统一的自动并行编程范式 + +- `神经网络编译器 `_ ：介绍了神经网络编译器自动优化的基本原理、架构和功能 + +- `高阶自动微分 `_ ：介绍了飞桨高阶自动微分在科学计算领域的应用 + +- `动转静 SOT 原理及使用 `_ ：介绍了动转静 SOT 原理及使用方式 + +- `PIR 基本概念和开发 `_ ：介绍了飞桨新一代中间表示（PIR）的设计和开发范式 + + + +.. toctree:: + :hidden: + + overview_cn.md + auto_parallel_cn.md + cinn_cn.md + higher_order_ad_cn.md + sot_cn.md + paddle_ir_cn.md diff --git a/docs/guides/paddle_v3_features/overview_cn.md b/docs/guides/paddle_v3_features/overview_cn.md new file mode 100644 index 00000000000..57169a34bee --- /dev/null +++ b/docs/guides/paddle_v3_features/overview_cn.md @@ -0,0 +1,359 @@ +# 飞桨框架 3.0 新特性 + +## 一、概述 + +深度学习框架作为基础软件，不仅促进了深度学习技术的飞速进步，更为人工智能技术的广泛应用铺设了坚实的基础。首先深度学习框架为开发者提供了便捷易用的开发接口，这些接口对数据和操作进行了高度抽象，使得开发者能够更专注于算法和模型的设计，而不必深陷底层数据的处理细节。通过这些接口，开发者无需直接感知和应对复杂的硬件底层开发细节，从而极大地提升了开发效率和体验。其次深度学习框架还提供了自动微分这一强大功能，开发者通常只需要编写前向传播网络的代码，而繁琐的反向传播网络则交由框架自动完成。 + +飞桨框架是我国首个自主研发、开源开放且功能丰富的深度学习框架，自 2016 年起正式对外开源。2018 年，我们发布了飞桨框架 1.0 版本，该版本默认使用静态图，并着手研发动态图功能。2021 年初，飞桨框架 2.0 版本问世，它默认采用动态图，并实现了动静统一与训推一体的设计。此版本进一步融合了动态图的灵活性与静态图的高效性，同时支持了千亿参数模型的混合并行训练。在此期间，飞桨还踏上了神经网络编译器技术的探索征程。随着大模型时代的到来，模型参数规模日益扩大，训练成本也随之上升，这对深度学习框架在大规模分布式训练和性能优化方面提出了更高要求。近期，我们推出了飞桨框架 3.0-Beta 版本，标志着飞桨新一代框架技术创新之路的开启。该版本的核心特性包括动静统一自动并行技术和神经网络编译器自动优化等新技术，旨在应对当前深度学习领域的新挑战。飞桨框架 3.x 版本延续了 2.x 版本动静统一、训推一体的设计理念，其开发接口全面兼容 2.x 版本。这意味着，使用 2.x 版本开发的代码，在绝大多数情况下无需修改，即可直接在 3.x 版本上运行。 + +以下是飞桨框架 3.x 的新特性： + +- **动静统一自动并行：** 为了降低大模型的编程难度，飞桨还优化了动静统一的半自动并行编程范式，显著简化了编程的复杂度。开发者无需深入研究手动并行编程的复杂概念和 API，只需进行少量的张量切分标注，即可完成混合并行模型的构建。框架能够自动推导分布式切分状态并添加通信算子，同时还支持一键动转静分布式训练，从而大幅简化了混合并行训练代码的开发过程。动静统一方面，飞桨通过采用基于字节码的动静转换技术，全面升级了其动转静训练能力，支持自适应的图构建功能。在 700 多个飞桨产业级模型上进行了验证，实现了一键动转静训练 100%的成功率。 +- **神经网络编译器自动优化：** 飞桨神经网络编译器 CINN（Compiler Infrastructure for Neural Networks）采用与框架一体化的设计，能够支持生成式模型、科学计算模型等多种模型的高效训练与可变形状推理，为计算灵活性与高性能之间提供了一个良好的平衡点。通过算子的自动融合和代码生成技术，Llama2 和 Stable Diffusion 模型的性能提升了 30%。 +- **高阶自动微分：** 为了更好支持科学计算等场景，飞桨框架设计并实现了基于组合算子机制的高阶自动微分技术，结合神经网络编译器自动优化技术，我们测试了超过 40 多个科学计算场景的微分方程，其求解速度领先业界同类产品 70%。 +- **高扩展中间表示** ：为了提升飞桨框架的可扩展性，我们研发了高扩展中间表示 PIR（Paddle Intermediate Representation）。这一表示系统性地抽象了底层核心概念，提供了灵活且高效的组件。PIR 作为基础设施，支撑着动转静、自动微分、自动并行、组合算子、图优化等多项技术，并广泛应用于分布式训练、模型压缩、推理部署等场景。通过 PIR 提供的 DRR（Declarative Rewrite Rule）机制，Pass 的开发成本可以降低 60%。我们对超过 900 个模型配置进行了测试，结果显示，在使用 PIR 后，推理的整体性能提升了超过 10%。 +- **多硬件适配：** 飞桨为大模型硬件适配提供了功能完善且低成本的方案。新硬件仅需适配 30 余个接口，即可支持大模型的训练、压缩与推理。同时，飞桨提供了基于编译器的硬件接入方式，硬件厂商只需以插件的形式实现编译器的代码生成后端，便能实现与飞桨框架的高效适配。 + +上述特性在飞桨框架 2.6 版本或更早版本时就已经开始开发，目前已达到外部可试用的阶段。由于这些新特性在使用体验、性能、二次开发便利度以及硬件适配能力等方面带来了显著提升，因此我们决定发布 3.0-Beta 版本。此版本包含了对框架 2.x 版本部分已有功能的改进，并且在不使用新特性的情况下，表现是成熟稳定的。展望未来，我们预计将在 2024 年 12 月发布飞桨框架 3.0 的正式版本。 + +## 二、设计思想 + +当前，AI 技术的发展正日新月异，引领着科技的前沿。深度学习框架的设计对于推动人工智能技术的发展至关重要，其核心设计目标是让深度学习技术的创新与应用更简单。那么如何做到这一点呢？我们需要从以下几个方面来考虑。 + +首先，框架向上对接开发者的需求。一个优秀的深度学习框架应当为开发者提供极致的开发体验。这不仅仅意味着提供一个用户友好的开发环境，更重要的是要能够大幅度减少开发者的学习成本和时间成本，同时显著提升开发的便利性。为此，飞桨框架提出了“动静统一、训推一体、自动并行”的理念，极大地提高了开发效率。 + +其次，框架向下对接硬件。现代深度学习应用往往需要在多样化的硬件平台上运行，因此，框架必须能够兼容并适配各种不同的硬件设备。这要求框架能够智能地隔离不同硬件接口之间的差异，实现广泛的硬件适配性。同时，为了充分发挥硬件的性能，框架还需要具备软硬件协同工作的能力，确保在利用硬件资源时能够达到最优的性能表现。 + +再者，框架需要考虑到 AI 技术发展的整体趋势。随着技术的不断进步，诸如 MOE（Mixture of Experts）、多模态以及科学智能（AI for Science）等前沿技术逐渐成为新的研究热点。深度学习框架应当能够紧跟这些技术发展的步伐，为研究者提供必要的支持和工具，以推动相关技术的快速发展和应用。在大模型领域，模型的显著特点是参数规模庞大、训练数据海量，以及对算力的巨大需求。随着模型复杂性的增加，计算瓶颈、存储瓶颈、访存瓶颈以及通信瓶颈等问题逐渐凸显。同时新的网络结构如 RWKV、Mamba 等也在不断涌现，为 AI 技术的发展注入了新的活力。为了解决这些问题，分布式训练和通用性能优化的需求日益迫切。在 AI for Science 领域，人工智能正引发科学发现和模式创新的深刻变革。以 AlphaFold 为代表的生物计算模型，GraphCast 等气象模型，物理信息神经网络（PINN）和傅里叶算子学习方法（FNO）都展示了 AI 在科学研究中的强大能力。为了支持科学计算模型，框架的设计需要能够支持高阶自动微分、复数运算、傅里叶变换等功能。 + +最后，框架需要能够支持产业的实际落地应用。在产业化方面，框架需要具备支持训练、压缩、推理一体化的全流程能力。这意味着，从模型的训练到优化，再到实际部署和推理，框架应当提供一套完整、高效的解决方案，以满足产业界对于深度学习技术的实际需求。 + +总的来说，飞桨将为开发者提供一个“动静统一、训推一体、自动并行、自动优化、广泛硬件适配”的深度学习框架，开发者可以像写单机代码一样写分布式代码，无需感知复杂的通信和调度逻辑，即可实现大模型的开发；可以像写数学公式一样用 Python 语言写神经网络，无需使用硬件开发语言编写复杂的算子内核代码，即可实现高效运行。 + +## 三、框架架构 + +为了实现深度学习框架的上述特性，我们必须对框架的架构进行精心设计，确保其能够支持各种复杂的模型构建，同时与多样化的芯片实现无缝对接。接下来，我们将通过直观的架构图，详细展示飞桨新一代框架内所涵盖的功能模块，以及这些模块之间的相互作用与联系。以下为飞桨框架 3.0 的架构图。 + + +
        + +
        + +飞桨框架对外提供了丰富的深度学习相关的各种开发接口，如张量表示、数学计算、模型组网、优化策略等。通过这些接口，开发者能够便捷地构建和训练自己的深度学习模型，无需深入到底层的技术细节中去。 + +在开发接口之下，飞桨框架可以划分为 4 个层次：表示层、调度层、算子层和适配层。 + +- 表示层专注于计算图的表达与转换，通过高可扩展中间表示 PIR，为动转静（动态图转为静态图）、自动微分、自动并行、组合算子以及计算图优化等核心功能提供坚实支撑。 +- 调度层则负责对代码或计算图进行智能编排与高效调度，并且能够根据实际需求进行显存和内存的管理优化，支持动态图和静态图高效执行。无论开发者选择使用动态图还是静态图进行模型开发，飞桨框架都能提供高效的执行环境，同时确保资源利用的最优化。 +- 算子层由神经网络编译器 CINN 和算子库 PHI 共同构成，涵盖了张量定义、算子定义、算子自动融合和算子内核实现等关键功能。 +- 适配层则用于实现与底层芯片适配，包括设备管理、算子适配、通信适配以及编译接入等功能。 + +## 四、动静统一自动并行 + +### 4.1 动静统一 + +我们来回顾下飞桨框架所提供的静态图和动态图两种开发模式。这两种模式在模型组网阶段的代码是完全一致的，因此我们称之为动静统一的组网方式。然而，它们之间的主要差异体现在计算图的构建和执行过程中。在静态图开发模式下，一旦计算图被创建，它将保持不变。这意味着，在运行阶段，不能再根据输入的计算数据作为判断条件，来调整计算图。相反，在动态图开发模式下，每当输入新的数据批次时，计算图会动态地生成和执行。这种灵活性使得动态图模式在现代深度学习任务中备受欢迎。然而，尽管动态图模式具有诸多优势，但也存在一个问题：由于计算图会频繁地创建和执行，这使得对其进行优化变得相当困难。特别是在推理部署场景下，动态图模式往往难以摆脱对 Python 解释器的依赖进行部署。而 Python 解释器的引入，在某些场景下，比如对性能要求很高的大模型推理部署场景或者资源受限的端侧场景，可能会导致效率低下或无法使用。为了克服这一难题，飞桨研发了动静转换技术，通过简单的一行命令（to_static），便能够将动态图的代码轻松转换为静态图代码。 + +飞桨采用的技术方案是源代码到源代码的转换，即分析并转写动态图 Python 源代码，进而生成对应的静态图 Python 源代码；在获取源代码后，使用静态 Python 解释器来执行这段静态图代码，从而得到计算图表示。动静转换技术的核心挑战在于对 Python 语法的支持程度。通过实际测试，我们发现飞桨对 Python 语法的支持率高达 94%，飞桨的动静转换功能在整图导出任务的成功率高达 95%。飞桨框架的优势在于它同时兼容动态图和静态图两种开发模式。因此，在进行动静转换时，仅需实现从动态图 Python 源代码到静态图 Python 源代码的转换。这一转换过程可以通过 Python 解释器进一步增强对 Python 语法的支持，从而大大降低了实现的难度。在训练场景，针对那些无法进行动静转换的情况，例如 Python 代码中调用 Numpy 等第三方库时，这些库的函数调用无法直接转换为静态图表示。为了解决这一问题，飞桨创新性地研发了“自适应图构建机制”。当遇到不支持的语法时，该机制会被触发，自动断开这些部分，并利用前后相邻的图进行重新构建。通过采用这种方案，我们在训练场景中可以实现 100%的动静转换成功率，从而为编译器等计算图优化技术提供了更广阔的空间。更多关于动静转换的信息，请参考以下链接：[《动转静 SOT 原理及使用》](./sot_cn.md) + +### 4.2 自动并行 + +在大模型开发场景中，多维混合并行显得尤为重要。对于百亿甚至千亿规模的大模型，一般需要使用张量模型并行、流水并行、数据并行、分组参数切片并行的混合并行方式进行训练。然而，多维混合并行的开发过程往往相当复杂。以数据并行、张量模型并行和流水线并行为例，开发者必须精心处理计算、通信、调度等多元逻辑，才能编写出正确的混合并行代码，这无疑提高了开发的难度。为了解决这一难题，我们提出了动静统一的自动并行方案。自动并行，开发者只需要提供模型结构和集群，以及少量的标记信息，飞桨框架可以根据模型结构和集群信息自动寻找合适的分布式训练策略。我们来看一下对分布式标记（DistAttr）的定义。通过使用 ProcessMesh 将一个设备（比如一块 GPU 卡）映射为一个进程，将多个设备映射为多个进程组成的一维或多维数组，下图展示了由 8 个设备构成的两种不同 ProcessMesh 抽象表示。 + +
        + +
        + +然后通过使用 Placement 来表示张量在不同设备上的切分状态，分为 Replicate、Shard 和 Partial 这 3 种切分状态。如下图所示，Replicate 表示张量在不同设备上会以复制的形式存在；Shard 表示按照特定的维度在不同设备上进行切分；Partial 表示设备上的张量不完整，需要进行 Reduce Sum 或者 Reduce Mean 等不同方式的操作后，才能得到完整的状态。 + +
        + +
        + +在完成分布式标记抽象后，我们通过调用`paddle.distributed.shard_tensor()`接口，实现对张量切分的标记。通过张量切分的标记，我们可以表示复杂的分布式混合并行，下图展示了一个具体的数据并行、张量模型并行、流水线并行组成的混合并行的例子。 + +
        + +
        + +以下代码展示了混合并行的具体例子。 + +```python +import paddle +import paddle.distributed as dist +from paddle.io import BatchSampler, DataLoader, Dataset +import numpy as np +... +mesh0 = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=['x', 'y']) +mesh1 = dist.ProcessMesh([[4, 5], [6, 7]], dim_names=['x', 'y']) +... +class MlpModel(paddle.nn.Layer): + def __init__(self): + super(MlpModel, self).__init__() + # 张量切分标记 + self.w0 = dist.shard_tensor( + self.create_parameter(shape=[1024, 4096]), + mesh0, [dist.Replicate(), dist.Shard(1)]) + self.w1 = dist.shard_tensor( + self.create_parameter(shape=[4096, 1024]), + mesh1, [dist.Replicate(), dist.Shard(0)]) + + def forward(self, x): + # 张量切分标记 + dist.shard_tensor(x, mesh0, [dist.Shard(0), dist.Replicate()]) + y = paddle.matmul(x, self.w0) + # 张量重切分 + y = dist.reshard(y, mesh1, [dist.Shard(0), dist.Shard(2)]) + z = paddle.matmul(y, self.w1) + return z +... +# 创建模型 +model = MlpModel() +opt = paddle.optimizer.AdamW(...) +... +# 动转静训练 +dist_model, dist_loader = dist.to_static(model, opt, ...) +for step, data in enumerate(dist_loader()): + ... + loss = dist_model(data) + ... +``` + +我们以具体的 Llama 模型训练为例，动态图手动并行的开发方式，它要求开发者不仅要选择合适的并行策略，还必须精心设计通信逻辑；通过采用自动并行的开发方式，开发者无需再考虑复杂的通信逻辑。其分布式训练核心代码量减少了 50%，从而大大降低了开发的难度；从我们的一些实验可知，当前这种自动并行的性能优于动态图手动并行的性能。未来，我们将进一步探索无需使用张量切分标记的全自动并行，让开发者可以像写单机代码一样写分布式代码，进一步提升大模型的开发体验。更多关于自动并行的信息，请参考以下文档：[《动静统一自动并行》](./auto_parallel_cn.md) + +## 五、神经网络编译器自动优化 + +编译器（compiler）是一种计算机程序，负责将用某种编程语言编写的源代码（原始语言）转换成另一种编程语言（目标语言）。以高级编程语言编译器为例，如 gcc，它能将 C 语言代码转换成 CPU 可执行的机器指令。类似地，神经网络编译器，通常也被称为深度学习编译器，是深度学习领域特有的工具，用于将一种神经网络中间表示(IR)转换为另一种中间表示(IR)。例如，飞桨神经网络编译器 CINN，能够将神经网络中间表示转换为其他形式的中间表示，如 CUDA C 语言代码、SyCL 语言代码，或 LLVM IR。之后，利用芯片软件栈提供的编程语言编译器，比如英伟达的 NVCC（NVIDIA CUDA Compiler）编译器或 NVRTC（NVIDIA CUDA Runtime Compilation）运行时编译库，将这些中间表示进一步转换为可在英伟达 GPU 上运行的机器指令。 + +### 5.1 从 RMSNorm 说起 + +为什么在深度学习框架中需要引入编译器技术呢？让我们通过一个实例来阐释这一点。我们以 Llama 模型中经常使用的 RMS Normalization （[Root Mean Square Layer Normalization](https://arxiv.org/abs/1910.07467)）为例，其计算公式相对简单明了。 + +
        + +
        + +假设我们需要是实现 RMS Normalization 的计算，最简单的办法是，我们可以使用飞桨框架提供的张量运算开发接口，调用平方、求和、除法、开根号等操作来完成，代码如下： + +```python +class RMSNorm(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.variance_epsilon = 1e-6 + self.size = 768 + self.weight = paddle.create_parameter( + shape=[self.size], + dtype=paddle.get_default_dtype(), + default_initializer=nn.initializer.Constant(1.0), + ) + + def forward(self, x): + variance = x.pow(2).mean(-1, keepdim=True) + x = paddle.rsqrt(variance + self.variance_epsilon) * x + return x * self.weight +``` + +从上述代码中，我们可以清晰地观察到代码与公式之间存在着良好的对应关系。具体来说，代码中的`weight`变量对应于公式中的`g`，`x`变量对应于公式中的`a`。此外，代码中的`pow`函数实现了平方运算，`mean`函数对应公式中的求和取平均操作，而`rsqrt`函数则实现了开根号后取倒数的计算。这种编写方式赋予了代码极高的灵活性和可维护性，使得开发者可以像书写数学公式一样编写代码，从而大大降低了代码的理解成本和维护成本。如果开发者希望采用新的 Normalization 策略，只需简单地修改代码即可实现新的计算公式。 + +尽管这种实现方式非常灵活，但它也面临着一个极具挑战的问题，即执行速度较慢。特别是在处理大型模型时，由于计算量巨大且算力成本高昂，这一问题尤为突出。速度慢的主要原因在于，每一次函数调用都会触发飞桨框架底层的一次算子调用。而算子作为深度学习框架的最小调度和执行单元，在执行过程中需要将显存中的数据搬运到寄存器中进行运算，并将计算结果写回到显存中。这种频繁的显存读写操作导致了计算密度降低，在访存带宽有限的情况下，显著拖慢了程序的运行速度。 + +为了解决这一问题，最简单的方法是，增加一个叫 RMSNorm 的算子，并且提供一个叫 RMSNorm 的 Python 层 API，这一方法在飞桨框架 1.x 版本就可以支持，比如我们采用以下代码实现： + +```python +class RMSNorm(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.variance_epsilon = 1e-6 + self.size = 768 + self.weight = paddle.create_parameter( + shape=[self.hidden_size], + dtype=paddle.get_default_dtype(), + default_initializer=nn.initializer.Constant(1.0), + ) + + def forward(self, x): + return paddle.incubate.nn.functional.fused_rms_norm( + x=x, + norm_weight=self.weight, + norm_bias=None, + epsilon=self.variance_epsilon, + begin_norm_axis=2, + ) +``` + +以上代码通过`fused_rms_norm`开发接口实现了对`rms_norm`的调用，这一改动带来了显著的性能提升，有效解决了之前版本运行速度慢的问题。然而，这一优化方案也带来了不少弊端。 + +最突出的是，它大大提高了开发者的门槛，因为开发者现在需要深入了解和掌握飞桨框架中关于张量、算子等核心概念，并熟悉算子开发与注册的全流程。此外，为了编写出性能优异的 reduce 求和操作，开发者还需精通 CUDA C 高性能程序开发的技巧，并对 Shared Memory、Warp Divergence、Bank Conflict 等高级概念有深刻的理解。 + +其次，该方案会增加框架开发接口的数量、并降低开发接口的可用性和可维护性。由于开发者的需求通常非常灵活多变，比如各种 Normalization 策略的变种，为每个特定操作同步增加一个 Python 开发接口，将导致框架的接口数量迅速增加，目前飞桨框架已经有接近 2000 个对外公开的开发接口。同时，随着算子融合粒度的增大，每个开发接口的参数数量也急剧上升，比如一些融合类的算子开发接口甚至可能包含多达 30 多个参数，这使得开发接口变得难以使用和维护。 + +再者，该方案的一个显著影响是导致框架算子库中的算子数量不断攀升，进而使得硬件适配的成本也随之增加。尽管飞桨框架已经对算子进行了清理和规范，且不考虑融合算子的情况，但当前飞桨框架的算子库仍然包含了超过 800 个算子，这为硬件适配工作带来了极大的挑战。这些新增加的算子需要使用 CUDA C 代码来实现，并且如果希望在其他类型的硬件上运行，还需要开发相应版本的代码。考虑到需要适配的硬件种类繁多，这无疑会大幅增加开发成本。 + +以下是截取的 CUDA C 代码实现片段，从中我们可以看出代码的实现变得复杂了许多。 + +```cpp + const ComputeType row_sum_square = + BlockAllReduce(thread_sum_square); + + // use multiply instead of divide. Author(zhengzekang). + ComputeType row_rms = row_sum_square * col_divisor; + ComputeType row_inv_rms = + Rsqrt(row_rms + static_cast(epsilon)); + // save for backward + if (inv_var_data != nullptr) { + inv_var_data[row] = row_inv_rms; + } + for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { + ComputeType pack[kPackSize]; +#pragma unroll + for (int i = 0; i < kPackSize; ++i) { + pack[i] = static_cast(buf[i * num_packs + pack_id]) * + row_inv_rms; + } + store.template store(pack, row, pack_id * kPackSize); + } +``` + +而借助神经网络编译器技术，我们能够在维持高度灵活性和易用性的基础上，实现性能的显著提升。以下 A100 平台上 RMSNorm 算子的性能测试结果便是一个明证：相较于采用 Python 开发接口组合实现的方式，经过编译优化后的算子运行速度提升了 4 倍；即便与手动算子融合的方式相比，也实现了 14%的性能提升。这一成果充分展示了飞桨框架在灵活性与性能之间寻找到的理想平衡点。 + +
        + +
        + +### 5.2 飞桨神经网络编译器 CINN + +飞桨神经网络编译器 CINN 采用了与框架一体化的设计，其基础设施是基于飞桨的高扩展中间表示 PIR。这一设计使得 CINN 能够同时支持训练和推理过程，并且具备处理动态可变形状输入的能力。在生成式大语言模型 Llama 和文生图模型 Stable Diffusion 上的实验结果显示，通过使用编译器的优化技术，相较于未采用手动性能优化的基础版本，推理速度分别实现了 36%和 30%的提升。那么，编译器究竟是如何实现深度学习任务的加速呢？以下，我们将通过一个由 Add 和 Relu 算子组成的例子来具体展示这一过程。 + +
        + +
        + +首先，该过程会利用组合算子机制，将原始的计算图拆解为由一系列基础算子构成的计算图，并在此过程中详细记录算子输入输出张量之间的形状关系，以确保其能够适应动态形状张量的复杂情况。随后，在神经网络编译器的前端部分，编译器会进行智能判断，识别出哪些基础算子具备融合潜力。对于这些可融合的基础算子，编译器会进一步调用基础的 Compute 函数，巧妙地将它们降级为由抽象语法树（AST）构成的低层中间表示（IR）。接下来，在神经网络编译器的后端部分，这些中间表示会被进一步精心转换成具体的代码实现，这既可能是 CUDA C 代码，也可能是 LLVM IR 代码，具体取决于目标平台的需求。最终，利用 NVCC 编译器或 LLVM 编译器，将这些代码转换成能够在芯片上高效运行的可执行代码，从而实现深度学习任务的显著加速。 + +更多关于神经网络编译器的信息，请参考文档[《神经网络编译器》](./cinn_cn.md)。 + +## 六、高阶自动微分 + +深度学习模型的训练过程，核心在于利用随机梯度下降（SGD）等优化算法来更新模型参数。在此过程中，深度学习框架的自动微分功能扮演着至关重要的角色，它基于链式法则自动计算出损失函数相对于模型参数的梯度。尽管在大多数深度学习任务中，仅需计算一阶导数，但在某些“AI for Science”的应用场景中，却需要计算高阶导数，这无疑大大增加了自动微分的复杂性。以 2D 矩形平板分布受载问题为例，该问题的内在机理需借助 4 阶微分方程来描述。因此，为了求解这类问题，深度学习框架必须提供高阶自动微分功能。然而，实现高阶自动微分面临着诸多挑战。具体来说，框架需要为每个算子编写高阶微分规则，而随着阶数的增加，这些微分规则的复杂性也随之上升。当阶数达到三阶或更高时，编写这些规则不仅变得极其困难，而且其正确性也难以保证。为了解决这一难题，我们提出了基于基础算子组合的高阶自动微分技术。该技术的核心思想是将复杂算子（如 log_softmax）拆解为多个基础算子的组合，然后对这些基础算子进行一阶自动微分变换。重要的是，基础算子经过一阶自动微分变换后，其所得的计算图仍然由基础算子构成。通过反复应用一阶自动微分规则，我们可以轻松地获得高阶自动微分的结果。 + +
        + +
        + +
        + +
        + +为了全面支持高阶自动微分，飞桨框架精心设计与实现了一套组合算子机制。这一机制不仅完美兼容动态图模式和静态图模式，而且在动态图模式下支持 N+1 阶微分的灵活拆分，同时在静态图模式下能够进行高效的编译器融合优化。我们创新性地设计并实现了动静一体的算子组合规则，这意味着同一套组合规则在动态图和静态图两种模式下均可无缝复用，从而有效避免了重复开发的繁琐。在构建基础算子体系时，我们以 Tensor 作为核心操作对象，严格确保了算子的原子性、实用性和完备性。此外，我们还提供了自定义反向操作和自动重计算功能，这些强大的特性不仅显著提升了模型的精度，还有效地减少了显存占用，为用户带来了更高效、更灵活的深度学习体验。 + +基于前期的工作积累，飞桨已开始积极探索科学智能（AI for Science）领域的相关工作。为了满足 AI for Science 任务的多样化需求，飞桨在框架层面实现了基于组合算子的高阶自动微分功能，并专门提供了针对科学计算的开发接口。此外，我们还实现了高阶优化器，如 LBFGS 等，以进一步提升科学计算的性能。在模型层面，我们成功研发了赛桨（PaddleScience）、螺旋桨（PaddleHelix）等系列开发套件，为科学计算提供了更为便捷、高效的解决方案。飞桨对国内外主流开源科学计算工具进行了广泛适配，如 DeepXDE、Modulus 等，并成为国际主流的科学计算深度学习库 DeepXDE 的默认推荐后端。在与 NVIDIA 合作适配 Modulus 的过程中，我们充分利用飞桨框架的高阶自动微分与编译优化技术，实现了方程求解类模型性能的大幅优化。相比 Modulus 现有的后端求解速度，我们的平均提升幅度达到了 71%。我们实现了物理信息网络（PINN）、傅里叶算子学习（FNO）等数据驱动、机理驱动以及数据机理融合的方法。这些方法在航空航天、汽车船舶、气象海洋、生命科学等多个领域都具有广泛的应用潜力，为科学研究和工程实践提供了有力的支持。 + +更多关于高阶自动微分和 AI for Science 的信息，请参考文档：[《高阶自动微分功能》](./higher_order_ad_cn.md)。 + +## 七、高扩展中间表示 PIR + +在通过动静转换技术获取计算图表示后，我们仍需对计算图进行一系列优化，如自动微分变换、分布式变换以及编译器加速等。为实现这些优化，我们需要一种“高扩展中间表示”PIR（Paddle Intermediate Representation）。PIR 具备灵活的基础组件，支持 Operation、Value、Attribute 等元素的定义，从而便于进行扩展。其中，Dialect 定义是 PIR 的核心组成部分，它类似于形式化语言中的一种表达，能够表示一个相对完整的体系，并支持开发者根据需求定制化扩展 Dialect，显著提升了框架的扩展性，这个体系涵盖了分布式、编译器、动态形状推理与控制流等多个方面。PIR 遵循 SSA（即 Static Single Assignment）原则，统一了顶层结构，实现“算子顺序性”和“计算图语义”的兼容表示。此外，PIR 还提供了更加简洁、低成本的 Pass 开发体系，并内置了一系列丰富且功能完备的 Pass 优化策略，为大模型的极致性能优化提供了强有力支撑。PIR 提供了 DRR 和 Pattern Rewriter 两种机制，以实现 IR 的灵活变化。为了验证 PIR 的有效性，我们比较了超过 900 个模型配置在使用 PIR 后的推理速度提升情况。结果显示，25%的模型推理速度提升了超过 30%，60%的模型提升了超过 10%。总体而言，使用 PIR 后，推理整体性能提升了超过 10%。这一显著提升主要归功于新 PIR 能够提前静态选择 Kernel，从而降低了调度成本和开销。此外，常量折叠策略的应用范围更广，Inplace Pass 策略机制也得到了更广泛的应用。采用新的 PIR 表示机制后，我们可以实现训推一体，展现出优异的性能和表现。更多关于 PIR 的信息，请参考文档：[《PIR 基本概念和开发》](./paddle_ir_cn.md)。 + +## 八、多硬件适配 + +深度学习框架在实现高效能计算的过程中，还面临着一个关键性挑战，即如何实现与各类硬件的有效适配。为了应对这一挑战，飞桨框架采取了全面的策略，并成功实现了多种不同的接入方式，以确保能够灵活满足不同芯片的适配需求。通过这些多样化的接入方法，飞桨框架不仅提升了深度学习应用的性能，还确保了广泛的硬件兼容性，从而为开发者提供了一个强大且灵活的工具，以适应不断变化的计算环境和需求。特别是针对大模型场景，飞桨提供了标准化硬件适配接口，只需要适配 30 余个接口，即可全面支持大模型训压推全流程；通过基础算子体系，减少硬件适配所需开发的算子数量；支持算子融合、显存复用等方式对大模型进行性能优化；支持通过神经网络编译器代码后端 CodeGen 的方式进行适配，实现算子自动融合和性能优化。 + +
        + +
        + +基于前述的先进技术，飞桨与芯片厂商携手，共同打造一个繁荣的硬件生态。这一过程可划分为三个核心阶段。首先是“共聚”阶段，我们联合多家芯片厂商，共同发起了飞桨硬件生态圈。其次是“共研”阶段，与芯片厂商携手实现软硬一体的联合优化。最后是“共创”阶段，与芯片厂商深度合作，共创繁荣生态。至今，我们已与 22 家硬件厂商伙伴成功联合推出了飞桨生态发行版，标志着合作的深入与成果的显现。同时，我们的生态圈已吸引超过 40 家成员单位加入，覆盖了主流硬件厂商，提供了极为丰富的硬件支持框架，为用户带来更加多样化的选择。 + +## 九、开始使用 + +接下来，欢迎大家使用飞桨框架 3.0-Beta 版本，并给我们反馈。在开始使用前，确认已安装飞桨框架 develop 版本。下面，我们通过一个矩阵乘和 Softmax 组成的例子来展示飞桨新一代框架是如何实现动静统一自动并行和编译器自动优化性能的。具体代码如下所示： + +```python +# test_demo.py +import paddle +import paddle.distributed as dist +import paddle.nn.functional as F +from paddle.io import Dataset, DataLoader +import numpy as np + +mesh = dist.ProcessMesh([0, 1], dim_names=["x"]) + +class DemoDataset(Dataset): + def __init__(self, num_samples): + self.num_samples = num_samples + + def __getitem__(self, idx): + return np.array([[1., 2.], [3., 4.],[5., 6.]]).astype('float32'), np.array([1.]) + + def __len__(self): + return self.num_samples + +class DemoLayer(paddle.nn.Layer): + def __init__(self): + super(DemoLayer, self).__init__() + self.w = dist.shard_tensor( + paddle.create_parameter(shape=[2, 4], dtype='float32'), + mesh, [dist.Shard(1)]) + self.b = paddle.to_tensor([0.1, 0.2, 0.3, 0.4]) + + def forward(self, x): + y = paddle.matmul(x, self.w) + z = F.softmax(y + self.b) + return z + +dataset = DemoDataset(10) +loader = DataLoader(dataset, batch_size=1) + +def loss_fn(logits, label): + loss = paddle.nn.MSELoss(reduction="sum") + logits = paddle.sum(logits, axis=[1, 2]) + return loss(logits, label) + +layer = DemoLayer() +dist_layer = dist.to_static(layer, loader, loss_fn) + +dist_layer.eval() +for data in loader(): + loss = dist_layer(data[0], data[1]) + print('loss', loss, flush=1) +``` + +因为一些功能还在开发中，为了避免对用户造成干扰，当前我们没有默认开启高扩展中间表示 PIR 和神经网络编译器自动优化功能，在开始执行前，我们需要进行环境变量设置以确保新功能生效，如下： + +```cpp +# 打开组合算子 +export FLAGS_prim_enable_dynamic=true && export FLAGS_prim_all=true + +# 打开 CINN 编译器相关 FLAG +export FLAGS_use_cinn=true +export FLAGS_cinn_new_group_scheduler=true +export FLAGS_group_schedule_tiling_first=true +export FLAGS_cinn_bucket_compile=true + +# 打开 PIR 模式 +export FLAGS_enable_pir_api=true + +# 是否打印 Program IR 信息 +export FLAGS_print_ir=false + +# 执行命令 +# python -u -m paddle.distributed.launch --gpus "0,1" test_demo.py +``` + +在设置环境变量后，我们即可正常使用飞桨框架。以上所展示例子的运行过程如下图所示： + +
        + +
        + +在开发者编写动态图代码时，利用`shard_tensor`分布式开发接口，可以轻松地标记张量切分方式。在此场景中，我们对矩阵乘法的参数`w`进行了列切分。 + +第 1 步，飞桨通过动转静技术，能够将动态图代码高效地转换为静态图代码，从而获取静态图中间表示。 + +第 2 步，通过切分推导规则，静态图中间表示可以自动转换成分布式中间表示。在这一过程中，我们可以观察到部分张量的切分标记发生了变化，并自动插入了分布式通信算子`allgather`。 + +第 3 步，通过组合算子机制，它能够将计算图中的复杂算子拆分为更小粒度的基础算子。例如，我们将`softmax`算子拆分成了`max`、`subtract`、`exp`、`sum`和`divide`等基础算子，为后续的性能优化提供了便利。 + +第 4 步，飞桨运用编译器自动优化技术，将这些基础算子自动融合，并生成高性能的内核代码，从而实现性能的提升。 + +从上述例子可以看到，基于飞桨新一代框架，开发者只需要少量张量切分标记，无需关注分布式通信逻辑，即可实现大模型的分布式训练；并且无需手写高性能算子内核代码，即可实现性能自动优化。 diff --git a/docs/guides/paddle_v3_features/paddle_ir_cn.md b/docs/guides/paddle_v3_features/paddle_ir_cn.md new file mode 100644 index 00000000000..8f5616e2b57 --- /dev/null +++ b/docs/guides/paddle_v3_features/paddle_ir_cn.md @@ -0,0 +1,127 @@ +# PIR 基本概念和开发 + +在 3.0 版本下，飞桨研发了基于 MLIR 范式的新一代中间表示技术，即 Paddle IR（下简称 PIR）。这项技术对底层的核心概念如 Operation、Attribute 等进行了系统性的抽象，为开发者提供了灵活的基础组件；同时，通过引入 Dialect 这一概念，飞桨能够全面、分层次管理框架各模块对中间表示的需求，并支持开发者根据需求定制化扩展 Dialect，显著提升了框架的扩展性。PIR 遵循 SSA（即 Static Single Assignment）原则，统一了顶层结构，实现“算子顺序性”和“计算图语义”的兼容表示。此外，PIR 还提供了更加简洁、低成本的 Pass 开发体系，并内置了一系列丰富且功能完备的 Pass 优化策略，为大模型的极致性能优化提供了强有力支撑。 + +## 一、基础概念 + +
        + +
        + +在深度学习框架 IR 概念中，「顺序性」和「图语义」是两个非常高频常用的概念。旧的中间表示体系由「顺序性」ProgramDesc 和「图语义」Graph 两个核心类共同承载。用户在静态图 API 或者动转静模块下，产生的中间表示是 Op-by-Op 的 Program，如果要应用更高层面的优化策略（比如算子融合、inplace 策略、剪枝等），框架会将由 Program 构造出 Graph，其由数据节点、算子节点和彼此关联的边构成。 +在新的 Paddle IR 中，飞桨在底层抽象了一套高度可扩展的基础组件，包括 Type、Attrbute、Op、Trait 和 Interface，并引入了 Dialect 的概念，支持开发者灵活扩展、自由定制，提供了完备鲁邦的语义表达能力；在模型表示层，通过多 Dialect 模块化管理，统一多端表示，实现了训推一体的全架构统一表示，无缝衔接组合算子和编译器，支持自动优化和多硬件适配；在图变换层，通过统一底层模块，简化基础概念，向用户提供了低成本开发、易用高性能、丰富可插拔的 Pass 优化机制。 +飞桨的新一代的 IR 表示坚持 SSA（静态单赋值）原则，模型等价于一个有向无环图。并以 Value、Operation 对计算图进行抽象， Operation 为节点，Value 为边。 + +* Operation 表示计算图中的节点：一个 Operation 表示一个算子，它里面包含了零个或多个 Region；Region 表示一个闭包，它里面包含了零个或多个 Block；Block 表示一个符合 SSA 的基本块，里面包含了零个或多个 Operation；三者循环嵌套，可以实现任意复杂的语法结构 +* Value 表示计算图中的有向边：用来将两个 Operaton 关联起来，描述了程序中的 UD 链（即 Use-Define 链）；OpResult 表示定义端，定义了一个 Value，OpOperand 表示使用端，描述了对一个 Value 的使用。 + +## 二、设计初衷 +计算图中间表示（Intermediate Representation，即 IR）是深度学习框架性能优化、推理部署、编译器等方向的重要基石。近些年来，越来越多的框架和研究者将编译器技术引入到深度学习的神经网络模型优化中，并在此基础上借助编译器的理念、技术和工具对神经网络进行自动优化和代码生成。飞桨历史上在架构层面并存着多套不同的中间表示体系，其表达能力各不相同、Pass 开发维护成本较高，代码复用性较差，缺乏统一规范，存在严重的框架稳定性问题。 + +
        + +
        + + +因此在 3.0 版本下，飞桨在基础架构层面规范了中间表示 IR 定义，实现全架构统一表示，实现上下游各个方向共享开发成果： ++ **推理部署** ：简化抽象计算图，解决有环问题，降低 Pass 的开发成本 ++ **分布式侧** ：多 Dialect 管理算子，支持分布式属性的灵活标记 ++ **编译器侧** ：严格遵循 SSA 原则，灵活支撑编译优化鲁棒性 + + +飞桨的新一代 IR 架构聚焦于高度灵活和高扩展性两个重要维度，通过更加完备且鲁邦的语义表达能力、训推全架构统一表示和高效可插拔的性能优化策略（Pass）开发机制，实现复杂语义支持，更便捷地支撑大模型自动并行下丰富的切分策略，无缝对接神经网络编译器实现自动性能优化和多硬件适配。 + +## 三、使用指南 + +飞桨新的一代 IR 是基础架构层面的升级，对于用户在 API 层面的使用是无感的，用户可保持之前动转静（即 paddle.jit.to_static）或静态图代码不变，在 3.0-Beta 下仅需额外通过 `export FLAGS_enable_pir_api=1` 开启新 IR 功能即可，如下是一个简单的使用样例。 + +```python +# test_add_relu.py + +import unittest +import numpy as np +import paddle +from paddle.static import InputSpec + +class SimpleNet(paddle.nn.Layer): + def __init__(self): + super().__init__() + + + def forward(self, x, y): + z = x + y + out = paddle.nn.functional.relu(z) + return out +# Step 1: 构建模型对象，并应用动转静策略 +specs = [InputSpec(shape=(-1, -1)), InputSpec(shape=(-1, -1))] +net = paddle.jit.to_static(SimpleNet(), specs) + +# Step 2: 准备输入，执行 forward +x = paddle.rand(shape=[16, 64], dtype=paddle.float32) +y = paddle.rand(shape=[16, 64], dtype=paddle.float32) +out = net(x, y) +print(out) +``` + +将上述文件保存为 test_add_relu.py，执行如下命令： `FLAGS_enable_pir_api=1 python test_add_relu.py` 即可。开发者可额外指定 GLOG_v=6 输出日志，查看新一代 IR 下的 Program 表示，如下所示，在动转静或静态图模式下，用户的代码经过组网 API 下会先生成 Operator Dialect 下计算图表示，在执行时飞桨会将其转换为给定硬件下的 Kernel Dialect，然后交给执行器去依次调度对应的 PHI 算子库，计算最终结果。 + +```python +{ // Operator Dialect + (%0) = "pd_op.data" () {dtype:(pd_op.DataType)float32,name:"x",place:(pd_op.Place)Place(undefined:0),shape:(pd_op.IntArray)[-1,-1],stop_gradient:[true]} : () -> builtin.tensor<-1x-1xf32> + (%1) = "pd_op.data" () {dtype:(pd_op.DataType)float32,name:"y",place:(pd_op.Place)Place(undefined:0),shape:(pd_op.IntArray)[-1,-1],stop_gradient:[true]} : () -> builtin.tensor<-1x-1xf32> + (%2) = "pd_op.add" (%0, %1) {stop_gradient:[true]} : (builtin.tensor<-1x-1xf32>, builtin.tensor<-1x-1xf32>) -> builtin.tensor<-1x-1xf32> + (%3) = "pd_op.relu" (%2) {stop_gradient:[true]} : (builtin.tensor<-1x-1xf32>) -> builtin.tensor<-1x-1xf32> + () = "builtin.shadow_output" (%3) {output_name:"output_0"} : (builtin.tensor<-1x-1xf32>) -> +} + +// IR after lowering +{ // Kernel Dialect + (%0) = "data(phi_kernel)" () {dtype:(pd_op.DataType)float32,kernel_key:,kernel_name:"data",name:"x",op_name:"pd_op.data",place:(pd_op.Place)Place(undefined:0),shape:(pd_op.IntArray)[-1,-1],stop_gradient:[true]} : () -> undefined_tensor<-1x-1xf32> + (%1) = "shadow_feed(phi_kernel)" (%0) {kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed"} : (undefined_tensor<-1x-1xf32>) -> gpu_tensor<-1x-1xf32> + (%2) = "data(phi_kernel)" () {dtype:(pd_op.DataType)float32,kernel_key:,kernel_name:"data",name:"y",op_name:"pd_op.data",place:(pd_op.Place)Place(undefined:0),shape:(pd_op.IntArray)[-1,-1],stop_gradient:[true]} : () -> undefined_tensor<-1x-1xf32> + (%3) = "shadow_feed(phi_kernel)" (%2) {kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed"} : (undefined_tensor<-1x-1xf32>) -> gpu_tensor<-1x-1xf32> + (%4) = "add(phi_kernel)" (%1, %3) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",stop_gradient:[true]} : (gpu_tensor<-1x-1xf32>, gpu_tensor<-1x-1xf32>) -> gpu_tensor<-1x-1xf32> + (%5) = "relu(phi_kernel)" (%4) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",stop_gradient:[true]} : (gpu_tensor<-1x-1xf32>) -> gpu_tensor<-1x-1xf32> + () = "builtin.shadow_output" (%5) {output_name:"output_0"} : (gpu_tensor<-1x-1xf32>) -> +} +``` + +## 四、架构原理 +在大模型场景下，对深度学习框架中间表示的灵活性、扩展性和完备性提出了全新的需求。飞桨通过抽象核心结构，引入 Dialect 概念，实现多 Dialect 模块化，并提供了易用高性能、低成本开发、丰富可插拔的 Pass 优化策略，串联 AI 编译器，适配支持多异构硬件，面向大模型训推流程优化提速。 + +
        + +
        + + +如上左图所示，新一代 IR 的整体设计自底向上分为三层： +### 1.灵活的基础组件 +飞桨提供了 Trait 和 Interface 两种重要机制实现了对算子 Op 的特征和接口的抽象标记。 比如 InplaceTrait 表示一个 Op 具有 Inplace 特征， InferShapeInterface 表示一个算子定义了 InferShape 函数接口等，这二者都是可以任意扩展的，只要派生自相应的基类、遵循相应的实现规则即可；并对算子体系下核心概念抽出 Type、Attrbute、Op，这三者是基于 Trait 和 Interface 进行定义的。它们会对关联自己所拥有的相应 Trait 和 Interface ；Dialect 用来对 Type、Attribtue、Op 做模块化管理， 比如 BuiltinDialect、PaddleDialect、CinnDialect 等等。一个 Dialect 里面包含了一系列的 Type、Attribtue、Op 的定义。相应的，每个 Type、Attribtue、Op 都是定义在某个唯一的 Dialect 里面。对整个 IR 框架而言， Dialect 是可以随意插拔的，也是可以任意扩展的。 + +这一层是 IR 适应多种场景的基础。这一层的每一个要素都是可定制化扩展的，一般情况下，针对一个具体的场景，比如分布式、编译器。都需要定义自己需要用到的 Trait、Interfce，然后定义自己的 Dialect，在自己的 Dialect 里面，定义自己需要用到的 Type、Attribute、Op。 + +### 2.多层级的 Dialect + +飞桨通过不同层级的 Dialect 来管理框架内不同领域的算子体系，比如 Built-in 下的 Shape Dialect 和 Control Flow Dialect，分别用户形状符号推导和控制流表示、与 PHI 算子库执行体系相关的 Operator Dialect 和 Kernel Dialect、与神经网络编译器领域相关的 CINN Dialect 等。在飞桨神经网络编译器中，主要以计算图 Operator Dialect 为输入，经过组合算子和 Pass Pipline 后，会转换为 CINN Dialect，并附加 Shape Dialect 中的符号信息，最后会 Lowering 成编译器的 AST IR。 +上述这些多层级的 Dialect 内的算子 Op 会组成 Program ，并用来表示一个具体的模型。它包含两部分：计算图 和 权重 。 +* Value、Operation 用来对计算图进行抽象。Value 表示计算图中的有向边，他用来将两个 Operaton 关联起来，描述了程序中的 UD 链 ，Operation 表示计算图中的节点。一个 Operation 表示一个算子，它里面包含了零个或多个 Region 。Region 表示一个闭包，它里面包含了零个或多个 Block。Block 表示一个符合 SSA 的基本块，里面包含了零个或多个 Operation 。三者循环嵌套，可以实现任意复杂的语法结构。 +* Weight 用来对模型的权重参数进行单独存储，这也是深度学习框架和传统编译器不一样的地方。传统编译器会将数据段内嵌到程序里面。这是因为传统编译器里面，数据和代码是强绑定的，不可分割。但是对神经网络而言，一个计算图的每个 epoch 都会存在一份权重参数，多个计算图也有可能共同一份权重参数，二者不是强绑定的 + +### 3.功能完善的 Pass 体系 + +Pass 的核心是子图匹配和替换（即图变换），是将一个 Program 通过某种规则转换为另一个新的 Program。IR 中包含了计算图中全局信息，如上下游算子的邻接关系等，更有利于进行图优化，比如常量折叠、算子融合，Inplace 策略等： + +
        + +
        + +飞桨内置了一系列计算图优化、显存优化、量化等通用 Pass，灵活可配置。并简化了基础概念，向用户提供了 2 种 Pass 开发范式：Pattern Rewriter 和 Declarative Rewrite Rule（简称 DRR），充分兼顾自定义灵活性和开发易用性，大幅降用户 Pass 优化策略的开发门槛和代码量。 + +
        + +
        + +## 五、参考资料 +1. [【方案设计】IR 底层基础类型系统设计文档](https://github.com/PaddlePaddle/community/blob/master/pfcc/paddle-code-reading/IR_Dialect/basic_concepts.md) +2. [【方案设计】IR 顶层模型结构表示设计文档](https://github.com/PaddlePaddle/community/blob/master/pfcc/paddle-code-reading/IR_Dialect/ir_program.md) +3. [【方案设计】控制流设计文档](https://github.com/PaddlePaddle/community/blob/master/pfcc/paddle-code-reading/IR_Dialect/control_flow.md) diff --git a/docs/guides/paddle_v3_features/sot_cn.md b/docs/guides/paddle_v3_features/sot_cn.md new file mode 100644 index 00000000000..11ce7043505 --- /dev/null +++ b/docs/guides/paddle_v3_features/sot_cn.md @@ -0,0 +1,237 @@ +# 动转静 SOT 原理及使用 + +## **一、背景与动机** + +因为动态图和静态图在性能和用户体验、二次开发上各有优劣，深度学习框架在架构层统一动静概念并实现用户的最佳一致性使用体验，是极具挑战性的。飞桨从用户体验角度出发，着眼于训练、推理，并紧跟大模型时代的场景需求，直面技术难题，解决用户在性能，部署和大模型定制化的痛点。但是随着技术的演进，传统的基于 AST 变换的动静统一策略开始无法处理日益复杂的用户模型，在一些不常用的语法和太过于动态的场景下会出现转写失败的情况，而转写一旦失败，意味着后续基于静态图的优化工作没有任何一个可以被运用。AST 转写方案虽然具有高层级，易于转写的特性，但由于 Python 是一门纯动态语言，以及 Paddle 静态化数据表示能力的有限性，现在的 AST 方案存在如下局限性： + +- **难以处理动态和静态相互混合的场景**。例如 numpy 和 tensor 的互相转换，见样例代码； + +- **控制流和容器的混合使用时有边界 case**，经常出现解析出错或者是无法完全表示的情况； + +- **不支持源码加密场景下使用，完备性存在上限**。比如在 C++ 端执行的 Python 代码无法进行 AST 转写，或者对于 `.pyc` 文件无法处理（某些加密场景等） + + +一个在套件和用户使用中经典的场景如下： + +```python +# 一个简单的 Case 如下： +@paddle.jit.to_static() +def unsupport_func(x): + x = 2 * x + t = x.numpy() # t 依赖了 x 的值，依赖静态图的执行结果 + t = np.ones(t) + return paddle.to_tensor(t) + +x = paddle.to_tensor([2]) +unsupport_func(x) # raise error +``` + +这里的 np.ones 因为动转静的使用，上述的 x 和 t 其实都是 Variable 类型，传入到 np.ones 中是无法获取到真实的 value 的，因此 numpy 接口会报错。而这样的 Case 也是 AST 动转静理论上无法解决的问题，本质原因是，AST 必须要求转写的函数可以**被整图的**静态图 IR 表示。 + +这些长尾的 case 虽然可以通过要求用户使用规范的做法来避免，但是这类问题还是层出不穷，因为用户不希望在写动态图时考虑动转静的场景。 + +为了解决极具灵活性的 Python 语言与深度学习框架中间表示巨大的差异性鸿沟问题，飞桨在动转静模块中引入了字节码符号化模拟执行机制（即 Symbolic OpCode Translator，简称 SOT），在字节码层级分析和模拟执行动态图模型代码，动态抽取静态组网代码，构建成为一个新的等价的 Python 函数，消除语言与表示之间的鸿沟，实现动态图到静态图的等价转写。同时这种字节码模拟执行机制，可以自适应选择触发子图级别的打断机制（即 Graph Break），实现控制流代码保持动态图运行的效果。 + +## 二、概要介绍 + +### **自适应打断机制：** + +在新的 SOT 方案中引入了自适应打断机制来获得 100% 理论动转静成功率。在旧的动转静 AST 方案中，是以源码转写的方式对整图进行转写，当遇到无法静态化的 Op 时，AST 整图转写失败。新的 SOT 方案中，首先将源码转写的方式升级为了字节码转写，当遇到无法静态化的 Op 时，我们将整图切分为子图，并使用**字节码进行粘连**，以达到转写成功的目的。在自适应打断机制加持下，用户动态图编写可以更加随意，并在子图层面享受动转静和编译器加速。 + +

        + +

        + + +### 执行流程： + +在新的 SOT 流程下，动转静是在字节码层面进行分析的，SOT 会先利用注册的 Python EvalFrame Hooker 获取到用户函数运行时的字节码和 PyFrame 上下文信息（包含了局部变量，参数等），然后使用内部实现的**字节码模拟执行器**来进行模拟执行，最后得到一个可以替换原来字节码的新 PyCodeObject 对象。模拟执行器会识别出用户函数中需要静态化的字节码和无法静态化的字节码，对于无法静态化的字节码使用打断功能会回退到动态图执行，对于可以静态化的字节码会生成一个静态图来进行替换。当第二次执行时，SOT 会先判断是否命中了上次转写的缓存，如果命中了缓存就可以直接获取上次转写的 PyCodeObject 重用。下图是整个 SOT 的执行流程。 + +

        + +

        + +## 三、框架架构 + +

        + +

        + + +上图展示了 SOT 的所有组件，针对一些名词和模块，这里进行一个简单的介绍： + +### 3.1 EvalFrame Hooker 模块 + +Python 在 2016 年的 PEP523 提案支持了自定义回调函数，将默认的执行器替换为用户自定义的解释函数。这个机制结合子图 fallback 方案的需求，我们在 Paddle 的 Pybind 层暴露了 `paddle.core.set_eval_frame` 接口。 + +### 3.2 字节码模拟器（OpcodeExecutor）模块 + +这个部分是 SOT 方案的核心，主要的功能是我们需要模拟获取到的 PyCodeObject，并进行动态和静态代码分离，因此字节码模拟器是将 Python 函数映射为新的 Python 函数的模块。对于不同的静态化程度的函数，**字节码模拟器**会将一个函数对应于下面几种可能的情况： + +1. 若能够**完全静态化**目标函数，则需要返回一个新的可执行函数，该函数能够构建目标函数对应的子图； +2. 若只能**部分静态化**目标函数，同样需要返回一个新的可执行函数，该函数将可静态化部分抽取为子图，并将无法静态化的部分抽取为子函数（可能代表着不同分支），通过 Eval Frame 机制进行递归的处理。 +3. 若完全**无法静态化**目标函数，则返回原本的目标函数，在动态图环境下进行计算。 + +我们在 SOT 项目中完成了一个完备的 Python 字节码解释器，具有如下的特点： + +- 设计良好，具备 Dispatch 机制，符合开闭原则，便于维护。 +- 支持随意触发打断和 Fallback 的能力。 +- 支持子函数递归模拟。 +- 完备的字节码支持，完备的版本支持。我们支持 python3.8 - python3.12 的几乎 90%常见字节码模拟。 + +### 3.3 自适应子图打断模块 + +**对于控制流 If、For 依赖 Tensor 的场景，需要打断构图并静态化部分函数，子图打断能力是 SOT 能够达到近 100%成功率的核心组件。** + +我们深入研究了打断的类型，设计和打断机制，并将所有的打断场景划分为了 2 个不同的行为： + +- BreakGraph ：触发子图打断，当前函数会产生一个子图和一个 resume function 进行下一轮的模拟。 +- Fallback：触发子图打断，当前函数不产生子图，直接动态图运行。 + +基于不同的场景我们设计了不同的异常传播途径和不同的处理逻辑。 + +### 3.4 Tracker、Guard、缓存模块 + +子图 Fallback 的整体实现可以认为是将用户函数原始字节码转换为新的字节码，**为了避免每次传入相同输入都会重新触发开销昂贵的字节码转换操作，我们需要增加缓存机制来复用之前转写过的代码，实现 JIT 的效果。** + +但并不是任何字节码成功转换一次后第二次都是可以直接复用的，因为我们字节码的转换是基于 Frame 的初始状态进行模拟执行得到的，也就是说**转换后的字节码强依赖于 Frame 的初始状态**。当初始状态发生改变，最后转换后的字节码很有可能发生改变，因此我们需要一种机制来根据 Frame 初始状态来判断缓存过的字节码是否有效。这种转换复用的机制我们称为 Guard 函数，而 Guard 函数生成依赖字节码模拟过程中记录的每个模拟变量的 Tracker。 + +### 3.5 副作用处理模块 + +**SideEffect 是指代码执行过程中除了函数返回值之外，还对调用方产生了额外的影响，比如修改全局变量、修改可变的共享变量等。** + +在模拟执行过程中，我们的代码是在虚拟环境下执行的，在该过程中不应该也不会对真实环境进行修改。而如果用户代码产生了 SideEffect，我们需要在生成的代码里反映出相应的 SideEffect，即在字节码生成步骤中增加 SideEffect 的处理部分。副作用模块就是专门记录并处理副作用正确性的功能模块。 + +### 3.6 StatementIR 模块 + +**StatementIR 是 Paddle 动转静模块与子图 FallBack 的一个『中间桥梁』，它达到了动转静复用的目的。** + +StatementIR 与 Program 类似，都是表征计算的一个结构。**在字节码执行过程中，我们需要将所有的组网代码都『临时记录』下来，并最后将他们组网成为一个 Program 。**这里的组网代码记录的载体就是 StatementIR 。在函数结束的时刻，我们会将记录下来的 StatementIR 转化为一个函数。与原来的用户代码不同，由 StatementIR 转化为的函数可以确保一定可以动转静。这样我们可以复用原来的动转静 to_static 函数来实现静态图的执行。 + +## 四、对比 AST 方案 + +SOT 方案相比于 AST 方案有如下的优势： + +1. 【成功率提升】SOT 在遇到不支持的语法时会自动打断，并将不支持部分运行在动态图下，因此理论上可以达到近 100% 的成功率。 +2. 【转写完备性】SOT 只依赖 Python 字节码，针对无法获取源码的场景，也可以得到运行，获取正确的结果。 +3. 【控制流支持】SOT 因为支持自适应子图打断，因此可以不静态图化某些容器操作，可以更好的处理控制流与容器。不需要在静态图底层支持太多的容器类结构，比如 TensorArray 或者是 TensorDict。 +4. 【自适应打断子图】SOT 支持自适应打断子图。在无法静态化时，主动打断组网、运行静态图并获取输出，然后在进行新一轮的组网。因此可以在自图层面享受静态图和编译器的加速收益。 + +**注意：在 Save/Load 模式下需要整图导出，会自动切换到 AST 模式进行运行。** + +## 五、开始使用 + +### 5.1 使用 SOT 模式（默认模式） + +目前 SOT 模式是动转静的默认转写模式。用户只需要使用默认的 paddle.jit.to_static 就可以，下面是一个 SOT 动转静的使用样例： + +```python +import paddle +from paddle.jit import to_static +from paddle.static import InputSpec +import numpy as np +import random + +# set seed for determinated output +paddle.seed(2024) +np.random.seed(2024) +random.seed(2024) + +class SimpleNet(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.linear = paddle.nn.Linear(10, 3) + + def forward(self, x, y): + x = self.linear(x) + x = x + y + np_x = x.numpy() + np_x = np.sum(np_x) * 2 + return paddle.to_tensor(np_x) + +net = SimpleNet() + +net = paddle.jit.to_static(net, full_graph=False) # 动静转换, full_graph=False 表示 SOT 模式 +x = paddle.randn((10, 10)) +y = paddle.randn((3,)) +out = net(x, y) +print(out) +``` + +输出如下： + +```bash +Tensor(shape=[], dtype=float64, place=Place(gpu:0), stop_gradient=True, + 54.16428375) +``` + +### 5.2 使用 AST 模式 + +如果确定自己的代码完全可以静态化，用户可以手动打开 AST 模式，通常 AST 模式成功率会更低，但是调度开销会更小，同时支持部署推理。 + +```python +import paddle +from paddle.jit import to_static +from paddle.static import InputSpec +import numpy as np +import random + +# set seed for determinated output +paddle.seed(2024) +np.random.seed(2024) +random.seed(2024) + +class SimpleNet(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.linear = paddle.nn.Linear(10, 3) + + def forward(self, x, y): + x = self.linear(x) + x = x + y + np_x = x.numpy() + np_x = np.sum(np_x) * 2 + return paddle.to_tensor(np_x) + +net = SimpleNet() + +net = paddle.jit.to_static(net, full_graph=True) # 动静转换, full_graph=True 表示 AST 模式 +x = paddle.randn((10, 10)) +y = paddle.randn((3,)) +out = net(x, y) +print(out) +``` + +在这个 Case 中，AST 模式下会报错，因为我们尝试混合使用 numpy 和 paddle api，导致无法整图静态化。 +```bash +Traceback (most recent call last): + File "ttt.py", line 29, in + out = net(x, y) + File "/home/ssd2/xiongkun/Paddle/build/python/paddle/nn/layer/layers.py", line 1484, in __call__ + return self.forward(*inputs, **kwargs) + File "/home/ssd2/xiongkun/Paddle/build/python/paddle/jit/dy2static/program_translator.py", line 502, in __call__ + return self._perform_call(*args, **kwargs) + File "/home/ssd2/xiongkun/Paddle/build/python/paddle/jit/dy2static/program_translator.py", line 822, in _perform_call + error_data.raise_new_exception() + File "/home/ssd2/xiongkun/Paddle/build/python/paddle/jit/dy2static/error.py", line 448, in raise_new_exception + raise new_exception from None +TypeError: In transformed code: + + File "ttt.py", line 21, in forward + x = x + y + np_x = x.numpy() + np_x = np.sum(np_x) * 2 + ~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE + return paddle.to_tensor(np_x) + + File "<__array_function__ internals>", line 200, in sum + + File "/root/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 2324, in sum + return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, + File "/root/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 84, in _wrapreduction + return reduction(axis=axis, out=out, **passkwargs) + + TypeError: Code 'np_x = np.sum(np_x) * 2' called numpy API np.sum, please use Paddle API to replace it. + values will be changed to variables by dy2static, numpy api can not handle variables + +``` diff --git a/docs/guides/performance_improving/amp_cn.md b/docs/guides/performance_improving/amp_cn.md index 113490fef34..edf92fa681f 100644 --- a/docs/guides/performance_improving/amp_cn.md +++ b/docs/guides/performance_improving/amp_cn.md @@ -721,7 +721,7 @@ print("使用 AMP-O2 模式耗时:{:.3f} sec".format(train_time/(epochs*nums_bat 可能原因 1：所用显卡并不支持 AMP 加速，可在训练日志中查看如下 warning 信息：`UserWarning: AMP only support NVIDIA GPU with Compute Capability 7.0 or higher, current GPU is: Tesla K40m, with Compute Capability: 3.5.`； - 可能原因 2：模型是轻计算、重调度的类型，计算负载较大的 matmul、conv 等操作占比较低，可通过 nvidia-smi 实时产看显卡显存利用率（Memory Usage 及 GPU_Util 参数）。 + 可能原因 2：模型是轻计算、重调度的类型，计算负载较大的 matmul、conv 等操作占比较低，可通过 nvidia-smi 实时查看显卡显存利用率（Memory Usage 及 GPU_Util 参数）。 针对上述原因，建议关闭混合精度训练。 diff --git a/docs/guides/performance_improving/analysis_tools/benchmark_cn.md b/docs/guides/performance_improving/analysis_tools/benchmark_cn.md deleted file mode 100644 index d98936676f7..00000000000 --- a/docs/guides/performance_improving/analysis_tools/benchmark_cn.md +++ /dev/null @@ -1,90 +0,0 @@ -如何进行基准测试 -=============== -本文介绍如何给深度学习框架做基准测试。基准测试主要包含验证模型的精度和性能两方面，下文包含搭建测试环境，选择基准测试模型，验证测试结果等几方面内容。 - -验证深度学习框架，可分为训练和测试两个阶段， 验证指标略有不同，本文只介绍训练阶段的指标验证。训练阶段关注的是模型训练集上的精度，训练集是完备的，因此关注大 batch\_size 下的训练速度,关注吞吐量，例如图像模型常用的 batch\_size=128, 多卡情况下会加大；预测阶段关注的是在测试集上的精度，线上服务测试数据不能提前收集，因此关注小 batch\_size 下的预测速度，关注延迟，例如预测服务常用的 batch\_size=1, 4 等。 - -[Fluid](https://github.com/PaddlePaddle/Paddle>)是 PaddlePaddle 从 0.11.0 版本开始引入的设计，本文的基准测试在该版本上完成。 - - -环境搭建 -======== - -基准测试中模型精度和硬件、框架无关，由模型结构和数据共同决定；性能方面由测试硬件和框架性能决定。框架基准测试为了对比框架之间的差异，控制硬件环境，系统库等版本一致。下文中的对比实验都在相同的硬件条件和系统环境条件下进行. - - -不同架构的 GPU 卡性能差异巨大，在验证模型在 GPU 上训练性能时，可使用 NVIDIA 提供的命令:```nvidia-smi``` 检验当前使用的 GPU 型号，如果测试多卡训练性能，需确认硬件连接是 [nvlink](https://zh.wikipedia.org/zh/NVLink)或 [PCIe](https://zh.wikipedia.org/zh-hans/PCI_Express)。 同样地，CPU 型号会极大影响模型在 CPU 上的训练性能。可读取`/proc/cpuinfo`中的参数，确认当前正在使用的 CPU 型号。 - -下载 GPU 对应的 Cuda Tool Kit 和 Cudnn，或者使用 NVIDIA 官方发布的 nvidia-docker 镜像 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), 镜像内包含了 Cuda 和 Cudnn，本文采用这种方式。 Cuda Tool Kit 包含了 GPU 代码使用到的基础库，影响在此基础上编译出的 Fluid 二进制运行性能。 - -准备好 Cuda 环境后，从 github 上下载 Paddle 代码并编译，会生成对应的最适合当前 GPU 的 sm\_arch 二进制[sm\_arch](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html)。另外，cudnn 对卷积类任务影响巨大，在基准测试中需要小版本一致，例如 Cudnn7.0.2 与 Cudnn7.1.4 在 Resnet 上有 5%以上差异。 - - -选择基准模型 -============ - -对框架做基准测试，需要覆盖不同训练任务和不同大小的模型，本文中选取了图像和 NLP 的最为常用的 5 个模型。 - -任务种类| 模型名称| 网络结构| 数据集 -:---:|:--:|:---:|:---: -图像生成| CycleGAN| GAN| horse2zebra -图像分类| SE-ResNeXt50| Resnet-50| image-net -语义分割| DeepLab_V3+| ResNets| cityscapes -自然语言| Bert| Transformer| Wikipedia -机器翻译| Transformer| Attention| Wikipedia - -CycleGAN, SE-ResNeXt50, DeepLab_V3+属于 CNN 模型, Bert, Transformer 是一种比传统 RNN 模型更好的 NLP 模型。 -[benchmark](https://github.com/PaddlePaddle/Paddle/tree/develop/benchmark/fluid) -基准模型测试脚本中，均跳过了前几个 batch 的训练过程，原因是加载数据和分配显存受系统当前运行情况影响，会导致统计性能不准确。运行完若干个轮次后，统计对应指标。 - - -基准模型的数据的选择方面，数据量大且验证效果多的公开数据集为首选。图像模型 CycleGAN 选择了 horse2zebra 数据集，SE-ResNeXt50 选择了[image-net](http://www.image-net.org/challenges/LSVRC/2012/nnoupb)数据集，图像大小预处理为和 Imagenet 相同大小，因此性能可直接对比。 -NLP 模型的公开且影响力大数据集较少，Bert 和 Transformer 模型都选择了[Wikipedia](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2)数据集。 - - -注意，图像模型每条样本大小相同，图像经过变换后大小一致，因此经过的计算路径基本相同，计算速度和显存占用波动较小，可以从若干个 batch 的数据中采样得到当前的训练性能数据。而 NLP 模型由于样本长度不定，计算路径和显存占用也不相同，因此只能完整运行若干个轮次后，统计速度和显存消耗。 -显存分配是特别耗时的操作，因此 Fluid 默认会占用所有可用显存空间形成显存池，用以加速计算过程中的显存分配。如果需要统计模型真实显存消耗，可设置环境变量`FLAGS_fraction_of_gpu_memory_to_use=0.0`，观察最大显存开销。 - - -测试过程 -======== - -- GPU 单机单卡测试 - -本教程使用了 Cuda9, Cudnn7.0.1。来源为:```nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04``` - -``` - nvidia-docker run -it --name CASE_NAME --security-opt seccomp=unconfined -v $PWD/benchmark:/benchmark -v /usr/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu paddlepaddle/paddle:latest-dev /bin/bash -``` -在单卡上测试，设置 CUDA 的环境变量使用一块 GPU，``CUDA_VISIBLE_DEVICES=0`` -然后代码中设置为使用 CUDAPlace，如果使用 Paddle 代码库中的脚本，只需要命令行参数传入 use_gpu=True 即可。 - -``` - >>> import paddle.fluid as fluid - >>> place = fluid.CUDAPlace(0) // 0 指第 0 块 GPU -``` - -测试结果 -======== - -本教程对比相同环境下的 Fluid1.4, PyTorch1.1.0 和 TensorFlow1.12.0 的性能表现。 -硬件环境为 CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, GPU: Tesla v100(volta) 21729MiB x 1, Nvidia-Driver 384.66。 -系统环境为 Ubuntu 16.04.3 LTS, 本文中采用了 docker 环境，系统版本为 nvidia-docker17.05.0-ce。 -测试的 Fluid 版本为[v.1.4.1](https://github.com/PaddlePaddle/Paddle/tree/v1.4.1) 。 -TensorFlow 版本为[v.1.12.0-rc2](https://github.com/tensorflow/tensorflow/tree/v1.12.0-rc2)。 -PyTorch 版本为[v.1.1.0](https://github.com/pytorch/pytorch/tree/v1.1.0)。 -使用的脚本和配置见[benchmark](https://github.com/PaddlePaddle/Paddle/tree/develop/benchmark/fluid) 。 -SE-ResNeXt50 对比的框架是 PyTorch，因为 TensorFlow 上没有对应的模型。 -图表中统计单位为 samples/秒。 - - - -- GPU 单机单卡测试结果 - - Model|Fluid GPU| TensorFlow/PyTorch GPU - :---:|:--:|:---: - CycleGAN| 7.3 samples/s| 6.1 samples/s - SE-ResNeXt50| 169.4 samples/s | 153.1 samples/s - DeepLab_V3+| 12.8 samples/s | 6.4 samples/s - Bert| 4.0 samples/s | 3.4 samples/s - Transformer| 4.9 samples/s | 4.7 samples/s diff --git a/docs/guides/performance_improving/analysis_tools/cpu_profiling_cn.md b/docs/guides/performance_improving/analysis_tools/cpu_profiling_cn.md deleted file mode 100644 index 8b9c492f162..00000000000 --- a/docs/guides/performance_improving/analysis_tools/cpu_profiling_cn.md +++ /dev/null @@ -1,183 +0,0 @@ -# CPU 性能调优 - -此教程会介绍如何使用 Python 的 cProfile 包、Python 库 yep、Google perftools 来进行性能分析 (profiling) 与调优（performance tuning）。 - -Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。 - -PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大部分 Python API 调用用 C++ 写的 libpaddle.so。所以 PaddlePaddle 的性能分析与调优分为两个部分: - -* Python 代码的性能分析 -* Python 与 C++ 混合代码的性能分析 - - -## Python 代码的性能分析 - -### 生成性能分析文件 - -Python 标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成 Python 性能分析的命令如下: - -```bash -python -m cProfile -o profile.out main.py -``` - -其中 `main.py` 是我们要分析的程序，`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印到标准输出。 - -### 查看性能分析文件 - -`cProfile` 在 main.py 运行完毕后输出`profile.out`。我们可以使用[`cprofilev`](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个 Python 的第三方库。使用它会开启一个 HTTP 服务，将性能分析结果以网页的形式展示出来： - -```bash -cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py -``` - -其中`-a`标识 HTTP 服务绑定的 IP。使用`0.0.0.0`允许外网访问这个 HTTP 服务。`-p`标识 HTTP 服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。 - -用 Web 浏览器访问对应网址，即可显示性能分析的结果： - -``` - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.284 0.284 29.514 29.514 main.py:1() - 4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/executor.py:20(run) - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14() -``` - -每一列的含义是: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        列名含义
        ncalls 函数的调用次数
        tottime 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间
        percall tottime 的每次调用平均时间
        cumtime 函数总时间。包含这个函数调用其他函数的时间
        percall cumtime 的每次调用平均时间
        filename:lineno(function) 文件名, 行号，函数名
        - - -### 寻找性能瓶颈 - -通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。 - -将性能分析结果按照 tottime 排序，效果如下: - -```text - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader) - 107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:219(__init__) - 4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) - 1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/__init__.py:1() -``` - -可以看到最耗时的函数是 C++端的`run`函数。这需要联合我们第二节`Python`与`C++`混合代码的性能分析来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。 - -```text -Called By: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> - -Function was called by... - ncalls tottime cumtime -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:487(clone) - 1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:534(append_backward) - - -Called: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> -``` - -通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。 - - - -## Python 与 C++混合代码的性能分析 - -### 生成性能分析文件 - -C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试 Python 中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而 Python 的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行 Python 与 C++混合代码的性能分析 - -使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu 下安装命令为 - -```bash -apt update -apt install libgoogle-perftools-dev -pip install yep -``` - -安装完毕后，我们可以通过 - -```bash -python -m yep -v main.py -``` - -生成性能分析文件。生成的性能分析文件为`main.py.prof`。 - -命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为 C++与 Python 不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施: - -1. 编译时指定`-g`生成调试信息。使用 cmake 的话，可以将 CMAKE_BUILD_TYPE 指定为`RelWithDebInfo`。 -2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。 -3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭 openmp 优化。 - -### 查看性能分析文件 - -在运行完性能分析后，会生成性能分析结果文件。我们可以使用[`pprof`](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有 web 服务界面，且展示效果更好。 - -安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下: - -```bash -go get github.com/google/pprof -``` - -进而我们可以使用如下命令开启一个 HTTP 服务: - -```bash -pprof -http=0.0.0.0:3213 `which python` ./main.py.prof -``` - -这行命令中，`-http`指开启 HTTP 服务。`which python`会产生当前 Python 二进制的完整路径，进而指定了 Python 可执行文件的路径。`./main.py.prof`输入了性能分析结果。 - -访问对应的网址，我们可以查看性能分析的结果。结果如下图所示: - -![result](./pprof_1.png) - - -### 寻找性能瓶颈 - -与寻找 Python 代码的性能瓶颈类似，寻找 Python 与 C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。 - -例如下图中， - -![kernel_perf](./pprof_2.png) - -在一次训练中，乘法和乘法梯度的计算占用 2%-4%左右的计算时间。而`MomentumOp`占用了 17%左右的计算时间。显然，`MomentumOp`的性能有问题。 - -在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。 diff --git a/docs/guides/performance_improving/analysis_tools/cpu_profiling_en.md b/docs/guides/performance_improving/analysis_tools/cpu_profiling_en.md deleted file mode 100644 index 216694965b3..00000000000 --- a/docs/guides/performance_improving/analysis_tools/cpu_profiling_en.md +++ /dev/null @@ -1,224 +0,0 @@ -# Tune CPU performance - -This tutorial introduces techniques we use to profile and tune the -CPU performance of PaddlePaddle. We will use Python packages -`cProfile` and `yep`, and Google's `perftools`. - -Profiling is the process that reveals performance bottlenecks, -which could be very different from what's in the developers' mind. -Performance tuning is done to fix these bottlenecks. Performance optimization -repeats the steps of profiling and tuning alternatively. - -PaddlePaddle users program AI applications by calling the Python API, which calls -into `libpaddle.so.` written in C++. In this tutorial, we focus on -the profiling and tuning of - -1. the Python code and -1. the mixture of Python and C++ code. - -## Profiling the Python Code - -### Generate the Performance Profiling File - -We can use Python standard -package, [`cProfile`](https://docs.python.org/2/library/profile.html), -to generate Python profiling file. For example: - -```bash -python -m cProfile -o profile.out main.py -``` - -where `main.py` is the program we are going to profile, `-o` specifies -the output file. Without `-o`, `cProfile` would outputs to standard -output. - -### Look into the Profiling File - -`cProfile` generates `profile.out` after `main.py` completes. We can -use [`cprofilev`](https://github.com/ymichael/cprofilev) to look into -the details: - -```bash -cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py -``` - -where `-a` specifies the HTTP IP, `-p` specifies the port, `-f` -specifies the profiling file, and `main.py` is the source file. - -Open the Web browser and points to the local IP and the specifies -port, we will see the output like the following: - -``` - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.284 0.284 29.514 29.514 main.py:1() - 4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/executor.py:20(run) - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14() -``` - -where each line corresponds to Python function, and the meaning of -each column is as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        columnmeaning
        ncalls the number of calls into a function
        tottime the total execution time of the function, not including the execution time of other functions called by the function
        percall tottime divided by ncalls
        cumtime the total execution time of the function, including the execution time of other functions being called
        percall cumtime divided by ncalls
        filename:lineno(function) where the function is define
        - -### Identify Performance Bottlenecks - -Usually, `tottime` and the related `percall` time is what we want to -focus on. We can sort above profiling file by tottime: - -```text - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader) - 107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:219(__init__) - 4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) - 1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/__init__.py:1() -``` - -We can see that the most time-consuming function is the `built-in -method run`, which is a C++ function in `libpaddle.so`. We will -explain how to profile C++ code in the next section. At this -moment, let's look into the third function `sync_with_cpp`, which is a -Python function. We can click it to understand more about it: - -``` -Called By: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> - -Function was called by... - ncalls tottime cumtime -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:487(clone) - 1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:534(append_backward) - - -Called: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> -``` - -The lists of the callers of `sync_with_cpp` might help us understand -how to improve the function definition. - -## Profiling Python and C++ Code - -### Generate the Profiling File - -To profile a mixture of Python and C++ code, we can use a Python -package, `yep`, that can work with Google's `perftools`, which is a -commonly-used profiler for C/C++ code. - -In Ubuntu systems, we can install `yep` and `perftools` by running the -following commands: - -```bash -apt update -apt install libgoogle-perftools-dev -pip install yep -``` - -Then we can run the following command - -```bash -python -m yep -v main.py -``` - -to generate the profiling file. The default filename is -`main.py.prof`. - -Please be aware of the `-v` command line option, which prints the -analysis results after generating the profiling file. By examining the - the print result, we'd know that if we stripped debug -information from `libpaddle.so` at build time. The following hints -help make sure that the analysis results are readable: - -1. Use GCC command line option `-g` when building `libpaddle.so` so to - include the debug information. The standard building system of - PaddlePaddle is CMake, so you might want to set - `CMAKE_BUILD_TYPE=RelWithDebInfo`. - -1. Use GCC command line option `-O2` or `-O3` to generate optimized - binary code. It doesn't make sense to profile `libpaddle.so` - without optimization, because it would anyway run slowly. - -1. Profiling the single-threaded binary file before the - multi-threading version, because the latter often generates tangled - profiling analysis result. You might want to set environment - variable `OMP_NUM_THREADS=1` to prevents OpenMP from automatically - starting multiple threads. - -### Examining the Profiling File - -The tool we used to examine the profiling file generated by -`perftools` is [`pprof`](https://github.com/google/pprof), which -provides a Web-based GUI like `cprofilev`. - -We can rely on the standard Go toolchain to retrieve the source code -of `pprof` and build it: - -```bash -go get github.com/google/pprof -``` - -Then we can use it to profile `main.py.prof` generated in the previous -section: - -```bash -pprof -http=0.0.0.0:3213 `which python` ./main.py.prof -``` - -Where `-http` specifies the IP and port of the HTTP service. -Directing our Web browser to the service, we would see something like -the following: - -![result](./pprof_1.png) - -### Identifying the Performance Bottlenecks - -Similar to how we work with `cprofilev`, we'd focus on `tottime` and -`cumtime`. - -![kernel_perf](./pprof_2.png) - -We can see that the execution time of multiplication and the computing -of the gradient of multiplication takes 2% to 4% of the total running -time, and `MomentumOp` takes about 17%. Obviously, we'd want to -optimize `MomentumOp`. - -`pprof` would mark performance critical parts of the program in -red. It's a good idea to follow the hints. diff --git a/docs/guides/performance_improving/analysis_tools/host_memory_profiling_en.md b/docs/guides/performance_improving/analysis_tools/host_memory_profiling_en.md deleted file mode 100644 index b1dbee1bd45..00000000000 --- a/docs/guides/performance_improving/analysis_tools/host_memory_profiling_en.md +++ /dev/null @@ -1,87 +0,0 @@ -# Heap Memory Profiling and Optimization - -Any computer program has the danger of memory leak. Generally, **Memory Leak** is caused by the unreleased heap memory allocated by the program. As the memory occupied by the program becomes larger and larger, it will affect the stability of the program, which may make the running speed slower or give rise to OoM(Out of Memory). It even compromises the stability of the machine in use, and leads to *downtime* . - - -There are many memory leak analysis tools at present. Typical ones include, [valgrind](http://valgrind.org/docs/manual/quick-start.html#quick-start.intro), [gperftools](https://gperftools.github.io/gperftools/). - -Because Fluid runs in C++ core driven by Python, It is very difficult for valgrind to analyze directly. You need to compile the debug version and dedicated Python version with valgrind support, and most of the output information is Python's own symbols and call information. In addition, valgrind will make the program run very slowly, so it is not recommended. - -Here we mainly introduce the use of [gperftools](https://gperftools.github.io/gperftools/) . - -gperftool mainly supports four functions: - -- thread-caching malloc -- heap-checking using tcmalloc -- heap-profiling using tcmalloc -- CPU profiler - -Paddle also provides a [tutorial on CPU performance analysis](./cpu_profiling_en.html) based on gperftool. - -For the analysis for heap, we mainly use thread-caching malloc and heap-profiling using tcmalloc. - -## Environment - -This tutorial is based on the Docker development environment paddlepaddle/paddle:latest-dev provided by paddle, based on the Ubuntu 16.04.4 LTS environment. - -## Manual - -- Install google-perftools - -``` -apt-get install libunwind-dev -apt-get install google-perftools -``` - -- Install pprof - -``` -go get -u github.com/google/pprof -``` - -- Configure Running Environment - -``` -export PPROF_PATH=/root/gopath/bin/pprof -export PPROF_BINARY_PATH=/root/gopath/bin/pprof -export LD_PRELOAD=/usr/lib/libtcmalloc.so.4 -``` - -- Use heap profile to run python program. The essence of it is to get a snapshot of the heap allocation periodically. - -``` -# HEAPPROFILE sets the directory and file prefix of the generated heap analysis file -# HEAP_PROFILE_ALLOCATION_INTERVAL Sets how many storage dumps are allocated for each dump, default 1GB -env HEAPPROFILE="./perf_log/test.log" HEAP_PROFILE_ALLOCATION_INTERVAL=209715200 python trainer.py -``` - -As the program runs, a lot of files will be generated in the perf_log folder as follows: - -``` --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0001.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0002.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0003.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0004.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0005.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0006.heap -``` - -- Analyze the heap files with pprof. There are two modes of analysis: - - Complete mode. An analysis of the current heap is performed, showing some of the call paths for the current allocation of memory. - - ``` - pprof --pdf python test.log.0012.heap - ``` - The command above will generate a file of profile00x.pdf, which can be opened directly, for example, [memory_cpu_allocator](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_cpu_allocator.pdf). As demonstrated in the chart below, during the running of the CPU version fluid, the module CPUAllocator is allocated with most memory. Other modules are allocated with relatively less memory, so they are ignored. It is very inconvenient for inspecting memory leak for memory leak is a chronic process which cannot be inspected in this picture. - ![result](https://user-images.githubusercontent.com/3048612/40964027-a54033e4-68dc-11e8-836a-144910c4bb8c.png) - - - Diff mode. You can do diff on the heap at two moments, which removes some modules whose memory allocation has not changed, and displays the incremental part. - ``` - pprof --pdf --base test.log.0010.heap python test.log.1045.heap - ``` - The generated result: [`memory_leak_protobuf`](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_leak_protobuf.pdf) - - As shown from the figure: The structure of ProgramDesc has increased by 200MB+ between the two versions, so there is a large possibility that memory leak happens here, and the final result does prove a leak here. - - ![result](https://user-images.githubusercontent.com/3048612/40964057-b434d5e4-68dc-11e8-894b-8ab62bcf26c2.png) - ![result](https://user-images.githubusercontent.com/3048612/40964063-b7dbee44-68dc-11e8-9719-da279f86477f.png) diff --git a/docs/guides/performance_improving/analysis_tools/index_cn.rst b/docs/guides/performance_improving/analysis_tools/index_cn.rst deleted file mode 100644 index 1e1a5d1cbe2..00000000000 --- a/docs/guides/performance_improving/analysis_tools/index_cn.rst +++ /dev/null @@ -1,18 +0,0 @@ -.. _api_guide_analysis_tools: - -############### -性能优化分析及工具 -############### - -.. toctree:: - :hidden: - - cpu_profiling_cn.md - host_memory_profiling_cn.md - timeline_cn.md - -本模块介绍 Fluid 使用过程中的调优方法，包括： - -- `CPU 性能调优 `_：介绍如何使用 cProfile 包、yep 库、Google perftools 进行性能分析与调优 -- `堆内存分析和优化 `_：介绍如何使用 gperftool 进行堆内存分析和优化，以解决内存泄漏的问题 -- `Timeline 工具简介 `_ ：介绍如何使用 Timeline 工具进行性能分析和调优 diff --git a/docs/guides/performance_improving/analysis_tools/index_en.rst b/docs/guides/performance_improving/analysis_tools/index_en.rst deleted file mode 100644 index abacd2fb5fe..00000000000 --- a/docs/guides/performance_improving/analysis_tools/index_en.rst +++ /dev/null @@ -1,18 +0,0 @@ -####################################### -Performance Profiling and Optimization -####################################### - -.. toctree:: - :hidden: - - - 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z4DG$X{>bS}w5R|4*9NoaF>R;GUt2Xx9Qf5A8xmz-A5UV+;f4v%h%YVSs}o8W{1wEv z^8K5*q=eW(GujvaVT+i6|9_zW!*B#5FK*y9+Eb)D_ECm38%{Z=RS~P4nqT^mtL`Lk zuMiO)PrdJNzVN>)y7<@PjJk`A3nhw|QUl(sg(LO9zTkPLi|v2qf2G本地使用 - -1. 在训练的主循环外加上`profiler.start_profiler(...)`和`profiler.stop_profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个 profile 的记录文件。 - - **提示：** - 请不要在 timeline 记录信息时运行太多次迭代，因为 timeline 中的记录数量和迭代次数是成正比的。 - - ```python - import numpy as np - import paddle - import paddle.fluid as fluid - from paddle.fluid import profiler - - place = fluid.CPUPlace() - - def reader(): - for i in range(100): - yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')], - - main_program = fluid.Program() - startup_program = fluid.Program() - - with fluid.program_guard(main_program, startup_program): - data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2]) - data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3]) - out = fluid.layers.fc(input=[data_1, data_2], size=2) - # ... - - feeder = fluid.DataFeeder([data_1, data_2], place) - exe = fluid.Executor(place) - exe.run(startup_program) - pass_num = 10 - - for pass_id in range(pass_num): - for batch_id, data in enumerate(reader()): - if pass_id == 0 and batch_id == 5: - profiler.start_profiler("All") - elif pass_id == 0 and batch_id == 10: - profiler.stop_profiler("total", "/tmp/profile") - outs = exe.run(program=main_program, - feed=feeder.feed(data), - fetch_list=[out]) - - ``` - -1. 运行`python paddle/tools/timeline.py`来处理`/tmp/profile`，这个程序默认会生成一个`/tmp/timeline`文件，你也可以用命令行参数来修改这个路径，请参考[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py)。 -```python -python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline -``` - -1. 打开 chrome 浏览器，访问，用`load`按钮来加载生成的`timeline`文件。 - - -1. 结果如下图所示，可以放大来查看 timeline 的细节信息。 - - ![chrome timeline](./timeline.jpeg) - -## 分布式使用 -一般来说，分布式的训练程序都会有两种程序：pserver 和 trainer。我们提供了把 pserver 和 trainer 的 profile 日志用 timeline 来显示的方式。 - -1. trainer 打开方式与[本地使用](#local)部分的第 1 步相同 - -1. pserver 可以通过加两个环境变量打开 profile，例如： -``` -FLAGS_rpc_server_profile_period=10 FLAGS_rpc_server_profile_path=./tmp/pserver python train.py -``` - -3. 把 pserver 和 trainer 的 profile 文件生成一个 timeline 文件，例如： -``` -python /paddle/tools/timeline.py - --profile_path trainer0=local_profile_10_pass0_0,trainer1=local_profile_10_pass0_1,pserver0=./pserver_0,pserver1=./pserver_1 - --timeline_path ./dist.timeline -``` - -4. 在 chrome 中加载 dist.timeline 文件，方法和[本地使用](#local)第 4 步相同。 diff --git a/docs/guides/performance_improving/analysis_tools/timeline_en.md b/docs/guides/performance_improving/analysis_tools/timeline_en.md deleted file mode 100644 index fb51802a168..00000000000 --- a/docs/guides/performance_improving/analysis_tools/timeline_en.md +++ /dev/null @@ -1,79 +0,0 @@ -# How to use timeline tool to do profile - -## Local - -1. Add `profiler.start_profiler(...)` and `profiler.stop_profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number. - - ```python - - import numpy as np - import paddle - import paddle.fluid as fluid - from paddle.fluid import profiler - - place = fluid.CPUPlace() - - def reader(): - for i in range(100): - yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')], - - main_program = fluid.Program() - startup_program = fluid.Program() - - with fluid.program_guard(main_program, startup_program): - data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2]) - data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3]) - out = fluid.layers.fc(input=[data_1, data_2], size=2) - # ... - - feeder = fluid.DataFeeder([data_1, data_2], place) - exe = fluid.Executor(place) - exe.run(startup_program) - pass_num = 10 - - for pass_id in range(pass_num): - for batch_id, data in enumerate(reader()): - if pass_id == 0 and batch_id == 5: - profiler.start_profiler("All") - elif pass_id == 0 and batch_id == 10: - profiler.stop_profiler("total", "/tmp/profile") - outs = exe.run(program=main_program, - feed=feeder.feed(data), - fetch_list=[out]) - - ``` - -2. Run `python paddle/tools/timeline.py` to process `/tmp/profile`, it will generate another -file `/tmp/timeline` by default. You can change the path by cmd parameter, please take a look at -[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py) for details. -```python -python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline -``` - -3. Open chrome and visit , use `load` button to load the generated `timeline` file. - - - - -4. The result timeline should be like: - - ![chrome timeline](./timeline.jpeg) - -## Distributed -This tool can support distributed train programs(pserver and trainer) too. - -1. Open traniner profiler just like how to use in [local](#local). - -2. Open pserver profiler: add two environment variables, e.g.: -``` -FLAGS_rpc_server_profile_period=10 FLAGS_rpc_server_profile_path=./tmp/pserver python train.py -``` - -3. Merge pservers' and trainers' profiler file, e.g.: -``` -python /paddle/tools/timeline.py - --profile_path trainer0=local_profile_10_pass0_0,trainer1=local_profile_10_pass0_1,pserver0=./pserver_0,pserver1=./pserver_1 - --timeline_path ./dist.timeline -``` - -4. Load `dist.timeline` in chrome just like the [fourth step in Local](#local_step_4) diff --git a/docs/guides/performance_improving/device_switching.md b/docs/guides/performance_improving/device_switching.md deleted file mode 100644 index 3c15919c503..00000000000 --- a/docs/guides/performance_improving/device_switching.md +++ /dev/null @@ -1,199 +0,0 @@ -# 运行时设备切换 - -Paddle 提供了[fluid.CUDAPlace](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/CUDAPlace_cn.html)以及[fluid.CPUPlace](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/CPUPlace_cn.html)用于指定运行时的设备。这两个接口用于指定全局的设备，从 1.8 版本开始，Paddle 提供了[device_guard](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/fluid_cn/device_guard_cn.html)接口，用于指定部分 OP 的运行设备，此教程会介绍 device_guard 的使用场景，以及如何使用该接口对模型进行优化。 - -如果使用了`fluid.CUDAPlace`设置了全局的执行设备，框架将尽可能地将 OP 设置在 GPU 上执行，因此有可能会遇到显存不够的情况。`device_guard`可以用于设置 OP 的执行设备，如果将部分层设置在 CPU 上运行，就能够充分利用 CPU 大内存的优势，避免显存超出。 - -有时尽管指定了全局的执行设备为 GPU，但框架在自动分配 OP 执行设备时，可能会将部分 OP 设置在 CPU 上执行。另外，个别 OP 会将输出存储在 CPU 上。在以上的场景中，常常会发生不同设备间的数据传输，可能会影响模型的性能。使用`device_guard`可以避免模型运行中不必要的数据传输。在下面的内容中，将会详细介绍如何通过[profile](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/profiler_cn.html)工具分析数据传输开销，以及如何使用`device_guard`避免不必要的数据传输，从而提升模型性能。 - -## 如何避免显存超出 - -下面示例代码中的`embedding`层，其参数`size`包含两个元素，第一个元素为`vocab_size` (词表大小), 第二个为`emb_size`（`embedding`层维度）。实际场景中，词表可能会非常大。示例代码中，词表大小被设置为 10000000。如果在 GPU 模式下运行，该层创建的权重矩阵的大小为(10000000, 150)，仅这一层就需要 5.59G 的显存，如果词表大小继续增加，极有可能会导致显存超出。 - -```python -import paddle.fluid as fluid - -data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64') -label = fluid.layers.fill_constant(shape=[1, 150], value=0.5, dtype='float32') -emb = fluid.embedding(input=data, size=(10000000, 150), dtype='float32') -out = fluid.layers.l2_normalize(x=emb, axis=-1) - -cost = fluid.layers.square_error_cost(input=out, label=label) -avg_cost = fluid.layers.mean(cost) -sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) -sgd_optimizer.minimize(avg_cost) - -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -result = exe.run(fluid.default_main_program(), fetch_list=[avg_cost]) -``` - -`embedding`是根据`input`中的`id`信息从`embedding`矩阵中查询对应`embedding`信息，在 CPU 上进行计算，其速度也是可接受的。因此，可以参考如下代码，使用`device_guard`将`embedding`层设置在 CPU 上，以利用 CPU 内存资源。那么，除了`embedding`层，其他各层都会在 GPU 上运行。 - -```python -import paddle.fluid as fluid - -data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64') -label = fluid.layers.fill_constant(shape=[1, 150], value=0.5, dtype='float32') -with fluid.device_guard("cpu"): - emb = fluid.embedding(input=data, size=(10000000, 150), dtype='float32') -out = fluid.layers.l2_normalize(x=emb, axis=-1) - -cost = fluid.layers.square_error_cost(input=out, label=label) -avg_cost = fluid.layers.mean(cost) -sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) -sgd_optimizer.minimize(avg_cost) - -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -result = exe.run(fluid.default_main_program(), fetch_list=[avg_cost]) -``` - -在显存足够的情况下，可不必进行这样的设置。 - -## 如何减少数据传输 -### 使用 profile 工具确认是否发生了数据传输 -首先对模型的性能数据进行分析，找到发生数据传输的原因。如下列代码所示，可以利用[profile](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/profiler_cn.html)工具进行分析。 - -```python -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.fluid.profiler as profiler - -data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') -data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32') -shape = fluid.layers.shape(data2) -shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) -out = fluid.layers.crop_tensor(data1, shape=shape) -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -compiled_prog = compiler.CompiledProgram(fluid.default_main_program()) -with profiler.profiler('All', 'total') as prof: - for i in range(10): - result = exe.run(program=compiled_prog, fetch_list=[out]) -``` - -在程序运行结束后，将会自动地打印出 profile report。在下面的 profile report 中，可以看到 `GpuMemCpy Summary`中给出了 2 项数据传输的调用耗时。在 OP 执行过程中，如果输入 Tensor 所在的设备与 OP 执行的设备不同，就会发生`GpuMemcpySync`，通常我们可以直接优化的就是这一项。进一步分析，可以看到`slice`和`crop_tensor`执行中都发生了`GpuMemcpySync`。尽管我们在程序中设置了 GPU 模式运行，但是框架中有些 OP，例如 shape，会将输出结果放在 CPU 上。 - -```text --------------------------> Profiling Report <------------------------- - -Note! This Report merge all thread info into one. -Place: All -Time unit: ms -Sorted by total time in descending order in the same thread - -Total time: 26.6328 - Computation time Total: 13.3133 Ratio: 49.9884% - Framework overhead Total: 13.3195 Ratio: 50.0116% - -------------------------- GpuMemCpy Summary ------------------------- - -GpuMemcpy Calls: 30 Total: 1.47508 Ratio: 5.5386% - GpuMemcpyAsync Calls: 10 Total: 0.443514 Ratio: 1.66529% - GpuMemcpySync Calls: 20 Total: 1.03157 Ratio: 3.87331% - -------------------------- Event Summary ------------------------- - -Event Calls Total CPU Time (Ratio) GPU Time (Ratio) Min. Max. Ave. Ratio. -FastThreadedSSAGraphExecutorPrepare 10 9.16493 9.152509 (0.998645) 0.012417 (0.001355) 0.025192 8.85968 0.916493 0.344122 -shape 10 8.33057 8.330568 (1.000000) 0.000000 (0.000000) 0.030711 7.99849 0.833057 0.312793 -fill_constant 20 4.06097 4.024522 (0.991025) 0.036449 (0.008975) 0.075087 0.888959 0.203049 0.15248 -slice 10 1.78033 1.750439 (0.983212) 0.029888 (0.016788) 0.148503 0.290851 0.178033 0.0668471 - GpuMemcpySync:CPU->GPU 10 0.45524 0.446312 (0.980388) 0.008928 (0.019612) 0.039089 0.060694 0.045524 0.0170932 -crop_tensor 10 1.67658 1.620542 (0.966578) 0.056034 (0.033422) 0.143906 0.258776 0.167658 0.0629515 - GpuMemcpySync:GPU->CPU 10 0.57633 0.552906 (0.959357) 0.023424 (0.040643) 0.050657 0.076322 0.057633 0.0216398 -Fetch 10 0.919361 0.895201 (0.973721) 0.024160 (0.026279) 0.082935 0.138122 0.0919361 0.0345199 - GpuMemcpyAsync:GPU->CPU 10 0.443514 0.419354 (0.945526) 0.024160 (0.054474) 0.040639 0.059673 0.0443514 0.0166529 -ScopeBufferedMonitor::post_local_exec_scopes_process 10 0.341999 0.341999 (1.000000) 0.000000 (0.000000) 0.028436 0.057134 0.0341999 0.0128413 -eager_deletion 30 0.287236 0.287236 (1.000000) 0.000000 (0.000000) 0.005452 0.022696 0.00957453 0.010785 -ScopeBufferedMonitor::pre_local_exec_scopes_process 10 0.047864 0.047864 (1.000000) 0.000000 (0.000000) 0.003668 0.011592 0.0047864 0.00179718 -InitLocalVars 1 0.022981 0.022981 (1.000000) 0.000000 (0.000000) 0.022981 0.022981 0.022981 0.000862883 -``` -### 通过 log 查看发生数据传输的具体位置 - -以上的示例程序比较简单，我们只用看 profile report 就能知道具体是哪些算子发生了数据传输。但是当模型比较复杂时，可能需要去查看更加详细的调试信息，可以打印出运行时的 log 去确定发生数据传输的具体位置。依然以上述程序为例，执行`GLOG_vmodule=operator=3 python test_case.py`，会得到如下 log 信息，会发现发生了 2 次数据传输： - -- `shape`输出的结果在 CPU 上，在`slice`运行时，`shape`的输出被拷贝到 GPU 上 -- `slice`执行完的结果在 GPU 上，当`crop_tensor`执行时，它会被拷贝到 CPU 上。 - -```text -I0406 14:56:23.286592 17516 operator.cc:180] CUDAPlace(0) Op(shape), inputs:{Input[fill_constant_1.tmp_0:float[1, 3, 5, 5]({})]}, outputs:{Out[shape_0.tmp_0:int[4]({})]}. -I0406 14:56:23.286628 17516 eager_deletion_op_handle.cc:107] Erase variable fill_constant_1.tmp_0 on CUDAPlace(0) -I0406 14:56:23.286725 17516 operator.cc:1210] Transform Variable shape_0.tmp_0 from data_type[int]:data_layout[NCHW]:place[CPUPlace]:library_type[PLAIN] to data_type[int]:data_layout[ANY_LAYOUT]:place[CUDAPlace(0)]:library_type[PLAIN] -I0406 14:56:23.286763 17516 scope.cc:169] Create variable shape_0.tmp_0 -I0406 14:56:23.286784 17516 data_device_transform.cc:21] DeviceTransform in, src_place CPUPlace dst_place: CUDAPlace(0) -I0406 14:56:23.286867 17516 tensor_util.cu:129] TensorCopySync 4 from CPUPlace to CUDAPlace(0) -I0406 14:56:23.287099 17516 operator.cc:180] CUDAPlace(0) Op(slice), inputs:{EndsTensor[], EndsTensorList[], Input[shape_0.tmp_0:int[4]({})], StartsTensor[], StartsTensorList[]}, outputs:{Out[slice_0.tmp_0:int[4]({})]}. -I0406 14:56:23.287140 17516 eager_deletion_op_handle.cc:107] Erase variable shape_0.tmp_0 on CUDAPlace(0) -I0406 14:56:23.287220 17516 tensor_util.cu:129] TensorCopySync 4 from CUDAPlace(0) to CPUPlace -I0406 14:56:23.287473 17516 operator.cc:180] CUDAPlace(0) Op(crop_tensor), inputs:{Offsets[], OffsetsTensor[], Shape[slice_0.tmp_0:int[4]({})], ShapeTensor[], X[fill_constant_0.tmp_0:float[1, 3, 8, 8]({})]}, outputs:{Out[crop_tensor_0.tmp_0:float[1, 3, 5, 5]({})]}. -``` - -### 使用 device_guard 避免不必要的数据传输 - -在上面的例子中，`shape`输出的是一个 1-D 的 Tensor，因此对于`slice`而言计算量很小。这种情况下如果将`slice`设置在 CPU 上运行，就可以避免 2 次数据传输。修改后的程序如下： - -```python -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.fluid.profiler as profiler - -data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') -data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32') -shape = fluid.layers.shape(data2) -with fluid.device_guard("cpu"): - shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) -out = fluid.layers.crop_tensor(data1, shape=shape) -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -compiled_prog = compiler.CompiledProgram(fluid.default_main_program()) -with profiler.profiler('All', 'total') as prof: - for i in range(10): - result = exe.run(program=compiled_prog, fetch_list=[out]) -``` -再次观察 profile report 中`GpuMemCpy Summary`的内容，可以看到`GpuMemCpySync`已经被消除。在实际的模型中，若`GpuMemCpySync` 调用耗时占比较大，并且可以通过设置`device_guard`避免，那么就能够带来一定的性能提升。 - -```text --------------------------> Profiling Report <------------------------- - -Note! This Report merge all thread info into one. -Place: All -Time unit: ms -Sorted by total time in descending order in the same thread - -Total time: 14.5345 - Computation time Total: 4.47587 Ratio: 30.7948% - Framework overhead Total: 10.0586 Ratio: 69.2052% - -------------------------- GpuMemCpy Summary ------------------------- - -GpuMemcpy Calls: 10 Total: 0.457033 Ratio: 3.14447% - GpuMemcpyAsync Calls: 10 Total: 0.457033 Ratio: 3.14447% - -------------------------- Event Summary ------------------------- - -Event Calls Total CPU Time (Ratio) GPU Time (Ratio) Min. Max. Ave. Ratio. -FastThreadedSSAGraphExecutorPrepare 10 7.70113 7.689066 (0.998433) 0.012064 (0.001567) 0.032657 7.39363 0.770113 0.529852 -fill_constant 20 2.62299 2.587022 (0.986287) 0.035968 (0.013713) 0.071097 0.342082 0.13115 0.180466 -shape 10 1.93504 1.935040 (1.000000) 0.000000 (0.000000) 0.026774 1.6016 0.193504 0.133134 -Fetch 10 0.880496 0.858512 (0.975032) 0.021984 (0.024968) 0.07392 0.140896 0.0880496 0.0605797 - GpuMemcpyAsync:GPU->CPU 10 0.457033 0.435049 (0.951898) 0.021984 (0.048102) 0.037836 0.071424 0.0457033 0.0314447 -crop_tensor 10 0.705426 0.671506 (0.951916) 0.033920 (0.048084) 0.05841 0.123901 0.0705426 0.0485346 -slice 10 0.324241 0.324241 (1.000000) 0.000000 (0.000000) 0.024299 0.07213 0.0324241 0.0223084 -eager_deletion 30 0.250524 0.250524 (1.000000) 0.000000 (0.000000) 0.004171 0.016235 0.0083508 0.0172365 -ScopeBufferedMonitor::post_local_exec_scopes_process 10 0.047794 0.047794 (1.000000) 0.000000 (0.000000) 0.003344 0.014131 0.0047794 0.00328831 -InitLocalVars 1 0.034629 0.034629 (1.000000) 0.000000 (0.000000) 0.034629 0.034629 0.034629 0.00238254 -ScopeBufferedMonitor::pre_local_exec_scopes_process 10 0.032231 0.032231 (1.000000) 0.000000 (0.000000) 0.002952 0.004076 0.0032231 0.00221755 -``` - -### 总结 - -- 使用 profile 工具对模型进行分析，看是否存在 GpuMemcpySync 的调用耗时。若存在，则进一步分析发生数据传输的原因。 -- 可以通过 profile report 找到发生 GpuMemcpySync 的 OP。如果需要，可以通过打印 log，找到 GpuMemcpySync 发生的具体位置。 -- 尝试使用`device_guard`设置部分 OP 的运行设备，来减少 GpuMemcpySync 的调用。 -- 最后可以通过比较修改前后模型的 profile report，或者其他用来衡量性能的指标，确认修改后是否带来了性能提升。 diff --git a/docs/guides/performance_improving/memory_optimize.rst b/docs/guides/performance_improving/memory_optimize.rst deleted file mode 100644 index f4a892e1dd8..00000000000 --- a/docs/guides/performance_improving/memory_optimize.rst +++ /dev/null @@ -1,156 +0,0 @@ -.. _api_guide_memory_optimize: - -########### -存储分配与优化 -########### - -1. PaddlePaddle 的显存分配策略 -=========================== - -1.1. 显存自增长 AutoGrowth 策略 --------------------------- -自 1.6+的版本起，PaddlePaddle 支持显存自增长 AutoGrowth 策略，按需分配显存，且已于 1.7+版本中默认开启，方便用户在同一张 GPU 卡上同时运行多个任务。 - -由于原生的 CUDA 系统调用 :code:`cudaMalloc` 和 :code:`cudaFree` 均是同步操作，非常耗时。 -因此显存自增长 AutoGrowth 策略会缓存已分配到的显存，供后续分配使用，具体方式为： - -- 在前几次显存分配时，框架会调用 :code:`cudaMalloc` 按需分配，但释放时不会调用 :code:`cudaFree` 返回给 GPU，而是在框架内部缓存起来。 - -- 在随后的显存分配时，框架会首先检查缓存的显存中是否有合适的块，若有则从中分割出所需的显存空间返回，否则才调用 :code:`cudaMalloc` 直接从 GPU 中分配。随后的显存释放亦会缓存起来供后续分配使用。 - -因此，显存自增长 AutoGrowth 策略会在前几个 batch 训练时分配较慢（因为频繁调用 :code:`cudaMalloc` ），在随后训练过程中基本不会影响模型训练速度。 - -1.2. 显存预分配策略 ----------------- - -除了显存自增长 AutoGrowth 策略以外，PaddlePaddle 还提供了显存预分配策略。显存预分配策略是 PaddlePaddle 1.7 版本前的默认显存分配策略。 - -显存预分配策略会在第一次分配时分配很大 chunk_size 的显存块，随后的显存分配大多从预分配的显存块中切分获得。 -其中，chunk_size 由环境变量 :code:`FLAGS_fraction_of_gpu_memory_to_use` 确定，chunk_size 的计算公式为： - -.. code-block:: python - - chunk_size = FLAGS_fraction_of_gpu_memory_to_use * 单张 GPU 卡的当前可用显存值 - -:code:`FLAGS_fraction_of_gpu_memory_to_use` 的默认值为 0.92，即框架预先分配显卡 92%的当前可用显存值。 - -显存预分配策略分配显存的具体方式为： - -- 在分配 requested_size 大小的显存时， - - 若 requested_size <= chunk_size，则框架会预先分配 chunk_size 大小的显存池 chunk，并从 chunk 中分出 requested_size 大小的块返回。之后每次申请显存都会从 chunk 中分配。 - - 若 requested_size > chunk_size，则框架会直接调用 :code:`cudaMalloc` 分配 requested_size 大小的显存返回。 - -- 在释放 free_size 大小的显存时， - - 若 free_size <= chunk_size，则框架会将该显存放回预分配的 chunk 中，而不是直接返回给 CUDA。 - - 若 free_size > chunk_size，则框架会直接调用 :code:`cudaFree` 将显存返回给 CUDA。 - -若你的 GPU 卡上有其他任务占用显存，你可以适当将 :code:`FLAGS_fraction_of_gpu_memory_to_use` 减少，保证框架能预分配到合适的显存块，例如： - -.. code-block:: shell - - export FLAGS_fraction_of_gpu_memory_to_use=0.4 # 预先 40%的 GPU 显存 - -若 :code:`FLAGS_fraction_of_gpu_memory_to_use` 设为 0，则每次显存分配和释放均会调用 :code:`cudaMalloc` 和 :code:`cudaFree` ，会严重影响性能，不建议你使用。 -只有当你想测量网络的实际显存占用量时，你可以设置 :code:`FLAGS_fraction_of_gpu_memory_to_use` 为 0，观察 nvidia-smi 显示的显存占用情况。 - -1.3. 显存分配策略的选择方式 ------------------------ -自 1.6+版本起，PaddlePaddle 同时支持显存自增长 AutoGrowth 策略和显存预分配策略，并通过环境变量 :code:`FLAGS_allocator_strategy` 控制。 - -选择显存自增长 AutoGrowth 的方式为： - -.. code-block:: shell - - export FLAGS_allocator_strategy=auto_growth # 选择显存自增长 AutoGrowth 策略 - -选择显存预分配策略的方式为： - -.. code-block:: shell - - export FLAGS_allocator_strategy=naive_best_fit # 选择显存预分配策略 - -此外，自 1.7.2+版本起，PaddlePaddle 提供了环境变量 :code:`FLAGS_gpu_memory_limit_mb` ，用于控制单个任务进程可分配的最大显存，单位是 MB。默认值是 0，表示没有限制，可分配全部显存。如果设置为大于 0 的值，则会在分配的显存超过限制时报错，即使此时系统还存在空闲的显存空间。 - -2. PaddlePaddle 的存储优化策略 -=========================== - -PaddlePaddle 提供了多种通用存储优化方法，优化你的网络的存储占用（包括显存和内存)。 - -2.1. GC 策略: 存储垃圾及时回收 -------------------------- - -GC（Garbage Collection）的原理是在网络运行阶段及时释放无用变量的存储空间，达到节省存储空间的目的。GC 适用于使用 Executor，ParallelExecutor 做模型训练/预测的场合，但不适用于 C++预测库接口。 - -**GC 策略已于 1.6+版本中默认开启。** - -GC 策略由三个环境变量控制： - - -- :code:`FLAGS_eager_delete_tensor_gb` - -GC 策略的使能开关，double 类型，在<1.6 的版本中默认值为-1，在 1.6+版本中默认值为 0。GC 策略会积攒一定大小的存储垃圾后再统一释放，:code:`FLAGS_eager_delete_tensor_gb` 控制的是存储垃圾的阈值，单位是 GB。**建议用户设置** :code:`FLAGS_eager_delete_tensor_gb=0` 。 - -若 :code:`FLAGS_eager_delete_tensor_gb=0` ，则一旦有存储垃圾则马上回收，最为节省存储空间。 - -若 :code:`FLAGS_eager_delete_tensor_gb=1` ，则存储垃圾积攒到 1G 后才触发回收。 - -若 :code:`FLAGS_eager_delete_tensor_gb<0` ，则 GC 策略关闭。 - - -- :code:`FLAGS_memory_fraction_of_eager_deletion` - -GC 策略的调节 flag，double 类型，默认值为 1，范围为[0,1]，仅适用于使用 ParallelExecutor 的场合。 -GC 内部会根据变量占用的存储空间大小，对变量进行降序排列，且仅回收前 :code:`FLAGS_memory_fraction_of_eager_deletion` 大的变量的存储空间。**建议用户维持默认值**，即 :code:`FLAGS_memory_fraction_of_eager_deletion=1` 。 - -若 :code:`FLAGS_memory_fraction_of_eager_deletion=0.6` ，则表示仅回收存储占用 60%大的变量的存储空间。 - -若 :code:`FLAGS_memory_fraction_of_eager_deletion=0` ，则表示不回收任何变量的存储空间，GC 策略关闭。 - -若 :code:`FLAGS_memory_fraction_of_eager_deletion=1` ，则表示回收所有变量的存储空间。 - - -- :code:`FLAGS_fast_eager_deletion_mode` - -快速 GC 策略的开关，bool 类型，默认值为 True，表示使用快速 GC 策略。快速 GC 策略会不等待 CUDA Kernel 结束直接释放显存。**建议用户维持默认值**，即 :code:`FLAGS_fast_eager_deletion_mode=True` 。 - - -2.2. Inplace 策略: Op 内部的输出复用输入 ----------------------------------- - -Inplace 策略的原理是 Op 的输出复用 Op 输入的存储空间。例如，reshape 操作的输出和输入可复用同一片存储空间。 - -Inplace 策略适用于使用 ParallelExecutor 的场合，通过 :code:`BuildStrategy` 设置。此策略不支持使用 Executor+Program 做单卡训练、使用 C++预测库接口等场合。 - -**Inplace 策略已于 1.6+版本中默认开启。** - -具体方式为: - -.. code-block:: python - - build_strategy = fluid.BuildStrategy() - build_strategy.enable_inplace = True # 开启 Inplace 策略 - - compiled_program = fluid.CompiledProgram(train_program, build_strategy=build_strategy) - - -在<1.6 的版本中，由于设计上的一些问题，在开启 Inplace 策略后，必须保证后续 exe.run 中 fetch_list 的变量是 persistable 的，即假如你后续需要 fetch 的变量为 loss 和 acc，则必须设置： - -.. code-block:: python - - loss.persistable = True - acc.persistable = True - - -**在 1.6+的版本中，无需设置 fetch 变量为 persistable。** - - -3. 存储优化 Best Practice -======================= - -我们推荐你的最佳存储优化策略为： - -- 开启 GC 策略：设置 :code:`FLAGS_eager_delete_tensor_gb=0` 。 - -- 开启 Inplace 策略：设置 :code:`build_strategy.enable_inplace = True` ，并在<1.6 版本中设置 fetch_list 中的 :code:`var.persistable = True` 。 - -**在 1.6+的版本中，上述最佳策略均已默认打开，无需手动配置，亦无需设置 fetch_list 变量为 persistable。** diff --git a/docs/guides/performance_improving/memory_optimize_en.rst b/docs/guides/performance_improving/memory_optimize_en.rst deleted file mode 100644 index 0a2eceb9bca..00000000000 --- a/docs/guides/performance_improving/memory_optimize_en.rst +++ /dev/null @@ -1,176 +0,0 @@ -.. _api_guide_memory_optimize_en: - -########### -Memory Allocation and Optimization -########### - -1. Memory Allocation Strategy -=========================== - -1.1. AutoGrowth Strategy --------------------------- - -Since version 1.6+, PaddlePaddle supports the AutoGrowth strategy, which allocates memory on demand. -AutoGrowth strategy has been enabled by default in version 1.7+, making it convenient for users to -run multiple tasks on the same GPU card at the same time. - -Because the native CUDA system calls :code:`cudaMalloc` and :code:`cudaFree` are synchronous operations, -which are very time-consuming, the AutoGrowth strategy will cache the allocated memory for subsequent allocation. -The specific methods are as follows: - -- In the first few memory allocations, PaddlePaddle framework will call :code:`cudaMalloc` and allocate memory on demand. When releasing the allocated memory, it will not call :code:`cudaFree` to return the memory to GPU, but cache the memory inside the framework. - -- In the subsequent allocations, PaddlePaddle framework will first check if there is a fit block (block size larger than the required memory size) in the cached memory. If there is, it will split the required memory from the fit block and return. Otherwise, it will call :code:`cudaMalloc` to allocate memory from GPU. The allocated memory are also cached when being released for subsequent allocation. - -Therefore, the AutoGrowth strategy may slow the speed in the first few batches of model training, -but will not affect the speed in the subsequent training process. - -1.2. Pre-Allocation Strategy ----------------- - -In addition to the AutoGrowth strategy, paddlepaddle also provides a Pre-Allocation strategy, -which is the default memory allocation strategy before paddlepaddle 1.7. - -The Pre-Allocation strategy allocates a large size chunk at the first allocation, and the subsequent memory allocation is mostly obtained from the pre allocated memory chunk. -Among them, the chunk size is determined by the environment variable :code:`FLAGS_fraction_of_gpu_memory_to_use`, and the calculation formula of chunk size is: - -.. code-block:: python - - chunk_size = FLAGS_fraction_of_gpu_memory_to_use * number of current available memory of a single GPU card - -The default value of :code:`FLAGS_fraction_of_gpu_memory_to_use` is 0.92, that is, the framework will pre allocates -92% of the currently available memory of the GPU card. - -The specific way of Pre-Allocation strategy to allocate GPU memory is: - -- When allocating memory of requested_size, - - If requested_size <= chunk_size, the framework will first allocate a memory chunk of chunk_size, then split a block of requested_size and return the block. Every subsequent memory allocation will be performed on the chunk. - - If requested_size > chunk_size, the framework will call :code:`cudaMalloc` to allocate memory block of requested_size and return. - -- When freeing memory of requested_size, - - If free_size <= chunk_size, the framework will put the memory block back into the pre-allocated chunk, instead of returning back to GPU. - - If free_size > chunk_size, the framework will call :code:`cudaFree` and return the memory back to GPU. - -If there are other tasks on your GPU card that occupy the memory, you can appropriately decrease :code:`FLAGS_fraction_of_gpu_memory_to_use` -to ensure that the framework can pre-allocate the memory block of appropriate size, for example - -.. code-block:: shell - - export FLAGS_fraction_of_gpu_memory_to_use=0.4 # Pre-allocate 40% memory of a single GPU card - -If :code:`FLAGS_fraction_of_gpu_memory_to_use` is set to 0, the framework will call :code:`cudaMalloc` and :code:`cudaFree` every time the memory is allocated and released, which will seriously affect the performance and is not recommended. Only when you want to measure the actual memory usage of the network, you could set :code:`FLAGS_fraction_of_gpu_memory_to_use` to 0, and observe the memory usage of command nvidia-smi display. - -1.3. Configuration of memory allocation strategy ------------------------ -Since version 1.6+, PaddlePaddle supports both the AutoGrowth strategy and the Pre-Allocation Strategy, and control the strategy used in framework by -the environment variable :code:`FLAGS_allocator_strategy`. - -Use AutoGrowth strategy: - -.. code-block:: shell - - export FLAGS_allocator_strategy=auto_growth # Use AutoGrowth strategy - -Use Pre-Allocation strategy: - -.. code-block:: shell - - export FLAGS_allocator_strategy=naive_best_fit # Use Pre-Allocation strategy - -Plus, since version 1.7.2+, PaddlePaddle provides an environment variable :code:`FLAGS_gpu_memory_limit_mb`, which controls the maximum gpu memory limit that the process can allocate. -If it is equal to 0, there would be no limit and all gpu memory would be available to the process. If it is larger than 0, the process would raise out of memory error if the allocated -memory exceeds the limit even though there is available memory on the gpu card. The unit is MB and default value is 0. - -2. Memory Optimization Strategy -=========================== - -Paddlepaddle provides several general memory optimization methods to optimize the memory usage of your network (including general memory and GPU memory). - -2.1. GC Strategy: memory garbage eager collection -------------------------- - -The principle of GC（Garbage Collection）is to release the memory space of useless variables eagerly during network running, -in order to save memory space. GC is suitable for training and inference using Executor or ParallelExecutor, but it is not suitable for C++ inference library. - -**Since version 1.6+, GC Strategy is enabled by default.** - -GC Strategy is controlled by 3 environment variable: - - -- :code:`FLAGS_eager_delete_tensor_gb` - -Variable to enable GC, its data type is double. The default value is -1 in PaddlePaddle with version < 1.6, -and is 0 in PaddlePaddle with version >= 1.6. GC Strategy will cache a certain amount of memory garbage and release it uniformly. -:code:`FLAGS_eager_delete_tensor_gb` means the threshold of cached memory garbage, the unit of which is GB. **It is recommended to set** :code:`FLAGS_eager_delete_tensor_gb=0`. - -If :code:`FLAGS_eager_delete_tensor_gb=0`, once there is memory garbage, it will be collected immediately to save memory. - -If :code:`FLAGS_eager_delete_tensor_gb=1`, the memory garbage is collected when the cached amount of garbage reaches 1GB. - -If :code:`FLAGS_eager_delete_tensor_gb<0`, GC Strategy is disabled. - - -- :code:`FLAGS_memory_fraction_of_eager_deletion` - -Variable to control GC Strategy, its data type is double. The default value is 1, range [0,1]. It is only suitable for ParallelExecutor. -GC will sort the variables in descending order according to the memory space occupied by the variables, -and only collect the memory space of top :code:`FLAGS_memory_fraction_of_eager_deletion` variables. -**It is recommended to remain default value**, that is :code:`FLAGS_memory_fraction_of_eager_deletion=1`. - -If :code:`FLAGS_memory_fraction_of_eager_deletion=0.6`, top 60% variables will be collected. - -If :code:`FLAGS_memory_fraction_of_eager_deletion=0`, no variable will be collected, GC Strategy is disabled. - -If :code:`FLAGS_memory_fraction_of_eager_deletion=1`, all variables will be collected. - - -- :code:`FLAGS_fast_eager_deletion_mode` - -Variable to enable fast GC Strategy, its type is bool. The default value is True, which means use fast GC Strategy. -Fast GC Strategy will collect the memory garbage immediately instead of waiting for CUDA Kernel finish. **It is recommended to remain default value**, that is :code:`FLAGS_fast_eager_deletion_mode=True`. - - -2.2. Inplace Strategy: output reuses input inside operator ----------------------------------- - -The principle of Inplace strategy is that the output of some operators can reuses the memory space of input. -For example, the output and input of operator :code:`reshape` can reuse the same memory space. - -Inplace Strategy is suitable for ParallelExecutor, which can be set through :code:`BuildStrategy`. -The Strategy is not suitable for Executor+Program or C++ inference library. - -**Since version 1.6+, Inplace Strategy is enabled by default.** - -The specific way of Inplace strategy is: - -.. code-block:: python - - build_strategy = fluid.BuildStrategy() - build_strategy.enable_inplace = True # Enable Inplace Strategy - - compiled_program = fluid.CompiledProgram(train_program, build_strategy=build_strategy) - - -In PaddlePaddle with version < 1.6, due to of some design problems, when the Inplace Strategy is enabled, -the variable in fetch_list in the subsequent :code:`exe.run` must be persistent. -That is, if you the variables you want to fetch are loss and acc, you must set: - -.. code-block:: python - - loss.persistable = True - acc.persistable = True - - -**Since version 1.6+, setting variables in fetch_list to persistable is not needed.** - - -3. Memory Optimization Best Practice -======================= - -We recommend the best memory optimization strategy as: - -- Enable GC strategy:set :code:`FLAGS_eager_delete_tensor_gb=0`. - -- Enable Inplace strategy:set :code:`build_strategy.enable_inplace = True`, and set variables in fetch_list to persistable using :code:`var.persistable = True` when the version of PaddlePaddle < 1.6. - -**Since version 1.6+, the above optimal strategy have been enabled by default and setting variables in fetch_list to persistable is not needed.** diff --git a/docs/guides/performance_improving/paddle_tensorrt_infer.md b/docs/guides/performance_improving/paddle_tensorrt_infer.md deleted file mode 100644 index 2890eceb4ab..00000000000 --- a/docs/guides/performance_improving/paddle_tensorrt_infer.md +++ /dev/null @@ -1,209 +0,0 @@ -# 使用 Paddle-TensorRT 库预测 - -NVIDIA TensorRT 是一个高性能的深度学习预测库，可为深度学习推理应用程序提供低延迟和高吞吐量。PaddlePaddle 采用子图的形式对 TensorRT 进行了集成，即我们可以使用该模块来提升 Paddle 模型的预测性能。该模块依旧在持续开发中，目前支持的模型如下表所示： - -|分类模型|检测模型|分割模型| -|---|---|---| -|mobilenetv1|yolov3|ICNET| -|resnet50|SSD|| -|vgg16|mask-rcnn|| -|resnext|faster-rcnn|| -|AlexNet|cascade-rcnn|| -|Se-ResNext|retinanet|| -|GoogLeNet|mobilenet-SSD|| -|DPN||| - -在这篇文档中，我们将会对 Paddle-TensorRT 库的获取、使用和原理进行介绍。 - -**Note:** - -1. 从源码编译时，TensorRT 预测库目前仅支持使用 GPU 编译，且需要设置编译选项 TENSORRT_ROOT 为 TensorRT 所在的路径。 -2. Windows 支持需要 TensorRT 版本 5.0 以上。 -3. Paddle-TRT 目前仅支持固定输入 shape。 -4. 下载安装 TensorRT 后，需要手动在`NvInfer.h`文件中为`class IPluginFactory`和`class IGpuAllocator`分别添加虚析构函数： - ``` c++ - virtual ~IPluginFactory() {}; - virtual ~IGpuAllocator() {}; - ``` - -## 内容 -- [Paddle-TRT 使用介绍](#Paddle-TRT 使用介绍) -- [Paddle-TRT 样例编译测试](#Paddle-TRT 样例编译测试) -- [Paddle-TRT INT8 使用](#Paddle-TRT_INT8 使用) -- [Paddle-TRT 子图运行原理](#Paddle-TRT 子图运行原理) -- [Paddle-TRT 性能测试](#Paddle-TRT 性能测试) - -## Paddle-TRT 使用介绍 - -在使用 AnalysisPredictor 时，我们通过配置 AnalysisConfig 中的接口 - -``` c++ -config->EnableTensorRtEngine(1 << 20 /* workspace_size*/, - batch_size /* max_batch_size*/, - 3 /* min_subgraph_size*/, - AnalysisConfig::Precision::kFloat32 /* precision*/, - false /* use_static*/, - false /* use_calib_mode*/); -``` -的方式来指定使用 Paddle-TRT 子图方式来运行。 -该接口中的参数的详细介绍如下： - -- **`workspace_size`**，类型：int，默认值为 1 << 20。指定 TensorRT 使用的工作空间大小，TensorRT 会在该大小限制下筛选合适的 kernel 执行预测运算。 -- **`max_batch_size`**，类型：int，默认值为 1。需要提前设置最大的 batch 大小，运行时 batch 大小不得超过此限定值。 -- **`min_subgraph_size`**，类型：int，默认值为 3。Paddle-TRT 是以子图的形式运行，为了避免性能损失，当子图内部节点个数大于`min_subgraph_size`的时候，才会使用 Paddle-TRT 运行。 -- **`precision`**，类型：`enum class Precision {kFloat32 = 0, kHalf, kInt8,};`, 默认值为`AnalysisConfig::Precision::kFloat32`。指定使用 TRT 的精度，支持 FP32（kFloat32），FP16（kHalf），Int8（kInt8）。若需要使用 Paddle-TRT int8 离线量化校准，需设定`precision`为 `AnalysisConfig::Precision::kInt8`, 且设置`use_calib_mode` 为 true。 -- **`use_static`**，类型：bool, 默认值为 false。如果指定为 true，在初次运行程序的时候会将 TRT 的优化信息进行序列化到磁盘上，下次运行时直接加载优化的序列化信息而不需要重新生成。 -- **`use_calib_mode`**，类型：bool, 默认值为 false。若要运行 Paddle-TRT int8 离线量化校准，需要将此选项设置为 true。 - -**Note：** Paddle-TRT 目前只支持固定 shape 的输入，不支持变化 shape 的输入。 - -## Paddle-TRT 样例编译测试 - -1. 下载或编译带有 TensorRT 的 paddle 预测库，参考[安装与编译 C++预测库](../../inference_deployment/inference/build_and_install_lib_cn.html)。 -2. 从[NVIDIA 官网](https://developer.nvidia.com/nvidia-tensorrt-download)下载对应本地环境中 cuda 和 cudnn 版本的 TensorRT，需要登陆 NVIDIA 开发者账号。 -3. 下载[预测样例](https://paddle-inference-dist.bj.bcebos.com/tensorrt_test/paddle_inference_sample_v1.7.tar.gz)并解压，进入`sample/paddle-TRT`目录下。 - - `paddle-TRT` 文件夹目录结构如下： - - ``` - paddle-TRT - ├── CMakeLists.txt - ├── mobilenet_test.cc - ├── fluid_generate_calib_test.cc - ├── fluid_int8_test.cc - ├── mobilenetv1 - │ ├── model - │ └── params - ├── run.sh - └── run_impl.sh - ``` - - - `mobilenet_test.cc` 为使用 paddle-TRT 预测的 C++源文件 - - `fluid_generate_calib_test.cc` 为使用 TRT int8 离线量化校准的 C++源文件 - - `fluid_int8_test.cc` 为使用 TRT 执行 int8 预测的 C++源文件 - - `mobilenetv1` 为模型文件夹 - - `run.sh` 为预测运行脚本文件 - - 在这里假设样例所在的目录为 `SAMPLE_BASE_DIR/sample/paddle-TRT` - -4. 配置编译与运行脚本 - - 编译运行预测样例之前，需要根据运行环境配置编译与运行脚本`run.sh`。`run.sh`的选项与路径配置的部分如下： - - ```shell - # 设置是否开启 MKL、GPU、TensorRT，如果要使用 TensorRT，必须打开 GPU - WITH_MKL=ON - WITH_GPU=ON - USE_TENSORRT=ON - - # 按照运行环境设置预测库路径、CUDA 库路径、CUDNN 库路径、TensorRT 路径、模型路径 - LIB_DIR=YOUR_LIB_DIR - CUDA_LIB_DIR=YOUR_CUDA_LIB_DIR - CUDNN_LIB_DIR=YOUR_CUDNN_LIB_DIR - TENSORRT_ROOT_DIR=YOUR_TENSORRT_ROOT_DIR - MODEL_DIR=YOUR_MODEL_DIR - ``` - - 按照实际运行环境配置`run.sh`中的选项开关和所需 lib 路径。 - -5. 编译与运行样例 - - -## Paddle-TRT INT8 使用 - -1. Paddle-TRT INT8 简介 - 神经网络的参数在一定程度上是冗余的，在很多任务上，我们可以在保证模型精度的前提下，将 Float32 的模型转换成 Int8 的模型。目前，Paddle-TRT 支持离线将预训练好的 Float32 模型转换成 Int8 的模型，具体的流程如下： - - 1) **生成校准表**（Calibration table）：我们准备 500 张左右的真实输入数据，并将数据输入到模型中去，Paddle-TRT 会统计模型中每个 op 输入和输出值的范围信息，并将其记录到校准表中，这些信息有效减少了模型转换时的信息损失。 - - 2) 生成校准表后，再次运行模型，**Paddle-TRT 会自动加载校准表**，并进行 INT8 模式下的预测。 - -2. 编译测试 INT8 样例 - 将`run.sh`文件中的`mobilenet_test`改为`fluid_generate_calib_test`，运行 - - ``` shell - sh run.sh - ``` - - 即可执行生成校准表样例，在该样例中，我们随机生成了 500 个输入来模拟这一过程，在实际业务中，建议大家使用真实样例。运行结束后，在 `SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/_opt_cache` 模型目录下会多出一个名字为 trt_calib_*的文件，即校准表。 - - 生成校准表后，将带校准表的模型文件拷贝到特定地址 - - ``` shell - cp -rf SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/ SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib - ``` - - 将`run.sh`文件中的`fluid_generate_calib_test`改为`fluid_int8_test`，将模型路径改为`SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib`，运行 - - ``` shell - sh run.sh - ``` - - 即可执行 int8 预测样例。 - -## Paddle-TRT 子图运行原理 - - PaddlePaddle 采用子图的形式对 TensorRT 进行集成，当模型加载后，神经网络可以表示为由变量和运算节点组成的计算图。Paddle TensorRT 实现的功能是对整个图进行扫描，发现图中可以使用 TensorRT 优化的子图，并使用 TensorRT 节点替换它们。在模型的推断期间，如果遇到 TensorRT 节点，Paddle 会调用 TensorRT 库对该节点进行优化，其他的节点调用 Paddle 的原生实现。TensorRT 在推断期间能够进行 Op 的横向和纵向融合，过滤掉冗余的 Op，并对特定平台下的特定的 Op 选择合适的 kernel 等进行优化，能够加快模型的预测速度。 - -下图使用一个简单的模型展示了这个过程： - -**原始网络** -

        - -

        - -**转换的网络** -

        - -

        - - - 我们可以在原始模型网络中看到，绿色节点表示可以被 TensorRT 支持的节点，红色节点表示网络中的变量，黄色表示 Paddle 只能被 Paddle 原生实现执行的节点。那些在原始网络中的绿色节点被提取出来汇集成子图，并由一个 TensorRT 节点代替，成为转换后网络中的`block-25` 节点。在网络运行过程中，如果遇到该节点，Paddle 将调用 TensorRT 库来对其执行。 - -## Paddle-TRT 性能测试 - -### 测试环境 -- CPU:Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU:Tesla P4 -- TensorRT4.0, CUDA8.0, CUDNNV7 -- 测试模型 ResNet50，MobileNet，ResNet101, Inception V3. - -### 测试对象 -**PaddlePaddle, PyTorch, TensorFlow** - -- 在测试中，PaddlePaddle 使用子图优化的方式集成了 TensorRT, 模型[地址](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification/models)。 -- PyTorch 使用了原生的实现, 模型[地址 1](https://github.com/pytorch/vision/tree/master/torchvision/models)、[地址 2](https://github.com/marvis/pytorch-mobilenet)。 -- 对 TensorFlow 测试包括了对 TF 的原生的测试，和对 TF—TRT 的测试，**对 TF—TRT 的测试并没有达到预期的效果，后期会对其进行补充**， 模型[地址](https://github.com/tensorflow/models)。 - - -#### ResNet50 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|4.64117 |16.3|10.878| -|5|6.90622| 22.9 |20.62| -|10|7.9758 |40.6|34.36| - -#### MobileNet - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1| 1.7541 | 7.8 |2.72| -|5| 3.04666 | 7.8 |3.19| -|10|4.19478 | 14.47 |4.25| - -#### ResNet101 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|8.95767| 22.48 |18.78| -|5|12.9811 | 33.88 |34.84| -|10|14.1463| 61.97 |57.94| - - -#### Inception v3 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|15.1613 | 24.2 |19.1| -|5|18.5373 | 34.8 |27.2| -|10|19.2781| 54.8 |36.7| diff --git a/docs/guides/performance_improving/paddle_tensorrt_infer_en.md b/docs/guides/performance_improving/paddle_tensorrt_infer_en.md deleted file mode 100644 index 0acc384ab2a..00000000000 --- a/docs/guides/performance_improving/paddle_tensorrt_infer_en.md +++ /dev/null @@ -1,200 +0,0 @@ -# Use Paddle-TensorRT Library for inference - -NVIDIA TensorRT is a is a platform for high-performance deep learning inference. It delivers low latency and high throughput for deep learning inference application. -Subgraph is used in PaddlePaddle to preliminarily integrate TensorRT, which enables TensorRT module to enhance inference performance of paddle models. The module is still under development. Currently supported models are as following: - -|classification|detection|segmentation| -|---|---|---| -|mobilenetv1|yolov3|ICNET| -|resnet50|SSD|| -|vgg16|mask-rcnn|| -|resnext|faster-rcnn|| -|AlexNet|cascade-rcnn|| -|Se-ResNext|retinanet|| -|GoogLeNet|mobilenet-SSD|| -|DPN||| - -We will introduce the obtaining, usage and theory of Paddle-TensorRT library in this documentation. - -**Note:** - -1. When compiling from source, TensorRT library currently only supports GPU compilation, and you need to set the compilation option TensorRT_ROOT to the path where tensorrt is located. -2. Windows support requires TensorRT version 5.0 or higher. -3. Paddle-TRT currently only supports fixed input shape. -4. After downloading and installing tensorrt, you need to manually add virtual destructors for `class IPluginFactory` and `class IGpuAllocator` in the `NvInfer.h` file: - ``` c++ - virtual ~IPluginFactory() {}; - virtual ~IGpuAllocator() {}; - ``` - -## Paddle-TRT interface usage - -When using AnalysisPredictor, we enable Paddle-TRT by setting - -``` c++ -config->EnableTensorRtEngine(1 << 20 /* workspace_size*/, - batch_size /* max_batch_size*/, - 3 /* min_subgraph_size*/, - AnalysisConfig::Precision::kFloat32 /* precision*/, - false /* use_static*/, - false /* use_calib_mode*/); -``` -The details of this interface is as following: - -- **`workspace_size`**: type:int, default is 1 << 20. Sets the max workspace size of TRT. TensorRT will choose kernels under this constraint. -- **`max_batch_size`**: type:int, default is 1. Sets the max batch size. Batch sizes during runtime cannot exceed this value. -- **`min_subgraph_size`**: type:int, default is 3. Subgraph is used to integrate TensorRT in PaddlePaddle. To avoid low performance, Paddle-TRT is only enabled when th number of nodes in th subgraph is more than `min_subgraph_size`. -- **`precision`**: type:`enum class Precision {kFloat32 = 0, kHalf, kInt8,};`, default is `AnalysisConfig::Precision::kFloat32`. Sets the precision of TRT, supporting FP32(kFloat32), FP16(kHalf), Int8(kInt8). Using Paddle-TRT int8 calibration requires setting `precision` to `AnalysisConfig::Precision::kInt8`, and `use_calib_mode` to true. -- **`use_static`**: type:bool, default is false. If set to true, Paddle-TRT will serialize optimization information to disk, to deserialize next time without optimizing again. -- **`use_calib_mode`**: type:bool, default is false. Using Paddle-TRT int8 calibration requires setting this option to true. - -**Note：** Paddle-TRT currently only supports fixed input shape. - -## Paddle-TRT example compiling test - -1. Download or compile Paddle Inference with TensorRT support, refer to [Install and Compile C++ Inference Library](../../inference_deployment/inference/build_and_install_lib_en.html). -2. Download NVIDIA TensorRT(with consistent version of cuda and cudnn in local environment) from [NVIDIA TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) with an NVIDIA developer account. -3. Download [Paddle Inference sample](https://paddle-inference-dist.bj.bcebos.com/tensorrt_test/paddle_inference_sample_v1.7.tar.gz) and uncompress, and enter `sample/paddle-TRT` directory. - - `paddle-TRT` directory structure is as following: - - ``` - paddle-TRT - ├── CMakeLists.txt - ├── mobilenet_test.cc - ├── fluid_generate_calib_test.cc - ├── fluid_int8_test.cc - ├── mobilenetv1 - │ ├── model - │ └── params - ├── run.sh - └── run_impl.sh - ``` - - - `mobilenet_test.cc` is the c++ source code of inference using Paddle-TRT - - `fluid_generate_calib_test.cc` is the c++ source code of inference using Paddle-TRT int8 calibration to generate calibration table - - `fluid_int8_test.cc` is the c++ source code of inference using Paddle-TRT int8 - - `mobilenetv1` is the model dir - - `run.sh` is the script for running inference - - Here we assume that the current directory is `SAMPLE_BASE_DIR/sample/paddle-TRT`. - - ``` shell - # set whether to enable MKL, GPU or TensorRT. Enabling TensorRT requires WITH_GPU being ON - WITH_MKL=ON - WITH_GPU=OFF - USE_TENSORRT=OFF - - # set path to CUDA lib dir, CUDNN lib dir, TensorRT root dir and model dir - LIB_DIR=YOUR_LIB_DIR - CUDA_LIB_DIR=YOUR_CUDA_LIB_DIR - CUDNN_LIB_DIR=YOUR_CUDNN_LIB_DIR - TENSORRT_ROOT_DIR=YOUR_TENSORRT_ROOT_DIR - MODEL_DIR=YOUR_MODEL_DIR - ``` - - Please configure `run.sh` depending on your environment. - -4. Build and run the sample. - - ``` shell - sh run.sh - ``` - -## Paddle-TRT INT8 usage - -1. Paddle-TRT INT8 introduction - The parameters of the neural network are redundant to some extent. In many tasks, we can turn the Float32 model into Int8 model on the premise of precision. At present, Paddle-TRT supports to turn the trained Float32 model into Int8 model off line. The specific processes are as follows: - - 1）**Create the calibration table**. We prepare about 500 real input data, and input the data to the model. Paddle-TRT will count the range information of each op input and output value in the model, and record in the calibration table. The information can reduce the information loss during model transformation. - - 2）After creating the calibration table, run the model again, **Paddle-TRT will load the calibration table automatically**, and conduct the inference in the INT8 mode. - -2. compile and test the INT8 example - - change the `mobilenet_test` in `run.sh` to `fluid_generate_calib_test` and run - - ``` shell - sh run.sh - ``` - - We generate 500 input data to simulate the process, and it's suggested that you use real example for experiment. After the running period, there will be a new file named trt_calib_* under the `SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/_opt_cache` model directory, which is the calibration table. - - Then copy the model dir with calibration infomation to path - - ``` shell - cp -rf SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/ SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib - ``` - - change `fluid_generate_calib_test` in `run.sh` to `fluid_int8_test`, and change model dir path to `SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib` and run - - ``` shell - sh run.sh - ``` - -## Paddle-TRT subgraph operation principle - - Subgraph is used to integrate TensorRT in PaddlePaddle. After model is loaded, neural network can be represented as a computing graph composed of variables and computing nodes. Functions Paddle TensorRT implements are to scan the whole picture, discover subgraphs that can be optimized with TensorRT and replace them with TensorRT nodes. During the inference of model, Paddle will call TensorRT library to optimize TensorRT nodes and call native library of Paddle to optimize other nodes. During the inference, TensorRT can integrate Op horizonally and vertically to filter redundant Ops and is able to choose appropriate kernel for specific Op in specific platform to speed up the inference of model. - - -A simple model expresses the process : - -**Original Network** -

        - -

        - -**Transformed Network** -

        - -

        - - We can see in the Original Network that the green nodes represent nodes supported by TensorRT, the red nodes represent variables in network and yellow nodes represent nodes which can only be operated by native functions in Paddle. Green nodes in original network are extracted to compose subgraph which is replaced by a single TensorRT node to be transformed into `block-25` node in network. When such nodes are encountered during the runtime, TensorRT library will be called to execute them. - -## Paddle-TRT benchmark - -### Test Environment -- CPU:Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU:Tesla P4 -- TensorRT 4.0, CUDA 8.0, CUDNN V7 -- models: ResNet50，MobileNet，ResNet101, Inception V3. - -### Test set -**PaddlePaddle, PyTorch, TensorFlow** - -- PaddlePaddle integrates TensorRT with subgraph, model[link](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification/models)。 -- PyTorch uses original kernels, model[link1](https://github.com/pytorch/vision/tree/master/torchvision/models), [link2](https://github.com/marvis/pytorch-mobilenet)。 -- We tested TF original and TF-TRT**对 TF—TRT 的测试并没有达到预期的效果，后期会对其进行补充**, model[link](https://github.com/tensorflow/models)。 - - -#### ResNet50 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|4.64117 |16.3|10.878| -|5|6.90622| 22.9 |20.62| -|10|7.9758 |40.6|34.36| - -#### MobileNet - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1| 1.7541 | 7.8 |2.72| -|5| 3.04666 | 7.8 |3.19| -|10|4.19478 | 14.47 |4.25| - -#### ResNet101 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|8.95767| 22.48 |18.78| -|5|12.9811 | 33.88 |34.84| -|10|14.1463| 61.97 |57.94| - - -#### Inception v3 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|15.1613 | 24.2 |19.1| -|5|18.5373 | 34.8 |27.2| -|10|19.2781| 54.8 |36.7| diff --git a/docs/guides/performance_improving/quantization.md b/docs/guides/performance_improving/quantization.md deleted file mode 100644 index c07c0b00d6f..00000000000 --- a/docs/guides/performance_improving/quantization.md +++ /dev/null @@ -1,169 +0,0 @@ -# 飞桨模型量化 - -深度学习技术飞速发展，在很多任务和领域都超越传统方法。但是，深度学习模型通常需要较大的存储空间和计算量，给部署应用带来了不小挑战。 - -模型量化作为一种常见的模型压缩方法，使用整数替代浮点数进行存储和计算，可以减少模型存储空间、加快推理速度、降低计算内存，助力深度学习应用的落地。 - -飞桨提供了模型量化全流程解决方案，首先使用 PaddleSlim 产出量化模型，然后使用 Paddle Inference 和 Paddle Lite 部署量化模型。 - -
        - missing -
        图 1. 飞桨模型量化全流程解决方案
        -
        - -## 产出量化模型 - -飞桨模型量化全流程解决方案中，PaddleSlim 负责产出量化模型。 - -PaddleSlim 支持三种模型量化方法：动态离线量化方法、静态离线量化方法和量化训练方法。这三种量化方法的特点如下图。 - -
        - missing -
        图 2. 量化方法概述
        -
        - -动态离线量化方法不需要使用样本数据，也不会对模型进行训练。在模型产出阶段，动态离线量化方法将模型权重从浮点数量化成整数。在模型部署阶段，将权重从整数反量化成浮点数，使用浮点数运算进行预测推理。这种方式主要减少模型存储空间，对权重读取费时的模型有一定加速作用，对模型精度影响较小。 - -静态离线量化方法要求有少量无标签样本数据，需要执行模型的前向计算，不会对模型进行训练。在模型产出阶段，静态离线量化方法使用样本数据执行模型的前向计算，同时对量化 OP 的输入输出进行采样，然后计算量化信息。在模型部署阶段，使用计算好的量化信息对输入进行量化，基于整数运算进行预测推理。静态离线量化方法可以减少模型存储空间、加快模型推理速度、降低计算内存，同时量化模型只存在较小的精度损失。 - -量化训练方法要求有大量有标签样本数据，需要对模型进行较长时间的训练。在模型产出阶段，量化训练方法使用模拟量化的思想，在模型训练过程中会更新权重，实现拟合、减少量化误差的目的。在模型部署阶段，量化训练方法和静态离线量化方法一致，采用相同的预测推理方式，在存储空间、推理速度、计算内存三方面实现相同的收益。更重要的是，量化训练方法对模型精度只有极小的影响。 - - -根据使用条件和压缩目的，大家可以参考下图选用不同的模型量化方法产出量化模型。 - -
        - missing -
        图 3. 选择量化方法
        -
        - -产出量化模型的使用方法、Demo 和 API，请参考[PaddleSlim 文档](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)。 - -## 部署量化模型 - -飞桨模型量化全流程解决方案中，Paddle Inference 负责在服务器端（X86 CPU 和 Nvidia GPU）部署量化模型，Paddle Lite 负责在移动端（ARM CPU）上部署量化模型。 - -X86 CPU 和 Nvidia GPU 上支持部署 PaddleSlim 静态离线量化方法和量化训练方法产出的量化模型。 -ARM CPU 上支持部署 PaddleSlim 动态离线量化方法、静态离线量化方法和量化训练方法产出的量化模型。 - -因为动态离线量化方法产出的量化模型主要是为了压缩模型体积，主要应用于移动端部署，所以在 X86 CPU 和 Nvidia GPU 上暂不支持这类量化模型。 - -### NV GPU 上部署量化模型 - -使用 PaddleSlim 静态离线量化方法和量化训练方法产出量化模型后，可以使用 Paddle Inference 在 Nvidia GPU 上部署该量化模型。 - -Nvidia GPU 上部署常规模型的流程是：准备 TensorRT 环境、配置 Config、创建 Predictor、执行。Nvidia GPU 上部署量化模型和常规模型大体相似，需要改动的是：指定 TensorRT 配置时将 precision_mode 设置为 paddle_infer.PrecisionType.Int8，将 use_calib_mode 设为 False。 - -``` -config.enable_tensorrt_engine( - workspace_size=1<<30, - max_batch_size=1, - min_subgraph_size=5, - precision_mode=paddle_infer.PrecisionType.Int8, - use_static=False, - use_calib_mode=False) -``` - -Paddle Inference 的详细说明，请参考[文档](https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html)。 - -Nvidia GPU 上部署量化模型的详细说明，请参考[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)。 - -### X86 CPU 上部署量化模型 - -使用 PaddleSlim 静态离线量化方法和量化训练方法产出量化模型后，可以使用 Paddle Inference 在 X86 CPU 上部署该量化模型。 - -X86 CPU 上部署量化模型，首先检查 X86 CPU 支持指令集，然后转换量化模型，最后在 X86 CPU 上执行预测。 - -Paddle Inference 的详细说明，请参考[文档](https://paddle-inference.readthedocs.io/en/latest/#)。 - -X86 CPU 上部署量化模型的详细说明，请参考[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)。 - -1）检查 X86 CPU 支持指令集 - -大家可以在命令行中输入 lscpu 查看本机支持指令。 - -在支持 avx512、不支持 avx512_vnni 的 X86 CPU 上（如：SkyLake, Model name：Intel(R) Xeon(R) Gold X1XX），量化模型性能为原始模型性能的 1.5 倍左右。 - -在支持 avx512 和 avx512_vnni 的 X86 CPU 上（如：Casecade Lake, Model name: Intel(R) Xeon(R) Gold X2XX），量化模型的精度和性能最高，量化模型性能为原始模型性能的 3~3.7 倍。 - -2）转换量化模型 - -下载[转换脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py)到本地. -``` -wget https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py -``` - -使用脚本转换量化模型，比如： -``` -python save_quant_model.py \ - --quant_model_path=/PATH/TO/PADDLESLIM/GENERATE/MODEL \ - --int8_model_save_path=/PATH/TO/SAVE/CONVERTED/MODEL -``` - -3）执行预测 - -准备预测库，加载转换后的量化模型，创建 Predictor，进行预测。 - -注意，在 X86 CPU 预测端部署量化模型，必须开启 MKLDNN，不要开启 IrOptim（模型已经转换好）。 - -4）数据展示 - -**图像分类 INT8 模型在 Intel(R) Xeon(R) Gold 6271 上精度** - -| Model | FP32 Top1 Accuracy | INT8 Top1 Accuracy | Top1 Diff | FP32 Top5 Accuracy | INT8 Top5 Accuracy | Top5 Diff | -|:------------:|:------------------:|:------------------:|:---------:|:------------------:|:------------------:|:---------:| -| MobileNet-V1 | 70.78% | 70.74% | -0.04% | 89.69% | 89.43% | -0.26% | -| MobileNet-V2 | 71.90% | 72.21% | 0.31% | 90.56% | 90.62% | 0.06% | -| ResNet101 | 77.50% | 77.60% | 0.10% | 93.58% | 93.55% | -0.03% | -| ResNet50 | 76.63% | 76.50% | -0.13% | 93.10% | 92.98% | -0.12% | -| VGG16 | 72.08% | 71.74% | -0.34% | 90.63% | 89.71% | -0.92% | -| VGG19 | 72.57% | 72.12% | -0.45% | 90.84% | 90.15% | -0.69% | - -**图像分类 INT8 模型在 Intel(R) Xeon(R) Gold 6271 单核上性能** - -| Model | FP32 (images/s) | INT8 (images/s) | Ratio (INT8/FP32) | -|:------------:|:---------------:|:---------------:|:-----------------:| -| MobileNet-V1 | 74.05 | 216.36 | 2.92 | -| MobileNet-V2 | 88.60 | 205.84 | 2.32 | -| ResNet101 | 7.20 | 26.48 | 3.68 | -| ResNet50 | 13.23 | 50.02 | 3.78 | -| VGG16 | 3.47 | 10.67 | 3.07 | -| VGG19 | 2.83 | 9.09 | 3.21 | - -**Ernie INT8 模型在 Intel(R) Xeon(R) Gold 6271 的精度结果** - -| Model | FP32 Accuracy | INT8 Accuracy | Accuracy Diff | -| :---: | :-----------: | :-----------: | :-----------: | -| Ernie | 80.20% | 79.44% | -0.76% | - - -**Ernie INT8 模型在 Intel(R) Xeon(R) Gold 6271 上单样本耗时** - -| Threads | FP32 Latency (ms) | INT8 Latency (ms) | Ratio (FP32/INT8) | -| :--------: | :---------------: | :---------------: | :---------------: | -| 1 thread | 237.21 | 79.26 | 2.99X | -| 20 threads | 22.08 | 12.57 | 1.76X | - - -### ARM CPU 上部署量化模型 - -Paddle Lite 可以在 ARM CPU 上部署 PaddleSlim 动态离线量化方法、静态离线量化方法和量化训练方法产出的量化模型。 - -Paddle Lite 部署量化模型的方法和常规非量化模型完全相同，主要包括使用 opt 工具进行模型优化、执行预测。 - -Paddle Lite 的详细说明，请参考[文档](https://www.paddlepaddle.org.cn/lite)。 - -Paddle Lite 部署动态离线量化方法产出的量化模型，请参考[文档](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant/quant_post_dynamic.html)。 - -Paddle Lite 部署静态离线量化方法产出的量化模型，请参考[文档](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant/quant_post_static.html)。 - -Paddle Lite 部署量化训练方法产出的量化模型，请参考[文档](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant_aware.html)。 - -**模型量化前后性能对比** - -| 骁龙 855 | armv7(ms) | armv7(ms) | armv7(ms) | armv8(ms) | armv8(ms) | armv8(ms) | -|:------:|:---------:|:---------: | :-------: | :--------:| :--------:| :--------:| -| threads num| 1 | 2 | 4 | 1 | 2 | 4 | -| mobilenet_v1_fp32 | 32.19 | 18.75 | 11.02 | 29.50 | 17.50 | 9.58 | -| mobilenet_v1_int8 | 19.00 | 10.93 | 5.97 | 13.08 | 7.68 | 3.98 | -| mobilenet_v2_fp32 | 23.77 | 14.23 | 8.52 | 19.98 | 12.19 | 7.44 | -| mobilenet_v2_int8 | 17.68 | 10.49 | 5.93 | 12.76 | 7.70 | 4.36 | diff --git a/docs/hardware_support/dcu/index_cn.rst b/docs/hardware_support/dcu/index_cn.rst new file mode 100644 index 00000000000..ec70f613e29 --- /dev/null +++ b/docs/hardware_support/dcu/index_cn.rst @@ -0,0 +1,20 @@ +.. _cn_rocm_information: + +#################### +海光 DCU 芯片 +#################### + +海光 Z100 系列芯片基于通用 GPGPU 架构设计，是海光（HYGON）推出的一款面向各种应用场景的通用 GPU 产品，更加适合为⼈⼯智能计算提供强⼤的算⼒。更多海光 DCU 芯片详情及技术指标请 `点击这里 `_ 。 + +飞桨框架支持基于海光 DCU 芯片的训练和推理，请参考以下内容快速体验： + +- `海光 DCU 基于框架的使用指南 <./paddle_tutorial_cn.html>`_ : 海光 DCU 基于框架的使用指南 +- `海光 DCU 基于套件的使用指南 <./suite_tutorial_cn.html>`_ : 海光 DCU 基于套件的使用指南 +- `海光 DCU 支持模型 <./support_cn.html>`_ : 海光 DCU 支持模型 + +.. toctree:: + :hidden: + + paddle_tutorial_cn.md + suite_tutorial_cn.md + support_cn.md diff --git a/docs/hardware_support/dcu/paddle_tutorial_cn.md b/docs/hardware_support/dcu/paddle_tutorial_cn.md new file mode 100644 index 00000000000..cc6cebd9a9f --- /dev/null +++ b/docs/hardware_support/dcu/paddle_tutorial_cn.md @@ -0,0 +1,111 @@ +# 海光 DCU 基于框架的使用指南 + +## 一、环境准备 + +### 环境说明 + +* 本教程介绍如何基于海光 DCU 进行 ResNet50 的训练，总共需要 1 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接： registry.baidubce.com/device/paddle-dcu:dtk23.10.1-kylinv10-gcc73-py310 + +### 环境安装 + +安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +*由于 dcu 代码位于飞桨主框架中，因此我们不需要安装额外的 Custom Device 包* + +```shell +python -m pip install --pre paddlepaddle-rocm -i https://www.paddlepaddle.org.cn/packages/nightly/dcu/ +``` + +## 二、运行示例 + +飞桨框架集成了经典的视觉模型用于帮助用户快速上手，我们将基于 ResNet50 结构，在 Cifar10 数据集上进行一次快速训练，用于帮助您了解如何基于海光 DCU 进行训练 + +注意： + +* *本教程主要用于快速入门，并未对参数进行细致调优，训练效果未必是最好的，您可以自行调整超参数进行效果调优* + +* *本教程预计使用单卡 DCU 训练 30 分钟* + +1. 导入必要的包 + +```python +import paddle +from paddle.vision import transforms +from paddle.vision.models import resnet50 +``` + +2. 设置运行设备 + +```python +# 注意：为了保持和 GPU 兼容，可以尽量少的修改模型代码，我们在命名上做了兼容处理，dcu 设备的名字同样叫做 gpu +# 1. 设定运行设备为 gpu +paddle.set_device("gpu") +``` + +3. 加载训练数据集 + +```python +# 2. 定义数据集、数据预处理方法与 DataLoader +transform = transforms.Compose([ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) +]) +train_set = paddle.vision.datasets.Cifar10(mode='train', transform=transform) +train_loader = paddle.io.DataLoader(train_set, batch_size=128, num_workers=8) +``` + +4. 定义网络结构和损失函数 + +```python +# 3. 定义网络结构 +net = resnet50(num_classes=10) +# 4. 定义损失函数 +net_loss = paddle.nn.CrossEntropyLoss() +# 5. 定义优化器 +optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters()) +``` + +5. 启动训练 + +训练过程中会打印 loss 的变化情况，可以观察到 loss 在初步下降，这意味着模型参数逐渐适应了该数据集。 + +```python +net.train() +for epoch in range(10): + for batch_idx, data in enumerate(train_loader, start=0): + inputs, labels = data + optimizer.clear_grad() + # 6. 前向传播并计算损失 + outputs = net(inputs) + loss = net_loss(outputs, labels) + # 7. 反向传播 + loss.backward() + # 8. 更新参数 + optimizer.step() + print('Epoch %d, Iter %d, Loss: %.5f' % (epoch + 1, batch_idx + 1, loss)) +print('Finished Training') +``` + +6. 测试模型效果 + +```python +test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform) + +# 测试 5 张图片效果 +for i in range(5): + test_image, gt = test_dataset[0] + # CHW -> NCHW + test_image = test_image.unsqueeze(0) + + # 取预测分布中的最大值 + res = net(test_image).argmax().numpy() + print(f"图像{i} 标签：{gt}") + print(f"模型预测结果：{res}") +``` diff --git a/docs/hardware_support/dcu/suite_tutorial_cn.md b/docs/hardware_support/dcu/suite_tutorial_cn.md new file mode 100644 index 00000000000..c914d5956e5 --- /dev/null +++ b/docs/hardware_support/dcu/suite_tutorial_cn.md @@ -0,0 +1,217 @@ +# 海光 DCU 基于套件的使用指南 + +## 环境准备 + +### 环境说明 + +* 本教程介绍如何基于海光 DCU 进行 ResNet50 的训练，总共需要 4 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接： registry.baidubce.com/device/paddle-dcu:dtk23.10.1-kylinv10-gcc73-py310 + +### 环境安装 + +安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +*由于 dcu 代码位于飞桨主框架中，因此我们不需要安装额外的 Custom Device 包* + +```shell +python -m pip install --pre paddlepaddle-rocm -i https://www.paddlepaddle.org.cn/packages/nightly/dcu/ +``` + +## 基于 PaddleClas 训练 ResNet50 + +### 一、安装 PaddleClas 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleClas.git -b release/2.5.1 +cd PaddleClas +python -m pip install -r requirements.txt +python -m pip install . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5.1/docs/zh_CN/models/ImageNet1k/ResNet.md#32-%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87) 准备 ImageNet1k 数据集，准备完成后解压到 PaddleClas/dataset/目录下，目录结构如下： + +``` +PaddleClas/dataset/ILSVRC2012/ +|_ train/ +| |_ n01440764 +| | |_ n01440764_10026.JPEG +| | |_ ... +| |_ ... +| | +| |_ n15075141 +| |_ ... +| |_ n15075141_9993.JPEG +|_ val/ +| |_ ILSVRC2012_val_00000001.JPEG +| |_ ... +| |_ ILSVRC2012_val_00050000.JPEG +|_ train_list.txt +|_ val_list.txt +``` + +### 三、模型训练 + +进入 PaddleClas 目录下，执行如下命令启动 4 卡 DCU（0 ~ 3 号卡）训练，其中： + +* 参数 `-o Global.device` 指定的是即将运行的设备，为了保持和 GPU 兼容，我们在命名上做了兼容处理，dcu 设备的名字同样叫做 gpu，因此这里需要传入的是 gpu ，通过指定该参数，PaddleSeg 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 dcu，在进行模型训练时，飞桨将自动调用 dcu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + +* 参数 `-c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 `ResNet50` + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.output_dir="output/ResNet50" \ + -o Global.device="gpu" +``` + +上述命令会在 PaddleClas 目录下产生一个 output/ResNet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最后一个 epoch 的权重放在 output/ResNet50/ 目录下的 epoch_120.pdparams 文件中，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export_model.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定./deploy/models/ResNet50 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export_model.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.pretrained_model=./output/ResNet50/epoch_120 \ + -o Global.save_inference_dir=./deploy/models/ResNet50 \ + -o Global.device=gpu +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleClas/deploy 目录下，执行下列命令进行 DCU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +cd deploy +export FLAGS_conv_workspace_size_limit=2000 +python python/predict_cls.py \ + -c ./configs/inference_cls.yaml \ + -o Global.inference_model_dir=./models/ResNet50 \ + -o Global.use_gpu=True \ + -o Global.infer_imgs=./images/ImageNet +``` + +#### 转换 ONNX 模型 + +如果您有额外的部署需求需要基于 ONNX 实现，我们也提供了专用的工具用于导出 ONNX 模型，参考如下步骤，即可将第一步导出的静态图模型转换为 ONNX 模型： + +a. 安装环境 + +```shell +# 安装 paddle2onnx，该工具支持将 PaddleInference 模型转换为 ONNX 格式 +python -m pip install paddle2onnx +``` + +b. 模型转换 + +```shell +paddle2onnx --model_dir=./deploy/models/ResNet50/ \ + --model_filename=inference.pdmodel \ + --params_filename=inference.pdiparams \ + --save_file=./deploy/models/ResNet50_onnx/inference.onnx \ + --opset_version=10 \ + --enable_onnx_checker=True +``` + +该命令会在 deploy/models/ResNet50_onnx 目录下生成 inference.onnx 文件，生成的文件可以基于 ONNX Runtime 进行推理，具体使用方式参考 [ONNX Runtime 官网](https://onnxruntime.ai/) + +## 基于 PaddleSeg 训练 DeepLabv3+ + +### 一、安装 PaddleSeg 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleSeg -b release/2.9 +cd PaddleSeg +python -m pip install -r requirements.txt +python -m pip install -e . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.9/docs/data/pre_data_cn.md#cityscapes%E6%95%B0%E6%8D%AE%E9%9B%86) 准备 Cityscapes 数据集，准备完成后解压到 PaddleSeg/data/目录下，目录结构如下： + +```shell +PaddleSeg/data/cityscapes +├── leftImg8bit +│ ├── train +│ ├── val +├── gtFine +│ ├── train +│ ├── val +``` + +### 三、模型训练 + +进入 PaddleSeg 目录下，执行如下命令启动 4 卡 DCU（0 ~ 3 号卡）训练，其中： + +* 参数 `--device` 指定的是即将运行的设备，为了保持和 GPU 兼容，我们在命名上做了兼容处理，dcu 设备的名字同样叫做 gpu，因此这里需要传入的是 gpu ，通过指定该参数，PaddleSeg 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 dcu，在进行模型训练时，飞桨将自动调用 dcu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + +* 参数 `--config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 DeepLabv3+ + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + --config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml \ + --num_workers 8 \ + --save_dir output/deeplabv3p_resnet50 \ + --log_iters 10 \ + --device gpu \ + --do_eval \ + --save_interval 1000 \ + --seed 2048 +``` + +上述命令会在 PaddleSeg 目录下产生一个 output/deeplabv3p_resnet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最优指标对应的权重放在 output/deeplabv3p_resnet50/best_model 目录下，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定 output/deeplabv3p_resnet50_inference_model 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export.py \ + --config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml \ + --model_path output/deeplabv3p_resnet50/best_model/model.pdparams \ + --save_dir output/deeplabv3p_resnet50_inference_model +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleSeg/deploy 目录下，执行下列命令进行 DCU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png + +export FLAGS_conv_workspace_size_limit=2000 +python deploy/python/infer.py \ + --config output/deeplabv3p_resnet50_inference_model/deploy.yaml \ + --image_path ./cityscapes_demo.png \ + --save_dir ./output \ + --device "gpu" +``` diff --git a/docs/hardware_support/dcu/support_cn.md b/docs/hardware_support/dcu/support_cn.md new file mode 100644 index 00000000000..599539d0e19 --- /dev/null +++ b/docs/hardware_support/dcu/support_cn.md @@ -0,0 +1,318 @@ +# 海光 DCU 验证模型 + +基于 2024Q2 devlop 版本，飞桨框架在海光 DCU 上通过精度验证的模型情况如下： + +| 模型库 | 模型名称 | 训练 | 推理 | +| - | - | - | - | +| PaddleClas | ResNet50 | √ | √ | +| PaddleClas | ResNet101 | √ | √ | +| PaddleClas | SE_ResNet50_vd | √ | √ | +| PaddleClas | VGG16 | √ | √ | +| PaddleClas | InceptionV4 | √ | √ | +| PaddleClas | AlexNet | √ | √ | +| PaddleClas | DenseNet121 | √ | √ | +| PaddleClas | CLIP_vit_base_patch16_224 | 未测试 | √ | +| PaddleClas | CSPDarkNet53 | 未测试 | √ | +| PaddleClas | CSWinTransformer_base_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_base_384 | 未测试 | √ | +| PaddleClas | CSWinTransformer_large_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_large_384 | 未测试 | √ | +| PaddleClas | CSWinTransformer_small_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_tiny_224 | 未测试 | √ | +| PaddleClas | ConvNeXt_small | 未测试 | √ | +| PaddleClas | ConvNeXt_tiny | 未测试 | √ | +| PaddleClas | CvT_13_224 | 未测试 | √ | +| PaddleClas | CvT_13_384 | 未测试 | √ | +| PaddleClas | CvT_21_224 | 未测试 | √ | +| PaddleClas | CvT_21_384 | 未测试 | √ | +| PaddleClas | DLA102 | 未测试 | √ | +| PaddleClas | DLA102x | 未测试 | √ | +| PaddleClas | DLA102x2 | 未测试 | √ | +| PaddleClas | DLA169 | 未测试 | √ | +| PaddleClas | DLA34 | 未测试 | √ | +| PaddleClas | DLA46_c | 未测试 | √ | +| PaddleClas | DLA46x_c | 未测试 | √ | +| PaddleClas | DLA60 | 未测试 | √ | +| PaddleClas | DLA60x | 未测试 | √ | +| PaddleClas | DLA60x_c | 未测试 | √ | +| PaddleClas | DPN107 | 未测试 | √ | +| PaddleClas | DPN131 | 未测试 | √ | +| PaddleClas | DPN68 | 未测试 | √ | +| PaddleClas | DPN92 | 未测试 | √ | +| PaddleClas | DPN98 | 未测试 | √ | +| PaddleClas | DSNet_base | 未测试 | √ | +| PaddleClas | DSNet_small | 未测试 | √ | +| PaddleClas | DSNet_tiny | 未测试 | √ | +| PaddleClas | DarkNet53 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_384 | 未测试 | √ | +| PaddleClas | DeiT_small_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_tiny_patch16_224 | 未测试 | √ | +| PaddleClas | DenseNet161 | 未测试 | √ | +| PaddleClas | DenseNet169 | 未测试 | √ | +| PaddleClas | DenseNet201 | 未测试 | √ | +| PaddleClas | DenseNet264 | 未测试 | √ | +| PaddleClas | DistillationModel | 未测试 | √ | +| PaddleClas | ESNet_x0_25 | 未测试 | √ | +| PaddleClas | ESNet_x0_5 | 未测试 | √ | +| PaddleClas | ESNet_x0_75 | 未测试 | √ | +| PaddleClas | ESNet_x1_0 | 未测试 | √ | +| PaddleClas | EfficientNetB0 | 未测试 | √ | +| PaddleClas | EfficientNetB1 | 未测试 | √ | +| PaddleClas | EfficientNetB2 | 未测试 | √ | +| PaddleClas | EfficientNetB3 | 未测试 | √ | +| PaddleClas | EfficientNetB4 | 未测试 | √ | +| PaddleClas | EfficientNetB5 | 未测试 | √ | +| PaddleClas | EfficientNetB6 | 未测试 | √ | +| PaddleClas | EfficientNetB7 | 未测试 | √ | +| PaddleClas | GeneralRecognitionV2_PPLCNetV2_base_ultra | 未测试 | √ | +| PaddleClas | GhostNet_x0_5 | 未测试 | √ | +| PaddleClas | GhostNet_x1_0 | 未测试 | √ | +| PaddleClas | GhostNet_x1_3 | 未测试 | √ | +| PaddleClas | GoogLeNet | 未测试 | √ | +| PaddleClas | HRNet_W18_C | 未测试 | √ | +| PaddleClas | HRNet_W30_C | 未测试 | √ | +| PaddleClas | HRNet_W32_C | 未测试 | √ | +| PaddleClas | HRNet_W40_C | 未测试 | √ | +| PaddleClas | HRNet_W44_C | 未测试 | √ | +| PaddleClas | HRNet_W48_C | 未测试 | √ | +| PaddleClas | HRNet_W64_C | 未测试 | √ | +| PaddleClas | HarDNet39_ds | 未测试 | √ | +| PaddleClas | HarDNet68 | 未测试 | √ | +| PaddleClas | HarDNet68_ds | 未测试 | √ | +| PaddleClas | HarDNet85 | 未测试 | √ | +| PaddleClas | InceptionV3 | 未测试 | √ | +| PaddleClas | LeViT_128 | 未测试 | √ | +| PaddleClas | LeViT_128S | 未测试 | √ | +| PaddleClas | LeViT_192 | 未测试 | √ | +| PaddleClas | LeViT_256 | 未测试 | √ | +| PaddleClas | LeViT_384 | 未测试 | √ | +| PaddleClas | MetaBIN_ResNet50 | 未测试 | √ | +| PaddleClas | MicroNet_M0 | 未测试 | √ | +| PaddleClas | MicroNet_M1 | 未测试 | √ | +| PaddleClas | MicroNet_M2 | 未测试 | √ | +| PaddleClas | MicroNet_M3 | 未测试 | √ | +| PaddleClas | MixNet_L | 未测试 | √ | +| PaddleClas | MixNet_M | 未测试 | √ | +| PaddleClas | MixNet_S | 未测试 | √ | +| PaddleClas | MobileNeXt_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV1 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_25 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_25 | 未测试 | √ | +| PaddleClas | MobileViTV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_0 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileViTV3_S | 未测试 | √ | +| PaddleClas | MobileViTV3_S_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XS | 未测试 | √ | +| PaddleClas | MobileViTV3_XS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_75 | 未测试 | √ | +| PaddleClas | MobileViTV3_x1_0 | 未测试 | √ | +| PaddleClas | MobileViT_S | 未测试 | √ | +| PaddleClas | MobileViT_XS | 未测试 | √ | +| PaddleClas | MobileViT_XXS | 未测试 | √ | +| PaddleClas | NextViT_base_224 | 未测试 | √ | +| PaddleClas | NextViT_base_384 | 未测试 | √ | +| PaddleClas | NextViT_large_224 | 未测试 | √ | +| PaddleClas | NextViT_large_384 | 未测试 | √ | +| PaddleClas | NextViT_small_224 | 未测试 | √ | +| PaddleClas | NextViT_small_384 | 未测试 | √ | +| PaddleClas | PPHGNet_small | 未测试 | √ | +| PaddleClas | PPHGNet_tiny | 未测试 | √ | +| PaddleClas | PPLCNetV2_base | 未测试 | √ | +| PaddleClas | PPLCNet_x0_25 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_35 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_75 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_5 | 未测试 | √ | +| PaddleClas | PVT_V2_B0 | 未测试 | √ | +| PaddleClas | PVT_V2_B1 | 未测试 | √ | +| PaddleClas | PVT_V2_B2 | 未测试 | √ | +| PaddleClas | PVT_V2_B2_Linear | 未测试 | √ | +| PaddleClas | PVT_V2_B3 | 未测试 | √ | +| PaddleClas | PVT_V2_B4 | 未测试 | √ | +| PaddleClas | PVT_V2_B5 | 未测试 | √ | +| PaddleClas | ReXNet_1_0 | 未测试 | √ | +| PaddleClas | ReXNet_1_3 | 未测试 | √ | +| PaddleClas | ReXNet_1_5 | 未测试 | √ | +| PaddleClas | ReXNet_2_0 | 未测试 | √ | +| PaddleClas | ReXNet_3_0 | 未测试 | √ | +| PaddleClas | RepVGG_B3 | 未测试 | √ | +| PaddleClas | Res2Net101_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net200_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_14w_8s | 未测试 | √ | +| PaddleClas | Res2Net50_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_vd_26w_4s | 未测试 | √ | +| PaddleClas | ResNeSt101 | 未测试 | √ | +| PaddleClas | ResNeSt50 | 未测试 | √ | +| PaddleClas | ResNeSt50_fast_1s1x64d | 未测试 | √ | +| PaddleClas | ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNet101_vd | 未测试 | √ | +| PaddleClas | ResNet152 | 未测试 | √ | +| PaddleClas | ResNet152_vd | 未测试 | √ | +| PaddleClas | ResNet18 | 未测试 | √ | +| PaddleClas | ResNet18_vd | 未测试 | √ | +| PaddleClas | ResNet200_vd | 未测试 | √ | +| PaddleClas | ResNet34 | 未测试 | √ | +| PaddleClas | ResNet34_vd | 未测试 | √ | +| PaddleClas | ResNet50_vd | 未测试 | √ | +| PaddleClas | SENet154_vd | 未测试 | √ | +| PaddleClas | SE_ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNet18_vd | 未测试 | √ | +| PaddleClas | SE_ResNet34_vd | 未测试 | √ | +| PaddleClas | ShuffleNetV2_swish | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_25 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_33 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_0 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x2_0 | 未测试 | √ | +| PaddleClas | SqueezeNet1_0 | 未测试 | √ | +| PaddleClas | SqueezeNet1_1 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_small_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_tiny_patch4_window7_224 | 未测试 | √ | +| PaddleClas | TNT_small | 未测试 | √ | +| PaddleClas | TinyNet_A | 未测试 | √ | +| PaddleClas | TinyNet_B | 未测试 | √ | +| PaddleClas | TinyNet_C | 未测试 | √ | +| PaddleClas | TinyNet_D | 未测试 | √ | +| PaddleClas | TinyNet_E | 未测试 | √ | +| PaddleClas | UniFormer_base | 未测试 | √ | +| PaddleClas | UniFormer_base_ls | 未测试 | √ | +| PaddleClas | UniFormer_small | 未测试 | √ | +| PaddleClas | UniFormer_small_plus | 未测试 | √ | +| PaddleClas | UniFormer_small_plus_dim64 | 未测试 | √ | +| PaddleClas | VAN_B0 | 未测试 | √ | +| PaddleClas | VAN_B1 | 未测试 | √ | +| PaddleClas | VGG11 | 未测试 | √ | +| PaddleClas | VGG13 | 未测试 | √ | +| PaddleClas | VGG19 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_base_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_small_patch16_224 | 未测试 | √ | +| PaddleClas | Xception41 | 未测试 | √ | +| PaddleClas | Xception41_deeplab | 未测试 | √ | +| PaddleClas | Xception65 | 未测试 | √ | +| PaddleClas | Xception65_deeplab | 未测试 | √ | +| PaddleClas | Xception71 | 未测试 | √ | +| PaddleClas | alt_gvt_base | 未测试 | √ | +| PaddleClas | alt_gvt_large | 未测试 | √ | +| PaddleClas | alt_gvt_small | 未测试 | √ | +| PaddleClas | cae_base_patch16_224 | 未测试 | √ | +| PaddleClas | pcpvt_base | 未测试 | √ | +| PaddleClas | pcpvt_large | 未测试 | √ | +| PaddleClas | pcpvt_small | 未测试 | √ | +| PaddleDetection | SOLOv2 | 未测试 | √ | +| PaddleDetection | TTFNet | 未测试 | √ | +| PaddleDetection | dark_hrnet_w32_256x192 | 未测试 | √ | +| PaddleDetection | fairmot_hrnetv2_w18_dlafpn_30e_576x320 | 未测试 | √ | +| PaddleDetection | hrnet_w32_256x192 | 未测试 | √ | +| PaddleDetection | ppyolo_r50vd_dcn_1x_coco | 未测试 | √ | +| PaddleDetection | ppyolov2_r50vd_dcn_365e_coco | 未测试 | √ | +| PaddleDetection | solov2_r50_enhance_coco | 未测试 | √ | +| PaddleDetection | tinypose_128x96 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv2_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv2_rec | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv3_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv3_rec | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_mobile_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_mobile_v2_0_det_0 | 未测试 | √ | +| PaddleOCR | ch_ppocr_mobile_v2_0_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_server_v2_0_det_0 | 未测试 | √ | +| PaddleOCR | ch_ppocr_server_v2_0_rec | 未测试 | √ | +| PaddleOCR | det_mv3_db_v2_0_0 | 未测试 | √ | +| PaddleOCR | det_r50_db_plusplus_0 | 未测试 | √ | +| PaddleOCR | det_r50_db_v2_0_0 | 未测试 | √ | +| PaddleOCR | en_table_structure | 未测试 | √ | +| PaddleOCR | rec_abinet | 未测试 | √ | +| PaddleOCR | rec_mtb_nrtr | 未测试 | √ | +| PaddleOCR | rec_mv3_none_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_tps_bilstm_att_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_tps_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r31_sar | 未测试 | √ | +| PaddleOCR | rec_r32_gaspin_bilstm_att | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_tps_bilstm_att_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_tps_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r50_fpn_vd_none_srn | 未测试 | √ | +| PaddleOCR | rec_resnet_rfl | 未测试 | √ | +| PaddleOCR | rec_svtrnet | 未测试 | √ | +| PaddleOCR | rec_vitstr | 未测试 | √ | +| PaddleOCR | slanet | 未测试 | √ | +| PaddleSeg | DANet | √ | √ | +| PaddleSeg | DeepLabV3+ | √ | √ | +| PaddleSeg | FCN | √ | √ | +| PaddleSeg | GCNet | √ | √ | +| PaddleSeg | U-Net | √ | √ | +| PaddleSeg | bisenetv2 | 未测试 | √ | +| PaddleSeg | deeplabv3p_resnet50_cityscapes | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18 | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18_small | 未测试 | √ | +| PaddleSeg | fcn_uhrnetw18_small | 未测试 | √ | +| PaddleSeg | ocrnet_hrnetw18 | 未测试 | √ | +| PaddleSeg | ocrnet_hrnetw48 | 未测试 | √ | +| PaddleSeg | pfpnnet | 未测试 | √ | +| PaddleSeg | pp_liteseg_stdc1 | 未测试 | √ | +| PaddleSeg | pphumanseg_lite | 未测试 | √ | +| PaddleSeg | pphumanseg_server | 未测试 | √ | +| PaddleSeg | ppmatting | 未测试 | √ | +| PaddleSeg | seaformer_base | 未测试 | √ | +| PaddleSeg | sfnet | 未测试 | √ | +| PaddleVideo | STGCN | 未测试 | √ | +| PaddleVideo | AGCN | 未测试 | √ | +| PaddleVideo | AGCN2s | 未测试 | √ | +| PaddleVideo | BMN | 未测试 | √ | +| PaddleVideo | PP-TSM | 未测试 | √ | +| PaddleVideo | PP-TSN | 未测试 | √ | +| PaddleVideo | SlowFast | 未测试 | √ | +| PaddleVideo | TSN | 未测试 | √ | +| PaddleNLP | Transformer | √ | 未测试 | +| PaddleGAN | msvsr | 未测试 | √ | diff --git a/docs/hardware_support/hardware_info_cn.md b/docs/hardware_support/hardware_info_cn.md new file mode 100644 index 00000000000..87caed5eeb6 --- /dev/null +++ b/docs/hardware_support/hardware_info_cn.md @@ -0,0 +1,79 @@ +# 飞桨产品硬件支持表 + +飞桨各个产品支持的硬件信息如下: + +## PaddlePaddle + +| 分类 | 架构 | 公司 | 型号 | 安装 | 源码编译 | 完全支持训练 | 支持部分模型 | +| ---- | ---- | ---- | ---- |---- | ---- |---- | ---- | +| 服务端 CPU | x86 | Intel | 常见 CPU 型号如 Xeon、Core 全系列 | [安装](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) | [源码编译](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/compile/linux-compile.html) | ✔️ | | +| 服务端 GPU | | NVIDIA | Ada Lovelace、Hopper、 Ampere、Turing、 Volta 架构 | [安装](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) | [源码编译](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/compile/linux-compile.html) | ✔️ | | +| AI 加速芯片 | 达芬奇 | 华为 | 昇腾 910 系列 | [安装](./npu/install_cn.html#wheel) | [源码编译](./npu/install_cn.html) | | ✔️ | +| AI 加速芯片 | GPGPU | 海光 | 海光 Z100 系列 | [安装](./dcu/install_cn.html#wheel) | [源码编译](./dcu/install_cn.html) | | [支持模型](./dcu/support_cn.html) | +| AI 加速芯片 | XPU | 昆仑芯 | 昆仑芯 R200、R300 等 | [安装](./xpu/install_cn.html#wheel) | [源码编译](./xpu/install_cn.html#xpu) | | [支持模型](./xpu/support_cn.html) | +| AI 加速芯片 | IPU | Graphcore | GC200 | | | | ✔️ | +| AI 加速芯片 | MLU | 寒武纪 | MLU370 系列 | [安装](./mlu/install_cn.html#wheel) | [源码编译](./mlu/install_cn.html) | | [支持模型](./mlu/support_cn.html) | +| AI 加速芯片 | | 天数智芯 | 天垓 100 | [安装](https://gitee.com/deep-spark/deepsparkhub/blob/master/docker/Iluvatar/README.md) || | | ✔️ | +| AI 加速芯片 | | 壁仞 | BR100、BR104 | | [源码编译](https://github.com/PaddlePaddle/PaddleCustomDevice/blob/develop/backends/biren_gpu/README_cn.md) | | ✔️ | +| AI 加速芯片 | | 燧原 | 云燧 T20 、i20| | [源码编译](https://github.com/PaddlePaddle/PaddleCustomDevice/blob/develop/backends/gcu/README_cn.md) | | ✔️ | +| AI 加速芯片 | | 太初 | 元碁系列 | | [源码编译](https://github.com/PaddlePaddle/PaddleTecoBackend) | | ✔️ | + +## Paddle Inference + +| 分类 | 架构 | 公司 | 型号 | 预编译库 | 源码编译 | 完全支持推理 | 支持部分模型 | +| ---- | ---- | ---- | ---- |---- | ---- |---- | ---- | +| 服务端 CPU | x86 | Intel | 常见 CPU 型号如 Xeon、Core 全系列以及 NUC | [预编译库](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html) | [源码编译](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html) | ✔️ | | +| 服务端 GPU | | NVIDIA | Ada Lovelace、Hopper、 Ampere、Turing、 Volta 架构 | [预编译库](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html) | [源码编译](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html) | ✔️ | | +| 移动端 GPU | | NVIDIA | Jetson 系列 | [预编译库](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html) | [源码编译](https://paddleinference.paddlepaddle.org.cn/user_guides/source_compile.html) | ✔️ | | +| AI 加速芯片 | 达芬奇 | 华为 | 昇腾 910 系列 | | [源码编译](./npu/install_cn.html) | | ✔️ | +| AI 加速芯片 | MLU | 寒武纪 | MLU370 系列 | | [源码编译](./mlu/install_cn.html) | | ✔️ | +| AI 加速芯片 | MUSA | 摩尔线程 | MTT S 系列 GPU | | | | | +| AI 加速芯片 | GPGPU | 海光 | 海光 Z100 系列 | | [源码编译](https://www.paddlepaddle.org.cn/inference/master/guides/hardware_support/dcu_hygon_cn.html) | | [支持模型](./dcu/support_cn.html) | +| AI 加速芯片 | XPU | 昆仑芯 | 昆仑芯 R200、R300 等 | | [源码编译](https://www.paddlepaddle.org.cn/inference/master/guides/hardware_support/xpu_kunlun_cn.html) | | [支持模型](./xpu/support_cn.html) | +| 服务端 CPU | ARM | 飞腾 | FT-2000+/64、S2500 | |[源码编译](../../install/compile/arm-compile.html#anchor-1) | | | +| 服务端 CPU | ARM | 华为 | 鲲鹏 920 2426SK | |[源码编译](../../install/compile/arm-compile.html) | | | +| 服务端 CPU | MIPS | 龙芯 | 龙芯 3A4000、3A5000、3C5000L | |[源码编译](../../install/compile/mips-compile.html#anchor-0) | | | +| 服务端 CPU | x86 | 兆芯 | 全系列 CPU | |[源码编译](../../install/compile/zhaoxin-compile.html#anchor-1) | | | +| 服务端 CPU | | 海光 | 海光 3000、5000、7000 系列 CPU | | | | | +| 服务端 CPU | | 申威 | 申威 SW6A、SW6B | |[源码编译](https://paddleinference.paddlepaddle.org.cn/user_guides/compile_SW.html) | | | + +## Paddle Lite + +| 分类 | 架构 | 公司 | 型号 | 预编译库 | 源码编译 | 完全支持推理 | 支持部分模型 | +| ---- | ---- | ---- | ---- |---- | ---- |---- | ---- | +| 移动端 CPU | ARM | ARM | Cortex-A 系列 | [预编译库](https://paddlelite.paddlepaddle.org.cn/quick_start/release_lib.html) | [源码编译](https://paddlelite.paddlepaddle.org.cn/source_compile/compile_env.html) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/introduction/support_model_list.html) | +| 移动端 GPU | | ARM | Mali 系列 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/opencl.html) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/introduction/support_model_list.html) | +| 移动端 GPU | | 高通 | Adreno 系列 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/opencl.html) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/introduction/support_model_list.html) | +| AI 加速芯片 | | 华为 | Kirin 810/990/9000 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/huawei_kirin_npu.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/huawei_kirin_npu.html#id1) | +| AI 加速芯片 | | 华为 | 昇腾 310 | | 即将提供 | | | +| AI 加速芯片 | | 瑞芯微 | RK18xx 系列 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/rockchip_npu.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/rockchip_npu.html#id1) | +| AI 加速芯片 | | 联发科 | NeuroPilot APU | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/mediatek_apu.html#id1) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/mediatek_apu.html#id1) | +| AI 加速芯片 | | Imagination | PowerVR 2NX | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/huawei_kirin_npu.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/huawei_kirin_npu.html#id1) | +| AI 加速芯片 | | 百度 | 昆仑芯 R200、R300 等 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id4) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id1) | +| AI 加速芯片 | | 寒武纪 | 思元系列芯片 | | [源码编译](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/cambricon_mlu.html#cankaoshiliyanshi) | | [支持模型](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/cambricon_mlu.html#paddle) | +| AI 加速芯片 | | 比特大陆 | 算丰 BM16 系列芯片 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/bitmain.html#id5) | | [支持模型](https://paddlelite.paddlepaddle.org.cn/demo_guides/bitmain.html#id1) | +| AI 加速芯片 | | 紫光展锐 | T820 | | [源码编译](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/android_nnapi.html#android-nnapi-paddle-lite) | | [支持模型](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/android_nnapi.html#paddle) | +| AI 加速芯片 | | 象帝先 | 天钧一号 GPU | |[源码编译](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/opencl.html#cankaoshiliyanshi) | | | +| PFGA | | Intel | 英特尔 Stratix 系列、Arria 系列、Cyclone 系列 | | [源码编译](https://www.paddlepaddle.org.cn/lite/v2.11/demo_guides/intel_fpga.html#cankaoshiliyanshi) | | [支持模型](https://www.paddlepaddle.org.cn/lite/v2.11/demo_guides/intel_fpga.html#paddle) | +| FPGA | | 百度 | 百度 Edgeboard 开发板 | | [源码编译](https://paddlelite.paddlepaddle.org.cn/demo_guides/baidu_xpu.html#id4) | | [支持模型](https://ai.baidu.com/ai-doc/HWCE/Qkda68drw) | + +## Paddle2ONNX + +| 分类 | 公司 | 型号 | 支持部分模型 | 模型库链接 | +| ---- | ---- | ---- | ---- | ---- | +| AI 加速芯片 | 登临 | Goldwasser 系列加速卡 | ✔️ | [模型库](https://github.com/denglin-github/DLPaddleModelZoo) | +| AI 加速芯片 | 墨芯 | Moffett S4 | ✔️ | [模型库](https://github.com/MoffettSystem/moffett-modelzoo-paddle) | +| AI 加速芯片 | 海飞科 | Compass C10 | ✔️ | [模型库](https://github.com/hexaflakeai/model_zoo) | +| AI 加速芯片 | 清微智能 | TX5368 | ✔️ | [模型库](https://github.com/tsingmicro-toolchain/ts.knight-modelzoo) | +| AI 加速芯片 | 爱芯元智 | AX620A | ✔️ | [模型库](https://github.com/AXERA-TECH/ax-samples/tree/main) | +| AI 加速芯片 | 沐曦 | N100 | ✔️ | [模型库](https://github.com/denglin-github/DLPaddleModelZoo) | + +## TVM + +| 分类 | 公司 | 型号 | 支持部分模型 | 模型库链接 | +| ---- | ---- | ---- | ---- | ---- | +| 嵌入式芯片 | Arm | Cortex-M 系列 | ✔️ | [模型库](https://github.com/ArmDeveloperEcosystem/Paddle-examples-for-AVH) | +| AI 加速芯片 | 安谋科技 | 周易 NPU | ✔️ | [模型库](https://github.com/Arm-China/PaddlePaddle_example_for_Zhouyi_NPU) | +| AI 加速芯片 | Imagination | PowerVR 2NX | ✔️ | [模型库](https://github.com/imaginationtech/PaddlePaddle_Model_zoo) | + +**注意:** 如果你想了解更多芯片支持的信息，请联系我们，邮箱为 Paddle-better@baidu.com。 diff --git a/docs/hardware_support/index_cn.rst b/docs/hardware_support/index_cn.rst new file mode 100644 index 00000000000..1ab20ff6446 --- /dev/null +++ b/docs/hardware_support/index_cn.rst @@ -0,0 +1,22 @@ +.. _cn_guides_hardware_support: + +########## +硬件支持 +########## + +你可以通过以下内容，了解飞桨框架硬件支持相关的内容: + +- `飞桨硬件支持 <./hardware_info_cn.html>`_ : 说明飞桨产品支持的硬件。 +- `昆仑芯 XPU 芯片运行飞桨 <./xpu/index_cn.html>`_ : 介绍如何在昆仑芯 XPU 芯片环境上安装和使用飞桨。 +- `海光 DCU 芯片运行飞桨 <./dcu/index_cn.html>`_ : 介绍如何在海光 DCU 芯片环境上安装和使用飞桨。 +- `昇腾 NPU 芯片运行飞桨 <./npu/index_cn.html>`_ : 介绍如何在昇腾 NPU 芯片环境上安装和使用飞桨。 +- `寒武纪 MLU 芯片运行飞桨 <./mlu/index_cn.html>`_ : 介绍如何在寒武纪 MLU 芯片环境上安装和使用飞桨。 + +.. toctree:: + :hidden: + + hardware_info_cn.md + xpu/index_cn.rst + dcu/index_cn.rst + npu/index_cn.rst + mlu/index_cn.rst diff --git a/docs/hardware_support/mlu/index_cn.rst b/docs/hardware_support/mlu/index_cn.rst new file mode 100644 index 00000000000..4e0fddcf2bb --- /dev/null +++ b/docs/hardware_support/mlu/index_cn.rst @@ -0,0 +1,21 @@ +.. _cn_mlu_information: + +#################### +寒武纪 MLU 芯片 +#################### + +思元 370 基于寒武纪芯片架构 MLUarch03 设计，是寒武纪（Cambricon）推出的人工智能领域高能效的通用智能芯片，支持 MLU-Link™多芯互联技术，可高效执行多芯多卡训练和分布式推理任务。更多寒武纪 MLU 芯片详情及技术指标请 `点击这里 `_ 。 + +飞桨框架支持基于寒武纪 MLU 芯片的训练和推理，请参考以下内容快速体验： + +- `寒武纪 MLU 基于框架的使用指南 <./paddle_tutorial_cn.html>`_ : 寒武纪 MLU 基于框架的使用指南 +- `寒武纪 MLU 基于套件的使用指南 <./suite_tutorial_cn.html>`_ : 寒武纪 MLU 基于套件的使用指南 +- `寒武纪 MLU 支持模型 <./support_cn.html>`_ : 寒武纪 MLU 支持模型 + + +.. toctree:: + :hidden: + + paddle_tutorial_cn.md + suite_tutorial_cn.md + support_cn.md diff --git a/docs/hardware_support/mlu/paddle_tutorial_cn.md b/docs/hardware_support/mlu/paddle_tutorial_cn.md new file mode 100644 index 00000000000..8c2de9b0bc4 --- /dev/null +++ b/docs/hardware_support/mlu/paddle_tutorial_cn.md @@ -0,0 +1,116 @@ +# 寒武纪 MLU 基于框架的使用指南 + +## 一、环境准备 + +### 环境说明 + +* 本教程介绍如何基于寒武纪 MLU 进行 ResNet50 的训练，总共需要 1 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接： registry.baidubce.com/device/paddle-mlu:ubuntu20-x86_64-gcc84-py310 + +### 环境安装 + +1. 安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +```shell +python -m pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ +``` + +2. 安装 CustomDevice + +*该命令会自动安装飞桨 Custom Device 每日自动构建的 nightly-build 版本* + +```shell +python -m pip install --pre paddle-custom-mlu -i https://www.paddlepaddle.org.cn/packages/nightly/mlu/ +``` + +## 二、运行示例 + +飞桨框架集成了经典的视觉模型用于帮助用户快速上手，我们将基于 ResNet50 结构，在 Cifar10 数据集上进行一次快速训练，用于帮助您了解如何基于寒武纪 MLU 进行训练（和 GPU 训练代码相比，差异点仅为 `paddle.set_device("mlu")`） + +注意： + +* *本教程主要用于快速入门，并未对参数进行细致调优，训练效果未必是最好的，您可以自行调整超参数进行效果调优* + +* *本教程预计使用单卡 MLU 训练 80 分钟* + +1. 导入必要的包 + +```python +import paddle +from paddle.vision import transforms +from paddle.vision.models import resnet50 +``` + +2. 设置运行设备 + +```python +# 1. 设定运行设备为 mlu +paddle.set_device("mlu") +``` + +3. 加载训练数据集 + +```python +# 2. 定义数据集、数据预处理方法与 DataLoader +transform = transforms.Compose([ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) +]) +train_set = paddle.vision.datasets.Cifar10(mode='train', transform=transform) +train_loader = paddle.io.DataLoader(train_set, batch_size=128, num_workers=8) +``` + +4. 定义网络结构和损失函数 + +```python +# 3. 定义网络结构 +net = resnet50(num_classes=10) +# 4. 定义损失函数 +net_loss = paddle.nn.CrossEntropyLoss() +# 5. 定义优化器 +optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters()) +``` + +5. 启动训练 + +训练过程中会打印 loss 的变化情况，可以观察到 loss 在初步下降，这意味着模型参数逐渐适应了该数据集。 + +```python +net.train() +for epoch in range(10): + for batch_idx, data in enumerate(train_loader, start=0): + inputs, labels = data + optimizer.clear_grad() + # 6. 前向传播并计算损失 + outputs = net(inputs) + loss = net_loss(outputs, labels) + # 7. 反向传播 + loss.backward() + # 8. 更新参数 + optimizer.step() + print('Epoch %d, Iter %d, Loss: %.5f' % (epoch + 1, batch_idx + 1, loss)) +print('Finished Training') +``` + +6. 测试模型效果 + +```python +test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform) + +# 测试 5 张图片效果 +for i in range(5): + test_image, gt = test_dataset[0] + # CHW -> NCHW + test_image = test_image.unsqueeze(0) + + # 取预测分布中的最大值 + res = net(test_image).argmax().numpy() + print(f"图像{i} 标签：{gt}") + print(f"模型预测结果：{res}") +``` diff --git a/docs/hardware_support/mlu/suite_tutorial_cn.md b/docs/hardware_support/mlu/suite_tutorial_cn.md new file mode 100644 index 00000000000..d15b9d0f1e6 --- /dev/null +++ b/docs/hardware_support/mlu/suite_tutorial_cn.md @@ -0,0 +1,221 @@ +# 寒武纪 MLU 基于套件的使用指南 + +## 环境准备 + +### 环境说明 + +* 本教程介绍如何基于寒武纪 MLU 进行 ResNet50 的训练，总共需要 4 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接： registry.baidubce.com/device/paddle-mlu:ubuntu20-x86_64-gcc84-py310 + +### 环境安装 + +1. 安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +```shell +python -m pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ +``` + +2. 安装 CustomDevice + +*该命令会自动安装飞桨 Custom Device 每日自动构建的 nightly-build 版本* + +```shell +python -m pip install --pre paddle-custom-mlu -i https://www.paddlepaddle.org.cn/packages/nightly/mlu/ +``` + +## 基于 PaddleClas 训练 ResNet50 + +### 一、安装 PaddleClas 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleClas.git -b release/2.5.1 +cd PaddleClas +python -m pip install -r requirements.txt +python -m pip install . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5.1/docs/zh_CN/models/ImageNet1k/ResNet.md#32-%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87) 准备 ImageNet1k 数据集，准备完成后解压到 PaddleClas/dataset/目录下，目录结构如下： +``` +PaddleClas/dataset/ILSVRC2012/ +|_ train/ +| |_ n01440764 +| | |_ n01440764_10026.JPEG +| | |_ ... +| |_ ... +| | +| |_ n15075141 +| |_ ... +| |_ n15075141_9993.JPEG +|_ val/ +| |_ ILSVRC2012_val_00000001.JPEG +| |_ ... +| |_ ILSVRC2012_val_00050000.JPEG +|_ train_list.txt +|_ val_list.txt +``` + +### 三、模型训练 + +进入 PaddleClas 目录下，执行如下命令启动 4 卡 MLU（0 ~ 3 号卡）训练，其中： + +* 参数 `-o Global.device` 指定的是即将运行的设备，这里需要传入的是 `mlu` ，通过指定该参数，PaddleClas 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 mlu，在进行模型训练时，飞桨将自动调用 mlu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + +* 参数 `-c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 `ResNet50` + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.output_dir="output/ResNet50" \ + -o Global.device="mlu" +``` + +上述命令会在 PaddleClas 目录下产生一个 output/ResNet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最后一个 epoch 的权重放在 output/ResNet50/ 目录下的 epoch_120.pdparams 文件中，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export_model.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定./deploy/models/ResNet50 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export_model.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.pretrained_model=./output/ResNet50/epoch_120 \ + -o Global.save_inference_dir=./deploy/models/ResNet50 \ + -o Global.device=mlu +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleClas/deploy 目录下，执行下列命令进行 MLU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +cd deploy +python python/predict_cls.py \ + -c ./configs/inference_cls.yaml \ + -o Global.inference_model_dir=./models/ResNet50 \ + -o Global.use_gpu=False \ + -o Global.use_mlu=True \ + -o Global.infer_imgs=./images/ImageNet +``` + +#### 转换 ONNX 模型 + +如果您有额外的部署需求需要基于 ONNX 实现，我们也提供了专用的工具用于导出 ONNX 模型，参考如下步骤，即可将第一步导出的静态图模型转换为 ONNX 模型： + +a. 安装环境 + +```shell +# 安装 paddle2onnx，该工具支持将 PaddleInference 模型转换为 ONNX 格式 +python -m pip install paddle2onnx +``` + +b. 模型转换 + +```shell +paddle2onnx --model_dir=./deploy/models/ResNet50/ \ + --model_filename=inference.pdmodel \ + --params_filename=inference.pdiparams \ + --save_file=./deploy/models/ResNet50_onnx/inference.onnx \ + --opset_version=10 \ + --enable_onnx_checker=True +``` + +该命令会在 deploy/models/ResNet50_onnx 目录下生成 inference.onnx 文件，生成的文件可以基于 ONNX Runtime 进行推理，具体使用方式参考 [ONNX Runtime 官网](https://onnxruntime.ai/) + +## 基于 PaddleSeg 训练 DeepLabv3+ + +### 一、安装 PaddleSeg 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleSeg -b release/2.9 +cd PaddleSeg +python -m pip install -r requirements.txt +python -m pip install -e . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.9/docs/data/pre_data_cn.md#cityscapes%E6%95%B0%E6%8D%AE%E9%9B%86) 准备 Cityscapes 数据集，准备完成后解压到 PaddleSeg/data/目录下，目录结构如下： + +```shell +PaddleSeg/data/cityscapes +├── leftImg8bit +│ ├── train +│ ├── val +├── gtFine +│ ├── train +│ ├── val +``` + +### 三、模型训练 + +进入 PaddleSeg 目录下，执行如下命令启动 4 卡 MLU（0 ~ 3 号卡）训练，其中： + +* 参数 `--device` 指定的是即将运行的设备，这里需要传入的是 mlu ，通过指定该参数，PaddleSeg 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 mlu，在进行模型训练时，飞桨将自动调用 mlu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + +* 参数 `--config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 DeepLabv3+ + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + --config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml \ + --num_workers 8 \ + --save_dir output/deeplabv3p_resnet50 \ + --log_iters 10 \ + --device mlu \ + --do_eval \ + --save_interval 1000 \ + --seed 2048 +``` + +上述命令会在 PaddleSeg 目录下产生一个 output/deeplabv3p_resnet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最优指标对应的权重放在 output/deeplabv3p_resnet50/best_model 目录下，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定 output/deeplabv3p_resnet50_inference_model 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export.py \ + --config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml \ + --model_path output/deeplabv3p_resnet50/best_model/model.pdparams \ + --save_dir output/deeplabv3p_resnet50_inference_model +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleSeg/deploy 目录下，执行下列命令进行 MLU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png + +python deploy/python/infer.py \ + --config output/deeplabv3p_resnet50_inference_model/deploy.yaml \ + --image_path ./cityscapes_demo.png \ + --save_dir ./output \ + --device "mlu" +``` diff --git a/docs/hardware_support/mlu/support_cn.md b/docs/hardware_support/mlu/support_cn.md new file mode 100644 index 00000000000..90fdefde4a7 --- /dev/null +++ b/docs/hardware_support/mlu/support_cn.md @@ -0,0 +1,316 @@ +# 寒武纪 MLU 验证模型 + +基于 2024Q2 devlop 版本，飞桨框架在寒武纪 MLU 上通过精度验证的模型情况如下： + +| 模型库 | 模型名称 | 训练 | 推理 | +| - | - | - | - | +| PaddleClas | ResNet50 | √ | √ | +| PaddleClas | VGG16 | √ | √ | +| PaddleClas | VGG19 | √ | √ | +| PaddleClas | InceptionV4 | √ | √ | +| PaddleClas | MobileNetV3 | √ | √ | +| PaddleClas | AlexNet | 未测试 | √ | +| PaddleClas | CLIP_vit_base_patch16_224 | 未测试 | √ | +| PaddleClas | CSPDarkNet53 | 未测试 | √ | +| PaddleClas | CSWinTransformer_base_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_base_384 | 未测试 | √ | +| PaddleClas | CSWinTransformer_large_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_large_384 | 未测试 | √ | +| PaddleClas | CSWinTransformer_small_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_tiny_224 | 未测试 | √ | +| PaddleClas | ConvNeXt_small | 未测试 | √ | +| PaddleClas | ConvNeXt_tiny | 未测试 | √ | +| PaddleClas | CvT_13_224 | 未测试 | √ | +| PaddleClas | CvT_13_384 | 未测试 | √ | +| PaddleClas | CvT_21_224 | 未测试 | √ | +| PaddleClas | CvT_21_384 | 未测试 | √ | +| PaddleClas | CycleGAN | 未测试 | √ | +| PaddleClas | DLA102 | 未测试 | √ | +| PaddleClas | DLA102x | 未测试 | √ | +| PaddleClas | DLA102x2 | 未测试 | √ | +| PaddleClas | DLA169 | 未测试 | √ | +| PaddleClas | DLA34 | 未测试 | √ | +| PaddleClas | DLA46_c | 未测试 | √ | +| PaddleClas | DLA46x_c | 未测试 | √ | +| PaddleClas | DLA60 | 未测试 | √ | +| PaddleClas | DLA60x | 未测试 | √ | +| PaddleClas | DLA60x_c | 未测试 | √ | +| PaddleClas | DPN107 | 未测试 | √ | +| PaddleClas | DPN131 | 未测试 | √ | +| PaddleClas | DPN68 | 未测试 | √ | +| PaddleClas | DPN92 | 未测试 | √ | +| PaddleClas | DPN98 | 未测试 | √ | +| PaddleClas | DSNet_base | 未测试 | √ | +| PaddleClas | DSNet_small | 未测试 | √ | +| PaddleClas | DSNet_tiny | 未测试 | √ | +| PaddleClas | DarkNet53 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_384 | 未测试 | √ | +| PaddleClas | DeiT_small_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_tiny_patch16_224 | 未测试 | √ | +| PaddleClas | DenseNet121 | 未测试 | √ | +| PaddleClas | DenseNet161 | 未测试 | √ | +| PaddleClas | DenseNet169 | 未测试 | √ | +| PaddleClas | DenseNet201 | 未测试 | √ | +| PaddleClas | DenseNet264 | 未测试 | √ | +| PaddleClas | DistillationModel | 未测试 | √ | +| PaddleClas | ESNet_x0_25 | 未测试 | √ | +| PaddleClas | ESNet_x0_5 | 未测试 | √ | +| PaddleClas | ESNet_x0_75 | 未测试 | √ | +| PaddleClas | ESNet_x1_0 | 未测试 | √ | +| PaddleClas | EfficientNetB0 | 未测试 | √ | +| PaddleClas | EfficientNetB1 | 未测试 | √ | +| PaddleClas | EfficientNetB2 | 未测试 | √ | +| PaddleClas | EfficientNetB3 | 未测试 | √ | +| PaddleClas | EfficientNetB4 | 未测试 | √ | +| PaddleClas | EfficientNetB5 | 未测试 | √ | +| PaddleClas | EfficientNetB6 | 未测试 | √ | +| PaddleClas | EfficientNetB7 | 未测试 | √ | +| PaddleClas | GeneralRecognitionV2_PPLCNetV2_base_ultra | 未测试 | √ | +| PaddleClas | GhostNet_x0_5 | 未测试 | √ | +| PaddleClas | GhostNet_x1_0 | 未测试 | √ | +| PaddleClas | GhostNet_x1_3 | 未测试 | √ | +| PaddleClas | GoogLeNet | 未测试 | √ | +| PaddleClas | HRNet_W18_C | 未测试 | √ | +| PaddleClas | HRNet_W30_C | 未测试 | √ | +| PaddleClas | HRNet_W32_C | 未测试 | √ | +| PaddleClas | HRNet_W40_C | 未测试 | √ | +| PaddleClas | HRNet_W44_C | 未测试 | √ | +| PaddleClas | HRNet_W48_C | 未测试 | √ | +| PaddleClas | HRNet_W64_C | 未测试 | √ | +| PaddleClas | HarDNet39_ds | 未测试 | √ | +| PaddleClas | HarDNet68 | 未测试 | √ | +| PaddleClas | HarDNet68_ds | 未测试 | √ | +| PaddleClas | HarDNet85 | 未测试 | √ | +| PaddleClas | InceptionV3 | 未测试 | √ | +| PaddleClas | LeViT_128 | 未测试 | √ | +| PaddleClas | LeViT_128S | 未测试 | √ | +| PaddleClas | LeViT_192 | 未测试 | √ | +| PaddleClas | LeViT_256 | 未测试 | √ | +| PaddleClas | LeViT_384 | 未测试 | √ | +| PaddleClas | MetaBIN_ResNet50 | 未测试 | √ | +| PaddleClas | MicroNet_M0 | 未测试 | √ | +| PaddleClas | MicroNet_M1 | 未测试 | √ | +| PaddleClas | MicroNet_M2 | 未测试 | √ | +| PaddleClas | MicroNet_M3 | 未测试 | √ | +| PaddleClas | MixNet_L | 未测试 | √ | +| PaddleClas | MixNet_M | 未测试 | √ | +| PaddleClas | MixNet_S | 未测试 | √ | +| PaddleClas | MobileNeXt_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV1 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_25 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_25 | 未测试 | √ | +| PaddleClas | MobileViTV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_0 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileViTV3_S | 未测试 | √ | +| PaddleClas | MobileViTV3_S_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XS | 未测试 | √ | +| PaddleClas | MobileViTV3_XS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_75 | 未测试 | √ | +| PaddleClas | MobileViTV3_x1_0 | 未测试 | √ | +| PaddleClas | MobileViT_S | 未测试 | √ | +| PaddleClas | MobileViT_XS | 未测试 | √ | +| PaddleClas | MobileViT_XXS | 未测试 | √ | +| PaddleClas | NextViT_base_224 | 未测试 | √ | +| PaddleClas | NextViT_base_384 | 未测试 | √ | +| PaddleClas | NextViT_large_224 | 未测试 | √ | +| PaddleClas | NextViT_large_384 | 未测试 | √ | +| PaddleClas | NextViT_small_224 | 未测试 | √ | +| PaddleClas | NextViT_small_384 | 未测试 | √ | +| PaddleClas | PPHGNet_small | 未测试 | √ | +| PaddleClas | PPHGNet_tiny | 未测试 | √ | +| PaddleClas | PPLCNetV2_base | 未测试 | √ | +| PaddleClas | PPLCNet_x0_25 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_35 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_75 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_5 | 未测试 | √ | +| PaddleClas | PVT_V2_B0 | 未测试 | √ | +| PaddleClas | PVT_V2_B1 | 未测试 | √ | +| PaddleClas | PVT_V2_B2 | 未测试 | √ | +| PaddleClas | PVT_V2_B2_Linear | 未测试 | √ | +| PaddleClas | PVT_V2_B3 | 未测试 | √ | +| PaddleClas | PVT_V2_B4 | 未测试 | √ | +| PaddleClas | PVT_V2_B5 | 未测试 | √ | +| PaddleClas | ReXNet_1_0 | 未测试 | √ | +| PaddleClas | ReXNet_1_3 | 未测试 | √ | +| PaddleClas | ReXNet_1_5 | 未测试 | √ | +| PaddleClas | ReXNet_2_0 | 未测试 | √ | +| PaddleClas | ReXNet_3_0 | 未测试 | √ | +| PaddleClas | RepVGG_B3 | 未测试 | √ | +| PaddleClas | Res2Net101_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net200_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_14w_8s | 未测试 | √ | +| PaddleClas | Res2Net50_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_vd_26w_4s | 未测试 | √ | +| PaddleClas | ResNeSt101 | 未测试 | √ | +| PaddleClas | ResNeSt50 | 未测试 | √ | +| PaddleClas | ResNeSt50_fast_1s1x64d | 未测试 | √ | +| PaddleClas | ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNet101 | 未测试 | √ | +| PaddleClas | ResNet101_vd | 未测试 | √ | +| PaddleClas | ResNet152 | 未测试 | √ | +| PaddleClas | ResNet152_vd | 未测试 | √ | +| PaddleClas | ResNet18 | 未测试 | √ | +| PaddleClas | ResNet18_vd | 未测试 | √ | +| PaddleClas | ResNet200_vd | 未测试 | √ | +| PaddleClas | ResNet34 | 未测试 | √ | +| PaddleClas | ResNet34_vd | 未测试 | √ | +| PaddleClas | ResNet50_vd | 未测试 | √ | +| PaddleClas | SENet154_vd | 未测试 | √ | +| PaddleClas | SE_ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNet18_vd | 未测试 | √ | +| PaddleClas | SE_ResNet34_vd | 未测试 | √ | +| PaddleClas | SE_ResNet50_vd | 未测试 | √ | +| PaddleClas | ShuffleNetV2_swish | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_25 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_33 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_0 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x2_0 | 未测试 | √ | +| PaddleClas | SlowFast | 未测试 | √ | +| PaddleClas | SqueezeNet1_0 | 未测试 | √ | +| PaddleClas | SqueezeNet1_1 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_small_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_tiny_patch4_window7_224 | 未测试 | √ | +| PaddleClas | TinyNet_A | 未测试 | √ | +| PaddleClas | TinyNet_B | 未测试 | √ | +| PaddleClas | TinyNet_C | 未测试 | √ | +| PaddleClas | TinyNet_D | 未测试 | √ | +| PaddleClas | TinyNet_E | 未测试 | √ | +| PaddleClas | UniFormer_base | 未测试 | √ | +| PaddleClas | UniFormer_base_ls | 未测试 | √ | +| PaddleClas | UniFormer_small | 未测试 | √ | +| PaddleClas | UniFormer_small_plus | 未测试 | √ | +| PaddleClas | UniFormer_small_plus_dim64 | 未测试 | √ | +| PaddleClas | VAN_B0 | 未测试 | √ | +| PaddleClas | VAN_B1 | 未测试 | √ | +| PaddleClas | VGG11 | 未测试 | √ | +| PaddleClas | VGG13 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_base_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_small_patch16_224 | 未测试 | √ | +| PaddleClas | Xception41 | 未测试 | √ | +| PaddleClas | Xception41_deeplab | 未测试 | √ | +| PaddleClas | Xception65 | 未测试 | √ | +| PaddleClas | Xception65_deeplab | 未测试 | √ | +| PaddleClas | Xception71 | 未测试 | √ | +| PaddleClas | alt_gvt_base | 未测试 | √ | +| PaddleClas | alt_gvt_large | 未测试 | √ | +| PaddleClas | alt_gvt_small | 未测试 | √ | +| PaddleClas | pcpvt_base | 未测试 | √ | +| PaddleClas | pcpvt_large | 未测试 | √ | +| PaddleClas | pcpvt_small | 未测试 | √ | +| PaddleDetection | YOLOv3 | √ | √ | +| PaddleDetection | SSD | √ | √ | +| PaddleDetection | dark_hrnet_w32_256x192 | 未测试 | √ | +| PaddleDetection | fairmot_hrnetv2_w18_dlafpn_30e_576x320 | 未测试 | √ | +| PaddleDetection | fcos_r50_fpn_1x_coco | 未测试 | √ | +| PaddleDetection | higherhrnet_hrnet_w32_512 | 未测试 | √ | +| PaddleDetection | hrnet_w32_256x192 | 未测试 | √ | +| PaddleDetection | picodet_lcnet_1_5x_416_coco | 未测试 | √ | +| PaddleDetection | picodet_s_320_coco | 未测试 | √ | +| PaddleDetection | ppyoloe_crn_s_300e_coco | 未测试 | √ | +| PaddleDetection | ppyoloe_plus_crn_s_80e_coco | 未测试 | √ | +| PaddleDetection | ppyoloe_plus_sod_crn_l_80e_coco | 未测试 | √ | +| PaddleDetection | ppyoloe_vit_base_csppan_cae_36e_coco | 未测试 | √ | +| PaddleDetection | ppyolov2_r50vd_dcn_365e_coco | 未测试 | √ | +| PaddleDetection | solov2_r50_enhance_coco | 未测试 | √ | +| PaddleDetection | tinypose_128x96 | 未测试 | √ | +| PaddleDetection | ttfnet_darknet53_1x_coco | 未测试 | √ | +| PaddleDetection | yolov5_s_300e_coco | 未测试 | √ | +| PaddleDetection | yolov7_tiny_300e_coco | 未测试 | √ | +| PaddleNLP | BERT | √ | √ | +| PaddleNLP | Transformer | √ | | +| PaddleNLP | Bi-LSTM | √ | √ | +| PaddleNLP | bisenetv2 | 未测试 | √ | +| PaddleOCR | OCR-Clas | √ | √ | +| PaddleOCR | OCR-E2E | √ | √ | +| PaddleOCR | ch_PP-OCRv2_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv3_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv3_rec | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_mobile_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_mobile_v2_0_det_0 | 未测试 | √ | +| PaddleOCR | ch_ppocr_server_v2_0_det_0 | 未测试 | √ | +| PaddleOCR | det_mv3_db_v2_0_0 | 未测试 | √ | +| PaddleOCR | det_r50_db_plusplus_0 | 未测试 | √ | +| PaddleOCR | det_r50_db_v2_0_0 | 未测试 | √ | +| PaddleOCR | det_r50_dcn_fce_ctw_v2_0_0 | 未测试 | √ | +| PaddleOCR | en_table_structure | 未测试 | √ | +| PaddleOCR | rec_abinet | 未测试 | √ | +| PaddleOCR | rec_mv3_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_resnet_rfl | 未测试 | √ | +| PaddleOCR | rec_svtrnet | 未测试 | √ | +| PaddleOCR | rec_vitstr | 未测试 | √ | +| PaddleOCR | slanet | 未测试 | √ | +| PaddleSeg | DeepLabV3+ | √ | √ | +| PaddleSeg | U-Net | √ | √ | +| PaddleSeg | deeplabv3p_resnet50_cityscapes | 未测试 | √ | +| PaddleSeg | fastscnn | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18 | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18_small | 未测试 | √ | +| PaddleSeg | fcn_uhrnetw18_small | 未测试 | √ | +| PaddleSeg | ocrnet_hrnetw18 | 未测试 | √ | +| PaddleSeg | ocrnet_hrnetw48 | 未测试 | √ | +| PaddleSeg | pfpnnet | 未测试 | √ | +| PaddleSeg | pp_liteseg_stdc2 | 未测试 | √ | +| PaddleSeg | pphumanseg_lite | 未测试 | √ | +| PaddleSeg | pphumanseg_server | 未测试 | √ | +| PaddleSeg | ppmatting | 未测试 | √ | +| PaddleSeg | seaformer_base | 未测试 | √ | +| PaddleVideo | AGCN | 未测试 | √ | +| PaddleVideo | AGCN2s | 未测试 | √ | +| PaddleVideo | BMN | 未测试 | √ | +| PaddleVideo | PP-TSM | 未测试 | √ | +| PaddleVideo | PP-TSN | 未测试 | √ | +| PaddleVideo | STGCN | 未测试 | √ | +| PaddleVideo | TSN | 未测试 | √ | diff --git a/docs/hardware_support/npu/index_cn.rst b/docs/hardware_support/npu/index_cn.rst new file mode 100644 index 00000000000..01ed8340009 --- /dev/null +++ b/docs/hardware_support/npu/index_cn.rst @@ -0,0 +1,20 @@ +.. _cn_npu_information: + +#################### +昇腾 NPU 芯片 +#################### + +昇腾 910 系列是华为昇腾（Ascend）推出的一款高能效、灵活可编程的人工智能处理器。采用自研华为达芬奇架构，集成丰富的计算单元, 提高 AI 计算完备度和效率，进而扩展该芯片的适用性。更多华为昇腾 NPU 芯片详情及技术指标请 `点击这里 `_ 。 + +飞桨框架支持基于昇腾 NPU 芯片的训练和推理，请参考以下内容快速体验： + +- `昇腾 NPU 基于框架的使用指南 <./paddle_tutorial_cn.html>`_ : 昇腾 NPU 基于框架的使用指南 +- `昇腾 NPU 基于套件的使用指南 <./suite_tutorial_cn.html>`_ : 昇腾 NPU 基于套件的使用指南 +- `昇腾 NPU 支持模型 <./support_cn.html>`_ : 昇腾 NPU 支持模型 + +.. toctree:: + :hidden: + + paddle_tutorial_cn.md + suite_tutorial_cn.md + support_cn.md diff --git a/docs/hardware_support/npu/paddle_tutorial_cn.md b/docs/hardware_support/npu/paddle_tutorial_cn.md new file mode 100644 index 00000000000..be3aa559f48 --- /dev/null +++ b/docs/hardware_support/npu/paddle_tutorial_cn.md @@ -0,0 +1,122 @@ +# 昇腾 NPU 基于框架的使用指南 + +## 一、环境准备 + +### 环境说明 + +* 本教程介绍如何基于昇腾 910B NPU 进行 ResNet50 的训练，总共需要 1 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接：registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39 + + * 镜像中已经默认安装了昇腾算子库 CANN-8.0.RC1 + +* 昇腾驱动版本为 23.0.3 + +### 环境安装 + +1. 安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +```shell +python -m pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ +``` + +2. 安装 CustomDevice + +*该命令会自动安装飞桨 Custom Device 每日自动构建的 nightly-build 版本* + +```shell +python -m pip install paddle-custom-npu -i https://www.paddlepaddle.org.cn/packages/nightly/npu/ +``` + +## 二、运行示例 + +飞桨框架集成了经典的视觉模型用于帮助用户快速上手，我们将基于 ResNet50 结构，在 Cifar10 数据集上进行一次快速训练，用于帮助您了解如何基于昇腾 NPU 进行训练（和 GPU 训练代码相比，差异点仅为 `paddle.set_device("npu")`） + +注意： + +* *本教程主要用于快速入门，并未对参数进行细致调优，训练效果未必是最好的，您可以自行调整超参数进行效果调优* + +* *昇腾 CANN 算子库在运行时，有些算子是即时编译生成的，因此第一次运行本教程时，会有 3 ~ 5 分钟的编译等待时间，这个速度取决于模型所用到的算子数量，Tensor shape 的变化情况，以及机器上的资源使用（如 CPU）* + +* *本教程预计使用单卡 910B 训练 20 分钟* + +1. 导入必要的包 + +```python +import paddle +from paddle.vision import transforms +from paddle.vision.models import resnet50 +``` + +2. 设置运行设备 + +```python +# 1. 设定运行设备为 npu +paddle.set_device("npu") +``` + +3. 加载训练数据集 + +```python +# 2. 定义数据集、数据预处理方法与 DataLoader +transform = transforms.Compose([ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) +]) +train_set = paddle.vision.datasets.Cifar10(mode='train', transform=transform) +train_loader = paddle.io.DataLoader(train_set, batch_size=128, num_workers=8) +``` + +4. 定义网络结构和损失函数 + +```python +# 3. 定义网络结构 +net = resnet50(num_classes=10) +# 4. 定义损失函数 +net_loss = paddle.nn.CrossEntropyLoss() +# 5. 定义优化器 +optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters()) +``` + +5. 启动训练 + +训练过程中会打印 loss 的变化情况，可以观察到 loss 在初步下降，这意味着模型参数逐渐适应了该数据集。 + +```python +net.train() +for epoch in range(10): + for batch_idx, data in enumerate(train_loader, start=0): + inputs, labels = data + optimizer.clear_grad() + # 6. 前向传播并计算损失 + outputs = net(inputs) + loss = net_loss(outputs, labels) + # 7. 反向传播 + loss.backward() + # 8. 更新参数 + optimizer.step() + print('Epoch %d, Iter %d, Loss: %.5f' % (epoch + 1, batch_idx + 1, loss)) +print('Finished Training') +``` + +6. 测试模型效果 + +```python +test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform) + +# 测试 5 张图片效果 +for i in range(5): + test_image, gt = test_dataset[0] + # CHW -> NCHW + test_image = test_image.unsqueeze(0) + + # 取预测分布中的最大值 + res = net(test_image).argmax().numpy() + print(f"图像{i} 标签：{gt}") + print(f"模型预测结果：{res}") +``` diff --git a/docs/hardware_support/npu/suite_tutorial_cn.md b/docs/hardware_support/npu/suite_tutorial_cn.md new file mode 100644 index 00000000000..a57f544f0e6 --- /dev/null +++ b/docs/hardware_support/npu/suite_tutorial_cn.md @@ -0,0 +1,300 @@ +# 昇腾 NPU 基于套件的使用指南 + +## 环境准备 + +### 环境说明 + +* 本教程介绍如何基于昇腾 910B NPU 进行 ResNet50 的训练，总共需要 4 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接：registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39 + + * 镜像中已经默认安装了昇腾算子库 CANN-8.0.RC1 + +* 昇腾驱动版本为 23.0.3 + +### 环境安装 + +1. 安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +```shell +python -m pip install paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ +``` + +2. 安装 CustomDevice + +*该命令会自动安装飞桨 Custom Device 每日自动构建的 nightly-build 版本* + +```shell +python -m pip install paddle-custom-npu -i https://www.paddlepaddle.org.cn/packages/nightly/npu/ +``` + +## 基于 PaddleClas 训练 ResNet50 + +### 一、安装 PaddleClas 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleClas.git -b release/2.5.1 +cd PaddleClas +python -m pip install -r requirements.txt +python -m pip install . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5.1/docs/zh_CN/models/ImageNet1k/ResNet.md#32-%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87) 准备 ImageNet1k 数据集，准备完成后解压到 PaddleClas/dataset/目录下，目录结构如下： +``` +PaddleClas/dataset/ILSVRC2012/ +|_ train/ +| |_ n01440764 +| | |_ n01440764_10026.JPEG +| | |_ ... +| |_ ... +| | +| |_ n15075141 +| |_ ... +| |_ n15075141_9993.JPEG +|_ val/ +| |_ ILSVRC2012_val_00000001.JPEG +| |_ ... +| |_ ILSVRC2012_val_00050000.JPEG +|_ train_list.txt +|_ val_list.txt +``` + +### 三、模型训练 + +进入 PaddleClas 目录下，执行如下命令启动 4 卡 NPU（0 ~ 3 号卡）训练，其中： + + * 参数 `-o Global.device` 指定的是即将运行的设备，这里需要传入的是 `npu` ，通过指定该参数，PaddleClas 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 npu，在进行模型训练时，飞桨将自动调用 npu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + + * 参数 `-c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 `ResNet50` + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.output_dir="output/ResNet50" \ + -o Global.device="npu" +``` + +上述命令会在 PaddleClas 目录下产生一个 output/ResNet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最后一个 epoch 的权重放在 output/ResNet50/ 目录下的 epoch_120.pdparams 文件中，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export_model.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定./deploy/models/ResNet50 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export_model.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.pretrained_model=./output/ResNet50/epoch_120 \ + -o Global.save_inference_dir=./deploy/models/ResNet50 +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleClas/deploy 目录下，执行下列命令进行 NPU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +cd deploy +python python/predict_cls.py \ + -c ./configs/inference_cls.yaml \ + -o Global.inference_model_dir=./models/ResNet50 \ + -o Global.use_gpu=False \ + -o Global.use_npu=True \ + -o Global.infer_imgs=./images/ImageNet +``` + +#### 转换 ONNX 模型 + +如果您有额外的部署需求需要基于 ONNX 实现，我们也提供了专用的工具用于导出 ONNX 模型，参考如下步骤，即可将第一步导出的静态图模型转换为 ONNX 模型： + +a. 安装环境 + +```shell +# 安装 paddle2onnx，该工具支持将 PaddleInference 模型转换为 ONNX 格式 +python -m pip install paddle2onnx +``` + +b. 模型转换 + +```shell +paddle2onnx --model_dir=./deploy/models/ResNet50/ \ + --model_filename=inference.pdmodel \ + --params_filename=inference.pdiparams \ + --save_file=./deploy/models/ResNet50_onnx/inference.onnx \ + --opset_version=10 \ + --enable_onnx_checker=True +``` + +该命令会在 deploy/models/ResNet50_onnx 目录下生成 inference.onnx 文件，生成的文件可以基于 ONNX Runtime 进行推理，具体使用方式参考 [ONNX Runtime 官网](https://onnxruntime.ai/) + +## 基于 PaddleDetection 训 PP-YOLOE+ + +### 一、安装 PaddleDetection 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleDetection.git -b release_2_7_npu +cd PaddleDetection +python -m pip install -r requirements.txt +python -m pip install -e . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/PrepareDataSet.md#coco%E6%95%B0%E6%8D%AE) 准备 COCO 2017 数据集，准备完成后解压到 PaddleDetection/dataset/目录下，目录结构如下： + +``` +PaddleDetection/dataset/coco/ +├── annotations +│ ├── instances_train2014.json +│ ├── instances_train2017.json +│ ├── instances_val2014.json +│ ├── instances_val2017.json +│ │ ... +├── train2017 +│ ├── 000000000009.jpg +│ ├── 000000580008.jpg +│ │ ... +├── val2017 +│ ├── 000000000139.jpg +│ ├── 000000000285.jpg +│ │ ... +``` + +### 三、模型训练 + +进入 PaddleDetection 目录下，执行如下命令启动 8 卡 NPU（0 ~ 7 号卡）训练，其中： + +* 参数 `--config configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 `PP-YOLOE+-l` + +```shell +export FLAGS_npu_jit_compile=0 +export FLAGS_use_stride_kernel=0 + +python -u -m paddle.distributed.launch --devices 0,1,2,3,4,5,6,7 \ + tools/train.py --eval --config configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml \ + --enable_ce True +``` + +上述命令会在 PaddleDetection 目录下产生一个 output/ppyoloe_plus_crn_l_80e_coco 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最优指标对应的权重放在 output/ppyoloe_plus_crn_l_80e_coco/pipeline/best_model/ 目录下，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export_model.py 执行的是 动转静 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定 output_inference/ppyoloe_plus_crn_l_80e_coco 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=output/ppyoloe_plus_crn_l_80e_coco/pipeline/best_model/model.pdparams +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleDetection/deploy 目录下，执行下列命令进行 NPU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=npu +``` + +## 基于 PaddleSeg 训练 DeepLabv3+ + +### 一、安装 PaddleSeg 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleSeg -b npu_cann8.0 +cd PaddleSeg +python -m pip install -r requirements.txt +python -m pip install -e . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.9/docs/data/pre_data_cn.md#cityscapes%E6%95%B0%E6%8D%AE%E9%9B%86) 准备 Cityscapes 数据集，准备完成后解压到 PaddleSeg/data/目录下，目录结构如下： + +```shell +PaddleSeg/data/cityscapes +├── leftImg8bit +│ ├── train +│ ├── val +├── gtFine +│ ├── train +│ ├── val +``` + +### 三、模型训练 + +进入 PaddleSeg 目录下，执行如下命令启动 4 卡 NPU（0 ~ 3 号卡）训练，其中： + +* 参数 `--device` 指定的是即将运行的设备，这里需要传入的是 npu ，通过指定该参数，PaddleSeg 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 npu，在进行模型训练时，飞桨将自动调用 npu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + +* 参数 `--config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 DeepLabv3+ + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + --config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml \ + --num_workers 8 \ + --save_dir output/deeplabv3p_resnet50 \ + --log_iters 10 \ + --device npu \ + --do_eval \ + --save_interval 1000 \ + --seed 2048 +``` + +上述命令会在 PaddleSeg 目录下产生一个 output/deeplabv3p_resnet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最优指标对应的权重放在 output/deeplabv3p_resnet50/best_model 目录下，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定 output/deeplabv3p_resnet50_inference_model 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export.py \ + --config configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml \ + --model_path output/deeplabv3p_resnet50/best_model/model.pdparams \ + --save_dir output/deeplabv3p_resnet50_inference_model +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleSeg/deploy 目录下，执行下列命令进行 NPU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png + +python deploy/python/infer.py \ + --config output/deeplabv3p_resnet50_inference_model/deploy.yaml \ + --image_path ./cityscapes_demo.png \ + --save_dir ./output \ + --device "npu" +``` diff --git a/docs/hardware_support/npu/support_cn.md b/docs/hardware_support/npu/support_cn.md new file mode 100644 index 00000000000..da9a98814ea --- /dev/null +++ b/docs/hardware_support/npu/support_cn.md @@ -0,0 +1,287 @@ +# 昇腾 NPU 验证模型 + +基于 2024Q2 devlop 版本，飞桨框架在昇腾 NPU 上通过精度验证的模型情况如下： + +| 模型库 | 模型名称 | 训练 | 推理 | +| - | - | - | - | +| PaddleClas | ResNet50 | √ | √ | +| PaddleClas | ResNet18 | √ | √ | +| PaddleClas | ResNet101 | √ | √ | +| PaddleClas | ResNet34 | √ | √ | +| PaddleClas | ResNet152 | √ | √ | +| PaddleClas | MobileNetV3_small_x1_0 | √ | √ | +| PaddleClas | convnext | √ | √ | +| PaddleClas | MobilenetV2 | √ | √ | +| PaddleClas | mobilnetv2_x0_5 | √ | √ | +| PaddleClas | mobilnetv2_x0_25 | √ | √ | +| PaddleClas | AlexNet | 未测试 | √ | +| PaddleClas | CLIP_vit_base_patch16_224 | 未测试 | √ | +| PaddleClas | CSPDarkNet53 | 未测试 | √ | +| PaddleClas | CSWinTransformer_base_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_base_384 | 未测试 | √ | +| PaddleClas | CSWinTransformer_large_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_large_384 | 未测试 | √ | +| PaddleClas | CSWinTransformer_small_224 | 未测试 | √ | +| PaddleClas | CSWinTransformer_tiny_224 | 未测试 | √ | +| PaddleClas | ConvNeXt_small | 未测试 | √ | +| PaddleClas | ConvNeXt_tiny | 未测试 | √ | +| PaddleClas | CvT_13_224 | 未测试 | √ | +| PaddleClas | CvT_13_384 | 未测试 | √ | +| PaddleClas | CvT_21_224 | 未测试 | √ | +| PaddleClas | CvT_21_384 | 未测试 | √ | +| PaddleClas | DLA102 | 未测试 | √ | +| PaddleClas | DLA102x | 未测试 | √ | +| PaddleClas | DLA102x2 | 未测试 | √ | +| PaddleClas | DLA169 | 未测试 | √ | +| PaddleClas | DLA34 | 未测试 | √ | +| PaddleClas | DLA46_c | 未测试 | √ | +| PaddleClas | DLA46x_c | 未测试 | √ | +| PaddleClas | DLA60 | 未测试 | √ | +| PaddleClas | DLA60x | 未测试 | √ | +| PaddleClas | DLA60x_c | 未测试 | √ | +| PaddleClas | DPN107 | 未测试 | √ | +| PaddleClas | DPN131 | 未测试 | √ | +| PaddleClas | DPN68 | 未测试 | √ | +| PaddleClas | DPN92 | 未测试 | √ | +| PaddleClas | DPN98 | 未测试 | √ | +| PaddleClas | DSNet_base | 未测试 | √ | +| PaddleClas | DSNet_tiny | 未测试 | √ | +| PaddleClas | DarkNet53 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_384 | 未测试 | √ | +| PaddleClas | DeiT_small_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_tiny_patch16_224 | 未测试 | √ | +| PaddleClas | DenseNet121 | 未测试 | √ | +| PaddleClas | DenseNet161 | 未测试 | √ | +| PaddleClas | DenseNet169 | 未测试 | √ | +| PaddleClas | DenseNet201 | 未测试 | √ | +| PaddleClas | DenseNet264 | 未测试 | √ | +| PaddleClas | DistillationModel | 未测试 | √ | +| PaddleClas | ESNet_x0_25 | 未测试 | √ | +| PaddleClas | ESNet_x0_5 | 未测试 | √ | +| PaddleClas | ESNet_x0_75 | 未测试 | √ | +| PaddleClas | ESNet_x1_0 | 未测试 | √ | +| PaddleClas | EfficientNetB0 | 未测试 | √ | +| PaddleClas | EfficientNetB1 | 未测试 | √ | +| PaddleClas | EfficientNetB2 | 未测试 | √ | +| PaddleClas | EfficientNetB3 | 未测试 | √ | +| PaddleClas | EfficientNetB4 | 未测试 | √ | +| PaddleClas | EfficientNetB5 | 未测试 | √ | +| PaddleClas | EfficientNetB6 | 未测试 | √ | +| PaddleClas | EfficientNetB7 | 未测试 | √ | +| PaddleClas | GhostNet_x0_5 | 未测试 | √ | +| PaddleClas | GhostNet_x1_0 | 未测试 | √ | +| PaddleClas | GhostNet_x1_3 | 未测试 | √ | +| PaddleClas | GoogLeNet | 未测试 | √ | +| PaddleClas | HRNet_W18_C | 未测试 | √ | +| PaddleClas | HRNet_W30_C | 未测试 | √ | +| PaddleClas | HRNet_W32_C | 未测试 | √ | +| PaddleClas | HRNet_W40_C | 未测试 | √ | +| PaddleClas | HRNet_W44_C | 未测试 | √ | +| PaddleClas | HRNet_W48_C | 未测试 | √ | +| PaddleClas | HRNet_W64_C | 未测试 | √ | +| PaddleClas | HarDNet39_ds | 未测试 | √ | +| PaddleClas | HarDNet68 | 未测试 | √ | +| PaddleClas | HarDNet68_ds | 未测试 | √ | +| PaddleClas | HarDNet85 | 未测试 | √ | +| PaddleClas | InceptionV3 | 未测试 | √ | +| PaddleClas | InceptionV4 | 未测试 | √ | +| PaddleClas | LeViT_128 | 未测试 | √ | +| PaddleClas | LeViT_128S | 未测试 | √ | +| PaddleClas | LeViT_192 | 未测试 | √ | +| PaddleClas | LeViT_256 | 未测试 | √ | +| PaddleClas | LeViT_384 | 未测试 | √ | +| PaddleClas | MetaBIN_ResNet50 | 未测试 | √ | +| PaddleClas | MixNet_L | 未测试 | √ | +| PaddleClas | MixNet_M | 未测试 | √ | +| PaddleClas | MixNet_S | 未测试 | √ | +| PaddleClas | MobileNeXt_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV1 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_25 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_25 | 未测试 | √ | +| PaddleClas | MobileViTV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_0 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileViTV3_S | 未测试 | √ | +| PaddleClas | MobileViTV3_S_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XS | 未测试 | √ | +| PaddleClas | MobileViTV3_XS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_75 | 未测试 | √ | +| PaddleClas | MobileViTV3_x1_0 | 未测试 | √ | +| PaddleClas | MobileViT_S | 未测试 | √ | +| PaddleClas | MobileViT_XS | 未测试 | √ | +| PaddleClas | MobileViT_XXS | 未测试 | √ | +| PaddleClas | NextViT_base_224 | 未测试 | √ | +| PaddleClas | NextViT_base_384 | 未测试 | √ | +| PaddleClas | NextViT_large_224 | 未测试 | √ | +| PaddleClas | NextViT_large_384 | 未测试 | √ | +| PaddleClas | NextViT_small_224 | 未测试 | √ | +| PaddleClas | NextViT_small_384 | 未测试 | √ | +| PaddleClas | PPHGNet_small | 未测试 | √ | +| PaddleClas | PPHGNet_tiny | 未测试 | √ | +| PaddleClas | PPLCNetV2_base | 未测试 | √ | +| PaddleClas | PPLCNet_x0_25 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_35 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_75 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_5 | 未测试 | √ | +| PaddleClas | PVT_V2_B0 | 未测试 | √ | +| PaddleClas | PVT_V2_B1 | 未测试 | √ | +| PaddleClas | PVT_V2_B2 | 未测试 | √ | +| PaddleClas | PVT_V2_B2_Linear | 未测试 | √ | +| PaddleClas | PVT_V2_B3 | 未测试 | √ | +| PaddleClas | PVT_V2_B4 | 未测试 | √ | +| PaddleClas | PVT_V2_B5 | 未测试 | √ | +| PaddleClas | ReXNet_1_0 | 未测试 | √ | +| PaddleClas | ReXNet_1_3 | 未测试 | √ | +| PaddleClas | ReXNet_1_5 | 未测试 | √ | +| PaddleClas | ReXNet_2_0 | 未测试 | √ | +| PaddleClas | ReXNet_3_0 | 未测试 | √ | +| PaddleClas | RepVGG_B3 | 未测试 | √ | +| PaddleClas | Res2Net101_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net200_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_14w_8s | 未测试 | √ | +| PaddleClas | Res2Net50_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_vd_26w_4s | 未测试 | √ | +| PaddleClas | ResNeSt101 | 未测试 | √ | +| PaddleClas | ResNeSt50 | 未测试 | √ | +| PaddleClas | ResNeSt50_fast_1s1x64d | 未测试 | √ | +| PaddleClas | ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNet101_vd | 未测试 | √ | +| PaddleClas | ResNet152_vd | 未测试 | √ | +| PaddleClas | ResNet18_vd | 未测试 | √ | +| PaddleClas | ResNet200_vd | 未测试 | √ | +| PaddleClas | ResNet34_vd | 未测试 | √ | +| PaddleClas | ResNet50_vd | 未测试 | √ | +| PaddleClas | SENet154_vd | 未测试 | √ | +| PaddleClas | SE_ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNet18_vd | 未测试 | √ | +| PaddleClas | SE_ResNet34_vd | 未测试 | √ | +| PaddleClas | SE_ResNet50_vd | 未测试 | √ | +| PaddleClas | ShuffleNetV2_swish | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_25 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_33 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_0 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x2_0 | 未测试 | √ | +| PaddleClas | SqueezeNet1_0 | 未测试 | √ | +| PaddleClas | SqueezeNet1_1 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_base_patch4_window16_256 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_base_patch4_window8_256 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_large_patch4_window16_256 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_small_patch4_window16_256 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_small_patch4_window8_256 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_tiny_patch4_window16_256 | 未测试 | √ | +| PaddleClas | SwinTransformerV2_tiny_patch4_window8_256 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_small_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_tiny_patch4_window7_224 | 未测试 | √ | +| PaddleClas | TNT_small | 未测试 | √ | +| PaddleClas | TinyNet_A | 未测试 | √ | +| PaddleClas | TinyNet_B | 未测试 | √ | +| PaddleClas | TinyNet_C | 未测试 | √ | +| PaddleClas | TinyNet_D | 未测试 | √ | +| PaddleClas | TinyNet_E | 未测试 | √ | +| PaddleClas | VAN_B0 | 未测试 | √ | +| PaddleClas | VAN_B1 | 未测试 | √ | +| PaddleClas | VGG13 | 未测试 | √ | +| PaddleClas | VGG16 | 未测试 | √ | +| PaddleClas | VGG19 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_base_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_small_patch16_224 | 未测试 | √ | +| PaddleClas | Xception41 | 未测试 | √ | +| PaddleClas | Xception41_deeplab | 未测试 | √ | +| PaddleClas | Xception65 | 未测试 | √ | +| PaddleClas | Xception65_deeplab | 未测试 | √ | +| PaddleClas | Xception71 | 未测试 | √ | +| PaddleClas | alt_gvt_base | 未测试 | √ | +| PaddleClas | alt_gvt_large | 未测试 | √ | +| PaddleClas | alt_gvt_small | 未测试 | √ | +| PaddleClas | cae_base_patch16_224 | 未测试 | √ | +| PaddleClas | pcpvt_base | 未测试 | √ | +| PaddleClas | pcpvt_large | 未测试 | √ | +| PaddleClas | pcpvt_small | 未测试 | √ | +| PaddleDetection | ppyoloe_vit_base_csppan_cae_36e_coco | 未测试 | √ | +| PaddleDetection | solov2_r50_enhance_coco | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv2_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv2_rec | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv3_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_mobile_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_mobile_v2_0_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_server_v2_0_rec | 未测试 | √ | +| PaddleOCR | det_mv3_db_v2_0_0 | 未测试 | √ | +| PaddleOCR | det_r50_db_plusplus_0 | 未测试 | √ | +| PaddleOCR | en_table_structure | 未测试 | √ | +| PaddleOCR | rec_mv3_none_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_tps_bilstm_att_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_tps_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r32_gaspin_bilstm_att | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_tps_bilstm_att_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_tps_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_svtrnet | 未测试 | √ | +| PaddleOCR | rec_vitstr | 未测试 | √ | +| PaddleOCR | slanet | 未测试 | √ | +| PaddleSeg | Deeplabv3_R50 | √ | √ | +| PaddleSeg | Deeplabv3_R101 | √ | √ | +| PaddleSeg | Deeplabv3Plus-R50 | √ | √ | +| PaddleSeg | Deeplabv3Plus-R101 | √ | √ | +| PaddleSeg | fcn_hrnetw18 | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18_small | 未测试 | √ | +| PaddleSeg | fcn_uhrnetw18_small | 未测试 | √ | +| PaddleSeg | ocrnet_hrnetw48 | 未测试 | √ | +| PaddleSeg | pfpnnet | 未测试 | √ | +| PaddleSeg | pphumanseg_lite | 未测试 | √ | +| PaddleSeg | pphumanseg_server | 未测试 | √ | +| PaddleSeg | ppmatting | 未测试 | √ | +| PaddleNLP | Bert | √ | √ | +| PaddleTS | DLinear | √ | √ | +| PaddleTS | RLinear | √ | √ | +| PaddleTS | NLinear | √ | √ | +| PaddleVideo | AGCN | 未测试 | √ | +| PaddleVideo | STGCN | 未测试 | √ | diff --git a/docs/hardware_support/xpu/index_cn.rst b/docs/hardware_support/xpu/index_cn.rst new file mode 100644 index 00000000000..61812c1f182 --- /dev/null +++ b/docs/hardware_support/xpu/index_cn.rst @@ -0,0 +1,24 @@ +.. _cn_xpu_information: + +#################### +昆仑芯 XPU 芯片 +#################### + +昆仑芯是百度集十年 AI 产业技术实践、自主研发的 AI 芯片。该产品拥有创新的 XPU 架构、领先的产品竞争力及丰富的场景落地实践等优势，目前已被广泛部署在百度搜索、小度、大模型等业务并在百度外有数百家客户，将 AI 算力赋能千行百业智能化升级。 + +昆仑芯和飞桨的适配自 2018 年启动，当前已携手共同完成了一套端到端的 AI 计算系统解决方案，并致力于携手打造一个全栈式软硬一体的 AI 生态，夯实算力基础设施建设。“昆仑芯+飞桨”方案已在智慧金融、工业质检等领域成功部署落地。 + +更多昆仑芯 XPU 芯片详情及技术指标请 `点击这里 `_ 。 + +飞桨框架支持基于昆仑芯 XPU 芯片的训练和推理，请参考以下内容快速体验： + +- `昆仑芯 XPU 基于框架的使用指南 <./paddle_tutorial_cn.html>`_ : 昆仑芯 XPU 基于框架的使用指南 +- `昆仑芯 XPU 基于套件的使用指南 <./suite_tutorial_cn.html>`_ : 昆仑芯 XPU 基于套件的使用指南 +- `昆仑芯 XPU 支持模型 <./support_cn.html>`_ : 昆仑芯 XPU 支持模型 + +.. toctree:: + :hidden: + + paddle_tutorial_cn.md + suite_tutorial_cn.md + support_cn.md diff --git a/docs/hardware_support/xpu/paddle_tutorial_cn.md b/docs/hardware_support/xpu/paddle_tutorial_cn.md new file mode 100644 index 00000000000..dddea1966ee --- /dev/null +++ b/docs/hardware_support/xpu/paddle_tutorial_cn.md @@ -0,0 +1,110 @@ +# 昆仑芯 XPU 基于框架的使用指南 + +## 一、环境准备 + +### 环境说明 + +* 本教程介绍如何基于昆仑芯 XPU 进行 ResNet50 的训练，总共需要 1 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接： registry.baidubce.com/device/paddle-xpu:ubuntu20-x86_64-gcc84-py310 + +### 环境安装 + +安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +*由于 xpu 代码位于飞桨主框架中，因此我们不需要安装额外的 Custom Device 包* + +```shell +python -m pip install paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu/ +``` + +## 二、运行示例 + +飞桨框架集成了经典的视觉模型用于帮助用户快速上手，我们将基于 ResNet50 结构，在 Cifar10 数据集上进行一次快速训练，用于帮助您了解如何基于昆仑芯 XPU 进行训练（和 GPU 训练代码相比，差异点仅为 `paddle.set_device("xpu")`） + +注意： + +* *本教程主要用于快速入门，并未对参数进行细致调优，训练效果未必是最好的，您可以自行调整超参数进行效果调优* + +* *本教程预计使用单卡 R300 训练 40 分钟* + +1. 导入必要的包 + +```python +import paddle +from paddle.vision import transforms +from paddle.vision.models import resnet50 +``` + +2. 设置运行设备 + +```python +# 1. 设定运行设备为 xpu +paddle.set_device("xpu") +``` + +3. 加载训练数据集 + +```python +# 2. 定义数据集、数据预处理方法与 DataLoader +transform = transforms.Compose([ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) +]) +train_set = paddle.vision.datasets.Cifar10(mode='train', transform=transform) +train_loader = paddle.io.DataLoader(train_set, batch_size=128, num_workers=8) +``` + +4. 定义网络结构和损失函数 + +```python +# 3. 定义网络结构 +net = resnet50(num_classes=10) +# 4. 定义损失函数 +net_loss = paddle.nn.CrossEntropyLoss() +# 5. 定义优化器 +optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters()) +``` + +5. 启动训练 + +训练过程中会打印 loss 的变化情况，可以观察到 loss 在初步下降，这意味着模型参数逐渐适应了该数据集。 + +```python +net.train() +for epoch in range(10): + for batch_idx, data in enumerate(train_loader, start=0): + inputs, labels = data + optimizer.clear_grad() + # 6. 前向传播并计算损失 + outputs = net(inputs) + loss = net_loss(outputs, labels) + # 7. 反向传播 + loss.backward() + # 8. 更新参数 + optimizer.step() + print('Epoch %d, Iter %d, Loss: %.5f' % (epoch + 1, batch_idx + 1, loss)) +print('Finished Training') +``` + +6. 测试模型效果 + +```python +test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform) + +# 测试 5 张图片效果 +for i in range(5): + test_image, gt = test_dataset[0] + # CHW -> NCHW + test_image = test_image.unsqueeze(0) + + # 取预测分布中的最大值 + res = net(test_image).argmax().numpy() + print(f"图像{i} 标签：{gt}") + print(f"模型预测结果：{res}") +``` diff --git a/docs/hardware_support/xpu/suite_tutorial_cn.md b/docs/hardware_support/xpu/suite_tutorial_cn.md new file mode 100644 index 00000000000..a21f76d99e0 --- /dev/null +++ b/docs/hardware_support/xpu/suite_tutorial_cn.md @@ -0,0 +1,134 @@ +# 昆仑芯 XPU 基于套件的使用指南 + +## 环境准备 + +### 环境说明 + +* 本教程介绍如何基于昆仑芯 XPU 进行 ResNet50 的训练，总共需要 4 卡进行训练 + +* 考虑到环境差异性，我们推荐使用教程提供的标准镜像完成环境准备： + + * 镜像链接： registry.baidubce.com/device/paddle-xpu:ubuntu20-x86_64-gcc84-py310 + +### 环境安装 + +安装 PaddlePaddle + +*该命令会自动安装飞桨主框架每日自动构建的 nightly-build 版本* + +*由于 xpu 代码位于飞桨主框架中，因此我们不需要安装额外的 Custom Device 包* + +```shell +python -m pip install paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu/ +``` + +## 基于 PaddleClas 训练 ResNet50 + +### 一、安装 PaddleClas 代码库 + +```shell +git clone https://github.com/PaddlePaddle/PaddleClas.git -b release/2.5.1 +cd PaddleClas +python -m pip install -r requirements.txt +python -m pip install . +``` + +### 二、数据准备 + +请根据 [数据说明文档](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5.1/docs/zh_CN/models/ImageNet1k/ResNet.md#32-%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87) 准备 ImageNet1k 数据集，准备完成后解压到 PaddleClas/dataset/目录下，目录结构如下： + +``` +PaddleClas/dataset/ILSVRC2012/ +|_ train/ +| |_ n01440764 +| | |_ n01440764_10026.JPEG +| | |_ ... +| |_ ... +| | +| |_ n15075141 +| |_ ... +| |_ n15075141_9993.JPEG +|_ val/ +| |_ ILSVRC2012_val_00000001.JPEG +| |_ ... +| |_ ILSVRC2012_val_00050000.JPEG +|_ train_list.txt +|_ val_list.txt +``` + +### 三、模型训练 + +进入 PaddleClas 目录下，执行如下命令启动 4 卡 XPU（0 ~ 3 号卡）训练，其中： + +* 参数 `-o Global.device` 指定的是即将运行的设备，这里需要传入的是 `xpu` ，通过指定该参数，PaddleClas 调用飞桨的设备指定接口 `paddle.set_device` 来指定运行设备为 xpu，在进行模型训练时，飞桨将自动调用 xpu 算子用于执行模型计算。关于设备指定的更多细节，可以参考官方 api [paddle.set_device](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/device/set_device_cn.html#set-device)。 + +* 参数 `-c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml` 表示读取指定目录下的配置文件，配置文件中指定了模型结构，训练超参等所有训练模型需要用到的配置，该文件中指定的模型结构为 `ResNet50` + +```shell +python -u -m paddle.distributed.launch --devices 0,1,2,3 tools/train.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.output_dir="output/ResNet50" \ + -o Global.device="xpu" +``` + +上述命令会在 PaddleClas 目录下产生一个 output/ResNet50 目录，该目录会存放训练过程中的模型参数 + +### 四、模型导出 & 推理 + +#### 模型导出 + +训练完成后，最后一个 epoch 的权重放在 output/ResNet50/ 目录下的 epoch_120.pdparams 文件中，执行以下命令将模型转成 Paddle 静态图格式存储，以获得更好的推理性能： + +* export_model.py 执行的是 `动转静` 操作，飞桨框架会对代码进行分析，将动态图代码（灵活易用）转为 静态图模型（高效），以达到更加高效的推理性能 + +* 该操作会在指定./deploy/models/ResNet50 下生成 inference.pdiparams、inference.pdiparams.info、inference.pdmodel 3 个文件 + +```shell +python tools/export_model.py \ + -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \ + -o Global.pretrained_model=./output/ResNet50/epoch_120 \ + -o Global.save_inference_dir=./deploy/models/ResNet50 \ + -o Global.device=xpu +``` + +#### 基于 PaddleInference 推理 + +推理代码位于 PaddleClas/deploy 目录下，执行下列命令进行 XPU 推理： + +* 该脚本将会加载上一步保存的静态图，使用飞桨预测库 PaddleInference 进行推理 + +* PaddleInference 内置了大量的高性能 Kernel，并且可以基于计算图分析，完成细粒度 OP 横向纵向融合，实现了高性能推理 + +```shell +cd deploy +python python/predict_cls.py \ + -c ./configs/inference_cls.yaml \ + -o Global.inference_model_dir=./models/ResNet50 \ + -o Global.use_gpu=False \ + -o Global.use_xpu=True \ + -o Global.infer_imgs=./images/ImageNet +``` + +#### 转换 ONNX 模型 + +如果您有额外的部署需求需要基于 ONNX 实现，我们也提供了专用的工具用于导出 ONNX 模型，参考如下步骤，即可将第一步导出的静态图模型转换为 ONNX 模型： + +a. 安装环境 + +```shell +# 安装 paddle2onnx，该工具支持将 PaddleInference 模型转换为 ONNX 格式 +python -m pip install paddle2onnx +``` + +b. 模型转换 + +```shell +paddle2onnx --model_dir=./deploy/models/ResNet50/ \ + --model_filename=inference.pdmodel \ + --params_filename=inference.pdiparams \ + --save_file=./deploy/models/ResNet50_onnx/inference.onnx \ + --opset_version=10 \ + --enable_onnx_checker=True +``` + +该命令会在 deploy/models/ResNet50_onnx 目录下生成 inference.onnx 文件，生成的文件可以基于 ONNX Runtime 进行推理，具体使用方式参考 [ONNX Runtime 官网](https://onnxruntime.ai/) diff --git a/docs/hardware_support/xpu/support_cn.md b/docs/hardware_support/xpu/support_cn.md new file mode 100644 index 00000000000..c5134619bb5 --- /dev/null +++ b/docs/hardware_support/xpu/support_cn.md @@ -0,0 +1,279 @@ +# 昆仑芯 XPU 支持模型 + +基于 2024Q2 devlop 版本，飞桨框架在昆仑芯 XPU 上通过精度验证的模型情况如下： + +| 模型库 | 模型名称 | 训练 | 推理 | +| - | - | - | - | +| PaddleClas | ResNet50 | √ | √ | +| PaddleClas | MobileNetV3 | √ | √ | +| PaddleClas | PP-LCNet | √ | √ | +| PaddleClas | AlexNet | 未测试 | √ | +| PaddleClas | CLIP_vit_base_patch16_224 | 未测试 | √ | +| PaddleClas | CSPDarkNet53 | 未测试 | √ | +| PaddleClas | ConvNeXt_small | 未测试 | √ | +| PaddleClas | ConvNeXt_tiny | 未测试 | √ | +| PaddleClas | CvT_13_224 | 未测试 | √ | +| PaddleClas | CvT_13_384 | 未测试 | √ | +| PaddleClas | CvT_21_224 | 未测试 | √ | +| PaddleClas | CvT_21_384 | 未测试 | √ | +| PaddleClas | DLA102 | 未测试 | √ | +| PaddleClas | DLA102x | 未测试 | √ | +| PaddleClas | DLA102x2 | 未测试 | √ | +| PaddleClas | DLA169 | 未测试 | √ | +| PaddleClas | DLA34 | 未测试 | √ | +| PaddleClas | DLA46_c | 未测试 | √ | +| PaddleClas | DLA46x_c | 未测试 | √ | +| PaddleClas | DLA60 | 未测试 | √ | +| PaddleClas | DLA60x | 未测试 | √ | +| PaddleClas | DLA60x_c | 未测试 | √ | +| PaddleClas | DPN107 | 未测试 | √ | +| PaddleClas | DPN131 | 未测试 | √ | +| PaddleClas | DPN68 | 未测试 | √ | +| PaddleClas | DPN92 | 未测试 | √ | +| PaddleClas | DPN98 | 未测试 | √ | +| PaddleClas | DSNet_base | 未测试 | √ | +| PaddleClas | DSNet_tiny | 未测试 | √ | +| PaddleClas | DarkNet53 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_base_patch16_384 | 未测试 | √ | +| PaddleClas | DeiT_small_patch16_224 | 未测试 | √ | +| PaddleClas | DeiT_tiny_patch16_224 | 未测试 | √ | +| PaddleClas | DenseNet121 | 未测试 | √ | +| PaddleClas | DenseNet161 | 未测试 | √ | +| PaddleClas | DenseNet169 | 未测试 | √ | +| PaddleClas | DenseNet201 | 未测试 | √ | +| PaddleClas | DenseNet264 | 未测试 | √ | +| PaddleClas | DistillationModel | 未测试 | √ | +| PaddleClas | ESNet_x0_25 | 未测试 | √ | +| PaddleClas | ESNet_x0_5 | 未测试 | √ | +| PaddleClas | ESNet_x0_75 | 未测试 | √ | +| PaddleClas | ESNet_x1_0 | 未测试 | √ | +| PaddleClas | EfficientNetB0 | 未测试 | √ | +| PaddleClas | EfficientNetB1 | 未测试 | √ | +| PaddleClas | EfficientNetB2 | 未测试 | √ | +| PaddleClas | EfficientNetB3 | 未测试 | √ | +| PaddleClas | EfficientNetB4 | 未测试 | √ | +| PaddleClas | EfficientNetB5 | 未测试 | √ | +| PaddleClas | EfficientNetB6 | 未测试 | √ | +| PaddleClas | EfficientNetB7 | 未测试 | √ | +| PaddleClas | GhostNet_x0_5 | 未测试 | √ | +| PaddleClas | GhostNet_x1_0 | 未测试 | √ | +| PaddleClas | GhostNet_x1_3 | 未测试 | √ | +| PaddleClas | GoogLeNet | 未测试 | √ | +| PaddleClas | HRNet_W18_C | 未测试 | √ | +| PaddleClas | HRNet_W30_C | 未测试 | √ | +| PaddleClas | HRNet_W32_C | 未测试 | √ | +| PaddleClas | HRNet_W40_C | 未测试 | √ | +| PaddleClas | HRNet_W44_C | 未测试 | √ | +| PaddleClas | HRNet_W48_C | 未测试 | √ | +| PaddleClas | HRNet_W64_C | 未测试 | √ | +| PaddleClas | HarDNet39_ds | 未测试 | √ | +| PaddleClas | HarDNet68 | 未测试 | √ | +| PaddleClas | HarDNet68_ds | 未测试 | √ | +| PaddleClas | HarDNet85 | 未测试 | √ | +| PaddleClas | InceptionV3 | 未测试 | √ | +| PaddleClas | InceptionV4 | 未测试 | √ | +| PaddleClas | LeViT_128 | 未测试 | √ | +| PaddleClas | LeViT_128S | 未测试 | √ | +| PaddleClas | LeViT_192 | 未测试 | √ | +| PaddleClas | LeViT_256 | 未测试 | √ | +| PaddleClas | LeViT_384 | 未测试 | √ | +| PaddleClas | MicroNet_M0 | 未测试 | √ | +| PaddleClas | MicroNet_M1 | 未测试 | √ | +| PaddleClas | MicroNet_M2 | 未测试 | √ | +| PaddleClas | MicroNet_M3 | 未测试 | √ | +| PaddleClas | MixNet_L | 未测试 | √ | +| PaddleClas | MixNet_M | 未测试 | √ | +| PaddleClas | MixNet_S | 未测试 | √ | +| PaddleClas | MobileNeXt_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV1 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV1_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_25 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileNetV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_large_x1_25 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_35 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_5 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x0_75 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_0 | 未测试 | √ | +| PaddleClas | MobileNetV3_small_x1_25 | 未测试 | √ | +| PaddleClas | MobileViTV2_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_0 | 未测试 | √ | +| PaddleClas | MobileViTV2_x1_5 | 未测试 | √ | +| PaddleClas | MobileViTV2_x2_0 | 未测试 | √ | +| PaddleClas | MobileViTV3_S | 未测试 | √ | +| PaddleClas | MobileViTV3_S_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XS | 未测试 | √ | +| PaddleClas | MobileViTV3_XS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS | 未测试 | √ | +| PaddleClas | MobileViTV3_XXS_L2 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_5 | 未测试 | √ | +| PaddleClas | MobileViTV3_x0_75 | 未测试 | √ | +| PaddleClas | MobileViTV3_x1_0 | 未测试 | √ | +| PaddleClas | NextViT_base_224 | 未测试 | √ | +| PaddleClas | NextViT_base_384 | 未测试 | √ | +| PaddleClas | NextViT_large_224 | 未测试 | √ | +| PaddleClas | NextViT_large_384 | 未测试 | √ | +| PaddleClas | NextViT_small_224 | 未测试 | √ | +| PaddleClas | NextViT_small_384 | 未测试 | √ | +| PaddleClas | PPHGNet_small | 未测试 | √ | +| PaddleClas | PPHGNet_tiny | 未测试 | √ | +| PaddleClas | PPLCNetV2_base | 未测试 | √ | +| PaddleClas | PPLCNet_x0_25 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_35 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x0_75 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x1_5 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_0 | 未测试 | √ | +| PaddleClas | PPLCNet_x2_5 | 未测试 | √ | +| PaddleClas | PVT_V2_B0 | 未测试 | √ | +| PaddleClas | PVT_V2_B1 | 未测试 | √ | +| PaddleClas | PVT_V2_B2 | 未测试 | √ | +| PaddleClas | PVT_V2_B2_Linear | 未测试 | √ | +| PaddleClas | PVT_V2_B3 | 未测试 | √ | +| PaddleClas | PVT_V2_B4 | 未测试 | √ | +| PaddleClas | PVT_V2_B5 | 未测试 | √ | +| PaddleClas | RepVGG_B3 | 未测试 | √ | +| PaddleClas | Res2Net101_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net200_vd_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_14w_8s | 未测试 | √ | +| PaddleClas | Res2Net50_26w_4s | 未测试 | √ | +| PaddleClas | Res2Net50_vd_26w_4s | 未测试 | √ | +| PaddleClas | ResNeSt101 | 未测试 | √ | +| PaddleClas | ResNeSt50 | 未测试 | √ | +| PaddleClas | ResNeSt50_fast_1s1x64d | 未测试 | √ | +| PaddleClas | ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt101_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt152_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_64x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | ResNeXt50_vd_64x4d | 未测试 | √ | +| PaddleClas | ResNet101 | 未测试 | √ | +| PaddleClas | ResNet101_vd | 未测试 | √ | +| PaddleClas | ResNet152 | 未测试 | √ | +| PaddleClas | ResNet152_vd | 未测试 | √ | +| PaddleClas | ResNet18 | 未测试 | √ | +| PaddleClas | ResNet18_vd | 未测试 | √ | +| PaddleClas | ResNet200_vd | 未测试 | √ | +| PaddleClas | ResNet34 | 未测试 | √ | +| PaddleClas | ResNet34_vd | 未测试 | √ | +| PaddleClas | ResNet50_vd | 未测试 | √ | +| PaddleClas | SENet154_vd | 未测试 | √ | +| PaddleClas | SE_ResNeXt101_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNeXt50_vd_32x4d | 未测试 | √ | +| PaddleClas | SE_ResNet18_vd | 未测试 | √ | +| PaddleClas | SE_ResNet34_vd | 未测试 | √ | +| PaddleClas | SE_ResNet50_vd | 未测试 | √ | +| PaddleClas | ShuffleNetV2_swish | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_25 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_33 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x0_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_0 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x1_5 | 未测试 | √ | +| PaddleClas | ShuffleNetV2_x2_0 | 未测试 | √ | +| PaddleClas | SlowFast | 未测试 | √ | +| PaddleClas | SqueezeNet1_0 | 未测试 | √ | +| PaddleClas | SqueezeNet1_1 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_base_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window12_384 | 未测试 | √ | +| PaddleClas | SwinTransformer_large_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_small_patch4_window7_224 | 未测试 | √ | +| PaddleClas | SwinTransformer_tiny_patch4_window7_224 | 未测试 | √ | +| PaddleClas | TNT_small | 未测试 | √ | +| PaddleClas | TinyNet_A | 未测试 | √ | +| PaddleClas | TinyNet_B | 未测试 | √ | +| PaddleClas | TinyNet_C | 未测试 | √ | +| PaddleClas | TinyNet_D | 未测试 | √ | +| PaddleClas | TinyNet_E | 未测试 | √ | +| PaddleClas | UniFormer_base | 未测试 | √ | +| PaddleClas | UniFormer_base_ls | 未测试 | √ | +| PaddleClas | UniFormer_small | 未测试 | √ | +| PaddleClas | UniFormer_small_plus | 未测试 | √ | +| PaddleClas | UniFormer_small_plus_dim64 | 未测试 | √ | +| PaddleClas | VAN_B0 | 未测试 | √ | +| PaddleClas | VAN_B1 | 未测试 | √ | +| PaddleClas | VGG13 | 未测试 | √ | +| PaddleClas | VGG16 | 未测试 | √ | +| PaddleClas | VGG19 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_base_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_base_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_224 | 未测试 | √ | +| PaddleClas | ViT_large_patch16_384 | 未测试 | √ | +| PaddleClas | ViT_large_patch32_384 | 未测试 | √ | +| PaddleClas | ViT_small_patch16_224 | 未测试 | √ | +| PaddleClas | Xception41 | 未测试 | √ | +| PaddleClas | Xception41_deeplab | 未测试 | √ | +| PaddleClas | Xception65 | 未测试 | √ | +| PaddleClas | Xception65_deeplab | 未测试 | √ | +| PaddleClas | Xception71 | 未测试 | √ | +| PaddleClas | alt_gvt_base | 未测试 | √ | +| PaddleClas | alt_gvt_large | 未测试 | √ | +| PaddleClas | alt_gvt_small | 未测试 | √ | +| PaddleClas | cae_base_patch16_224 | 未测试 | √ | +| PaddleClas | pcpvt_base | 未测试 | √ | +| PaddleClas | pcpvt_large | 未测试 | √ | +| PaddleClas | pcpvt_small | 未测试 | √ | +| PaddleDetection | cascade_mask_rcnn_r50_fpn_1x_coco | 未测试 | √ | +| PaddleDetection | cascade_rcnn_r50_fpn_1x_coco | 未测试 | √ | +| PaddleDetection | faster_rcnn_r50_1x_coco | 未测试 | √ | +| PaddleDetection | faster_rcnn_r50_fpn_1x_coco | 未测试 | √ | +| PaddleDetection | mask_rcnn_r50_1x_coco | 未测试 | √ | +| PaddleNLP | BERT | √ | √ | +| PaddleNLP | ERINE3.0 | √ | √ | +| PaddleNLP | UIE | √ | √ | +| PaddleOCR | PP-OCRv4-mobile-det | √ | √ | +| PaddleOCR | PP-OCRv4-server-det | √ | √ | +| PaddleOCR | ch_PP-OCRv2_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv2_rec | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv3_det_0 | 未测试 | √ | +| PaddleOCR | ch_PP-OCRv4_server_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_mobile_v2_0_rec | 未测试 | √ | +| PaddleOCR | ch_ppocr_server_v2_0_rec | 未测试 | √ | +| PaddleOCR | det_mv3_db_v2_0_0 | 未测试 | √ | +| PaddleOCR | det_r50_db_plusplus_0 | 未测试 | √ | +| PaddleOCR | en_table_structure | 未测试 | √ | +| PaddleOCR | rec_mv3_none_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_tps_bilstm_att_v2_0 | 未测试 | √ | +| PaddleOCR | rec_mv3_tps_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r31_sar | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_bilstm_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_r34_vd_none_none_ctc_v2_0 | 未测试 | √ | +| PaddleOCR | rec_vitstr | 未测试 | √ | +| PaddleOCR | slanet | 未测试 | √ | +| PaddleSeg | PP-LiteSeg | √ | √ | +| PaddleSeg | SFNet | √ | √ | +| PaddleSeg | deeplabv3p_resnet50 | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18 | 未测试 | √ | +| PaddleSeg | fcn_hrnetw18_small | 未测试 | √ | +| PaddleSeg | fcn_uhrnetw18_small | 未测试 | √ | +| PaddleSeg | fcos_r50_fpn_1x_coco | 未测试 | √ | +| PaddleSeg | ocrnet_hrnetw48 | 未测试 | √ | +| PaddleSeg | pphumanseg_lite | 未测试 | √ | +| PaddleSeg | pphumanseg_server | 未测试 | √ | +| PaddleTS | DLinear | √ | 未测试 | +| PaddleTS | NLinear | √ | 未测试 | +| PaddleTS | RLinear | √ | 未测试 | +| PaddleTS | TiDE | √ | 未测试 | +| PaddleTS | PatchTST | √ | 未测试 | +| PaddleVideo | AGCN | 未测试 | √ | +| PaddleVideo | AGCN2s | 未测试 | √ | +| PaddleVideo | BMN | 未测试 | √ | diff --git a/docs/images/api_legend/reshape.png b/docs/images/api_legend/reshape.png new file mode 100644 index 0000000000000000000000000000000000000000..0dc6ccc488642e9c36d0037dbb15d2b4eaf29b6e GIT binary patch literal 166671 zcmcG$2RN4P|37{k(U4IICBoB|2xU_g5=E&LGLlI4%uX38QkfYIQOHVUuR;_>MYfP+ zQ%2VRb*i4{Grqsye;vR3IK&u#NxwllmN7h8zFjS@UEBy25O8w$% zOU3)oLt|{!V&8uroK*kkp5Eux@gSeX8C_XfIqWDEB=8_IER6m0=g+)4-V29t*;8kF zonB3W4>c7F9!ql5(|1PfRyC;F-3rd>cN=BpzM8Dneu+lQFQ|FmZkh8+pf|gs0~VY5JQc4zFKN-a>Ta 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zdo$FP<#549C!TR;FbY#62fiS2r$Ko>FhQEIv?zIRd_QR_xsLQi&5!-qmpCvid2IE) zsQPQ{9W>~mnn27nPZM(tA&4c#g1)MD`5dOn%^=`4|$QwdjnnQAn&4HuX3jp7NYD-i~}hTCvW58HLH zf`qX5!bd|X)|fGoQGh6LjYFn!3P;6%6wGJC^bpIo)RFQ^dTDGR?j#lm6Wu;{7vxKr z3c^w#u?wvDQ0Z)s!4pNB92?Svt@W)OLl3VVQMDN~rVE|?bg@(lo*rUN_b?Gu>twOW z++w)t8?dqS{PMj?^RXTN9$gS1NBKhYWa|CoUzwpx%ue);N&voT88&E7pJ3INXm{WihF%!0#RqBFeO9welLI{KKMem0wTdL$0!Dj=TTy;0nS zb^r*PU_PFFQ*l}CkrfS__=mt;#5{6zj$C@|%9Z^lbky!NGeCi6vpO|%9~#NuxM6Xw zP<}vH;vhl^qVP{~egPfcXJhoMA6fM(9q+P0B3Md0FGo$teRPtR31RNc4W6^=`rsF= z(o8js8t9X6AruyffS23$@gZn$Tq5fZ3k|h>oU)Z_QdVwZw`@U5R_bSIhjKP zLrbxEu7AHPKBAqm44^7nW` znu)N?muDH1CU;kJ`I`=E&$A(}Ua{lfw+x!HqBWO(ZCQz$OQRNmf_!phqT|nY`}Uo% zX!sXBk)N`MRDF8&Uue{llU8&2ddeH-)ZU#@gKG1ef9SkRCA#KHuZIok(x`tc7PNhhYp?N=Qw%plZdTV zJ&yhX)vC}ehnb9KS-zZ{XO zoT_G>{-+~S-y$P-{0G1Pdfl2IyD@sw`o`G`6&?SRw$+ixKYVz@iwmM-e)-(9$I|luE-`xJ^`=+^GuFd7oJJkLnI&K6Re|*?^{^l9Su8*FSu|IyX!e{4S6EF9_c>GQe z7P+ -# **FAQ** - -- 报错“nccl.h 找不到” - - > 请[安装 nccl2](https://developer.nvidia.com/nccl/nccl-download) - - - -- Ubuntu18.04 下 libidn11 找不到？ - - > 使用以下指令： - - apt install libidn11 - -- Ubuntu 编译时出现大量的代码段不能识别？ - - > 这可能是由于 cmake 版本不匹配造成的，请在 gcc 的安装目录下使用以下指令： - - apt install gcc-4.8 g++-4.8 - cp gcc gcc.bak - cp g++ g++.bak - rm gcc - rm g++ - ln -s gcc-4.8 gcc - ln -s g++-4.8 g++ - -- 遇到 paddlepaddle.whl is not a supported wheel on this platform？ - - > 出现这个问题的主要原因是，没有找到和当前系统匹配的 paddlepaddle 安装包。 请检查 Python 版本是否为 2.7 系列。另外最新的 pip 官方源中的安装包默认是 manylinux1 标准， 需要使用最新的 pip (>9.0.0) 才可以安装。您可以执行以下指令更新您的 pip： - - pip install --upgrade pip - 或者 - - python -c "import pip; print(pip.pep425tags.get_supported())" - - > 如果系统支持的是 linux_x86_64 而安装包是 manylinux1_x86_64 ，需要升级 pip 版本到最新； 如果系统支持 manylinux1_x86_64 而安装包 （本地）是 linux_x86_64， 可以重命名这个 whl 包为 manylinux1_x86_64 再安装。 - -- 使用 Docker 编译出现问题？ - - > 请参照 GitHub 上[Issue12079](https://github.com/PaddlePaddle/Paddle/issues/12079) - -- 可以用 IDE 吗？ - - > 当然可以，因为源码就在本机上。IDE 默认调用 make 之类的程序来编译源码，我们只需要配置 IDE 来调用 Docker 命令编译源码即可。 - 很多 PaddlePaddle 开发者使用 Emacs。他们在自己的 `~/.emacs` 配置文件里加两行 - `global-set-key "\C-cc" 'compile` - `setq compile-command "docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev"` - 就可以按 `Ctrl-C` 和 `c` 键来启动编译了。 - -- 可以并行编译吗？ - - > 是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/paddle_build.sh)。这个脚本调用`make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。 - -- 在 Windows/macOS 上编译很慢？ - - > Docker 在 Windows 和 macOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考[issue627](https://github.com/PaddlePaddle/Paddle/issues/627)。 - -- 磁盘不够？ - - > 本文中的例子里，`docker run` 命令里都用了 `--rm` 参数，这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果，是没有名字的 images，也会占用磁盘。可以参考 [这篇文章](https://zaiste.net/posts/removing_docker_containers) 来清理这些内容。 - -- 在 DockerToolbox 下使用 book 时`http://localhost:8888/`无法打开？ - - > 需要将 localhost 替换成虚拟机 ip，一般需要在浏览器中输入：`http://192.168.99.100:8888/` - -- pip install gpu 版本的 PaddlePaddle 后运行出现 SegmentFault 如下： - - @ 0x7f6c8d214436 paddle::platform::EnforceNotMet::EnforceNotMet() - - @ 0x7f6c8dfed666 paddle::platform::GetCUDADeviceCount() - - @ 0x7f6c8d2b93b6 paddle::framework::InitDevices() - - - > 出现这个问题原因主要是由于您的显卡驱动低于对应 CUDA 版本的要求，请保证您的显卡驱动支持所使用的 CUDA 版本 - - - -- macOS 下使用自定义的 openblas 详见 issue： - - > [ISSUE 13217](https://github.com/PaddlePaddle/Paddle/issues/13721) - -- 已经安装 swig 但是仍旧出现 swig 找不到的问题 详见 issue： - - > [ISSUE 13759](https://github.com/PaddlePaddle/Paddle/issues/13759) - -- 出现 “target pattern contain no '%'.”的问题 详见 issue： - - > [ISSUE 13806](https://github.com/PaddlePaddle/Paddle/issues/13806) diff --git a/docs/install/FAQ_en.md b/docs/install/FAQ_en.md deleted file mode 100644 index c4a5572f3b7..00000000000 --- a/docs/install/FAQ_en.md +++ /dev/null @@ -1,111 +0,0 @@ -*** - - -# **FAQ** - -- Ubuntu18.04 under libidn11 can not be found? - - > Use the following instructions: - - apt install libidn11 - -- When Ubuntu compiles, a lot of code segments are not recognized? - - > This may be caused by a mismatch in the cmake version. Please use the following command in the gcc installation directory: - - apt install gcc-4.8 g++-4.8 - cp gcc gcc.bak - cp g++ g++.bak - rm gcc - rm g++ - ln -s gcc-4.8 gcc - ln -s g++-4.8 g++ - - - - -- Encountered paddlepaddle*.whl is not a supported wheel on this platform? - - > The main reason for this problem is that there is no paddlepaddle installation package that matches the current system. Please check if the Python version is 2.7 series. In addition, the latest pip official source installation package defaults to the manylinux1 standard, you need to use the latest pip (>9.0.0) to install. You can update your pip by following these instructions: - - pip install --upgrade pip - or - - python -c "import pip; print(pip.pep425tags.get_supported())" - - > If the system supports linux_x86_64 and the installation package is manylinux1_x86_64, you need to upgrade the pip version to the latest; if the system supports manylinux1_x86_64 and the installation package (local) is linux_x86_64, you can rename this whl package to manylinux1_x86_64 and install it again. - -- Is there a problem with Docker compilation? - - > Please refer to [Issue12079](https://github.com/PaddlePaddle/Paddle/issues/12079) on GitHub. - -- What is Docker? - - > If you haven't heard of Docker, you can think of it as a virtualenv-like system, but it virtualises more than the Python runtime environment. - -- Is Docker still a virtual machine? - - > Someone uses a virtual machine to analogize to Docker. It should be emphasized that Docker does not virtualize any hardware. The compiler tools running in the Docker container are actually run directly on the native CPU and operating system. The performance is the same as installing the compiler on the machine. - -- Why use Docker? - - > Installing the tools and configurations in a Docker image standardizes the build environment. This way, if you encounter problems, others can reproduce the problem to help. In addition, for developers accustomed to using Windows and macOS, there is no need to configure a cross-compilation environment using Docker. - -- Can I choose not to use Docker? - - > Of course you can. You can install development tools to the machine in the same way that you install them into Docker image. This document describes the Docker-based development process because it is easier than the other methods. - -- How hard is it to learn Docker? - - > It's not difficult to understand Docker. It takes about ten minutes to read this [article](https://zhuanlan.zhihu.com/p/19902938). - This can save you an hour of installing and configuring various development tools, as well as the need for new installations when switching machines. Don't forget that PaddlePaddle updates may lead to the need for new development tools. Not to mention the benefits of simplifying the recurrence of problems. - -- Can I use an IDE? - - > Of course, because the source code is on the machine. By default, the IDE calls a program like make to compile the source code. We only need to configure the IDE to call the Docker command to compile the source code. - Many PaddlePaddle developers use Emacs. They add two lines to their `~/.emacs` configuration file. - `global-set-key "\C-cc" 'compile` - `setq compile-command "docker run --rm -it -v $(git rev-parse --show- Toplevel): /paddle paddle:dev"` - You can start the compilation by pressing `Ctrl-C` and` c`. - -- Can I compile in parallel? - - > Yes. Our Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/paddle_build.sh). This script calls `make -j$(nproc)` to start as many processes as the CPU cores to compile in parallel. - -- Docker needs sudo? - - > If you develop with your own computer, you will naturally have admin privileges (sudo). If you are developing from a public computer, you need to ask the administrator to install and configure Docker. In addition, the PaddlePaddle project is working hard to support other container technologies that don't require sudo, such as rkt. - -- Is compiling slow on Windows/macOS? - - > Docker runs on both Windows and macOS. However, it is actually running on a Linux virtual machine. It may be necessary to pay attention to allocate more CPU and memory to this virtual machine to ensure efficient compilation. Please refer to [issue627](https://github.com/PaddlePaddle/Paddle/issues/627) for details. - -- Not enough disk? - - > In the example in this article, the `--rm` parameter is used in the `docker run`command to ensure that containers after the end of the run are not retained on disk. You can use the `docker ps -a` command to see containers that are stopped but not deleted. The `docker build` command sometimes produces some intermediate results, an image with no name, and it also occupies the disk. You can refer to this [article](https://zaiste.net/removing_docker_containers/) to clean up this content. - -- Can't I open `http://localhost:8888/` when using the book under DockerToolbox? - - > You need to replace localhost with virtual machine ip. Generally type this in the browser: `http://192.168.99.100:8888/` - -- After the pip install gpu version of PaddlePaddle runing, the SegmentFault appears as follows: - - @ 0x7f6c8d214436 paddle::platform::EnforceNotMet::EnforceNotMet() - - @ 0x7f6c8dfed666 paddle::platform::GetCUDADeviceCount() - - @ 0x7f6c8d2b93b6 paddle::framework::InitDevices() - - > The main reason for this problem is that your graphics card driver is lower than the corresponding CUDA version. Please ensure that your graphics card driver supports the CUDA version used. - -- Use customized openblas under macOS. See issue for details: - - >[ISSUE 13217](https://github.com/PaddlePaddle/Paddle/issues/13721) - -- Swig has been installed but there is still a problem that swig can't find. See issue for details: - - >[ISSUE 13759](https://github.com/PaddlePaddle/Paddle/issues/13759) - -- The question "target pattern contain no '%'." appears. See issue for details: - - >[ISSUE 13806](https://github.com/PaddlePaddle/Paddle/issues/13806) diff --git a/docs/install/Tables.md b/docs/install/Tables.md index e7d2405d820..64c7524d2ac 100644 --- a/docs/install/Tables.md +++ b/docs/install/Tables.md @@ -290,11 +290,11 @@ PaddePaddle 通过编译时指定路径来实现引用各种 BLAS/CUDA/cuDNN 库 - paddlepaddle==[版本号] 例如 paddlepaddle==2.6.1 + paddlepaddle==[版本号] 例如 paddlepaddle==3.0.0b1 只支持 CPU 对应版本的 PaddlePaddle，具体版本请参见Pypi - paddlepaddle-gpu==[版本号] 例如 paddlepaddle-gpu==2.6.1 + paddlepaddle-gpu==[版本号] 例如 paddlepaddle-gpu==3.0.0b1 默认安装支持 CUDA 11.8 和 cuDNN 8 的对应[版本号]的 PaddlePaddle 安装包 @@ -302,9 +302,8 @@ PaddePaddle 通过编译时指定路径来实现引用各种 BLAS/CUDA/cuDNN 库

        您可以在 [Release History](https://pypi.org/project/paddlepaddle-gpu/#history) 中找到 PaddlePaddle-gpu 的各个发行版本。 -> 其中`postXX` 对应的是 CUDA 和 cuDNN 的版本，`postXX`之前的数字代表 Paddle 的版本 -需要注意的是，命令中 paddlepaddle-gpu==2.6.1 在 windows 环境下，会默认安装支持 CUDA 11.8 和 cuDNN 8 的对应[版本号]的 PaddlePaddle 安装包 +需要注意的是，命令中 paddlepaddle-gpu==3.0.0b1 在 windows 环境下，会默认安装支持 CUDA 11.8 和 cuDNN 8 的对应[版本号]的 PaddlePaddle 安装包

        @@ -326,181 +325,86 @@ PaddePaddle 通过编译时指定路径来实现引用各种 BLAS/CUDA/cuDNN 库 cpu-mkl-avx - paddlepaddle-2.6.1-cp38-cp38-linux_x86_64.whl - paddlepaddle-2.6.1-cp39-cp39-linux_x86_64.whl - paddlepaddle-2.6.1-cp310-cp310-linux_x86_64.whl - paddlepaddle-2.6.1-cp311-cp311-linux_x86_64.whl - paddlepaddle-2.6.1-cp312-cp312-linux_x86_64.whl - - - cpu-openblas-avx - paddlepaddle-2.6.1-cp38-cp38-linux_x86_64.whl - - - - - - - - - - - cuda11.2-cudnn8.1-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1.post112-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp312-cp312-linux_x86_64.whl - - - cuda11.6-cudnn8.4-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1.post116-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp312-cp312-linux_x86_64.whl - - - cuda11.7-cudnn8.4-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1.post117-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp310-cp310-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp38-cp38-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp39-cp39-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp310-cp310-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp311-cp311-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp312-cp312-linux_x86_64.whl cuda11.8-cudnn8.6-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp312-cp312-linux_x86_64.whl - - cuda12.0-cudnn8.9-mkl-gcc12.2-avx - - paddlepaddle_gpu-2.6.1.post120-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp312-cp312-linux_x86_64.whl - - - macos-cpu-openblas - - paddlepaddle-2.6.1-cp38-cp38-macosx_10_14_x86_64.whl - - paddlepaddle-2.6.1-cp39-cp39-macosx_10_14_x86_64.whl - - paddlepaddle-2.6.1-cp310-cp310-macosx_10_14_universal2.whl - - paddlepaddle-2.6.1-cp311-cp311-macosx_10_14_universal2.whl - - paddlepaddle-2.6.1-cp312-cp312-macosx_10_14_universal2.whl - - - macos-cpu-openblas-m1 - - paddlepaddle-2.6.1-cp38-cp38-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp39-cp39-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp310-cp310-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp311-cp311-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp312-cp312-macosx_11_0_arm64.whl + + paddlepaddle_gpu-3.0.0b1-cp38-cp38-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp39-cp39-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp310-cp310-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp311-cp311-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp312-cp312-linux_x86_64.whl + + cuda12.3-cudnn9.0-mkl-gcc12.2-avx + + paddlepaddle_gpu-3.0.0b1-cp38-cp38-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp39-cp39-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp310-cp310-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp311-cp311-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp312-cp312-linux_x86_64.whl + + + macos-cpu-x86 + + paddlepaddle-3.0.0b1-cp38-cp38-macosx_10_9_x86_64.whl + + paddlepaddle-3.0.0b1-cp39-cp39-macosx_10_9_x86_64.whl + + paddlepaddle-3.0.0b1-cp310-cp310-macosx_10_9_universal2.whl + + paddlepaddle-3.0.0b1-cp311-cp311-macosx_10_9_universal2.whl + + paddlepaddle-3.0.0b1-cp312-cp312-macosx_10_9_universal2.whl + + + macos-cpu-arm + + paddlepaddle-3.0.0b1-cp38-cp38-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp39-cp39-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp310-cp310-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp311-cp311-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp312-cp312-macosx_11_0_arm64.whl win-cpu-mkl-avx - paddlepaddle-2.6.1-cp38-cp38-win_amd64.whl - paddlepaddle-2.6.1-cp39-cp39-win_amd64.whl - paddlepaddle-2.6.1-cp310-cp310-win_amd64.whl - paddlepaddle-2.6.1-cp311-cp311-win_amd64.whl - paddlepaddle-2.6.1-cp312-cp312-win_amd64.whl - - - win-cpu-openblas-avx - paddlepaddle-2.6.1-cp38-cp38-win_amd64.whl - - - - - - - - - - - win-cuda11.2-cudnn8.2-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post112-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp312-cp312-win_amd64.whl - - - win-cuda11.6-cudnn8.4-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post116-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp312-cp312-win_amd64.whl - - - win-cuda11.7-cudnn8.4-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post117-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp312-cp312-win_amd64.whl + paddlepaddle-3.0.0b1-cp38-cp38-win_amd64.whl + paddlepaddle-3.0.0b1-cp39-cp39-win_amd64.whl + paddlepaddle-3.0.0b1-cp310-cp310-win_amd64.whl + paddlepaddle-3.0.0b1-cp311-cp311-win_amd64.whl + paddlepaddle-3.0.0b1-cp312-cp312-win_amd64.whl win-cuda11.8-cudnn8.6-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp312-cp312-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp38-cp38-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp39-cp39-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp310-cp310-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp311-cp311-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp312-cp312-win_amd64.whl - win-cuda12.0-cudnn8.9-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post120-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp312-cp312-win_amd64.whl - - - linux-cinn-cuda11.2-cudnn8-mkl-gcc8.2-avx - paddlepaddle_gpu-2.6.1.post112-cp38-cp38-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp39-cp39-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp310-cp310-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp311-cp311-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp312-cp312-linux_x86_64.whl - - - linux-cuda11.2-cudnn8-mkl-gcc8.2-avx-pascal - paddlepaddle_gpu-2.6.1-cp38-cp38-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp39-cp39-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp310-cp310-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp311-cp311-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp312-cp312-linux_x86_64.whl + win-cuda12.3-cudnn9.0-mkl-vs2019-avx + paddlepaddle_gpu-3.0.0b1-cp38-cp38-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp39-cp39-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp310-cp310-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp311-cp311-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp312-cp312-win_amd64.whl @@ -539,155 +443,6 @@ abi tag: 类似'cp33m', 'abi3', 'none' platform tag: 类似 'linux_x86_64', 'any' - -

        -## **多版本 whl 包列表-develop** -

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        版本说明 cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311 cp312-cp312
        linux-cpu-mkl-avx paddlepaddle-latest-cp38-cp38-linux_x86_64.whl paddlepaddle-latest-cp39-cp39-linux_x86_64.whl paddlepaddle-latest-cp310-cp310-linux_x86_64.whl paddlepaddle-latest-cp311-cp311-linux_x86_64.whl paddlepaddle-latest-cp312-cp312-linux_x86_64.whl
        linux-cpu-openblas-avx paddlepaddle-latest-cp38-cp38-linux_x86_64.whl - - - -
        cuda11.2-cudnn8.1-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda11.6-cudnn8.4-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda11.7-cudnn8.4-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda11.8-cudnn8.6-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda12.0-cudnn8.9-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        mac-cpu paddlepaddle-cp38-cp38-macosx_10_9_x86_64.whl paddlepaddle-cp39-cp39-macosx_10_9_x86_64.whl paddlepaddle-cp310-cp310-macosx_10_9_x86_64.whl paddlepaddle-cp311-cp311-macosx_10_9_x86_64.whl paddlepaddle-cp312-cp312-macosx_10_9_x86_64.whl
        macos-cpu-openblas-m1 paddlepaddle-cp38-cp38-macosx_11_0_arm64.whl paddlepaddle-cp39-cp39-macosx_11_0_arm64.whl paddlepaddle-cp310-cp310-macosx_11_0_arm64.whl paddlepaddle-cp311-cp311-macosx_11_0_arm64.whl paddlepaddle-cp312-cp312-macosx_11_0_arm64.whl
        win-cpu-mkl-avx paddlepaddle-latest-cp38-cp38-win_amd64.whl paddlepaddle-latest-cp39-cp39-win_amd64.whl paddlepaddle-latest-cp310-cp310-win_amd64.whl paddlepaddle-latest-cp311-cp311-win_amd64.whl paddlepaddle-latest-cp312-cp312-win_amd64.whl
        win-cpu-openblas-avx paddlepaddle-latest-cp38-cp38-win_amd64.whl - - - -
        win-cuda11.2-cudnn8.2-mkl-vs2019-avx paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda11.6-cudnn8.4.0-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda11.7-cudnn8.4.1-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda11.8-cudnn8.6.0-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda12.0-cudnn8.9.1-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        -

        - diff --git a/docs/install/Tables_en.md b/docs/install/Tables_en.md index 9a651c5cd0a..453e45ffeec 100644 --- a/docs/install/Tables_en.md +++ b/docs/install/Tables_en.md @@ -282,11 +282,11 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif - paddlepaddle==[version code] such as paddlepaddle==2.6.1 + paddlepaddle==[version code] such as paddlepaddle==3.0.0b1 Only support the corresponding version of the CPU PaddlePaddle, please refer to Pypi for the specific version. - paddlepaddle-gpu==[version code], such as paddlepaddle-gpu==2.6.1 + paddlepaddle-gpu==[version code], such as paddlepaddle-gpu==3.0.0b1 The default installation supports the PaddlePaddle installation package corresponding to [version number] of CUDA 11.2 and cuDNN 8 @@ -294,9 +294,8 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif

        You can find various distributions of PaddlePaddle-gpu in [the Release History](https://pypi.org/project/paddlepaddle-gpu/#history). -> 'postxx' corresponds to CUDA and cuDNN versions, and the number before 'postxx' represents the version of Paddle -Please note that: in the commands, paddlepaddle-gpu==2.6.1 will install the installation package of PaddlePaddle that supports CUDA 11.2 and cuDNN 8 by default under Windows environment. +Please note that: in the commands, paddlepaddle-gpu==3.0.0b1 will install the installation package of PaddlePaddle that supports CUDA 11.2 and cuDNN 8 by default under Windows environment. @@ -320,181 +319,86 @@ Please note that: in the commands, paddlepaddle-gpu==2.6.1 will i cpu-mkl-avx - paddlepaddle-2.6.1-cp38-cp38-linux_x86_64.whl - paddlepaddle-2.6.1-cp39-cp39-linux_x86_64.whl - paddlepaddle-2.6.1-cp310-cp310-linux_x86_64.whl - paddlepaddle-2.6.1-cp311-cp311-linux_x86_64.whl - paddlepaddle-2.6.1-cp312-cp312-linux_x86_64.whl - - - cpu-openblas-avx - paddlepaddle-2.6.1-cp38-cp38-linux_x86_64.whl - - - - - - - - - - - cuda11.2-cudnn8.1-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1.post112-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post112-cp312-cp312-linux_x86_64.whl - - - cuda11.6-cudnn8.4-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1.post116-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post116-cp312-cp312-linux_x86_64.whl - - - cuda11.7-cudnn8.4-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1.post117-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post117-cp312-cp312-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp38-cp38-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp39-cp39-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp310-cp310-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp311-cp311-linux_x86_64.whl + paddlepaddle-3.0.0b1-cp312-cp312-linux_x86_64.whl cuda11.8-cudnn8.6-mkl-gcc8.2-avx - - paddlepaddle_gpu-2.6.1-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1-cp312-cp312-linux_x86_64.whl - - cuda12.0-cudnn8.9-mkl-gcc12.2-avx - - paddlepaddle_gpu-2.6.1.post120-cp38-cp38-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp39-cp39-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp310-cp310-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp311-cp311-linux_x86_64.whl - - paddlepaddle_gpu-2.6.1.post120-cp312-cp312-linux_x86_64.whl - - - macos-cpu-openblas - - paddlepaddle-2.6.1-cp38-cp38-macosx_10_14_x86_64.whl - - paddlepaddle-2.6.1-cp39-cp39-macosx_10_14_x86_64.whl - - paddlepaddle-2.6.1-cp310-cp310-macosx_10_14_universal2.whl - - paddlepaddle-2.6.1-cp311-cp311-macosx_10_14_universal2.whl - - paddlepaddle-2.6.1-cp312-cp312-macosx_10_14_universal2.whl - - - macos-cpu-openblas-m1 - - paddlepaddle-2.6.1-cp38-cp38-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp39-cp39-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp310-cp310-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp311-cp311-macosx_11_0_arm64.whl - - paddlepaddle-2.6.1-cp312-cp312-macosx_11_0_arm64.whl + + paddlepaddle_gpu-3.0.0b1-cp38-cp38-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp39-cp39-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp310-cp310-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp311-cp311-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp312-cp312-linux_x86_64.whl + + cuda12.3-cudnn9.0-mkl-gcc12.2-avx + + paddlepaddle_gpu-3.0.0b1-cp38-cp38-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp39-cp39-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp310-cp310-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp311-cp311-linux_x86_64.whl + + paddlepaddle_gpu-3.0.0b1-cp312-cp312-linux_x86_64.whl + + + macos-cpu-x86 + + paddlepaddle-3.0.0b1-cp38-cp38-macosx_10_9_x86_64.whl + + paddlepaddle-3.0.0b1-cp39-cp39-macosx_10_9_x86_64.whl + + paddlepaddle-3.0.0b1-cp310-cp310-macosx_10_9_universal2.whl + + paddlepaddle-3.0.0b1-cp311-cp311-macosx_10_9_universal2.whl + + paddlepaddle-3.0.0b1-cp312-cp312-macosx_10_9_universal2.whl + + + macos-cpu-arm + + paddlepaddle-3.0.0b1-cp38-cp38-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp39-cp39-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp310-cp310-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp311-cp311-macosx_11_0_arm64.whl + + paddlepaddle-3.0.0b1-cp312-cp312-macosx_11_0_arm64.whl win-cpu-mkl-avx - paddlepaddle-2.6.1-cp38-cp38-win_amd64.whl - paddlepaddle-2.6.1-cp39-cp39-win_amd64.whl - paddlepaddle-2.6.1-cp310-cp310-win_amd64.whl - paddlepaddle-2.6.1-cp311-cp311-win_amd64.whl - paddlepaddle-2.6.1-cp312-cp312-win_amd64.whl - - - win-cpu-openblas-avx - paddlepaddle-2.6.1-cp38-cp38-win_amd64.whl - - - - - - - - - - - win-cuda11.2-cudnn8.2-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post112-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post112-cp312-cp312-win_amd64.whl - - - win-cuda11.6-cudnn8.4-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post116-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post116-cp312-cp312-win_amd64.whl - - - win-cuda11.7-cudnn8.4-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post117-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post117-cp312-cp312-win_amd64.whl + paddlepaddle-3.0.0b1-cp38-cp38-win_amd64.whl + paddlepaddle-3.0.0b1-cp39-cp39-win_amd64.whl + paddlepaddle-3.0.0b1-cp310-cp310-win_amd64.whl + paddlepaddle-3.0.0b1-cp311-cp311-win_amd64.whl + paddlepaddle-3.0.0b1-cp312-cp312-win_amd64.whl win-cuda11.8-cudnn8.6-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1-cp312-cp312-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp38-cp38-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp39-cp39-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp310-cp310-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp311-cp311-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp312-cp312-win_amd64.whl - win-cuda12.0-cudnn8.9-mkl-vs2019-avx - paddlepaddle_gpu-2.6.1.post120-cp38-cp38-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp39-cp39-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp310-cp310-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp311-cp311-win_amd64.whl - paddlepaddle_gpu-2.6.1.post120-cp312-cp312-win_amd64.whl - - - linux-cinn-cuda11.2-cudnn8-mkl-gcc8.2-avx - paddlepaddle_gpu-2.6.1.post112-cp38-cp38-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp39-cp39-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp310-cp310-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp311-cp311-linux_x86_64.whl - paddlepaddle_gpu-2.6.1.post112-cp312-cp312-linux_x86_64.whl - - - linux-cuda11.2-cudnn8-mkl-gcc8.2-avx-pascal - paddlepaddle_gpu-2.6.1-cp38-cp38-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp39-cp39-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp310-cp310-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp311-cp311-linux_x86_64.whl - paddlepaddle_gpu-2.6.1-cp312-cp312-linux_x86_64.whl + win-cuda12.3-cudnn9.0-mkl-vs2019-avx + paddlepaddle_gpu-3.0.0b1-cp38-cp38-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp39-cp39-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp310-cp310-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp311-cp311-win_amd64.whl + paddlepaddle_gpu-3.0.0b1-cp312-cp312-win_amd64.whl @@ -537,155 +441,6 @@ abi tag: similar to 'cp33m', 'abi3', 'none' platform tag: similar to 'linux_x86_64', 'any' - -

        -## **Multi-version whl package list - dev** -

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        Release Instruction cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311 cp312-cp312
        linux-cpu-mkl-avx paddlepaddle-latest-cp38-cp38-linux_x86_64.whl paddlepaddle-latest-cp39-cp39-linux_x86_64.whl paddlepaddle-latest-cp310-cp310-linux_x86_64.whl paddlepaddle-latest-cp311-cp311-linux_x86_64.whl paddlepaddle-latest-cp312-cp312-linux_x86_64.whl
        linux-cpu-openblas-avx paddlepaddle-latest-cp38-cp38-linux_x86_64.whl - - - -
        cuda11.2-cudnn8.1-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda11.6-cudnn8.4-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda11.7-cudnn8.4-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda11.8-cudnn8.6-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        cuda12.0-cudnn8.9-mkl paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl paddlepaddle_gpu-latest-cp310-cp310-linux_x86_64.whl paddlepaddle_gpu-latest-cp311-cp311-linux_x86_64.whl paddlepaddle_gpu-latest-cp312-cp312-linux_x86_64.whl
        mac-cpu paddlepaddle-cp38-cp38-macosx_10_9_x86_64.whl paddlepaddle-cp39-cp39-macosx_10_9_x86_64.whl paddlepaddle-cp310-cp310-macosx_10_9_x86_64.whl paddlepaddle-cp311-cp311-macosx_10_9_x86_64.whl paddlepaddle-cp312-cp312-macosx_10_9_x86_64.whl
        macos-cpu-openblas-m1 paddlepaddle-cp38-cp38-macosx_11_0_arm64.whl paddlepaddle-cp39-cp39-macosx_11_0_arm64.whl paddlepaddle-cp310-cp310-macosx_11_0_arm64.whl paddlepaddle-cp311-cp311-macosx_11_0_arm64.whl paddlepaddle-cp312-cp312-macosx_11_0_arm64.whl
        win-cpu-mkl-avx paddlepaddle-latest-cp38-cp38-win_amd64.whl paddlepaddle-latest-cp39-cp39-win_amd64.whl paddlepaddle-latest-cp310-cp310-win_amd64.whl paddlepaddle-latest-cp311-cp311-win_amd64.whl paddlepaddle-latest-cp312-cp312-win_amd64.whl
        win-cpu-openblas-avx paddlepaddle-latest-cp38-cp38-win_amd64.whl - - - -
        win-cuda11.2-cudnn8.2-mkl-vs2019-avx paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda11.6-cudnn8.4.0-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda11.7-cudnn8.4.1-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda11.8-cudnn8.6.0-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        win-cuda12.0-cudnn8.9.1-mkl-avx-vs2019 paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl paddlepaddle_gpu-latest-cp310-cp310-win_amd64.whl paddlepaddle_gpu-latest-cp311-cp311-win_amd64.whl paddlepaddle_gpu-latest-cp312-cp312-win_amd64.whl
        -

        - - diff --git a/docs/install/conda/linux-conda.md b/docs/install/conda/linux-conda.md index fc55e0a4929..5b97ec80da0 100644 --- a/docs/install/conda/linux-conda.md +++ b/docs/install/conda/linux-conda.md @@ -86,39 +86,29 @@ python3 -c "import platform;print(platform.architecture()[0]);print(platform.mac #### CPU 版的 PaddlePaddle + 如果您的计算机没有 NVIDIA® GPU，请安装 CPU 版的 PaddlePaddle ``` -conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ +conda install paddlepaddle==3.0.0b1 -c paddle ``` - #### GPU 版的 PaddlePaddle - -* 对于 `CUDA 11.2`，需要搭配 cuDNN 8.2.1(多卡环境下 NCCL>=2.7)，安装命令为: +* 对于 `CUDA 11.8` 安装命令为: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=11.8 -c paddle -c nvidia ``` -* 对于 `CUDA 11.6`，需要搭配 cuDNN 8.4.0(多卡环境下 NCCL>=2.7)，安装命令为: +* 对于 `CUDA 12.3` 安装命令为: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=12.3 -c paddle -c nvidia ``` -* 对于 `CUDA 11.7`，需要搭配 cuDNN 8.4.1(多卡环境下 NCCL>=2.7)，安装命令为: - - ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge - ``` - -您可参考 NVIDIA 官方文档了解 CUDA 和 CUDNN 的安装流程和配置方法，请见[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) - - ## **三、验证安装** diff --git a/docs/install/conda/linux-conda_en.md b/docs/install/conda/linux-conda_en.md index 14916f84957..6a2d3d1d8e7 100644 --- a/docs/install/conda/linux-conda_en.md +++ b/docs/install/conda/linux-conda_en.md @@ -91,37 +91,29 @@ You can choose the following version of PaddlePaddle to start installation: #### CPU Version of PaddlePaddle + If your computer doesn't have NVIDIA® GPU, please install `the CPU Version of PaddlePaddle` ``` -conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ +conda install paddlepaddle==3.0.0b1 -c paddle ``` - #### GPU Version of PaddlePaddle -* If you are using CUDA 11.2，cuDNN 8.2.1(for multi card support, NCCL>=2.7): +* If you are using CUDA 11.8: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=11.8 -c paddle -c nvidia ``` -* If you are using CUDA 11.6，cuDNN 8.4.0(for multi card support, NCCL>=2.7): +* If you are using CUDA 12.3: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=12.3 -c paddle -c nvidia ``` -* If you are using CUDA 11.7，cuDNN 8.4.1(for multi card support, NCCL>=2.7): - - ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge - ``` - -You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) - ## Verify installation diff --git a/docs/install/conda/macos-conda.md b/docs/install/conda/macos-conda.md index 30600afbf54..4adb5843edc 100644 --- a/docs/install/conda/macos-conda.md +++ b/docs/install/conda/macos-conda.md @@ -83,7 +83,7 @@ python3 -c "import platform;print(platform.architecture()[0]);print(platform.mac * 目前在 macOS 环境仅支持 CPU 版 PaddlePaddle，请参考如下命令安装 Paddle: ``` - conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + conda install paddlepaddle==3.0.0b1 -c paddle ``` ## **三、验证安装** diff --git a/docs/install/conda/macos-conda_en.md b/docs/install/conda/macos-conda_en.md index 371e218972e..d938ea6408b 100644 --- a/docs/install/conda/macos-conda_en.md +++ b/docs/install/conda/macos-conda_en.md @@ -87,7 +87,7 @@ conda config --set show_channel_urls yes * Currently, only the CPU version of PaddlePaddle is supported in the macOS environment. Please use the following command to install PaddlePaddle： ``` - conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + conda install paddlepaddle==3.0.0b1 -c paddle ``` diff --git a/docs/install/conda/windows-conda.md b/docs/install/conda/windows-conda.md index 29891f4c291..8f165027654 100644 --- a/docs/install/conda/windows-conda.md +++ b/docs/install/conda/windows-conda.md @@ -90,35 +90,27 @@ python -c "import platform;print(platform.architecture()[0]);print(platform.mach 如果您的计算机没有 NVIDIA® GPU，请安装 CPU 版的 PaddlePaddle + ``` -conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ +conda install paddlepaddle==3.0.0b1 -c paddle ``` - #### GPU 版的 PaddlePaddle -* 对于 `CUDA 11.2`，需要搭配 cuDNN 8.2.1，安装命令为: +* 对于 `CUDA 11.8` 安装命令为: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=11.8 -c paddle -c nvidia ``` -* 对于 `CUDA 11.6`，需要搭配 cuDNN 8.4.0，安装命令为: +* 对于 `CUDA 12.3` 安装命令为: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=12.3 -c paddle -c nvidia ``` -* 对于 `CUDA 11.7`，需要搭配 cuDNN 8.4.1，安装命令为: - - ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge - ``` - -您可参考 NVIDIA 官方文档了解 CUDA 和 CUDNN 的安装流程和配置方法，请见[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) - ## **三、验证安装** diff --git a/docs/install/conda/windows-conda_en.md b/docs/install/conda/windows-conda_en.md index 736e0da59a7..f3f0a2b2df3 100644 --- a/docs/install/conda/windows-conda_en.md +++ b/docs/install/conda/windows-conda_en.md @@ -93,10 +93,11 @@ You can choose the following version of PaddlePaddle to start installation: #### CPU Version of PaddlePaddle + If your computer doesn't have NVIDIA® GPU, please install `the CPU Version of PaddlePaddle` ``` -conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ +conda install paddlepaddle==3.0.0b1 -c paddle ``` @@ -105,26 +106,18 @@ conda install paddlepaddle==2.6.1 --channel https://mirrors.tuna.tsinghua.edu.cn #### GPU Version of PaddlePaddle -* If you are using CUDA 11.2，cuDNN 8.2.1: - - ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge - ``` - -* If you are using CUDA 11.6，cuDNN 8.4.0: +* If you are using CUDA 11.8: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=11.8 -c paddle -c nvidia ``` -* If you are using CUDA 11.7，cuDNN 8.4.1: +* If you are using CUDA 12.3: ``` - conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge + conda install paddlepaddle-gpu==3.0.0b1 paddlepaddle-cuda=12.3 -c paddle -c nvidia ``` -You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) - ## Verify installation diff --git a/docs/install/docker/linux-docker.md b/docs/install/docker/linux-docker.md index b601163b7f1..defc14b2f51 100644 --- a/docs/install/docker/linux-docker.md +++ b/docs/install/docker/linux-docker.md @@ -21,46 +21,40 @@ * CPU 版的 PaddlePaddle： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 ``` * CPU 版的 PaddlePaddle，且镜像中预装好了 jupyter： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` * GPU 版的 PaddlePaddle(**建议拉取最新版本镜像，并确保已经成功安装 NVIDIA Container Toolkit**)： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.2-cudnn8.2-trt8.0 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda11.8-cudnn8.6-trt8.5 ``` ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.7-cudnn8.4-trt8.4 - ``` - ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda12.0-cudnn8.9-trt8.6 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 ``` 如果您的机器不在中国大陆地区，可以直接从 DockerHub 拉取镜像： * CPU 版的 PaddlePaddle： ``` - docker pull paddlepaddle/paddle:2.6.1 + docker pull paddlepaddle/paddle:3.0.0b1 ``` * CPU 版的 PaddlePaddle，且镜像中预装好了 jupyter： ``` - docker pull paddlepaddle/paddle:2.6.1-jupyter + docker pull paddlepaddle/paddle:3.0.0b1-jupyter ``` * GPU 版的 PaddlePaddle(**建议拉取最新版本镜像，并确保已经成功安装 NVIDIA Container Toolkit**)： ``` - docker pull paddlepaddle/paddle:2.6.1-gpu-cuda11.2-cudnn8.2-trt8.0 - ``` + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda11.8-cudnn8.6-trt8.5 ``` - docker pull paddlepaddle/paddle:2.6.1-gpu-cuda11.7-cudnn8.4-trt8.4 ``` - ``` - docker pull paddlepaddle/paddle:2.6.1-gpu-cuda12.0-cudnn8.9-trt8.6 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 ``` 您还可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取更多镜像。 @@ -72,7 +66,7 @@ ``` - docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1 /bin/bash + docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 /bin/bash ``` - `--name paddle_docker`：设定 Docker 的名称，`paddle_docker` 是自己设置的名称； @@ -83,7 +77,7 @@ - `-v $PWD:/paddle`：指定将当前路径（PWD 变量会展开为当前路径的绝对路径）挂载到容器内部的 /paddle 目录； - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1`：指定需要使用的 image 名称，您可以通过`docker images`命令查看；/bin/bash 是在 Docker 中要执行的命令 + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1`：指定需要使用的 image 名称，您可以通过`docker images`命令查看；/bin/bash 是在 Docker 中要执行的命令 * 使用 CPU 版本的 PaddlePaddle，且镜像中预装好了 jupyter： @@ -98,24 +92,24 @@ cd ./jupyter_docker ``` ``` - docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` - `--rm`：关闭容器后删除容器； - - `--env USER_PASSWD="password you set"`：为 jupyter 设置登录密码，`password you set` 是自己设置的密码； + - `--env USER_PASSWD="password you set"`：为登入用户 ["jovyan", "paddle"] 设置登录密码，`password you set` 是自己设置的密码； - `-v $PWD:/home/paddle`：指定将当前路径（PWD 变量会展开为当前路径的绝对路径）挂载到容器内部的 /home/paddle 目录； - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter`：指定需要使用的 image 名称，您可以通过`docker images`命令查看 + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter`：指定需要使用的 image 名称，您可以通过`docker images`命令查看 * 使用 GPU 版本的 PaddlePaddle： ``` - docker run --gpus all --name paddle_docker -v $PWD:/paddle --network=host -it registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda12.0-cudnn8.9-trt8.6-gcc12.2 /bin/bash + docker run --gpus all --name paddle_docker -v $PWD:/paddle --network=host -it registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 /bin/bash ``` - `--gpus all`: 在 Docker 容器中允许使用 gpu; @@ -127,7 +121,7 @@ - `-it`： 与宿主机保持交互状态; - - `registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda12.0-cudnn8.9-trt8.6-gcc12.2`：使用名为`registry.baidubce.com/paddlepaddle/paddle`, tag 为`latest-dev-cuda12.0-cudnn8.9-trt8.6-gcc12.2`的镜像创建 Docker 容器，/bin/bash 进入容器后启动/bin/bash 命令。 + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6`：使用名为`registry.baidubce.com/paddlepaddle/paddle`, tag 为`3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6`的镜像创建 Docker 容器，/bin/bash 进入容器后启动/bin/bash 命令。 @@ -146,24 +140,20 @@ - registry.baidubce.com/paddlepaddle/paddle:2.6.1 - 安装了 2.6.1 版本 paddle 的 CPU 镜像 - - - registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter - 安装了 2.6.1 版本 paddle 的 CPU 镜像，且镜像中预装好了 jupyter，启动 docker 即运行 jupyter 服务 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 + 安装了 3.0.0b1 版本 paddle 的 CPU 镜像 - registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda12.0-cudnn8.9-trt8.6 - 安装了 2.6.1 版本 paddle 的 GPU 镜像，cuda 版本为 12.0，cudnn 版本为 8.9，trt 版本为 8.6 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter + 安装了 3.0.0b1 版本 paddle 的 CPU 镜像，且镜像中预装好了 jupyter，启动 docker 即运行 jupyter 服务 - registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.7-cudnn8.4-trt8.4 - 安装了 2.6.1 版本 paddle 的 GPU 镜像，cuda 版本为 11.7，cudnn 版本为 8.4，trt 版本为 8.4 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda11.8-cudnn8.6-trt8.5 + 安装了 3.0.0b1 版本 paddle 的 GPU 镜像，cuda 版本为 11.8，cudnn 版本为 8.6，trt 版本为 8.5 - registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.2-cudnn8.2-trt8.0 - 安装了 2.6.1 版本 paddle 的 GPU 镜像，cuda 版本为 11.2，cudnn 版本为 8.2，trt 版本为 8.0 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 + 安装了 3.0.0b1 版本 paddle 的 GPU 镜像，cuda 版本为 12.3，cudnn 版本为 9.0，trt 版本为 8.6 diff --git a/docs/install/docker/linux-docker_en.md b/docs/install/docker/linux-docker_en.md index 71f66e14a35..30b83ee6e8f 100644 --- a/docs/install/docker/linux-docker_en.md +++ b/docs/install/docker/linux-docker_en.md @@ -21,46 +21,40 @@ For domestic users, when downloading docker is slow due to network problems, you * CPU version of PaddlePaddle： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 ``` * CPU version of PaddlePaddle, and the image is pre-installed with jupyter： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` * GPU version of PaddlePaddle(**Latest version of gpu image is recommended， and make sure NVIDIA Container Toolkit is installed successfully**)： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.2-cudnn8.2-trt8.0 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 ``` ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.7-cudnn8.4-trt8.4 - ``` - ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda12.0-cudnn8.9-trt8.6 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda11.8-cudnn8.6-trt8.5 ``` If your machine is not in mainland China, you can pull the image directly from DockerHub: * CPU version of PaddlePaddle： ``` - docker pull paddlepaddle/paddle:2.6.1 + docker pull paddlepaddle/paddle:3.0.0b1 ``` * CPU version of PaddlePaddle, and the image is pre-installed with jupyter： ``` - docker pull paddlepaddle/paddle:2.6.1-jupyter + docker pull paddlepaddle/paddle:3.0.0b1-jupyter ``` * GPU version of PaddlePaddle(**Latest version of gpu image is recommended， and make sure NVIDIA Container Toolkit is installed successfully**)： ``` - docker pull paddlepaddle/paddle:2.6.1-gpu-cuda11.2-cudnn8.2-trt8.0 - ``` + docker pull paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 ``` - docker pull paddlepaddle/paddle:2.6.1-gpu-cuda11.7-cudnn8.4-trt8.4 ``` - ``` - docker pull paddlepaddle/paddle:2.6.1-gpu-cuda12.0-cudnn8.9-trt8.6 + docker pull paddlepaddle/paddle:3.0.0b1-gpu-cuda11.8-cudnn8.6-trt8.5 ``` You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to get more images. @@ -72,7 +66,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g ``` - docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1 /bin/bash + docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 /bin/bash ``` - `--name paddle_docker`: set name of Docker, `paddle_docker` is name of docker you set; @@ -83,7 +77,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g - `-v $PWD:/paddle`: Specifies to mount the current path of the host (PWD variable in Linux will expand to the absolute path of the current path) to the /paddle directory inside the container; - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1`: Specify the name of the image to be used. You can view it through the 'docker images' command. /bin/Bash is the command to be executed in Docker + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1`: Specify the name of the image to be used. You can view it through the 'docker images' command. /bin/Bash is the command to be executed in Docker * Use GPU version of PaddlePaddle： @@ -91,7 +85,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g ``` - docker run --gpus all --name paddle_docker -v $PWD:/paddle --network=host -it registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda12.0-cudnn8.9-trt8.6-gcc12.2 /bin/bash + docker run --gpus all --name paddle_docker -v $PWD:/paddle --network=host -it registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 /bin/bash ``` - `--gpus all`: gpu resources can be used in Docker container; @@ -104,7 +98,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g - `-v $PWD:/paddle`: Specifies to mount the current path of the host (PWD variable in Linux will expand to the absolute path of the current path) to the /paddle directory inside the container; - - `registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda12.0-cudnn8.9-trt8.6-gcc12.2`: Specify the name of the image to be used. You can view it through the 'docker images' command. /bin/Bash is the command to be executed in Docker + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6`: Specify the name of the image to be used. You can view it through the 'docker images' command. /bin/Bash is the command to be executed in Docker * Use CPU version of PaddlePaddle with jupyter： @@ -120,7 +114,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g cd ./jupyter_docker ``` ``` - docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` - `--rm`: Delete the container after closing it; @@ -131,7 +125,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g - `-v $PWD:/home/paddle`: Specifies to mount the current path (the PWD variable will be expanded to the absolute path of the current path) to the /home/paddle directory inside the container; - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter`: Specify the name of the image to be used, you can view it through the `docker images` command + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter`: Specify the name of the image to be used, you can view it through the `docker images` command Now you have successfully used Docker to install PaddlePaddle. For more information about using Docker, see[Docker official documents](https://docs.docker.com) @@ -149,24 +143,20 @@ Now you have successfully used Docker to install PaddlePaddle. For more informat - registry.baidubce.com/paddlepaddle/paddle:2.6.1 - CPU image with 2.6.1 version of paddle installed - - - registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter - CPU image of paddle version 2.6.1 is installed, and jupyter is pre-installed in the image. Start the docker to run the jupyter service + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 + CPU image with 3.0.0b1 version of paddle installed - registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda12.0-cudnn8.9-trt8.6 - GPU image of paddle version 2.6.1 is installed, cuda version is 12.0, cudnn version is 8.9, trt version is 8.6 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter + CPU image of paddle version 3.0.0b1 is installed, and jupyter is pre-installed in the image. Start the docker to run the jupyter service - registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.7-cudnn8.4-trt8.4 - GPU image of paddle version 2.6.1 is installed, cuda version is 11.7, cudnn version is 8.4, trt version is 8.4 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda12.3-cudnn9.0-trt8.6 + GPU image of paddle version 3.0.0b1 is installed, cuda version is 12.3, cudnn version is 9.0, trt version is 8.6 - registry.baidubce.com/paddlepaddle/paddle:2.6.1-gpu-cuda11.2-cudnn8.2-trt8.0 - GPU image of paddle version 2.6.1 is installed, cuda version is 11.2, cudnn version is 8.2, trt version is 8.0 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-gpu-cuda11.8-cudnn8.6-trt8.5 + GPU image of paddle version 3.0.0b1 is installed, cuda version is 11.8, cudnn version is 8.6, trt version is 8.5 diff --git a/docs/install/docker/macos-docker.md b/docs/install/docker/macos-docker.md index 348b66bb22a..d5590b4c859 100644 --- a/docs/install/docker/macos-docker.md +++ b/docs/install/docker/macos-docker.md @@ -19,24 +19,24 @@ * CPU 版的 PaddlePaddle： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 ``` * CPU 版的 PaddlePaddle，且镜像中预装好了 jupyter： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` 如果您的机器不在中国大陆地区，可以直接从 DockerHub 拉取镜像： * CPU 版的 PaddlePaddle： ``` - docker pull paddlepaddle/paddle:2.6.1 + docker pull paddlepaddle/paddle:3.0.0b1 ``` * CPU 版的 PaddlePaddle，且镜像中预装好了 jupyter： ``` - docker pull paddlepaddle/paddle:2.6.1-jupyter + docker pull paddlepaddle/paddle:3.0.0b1-jupyter ``` 您还可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取更多镜像。 @@ -48,7 +48,7 @@ ``` - docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1 /bin/bash + docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 /bin/bash ``` - `--name paddle_docker`：设定 Docker 的名称，`paddle_docker` 是自己设置的名称； @@ -59,7 +59,7 @@ - `-v $PWD:/paddle`：指定将当前路径（PWD 变量会展开为当前路径的绝对路径）挂载到容器内部的 /paddle 目录； - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1`：指定需要使用的 image 名称，您可以通过`docker images`命令查看；/bin/bash 是在 Docker 中要执行的命令 + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1`：指定需要使用的 image 名称，您可以通过`docker images`命令查看；/bin/bash 是在 Docker 中要执行的命令 * 使用 CPU 版本的 PaddlePaddle，且镜像中预装好了 jupyter： @@ -73,7 +73,7 @@ cd ./jupyter_docker ``` ``` - docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` - `--rm`：关闭容器后删除容器； @@ -84,7 +84,7 @@ - `-v $PWD:/home/paddle`：指定将当前路径（PWD 变量会展开为当前路径的绝对路径）挂载到容器内部的 /home/paddle 目录； - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter`：指定需要使用的 image 名称，您可以通过`docker images`命令查看 + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter`：指定需要使用的 image 名称，您可以通过`docker images`命令查看 @@ -104,12 +104,12 @@ - registry.baidubce.com/paddlepaddle/paddle:2.6.1 - 安装了 2.6.1 版本 paddle 的 CPU 镜像 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 + 安装了 3.0.0b1 版本 paddle 的 CPU 镜像 - registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter - 安装了 2.6.1 版本 paddle 的 CPU 镜像，且镜像中预装好了 jupyter，启动 docker 即运行 jupyter 服务 + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter + 安装了 3.0.0b1 版本 paddle 的 CPU 镜像，且镜像中预装好了 jupyter，启动 docker 即运行 jupyter 服务 diff --git a/docs/install/docker/macos-docker_en.md b/docs/install/docker/macos-docker_en.md index fadf8883c33..d8d2c4bcd77 100644 --- a/docs/install/docker/macos-docker_en.md +++ b/docs/install/docker/macos-docker_en.md @@ -19,24 +19,24 @@ For domestic users, when downloading docker is slow due to network problems, you * CPU version of PaddlePaddle： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1 + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 ``` * CPU version of PaddlePaddle, and the image is pre-installed with jupyter： ``` - docker pull registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker pull registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` If your machine is not in mainland China, you can pull the image directly from DockerHub: * CPU version of PaddlePaddle： ``` - docker pull paddlepaddle/paddle:2.6.1 + docker pull paddlepaddle/paddle:3.0.0b1 ``` * CPU version of PaddlePaddle, and the image is pre-installed with jupyter： ``` - docker pull paddlepaddle/paddle:2.6.1-jupyter + docker pull paddlepaddle/paddle:3.0.0b1-jupyter ``` You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to get more images. @@ -48,7 +48,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g ``` - docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1 /bin/bash + docker run --name paddle_docker -it -v $PWD:/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 /bin/bash ``` - `--name paddle_docker`: set name of Docker, `paddle_docker` is name of docker you set; @@ -59,7 +59,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g - `-v $PWD:/paddle`: Specifies to mount the current path of the host (PWD variable in Linux will expand to the absolute path of the current path) to the /paddle directory inside the container; - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1`: Specify the name of the image to be used. You can view it through the 'docker images' command. /bin/Bash is the command to be executed in Docker + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1`: Specify the name of the image to be used. You can view it through the 'docker images' command. /bin/Bash is the command to be executed in Docker * Use CPU version of PaddlePaddle with jupyter： @@ -75,7 +75,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g cd ./jupyter_docker ``` ``` - docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter + docker run -p 80:80 --rm --env USER_PASSWD="password you set" -v $PWD:/home/paddle registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter ``` - `--rm`: Delete the container after closing it; @@ -86,7 +86,7 @@ You can see [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to g - `-v $PWD:/home/paddle`: Specifies to mount the current path (the PWD variable will be expanded to the absolute path of the current path) to the /home/paddle directory inside the container; - - `registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter`: Specify the name of the image to be used, you can view it through the `docker images` command + - `registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter`: Specify the name of the image to be used, you can view it through the `docker images` command @@ -105,12 +105,12 @@ Now you have successfully used Docker to install PaddlePaddle. For more informat - registry.baidubce.com/paddlepaddle/paddle:2.6.1 - CPU image with 2.6.1 version of paddle installed + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1 + CPU image with 3.0.0b1 version of paddle installed - registry.baidubce.com/paddlepaddle/paddle:2.6.1-jupyter - CPU image of paddle version 2.6.1 is installed, and jupyter is pre-installed in the image. Start the docker to run the jupyter service + registry.baidubce.com/paddlepaddle/paddle:3.0.0b1-jupyter + CPU image of paddle version 3.0.0b1 is installed, and jupyter is pre-installed in the image. Start the docker to run the jupyter service diff --git a/docs/install/install_script.md b/docs/install/install_script.md deleted file mode 100644 index 72300a97a7e..00000000000 --- a/docs/install/install_script.md +++ /dev/null @@ -1,52 +0,0 @@ -# 辅助安装脚本 - -## 使用方法 - -下载脚本至本地后，使用命令`/bin/bash fast_install.sh`启动脚本 - -### Ubuntu 和 CentOS - -脚本会执行以下几步： - -1. GPU 检测 - - 检测您的机器是否含有我们支持的 GPU，如果有，会安装 GPU 版本的 PaddlePaddle，否则会安装 CPU 版本。 - （PaddlePaddle 目前支持 NVIDIA[官网](https://developer.nvidia.com/cuda-gpus#collapseOne)列出的，算力 7.0 以下的 GPU 和 v100 系列的 GPU） - -2. CUDA，cuDNN 检测 - - 检测您的机器是否安装我们支持的 CUDA，cuDNN，具体地： - - 1. 在`/usr/local/` 及其子目录下寻找 `cuda10.1/cuda10.2/cuda11.0/cuda11.2` 目录下的`version.txt`文件（通常如果您以默认方式安装了 CUDA）。 如果提示未找到 CUDA 请使用命令`find / -name version.txt`找到您所需要的 CUDA 目录下的“version.txt”路径，然后按照提示输入。 - 2. 在`/usr` 及其子目录下寻找文件 `cudnn.h` , 如果您的 cuDNN 未安装在默认路径请使用命令`find / -name cudnn.h`寻找您希望使用的 cuDNN 版本的`cudnn.h`路径并按提示输入 - - 如果未找到相应文件，则会安装 CPU 版本的 PaddlePaddle - -3. 选择数学库 -脚本默认会为您安装支持[MKL](https://software.intel.com/en-us/mkl)数学库的 PaddlePaddle，如果您的机器不支持`MKL`，请选择安装支持[OPENBLAS](https://www.openblas.net)的 PaddlePaddle - -4. 选择 PaddlePaddle 版本 -我们为您提供 2 种版本：开发版和稳定版，推荐您选择测试验证过的稳定版 - -5. 选择 Python 版本 -脚本默认会使用您机器中的 Python，您也可以输入您希望使用的 Python 的路径 - -6. 检查[AVX](https://zh.wikipedia.org/zh-hans/AVX 指令集)指令集 - -7. 使用[Python virtualenv](https://virtualenv.pypa.io/en/latest/) -脚本也支持按您的需求创建 Python 的虚拟环境 - -以上检查完成后就会为您安装对应您系统的 PaddlePaddle 了，安装一般需要 1~2 分钟会根据您的网络来决定，请您耐心等待。 - - -### macOS - -脚本会执行以下几步： - -1. 选择 PaddlePaddle 版本 -我们为您提供 2 种版本：开发版和稳定版，推荐您选择测试验证过的稳定版 - -2. 检查 Python 版本 -由于 macOS 自带的 Python 通常依赖于系统环境，因此我们不支持 macOS 自带的 Python 环境，请重新从 Python.org 安装 Python，然后根据提示输入您希望使用的 Python 的路径 - -3. 检查是否支持[AVX](https://zh.wikipedia.org/zh-hans/AVX 指令集)指令集 diff --git a/docs/install/install_xpu_cn.md b/docs/install/install_xpu_cn.md index 612d61e8c9b..a7e70768aba 100644 --- a/docs/install/install_xpu_cn.md +++ b/docs/install/install_xpu_cn.md @@ -1,10 +1,14 @@ -# 昆仑 XPU 芯片 +# 昆仑芯 XPU 芯片 -百度昆仑 AI 计算处理器（Baidu KUNLUN AI Computing Processor）是百度集十年 AI 产业技术实践于 2019 年推出的全功能 AI 芯片。基于自主研发的先进 XPU 架构，为云端和边缘端的人工智能业务而设计。 百度昆仑与飞桨及其他国产软硬件强强组合，打造一个全面领先的国产化 AI 技术生态，部署和应用于诸多 “人工智能+“的行业领域，包括智能云和高性能计算，智慧制造、智慧城市和安防等。更多昆仑 XPU 芯片详情及技术指标请 [点击这里](https://www.kunlunxin.com/) 。 +昆仑芯是百度集十年 AI 产业技术实践、自主研发的 AI 芯片。该产品拥有创新的 XPU 架构、领先的产品竞争力及丰富的场景落地实践等优势，目前已被广泛部署在百度搜索、小度、大模型等业务并在百度外有数百家客户，将 AI 算力赋能千行百业智能化升级。 -飞桨框架支持基于昆仑 XPU 芯片的训练和推理，请参考以下内容快速体验： +昆仑芯和飞桨的适配自 2018 年启动，当前已携手共同完成了一套端到端的 AI 计算系统解决方案，并致力于携手打造一个全栈式软硬一体的 AI 生态，夯实算力基础设施建设。“昆仑芯+飞桨”方案已在智慧金融、工业质检等领域成功部署落地。 -- [昆仑 XPU 安装说明](../guides/hardware_support/xpu/install_cn.html) -- [昆仑 XPU 运行示例](../guides/hardware_support/xpu/example_cn.html) -- [昆仑 XPU 支持模型](../guides/hardware_support/xpu/support_cn.html) +更多昆仑芯 XPU 芯片详情及技术指标请 [点击这里](https://www.kunlunxin.com/) 。 + +飞桨框架支持基于昆仑芯 XPU 芯片的训练和推理，请参考以下内容快速体验： + +- [昆仑芯 XPU 安装说明](../guides/hardware_support/xpu/install_cn.html) +- [昆仑芯 XPU 运行示例](../guides/hardware_support/xpu/example_cn.html) +- [昆仑芯 XPU 支持模型](../guides/hardware_support/xpu/support_cn.html) diff --git a/docs/install/pip/linux-pip.md b/docs/install/pip/linux-pip.md index dadf3f415fd..9ab6ffd0b09 100644 --- a/docs/install/pip/linux-pip.md +++ b/docs/install/pip/linux-pip.md @@ -52,7 +52,7 @@ * 默认提供的安装包需要计算机支持 MKL -* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install/install_script.md)。 +* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/docs/install/install_script.md)。 @@ -64,131 +64,43 @@ * 如果您的计算机有 NVIDIA® GPU，请确保满足以下条件并且安装[GPU 版 PaddlePaddle](#gpu)，依赖库环境版本要求如下： - * **CUDA 工具包 11.2 配合 cuDNN v8.2.1, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.0.3.4** + * **CUDA 工具包 11.8 配合 cuDNN v8.6.0, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.5.3.1** - * **CUDA 工具包 11.6 配合 cuDNN v8.4.0, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.4.0.6** - - * **CUDA 工具包 11.7 配合 cuDNN v8.4.1, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.4.2.4** - - * **CUDA 工具包 11.8 配合 cuDNN v8.6.0, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.5.1.7** - - * **CUDA 工具包 12.0 配合 cuDNN v8.9.1, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.6.1.6** - - * **如需使用分布式多卡环境，需配合 NCCL>=2.7** + * **CUDA 工具包 12.3 配合 cuDNN v9.0.0, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.6.1.6** * **GPU 运算能力超过 6.0 的硬件设备** 您可参考 NVIDIA 官方文档了解 CUDA、CUDNN 和 TensorRT 的安装流程和配置方法，请见[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)，[TensorRT](https://developer.nvidia.com/tensorrt) -* 如果您需要使用多卡环境请确保您已经正确安装 nccl2，或者按照以下指令安装 nccl2（这里提供的是 CUDA11.2，cuDNN7 下 nccl2 的安装指令，更多版本的安装信息请参考 NVIDIA[官方网站](https://developer.nvidia.com/nccl)）: - - - ``` - rm -f /usr/local/lib/libnccl.so - wget --no-check-certificate -q https://nccl2-deb.cdn.bcebos.com/libnccl-2.10.3-1+cuda11.4.x86_64.rpm - wget --no-check-certificate -q https://nccl2-deb.cdn.bcebos.com/libnccl-devel-2.10.3-1+cuda11.4.x86_64.rpm - wget --no-check-certificate -q https://nccl2-deb.cdn.bcebos.com/libnccl-static-2.10.3-1+cuda11.4.x86_64.rpm - rpm -ivh libnccl-2.10.3-1+cuda11.4.x86_64.rpm - rpm -ivh libnccl-devel-2.10.3-1+cuda11.4.x86_64.rpm - rpm -ivh libnccl-static-2.10.3-1+cuda11.4.x86_64.rpm - ``` #### 2.1 CPU 版的 PaddlePaddle ``` - python3 -m pip install paddlepaddle==2.6.1 -i https://mirror.baidu.com/pypi/simple + python3 -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` - #### 2.2 GPU 版的 PaddlePaddle - -2.2.1 CUDA11.2 的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html - ``` - - - CUDA11.2 包含 cuDNN 动态链接库的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - -2.2.3 CUDA11.6 的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html - ``` - - - CUDA11.6 包含 cuDNN 动态链接库的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post116 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - -2.2.4 CUDA11.7 的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post117 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html - ``` - - - CUDA11.7 包含 cuDNN 动态链接库的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post117 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - -2.2.5 CUDA11.8 的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1 -i https://mirror.baidu.com/pypi/simple - ``` - - - CUDA11.8 包含 cuDNN 动态链接库的 PaddlePaddle，需要先使用如下命令将 wheel 包下载到本地，再使用`python3 -m pip install [name].whl`本地安装（[name]为 wheel 包名称）： +2.2.1 CUDA11.8 的 PaddlePaddle ``` - python3 -m pip download paddlepaddle-gpu==2.6.1 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html --no-index --no-deps - + python3 -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ ``` -2.2.6 CUDA12.0 的 PaddlePaddle +2.2.2 CUDA12.3 的 PaddlePaddle ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html + python3 -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/ ``` - CUDA12.0 包含 cuDNN 动态链接库的 PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - - 注： * 飞桨对于主流各 python 版本均提供了对应的安装包，而您环境中可能有多个 Python，请确认你想使用的 python 版本并下载对应的 paddlepaddle 安装包。例如您想使用 python3.10 的环境，则安装命令为 python3.10 -m pip install paddlepaddle。 @@ -201,7 +113,7 @@ * 如果你想安装`avx`、`openblas`的 Paddle 包，可以通过以下命令将 wheel 包下载到本地，再使用`python3 -m pip install [name].whl`本地安装（[name]为 wheel 包名称）： ``` - python3 -m pip download paddlepaddle==2.6.1 -f https://www.paddlepaddle.org.cn/whl/linux/openblas/avx/stable.html --no-index --no-deps + python3 -m pip install https://paddle-wheel.bj.bcebos.com/3.0.0-beta0/linux/linux-cpu-openblas-avx/paddlepaddle-3.0.0b1-cp38-cp38-linux_x86_64.whl ``` diff --git a/docs/install/pip/linux-pip_en.md b/docs/install/pip/linux-pip_en.md index c032bbb1603..a59c7ef1d02 100644 --- a/docs/install/pip/linux-pip_en.md +++ b/docs/install/pip/linux-pip_en.md @@ -63,33 +63,14 @@ * If your computer has NVIDIA® GPU, please make sure that the following conditions are met and install [the GPU Version of PaddlePaddle](#gpu) - * **CUDA toolkit 11.2 with cuDNN v8.2.1(for multi card support, NCCL2.7 or higher；for PaddleTensorRT deployment, TensorRT8.0.3.4)** + * **CUDA toolkit 11.8 with cuDNN v8.6.0(for PaddleTensorRT deployment, TensorRT8.5.1.7)** - * **CUDA toolkit 11.6 with cuDNN v8.4.0(for multi card support, NCCL2.7 or higher；for PaddleTensorRT deployment, TensorRT8.4.0.6)** + * **CUDA toolkit 12.3 with cuDNN v9.0.0(for PaddleTensorRT deployment, TensorRT8.6.1.6)** - * **CUDA toolkit 11.7 with cuDNN v8.4.1(for multi card support, NCCL2.7 or higher；for PaddleTensorRT deployment, TensorRT8.4.2.4)** - - * **CUDA toolkit 11.8 with cuDNN v8.6.0(for multi card support, NCCL2.7 or higher；for PaddleTensorRT deployment, TensorRT8.5.1.7)** - - * **CUDA toolkit 12.0 with cuDNN v8.9.1(for multi card support, NCCL2.7 or higher；for PaddleTensorRT deployment, TensorRT8.6.1.6)** - - * **Hardware devices with GPU computing power over 3.5** + * **Hardware devices with GPU computing power over 6.0** You can refer to NVIDIA official documents for installation process and configuration method of CUDA, cuDNN and TensorRT. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)，[TensorRT](https://developer.nvidia.com/tensorrt) -* If you need to use a multi-card environment, please make sure that you have installed nccl2 correctly, or install nccl2 according to the following instructions (here are the installation instructions of nccl2 under CUDA11.2 and cuDNN7. For more version installation information, please refer to NVIDIA [Official Website](https://developer.nvidia.com/nccl)): - - - ``` - rm -f /usr/local/lib/libnccl.so - wget --no-check-certificate -q https://nccl2-deb.cdn.bcebos.com/libnccl-2.10.3-1+cuda11.4.x86_64.rpm - wget --no-check-certificate -q https://nccl2-deb.cdn.bcebos.com/libnccl-devel-2.10.3-1+cuda11.4.x86_64.rpm - wget --no-check-certificate -q https://nccl2-deb.cdn.bcebos.com/libnccl-static-2.10.3-1+cuda11.4.x86_64.rpm - rpm -ivh libnccl-2.10.3-1+cuda11.4.x86_64.rpm - rpm -ivh libnccl-devel-2.10.3-1+cuda11.4.x86_64.rpm - rpm -ivh libnccl-static-2.10.3-1+cuda11.4.x86_64.rpm - ``` - ## Installation Step @@ -102,93 +83,26 @@ You can choose the following version of PaddlePaddle to start installation: ``` - python3 -m pip install paddlepaddle==2.6.1 -i https://mirror.baidu.com/pypi/simple + python3 -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` - #### 2.2 GPU Version of PaddlePaddle - -2.2.1 If you are using CUDA 11.2 - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post112 -i https://mirror.baidu.com/pypi/simple - ``` - - - CUDA11.2 with cuDNN dynamic library PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - -2.2.2 If you are using CUDA 11.6 - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html - ``` - - - CUDA11.6 with cuDNN dynamic library PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post116 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - -2.2.3 If you are using CUDA 11.7 - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post117 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html - ``` - - - CUDA11.7 with cuDNN dynamic library PaddlePaddle - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post117 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html - ``` - - 2.2.4 If you are using CUDA 11.8 ``` - python3 -m pip install paddlepaddle-gpu==2.6.1 -i https://mirror.baidu.com/pypi/simple - ``` - - - CUDA11.8 with cuDNN dynamic library PaddlePaddle, you can use the following command to download the wheel package to the local, and then use `python3 -m pip install [name].whl` to install locally ([name] is the name of the wheel package) - - - ``` - python3 -m pip download paddlepaddle-gpu==2.6.1 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html --no-index --no-deps - - ``` - - -2.2.5 If you are using CUDA 12.0 - - - ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html + python3 -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ ``` - CUDA12.0 with cuDNN dynamic library PaddlePaddle +2.2.5 If you are using CUDA 12.3 ``` - python3 -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/cudnnin/stable.html + python3 -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/ ``` @@ -204,7 +118,7 @@ Note： * If you want to install the Paddle package with `avx` and `openblas`, you can use the following command to download the wheel package to the local, and then use `python3 -m pip install [name].whl` to install locally ([name] is the name of the wheel package): ``` - python3 -m pip download paddlepaddle==2.6.1 -f https://www.paddlepaddle.org.cn/whl/linux/openblas/avx/stable.html --no-index --no-deps + python3 -m pip install https://paddle-wheel.bj.bcebos.com/3.0.0-beta0/linux/linux-cpu-openblas-avx/paddlepaddle-3.0.0b1-cp38-cp38-linux_x86_64.whl ``` diff --git a/docs/install/pip/macos-pip.md b/docs/install/pip/macos-pip.md index c28921cb6c4..ab472f326f8 100644 --- a/docs/install/pip/macos-pip.md +++ b/docs/install/pip/macos-pip.md @@ -53,7 +53,7 @@ -* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install/install_script.md)。 +* 如果您对机器环境不了解，请下载使用[快速安装脚本](https://fast-install.bj.bcebos.com/fast_install.sh)，配套说明请参考[这里](https://github.com/PaddlePaddle/FluidDoc/tree/develop/docs/install/install_script.md)。 @@ -70,12 +70,11 @@ ``` - python3 -m pip install paddlepaddle==2.6.1 -i https://mirror.baidu.com/pypi/simple + python3 -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` 注: -* macOS 上您需要安装 unrar 以支持 PaddlePaddle，可以使用命令`brew install unrar` * 请确认需要安装 PaddlePaddle 的 Python 是您预期的位置，因为您计算机可能有多个 Python。根据您的环境您可能需要将说明中所有命令行中的 python3 替换为具体的 Python 路径。 * 默认下载最新稳定版的安装包，如需获取 develop 版本 nightly build 的安装包，请参考[这里](https://www.paddlepaddle.org.cn/install/quick/zh/1.8.5-windows-pip) * 使用 macOS 中自带 Python 可能会导致安装失败。请使用[python 官网](https://www.python.org/downloads/mac-osx/)提供的 python3.8.x、python3.9.x、python3.10.x、python3.11.x、python3.12.x。 diff --git a/docs/install/pip/macos-pip_en.md b/docs/install/pip/macos-pip_en.md index 4601c70efba..230cf22ba63 100644 --- a/docs/install/pip/macos-pip_en.md +++ b/docs/install/pip/macos-pip_en.md @@ -47,7 +47,7 @@ ``` -* If you do not know the machine environment, please download and use[Quick install script](https://fast-install.bj.bcebos.com/fast_install.sh), for instructions please refer to[here](https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/install/install_script.md)。 +* If you do not know the machine environment, please download and use[Quick install script](https://fast-install.bj.bcebos.com/fast_install.sh), for instructions please refer to[here](https://github.com/PaddlePaddle/FluidDoc/tree/develop/docs/install/install_script.md)。 @@ -66,7 +66,7 @@ You can choose the following version of PaddlePaddle to start installation: ``` -python3 -m pip install paddlepaddle==2.6.1 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` Note： diff --git a/docs/install/pip/windows-pip.md b/docs/install/pip/windows-pip.md index 48946f16bb0..ebb007b0109 100644 --- a/docs/install/pip/windows-pip.md +++ b/docs/install/pip/windows-pip.md @@ -46,17 +46,11 @@ * 如果您的计算机有 NVIDIA® GPU，请确保满足以下条件并且安装 GPU 版 PaddlePaddle - * **CUDA 工具包 11.2 配合 cuDNN v8.2.1，如需使用 PaddleTensorRT 推理，需配合 TensorRT8.2.4.2** - - * **CUDA 工具包 11.6 配合 cuDNN v8.4.0，如需使用 PaddleTensorRT 推理，需配合 TensorRT8.4.0.6** - - * **CUDA 工具包 11.7 配合 cuDNN v8.4.1，如需使用 PaddleTensorRT 推理，需配合 TensorRT8.4.2.4** - * **CUDA 工具包 11.8 配合 cuDNN v8.6.0，如需使用 PaddleTensorRT 推理，需配合 TensorRT8.5.1.7** - * **CUDA 工具包 12.0 配合 cuDNN v8.9.1, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.6.1.6** + * **CUDA 工具包 12.3 配合 cuDNN v9.0.0, 如需使用 PaddleTensorRT 推理，需配合 TensorRT8.6.1.6** - * **GPU 运算能力超过 3.5 的硬件设备** + * **GPU 运算能力超过 6.0 的硬件设备** * 注：目前官方发布的 windows 安装包仅包含 CUDA 11.2/11.6/11.7/11.8/12.0，如需使用其他 cuda 版本，请通过源码自行编译。您可参考 NVIDIA 官方文档了解 CUDA、CUDNN 和 TensorRT 的安装流程和配置方法，请见[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)，[TensorRT](https://developer.nvidia.com/tensorrt) @@ -71,41 +65,23 @@ ``` - python -m pip install paddlepaddle==2.6.1 -i https://mirror.baidu.com/pypi/simple + python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` -#### 2.2 GPU 版的 PaddlePaddle - +#### 2.2 GPU 版的 PaddlePaddle -2.2.1 CUDA11.2 的 PaddlePaddle - - ``` - python -m pip install paddlepaddle-gpu==2.6.1.post112 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` - -2.2.2 CUDA11.6 的 PaddlePaddle - - ``` - python -m pip install paddlepaddle-gpu==2.6.1.post116 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` - -2.2.3 CUDA11.7 的 PaddlePaddle - - ``` - python -m pip install paddlepaddle-gpu==2.6.1.post117 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` 2.2.4 CUDA11.8 的 PaddlePaddle ``` - python -m pip install paddlepaddle-gpu==2.6.1 -i https://mirror.baidu.com/pypi/simple + python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ ``` -2.2.5 CUDA12.0 的 PaddlePaddle +2.2.5 CUDA12.3 的 PaddlePaddle ``` - python -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html + python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/ ``` 注： @@ -117,7 +93,7 @@ * 如果你想安装`avx`、`openblas`的 Paddle 包，可以通过以下命令将 wheel 包下载到本地，再使用`python -m pip install [name].whl`本地安装（[name]为 wheel 包名称）： ``` - python -m pip download paddlepaddle==2.6.1 -f https://www.paddlepaddle.org.cn/whl/windows/openblas/avx/stable.html --no-index --no-deps + python -m pip install https://paddle-wheel.bj.bcebos.com/3.0.0-beta0/windows/windows-cpu-avx-openblas-vs2017/paddlepaddle-3.0.0b1-cp38-cp38-win_amd64.whl ``` diff --git a/docs/install/pip/windows-pip_en.md b/docs/install/pip/windows-pip_en.md index e21c9fd5497..1bf2e8843fe 100644 --- a/docs/install/pip/windows-pip_en.md +++ b/docs/install/pip/windows-pip_en.md @@ -43,17 +43,11 @@ If you installed Python via Homebrew or the Python website, `pip` was installed * If your computer has NVIDIA® GPU, please make sure that the following conditions are met and install [the GPU Version of PaddlePaddle](#gpu) - * **CUDA toolkit 11.2 with cuDNN v8.2.1(for PaddleTensorRT deployment, TensorRT8.2.4.2)** - - * **CUDA toolkit 11.6 with cuDNN v8.4.0(for PaddleTensorRT deployment, TensorRT8.4.0.6)** - - * **CUDA toolkit 11.7 with cuDNN v8.4.1(for PaddleTensorRT deployment, TensorRT8.4.2.4)** - * **CUDA toolkit 11.8 with cuDNN v8.6.0(for PaddleTensorRT deployment, TensorRT8.5.1.7)** - * **CUDA toolkit 12.0 with cuDNN v8.9.1(for multi card support, NCCL2.7 or higher；for PaddleTensorRT deployment, TensorRT8.6.1.6)** + * **CUDA toolkit 12.3 with cuDNN v9.0.0(for PaddleTensorRT deployment, TensorRT8.6.1.6)** - * **GPU CUDA capability over 3.5** + * **GPU CUDA capability over 6.0** You can refer to NVIDIA official documents for installation process and configuration method of CUDA, cuDNN and TensorRT. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)，[TensorRT](https://developer.nvidia.com/tensorrt) @@ -68,43 +62,23 @@ You can choose the following version of PaddlePaddle to start installation: ``` - python -m pip install paddlepaddle==2.6.1 -i https://mirror.baidu.com/pypi/simple + python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` - #### 2.2 GPU Version of PaddlePaddle - -2.2.1 If you are using CUDA 11.2 - - ``` - python -m pip install paddlepaddle-gpu==2.6.1.post112 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` - -2.2.2 If you are using CUDA 11.6 - - ``` - python -m pip install paddlepaddle-gpu==2.6.1.post116 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` - -2.2.3 If you are using CUDA 11.7 - - ``` - python -m pip install paddlepaddle-gpu==2.6.1.post117 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - ``` - 2.2.4 If you are using CUDA 11.8 ``` - python -m pip install paddlepaddle-gpu==2.6.1 -i https://mirror.baidu.com/pypi/simple + python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ ``` -2.2.5 If you are using CUDA 12.0 +2.2.5 If you are using CUDA 12.3 ``` - python -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html + python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/ ``` Note： @@ -117,7 +91,7 @@ Note： * If you want to install the Paddle package with `avx` and `openblas`, you can use the following command to download the wheel package to the local, and then use `python -m pip install [name].whl` to install locally ([name] is the name of the wheel package): ``` - python -m pip download paddlepaddle==2.6.1 -f https://www.paddlepaddle.org.cn/whl/windows/openblas/avx/stable.html --no-index --no-deps + python -m pip install https://paddle-wheel.bj.bcebos.com/3.0.0-beta0/windows/windows-cpu-avx-openblas-vs2017/paddlepaddle-3.0.0b1-cp38-cp38-win_amd64.whl ``` ## Verify installation diff --git a/docs/practices/cv/super_resolution_sub_pixel.ipynb b/docs/practices/cv/super_resolution_sub_pixel.ipynb index cdbcade5fe2..c28fd9d228c 100755 --- a/docs/practices/cv/super_resolution_sub_pixel.ipynb +++ b/docs/practices/cv/super_resolution_sub_pixel.ipynb @@ -143,7 +143,7 @@ "### 3.3 数据集类定义\n", "飞桨（PaddlePaddle）数据集加载方案是统一使用Dataset（数据集定义） + DataLoader（多进程数据集加载）。\n", "\n", - "首先先进行数据集的定义，数据集定义主要是实现一个新的Dataset类，继承父类paddle.io.Dataset，并实现父类中以下两个抽象方法，__getitem__和__len__：\n", + "首先先进行数据集的定义，数据集定义主要是实现一个新的Dataset类，继承父类paddle.io.Dataset，并实现父类中以下两个抽象方法，`__getitem__`和`__len__`：\n", "```python\n", "class MyDataset(Dataset):\n", " def __init__(self):\n", @@ -301,7 +301,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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\n", 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", "text/plain": [ "
        " ] diff --git a/docs/practices/nlp/n_gram_model.ipynb b/docs/practices/nlp/n_gram_model.ipynb index 4e69c308370..36120b500ca 100644 --- a/docs/practices/nlp/n_gram_model.ipynb +++ b/docs/practices/nlp/n_gram_model.ipynb @@ -137,7 +137,7 @@ "collapsed": false }, "source": [ - "由于词表的的长尾，会降低模型训练的速度与精度。因此取词频前2500的单词作为词表，如果不在词表中的单词都用 '' 替换。" + "由于词表中的长尾效应，会降低模型训练的速度与精度。因此取词频前2500的单词作为词表，如果不在词表中的单词都用 '' 替换。" ] }, { @@ -449,7 +449,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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        " ] diff --git a/docs/release_note_cn.md b/docs/release_note_cn.md index 5abf718f880..0aaa2b14b52 100644 --- a/docs/release_note_cn.md +++ b/docs/release_note_cn.md @@ -1,3486 +1,462 @@ -# 2.6.0 Release Note +# 3.0 Beta Release Note +本版本的核心特性主要包括动静统一自动并行技术和神经网络编译器自动优化等新技术，旨在应对当前深度学习领域的新挑战。飞桨框架 3.0 Beta 版本延续了 2.x 版本动静统一、训推一体的设计理念，其开发接口全面兼容 2.x 版本。这意味着，使用 2.x 版本开发的代码，在绝大多数情况下无需修改，即可直接在 3.x 版本上运行。几个重点特性具体展开说明如下： +- 动静统一自动并行：为了降低大模型的编程难度，飞桨还优化了动静统一的半自动并行编程范式，显著简化了编程的复杂度。开发者无需深入研究手动并行编程的复杂概念和 API，只需进行少量的张量切分标注，即可完成混合并行模型的构建。框架能够自动推导分布式切分状态并添加通信算子，同时还支持一键动转静分布式训练，从而大幅简化了混合并行训练代码的开发过程。动静统一方面，飞桨通过采用基于字节码的动静转换技术，全面升级了其动转静训练能力，支持自适应的图构建功能。在 700 多个飞桨产业级模型上进行了验证，实现了一键动转静训练 100%的成功率。 +- 神经网络编译器自动优化：飞桨神经网络编译器 CINN（Compiler Infrastructure for Neural Networks）采用与框架一体化的设计，能够支持生成式模型、科学计算模型等多种模型的高效训练与可变形状推理，为计算灵活性与高性能之间提供了一个良好的平衡点。通过算子的自动融合和代码生成技术，Llama2 和 Stable Diffusion 模型的性能提升了 30%。 +- 高阶自动微分：为了更好支持科学计算等场景，飞桨框架设计并实现了基于组合算子机制的高阶自动微分技术，结合神经网络编译器自动优化技术，我们测试了超过 40 多个科学计算场景的微分方程，其求解速度领先业界同类产品 70%。 +- 高扩展中间表示 ：为了提升飞桨框架的可扩展性，我们研发了高扩展中间表示 PIR（Paddle Intermediate Representation）。这一表示系统性地抽象了底层核心概念，提供了灵活且高效的组件。PIR 作为基础设施，支撑着动转静、自动微分、自动并行、组合算子、图优化等多项技术，并广泛应用于分布式训练、模型压缩、推理部署等场景。通过 PIR 提供的 DRR（Declarative Rewrite Rule）机制，Pass 的开发成本可以降低 60%。我们对超过 900 个模型配置进行了测试，结果显示，在使用 PIR 后，推理的整体性能提升了超过 10%。 +- 多硬件适配：飞桨为大模型硬件适配提供了功能完善且低成本的方案。新硬件仅需适配 30 余个接口，即可支持大模型的训练、压缩与推理。同时，飞桨提供了基于编译器的硬件接入方式，硬件厂商只需以插件的形式实现编译器的代码生成后端，便能实现与飞桨框架的高效适配。飞桨硬件接入本次新增了对 4 款硬件昆仑 XPU、昇腾 NPU、海光 DCU 和寒武纪 MLU 的日常发版支持。 + +此版本包含了对框架 2.x 版本部分已有功能的持续改进，同时本版本的新特性在使用体验、性能、二次开发便利度以及硬件适配能力等方面带来了显著提升。除了上述核心特性外，此版本在用户体验层面持续丰富并增强了满足更多场景的 API 功能，针对大模型场景优化完善了分布式并行策略优化和推理功能增强，在编译安装方面做了比较彻底的易用性改进，对依赖包的安装方式和版本进行了全新同步升级，对系统安全进行了全面加固，对产品文档也进行了全面的纠错检查，同时也对一些废弃代码做了大量的清理以保证架构的简洁性。飞桨 3.0 Beta 版本在不使用新特性的情况下，表现仍然是成熟稳定的，每个新特性都提供了可灵活进行控制的开关，方便用户快速了解相关产品功能和体验对比。 + +## 1.用户体验升级 + +### 不兼容升级 +- 飞桨 API 支持隐式类型提升。在加减乘除等最常用的计算中，如果两个输入的数据类型不一样，就需要确定输出的数据类型问题。飞桨历史上的现状是部分支持且实际规则并不清楚，客观上表现为动静不一致、API 和运算符重载不一致 及 不符合交换率，特别是在大模型广泛使用 bf16/fp16 与 fp32 进行混合计算时容易出现非预期问题且难以定位。飞桨从 3.0 beta 版本开始，明确了[隐式数据类型提升规则](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/advanced/auto_type_promotion_cn.html)，其中详细定义了 Tensor 与 Tensor 和 Tensor 与 1 个数(Scalar)计算结果的类型，保证了计算符合交换律，运算符重载与二元 API 结果一致，动态图与静态图结果一致。更符合用户理解和业界习惯。[#60638](https://github.com/PaddlePaddle/Paddle/pull/60638), [#63842](https://github.com/PaddlePaddle/Paddle/pull/63842), [#60011](https://github.com/PaddlePaddle/Paddle/pull/60011) + +### 废弃功能 +- 支持 0 维 Tensor 已经稳定了 2 个版本，本版本取消了在一些情况下将 0 维 Tensor 转成只含 1 个元素的 1 维 Tensor 的开关`FLAGS_set_to_1d`，这个开关是为了兼容一些套件中用 1 个元素的 1 维 Tensor 表示 0 维 Tensor 的不正确写法。即当前飞桨完全区分 0 维 Tensor 和只含 1 个元素的 1 维 Tensor 的语义，两者不等价。[#61227](https://github.com/PaddlePaddle/Paddle/pull/61227) + +### 新增 API 功能 +此版本相比上一个版本新增 126 个 API，API 功能更加丰富，以更好支持大模型、科学计算等需求，包括： +- 新增 Tensor 计算类 API。`paddle.gammaln`, `paddle.gammainc`, `paddle.gammaincc`, `paddle.sinc`, `paddle.pdist`, `paddle.histogramdd`,`paddle.signbit`, `paddle.copysign`, `paddle.bitwise_right_shift/bitwise_left_shift`, `paddle.isposinf/isneginf/isreal`, `paddle.isin`, `paddle.hsplit/dsplit`, `paddle.column_stack/row_stack/dstack/hstack/vstack`, `paddle.slice_scatter`, `paddle.masked_scatter` [#60553](https://github.com/PaddlePaddle/Paddle/pull/60553), [#59311](https://github.com/PaddlePaddle/Paddle/pull/59311), [#59357](https://github.com/PaddlePaddle/Paddle/pull/59357), [#63521](https://github.com/PaddlePaddle/Paddle/pull/63521), [#57869](https://github.com/PaddlePaddle/Paddle/pull/57869), [#57880](https://github.com/PaddlePaddle/Paddle/pull/57880), [#57882](https://github.com/PaddlePaddle/Paddle/pull/57882), [#60150](https://github.com/PaddlePaddle/Paddle/pull/60150), [#57785](https://github.com/PaddlePaddle/Paddle/pull/57785), [#58092](https://github.com/PaddlePaddle/Paddle/pull/58092), [#63523](https://github.com/PaddlePaddle/Paddle/pull/63523), [#64001](https://github.com/PaddlePaddle/Paddle/pull/64001), [#58917](https://github.com/PaddlePaddle/Paddle/pull/58917), [#59127](https://github.com/PaddlePaddle/Paddle/pull/59127), [#59973](https://github.com/PaddlePaddle/Paddle/pull/59973), [#59383](https://github.com/PaddlePaddle/Paddle/pull/59383) +- 新增概率分布类 API。`paddle.distribution.ContinuousBernoulli`, `paddle.distribution.MultivariateNormal`, `paddle.distribution.Exponential`, `paddle.distribution.Gamma`, `paddle.distribution.Binomial`, `paddle.distribution.Poisson` [#58004](https://github.com/PaddlePaddle/Paddle/pull/58004), [#57899](https://github.com/PaddlePaddle/Paddle/pull/57899), [#57856](https://github.com/PaddlePaddle/Paddle/pull/57856) +- 新增优化器类 API。`paddle.optimizer.ASGD`, `paddle.optimizer.NAdam`, `paddle.optimizer.RAdam`, `paddle.optimizer.Rprop` [#58834](https://github.com/PaddlePaddle/Paddle/pull/58834), [#63671](https://github.com/PaddlePaddle/Paddle/pull/63671), [#58851](https://github.com/PaddlePaddle/Paddle/pull/58851) +- 新增线性代数类 API。`paddle.linalg.matrix_exp` [#59715](https://github.com/PaddlePaddle/Paddle/pull/59715) +- 新增其他 API。`paddle.bernoulli_`, `paddle.nn.ZeroPad1D/ZeroPad3D`, `paddle.nn.AdaptiveLogSoftmaxWithLoss`, `paddle.Tensor.apply` [#64252](https://github.com/PaddlePaddle/Paddle/pull/64252), [#59690](https://github.com/PaddlePaddle/Paddle/pull/59690), [#63728](https://github.com/PaddlePaddle/Paddle/pull/63728), [#63302](https://github.com/PaddlePaddle/Paddle/pull/63302), [#59374](https://github.com/PaddlePaddle/Paddle/pull/59374),[#63227](https://github.com/PaddlePaddle/Paddle/pull/63227) + +### 部分 API 功能增强 +- 增强了约 30 个 API 以支持复数计算，如`paddle.log`, `paddle.log1p`, `paddle.square`, `paddle.reciprocal`等，进而扩展对更多科学计算场景的支持。[#62448](https://github.com/PaddlePaddle/Paddle/pull/62448), [#60821](https://github.com/PaddlePaddle/Paddle/pull/60821), [#60897](https://github.com/PaddlePaddle/Paddle/pull/60897), [#62764](https://github.com/PaddlePaddle/Paddle/pull/62764), [#59536](https://github.com/PaddlePaddle/Paddle/pull/59536), [#59529](https://github.com/PaddlePaddle/Paddle/pull/59529), [#63207](https://github.com/PaddlePaddle/Paddle/pull/63207), [#62237](https://github.com/PaddlePaddle/Paddle/pull/62237), [#64684](https://github.com/PaddlePaddle/Paddle/pull/64684) +- 增强了 46 个 API 的功能，使得已有 API 更易用，也更容易进行代码转换。包括但不限于增加 API 参数，扩展 API 支持的数据类型，以及修正原有不合理设计等。[#59890](https://github.com/PaddlePaddle/Paddle/pull/59890), [#63513](https://github.com/PaddlePaddle/Paddle/pull/63513), [#59674](https://github.com/PaddlePaddle/Paddle/pull/59674), [#62778](https://github.com/PaddlePaddle/Paddle/pull/62778), [#64110](https://github.com/PaddlePaddle/Paddle/pull/64110), [#63222](https://github.com/PaddlePaddle/Paddle/pull/63222), [#64331](https://github.com/PaddlePaddle/Paddle/pull/64331), [#64715](https://github.com/PaddlePaddle/Paddle/pull/64715), [#61155](https://github.com/PaddlePaddle/Paddle/pull/61155), [#60070](https://github.com/PaddlePaddle/Paddle/pull/60070), [#61974](https://github.com/PaddlePaddle/Paddle/pull/61974), [#62407](https://github.com/PaddlePaddle/Paddle/pull/62407), [#62672](https://github.com/PaddlePaddle/Paddle/pull/62672),[#62722](https://github.com/PaddlePaddle/Paddle/pull/62722)， [#62876](https://github.com/PaddlePaddle/Paddle/pull/62876), [#63284](https://github.com/PaddlePaddle/Paddle/pull/63284), [#63860](https://github.com/PaddlePaddle/Paddle/pull/63860), [#60466](https://github.com/PaddlePaddle/Paddle/pull/60466), [#63690](https://github.com/PaddlePaddle/Paddle/pull/63690), [#63953](https://github.com/PaddlePaddle/Paddle/pull/63953), [#63901](https://github.com/PaddlePaddle/Paddle/pull/63901), [#62624](https://github.com/PaddlePaddle/Paddle/pull/62624), [#59857](https://github.com/PaddlePaddle/Paddle/pull/59857), [#60084](https://github.com/PaddlePaddle/Paddle/pull/60084), [#60766](https://github.com/PaddlePaddle/Paddle/pull/60766), [#62788](https://github.com/PaddlePaddle/Paddle/pull/62788), [#62937](https://github.com/PaddlePaddle/Paddle/pull/62937), [#63134](https://github.com/PaddlePaddle/Paddle/pull/63134), [#62966](https://github.com/PaddlePaddle/Paddle/pull/62966), [#63648](https://github.com/PaddlePaddle/Paddle/pull/63648), [#63881](https://github.com/PaddlePaddle/Paddle/pull/63881), [#64358](https://github.com/PaddlePaddle/Paddle/pull/64358), [#60503](https://github.com/PaddlePaddle/Paddle/pull/60503), [#63604](https://github.com/PaddlePaddle/Paddle/pull/63604), [#62338](https://github.com/PaddlePaddle/Paddle/pull/62338) +- 增强了高阶微分的单测基础设施，能够更容易地添加高阶微分的单测用例。[#62074](https://github.com/PaddlePaddle/Paddle/pull/62074) + +### API 性能提升 +- 对 Tensor 基础索引、高级索引和联合索引的性能进行了集中优化，在 GPU 上的计算性能较此前提升 2 到 31 倍，CPU 上提升 1.8 到 1004 倍。[#60254](https://github.com/PaddlePaddle/Paddle/pull/60254), [#60276](https://github.com/PaddlePaddle/Paddle/pull/60276), [#60452](https://github.com/PaddlePaddle/Paddle/pull/60452), [#60771](https://github.com/PaddlePaddle/Paddle/pull/60771), [#61021](https://github.com/PaddlePaddle/Paddle/pull/61021), [#60983](https://github.com/PaddlePaddle/Paddle/pull/60983), [#61060](https://github.com/PaddlePaddle/Paddle/pull/61060), [#60618](https://github.com/PaddlePaddle/Paddle/pull/60618) + +### Bug 修复 +- 修复 `paddle.optimizer.LBFGS` 中使用非 Tensor 进行计算导致的报错。 [#60219](https://github.com/PaddlePaddle/Paddle/pull/60219) +- 修复 `paddle.optimizer.LBFGS` 中随机数不能固定的问题。 [#60591](https://github.com/PaddlePaddle/Paddle/pull/60591) +- 修复 `set_value` 算子梯度计算不正确的问题。 [#59034](https://github.com/PaddlePaddle/Paddle/pull/59034) +- 修复 Tensor 基础索引适配 PIR 的问题。 [#60259](https://github.com/PaddlePaddle/Paddle/pull/60259), [#61103](https://github.com/PaddlePaddle/Paddle/pull/61103) +- 修复 Tensor 联合索引赋值时的问题。[#60376](https://github.com/PaddlePaddle/Paddle/issues/60376), [#60447](https://github.com/PaddlePaddle/Paddle/pull/60447) +- 修复 Tensor 联合索引取值时的问题。 [#61922](https://github.com/PaddlePaddle/Paddle/pull/61922) +- 修复 `paddle.flatten` stride 计算错误问题，并能够新增`paddle.flatten_` 。[#63084](https://github.com/PaddlePaddle/Paddle/pull/63084) +- 修复 `paddle.index_fill` 和 `paddle.index_fill_` 结果不一致问题。 [#59863](https://github.com/PaddlePaddle/Paddle/pull/59863) +- 修复 `paddle.masked_scatter`报错问题。 [#60835](https://github.com/PaddlePaddle/Paddle/pull/60835) +- 修复 `paddle.histogramdd` cpu 报错问题。 [#61891](https://github.com/PaddlePaddle/Paddle/pull/61891) +- 修复 `paddle.cast_` 在 cpu 上连续使用导致结果错误的 bug。 [#60054](https://github.com/PaddlePaddle/Paddle/pull/60054) +- 修复 `paddle.put_along_axis` 在输入 size 很大的时候存在 bug 的问题。 [#60551](https://github.com/PaddlePaddle/Paddle/pull/60551) +- 修复 `paddle.nanmedian` cpu 报错问题。 [#63221](https://github.com/PaddlePaddle/Paddle/pull/63221) +- 修复 `paddle.median` 在 min 分支下不支持输入为除浮点类型以外的类型。 [#64444](https://github.com/PaddlePaddle/Paddle/pull/64444) +- 修复 分布式场景中的 dataloader 问题。 [#62696](https://github.com/PaddlePaddle/Paddle/pull/62696), [#63378](https://github.com/PaddlePaddle/Paddle/pull/63378) +- 修复 error 提示的格式问题。 [#63106](https://github.com/PaddlePaddle/Paddle/pull/63106), [#63144](https://github.com/PaddlePaddle/Paddle/pull/63144) +- 修复 GLOG_v>=6 下格式问题。 [#63345](https://github.com/PaddlePaddle/Paddle/pull/63345) + +### 安全改善 +- 增强对 parent_ids 的检查。 [#62826](https://github.com/PaddlePaddle/Paddle/pull/62826) + +## 2.基础执行架构 + +PIR 基础功能全面升级完善，成熟度大幅提升，基于 PIR 使飞桨基础架构设计更合理、保证了框架卓越的性能表现和良好的拓展性。在此版本中，完成了 PIR 多场景的推全验证：单机场景完成动转静场景 PIR 后端切换；推理场景完成全部存量模型验证，并在 84.2%模型有 10%+收益；完成分布式场景基于 PIR 的验证。同时基于 PIR 完成控制流、backward 逻辑、save/load、OneDNN 适配等核心模块的开发验证，为飞桨 PIR 切换为默认模式，奠定了坚实的基础。对飞桨框架算子体系的功能完备性、执行效率和稳定性进一步提升，给开发者带来更好的使用和开发体验。 -## 1. 重要更新 - -- **新一代中间表示 PIR**：为了进一步提升飞桨框架的可扩展性，研制了新一代中间表示 PIR（Paddle Intermediate Representation）。实现系统性的抽象飞桨框架底层核心概念，如：Operation、Attribute 和 Type 等，为开发者提供了灵活、高效的基础组件。通过引入 Dialect 机制，可以全面、分层次地满足各模块对中间表示的需求，从而极大地提升了框架的扩展性。PIR 严格遵循 SSA（Static Single Assignment）原则，在实现了顶层结构的统一的同时，还确保了“算子顺序性”与“计算图语义”的和谐共存。此外，PIR 提供了更为简洁、低成本的 Pass 开发流程，内置了一系列丰富且功能完备的 Pass 优化策略，为大型模型的极致性能优化提供了技术支撑。 -- **动转静编译优化架构**：为了进一步提升框架的模型开发性能，飞桨动转静训练能力全面升级，支持自适应的图构建能力，在 700 多个飞桨产业级模型上验证，一键动转静训练成功率达到 100%。同时，飞桨框架的神经网络编译器 CINN 整合入飞桨主 Repo，使得编译器与飞桨更加融为一体。CINN 完成了架构的梳理和扩展能力的完善，提升系统稳定性。基于 PIR 完成动转静、组合算子、执行器和编译器的紧密联动，为飞桨框架整体性能的提升提供了更大的空间。 -- **增强动态图分布式能力**：大模型对框架的分布式训练性能提出了更高的需求。飞桨在通信库、图分析、分布式策略和任务启停等维度进行了全面优化，增强了飞桨动态图的分布式计算能力，为大型模型高效训练提供了支持。在性能方面，通过减少流水线 GPU 显存占用、采用 TensorFusion 技术、实现通信计算 overlap 以及减少非必要的数据同步拷贝等方式，进一步提升了训练性能。同时，通过环境变量控制 Optimizer 等方式提高了混合并行调试的灵活性。此外，通过相关 Bug 的修复，显著提升了系统的稳定性。 -- **动静统一自动并行架构**：为了进一步降低大模型编程和优化难度，飞桨对动静统一的半自动并行（Auto Parallel）编程范式进行了全面的优化，简化了开发者的编程复杂度。开发者无需深入了解手动并行编程范式下的复杂概念和 API 接口，如行切分、列切分等，仅需通过少量的张量切分标注即可完成混合并行模型的构建，框架便能够自动推导出所有张量和算子的分布式切分状态，并添加合适的通信算子。同时支持一键动转静进行分布式训练，使开发者能够高效地实现任意混合并行策略，大幅简化了混合并行训练代码的开发过程。 -- **硬件适配方案(CustomDevice)**：大模型场景下新硬件并行训练需求增加，飞桨新增了对分布式高级策略、自定义算子和自定义融合策略的支持。升级了分布式通信库，新增了对 MP、GroupShared、PP、SP 和 MOE 等多项高级分布式策略的支持。同时，支持厂商灵活接入不同颗粒度的 Transformer 算子库并通过融合 Pass 修改计算图进行性能加速。 -- **安装和开发体验**：采用模块化编译的方式优化了 CMake 代码的逻辑，提升了飞桨全量编译和增量编译的效率，提升了 RD 开发效率，同时支持了 Python3.12，CUDA12，Hopper 架构编译，并引入 Clang 等工具全面优化了代码格式。此外，将 C++单测从链接静态库的方式转变为链接动态库，减小编译体积。这些改进措施为用户提供更加流畅、高效的安装和开发体验。 - -## 2. 不兼容升级 - -- 为了避免误用，去除了 0 维 Tensor 兼容态开关，实现 API 行为和业界主流习惯一致。在上一个版本中，我们已经支持 0 维 Tensor，但是考虑到尽量避免部分模型的报错，添加了兼容态开关。即在一些模型套件使用较多且没有修改完成的场景中还是默认使用只有 1 个元素的 1 维 Tensor 来替代 0 维 Tensor。这个版本去除了兼容态开关，在任何场景中都不会再使用只有 1 个元素的 1 维 Tensor 来替代 0 维 Tensor，应该支持 0 维 Tensor 的 376 个 API 的行为都完成了修正和统一，彻底完成对 0 维 Tensor 的支持。[#57036](https://github.com/PaddlePaddle/Paddle/pull/57036), [#54581](https://github.com/PaddlePaddle/Paddle/pull/54581), [#54500](https://github.com/PaddlePaddle/Paddle/pull/54500) -- 为了提升 API 易用性，将 paddle.nn.functional.diag_embed 精简为 paddle.diag_embed，并支持 Tensor.diag_embed 方式使用。 [#58223](https://github.com/PaddlePaddle/Paddle/pull/58223) -- 为了解决在静态图下 Tensor 索引写（如 tensor[0] = 10）导致的微分计算错误问题，并符合静态图的规范，本版本引入了 paddle.static.setitem API。在静态图环境中，更推荐使用此 API 来支持 tensor 的索引写操作，而非下标运算符。这一变化并不影响动态图环境，其中仍允许使用下标运算符进行索引写操作。[#53682](https://github.com/PaddlePaddle/Paddle/pull/53682) -- 本版本中 paddle.fluid API 全面退出历史舞台。在本次更新中，我们彻底移除了所有 paddle.fluid API，并删除了 fluid 目录。同时，飞桨底层的少量公共组件已被整合至 paddle.base 目录中。使得飞桨用户无需再关注 fluid 相关概念和接口，进一步简化了飞桨 API 体系，提升可读性。[#56576](https://github.com/PaddlePaddle/Paddle/pull/56576), [#54424](https://github.com/PaddlePaddle/Paddle/pull/54424), [#54829](https://github.com/PaddlePaddle/Paddle/pull/54829), [#53992](https://github.com/PaddlePaddle/Paddle/pull/53992), [#54806](https://github.com/PaddlePaddle/Paddle/pull/54806), [#55754](https://github.com/PaddlePaddle/Paddle/pull/55754), [#55986](https://github.com/PaddlePaddle/Paddle/pull/55986), [#55345](https://github.com/PaddlePaddle/Paddle/pull/55345), [#56099](https://github.com/PaddlePaddle/Paddle/pull/56099), [#51717](https://github.com/PaddlePaddle/Paddle/pull/51717), [#54152](https://github.com/PaddlePaddle/Paddle/pull/54152), [#55522](https://github.com/PaddlePaddle/Paddle/pull/55522), [#55757](https://github.com/PaddlePaddle/Paddle/pull/55757), [#58521](https://github.com/PaddlePaddle/Paddle/pull/58521), [#54936](https://github.com/PaddlePaddle/Paddle/pull/54936), [#55007](https://github.com/PaddlePaddle/Paddle/pull/55007), [#55661](https://github.com/PaddlePaddle/Paddle/pull/55661), [#55970](https://github.com/PaddlePaddle/Paddle/pull/55970) - -## 3. 训练框架（含分布式） - -### Python API - -#### 升级 Tensor 索引机制 - -本版本全面优化了 Tensor 的基础索引、高级索引以及联合索引功能，以更好地符合业界标准与用户习惯。具体包括：在基础索引中增加了对 view 的支持，修正了高级索引中的一些错误行为，并实现了联合索引的读取功能。此外，我们还将索引解析下沉到 C++层面，改进了高级索引算子的性能，并移除了 bool 索引中的冗余计算。通过这些优化措施，Tensor 的基础索引、高级索引和联合索引性能得到了全面提升。[#56893](https://github.com/PaddlePaddle/Paddle/pull/56893), [#58643](https://github.com/PaddlePaddle/Paddle/pull/58643), [#57986](https://github.com/PaddlePaddle/Paddle/pull/57986), [#56272](https://github.com/PaddlePaddle/Paddle/pull/56272), [#58856](https://github.com/PaddlePaddle/Paddle/pull/58856), [#55211](https://github.com/PaddlePaddle/Paddle/pull/55211), [#57023](https://github.com/PaddlePaddle/Paddle/pull/57023), [#56613](https://github.com/PaddlePaddle/Paddle/pull/56613), [#55602](https://github.com/PaddlePaddle/Paddle/pull/55602), [#59281](https://github.com/PaddlePaddle/Paddle/pull/59281), [#57737](https://github.com/PaddlePaddle/Paddle/pull/57737) - -#### 升级 Inplace 机制 - -在之前的版本中，为了确保反向微分计算的正确性，当某个 API 的反向计算依赖于其前向输入数据时，飞桨会避免使用 Inplace 操作方式，因为这种方法可能会覆盖原始输入数据。虽然这种机制简化了实现过程，但也限制了许多 API 实现 Inplace 功能，从而影响了用户体验。 -在本版本中，飞桨对 Inplace 机制进行了全面升级。实现自动检测反向计算对前向输入的依赖关系，并在需要时保存这些输入数据，从而支持更多的 Inplace 操作。这一改进不仅提升了内存使用效率，还增强了 API 的功能性。 -此外，我们新增了 109 个支持 Inplace 操作的 API，包括 paddle.abs_、paddle.sin_/cos_/tan_、比较操作如 paddle.greater_than_/less_than_/equal_、逻辑操作如 paddle.logical_and_/logical_or_/logical_not_，以及 paddle.neg_和 paddle.log_等。在丰富飞桨的功能集同时，提升了用户在数值计算和深度学习任务中的效率与便捷性。[#54683](https://github.com/PaddlePaddle/Paddle/pull/54683), [#55078](https://github.com/PaddlePaddle/Paddle/pull/55078), [#55576](https://github.com/PaddlePaddle/Paddle/pull/55576), [#56888](https://github.com/PaddlePaddle/Paddle/pull/56888), [#55509](https://github.com/PaddlePaddle/Paddle/pull/55509), [#57093](https://github.com/PaddlePaddle/Paddle/pull/57093) - -#### 其他新增 API - -- 新增 paddle.nn.functional.scaled_dot_product_attention，显著提升大模型中注意力（attention）机制的计算效率，更好地满足大规模深度学习模型对高性能计算的需求。。[#55242](https://github.com/PaddlePaddle/Paddle/pull/55242) -- 新增了一系列科学计算相关 API，包括 paddle.cummax 和 paddle.cummin 用于累积最大值和最小值的计算，paddle.index_fill 和 paddle.masked_fill 用于按索引或掩码填充张量，paddle.linalg.pca_lowrank 用于低秩主成分分析，paddle.hypot 用于计算直角三角形的斜边长，以及 paddle.atleast_1d、paddle.atleast_2d 和 paddle.atleast_3d 用于确保张量至少有一维、二维或三维。同时，我们还提供了 paddle.select_scatter 和 paddle.diagonal_scatter 用于更灵活地选择和散列张量数据，以及 paddle.multigammaln 用于计算多伽马函数的自然对数。此外，本版本新增优化器相关 API，包括：paddle.optimizer.lr.LinearLR 和 paddle.optimizer.lr.CosineAnnealingWarmRestarts 学习率调度策略；引入了 paddle.io.SubsetRandomSampler 以支持从数据子集中进行随机采样。这些新增 API 将进一步提升飞桨在各类应用场景中的灵活性和高效性。。 [#57416](https://github.com/PaddlePaddle/Paddle/pull/57416), [#53546](https://github.com/PaddlePaddle/Paddle/pull/53546), [#53743](https://github.com/PaddlePaddle/Paddle/pull/53743), [#57295](https://github.com/PaddlePaddle/Paddle/pull/57295), [#57726](https://github.com/PaddlePaddle/Paddle/pull/57726), [#58764](https://github.com/PaddlePaddle/Paddle/pull/58764), [#58323](https://github.com/PaddlePaddle/Paddle/pull/58323), [#57720](https://github.com/PaddlePaddle/Paddle/pull/57720), [#58209](https://github.com/PaddlePaddle/Paddle/pull/58209), [#58214](https://github.com/PaddlePaddle/Paddle/pull/58214), [#57792](https://github.com/PaddlePaddle/Paddle/pull/57792), [#51395](https://github.com/PaddlePaddle/Paddle/pull/51395), [#57724](https://github.com/PaddlePaddle/Paddle/pull/57724), [#57355](https://github.com/PaddlePaddle/Paddle/pull/57355), [#57744](https://github.com/PaddlePaddle/Paddle/pull/57744), [#58244](https://github.com/PaddlePaddle/Paddle/pull/58244), [#57599](https://github.com/PaddlePaddle/Paddle/pull/57599), [#59343](https://github.com/PaddlePaddle/Paddle/pull/59343), [#57879](https://github.com/PaddlePaddle/Paddle/pull/57879) - -### 新一代中间表示（PIR） - -PIR（Paddle Intermediate Representation）对底层的核心概念如 Operation、Attribute 和 Type 等进行了系统性的抽象，为开发者构建了一套灵活且强大的基础组件。此外，通过引入 Dialect 这一概念，飞桨框架能够全面且分层次地管理各模块对中间表示（IR）的需求，并支持开发者根据特定需求定制化扩展 Dialect，从而显著提升了框架的扩展性和适应性。在设计上，PIR 严格遵循 SSA（Static Single Assignment）原则，统一了顶层结构，实现了“算子顺序性”与“计算图语义”的兼容表示，为复杂的计算流程提供了清晰且一致的视图。为了进一步优化大模型的性能，PIR 还提供了一套更加简洁、低成本的 Pass 开发流程，包括 DRR（Declarative Rewrite Rule）和模式重写器（Pattern Rewriter）。同时，内置了一系列丰富且功能完备的 Pass 优化策略，这些策略能够针对大模型的特点进行深度优化，从而为大模型的极致性能提供了强有力支撑。通过这些创新设计和优化手段，PIR 为飞桨框架的高效运行和持续扩展奠定了坚实基础。 - -#### 新功能 - -- 系统抽象了 IR 底层的核心概念，为开发者提供了灵活的基础组件，如 Operation、Attribute、Value、Type、Trait、Interface 等。[#56354](https://github.com/PaddlePaddle/Paddle/pull/56354),[#57106](https://github.com/PaddlePaddle/Paddle/pull/57106),[#57349](https://github.com/PaddlePaddle/Paddle/pull/57349),[#54844](https://github.com/PaddlePaddle/Paddle/pull/54844),[#54984](https://github.com/PaddlePaddle/Paddle/pull/54984),[#54565](https://github.com/PaddlePaddle/Paddle/pull/54565),[#54562](https://github.com/PaddlePaddle/Paddle/pull/54562),[#57249](https://github.com/PaddlePaddle/Paddle/pull/57249),[#57550](https://github.com/PaddlePaddle/Paddle/pull/57550),[#59278](https://github.com/PaddlePaddle/Paddle/pull/59278),[#54875](https://github.com/PaddlePaddle/Paddle/pull/54875),[#55041](https://github.com/PaddlePaddle/Paddle/pull/55041),[#54987](https://github.com/PaddlePaddle/Paddle/pull/54987),[#55903](https://github.com/PaddlePaddle/Paddle/pull/55903),[#57582](https://github.com/PaddlePaddle/Paddle/pull/57582),[#57580](https://github.com/PaddlePaddle/Paddle/pull/57580),[#58052](https://github.com/PaddlePaddle/Paddle/pull/58052),[#55322](https://github.com/PaddlePaddle/Paddle/pull/55322),[#57418](https://github.com/PaddlePaddle/Paddle/pull/57418),[#57635](https://github.com/PaddlePaddle/Paddle/pull/57635),[#55328](https://github.com/PaddlePaddle/Paddle/pull/55328),[#57463](https://github.com/PaddlePaddle/Paddle/pull/57463),[#59791](https://github.com/PaddlePaddle/Paddle/pull/59791),[#59821](https://github.com/PaddlePaddle/Paddle/pull/59821),[#59115](https://github.com/PaddlePaddle/Paddle/pull/59115),[#57461](https://github.com/PaddlePaddle/Paddle/pull/57461),[#59392](https://github.com/PaddlePaddle/Paddle/pull/59392),[#57373](https://github.com/PaddlePaddle/Paddle/pull/57373),[#59118](https://github.com/PaddlePaddle/Paddle/pull/59118) -- 新增引入 Dialect 机制，支持全面、分层次管理框架各个模块对中间表示的需求，且内置了 Builtin Dialect，支持开发者根据需求自定义化扩展 Dialect。 [#56325](https://github.com/PaddlePaddle/Paddle/pull/56325),[#57539](https://github.com/PaddlePaddle/Paddle/pull/57539),[#54682](https://github.com/PaddlePaddle/Paddle/pull/54682),[#55381](https://github.com/PaddlePaddle/Paddle/pull/55381),[#56156](https://github.com/PaddlePaddle/Paddle/pull/56156),[#56431](https://github.com/PaddlePaddle/Paddle/pull/56431),[#56615](https://github.com/PaddlePaddle/Paddle/pull/56615),[#57103](https://github.com/PaddlePaddle/Paddle/pull/57103),[#57209](https://github.com/PaddlePaddle/Paddle/pull/57209) -- 规范化了飞桨静态图算子体系，新增 OperatorDialect、KernelDialect，以 Dialect 形式分层管理编译期和执行期的算子表示概念差异性，架构更加清晰。[#56284](https://github.com/PaddlePaddle/Paddle/pull/56284),[#54469](https://github.com/PaddlePaddle/Paddle/pull/54469),[#58660](https://github.com/PaddlePaddle/Paddle/pull/58660),[#58975](https://github.com/PaddlePaddle/Paddle/pull/58975),[#56680](https://github.com/PaddlePaddle/Paddle/pull/56680),[#54790](https://github.com/PaddlePaddle/Paddle/pull/54790),[#54826](https://github.com/PaddlePaddle/Paddle/pull/54826),[#54840](https://github.com/PaddlePaddle/Paddle/pull/54840),[#55699](https://github.com/PaddlePaddle/Paddle/pull/55699),[#55648](https://github.com/PaddlePaddle/Paddle/pull/55648),[#55880](https://github.com/PaddlePaddle/Paddle/pull/55880),[#56101](https://github.com/PaddlePaddle/Paddle/pull/56101),[#56754](https://github.com/PaddlePaddle/Paddle/pull/56754),[#54944](https://github.com/PaddlePaddle/Paddle/pull/54944),[#56836](https://github.com/PaddlePaddle/Paddle/pull/56836),[#57185](https://github.com/PaddlePaddle/Paddle/pull/57185),[#58757](https://github.com/PaddlePaddle/Paddle/pull/58757),[#56243](https://github.com/PaddlePaddle/Paddle/pull/56243),[#56436](https://github.com/PaddlePaddle/Paddle/pull/56436),[#57741](https://github.com/PaddlePaddle/Paddle/pull/57741),[#59124](https://github.com/PaddlePaddle/Paddle/pull/59124),[#57054](https://github.com/PaddlePaddle/Paddle/pull/57054),[#56984](https://github.com/PaddlePaddle/Paddle/pull/56984),[#57403](https://github.com/PaddlePaddle/Paddle/pull/57403),[#57904](https://github.com/PaddlePaddle/Paddle/pull/57904),[#58031](https://github.com/PaddlePaddle/Paddle/pull/58031),[#56924](https://github.com/PaddlePaddle/Paddle/pull/56924),[#59270](https://github.com/PaddlePaddle/Paddle/pull/59270),[#55343](https://github.com/PaddlePaddle/Paddle/pull/55343),[#56557](https://github.com/PaddlePaddle/Paddle/pull/56557),[#55693](https://github.com/PaddlePaddle/Paddle/pull/55693),[#54428](https://github.com/PaddlePaddle/Paddle/pull/54428) -- 新增 ShapeDialect，内置了丰富的 Shape 操作算子，用于面向 AI 编译器的动态 Shape 约束关系和表达式的构建。[#56727](https://github.com/PaddlePaddle/Paddle/pull/56727),[#59254](https://github.com/PaddlePaddle/Paddle/pull/59254),[#58368](https://github.com/PaddlePaddle/Paddle/pull/58368),[#57069](https://github.com/PaddlePaddle/Paddle/pull/57069),[#57337](https://github.com/PaddlePaddle/Paddle/pull/57337),[#56351](https://github.com/PaddlePaddle/Paddle/pull/56351),[#57029](https://github.com/PaddlePaddle/Paddle/pull/57029),[#58036](https://github.com/PaddlePaddle/Paddle/pull/58036),[#59032](https://github.com/PaddlePaddle/Paddle/pull/59032),[#57961](https://github.com/PaddlePaddle/Paddle/pull/57961),[#56427](https://github.com/PaddlePaddle/Paddle/pull/56427),[#57459](https://github.com/PaddlePaddle/Paddle/pull/57459) -- 统一了框架 Program 顶层结构，支持兼容表示“算子顺序性”和“计算图语义”，解耦对 ir::Graph 的依赖，且严格遵循 SSA （即 Static Single Assignment）原则。[#59369](https://github.com/PaddlePaddle/Paddle/pull/59369),[#54563](https://github.com/PaddlePaddle/Paddle/pull/54563),[#57051](https://github.com/PaddlePaddle/Paddle/pull/57051),[#57306](https://github.com/PaddlePaddle/Paddle/pull/57306),[#57857](https://github.com/PaddlePaddle/Paddle/pull/57857) -- 新增了 IrPrinter 和 IrPaser 组件，支持 PIR Program 的序列化和反序列化功能，提供了友好的 PIR 开发调试体验。[#55695](https://github.com/PaddlePaddle/Paddle/pull/55695),[#59449](https://github.com/PaddlePaddle/Paddle/pull/59449),[#54369](https://github.com/PaddlePaddle/Paddle/pull/54369),[#54499](https://github.com/PaddlePaddle/Paddle/pull/54499),[#55518](https://github.com/PaddlePaddle/Paddle/pull/55518),[#55784](https://github.com/PaddlePaddle/Paddle/pull/55784),[#57180](https://github.com/PaddlePaddle/Paddle/pull/57180),[#57471](https://github.com/PaddlePaddle/Paddle/pull/57471),[#54859](https://github.com/PaddlePaddle/Paddle/pull/54859),[#54968](https://github.com/PaddlePaddle/Paddle/pull/54968),[#55209](https://github.com/PaddlePaddle/Paddle/pull/55209),[#57314](https://github.com/PaddlePaddle/Paddle/pull/57314),[#57969](https://github.com/PaddlePaddle/Paddle/pull/57969) -- 基于 DRR（即 Declarative Rewrite Rule） 和 Pattern Rewriter 构建了全新、简洁、低成本的 Pass 开发体系，并内置了一系列丰富且功能完备的 Pass 优化策略，加速训练和推理执行过程。[#54385](https://github.com/PaddlePaddle/Paddle/pull/54385),[#54738](https://github.com/PaddlePaddle/Paddle/pull/54738),[#55859](https://github.com/PaddlePaddle/Paddle/pull/55859),[#56638](https://github.com/PaddlePaddle/Paddle/pull/56638),[#57090](https://github.com/PaddlePaddle/Paddle/pull/57090),[#58673](https://github.com/PaddlePaddle/Paddle/pull/58673),[#59415](https://github.com/PaddlePaddle/Paddle/pull/59415),[#56729](https://github.com/PaddlePaddle/Paddle/pull/56729),[#58655](https://github.com/PaddlePaddle/Paddle/pull/58655) -- 新增 ProgramTranslator 组件，支持由 ProgramDesc 一键转换为飞桨新一代 IR 表示，并提供了易用的 C++和 Python 接口。[#55433](https://github.com/PaddlePaddle/Paddle/pull/55433),[#54470](https://github.com/PaddlePaddle/Paddle/pull/54470),[#58044](https://github.com/PaddlePaddle/Paddle/pull/58044),[#58390](https://github.com/PaddlePaddle/Paddle/pull/58390),[#58100](https://github.com/PaddlePaddle/Paddle/pull/58100),[#55403](https://github.com/PaddlePaddle/Paddle/pull/55403),[#55406](https://github.com/PaddlePaddle/Paddle/pull/55406),[#54719](https://github.com/PaddlePaddle/Paddle/pull/54719),[#56550](https://github.com/PaddlePaddle/Paddle/pull/56550),[#55448](https://github.com/PaddlePaddle/Paddle/pull/55448),[#55453](https://github.com/PaddlePaddle/Paddle/pull/55453),[#56294](https://github.com/PaddlePaddle/Paddle/pull/56294),[#56308](https://github.com/PaddlePaddle/Paddle/pull/56308),[#56842](https://github.com/PaddlePaddle/Paddle/pull/56842),[#58517](https://github.com/PaddlePaddle/Paddle/pull/58517) -- 借助自动代码生成技术，一键生成飞桨框架全量静态图算子表示。将静态图组网逻辑下沉至 C++端，统一绑定到\_C_ops 模块，大幅精简 Python 端 API 代码，实现飞桨框架 API 的极致化动静统一，升级了诸多 Python API 以支持新 IR 静态图组网。[#56570](https://github.com/PaddlePaddle/Paddle/pull/56570),[#55745](https://github.com/PaddlePaddle/Paddle/pull/55745),[#56955](https://github.com/PaddlePaddle/Paddle/pull/56955),[#57298](https://github.com/PaddlePaddle/Paddle/pull/57298),[#57946](https://github.com/PaddlePaddle/Paddle/pull/57946),[#57248](https://github.com/PaddlePaddle/Paddle/pull/57248),[#56080](https://github.com/PaddlePaddle/Paddle/pull/56080),[#54396](https://github.com/PaddlePaddle/Paddle/pull/54396),[#54551](https://github.com/PaddlePaddle/Paddle/pull/54551),[#56520](https://github.com/PaddlePaddle/Paddle/pull/56520),[#55002](https://github.com/PaddlePaddle/Paddle/pull/55002),[#57067](https://github.com/PaddlePaddle/Paddle/pull/57067),[#59320](https://github.com/PaddlePaddle/Paddle/pull/59320),[#59348](https://github.com/PaddlePaddle/Paddle/pull/59348),[#57164](https://github.com/PaddlePaddle/Paddle/pull/57164),[#57267](https://github.com/PaddlePaddle/Paddle/pull/57267),[#59064](https://github.com/PaddlePaddle/Paddle/pull/59064),[#54340](https://github.com/PaddlePaddle/Paddle/pull/54340),[#54895](https://github.com/PaddlePaddle/Paddle/pull/54895),[#55004](https://github.com/PaddlePaddle/Paddle/pull/55004),[#56196](https://github.com/PaddlePaddle/Paddle/pull/56196),[#56862](https://github.com/PaddlePaddle/Paddle/pull/56862),[#58991](https://github.com/PaddlePaddle/Paddle/pull/58991),[#55428](https://github.com/PaddlePaddle/Paddle/pull/55428),[#55909](https://github.com/PaddlePaddle/Paddle/pull/55909),[#56241](https://github.com/PaddlePaddle/Paddle/pull/56241),[#56526](https://github.com/PaddlePaddle/Paddle/pull/56526),[#56571](https://github.com/PaddlePaddle/Paddle/pull/56571),[#56518](https://github.com/PaddlePaddle/Paddle/pull/56518),[#57016](https://github.com/PaddlePaddle/Paddle/pull/57016),[#56653](https://github.com/PaddlePaddle/Paddle/pull/56653),[#56809](https://github.com/PaddlePaddle/Paddle/pull/56809),[#57158](https://github.com/PaddlePaddle/Paddle/pull/57158),[#55422](https://github.com/PaddlePaddle/Paddle/pull/55422),[#55458](https://github.com/PaddlePaddle/Paddle/pull/55458),[#55432](https://github.com/PaddlePaddle/Paddle/pull/55432),[#55467](https://github.com/PaddlePaddle/Paddle/pull/55467),[#55483](https://github.com/PaddlePaddle/Paddle/pull/55483),[#55419](https://github.com/PaddlePaddle/Paddle/pull/55419),[#55517](https://github.com/PaddlePaddle/Paddle/pull/55517),[#55500](https://github.com/PaddlePaddle/Paddle/pull/55500),[#56674](https://github.com/PaddlePaddle/Paddle/pull/56674),[#57693](https://github.com/PaddlePaddle/Paddle/pull/57693),[#55008](https://github.com/PaddlePaddle/Paddle/pull/55008),[#57166](https://github.com/PaddlePaddle/Paddle/pull/57166),[#57157](https://github.com/PaddlePaddle/Paddle/pull/57157),[#57159](https://github.com/PaddlePaddle/Paddle/pull/57159),[#57175](https://github.com/PaddlePaddle/Paddle/pull/57175),[#57325](https://github.com/PaddlePaddle/Paddle/pull/57325),[#57330](https://github.com/PaddlePaddle/Paddle/pull/57330),[#57415](https://github.com/PaddlePaddle/Paddle/pull/57415),[#57122](https://github.com/PaddlePaddle/Paddle/pull/57122),[#57393](https://github.com/PaddlePaddle/Paddle/pull/57393),[#57344](https://github.com/PaddlePaddle/Paddle/pull/57344),[#57667](https://github.com/PaddlePaddle/Paddle/pull/57667),[#57348](https://github.com/PaddlePaddle/Paddle/pull/57348),[#57700](https://github.com/PaddlePaddle/Paddle/pull/57700),[#58093](https://github.com/PaddlePaddle/Paddle/pull/58093),[#58005](https://github.com/PaddlePaddle/Paddle/pull/58005),[#58081](https://github.com/PaddlePaddle/Paddle/pull/58081),[#58094](https://github.com/PaddlePaddle/Paddle/pull/58094),[#58137](https://github.com/PaddlePaddle/Paddle/pull/58137),[#58287](https://github.com/PaddlePaddle/Paddle/pull/58287),[#58352](https://github.com/PaddlePaddle/Paddle/pull/58352),[#58340](https://github.com/PaddlePaddle/Paddle/pull/58340),[#58363](https://github.com/PaddlePaddle/Paddle/pull/58363),[#58331](https://github.com/PaddlePaddle/Paddle/pull/58331),[#58343](https://github.com/PaddlePaddle/Paddle/pull/58343),[#58317](https://github.com/PaddlePaddle/Paddle/pull/58317),[#58450](https://github.com/PaddlePaddle/Paddle/pull/58450),[#58377](https://github.com/PaddlePaddle/Paddle/pull/58377),[#58466](https://github.com/PaddlePaddle/Paddle/pull/58466),[#58470](https://github.com/PaddlePaddle/Paddle/pull/58470),[#58491](https://github.com/PaddlePaddle/Paddle/pull/58491),[#58546](https://github.com/PaddlePaddle/Paddle/pull/58546),[#58587](https://github.com/PaddlePaddle/Paddle/pull/58587),[#58453](https://github.com/PaddlePaddle/Paddle/pull/58453),[#58634](https://github.com/PaddlePaddle/Paddle/pull/58634),[#58604](https://github.com/PaddlePaddle/Paddle/pull/58604),[#58605](https://github.com/PaddlePaddle/Paddle/pull/58605),[#58593](https://github.com/PaddlePaddle/Paddle/pull/58593),[#58675](https://github.com/PaddlePaddle/Paddle/pull/58675),[#58699](https://github.com/PaddlePaddle/Paddle/pull/58699),[#58384](https://github.com/PaddlePaddle/Paddle/pull/58384),[#58629](https://github.com/PaddlePaddle/Paddle/pull/58629),[#58579](https://github.com/PaddlePaddle/Paddle/pull/58579),[#58695](https://github.com/PaddlePaddle/Paddle/pull/58695),[#58548](https://github.com/PaddlePaddle/Paddle/pull/58548),[#58688](https://github.com/PaddlePaddle/Paddle/pull/58688),[#58792](https://github.com/PaddlePaddle/Paddle/pull/58792),[#58843](https://github.com/PaddlePaddle/Paddle/pull/58843),[#58840](https://github.com/PaddlePaddle/Paddle/pull/58840),[#58718](https://github.com/PaddlePaddle/Paddle/pull/58718),[#58883](https://github.com/PaddlePaddle/Paddle/pull/58883),[#58785](https://github.com/PaddlePaddle/Paddle/pull/58785),[#58608](https://github.com/PaddlePaddle/Paddle/pull/58608),[#58781](https://github.com/PaddlePaddle/Paddle/pull/58781),[#58783](https://github.com/PaddlePaddle/Paddle/pull/58783),[#58429](https://github.com/PaddlePaddle/Paddle/pull/58429),[#58685](https://github.com/PaddlePaddle/Paddle/pull/58685),[#58696](https://github.com/PaddlePaddle/Paddle/pull/58696),[#58690](https://github.com/PaddlePaddle/Paddle/pull/58690),[#58831](https://github.com/PaddlePaddle/Paddle/pull/58831),[#58929](https://github.com/PaddlePaddle/Paddle/pull/58929),[#58740](https://github.com/PaddlePaddle/Paddle/pull/58740),[#58937](https://github.com/PaddlePaddle/Paddle/pull/58937),[#58782](https://github.com/PaddlePaddle/Paddle/pull/58782),[#58833](https://github.com/PaddlePaddle/Paddle/pull/58833),[#58882](https://github.com/PaddlePaddle/Paddle/pull/58882),[#58935](https://github.com/PaddlePaddle/Paddle/pull/58935),[#58931](https://github.com/PaddlePaddle/Paddle/pull/58931),[#59041](https://github.com/PaddlePaddle/Paddle/pull/59041),[#59040](https://github.com/PaddlePaddle/Paddle/pull/59040),[#58877](https://github.com/PaddlePaddle/Paddle/pull/58877),[#58888](https://github.com/PaddlePaddle/Paddle/pull/58888),[#59042](https://github.com/PaddlePaddle/Paddle/pull/59042),[#58780](https://github.com/PaddlePaddle/Paddle/pull/58780),[#58682](https://github.com/PaddlePaddle/Paddle/pull/58682),[#58815](https://github.com/PaddlePaddle/Paddle/pull/58815),[#58676](https://github.com/PaddlePaddle/Paddle/pull/58676),[#58678](https://github.com/PaddlePaddle/Paddle/pull/58678),[#58446](https://github.com/PaddlePaddle/Paddle/pull/58446),[#59077](https://github.com/PaddlePaddle/Paddle/pull/59077),[#59091](https://github.com/PaddlePaddle/Paddle/pull/59091),[#58661](https://github.com/PaddlePaddle/Paddle/pull/58661),[#58832](https://github.com/PaddlePaddle/Paddle/pull/58832),[#58642](https://github.com/PaddlePaddle/Paddle/pull/58642),[#58698](https://github.com/PaddlePaddle/Paddle/pull/58698),[#59313](https://github.com/PaddlePaddle/Paddle/pull/59313),[#59371](https://github.com/PaddlePaddle/Paddle/pull/59371),[#58700](https://github.com/PaddlePaddle/Paddle/pull/58700),[#58953](https://github.com/PaddlePaddle/Paddle/pull/58953),[#58879](https://github.com/PaddlePaddle/Paddle/pull/58879),[#59469](https://github.com/PaddlePaddle/Paddle/pull/59469),[#59573](https://github.com/PaddlePaddle/Paddle/pull/59573),[#59481](https://github.com/PaddlePaddle/Paddle/pull/59481),[#59419](https://github.com/PaddlePaddle/Paddle/pull/59419),[#59509](https://github.com/PaddlePaddle/Paddle/pull/59509),[#58735](https://github.com/PaddlePaddle/Paddle/pull/58735),[#59616](https://github.com/PaddlePaddle/Paddle/pull/59616),[#59582](https://github.com/PaddlePaddle/Paddle/pull/59582),[#59420](https://github.com/PaddlePaddle/Paddle/pull/59420),[#59500](https://github.com/PaddlePaddle/Paddle/pull/59500),[#58911](https://github.com/PaddlePaddle/Paddle/pull/58911),[#59535](https://github.com/PaddlePaddle/Paddle/pull/59535),[#54891](https://github.com/PaddlePaddle/Paddle/pull/54891),[#56794](https://github.com/PaddlePaddle/Paddle/pull/56794),[#57477](https://github.com/PaddlePaddle/Paddle/pull/57477),[#57929](https://github.com/PaddlePaddle/Paddle/pull/57929),[#57765](https://github.com/PaddlePaddle/Paddle/pull/57765),[#58693](https://github.com/PaddlePaddle/Paddle/pull/58693),[#58603](https://github.com/PaddlePaddle/Paddle/pull/58603),[#56291](https://github.com/PaddlePaddle/Paddle/pull/56291),[#57123](https://github.com/PaddlePaddle/Paddle/pull/57123),[#57317](https://github.com/PaddlePaddle/Paddle/pull/57317),[#57341](https://github.com/PaddlePaddle/Paddle/pull/57341),[#57020](https://github.com/PaddlePaddle/Paddle/pull/57020),[#57324](https://github.com/PaddlePaddle/Paddle/pull/57324),[#57761](https://github.com/PaddlePaddle/Paddle/pull/57761),[#57762](https://github.com/PaddlePaddle/Paddle/pull/57762),[#57907](https://github.com/PaddlePaddle/Paddle/pull/57907),[#57909](https://github.com/PaddlePaddle/Paddle/pull/57909),[#58099](https://github.com/PaddlePaddle/Paddle/pull/58099),[#58110](https://github.com/PaddlePaddle/Paddle/pull/58110),[#58114](https://github.com/PaddlePaddle/Paddle/pull/58114),[#58139](https://github.com/PaddlePaddle/Paddle/pull/58139),[#58144](https://github.com/PaddlePaddle/Paddle/pull/58144),[#58165](https://github.com/PaddlePaddle/Paddle/pull/58165),[#58194](https://github.com/PaddlePaddle/Paddle/pull/58194),[#58138](https://github.com/PaddlePaddle/Paddle/pull/58138),[#58113](https://github.com/PaddlePaddle/Paddle/pull/58113),[#58245](https://github.com/PaddlePaddle/Paddle/pull/58245),[#58318](https://github.com/PaddlePaddle/Paddle/pull/58318),[#58105](https://github.com/PaddlePaddle/Paddle/pull/58105),[#58348](https://github.com/PaddlePaddle/Paddle/pull/58348),[#58235](https://github.com/PaddlePaddle/Paddle/pull/58235),[#58354](https://github.com/PaddlePaddle/Paddle/pull/58354),[#58341](https://github.com/PaddlePaddle/Paddle/pull/58341),[#58445](https://github.com/PaddlePaddle/Paddle/pull/58445),[#58418](https://github.com/PaddlePaddle/Paddle/pull/58418),[#58239](https://github.com/PaddlePaddle/Paddle/pull/58239),[#58473](https://github.com/PaddlePaddle/Paddle/pull/58473),[#58239](https://github.com/PaddlePaddle/Paddle/pull/58239),[#58391](https://github.com/PaddlePaddle/Paddle/pull/58391),[#58501](https://github.com/PaddlePaddle/Paddle/pull/58501),[#58519](https://github.com/PaddlePaddle/Paddle/pull/58519),[#58416](https://github.com/PaddlePaddle/Paddle/pull/58416),[#58588](https://github.com/PaddlePaddle/Paddle/pull/58588),[#58531](https://github.com/PaddlePaddle/Paddle/pull/58531),[#58730](https://github.com/PaddlePaddle/Paddle/pull/58730),[#58773](https://github.com/PaddlePaddle/Paddle/pull/58773),[#58862](https://github.com/PaddlePaddle/Paddle/pull/58862),[#58946](https://github.com/PaddlePaddle/Paddle/pull/58946),[#58500](https://github.com/PaddlePaddle/Paddle/pull/58500),[#56585](https://github.com/PaddlePaddle/Paddle/pull/56585),[#57480](https://github.com/PaddlePaddle/Paddle/pull/57480),[#57433](https://github.com/PaddlePaddle/Paddle/pull/57433),[#58498](https://github.com/PaddlePaddle/Paddle/pull/58498) - -#### 功能优化 - -- 升级了静态图执行器，扩展了更多 Kernel Instruction 类型，支持加载 PIR 并高效调度执行，在训练、推理环节都有显存和性能收益。[#54570](https://github.com/PaddlePaddle/Paddle/pull/54570),[#58665](https://github.com/PaddlePaddle/Paddle/pull/58665),[#57291](https://github.com/PaddlePaddle/Paddle/pull/57291),[#54452](https://github.com/PaddlePaddle/Paddle/pull/54452),[#57431](https://github.com/PaddlePaddle/Paddle/pull/57431),[#54692](https://github.com/PaddlePaddle/Paddle/pull/54692),[#55112](https://github.com/PaddlePaddle/Paddle/pull/55112),[#55210](https://github.com/PaddlePaddle/Paddle/pull/55210),[#55401](https://github.com/PaddlePaddle/Paddle/pull/55401),[#55772](https://github.com/PaddlePaddle/Paddle/pull/55772),[#55828](https://github.com/PaddlePaddle/Paddle/pull/55828),[#56148](https://github.com/PaddlePaddle/Paddle/pull/56148),[#54763](https://github.com/PaddlePaddle/Paddle/pull/54763),[#56886](https://github.com/PaddlePaddle/Paddle/pull/56886),[#57284](https://github.com/PaddlePaddle/Paddle/pull/57284),[#57268](https://github.com/PaddlePaddle/Paddle/pull/57268),[#57791](https://github.com/PaddlePaddle/Paddle/pull/57791),[#56789](https://github.com/PaddlePaddle/Paddle/pull/56789),[#56704](https://github.com/PaddlePaddle/Paddle/pull/56704),[#57594](https://github.com/PaddlePaddle/Paddle/pull/57594),[#58397](https://github.com/PaddlePaddle/Paddle/pull/58397),[#58337](https://github.com/PaddlePaddle/Paddle/pull/58337),[#58756](https://github.com/PaddlePaddle/Paddle/pull/58756),[#58371](https://github.com/PaddlePaddle/Paddle/pull/58371) -- 面向 PIR 重构了自动微分模块，迁移适配了高阶自动微分功能，优化了 Stop Gradient 传递机制，逻辑更加清晰，功能更加鲁棒。[#55660](https://github.com/PaddlePaddle/Paddle/pull/55660),[#57084](https://github.com/PaddlePaddle/Paddle/pull/57084),[#56890](https://github.com/PaddlePaddle/Paddle/pull/56890),[#58942](https://github.com/PaddlePaddle/Paddle/pull/58942),[#59373](https://github.com/PaddlePaddle/Paddle/pull/59373),[#57206](https://github.com/PaddlePaddle/Paddle/pull/57206),[#58145](https://github.com/PaddlePaddle/Paddle/pull/58145),[#55235](https://github.com/PaddlePaddle/Paddle/pull/55235),[#57255](https://github.com/PaddlePaddle/Paddle/pull/57255),[#56925](https://github.com/PaddlePaddle/Paddle/pull/56925),[#55957](https://github.com/PaddlePaddle/Paddle/pull/55957),[#56163](https://github.com/PaddlePaddle/Paddle/pull/56163),[#56316](https://github.com/PaddlePaddle/Paddle/pull/56316),[#57294](https://github.com/PaddlePaddle/Paddle/pull/57294),[#57449](https://github.com/PaddlePaddle/Paddle/pull/57449),[#59520](https://github.com/PaddlePaddle/Paddle/pull/59520),[#59565](https://github.com/PaddlePaddle/Paddle/pull/59565),[#56265](https://github.com/PaddlePaddle/Paddle/pull/56265),[#56512](https://github.com/PaddlePaddle/Paddle/pull/56512),[#56650](https://github.com/PaddlePaddle/Paddle/pull/56650),[#57183](https://github.com/PaddlePaddle/Paddle/pull/57183),[#57956](https://github.com/PaddlePaddle/Paddle/pull/57956),[#59100](https://github.com/PaddlePaddle/Paddle/pull/59100) -- 优化了控制流前向,反向算子的设计和表示，引入 ControlFlow Dialect，并支持 ProgramDesc 下控制流算子到 PIR 的转换和执行。[#58729](https://github.com/PaddlePaddle/Paddle/pull/58729),[#57364](https://github.com/PaddlePaddle/Paddle/pull/57364),[#58625](https://github.com/PaddlePaddle/Paddle/pull/58625),[#57475](https://github.com/PaddlePaddle/Paddle/pull/57475),[#57265](https://github.com/PaddlePaddle/Paddle/pull/57265),[#56799](https://github.com/PaddlePaddle/Paddle/pull/56799),[#59033](https://github.com/PaddlePaddle/Paddle/pull/59033),[#57342](https://github.com/PaddlePaddle/Paddle/pull/57342),[#57801](https://github.com/PaddlePaddle/Paddle/pull/57801),[#57958](https://github.com/PaddlePaddle/Paddle/pull/57958),[#57949](https://github.com/PaddlePaddle/Paddle/pull/57949),[#57937](https://github.com/PaddlePaddle/Paddle/pull/57937),[#59231](https://github.com/PaddlePaddle/Paddle/pull/59231),[#59496](https://github.com/PaddlePaddle/Paddle/pull/59496),[#59321](https://github.com/PaddlePaddle/Paddle/pull/59321),[#58088](https://github.com/PaddlePaddle/Paddle/pull/58088),[#58198](https://github.com/PaddlePaddle/Paddle/pull/58198),[#58024](https://github.com/PaddlePaddle/Paddle/pull/58024),[#58089](https://github.com/PaddlePaddle/Paddle/pull/58089),[#58086](https://github.com/PaddlePaddle/Paddle/pull/58086),[#59175](https://github.com/PaddlePaddle/Paddle/pull/59175),[#59423](https://github.com/PaddlePaddle/Paddle/pull/59423),[#59567](https://github.com/PaddlePaddle/Paddle/pull/59567),[#58098](https://github.com/PaddlePaddle/Paddle/pull/58098),[#58163](https://github.com/PaddlePaddle/Paddle/pull/58163),[#58250](https://github.com/PaddlePaddle/Paddle/pull/58250),[#58277](https://github.com/PaddlePaddle/Paddle/pull/58277),[#58355](https://github.com/PaddlePaddle/Paddle/pull/58355),[#59020](https://github.com/PaddlePaddle/Paddle/pull/59020),[#59200](https://github.com/PaddlePaddle/Paddle/pull/59200),[#59585](https://github.com/PaddlePaddle/Paddle/pull/59585),[#58109](https://github.com/PaddlePaddle/Paddle/pull/58109) -- 动转静执行流程升级支持 PIR，优化了动转静子图 Pass 机制，支持用户在@to_static 功能下尝鲜使用 PIR 体系下功能。[#57566](https://github.com/PaddlePaddle/Paddle/pull/57566),[#55620](https://github.com/PaddlePaddle/Paddle/pull/55620),[#56791](https://github.com/PaddlePaddle/Paddle/pull/56791),[#57357](https://github.com/PaddlePaddle/Paddle/pull/57357),[#59152](https://github.com/PaddlePaddle/Paddle/pull/59152),[#59312](https://github.com/PaddlePaddle/Paddle/pull/59312),[#58630](https://github.com/PaddlePaddle/Paddle/pull/58630),[#56035](https://github.com/PaddlePaddle/Paddle/pull/56035),[#59447](https://github.com/PaddlePaddle/Paddle/pull/59447),[#57361](https://github.com/PaddlePaddle/Paddle/pull/57361),[#59261](https://github.com/PaddlePaddle/Paddle/pull/59261),[#59774](https://github.com/PaddlePaddle/Paddle/pull/59774) -- 升级了组合算子功能，引入 Backend 概念分层管理动、静态图组合算子模块逻辑，将必要组件和算子拆分规则下沉至 C++，大幅降低了维护成本。[#58153](https://github.com/PaddlePaddle/Paddle/pull/58153),[#56391](https://github.com/PaddlePaddle/Paddle/pull/56391),[#56614](https://github.com/PaddlePaddle/Paddle/pull/56614),[#57030](https://github.com/PaddlePaddle/Paddle/pull/57030),[#57554](https://github.com/PaddlePaddle/Paddle/pull/57554),[#58018](https://github.com/PaddlePaddle/Paddle/pull/58018),[#58130](https://github.com/PaddlePaddle/Paddle/pull/58130),[#58581](https://github.com/PaddlePaddle/Paddle/pull/58581),[#58679](https://github.com/PaddlePaddle/Paddle/pull/58679),[#59054](https://github.com/PaddlePaddle/Paddle/pull/59054),[#55480](https://github.com/PaddlePaddle/Paddle/pull/55480),[#58451](https://github.com/PaddlePaddle/Paddle/pull/58451),[#55647](https://github.com/PaddlePaddle/Paddle/pull/55647),[#56342](https://github.com/PaddlePaddle/Paddle/pull/56342),[#56798](https://github.com/PaddlePaddle/Paddle/pull/56798),[#57561](https://github.com/PaddlePaddle/Paddle/pull/57561),[#58023](https://github.com/PaddlePaddle/Paddle/pull/58023),[#57722](https://github.com/PaddlePaddle/Paddle/pull/57722) - -#### 性能优化 - -- 新增 DCE、constant_folding_pass 等 PIR Program 算子和结构优化的 Pass。[#54935](https://github.com/PaddlePaddle/Paddle/pull/54935),[#59430](https://github.com/PaddlePaddle/Paddle/pull/59430),[#58753](https://github.com/PaddlePaddle/Paddle/pull/58753),[#58732](https://github.com/PaddlePaddle/Paddle/pull/58732) +### 功能优化 +- 完善 PIR 的基础功能，包含基础的类型系统增强、调试、打印、Pass 开发、AMP 支持等，提升 PIR 的研发效率。[#60723](https://github.com/PaddlePaddle/Paddle/pull/60723), [#60677](https://github.com/PaddlePaddle/Paddle/pull/60677), [#60783](https://github.com/PaddlePaddle/Paddle/pull/60783), [#60798](https://github.com/PaddlePaddle/Paddle/pull/60798), [#61053](https://github.com/PaddlePaddle/Paddle/pull/61053), [#61366](https://github.com/PaddlePaddle/Paddle/pull/61366), [#61446](https://github.com/PaddlePaddle/Paddle/pull/61446), [#60024](https://github.com/PaddlePaddle/Paddle/pull/60024), [#59939](https://github.com/PaddlePaddle/Paddle/pull/59939), [#63376](https://github.com/PaddlePaddle/Paddle/pull/63376), [#61853](https://github.com/PaddlePaddle/Paddle/pull/61853), [#63914](https://github.com/PaddlePaddle/Paddle/pull/63914), [#60170](https://github.com/PaddlePaddle/Paddle/pull/60170), [#60678](https://github.com/PaddlePaddle/Paddle/pull/60678), [#64093](https://github.com/PaddlePaddle/Paddle/pull/64093), [#64065](https://github.com/PaddlePaddle/Paddle/pull/64065), [#62451](https://github.com/PaddlePaddle/Paddle/pull/62451), [#59784](https://github.com/PaddlePaddle/Paddle/pull/59784), [#60136](https://github.com/PaddlePaddle/Paddle/pull/60136), [#63336](https://github.com/PaddlePaddle/Paddle/pull/63336), [#62108](https://github.com/PaddlePaddle/Paddle/pull/62108), [#60860](https://github.com/PaddlePaddle/Paddle/pull/60860), [#60536](https://github.com/PaddlePaddle/Paddle/pull/60536), [#60590](https://github.com/PaddlePaddle/Paddle/pull/60590), [#60752](https://github.com/PaddlePaddle/Paddle/pull/60752), [#61435](https://github.com/PaddlePaddle/Paddle/pull/61435), [#62977](https://github.com/PaddlePaddle/Paddle/pull/62977), [#62139](https://github.com/PaddlePaddle/Paddle/pull/62139), [#60432](https://github.com/PaddlePaddle/Paddle/pull/60432), [#61452](https://github.com/PaddlePaddle/Paddle/pull/61452), [#61978](https://github.com/PaddlePaddle/Paddle/pull/61978), [#62262](https://github.com/PaddlePaddle/Paddle/pull/62262), [#62422](https://github.com/PaddlePaddle/Paddle/pull/62422), [#60359](https://github.com/PaddlePaddle/Paddle/pull/60359), [#62989](https://github.com/PaddlePaddle/Paddle/pull/62989), [#61297](https://github.com/PaddlePaddle/Paddle/pull/61297), [#61399](https://github.com/PaddlePaddle/Paddle/pull/61399), [#61871](https://github.com/PaddlePaddle/Paddle/pull/61871), [#61496](https://github.com/PaddlePaddle/Paddle/pull/61496), [#62413](https://github.com/PaddlePaddle/Paddle/pull/62413) +- 优化飞桨执行器执行逻辑，完善 Pass 体系，提升训推性能表现，并更好的支持分布式并行的逻辑运行。 [#60182](https://github.com/PaddlePaddle/Paddle/pull/60182), [#60516](https://github.com/PaddlePaddle/Paddle/pull/60516), [#63573](https://github.com/PaddlePaddle/Paddle/pull/63573), [#60181](https://github.com/PaddlePaddle/Paddle/pull/60181), [#59792](https://github.com/PaddlePaddle/Paddle/pull/59792), [#62025](https://github.com/PaddlePaddle/Paddle/pull/62025), [#61160](https://github.com/PaddlePaddle/Paddle/pull/61160), [#61188](https://github.com/PaddlePaddle/Paddle/pull/61188), [#61277](https://github.com/PaddlePaddle/Paddle/pull/61277), [#61669](https://github.com/PaddlePaddle/Paddle/pull/61669), [#60823](https://github.com/PaddlePaddle/Paddle/pull/60823), [#61310](https://github.com/PaddlePaddle/Paddle/pull/61310), [#60892](https://github.com/PaddlePaddle/Paddle/pull/60892), [#60578](https://github.com/PaddlePaddle/Paddle/pull/60578), [#61657](https://github.com/PaddlePaddle/Paddle/pull/61657), [#62638](https://github.com/PaddlePaddle/Paddle/pull/62638), [#63960](https://github.com/PaddlePaddle/Paddle/pull/63960), [#64234](https://github.com/PaddlePaddle/Paddle/pull/64234) -2. 新增 fused_attention,fused_dropout_add,fused_gemm_epilogue_pass,fused_linear_param_grad_add_pass,fused_weight_only_linear_pass,fused_softmax_mask_upper_triangle 等优化算子融合类 Pass，提升训练和推理性能。[#57557](https://github.com/PaddlePaddle/Paddle/pull/57557),[#58272](https://github.com/PaddlePaddle/Paddle/pull/58272),[#58188](https://github.com/PaddlePaddle/Paddle/pull/58188),[#58401](https://github.com/PaddlePaddle/Paddle/pull/58401),[#59366](https://github.com/PaddlePaddle/Paddle/pull/59366),[#57655](https://github.com/PaddlePaddle/Paddle/pull/57655),[#57360](https://github.com/PaddlePaddle/Paddle/pull/57360),[#56672](https://github.com/PaddlePaddle/Paddle/pull/56672),[#58537](https://github.com/PaddlePaddle/Paddle/pull/58537),[#56247](https://github.com/PaddlePaddle/Paddle/pull/56247),[#59391](https://github.com/PaddlePaddle/Paddle/pull/59391),[#58897](https://github.com/PaddlePaddle/Paddle/pull/58897),[#54933](https://github.com/PaddlePaddle/Paddle/pull/54933) +### PIR 新功能 +- 基于 PIR 实现反向逻辑，直接生成反向计算图，同时支持高阶微分。 [#60174](https://github.com/PaddlePaddle/Paddle/pull/60174), [#60328](https://github.com/PaddlePaddle/Paddle/pull/60328), [#60818](https://github.com/PaddlePaddle/Paddle/pull/60818), [#61352](https://github.com/PaddlePaddle/Paddle/pull/61352), [#61661](https://github.com/PaddlePaddle/Paddle/pull/61661), [#61927](https://github.com/PaddlePaddle/Paddle/pull/61927), [#62772](https://github.com/PaddlePaddle/Paddle/pull/62772), [#60360](https://github.com/PaddlePaddle/Paddle/pull/60360), [#60866](https://github.com/PaddlePaddle/Paddle/pull/60866), [#60970](https://github.com/PaddlePaddle/Paddle/pull/60970), [#60810](https://github.com/PaddlePaddle/Paddle/pull/60810), [#64696](https://github.com/PaddlePaddle/Paddle/pull/64696), [#59844](https://github.com/PaddlePaddle/Paddle/pull/59844), [#59999](https://github.com/PaddlePaddle/Paddle/pull/59999), [#60262](https://github.com/PaddlePaddle/Paddle/pull/60262), [#60338](https://github.com/PaddlePaddle/Paddle/pull/60338), [#59935](https://github.com/PaddlePaddle/Paddle/pull/59935), [#59982](https://github.com/PaddlePaddle/Paddle/pull/59982), [#60221](https://github.com/PaddlePaddle/Paddle/pull/60221), [#62621](https://github.com/PaddlePaddle/Paddle/pull/62621), [#60044](https://github.com/PaddlePaddle/Paddle/pull/60044), [#59790](https://github.com/PaddlePaddle/Paddle/pull/59790), [#60529](https://github.com/PaddlePaddle/Paddle/pull/60529), [#61378](https://github.com/PaddlePaddle/Paddle/pull/61378), [#61584](https://github.com/PaddlePaddle/Paddle/pull/61584) +- 基于 PIR 实现控制流逻辑，提升 PIR 的表达能力，更好的支持训练和推理等多场景业务。[#61396](https://github.com/PaddlePaddle/Paddle/pull/61396), [#64045](https://github.com/PaddlePaddle/Paddle/pull/64045), [#60953](https://github.com/PaddlePaddle/Paddle/pull/60953), [#61091](https://github.com/PaddlePaddle/Paddle/pull/61091), [#61304](https://github.com/PaddlePaddle/Paddle/pull/61304), [#62093](https://github.com/PaddlePaddle/Paddle/pull/62093), [#64710](https://github.com/PaddlePaddle/Paddle/pull/64710), [#60668](https://github.com/PaddlePaddle/Paddle/pull/60668), [#60433](https://github.com/PaddlePaddle/Paddle/pull/60433), [#60963](https://github.com/PaddlePaddle/Paddle/pull/60963), [#61192](https://github.com/PaddlePaddle/Paddle/pull/61192), [#60895](https://github.com/PaddlePaddle/Paddle/pull/60895), [#60017](https://github.com/PaddlePaddle/Paddle/pull/60017), [#60369](https://github.com/PaddlePaddle/Paddle/pull/60369), [#60330](https://github.com/PaddlePaddle/Paddle/pull/60330), [#60364](https://github.com/PaddlePaddle/Paddle/pull/60364), [#61416](https://github.com/PaddlePaddle/Paddle/pull/61416), [#60460](https://github.com/PaddlePaddle/Paddle/pull/60460), [#60703](https://github.com/PaddlePaddle/Paddle/pull/60703), [#61027](https://github.com/PaddlePaddle/Paddle/pull/61027) +- 基于 PIR 实现 save/load 逻辑，打通 PIR 和上下游训练和推理业务的流程。 [#63438](https://github.com/PaddlePaddle/Paddle/pull/63438), [#63574](https://github.com/PaddlePaddle/Paddle/pull/63574), [#64281](https://github.com/PaddlePaddle/Paddle/pull/64281), [#64327](https://github.com/PaddlePaddle/Paddle/pull/64327), [#63622](https://github.com/PaddlePaddle/Paddle/pull/63622), [#64507](https://github.com/PaddlePaddle/Paddle/pull/64507), [#63389](https://github.com/PaddlePaddle/Paddle/pull/63389), [#63539](https://github.com/PaddlePaddle/Paddle/pull/63539), [#63749](https://github.com/PaddlePaddle/Paddle/pull/63749), [#63957](https://github.com/PaddlePaddle/Paddle/pull/63957), [#64044](https://github.com/PaddlePaddle/Paddle/pull/64044), [#64121](https://github.com/PaddlePaddle/Paddle/pull/64121), [#64239](https://github.com/PaddlePaddle/Paddle/pull/64239), [#63818](https://github.com/PaddlePaddle/Paddle/pull/63818), [#63910](https://github.com/PaddlePaddle/Paddle/pull/63910),[#63380](https://github.com/PaddlePaddle/Paddle/pull/63380)[#63380](https://github.com/PaddlePaddle/Paddle/pull/63380),[#63275](https://github.com/PaddlePaddle/Paddle/pull/63275),[#63663](https://github.com/PaddlePaddle/Paddle/pull/63663),[#64692](https://github.com/PaddlePaddle/Paddle/pull/64692),[#63958](https://github.com/PaddlePaddle/Paddle/pull/63958) +- 完成 OneDNN 相关基础功能开发和验证，为 OneDNN 全面切换做准备。 [#60680](https://github.com/PaddlePaddle/Paddle/pull/60680), [#60665](https://github.com/PaddlePaddle/Paddle/pull/60665), [#63162](https://github.com/PaddlePaddle/Paddle/pull/63162), [#59917](https://github.com/PaddlePaddle/Paddle/pull/59917), [#62901](https://github.com/PaddlePaddle/Paddle/pull/62901), [#59918](https://github.com/PaddlePaddle/Paddle/pull/59918), [#60257](https://github.com/PaddlePaddle/Paddle/pull/60257), [#60502](https://github.com/PaddlePaddle/Paddle/pull/60502), [#61062](https://github.com/PaddlePaddle/Paddle/pull/61062), [#61170](https://github.com/PaddlePaddle/Paddle/pull/61170), [#61474](https://github.com/PaddlePaddle/Paddle/pull/61474), [#60874](https://github.com/PaddlePaddle/Paddle/pull/60874), [#61495](https://github.com/PaddlePaddle/Paddle/pull/61495), [#61664](https://github.com/PaddlePaddle/Paddle/pull/61664), [#61649](https://github.com/PaddlePaddle/Paddle/pull/61649), [#61592](https://github.com/PaddlePaddle/Paddle/pull/61592), [#61667](https://github.com/PaddlePaddle/Paddle/pull/61667), [#61137](https://github.com/PaddlePaddle/Paddle/pull/61137), [#60952](https://github.com/PaddlePaddle/Paddle/pull/60952), [#61651](https://github.com/PaddlePaddle/Paddle/pull/61651), [#62126](https://github.com/PaddlePaddle/Paddle/pull/62126), [#62187](https://github.com/PaddlePaddle/Paddle/pull/62187), [#61307](https://github.com/PaddlePaddle/Paddle/pull/61307), [#62734](https://github.com/PaddlePaddle/Paddle/pull/62734), [#60974](https://github.com/PaddlePaddle/Paddle/pull/60974), [#61451](https://github.com/PaddlePaddle/Paddle/pull/61451), [#61011](https://github.com/PaddlePaddle/Paddle/pull/61011), [#61218](https://github.com/PaddlePaddle/Paddle/pull/61218), [#61623](https://github.com/PaddlePaddle/Paddle/pull/61623), [#61893](https://github.com/PaddlePaddle/Paddle/pull/61893), [#61876](https://github.com/PaddlePaddle/Paddle/pull/61876), [#61892](https://github.com/PaddlePaddle/Paddle/pull/61892), [#62085](https://github.com/PaddlePaddle/Paddle/pull/62085), [#62220](https://github.com/PaddlePaddle/Paddle/pull/62220), [#62244](https://github.com/PaddlePaddle/Paddle/pull/62244), [#62265](https://github.com/PaddlePaddle/Paddle/pull/62265), [#60754](https://github.com/PaddlePaddle/Paddle/pull/60754), [#60896](https://github.com/PaddlePaddle/Paddle/pull/60896), [#61868](https://github.com/PaddlePaddle/Paddle/pull/61868), [#61659](https://github.com/PaddlePaddle/Paddle/pull/61659), [#62241](https://github.com/PaddlePaddle/Paddle/pull/62241), [#62471](https://github.com/PaddlePaddle/Paddle/pull/62471), [#61165](https://github.com/PaddlePaddle/Paddle/pull/61165),[#64441](https://github.com/PaddlePaddle/Paddle/pull/64441),[#63141](https://github.com/PaddlePaddle/Paddle/pull/63141),[#63145](https://github.com/PaddlePaddle/Paddle/pull/63145),[#63592](https://github.com/PaddlePaddle/Paddle/pull/63592),[#63617](https://github.com/PaddlePaddle/Paddle/pull/63617),[#63518](https://github.com/PaddlePaddle/Paddle/pull/63518),[#63726](https://github.com/PaddlePaddle/Paddle/pull/63726),[#63853](https://github.com/PaddlePaddle/Paddle/pull/63853),[#63812](https://github.com/PaddlePaddle/Paddle/pull/63812),[#63811](https://github.com/PaddlePaddle/Paddle/pull/63811),[#64524](https://github.com/PaddlePaddle/Paddle/pull/64524),[#62993](https://github.com/PaddlePaddle/Paddle/pull/62993),[#63516](https://github.com/PaddlePaddle/Paddle/pull/63516),[#62998](https://github.com/PaddlePaddle/Paddle/pull/62998),[#63151](https://github.com/PaddlePaddle/Paddle/pull/63151),[#64661](https://github.com/PaddlePaddle/Paddle/pull/64661),[#64433](https://github.com/PaddlePaddle/Paddle/pull/64433),[#64448](https://github.com/PaddlePaddle/Paddle/pull/64448),[#63201](https://github.com/PaddlePaddle/Paddle/pull/63201),[#63230](https://github.com/PaddlePaddle/Paddle/pull/63230),[#63233](https://github.com/PaddlePaddle/Paddle/pull/63233),[#63281](https://github.com/PaddlePaddle/Paddle/pull/63281),[#64671](https://github.com/PaddlePaddle/Paddle/pull/64671),[#63274](https://github.com/PaddlePaddle/Paddle/pull/63274) +- 基于 PIR 实现 Sparse 相关逻辑，包含基础的 Type 类型和算子表达，并完成 Sparse 重点功能验证。 [#62868](https://github.com/PaddlePaddle/Paddle/pull/62868), [#63015](https://github.com/PaddlePaddle/Paddle/pull/63015), [#62894](https://github.com/PaddlePaddle/Paddle/pull/62894) -### 动转静能力增强 +### 动转静功能优化 +优化动转静基础能力，适配 SOT 训练场景下的动态维度，支持 Python3.12。 +- 完成动转静场景的 PIR 适配。[#60988](https://github.com/PaddlePaddle/Paddle/pull/60988), [#61936](https://github.com/PaddlePaddle/Paddle/pull/61936), [#59929](https://github.com/PaddlePaddle/Paddle/pull/59929), [#61790](https://github.com/PaddlePaddle/Paddle/pull/61790), [#64323](https://github.com/PaddlePaddle/Paddle/pull/64323), [#62030](https://github.com/PaddlePaddle/Paddle/pull/62030), [#61143](https://github.com/PaddlePaddle/Paddle/pull/61143), [#62680](https://github.com/PaddlePaddle/Paddle/pull/62680), [#63309](https://github.com/PaddlePaddle/Paddle/pull/63309), [#63311](https://github.com/PaddlePaddle/Paddle/pull/63311), [#62199](https://github.com/PaddlePaddle/Paddle/pull/62199) +- SOT 适配 Python 3.12 版本字节码，动转静 SOT 功能能够在 Python 3.12 版本使用。[#61414](https://github.com/PaddlePaddle/Paddle/pull/61414), [#59562](https://github.com/PaddlePaddle/Paddle/pull/59562), [#61031](https://github.com/PaddlePaddle/Paddle/pull/61031), [#61272](https://github.com/PaddlePaddle/Paddle/pull/61272), [#61412](https://github.com/PaddlePaddle/Paddle/pull/61412), [#61305](https://github.com/PaddlePaddle/Paddle/pull/61305), [#61964](https://github.com/PaddlePaddle/Paddle/pull/61964), [#62008](https://github.com/PaddlePaddle/Paddle/pull/62008), [#62028](https://github.com/PaddlePaddle/Paddle/pull/62028), [#61995](https://github.com/PaddlePaddle/Paddle/pull/61995), [#62073](https://github.com/PaddlePaddle/Paddle/pull/62073), [#62120](https://github.com/PaddlePaddle/Paddle/pull/62120), [#62218](https://github.com/PaddlePaddle/Paddle/pull/62218), [#62155](https://github.com/PaddlePaddle/Paddle/pull/62155) +- SOT 完成训练场景动态维度的适配，避免维度发生改变，触发重复构图，提升运行效率。[#64278](https://github.com/PaddlePaddle/Paddle/pull/64278), [#64435](https://github.com/PaddlePaddle/Paddle/pull/64435), [#64499](https://github.com/PaddlePaddle/Paddle/pull/64499), [#64500](https://github.com/PaddlePaddle/Paddle/pull/64500), [#62080](https://github.com/PaddlePaddle/Paddle/pull/62080) -动态图到静态图的转换是深度学习框架中的一项关键技术，它允许开发者在灵活性和训练效率之间找到最佳平衡。本版本飞桨对动转静核心功能进行了全面升级，在飞桨产业级模型库的 700 多个模型中，动转静训练的成功率高达 100%。 +### 算子机制 +针对飞桨框架部分算子 Kernel 实现不完备、计算逻辑不高效等问题，我们对飞桨的部分算子功能和算子体系内部机制做了进一步的完善优化，修复部分已知问题，并新增了一些特性支持。 +- 针对 XPU Kernel，优化了`numel`、`concat`、`slice`等算子的数据类型支持，以及`AdamW`优化器的混合精度训练支持等。[#63715](https://github.com/PaddlePaddle/Paddle/pull/63715), [#61617](https://github.com/PaddlePaddle/Paddle/pull/61617), [#61694](https://github.com/PaddlePaddle/Paddle/pull/61694), [#64542](https://github.com/PaddlePaddle/Paddle/pull/64542), [#63644](https://github.com/PaddlePaddle/Paddle/pull/63644), [#61340](https://github.com/PaddlePaddle/Paddle/pull/61340), [#63108](https://github.com/PaddlePaddle/Paddle/pull/63108) +- 对部分算子进行了功能和性能的改进。[#59413](https://github.com/PaddlePaddle/Paddle/pull/59413), [#60295](https://github.com/PaddlePaddle/Paddle/pull/60295), [#64304](https://github.com/PaddlePaddle/Paddle/pull/64304), [#60979](https://github.com/PaddlePaddle/Paddle/pull/60979), [#63556](https://github.com/PaddlePaddle/Paddle/pull/63556), [#63061](https://github.com/PaddlePaddle/Paddle/pull/63061), [#62533](https://github.com/PaddlePaddle/Paddle/pull/62533) +- 完善组合算子的内部实现机制，并且为部分算子新增和优化组合拆分逻辑。[#59448](https://github.com/PaddlePaddle/Paddle/pull/59448), [#60505](https://github.com/PaddlePaddle/Paddle/pull/60505), [#59891](https://github.com/PaddlePaddle/Paddle/pull/59891), [#63161](https://github.com/PaddlePaddle/Paddle/pull/63161), [#63245](https://github.com/PaddlePaddle/Paddle/pull/63245), [#63782](https://github.com/PaddlePaddle/Paddle/pull/63782), [#64346](https://github.com/PaddlePaddle/Paddle/pull/64346), [#63156](https://github.com/PaddlePaddle/Paddle/pull/63156), [#63171](https://github.com/PaddlePaddle/Paddle/pull/63171), [#61315](https://github.com/PaddlePaddle/Paddle/pull/61315), [#61701](https://github.com/PaddlePaddle/Paddle/pull/61701), [#61874](https://github.com/PaddlePaddle/Paddle/pull/61874), [#61873](https://github.com/PaddlePaddle/Paddle/pull/61873), [#62059](https://github.com/PaddlePaddle/Paddle/pull/62059), [#61912](https://github.com/PaddlePaddle/Paddle/pull/61912), [#62112](https://github.com/PaddlePaddle/Paddle/pull/62112), [#63011](https://github.com/PaddlePaddle/Paddle/pull/63011), [#63009](https://github.com/PaddlePaddle/Paddle/pull/63009), [#64714](https://github.com/PaddlePaddle/Paddle/pull/64714) -#### 新功能 +### Bug 修复 +- 修复 PIR、执行器、动转静等相关的 Bug。[#64442](https://github.com/PaddlePaddle/Paddle/pull/64442), [#60443](https://github.com/PaddlePaddle/Paddle/pull/60443), [#60122](https://github.com/PaddlePaddle/Paddle/pull/60122), [#60625](https://github.com/PaddlePaddle/Paddle/pull/60625), [#60607](https://github.com/PaddlePaddle/Paddle/pull/60607), [#60705](https://github.com/PaddlePaddle/Paddle/pull/60705), [#61110](https://github.com/PaddlePaddle/Paddle/pull/61110), [#61278](https://github.com/PaddlePaddle/Paddle/pull/61278), [#61448](https://github.com/PaddlePaddle/Paddle/pull/61448), [#61491](https://github.com/PaddlePaddle/Paddle/pull/61491), [#61692](https://github.com/PaddlePaddle/Paddle/pull/61692), [#62100](https://github.com/PaddlePaddle/Paddle/pull/62100), [#62239](https://github.com/PaddlePaddle/Paddle/pull/62239), [#62365](https://github.com/PaddlePaddle/Paddle/pull/62365), [#62758](https://github.com/PaddlePaddle/Paddle/pull/62758), [#63395](https://github.com/PaddlePaddle/Paddle/pull/63395), [#64272](https://github.com/PaddlePaddle/Paddle/pull/64272), [#62165](https://github.com/PaddlePaddle/Paddle/pull/62165), [#64151](https://github.com/PaddlePaddle/Paddle/pull/64151), [#64204](https://github.com/PaddlePaddle/Paddle/pull/64204), [#64815](https://github.com/PaddlePaddle/Paddle/pull/64815), [#63757](https://github.com/PaddlePaddle/Paddle/pull/63757), [#61972](https://github.com/PaddlePaddle/Paddle/pull/61972), [#64806](https://github.com/PaddlePaddle/Paddle/pull/64806), [#60010](https://github.com/PaddlePaddle/Paddle/pull/60010), [#60461](https://github.com/PaddlePaddle/Paddle/pull/60461), [#60310](https://github.com/PaddlePaddle/Paddle/pull/60310), [#62006](https://github.com/PaddlePaddle/Paddle/pull/62006), [#61591](https://github.com/PaddlePaddle/Paddle/pull/61591), [#60327](https://github.com/PaddlePaddle/Paddle/pull/60327), [#60720](https://github.com/PaddlePaddle/Paddle/pull/60720), [#64656](https://github.com/PaddlePaddle/Paddle/pull/64656), [#60236](https://github.com/PaddlePaddle/Paddle/pull/60236), [#60684](https://github.com/PaddlePaddle/Paddle/pull/60684), [#60790](https://github.com/PaddlePaddle/Paddle/pull/60790), [#60944](https://github.com/PaddlePaddle/Paddle/pull/60944), [#62056](https://github.com/PaddlePaddle/Paddle/pull/62056), [#62891](https://github.com/PaddlePaddle/Paddle/pull/62891), [#64676](https://github.com/PaddlePaddle/Paddle/pull/64676), [#60271](https://github.com/PaddlePaddle/Paddle/pull/60271), [#60634](https://github.com/PaddlePaddle/Paddle/pull/60634), [#60663](https://github.com/PaddlePaddle/Paddle/pull/60663), [#60827](https://github.com/PaddlePaddle/Paddle/pull/60827), [#60845](https://github.com/PaddlePaddle/Paddle/pull/60845), [#60905](https://github.com/PaddlePaddle/Paddle/pull/60905), [#60945](https://github.com/PaddlePaddle/Paddle/pull/60945), [#60949](https://github.com/PaddlePaddle/Paddle/pull/60949), [#61107](https://github.com/PaddlePaddle/Paddle/pull/61107), [#61111](https://github.com/PaddlePaddle/Paddle/pull/61111), [#61117](https://github.com/PaddlePaddle/Paddle/pull/61117), [#61158](https://github.com/PaddlePaddle/Paddle/pull/61158), [#61177](https://github.com/PaddlePaddle/Paddle/pull/61177), [#61355](https://github.com/PaddlePaddle/Paddle/pull/61355), [#61593](https://github.com/PaddlePaddle/Paddle/pull/61593), [#61666](https://github.com/PaddlePaddle/Paddle/pull/61666), [#61934](https://github.com/PaddlePaddle/Paddle/pull/61934), [#62216](https://github.com/PaddlePaddle/Paddle/pull/62216), [#62491](https://github.com/PaddlePaddle/Paddle/pull/62491), [#62515](https://github.com/PaddlePaddle/Paddle/pull/62515), [#62594](https://github.com/PaddlePaddle/Paddle/pull/62594), [#62605](https://github.com/PaddlePaddle/Paddle/pull/62605), [#62895](https://github.com/PaddlePaddle/Paddle/pull/62895), [#62913](https://github.com/PaddlePaddle/Paddle/pull/62913), [#64413](https://github.com/PaddlePaddle/Paddle/pull/64413), [#59947](https://github.com/PaddlePaddle/Paddle/pull/59947), [#60264](https://github.com/PaddlePaddle/Paddle/pull/60264), [#60721](https://github.com/PaddlePaddle/Paddle/pull/60721), [#63113](https://github.com/PaddlePaddle/Paddle/pull/63113), [#63629](https://github.com/PaddlePaddle/Paddle/pull/63629), [#64300](https://github.com/PaddlePaddle/Paddle/pull/64300), [#64450](https://github.com/PaddlePaddle/Paddle/pull/64450), [#64532](https://github.com/PaddlePaddle/Paddle/pull/64532), [#64561](https://github.com/PaddlePaddle/Paddle/pull/64561), [#64625](https://github.com/PaddlePaddle/Paddle/pull/64625), [#64731](https://github.com/PaddlePaddle/Paddle/pull/64731), [#60059](https://github.com/PaddlePaddle/Paddle/pull/60059), [#60487](https://github.com/PaddlePaddle/Paddle/pull/60487), [#60423](https://github.com/PaddlePaddle/Paddle/pull/60423), [#61599](https://github.com/PaddlePaddle/Paddle/pull/61599), [#62032](https://github.com/PaddlePaddle/Paddle/pull/62032), [#62686](https://github.com/PaddlePaddle/Paddle/pull/62686), [#64055](https://github.com/PaddlePaddle/Paddle/pull/64055), [#60751](https://github.com/PaddlePaddle/Paddle/pull/60751), [#61646](https://github.com/PaddlePaddle/Paddle/pull/61646), [#60454](https://github.com/PaddlePaddle/Paddle/pull/60454), [#62530](https://github.com/PaddlePaddle/Paddle/pull/62530), [#62821](https://github.com/PaddlePaddle/Paddle/pull/62821), [#64454](https://github.com/PaddlePaddle/Paddle/pull/64454), [#64754](https://github.com/PaddlePaddle/Paddle/pull/64754), [#59860](https://github.com/PaddlePaddle/Paddle/pull/59860), [#60280](https://github.com/PaddlePaddle/Paddle/pull/60280), [#60357](https://github.com/PaddlePaddle/Paddle/pull/60357), [#60363](https://github.com/PaddlePaddle/Paddle/pull/60363), [#60900](https://github.com/PaddlePaddle/Paddle/pull/60900), [#61185](https://github.com/PaddlePaddle/Paddle/pull/61185), [#61505](https://github.com/PaddlePaddle/Paddle/pull/61505), [#61644](https://github.com/PaddlePaddle/Paddle/pull/61644), [#62256](https://github.com/PaddlePaddle/Paddle/pull/62256), [#62396](https://github.com/PaddlePaddle/Paddle/pull/62396), [#63040](https://github.com/PaddlePaddle/Paddle/pull/63040), [#63409](https://github.com/PaddlePaddle/Paddle/pull/63409), [#63764](https://github.com/PaddlePaddle/Paddle/pull/63764), [#59571](https://github.com/PaddlePaddle/Paddle/pull/59571), [#59894](https://github.com/PaddlePaddle/Paddle/pull/59894), [#59569](https://github.com/PaddlePaddle/Paddle/pull/59569), [#59896](https://github.com/PaddlePaddle/Paddle/pull/59896), [#60015](https://github.com/PaddlePaddle/Paddle/pull/60015), [#60081](https://github.com/PaddlePaddle/Paddle/pull/60081), [#60164](https://github.com/PaddlePaddle/Paddle/pull/60164), [#60200](https://github.com/PaddlePaddle/Paddle/pull/60200), [#60211](https://github.com/PaddlePaddle/Paddle/pull/60211), [#60267](https://github.com/PaddlePaddle/Paddle/pull/60267), [#60458](https://github.com/PaddlePaddle/Paddle/pull/60458), [#60395](https://github.com/PaddlePaddle/Paddle/pull/60395), [#60907](https://github.com/PaddlePaddle/Paddle/pull/60907), [#60707](https://github.com/PaddlePaddle/Paddle/pull/60707), [#60993](https://github.com/PaddlePaddle/Paddle/pull/60993), [#61401](https://github.com/PaddlePaddle/Paddle/pull/61401), [#61433](https://github.com/PaddlePaddle/Paddle/pull/61433), [#61450](https://github.com/PaddlePaddle/Paddle/pull/61450), [#61577](https://github.com/PaddlePaddle/Paddle/pull/61577), [#61575](https://github.com/PaddlePaddle/Paddle/pull/61575), [#61703](https://github.com/PaddlePaddle/Paddle/pull/61703), [#61711](https://github.com/PaddlePaddle/Paddle/pull/61711), [#61883](https://github.com/PaddlePaddle/Paddle/pull/61883), [#61822](https://github.com/PaddlePaddle/Paddle/pull/61822), [#62012](https://github.com/PaddlePaddle/Paddle/pull/62012), [#61858](https://github.com/PaddlePaddle/Paddle/pull/61858), [#62176](https://github.com/PaddlePaddle/Paddle/pull/62176), [#62257](https://github.com/PaddlePaddle/Paddle/pull/62257), [#62470](https://github.com/PaddlePaddle/Paddle/pull/62470), [#62536](https://github.com/PaddlePaddle/Paddle/pull/62536), [#62606](https://github.com/PaddlePaddle/Paddle/pull/62606), [#62808](https://github.com/PaddlePaddle/Paddle/pull/62808), [#62854](https://github.com/PaddlePaddle/Paddle/pull/62854), [#62879](https://github.com/PaddlePaddle/Paddle/pull/62879), [#62864](https://github.com/PaddlePaddle/Paddle/pull/62864), [#63063](https://github.com/PaddlePaddle/Paddle/pull/63063), [#62958](https://github.com/PaddlePaddle/Paddle/pull/62958), [#63397](https://github.com/PaddlePaddle/Paddle/pull/63397), [#63805](https://github.com/PaddlePaddle/Paddle/pull/63805), [#63694](https://github.com/PaddlePaddle/Paddle/pull/63694), [#64168](https://github.com/PaddlePaddle/Paddle/pull/64168), [#64184](https://github.com/PaddlePaddle/Paddle/pull/64184), [#64174](https://github.com/PaddlePaddle/Paddle/pull/64174), [#64315](https://github.com/PaddlePaddle/Paddle/pull/64315), [#64362](https://github.com/PaddlePaddle/Paddle/pull/64362), [#64400](https://github.com/PaddlePaddle/Paddle/pull/64400), [#64475](https://github.com/PaddlePaddle/Paddle/pull/64475), [#64458](https://github.com/PaddlePaddle/Paddle/pull/64458), [#64548](https://github.com/PaddlePaddle/Paddle/pull/64548), [#59858](https://github.com/PaddlePaddle/Paddle/pull/59858), [#61132](https://github.com/PaddlePaddle/Paddle/pull/61132), [#62010](https://github.com/PaddlePaddle/Paddle/pull/62010), [#62069](https://github.com/PaddlePaddle/Paddle/pull/62069), [#62707](https://github.com/PaddlePaddle/Paddle/pull/62707), [#62921](https://github.com/PaddlePaddle/Paddle/pull/62921), [#63085](https://github.com/PaddlePaddle/Paddle/pull/63085), [#63321](https://github.com/PaddlePaddle/Paddle/pull/63321), [#63351](https://github.com/PaddlePaddle/Paddle/pull/63351), [#63549](https://github.com/PaddlePaddle/Paddle/pull/63549), [#64567](https://github.com/PaddlePaddle/Paddle/pull/64567), [#59936](https://github.com/PaddlePaddle/Paddle/pull/59936), [#60269](https://github.com/PaddlePaddle/Paddle/pull/60269), [#60879](https://github.com/PaddlePaddle/Paddle/pull/60879), [#61314](https://github.com/PaddlePaddle/Paddle/pull/61314), [#61391](https://github.com/PaddlePaddle/Paddle/pull/61391), [#61479](https://github.com/PaddlePaddle/Paddle/pull/61479), [#61789](https://github.com/PaddlePaddle/Paddle/pull/61789), [#61832](https://github.com/PaddlePaddle/Paddle/pull/61832), [#61864](https://github.com/PaddlePaddle/Paddle/pull/61864), [#61917](https://github.com/PaddlePaddle/Paddle/pull/61917), [#62052](https://github.com/PaddlePaddle/Paddle/pull/62052), [#62068](https://github.com/PaddlePaddle/Paddle/pull/62068), [#62293](https://github.com/PaddlePaddle/Paddle/pull/62293), [#62479](https://github.com/PaddlePaddle/Paddle/pull/62479), [#62506](https://github.com/PaddlePaddle/Paddle/pull/62506), [#59948](https://github.com/PaddlePaddle/Paddle/pull/59948), [#64118](https://github.com/PaddlePaddle/Paddle/pull/64118), [#64126](https://github.com/PaddlePaddle/Paddle/pull/64126), [#64195](https://github.com/PaddlePaddle/Paddle/pull/64195), [#64307](https://github.com/PaddlePaddle/Paddle/pull/64307), [#64314](https://github.com/PaddlePaddle/Paddle/pull/64314), [#64276](https://github.com/PaddlePaddle/Paddle/pull/64276), [#64312](https://github.com/PaddlePaddle/Paddle/pull/64312), [#64350](https://github.com/PaddlePaddle/Paddle/pull/64350), [#64319](https://github.com/PaddlePaddle/Paddle/pull/64319), [#64463](https://github.com/PaddlePaddle/Paddle/pull/64463), [#64457](https://github.com/PaddlePaddle/Paddle/pull/64457), [#64455](https://github.com/PaddlePaddle/Paddle/pull/64455), [#64487](https://github.com/PaddlePaddle/Paddle/pull/64487), [#64645](https://github.com/PaddlePaddle/Paddle/pull/64645), [#63155](https://github.com/PaddlePaddle/Paddle/pull/63155), [#59893](https://github.com/PaddlePaddle/Paddle/pull/59893), [#63332](https://github.com/PaddlePaddle/Paddle/pull/63332), [#63332](https://github.com/PaddlePaddle/Paddle/pull/63332), [#64786](https://github.com/PaddlePaddle/Paddle/pull/64786), [#60515](https://github.com/PaddlePaddle/Paddle/pull/60515), [#60627](https://github.com/PaddlePaddle/Paddle/pull/60627), [#60863](https://github.com/PaddlePaddle/Paddle/pull/60863), [#60854](https://github.com/PaddlePaddle/Paddle/pull/60854), [#61447](https://github.com/PaddlePaddle/Paddle/pull/61447), [#61440](https://github.com/PaddlePaddle/Paddle/pull/61440), [#61932](https://github.com/PaddlePaddle/Paddle/pull/61932), [#62131](https://github.com/PaddlePaddle/Paddle/pull/62131), [#62252](https://github.com/PaddlePaddle/Paddle/pull/62252), [#62283](https://github.com/PaddlePaddle/Paddle/pull/62283), [#62358](https://github.com/PaddlePaddle/Paddle/pull/62358), [#62411](https://github.com/PaddlePaddle/Paddle/pull/62411), [#62424](https://github.com/PaddlePaddle/Paddle/pull/62424), [#62810](https://github.com/PaddlePaddle/Paddle/pull/62810), [#62811](https://github.com/PaddlePaddle/Paddle/pull/62811), [#62896](https://github.com/PaddlePaddle/Paddle/pull/62896), [#62947](https://github.com/PaddlePaddle/Paddle/pull/62947), [#63182](https://github.com/PaddlePaddle/Paddle/pull/63182), [#63190](https://github.com/PaddlePaddle/Paddle/pull/63190), [#63294](https://github.com/PaddlePaddle/Paddle/pull/63294), [#63306](https://github.com/PaddlePaddle/Paddle/pull/63306), [#63352](https://github.com/PaddlePaddle/Paddle/pull/63352), [#63404](https://github.com/PaddlePaddle/Paddle/pull/63404), [#63474](https://github.com/PaddlePaddle/Paddle/pull/63474), [#64013](https://github.com/PaddlePaddle/Paddle/pull/64013), [#64674](https://github.com/PaddlePaddle/Paddle/pull/64674),[#60055](https://github.com/PaddlePaddle/Paddle/pull/60055),[#62050](https://github.com/PaddlePaddle/Paddle/pull/62050),[#62770](https://github.com/PaddlePaddle/Paddle/pull/62770),[#63234](https://github.com/PaddlePaddle/Paddle/pull/63234),[#63374](https://github.com/PaddlePaddle/Paddle/pull/63374),[#64277](https://github.com/PaddlePaddle/Paddle/pull/64277), [#63420](https://github.com/PaddlePaddle/Paddle/pull/63420), [#60312](https://github.com/PaddlePaddle/Paddle/pull/60312), [#63810](https://github.com/PaddlePaddle/Paddle/pull/63810), [#64631](https://github.com/PaddlePaddle/Paddle/pull/64631), [#63970](https://github.com/PaddlePaddle/Paddle/pull/63970), [#63708](https://github.com/PaddlePaddle/Paddle/pull/63708), [#62062](https://github.com/PaddlePaddle/Paddle/pull/62062), [#60898](https://github.com/PaddlePaddle/Paddle/pull/60898), [#62373](https://github.com/PaddlePaddle/Paddle/pull/62373), [#59878](https://github.com/PaddlePaddle/Paddle/pull/59878) +- 修复部分算子机制、算子实现逻辑和相关单测的 Bug。[#63792](https://github.com/PaddlePaddle/Paddle/pull/63792), [#60570](https://github.com/PaddlePaddle/Paddle/pull/60570), [#61572](https://github.com/PaddlePaddle/Paddle/pull/61572), [#59971](https://github.com/PaddlePaddle/Paddle/pull/59971), [#61336](https://github.com/PaddlePaddle/Paddle/pull/61336), [#63276](https://github.com/PaddlePaddle/Paddle/pull/63276), [#63251](https://github.com/PaddlePaddle/Paddle/pull/63251), [#63697](https://github.com/PaddlePaddle/Paddle/pull/63697), [#63706](https://github.com/PaddlePaddle/Paddle/pull/63706), [#64685](https://github.com/PaddlePaddle/Paddle/pull/64685), [#64009](https://github.com/PaddlePaddle/Paddle/pull/64009), [#62461](https://github.com/PaddlePaddle/Paddle/pull/62461), [#61568](https://github.com/PaddlePaddle/Paddle/pull/61568), [#63912](https://github.com/PaddlePaddle/Paddle/pull/63912), [#60475](https://github.com/PaddlePaddle/Paddle/pull/60475), [#60222](https://github.com/PaddlePaddle/Paddle/pull/60222), [#63961](https://github.com/PaddlePaddle/Paddle/pull/63961), [#63593](https://github.com/PaddlePaddle/Paddle/pull/63593) -- 采用 Python Eval Frame 和虚拟机模拟执行技术，创新性地实现了自适应的 Graph Break 机制。这一机制特别针对控制流场景，通过引入 CallLayer 机制，充分利用飞桨动静统一的优势，支持 AST（抽象语法树）与字节码模拟的混合模式，有效捕获控制流算子，从而大幅度提高了计算图的静态化能力。在缓存优化层面，融合了公共子表达式消除等高级优化技术，显著提升了 Guard 的执行效率。这些优化措施不仅减少了冗余计算，还提高了整体系统的运行速度。为了增强系统的鲁棒性，设计了一个简洁高效的数据中间层结构。这一结构支持 SideEffects 的正确性恢复，确保了系统在复杂环境下的稳定性和可靠性。此外，广泛兼容 Python 3.8 至 3.11 的主流解释器版本，为用户提供了广泛的适用性。[#57824](https://github.com/PaddlePaddle/Paddle/pull/57824),[#55887](https://github.com/PaddlePaddle/Paddle/pull/55887),[#58155](https://github.com/PaddlePaddle/Paddle/pull/58155),[#56107](https://github.com/PaddlePaddle/Paddle/pull/56107),[#57490](https://github.com/PaddlePaddle/Paddle/pull/57490),[#58829](https://github.com/PaddlePaddle/Paddle/pull/58829),[#57240](https://github.com/PaddlePaddle/Paddle/pull/57240),[#57588](https://github.com/PaddlePaddle/Paddle/pull/57588),[#58117](https://github.com/PaddlePaddle/Paddle/pull/58117),[#59823](https://github.com/PaddlePaddle/Paddle/pull/59823),[#56077](https://github.com/PaddlePaddle/Paddle/pull/56077),[#58956](https://github.com/PaddlePaddle/Paddle/pull/58956),[#57653](https://github.com/PaddlePaddle/Paddle/pull/57653),[#59855](https://github.com/PaddlePaddle/Paddle/pull/59855),[#59017](https://github.com/PaddlePaddle/Paddle/pull/59017),[#58424](https://github.com/PaddlePaddle/Paddle/pull/58424),[#58187](https://github.com/PaddlePaddle/Paddle/pull/58187),[#57793](https://github.com/PaddlePaddle/Paddle/pull/57793),[#59698](https://github.com/PaddlePaddle/Paddle/pull/59698),[#59747](https://github.com/PaddlePaddle/Paddle/pull/59747),[#59710](https://github.com/PaddlePaddle/Paddle/pull/59710),[#59297](https://github.com/PaddlePaddle/Paddle/pull/59297),[#58423](https://github.com/PaddlePaddle/Paddle/pull/58423),[#56262](https://github.com/PaddlePaddle/Paddle/pull/56262),[#58103](https://github.com/PaddlePaddle/Paddle/pull/58103),[#58538](https://github.com/PaddlePaddle/Paddle/pull/58538),[#58771](https://github.com/PaddlePaddle/Paddle/pull/58771),[#59191](https://github.com/PaddlePaddle/Paddle/pull/59191),[#57754](https://github.com/PaddlePaddle/Paddle/pull/57754),[#59439](https://github.com/PaddlePaddle/Paddle/pull/59439),[#59816](https://github.com/PaddlePaddle/Paddle/pull/59816),[#59035](https://github.com/PaddlePaddle/Paddle/pull/59035) -- 新增对 PyLayer 功能的动转静语法转写解析，使得 PyLayer 在动态图与静态图之间的转换更加顺畅。现在，用户可以在 PyLayer 下无缝地进行动转静的训练，并轻松导出推理模型。[#56108](https://github.com/PaddlePaddle/Paddle/pull/56108),[#56531](https://github.com/PaddlePaddle/Paddle/pull/56531),[#57066](https://github.com/PaddlePaddle/Paddle/pull/57066),[#57633](https://github.com/PaddlePaddle/Paddle/pull/57633) +### 开发者相关内容 +- 开发者相关内容，包含 PIR 切换、单测开启、功能验证等 PR。 [#60621](https://github.com/PaddlePaddle/Paddle/pull/60621), [#59703](https://github.com/PaddlePaddle/Paddle/pull/59703), [#59694](https://github.com/PaddlePaddle/Paddle/pull/59694), [#59717](https://github.com/PaddlePaddle/Paddle/pull/59717), [#59729](https://github.com/PaddlePaddle/Paddle/pull/59729), [#59730](https://github.com/PaddlePaddle/Paddle/pull/59730), [#60216](https://github.com/PaddlePaddle/Paddle/pull/60216), [#60238](https://github.com/PaddlePaddle/Paddle/pull/60238), [#60246](https://github.com/PaddlePaddle/Paddle/pull/60246), [#60343](https://github.com/PaddlePaddle/Paddle/pull/60343), [#60302](https://github.com/PaddlePaddle/Paddle/pull/60302), [#60870](https://github.com/PaddlePaddle/Paddle/pull/60870), [#59956](https://github.com/PaddlePaddle/Paddle/pull/59956), [#60795](https://github.com/PaddlePaddle/Paddle/pull/60795), [#62528](https://github.com/PaddlePaddle/Paddle/pull/62528), [#59932](https://github.com/PaddlePaddle/Paddle/pull/59932), [#59636](https://github.com/PaddlePaddle/Paddle/pull/59636), [#59959](https://github.com/PaddlePaddle/Paddle/pull/59959), [#59734](https://github.com/PaddlePaddle/Paddle/pull/59734), [#60287](https://github.com/PaddlePaddle/Paddle/pull/60287), [#60347](https://github.com/PaddlePaddle/Paddle/pull/60347), [#60335](https://github.com/PaddlePaddle/Paddle/pull/60335), [#60332](https://github.com/PaddlePaddle/Paddle/pull/60332), [#59631](https://github.com/PaddlePaddle/Paddle/pull/59631), [#60255](https://github.com/PaddlePaddle/Paddle/pull/60255), [#60329](https://github.com/PaddlePaddle/Paddle/pull/60329), [#60401](https://github.com/PaddlePaddle/Paddle/pull/60401), [#60522](https://github.com/PaddlePaddle/Paddle/pull/60522), [#60792](https://github.com/PaddlePaddle/Paddle/pull/60792), [#59617](https://github.com/PaddlePaddle/Paddle/pull/59617), [#60277](https://github.com/PaddlePaddle/Paddle/pull/60277), [#60584](https://github.com/PaddlePaddle/Paddle/pull/60584), [#60911](https://github.com/PaddlePaddle/Paddle/pull/60911), [#61322](https://github.com/PaddlePaddle/Paddle/pull/61322), [#60838](https://github.com/PaddlePaddle/Paddle/pull/60838), [#60602](https://github.com/PaddlePaddle/Paddle/pull/60602), [#61458](https://github.com/PaddlePaddle/Paddle/pull/61458), [#61607](https://github.com/PaddlePaddle/Paddle/pull/61607), [#61960](https://github.com/PaddlePaddle/Paddle/pull/61960), [#60484](https://github.com/PaddlePaddle/Paddle/pull/60484), [#61662](https://github.com/PaddlePaddle/Paddle/pull/61662), [#62263](https://github.com/PaddlePaddle/Paddle/pull/62263), [#62270](https://github.com/PaddlePaddle/Paddle/pull/62270), [#62469](https://github.com/PaddlePaddle/Paddle/pull/62469), [#62416](https://github.com/PaddlePaddle/Paddle/pull/62416), [#62443](https://github.com/PaddlePaddle/Paddle/pull/62443), [#62412](https://github.com/PaddlePaddle/Paddle/pull/62412), [#62541](https://github.com/PaddlePaddle/Paddle/pull/62541), [#62634](https://github.com/PaddlePaddle/Paddle/pull/62634), [#62369](https://github.com/PaddlePaddle/Paddle/pull/62369), [#60805](https://github.com/PaddlePaddle/Paddle/pull/60805), [#62644](https://github.com/PaddlePaddle/Paddle/pull/62644), [#62494](https://github.com/PaddlePaddle/Paddle/pull/62494), [#62767](https://github.com/PaddlePaddle/Paddle/pull/62767), [#62735](https://github.com/PaddlePaddle/Paddle/pull/62735), [#62802](https://github.com/PaddlePaddle/Paddle/pull/62802), [#62801](https://github.com/PaddlePaddle/Paddle/pull/62801), [#62783](https://github.com/PaddlePaddle/Paddle/pull/62783), [#62579](https://github.com/PaddlePaddle/Paddle/pull/62579), [#62833](https://github.com/PaddlePaddle/Paddle/pull/62833), [#62668](https://github.com/PaddlePaddle/Paddle/pull/62668), [#62972](https://github.com/PaddlePaddle/Paddle/pull/62972), [#62505](https://github.com/PaddlePaddle/Paddle/pull/62505), [#63005](https://github.com/PaddlePaddle/Paddle/pull/63005), [#62900](https://github.com/PaddlePaddle/Paddle/pull/62900), [#60577](https://github.com/PaddlePaddle/Paddle/pull/60577), [#60877](https://github.com/PaddlePaddle/Paddle/pull/60877), [#61076](https://github.com/PaddlePaddle/Paddle/pull/61076), [#61038](https://github.com/PaddlePaddle/Paddle/pull/61038), [#61112](https://github.com/PaddlePaddle/Paddle/pull/61112), [#61120](https://github.com/PaddlePaddle/Paddle/pull/61120), [#61582](https://github.com/PaddlePaddle/Paddle/pull/61582), [#61119](https://github.com/PaddlePaddle/Paddle/pull/61119), [#61036](https://github.com/PaddlePaddle/Paddle/pull/61036), [#61289](https://github.com/PaddlePaddle/Paddle/pull/61289), [#60695](https://github.com/PaddlePaddle/Paddle/pull/60695), [#61039](https://github.com/PaddlePaddle/Paddle/pull/61039), [#61963](https://github.com/PaddlePaddle/Paddle/pull/61963), [#62118](https://github.com/PaddlePaddle/Paddle/pull/62118), [#62797](https://github.com/PaddlePaddle/Paddle/pull/62797), [#62807](https://github.com/PaddlePaddle/Paddle/pull/62807), [#62887](https://github.com/PaddlePaddle/Paddle/pull/62887), [#62830](https://github.com/PaddlePaddle/Paddle/pull/62830), [#62849](https://github.com/PaddlePaddle/Paddle/pull/62849), [#62750](https://github.com/PaddlePaddle/Paddle/pull/62750), [#62965](https://github.com/PaddlePaddle/Paddle/pull/62965), [#59742](https://github.com/PaddlePaddle/Paddle/pull/59742), [#59867](https://github.com/PaddlePaddle/Paddle/pull/59867), [#60836](https://github.com/PaddlePaddle/Paddle/pull/60836), [#60902](https://github.com/PaddlePaddle/Paddle/pull/60902), [#61228](https://github.com/PaddlePaddle/Paddle/pull/61228), [#60037](https://github.com/PaddlePaddle/Paddle/pull/60037), [#60079](https://github.com/PaddlePaddle/Paddle/pull/60079), [#60173](https://github.com/PaddlePaddle/Paddle/pull/60173), [#60373](https://github.com/PaddlePaddle/Paddle/pull/60373), [#60380](https://github.com/PaddlePaddle/Paddle/pull/60380), [#60381](https://github.com/PaddlePaddle/Paddle/pull/60381), [#60750](https://github.com/PaddlePaddle/Paddle/pull/60750), [#61065](https://github.com/PaddlePaddle/Paddle/pull/61065), [#61122](https://github.com/PaddlePaddle/Paddle/pull/61122), [#61074](https://github.com/PaddlePaddle/Paddle/pull/61074), [#61204](https://github.com/PaddlePaddle/Paddle/pull/61204), [#61191](https://github.com/PaddlePaddle/Paddle/pull/61191), [#61182](https://github.com/PaddlePaddle/Paddle/pull/61182), [#61219](https://github.com/PaddlePaddle/Paddle/pull/61219), [#61296](https://github.com/PaddlePaddle/Paddle/pull/61296), [#61503](https://github.com/PaddlePaddle/Paddle/pull/61503), [#61484](https://github.com/PaddlePaddle/Paddle/pull/61484), [#61513](https://github.com/PaddlePaddle/Paddle/pull/61513), [#61476](https://github.com/PaddlePaddle/Paddle/pull/61476), [#61510](https://github.com/PaddlePaddle/Paddle/pull/61510), [#61511](https://github.com/PaddlePaddle/Paddle/pull/61511), [#61526](https://github.com/PaddlePaddle/Paddle/pull/61526), [#61524](https://github.com/PaddlePaddle/Paddle/pull/61524), [#61525](https://github.com/PaddlePaddle/Paddle/pull/61525), [#61466](https://github.com/PaddlePaddle/Paddle/pull/61466), [#61497](https://github.com/PaddlePaddle/Paddle/pull/61497), [#61538](https://github.com/PaddlePaddle/Paddle/pull/61538), [#61533](https://github.com/PaddlePaddle/Paddle/pull/61533), [#61530](https://github.com/PaddlePaddle/Paddle/pull/61530), [#61468](https://github.com/PaddlePaddle/Paddle/pull/61468), [#61527](https://github.com/PaddlePaddle/Paddle/pull/61527), [#61535](https://github.com/PaddlePaddle/Paddle/pull/61535), [#61512](https://github.com/PaddlePaddle/Paddle/pull/61512), [#61531](https://github.com/PaddlePaddle/Paddle/pull/61531), [#61539](https://github.com/PaddlePaddle/Paddle/pull/61539), [#61532](https://github.com/PaddlePaddle/Paddle/pull/61532), [#61521](https://github.com/PaddlePaddle/Paddle/pull/61521), [#61517](https://github.com/PaddlePaddle/Paddle/pull/61517), [#61518](https://github.com/PaddlePaddle/Paddle/pull/61518), [#61550](https://github.com/PaddlePaddle/Paddle/pull/61550), [#61545](https://github.com/PaddlePaddle/Paddle/pull/61545), [#61548](https://github.com/PaddlePaddle/Paddle/pull/61548), [#61519](https://github.com/PaddlePaddle/Paddle/pull/61519), [#61549](https://github.com/PaddlePaddle/Paddle/pull/61549), [#61574](https://github.com/PaddlePaddle/Paddle/pull/61574), [#61585](https://github.com/PaddlePaddle/Paddle/pull/61585), [#61581](https://github.com/PaddlePaddle/Paddle/pull/61581), [#61553](https://github.com/PaddlePaddle/Paddle/pull/61553), [#61504](https://github.com/PaddlePaddle/Paddle/pull/61504), [#61603](https://github.com/PaddlePaddle/Paddle/pull/61603), [#61534](https://github.com/PaddlePaddle/Paddle/pull/61534), [#61567](https://github.com/PaddlePaddle/Paddle/pull/61567), [#61523](https://github.com/PaddlePaddle/Paddle/pull/61523), [#61565](https://github.com/PaddlePaddle/Paddle/pull/61565), [#61564](https://github.com/PaddlePaddle/Paddle/pull/61564), [#61707](https://github.com/PaddlePaddle/Paddle/pull/61707), [#61560](https://github.com/PaddlePaddle/Paddle/pull/61560), [#61684](https://github.com/PaddlePaddle/Paddle/pull/61684), [#61706](https://github.com/PaddlePaddle/Paddle/pull/61706), [#61724](https://github.com/PaddlePaddle/Paddle/pull/61724), [#61719](https://github.com/PaddlePaddle/Paddle/pull/61719), [#61729](https://github.com/PaddlePaddle/Paddle/pull/61729), [#61763](https://github.com/PaddlePaddle/Paddle/pull/61763), [#61755](https://github.com/PaddlePaddle/Paddle/pull/61755), [#61737](https://github.com/PaddlePaddle/Paddle/pull/61737), [#61750](https://github.com/PaddlePaddle/Paddle/pull/61750), [#61753](https://github.com/PaddlePaddle/Paddle/pull/61753), [#61756](https://github.com/PaddlePaddle/Paddle/pull/61756), [#61777](https://github.com/PaddlePaddle/Paddle/pull/61777), [#61758](https://github.com/PaddlePaddle/Paddle/pull/61758), [#61731](https://github.com/PaddlePaddle/Paddle/pull/61731), [#61771](https://github.com/PaddlePaddle/Paddle/pull/61771), [#61739](https://github.com/PaddlePaddle/Paddle/pull/61739), [#61559](https://github.com/PaddlePaddle/Paddle/pull/61559), [#61717](https://github.com/PaddlePaddle/Paddle/pull/61717), [#61733](https://github.com/PaddlePaddle/Paddle/pull/61733), [#61563](https://github.com/PaddlePaddle/Paddle/pull/61563), [#61546](https://github.com/PaddlePaddle/Paddle/pull/61546), [#61566](https://github.com/PaddlePaddle/Paddle/pull/61566), [#61562](https://github.com/PaddlePaddle/Paddle/pull/61562), [#61793](https://github.com/PaddlePaddle/Paddle/pull/61793), [#61902](https://github.com/PaddlePaddle/Paddle/pull/61902), [#61905](https://github.com/PaddlePaddle/Paddle/pull/61905), [#61904](https://github.com/PaddlePaddle/Paddle/pull/61904), [#62227](https://github.com/PaddlePaddle/Paddle/pull/62227), [#62332](https://github.com/PaddlePaddle/Paddle/pull/62332), [#62653](https://github.com/PaddlePaddle/Paddle/pull/62653), [#62681](https://github.com/PaddlePaddle/Paddle/pull/62681), [#62709](https://github.com/PaddlePaddle/Paddle/pull/62709), [#62794](https://github.com/PaddlePaddle/Paddle/pull/62794), [#62938](https://github.com/PaddlePaddle/Paddle/pull/62938), [#63185](https://github.com/PaddlePaddle/Paddle/pull/63185), [#63754](https://github.com/PaddlePaddle/Paddle/pull/63754), [#63769](https://github.com/PaddlePaddle/Paddle/pull/63769), [#63793](https://github.com/PaddlePaddle/Paddle/pull/63793), [#63830](https://github.com/PaddlePaddle/Paddle/pull/63830), [#63939](https://github.com/PaddlePaddle/Paddle/pull/63939), [#64340](https://github.com/PaddlePaddle/Paddle/pull/64340), [#64657](https://github.com/PaddlePaddle/Paddle/pull/64657), [#62527](https://github.com/PaddlePaddle/Paddle/pull/62527), [#64088](https://github.com/PaddlePaddle/Paddle/pull/64088), [#60203](https://github.com/PaddlePaddle/Paddle/pull/60203), [#60372](https://github.com/PaddlePaddle/Paddle/pull/60372), [#60685](https://github.com/PaddlePaddle/Paddle/pull/60685), [#60815](https://github.com/PaddlePaddle/Paddle/pull/60815), [#60791](https://github.com/PaddlePaddle/Paddle/pull/60791), [#60864](https://github.com/PaddlePaddle/Paddle/pull/60864), [#60851](https://github.com/PaddlePaddle/Paddle/pull/60851), [#60844](https://github.com/PaddlePaddle/Paddle/pull/60844), [#60694](https://github.com/PaddlePaddle/Paddle/pull/60694), [#60855](https://github.com/PaddlePaddle/Paddle/pull/60855), [#60869](https://github.com/PaddlePaddle/Paddle/pull/60869), [#60948](https://github.com/PaddlePaddle/Paddle/pull/60948), [#61042](https://github.com/PaddlePaddle/Paddle/pull/61042), [#61455](https://github.com/PaddlePaddle/Paddle/pull/61455), [#61580](https://github.com/PaddlePaddle/Paddle/pull/61580), [#61589](https://github.com/PaddlePaddle/Paddle/pull/61589), [#61609](https://github.com/PaddlePaddle/Paddle/pull/61609), [#61616](https://github.com/PaddlePaddle/Paddle/pull/61616), [#61715](https://github.com/PaddlePaddle/Paddle/pull/61715), [#61716](https://github.com/PaddlePaddle/Paddle/pull/61716), [#61759](https://github.com/PaddlePaddle/Paddle/pull/61759), [#61555](https://github.com/PaddlePaddle/Paddle/pull/61555), [#61492](https://github.com/PaddlePaddle/Paddle/pull/61492), [#61805](https://github.com/PaddlePaddle/Paddle/pull/61805), [#61712](https://github.com/PaddlePaddle/Paddle/pull/61712), [#61615](https://github.com/PaddlePaddle/Paddle/pull/61615), [#61713](https://github.com/PaddlePaddle/Paddle/pull/61713), [#62129](https://github.com/PaddlePaddle/Paddle/pull/62129), [#59294](https://github.com/PaddlePaddle/Paddle/pull/59294), [#59865](https://github.com/PaddlePaddle/Paddle/pull/59865), [#60270](https://github.com/PaddlePaddle/Paddle/pull/60270), [#60547](https://github.com/PaddlePaddle/Paddle/pull/60547), [#60698](https://github.com/PaddlePaddle/Paddle/pull/60698), [#60762](https://github.com/PaddlePaddle/Paddle/pull/60762), [#60753](https://github.com/PaddlePaddle/Paddle/pull/60753), [#60966](https://github.com/PaddlePaddle/Paddle/pull/60966), [#60976](https://github.com/PaddlePaddle/Paddle/pull/60976), [#61100](https://github.com/PaddlePaddle/Paddle/pull/61100), [#61203](https://github.com/PaddlePaddle/Paddle/pull/61203), [#61210](https://github.com/PaddlePaddle/Paddle/pull/61210), [#61424](https://github.com/PaddlePaddle/Paddle/pull/61424), [#61213](https://github.com/PaddlePaddle/Paddle/pull/61213), [#61275](https://github.com/PaddlePaddle/Paddle/pull/61275), [#61276](https://github.com/PaddlePaddle/Paddle/pull/61276), [#61279](https://github.com/PaddlePaddle/Paddle/pull/61279), [#61292](https://github.com/PaddlePaddle/Paddle/pull/61292), [#61295](https://github.com/PaddlePaddle/Paddle/pull/61295), [#61298](https://github.com/PaddlePaddle/Paddle/pull/61298), [#61299](https://github.com/PaddlePaddle/Paddle/pull/61299), [#61301](https://github.com/PaddlePaddle/Paddle/pull/61301), [#61302](https://github.com/PaddlePaddle/Paddle/pull/61302), [#61329](https://github.com/PaddlePaddle/Paddle/pull/61329), [#61804](https://github.com/PaddlePaddle/Paddle/pull/61804), [#62745](https://github.com/PaddlePaddle/Paddle/pull/62745), [#62909](https://github.com/PaddlePaddle/Paddle/pull/62909), [#64247](https://github.com/PaddlePaddle/Paddle/pull/64247), [#64308](https://github.com/PaddlePaddle/Paddle/pull/64308), [#60690](https://github.com/PaddlePaddle/Paddle/pull/60690), [#61149](https://github.com/PaddlePaddle/Paddle/pull/61149), [#61145](https://github.com/PaddlePaddle/Paddle/pull/61145), [#61193](https://github.com/PaddlePaddle/Paddle/pull/61193), [#61207](https://github.com/PaddlePaddle/Paddle/pull/61207), [#61229](https://github.com/PaddlePaddle/Paddle/pull/61229), [#61236](https://github.com/PaddlePaddle/Paddle/pull/61236), [#61244](https://github.com/PaddlePaddle/Paddle/pull/61244), [#61242](https://github.com/PaddlePaddle/Paddle/pull/61242), [#61263](https://github.com/PaddlePaddle/Paddle/pull/61263), [#61370](https://github.com/PaddlePaddle/Paddle/pull/61370), [#61410](https://github.com/PaddlePaddle/Paddle/pull/61410), [#61480](https://github.com/PaddlePaddle/Paddle/pull/61480), [#61522](https://github.com/PaddlePaddle/Paddle/pull/61522), [#61540](https://github.com/PaddlePaddle/Paddle/pull/61540), [#61520](https://github.com/PaddlePaddle/Paddle/pull/61520), [#61625](https://github.com/PaddlePaddle/Paddle/pull/61625), [#61700](https://github.com/PaddlePaddle/Paddle/pull/61700), [#61708](https://github.com/PaddlePaddle/Paddle/pull/61708), [#61736](https://github.com/PaddlePaddle/Paddle/pull/61736), [#61889](https://github.com/PaddlePaddle/Paddle/pull/61889), [#61952](https://github.com/PaddlePaddle/Paddle/pull/61952), [#62033](https://github.com/PaddlePaddle/Paddle/pull/62033), [#62637](https://github.com/PaddlePaddle/Paddle/pull/62637), [#62777](https://github.com/PaddlePaddle/Paddle/pull/62777), [#62779](https://github.com/PaddlePaddle/Paddle/pull/62779), [#63226](https://github.com/PaddlePaddle/Paddle/pull/63226), [#63287](https://github.com/PaddlePaddle/Paddle/pull/63287), [#63398](https://github.com/PaddlePaddle/Paddle/pull/63398), [#63431](https://github.com/PaddlePaddle/Paddle/pull/63431), [#64000](https://github.com/PaddlePaddle/Paddle/pull/64000), [#64058](https://github.com/PaddlePaddle/Paddle/pull/64058), [#64059](https://github.com/PaddlePaddle/Paddle/pull/64059), [#64063](https://github.com/PaddlePaddle/Paddle/pull/64063), [#64066](https://github.com/PaddlePaddle/Paddle/pull/64066), [#64089](https://github.com/PaddlePaddle/Paddle/pull/64089), [#64170](https://github.com/PaddlePaddle/Paddle/pull/64170), [#64235](https://github.com/PaddlePaddle/Paddle/pull/64235), [#64237](https://github.com/PaddlePaddle/Paddle/pull/64237), [#64243](https://github.com/PaddlePaddle/Paddle/pull/64243), [#64242](https://github.com/PaddlePaddle/Paddle/pull/64242), [#64286](https://github.com/PaddlePaddle/Paddle/pull/64286), [#64322](https://github.com/PaddlePaddle/Paddle/pull/64322), [#64317](https://github.com/PaddlePaddle/Paddle/pull/64317), [#64490](https://github.com/PaddlePaddle/Paddle/pull/64490), [#60138](https://github.com/PaddlePaddle/Paddle/pull/60138), [#62384](https://github.com/PaddlePaddle/Paddle/pull/62384), [#59702](https://github.com/PaddlePaddle/Paddle/pull/59702), [#60341](https://github.com/PaddlePaddle/Paddle/pull/60341), [#60636](https://github.com/PaddlePaddle/Paddle/pull/60636), [#60714](https://github.com/PaddlePaddle/Paddle/pull/60714), [#60716](https://github.com/PaddlePaddle/Paddle/pull/60716), [#60700](https://github.com/PaddlePaddle/Paddle/pull/60700), [#60702](https://github.com/PaddlePaddle/Paddle/pull/60702), [#60704](https://github.com/PaddlePaddle/Paddle/pull/60704), [#60715](https://github.com/PaddlePaddle/Paddle/pull/60715), [#60713](https://github.com/PaddlePaddle/Paddle/pull/60713), [#60711](https://github.com/PaddlePaddle/Paddle/pull/60711), [#60724](https://github.com/PaddlePaddle/Paddle/pull/60724), [#60803](https://github.com/PaddlePaddle/Paddle/pull/60803), [#61331](https://github.com/PaddlePaddle/Paddle/pull/61331), [#63286](https://github.com/PaddlePaddle/Paddle/pull/63286), [#60473](https://github.com/PaddlePaddle/Paddle/pull/60473), [#61046](https://github.com/PaddlePaddle/Paddle/pull/61046), [#61859](https://github.com/PaddlePaddle/Paddle/pull/61859), [#60675](https://github.com/PaddlePaddle/Paddle/pull/60675), [#60719](https://github.com/PaddlePaddle/Paddle/pull/60719), [#62863](https://github.com/PaddlePaddle/Paddle/pull/62863), [#63013](https://github.com/PaddlePaddle/Paddle/pull/63013), [#61293](https://github.com/PaddlePaddle/Paddle/pull/61293), [#62781](https://github.com/PaddlePaddle/Paddle/pull/62781), [#62935](https://github.com/PaddlePaddle/Paddle/pull/62935), [#63014](https://github.com/PaddlePaddle/Paddle/pull/63014), [#64203](https://github.com/PaddlePaddle/Paddle/pull/64203), [#63349](https://github.com/PaddlePaddle/Paddle/pull/63349), [#59572](https://github.com/PaddlePaddle/Paddle/pull/59572), [#59911](https://github.com/PaddlePaddle/Paddle/pull/59911), [#59861](https://github.com/PaddlePaddle/Paddle/pull/59861), [#60014](https://github.com/PaddlePaddle/Paddle/pull/60014), [#59913](https://github.com/PaddlePaddle/Paddle/pull/59913), [#58889](https://github.com/PaddlePaddle/Paddle/pull/58889), [#60114](https://github.com/PaddlePaddle/Paddle/pull/60114), [#59928](https://github.com/PaddlePaddle/Paddle/pull/59928), [#60180](https://github.com/PaddlePaddle/Paddle/pull/60180), [#60168](https://github.com/PaddlePaddle/Paddle/pull/60168), [#60166](https://github.com/PaddlePaddle/Paddle/pull/60166), [#60250](https://github.com/PaddlePaddle/Paddle/pull/60250), [#60247](https://github.com/PaddlePaddle/Paddle/pull/60247), [#60172](https://github.com/PaddlePaddle/Paddle/pull/60172), [#59661](https://github.com/PaddlePaddle/Paddle/pull/59661), [#58880](https://github.com/PaddlePaddle/Paddle/pull/58880), [#60291](https://github.com/PaddlePaddle/Paddle/pull/60291), [#58881](https://github.com/PaddlePaddle/Paddle/pull/58881), [#58955](https://github.com/PaddlePaddle/Paddle/pull/58955), [#58684](https://github.com/PaddlePaddle/Paddle/pull/58684), [#58708](https://github.com/PaddlePaddle/Paddle/pull/58708), [#60323](https://github.com/PaddlePaddle/Paddle/pull/60323), [#58762](https://github.com/PaddlePaddle/Paddle/pull/58762), [#60048](https://github.com/PaddlePaddle/Paddle/pull/60048), [#60345](https://github.com/PaddlePaddle/Paddle/pull/60345), [#60325](https://github.com/PaddlePaddle/Paddle/pull/60325), [#59627](https://github.com/PaddlePaddle/Paddle/pull/59627), [#60416](https://github.com/PaddlePaddle/Paddle/pull/60416), [#60434](https://github.com/PaddlePaddle/Paddle/pull/60434), [#59801](https://github.com/PaddlePaddle/Paddle/pull/59801), [#60619](https://github.com/PaddlePaddle/Paddle/pull/60619), [#60445](https://github.com/PaddlePaddle/Paddle/pull/60445), [#60666](https://github.com/PaddlePaddle/Paddle/pull/60666), [#60353](https://github.com/PaddlePaddle/Paddle/pull/60353), [#60733](https://github.com/PaddlePaddle/Paddle/pull/60733), [#60693](https://github.com/PaddlePaddle/Paddle/pull/60693), [#60350](https://github.com/PaddlePaddle/Paddle/pull/60350), [#61096](https://github.com/PaddlePaddle/Paddle/pull/61096), [#61121](https://github.com/PaddlePaddle/Paddle/pull/61121), [#61164](https://github.com/PaddlePaddle/Paddle/pull/61164), [#62054](https://github.com/PaddlePaddle/Paddle/pull/62054), [#62136](https://github.com/PaddlePaddle/Paddle/pull/62136), [#62508](https://github.com/PaddlePaddle/Paddle/pull/62508), [#62988](https://github.com/PaddlePaddle/Paddle/pull/62988), [#63472](https://github.com/PaddlePaddle/Paddle/pull/63472), [#60193](https://github.com/PaddlePaddle/Paddle/pull/60193), [#60197](https://github.com/PaddlePaddle/Paddle/pull/60197), [#60198](https://github.com/PaddlePaddle/Paddle/pull/60198), [#60346](https://github.com/PaddlePaddle/Paddle/pull/60346), [#60318](https://github.com/PaddlePaddle/Paddle/pull/60318), [#60645](https://github.com/PaddlePaddle/Paddle/pull/60645), [#60650](https://github.com/PaddlePaddle/Paddle/pull/60650), [#60660](https://github.com/PaddlePaddle/Paddle/pull/60660), [#60706](https://github.com/PaddlePaddle/Paddle/pull/60706), [#60799](https://github.com/PaddlePaddle/Paddle/pull/60799), [#60837](https://github.com/PaddlePaddle/Paddle/pull/60837), [#60817](https://github.com/PaddlePaddle/Paddle/pull/60817), [#60820](https://github.com/PaddlePaddle/Paddle/pull/60820), [#60894](https://github.com/PaddlePaddle/Paddle/pull/60894), [#61079](https://github.com/PaddlePaddle/Paddle/pull/61079), [#61087](https://github.com/PaddlePaddle/Paddle/pull/61087), [#61073](https://github.com/PaddlePaddle/Paddle/pull/61073), [#61072](https://github.com/PaddlePaddle/Paddle/pull/61072), [#61127](https://github.com/PaddlePaddle/Paddle/pull/61127), [#61097](https://github.com/PaddlePaddle/Paddle/pull/61097), [#61365](https://github.com/PaddlePaddle/Paddle/pull/61365), [#61456](https://github.com/PaddlePaddle/Paddle/pull/61456), [#61846](https://github.com/PaddlePaddle/Paddle/pull/61846), [#62217](https://github.com/PaddlePaddle/Paddle/pull/62217), [#62519](https://github.com/PaddlePaddle/Paddle/pull/62519), [#62881](https://github.com/PaddlePaddle/Paddle/pull/62881), [#62880](https://github.com/PaddlePaddle/Paddle/pull/62880), [#59723](https://github.com/PaddlePaddle/Paddle/pull/59723), [#59722](https://github.com/PaddlePaddle/Paddle/pull/59722), [#59797](https://github.com/PaddlePaddle/Paddle/pull/59797), [#59960](https://github.com/PaddlePaddle/Paddle/pull/59960), [#59761](https://github.com/PaddlePaddle/Paddle/pull/59761), [#59996](https://github.com/PaddlePaddle/Paddle/pull/59996), [#60009](https://github.com/PaddlePaddle/Paddle/pull/60009), [#58896](https://github.com/PaddlePaddle/Paddle/pull/58896), [#60051](https://github.com/PaddlePaddle/Paddle/pull/60051), [#60410](https://github.com/PaddlePaddle/Paddle/pull/60410), [#60420](https://github.com/PaddlePaddle/Paddle/pull/60420), [#60548](https://github.com/PaddlePaddle/Paddle/pull/60548), [#60575](https://github.com/PaddlePaddle/Paddle/pull/60575), [#60726](https://github.com/PaddlePaddle/Paddle/pull/60726), [#60809](https://github.com/PaddlePaddle/Paddle/pull/60809), [#61346](https://github.com/PaddlePaddle/Paddle/pull/61346), [#61222](https://github.com/PaddlePaddle/Paddle/pull/61222), [#61099](https://github.com/PaddlePaddle/Paddle/pull/61099), [#62254](https://github.com/PaddlePaddle/Paddle/pull/62254), [#62269](https://github.com/PaddlePaddle/Paddle/pull/62269), [#62362](https://github.com/PaddlePaddle/Paddle/pull/62362) +- 完善飞桨底层报错检查等机制，方便开发者调试。[#62571](https://github.com/PaddlePaddle/Paddle/pull/62571), [#62602](https://github.com/PaddlePaddle/Paddle/pull/62602), [#60903](https://github.com/PaddlePaddle/Paddle/pull/60903), [#64695](https://github.com/PaddlePaddle/Paddle/pull/64695), [#59907](https://github.com/PaddlePaddle/Paddle/pull/59907), [#62018](https://github.com/PaddlePaddle/Paddle/pull/62018), [#62839](https://github.com/PaddlePaddle/Paddle/pull/62839), [#60651](https://github.com/PaddlePaddle/Paddle/pull/60651), [#61488](https://github.com/PaddlePaddle/Paddle/pull/61488), [#64064](https://github.com/PaddlePaddle/Paddle/pull/64064), [#63192](https://github.com/PaddlePaddle/Paddle/pull/63192), [#63525](https://github.com/PaddlePaddle/Paddle/pull/63525)。 -#### Bug Fix +### 漏洞修复 +- 修复潜在的安全漏洞。[#59957](https://github.com/PaddlePaddle/Paddle/pull/59957), [#61032](https://github.com/PaddlePaddle/Paddle/pull/61032), [#61356](https://github.com/PaddlePaddle/Paddle/pull/61356), [#61573](https://github.com/PaddlePaddle/Paddle/pull/61573), [#61671](https://github.com/PaddlePaddle/Paddle/pull/61671), [#62345](https://github.com/PaddlePaddle/Paddle/pull/62345), [#60097](https://github.com/PaddlePaddle/Paddle/pull/60097), [#61161](https://github.com/PaddlePaddle/Paddle/pull/61161), [#61294](https://github.com/PaddlePaddle/Paddle/pull/61294), [#61349](https://github.com/PaddlePaddle/Paddle/pull/61349), [#61344](https://github.com/PaddlePaddle/Paddle/pull/61344), [#61162](https://github.com/PaddlePaddle/Paddle/pull/61162), [#61285](https://github.com/PaddlePaddle/Paddle/pull/61285), [#61826](https://github.com/PaddlePaddle/Paddle/pull/61826), [#59967](https://github.com/PaddlePaddle/Paddle/pull/59967), [#59976](https://github.com/PaddlePaddle/Paddle/pull/59976), [#59979](https://github.com/PaddlePaddle/Paddle/pull/59979)[#60527](https://github.com/PaddlePaddle/Paddle/pull/60527),[#60646](https://github.com/PaddlePaddle/Paddle/pull/60646),[#61827](https://github.com/PaddlePaddle/Paddle/pull/61827) -- 修复了动转静在 is_test=True 模式部分场景下出现显存异常的问题。[#58350](https://github.com/PaddlePaddle/Paddle/pull/58350) -- 修复了被@to_static 装饰的函数在类似 foo(x,x,y) 场景下 jit.save 模型导出的问题。[#55963](https://github.com/PaddlePaddle/Paddle/pull/55963) -- 修复了部分 API 行为动静逻辑不统一问题，提升了动转静整图转换成功率和使用体验。[#56092](https://github.com/PaddlePaddle/Paddle/pull/56092) +### 废弃功能 +- 清理废弃的执行器等逻辑，减少冗余代码。[#64822](https://github.com/PaddlePaddle/Paddle/pull/64822), [#60941](https://github.com/PaddlePaddle/Paddle/pull/60941) -#### 漏洞修复 +## 3.编译器架构 +在 3.0 版本下，编译器架构进行了重要升级。基于 Shape Dialect 构建了符号自动推导和化简体系，支持符号表达、约束构建，支撑了编译器动态形状下的端到端执行。同时飞桨编译器 CINN 全新升级了子图自动融合和 Pass Pipline 机制，合并了动、静态形状的核心模块，合并迭代路径，架构清晰统一。在此版本下，编译器在 AST Compute、Schedule 策略、Tiling 等重要后端模块进行了重构，提升了编译器的通用优化能力，在飞桨产业套件模型子图和典型大模型 Llama2-13B、Stable Diffusion 模型上验证了动形状的训练、推理正确性和提速性能。 -- 修复了动转静语法转写模块使用 eval()存在的潜在安全漏洞问题。[#60100](https://github.com/PaddlePaddle/Paddle/pull/60100) +### 新功能 +1. 升级了全新的子图自动融合机制，创新性提出了 TrivialOp 和 ReduceOp 融合理论，支持更广泛的垂直融合和水平融合范围，保障了子图融合的正确性和鲁棒性，充分发挥神经网络编译器的融合潜力（[#63340](https://github.com/PaddlePaddle/Paddle/pull/63340)、[#63913](https://github.com/PaddlePaddle/Paddle/pull/63913)、[#63579](https://github.com/PaddlePaddle/Paddle/pull/63579)、[#63605](https://github.com/PaddlePaddle/Paddle/pull/63605)、[#60769](https://github.com/PaddlePaddle/Paddle/pull/60769)、[#62088](https://github.com/PaddlePaddle/Paddle/pull/62088)、[#63124](https://github.com/PaddlePaddle/Paddle/pull/63124)、[#63658](https://github.com/PaddlePaddle/Paddle/pull/63658)、[#64557](https://github.com/PaddlePaddle/Paddle/pull/64557)、[#63318](https://github.com/PaddlePaddle/Paddle/pull/63318)、[#62545](https://github.com/PaddlePaddle/Paddle/pull/62545)） +2. 新增支持了动态形状的符号推导功能，基于 Shape Dialect 实现了动态符号构建、自动推导、约束表达、符号化简等机制，引入 DimExpr 概念，升级支持了飞桨框架 150+个典型基础算子的 InferSymbolicShape 逻辑，为编译器支持动态形状下的训练和推理提供更多信息（[#60843](https://github.com/PaddlePaddle/Paddle/pull/60843)、[#62662](https://github.com/PaddlePaddle/Paddle/pull/62662)、[#63790](https://github.com/PaddlePaddle/Paddle/pull/63790)、[#60098](https://github.com/PaddlePaddle/Paddle/pull/60098)、[#60511](https://github.com/PaddlePaddle/Paddle/pull/60511)、[#61232](https://github.com/PaddlePaddle/Paddle/pull/61232)、[#61939](https://github.com/PaddlePaddle/Paddle/pull/61939)、[#62798](https://github.com/PaddlePaddle/Paddle/pull/62798)、[#62955](https://github.com/PaddlePaddle/Paddle/pull/62955)、[#63029](https://github.com/PaddlePaddle/Paddle/pull/63029)、[#60572](https://github.com/PaddlePaddle/Paddle/pull/60572)、[#61035](https://github.com/PaddlePaddle/Paddle/pull/61035)、[#61224](https://github.com/PaddlePaddle/Paddle/pull/61224)、[#61587](https://github.com/PaddlePaddle/Paddle/pull/61587)、[#61937](https://github.com/PaddlePaddle/Paddle/pull/61937)、[#62314](https://github.com/PaddlePaddle/Paddle/pull/62314)、[#62394](https://github.com/PaddlePaddle/Paddle/pull/62394)、[#62569](https://github.com/PaddlePaddle/Paddle/pull/62569)、[#62495](https://github.com/PaddlePaddle/Paddle/pull/62495)、[#62844](https://github.com/PaddlePaddle/Paddle/pull/62844)、[#63000](https://github.com/PaddlePaddle/Paddle/pull/63000)、[#63016](https://github.com/PaddlePaddle/Paddle/pull/63016)、[#64222](https://github.com/PaddlePaddle/Paddle/pull/64222)、[#60129](https://github.com/PaddlePaddle/Paddle/pull/60129)、[#60899](https://github.com/PaddlePaddle/Paddle/pull/60899)、[#61342](https://github.com/PaddlePaddle/Paddle/pull/61342)、[#61439](https://github.com/PaddlePaddle/Paddle/pull/61439)、[#62766](https://github.com/PaddlePaddle/Paddle/pull/62766)、[#61133](https://github.com/PaddlePaddle/Paddle/pull/61133)、[#61430](https://github.com/PaddlePaddle/Paddle/pull/61430)、[#61498](https://github.com/PaddlePaddle/Paddle/pull/61498)、[#61680](https://github.com/PaddlePaddle/Paddle/pull/61680)、[#63367](https://github.com/PaddlePaddle/Paddle/pull/63367)、[#62151](https://github.com/PaddlePaddle/Paddle/pull/62151)、[#62665](https://github.com/PaddlePaddle/Paddle/pull/62665)、[#61407](https://github.com/PaddlePaddle/Paddle/pull/61407)、[#61502](https://github.com/PaddlePaddle/Paddle/pull/61502)、[#61655](https://github.com/PaddlePaddle/Paddle/pull/61655)、[#64115](https://github.com/PaddlePaddle/Paddle/pull/64115)、[#61791](https://github.com/PaddlePaddle/Paddle/pull/61791)、[#62141](https://github.com/PaddlePaddle/Paddle/pull/62141)、[#63422](https://github.com/PaddlePaddle/Paddle/pull/63422)、[#63577](https://github.com/PaddlePaddle/Paddle/pull/63577)、[#63978](https://github.com/PaddlePaddle/Paddle/pull/63978)、[#63576](https://github.com/PaddlePaddle/Paddle/pull/63576)、[#63947](https://github.com/PaddlePaddle/Paddle/pull/63947)、[#64332](https://github.com/PaddlePaddle/Paddle/pull/64332)、[#63990](https://github.com/PaddlePaddle/Paddle/pull/63990)） +3. 新增了 Pass Pipline 功能，包括 PdToCinn、CinnPreprocess、BuildGroupOp、DivideGroupOp、CinnLowering、精度检查等 Pass 策略，统一支持动、静形状下子图的 Lowering 和执行，架构清晰（[#61611](https://github.com/PaddlePaddle/Paddle/pull/61611)、[#62612](https://github.com/PaddlePaddle/Paddle/pull/62612)、[#64354](https://github.com/PaddlePaddle/Paddle/pull/64354)、[#61848](https://github.com/PaddlePaddle/Paddle/pull/61848)、[#62316](https://github.com/PaddlePaddle/Paddle/pull/62316)、[#64152](https://github.com/PaddlePaddle/Paddle/pull/64152)、[#61619](https://github.com/PaddlePaddle/Paddle/pull/61619)、[#62318](https://github.com/PaddlePaddle/Paddle/pull/62318)、[#61977](https://github.com/PaddlePaddle/Paddle/pull/61977)、[#62211](https://github.com/PaddlePaddle/Paddle/pull/62211)、[#63972](https://github.com/PaddlePaddle/Paddle/pull/63972)、[#63686](https://github.com/PaddlePaddle/Paddle/pull/63686)、[#64505](https://github.com/PaddlePaddle/Paddle/pull/64505)） +4. 新增支持了 BuketLower 和 DyShapeSchdule 功能，根据动态形状的范围实现自动分桶编译优化；并适配升级了 CodeGen 模块逻辑，支持 InferShape 函数生成和 Host 函数的条件分支分发功能，支撑大模型的动态 Shape 下训练推理加速（[#62730](https://github.com/PaddlePaddle/Paddle/pull/62730)、[#61115](https://github.com/PaddlePaddle/Paddle/pull/61115)、[#59941](https://github.com/PaddlePaddle/Paddle/pull/59941)、[#62207](https://github.com/PaddlePaddle/Paddle/pull/62207)、[#64318](https://github.com/PaddlePaddle/Paddle/pull/64318)、[#64345](https://github.com/PaddlePaddle/Paddle/pull/64345)、[#60519](https://github.com/PaddlePaddle/Paddle/pull/60519)、[#62584](https://github.com/PaddlePaddle/Paddle/pull/62584)、[#60828](https://github.com/PaddlePaddle/Paddle/pull/60828)、[#60533](https://github.com/PaddlePaddle/Paddle/pull/60533)、[#61436](https://github.com/PaddlePaddle/Paddle/pull/61436)、[#62071](https://github.com/PaddlePaddle/Paddle/pull/62071)、[#63971](https://github.com/PaddlePaddle/Paddle/pull/63971)、[#61656](https://github.com/PaddlePaddle/Paddle/pull/61656)、[#63083](https://github.com/PaddlePaddle/Paddle/pull/63083)、[#64405](https://github.com/PaddlePaddle/Paddle/pull/64405)、[#63047](https://github.com/PaddlePaddle/Paddle/pull/63047)、[#64655](https://github.com/PaddlePaddle/Paddle/pull/64655)、[#63095](https://github.com/PaddlePaddle/Paddle/pull/63095)、[#63829](https://github.com/PaddlePaddle/Paddle/pull/63829)、[#63572](https://github.com/PaddlePaddle/Paddle/pull/63572)） +5. 新增支持了编译缓存策略，自动识别、合并和复用相同子图结构的编译结果，使用多线程提升编译效率，提升用户的使用体验（[#62952](https://github.com/PaddlePaddle/Paddle/pull/62952)、[#63269](https://github.com/PaddlePaddle/Paddle/pull/63269)、[#64718](https://github.com/PaddlePaddle/Paddle/pull/64718)、[#61367](https://github.com/PaddlePaddle/Paddle/pull/61367)、[#63305](https://github.com/PaddlePaddle/Paddle/pull/63305)、[#63750](https://github.com/PaddlePaddle/Paddle/pull/63750)、[#63871](https://github.com/PaddlePaddle/Paddle/pull/63871)、[#64893](https://github.com/PaddlePaddle/Paddle/pull/64893)） +6. 新增支持了 GenerateShape 机制，添加了对应的 AST Compute 算子定义，支持动态符号的自动解析，以及在 Lowering 阶段自动生成 ShapeOp（[#64167](https://github.com/PaddlePaddle/Paddle/pull/64167)、[#64636](https://github.com/PaddlePaddle/Paddle/pull/64636)、[#61993](https://github.com/PaddlePaddle/Paddle/pull/61993)、[#64843](https://github.com/PaddlePaddle/Paddle/pull/64843)、[#62587](https://github.com/PaddlePaddle/Paddle/pull/62587)） -### 动态图分布式能力增强 +### 功能优化 +1. 优化了 BuildCinnPass 逻辑，升级编译器对黑白名单算子的感知策略，提升了 Pass 逻辑的鲁棒性（[#62372](https://github.com/PaddlePaddle/Paddle/pull/62372)、[#61081](https://github.com/PaddlePaddle/Paddle/pull/61081)、[#61225](https://github.com/PaddlePaddle/Paddle/pull/61225)、[#58863](https://github.com/PaddlePaddle/Paddle/pull/58863)） +2. 优化了 OpLoweringGroup 数据结构，移除了不必要的接口和成员，降低上下游模块的耦合度（[#62339](https://github.com/PaddlePaddle/Paddle/pull/62339)） +3. 优化了编译器关于架构 Arch 的组件设计，抽象硬件概念，降低国产硬件的适配成本（[#63530](https://github.com/PaddlePaddle/Paddle/pull/63530)、[#64347](https://github.com/PaddlePaddle/Paddle/pull/64347)、[#64506](https://github.com/PaddlePaddle/Paddle/pull/64506)、[#64587](https://github.com/PaddlePaddle/Paddle/pull/64587)） +4. 升级了编译器后端算子 AST Compute 模块，适配支持了动态 Shape 的计算逻辑（[#62488](https://github.com/PaddlePaddle/Paddle/pull/62488)、[#63581](https://github.com/PaddlePaddle/Paddle/pull/63581)、[#63687](https://github.com/PaddlePaddle/Paddle/pull/63687)、[#63654](https://github.com/PaddlePaddle/Paddle/pull/63654)、[#64217](https://github.com/PaddlePaddle/Paddle/pull/64217)） + +### 性能优化 +1. 优化了 AST IR 的 Schedule 逻辑，重构了 Vectorize、Unroll、AxisBind、ComputeAt 等核心模块，合并动静形状迭代路径，降低开发维护成本（[#60449](https://github.com/PaddlePaddle/Paddle/pull/60449)、[#60155](https://github.com/PaddlePaddle/Paddle/pull/60155)、[#60342](https://github.com/PaddlePaddle/Paddle/pull/60342)、[#60498](https://github.com/PaddlePaddle/Paddle/pull/60498)、[#60538](https://github.com/PaddlePaddle/Paddle/pull/60538)、[#60190](https://github.com/PaddlePaddle/Paddle/pull/60190)、[#61197](https://github.com/PaddlePaddle/Paddle/pull/61197)、[#63140](https://github.com/PaddlePaddle/Paddle/pull/63140)、[#61156](https://github.com/PaddlePaddle/Paddle/pull/61156)） +2. 优化了 Tiling 策略和 temp Buffer 功能，支持 warp-level 内存连续 Read 和 cache_read cache_write 功能， 提升子图执行性能（[#64240](https://github.com/PaddlePaddle/Paddle/pull/64240)、[#60562](https://github.com/PaddlePaddle/Paddle/pull/60562)、[#64711](https://github.com/PaddlePaddle/Paddle/pull/64711)、[#62856](https://github.com/PaddlePaddle/Paddle/pull/62856)、[#61576](https://github.com/PaddlePaddle/Paddle/pull/61576)、[#61901](https://github.com/PaddlePaddle/Paddle/pull/61901)、[#62581](https://github.com/PaddlePaddle/Paddle/pull/62581)、[#61987](https://github.com/PaddlePaddle/Paddle/pull/61987)、[#60190](https://github.com/PaddlePaddle/Paddle/pull/60190)、[#63138](https://github.com/PaddlePaddle/Paddle/pull/63138)、[#62517](https://github.com/PaddlePaddle/Paddle/pull/62517)） +3. 支持 Schedule 配置的自动搜索功能，AOT 式离线保存机制实现子图 Kernel 的性能加速（[#64271](https://github.com/PaddlePaddle/Paddle/pull/64271)、[#64588](https://github.com/PaddlePaddle/Paddle/pull/64588)、[#64694](https://github.com/PaddlePaddle/Paddle/pull/64694)、[#64620](https://github.com/PaddlePaddle/Paddle/pull/64620)、[#64702](https://github.com/PaddlePaddle/Paddle/pull/64702)、[#63086](https://github.com/PaddlePaddle/Paddle/pull/63086)） +4. 支持了 OptimizeReductionTactic 优化策略，提升 Reduce 场景下的 kernel 性能（[#6066](https://github.com/PaddlePaddle/Paddle/pull/60661)、[#61363](https://github.com/PaddlePaddle/Paddle/pull/61363)、[#60881](https://github.com/PaddlePaddle/Paddle/pull/60881)、[#63859](https://github.com/PaddlePaddle/Paddle/pull/63859)） +5. 增强了 DCE Pass 功能，移除了多余的 If/For 分支代码，提升执行效率（[#61682](https://github.com/PaddlePaddle/Paddle/pull/61682)） +6. 新增支持了 FuseParallelMatmulPass Pass，可融合多个 Matmul 算子实现加速（[#63623](https://github.com/PaddlePaddle/Paddle/pull/63623)） + +### Bug 修复 +1. 修复了部分特殊算子在 Lowering 到编译器时 BUG，提升了端到端使用的用户体验（[#60800](https://github.com/PaddlePaddle/Paddle/pull/60800)、[#64720](https://github.com/PaddlePaddle/Paddle/pull/64720)、[#62593](https://github.com/PaddlePaddle/Paddle/pull/62593)、[#62661](https://github.com/PaddlePaddle/Paddle/pull/62661)、[#64626](https://github.com/PaddlePaddle/Paddle/pull/64626)、[#63320](https://github.com/PaddlePaddle/Paddle/pull/63320)、[#64581](https://github.com/PaddlePaddle/Paddle/pull/64581)、[#61608](https://github.com/PaddlePaddle/Paddle/pull/61608)、[#64135](https://github.com/PaddlePaddle/Paddle/pull/64135)、[#64659](https://github.com/PaddlePaddle/Paddle/pull/64659)、[#62391](https://github.com/PaddlePaddle/Paddle/pull/62391)、[#62490](https://github.com/PaddlePaddle/Paddle/pull/62490)、[#63891](https://github.com/PaddlePaddle/Paddle/pull/63891)、[#64529](https://github.com/PaddlePaddle/Paddle/pull/64529)） +2. 修复了部分算子符号推导实现逻辑的 BUG（[#62141](https://github.com/PaddlePaddle/Paddle/pull/62141)、[#62376](https://github.com/PaddlePaddle/Paddle/pull/62376)、[#62941](https://github.com/PaddlePaddle/Paddle/pull/62941)、[#63322](https://github.com/PaddlePaddle/Paddle/pull/63322)、[#64672](https://github.com/PaddlePaddle/Paddle/pull/64672)、[#64407](https://github.com/PaddlePaddle/Paddle/pull/64407)、[#60241](https://github.com/PaddlePaddle/Paddle/pull/60241)、[#60440](https://github.com/PaddlePaddle/Paddle/pull/60440)、[#62503](https://github.com/PaddlePaddle/Paddle/pull/62503)、[#62997](https://github.com/PaddlePaddle/Paddle/pull/62997)、[#63169](https://github.com/PaddlePaddle/Paddle/pull/63169)、[#61098](https://github.com/PaddlePaddle/Paddle/pull/61098)、[#63973](https://github.com/PaddlePaddle/Paddle/pull/63973)、[#62248](https://github.com/PaddlePaddle/Paddle/pull/62248)、[#62321](https://github.com/PaddlePaddle/Paddle/pull/62321)、[#63755](https://github.com/PaddlePaddle/Paddle/pull/63755)、[#63917](https://github.com/PaddlePaddle/Paddle/pull/63917)、[#63903](https://github.com/PaddlePaddle/Paddle/pull/63903)、[#64173](https://github.com/PaddlePaddle/Paddle/pull/64173)、[#64525](https://github.com/PaddlePaddle/Paddle/pull/64525)、[#64615](https://github.com/PaddlePaddle/Paddle/pull/64615)、[#62247](https://github.com/PaddlePaddle/Paddle/pull/62247)、[#62455](https://github.com/PaddlePaddle/Paddle/pull/62455)、[#62898](https://github.com/PaddlePaddle/Paddle/pull/62898)、[#62867](https://github.com/PaddlePaddle/Paddle/pull/62867)、[#63608](https://github.com/PaddlePaddle/Paddle/pull/63608)、[#63789](https://github.com/PaddlePaddle/Paddle/pull/63789)、[#64085](https://github.com/PaddlePaddle/Paddle/pull/64085)、[#64136](https://github.com/PaddlePaddle/Paddle/pull/64136)、[#64181](https://github.com/PaddlePaddle/Paddle/pull/64181)） +3. 修复了动静形状下编译器执行结果错误的诸多问题，提升了框架机制的鲁棒性（[#60813](https://github.com/PaddlePaddle/Paddle/pull/60813)、[#61877](https://github.com/PaddlePaddle/Paddle/pull/61877)、[#61909](https://github.com/PaddlePaddle/Paddle/pull/61909)、[#62954](https://github.com/PaddlePaddle/Paddle/pull/62954)、[#63614](https://github.com/PaddlePaddle/Paddle/pull/63614)、[#60339](https://github.com/PaddlePaddle/Paddle/pull/60339)、[#60623](https://github.com/PaddlePaddle/Paddle/pull/60623)、[#60658](https://github.com/PaddlePaddle/Paddle/pull/60658)、[#60669](https://github.com/PaddlePaddle/Paddle/pull/60669)、[#58823](https://github.com/PaddlePaddle/Paddle/pull/58823)、[#62483](https://github.com/PaddlePaddle/Paddle/pull/62483)、[#62742](https://github.com/PaddlePaddle/Paddle/pull/62742)、[#61797](https://github.com/PaddlePaddle/Paddle/pull/61797)、[#63411](https://github.com/PaddlePaddle/Paddle/pull/63411)、[#64077](https://github.com/PaddlePaddle/Paddle/pull/64077)、[#62736](https://github.com/PaddlePaddle/Paddle/pull/62736)、[#62390](https://github.com/PaddlePaddle/Paddle/pull/62390)、[#63689](https://github.com/PaddlePaddle/Paddle/pull/63689)） + +### 废弃功能 +1. 移除了 adt DimExpr、SymbolicDimExpr、ShapedTypeInterface 等无用的符号相关组件（[#60901](https://github.com/PaddlePaddle/Paddle/pull/60901)、[#60933](https://github.com/PaddlePaddle/Paddle/pull/60933)、[#60744](https://github.com/PaddlePaddle/Paddle/pull/60744)、[#64176](https://github.com/PaddlePaddle/Paddle/pull/64176)、[#64140](https://github.com/PaddlePaddle/Paddle/pull/64140)） +2. 移除了旧的 Group Cluster、旧 IR 下的前端表示等相关组件，提升架构层面的简洁性（[#63683](https://github.com/PaddlePaddle/Paddle/pull/63683)、[#64630](https://github.com/PaddlePaddle/Paddle/pull/64630)、[#61380](https://github.com/PaddlePaddle/Paddle/pull/61380)） + +## 4.自动并行架构 +为了进一步增强自动并行（Auto Parallel）架构在大模型训练场景的可用性，飞桨完善了动-静态图自动并行的功能，包括新增 Sharding、interleaved pipeline 等并行策略，支持 lazy 初始化参数，新增和完善部分算子的切分推导规则等，并在多个主流大语言模型中全面验证了自动并行架构。同时，为打造飞桨全新 3.0 架构，静态图自动并行架构基于新一代中间表示 PIR 进行了全面升级，扩展实现了 DistDialect，在计算图表示中原生支持了分布式属性（DistAttr）和分布式张量（DistTensor），并打通了静态图自动并行全流程，进一步增强了自动并行的动静统一和飞桨架构的统一性。最后，新增和完善了多项性能优化技术，包括 zero bubble pipeline 调度策略等，在 Llama-2 13B/70B 等典型大模型上实现端到端训练性能持平或领先手动并行方式。 + +### 功能完善 +- 新增 dtensor_from_local 接口，用于从切分后的局部张量创建 DistTensor（与之对应的，shard_tensor 是从切分前的全局张量创建 DistTensor）。[#60206](https://github.com/PaddlePaddle/Paddle/pull/60206) +- 新增 unshard_tensor 接口，用于将 DistTensor 转为全局张量，该接口与 shard_tensor 是互逆操作。[#60272](https://github.com/PaddlePaddle/Paddle/pull/60272) +- 为减少训练时的显存占用，新增 Sharding 策略，包括 stage1，stage2 和 stage3。[#61926](https://github.com/PaddlePaddle/Paddle/pull/61926), [#62711](https://github.com/PaddlePaddle/Paddle/pull/62711), [#62486](https://github.com/PaddlePaddle/Paddle/pull/62486), [#62230](https://github.com/PaddlePaddle/Paddle/pull/62230) +- 为解决先初始化参数再切分参数时可能出现的显存不足问题，新增自动并行参数 LazyInit 功能，支持先切分参数，再初始化参数。[#60316](https://github.com/PaddlePaddle/Paddle/pull/60316), [#60441](https://github.com/PaddlePaddle/Paddle/pull/60441), [#60563](https://github.com/PaddlePaddle/Paddle/pull/60563), [#61792](https://github.com/PaddlePaddle/Paddle/pull/61792) +- 为减少流水线并行的 bubble，新增 interleaved pipeline 并行策略，同时支持通过配置的方式自动将用户组网的 pipeline 并行自动转为 interleaved pipeline 并行，让用户无需在组网中进行复杂的标记。[#59751](https://github.com/PaddlePaddle/Paddle/pull/59751), [#60050](https://github.com/PaddlePaddle/Paddle/pull/60050), [#60467](https://github.com/PaddlePaddle/Paddle/pull/60467), [#60868](https://github.com/PaddlePaddle/Paddle/pull/60868), [#60187](https://github.com/PaddlePaddle/Paddle/pull/60187), [#62884](https://github.com/PaddlePaddle/Paddle/pull/62884), [#60560](https://github.com/PaddlePaddle/Paddle/pull/60560), [#61541](https://github.com/PaddlePaddle/Paddle/pull/61541) +- 新增 stack, gather, scatter_grad, cumsum, unbind, swiglu, fused_linear_param_grad 等算子的切分推导规则，完善和优化 fused_rope, reshape, flatten, fused_rms_norm, slice, tile, flash_attn, cross_entropy 等算子切分推导规则实现，解决在部分模型组网场景中不兼容的问题。[#62720](https://github.com/PaddlePaddle/Paddle/pull/62720), [#64202](https://github.com/PaddlePaddle/Paddle/pull/64202), [#63361](https://github.com/PaddlePaddle/Paddle/pull/63361), [#63290](https://github.com/PaddlePaddle/Paddle/pull/63290), [#61460](https://github.com/PaddlePaddle/Paddle/pull/61460), [#59986](https://github.com/PaddlePaddle/Paddle/pull/59986), [#61184](https://github.com/PaddlePaddle/Paddle/pull/61184), [#60144](https://github.com/PaddlePaddle/Paddle/pull/60144), [#62525](https://github.com/PaddlePaddle/Paddle/pull/62525), [#62053](https://github.com/PaddlePaddle/Paddle/pull/62053), [#60709](https://github.com/PaddlePaddle/Paddle/pull/60709), [#60111](https://github.com/PaddlePaddle/Paddle/pull/60111), [#63681](https://github.com/PaddlePaddle/Paddle/pull/63681), [#62180](https://github.com/PaddlePaddle/Paddle/pull/62180), [#60794](https://github.com/PaddlePaddle/Paddle/pull/60794), [#60632](https://github.com/PaddlePaddle/Paddle/pull/60632), [#62439](https://github.com/PaddlePaddle/Paddle/pull/62439) +- 完善分布式 checkpoint 存储和加载功能，支持 master_weights 存储，修复随机挂问题。[#60027](https://github.com/PaddlePaddle/Paddle/pull/60027), [#59872](https://github.com/PaddlePaddle/Paddle/pull/59872) +- 为支持任意 shape 张量的自动并行，新增支持张量非均匀切分特性。[#62611](https://github.com/PaddlePaddle/Paddle/pull/62611), [#61432](https://github.com/PaddlePaddle/Paddle/pull/61432) +- 为支持用户在自动并行组网中使用自定义算子，支持用户在框架外注册自定义该类算子的切分推导规则。 [#60509](https://github.com/PaddlePaddle/Paddle/pull/60509) +- 完善切分转换规则，支持从任意状态转为 replicate 以及从 replicate 状态转换为任意状态。[#60281](https://github.com/PaddlePaddle/Paddle/pull/60281), [#59869](https://github.com/PaddlePaddle/Paddle/pull/59869) +- 新增 MoE 专家并行策略（experimental），目前仅支持动态图自动并行。[#63904](https://github.com/PaddlePaddle/Paddle/pull/63904) +- 修复自动并行与动态图执行、动转静等流程适配的部分问题。[#60214](https://github.com/PaddlePaddle/Paddle/pull/60214), [#60546](https://github.com/PaddlePaddle/Paddle/pull/60546), [#62082](https://github.com/PaddlePaddle/Paddle/pull/62082), [#61313](https://github.com/PaddlePaddle/Paddle/pull/61313), [#61840](https://github.com/PaddlePaddle/Paddle/pull/61840), [#60614](https://github.com/PaddlePaddle/Paddle/pull/60614), [#60234](https://github.com/PaddlePaddle/Paddle/pull/60234), [#64813](https://github.com/PaddlePaddle/Paddle/pull/64813), [#61606](https://github.com/PaddlePaddle/Paddle/pull/61606), [#63405](https://github.com/PaddlePaddle/Paddle/pull/63405), [#64334](https://github.com/PaddlePaddle/Paddle/pull/64334), [#60504](https://github.com/PaddlePaddle/Paddle/pull/60504) + +### 性能优化 +- 为减少流水线并行中的 bubble，支持 backward 中参数和激活的反向计算拆分，新增 zero bubble pipeline 调度策略，提升训练性能。[#62865](https://github.com/PaddlePaddle/Paddle/pull/62865), [#62737](https://github.com/PaddlePaddle/Paddle/pull/62737), [#64534](https://github.com/PaddlePaddle/Paddle/pull/64534), +- 为提升序列并行（sequence parallel）的性能，对相关通信操作和计算操作进行 fusion，并优化冗余的 transopse 操作。[#64807](https://github.com/PaddlePaddle/Paddle/pull/64807), [#63948](https://github.com/PaddlePaddle/Paddle/pull/63948), [#64316](https://github.com/PaddlePaddle/Paddle/pull/64316), [#64119](https://github.com/PaddlePaddle/Paddle/pull/64119) +- 优化静态图自动并行图优化耗时，减少从启动训练到第一个 step 完成的延时。[#59912](https://github.com/PaddlePaddle/Paddle/pull/59912), [#61817](https://github.com/PaddlePaddle/Paddle/pull/61817), [#60022](https://github.com/PaddlePaddle/Paddle/pull/60022), [#60125](https://github.com/PaddlePaddle/Paddle/pull/60125) +- 优化混合并行场景下相关通信操作的耗时。[#62157](https://github.com/PaddlePaddle/Paddle/pull/62157), [#61622](https://github.com/PaddlePaddle/Paddle/pull/61622) +- 优化自动并行动转静下参数的的冗余显存占用。[#62746](https://github.com/PaddlePaddle/Paddle/pull/62746) +- 完善自动并行的混合精度训练功能，支持设置局部 auto_cast 和黑白名单，支持 master grad 功能，适配不同的并行策略等。[60158](https://github.com/PaddlePaddle/Paddle/pull/60158), [#59987](https://github.com/PaddlePaddle/Paddle/pull/59987), [#62629](https://github.com/PaddlePaddle/Paddle/pull/62629), [#60385](https://github.com/PaddlePaddle/Paddle/pull/60385), [#62015](https://github.com/PaddlePaddle/Paddle/pull/62015), [#60514](https://github.com/PaddlePaddle/Paddle/pull/60514), [#61221](https://github.com/PaddlePaddle/Paddle/pull/61221), [#60779](https://github.com/PaddlePaddle/Paddle/pull/60779), [#63228](https://github.com/PaddlePaddle/Paddle/pull/63228) +- 优化 type promotion 和 amp 带来的非必要的 cast，提升性能。[#63293](https://github.com/PaddlePaddle/Paddle/pull/63293), [#63228](https://github.com/PaddlePaddle/Paddle/pull/63228) + +### 静态图自动并行架构升级 +- 基于新一代中间表示 PIR，新增 DistDialect，在计算图表示中原生支持了分布式属性（DistAttr）和分布式张量（DistTensor），实现了分布式属性和张量或算子的直接绑定，使自动并行架构更简洁统一。[#63828](https://github.com/PaddlePaddle/Paddle/pull/63828), [#64299](https://github.com/PaddlePaddle/Paddle/pull/64299), [#63870](https://github.com/PaddlePaddle/Paddle/pull/63870), [#64144](https://github.com/PaddlePaddle/Paddle/pull/64144), [#62524](https://github.com/PaddlePaddle/Paddle/pull/62524), [#62630](https://github.com/PaddlePaddle/Paddle/pull/62630), [#62897](https://github.com/PaddlePaddle/Paddle/pull/62897), [#60478](https://github.com/PaddlePaddle/Paddle/pull/60478), [#60574](https://github.com/PaddlePaddle/Paddle/pull/60574), [#63876](https://github.com/PaddlePaddle/Paddle/pull/63876), [#63798](https://github.com/PaddlePaddle/Paddle/pull/63798), [#62560](https://github.com/PaddlePaddle/Paddle/pull/62560), [#63676](https://github.com/PaddlePaddle/Paddle/pull/63676) +- 完成自动并行 PIR 新架构对 shard_tensor、reshard、to_static 等 API 的适配，支持用户将动态图模型组网直接转成 PIR 静态计算图进行优化和训练。[#62945](https://github.com/PaddlePaddle/Paddle/pull/62945), [#62356](https://github.com/PaddlePaddle/Paddle/pull/62356), [#60175](https://github.com/PaddlePaddle/Paddle/pull/60175), [#62654](https://github.com/PaddlePaddle/Paddle/pull/62654), [#63347](https://github.com/PaddlePaddle/Paddle/pull/63347) +- 优化静态图自动并行的图优化编译过程，通过重构优化静半中计算图切分和通信解析两个主要过程的实现，减少静态图编译优化耗时。[#64137](https://github.com/PaddlePaddle/Paddle/pull/64137), [#62201](https://github.com/PaddlePaddle/Paddle/pull/62201), [#64143](https://github.com/PaddlePaddle/Paddle/pull/64143), [#62560](https://github.com/PaddlePaddle/Paddle/pull/62560) +- 优化静态图中切分推导规则的调用流程，实现切分推导结果在动-静态图下的一致，提升了架构的统一性和稳定性。 [#62659](https://github.com/PaddlePaddle/Paddle/pull/62659), [#62547](https://github.com/PaddlePaddle/Paddle/pull/62547), [#63117](https://github.com/PaddlePaddle/Paddle/pull/63117), [#63434](https://github.com/PaddlePaddle/Paddle/pull/63434), [#63770](https://github.com/PaddlePaddle/Paddle/pull/63770), [#64361](https://github.com/PaddlePaddle/Paddle/pull/64361), [#63073](https://github.com/PaddlePaddle/Paddle/pull/63073) +- 升级静态图中张量切分转换的实现，动-静态图下使用一致的切分转换通信规则，保障动-静态图下张量切分转换执行逻辑和结果的一致性，提升用户体验。[#62718](https://github.com/PaddlePaddle/Paddle/pull/62718), [#62694](https://github.com/PaddlePaddle/Paddle/pull/62694), [#60215](https://github.com/PaddlePaddle/Paddle/pull/60215), [#63362](https://github.com/PaddlePaddle/Paddle/pull/63362), [#63072](https://github.com/PaddlePaddle/Paddle/pull/63072), [#63962](https://github.com/PaddlePaddle/Paddle/pull/63962), [#64223](https://github.com/PaddlePaddle/Paddle/pull/64223), [#61796](https://github.com/PaddlePaddle/Paddle/pull/61796), [#64465](https://github.com/PaddlePaddle/Paddle/pull/64465), [#64623](https://github.com/PaddlePaddle/Paddle/pull/64623), [#64418](https://github.com/PaddlePaddle/Paddle/pull/64418) + +### 训练策略自动搜索和调优 +为提升训练策略自动搜索和调优工具（AutoTuner）的易用性，支持用户自定义搜索项，支持设置搜索项的优先级，支持用户配置不合法的策略组合，全面增强了运行时和运行后日志中的报错信息，支持在 NPU 设备上进行 AutoTuner。[#60101](https://github.com/PaddlePaddle/Paddle/pull/60101), [#60294](https://github.com/PaddlePaddle/Paddle/pull/60294), [#61898](https://github.com/PaddlePaddle/Paddle/pull/61898), [#60248](https://github.com/PaddlePaddle/Paddle/pull/60248), [#60417](https://github.com/PaddlePaddle/Paddle/pull/60417), [#60954](https://github.com/PaddlePaddle/Paddle/pull/60954), [#61499](https://github.com/PaddlePaddle/Paddle/pull/61499), [#62724](https://github.com/PaddlePaddle/Paddle/pull/62724), [#60954](https://github.com/PaddlePaddle/Paddle/pull/60954), [#63693](https://github.com/PaddlePaddle/Paddle/pull/63693), [#62853](https://github.com/PaddlePaddle/Paddle/pull/62853), [#62984](https://github.com/PaddlePaddle/Paddle/pull/62984) + +## 5.Cuda 训练性能优化 +本次升级从算子计算效率、分布式通信优化、显存优化等多个角度实现了大模型训练效率的提升。 + +### 功能完善 +- FlashAttention 算子功能增强，包含支持 NVIDIA SM90 GPU 编译，支持 Group Query Attention，支持 cuDNN 接入，支持 QKV-packed 形式输入等。[#59820](https://github.com/PaddlePaddle/Paddle/pull/59820)，[#60776](https://github.com/PaddlePaddle/Paddle/pull/60776)，[#58680](https://github.com/PaddlePaddle/Paddle/pull/58680)，[#63289](https://github.com/PaddlePaddle/Paddle/pull/63289) +- repeat_interleave 算子添加 BFloat16 数据类型的支持。[#61854](https://github.com/PaddlePaddle/Paddle/pull/61854) +- 针对 fused_scale_bias_add_relu、fused_scale_bias_relu_conv_bn、fused_dconv_drelu_dbn 等 ResNet 类模型接口参数多、算子易用性查等问题，添加了 fuse_resunit pass，支持上述算子的自动融合，实现通用性能优化。([#59771](https://github.com/PaddlePaddle/Paddle/pull/59771)) -为了满足大型模型的需求，本版本重点提升了飞桨动态图的分布式计算能力。在通信库、图分析、分布式策略和任务启停等方面进行了多方面的改进，为大型模型训练提供了全面的支持。在性能方面，我们通过减少流水并行 GPU 显存占用、采用 TensorFusion 技术、实现通信计算 overlap 以及减少非必要的数据同步拷贝等方式，进一步提升了训练性能。同时，通过环境变量控制 Optimizer 等方式提高了混合并行调试的灵活性。此外，通过修复相关 Bug，进一步提升了系统的稳定性。 +### 性能提升 +- 针对 Llama 类模型 SwiGLU 激活模块计算过程显存占用较大的问题，新增了 SwiGLU 融合算子，节省中间变量的显存占用，从而降低大模型训练过程显存开销，减少重计算以提升性能，Llama-70B 模型性能提升 9%。 [#61508](https://github.com/PaddlePaddle/Paddle/pull/61508) +- 针对序列并行（Sequence Parallel）过程通信占比较高的问题，实现了序列并行反向过程通信与 Matmul 计算的 overlap，节省端到端耗时，在大模型训练场景端到端性能提升 1%~2%。[#62284](https://github.com/PaddlePaddle/Paddle/pull/62284)，[#63531](https://github.com/PaddlePaddle/Paddle/pull/63531) +- 针对 Sharding 反向通信后仍需要除以 nranks 导致训练速度慢的问题，支持了反向通信与除以 nranks 运算的融合，支持 ReduceScatter Average 的模式，提升大模型训练性能。[#62623](https://github.com/PaddlePaddle/Paddle/pull/62623) +- 针对张量模型并行过程输入数据广播过程导致训练速度抖动的问题，修复了数据广播过程的不必要的 CPU 和 GPU 间的同步，保证训练速度的稳定性。[#60816](https://github.com/PaddlePaddle/Paddle/pull/60816) +- 针对流水线模型并行 P2P 通信时间较长导致训练速度低下的问题，实现了 P2P 通信与前反向计算的 overlap，大模型端到端训练性能提升 2%~3%。[#61935](https://github.com/PaddlePaddle/Paddle/pull/61935),[#62051](https://github.com/PaddlePaddle/Paddle/pull/62051，[#62051](https://github.com/PaddlePaddle/Paddle/pull/62051)) +- 针对 fused_linear_param_grad_add 算子 bias 梯度计算效率低下问题，优化了 bias 梯度计算环节的计算效率，大模型端到端训练性能提升 0.2%。[#63114](https://github.com/PaddlePaddle/Paddle/pull/63114) +- 针对 Sharding 反向计算结束后参数广播过程耗时较长的问题，实现了参数广播与下一个 step 计算的 overlap，大模型端到端训练性能提升 2%以上。[#63945](https://github.com/PaddlePaddle/Paddle/pull/63945) +- 针对流水线并行训练过程梯度占用显存过高从而引入过多重计算导致训练速度慢的问题，实现了梯度动态释放技术，大模型端到端训练性能提升 3.4%。[#59739](https://github.com/PaddlePaddle/Paddle/pull/59739) + +### Bug 修复 +- 修复 StreamSafeCUDAAllocator CUDA Event 资源泄露导致大模型训练降速等问题。[#64621](https://github.com/PaddlePaddle/Paddle/pull/64621) +- 修复 fused_rotary_position_embedding 算子反向计算错误的 bug。[#60217](https://github.com/PaddlePaddle/Paddle/pull/60217) +- 修复自定义算子在 AMP 场景下无法通过黑白名单控制计算精度的 bug。[#60052](https://github.com/PaddlePaddle/Paddle/pull/60052) +- 修复 add_、divide_等原生支持不同数据类型运算的算子在类型提升时发生预期外的类型提升的 bug。[#64302](https://github.com/PaddlePaddle/Paddle/pull/64302) + +## 6.分布式策略增强 +重点强化了飞桨动态图分布式计算功能体验，对 AutoTuner、流水线并行、Sharding 等并行策略做了多方面的功能改进，增强了大模型训练的灵活性；新增 Flash Attention Mask 等功能，显著降低大模型训练特别是长 sequence 训练的显存占用，提升训练性能，为大模型训练提供更强的能力支持；另外修复了若干 Bug 以及潜在的安全性风险，显著提升了系统整体稳定性。 -#### 新功能 +### 功能优化 +- 优化了 Autotuner 的搜索空间，大幅提升了搜索的性能。[#62608](https://github.com/PaddlePaddle/Paddle/pull/62608) +- 针对流水线并行中由于在 eval 过程检查发送类型，导致训练可能出错的问题，增加训练配置，跳过流水线发送的冗余接收检查，灵活性更高、性能更好。[#63001](https://github.com/PaddlePaddle/Paddle/pull/63001) +- 在动态图流水并行中，增加了发送和接收数据的大小和类型的的检查，增加报错信息，使得鲁棒性、可调试性更好。[#59405](https://github.com/PaddlePaddle/Paddle/pull/59405) +- 支持动态图流水并行设定多个损失函数，并返回多个 loss，提升了动态图流水线的灵活性。[#63167](https://github.com/PaddlePaddle/Paddle/pull/63167) +- 在动态图流水并行中，增加流水线缓存清除配置选项，可以及时清除流水线中发送和接受的 cache，更好的支持动态 batchsize 训练。[#62277](https://github.com/PaddlePaddle/Paddle/pull/62277) +- 针对 sharding stage3 策略无法逐位对齐的问题，将无序的 set 集合换成了有序的 OrderedSet，避免了累加顺序导致的误差，修复完后可以逐位对齐。[#60085](https://github.com/PaddlePaddle/Paddle/pull/60085) +- 为了进一步降低针对序列并行中显存占用，新增重计算 allgather 的方法，减少 allgather 的 activation 的显存大小。[#64244](https://github.com/PaddlePaddle/Paddle/pull/64244) + +### 动态图新功能 +- 针对 autotuner 的搜索空间，新增了 refined recompute 的搜索维度，使得搜索结果更精准，调优模型的门槛更低。[#62430](https://github.com/PaddlePaddle/Paddle/pull/62430) +- 针对虚拟流水线并行中，需要限制训练批大小的问题，修改了流水线调度方式，解除批大小限制，支持更灵活的批大小。[#61561](https://github.com/PaddlePaddle/Paddle/pull/61561),[#60314](https://github.com/PaddlePaddle/Paddle/pull/60134) +- 针对使用 flash attention 具有 mask 时，mask 的显存占用随序列长度呈二次方复杂度、性能低的问题，使用稀疏的 mask 表达、优化 mask 的显存，显存复杂度从序列长度的二次方降低为一次方，减少了存储的访问次数，同时使用 share memory 加速访存，大幅提升性能。[#62029](https://github.com/PaddlePaddle/Paddle/pull/62029) +- 动态图 Sharding 并行策略新增完善通信和计算 overlap 功能，提升训练过程中的性能。[#60455](https://github.com/PaddlePaddle/Paddle/pull/60455) + +### 通信库功能优化 +- 增强 NCCL 通信库的功能，支持初始化时传入额外的初始化参数以支持定制的 NCCL 库的初始化。[#62193](https://github.com/PaddlePaddle/Paddle/pull/62193) +- 增加 NCCL 库路径查找功能，支持更灵活的 NCCL 库查找方式。[#62492](https://github.com/PaddlePaddle/Paddle/pull/62492) + +### Bug 修复 +- 修复 fused\_linear\_param\_grad\_add\_kernel 算子 dbias_out 空间申请问题，同时增加梯度地址检查逻辑，使得报错信息更易调试。[#363433](https://github.com/PaddlePaddle/Paddle/pull/63433),[#64460](https://github.com/PaddlePaddle/Paddle/pull/64460) +- 修复 sharding 策略在支持 reduce_avg 操作中、comm_overlap 在关闭时未对梯度进行缩放的问题。[#62702](https://github.com/PaddlePaddle/Paddle/pull/62702) +- 解决 Stage2 中 main grad 计算顺序、fusion 相关的 bug。[#59142](https://github.com/PaddlePaddle/Paddle/pull/59142) +- 修复 sharding 策略下，当开启 reduce_avg 通信操作时，无法找到该开关属性的问题。[#62502](https://github.com/PaddlePaddle/Paddle/pull/62502) +- sharding stage1 训练支持非训练参数训练，解决部分参数设置 stop_gradient=True 的问题。[#62616](https://github.com/PaddlePaddle/Paddle/pull/62616) +- 修正 TCP 关闭时打印的信息，防止误导用户。[#62631](https://github.com/PaddlePaddle/Paddle/pull/62631) +- 针对数据并行训练中，部分梯度没有初始化，出现 segmentation fault 错误，修改 DataParallel 训练问题，解决多卡训练出错的问题。[#62299](https://github.com/PaddlePaddle/Paddle/pull/62299) +- 针对开启序列并行的场景，修复了部分模型因为权重冻结而导致的 bug。[#63596](https://github.com/PaddlePaddle/Paddle/pull/63596) +- 针对单路 dp 的 autotuner 场景，修复了一些 bug。[#60757](https://github.com/PaddlePaddle/Paddle/pull/60757) +- 修复流水并行策略 aadiff bug。 ([#64716](https://github.com/PaddlePaddle/Paddle/pull/64716)) +- 移除部分分布式单测。 ([#62762](https://github.com/PaddlePaddle/Paddle/pull/62762)) + +### 安全风险修复 +- 针对 prune\_by\_memory\_estimation 算子中存在安全泄露风险，修补安全漏洞。[#61320](https://github.com/PaddlePaddle/Paddle/pull/61320) + +## 7.参数服务器 +本次更新主要修复了参数服务器使用过程的若干 bug 以及编译安装等问题。 + +### Bug 修复 +- 针对 unique 算子读写越界的问题，修复了 unique 算子计算过程长度设置错误问题，保证 unique 算子运算正确性。[#60840](https://github.com/PaddlePaddle/Paddle/pull/60840) +- 针对 PGLBox 训练过程 save/load 功能缺失以及编译错误等问题，修复了 PGLBox save/load 和编译过程的若干 bug，保证了 PGLBox 功能的正确性。[#63905](https://github.com/PaddlePaddle/Paddle/pull/63905) +- 针对 CPUPS 训练过程触发 GPUPS 逻辑导致训练挂掉的问题，修复了 CPUPS 中 use_ps_gpu 的设置值，保证 CPUPS 训练流程的正确性。[#61406](https://github.com/PaddlePaddle/Paddle/pull/61406) +- 针对 GPUPS 在 CUDA 12.3 中训练出 cudaErrorInvalidResourceHandle 错误的问题，加入了 device id 切换机制，保证在正确的设备上进行对应的资源操作。[#63391](https://github.com/PaddlePaddle/Paddle/pull/63391) +- 针对 PGLBox Embedding Dump 过程出现乱码的问题，修复了 C++ std::string 使用不当的 bug，保证 Embedding Dump 结果的正确性。[#65179](https://github.com/PaddlePaddle/Paddle/pull/65179) + +### 文档完善 +- 在 RPC 接口文档中接入安全警告，提醒用户需要在安全的网络条件下使用此接口。[#64100](https://github.com/PaddlePaddle/Paddle/pull/64100) + +### 安全加强 +- 修复若干代码安全问题，防止恶意代码注入。[#60023](https://github.com/PaddlePaddle/Paddle/pull/60023)，[#60544](https://github.com/PaddlePaddle/Paddle/pull/60544)，[#60615](https://github.com/PaddlePaddle/Paddle/pull/60615) + +## 8.推理部署 +推理框架基于 PIR 升级了 GPU、XPU、CPU 硬件下 PASS，相比上个版本可大幅减少代码行数，提升开发效率。底层执行器升级到了新版异步执行器，在大多数模型上提升推理性能。完成基于 CINN 编译器进行推理加速的适配对接。针对这些特性增加了开关，用户可设置开启。此外，Paddle Inference 还支持了原生与 TensorRT 子图混合推理下直接加载优化后的序列化模型，可以减少启动时耗时。针对 Paddle-TensorRT 增加灵活控制节点计算精度、子图是否进入 TensorRT 计算等接口，方便调试。 性能优化上，GPU、XPU、CPU 都增加了较多 Transformer 及 LLM 计算加速的融合算子，如分组注意力机制融合算子、GQA 结构、WINT4 等支持，并支持通过 PASS 自动匹配。 + +### 新增功能 +- Paddle-TensorRT + - Paddle-TensorRT 底层调用的 API 升级，在 TensorRT 版本大于 8.5 以上时，调用的 EnqueueV2 API (后续会被废弃)升级为 EnqueueV3 API。[#60807](https://github.com/PaddlePaddle/Paddle/pull/60807) + - 增加配置 config.exp_disable_tensorrt_subgraph()可以设置一些子图不进入 TensorRT。[#61967](https://github.com/PaddlePaddle/Paddle/pull/61967) + - 增加配置 config.exp_disable_tensorrt_dynamic_shape_ops()可设置动态 shape 输入的算子不进入 TensorRT，默认值为 False。[#62352](https://github.com/PaddlePaddle/Paddle/pull/62352) + - 增加配置 config.exp_specify_tensorrt_subgraph_precision()可以设置节点跑不同的精度类型。[#62402](https://github.com/PaddlePaddle/Paddle/pull/62402) +- Inference 中增加开启 CINN 编译器的开关，配置推理 config 时，通过 config.enable_cinn()开启 CINN。[#61949](https://github.com/PaddlePaddle/Paddle/pull/61949) +- Inference 升级使用 PIR 机制 + - config 增加 enable_new_ir()接口使能 PIR。[#61968](https://github.com/PaddlePaddle/Paddle/pull/61968) + - config 增加 set_optimization_level()接口可设置不同优化等级。[#61968](https://github.com/PaddlePaddle/Paddle/pull/61968) + - PIR 机制下 PASS 功能支持自定义 C++PASS。[#62468](https://github.com/PaddlePaddle/Paddle/pull/62468) + - 推理库对外暴露 PIR 相关实现头文件，支持用户基于 PIR 的二次开发，如自定义 Pass 开发等。[#61863](https://github.com/PaddlePaddle/Paddle/pull/61863),[#62293](https://github.com/PaddlePaddle/Paddle/pull/62293) + - PIR 机制下支持通过对 Predictor 注册 Hook 操作算子的输入输出。[#63101](https://github.com/PaddlePaddle/Paddle/pull/63101) +- 多层 Transformer 融合算子 fused_multi_transformer_op 融合算子支持 GQA 计算。[#64125](https://github.com/PaddlePaddle/Paddle/pull/64125) + +### 功能完善 +- 推理支持直接加载优化后的模型，使得可以完全跳过 IR 优化，使用该方式部署可以最大程度降低框架开销。[#61598](https://github.com/PaddlePaddle/Paddle/pull/61598) +- 支持加载保存下来的经过 IR PASS 优化后的模型推理时，重新指定 shape 范围信息文件。[#60457](https://github.com/PaddlePaddle/Paddle/pull/60457) +- 控制流算子的子图内可收集 Shape 信息，支持使用 Paddle-TensorRT 推理加速。[#60451](https://github.com/PaddlePaddle/Paddle/pull/60451) ,[#59588](https://github.com/PaddlePaddle/Paddle/pull/59588) +- GPU 原生推理的混合精度 PASS(auto_mixed_precision_pass)支持处理稀疏 Tensor。[#62656](https://github.com/PaddlePaddle/Paddle/pull/62656) +- XPU 硬件相关 + - XPU 针对 Conv 和 FC 的融合 PASS 支持 Float 到 INT31 类型的转换。[#59981](https://github.com/PaddlePaddle/Paddle/pull/59981) + - XPU 的 strided slice 算子支持设置 strides 未负数。 [#62268](https://github.com/PaddlePaddle/Paddle/pull/62268) + - XPU 的多层 Encoder 融合 PASS 可以自适应序列长度并支持变长 [#63825](https://github.com/PaddlePaddle/Paddle/pull/63825) +- Paddle TensorRT INT8 计算模式下支持 tile 算子进入 TensorRT 计算，提升部分模型 INT8 性能。 [#60189](https://github.com/PaddlePaddle/Paddle/pull/60189) + +### 模型压缩 +主要针对训练后量化（Post Training Quantization，PTQ）和量化训练（Quantization Aware Trainig，QAT）做了 bug 修复和功能优化。 +- 支持模按照通道内分组的模拟量化[#61828](https://github.com/PaddlePaddle/Paddle/pull/61828) +- 支持动态图下自动保存量化 scale 到模型参数文件中[#59441](https://github.com/PaddlePaddle/Paddle/pull/59441) +- 去除中 dataloader 必须是 DataLoader 实例的限制[#61798](https://github.com/PaddlePaddle/Paddle/pull/61798) + +### 性能优化 +- 推理执行器升级，保正性能不变情况下，大幅度降低运行时显存占用，可通过 config.enable_use_executor(True)来使用。[#57920](https://github.com/PaddlePaddle/Paddle/pull/57920),[#58452](https://github.com/PaddlePaddle/Paddle/pull/58452),[#63350](https://github.com/PaddlePaddle/Paddle/pull/63350),[#64466](https://github.com/PaddlePaddle/Paddle/pull/64466) +- 升级 paddle inference 的 oneDNN 版本到 v3.4，其中整体性能相比 v3.3 版本有提升。 [#64661](https://github.com/PaddlePaddle/Paddle/pull/64661) +- 升级基于 CUTLASS 支持矩阵乘与激活的融合计算。 ([#61925](https://github.com/PaddlePaddle/Paddle/pull/61925)) + +#### PIR 机制下新增通用 PASS +- 添加 identity_op_clean_pass 和 matmul_scale_fuse_pass。 [#59840](https://github.com/PaddlePaddle/Paddle/pull/59840) +- 添加 fused_flash_attn_pass，该 pass 会调用 flash_attention 替换原始的 attention 计算。[#64213](https://github.com/PaddlePaddle/Paddle/pull/64213),[#64707](https://github.com/PaddlePaddle/Paddle/pull/64707),[#63304](https://github.com/PaddlePaddle/Paddle/pull/63304) +- 推理 PIR 新架构下全新升级 layout 布局调整算法，支持 conv 类、norm 类等算子的 NHWC 推理，在 SD 模型上测试大幅提升性能。[#63628](https://github.com/PaddlePaddle/Paddle/pull/63628),[#64634](https://github.com/PaddlePaddle/Paddle/pull/64634),[#64658](https://github.com/PaddlePaddle/Paddle/pull/64658),[#64708](https://github.com/PaddlePaddle/Paddle/pull/64708),[#64830](https://github.com/PaddlePaddle/Paddle/pull/64830),[#64896](https://github.com/PaddlePaddle/Paddle/pull/64896) +- 增加 remove_redundant_transpose PASS。 [#63357](https://github.com/PaddlePaddle/Paddle/pull/63357) +- 在推理中使能 CSE PASS，提升推理性能。[#64523](https://github.com/PaddlePaddle/Paddle/pull/64523) -- 通信库新增 TraceHang 功能，当集群训练出现 Hang 的问题时，能够快速的定位到出现问题的节点。[#59217](https://github.com/PaddlePaddle/Paddle/pull/59217) -- 为了提升训练效率和降低显存，动态图支持 stride 机制。[#55156](https://github.com/PaddlePaddle/Paddle/pull/55156),[#54762](https://github.com/PaddlePaddle/Paddle/pull/54762),[#55850](https://github.com/PaddlePaddle/Paddle/pull/55850),[#59190](https://github.com/PaddlePaddle/Paddle/pull/59190),[#57005](https://github.com/PaddlePaddle/Paddle/pull/57005),[#57005](https://github.com/PaddlePaddle/Paddle/pull/57005),[#57331](https://github.com/PaddlePaddle/Paddle/pull/57331),[#58033](https://github.com/PaddlePaddle/Paddle/pull/58033),[#58033](https://github.com/PaddlePaddle/Paddle/pull/58033),[#58303](https://github.com/PaddlePaddle/Paddle/pull/58303),[#57835](https://github.com/PaddlePaddle/Paddle/pull/57835),[#57189](https://github.com/PaddlePaddle/Paddle/pull/57189) -- 为了方便计算图的分析，增强 paddleviz 功能。[#56837](https://github.com/PaddlePaddle/Paddle/pull/56837),[#57626](https://github.com/PaddlePaddle/Paddle/pull/57626) -- 分布式 Sharding 策略(Stage1,2,3)新增 main_grad 功能，以支持更高精度的梯度累加，减少低精度累加带来的精度损失。[#57972](https://github.com/PaddlePaddle/Paddle/pull/57972),[#57934](https://github.com/PaddlePaddle/Paddle/pull/57934),[#57473](https://github.com/PaddlePaddle/Paddle/pull/57473),[#57537](https://github.com/PaddlePaddle/Paddle/pull/57537),[#59611](https://github.com/PaddlePaddle/Paddle/pull/59611),[#57960](https://github.com/PaddlePaddle/Paddle/pull/57960) -- Sharding Stage1 策略新增开关变量，可以控制是否对 Optimizer 进行 fusion 计算。[#58790](https://github.com/PaddlePaddle/Paddle/pull/58790) -- Recompute 功能新增对 Tuple 输入参数的支持，增强了 Recompute 接口的调用能力。[#56793](https://github.com/PaddlePaddle/Paddle/pull/56793) -- 增强 Launch 功能，动态图下无需指定 endpoints 也可以进行分布式训练。 [#54636](https://github.com/PaddlePaddle/Paddle/pull/54636) +#### GPU 性能优化 +含新增融合算子及 PIR 机制下新增 PASS。 +- 稀疏卷积算子(sparse conv)性能优化，提升 BEV 等模型的推理性能。[#63067](https://github.com/PaddlePaddle/Paddle/pull/63067) +- 新增基于 flash attention 的融合 PASS。 [#63220](https://github.com/PaddlePaddle/Paddle/pull/63220) +- 理支持 elementwise_add+group_norm+silu 激活的算子融合 pattern 及其对应融合 kernel。[#64199](https://github.com/PaddlePaddle/Paddle/pull/64199) +- 矩阵乘计算支持 groupwise 的 Weight only INT4 计算。[#60422](https://github.com/PaddlePaddle/Paddle/pull/60422) 、[#63212](https://github.com/PaddlePaddle/Paddle/pull/63212) 、[#60204](https://github.com/PaddlePaddle/Paddle/pull/60204)) +- 分组注意力机制融合算子 block_multi_head_attention 的算子实现支持 KV Cache 量化。[#59951](https://github.com/PaddlePaddle/Paddle/pull/59951)) +- 推理使用 CUTLASS 升级 conv 融合算子实现并支持 PASS 自动融合支持 bias 与 activation，新算子相较原先 cuDNN 实现有显著的性能加速。需通过 config.exp_enable_use_cutlass(True)使用。[#64201](https://github.com/PaddlePaddle/Paddle/pull/64201)、[#64641](https://github.com/PaddlePaddle/Paddle/pull/64641) +- 添加 blha_get_max_len 算子并去除了 block_multihead_attention 中每次调用 get_max_len 的行为，该功能应用于大模型动态推理加速。[#64246](https://github.com/PaddlePaddle/Paddle/pull/64246) +- 数据排布优化 PASS 禁止 conv 融合算子 FP32 精度类型时使用 NHWC 模式计算，原因是 cuDNN 在此条件下会导致性能退化。[#63400](https://github.com/PaddlePaddle/Paddle/pull/63400) +- GPU 峰值显存优化，升级底层接口 TryShrinkMemory 升级支持 GPU place 下支持释放显存池空闲显存，某些场景下可大幅度削减峰值显存。[#61319](https://github.com/PaddlePaddle/Paddle/pull/61319) -#### 功能优化 +#### CPU 性能优化 +含新增融合算子及 PIR 机制下新增 PASS 并优化部分 Kernel。 +- 添加 scale_matmul_fuse_pass [#63313](https://github.com/PaddlePaddle/Paddle/pull/63313) +- 融合算子 fused_bias_residual_layernorm 和 fused_rms_norm 添加 CPU 实现，大幅度推理速度。[#63196](https://github.com/PaddlePaddle/Paddle/pull/63196)、[#63165](https://github.com/PaddlePaddle/Paddle/pull/63165) +- 新增 Deconvolution kernel 的缓存优化，从而大大提升该算子的执行速度。 [#60922](https://github.com/PaddlePaddle/Paddle/pull/60922) +- PIR 下新增 depthwise_conv 融合 PASS，将 depthwise_conv 算子转换为 conv2d，从而使用 onednn conv2d 的 kernel 优化，提升该算子推理速度。 [#63051](https://github.com/PaddlePaddle/Paddle/pull/63051) +- PIR 下新增 Conv 与激活的融合 PASS(conv_activation_mkldnn_fuse_pass)，支持 conv 和 13 种激活函数进行融合，大大提升 conv 相关算子的推理速度。 [#63145](https://github.com/PaddlePaddle/Paddle/pull/63145) +- PIR 下新增多种算子和 unsqueeze 的算子融合 PASS(operator_unsqueeze_onednn_fuse_pass)，提升推理速度。 [#63592](https://github.com/PaddlePaddle/Paddle/pull/63592) +- PIR 下新增将 reshape 融合进多个算子的 PASS (operator_reshape_onednn_fuse_pass)。 [#63812](https://github.com/PaddlePaddle/Paddle/pull/63812) +- PIR 下新增 scale 融合 PASS (operator_scale_onednn_fuse_pass)。 [#63811](https://github.com/PaddlePaddle/Paddle/pull/63811) +- PIR 下新增 conv 与 bias 融合的 PASS (conv2d_transpose_bias 算子) 。 [#62241](https://github.com/PaddlePaddle/Paddle/pull/62241) +- PIR 下新增 onednn_placement_pass，支持了 151 种算子从 Phi 算子转换为 oneDNN 算子，从而使用 oneDNN 高性能库进行优化，提升推理速度。 [#63982](https://github.com/PaddlePaddle/Paddle/pull/63982) +- PIR 下新增 elementwise 类型算子和 13 种激活函数的融合，大大提升 cpu 下开启 onednn 的推理速度。 [#63516](https://github.com/PaddlePaddle/Paddle/pull/63516) +- PIR 下新增多个 conv + concat + 激活函数和 fused_conv + concat + 激活函数的融合，大大提升了 conv 下有 concat 和激活函数的情况下推理速度。 [#62993](https://github.com/PaddlePaddle/Paddle/pull/62993)、 [#62713](https://github.com/PaddlePaddle/Paddle/pull/62713) +- PIR 下新增 matmul+add 算子融合 PASS (matmul_elementwise_add_fuse_pass)。[#62715](https://github.com/PaddlePaddle/Paddle/pull/62715) +- PIR 下新增 scale 参数折叠 PASS(scale_matmul_fuse_pass)。[#63313](https://github.com/PaddlePaddle/Paddle/pull/63313) +- PIR 下新增 softplus 与 12 种激活函数融合 PASS(softplus_activation_fuse_pass)。[#63617](https://github.com/PaddlePaddle/Paddle/pull/63617) +- PIR 下新增 fc 算子转换 PASS(fc_onednn_enable_pass)。[#63518](https://github.com/PaddlePaddle/Paddle/pull/63518) +- PIR 下新增自注意力算子融合 PASS(self_attention_fuse_pass)。[#63726](https://github.com/PaddlePaddle/Paddle/pull/63726) +- PIR 下新增 fc 与 12 种激活函数融合 PASS(fc_activation_fuse_pass)。[#63853](https://github.com/PaddlePaddle/Paddle/pull/63853) +- PIR 下新增 BatchNorm 折叠 PASS(conv2d_bn_onednn_fuse_pass)，扩增后续 pass 的融合几率。[#64524](https://github.com/PaddlePaddle/Paddle/pull/64524) +- PIR 下新增 matmul 与 12 种激活函数融合 PASS(matmul_activation_fuse_pass)。[#62901](https://github.com/PaddlePaddle/Paddle/pull/62901) +- PIR 下新增 reshape + transpose + reshape 融合 PASS(shuffle_channel_detect_pass)，在特定条件下融合为 shuffle_channel 算子。[#64053](https://github.com/PaddlePaddle/Paddle/pull/64053) +- PIR 下新增 reshape + transpose + matmul 融合 PASS(reshape_transpose_matmul_fuse_pass)。[#62998](https://github.com/PaddlePaddle/Paddle/pull/62998) +- PIR 下新增 matmul + transpose + reshape 融合 PASS(matmul_transpose_reshape_fuse_pass)，在部分场景下显著提升性能。[#63151](https://github.com/PaddlePaddle/Paddle/pull/63151)(https://github.com/PaddlePaddle/Paddle/pull/63151) + +### Bug 修复 +- 修复 faster_rcnn_swin_tiny_fpn_1x_coco 等模型中的混合精度转换问题，解决了 mixed_precision_pass 的错误。 [#64673](https://github.com/PaddlePaddle/Paddle/pull/64673) +- 阻止 fused_conv2d_add_act pass 在激活函数为 sigmoid 中被生效（cudnn 版本 8.0~8.7 之间时，融合 conv2d 和 sigmoid 会导致性能退化）。[#64717](https://github.com/PaddlePaddle/Paddle/pull/64717) +- 修复 self_dp_attention 和 fused_layer_norm_avx_kernel 在 Clang12 中的编译问题。 [#63414](https://github.com/PaddlePaddle/Paddle/pull/63414) +- 修复部分模型在 IR/Pass 阶段 qdq 算子中的 scale 和 zeroPoint 过早删除的问题。 [#62225](https://github.com/PaddlePaddle/Paddle/pull/62225) +- 修复同时开启 Config.UseOptimizedModel()和 config.EnableMemoryOptim()时导致报错的问题。 [#62501](https://github.com/PaddlePaddle/Paddle/pull/62501) +- 增加 matmul_scale_fuse_pass 的约束，其中输入 w 必须是权重，否则不会匹配该 pass。 [#62850](https://github.com/PaddlePaddle/Paddle/pull/62850) +- 保持 inference 模型输出键顺序保证与动态图模型导出时的顺序一致。 [#63791](https://github.com/PaddlePaddle/Paddle/pull/63791) +- 修复子图在常量折节 PASS 在"被折叠的 op 和其输入输出不在一个子图时"出错问题。 [#62148](https://github.com/PaddlePaddle/Paddle/pull/62148) +- 修复 PaddleTRT 模式下若干运行时问题。包括 int8 模式下 yolo_box 算子引起的量化校准表生成失败、reduce 算子 dim 属性数据类型未正确处理引起的报错。[#61596](https://github.com/PaddlePaddle/Paddle/pull/61596) +- 修复混合精度推理模式下若干运行时报错问题。包括 fused conv2d 算子间共享权重未正确转换权重 layout、fused conv2d 算子 backend 未正常选择为 cuDNN、fused conv2d 算子在 NHWC 下错误处理 bias 维度、错误处理 norm 类算子的输入数据类型引起的报错。[#60955](https://github.com/PaddlePaddle/Paddle/pull/60955)、[#60076](https://github.com/PaddlePaddle/Paddle/pull/60076)、[#63007](https://github.com/PaddlePaddle/Paddle/pull/63007)、[#63988](https://github.com/PaddlePaddle/Paddle/pull/63988) +- 修复 config.delete_pass 功能未生效问题。[#61056](https://github.com/PaddlePaddle/Paddle/pull/61056) +- PIR 中修复 While 控制流的 GC 机制，提前回收不需要的输入，减少峰值显存，例如在 LLaMA 7B 模型中减少 2GB 显存。[#63062](https://github.com/PaddlePaddle/Paddle/pull/63062) +- 修正了 OneDNN mean kernel 回退错误。 [#64676](https://github.com/PaddlePaddle/Paddle/pull/64676) +- 修正 conv_bias_fuse_pass 新增了若干强约束， 例如 bias 的 shape 不能为 1，从而保证 pass 推理结果稳定。 [#64412](https://github.com/PaddlePaddle/Paddle/pull/64412) +- 修正 conv_elementwise_add_onednn_fuse_pass 新增了若干强约束，例如 conv2d_out 和 residual_param 的尺寸必须一致，从而保证 pass 推理稳定。 [#64448](https://github.com/PaddlePaddle/Paddle/pull/64448) +- 修复在特定情况下，反复插入量化反量化算子的问题 [#63082](https://github.com/PaddlePaddle/Paddle/pull/63082) + +## 9.硬件适配 + +### 适配方案 (Custom Device) +飞桨硬件接入本次新增了对 4 款硬件昆仑 XPU、昇腾 NPU、海光 DCU 和寒武纪 MLU 的日常发版支持，同时通过大模型训练和推理部署的打磨修复了分布式通信中存在的问题，并通过显存优化、计算和通信的 overlap 等功能进行性能优化。其次、本次各个硬件还新增了大量 BFloat16 数据类型的算子支持，以及众多算子融合 Pass 和各个硬件上的融合算子，通过软硬联合的方式接入硬件大 Transformer 算子库来充分提升大模型性能。 -- 实现动静统一的新通信库，通信算子全面适配 PHI 算子体系，减少开发和维护成本，更好地支持动态图和自动并行架构升级。[#54417](https://github.com/PaddlePaddle/Paddle/pull/54417),[#57768](https://github.com/PaddlePaddle/Paddle/pull/57768),[#57897](https://github.com/PaddlePaddle/Paddle/pull/57897),[#55537](https://github.com/PaddlePaddle/Paddle/pull/55537),[#56604](https://github.com/PaddlePaddle/Paddle/pull/56604),[#57519](https://github.com/PaddlePaddle/Paddle/pull/57519),[#56088](https://github.com/PaddlePaddle/Paddle/pull/56088),[#57153](https://github.com/PaddlePaddle/Paddle/pull/57153),[#57161](https://github.com/PaddlePaddle/Paddle/pull/57161),[#57252](https://github.com/PaddlePaddle/Paddle/pull/57252),[#57251](https://github.com/PaddlePaddle/Paddle/pull/57251),[#57208](https://github.com/PaddlePaddle/Paddle/pull/57208),[#57305](https://github.com/PaddlePaddle/Paddle/pull/57305),[#57424](https://github.com/PaddlePaddle/Paddle/pull/57424),[#57548](https://github.com/PaddlePaddle/Paddle/pull/57548),[#57560](https://github.com/PaddlePaddle/Paddle/pull/57560),[#57564](https://github.com/PaddlePaddle/Paddle/pull/57564),[#57233](https://github.com/PaddlePaddle/Paddle/pull/57233),[#55726](https://github.com/PaddlePaddle/Paddle/pull/55726),[#58073](https://github.com/PaddlePaddle/Paddle/pull/58073) -- TCPStore 改为单例以便更灵活地支持动态图和自动并行功能。[#55956](https://github.com/PaddlePaddle/Paddle/pull/55956) -- 改善了 MP/PP/SP 等分布式策略的可维护性和灵活性，包含增加打印 warning、报错信息，对代码文件进行结构清理，梳理 PP 对输入的限制等。[#54448](https://github.com/PaddlePaddle/Paddle/pull/54448),[#59762](https://github.com/PaddlePaddle/Paddle/pull/59762),[#55462](https://github.com/PaddlePaddle/Paddle/pull/55462),[#54788](https://github.com/PaddlePaddle/Paddle/pull/54788),[#54664](https://github.com/PaddlePaddle/Paddle/pull/54664),[#56456](https://github.com/PaddlePaddle/Paddle/pull/56456),[#55540](https://github.com/PaddlePaddle/Paddle/pull/55540) -- PP 策略中增加可以在计算流中进行 P2P 通信的支持，通信模式更加灵活。[#54747](https://github.com/PaddlePaddle/Paddle/pull/54747) -- Sharding 策略支持对梯度进行 reduce 操作。[#58842](https://github.com/PaddlePaddle/Paddle/pull/58842),[#57967](https://github.com/PaddlePaddle/Paddle/pull/57967),[#55495](https://github.com/PaddlePaddle/Paddle/pull/55495) +#### 新增功能 +- 新增分布式策略 sharding stage1 v2 的支持。[#61500](https://github.com/PaddlePaddle/Paddle/pull/61500) +- 支持分布式通信模块支持 BF16 数据类型。新增部分算子对 BF16 数据类型的支持，如 empty、shape 等。[#60768](https://github.com/PaddlePaddle/Paddle/pull/60768),[#62140](https://github.com/PaddlePaddle/Paddle/pull/62140),[#62604](https://github.com/PaddlePaddle/Paddle/pull/62604) +- 新增 get_comm_name 接口的支持，对 memory stat 功能支持， 支持 Profiler 对内存时间的记录。[#62556](https://github.com/PaddlePaddle/Paddle/pull/62556),[#61030](https://github.com/PaddlePaddle/Paddle/pull/61030),[#62292](https://github.com/PaddlePaddle/Paddle/pull/62292) +- 新增部分融合策略和算子的支持，包括 silu_fuse_pass, conv_elementwise_add_act_fuse_pass, generator offset 的支持。 [#60595](https://github.com/PaddlePaddle/Paddle/pull/60595),[#60708](https://github.com/PaddlePaddle/Paddle/pull/60708),[#60616](https://github.com/PaddlePaddle/Paddle/pull/60616) #### 性能优化 - -- 实现 PP 策略的最后一层及时释放 output，以节约显存。[#54505](https://github.com/PaddlePaddle/Paddle/pull/54505) -- MP 策略 Tensor fusion 支持传入 params group，增强 Tensor fusion 功能；增加 allreduce 异步通信性能，通过计算和通信的 overlap 提升训练性能。[#57690](https://github.com/PaddlePaddle/Paddle/pull/57690),[#55662](https://github.com/PaddlePaddle/Paddle/pull/55662) -- Sharding 策略反向计算和梯度通信进行 overlap 以提升训练性能。Sharding stage1 新增 Tensor fusion 和 fuse grad clip，optimizer 等优化提高计算效率。支持 VPP 与 DP/Sharding Stage1 的 overlap，提升通信计算并行度。优化 Sharding Stage1 在 FP16 下的性能，在 check finite 阶段只对本 sharding rank 负责的梯度进行检查，降低计算开销；增加环境变量，控制是否进行 Optimize，以节约显存支持，实现使用更少的资源进行模型训练调试。[#55598](https://github.com/PaddlePaddle/Paddle/pull/55598),[#55427](https://github.com/PaddlePaddle/Paddle/pull/55427),[#56063](https://github.com/PaddlePaddle/Paddle/pull/56063),[#55766](https://github.com/PaddlePaddle/Paddle/pull/55766),[#59848](https://github.com/PaddlePaddle/Paddle/pull/59848) -- 混合并行策略将 PP/VPP 下的 Tensor fusion 提到运行前，解决运行时 fuse 对显存额外开销的问题。通过减少非必需的同步 memcpy，以提升模型训练性能。[#54403](https://github.com/PaddlePaddle/Paddle/pull/54403),[#57215](https://github.com/PaddlePaddle/Paddle/pull/57215) - -#### Bug Fix - -- 修复了 PP、Launch 功能、MP 策略以及 fuse_rope 等 13 个 bug，增强了分布式策略的稳定性；机制层面，修复 inplace，tensor 引用的错误，提升稳定性。[#55116](https://github.com/PaddlePaddle/Paddle/pull/55116),[#55782](https://github.com/PaddlePaddle/Paddle/pull/55782),[#59609](https://github.com/PaddlePaddle/Paddle/pull/59609),[#57394](https://github.com/PaddlePaddle/Paddle/pull/57394),[#55864](https://github.com/PaddlePaddle/Paddle/pull/55864),[#58482](https://github.com/PaddlePaddle/Paddle/pull/58482),[#54571](https://github.com/PaddlePaddle/Paddle/pull/54571),[#55896](https://github.com/PaddlePaddle/Paddle/pull/55896),[#54648](https://github.com/PaddlePaddle/Paddle/pull/54648),[#58307](https://github.com/PaddlePaddle/Paddle/pull/58307),[#55679](https://github.com/PaddlePaddle/Paddle/pull/55679),[#58133](https://github.com/PaddlePaddle/Paddle/pull/58133),[#58408](https://github.com/PaddlePaddle/Paddle/pull/58408),[#59707](https://github.com/PaddlePaddle/Paddle/pull/59707),[#55342](https://github.com/PaddlePaddle/Paddle/pull/55342),[#54703](https://github.com/PaddlePaddle/Paddle/pull/54703),[#54869](https://github.com/PaddlePaddle/Paddle/pull/54869),[#55568](https://github.com/PaddlePaddle/Paddle/pull/55568),[#55233](https://github.com/PaddlePaddle/Paddle/pull/55233),[#56418](https://github.com/PaddlePaddle/Paddle/pull/56418),[#56428](https://github.com/PaddlePaddle/Paddle/pull/56428),[#56892](https://github.com/PaddlePaddle/Paddle/pull/56892),[#57192](https://github.com/PaddlePaddle/Paddle/pull/57192),[#59161](https://github.com/PaddlePaddle/Paddle/pull/59161),[#59340](https://github.com/PaddlePaddle/Paddle/pull/59340),[#57006](https://github.com/PaddlePaddle/Paddle/pull/57006),[#57353](https://github.com/PaddlePaddle/Paddle/pull/57353),[#57352](https://github.com/PaddlePaddle/Paddle/pull/57352),[#59088](https://github.com/PaddlePaddle/Paddle/pull/59088) -- 修复了 PP 策略无法及时释放单层 output 的 bug，以及初始化过程中可能会 Hang 的 bug。 [#54624](https://github.com/PaddlePaddle/Paddle/pull/54624),[#58844](https://github.com/PaddlePaddle/Paddle/pull/58844),[#54673](https://github.com/PaddlePaddle/Paddle/pull/54673),[#58376](https://github.com/PaddlePaddle/Paddle/pull/58376) -- 修复了 MP 策略下，当输入数据类型不统一时计算出错的 bug，修复了 MP 策略下参数同步的 bug 和没有正确使用用户输入 config 的 bug。[#58858](https://github.com/PaddlePaddle/Paddle/pull/58858),[#57918](https://github.com/PaddlePaddle/Paddle/pull/57918),[#58037](https://github.com/PaddlePaddle/Paddle/pull/58037) -- 统一 dygraph 和 dynamic 模式的判断方法。[#54633](https://github.com/PaddlePaddle/Paddle/pull/54633) -- 修复了 fuse_rope 中 sin 和 cos 的 Shape 不对的 bug。[#56132](https://github.com/PaddlePaddle/Paddle/pull/56132) -- 修复了 Luanch 功能分布式场景下 endpoints 太长导致不能启动任务的 bug，同时修复了 endpoints 可能乱序的 bug。 [#55011](https://github.com/PaddlePaddle/Paddle/pull/55011),[#55478](https://github.com/PaddlePaddle/Paddle/pull/55478) -- 修复了 MEA 功能可能导致 segmentation fault error 的 bug。[#55408](https://github.com/PaddlePaddle/Paddle/pull/55408) - -### 自动并行 - -本版本对动静统一自动并行（Auto Parallel）编程范式进行了全面的优化，简化了开发者的编程复杂度。开发者无需深入了解手动并行编程范式下的复杂概念和 API 接口，如行切分、列切分等。仅需通过少量的张量切分标注即可完成混合并行模型的构建。框架能够自动推导出所有张量和算子的分布式切分状态，并添加合适的通信算子。同时支持一键动转静进行分布式训练，使开发者能够高效轻松地实现任意混合并行策略，大幅降低了混合并行训练代码的开发成本。 - -#### 完善了自动并行核心功能 - -- 实现 process_mesh、placement、shard_tensor、reshard、dtensor_from_fn、unshard_dtensor、shard_layer、to_static 等自动并行核心接口 [#55494](https://github.com/PaddlePaddle/Paddle/pull/55494),[#59059](https://github.com/PaddlePaddle/Paddle/pull/59059),[#56561](https://github.com/PaddlePaddle/Paddle/pull/56561),[#54425](https://github.com/PaddlePaddle/Paddle/pull/54425),[#59557](https://github.com/PaddlePaddle/Paddle/pull/59557),[#59682](https://github.com/PaddlePaddle/Paddle/pull/59682),[#56565](https://github.com/PaddlePaddle/Paddle/pull/56565),[#59862](https://github.com/PaddlePaddle/Paddle/pull/59862),[#59856](https://github.com/PaddlePaddle/Paddle/pull/59856),[#59342](https://github.com/PaddlePaddle/Paddle/pull/59342),[#59575](https://github.com/PaddlePaddle/Paddle/pull/59575),[#57604](https://github.com/PaddlePaddle/Paddle/pull/57604),[#57293](https://github.com/PaddlePaddle/Paddle/pull/57293),[#57278](https://github.com/PaddlePaddle/Paddle/pull/57278) -- 实现基于 Enisum 表达式的切分推导规则，并完成 20+类算子切分推导规则，覆盖 LLaMA、GPT 等主流生成式大语言模型。[#55196](https://github.com/PaddlePaddle/Paddle/pull/55196),[#53863](https://github.com/PaddlePaddle/Paddle/pull/53863),[#56257](https://github.com/PaddlePaddle/Paddle/pull/56257),[#55394](https://github.com/PaddlePaddle/Paddle/pull/55394),[#54810](https://github.com/PaddlePaddle/Paddle/pull/54810),[#55508](https://github.com/PaddlePaddle/Paddle/pull/55508),[#56257](https://github.com/PaddlePaddle/Paddle/pull/56257),[#57813](https://github.com/PaddlePaddle/Paddle/pull/57813),[#58149](https://github.com/PaddlePaddle/Paddle/pull/58149),[#58506](https://github.com/PaddlePaddle/Paddle/pull/58506),[#58563](https://github.com/PaddlePaddle/Paddle/pull/58563),[#58360](https://github.com/PaddlePaddle/Paddle/pull/58360),[#58920](https://github.com/PaddlePaddle/Paddle/pull/58920),[#59050](https://github.com/PaddlePaddle/Paddle/pull/59050),[#58760](https://github.com/PaddlePaddle/Paddle/pull/58760),[#59083](https://github.com/PaddlePaddle/Paddle/pull/59083),[#59236](https://github.com/PaddlePaddle/Paddle/pull/59236),[#59350](https://github.com/PaddlePaddle/Paddle/pull/59350),[#59411](https://github.com/PaddlePaddle/Paddle/pull/59411),[#59260](https://github.com/PaddlePaddle/Paddle/pull/59260),[#54373](https://github.com/PaddlePaddle/Paddle/pull/54373),[#54991](https://github.com/PaddlePaddle/Paddle/pull/54991),[#55397](https://github.com/PaddlePaddle/Paddle/pull/55397),[#55350](https://github.com/PaddlePaddle/Paddle/pull/55350),[#55177](https://github.com/PaddlePaddle/Paddle/pull/55177),[#56443](https://github.com/PaddlePaddle/Paddle/pull/56443),[#58097](https://github.com/PaddlePaddle/Paddle/pull/58097),[#56509](https://github.com/PaddlePaddle/Paddle/pull/56509),[#56502](https://github.com/PaddlePaddle/Paddle/pull/56502),[#56504](https://github.com/PaddlePaddle/Paddle/pull/56504),[#56506](https://github.com/PaddlePaddle/Paddle/pull/56506),[#56507](https://github.com/PaddlePaddle/Paddle/pull/56507),[#56505](https://github.com/PaddlePaddle/Paddle/pull/56505),[#57176](https://github.com/PaddlePaddle/Paddle/pull/57176),[#57374](https://github.com/PaddlePaddle/Paddle/pull/57374),[#57573](https://github.com/PaddlePaddle/Paddle/pull/57573),[#57545](https://github.com/PaddlePaddle/Paddle/pull/57545),[#57875](https://github.com/PaddlePaddle/Paddle/pull/57875),[#57866](https://github.com/PaddlePaddle/Paddle/pull/57866),[#58854](https://github.com/PaddlePaddle/Paddle/pull/58854),[#59109](https://github.com/PaddlePaddle/Paddle/pull/59109),[#59185](https://github.com/PaddlePaddle/Paddle/pull/59185),[#58913](https://github.com/PaddlePaddle/Paddle/pull/58913),[#59547](https://github.com/PaddlePaddle/Paddle/pull/59547),[#58296](https://github.com/PaddlePaddle/Paddle/pull/58296),[#59545](https://github.com/PaddlePaddle/Paddle/pull/59545),[#59039](https://github.com/PaddlePaddle/Paddle/pull/59039),[#59002](https://github.com/PaddlePaddle/Paddle/pull/59002),[#58087](https://github.com/PaddlePaddle/Paddle/pull/58087),[#56367](https://github.com/PaddlePaddle/Paddle/pull/56367),[#57877](https://github.com/PaddlePaddle/Paddle/pull/57877),[#56839](https://github.com/PaddlePaddle/Paddle/pull/56839),[#59003](https://github.com/PaddlePaddle/Paddle/pull/59003),[#57269](https://github.com/PaddlePaddle/Paddle/pull/57269),[#55130](https://github.com/PaddlePaddle/Paddle/pull/55130),[#58474](https://github.com/PaddlePaddle/Paddle/pull/58474),[#57197](https://github.com/PaddlePaddle/Paddle/pull/57197),[#57467](https://github.com/PaddlePaddle/Paddle/pull/57467),[#57259](https://github.com/PaddlePaddle/Paddle/pull/57259),[#57280](https://github.com/PaddlePaddle/Paddle/pull/57280),[#56508](https://github.com/PaddlePaddle/Paddle/pull/56508) -- 实现动静统一的分布式 checkpoint 存储和加载，支持任意按切分状态存储和加载时重切分。[#59659](https://github.com/PaddlePaddle/Paddle/pull/59659),[#59843](https://github.com/PaddlePaddle/Paddle/pull/59843),[#60033](https://github.com/PaddlePaddle/Paddle/pull/60033),[#60034](https://github.com/PaddlePaddle/Paddle/pull/60034) - -#### 增强动态图半自动并行能力 - -- 基础数据结构补充：C++端新增 DistTensor、Placements 等分布式特有的基础数据结构，并暴露到 Python 端，支持对相关属性和值的调试打印。[#58930](https://github.com/PaddlePaddle/Paddle/pull/58930),[#59068](https://github.com/PaddlePaddle/Paddle/pull/59068),[#55436](https://github.com/PaddlePaddle/Paddle/pull/55436),[#56449](https://github.com/PaddlePaddle/Paddle/pull/56449),[#59683](https://github.com/PaddlePaddle/Paddle/pull/59683),[#55593](https://github.com/PaddlePaddle/Paddle/pull/55593),[#58032](https://github.com/PaddlePaddle/Paddle/pull/58032),[#56368](https://github.com/PaddlePaddle/Paddle/pull/56368),[#59086](https://github.com/PaddlePaddle/Paddle/pull/59086) -- 在前、反向算子执行流程中添加 SPMD 推导与 Reshard 的生成逻辑，适配 vector、optional 等多类型输入输出以及 cpu fallback、多 kernel 选择等特殊机制。[#56602](https://github.com/PaddlePaddle/Paddle/pull/56602),[#57321](https://github.com/PaddlePaddle/Paddle/pull/57321),[#57092](https://github.com/PaddlePaddle/Paddle/pull/57092),[#56831](https://github.com/PaddlePaddle/Paddle/pull/56831),[#57119](https://github.com/PaddlePaddle/Paddle/pull/57119),[#58819](https://github.com/PaddlePaddle/Paddle/pull/58819),[#58254](https://github.com/PaddlePaddle/Paddle/pull/58254),[#55698](https://github.com/PaddlePaddle/Paddle/pull/55698),[#59241](https://github.com/PaddlePaddle/Paddle/pull/59241),[#59328](https://github.com/PaddlePaddle/Paddle/pull/59328),[#58644](https://github.com/PaddlePaddle/Paddle/pull/58644),[#56202](https://github.com/PaddlePaddle/Paddle/pull/56202),[#59159](https://github.com/PaddlePaddle/Paddle/pull/59159),[#58573](https://github.com/PaddlePaddle/Paddle/pull/58573),[#59246](https://github.com/PaddlePaddle/Paddle/pull/59246),[#59133](https://github.com/PaddlePaddle/Paddle/pull/59133),[#59186](https://github.com/PaddlePaddle/Paddle/pull/59186),[#57505](https://github.com/PaddlePaddle/Paddle/pull/57505),[#57241](https://github.com/PaddlePaddle/Paddle/pull/57241),[#58928](https://github.com/PaddlePaddle/Paddle/pull/58928) - -- 对 custom 算子、手写算子等特殊类型的算子，适配自动并行的执行逻辑。支持 DistTensor 和 DenseTensor 作为混合输入时的自动转换。[#57774](https://github.com/PaddlePaddle/Paddle/pull/57774),[#59108](https://github.com/PaddlePaddle/Paddle/pull/59108),[#58436](https://github.com/PaddlePaddle/Paddle/pull/58436),[#59523](https://github.com/PaddlePaddle/Paddle/pull/59523),[#59136](https://github.com/PaddlePaddle/Paddle/pull/59136),[#59352](https://github.com/PaddlePaddle/Paddle/pull/59352),[#59062](https://github.com/PaddlePaddle/Paddle/pull/59062),[#58434](https://github.com/PaddlePaddle/Paddle/pull/58434),[#59148](https://github.com/PaddlePaddle/Paddle/pull/59148),[#58553](https://github.com/PaddlePaddle/Paddle/pull/58553),[#58716](https://github.com/PaddlePaddle/Paddle/pull/58716),[#58369](https://github.com/PaddlePaddle/Paddle/pull/58369),[#59061](https://github.com/PaddlePaddle/Paddle/pull/59061),[#58841](https://github.com/PaddlePaddle/Paddle/pull/58841),[#59139](https://github.com/PaddlePaddle/Paddle/pull/59139),[#59141](https://github.com/PaddlePaddle/Paddle/pull/59141),[#58837](https://github.com/PaddlePaddle/Paddle/pull/58837),[#59137](https://github.com/PaddlePaddle/Paddle/pull/59137),[#59143](https://github.com/PaddlePaddle/Paddle/pull/59143) - -- 动态图执行体系完善：适配 Autograd 执行过程，支持动态图的反向梯度聚合、AMP、Hook、PyLayer、View、自定义算子等周围机制。[#58437](https://github.com/PaddlePaddle/Paddle/pull/58437),[#58769](https://github.com/PaddlePaddle/Paddle/pull/58769),[#58796](https://github.com/PaddlePaddle/Paddle/pull/58796),[#58339](https://github.com/PaddlePaddle/Paddle/pull/58339),[#58409](https://github.com/PaddlePaddle/Paddle/pull/58409),[#58772](https://github.com/PaddlePaddle/Paddle/pull/58772),[#58380](https://github.com/PaddlePaddle/Paddle/pull/58380),[#58447](https://github.com/PaddlePaddle/Paddle/pull/58447),[#58706](https://github.com/PaddlePaddle/Paddle/pull/58706),[#58656](https://github.com/PaddlePaddle/Paddle/pull/58656),[#58172](https://github.com/PaddlePaddle/Paddle/pull/58172),[#59401](https://github.com/PaddlePaddle/Paddle/pull/59401),[#58727](https://github.com/PaddlePaddle/Paddle/pull/58727),[#58238](https://github.com/PaddlePaddle/Paddle/pull/58238),[#59243](https://github.com/PaddlePaddle/Paddle/pull/59243),[#58469](https://github.com/PaddlePaddle/Paddle/pull/58469),[#58442](https://github.com/PaddlePaddle/Paddle/pull/58442),[#58487](https://github.com/PaddlePaddle/Paddle/pull/58487),[#58476](https://github.com/PaddlePaddle/Paddle/pull/58476),[#59706](https://github.com/PaddlePaddle/Paddle/pull/59706) - -- 新增对 PP、SP 等分布式策略的支持。[#58126](https://github.com/PaddlePaddle/Paddle/pull/58126),[#59766](https://github.com/PaddlePaddle/Paddle/pull/59766),[#59060](https://github.com/PaddlePaddle/Paddle/pull/59060),[#59841](https://github.com/PaddlePaddle/Paddle/pull/59841),[#58609](https://github.com/PaddlePaddle/Paddle/pull/58609),[#59688](https://github.com/PaddlePaddle/Paddle/pull/59688),[#58449](https://github.com/PaddlePaddle/Paddle/pull/58449)、[#59598](https://github.com/PaddlePaddle/Paddle/pull/59598) -- 新增多种 Reshard 策略，支持张量在不同分布式状态间的转换。[#58592](https://github.com/PaddlePaddle/Paddle/pull/58592),[#59138](https://github.com/PaddlePaddle/Paddle/pull/59138),[#59367](https://github.com/PaddlePaddle/Paddle/pull/59367),[#59621](https://github.com/PaddlePaddle/Paddle/pull/59621),[#59758](https://github.com/PaddlePaddle/Paddle/pull/59758),[#59777](https://github.com/PaddlePaddle/Paddle/pull/59777),[#56975](https://github.com/PaddlePaddle/Paddle/pull/56975),[#58550](https://github.com/PaddlePaddle/Paddle/pull/58550),[#58703](https://github.com/PaddlePaddle/Paddle/pull/58703),[#57210](https://github.com/PaddlePaddle/Paddle/pull/57210),[#58734](https://github.com/PaddlePaddle/Paddle/pull/58734),[#56833](https://github.com/PaddlePaddle/Paddle/pull/56833),[#59292](https://github.com/PaddlePaddle/Paddle/pull/59292),[#57432](https://github.com/PaddlePaddle/Paddle/pull/57432),[#57568](https://github.com/PaddlePaddle/Paddle/pull/57568),[#56553](https://github.com/PaddlePaddle/Paddle/pull/56553),[#58284](https://github.com/PaddlePaddle/Paddle/pull/58284),[#56039](https://github.com/PaddlePaddle/Paddle/pull/56039),[#55552](https://github.com/PaddlePaddle/Paddle/pull/55552),[#56149](https://github.com/PaddlePaddle/Paddle/pull/56149) - -#### 静态图半自动并行能力增强 - -- 新增 Sequence Parallel 并行策略；流水线并行新增: FThenB、Interleaved 1F1B、Eager 1F1B、VPP 等调度模式，支持流水线调度的可视化，并支持上述策略与原有并行策略的混合并行；升级梯度同步机制，支持数据在任意 broadcast 维度后需要的梯度同步。[#57605](https://github.com/PaddlePaddle/Paddle/pull/57605),[#54727](https://github.com/PaddlePaddle/Paddle/pull/54727),[#54409](https://github.com/PaddlePaddle/Paddle/pull/54409),[#54787](https://github.com/PaddlePaddle/Paddle/pull/54787),[#58313](https://github.com/PaddlePaddle/Paddle/pull/58313),[#59179](https://github.com/PaddlePaddle/Paddle/pull/59179),[#59416](https://github.com/PaddlePaddle/Paddle/pull/59416),[#59719](https://github.com/PaddlePaddle/Paddle/pull/59719),[#59822](https://github.com/PaddlePaddle/Paddle/pull/59822),[#59057](https://github.com/PaddlePaddle/Paddle/pull/59057),[#59522](https://github.com/PaddlePaddle/Paddle/pull/59522),[#57061](https://github.com/PaddlePaddle/Paddle/pull/57061) -- 执行体系与 PIR 进一步适配，打通 PIR 的优化 Pass，分布式场景下支持了 fuse_linear fuse 优化，实现性能提升。[#58459](https://github.com/PaddlePaddle/Paddle/pull/58459),[#58528](https://github.com/PaddlePaddle/Paddle/pull/58528),[#55555](https://github.com/PaddlePaddle/Paddle/pull/55555),[#59757](https://github.com/PaddlePaddle/Paddle/pull/59757),[#59102](https://github.com/PaddlePaddle/Paddle/pull/59102),[#57917](https://github.com/PaddlePaddle/Paddle/pull/57917) -- 底层架构升级: 执行器升级支持图依赖信息复用和静态化 kernel 选择；整图切分补全机制升级，切换新切分推导规则并支持更多长尾 cases 的正确切分补全；优化了静态图分布式下对控制流的支持，适配更多场景；优化了整图编译速度、日志信息格式等提升用户体验。 [#55389](https://github.com/PaddlePaddle/Paddle/pull/55389),[#55650](https://github.com/PaddlePaddle/Paddle/pull/55650),[#54938](https://github.com/PaddlePaddle/Paddle/pull/54938),[#57447](https://github.com/PaddlePaddle/Paddle/pull/57447),[#57751](https://github.com/PaddlePaddle/Paddle/pull/57751),[#57742](https://github.com/PaddlePaddle/Paddle/pull/57742),[#59524](https://github.com/PaddlePaddle/Paddle/pull/59524),[#59526](https://github.com/PaddlePaddle/Paddle/pull/59526),[#58669](https://github.com/PaddlePaddle/Paddle/pull/58669),[#57616](https://github.com/PaddlePaddle/Paddle/pull/57616),[#56511](https://github.com/PaddlePaddle/Paddle/pull/56511),[#55727](https://github.com/PaddlePaddle/Paddle/pull/55727),[#58906](https://github.com/PaddlePaddle/Paddle/pull/58906),[#56016](https://github.com/PaddlePaddle/Paddle/pull/56016),[#54897](https://github.com/PaddlePaddle/Paddle/pull/54897) -- 优化静态图显存管理，新增精细化重计算策略；优化混合精度适配，支持用户手动指定 cast 范围等场景；支持 Cross Entropy 的并行计算；支持 scaled_dot_product_attention、fuse_rope 等融合算子；执行调度优化，支持张量并行、流水线并行中通信计算间更好地 Overlap。[#58421](https://github.com/PaddlePaddle/Paddle/pull/58421),[#58533](https://github.com/PaddlePaddle/Paddle/pull/58533),[#59498](https://github.com/PaddlePaddle/Paddle/pull/59498),[#59498](https://github.com/PaddlePaddle/Paddle/pull/59498),[#59187](https://github.com/PaddlePaddle/Paddle/pull/59187),[#59188](https://github.com/PaddlePaddle/Paddle/pull/59188),[#58172](https://github.com/PaddlePaddle/Paddle/pull/58172),[#58628](https://github.com/PaddlePaddle/Paddle/pull/58628),[#56185](https://github.com/PaddlePaddle/Paddle/pull/56185),[#56696](https://github.com/PaddlePaddle/Paddle/pull/56696),[#59497](https://github.com/PaddlePaddle/Paddle/pull/59497),[#58304](https://github.com/PaddlePaddle/Paddle/pull/58304),[#58977](https://github.com/PaddlePaddle/Paddle/pull/58977) - -#### AutoTuner - -本版本实现基于 Profiling 的并行策略自动搜索和调优工具 AutoTuner，能够在给定模型和硬件资源的条件下，自动将并行策略和优化策略进行组合，并选取有效的组合配置运行实验，从而搜索出大模型训练和推理的最佳配置。此外，AutoTuner 实现了多种剪枝优化策略，包括显存建模等，能够大幅度减少搜索空间和搜索时间。[#54460](https://github.com/PaddlePaddle/Paddle/pull/54460),[#54668](https://github.com/PaddlePaddle/Paddle/pull/54668),[#59794](https://github.com/PaddlePaddle/Paddle/pull/59794),[#59727](https://github.com/PaddlePaddle/Paddle/pull/59727),[#59782](https://github.com/PaddlePaddle/Paddle/pull/59782),[#54834](https://github.com/PaddlePaddle/Paddle/pull/54834),[#58127](https://github.com/PaddlePaddle/Paddle/pull/58127),[#56968](https://github.com/PaddlePaddle/Paddle/pull/56968),[#55466](https://github.com/PaddlePaddle/Paddle/pull/55466),[#56939](https://github.com/PaddlePaddle/Paddle/pull/56939),[#58183](https://github.com/PaddlePaddle/Paddle/pull/58183),[#58314](https://github.com/PaddlePaddle/Paddle/pull/58314),[#55499](https://github.com/PaddlePaddle/Paddle/pull/55499),[#59748](https://github.com/PaddlePaddle/Paddle/pull/59748) - -### 算子库 - -#### 不兼容升级 - -为了提升飞桨框架的可维护性，删除框架中部分废弃的算子(如 diag_v1, isfinite_v1, pad2d_v1 等)，通过飞桨 1.x 版本训练所保存的使用到这些算子的模型将无法在飞桨新版本上进行推理。[#57895](https://github.com/PaddlePaddle/Paddle/pull/57895),[#57892](https://github.com/PaddlePaddle/Paddle/pull/57892),[#57898](https://github.com/PaddlePaddle/Paddle/pull/57898),[#57730](https://github.com/PaddlePaddle/Paddle/pull/57730),[#57732](https://github.com/PaddlePaddle/Paddle/pull/57732),[#57810](https://github.com/PaddlePaddle/Paddle/pull/57810),[#57884](https://github.com/PaddlePaddle/Paddle/pull/57884),[#57794](https://github.com/PaddlePaddle/Paddle/pull/57794),[#57926](https://github.com/PaddlePaddle/Paddle/pull/57926),[#57925](https://github.com/PaddlePaddle/Paddle/pull/57925),[#57807](https://github.com/PaddlePaddle/Paddle/pull/57807),[#57808](https://github.com/PaddlePaddle/Paddle/pull/57808) - -#### 算子库功能增强 - -- 飞桨 PHI 算子库复数计算功能进一步增强，累计新增支持复数计算 Kernel 40+。[#55380](https://github.com/PaddlePaddle/Paddle/pull/55380), [#56349](https://github.com/PaddlePaddle/Paddle/pull/56349), [#56412](https://github.com/PaddlePaddle/Paddle/pull/56412), [#56323](https://github.com/PaddlePaddle/Paddle/pull/56323), [#56723](https://github.com/PaddlePaddle/Paddle/pull/56723), [#56457](https://github.com/PaddlePaddle/Paddle/pull/56457), [#56903](https://github.com/PaddlePaddle/Paddle/pull/56903)[#56914](https://github.com/PaddlePaddle/Paddle/pull/56914), [#57116](https://github.com/PaddlePaddle/Paddle/pull/57116), [#56048](https://github.com/PaddlePaddle/Paddle/pull/56048), [#57244](https://github.com/PaddlePaddle/Paddle/pull/57244), [#57639](https://github.com/PaddlePaddle/Paddle/pull/57639), [#57638](https://github.com/PaddlePaddle/Paddle/pull/57638), [#57540](https://github.com/PaddlePaddle/Paddle/pull/57540), [#58545](https://github.com/PaddlePaddle/Paddle/pull/58545), [#58336](https://github.com/PaddlePaddle/Paddle/pull/58336), [#58532](https://github.com/PaddlePaddle/Paddle/pull/58532), [#58839](https://github.com/PaddlePaddle/Paddle/pull/58839), [#59079](https://github.com/PaddlePaddle/Paddle/pull/59079), [#59277](https://github.com/PaddlePaddle/Paddle/pull/59277), [#59122](https://github.com/PaddlePaddle/Paddle/pull/59122), [#57058](https://github.com/PaddlePaddle/Paddle/pull/57058) - -- 优化和新增部分算子的 XPU Kernel，并增强了 XPU Kernel 对 bfloat16 等数据类型的运算支持。[#54478](https://github.com/PaddlePaddle/Paddle/pull/54478), [#57740](https://github.com/PaddlePaddle/Paddle/pull/57740), [#58346](https://github.com/PaddlePaddle/Paddle/pull/58346), [#58456](https://github.com/PaddlePaddle/Paddle/pull/58456), [#58662](https://github.com/PaddlePaddle/Paddle/pull/58662), [#59066](https://github.com/PaddlePaddle/Paddle/pull/59066), [#59263](https://github.com/PaddlePaddle/Paddle/pull/59263)), [#59375](https://github.com/PaddlePaddle/Paddle/pull/59375), [#59505](https://github.com/PaddlePaddle/Paddle/pull/59505), [#59653](https://github.com/PaddlePaddle/Paddle/pull/59653), [#55001](https://github.com/PaddlePaddle/Paddle/pull/55001), [#57272](https://github.com/PaddlePaddle/Paddle/pull/57272), [#56169](https://github.com/PaddlePaddle/Paddle/pull/56169), [#59454](https://github.com/PaddlePaddle/Paddle/pull/59454), [#59480](https://github.com/PaddlePaddle/Paddle/pull/59480), [#55914](https://github.com/PaddlePaddle/Paddle/pull/55914), [#54758](https://github.com/PaddlePaddle/Paddle/pull/54758), [#54827](https://github.com/PaddlePaddle/Paddle/pull/54827), [#58364](https://github.com/PaddlePaddle/Paddle/pull/58364), [#58419](https://github.com/PaddlePaddle/Paddle/pull/58419), [#58982](https://github.com/PaddlePaddle/Paddle/pull/58982), [#57216](https://github.com/PaddlePaddle/Paddle/pull/57216), [#59166](https://github.com/PaddlePaddle/Paddle/pull/59166), [#55033](https://github.com/PaddlePaddle/Paddle/pull/55033), [#55375](https://github.com/PaddlePaddle/Paddle/pull/55375), [#58805](https://github.com/PaddlePaddle/Paddle/pull/58805), [#59389](https://github.com/PaddlePaddle/Paddle/pull/59389), [#57077](https://github.com/PaddlePaddle/Paddle/pull/57077), [#55166](https://github.com/PaddlePaddle/Paddle/pull/55166), [#56773](https://github.com/PaddlePaddle/Paddle/pull/56773) - -- 新增了用于优化大模型训练和推理性能的常见算子。[#55758](https://github.com/PaddlePaddle/Paddle/pull/55758), [#54998](https://github.com/PaddlePaddle/Paddle/pull/54998), [#55400](https://github.com/PaddlePaddle/Paddle/pull/55400), [#54630](https://github.com/PaddlePaddle/Paddle/pull/54630), [#55969](https://github.com/PaddlePaddle/Paddle/pull/55969), [#55026](https://github.com/PaddlePaddle/Paddle/pull/55026), [#58986](https://github.com/PaddlePaddle/Paddle/pull/58986) - -- 完善算子库 Tensor Strided 机制。[#59422](https://github.com/PaddlePaddle/Paddle/pull/59422), [#59325](https://github.com/PaddlePaddle/Paddle/pull/59325), [#56863](https://github.com/PaddlePaddle/Paddle/pull/56863), [#56882](https://github.com/PaddlePaddle/Paddle/pull/56882), [#56947](https://github.com/PaddlePaddle/Paddle/pull/56947) - -- 对算子 Kernel 中的函数实现以及模板调用接口进行了编译优化，降低算子库编包体积。[#57083](https://github.com/PaddlePaddle/Paddle/pull/57083), [#57299](https://github.com/PaddlePaddle/Paddle/pull/57299), [#57261](https://github.com/PaddlePaddle/Paddle/pull/57261), [#57290](https://github.com/PaddlePaddle/Paddle/pull/57290), [#57118](https://github.com/PaddlePaddle/Paddle/pull/57118), [#57551](https://github.com/PaddlePaddle/Paddle/pull/57551), [#57509](https://github.com/PaddlePaddle/Paddle/pull/57509), [#57558](https://github.com/PaddlePaddle/Paddle/pull/57558), [#57064](https://github.com/PaddlePaddle/Paddle/pull/57064), [#57365](https://github.com/PaddlePaddle/Paddle/pull/57365), [#57327](https://github.com/PaddlePaddle/Paddle/pull/57327), [#57603](https://github.com/PaddlePaddle/Paddle/pull/57603), [#57671](https://github.com/PaddlePaddle/Paddle/pull/57671), [#57672](https://github.com/PaddlePaddle/Paddle/pull/57672), [#57631](https://github.com/PaddlePaddle/Paddle/pull/57631), [#57082](https://github.com/PaddlePaddle/Paddle/pull/57082), [#57721](https://github.com/PaddlePaddle/Paddle/pull/57721), [#57823](https://github.com/PaddlePaddle/Paddle/pull/57823), [#57821](https://github.com/PaddlePaddle/Paddle/pull/57821), [#57815](https://github.com/PaddlePaddle/Paddle/pull/57815), [#57822](https://github.com/PaddlePaddle/Paddle/pull/57822), [#57541](https://github.com/PaddlePaddle/Paddle/pull/57541), [#57817](https://github.com/PaddlePaddle/Paddle/pull/57817), [#57838](https://github.com/PaddlePaddle/Paddle/pull/57838) +- 分布式通信策略 Sharing 在 Broadcast 参数采用异步策略，提升计算和通信的 overlap。 [#59745](https://github.com/PaddlePaddle/Paddle/pull/59745) +- 新增 STRIDED Layout 算子支持，提升算子性能。[#62532](https://github.com/PaddlePaddle/Paddle/pull/62532),[#62697](https://github.com/PaddlePaddle/Paddle/pull/62697),[#62649](https://github.com/PaddlePaddle/Paddle/pull/62649) +- 优化 elementwise_mul 算子内存使用。[#62377](https://github.com/PaddlePaddle/Paddle/pull/62377) #### Bug 修复 - -- 修复了飞桨框架适配 CUDA 12 的一些问题。[#54640](https://github.com/PaddlePaddle/Paddle/pull/54640), [#57820](https://github.com/PaddlePaddle/Paddle/pull/57820), [#58958](https://github.com/PaddlePaddle/Paddle/pull/58958), [#58179](https://github.com/PaddlePaddle/Paddle/pull/58179), [#55594](https://github.com/PaddlePaddle/Paddle/pull/55594) - -### CUDA - -#### 新功能 - -- 新增调试类 API paddle.amp.debugging.check_check_numerics，计算并返回这个 Tensor 数值中异常值(NaN、Inf)和零元素的数量。[#54301](https://github.com/PaddlePaddle/Paddle/pull/54301) -- 新增 fused_rope 融合算子，加速 LLaMA 类大模型训练。[#54351](https://github.com/PaddlePaddle/Paddle/pull/54351) -- 更新 CUDNN Frontend API 版本到 v0.9.1，并新增加速 ResNet 网络的 fused_scale_bias_add_relu 融合算子。注意该功能处于实验期，默认不开启。[#58367](https://github.com/PaddlePaddle/Paddle/pull/58367), [#54949](https://github.com/PaddlePaddle/Paddle/pull/54949), [#58504](https://github.com/PaddlePaddle/Paddle/pull/58504) -- 基于 Flash-Attention v2，添加 Tensor 类似 Mask 功能支持，反向算子支持确定性计算，便于调试。[#57276](https://github.com/PaddlePaddle/Paddle/pull/57276), [#56363](https://github.com/PaddlePaddle/Paddle/pull/56363) -- 修改稀疏 conv3d 后端实现以支持 2d 形状，避免前端 reshape 的开销。[#54707](https://github.com/PaddlePaddle/Paddle/pull/54707) -- 新增 matmul_int8 算子。([#55228](https://github.com/PaddlePaddle/Paddle/pull/55228)) - -#### 功能优化 - -- 优化 CUDA Graph 对随机数算子的支持。[#58310](https://github.com/PaddlePaddle/Paddle/pull/58310) -- 自动混合精度训练默认功能加强，包括： - - 优化自动混合精度训练接口的使用体验。[#58152](https://github.com/PaddlePaddle/Paddle/pull/58152),[#55364](https://github.com/PaddlePaddle/Paddle/pull/55364),[#57903](https://github.com/PaddlePaddle/Paddle/pull/57903) - - 将 fused_attention、fused_feedforward、fused_gemm_epilogue 等矩阵计算类算子加入框架默认的白名单，并统一动静态图默认黑白名单设置。[#55373](https://github.com/PaddlePaddle/Paddle/pull/55373), [#55713](https://github.com/PaddlePaddle/Paddle/pull/55713) - - argsort、dist、erfinv、nanmedian、poisson 算子和 lamb 优化器算子支持 FP16、BF16 低精度计算。[#51662](https://github.com/PaddlePaddle/Paddle/pull/51662), [#55105](https://github.com/PaddlePaddle/Paddle/pull/55105), [#55287](https://github.com/PaddlePaddle/Paddle/pull/55287), [#55824](https://github.com/PaddlePaddle/Paddle/pull/55824), [#56056](https://github.com/PaddlePaddle/Paddle/pull/56056), [#56184](https://github.com/PaddlePaddle/Paddle/pull/56184), [#55641](https://github.com/PaddlePaddle/Paddle/pull/55641) - - 修复 elementwise_max 算子低精度实现，改成使用 FP32 类型进行数值计算，减少精度损失。[#54799](https://github.com/PaddlePaddle/Paddle/pull/54799) - - 将 Reduce 类算子计算需要的临时结果 Tensor 改成 FP32 类型，避免将中间结果转换成低精度带来的精度损失。[#55709](https://github.com/PaddlePaddle/Paddle/pull/55709)) -- flip、roll & roll_grad、index_put & index_put_grad 等算子 GPU 代码实现优化，在性能不下降的前提下移除不必要的 C++模板，优化算子编译耗时并减少编译生成的二进制体积。[#57309](https://github.com/PaddlePaddle/Paddle/pull/57309), [#57525](https://github.com/PaddlePaddle/Paddle/pull/57525) -- bernoulli 算子增加对输入概率合法性的检查。[#59174](https://github.com/PaddlePaddle/Paddle/pull/59174) - -#### 性能优化 - -- 优化 BroadcastKernel 对大 Tensor 的支持，改成对大 Tensor 切片多次调用 INT32 版本实现的方式，算子性能提升 7.27x。[#57313](https://github.com/PaddlePaddle/Paddle/pull/57313), [#57996](https://github.com/PaddlePaddle/Paddle/pull/57996) -- 优化 Tensor 保存接口的性能，通过先将 Tensor 拷贝到 CPU 再转 numpy，避免 Tensor 不连续时自动转换成连续 Tensor 的开销。[#57040](https://github.com/PaddlePaddle/Paddle/pull/57040) - -#### Bug Fix - -- 修复 memmory_efficient_attention 算子对 sm_90 的支持。[#58070](https://github.com/PaddlePaddle/Paddle/pull/58070) -- 修复 softmax 算子，当 axis=-1 且长度大于 100000 的实现出现的 NaN 问题。[#57851](https://github.com/PaddlePaddle/Paddle/pull/57851) -- 修复 set_constant 算子在一些情况下出现 GPU 访存错误问题。[#59905](https://github.com/PaddlePaddle/Paddle/pull/59905) -- 修复 layer_norm 算子快速实现版本中出现的 GPU 存储读写竞争问题。[#56435](https://github.com/PaddlePaddle/Paddle/pull/56435) - -### 拓展神经网络编译器 CINN 架构能力 - -在本次更新中，飞桨神经网络编译器 CINN 的重点在于架构的梳理和能力的全面扩展。鉴于大模型对动态 Shape 的需求日益增长，初步探索并实现了在动态 shape 下编译器的有效运行和优化策略。 -在架构层面，引入了 Python DSL，这一举措显著提升了 CINN 的开发便捷性和 Debug 能力，使得开发者能够更高效地编写和调试代码。同时，对 Schedule 的逻辑进行了重构，以 GroupSchedule 为主导，从而在算子 Group 层面实现更加通用且稳定的优化策略。为了增强 CINN 的稳定性，探索并引入了强约束组件，这一组件能够有效减少系统中的不确定性和潜在错误。此外，对 CINN 的历史工具类和软件结构进行了系统性的整理、优化和改进，进一步提升了代码的可读性和可维护性。在与飞桨其他组件的整合方面，进一步加强了 CINN 与 PIR、Paddle 的紧密结合，使得编译器与飞桨整体框架更加协调一致。这一改进不仅提升了编译器的性能，还为开发者提供了更加流畅和统一的开发体验。 - -#### 兼容性升级 - -- 更新存储读取接口至兼容 Paddle 2.0。 [#55836](https://github.com/PaddlePaddle/Paddle/pull/55836) -- 更新 relu6 Op Mapper 的兼容性。 [#55611](https://github.com/PaddlePaddle/Paddle/pull/55611) - -#### 改造废弃 - -- 删除旧的 Schedule 形式。 [#55566](https://github.com/PaddlePaddle/Paddle/pull/55566),[#55391](https://github.com/PaddlePaddle/Paddle/pull/55391) -- 删除一些过时测试。 [#56245](https://github.com/PaddlePaddle/Paddle/pull/56245),[#57987](https://github.com/PaddlePaddle/Paddle/pull/57987) -- 删除不再适用的 remove_nested_block Visitor 工具。 [#56972](https://github.com/PaddlePaddle/Paddle/pull/56972) -- 删除其他无用代码。 [#55413](https://github.com/PaddlePaddle/Paddle/pull/55413) - -#### 新功能 - -- 增加飞桨端 CINN paddle.framework.core.is_run_with_cinn()运行接口。 [#54355](https://github.com/PaddlePaddle/Paddle/pull/54355) -- 增加 CINN 相关算子逻辑，包括各种组合算子拆解逻辑。 [#56072](https://github.com/PaddlePaddle/Paddle/pull/56072),[#58210](https://github.com/PaddlePaddle/Paddle/pull/58210),[#58502](https://github.com/PaddlePaddle/Paddle/pull/58502), [#58591](https://github.com/PaddlePaddle/Paddle/pull/58591), [#58981](https://github.com/PaddlePaddle/Paddle/pull/58981), [#59135](https://github.com/PaddlePaddle/Paddle/pull/59135), [#59274](https://github.com/PaddlePaddle/Paddle/pull/59274), [#59306](https://github.com/PaddlePaddle/Paddle/pull/59306), [#59202](https://github.com/PaddlePaddle/Paddle/pull/59202), [#59176](https://github.com/PaddlePaddle/Paddle/pull/59176), [#59534](https://github.com/PaddlePaddle/Paddle/pull/59534), [#59713](https://github.com/PaddlePaddle/Paddle/pull/59713), [#59798](https://github.com/PaddlePaddle/Paddle/pull/59798)；支持 bf16、amp 等形式[#54399](https://github.com/PaddlePaddle/Paddle/pull/54399), [#54368](https://github.com/PaddlePaddle/Paddle/pull/54368), [#54608](https://github.com/PaddlePaddle/Paddle/pull/54608)；支持算子零维能力[#54892](https://github.com/PaddlePaddle/Paddle/pull/54892), [#54919](https://github.com/PaddlePaddle/Paddle/pull/54919), [#54907](https://github.com/PaddlePaddle/Paddle/pull/54907), [#54966](https://github.com/PaddlePaddle/Paddle/pull/54966) -- CINN 和飞桨 PIR、组合算子交界运行方式，使新增 PIR 和 CINN 运行浑然一体。 [#54732](https://github.com/PaddlePaddle/Paddle/pull/54732), [#56074](https://github.com/PaddlePaddle/Paddle/pull/56074), [#58216](https://github.com/PaddlePaddle/Paddle/pull/58216), [#55680](https://github.com/PaddlePaddle/Paddle/pull/55680), [#56302](https://github.com/PaddlePaddle/Paddle/pull/56302), [#59037](https://github.com/PaddlePaddle/Paddle/pull/59037), [#55186](https://github.com/PaddlePaddle/Paddle/pull/55186), [#58641](https://github.com/PaddlePaddle/Paddle/pull/58641) -- 对 CINN 变化起到稳定作用的强约束组件。 [#58719](https://github.com/PaddlePaddle/Paddle/pull/58719), [#59309](https://github.com/PaddlePaddle/Paddle/pull/59309), [#58993](https://github.com/PaddlePaddle/Paddle/pull/58993) -- Group Schedule 相关的 CINN 架构流程添加。 [#58399](https://github.com/PaddlePaddle/Paddle/pull/58399), [#56444](https://github.com/PaddlePaddle/Paddle/pull/56444) -- CINN 架构功能初步增加 CUTLASS、报错处理、NVRTC Cubin Fmad 选项。 [#58079](https://github.com/PaddlePaddle/Paddle/pull/58079), [#57198](https://github.com/PaddlePaddle/Paddle/pull/57198), [#58794](https://github.com/PaddlePaddle/Paddle/pull/58794) -- CINN 增加 Python 界面语言。 [#57731](https://github.com/PaddlePaddle/Paddle/pull/57731), [#57515](https://github.com/PaddlePaddle/Paddle/pull/57515), [#57644](https://github.com/PaddlePaddle/Paddle/pull/57644), [#57981](https://github.com/PaddlePaddle/Paddle/pull/57981), [#58009](https://github.com/PaddlePaddle/Paddle/pull/58009) -- CINN 增加动态 Shape 功能，涵盖 ASTGen 可以代替 ISL 产生动态 Shape 符号 [#56360](https://github.com/PaddlePaddle/Paddle/pull/56360), [#57207](https://github.com/PaddlePaddle/Paddle/pull/57207), [#57454](https://github.com/PaddlePaddle/Paddle/pull/57454)；增加分桶条件编译功能 [#59165](https://github.com/PaddlePaddle/Paddle/pull/59165)；增加 Schedule、Device、IR 层面支持动态 shape 的功能 [#58988](https://github.com/PaddlePaddle/Paddle/pull/58988), [#59493](https://github.com/PaddlePaddle/Paddle/pull/59493), [#58717](https://github.com/PaddlePaddle/Paddle/pull/58717), [#58602](https://github.com/PaddlePaddle/Paddle/pull/58602), [#59196](https://github.com/PaddlePaddle/Paddle/pull/59196) -- CINN Group Schedule 算子 Group 层面做更通用稳定的 Schedule 优化。 [#56122](https://github.com/PaddlePaddle/Paddle/pull/56122), [#57777](https://github.com/PaddlePaddle/Paddle/pull/57777), [#57569](https://github.com/PaddlePaddle/Paddle/pull/57569) - -#### 功能优化 - -- 丰富或改善算子功能，包括修理反向、FP16、Infershape、算子单测等各种算子过程的改善。 [#56320](https://github.com/PaddlePaddle/Paddle/pull/56320), [#56845](https://github.com/PaddlePaddle/Paddle/pull/56845), [#54939](https://github.com/PaddlePaddle/Paddle/pull/54939),[#54378](https://github.com/PaddlePaddle/Paddle/pull/54378),[#55321](https://github.com/PaddlePaddle/Paddle/pull/55321),[#55336](https://github.com/PaddlePaddle/Paddle/pull/55336),[#55337](https://github.com/PaddlePaddle/Paddle/pull/55337),[#55442](https://github.com/PaddlePaddle/Paddle/pull/55442),[#55470](https://github.com/PaddlePaddle/Paddle/pull/55470),[#55489](https://github.com/PaddlePaddle/Paddle/pull/55489),[#55510](https://github.com/PaddlePaddle/Paddle/pull/55510),[#55547](https://github.com/PaddlePaddle/Paddle/pull/55547),[#55505](https://github.com/PaddlePaddle/Paddle/pull/55505),[#55563](https://github.com/PaddlePaddle/Paddle/pull/55563),[#54280](https://github.com/PaddlePaddle/Paddle/pull/54280),[#59650](https://github.com/PaddlePaddle/Paddle/pull/59650),[#54862](https://github.com/PaddlePaddle/Paddle/pull/54862),[#55135](https://github.com/PaddlePaddle/Paddle/pull/55135),[#55292](https://github.com/PaddlePaddle/Paddle/pull/55292),[#55333](https://github.com/PaddlePaddle/Paddle/pull/55333),[#55316](https://github.com/PaddlePaddle/Paddle/pull/55316),[#55379](https://github.com/PaddlePaddle/Paddle/pull/55379),[#55326](https://github.com/PaddlePaddle/Paddle/pull/55326) -- CINN、飞桨、PIR、组合算子交界运行方式改善，主要包括各种和 PIR 及其执行器接口和 CINN 互相支持。 [#59170](https://github.com/PaddlePaddle/Paddle/pull/59170),[#58766](https://github.com/PaddlePaddle/Paddle/pull/58766),[#59255](https://github.com/PaddlePaddle/Paddle/pull/59255),[#59203](https://github.com/PaddlePaddle/Paddle/pull/59203),[#59024](https://github.com/PaddlePaddle/Paddle/pull/59024),[#57829](https://github.com/PaddlePaddle/Paddle/pull/57829),[#58135](https://github.com/PaddlePaddle/Paddle/pull/58135),[#58193](https://github.com/PaddlePaddle/Paddle/pull/58193),[#58207](https://github.com/PaddlePaddle/Paddle/pull/58207),[#58606](https://github.com/PaddlePaddle/Paddle/pull/58606),[#59437](https://github.com/PaddlePaddle/Paddle/pull/59437),[#59759](https://github.com/PaddlePaddle/Paddle/pull/59759),[#55075](https://github.com/PaddlePaddle/Paddle/pull/55075),[#56805](https://github.com/PaddlePaddle/Paddle/pull/56805),[#57764](https://github.com/PaddlePaddle/Paddle/pull/57764),[#58620](https://github.com/PaddlePaddle/Paddle/pull/58620),[#59769](https://github.com/PaddlePaddle/Paddle/pull/59769),[#58702](https://github.com/PaddlePaddle/Paddle/pull/58702),[#58749](https://github.com/PaddlePaddle/Paddle/pull/58749),[#59025](https://github.com/PaddlePaddle/Paddle/pull/59025),[#58820](https://github.com/PaddlePaddle/Paddle/pull/58820),[#58908](https://github.com/PaddlePaddle/Paddle/pull/58908),[#58169](https://github.com/PaddlePaddle/Paddle/pull/58169) -- 对 CINN 改善稳定作用的强约束组件。 [#55090](https://github.com/PaddlePaddle/Paddle/pull/55090),[#55705](https://github.com/PaddlePaddle/Paddle/pull/55705),[#57587](https://github.com/PaddlePaddle/Paddle/pull/57587),[#59501](https://github.com/PaddlePaddle/Paddle/pull/59501) -- CINN IR 和相关工具代码改善。 [#55145](https://github.com/PaddlePaddle/Paddle/pull/55145),[#55955](https://github.com/PaddlePaddle/Paddle/pull/55955),[#56307](https://github.com/PaddlePaddle/Paddle/pull/56307),[#55519](https://github.com/PaddlePaddle/Paddle/pull/55519),[#56958](https://github.com/PaddlePaddle/Paddle/pull/56958),[#57019](https://github.com/PaddlePaddle/Paddle/pull/57019),[#57230](https://github.com/PaddlePaddle/Paddle/pull/57230),[#57531](https://github.com/PaddlePaddle/Paddle/pull/57531),[#57532](https://github.com/PaddlePaddle/Paddle/pull/57532),[#57524](https://github.com/PaddlePaddle/Paddle/pull/57524),[#58770](https://github.com/PaddlePaddle/Paddle/pull/58770),[#59337](https://github.com/PaddlePaddle/Paddle/pull/59337),[#59096](https://github.com/PaddlePaddle/Paddle/pull/59096),[#56274](https://github.com/PaddlePaddle/Paddle/pull/56274),[#56350](https://github.com/PaddlePaddle/Paddle/pull/56350),[#57312](https://github.com/PaddlePaddle/Paddle/pull/57312),[#55171](https://github.com/PaddlePaddle/Paddle/pull/55171) -- CINN Group Schedule 算子 Group 层面做更通用稳定的 Schedule 优化。 [#54982](https://github.com/PaddlePaddle/Paddle/pull/54982),[#57963](https://github.com/PaddlePaddle/Paddle/pull/57963),[#58220](https://github.com/PaddlePaddle/Paddle/pull/58220),[#55484](https://github.com/PaddlePaddle/Paddle/pull/55484),[#55935](https://github.com/PaddlePaddle/Paddle/pull/55935),[#55590](https://github.com/PaddlePaddle/Paddle/pull/55590),[#56530](https://github.com/PaddlePaddle/Paddle/pull/56530),[#58344](https://github.com/PaddlePaddle/Paddle/pull/58344),[#59810](https://github.com/PaddlePaddle/Paddle/pull/59810) -- CINN 架构功能改善，包括并行编译、低层存储分配方式、打印信息、Group 结构、Pass 结构等。[#56282](https://github.com/PaddlePaddle/Paddle/pull/56282), [#59014](https://github.com/PaddlePaddle/Paddle/pull/59014),[#59209](https://github.com/PaddlePaddle/Paddle/pull/59209),[#52660](https://github.com/PaddlePaddle/Paddle/pull/52660),[#54749](https://github.com/PaddlePaddle/Paddle/pull/54749),[#58694](https://github.com/PaddlePaddle/Paddle/pull/58694),[#58940](https://github.com/PaddlePaddle/Paddle/pull/58940),[#59504](https://github.com/PaddlePaddle/Paddle/pull/59504),[#56123](https://github.com/PaddlePaddle/Paddle/pull/56123) -- CINN 改善 codegen、jit instruction、dim args、host kernel 等以支持动态 Shape 功能。 [#58825](https://github.com/PaddlePaddle/Paddle/pull/58825),[#59395](https://github.com/PaddlePaddle/Paddle/pull/59395),[#59398](https://github.com/PaddlePaddle/Paddle/pull/59398),[#59540](https://github.com/PaddlePaddle/Paddle/pull/59540),[#59470](https://github.com/PaddlePaddle/Paddle/pull/59470),[#59640](https://github.com/PaddlePaddle/Paddle/pull/59640) -- CINN 报错优化。 [#54983](https://github.com/PaddlePaddle/Paddle/pull/54983),[#55544](https://github.com/PaddlePaddle/Paddle/pull/55544) -- CINN 其他代码清理改善、包括 CI、文件路径、C++17、Flags、第三方库、Docker 等 [#55018](https://github.com/PaddlePaddle/Paddle/pull/55018),[#55121](https://github.com/PaddlePaddle/Paddle/pull/55121),[#55009](https://github.com/PaddlePaddle/Paddle/pull/55009),[#55888](https://github.com/PaddlePaddle/Paddle/pull/55888),[#56168](https://github.com/PaddlePaddle/Paddle/pull/56168),[#56192](https://github.com/PaddlePaddle/Paddle/pull/56192),[#56896](https://github.com/PaddlePaddle/Paddle/pull/56896),[#53861](https://github.com/PaddlePaddle/Paddle/pull/53861),[#55208](https://github.com/PaddlePaddle/Paddle/pull/55208) - -#### 性能优化 - -- 对 vit attention 进行融合。 [#54139](https://github.com/PaddlePaddle/Paddle/pull/54139) -- 优化 block reduce。 [#58196](https://github.com/PaddlePaddle/Paddle/pull/58196) - -#### bug 修复 - -- 算子相关 bug 修复。 [#56280](https://github.com/PaddlePaddle/Paddle/pull/56280),[#57767](https://github.com/PaddlePaddle/Paddle/pull/57767),[#58406](https://github.com/PaddlePaddle/Paddle/pull/58406),[#54406](https://github.com/PaddlePaddle/Paddle/pull/54406),[#54494](https://github.com/PaddlePaddle/Paddle/pull/54494),[#54751](https://github.com/PaddlePaddle/Paddle/pull/54751),[#55674](https://github.com/PaddlePaddle/Paddle/pull/55674),[#55684](https://github.com/PaddlePaddle/Paddle/pull/55684),[#55683](https://github.com/PaddlePaddle/Paddle/pull/55683),[#57798](https://github.com/PaddlePaddle/Paddle/pull/57798),[#57816](https://github.com/PaddlePaddle/Paddle/pull/57816),[#57687](https://github.com/PaddlePaddle/Paddle/pull/57687),[#56719](https://github.com/PaddlePaddle/Paddle/pull/56719),[#59756](https://github.com/PaddlePaddle/Paddle/pull/59756),[#59770](https://github.com/PaddlePaddle/Paddle/pull/59770),[#58811](https://github.com/PaddlePaddle/Paddle/pull/58811) -- 流程架构相关 bug 修复。 [#54899](https://github.com/PaddlePaddle/Paddle/pull/54899),[#59737](https://github.com/PaddlePaddle/Paddle/pull/59737),[#59356](https://github.com/PaddlePaddle/Paddle/pull/59356),[#56105](https://github.com/PaddlePaddle/Paddle/pull/56105),[#56662](https://github.com/PaddlePaddle/Paddle/pull/56662),[#58146](https://github.com/PaddlePaddle/Paddle/pull/58146),[#58910](https://github.com/PaddlePaddle/Paddle/pull/58910),[#58121](https://github.com/PaddlePaddle/Paddle/pull/58121),[#58943](https://github.com/PaddlePaddle/Paddle/pull/58943),[#58886](https://github.com/PaddlePaddle/Paddle/pull/58886),[#59642](https://github.com/PaddlePaddle/Paddle/pull/59642),[#56164](https://github.com/PaddlePaddle/Paddle/pull/56164),[#56338](https://github.com/PaddlePaddle/Paddle/pull/56338),[#56966](https://github.com/PaddlePaddle/Paddle/pull/56966),[#59112](https://github.com/PaddlePaddle/Paddle/pull/59112),[#55820](https://github.com/PaddlePaddle/Paddle/pull/55820),[#56660](https://github.com/PaddlePaddle/Paddle/pull/56660),[#57307](https://github.com/PaddlePaddle/Paddle/pull/57307),[#57530](https://github.com/PaddlePaddle/Paddle/pull/57530),[#58236](https://github.com/PaddlePaddle/Paddle/pull/58236),[#55190](https://github.com/PaddlePaddle/Paddle/pull/55190),[#55043](https://github.com/PaddlePaddle/Paddle/pull/55043),[#55667](https://github.com/PaddlePaddle/Paddle/pull/55667) -- 其他 bug 修复。 [#57239](https://github.com/PaddlePaddle/Paddle/pull/57239),[#55530](https://github.com/PaddlePaddle/Paddle/pull/55530),[#56605](https://github.com/PaddlePaddle/Paddle/pull/56605),[#58243](https://github.com/PaddlePaddle/Paddle/pull/58243),[#58197](https://github.com/PaddlePaddle/Paddle/pull/58197),[#58197](https://github.com/PaddlePaddle/Paddle/pull/58197),[#56086](https://github.com/PaddlePaddle/Paddle/pull/56086),[#56065](https://github.com/PaddlePaddle/Paddle/pull/56065),[#58775](https://github.com/PaddlePaddle/Paddle/pull/58775),[#54750](https://github.com/PaddlePaddle/Paddle/pull/54750),[#58595](https://github.com/PaddlePaddle/Paddle/pull/58595),[#58873](https://github.com/PaddlePaddle/Paddle/pull/58873) - -#### 文档 - -- 增加 README 文件。 [#58349](https://github.com/PaddlePaddle/Paddle/pull/58349) - -## 4. 部署方向（Paddle Inference） - -### 通用推理优化 - -本版本升级提升了推理引擎在 GPU 和 CPU 上性能和易用性，降低了用户使用成本和线上推理的应用成本。在 GPU 上支持了高性能的多线程异步执行器，各模型推理性能提升 5%~10%；同时支持新版本 TensorRT 和 BF16 推理能力，TensorRT 推理性能和易用性进一步提升；在 CPU 上，支持最新版本的 OneDNN 高性能推理，在 SwinTransformer、FastRCNN 等系列模型上性能大幅提升。 - -- matmul 支持 transpose、broadcast 操作。 [#56827](https://github.com/PaddlePaddle/Paddle/pull/56827) -- TruncatedNormal and Assign 支持 FP64 数据类型。[#57507](https://github.com/PaddlePaddle/Paddle/pull/57507) -- 支持 conv2d 显式量化推理。[#57160](https://github.com/PaddlePaddle/Paddle/pull/57160),[#58015](https://github.com/PaddlePaddle/Paddle/pull/58015) -- 新增 conv_fuse_pass，支持 conv + bn 融合，conv2d_fusion 融合重命名为 fused_conv2d_add_act。 [#58724](https://github.com/PaddlePaddle/Paddle/pull/58724),[#55374](https://github.com/PaddlePaddle/Paddle/pull/55374),[#54477](https://github.com/PaddlePaddle/Paddle/pull/54477),[#59431](https://github.com/PaddlePaddle/Paddle/pull/59431) -- 混合精度推理支持 OP 白名单。[#56535](https://github.com/PaddlePaddle/Paddle/pull/56535) -- 默认开启 OneDNN 优化，支持 SwinTransformer、FastRCNNd 等推理优化。[#58560](https://github.com/PaddlePaddle/Paddle/pull/58560),[#59394](https://github.com/PaddlePaddle/Paddle/pull/59394),[#59421](https://github.com/PaddlePaddle/Paddle/pull/59421),[#58435](https://github.com/PaddlePaddle/Paddle/pull/58435),[#58488](https://github.com/PaddlePaddle/Paddle/pull/58488),[#59259](https://github.com/PaddlePaddle/Paddle/pull/59259),[#56303](https://github.com/PaddlePaddle/Paddle/pull/56303),[#56782](https://github.com/PaddlePaddle/Paddle/pull/56782),[#57598](https://github.com/PaddlePaddle/Paddle/pull/57598),[#58361](https://github.com/PaddlePaddle/Paddle/pull/58361),[#59641](https://github.com/PaddlePaddle/Paddle/pull/59641),[#59527](https://github.com/PaddlePaddle/Paddle/pull/59527),[#59663](https://github.com/PaddlePaddle/Paddle/pull/59663),[#59744](https://github.com/PaddlePaddle/Paddle/pull/59744) -- 新增 share_data 支持传入指定数据。[#57933](https://github.com/PaddlePaddle/Paddle/pull/57933) - -### 大模型推理优化 - -实现了生成式大模型的细粒度融合推理优化，该优化方案既保证了高性能的推理能力，又具备良好的可拓展性。用户可以根据需要，灵活运用各种细粒度融合算子和飞桨原生算子，自由组合构建生成式大模型的网络结构，从而实现高效且低成本的推理。此外，我们的方案还支持主流的生成式大模型结构，显著降低了这类模型的推理部署成本，为生成式大模型的高效、低成本落地提供了有力支持。 - -- 支持 FMHA/MMHA 对 CacheKV 划分 block 调度。[#59462](https://github.com/PaddlePaddle/Paddle/pull/59462) -- RoPE 编码融合算子支持输入 sin/cos 值。[#55415](https://github.com/PaddlePaddle/Paddle/pull/55415) -- 新增细粒度融合算子支持生成式大模型高性能推理优化，新增 quant_linear、weight_quantize、linear_compress 等算子支持大模型量化推理。[#57852](https://github.com/PaddlePaddle/Paddle/pull/57852),[#55128](https://github.com/PaddlePaddle/Paddle/pull/55128),[#59090](https://github.com/PaddlePaddle/Paddle/pull/59090),[#56706](https://github.com/PaddlePaddle/Paddle/pull/56706),[#59951](https://github.com/PaddlePaddle/Paddle/pull/59951),[#55490](https://github.com/PaddlePaddle/Paddle/pull/55490),[#59291](https://github.com/PaddlePaddle/Paddle/pull/59291),[#59441](https://github.com/PaddlePaddle/Paddle/pull/59441),[#59778](https://github.com/PaddlePaddle/Paddle/pull/59778),[#59651](https://github.com/PaddlePaddle/Paddle/pull/59651)[#55301](https://github.com/PaddlePaddle/Paddle/pull/55301),[#58637](https://github.com/PaddlePaddle/Paddle/pull/58637),[#56673](https://github.com/PaddlePaddle/Paddle/pull/56673),[#56401](https://github.com/PaddlePaddle/Paddle/pull/56401) -- 支持变长推理系列 API。[#57948](https://github.com/PaddlePaddle/Paddle/pull/57948) -- 支持 GQA 推理。[#58472](https://github.com/PaddlePaddle/Paddle/pull/58472),[#58836](https://github.com/PaddlePaddle/Paddle/pull/58836) -- 新增 masked multihead attention 支持高性能 MMHA 推理。[#55344](https://github.com/PaddlePaddle/Paddle/pull/55344),[#56411](https://github.com/PaddlePaddle/Paddle/pull/56411),[#58134](https://github.com/PaddlePaddle/Paddle/pull/58134),[#57936](https://github.com/PaddlePaddle/Paddle/pull/57936) -- weight_quantize/weight_only_linear 支持 Volta 架构。[#58082](https://github.com/PaddlePaddle/Paddle/pull/58082) -- 新增 weight_only_linear_grad 支持大模型 weight only 量化梯度回传。[#57685](https://github.com/PaddlePaddle/Paddle/pull/57685) -- 修复大模型动转静问题，优化静态图卡间通信初始化逻辑。[#56390](https://github.com/PaddlePaddle/Paddle/pull/56390),[#57169](https://github.com/PaddlePaddle/Paddle/pull/57169),[#56688](https://github.com/PaddlePaddle/Paddle/pull/56688),[#56592](https://github.com/PaddlePaddle/Paddle/pull/56592),[#58868](https://github.com/PaddlePaddle/Paddle/pull/58868) -- 优化 top_p_sampling 随机数生成逻辑。[#59494](https://github.com/PaddlePaddle/Paddle/pull/59494) - -### Paddle-TensorRT 推理优化 - -- elementwise_add 融合支持 NHWC 格式。 [#56795](https://github.com/PaddlePaddle/Paddle/pull/56795) -- conv2d 支持 filter 作为输入。[#55246](https://github.com/PaddlePaddle/Paddle/pull/55246)。 -- 支持 BF16、FP64 推理。[#59765](https://github.com/PaddlePaddle/Paddle/pull/59765),[#55520](https://github.com/PaddlePaddle/Paddle/pull/55520) -- 新增 MarkTrtEngineOutputs API 支持指定 TensorRT Engine 输出。 [#56858](https://github.com/PaddlePaddle/Paddle/pull/56858),[#56188](https://github.com/PaddlePaddle/Paddle/pull/56188),[#57407](https://github.com/PaddlePaddle/Paddle/pull/57407) -- 支持自定义 OP 自动生成 TensorRT Plugin。[#58976](https://github.com/PaddlePaddle/Paddle/pull/58976),[#56037](https://github.com/PaddlePaddle/Paddle/pull/56037) -- TensorRT 推理支持指定输入 hook，优化 shape 收集流程。[#59466](https://github.com/PaddlePaddle/Paddle/pull/59466),[#54841](https://github.com/PaddlePaddle/Paddle/pull/54841),[#57498](https://github.com/PaddlePaddle/Paddle/pull/57498),[#54861](https://github.com/PaddlePaddle/Paddle/pull/54861),[#54432](https://github.com/PaddlePaddle/Paddle/pull/54432),[#55503](https://github.com/PaddlePaddle/Paddle/pull/55503) -- TensorRT 推理支持保存 Tuning 后的推理模型。[#55893](https://github.com/PaddlePaddle/Paddle/pull/55893),[#56952](https://github.com/PaddlePaddle/Paddle/pull/56952),[#57031](https://github.com/PaddlePaddle/Paddle/pull/57031) -- 支持变长 Transformer 模型 PromptTuning。[#57034](https://github.com/PaddlePaddle/Paddle/pull/57034) -- 新增 bitwise_and、bitwise_or、bitwise_not、cumsum、einsum、lookup_table、assign、flip、size、scatter、solve、unbind、reduce、argsort 算子支持，优化已有算子支持。[#59214](https://github.com/PaddlePaddle/Paddle/pull/59214),[#59293](https://github.com/PaddlePaddle/Paddle/pull/59293),[#54882](https://github.com/PaddlePaddle/Paddle/pull/54882),[#54097](https://github.com/PaddlePaddle/Paddle/pull/54097),[#54860](https://github.com/PaddlePaddle/Paddle/pull/54860),[#55426](https://github.com/PaddlePaddle/Paddle/pull/55426),[#54372](https://github.com/PaddlePaddle/Paddle/pull/54372),[#55688](https://github.com/PaddlePaddle/Paddle/pull/55688),[#56069](https://github.com/PaddlePaddle/Paddle/pull/56069),[#59563](https://github.com/PaddlePaddle/Paddle/pull/59563),[#59317](https://github.com/PaddlePaddle/Paddle/pull/59317),[#59424](https://github.com/PaddlePaddle/Paddle/pull/59424),[#55476](https://github.com/PaddlePaddle/Paddle/pull/55476),[#56043](https://github.com/PaddlePaddle/Paddle/pull/56043),[#58549](https://github.com/PaddlePaddle/Paddle/pull/58549),[#57326](https://github.com/PaddlePaddle/Paddle/pull/57326),[#59409](https://github.com/PaddlePaddle/Paddle/pull/59409)) -- TensorRT 默认开启显存共享。[#59495](https://github.com/PaddlePaddle/Paddle/pull/59495),[#58251](https://github.com/PaddlePaddle/Paddle/pull/58251) -- PrelnResidualBiasPluginDynamic 支持 4D 输入。[#56304](https://github.com/PaddlePaddle/Paddle/pull/56304) -- 新增 SM80 以下架构 Paddle-TRT 推理对 FlashAttention 的支持。[#56492](https://github.com/PaddlePaddle/Paddle/pull/56492) - -### 改造废弃 - -- OneDNN 中删除 fc_elementwise_add 融合。[#55504](https://github.com/PaddlePaddle/Paddle/pull/55504) -- 删除 redunant op。 [#54442](https://github.com/PaddlePaddle/Paddle/pull/54442) - -### Bug Fix - -- 修复 Inference so 链接 flags 冲突问题。[#59755](https://github.com/PaddlePaddle/Paddle/pull/59755) -- 修复 constant_folding pass 执行报错。[#55556](https://github.com/PaddlePaddle/Paddle/pull/55556) -- 修复 softmax 前向速度问题及反向精度问题。[#56036](https://github.com/PaddlePaddle/Paddle/pull/56036),[#57858](https://github.com/PaddlePaddle/Paddle/pull/57858)[#57538](https://github.com/PaddlePaddle/Paddle/pull/57538) -- 修复自定义 OP while 报错及导出问题。[#58898](https://github.com/PaddlePaddle/Paddle/pull/58898),[#59318](https://github.com/PaddlePaddle/Paddle/pull/59318) -- 修复 Windows 平台 CUDA 12.0 编译问题。[#59852](https://github.com/PaddlePaddle/Paddle/pull/59852) -- 修复 TensorRT 版本大于等于 8.6 时推理部分算子报错问题。[#54379](https://github.com/PaddlePaddle/Paddle/pull/54379),[#54679](https://github.com/PaddlePaddle/Paddle/pull/54679),[#54251](https://github.com/PaddlePaddle/Paddle/pull/54251) -- 修复、删除推理融合 Pass。[#54846](https://github.com/PaddlePaddle/Paddle/pull/54846),[#54887](https://github.com/PaddlePaddle/Paddle/pull/54887),[#55573](https://github.com/PaddlePaddle/Paddle/pull/55573),[#56434](https://github.com/PaddlePaddle/Paddle/pull/56434),[#56326](https://github.com/PaddlePaddle/Paddle/pull/56326),[#56753](https://github.com/PaddlePaddle/Paddle/pull/56753),[#57491](https://github.com/PaddlePaddle/Paddle/pull/57491),[#56909](https://github.com/PaddlePaddle/Paddle/pull/56909),[#54536](https://github.com/PaddlePaddle/Paddle/pull/54536),[#55073](https://github.com/PaddlePaddle/Paddle/pull/55073),[#55081](https://github.com/PaddlePaddle/Paddle/pull/55081),[#55240](https://github.com/PaddlePaddle/Paddle/pull/55240),[#56439](https://github.com/PaddlePaddle/Paddle/pull/56439),[#59009](https://github.com/PaddlePaddle/Paddle/pull/59009) -- 修复多 Stream 推理上下文切换报错问题。[#57629](https://github.com/PaddlePaddle/Paddle/pull/57629),[#58048](https://github.com/PaddlePaddle/Paddle/pull/58048),[#54994](https://github.com/PaddlePaddle/Paddle/pull/54994) - -## 5. 硬件适配 - -### 硬件适配方案 (Custom Device) - -在本次更新中，新增了对分布式高级策略、自定义算子和自定义融合策略的支持。通过升级分布式通信库，新增了对 MP、GroupShared、PP、SP 和 MOE 等多项高级分布式策略的支持。同时支持厂商灵活接入不同颗粒度的 Transformer 算子库并通过融合 Pass 修改计算图进行性能加速。 - -#### 新功能 - -- CustomDevice 升级对 Paddle 最新分布式通信库 CommContext 的支持，并新增了多种高级分布式策略 GroupShared 和 MOE 等策略。[#56301](https://github.com/PaddlePaddle/Paddle/pull/56301),[#54671](https://github.com/PaddlePaddle/Paddle/pull/54671),[#57957](https://github.com/PaddlePaddle/Paddle/pull/57957),[#56669](https://github.com/PaddlePaddle/Paddle/pull/56669),[#54384](https://github.com/PaddlePaddle/Paddle/pull/54384),[#54572](https://github.com/PaddlePaddle/Paddle/pull/54572),[#54573](https://github.com/PaddlePaddle/Paddle/pull/54573),[#54676](https://github.com/PaddlePaddle/Paddle/pull/54676) -- 新增 CustomDevice 对 CustomOP 的支持，并可注册 Paddle PHI 算子库中尚未定义的算子,同时新增 CustomDevice 通过 CAPI 支持 CustomOP。[#57038](https://github.com/PaddlePaddle/Paddle/pull/57038),[#55532](https://github.com/PaddlePaddle/Paddle/pull/55532),[#56755](https://github.com/PaddlePaddle/Paddle/pull/56755),[#55532](https://github.com/PaddlePaddle/Paddle/pull/55532),[#55533](https://github.com/PaddlePaddle/Paddle/pull/55533),[#55659](https://github.com/PaddlePaddle/Paddle/pull/55659) -- 新增 CustomDevice 对 CustomPass 功能的功能，支持通过 Python API 修改计算图 IR。[#55511](https://github.com/PaddlePaddle/Paddle/pull/55511),[#55728](https://github.com/PaddlePaddle/Paddle/pull/55728) -- 新增 CustomDevice 对 Paddle run_check 健康功能检查的支持。[#56318](https://github.com/PaddlePaddle/Paddle/pull/56318) -- 新增 CustomDevice 对 StreamSafeAllocator 的支持。[#55393](https://github.com/PaddlePaddle/Paddle/pull/55393),[#56380](https://github.com/PaddlePaddle/Paddle/pull/56380),[#56536](https://github.com/PaddlePaddle/Paddle/pull/56536),[#58035](https://github.com/PaddlePaddle/Paddle/pull/58035) -- 新增 CustomDevice 对 DataTransform 的支持。[#56627](https://github.com/PaddlePaddle/Paddle/pull/56627) - -#### 功能优化 - -- 新增 CustomDevice，支持飞桨更多的接口，包括 Variable.set_value,adamw,share_external_data,mp_allreduce_sum,tensor.numpy,get_paddle_place, GeneratorState。[#55272](https://github.com/PaddlePaddle/Paddle/pull/55272), [#56386](https://github.com/PaddlePaddle/Paddle/pull/56386), [#57253](https://github.com/PaddlePaddle/Paddle/pull/57253), [#56927](https://github.com/PaddlePaddle/Paddle/pull/56927),[#56189](https://github.com/PaddlePaddle/Paddle/pull/56189),[#55225](https://github.com/PaddlePaddle/Paddle/pull/55225),[#55247](https://github.com/PaddlePaddle/Paddle/pull/55247) -- 修改 CustomDevice 动态库加载方式，从 RTLD_NOW 改为 RTLD_LAZY，方便后续检查 Custom Device 相关软件栈版本的兼容性。 [#57544](https://github.com/PaddlePaddle/Paddle/pull/57544) -- 新增 CustomDevice 在混合精度训练下对 FP16 算子的检测功能。[#56053](https://github.com/PaddlePaddle/Paddle/pull/56053),[#56176](https://github.com/PaddlePaddle/Paddle/pull/56176) - -#### Bug Fix - -- 修复 CustomDevice 对分布式通信库支持上的一些问题。[#55293](https://github.com/PaddlePaddle/Paddle/pull/55293),[#58038](https://github.com/PaddlePaddle/Paddle/pull/58038),[#59800](https://github.com/PaddlePaddle/Paddle/pull/59800) -- 修复 CustomDevice 在部分算子上的问题，包括 c_softmax_with_cross_entropy,data loader,SplitDenseTensor,grad accumulation,atan2 grad。[#56486](https://github.com/PaddlePaddle/Paddle/pull/56486),[#55541](https://github.com/PaddlePaddle/Paddle/pull/55541),[#55615](https://github.com/PaddlePaddle/Paddle/pull/55615),[#56052](https://github.com/PaddlePaddle/Paddle/pull/56052),[#56067](https://github.com/PaddlePaddle/Paddle/pull/56067) -- 修复 CustomDevice 中设备管理的一些问题，包括设备异常 ([#56556](https://github.com/PaddlePaddle/Paddle/pull/56556),[#58639](https://github.com/PaddlePaddle/Paddle/pull/58639),[#55173](https://github.com/PaddlePaddle/Paddle/pull/55173)), 异常事件([#56745](https://github.com/PaddlePaddle/Paddle/pull/56745),[#58059](https://github.com/PaddlePaddle/Paddle/pull/58059)), 显存异常([#56977](https://github.com/PaddlePaddle/Paddle/pull/56977),[#59247](https://github.com/PaddlePaddle/Paddle/pull/59247),[#54606](https://github.com/PaddlePaddle/Paddle/pull/54606)), 设备初始化 ([#57099](https://github.com/PaddlePaddle/Paddle/pull/57099),[#57994](https://github.com/PaddlePaddle/Paddle/pull/57994))，设备释放([#54932](https://github.com/PaddlePaddle/Paddle/pull/54932),[#55351](https://github.com/PaddlePaddle/Paddle/pull/55351),[#55783](https://github.com/PaddlePaddle/Paddle/pull/55783))，和设备资源池等。([#55229](https://github.com/PaddlePaddle/Paddle/pull/55229),[#56580](https://github.com/PaddlePaddle/Paddle/pull/56580)) -- 修复 CustomDevice 编译相关问题。[#56760](https://github.com/PaddlePaddle/Paddle/pull/56760),[#56766](https://github.com/PaddlePaddle/Paddle/pull/56766) +- 修复分布式策略 Sharing 下的错误。[#61942](https://github.com/PaddlePaddle/Paddle/pull/61942),[#62236](https://github.com/PaddlePaddle/Paddle/pull/62236),[#62305](https://github.com/PaddlePaddle/Paddle/pull/62305),[#62535](https://github.com/PaddlePaddle/Paddle/pull/62535),[#62572](https://github.com/PaddlePaddle/Paddle/pull/62572),[#61601](https://github.com/PaddlePaddle/Paddle/pull/61601) +- 修复 c_embedding 算子不在 PHI namespace 下导致的算子无法注册的问题。[#60774](https://github.com/PaddlePaddle/Paddle/pull/60774) +- 修复 xccl_comm 释放问题。[#60465](https://github.com/PaddlePaddle/Paddle/pull/60465) +- 修复 index_put 算子 fallbacking cpu 时导致的数据地址错误。[#61842](https://github.com/PaddlePaddle/Paddle/pull/61842) +- 修复 stream_safe_custom_device_allocator 的问题。[#63369](https://github.com/PaddlePaddle/Paddle/pull/63369) +- 修复分布式下 worker 端口冲突问题。[#61409](https://github.com/PaddlePaddle/Paddle/pull/61409) +- 修复 comm 数据类型以提升设备兼容性。[#62306](https://github.com/PaddlePaddle/Paddle/pull/62306) +- 统一通信数据类型的使用为 phi::DataType。[#62464](https://github.com/PaddlePaddle/Paddle/pull/62464),[#62562](https://github.com/PaddlePaddle/Paddle/pull/62562) +- 修复 PD_ConfigEnableCustomDevice 缺少 precision 参数问题。[#63702](https://github.com/PaddlePaddle/Paddle/pull/63702) ### 昆仑 XPU -#### 新功能 - -- 新增 XPTI (XPU Profiling Tool Interface) 支持运行时性能数据的采集和分析功能。[#54685](https://github.com/PaddlePaddle/Paddle/pull/54685),[#54690](https://github.com/PaddlePaddle/Paddle/pull/54690),[#54800](https://github.com/PaddlePaddle/Paddle/pull/54800) -- 完成对 Paddle 最新分布式通信库 CommContext 的支持。[#59418](https://github.com/PaddlePaddle/Paddle/pull/59418) -- 新增 XPU 融合算子包括 fast_where。[#55628](https://github.com/PaddlePaddle/Paddle/pull/55628) -- 新增 XPU Plugin 功能支持，方便用户可通过 XTDK 编程方式开发 XPU 自定义算子。[#55101](https://github.com/PaddlePaddle/Paddle/pull/55101),[#59326](https://github.com/PaddlePaddle/Paddle/pull/59326) -- 新增 XPU 对 AutoGrowthAllocator 的支持。[#54121](https://github.com/PaddlePaddle/Paddle/pull/54121) -- 新增昆仑 3 的算子支持列表。[#57683](https://github.com/PaddlePaddle/Paddle/pull/57683) - -#### 功能优化 - -- 对 XPU Inference API 进行升级。[#54342](https://github.com/PaddlePaddle/Paddle/pull/54342) -- 优化部分 XPU 算子性能和新增部分 XPU 算子对 bf16 的的支持，包括 unique/index_put,squeeze/unsqueeze kernels,swish/swish_grad,scatter_nd_add_grad/slice,rsqrt/bitwise_or/arange_tensor,where,collective 算子等。[#56582](https://github.com/PaddlePaddle/Paddle/pull/56582),[#58161](https://github.com/PaddlePaddle/Paddle/pull/58161),[#58440](https://github.com/PaddlePaddle/Paddle/pull/58440),[#58580](https://github.com/PaddlePaddle/Paddle/pull/58580),[#58950](https://github.com/PaddlePaddle/Paddle/pull/58950),[#58616](https://github.com/PaddlePaddle/Paddle/pull/58616),[#59273](https://github.com/PaddlePaddle/Paddle/pull/59273) -- 优化 XPU 内存管理，避免内存泄漏。[#59334](https://github.com/PaddlePaddle/Paddle/pull/59334),[#54847](https://github.com/PaddlePaddle/Paddle/pull/54847) -- 支持 INT8 推理。[#57258](https://github.com/PaddlePaddle/Paddle/pull/57258) -- 新增 FP16 系列推理算子支持。[#55642](https://github.com/PaddlePaddle/Paddle/pull/55642),[#54410](https://github.com/PaddlePaddle/Paddle/pull/54410) -- 支持 share_external_memory 接口传入输入输出。[#55170](https://github.com/PaddlePaddle/Paddle/pull/55170) -- 开源量化模型 XPU 推理支持。[#58568](https://github.com/PaddlePaddle/Paddle/pull/58568) -- 新增 context_gm_size 配置代替在 Pass 中分配 global memory。[#54674](https://github.com/PaddlePaddle/Paddle/pull/54674) -- 新增 embedding、fast_gather_nd plugin。[#56488](https://github.com/PaddlePaddle/Paddle/pull/56488),[#56103](https://github.com/PaddlePaddle/Paddle/pull/56103) -- 支持 fast_layternorm + leaky_relu 融合。[#57113](https://github.com/PaddlePaddle/Paddle/pull/57113) -- KL1 和 KL2 精度下 elementwise_min/max/floordiv/where 推理支持。[#58422](https://github.com/PaddlePaddle/Paddle/pull/58422) -- 支持 fc 和 conv2d 算子 autotune 配置。[#58801](https://github.com/PaddlePaddle/Paddle/pull/58801) -- 支持 conv 和 fc 动态量化。[#59307](https://github.com/PaddlePaddle/Paddle/pull/59307) -- fc + act 融合支持 sigmoid, swish and relu6。[#54486](https://github.com/PaddlePaddle/Paddle/pull/54486) -- elementwise_sub/elementwise_div 支持 int 数据类型。[#55920](https://github.com/PaddlePaddle/Paddle/pull/55920) - -#### Bug Fix - -- 修复 XPU 通信库问题和部分算子问题包括 rnn、layer_norm_grad、yolo_box。 [#55475](https://github.com/PaddlePaddle/Paddle/pull/55475),[#55515](https://github.com/PaddlePaddle/Paddle/pull/55515) [#55656](https://github.com/PaddlePaddle/Paddle/pull/55656),[#54669](https://github.com/PaddlePaddle/Paddle/pull/54669),[#55310](https://github.com/PaddlePaddle/Paddle/pull/55310) - -### 海光 DCU - -#### Bug Fix - -- 修复海光 DCU 部分算子问题，包括 rnn,concat/split,fft 等。[#59402](https://github.com/PaddlePaddle/Paddle/pull/59402),[#55821](https://github.com/PaddlePaddle/Paddle/pull/55821),[#56340](https://github.com/PaddlePaddle/Paddle/pull/56340) -- 修复海光 DCU 通信库相关问题。[#57110](https://github.com/PaddlePaddle/Paddle/pull/57110) -- 修复海光 DCU 编译相关问题。[#59775](https://github.com/PaddlePaddle/Paddle/pull/59775),[#55507](https://github.com/PaddlePaddle/Paddle/pull/55507),[#55612](https://github.com/PaddlePaddle/Paddle/pull/55612),[#54952](https://github.com/PaddlePaddle/Paddle/pull/54952),[#55076](https://github.com/PaddlePaddle/Paddle/pull/55076),[#56079](https://github.com/PaddlePaddle/Paddle/pull/56079),[#54874](https://github.com/PaddlePaddle/Paddle/pull/54874) -- 修复海光 DCU 对 BF16 数据类型的支持问题。[#56517](https://github.com/PaddlePaddle/Paddle/pull/56517) - -## 6. 环境适配 - -采用模块化编译的方式优化了 CMake 代码的逻辑，提升了飞桨全量编译和增量编译的效率，提升了 RD 本地开发效率，同时支持了 Python3.12，CUDA12，Hopper 架构编译，并引入 Clang 等工具全面优化了代码格式。此外，将 C++单测从链接静态库的方式转变为链接动态库，减小编译体积。这些改进措施为用户提供更加流畅、高效地安装和开发体验。 - -- CMake 代码优化：分模块和目录编译成独立的静态库，并减少编译依赖，提升增量编译效率。[#59095](https://github.com/PaddlePaddle/Paddle/pull/59095), [#58960](https://github.com/PaddlePaddle/Paddle/pull/58960),[#56591](https://github.com/PaddlePaddle/Paddle/pull/56591),[#58484](https://github.com/PaddlePaddle/Paddle/pull/58484) -- CMake 编译分层：将公共组件拆分到公有 common 库，自下而上实现飞桨架构的编译分层，提高编译效率。[#56442](https://github.com/PaddlePaddle/Paddle/pull/56442),[#54729](https://github.com/PaddlePaddle/Paddle/pull/54729),[#55733](https://github.com/PaddlePaddle/Paddle/pull/55733),[#56352](https://github.com/PaddlePaddle/Paddle/pull/56352),[#55109](https://github.com/PaddlePaddle/Paddle/pull/55109),[#54992](https://github.com/PaddlePaddle/Paddle/pull/54992),[#57698](https://github.com/PaddlePaddle/Paddle/pull/57698),[#55147](https://github.com/PaddlePaddle/Paddle/pull/55147),[#55113](https://github.com/PaddlePaddle/Paddle/pull/55113),[#56691](https://github.com/PaddlePaddle/Paddle/pull/56691),[#58618](https://github.com/PaddlePaddle/Paddle/pull/58618),[#58899](https://github.com/PaddlePaddle/Paddle/pull/58899),[#59140](https://github.com/PaddlePaddle/Paddle/pull/59140),[#59129](https://github.com/PaddlePaddle/Paddle/pull/59129),[#59222](https://github.com/PaddlePaddle/Paddle/pull/59222),[#59105](https://github.com/PaddlePaddle/Paddle/pull/59105),[#59711](https://github.com/PaddlePaddle/Paddle/pull/59711) -- 第三方库离线编译：将第三方依赖库离线编译，CI/CE 系统无需每次编译重复下载第三方库，提升 CI/CE 系统运行效率。[#54344](https://github.com/PaddlePaddle/Paddle/pull/54344),[#54370](https://github.com/PaddlePaddle/Paddle/pull/54370),[#54466](https://github.com/PaddlePaddle/Paddle/pull/54466),[#54438](https://github.com/PaddlePaddle/Paddle/pull/54438),[#54388](https://github.com/PaddlePaddle/Paddle/pull/54388),[#54436](https://github.com/PaddlePaddle/Paddle/pull/54436),[#54392](https://github.com/PaddlePaddle/Paddle/pull/54392),[#54646](https://github.com/PaddlePaddle/Paddle/pull/54646),[#54380](https://github.com/PaddlePaddle/Paddle/pull/54380),[#55501](https://github.com/PaddlePaddle/Paddle/pull/55501),[#55136](https://github.com/PaddlePaddle/Paddle/pull/55136),[#54451](https://github.com/PaddlePaddle/Paddle/pull/54451),[#55631](https://github.com/PaddlePaddle/Paddle/pull/55631),[#55549](https://github.com/PaddlePaddle/Paddle/pull/55549),[#56165](https://github.com/PaddlePaddle/Paddle/pull/56165),[#54391](https://github.com/PaddlePaddle/Paddle/pull/54391),[#54614](https://github.com/PaddlePaddle/Paddle/pull/54614),[#54522](https://github.com/PaddlePaddle/Paddle/pull/54522),[#54764](https://github.com/PaddlePaddle/Paddle/pull/54764),[#54400](https://github.com/PaddlePaddle/Paddle/pull/54400),[#54322](https://github.com/PaddlePaddle/Paddle/pull/54322) -- 飞桨支持 Python 3.12。[#59396](https://github.com/PaddlePaddle/Paddle/pull/59396),[#58069](https://github.com/PaddlePaddle/Paddle/pull/58069) -- 使用 Clang 等工具对于源代码进行优化，提升代码质量。[#59626](https://github.com/PaddlePaddle/Paddle/pull/59626),[#55895](https://github.com/PaddlePaddle/Paddle/pull/55895),[#56632](https://github.com/PaddlePaddle/Paddle/pull/56632),[#54449](https://github.com/PaddlePaddle/Paddle/pull/54449),[#54523](https://github.com/PaddlePaddle/Paddle/pull/54523),[#54796](https://github.com/PaddlePaddle/Paddle/pull/54796),[#55847](https://github.com/PaddlePaddle/Paddle/pull/55847),[#55807](https://github.com/PaddlePaddle/Paddle/pull/55807),[#56261](https://github.com/PaddlePaddle/Paddle/pull/56261),[#57522](https://github.com/PaddlePaddle/Paddle/pull/57522),[#57868](https://github.com/PaddlePaddle/Paddle/pull/57868),[#57809](https://github.com/PaddlePaddle/Paddle/pull/57809),[#55658](https://github.com/PaddlePaddle/Paddle/pull/55658),[#58285](https://github.com/PaddlePaddle/Paddle/pull/58285),[#55491](https://github.com/PaddlePaddle/Paddle/pull/55491),[#55506](https://github.com/PaddlePaddle/Paddle/pull/55506),[#55279](https://github.com/PaddlePaddle/Paddle/pull/55279),[#55741](https://github.com/PaddlePaddle/Paddle/pull/55741),[#55894](https://github.com/PaddlePaddle/Paddle/pull/55894),[#55704](https://github.com/PaddlePaddle/Paddle/pull/55704),[#55800](https://github.com/PaddlePaddle/Paddle/pull/55800),[#55799](https://github.com/PaddlePaddle/Paddle/pull/55799),[#55983](https://github.com/PaddlePaddle/Paddle/pull/55983),[#55954](https://github.com/PaddlePaddle/Paddle/pull/55954),[#55764](https://github.com/PaddlePaddle/Paddle/pull/55764),[#56246](https://github.com/PaddlePaddle/Paddle/pull/56246),[#56219](https://github.com/PaddlePaddle/Paddle/pull/56219),[#56217](https://github.com/PaddlePaddle/Paddle/pull/56217),[#56216](https://github.com/PaddlePaddle/Paddle/pull/56216),[#56208](https://github.com/PaddlePaddle/Paddle/pull/56208),[#56134](https://github.com/PaddlePaddle/Paddle/pull/56134),[#56253](https://github.com/PaddlePaddle/Paddle/pull/56253),[#56255](https://github.com/PaddlePaddle/Paddle/pull/56255),[#56693](https://github.com/PaddlePaddle/Paddle/pull/56693),[#56692](https://github.com/PaddlePaddle/Paddle/pull/56692),[#56637](https://github.com/PaddlePaddle/Paddle/pull/56637),[#56636](https://github.com/PaddlePaddle/Paddle/pull/56636),[#56647](https://github.com/PaddlePaddle/Paddle/pull/56647),[#56218](https://github.com/PaddlePaddle/Paddle/pull/56218),[#56640](https://github.com/PaddlePaddle/Paddle/pull/56640),[#56635](https://github.com/PaddlePaddle/Paddle/pull/56635),[#55675](https://github.com/PaddlePaddle/Paddle/pull/55675),[#56601](https://github.com/PaddlePaddle/Paddle/pull/56601),[#56485](https://github.com/PaddlePaddle/Paddle/pull/56485),[#56648](https://github.com/PaddlePaddle/Paddle/pull/56648),[#56747](https://github.com/PaddlePaddle/Paddle/pull/56747),[#56676](https://github.com/PaddlePaddle/Paddle/pull/56676),[#56649](https://github.com/PaddlePaddle/Paddle/pull/56649),[#56895](https://github.com/PaddlePaddle/Paddle/pull/56895),[#56994](https://github.com/PaddlePaddle/Paddle/pull/56994),[#56904](https://github.com/PaddlePaddle/Paddle/pull/56904),[#56744](https://github.com/PaddlePaddle/Paddle/pull/56744),[#56954](https://github.com/PaddlePaddle/Paddle/pull/56954),[#57114](https://github.com/PaddlePaddle/Paddle/pull/57114),[#57343](https://github.com/PaddlePaddle/Paddle/pull/57343),[#57483](https://github.com/PaddlePaddle/Paddle/pull/57483),[#57871](https://github.com/PaddlePaddle/Paddle/pull/57871),[#57861](https://github.com/PaddlePaddle/Paddle/pull/57861),[#58028](https://github.com/PaddlePaddle/Paddle/pull/58028),[#57627](https://github.com/PaddlePaddle/Paddle/pull/57627),[#59072](https://github.com/PaddlePaddle/Paddle/pull/59072) -- C++从链接静态库转变为链接动态库，减小编译体积，提升编译效率。[#59477](https://github.com/PaddlePaddle/Paddle/pull/59477),[#56630](https://github.com/PaddlePaddle/Paddle/pull/56630),[#57789](https://github.com/PaddlePaddle/Paddle/pull/57789),[#54257](https://github.com/PaddlePaddle/Paddle/pull/54257),[#59620](https://github.com/PaddlePaddle/Paddle/pull/59620),[#59384](https://github.com/PaddlePaddle/Paddle/pull/59384),[#59619](https://github.com/PaddlePaddle/Paddle/pull/59619),[#58583](https://github.com/PaddlePaddle/Paddle/pull/58583),[#58821](https://github.com/PaddlePaddle/Paddle/pull/58821),[#58710](https://github.com/PaddlePaddle/Paddle/pull/58710)，[#58619](https://github.com/PaddlePaddle/Paddle/pull/58619) -- 修复源代码编译相关的问题，提升编译安装效率。[#56617](https://github.com/PaddlePaddle/Paddle/pull/56617),[#58195](https://github.com/PaddlePaddle/Paddle/pull/58195),[#56136](https://github.com/PaddlePaddle/Paddle/pull/56136),[#54540](https://github.com/PaddlePaddle/Paddle/pull/54540),[#57172](https://github.com/PaddlePaddle/Paddle/pull/57172),[#54429](https://github.com/PaddlePaddle/Paddle/pull/54429),[#55603](https://github.com/PaddlePaddle/Paddle/pull/55603),[#54807](https://github.com/PaddlePaddle/Paddle/pull/54807),[#56102](https://github.com/PaddlePaddle/Paddle/pull/56102),[#56829](https://github.com/PaddlePaddle/Paddle/pull/56829),[#56951](https://github.com/PaddlePaddle/Paddle/pull/56951),[#56555](https://github.com/PaddlePaddle/Paddle/pull/56555),[#57781](https://github.com/PaddlePaddle/Paddle/pull/57781),[#57836](https://github.com/PaddlePaddle/Paddle/pull/57836),[#58807](https://github.com/PaddlePaddle/Paddle/pull/58807),[#54535](https://github.com/PaddlePaddle/Paddle/pull/54535),[#54946](https://github.com/PaddlePaddle/Paddle/pull/54946),[#54437](https://github.com/PaddlePaddle/Paddle/pull/54437),[#54411](https://github.com/PaddlePaddle/Paddle/pull/54411),[#54411](https://github.com/PaddlePaddle/Paddle/pull/54411),[#54391](https://github.com/PaddlePaddle/Paddle/pull/54391),[#54466](https://github.com/PaddlePaddle/Paddle/pull/54466),[#54480](https://github.com/PaddlePaddle/Paddle/pull/54480),[#54480](https://github.com/PaddlePaddle/Paddle/pull/54480),[#54724](https://github.com/PaddlePaddle/Paddle/pull/54724),[#59193](https://github.com/PaddlePaddle/Paddle/pull/59193),[#54735](https://github.com/PaddlePaddle/Paddle/pull/54735),[#54812](https://github.com/PaddlePaddle/Paddle/pull/54812),[#56430](https://github.com/PaddlePaddle/Paddle/pull/56430),[#56655](https://github.com/PaddlePaddle/Paddle/pull/56655),[#56684](https://github.com/PaddlePaddle/Paddle/pull/56684),[#56774](https://github.com/PaddlePaddle/Paddle/pull/56774),[#56936](https://github.com/PaddlePaddle/Paddle/pull/56936),[#56949](https://github.com/PaddlePaddle/Paddle/pull/56949),[#56974](https://github.com/PaddlePaddle/Paddle/pull/56974),[#57171](https://github.com/PaddlePaddle/Paddle/pull/57171),[#57712](https://github.com/PaddlePaddle/Paddle/pull/57712),[#56617](https://github.com/PaddlePaddle/Paddle/pull/56617),[#58181](https://github.com/PaddlePaddle/Paddle/pull/58181),[#58253](https://github.com/PaddlePaddle/Paddle/pull/58253),[#58268](https://github.com/PaddlePaddle/Paddle/pull/58268),[#59051](https://github.com/PaddlePaddle/Paddle/pull/59051),[#59048](https://github.com/PaddlePaddle/Paddle/pull/59048),[#59081](https://github.com/PaddlePaddle/Paddle/pull/59081),[#59076](https://github.com/PaddlePaddle/Paddle/pull/59076),[#59155](https://github.com/PaddlePaddle/Paddle/pull/59155),[#59253](https://github.com/PaddlePaddle/Paddle/pull/59253),[#59347](https://github.com/PaddlePaddle/Paddle/pull/59347),[#58957](https://github.com/PaddlePaddle/Paddle/pull/58957),[#59443](https://github.com/PaddlePaddle/Paddle/pull/59443),[#58998](https://github.com/PaddlePaddle/Paddle/pull/58998),[#57574](https://github.com/PaddlePaddle/Paddle/pull/57574),[#55889](https://github.com/PaddlePaddle/Paddle/pull/55889),[#59078](https://github.com/PaddlePaddle/Paddle/pull/59078),[#55762](https://github.com/PaddlePaddle/Paddle/pull/55762),[#56252](https://github.com/PaddlePaddle/Paddle/pull/56252),[#56715](https://github.com/PaddlePaddle/Paddle/pull/56715),[#54905](https://github.com/PaddlePaddle/Paddle/pull/54905),[#56978](https://github.com/PaddlePaddle/Paddle/pull/56978),[#57032](https://github.com/PaddlePaddle/Paddle/pull/57032),[#57179](https://github.com/PaddlePaddle/Paddle/pull/57179),[#57179](https://github.com/PaddlePaddle/Paddle/pull/57179),[#58996](https://github.com/PaddlePaddle/Paddle/pull/58996),[#59915](https://github.com/PaddlePaddle/Paddle/pull/59915),[#54883](https://github.com/PaddlePaddle/Paddle/pull/54883),[#56746](https://github.com/PaddlePaddle/Paddle/pull/56746),[#57674](https://github.com/PaddlePaddle/Paddle/pull/57674),[#60117](https://github.com/PaddlePaddle/Paddle/pull/60117),[#55627](https://github.com/PaddlePaddle/Paddle/pull/55627),[#54568](https://github.com/PaddlePaddle/Paddle/pull/54568),[#54450](https://github.com/PaddlePaddle/Paddle/pull/54450),[#54513](https://github.com/PaddlePaddle/Paddle/pull/54513),[#54615](https://github.com/PaddlePaddle/Paddle/pull/54615),[#54913](https://github.com/PaddlePaddle/Paddle/pull/54913),[#54916](https://github.com/PaddlePaddle/Paddle/pull/54916),[#55148](https://github.com/PaddlePaddle/Paddle/pull/55148),[#55125](https://github.com/PaddlePaddle/Paddle/pull/55125),[#55479](https://github.com/PaddlePaddle/Paddle/pull/55479),[#55723](https://github.com/PaddlePaddle/Paddle/pull/55723),[#55831](https://github.com/PaddlePaddle/Paddle/pull/55831),[#55904](https://github.com/PaddlePaddle/Paddle/pull/55904),[#56085](https://github.com/PaddlePaddle/Paddle/pull/56085),[#56259](https://github.com/PaddlePaddle/Paddle/pull/56259),[#56366](https://github.com/PaddlePaddle/Paddle/pull/56366),[#56366](https://github.com/PaddlePaddle/Paddle/pull/56366),[#56546](https://github.com/PaddlePaddle/Paddle/pull/56546),[#56679](https://github.com/PaddlePaddle/Paddle/pull/56679),[#57222](https://github.com/PaddlePaddle/Paddle/pull/57222),[#57387](https://github.com/PaddlePaddle/Paddle/pull/57387),[#57993](https://github.com/PaddlePaddle/Paddle/pull/57993),[#59556](https://github.com/PaddlePaddle/Paddle/pull/59556),[#57931](https://github.com/PaddlePaddle/Paddle/pull/57931),[#58112](https://github.com/PaddlePaddle/Paddle/pull/58112),[#54228](https://github.com/PaddlePaddle/Paddle/pull/54228),[#56913](https://github.com/PaddlePaddle/Paddle/pull/56913),[#56993](https://github.com/PaddlePaddle/Paddle/pull/56993),[#55042](https://github.com/PaddlePaddle/Paddle/pull/55042),[#55305](https://github.com/PaddlePaddle/Paddle/pull/55305),[#55286](https://github.com/PaddlePaddle/Paddle/pull/55286),[#56634](https://github.com/PaddlePaddle/Paddle/pull/56634),[#57778](https://github.com/PaddlePaddle/Paddle/pull/57778),[#58374](https://github.com/PaddlePaddle/Paddle/pull/58374),[#58640](https://github.com/PaddlePaddle/Paddle/pull/58640),[#58822](https://github.com/PaddlePaddle/Paddle/pull/58822),[#59055](https://github.com/PaddlePaddle/Paddle/pull/59055),[#59303](https://github.com/PaddlePaddle/Paddle/pull/59303),[#59487](https://github.com/PaddlePaddle/Paddle/pull/59487),[#58400](https://github.com/PaddlePaddle/Paddle/pull/58400),[#59283](https://github.com/PaddlePaddle/Paddle/pull/59283),[#54791](https://github.com/PaddlePaddle/Paddle/pull/54791),[#59134](https://github.com/PaddlePaddle/Paddle/pull/59134),[#56206](https://github.com/PaddlePaddle/Paddle/pull/56206),[#56199](https://github.com/PaddlePaddle/Paddle/pull/56199),[#56670](https://github.com/PaddlePaddle/Paddle/pull/56670),[#58923](https://github.com/PaddlePaddle/Paddle/pull/58923) -- 修复 Paddle ARM 编译相关问题。[#55416](https://github.com/PaddlePaddle/Paddle/pull/55416),[#55548](https://github.com/PaddlePaddle/Paddle/pull/55548) - -## Thanks to Our Contributors - -Azure-Tang, zhaoyinglia, From00, JZ-LIANG, xysheng-baidu, SylarTiaNII, kuizhiqing, zhiqiu, FeixLiu, liuzhenhai93, GhostScreaming, pangengzheng, xiaoyewww, wanghuancoder, ForFishes, hitywt, danleifeng, tianshuo78520a, ykkk2333, houj04, lj970926, XiaociZhang, HarperCy, cqulilujia, runzhech, RuohengMa, Caozhou1995, kangguangli, heavyrain-lzy, zyfncg, SigureMo, YuanRisheng, lchdl, LiYuRio, AndSonder, Wennie396, zhangbo9674, liudongxue01, risemeup1, phlrain, winter-wang, yuanlehome, NALLEIN, Liujie0926, yuguo-Jack, gitliuyf, zh794390558, Aurelius84, 6clc, GGBond8488, xiaoguoguo626807, Wong4j, iosmers, xiaoxiaohehe001, LielinJiang, carryyu, Difers, yangxiaoyu14, xuxinyi389, cxxly, gongshaotian, jjyaoao, lijialin03, lxd-cumt, cyber-pioneer, HydrogenSulfate, MayYouBeProsperous, Charles-hit, Patrick-Star125, ScottWong98, huangjiyi, DrRyanHuang, jinyouzhi, BeingGod, Wanglongzhi2001, yangguohao, zyt1024, longranger2, 2742195759, megemini, thisjiang, kevincheng2, zhoutianzi666, Wangzheee, ming1753, tianhaodongbd, freeliuzc, zhenyun-li, MARD1NO, RichardWooSJTU, eee4017, leo0519, csy0225, wwbitejotunn, bukejiyu, jiweibo, iamsonderr, ckl117, ronny1996, zhanglirong1999, LLee233, ZHUI, wangxn12138, zhwesky2010, Courtesy-Xs, zoooo0820, llyyxx0413, Asthestarsfalll, zxcd, pkuzyc, idontkonwher, sneaxiy, hong19860320, ZibinGuo, leolishaohao, MuShangCC, zhupengyang, shentanyue, Travis-Lee, wz1qqx, frank-oops, newway, QingshuChen, zhangyk0314, HandSomeLEEw, Shixiaowei02, zhangyuqin1998, Xing-lil, zhhsplendid, jiahy0825, xinyu-intel, MarioLulab, 0x45f, Tom-Zheng, xingmingyyj, zhangbopd, gouzil, zeroRains, BiynXu, WintersMontagne10335, wuhuachaocoding, GreatV, chenwhql, deepllz, parap1uie-s, ozogxyz, FisherWY, changeyoung98, zhiboniu, YangQun1 dynamicheart, Xreki, liugddx, Lylinnnnn, YSF-A, zzjjay, YanhuiDua, lishicheng1996, USTCKAY, abenmao, cocoshe, HermitSun, ccsuzzh, sanbuphy, enkilee, RedContritio, Liyulingyue, zrr1999, chen2016013, Galaxy1458, chalsliu, mrcangye, XieYunshen, zhiheng-liu, haohongxiang, ZzSean, JamesLim-sy, yuehuayingxueluo, niuliling123, umiswing, sijunhe, littsk, SecretXV, zhurou603, zhangjun, caizejun, yangjianfengo1, vivienfanghuagood, Xinyu302, lizexu123, yghstill, Li-fAngyU, VigiZhang, co63oc, dhanush-2501, ooooo-create, PommesPeter, zeus2x7, akshatvishu, jzhang533, Sekiro-x, gumblex, BernieHuang2008, YibinLiu666, qiuwenbogdut, XavierZXY, MqLeet, zhangting2020, mingxu1067, Ainavo, SSKlearns, yuchen202, silverling, zade23, wenxiaohahaha, NKNaN, Tsaiyue, fsczz, Tomoko-hjf, rhmaaa, zbt78, Hhankyangg, wangzhen38, zhengqiwen1997, engineer1109, onepick, qili93, Rane2021, nemonameless, DesmonDay, RachelXu7, ceci3, lyuwenyu, liuruyan, LokeZhou, shiyutang, lanxianghit, feifei-111, Sahala08, sunzhongkai588, Kaedeharai, Candy2Tang, liyongchao911, whisky-12, InsaneOnion, yoyoIcy, KongAKun, linzeyang, MuhammadNizamani, eltociear, Ligoml, LUZY0726, Windfarer, FlyingQianMM, jeng1220, junelotus, zlsh80826, Vvsmile, Frida-a, TonibMw, guoshengCS, zhink, ZhangYulongg, AlbertVan, fengxin-hello, mjp9527, entired, DanGuge. - - -# 2.5.0 Release Note - -## 1. 重要更新 -- **动静统一新架构**：实现基础算子组合的动转静加编译器执行新模式，在 ResNet50&Bert 模型上完成动转静、组合算子、神经网络编译器优化加速全流程。动转静完成整图 fallback 核心功能开发，支持动转静失败时回退到动态图训练执行；组合算子设计一套包含 150 多个基础算子的基础算子体系，实现 python 层前向算子拆分机制和支持动、静态图的反向算子拆分机制，实现 70 多个常用前、反向算子的拆分；CINN 编译器修复正确性问题，开发关键 Pass，添加手工 Schedule 规则，实现内核代码自动生成，ResNet50 模型性能提升 12%，Bert 模型性能提升 10%。 -- **PHI 算子库算子架构统一**：将原算子体系下剩余的 350+算子内核全部统一到 PHI 算子库中，以及原算子体系中的算子定义方式也都统一为 PHI 算子库的算子定义形式（基于 YAML 配置定义算子），提升了架构统一性，降低了框架开发的理解成本；将 PHI 算子库依赖的 Fluid 头文件全部解耦，并独立编译为动态链接库，为框架的二次开发提供更轻量的算子库复用方式；继续对飞桨框架中不规范的算子以及算子内核进行规范化调整，便于开发者理解，降低了硬件的接入成本。 -- **静态图新执行器全面上线**：静态图新执行器实现多项功能和性能优化，完成对原有多套旧执行器的统一和替换，成为静态图单卡和分布式训练 python 端入口以及动转静、控制流、CINN 等后端默认使用的执行引擎，大幅提升框架调度性能，功能架构更加清晰，二次开发能力显著增强。 -- **Python API 支持 0 维 tensor**：为形状为`[1,]` 及形状为 `[]` 的张量定义了清晰的语义。 -- **新的环境适配**：适配了 CUDA 12，并支持使用 gcc12 进行编译。 - -## 2. 不兼容升级 -- 飞桨 API 支持 0 维 tensor。飞桨之前用 shape 为[1]的 1 维 tensor 来替代 0 维 tensor，这种替代方式和当前主流习惯有差异，增加模型的开发调试成本，有时还会导致非预期错误。本版本对需支持 0 维 tensor 的 376 个 API 进行了修正，和社区广泛使用的工具如 EinOps 等实现。例如，在之前的情况下，模型训练中输出的 loss 为 1 维 tensor，如果要取出或打印 loss，往往需要使用 `loss.numpy()[0]` 这样的代码。经过本次修改后，模型训练中输出的 loss 为 0 维 tensor，使用 `loss.numpy()` 即可取出或打印 loss，代码简短、易懂且符合业界使用习惯。 -- `paddle.fluid` API 全面退场。按照上个版本已预告的计划，本次退场了 1116 个`paddle.fluid`API 及相关内部接口，剩余少量相关内部接口会在下个版本全部清理完成。fluid API 属于飞桨 2.0 本计划移除但考虑到兼容性等因素延缓清理的历史 API，本次退场清理不会影响基于飞桨 2.0 开发的程序，飞桨 API 体系也会更加简洁易懂。 -- 旧版动态图 Python 端代码完成清理。至此，Python 端仅使用新版动态图调用 C++核心逻辑。 -- 为统一静态图模型数据并行的训练方式，废弃原有的单进程多卡训练方式，包括 `paddle.static.ParallelExecutor` 和 `paddle.static.CompiledProgram().with_data_parallel()` 两个接口，原因是这套接口只支持单机多卡，不支持多机多卡，且底层执行性能较差。推荐统一使用多进程多卡训练方式，即 `paddle.distributed.launch` 接口来进行数据并行的分布式训练。该升级只影响静态图，不影响动态图和动转静训练，如果使用了废弃接口，请参考 [数据并行](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/06_distributed_training/cluster_quick_start_collective_cn.html) 的文档修改模型代码。[#50351](https://github.com/PaddlePaddle/Paddle/pull/50351)，[#50501](https://github.com/PaddlePaddle/Paddle/pull/50501)，[#51240](https://github.com/PaddlePaddle/Paddle/pull/51240)，[#51701](https://github.com/PaddlePaddle/Paddle/pull/51701)，[#51616](https://github.com/PaddlePaddle/Paddle/pull/51616)，[#51369](https://github.com/PaddlePaddle/Paddle/pull/51369)，[#52671](https://github.com/PaddlePaddle/Paddle/pull/52671) -- 移除框架中原有的昇腾 NPU 和寒武纪 MLU 的适配代码，全部升级为 CustomDevice 插件式适配方式，并将昇腾 NPU 和寒武纪 MLU 的适配代码迁移至 PaddleCustomDevice 仓库。 - -## 3. 训练框架（含分布式） -### Python API -#### API 支持 0 维 tensor -- API 输入支持 0 维 tensor，涉及 `paddle.reshape`、`paddle.trace`、`paddle.linalg.norm` 等 286 个 API。[#53208](https://github.com/PaddlePaddle/Paddle/pull/53208), [#53592](https://github.com/PaddlePaddle/Paddle/pull/53592), [#47074](https://github.com/PaddlePaddle/Paddle/pull/47074), [#53186](https://github.com/PaddlePaddle/Paddle/pull/53186), [#47677](https://github.com/PaddlePaddle/Paddle/pull/47677), [#49357](https://github.com/PaddlePaddle/Paddle/pull/49357), [#50237](https://github.com/PaddlePaddle/Paddle/pull/50237), [#46555](https://github.com/PaddlePaddle/Paddle/pull/46555), [#47219](https://github.com/PaddlePaddle/Paddle/pull/47219), [#47501](https://github.com/PaddlePaddle/Paddle/pull/47501), [#47858](https://github.com/PaddlePaddle/Paddle/pull/47858), [#47961](https://github.com/PaddlePaddle/Paddle/pull/47961), [#48058](https://github.com/PaddlePaddle/Paddle/pull/48058), [#48007](https://github.com/PaddlePaddle/Paddle/pull/48007), [#49755](https://github.com/PaddlePaddle/Paddle/pull/49755), [#51024](https://github.com/PaddlePaddle/Paddle/pull/51024), [#51566](https://github.com/PaddlePaddle/Paddle/pull/51566), [#51899](https://github.com/PaddlePaddle/Paddle/pull/51899), [#49813](https://github.com/PaddlePaddle/Paddle/pull/49813), [#47812](https://github.com/PaddlePaddle/Paddle/pull/47812), [#47849](https://github.com/PaddlePaddle/Paddle/pull/47849), [#47251](https://github.com/PaddlePaddle/Paddle/pull/47251), [#53125](https://github.com/PaddlePaddle/Paddle/pull/53125), [#53828](https://github.com/PaddlePaddle/Paddle/pull/53828), [#51265](https://github.com/PaddlePaddle/Paddle/pull/51265), [#47689](https://github.com/PaddlePaddle/Paddle/pull/47689), [#48452](https://github.com/PaddlePaddle/Paddle/pull/48452), [#49072](https://github.com/PaddlePaddle/Paddle/pull/49072), [#48638](https://github.com/PaddlePaddle/Paddle/pull/48638), [#49175](https://github.com/PaddlePaddle/Paddle/pull/49175), [#49279](https://github.com/PaddlePaddle/Paddle/pull/49279), [#50857](https://github.com/PaddlePaddle/Paddle/pull/50857), [#49805](https://github.com/PaddlePaddle/Paddle/pull/49805), [#47734](https://github.com/PaddlePaddle/Paddle/pull/47734), [#45992](https://github.com/PaddlePaddle/Paddle/pull/45992), [#49616](https://github.com/PaddlePaddle/Paddle/pull/49616), [#49959](https://github.com/PaddlePaddle/Paddle/pull/49959), [#50536](https://github.com/PaddlePaddle/Paddle/pull/50536), [#49544](https://github.com/PaddlePaddle/Paddle/pull/49544), [#49842](https://github.com/PaddlePaddle/Paddle/pull/49842), [#46909](https://github.com/PaddlePaddle/Paddle/pull/46909), [#49361](https://github.com/PaddlePaddle/Paddle/pull/49361), [#50169](https://github.com/PaddlePaddle/Paddle/pull/50169), [#48314](https://github.com/PaddlePaddle/Paddle/pull/48314), [#48735](https://github.com/PaddlePaddle/Paddle/pull/48735), [#49122](https://github.com/PaddlePaddle/Paddle/pull/49122), [#49122](https://github.com/PaddlePaddle/Paddle/pull/49122), [#49177](https://github.com/PaddlePaddle/Paddle/pull/49177), [#49501](https://github.com/PaddlePaddle/Paddle/pull/49501), [#49562](https://github.com/PaddlePaddle/Paddle/pull/49562), [#49340](https://github.com/PaddlePaddle/Paddle/pull/49340), [#49550](https://github.com/PaddlePaddle/Paddle/pull/49550), [#49596](https://github.com/PaddlePaddle/Paddle/pull/49596), [#49730](https://github.com/PaddlePaddle/Paddle/pull/49730), [#49667](https://github.com/PaddlePaddle/Paddle/pull/49667), [#49692](https://github.com/PaddlePaddle/Paddle/pull/49692), [#49854](https://github.com/PaddlePaddle/Paddle/pull/49854), [#49845](https://github.com/PaddlePaddle/Paddle/pull/49845), [#49803](https://github.com/PaddlePaddle/Paddle/pull/49803), [#49889](https://github.com/PaddlePaddle/Paddle/pull/49889), [#49904](https://github.com/PaddlePaddle/Paddle/pull/49904), [#49518](https://github.com/PaddlePaddle/Paddle/pull/49518), [#49884](https://github.com/PaddlePaddle/Paddle/pull/49884), [#49880](https://github.com/PaddlePaddle/Paddle/pull/49880), [#49862](https://github.com/PaddlePaddle/Paddle/pull/49862), [#49921](https://github.com/PaddlePaddle/Paddle/pull/49921), [#49260](https://github.com/PaddlePaddle/Paddle/pull/49260), [#49929](https://github.com/PaddlePaddle/Paddle/pull/49929), [#49570](https://github.com/PaddlePaddle/Paddle/pull/49570), [#49882](https://github.com/PaddlePaddle/Paddle/pull/49882), [#50213](https://github.com/PaddlePaddle/Paddle/pull/50213), [#49780](https://github.com/PaddlePaddle/Paddle/pull/49780), [#50271](https://github.com/PaddlePaddle/Paddle/pull/50271), [#50289](https://github.com/PaddlePaddle/Paddle/pull/50289), [#50293](https://github.com/PaddlePaddle/Paddle/pull/50293), [#49735](https://github.com/PaddlePaddle/Paddle/pull/49735), [#50433](https://github.com/PaddlePaddle/Paddle/pull/50433), [#49847](https://github.com/PaddlePaddle/Paddle/pull/49847), [#50635](https://github.com/PaddlePaddle/Paddle/pull/50635), [#50950](https://github.com/PaddlePaddle/Paddle/pull/50950), [#50947](https://github.com/PaddlePaddle/Paddle/pull/50947), [#49460](https://github.com/PaddlePaddle/Paddle/pull/49460), [#53087](https://github.com/PaddlePaddle/Paddle/pull/53087), [#51687](https://github.com/PaddlePaddle/Paddle/pull/51687), [#52185](https://github.com/PaddlePaddle/Paddle/pull/52185), [#54649](https://github.com/PaddlePaddle/Paddle/pull/54649) -- API 输出支持 0 维 tensor，涉及 `paddle.sum`、`paddle.min/max`、`paddle.any/all` 等 90 个 API。[#52891](https://github.com/PaddlePaddle/Paddle/pull/52891), [#52861](https://github.com/PaddlePaddle/Paddle/pull/52861), [#52775](https://github.com/PaddlePaddle/Paddle/pull/52775), [#52850](https://github.com/PaddlePaddle/Paddle/pull/52850), [#52843](https://github.com/PaddlePaddle/Paddle/pull/52843), [#52857](https://github.com/PaddlePaddle/Paddle/pull/52857), [#51721](https://github.com/PaddlePaddle/Paddle/pull/51721), [#53051](https://github.com/PaddlePaddle/Paddle/pull/53051), [#53192](https://github.com/PaddlePaddle/Paddle/pull/53192), [#52739](https://github.com/PaddlePaddle/Paddle/pull/52739), [#52741](https://github.com/PaddlePaddle/Paddle/pull/52741), [#53175](https://github.com/PaddlePaddle/Paddle/pull/53175), [#51889](https://github.com/PaddlePaddle/Paddle/pull/51889), [#53199](https://github.com/PaddlePaddle/Paddle/pull/53199), [#53242](https://github.com/PaddlePaddle/Paddle/pull/53242), [#53421](https://github.com/PaddlePaddle/Paddle/pull/53421) -- 支持 0 维 tensor 后，修正原有不规范的代码，及对模型代码中的非规范用法进行提示和兼容。[#51562](https://github.com/PaddlePaddle/Paddle/pull/51562), [#51586](https://github.com/PaddlePaddle/Paddle/pull/51586), [#51757](https://github.com/PaddlePaddle/Paddle/pull/51757), [#52197](https://github.com/PaddlePaddle/Paddle/pull/52197), [#54117](https://github.com/PaddlePaddle/Paddle/pull/54117)。 - -#### new API -- 新增 jacobian 和 hessian API，用于科学计算。[#53331](https://github.com/PaddlePaddle/Paddle/pull/53331) -- 新增稀疏计算 API。例如 `paddle.sparse.reshape`、`paddle.sparse.sum` 和 `paddle.sparse.slice` 等。[#46694](https://github.com/PaddlePaddle/Paddle/pull/46694), [#51513](https://github.com/PaddlePaddle/Paddle/pull/51513), [#53794](https://github.com/PaddlePaddle/Paddle/pull/53794), [#51406](https://github.com/PaddlePaddle/Paddle/pull/51406) -- 新增其它 API。例如 `paddle.optimizer.LBFGS`、`paddle.index_put` 和 `paddle.logaddexp` 等。[#53314](https://github.com/PaddlePaddle/Paddle/pull/53314), [#51912](https://github.com/PaddlePaddle/Paddle/pull/51912), [#52886](https://github.com/PaddlePaddle/Paddle/pull/52886), [#50843](https://github.com/PaddlePaddle/Paddle/pull/50843), [#47282](https://github.com/PaddlePaddle/Paddle/pull/47282), [#52284](https://github.com/PaddlePaddle/Paddle/pull/52284) - -### 动态图 -#### 新功能 -- 新增了 paddle.nn.utils.clip_grad_norm_用于支持梯度裁剪和 paddle.Tensor.data_ptr 用于获取 Tensor 数据的内存/显存的地址 [PR49935](https://github.com/PaddlePaddle/Paddle/pull/49935)[, PR48235](https://github.com/PaddlePaddle/Paddle/pull/48235), [PR49173](https://github.com/PaddlePaddle/Paddle/pull/49173) -- 新增了 saved_tensors_hooks 机制，用于临时存放和取回用于反向计算使用的前向 Tensor。 [PR45763](https://github.com/PaddlePaddle/Paddle/pull/45763), [PR46215](https://github.com/PaddlePaddle/Paddle/pull/46215), [PR48124](https://github.com/PaddlePaddle/Paddle/pull/48124) -- Tensor 支持了 pickler，用于支持 Tensor 的序列化。 [PR47025](https://github.com/PaddlePaddle/Paddle/pull/47025), [PR48179](https://github.com/PaddlePaddle/Paddle/pull/48179) -- 新增了调试日志，反向出现 nan/inf 时打印前向 Python 堆栈 [PR53217](https://github.com/PaddlePaddle/Paddle/pull/53217) [PR52639](https://github.com/PaddlePaddle/Paddle/pull/52639) [PR52729](https://github.com/PaddlePaddle/Paddle/pull/52729) -- 新增了对 expand_v2, tile, concat, assign, slice 高阶微分的支持。[PR45941](https://github.com/PaddlePaddle/Paddle/pull/45941)[, PR45942](https://github.com/PaddlePaddle/Paddle/pull/45942)[, PR45940](https://github.com/PaddlePaddle/Paddle/pull/45940)[, PR45879](https://github.com/PaddlePaddle/Paddle/pull/45879), [PR45960](https://github.com/PaddlePaddle/Paddle/pull/45960) - -#### 功能优化 -- 优化了动态图的日志打印，包括日志内容优化、VLog 级别优化、报错内容优化等。[PR45783](https://github.com/PaddlePaddle/Paddle/pull/45783), [PR46349](https://github.com/PaddlePaddle/Paddle/pull/46349), [PR46934](https://github.com/PaddlePaddle/Paddle/pull/46934), [PR47724](https://github.com/PaddlePaddle/Paddle/pull/47724) -- 新增了 FLAGS_auto_growth_chunk_size_in_mb 用于 auto_growth_allocator 最小 chunk size 的设置 [PR52204](https://github.com/PaddlePaddle/Paddle/pull/52204) - -#### bug fix -- 修复了一些算子的 bug，包括：batch_norm, slice, set_value, scale, multinomial, adam, conv, transpose2_grad, conv2d_transpose_double_grad。[PR47802](https://github.com/PaddlePaddle/Paddle/pull/47802), [PR47634](https://github.com/PaddlePaddle/Paddle/pull/47634), [PR47349](https://github.com/PaddlePaddle/Paddle/pull/47349), [PR46124](https://github.com/PaddlePaddle/Paddle/pull/46124), [PR46147](https://github.com/PaddlePaddle/Paddle/pull/46147), [PR50388](https://github.com/PaddlePaddle/Paddle/pull/50388), [PR48626](https://github.com/PaddlePaddle/Paddle/pull/48626), [PR48519](https://github.com/PaddlePaddle/Paddle/pull/48519), [PR50386](https://github.com/PaddlePaddle/Paddle/pull/50386), [PR48432](https://github.com/PaddlePaddle/Paddle/pull/48432), [PR51851](https://github.com/PaddlePaddle/Paddle/pull/51851) -- 修复了 PyLayer 的一些错误问题。[PR51740](https://github.com/PaddlePaddle/Paddle/pull/51740), [PR47154](https://github.com/PaddlePaddle/Paddle/pull/47154), [PR47323](https://github.com/PaddlePaddle/Paddle/pull/47323), [PR54041](https://github.com/PaddlePaddle/Paddle/pull/54041), [PR48533](https://github.com/PaddlePaddle/Paddle/pull/48533) -- 确保 sync_batch_norm 在反向有序，防止错序导致 hang 或精度错误。[PR52268](https://github.com/PaddlePaddle/Paddle/pull/52268), [PR52860](https://github.com/PaddlePaddle/Paddle/pull/52860), [PR52779](https://github.com/PaddlePaddle/Paddle/pull/52779) -- 修复了 linspace 在 AMP 下的 bug。[PR46088](https://github.com/PaddlePaddle/Paddle/pull/46088) -- 修复了 Python C API 错误调用导致 Windows 崩溃的问题。[PR46833](https://github.com/PaddlePaddle/Paddle/pull/46833) -- 修复了 DataLoader 可能遗漏删除/dev/shm 的问题。[PR48511](https://github.com/PaddlePaddle/Paddle/pull/48511) -- 修复了 paddle.grad 的一些问题。[PR47151](https://github.com/PaddlePaddle/Paddle/pull/47151) -- 为不支持高阶微分的算子添加报错信息。[PR47231](https://github.com/PaddlePaddle/Paddle/pull/47231) -- 为 python 运算符添加 numpyarray 的支持。[PR48229](https://github.com/PaddlePaddle/Paddle/pull/48229) -- 有两处 element_size 接口，删除其中之一。[PR49631](https://github.com/PaddlePaddle/Paddle/pull/49631) -- 修复老动态图开 VLOG 崩溃问题。[PR47115](https://github.com/PaddlePaddle/Paddle/pull/47115) -- XPU，d2d 时，改成 d2h+h2d，规避多线程问题 。[PR48373](https://github.com/PaddlePaddle/Paddle/pull/48373) - -#### 性能优化 -- Python 运算符下沉到 C++实现，以提升 API 性能, 下沉后该类 API 有 3~6 倍性能提升。[PR45811](https://github.com/PaddlePaddle/Paddle/pull/45811), [PR46326](https://github.com/PaddlePaddle/Paddle/pull/46326), [PR46329](https://github.com/PaddlePaddle/Paddle/pull/46329), [PR46520](https://github.com/PaddlePaddle/Paddle/pull/46520), [PR46542](https://github.com/PaddlePaddle/Paddle/pull/46542), [PR46565](https://github.com/PaddlePaddle/Paddle/pull/46565), [PR47060](https://github.com/PaddlePaddle/Paddle/pull/47060), [PR47077](https://github.com/PaddlePaddle/Paddle/pull/47077), [PR47174](https://github.com/PaddlePaddle/Paddle/pull/47174), [PR47315](https://github.com/PaddlePaddle/Paddle/pull/47315) -- 优化了 Optimizer CPU 调度性能，可减少 Optimizer 阶段导致的 GPU Gap。 [PR49787](https://github.com/PaddlePaddle/Paddle/pull/49787), [PR50188](https://github.com/PaddlePaddle/Paddle/pull/50188)[, PR51340](https://github.com/PaddlePaddle/Paddle/pull/51340), [PR49864](https://github.com/PaddlePaddle/Paddle/pull/49864), [PR50158](https://github.com/PaddlePaddle/Paddle/pull/50158), [PR50335](https://github.com/PaddlePaddle/Paddle/pull/50335) -- API 中可下沉到 C++的逻辑，下沉到 C++，以提升 API 性能。[PR46412](https://github.com/PaddlePaddle/Paddle/pull/46412), [PR46190](https://github.com/PaddlePaddle/Paddle/pull/46190) -- 优化动态图下 Python 端不必要的调用逻辑，以提升 API 性能。[PR46221](https://github.com/PaddlePaddle/Paddle/pull/46221), [PR49473](https://github.com/PaddlePaddle/Paddle/pull/49473), [PR49574](https://github.com/PaddlePaddle/Paddle/pull/49574), [PR49589](https://github.com/PaddlePaddle/Paddle/pull/49589), [PR49612](https://github.com/PaddlePaddle/Paddle/pull/49612), [PR49717](https://github.com/PaddlePaddle/Paddle/pull/49717)[, PR49733](https://github.com/PaddlePaddle/Paddle/pull/49733), [PR49823](https://github.com/PaddlePaddle/Paddle/pull/49823)[, PR49508](https://github.com/PaddlePaddle/Paddle/pull/49508), [PR46840](https://github.com/PaddlePaddle/Paddle/pull/46840) -- 优化了 Allocator 的使用，以提升动态图 API 调度性能。[PR47125](https://github.com/PaddlePaddle/Paddle/pull/47125), [PR48548](https://github.com/PaddlePaddle/Paddle/pull/48548), [PR50995](https://github.com/PaddlePaddle/Paddle/pull/50995), [PR47731](https://github.com/PaddlePaddle/Paddle/pull/47731) -- 优化了 fused_attention 算子性能。[PR48902](https://github.com/PaddlePaddle/Paddle/pull/48902) -- optimizer 的_add_accumulator，如果 device 是 CPU，且在动态图下，直接使用 full 初始化 var。[PR48189](https://github.com/PaddlePaddle/Paddle/pull/48189) -- 对反向图不必要执行的 subgraph 进行剪枝以提升性能。[PR47827](https://github.com/PaddlePaddle/Paddle/pull/47827) -- 优化了 initalizers 的性能。[PR46033](https://github.com/PaddlePaddle/Paddle/pull/46033) -- 新增 fused dropout add 算子，提升 dropout 和 add 一起计算的性能。[#52903](https://github.com/PaddlePaddle/Paddle/pull/52903) - -### 静态图 -#### 静态图新执行器全面上线 -静态图新执行器实现多项功能和性能优化，完成对原有多套旧执行器的统一和替换，成为静态图单卡和分布式训练 python 端入口以及动转静、控制流、CINN 等后端默认使用的执行引擎，大幅提升框架调度性能，功能架构更加清晰，二次开发能力显著增强。[#45913](https://github.com/PaddlePaddle/Paddle/pull/45913)，[#46025](https://github.com/PaddlePaddle/Paddle/pull/46025)，[#48911](https://github.com/PaddlePaddle/Paddle/pull/48911)，[#50239](https://github.com/PaddlePaddle/Paddle/pull/50239)，[#45696](https://github.com/PaddlePaddle/Paddle/pull/45696)，[#46092](https://github.com/PaddlePaddle/Paddle/pull/46092)，[#48158](https://github.com/PaddlePaddle/Paddle/pull/48158),[#51389](https://github.com/PaddlePaddle/Paddle/pull/51389)，[#49708](https://github.com/PaddlePaddle/Paddle/pull/49708)，[#49275](https://github.com/PaddlePaddle/Paddle/pull/49275),[#48789](https://github.com/PaddlePaddle/Paddle/pull/48789)，[#49939](https://github.com/PaddlePaddle/Paddle/pull/49939)，[#51149](https://github.com/PaddlePaddle/Paddle/pull/51149)，[#52652](https://github.com/PaddlePaddle/Paddle/pull/52652) - -### 算子库 -#### 自定义算子等功能增强 -包括：全新支持了自定义扩展机制，实现将 C++ 扩展的运算函数绑定至 Python 端使用，进一步提升了框架的二次开发能力；扩展支持自定义硬件上使用自定义算子机制，以满足硬件厂商实现非 Paddle 已有算子的需求；扩展支持了在自定义算子中实现`inplace`、`vector`输出、`optional`输入等高阶机制；优化了自定义算子在动态图模式下的调度性能，多输入参数的算子性能提升 25.4%；为自定义算子 Tensor 扩展新增了常用运算符及 API，支持链式调用，简化代码写法。对算子内核选择机制进行了优化；对部分算子内核进行了逻辑完善、支持数据类型增强以及性能优化；新增以及完善 XPU 内核 100+；修复各项 Bug 累计 170+。 -[#49222](https://github.com/PaddlePaddle/Paddle/pull/49222), [#51773](https://github.com/PaddlePaddle/Paddle/pull/51773), [#51923](https://github.com/PaddlePaddle/Paddle/pull/51923), [#53080](https://github.com/PaddlePaddle/Paddle/pull/53080), [#50731](https://github.com/PaddlePaddle/Paddle/pull/50731), [#50563](https://github.com/PaddlePaddle/Paddle/pull/50563), [#50840](https://github.com/PaddlePaddle/Paddle/pull/50840), [#50983](https://github.com/PaddlePaddle/Paddle/pull/50983), [#51713](https://github.com/PaddlePaddle/Paddle/pull/51713), [#48733](https://github.com/PaddlePaddle/Paddle/pull/48733), [#50558](https://github.com/PaddlePaddle/Paddle/pull/50558), [#50764](https://github.com/PaddlePaddle/Paddle/pull/50764), [#51973](https://github.com/PaddlePaddle/Paddle/pull/51973), [#52216](https://github.com/PaddlePaddle/Paddle/pull/52216), [#51027](https://github.com/PaddlePaddle/Paddle/pull/51027), [#50745](https://github.com/PaddlePaddle/Paddle/pull/50745), [#50756](https://github.com/PaddlePaddle/Paddle/pull/50756), [#50886](https://github.com/PaddlePaddle/Paddle/pull/50886), [#50813](https://github.com/PaddlePaddle/Paddle/pull/50813), [#50869](https://github.com/PaddlePaddle/Paddle/pull/50869), [#51085](https://github.com/PaddlePaddle/Paddle/pull/51085), [#51646](https://github.com/PaddlePaddle/Paddle/pull/51646), [#51620](https://github.com/PaddlePaddle/Paddle/pull/51620), [#51844](https://github.com/PaddlePaddle/Paddle/pull/51844), [#52421](https://github.com/PaddlePaddle/Paddle/pull/52421), [#52872](https://github.com/PaddlePaddle/Paddle/pull/52872), [#52597](https://github.com/PaddlePaddle/Paddle/pull/52597), [#50582](https://github.com/PaddlePaddle/Paddle/pull/50582), [#52114](https://github.com/PaddlePaddle/Paddle/pull/52114), [#52915](https://github.com/PaddlePaddle/Paddle/pull/52915), [#50928](https://github.com/PaddlePaddle/Paddle/pull/50928), [#48272](https://github.com/PaddlePaddle/Paddle/pull/48272), [#48702](https://github.com/PaddlePaddle/Paddle/pull/48702), [#52191](https://github.com/PaddlePaddle/Paddle/pull/52191), [#52191](https://github.com/PaddlePaddle/Paddle/pull/52191), [#47374](https://github.com/PaddlePaddle/Paddle/pull/47374), [#47375](https://github.com/PaddlePaddle/Paddle/pull/47375), [#47378](https://github.com/PaddlePaddle/Paddle/pull/47378), [#54126](https://github.com/PaddlePaddle/Paddle/pull/54126), [#47638](https://github.com/PaddlePaddle/Paddle/pull/47638), [#47661](https://github.com/PaddlePaddle/Paddle/pull/47661), [#50606](https://github.com/PaddlePaddle/Paddle/pull/50606), [#53528](https://github.com/PaddlePaddle/Paddle/pull/53528), [#50599](https://github.com/PaddlePaddle/Paddle/pull/50599), [#51727](https://github.com/PaddlePaddle/Paddle/pull/51727), [#50825](https://github.com/PaddlePaddle/Paddle/pull/50825), [#50773](https://github.com/PaddlePaddle/Paddle/pull/50773), [#50979](https://github.com/PaddlePaddle/Paddle/pull/50979), [#53336](https://github.com/PaddlePaddle/Paddle/pull/53336), [#53555](https://github.com/PaddlePaddle/Paddle/pull/53555), [#53716](https://github.com/PaddlePaddle/Paddle/pull/53716), [#53753](https://github.com/PaddlePaddle/Paddle/pull/53753), [#53981](https://github.com/PaddlePaddle/Paddle/pull/53981), [#53977](https://github.com/PaddlePaddle/Paddle/pull/53977), [#53980](https://github.com/PaddlePaddle/Paddle/pull/53980), [#54043](https://github.com/PaddlePaddle/Paddle/pull/54043), [#54066](https://github.com/PaddlePaddle/Paddle/pull/54066), [#52866](https://github.com/PaddlePaddle/Paddle/pull/52866), [#53043](https://github.com/PaddlePaddle/Paddle/pull/53043), [#53325](https://github.com/PaddlePaddle/Paddle/pull/53325), [#54323](https://github.com/PaddlePaddle/Paddle/pull/54323), [#54367](https://github.com/PaddlePaddle/Paddle/pull/54367), [#51353](https://github.com/PaddlePaddle/Paddle/pull/51353), [#53749](https://github.com/PaddlePaddle/Paddle/pull/53749), [#50013](https://github.com/PaddlePaddle/Paddle/pull/50013), [#47570](https://github.com/PaddlePaddle/Paddle/pull/47570), [#50997](https://github.com/PaddlePaddle/Paddle/pull/50997), [#51241](https://github.com/PaddlePaddle/Paddle/pull/51241), [#49537](https://github.com/PaddlePaddle/Paddle/pull/49537) - -#### 算子体系架构统一 -具体包括：将原算子体系下剩余的 350+算子内核全部统一到 PHI 算子库中，以及原算子体系中的算子定义方式也都统一为 PHI 算子库的算子定义形式（基于 YAML 配置定义算子），提升了架构统一性，降低了框架开发的理解成本；将 PHI 算子库依赖的 Fluid 头文件全部解耦，并独立编译为动态链接库，为框架的二次开发提供更轻量的算子库复用方式；继续对飞桨框架中不规范的算子以及算子内核进行规范化调整，便于开发者理解，降低了硬件的接入成本。 -[#47856](https://github.com/PaddlePaddle/Paddle/pull/47856), [#49328](https://github.com/PaddlePaddle/Paddle/pull/49328), [#49138](https://github.com/PaddlePaddle/Paddle/pull/49138), [#52014](https://github.com/PaddlePaddle/Paddle/pull/52014), [#52044](https://github.com/PaddlePaddle/Paddle/pull/52044), [#52116](https://github.com/PaddlePaddle/Paddle/pull/52116), [#52486](https://github.com/PaddlePaddle/Paddle/pull/52486), [#52101](https://github.com/PaddlePaddle/Paddle/pull/52101), [#52882](https://github.com/PaddlePaddle/Paddle/pull/52882), [#53003](https://github.com/PaddlePaddle/Paddle/pull/53003), [#53034](https://github.com/PaddlePaddle/Paddle/pull/53034), [#51914](https://github.com/PaddlePaddle/Paddle/pull/51914), [#49116](https://github.com/PaddlePaddle/Paddle/pull/49116), [#52626](https://github.com/PaddlePaddle/Paddle/pull/52626), [#52878](https://github.com/PaddlePaddle/Paddle/pull/52878), [#52879](https://github.com/PaddlePaddle/Paddle/pull/52879), [#52880](https://github.com/PaddlePaddle/Paddle/pull/52880), [#52875](https://github.com/PaddlePaddle/Paddle/pull/52875), [#51600](https://github.com/PaddlePaddle/Paddle/pull/51600), [#51601](https://github.com/PaddlePaddle/Paddle/pull/51601), [#51590](https://github.com/PaddlePaddle/Paddle/pull/51590), [#51887](https://github.com/PaddlePaddle/Paddle/pull/51887), [#51891](https://github.com/PaddlePaddle/Paddle/pull/51891), [#52036](https://github.com/PaddlePaddle/Paddle/pull/52036), [#52130](https://github.com/PaddlePaddle/Paddle/pull/52130), [#52134](https://github.com/PaddlePaddle/Paddle/pull/52134), [#51951](https://github.com/PaddlePaddle/Paddle/pull/51951), [#51886](https://github.com/PaddlePaddle/Paddle/pull/51886), [#52274](https://github.com/PaddlePaddle/Paddle/pull/52274), [#52263](https://github.com/PaddlePaddle/Paddle/pull/52263), [#51913](https://github.com/PaddlePaddle/Paddle/pull/51913), [#52145](https://github.com/PaddlePaddle/Paddle/pull/52145), [#52347](https://github.com/PaddlePaddle/Paddle/pull/52347), [#52370](https://github.com/PaddlePaddle/Paddle/pull/52370), [#52437](https://github.com/PaddlePaddle/Paddle/pull/52437), [#52424](https://github.com/PaddlePaddle/Paddle/pull/52424), [#52231](https://github.com/PaddlePaddle/Paddle/pull/52231), [#52522](https://github.com/PaddlePaddle/Paddle/pull/52522), [#52529](https://github.com/PaddlePaddle/Paddle/pull/52529), [#52802](https://github.com/PaddlePaddle/Paddle/pull/52802), [#52799](https://github.com/PaddlePaddle/Paddle/pull/52799), [#52855](https://github.com/PaddlePaddle/Paddle/pull/52855), [#52711](https://github.com/PaddlePaddle/Paddle/pull/52711), [#52940](https://github.com/PaddlePaddle/Paddle/pull/52940), [#53309](https://github.com/PaddlePaddle/Paddle/pull/53309), [#47817](https://github.com/PaddlePaddle/Paddle/pull/47817), [#48001](https://github.com/PaddlePaddle/Paddle/pull/48001), [#48063](https://github.com/PaddlePaddle/Paddle/pull/48063), [#48049](https://github.com/PaddlePaddle/Paddle/pull/48049), [#48168](https://github.com/PaddlePaddle/Paddle/pull/48168), [#48415](https://github.com/PaddlePaddle/Paddle/pull/48415), [#48696](https://github.com/PaddlePaddle/Paddle/pull/48696), [#48970](https://github.com/PaddlePaddle/Paddle/pull/48970), [#50183](https://github.com/PaddlePaddle/Paddle/pull/50183), [#50407](https://github.com/PaddlePaddle/Paddle/pull/50407), [#50498](https://github.com/PaddlePaddle/Paddle/pull/50498), [#50419](https://github.com/PaddlePaddle/Paddle/pull/50419), [#50282](https://github.com/PaddlePaddle/Paddle/pull/50282), [#50870](https://github.com/PaddlePaddle/Paddle/pull/50870), [#50911](https://github.com/PaddlePaddle/Paddle/pull/50911), [#50865](https://github.com/PaddlePaddle/Paddle/pull/50865), [#51288](https://github.com/PaddlePaddle/Paddle/pull/51288), [#53735](https://github.com/PaddlePaddle/Paddle/pull/53735), [#47248](https://github.com/PaddlePaddle/Paddle/pull/47248), [#47787](https://github.com/PaddlePaddle/Paddle/pull/47787), [#52202](https://github.com/PaddlePaddle/Paddle/pull/52202), -[#47579](https://github.com/PaddlePaddle/Paddle/pull/47579), [#49444](https://github.com/PaddlePaddle/Paddle/pull/49444), [#45772](https://github.com/PaddlePaddle/Paddle/pull/45772), [#51264](https://github.com/PaddlePaddle/Paddle/pull/51264), [#51634](https://github.com/PaddlePaddle/Paddle/pull/51634), [#51631](https://github.com/PaddlePaddle/Paddle/pull/51631), [#47385](https://github.com/PaddlePaddle/Paddle/pull/47385), [#46342](https://github.com/PaddlePaddle/Paddle/pull/46342), [#47510](https://github.com/PaddlePaddle/Paddle/pull/47510), [#47532](https://github.com/PaddlePaddle/Paddle/pull/47532), [#47702](https://github.com/PaddlePaddle/Paddle/pull/47702), [#47860](https://github.com/PaddlePaddle/Paddle/pull/47860), [#49470](https://github.com/PaddlePaddle/Paddle/pull/49470), [#50358](https://github.com/PaddlePaddle/Paddle/pull/50358), [#49121](https://github.com/PaddlePaddle/Paddle/pull/49121), [#50190](https://github.com/PaddlePaddle/Paddle/pull/50190), [#52374](https://github.com/PaddlePaddle/Paddle/pull/52374), [#52372](https://github.com/PaddlePaddle/Paddle/pull/52372), [#52375](https://github.com/PaddlePaddle/Paddle/pull/52375), [#52371](https://github.com/PaddlePaddle/Paddle/pull/52371) - -### 动转静加组合算子 -#### 新功能 -- 组合算子添加 dropout, silu, stack, relu, expand, unsqueeze, pow, squeeze, meshgrid, batch_norm, layer_norm, group_norm, instance_norm, full_like, split, split_with_num, gelu, mean, flatten, rsqrt, hadswish 算子的组合规则 [#50497](https://github.com/PaddlePaddle/Paddle/pull/50497), [#50838](https://github.com/PaddlePaddle/Paddle/pull/50838), [#50861](https://github.com/PaddlePaddle/Paddle/pull/50861), [#50819](https://github.com/PaddlePaddle/Paddle/pull/50819), [#50810](https://github.com/PaddlePaddle/Paddle/pull/50810), [#51527](https://github.com/PaddlePaddle/Paddle/pull/51527), [#51070](https://github.com/PaddlePaddle/Paddle/pull/51070), [#51539](https://github.com/PaddlePaddle/Paddle/pull/51539), [#51061](https://github.com/PaddlePaddle/Paddle/pull/51061), [#49894](https://github.com/PaddlePaddle/Paddle/pull/49894), [#50422](https://github.com/PaddlePaddle/Paddle/pull/50422), [#51874](https://github.com/PaddlePaddle/Paddle/pull/51874), [#51341](https://github.com/PaddlePaddle/Paddle/pull/51341), [#50295](https://github.com/PaddlePaddle/Paddle/pull/50295), [#50298](https://github.com/PaddlePaddle/Paddle/pull/50298), [#50672](https://github.com/PaddlePaddle/Paddle/pull/50672), [#51432](https://github.com/PaddlePaddle/Paddle/pull/51432), [#51003](https://github.com/PaddlePaddle/Paddle/pull/51003) -- 组合算子添加 gather_nd, reduce_max, group_norm, relu, reduce_max, gather, topk, sqrt, elementwise_pow, softmax, batch_norm, prod, multiply, expand, div, relu, slice, cumsum, sigmoid, layer_norm, sin, cos, roll, instance_norm, abs, assign, tile, scatter_nd_add, erf, floor, log, silu, leaky_relu, pad 算子的 vjp 规则 [#50966](https://github.com/PaddlePaddle/Paddle/pull/50966), [#51653](https://github.com/PaddlePaddle/Paddle/pull/51653), [#52663](https://github.com/PaddlePaddle/Paddle/pull/52663), [#51742](https://github.com/PaddlePaddle/Paddle/pull/51742), [#52203](https://github.com/PaddlePaddle/Paddle/pull/52203), [#50794](https://github.com/PaddlePaddle/Paddle/pull/50794), [#50305](https://github.com/PaddlePaddle/Paddle/pull/50305), [#50786](https://github.com/PaddlePaddle/Paddle/pull/50786), [#50679](https://github.com/PaddlePaddle/Paddle/pull/50679), [#51045](https://github.com/PaddlePaddle/Paddle/pull/51045), [#51230](https://github.com/PaddlePaddle/Paddle/pull/51230), [#51474](https://github.com/PaddlePaddle/Paddle/pull/51474), [#51283](https://github.com/PaddlePaddle/Paddle/pull/51283), [#51238](https://github.com/PaddlePaddle/Paddle/pull/51238), [#49831](https://github.com/PaddlePaddle/Paddle/pull/49831), [#51838](https://github.com/PaddlePaddle/Paddle/pull/51838), [#50771](https://github.com/PaddlePaddle/Paddle/pull/50771), [#50565](https://github.com/PaddlePaddle/Paddle/pull/50565), [#51768](https://github.com/PaddlePaddle/Paddle/pull/51768), [#51750](https://github.com/PaddlePaddle/Paddle/pull/51750), [#51748](https://github.com/PaddlePaddle/Paddle/pull/51748), [#52532](https://github.com/PaddlePaddle/Paddle/pull/52532), [#52935](https://github.com/PaddlePaddle/Paddle/pull/52935), [#50963](https://github.com/PaddlePaddle/Paddle/pull/50963), [#51430](https://github.com/PaddlePaddle/Paddle/pull/51430), [#53141](https://github.com/PaddlePaddle/Paddle/pull/53141), [#52469](https://github.com/PaddlePaddle/Paddle/pull/52469), [#50436](https://github.com/PaddlePaddle/Paddle/pull/50436), [#51059](https://github.com/PaddlePaddle/Paddle/pull/51059), [#51296](https://github.com/PaddlePaddle/Paddle/pull/51296), [#52533](https://github.com/PaddlePaddle/Paddle/pull/52533), [#53374](https://github.com/PaddlePaddle/Paddle/pull/53374) -- 组合算子添加 matmul, tanh, elementwise 二阶微分规则 [#50452](https://github.com/PaddlePaddle/Paddle/pull/50452), [#52192](https://github.com/PaddlePaddle/Paddle/pull/52192), [#53014](https://github.com/PaddlePaddle/Paddle/pull/53014) -- 组合算子添加 exp, reduce_mean, softmax, divide, cast, layer_norm, prod, meshgrid, expand_as, dropout, concat, gather_nd, elementwise_max, elementwise_pow, reduce_max 组合算子 bf16 数据类型支持 [#54263](https://github.com/PaddlePaddle/Paddle/pull/54263)， [#54236](https://github.com/PaddlePaddle/Paddle/pull/54236), [#53865](https://github.com/PaddlePaddle/Paddle/pull/53865), [#54175](https://github.com/PaddlePaddle/Paddle/pull/54175), [#54399](https://github.com/PaddlePaddle/Paddle/pull/54399) -- 动转静新增控制流中的容器添加赋值语义支持 [#51248](https://github.com/PaddlePaddle/Paddle/pull/51248) -- 动转静新增全图回退功能，当动转静转换失败时，可全图回退到动态图方式执行; 回退机制增加 set_eval_frame 接口 [#50111](https://github.com/PaddlePaddle/Paddle/pull/50111), [#52006](https://github.com/PaddlePaddle/Paddle/pull/52006) -- 动转静 to_static 支持算子组合机制；支持被 to_static 装饰下使用 register_hook 的场景； [#49836](https://github.com/PaddlePaddle/Paddle/pull/49836), [#52948](https://github.com/PaddlePaddle/Paddle/pull/52948), [#53572](https://github.com/PaddlePaddle/Paddle/pull/53572) -- 动转静 to_static 接口增加 backend 参数， 可以指定为 `CINN` 或者 None，当该参数指定为 `CINN` 时，将会使用 CINN 编译器来加速训练和推理 [#52596](https://github.com/PaddlePaddle/Paddle/pull/52596) -- 新增 primitive 接口代码自动生成功能，根据 ops.yaml 和 legacy_ops.yaml 中的算子定义；自动生成 primitive 接口的代码；自动生成 Tensor 运算接口 [#50315](https://github.com/PaddlePaddle/Paddle/pull/50315), [#49654](https://github.com/PaddlePaddle/Paddle/pull/49654), [#50642](https://github.com/PaddlePaddle/Paddle/pull/50642) -- 新增算子前向组合功能，通过注册前向算子的组合规则，实现将前向算子拆分成基础算子 [#49605](https://github.com/PaddlePaddle/Paddle/pull/49605) -- 新增组合算子开关，可以在 shell 中通过设置环境变量，实现算子按照不同方式进行拆分 [#50309](https://github.com/PaddlePaddle/Paddle/pull/50309) -- 添加`OpTest`新增组合测试功能，对算子精度进行保障；添加 elementwise 类基础算子单测；添加 batch_norm 的 CINN 单测 [#50509](https://github.com/PaddlePaddle/Paddle/pull/50509), [#50807](https://github.com/PaddlePaddle/Paddle/pull/50807), [#52815](https://github.com/PaddlePaddle/Paddle/pull/52815) - -#### 功能优化 -- 添加组合算子支持 FP16 运算和 AMP O1 运算；添加 softmax 和 layer_norm 算子 AMP 逻辑 [#52397](https://github.com/PaddlePaddle/Paddle/pull/52397), [#52598](https://github.com/PaddlePaddle/Paddle/pull/52598), [#51473](https://github.com/PaddlePaddle/Paddle/pull/51473) -- 简化组合算子 batch_norm 的组合规则和 vjp 规则 [#54012](https://github.com/PaddlePaddle/Paddle/pull/54012), [#51827](https://github.com/PaddlePaddle/Paddle/pull/51827), [#51933](https://github.com/PaddlePaddle/Paddle/pull/51933), -- 组合算子优化组合规则，提升含 scalar 组合规则的性能；优化组合算子日志打印 [#51960](https://github.com/PaddlePaddle/Paddle/pull/51960), [#50160](https://github.com/PaddlePaddle/Paddle/pull/50160) -- 组合算子支持 jit.save 接口；新增自定义 VJP 规则接口 [#52344](https://github.com/PaddlePaddle/Paddle/pull/52344), [#50885](https://github.com/PaddlePaddle/Paddle/pull/50885) -- 组合算子 gather_grad 删除 overwrite 参数。 [#52707](https://github.com/PaddlePaddle/Paddle/pull/52707) -- 动转静代码风格清理，报错信息优化，规范日志 [#48637](https://github.com/PaddlePaddle/Paddle/pull/48637), [#46128](https://github.com/PaddlePaddle/Paddle/pull/46128), [#52527](https://github.com/PaddlePaddle/Paddle/pull/52527), [#46800](https://github.com/PaddlePaddle/Paddle/pull/46800),[#46415](https://github.com/PaddlePaddle/Paddle/pull/46415) -- 动转静通过调用 append backward 的方式获取`grad var name`以修复高阶梯度计算时的错误 [#53250](https://github.com/PaddlePaddle/Paddle/pull/53250) -- 动转静功能升级，清理 to_static 的临时目录以加速代码转换；增强 to_static 自动略过内部接口；支持在程序使用 to_static 装饰器 [#47102](https://github.com/PaddlePaddle/Paddle/pull/47102), [#50596](https://github.com/PaddlePaddle/Paddle/pull/50596), [#45768](https://github.com/PaddlePaddle/Paddle/pull/45768) -- 动转静优化`print`函数转换以支持在组网阶段打印 Tensor 参数；升级参数收集机制 [#48672](https://github.com/PaddlePaddle/Paddle/pull/48672), [#50336](https://github.com/PaddlePaddle/Paddle/pull/50336) - -#### bug fix -- 组合算子修复 cmake 编译错误；修复 cuda 12 测试错误；修复若干算子如 meshgird, expand_as, concat, conv, arrange 等错误[#49643](https://github.com/PaddlePaddle/Paddle/pull/49643), [#54622](https://github.com/PaddlePaddle/Paddle/pull/54622), [#53951](https://github.com/PaddlePaddle/Paddle/pull/53951), [#53951](https://github.com/PaddlePaddle/Paddle/pull/53951), [#53350](https://github.com/PaddlePaddle/Paddle/pull/53350), [#51486](https://github.com/PaddlePaddle/Paddle/pull/51486), [#52764](https://github.com/PaddlePaddle/Paddle/pull/52764) -- 组合算子修复若干 rank=1, shape=-1, amp, 多进程等场景下的 bug；[#51413](https://github.com/PaddlePaddle/Paddle/pull/51413), [#51435](https://github.com/PaddlePaddle/Paddle/pull/51435), [#50518](https://github.com/PaddlePaddle/Paddle/pull/50518), [#47301](https://github.com/PaddlePaddle/Paddle/pull/47301), -- 组合算子修复 composite grad maker 和 static prim api 自动代码生成 bug; 修复 op 创建属性丢失和部分组合规则不生效的 bug [#50854](https://github.com/PaddlePaddle/Paddle/pull/50854), [#51445](https://github.com/PaddlePaddle/Paddle/pull/51445), [#50780](https://github.com/PaddlePaddle/Paddle/pull/50780), [#52120](https://github.com/PaddlePaddle/Paddle/pull/52120) -- 组合算子修复一些其他 bug [#50086](https://github.com/PaddlePaddle/Paddle/pull/50086), [#51208](https://github.com/PaddlePaddle/Paddle/pull/51208), [#51577](https://github.com/PaddlePaddle/Paddle/pull/51577), [#53598](https://github.com/PaddlePaddle/Paddle/pull/53598), [#47500](https://github.com/PaddlePaddle/Paddle/pull/47500), [#52119](https://github.com/PaddlePaddle/Paddle/pull/52119), [#50397](https://github.com/PaddlePaddle/Paddle/pull/50397), [#50527](https://github.com/PaddlePaddle/Paddle/pull/50527), [#50788](https://github.com/PaddlePaddle/Paddle/pull/50788), [#51014](https://github.com/PaddlePaddle/Paddle/pull/51014), [#52154](https://github.com/PaddlePaddle/Paddle/pull/52154), [#52752](https://github.com/PaddlePaddle/Paddle/pull/52752) -- 动转静修复 dataloader, cond 输入 dict, transformer 导入, T5 模型内存泄露, grad var name 解析错误等 bug [#49821](https://github.com/PaddlePaddle/Paddle/pull/49821)， [#47299](https://github.com/PaddlePaddle/Paddle/pull/47299), [#50776](https://github.com/PaddlePaddle/Paddle/pull/50776), [#50883](https://github.com/PaddlePaddle/Paddle/pull/50883), [#51100](https://github.com/PaddlePaddle/Paddle/pull/51100), [#51464](https://github.com/PaddlePaddle/Paddle/pull/51464), [#51966](https://github.com/PaddlePaddle/Paddle/pull/51966), [#52110](https://github.com/PaddlePaddle/Paddle/pull/52110), [#52821](https://github.com/PaddlePaddle/Paddle/pull/52821) -- 动转静修复 Lazy 初始化，Windows 训练，is_paddle_func 失效，recurrent op 删除 pass 失败等错误 [#50785](https://github.com/PaddlePaddle/Paddle/pull/50785), [#52580](https://github.com/PaddlePaddle/Paddle/pull/52580), [#51585](https://github.com/PaddlePaddle/Paddle/pull/51585), [#51763](https://github.com/PaddlePaddle/Paddle/pull/51763), [#51763](https://github.com/PaddlePaddle/Paddle/pull/51763) +#### 新增功能 +- 新增部分算子对 BF16 数据类型的支持，包括 compare_kernel 与 add reduce_all_kernel([#63602](https://github.com/PaddlePaddle/Paddle/pull/63602))、empty([#60212](https://github.com/PaddlePaddle/Paddle/pull/60212))、hybrid_parallel_optimizer([#60213](https://github.com/PaddlePaddle/Paddle/pull/60213))、reduce_max/reduce_min([#60453](https://github.com/PaddlePaddle/Paddle/pull/60453))、all_reduce/concat/split([#62364](https://github.com/PaddlePaddle/Paddle/pull/62364))、tile/tile_grad([#63075](https://github.com/PaddlePaddle/Paddle/pull/63075))、accuracy([#63863](https://github.com/PaddlePaddle/Paddle/pull/63863)), swiglu/set_value([#64070](https://github.com/PaddlePaddle/Paddle/pull/64070))、amp_master_grad([#63865](https://github.com/PaddlePaddle/Paddle/pull/63865))、c_concat ([#63403](https://github.com/PaddlePaddle/Paddle/pull/63403))、flatten ([#63997](https://github.com/PaddlePaddle/Paddle/pull/63997))、compare_op ([#64473](https://github.com/PaddlePaddle/Paddle/pull/64473))、moment1/moment2 ([#62688](https://github.com/PaddlePaddle/Paddle/pull/62688))、fused_rope ([#60064](https://github.com/PaddlePaddle/Paddle/pull/60064))、c_softmax_with_cross_entropy ([#60472](https://github.com/PaddlePaddle/Paddle/pull/60472))、elementwise_pow/square/sin/cos ([#60402](https://github.com/PaddlePaddle/Paddle/pull/60402))、strided_slice ([#60382](https://github.com/PaddlePaddle/Paddle/pull/60382))、tile/sigmoid_grad ([#60119](https://github.com/PaddlePaddle/Paddle/pull/60119))、 elementwise_sub/elementwise_div ([#60386](https://github.com/PaddlePaddle/Paddle/pull/60386))、softmax_with_cross_entropy ([#63759](https://github.com/PaddlePaddle/Paddle/pull/63759)) +- 新增部分算子对 INT8 数据类型的支持，包括 multi_encoder_xpu ([#61212](https://github.com/PaddlePaddle/Paddle/pull/61212))、qkv_attention ([#63105](https://github.com/PaddlePaddle/Paddle/pull/63105)) +- 更新昆仑 SDK 版本包括 BKCL、XHPC、XCCL 等。 [#59895](https://github.com/PaddlePaddle/Paddle/pull/59895)、[#59888](https://github.com/PaddlePaddle/Paddle/pull/59888)、[#63624](https://github.com/PaddlePaddle/Paddle/pull/63624), [#60305](https://github.com/PaddlePaddle/Paddle/pull/60305), [#62076](https://github.com/PaddlePaddle/Paddle/pull/62076), [#62646](https://github.com/PaddlePaddle/Paddle/pull/62646), [#63520](https://github.com/PaddlePaddle/Paddle/pull/63520), [#64163](https://github.com/PaddlePaddle/Paddle/pull/64163), [#64326](https://github.com/PaddlePaddle/Paddle/pull/64326), [#60617](https://github.com/PaddlePaddle/Paddle/pull/60617), [#60377](https://github.com/PaddlePaddle/Paddle/pull/60377), [#60421](https://github.com/PaddlePaddle/Paddle/pull/60421), [#60598](https://github.com/PaddlePaddle/Paddle/pull/60598), [#61199](https://github.com/PaddlePaddle/Paddle/pull/61199) +- 新增对 memory stat 功能支持。[#61116](https://github.com/PaddlePaddle/Paddle/pull/61116) +- 新增多 stream 支持，且可以给每个 stream 分配默认的 l3/gm buffer 大小。 [#62729](https://github.com/PaddlePaddle/Paddle/pull/62729) +- 新增 nonzero 算子支持支持 simulator XPUSIM_SKIP_RUN 模式。[#60224](https://github.com/PaddlePaddle/Paddle/pull/60224)。[#60388](https://github.com/PaddlePaddle/Paddle/pull/60388) +- 新增 stride_slice 和 stride_slice_grad 算子支持 strides < 0。 [#62749](https://github.com/PaddlePaddle/Paddle/pull/62749) +- 新增 rotary_embedding 对 use_neox_rotary_style == True 的支持。[#64090](https://github.com/PaddlePaddle/Paddle/pull/64090) +- 新增融合 Pass 和融合算子，包括 cross_attention ([#63203](https://github.com/PaddlePaddle/Paddle/pull/63203))、fused_bias_act ([#62232](https://github.com/PaddlePaddle/Paddle/pull/62232))、fused_layernorm ([#62228](https://github.com/PaddlePaddle/Paddle/pull/62228))、group_norm_silu_xpu_fuse_pass ([#63342](https://github.com/PaddlePaddle/Paddle/pull/63342)) +- 新增对分布式策略 sharding stage3 的支持。 [#57457](https://github.com/PaddlePaddle/Paddle/pull/57457) +- 新增 tf32 fc quantization 模式的支持。[#62273](https://github.com/PaddlePaddle/Paddle/pull/62273) +- 新增 flash attention 算子。[#60065](https://github.com/PaddlePaddle/Paddle/pull/60065) +- 新增 roformer relative embedding pass & kernel 并支持 multi_encoder_xpu。[#62089](https://github.com/PaddlePaddle/Paddle/pull/62089) +- 新增 pp + sharding 策略支持。[#63640](https://github.com/PaddlePaddle/Paddle/pull/63640) +- 升级 XPU 通信库架构以支持动静统一的通信库功能。[#63817](https://github.com/PaddlePaddle/Paddle/pull/63817) #### 性能优化 -- 动转静调用 run_program_op 的执行过程中，增加 scope 缓存和复用机制，避免每个 step 都会传入新的 scope [#45813](https://github.com/PaddlePaddle/Paddle/pull/45813) - -### 分布式训练 -#### 动态图分布式 -- 去除旧动态图分布式 sharding 功能 API [#49334](https://github.com/PaddlePaddle/Paddle/pull/49334) -- fleet 升级到 distributed 目录 [#50834](https://github.com/PaddlePaddle/Paddle/pull/50834) -- 优化分布式策略的日志打印。[#47761](https://github.com/PaddlePaddle/Paddle/pull/47761) -- 重计算支持 hook 模式、inplace 功能、stop_gradient 模式，支持更灵活的使用。 [#48471](https://github.com/PaddlePaddle/Paddle/pull/48471), [#47985](https://github.com/PaddlePaddle/Paddle/pull/47985) -- 数据并行 - - 数据并行支持 no_sync 接口，用于屏蔽参数梯度通信；参数同步功能；添加 scale 接口，缩放参数。[#47536](https://github.com/PaddlePaddle/Paddle/pull/47536),[#51895](https://github.com/PaddlePaddle/Paddle/pull/51895),[#47519](https://github.com/PaddlePaddle/Paddle/pull/47519) - - 修复数据并行下显存泄露问题。[#47369](https://github.com/PaddlePaddle/Paddle/pull/47369),[#47444](https://github.com/PaddlePaddle/Paddle/pull/47444),[#48668](https://github.com/PaddlePaddle/Paddle/pull/48668) - - 支持 sparse 参数梯度同步。[#52785](https://github.com/PaddlePaddle/Paddle/pull/52785) -- 流水线并行 - - 优化流水线性能，去除通信等待，优化调度，通信 overlap。[#46209](https://github.com/PaddlePaddle/Paddle/pull/46209),[#54003](https://github.com/PaddlePaddle/Paddle/pull/54003),[#54312](https://github.com/PaddlePaddle/Paddle/pull/54312),[#53384](https://github.com/PaddlePaddle/Paddle/pull/53384),[#54310](https://github.com/PaddlePaddle/Paddle/pull/54310),[#46399](https://github.com/PaddlePaddle/Paddle/pull/46399),[#46483](https://github.com/PaddlePaddle/Paddle/pull/46483),[#46780](https://github.com/PaddlePaddle/Paddle/pull/46780),[#46116](https://github.com/PaddlePaddle/Paddle/pull/46116) - - 支持自定义切分，日志打印，随机种子设置，timer 耗时打印。[#53344](https://github.com/PaddlePaddle/Paddle/pull/53344), [#47670](https://github.com/PaddlePaddle/Paddle/pull/47670),[#47336](https://github.com/PaddlePaddle/Paddle/pull/47336),[#52656](https://github.com/PaddlePaddle/Paddle/pull/52656),[#53831](https://github.com/PaddlePaddle/Paddle/pull/53831) - - 优化流水线调度中的显存释放逻辑，提前释放中间变量和数据。[#54557](https://github.com/PaddlePaddle/Paddle/pull/54557), [#47199](https://github.com/PaddlePaddle/Paddle/pull/47199),[#47497](https://github.com/PaddlePaddle/Paddle/pull/47497),[#48045](https://github.com/PaddlePaddle/Paddle/pull/48045),[#54672](https://github.com/PaddlePaddle/Paddle/pull/54672) - - 支持流水线并行的 VPP 模式，模型保存。[#54196](https://github.com/PaddlePaddle/Paddle/pull/54196), [#52927](https://github.com/PaddlePaddle/Paddle/pull/52927),[#47801](https://github.com/PaddlePaddle/Paddle/pull/47801),[#45922](https://github.com/PaddlePaddle/Paddle/pull/45922),[#47242](https://github.com/PaddlePaddle/Paddle/pull/47242) -- 分组切分并行 - - sharding stage2 并行支持量化功能，混合并行训练，梯度累加，XPU 硬件，BF16 低精度计算、优化器学习率设置、offload 功能、数据并行。[#47169](https://github.com/PaddlePaddle/Paddle/pull/47169),[#47535](https://github.com/PaddlePaddle/Paddle/pull/47535), [#46795](https://github.com/PaddlePaddle/Paddle/pull/46795),[#47711](https://github.com/PaddlePaddle/Paddle/pull/47711),[#48310](https://github.com/PaddlePaddle/Paddle/pull/48310),[#46846](https://github.com/PaddlePaddle/Paddle/pull/46846),[#48857](https://github.com/PaddlePaddle/Paddle/pull/48857),[#49196](https://github.com/PaddlePaddle/Paddle/pull/49196),[#49931](https://github.com/PaddlePaddle/Paddle/pull/49931),[#47114](https://github.com/PaddlePaddle/Paddle/pull/47114),[#49767](https://github.com/PaddlePaddle/Paddle/pull/49767) - - sharing stage2 性能优化，支持通信计算 overlap。[#46495](https://github.com/PaddlePaddle/Paddle/pull/46495),[#46894](https://github.com/PaddlePaddle/Paddle/pull/46894) - - sharding stage3 支持共享参数、不可训练参数。[#48695](https://github.com/PaddlePaddle/Paddle/pull/48695),[#48577](https://github.com/PaddlePaddle/Paddle/pull/48577) -- 张量模型并行 - - 张量模型并行性能优化，减少 stream 切流对性能的影响。[#47715](https://github.com/PaddlePaddle/Paddle/pull/47715),[#51617](https://github.com/PaddlePaddle/Paddle/pull/51617) - - 支持参数、优化器状体、梯度同步。[#51428](https://github.com/PaddlePaddle/Paddle/pull/51428),[#53254](https://github.com/PaddlePaddle/Paddle/pull/53254), [#53335](https://github.com/PaddlePaddle/Paddle/pull/53335),[#45803](https://github.com/PaddlePaddle/Paddle/pull/45803),[#46303](https://github.com/PaddlePaddle/Paddle/pull/46303),[#52293](https://github.com/PaddlePaddle/Paddle/pull/52293) - - 优化张量模型并行算子，如 c_embedding、softmax_with_corss_entropy。[#53197](https://github.com/PaddlePaddle/Paddle/pull/53197),[#53547](https://github.com/PaddlePaddle/Paddle/pull/53547),[#53541](https://github.com/PaddlePaddle/Paddle/pull/53541),[#52789](https://github.com/PaddlePaddle/Paddle/pull/52789),[#46491](https://github.com/PaddlePaddle/Paddle/pull/46491),[#52742](https://github.com/PaddlePaddle/Paddle/pull/52742),[#53419](https://github.com/PaddlePaddle/Paddle/pull/53419) -- Launch 启动 - - 支持分布式 Launch 功能，保存独立日志。[#53207](https://github.com/PaddlePaddle/Paddle/pull/53207),[#50405](https://github.com/PaddlePaddle/Paddle/pull/50405) - - 新增框架打印环境变量功能，日志覆盖功能，日志返回，环境检查，便于 debug 环境变量的改动。[#53243](https://github.com/PaddlePaddle/Paddle/pull/53243),[#53243](https://github.com/PaddlePaddle/Paddle/pull/53243), [#51803](https://github.com/PaddlePaddle/Paddle/pull/51803), [#53990](https://github.com/PaddlePaddle/Paddle/pull/53990) -- 通信库 - - 增加自定义混合并行通信组，拓扑结构信息打印，自定义通信拓扑顺序。[#47021](https://github.com/PaddlePaddle/Paddle/pull/47021),[#54000](https://github.com/PaddlePaddle/Paddle/pull/54000),[#51781](https://github.com/PaddlePaddle/Paddle/pull/51781) - - 去除通信库对 Place 信息依赖 [#47857](https://github.com/PaddlePaddle/Paddle/pull/47857) - - 增加通信库对 GLOO 算子支持，支持 send/recv/gather。 [#52221](https://github.com/PaddlePaddle/Paddle/pull/52221), [#52334](https://github.com/PaddlePaddle/Paddle/pull/52334),[#49084](https://github.com/PaddlePaddle/Paddle/pull/49084) - - 禁止通信算子的反向计算。[#47636](https://github.com/PaddlePaddle/Paddle/pull/47636) - - 新增通信库静态 shape check，帮助判别通信量是否匹配。[#48256](https://github.com/PaddlePaddle/Paddle/pull/48256),[#48915](https://github.com/PaddlePaddle/Paddle/pull/48915),[#48646](https://github.com/PaddlePaddle/Paddle/pull/48646) - - 支持通信 python object 类型，BF16 类型，alltoall，reduce，allgather，group call，global gather，broadcast，scatter 通信方式，XPU 设备通信支持。[#51765](https://github.com/PaddlePaddle/Paddle/pull/51765),[#45844](https://github.com/PaddlePaddle/Paddle/pull/45844),[#48059](https://github.com/PaddlePaddle/Paddle/pull/48059),[#48115](https://github.com/PaddlePaddle/Paddle/pull/48115), [#48339](https://github.com/PaddlePaddle/Paddle/pull/48339),[#49252](https://github.com/PaddlePaddle/Paddle/pull/49252),[#49451](https://github.com/PaddlePaddle/Paddle/pull/49451),[#50085](https://github.com/PaddlePaddle/Paddle/pull/50085),[#50701](https://github.com/PaddlePaddle/Paddle/pull/50701),[#48208](https://github.com/PaddlePaddle/Paddle/pull/48208),[#48736](https://github.com/PaddlePaddle/Paddle/pull/48736),[#51762](https://github.com/PaddlePaddle/Paddle/pull/51762),[#52495](https://github.com/PaddlePaddle/Paddle/pull/52495),[#53514](https://github.com/PaddlePaddle/Paddle/pull/53514),[#48232](https://github.com/PaddlePaddle/Paddle/pull/48232),[#49896](https://github.com/PaddlePaddle/Paddle/pull/49896),[#49941](https://github.com/PaddlePaddle/Paddle/pull/49941),[#45584](https://github.com/PaddlePaddle/Paddle/pull/45584) - - 新增对计算流通信功能。[#46182](https://github.com/PaddlePaddle/Paddle/pull/46182),[#46023](https://github.com/PaddlePaddle/Paddle/pull/46023),[#46295](https://github.com/PaddlePaddle/Paddle/pull/46295),[#46761](https://github.com/PaddlePaddle/Paddle/pull/46761),[#47481](https://github.com/PaddlePaddle/Paddle/pull/47481),[#47740](https://github.com/PaddlePaddle/Paddle/pull/47740),[#47976](https://github.com/PaddlePaddle/Paddle/pull/47976),[#48163](https://github.com/PaddlePaddle/Paddle/pull/48163),[#48396](https://github.com/PaddlePaddle/Paddle/pull/48396),[#48308](https://github.com/PaddlePaddle/Paddle/pull/48308),[#47110](https://github.com/PaddlePaddle/Paddle/pull/47110),[#53089](https://github.com/PaddlePaddle/Paddle/pull/53089) - - 优化通信库 TCP 建联时间。[#49810](https://github.com/PaddlePaddle/Paddle/pull/49810),[#47184](https://github.com/PaddlePaddle/Paddle/pull/47184) - -#### 自动并行 -- 静态图半自动并行功能完善： - - 新增多个算子的 FLOPs 计算函数，并新增基于 FLOPs 的计算 Cost 建模 [#48083](https://github.com/PaddlePaddle/Paddle/pull/48083),[#47978](https://github.com/PaddlePaddle/Paddle/pull/47978),[#47595](https://github.com/PaddlePaddle/Paddle/pull/47595),[#48083](https://github.com/PaddlePaddle/Paddle/pull/48083),[#48084](https://github.com/PaddlePaddle/Paddle/pull/48084),[#47816](https://github.com/PaddlePaddle/Paddle/pull/47816) - - 接口易用性提升，完善 DistAttr, Process Mesh, Engine API、信息打印、输入输出等模块；执行 Engine 新增 cost 接口，可用于理论分析模型运行的时间和显存开销 [#47503](https://github.com/PaddlePaddle/Paddle/pull/47503),[#46416](https://github.com/PaddlePaddle/Paddle/pull/46416),[#46554](https://github.com/PaddlePaddle/Paddle/pull/46554), [#46633](https://github.com/PaddlePaddle/Paddle/pull/46633),[#49214](https://github.com/PaddlePaddle/Paddle/pull/49214),[#53848](https://github.com/PaddlePaddle/Paddle/pull/53848),[#46552](https://github.com/PaddlePaddle/Paddle/pull/46552), [#47043](https://github.com/PaddlePaddle/Paddle/pull/47043), [#49665](https://github.com/PaddlePaddle/Paddle/pull/49665), [#52912](https://github.com/PaddlePaddle/Paddle/pull/52912), [#45776](https://github.com/PaddlePaddle/Paddle/pull/45776), [#47263](https://github.com/PaddlePaddle/Paddle/pull/47263) - - 优化 Pass 的通用性和易用性升级，支持更多场景、减少 Pass 预分析耗时 [#46519](https://github.com/PaddlePaddle/Paddle/pull/46519),[#47358](https://github.com/PaddlePaddle/Paddle/pull/47358),[#46391](https://github.com/PaddlePaddle/Paddle/pull/46391), [#51035](https://github.com/PaddlePaddle/Paddle/pull/51035) - - 调试能力增强，添加分布式随机性控制机制和混合并行精度对齐工具 [#52903](https://github.com/PaddlePaddle/Paddle/pull/52903),[#49865](https://github.com/PaddlePaddle/Paddle/pull/49865) - - 支持推理生成任务组网的自动切分, 适配生成模型中的控制流、conditional block 等特殊用法 [#46771](https://github.com/PaddlePaddle/Paddle/pull/46771), [#54067](https://github.com/PaddlePaddle/Paddle/pull/54067) - - 完善 grad_clip，支持了数据并行场景下的负载均衡。[#49510](https://github.com/PaddlePaddle/Paddle/pull/49510), [#49249](https://github.com/PaddlePaddle/Paddle/pull/49249) -- 静态图半自动并行性能提升： - - 新增 Sharding Pass 自动化通信 Fuse 和 多流通信功能，GPT 6.7B 模型两机上吞吐性能提升 26% [#48604](https://github.com/PaddlePaddle/Paddle/pull/48604), [#47180](https://github.com/PaddlePaddle/Paddle/pull/47180),[#46180](https://github.com/PaddlePaddle/Paddle/pull/46180) - - 新增 Recompute 优化策略调优功能，支持根据显存和模型大小选择最优 recompute checkpoint 设置 [#48608](https://github.com/PaddlePaddle/Paddle/pull/48608),[#47846](https://github.com/PaddlePaddle/Paddle/pull/47846),[#49010](https://github.com/PaddlePaddle/Paddle/pull/49010) - - 流水线并行新增 1F1B 调度优化 Pass [#54260](https://github.com/PaddlePaddle/Paddle/pull/54260), [#45915](https://github.com/PaddlePaddle/Paddle/pull/45915) - - 数据并行优化，支持融合通信和通信计算 Overlap 等优化, GPT 1.3B 模型内性能提升 5% [#48092](https://github.com/PaddlePaddle/Paddle/pull/48092),[#45643](https://github.com/PaddlePaddle/Paddle/pull/45643),[#49744](https://github.com/PaddlePaddle/Paddle/pull/49744), [#47578](https://github.com/PaddlePaddle/Paddle/pull/47578) - - 优化 Reshard 模块 concate 性能，减少部分场景下 concate 次数。[#47809](https://github.com/PaddlePaddle/Paddle/pull/47809) - - 混合精度优化 Pass 性能升级, 支持 BF16 低精度, 适配 while 循环控制流的自动混合并行等 [#51285](https://github.com/PaddlePaddle/Paddle/pull/51285),[#51147](https://github.com/PaddlePaddle/Paddle/pull/51147), [#49219](https://github.com/PaddlePaddle/Paddle/pull/49219), [#49079](https://github.com/PaddlePaddle/Paddle/pull/49079) -- 静态图全自动并行功能完善： - - 新增基于规则的全自动搜索策略 [#51859](https://github.com/PaddlePaddle/Paddle/pull/51859),[#51908](https://github.com/PaddlePaddle/Paddle/pull/51908),[#52053](https://github.com/PaddlePaddle/Paddle/pull/52053),[#48316](https://github.com/PaddlePaddle/Paddle/pull/48316),[#48464](https://github.com/PaddlePaddle/Paddle/pull/48464), [#52041](https://github.com/PaddlePaddle/Paddle/pull/52041) - - 自动并行建模能力完善，丰富单节点内拓扑建模、通信量建模等。 [#52723](https://github.com/PaddlePaddle/Paddle/pull/52723),[#46387](https://github.com/PaddlePaddle/Paddle/pull/46387),[#47043](https://github.com/PaddlePaddle/Paddle/pull/47043) - -#### 参数服务器 -- 清空 ps 目录下 all 列表，其中 API 不暴露 [#51289](https://github.com/PaddlePaddle/Paddle/pull/51289) -- 清理 cvm 算子 [#48989](https://github.com/PaddlePaddle/Paddle/pull/48989) -- GPUPS 新增对 AFS 支持。[#46611](https://github.com/PaddlePaddle/Paddle/pull/46611) -- PGLBOX2.0 日志降级、修复 dense 参数卡住问题、修复 barrier 不生效的问题、增加 get_epoch_finish python 端接口[#49946](https://github.com/PaddlePaddle/Paddle/pull/49946),[#50166](https://github.com/PaddlePaddle/Paddle/pull/50166),[#50349](https://github.com/PaddlePaddle/Paddle/pull/50349) -- GPUPs 运行切换到指定模式。[#51115](https://github.com/PaddlePaddle/Paddle/pull/51115) -- GPUPS 加入 benchmark。[#49587](https://github.com/PaddlePaddle/Paddle/pull/49587),[#49649](https://github.com/PaddlePaddle/Paddle/pull/49649) -- GPUPS 优化器选择问题修复，修复 reader 读取问题，修复 RPC 编译问题。 [#47026](https://github.com/PaddlePaddle/Paddle/pull/47026),[#47192](https://github.com/PaddlePaddle/Paddle/pull/47192),[#49878](https://github.com/PaddlePaddle/Paddle/pull/49878), [#46356](https://github.com/PaddlePaddle/Paddle/pull/46356),[#46575](https://github.com/PaddlePaddle/Paddle/pull/46575),[#49389](https://github.com/PaddlePaddle/Paddle/pull/49389),[#46258](https://github.com/PaddlePaddle/Paddle/pull/46258),[#50136](https://github.com/PaddlePaddle/Paddle/pull/50136) -- 增加 rocksdb 编译方式。[#46074](https://github.com/PaddlePaddle/Paddle/pull/46074) - -### CUDA -#### 新功能 -- 新增对 CUDA 12.0 的编译支持，并修复相关单测 ([#49539](https://github.com/PaddlePaddle/Paddle/pull/49539), [#54542](https://github.com/PaddlePaddle/Paddle/pull/54542)) -- 新增 CUDNN Frontend API 的编译支持及相关单测，可以使用`WITH_CUDNN_FRONTEND=ON` 的编译选项进行开启。([#47524](https://github.com/PaddlePaddle/Paddle/pull/47524), [#47612](https://github.com/PaddlePaddle/Paddle/pull/47612)) - -#### 功能优化 -- 混合精度策略及精度优化： - - 新增及优化了框架 200 余个算子的 FP16、BF16 数据类型支持，包括 logsumexp，reduce_max，cumprod，sync_batch_norm，compare 类 OP 等，并对所有 FP16、BF16 算子进行了精度优化及单测覆盖，针对低精度算子完善单测框架功能，确保在大模型训推过程中精度无损。([#51193](https://github.com/PaddlePaddle/Paddle/pull/51193), [#51114](https://github.com/PaddlePaddle/Paddle/pull/51114), [#45817](https://github.com/PaddlePaddle/Paddle/pull/45817), [#52862](https://github.com/PaddlePaddle/Paddle/pull/52862), [#52919](https://github.com/PaddlePaddle/Paddle/pull/52919), [#52921](https://github.com/PaddlePaddle/Paddle/pull/52921), [#46413](https://github.com/PaddlePaddle/Paddle/pull/46413), [#48205](https://github.com/PaddlePaddle/Paddle/pull/48205), [#54193](https://github.com/PaddlePaddle/Paddle/pull/54193), [#48041](https://github.com/PaddlePaddle/Paddle/pull/48041), [#48121](https://github.com/PaddlePaddle/Paddle/pull/48121), [#46364](https://github.com/PaddlePaddle/Paddle/pull/46364), [#51153](https://github.com/PaddlePaddle/Paddle/pull/51153), [#53023](https://github.com/PaddlePaddle/Paddle/pull/53023), [#53079](https://github.com/PaddlePaddle/Paddle/pull/53079), [#53137](https://github.com/PaddlePaddle/Paddle/pull/53137), [#46212](https://github.com/PaddlePaddle/Paddle/pull/46212), [#50908](https://github.com/PaddlePaddle/Paddle/pull/50908), [#52555](https://github.com/PaddlePaddle/Paddle/pull/52555), [#51582](https://github.com/PaddlePaddle/Paddle/pull/51582), [#47897](https://github.com/PaddlePaddle/Paddle/pull/47897), [#45601](https://github.com/PaddlePaddle/Paddle/pull/45601), [#53522](https://github.com/PaddlePaddle/Paddle/pull/53522), [#52666](https://github.com/PaddlePaddle/Paddle/pull/52666), [#50101](https://github.com/PaddlePaddle/Paddle/pull/50101), [#48315](https://github.com/PaddlePaddle/Paddle/pull/48315), [#50847](https://github.com/PaddlePaddle/Paddle/pull/50847), [#50905](https://github.com/PaddlePaddle/Paddle/pull/50905), [#50906](https://github.com/PaddlePaddle/Paddle/pull/50906), [#50909](https://github.com/PaddlePaddle/Paddle/pull/50909), [#50916](https://github.com/PaddlePaddle/Paddle/pull/50916), [#50917](https://github.com/PaddlePaddle/Paddle/pull/50917), [#50920](https://github.com/PaddlePaddle/Paddle/pull/50920), [#50919](https://github.com/PaddlePaddle/Paddle/pull/50919), [#50904](https://github.com/PaddlePaddle/Paddle/pull/50904), [#50918](https://github.com/PaddlePaddle/Paddle/pull/50918), [#50938](https://github.com/PaddlePaddle/Paddle/pull/50938), [#50858](https://github.com/PaddlePaddle/Paddle/pull/50858), [#50933](https://github.com/PaddlePaddle/Paddle/pull/50933), [#50945](https://github.com/PaddlePaddle/Paddle/pull/50945), [#50936](https://github.com/PaddlePaddle/Paddle/pull/50936), [#51168](https://github.com/PaddlePaddle/Paddle/pull/51168), [#51493](https://github.com/PaddlePaddle/Paddle/pull/51493), [#50924](https://github.com/PaddlePaddle/Paddle/pull/50924), [#50923](https://github.com/PaddlePaddle/Paddle/pull/50923), [#50926](https://github.com/PaddlePaddle/Paddle/pull/50926), [#50925](https://github.com/PaddlePaddle/Paddle/pull/50925), [#50930](https://github.com/PaddlePaddle/Paddle/pull/50930), [#53284](https://github.com/PaddlePaddle/Paddle/pull/53284), [#53286](https://github.com/PaddlePaddle/Paddle/pull/53286), [#53285](https://github.com/PaddlePaddle/Paddle/pull/53285), [#50976](https://github.com/PaddlePaddle/Paddle/pull/50976), [#50915](https://github.com/PaddlePaddle/Paddle/pull/50915), [#50915](https://github.com/PaddlePaddle/Paddle/pull/50915), [#48192](https://github.com/PaddlePaddle/Paddle/pull/48192), [#50993](https://github.com/PaddlePaddle/Paddle/pull/50993)， [#50998](https://github.com/PaddlePaddle/Paddle/pull/50998), [#51380](https://github.com/PaddlePaddle/Paddle/pull/51380), [#51137](https://github.com/PaddlePaddle/Paddle/pull/51137), [#51106](https://github.com/PaddlePaddle/Paddle/pull/51106), [#51197](https://github.com/PaddlePaddle/Paddle/pull/51197), [#51159](https://github.com/PaddlePaddle/Paddle/pull/51159), [#51552](https://github.com/PaddlePaddle/Paddle/pull/51552), [#51151](https://github.com/PaddlePaddle/Paddle/pull/51151), [#51005](https://github.com/PaddlePaddle/Paddle/pull/51005), [#51565](https://github.com/PaddlePaddle/Paddle/pull/51565), [#51036](https://github.com/PaddlePaddle/Paddle/pull/51036), [#51185](https://github.com/PaddlePaddle/Paddle/pull/51185), [#51791](https://github.com/PaddlePaddle/Paddle/pull/51791), [#51083](https://github.com/PaddlePaddle/Paddle/pull/51083), [#51694](https://github.com/PaddlePaddle/Paddle/pull/51694), [#51689](https://github.com/PaddlePaddle/Paddle/pull/51689), [#51009](https://github.com/PaddlePaddle/Paddle/pull/51009), [#51051](https://github.com/PaddlePaddle/Paddle/pull/51051), [#51532](https://github.com/PaddlePaddle/Paddle/pull/51532), [#51978](https://github.com/PaddlePaddle/Paddle/pull/51978), [#51903](https://github.com/PaddlePaddle/Paddle/pull/51903), [#51888](https://github.com/PaddlePaddle/Paddle/pull/51888), [#52016](https://github.com/PaddlePaddle/Paddle/pull/52016), [#52035](https://github.com/PaddlePaddle/Paddle/pull/52035), [#52184](https://github.com/PaddlePaddle/Paddle/pull/52184), [#52018](https://github.com/PaddlePaddle/Paddle/pull/52018), [#51787](https://github.com/PaddlePaddle/Paddle/pull/51787), [#51640](https://github.com/PaddlePaddle/Paddle/pull/51640), [#52172](https://github.com/PaddlePaddle/Paddle/pull/52172), [#52193](https://github.com/PaddlePaddle/Paddle/pull/52193), [#51160](https://github.com/PaddlePaddle/Paddle/pull/51160), [#51809](https://github.com/PaddlePaddle/Paddle/pull/51809), [#51678](https://github.com/PaddlePaddle/Paddle/pull/51678), [#52158](https://github.com/PaddlePaddle/Paddle/pull/52158), [#51015](https://github.com/PaddlePaddle/Paddle/pull/51015), [#52240](https://github.com/PaddlePaddle/Paddle/pull/52240), [#52276](https://github.com/PaddlePaddle/Paddle/pull/52276), [#52233](https://github.com/PaddlePaddle/Paddle/pull/52233), [#52220](https://github.com/PaddlePaddle/Paddle/pull/52220), [#52107](https://github.com/PaddlePaddle/Paddle/pull/52107), [#52282](https://github.com/PaddlePaddle/Paddle/pull/52282), [#52311](https://github.com/PaddlePaddle/Paddle/pull/52311), [#52315](https://github.com/PaddlePaddle/Paddle/pull/52315), [#52357](https://github.com/PaddlePaddle/Paddle/pull/52357), [#52256](https://github.com/PaddlePaddle/Paddle/pull/52256), [#51649](https://github.com/PaddlePaddle/Paddle/pull/51649), [#52413](https://github.com/PaddlePaddle/Paddle/pull/52413), [#52369](https://github.com/PaddlePaddle/Paddle/pull/52369), [#51837](https://github.com/PaddlePaddle/Paddle/pull/51837), [#52112](https://github.com/PaddlePaddle/Paddle/pull/52112), [#51819](https://github.com/PaddlePaddle/Paddle/pull/51819), [#52388](https://github.com/PaddlePaddle/Paddle/pull/52388), [#52411](https://github.com/PaddlePaddle/Paddle/pull/52411), [#52521](https://github.com/PaddlePaddle/Paddle/pull/52521), [#51300](https://github.com/PaddlePaddle/Paddle/pull/51300), [#51117](https://github.com/PaddlePaddle/Paddle/pull/51117), [#52380](https://github.com/PaddlePaddle/Paddle/pull/52380), [#52317](https://github.com/PaddlePaddle/Paddle/pull/52317), [#51263](https://github.com/PaddlePaddle/Paddle/pull/51263), [#52668](https://github.com/PaddlePaddle/Paddle/pull/52668), [#52259](https://github.com/PaddlePaddle/Paddle/pull/52259), [#50999](https://github.com/PaddlePaddle/Paddle/pull/50999), [#52407](https://github.com/PaddlePaddle/Paddle/pull/52407), [#52288](https://github.com/PaddlePaddle/Paddle/pull/52288), [#52845](https://github.com/PaddlePaddle/Paddle/pull/52845), [#50953](https://github.com/PaddlePaddle/Paddle/pull/50953), [#52667](https://github.com/PaddlePaddle/Paddle/pull/52667), [#52582](https://github.com/PaddlePaddle/Paddle/pull/52582), [#52426](https://github.com/PaddlePaddle/Paddle/pull/52426), [#51884](https://github.com/PaddlePaddle/Paddle/pull/51884), [#52630](https://github.com/PaddlePaddle/Paddle/pull/52630), [#52136](https://github.com/PaddlePaddle/Paddle/pull/52136), [#52604](https://github.com/PaddlePaddle/Paddle/pull/52604), [#51615](https://github.com/PaddlePaddle/Paddle/pull/51615), [#51275](https://github.com/PaddlePaddle/Paddle/pull/51275), [#52898](https://github.com/PaddlePaddle/Paddle/pull/52898), [#52918](https://github.com/PaddlePaddle/Paddle/pull/52918), [#52572](https://github.com/PaddlePaddle/Paddle/pull/52572), [#52683](https://github.com/PaddlePaddle/Paddle/pull/52683), [#52956](https://github.com/PaddlePaddle/Paddle/pull/52956), [#52963](https://github.com/PaddlePaddle/Paddle/pull/52963), [#52954](https://github.com/PaddlePaddle/Paddle/pull/52954), [#52444](https://github.com/PaddlePaddle/Paddle/pull/52444), [#52314](https://github.com/PaddlePaddle/Paddle/pull/52314), [#52887](https://github.com/PaddlePaddle/Paddle/pull/52887), [#52195](https://github.com/PaddlePaddle/Paddle/pull/52195), [#53100](https://github.com/PaddlePaddle/Paddle/pull/53100), [#52961](https://github.com/PaddlePaddle/Paddle/pull/52961), [#52953](https://github.com/PaddlePaddle/Paddle/pull/52953), [#53111](https://github.com/PaddlePaddle/Paddle/pull/53111), [#53549](https://github.com/PaddlePaddle/Paddle/pull/53549), [#53736](https://github.com/PaddlePaddle/Paddle/pull/53736), [#52920](https://github.com/PaddlePaddle/Paddle/pull/52920), [#53195](https://github.com/PaddlePaddle/Paddle/pull/53195), [#53535](https://github.com/PaddlePaddle/Paddle/pull/53535), [#53876](https://github.com/PaddlePaddle/Paddle/pull/53876), [#53785](https://github.com/PaddlePaddle/Paddle/pull/53785), [#53722](https://github.com/PaddlePaddle/Paddle/pull/53722), [#54285](https://github.com/PaddlePaddle/Paddle/pull/54285), [#54232](https://github.com/PaddlePaddle/Paddle/pull/54232), [#53922](https://github.com/PaddlePaddle/Paddle/pull/53922), [#47277](https://github.com/PaddlePaddle/Paddle/pull/47277), [#50811](https://github.com/PaddlePaddle/Paddle/pull/50811), [#54571](https://github.com/PaddlePaddle/Paddle/pull/54571), [#50129](https://github.com/PaddlePaddle/Paddle/pull/50129), [#50340](https://github.com/PaddlePaddle/Paddle/pull/50340), [#50848](https://github.com/PaddlePaddle/Paddle/pull/50848), [#50849](https://github.com/PaddlePaddle/Paddle/pull/50849), [#50868](https://github.com/PaddlePaddle/Paddle/pull/50868), [#50878](https://github.com/PaddlePaddle/Paddle/pull/50878), [#50929](https://github.com/PaddlePaddle/Paddle/pull/50929), [#50939](https://github.com/PaddlePaddle/Paddle/pull/50939), [#50973](https://github.com/PaddlePaddle/Paddle/pull/50973), [#50913](https://github.com/PaddlePaddle/Paddle/pull/50913), [#51145](https://github.com/PaddlePaddle/Paddle/pull/51145), [#51090](https://github.com/PaddlePaddle/Paddle/pull/51090), [#51098](https://github.com/PaddlePaddle/Paddle/pull/51098), [#51094](https://github.com/PaddlePaddle/Paddle/pull/51094), [#51216](https://github.com/PaddlePaddle/Paddle/pull/51216), [#51736](https://github.com/PaddlePaddle/Paddle/pull/51736), [#51684](https://github.com/PaddlePaddle/Paddle/pull/51684), [#51925](https://github.com/PaddlePaddle/Paddle/pull/51925), [#54030](https://github.com/PaddlePaddle/Paddle/pull/54030), [#50700](https://github.com/PaddlePaddle/Paddle/pull/50700), [#52264](https://github.com/PaddlePaddle/Paddle/pull/52264), [#51069](https://github.com/PaddlePaddle/Paddle/pull/51069), [#51101](https://github.com/PaddlePaddle/Paddle/pull/51101), [#51286](https://github.com/PaddlePaddle/Paddle/pull/51286), [#53582](https://github.com/PaddlePaddle/Paddle/pull/53582),[#49869](https://github.com/PaddlePaddle/Paddle/pull/49869))) -- 混合精度策略（AMP）优化：在混合精度训练的易用性、精度稳定性及可调试性方面进行了全面的升级和优化，能够更好的支持大模型训练加速。易用性方面统一了动静态图 API，并新增 model.float()、model.float16()、model.bfloat16()等转换接口；精度稳定性方面增强了针对 BF16 类型的策略自动调整，优化了黑名单设置，增强了优化器算子 Adagrad、Adamax、Adadelta、RMSProp 等对 multi_precision 功能的支持，在 O2 模式下，完善了 master grad 机制，并新增类型提升机制，以及新增参数对特定模块使用 float32 计算以保障精度；在可调式性方面，新增 paddle.amp.debugging 模块，提供算子统计、异常值检测、精度对比等功能。( [#50132](https://github.com/PaddlePaddle/Paddle/pull/50132), [#50078](https://github.com/PaddlePaddle/Paddle/pull/50078), [#50131](https://github.com/PaddlePaddle/Paddle/pull/50131), [#49705](https://github.com/PaddlePaddle/Paddle/pull/49705), [#52936](https://github.com/PaddlePaddle/Paddle/pull/52936), [#52871](https://github.com/PaddlePaddle/Paddle/pull/52871), [#53289](https://github.com/PaddlePaddle/Paddle/pull/53289), [#53362](https://github.com/PaddlePaddle/Paddle/pull/53362), [#54240](https://github.com/PaddlePaddle/Paddle/pull/54240), [#53768](https://github.com/PaddlePaddle/Paddle/pull/53768), [#48041](https://github.com/PaddlePaddle/Paddle/pull/48041), [#47672](https://github.com/PaddlePaddle/Paddle/pull/47672), [#48843](https://github.com/PaddlePaddle/Paddle/pull/48843), [#49391](https://github.com/PaddlePaddle/Paddle/pull/49391), [#51635](https://github.com/PaddlePaddle/Paddle/pull/51635), [#45541](https://github.com/PaddlePaddle/Paddle/pull/45541), [#53742](https://github.com/PaddlePaddle/Paddle/pull/53742), [#51020](https://github.com/PaddlePaddle/Paddle/pull/51020), [#51063](https://github.com/PaddlePaddle/Paddle/pull/51063), [#52514](https://github.com/PaddlePaddle/Paddle/pull/52514), [#50940](https://github.com/PaddlePaddle/Paddle/pull/50940), [#52936](https://github.com/PaddlePaddle/Paddle/pull/52936), [#53439](https://github.com/PaddlePaddle/Paddle/pull/53439), [#53712](https://github.com/PaddlePaddle/Paddle/pull/53712), [#48238](https://github.com/PaddlePaddle/Paddle/pull/48238), [#52215](https://github.com/PaddlePaddle/Paddle/pull/52215), [#53012](https://github.com/PaddlePaddle/Paddle/pull/53012), [#52918](https://github.com/PaddlePaddle/Paddle/pull/52918), [#54571](https://github.com/PaddlePaddle/Paddle/pull/54571)) -- GroupNorm 算子新增对 NHWC 数据格式的支持 ([#47533](https://github.com/PaddlePaddle/Paddle/pull/47533)) -- index_put 算子新增对 bool 和 int 的混合数据类型支持 ([#54195](https://github.com/PaddlePaddle/Paddle/pull/54195)) -- 新增 sparse.is_nan API 用于判断 sparse tensor 中是否含有 NaN 元素。 ([#51513](https://github.com/PaddlePaddle/Paddle/pull/51513)) - -#### bug fix -- 修复 trace、roll、dropout_nd、log_softmax 等多个算子计算出错、栈溢出，以及部分单测问题。([#50243](https://github.com/PaddlePaddle/Paddle/pull/50243), [#52012](https://github.com/PaddlePaddle/Paddle/pull/52012), [#53795](https://github.com/PaddlePaddle/Paddle/pull/53795), [#53149](https://github.com/PaddlePaddle/Paddle/pull/53149), [#53654](https://github.com/PaddlePaddle/Paddle/pull/53654), [#51054](https://github.com/PaddlePaddle/Paddle/pull/51054), [#49373](https://github.com/PaddlePaddle/Paddle/pull/49373), [#53038](https://github.com/PaddlePaddle/Paddle/pull/53038)) -- 修复 conv 算子穷举搜索在部分场景不生效的问题。([#47065](https://github.com/PaddlePaddle/Paddle/pull/47065)) -- 修复 collective_reduce_scatter 等算子在 A100 上出现 timeout 的问题。([#54513](https://github.com/PaddlePaddle/Paddle/pull/54513)) -- 修复 FusedLinear 单测中属性错误的问题。 ([#50359](https://github.com/PaddlePaddle/Paddle/pull/50359)) -- 修复在使用 Profiler 时可能出现的 OOM 等问题 ([#46089](https://github.com/PaddlePaddle/Paddle/pull/46089)) - -#### 性能提升 -- 进一步优化框架大量算子的 GPU Kernel 以及 eigen 实现方式，包括 max_pool3d, dropout, adaptive_pooling, depthwise_conv2d、transpose, eigh, broadcast 类计算，reduce 类计算，prelu，logsumexp，以及 sparse 类算子等，在更多配置场景下达到更优性能。([#45820](https://github.com/PaddlePaddle/Paddle/pull/45820), [#45959](https://github.com/PaddlePaddle/Paddle/pull/45959), [#45934](https://github.com/PaddlePaddle/Paddle/pull/45934), [#46332](https://github.com/PaddlePaddle/Paddle/pull/46332), [#46287](https://github.com/PaddlePaddle/Paddle/pull/46287), [#47233](https://github.com/PaddlePaddle/Paddle/pull/47233), [#48855](https://github.com/PaddlePaddle/Paddle/pull/48855), [#48560](https://github.com/PaddlePaddle/Paddle/pull/48560), [#49419](https://github.com/PaddlePaddle/Paddle/pull/49419), [#49748](https://github.com/PaddlePaddle/Paddle/pull/49748), [#50348](https://github.com/PaddlePaddle/Paddle/pull/50348), [#52401](https://github.com/PaddlePaddle/Paddle/pull/52401), [#51131](https://github.com/PaddlePaddle/Paddle/pull/51131), [#51141](https://github.com/PaddlePaddle/Paddle/pull/51141), [#51479](https://github.com/PaddlePaddle/Paddle/pull/51479), [#51835](https://github.com/PaddlePaddle/Paddle/pull/51835), [#52509](https://github.com/PaddlePaddle/Paddle/pull/52509), [#52482](https://github.com/PaddlePaddle/Paddle/pull/52482), [#52700](https://github.com/PaddlePaddle/Paddle/pull/52700), [#53112](https://github.com/PaddlePaddle/Paddle/pull/53112), [#53659](https://github.com/PaddlePaddle/Paddle/pull/53659), [#53658](https://github.com/PaddlePaddle/Paddle/pull/53658), [#53154](https://github.com/PaddlePaddle/Paddle/pull/53154), [#54071](https://github.com/PaddlePaddle/Paddle/pull/54071), [#53622](https://github.com/PaddlePaddle/Paddle/pull/53622), [#52952](https://github.com/PaddlePaddle/Paddle/pull/52952), [#46046](https://github.com/PaddlePaddle/Paddle/pull/46046), [#46119](https://github.com/PaddlePaddle/Paddle/pull/46119), [#45946](https://github.com/PaddlePaddle/Paddle/pull/45946), [#47212](https://github.com/PaddlePaddle/Paddle/pull/47212), [#47791](https://github.com/PaddlePaddle/Paddle/pull/47791), [#47454](https://github.com/PaddlePaddle/Paddle/pull/47454), [#45230](https://github.com/PaddlePaddle/Paddle/pull/45230), [#48899](https://github.com/PaddlePaddle/Paddle/pull/48899), [#33051](https://github.com/PaddlePaddle/Paddle/pull/33051), [#49040](https://github.com/PaddlePaddle/Paddle/pull/49040), [#48992](https://github.com/PaddlePaddle/Paddle/pull/48992), [#49086](https://github.com/PaddlePaddle/Paddle/pull/49086), [#50808](https://github.com/PaddlePaddle/Paddle/pull/50808), [#46431](https://github.com/PaddlePaddle/Paddle/pull/46431), [#50931](https://github.com/PaddlePaddle/Paddle/pull/50931), [#48056](https://github.com/PaddlePaddle/Paddle/pull/48056), [#46071](https://github.com/PaddlePaddle/Paddle/pull/46071), [#49231](https://github.com/PaddlePaddle/Paddle/pull/49231), [#38660](https://github.com/PaddlePaddle/Paddle/pull/38660), [#50287](https://github.com/PaddlePaddle/Paddle/pull/50287), [#46111](https://github.com/PaddlePaddle/Paddle/pull/46111), [#46997](https://github.com/PaddlePaddle/Paddle/pull/46997), [#45854](https://github.com/PaddlePaddle/Paddle/pull/45854), [#47738](https://github.com/PaddlePaddle/Paddle/pull/47738), [#48635](https://github.com/PaddlePaddle/Paddle/pull/48635), [#50353](https://github.com/PaddlePaddle/Paddle/pull/50353), [#50362](https://github.com/PaddlePaddle/Paddle/pull/50362), [#51934](https://github.com/PaddlePaddle/Paddle/pull/51934), [#54045](https://github.com/PaddlePaddle/Paddle/pull/54045), [#46679](https://github.com/PaddlePaddle/Paddle/pull/46679), [#52093](https://github.com/PaddlePaddle/Paddle/pull/52093), [#52969](https://github.com/PaddlePaddle/Paddle/pull/52969)) -- 提供更多融合算子实现，以及相关融合 Pass，如 fused_feed_forward，gather-gemm-scatter，matmul + bias，layernorm_shift_partition + element_add，elementwise 类融合等模式，进一步提升使用该模式的模型性能。( [#50423](https://github.com/PaddlePaddle/Paddle/pull/50423), [#50091](https://github.com/PaddlePaddle/Paddle/pull/50091), [#50364](https://github.com/PaddlePaddle/Paddle/pull/50364), [#53017](https://github.com/PaddlePaddle/Paddle/pull/53017), [#50755](https://github.com/PaddlePaddle/Paddle/pull/50755), [#50050](https://github.com/PaddlePaddle/Paddle/pull/50050), [#47099](https://github.com/PaddlePaddle/Paddle/pull/47099), [#48848](https://github.com/PaddlePaddle/Paddle/pull/48848), [#49383](https://github.com/PaddlePaddle/Paddle/pull/49383), [#50809](https://github.com/PaddlePaddle/Paddle/pull/50809), [#52361](https://github.com/PaddlePaddle/Paddle/pull/52361), [#52028](https://github.com/PaddlePaddle/Paddle/pull/52028), [#48439](https://github.com/PaddlePaddle/Paddle/pull/48439), [#49009](https://github.com/PaddlePaddle/Paddle/pull/49009), [#51427](https://github.com/PaddlePaddle/Paddle/pull/51427), [#52731](https://github.com/PaddlePaddle/Paddle/pull/52731), [#51805](https://github.com/PaddlePaddle/Paddle/pull/51805)) - -#### 文档 -- 修复 index_put 文档中的错误 ([#53727](https://github.com/PaddlePaddle/Paddle/pull/53727)) - -### Intermediate Representation -为了飞桨 IR 体系存在的稳定性、降低研发成本问题，孵化了飞桨新的 IR 体系，完成了基础的数据结构定义、算子定义生成和执行体系适配。为了更好的支持科学计算场景的高阶需求，完成了 silu、cast 等算子的高阶适配。 -- 完成了 IR 数据数据结构定义，包含类型系统，算子定义；打通了和 phi kernel 的执行适配。[#51112](https://github.com/PaddlePaddle/Paddle/pull/51112)， [#51992](https://github.com/PaddlePaddle/Paddle/pull/51992), [#50412](https://github.com/PaddlePaddle/Paddle/pull/50412), [#53557](https://github.com/PaddlePaddle/Paddle/pull/53557), [#53953](https://github.com/PaddlePaddle/Paddle/pull/53953), [#50959](https://github.com/PaddlePaddle/Paddle/pull/50959), [#54250](https://github.com/PaddlePaddle/Paddle/pull/54250), [#54197](https://github.com/PaddlePaddle/Paddle/pull/54197), [#54289](https://github.com/PaddlePaddle/Paddle/pull/54289), [#51636](https://github.com/PaddlePaddle/Paddle/pull/51636), [#52846](https://github.com/PaddlePaddle/Paddle/pull/52846), [#53988](https://github.com/PaddlePaddle/Paddle/pull/53988), [#54143](https://github.com/PaddlePaddle/Paddle/pull/54143), [#54035](https://github.com/PaddlePaddle/Paddle/pull/54035), [#54052](https://github.com/PaddlePaddle/Paddle/pull/54052), [#54340](https://github.com/PaddlePaddle/Paddle/pull/54340), [#54356](https://github.com/PaddlePaddle/Paddle/pull/54356), [#54068](https://github.com/PaddlePaddle/Paddle/pull/54068), [#53894](https://github.com/PaddlePaddle/Paddle/pull/53894), [#53707](https://github.com/PaddlePaddle/Paddle/pull/53707), [#54185](https://github.com/PaddlePaddle/Paddle/pull/54185), [#54031](https://github.com/PaddlePaddle/Paddle/pull/54031), [#54220](https://github.com/PaddlePaddle/Paddle/pull/54220), [#54275](https://github.com/PaddlePaddle/Paddle/pull/54275), [#54281](https://github.com/PaddlePaddle/Paddle/pull/54281), [#54186](https://github.com/PaddlePaddle/Paddle/pull/54186), [#54259](https://github.com/PaddlePaddle/Paddle/pull/54259), [#54124](https://github.com/PaddlePaddle/Paddle/pull/54124), [#54292](https://github.com/PaddlePaddle/Paddle/pull/54292), [#48068](https://github.com/PaddlePaddle/Paddle/pull/48068), [#53978](https://github.com/PaddlePaddle/Paddle/pull/53978) -- 完善 pass 基础设置，包含基础的 pass 定义，pass 注册管理等。 [#54023](https://github.com/PaddlePaddle/Paddle/pull/54023),[#54170](https://github.com/PaddlePaddle/Paddle/pull/54170), [#54170](https://github.com/PaddlePaddle/Paddle/pull/54170), [#54308](https://github.com/PaddlePaddle/Paddle/pull/54308), [#54348](https://github.com/PaddlePaddle/Paddle/pull/54348), [#54385](https://github.com/PaddlePaddle/Paddle/pull/54385) -- 完善高阶算子的适配，主要包含基础模块改造和 silu、cast 算子适配等。 [#52005](https://github.com/PaddlePaddle/Paddle/pull/52005), [#53425](https://github.com/PaddlePaddle/Paddle/pull/53425), [#53417](https://github.com/PaddlePaddle/Paddle/pull/53417), [#53417](https://github.com/PaddlePaddle/Paddle/pull/53417), [#53498](https://github.com/PaddlePaddle/Paddle/pull/53498), [#53171](https://github.com/PaddlePaddle/Paddle/pull/53171), [#53632](https://github.com/PaddlePaddle/Paddle/pull/53632), [#53605](https://github.com/PaddlePaddle/Paddle/pull/53605), [#53746](https://github.com/PaddlePaddle/Paddle/pull/53746), [#53874](https://github.com/PaddlePaddle/Paddle/pull/53874), [#54164](https://github.com/PaddlePaddle/Paddle/pull/54164), [#45888](https://github.com/PaddlePaddle/Paddle/pull/45888), [#46024](https://github.com/PaddlePaddle/Paddle/pull/46024), [#46446](https://github.com/PaddlePaddle/Paddle/pull/46446), [#46960](https://github.com/PaddlePaddle/Paddle/pull/46960) - -### CINN 编译器 -#### 新功能 -- 新增 CINN 对 0D-Tensor 的支持，目前为配合主框架升级，暂时采用增加 pass 的临时方案进行支持，后续会对该方案进行替换升级。 ([#53382](https://github.com/PaddlePaddle/Paddle/pull/53382), [#53955](https://github.com/PaddlePaddle/Paddle/pull/53955), [#54064](https://github.com/PaddlePaddle/Paddle/pull/54064), [#54118](https://github.com/PaddlePaddle/Paddle/pull/54118), [#54216](https://github.com/PaddlePaddle/Paddle/pull/54216), [#53454](https://github.com/PaddlePaddle/Paddle/pull/53454)) -- 新增 CINN 对 int8/uint8/int16/uint16/bf16 等数据类型的支持 ([#50566](https://github.com/PaddlePaddle/Paddle/pull/50566), [#53637](https://github.com/PaddlePaddle/Paddle/pull/53637)) -- 新增 CINN expand 算子的支持 ([#46776](https://github.com/PaddlePaddle/Paddle/pull/46776)) -- 新增 CINN 对 PaddleInference 的支持. ([#45009](https://github.com/PaddlePaddle/Paddle/pull/45009)) - -#### 功能优化 -- CINN 编译器，传递 skip_gc_vars 属性到 CINN 子图；CINN 为 skip_gc_vars 添加 fetch 算子 [#49471](https://github.com/PaddlePaddle/Paddle/pull/49471), [#49553](https://github.com/PaddlePaddle/Paddle/pull/49553) -- CINN 编译器，conv2d 和 conv2d_grad 默认不使用 cinn 算子 [#51645](https://github.com/PaddlePaddle/Paddle/pull/51645) -- 将 build_cinn_pass 添加到 BuildStrategy，以便于在动转静中使用 ([#49496](https://github.com/PaddlePaddle/Paddle/pull/49496)) -- 增加 reshape 算子在组合算子机制下的单测 ([#51276](https://github.com/PaddlePaddle/Paddle/pull/51276)) -- 主框架联编 CINN 的版本从固定 commit 改为 develop ([#49775](https://github.com/PaddlePaddle/Paddle/pull/49775)) -- 为 CINN 设置默认 Target 参数 ([#50182](https://github.com/PaddlePaddle/Paddle/pull/50182)) - -#### bug fix -- 修复 CINN 符号化过程中拓扑排序后的出现的算子顺序不一致的问题。 ([#52556](https://github.com/PaddlePaddle/Paddle/pull/52556)) -- 修复一些算子计算错误、精度下降，以及单测相关问题 ([#53859](https://github.com/PaddlePaddle/Paddle/pull/53859), [#54261](https://github.com/PaddlePaddle/Paddle/pull/54261), [#46801](https://github.com/PaddlePaddle/Paddle/pull/46801), [#53676](https://github.com/PaddlePaddle/Paddle/pull/53676), [#53772](https://github.com/PaddlePaddle/Paddle/pull/53772)) -- 修复 CINN 对 float16 类型支持的问题。([#48249](https://github.com/PaddlePaddle/Paddle/pull/48249)) -- 修复 build_cinn_pass 中的问题。 ([#46843](https://github.com/PaddlePaddle/Paddle/pull/46843)) -- 修复了组合算子+动转静 在开启 CINN 时，出现反向因误被 GC 而导致的无数据区的问题 ([#50116](https://github.com/PaddlePaddle/Paddle/pull/50116)) -- 修复编译器 dropout amp 出错，组合算子跑 resnet 出错，inplace 变量未找到等问题 [#51688](https://github.com/PaddlePaddle/Paddle/pull/51688), [#52813](https://github.com/PaddlePaddle/Paddle/pull/52813), [#51769](https://github.com/PaddlePaddle/Paddle/pull/51769) - -#### 性能提升 -- 优化 reshape 相关融合策略 ([#53066](https://github.com/PaddlePaddle/Paddle/pull/53066)) -- 优化 BuildCINNPass 的性能 ([#49696](https://github.com/PaddlePaddle/Paddle/pull/49696)) -- 优化子图检测模块的性能 ([#45040](https://github.com/PaddlePaddle/Paddle/pull/45040), [#46937](https://github.com/PaddlePaddle/Paddle/pull/46937)) - -### 硬件接入 -#### CustomDevice -- 训练侧新增分布式策略 MP/Sharding/PP/MoE 以及 recompute 重计算功能的支持，推理侧新增分布式策略 MP 的支持，支持通过 CustomDevice 接入的硬件昇腾 NPU 和寒武纪 MLU 无需修改任何代码即可自动继承 CustomDevice 新增的所有分布式策略。 [#52872](https://github.com/PaddlePaddle/Paddle/pull/52872), [#54384](https://github.com/PaddlePaddle/Paddle/pull/54384), [#53220](https://github.com/PaddlePaddle/Paddle/pull/53220), [#54572](https://github.com/PaddlePaddle/Paddle/pull/54572), [#54573](https://github.com/PaddlePaddle/Paddle/pull/54573), [#54676](https://github.com/PaddlePaddle/Paddle/pull/54676), [#53044](https://github.com/PaddlePaddle/Paddle/pull/53044), [#53719](https://github.com/PaddlePaddle/Paddle/pull/53719), [#53701](https://github.com/PaddlePaddle/Paddle/pull/53701), [#53702](https://github.com/PaddlePaddle/Paddle/pull/53702), [#53703](https://github.com/PaddlePaddle/Paddle/pull/53703) -- 新增 API paddle.device.is_compiled_with_custom_device，方便用户判断当前环境是否支持某硬件的插件式设备后端 [#49271](https://github.com/PaddlePaddle/Paddle/pull/49721) -- 增加环境变量 CUSTOM_DEVICE_BLACK_LIST 设置，支持黑名单内的算子自动异构到 CPU 上运行 [#50409](https://github.com/PaddlePaddle/Paddle/pull/50409), [#50666](https://github.com/PaddlePaddle/Paddle/pull/50666) -- 优化 CustomDevice 性能，减少对 runtime 中 get_device_count 接口的调用次数 [#46963](https://github.com/PaddlePaddle/Paddle/pull/46963) - -#### 昆仑芯 XPU -- 训练侧使用了新版动态图并新增分布式策略 MP/Sharding/PP 以及 recompute 重计算功能,通信库通信的支持；推理侧新增分布式策略 MP 的支持，并增加对 XPU FasterTransformer 算子加速库的支持；[#49531](https://github.com/PaddlePaddle/Paddle/pull/49531), [#49815](https://github.com/PaddlePaddle/Paddle/pull/49815), [#48897](https://github.com/PaddlePaddle/Paddle/pull/48897), [#50717](https://github.com/PaddlePaddle/Paddle/pull/50717), [#51082](https://github.com/PaddlePaddle/Paddle/pull/51082), [#49757](https://github.com/PaddlePaddle/Paddle/pull/49757), [#51399](https://github.com/PaddlePaddle/Paddle/pull/51399), [#50329](https://github.com/PaddlePaddle/Paddle/pull/50329), [#48369](https://github.com/PaddlePaddle/Paddle/pull/48369), [#47838](https://github.com/PaddlePaddle/Paddle/pull/47838),[#48076](https://github.com/PaddlePaddle/Paddle/pull/48076),[#47882](https://github.com/PaddlePaddle/Paddle/pull/47882),[#48961](https://github.com/PaddlePaddle/Paddle/pull/48961),[#49043](https://github.com/PaddlePaddle/Paddle/pull/49043),[#49749](https://github.com/PaddlePaddle/Paddle/pull/49749),[#49806](https://github.com/PaddlePaddle/Paddle/pull/49806),[#53427](https://github.com/PaddlePaddle/Paddle/pull/53427),[#48470](https://github.com/PaddlePaddle/Paddle/pull/48470),[#49207](https://github.com/PaddlePaddle/Paddle/pull/49207),[#52296](https://github.com/PaddlePaddle/Paddle/pull/52296),[#51785](https://github.com/PaddlePaddle/Paddle/pull/51785),[#47168](https://github.com/PaddlePaddle/Paddle/pull/47168),[#47445](https://github.com/PaddlePaddle/Paddle/pull/47445),[#50200](https://github.com/PaddlePaddle/Paddle/pull/50200),[#49934](https://github.com/PaddlePaddle/Paddle/pull/49934),[#50792](https://github.com/PaddlePaddle/Paddle/pull/50792),[#52228](https://github.com/PaddlePaddle/Paddle/pull/52228),[#53337](https://github.com/PaddlePaddle/Paddle/pull/53337),[#53389](https://github.com/PaddlePaddle/Paddle/pull/53389),[#53496](https://github.com/PaddlePaddle/Paddle/pull/53496),[#53609](https://github.com/PaddlePaddle/Paddle/pull/53609),[#53697](https://github.com/PaddlePaddle/Paddle/pull/53697),[#53496](https://github.com/PaddlePaddle/Paddle/pull/53496),[#53720](https://github.com/PaddlePaddle/Paddle/pull/53720),[#53734](https://github.com/PaddlePaddle/Paddle/pull/53734),[#54172](https://github.com/PaddlePaddle/Paddle/pull/54172),[PR46227](https://github.com/PaddlePaddle/Paddle/pull/46227) - -## 4. 部署方向（Paddle Inference） -### 新功能 -- 支持 Paddle TensorRT 多个子图 TensorRT engine 或者不同 Predictor 的之间的 TensorRT engine 共享显存，以便节约显存。[#45842](https://github.com/PaddlePaddle/Paddle/pull/45842) [#47631](https://github.com/PaddlePaddle/Paddle/pull/47631) -- C++ API 增加获取输入 Tensor 的 Shape 和数据类型接口，增加获取输出 Tensor 的 Shape 和数据类型接口。C API 增加 SetExecStream、EnableMkldnnInt8 等 C++已有接口，用于服务化部署。 [#49758](https://github.com/PaddlePaddle/Paddle/pull/49758) -- 新增 paddle.inference.Predictor.register_output_hook()接口，可支持调试时打印 GPU 推理下每层的输出，同时也支持在 While 等控制流模型中使用。注意此接口不支持 Paddle-TensorRT。[#54433](https://github.com/PaddlePaddle/Paddle/pull/54433) ，[#47050](https://github.com/PaddlePaddle/Paddle/pull/47050) ， [#54254](https://github.com/PaddlePaddle/Paddle/pull/54254) 。 -- Paddle Inference 推理的 Predictor 接口支持 paddle::Tensor 作为输入和输出，以便用户直接复用飞桨动态图做推理前、后处理。 ([#50445](https://github.com/PaddlePaddle/Paddle/pull/50445)) -- 增强 Paddle TensorRT 动态 shape 运行能力，config.enable_tuned_tensorrt_dynamic_shape()接口，不传任何参数时，在运行时构建 TensorRT Engine。不再需要先收集 shape 信息再运行，但为了避免运行时的重新构建，需要在前几次运行时，覆盖最小及最大 Shape 的情况， [#52162](https://github.com/PaddlePaddle/Paddle/pull/52162) 。 -- Paddle-TensorRT 支持 NHWC 格式的模型输入，[#49633](https://github.com/PaddlePaddle/Paddle/pull/49633) 。 -- 扩展 config.Exp_DisableTensorRtOPs 接口通过指定 Tensor 变量的名字来禁止进入 TensorRT，[#49497](https://github.com/PaddlePaddle/Paddle/pull/49497) 。 +- 新增 XHPC buffer manager 以提升 Paddle 和 XHPC 内存协同性能。 [#63924](https://github.com/PaddlePaddle/Paddle/pull/63924) +- 提升 TensorSetConstantXPU 性能，并支持 BF16 数据类型。[#63920](https://github.com/PaddlePaddle/Paddle/pull/63920),[#61818](https://github.com/PaddlePaddle/Paddle/pull/61818) +- 融合多个 group norm + silu + conv 模块, 压缩显存。[#62892](https://github.com/PaddlePaddle/Paddle/pull/62892) +- 优化 comm manager 中 XPU 显存分配。[#64139](https://github.com/PaddlePaddle/Paddle/pull/64139) +- 优化算子性能，包括 mean_all_grad ([#61148](https://github.com/PaddlePaddle/Paddle/pull/61148))、dropout_v2 ([#61029](https://github.com/PaddlePaddle/Paddle/pull/61029))、fused_rotary_position_embedding ([#62846](https://github.com/PaddlePaddle/Paddle/pull/62846))、cross_entropy ([#63159](https://github.com/PaddlePaddle/Paddle/pull/63159))、elementwise_add ([#64289](https://github.com/PaddlePaddle/Paddle/pull/64289))、fused_gemm_epilogue ([#61350](https://github.com/PaddlePaddle/Paddle/pull/61350)、check_nan_or_inf ([#60853](https://github.com/PaddlePaddle/Paddle/pull/60853)) +- XPU 硬件下新增 qk_qkv_attention_xpu_fuse_pass 和 qkv_attention_xpu_kernel。 [#60089](https://github.com/PaddlePaddle/Paddle/pull/60089) +- XPU 硬件下新增 rotary position 编码的融合算子支持 elementwise_mul + strided_slice + sin/cos+ stack 融合为 1 个算子。 [#60025](https://github.com/PaddlePaddle/Paddle/pull/60025) +- 添加 group_norm_silu_xpu_fuse_pass。 [#62689](https://github.com/PaddlePaddle/Paddle/pull/62689) +- 添加 weight_only_linear_xpu_pass。 [#64185](https://github.com/PaddlePaddle/Paddle/pull/64185) +- 新增 block_multihead_attention 算子及 PASS，支持 LLaMA2 模型在 XPU 设备中的大模型推理。 [#65036](https://github.com/PaddlePaddle/Paddle/pull/65036) +- 支持 squeeze_excitation_block_xpu_kernel 的 float16 类型。 [#61023](https://github.com/PaddlePaddle/Paddle/pull/61023) -### 功能优化 -- GPU 混合精度推理(非 Paddle TensorRT 场景)功能增强，Config.enable_use_gpu 增强可设置精度类型。 [#47993](https://github.com/PaddlePaddle/Paddle/pull/47993) -- 支持 double 类型输入进行推理， [#51786](https://github.com/PaddlePaddle/Paddle/pull/51786) 。 -- 由于 TensorRT 算子不支持 INT64 类型导致模型中存在 INT64 数据类型式运行失败问题，Paddle-TensorRT 做了增强，当模型中包含 INT64 数据类型时，进行自动转换，降低到 INT32 类型运行。 [#45547](https://github.com/PaddlePaddle/Paddle/pull/45547) -- Paddle-TensorRT 支持更多算子进入 TensorRT 推理，包含： - - expand_v2，gather_nd，rsqrt，sign，not，onehot，arg_min，temporal_shift，expend_as_v2，setvalue，index_select，round，acosh，square，reduce_max，not_equal，reduce_min，reduce_prod，grid_sampler，elementwise_mod，pad3d ，greater_equal，bitwise，cumsum，matmul_v2，reciprocal，where，bmm，take_along_axis，less_than，greater_than， logical_or， logical_xor， logical_and， less_equal，range，reduce_all，reduce_any ，fill_any_like ，pow - - [#47002](https://github.com/PaddlePaddle/Paddle/pull/47002) , [#47589](https://github.com/PaddlePaddle/Paddle/pull/47589) ，[#48223](https://github.com/PaddlePaddle/Paddle/pull/48223) ，[#48557](https://github.com/PaddlePaddle/Paddle/pull/48557) ， [#48655](https://github.com/PaddlePaddle/Paddle/pull/48655) ， [#49113](https://github.com/PaddlePaddle/Paddle/pull/49113) ， [#51207](https://github.com/PaddlePaddle/Paddle/pull/51207) ，[#51028](https://github.com/PaddlePaddle/Paddle/pull/51028) ，[#50341](https://github.com/PaddlePaddle/Paddle/pull/50341) ，[#51498](https://github.com/PaddlePaddle/Paddle/pull/51498) ，[#48534](https://github.com/PaddlePaddle/Paddle/pull/48534) ，[#48684](https://github.com/PaddlePaddle/Paddle/pull/48684) ， [#49393](https://github.com/PaddlePaddle/Paddle/pull/49393) ， [#49615](https://github.com/PaddlePaddle/Paddle/pull/49615) ，[#50934](https://github.com/PaddlePaddle/Paddle/pull/50934) ，[#50974](https://github.com/PaddlePaddle/Paddle/pull/50974)，[#50986](https://github.com/PaddlePaddle/Paddle/pull/50986) ， [#52000](https://github.com/PaddlePaddle/Paddle/pull/52000) ，[#51971](https://github.com/PaddlePaddle/Paddle/pull/51971) ， [#52518](https://github.com/PaddlePaddle/Paddle/pull/52518) ，[#44918](https://github.com/PaddlePaddle/Paddle/pull/44918) ，[#48230](https://github.com/PaddlePaddle/Paddle/pull/48230) ，[#47820](https://github.com/PaddlePaddle/Paddle/pull/47820) ， [#46877](https://github.com/PaddlePaddle/Paddle/pull/46877) ， [#48358](https://github.com/PaddlePaddle/Paddle/pull/48358) ， [#48592](https://github.com/PaddlePaddle/Paddle/pull/48592) ，[#48697](https://github.com/PaddlePaddle/Paddle/pull/48697) , [#53088](https://github.com/PaddlePaddle/Paddle/pull/53088) ， [#47974](https://github.com/PaddlePaddle/Paddle/pull/47974) ， [#53462](https://github.com/PaddlePaddle/Paddle/pull/53462) -- 增强 Paddle-TensorRT 映射算子 strided_slice，instance_norm，prelu，argmax，cast，nearest_interp_v2，elementwise，bilinear 实现，[#46819](https://github.com/PaddlePaddle/Paddle/pull/46819) ，[#47998](https://github.com/PaddlePaddle/Paddle/pull/47998) ，[#48043](https://github.com/PaddlePaddle/Paddle/pull/48043) ，[#48998](https://github.com/PaddlePaddle/Paddle/pull/48998) ， [#49675](https://github.com/PaddlePaddle/Paddle/pull/49675) , [#47495](https://github.com/PaddlePaddle/Paddle/pull/47495) -- Paddle-TensorRT 部分算子(scale, square, sum, swish, expand_as_v2, prelu, gelu, hard_swish, hard_sigmoid, leaky_relu,softmax, stack, clip, cast, flatten_contiguous_range，unary，equal, elementwise_op) 支持 0 维 Tensor，[#53660](https://github.com/PaddlePaddle/Paddle/pull/53660) ，[#53627](https://github.com/PaddlePaddle/Paddle/pull/53627) ， [#53634](https://github.com/PaddlePaddle/Paddle/pull/53634) ， [#53714](https://github.com/PaddlePaddle/Paddle/pull/53714) ， [#53729](https://github.com/PaddlePaddle/Paddle/pull/53729) ，[#53769](https://github.com/PaddlePaddle/Paddle/pull/53769) ，[#53506](https://github.com/PaddlePaddle/Paddle/pull/53506) ，[#53704](https://github.com/PaddlePaddle/Paddle/pull/53704) -- 支持 GCC12 + CUDA 12.0 以下版本编译， [#50106](https://github.com/PaddlePaddle/Paddle/pull/50106) -- Paddle-TensorRT 的 DeformableConv 插件支持动态 Shape 输入，[#50698](https://github.com/PaddlePaddle/Paddle/pull/50698) -- Paddle-TensorRT 增加 lookup_table 算子的插件支持， [#46613](https://github.com/PaddlePaddle/Paddle/pull/46613) -- 新增 config.enable_low_precision_io()接口支持 Paddle-TensorRT 场景下低精度类型输入， [#52485](https://github.com/PaddlePaddle/Paddle/pull/52485) -- Paddle-TensorRT 的 LayerNorm 插件支持 FP16 计算， [#45043](https://github.com/PaddlePaddle/Paddle/pull/45043) -- Predictor 的输入数据 paddle_infer::Tensor 支持 bool 类型，[#49388](https://github.com/PaddlePaddle/Paddle/pull/49388) -- Paddle-TensorRT 增强 Convolution 实现采用 ConvolutionNd，[#47653](https://github.com/PaddlePaddle/Paddle/pull/47653) -- conv2d_fusion 融合算子支持 NHWC 格式，[#49047](https://github.com/PaddlePaddle/Paddle/pull/49047) -- 调整 C++推理库下 Phi 算子相关目录结构，[#53091](https://github.com/PaddlePaddle/Paddle/pull/53091) -- 当 TensorRT 序列化和加载版本不匹配时，支持重新构建 TensorRT Engine，而不是报错，[#50775](https://github.com/PaddlePaddle/Paddle/pull/50775) 。 -- 优化 Paddle-TensorRT 运行时打印日志信息，[#50181](https://github.com/PaddlePaddle/Paddle/pull/50181) -- 基于 oneDNN 的 CPU 推理支持 elementwise 的 0 维 Tensor 输入，[#51656](https://github.com/PaddlePaddle/Paddle/pull/51656) -- 清理和规范化 Paddle-TensorRT 的 FC、matmul、matmul_v2 算子的支持，统一升级到使用 TensorRT 的 IMatrixMultiplyLayer 进行支持，[#52222](https://github.com/PaddlePaddle/Paddle/pull/52222) +#### Bug 修复 +- 修复 tile 算子对 0 维 Tensor 的支持。 [#64279](https://github.com/PaddlePaddle/Paddle/pull/64279) +- 修复 group_norm_silu_fuse_pass。 [#63449](https://github.com/PaddlePaddle/Paddle/pull/63449) +- 修复 XPU API GM 显存问题。[#60260](https://github.com/PaddlePaddle/Paddle/pull/60260),[#60387](https://github.com/PaddlePaddle/Paddle/pull/60387),[#62940](https://github.com/PaddlePaddle/Paddle/pull/62940) +- 修复分布式策略 Sharing stage1 v2 的错误。[#64209](https://github.com/PaddlePaddle/Paddle/pull/64209) +- 修复 XPU constant 问题。[#60763](https://github.com/PaddlePaddle/Paddle/pull/60763) +- 修复部分算子问题，包括 AdamW ([#62251](https://github.com/PaddlePaddle/Paddle/pull/62251))、dropout_v3 ([#62726](https://github.com/PaddlePaddle/Paddle/pull/62726))、softmax([#63780](https://github.com/PaddlePaddle/Paddle/pull/63780)) 、fused rope embedding ([#62143](https://github.com/PaddlePaddle/Paddle/pull/62143))、elementwise_add ([#60252](https://github.com/PaddlePaddle/Paddle/pull/60252))、resnet_basic_block ([#62914](https://github.com/PaddlePaddle/Paddle/pull/62914)) +- 修复 XPU 运行和安装相关问题。[#60028](https://github.com/PaddlePaddle/Paddle/pull/60028),[#61970](https://github.com/PaddlePaddle/Paddle/pull/61970) +- 修复 XPU 编译 bug。[#63307](https://github.com/PaddlePaddle/Paddle/pull/63307) +- 修复 XPU 通信库初始化时端侧内存相关的 bug。[#64396](https://github.com/PaddlePaddle/Paddle/pull/64396) -### 性能提升 -- 支持多个 lookup_tables 进入 Paddle-TensorRT 的 Embedding+Eltwise+LayerNorm 的融合 [#46243](https://github.com/PaddlePaddle/Paddle/pull/46243) ，[#46230](https://github.com/PaddlePaddle/Paddle/pull/46230) -- 增加 MoE 融合 Phi 算子，提升 MoE 模型性能推理性能， [#48703](https://github.com/PaddlePaddle/Paddle/pull/48703) -- 在 INT8 量化推理的场景下，Paddle-TensorRT 插件 fallback 到 FP16 计算而不是 FP32 计算，[#50554](https://github.com/PaddlePaddle/Paddle/pull/50554) -- 优化推理时内存、显存， [#49051](https://github.com/PaddlePaddle/Paddle/pull/49051) ， [#49046](https://github.com/PaddlePaddle/Paddle/pull/49046) ，[#53930](https://github.com/PaddlePaddle/Paddle/pull/53930) -- Layout 排布优化 Pass 增强， [#52997](https://github.com/PaddlePaddle/Paddle/pull/52997) -- 支持对算子 Shape 推断进行缓存，提升模型推理性能， [#48312](https://github.com/PaddlePaddle/Paddle/pull/48312) -- 使用 half2 指令优化 bias+add+relu 融合，[#49048](https://github.com/PaddlePaddle/Paddle/pull/49048) -- 使用向量化操作优化多个输入的 Concat Kernel，[#49540](https://github.com/PaddlePaddle/Paddle/pull/49540) -- 基于 CUTLASS 实现 Convolution、Depthwise Convolution 及相关融合算子，提升推理速度。 [#47989](https://github.com/PaddlePaddle/Paddle/pull/47989) ，[#50603](https://github.com/PaddlePaddle/Paddle/pull/50603) ，[#51792](https://github.com/PaddlePaddle/Paddle/pull/51792) ，[#50603](https://github.com/PaddlePaddle/Paddle/pull/50603) -- Paddle-TensorRT 支持 FlashAttention 的插件，提升 StableDiffusion 等模型的推理速度，[#49438](https://github.com/PaddlePaddle/Paddle/pull/49438) 。 -- 增加 Transpose+LayerNorm 的融合 PASS，提升 StableDiffusion 等模型的推理速度，[#50082](https://github.com/PaddlePaddle/Paddle/pull/50082) 。 -- 增加 Elementwise+Transpose 的融合，[#50081](https://github.com/PaddlePaddle/Paddle/pull/50081) -- 优化 Paddle-TensorRT Group Norm 插件实现 ，[#49160](https://github.com/PaddlePaddle/Paddle/pull/49160) -- Config.EnableTensorRtEngine()接口增加 use_cuda_graph 参数，可以支持开启 CUDA Graph，注意在使用时，需要保证模型输入 shape 不变，可以降低运行时耗时，[#53406](https://github.com/PaddlePaddle/Paddle/pull/53406) -- 支持对 Reshape 的 inplace 操作减少模型运行时的拷贝耗时， [#49146](https://github.com/PaddlePaddle/Paddle/pull/49146) -- 基于 oneDNN 优化 LayerNorm kernel 实现，[#47782](https://github.com/PaddlePaddle/Paddle/pull/47782) -- 基于 oneDNN 支持 quantize+transpose 以及 transpose+dequantize 融合，[#49509](https://github.com/PaddlePaddle/Paddle/pull/49509) -- CPU 推理下当开启 MKLDNN 时，默认开启 FC 相关的融合 Pass，提升性能，[#45704](https://github.com/PaddlePaddle/Paddle/pull/45704) -- CPU 的 OneDNN 推理支持 suqeeze2 + transpose2 融合，[#47592](https://github.com/PaddlePaddle/Paddle/pull/47592) - -### XPU 推理提升和性能优化 -- 新增 ExpRunWithRuntimeConfig 接口与 XpuRuntimeConfig 允许推理期间设置外部流、L3 cache 等参数；GetExecStream 接口支持获得昆仑外部流对象；输入、输出支持昆仑设备内存减少 D2H 和 H2D 开销，[#53334](https://github.com/PaddlePaddle/Paddle/pull/53334)、 [#52466](https://github.com/PaddlePaddle/Paddle/pull/52466)、 [#53240](https://github.com/PaddlePaddle/Paddle/pull/53240) -- 新增 multi-encoder, fused_multi_transformer 算子和融合 pass，提升 ERNIE 和 Transformer 类模型性能，[#50570](https://github.com/PaddlePaddle/Paddle/pull/50570)、[#51346](https://github.com/PaddlePaddle/Paddle/pull/51346)、 [#50499](https://github.com/PaddlePaddle/Paddle/pull/50499)、[#53982](https://github.com/PaddlePaddle/Paddle/pull/53982)、[#50759](https://github.com/PaddlePaddle/Paddle/pull/50759)、[#51571](https://github.com/PaddlePaddle/Paddle/pull/51571)、 [#53144](https://github.com/PaddlePaddle/Paddle/pull/53144)、[#53306](https://github.com/PaddlePaddle/Paddle/pull/53306) -- 优化 BeamSearch 性能，当 beam_size=1 时对 write_read_array, gather 等细粒度算子进行变换、去除和融合提升模型性能，[#53130](https://github.com/PaddlePaddle/Paddle/pull/53130) -- 多个相同输入的 stack 算子变换为支持 broadcast 的 unsqueeze 算子，unsquee/squeeze 支持 inplace 计算， [#52099](https://github.com/PaddlePaddle/Paddle/pull/52099) -- 新增支持导出适用于昆仑芯的多卡推理模型， [#50490](https://github.com/PaddlePaddle/Paddle/pull/50490) -- 新增 embedding_with_eltwise_add 融合 pass 及算子 phi kernel，减小显存占用并提升推理性能， [#50590](https://github.com/PaddlePaddle/Paddle/pull/50590) -- interpolate 类算子 phi kernel 支持 FP16， [#52358](https://github.com/PaddlePaddle/Paddle/pull/52358) -- argmax 算子支持 INT32 类型输出， [#51303](https://github.com/PaddlePaddle/Paddle/pull/51303) -- 修复开启混合精度推理模式后, 保存序列化模型时只有 model 文件时的报错， [#52994](https://github.com/PaddlePaddle/Paddle/pull/52994) -- 修复 instance_norm 在 scale 和 bias 为空时出现的段错误， [#52627](https://github.com/PaddlePaddle/Paddle/pull/52627) -- conv_transpose 算子支持 FP16，[#53626](https://github.com/PaddlePaddle/Paddle/pull/53626) -- 添加 yolo_box_xpu 融合 pass 及算子 phi kernel，优化 YOLO 模型通用子结构， [#54163](https://github.com/PaddlePaddle/Paddle/pull/54163) -- 添加 conv2d_xpu 融合 pass 以及算子 phi kernel，并支持 FP16 推理，优化卷积操作推理耗时，[#52247](https://github.com/PaddlePaddle/Paddle/pull/52247) ，[#53626](https://github.com/PaddlePaddle/Paddle/pull/53626) -- 添加 sigmoid_elementmul 通用融合 pass，融合为 swish 算子以匹配 conv2d_fusion pass 提升 YOLO 模型推理性能， [#53580](https://github.com/PaddlePaddle/Paddle/pull/53580) -- 添加 act_add 融合 pass 及算子 phi kernel 提升推理性能，[#53965](https://github.com/PaddlePaddle/Paddle/pull/53965) -- 添加 fold_interp_outsize 融合 pass 提升推理性能， [#54245](https://github.com/PaddlePaddle/Paddle/pull/54245) -- 解决当 FC 存在共享 weight 时因重复融合导致结果错误的问题。 [#51108](https://github.com/PaddlePaddle/Paddle/pull/51108)、[#51039](https://github.com/PaddlePaddle/Paddle/pull/51039) -- 删除算子仅用于训练的 op_device 属性，防止在推理期间错误的选择训练时的 place， [#51029](https://github.com/PaddlePaddle/Paddle/pull/51029) -- 支持优化后模型的保存，允许再次推理时跳过 PASS 优化减少第一次推理时间， [#53696](https://github.com/PaddlePaddle/Paddle/pull/53696) -- 解决算子 Kernel 的 CPUPlace 输入被强制拷贝到 XPU 而导致的计算错误问题， [#51306](https://github.com/PaddlePaddle/Paddle/pull/51306) -- subblock 支持参数 H2D 提前拷贝以提升推理性能。[#51876](https://github.com/PaddlePaddle/Paddle/pull/51876) -- 修复昆仑芯 2 代芯片输出激活的 scale 存储空间大小。 [#53505](https://github.com/PaddlePaddle/Paddle/pull/53505) -- 新执行器昆仑芯 D2D 拷贝支持异步执行， [#51876](https://github.com/PaddlePaddle/Paddle/pull/51876) -- 删除只有一个输入的 concat 算子，[#52304](https://github.com/PaddlePaddle/Paddle/pull/52304) -- lookup_table_v2 支持 FP16 删除冗余 cast 算子， [#52888](https://github.com/PaddlePaddle/Paddle/pull/52888) -- 控制流 While 算子支持缓存 scope，降低每次新建 scope 的开销， [#52628](https://github.com/PaddlePaddle/Paddle/pull/52628) -- scatter 新增支持 FP16，删除冗余 cast 算子以及某一个输入为 1 的 elementwise_mul 算子。[#52831](https://github.com/PaddlePaddle/Paddle/pull/52831) - -### 模型量化 -- 动态图量化功能全面升级 - - 新增动态图模型下量化训练的 API 为 ```paddle.quantization.QAT``` ，支持通过配置传入量化相关参数，简化量化训练使用流程和二次开发难度 ([#49398](https://github.com/PaddlePaddle/Paddle/pull/49398)) - - 新增离线量化的 API 为 ```paddle.quantization.PTQ``` ，支持量化模型导出成推理支持的模型格式 ([#50107](https://github.com/PaddlePaddle/Paddle/pull/50107)) - - 新增 STUB 算子，在训练过程中模拟实际的量化操作([#50510](https://github.com/PaddlePaddle/Paddle/pull/50510)) -- 支持量化训练模型加载离线量化模型的参数，支持更多算子量化，包含 matmul, scale,conv1d，[#47892](https://github.com/PaddlePaddle/Paddle/pull/47892)， [#45911](https://github.com/PaddlePaddle/Paddle/pull/45911)，[#48912](https://github.com/PaddlePaddle/Paddle/pull/48912) -- 支持静态图量化训练的混合并行训练，[#52219](https://github.com/PaddlePaddle/Paddle/pull/52219) -- 修复动态图量化过程中的问题： - - 导出量化训练模型时候重复插入量化节点，[#48751](https://github.com/PaddlePaddle/Paddle/pull/48751) - - 修复给模型输入插入量化节点的问题，[#49926](https://github.com/PaddlePaddle/Paddle/pull/49926) - -## 5. 环境适配 -为提升源码编译效率，完善和推广 setuptools + ninja 编译方式，提升开发效率，CPU 场景下，全量编译耗时减少 20min，编译速度提升 24.52%，GPU 场景下全量编译耗时减少 22min，编译速度提升 29.31%; 为了适配较为主流的开发环境，飞桨在源码编译支持了 gcc12 编译和 C++17 标准，适配了最新的 CUDA12； 代码质量完成了编译 warning 的清理，提升编译体验；第三方依赖层级，为减少依赖冲突，升级了底层的 protobuf 版本，并清理了一些低版本依赖库的废弃属性和老旧的代码格式，并移除了对于 python2.x 的支持。 -- ninja 编译适配，提升编译速度。[#52433](https://github.com/PaddlePaddle/Paddle/pull/52433),[#48932](https://github.com/PaddlePaddle/Paddle/pull/48932),[#49420](https://github.com/PaddlePaddle/Paddle/pull/49420),[#48435](https://github.com/PaddlePaddle/Paddle/pull/48435),[#49303](https://github.com/PaddlePaddle/Paddle/pull/49303),[#49448](https://github.com/PaddlePaddle/Paddle/pull/49448),[#49838](https://github.com/PaddlePaddle/Paddle/pull/49838),[#50067](https://github.com/PaddlePaddle/Paddle/pull/50067),[#52796](https://github.com/PaddlePaddle/Paddle/pull/52796),[#50431](https://github.com/PaddlePaddle/Paddle/pull/50431),[#49181](https://github.com/PaddlePaddle/Paddle/pull/49181),[#48867](https://github.com/PaddlePaddle/Paddle/pull/48867),[#48490](https://github.com/PaddlePaddle/Paddle/pull/48490),[#48211](https://github.com/PaddlePaddle/Paddle/pull/48211),[#49499](https://github.com/PaddlePaddle/Paddle/pull/49499),[#53076](https://github.com/PaddlePaddle/Paddle/pull/53076) -- setuptools 编译打包一体化适配。[#48770](https://github.com/PaddlePaddle/Paddle/pull/48770),[#46957](https://github.com/PaddlePaddle/Paddle/pull/46957),[#49583](https://github.com/PaddlePaddle/Paddle/pull/49583),[#47602](https://github.com/PaddlePaddle/Paddle/pull/47602),[#48301](https://github.com/PaddlePaddle/Paddle/pull/48301),[#50800](https://github.com/PaddlePaddle/Paddle/pull/50800),[#42575](https://github.com/PaddlePaddle/Paddle/pull/42575)),[#49826](https://github.com/PaddlePaddle/Paddle/pull/49826),[#49002](https://github.com/PaddlePaddle/Paddle/pull/49002),[#51443](https://github.com/PaddlePaddle/Paddle/pull/51443),[#51528](https://github.com/PaddlePaddle/Paddle/pull/51528),[#52621](https://github.com/PaddlePaddle/Paddle/pull/52621),[#52465](https://github.com/PaddlePaddle/Paddle/pull/52465) -- gcc12 支持。[#52960](https://github.com/PaddlePaddle/Paddle/pull/52960),[#52265](https://github.com/PaddlePaddle/Paddle/pull/52265),[#46546](https://github.com/PaddlePaddle/Paddle/pull/46546),[#52318](https://github.com/PaddlePaddle/Paddle/pull/52318),[#46808](https://github.com/PaddlePaddle/Paddle/pull/46808),[#47466](https://github.com/PaddlePaddle/Paddle/pull/47466),[#52083](https://github.com/PaddlePaddle/Paddle/pull/52083),[#48176](https://github.com/PaddlePaddle/Paddle/pull/48176),[#49423](https://github.com/PaddlePaddle/Paddle/pull/49423),[#49452](https://github.com/PaddlePaddle/Paddle/pull/49452),[#51037](https://github.com/PaddlePaddle/Paddle/pull/51037),[#52007](https://github.com/PaddlePaddle/Paddle/pull/52007),[#52441](https://github.com/PaddlePaddle/Paddle/pull/52441),[#52085](https://github.com/PaddlePaddle/Paddle/pull/52085),[#50817](https://github.com/PaddlePaddle/Paddle/pull/50817),[#52646](https://github.com/PaddlePaddle/Paddle/pull/52646),[#50777](https://github.com/PaddlePaddle/Paddle/pull/50777),[#53288](https://github.com/PaddlePaddle/Paddle/pull/53288),[#54009](https://github.com/PaddlePaddle/Paddle/pull/54009) -- c++17 标准支持。[#53345](https://github.com/PaddlePaddle/Paddle/pull/53345),[#53892](https://github.com/PaddlePaddle/Paddle/pull/53892),[#54282](https://github.com/PaddlePaddle/Paddle/pull/54282),[#49017](https://github.com/PaddlePaddle/Paddle/pull/49017),[#47635](https://github.com/PaddlePaddle/Paddle/pull/47635),[#54258](https://github.com/PaddlePaddle/Paddle/pull/54258) -- cuda12 支持。[#52285](https://github.com/PaddlePaddle/Paddle/pull/52285),[#49592](https://github.com/PaddlePaddle/Paddle/pull/49592),[#52232](https://github.com/PaddlePaddle/Paddle/pull/52232),[#52654](https://github.com/PaddlePaddle/Paddle/pull/52654),[#54641](https://github.com/PaddlePaddle/Paddle/pull/54641) -- CodeStyle。[#45909](https://github.com/PaddlePaddle/Paddle/pull/45909),[#47772](https://github.com/PaddlePaddle/Paddle/pull/47772),[#48538](https://github.com/PaddlePaddle/Paddle/pull/48538),[#49522](https://github.com/PaddlePaddle/Paddle/pull/49522),[#47264](https://github.com/PaddlePaddle/Paddle/pull/47264),[#49558](https://github.com/PaddlePaddle/Paddle/pull/49558) -- 编译 Warning 消除。[#47163](https://github.com/PaddlePaddle/Paddle/pull/47163),[#47216](https://github.com/PaddlePaddle/Paddle/pull/47216),[#47309](https://github.com/PaddlePaddle/Paddle/pull/47309)，[#47252](https://github.com/PaddlePaddle/Paddle/pull/47252)，[#47341](https://github.com/PaddlePaddle/Paddle/pull/47341)，[#47399](https://github.com/PaddlePaddle/Paddle/pull/47399)，[#47513](https://github.com/PaddlePaddle/Paddle/pull/47513)，[#47558](https://github.com/PaddlePaddle/Paddle/pull/47558)，[#47706](https://github.com/PaddlePaddle/Paddle/pull/47706)，[#52717](https://github.com/PaddlePaddle/Paddle/pull/52717)，[#51203](https://github.com/PaddlePaddle/Paddle/pull/51203)，[#51336](https://github.com/PaddlePaddle/Paddle/pull/51336)，[#51608](https://github.com/PaddlePaddle/Paddle/pull/51608)，[#51633](https://github.com/PaddlePaddle/Paddle/pull/51633),[#46644](https://github.com/PaddlePaddle/Paddle/pull/46644),[#53092](https://github.com/PaddlePaddle/Paddle/pull/53092),[#53185](https://github.com/PaddlePaddle/Paddle/pull/53185),[#53246](https://github.com/PaddlePaddle/Paddle/pull/53246),[#53650](https://github.com/PaddlePaddle/Paddle/pull/53650),[#53683](https://github.com/PaddlePaddle/Paddle/pull/53683),[#53687](https://github.com/PaddlePaddle/Paddle/pull/53687),[#53886](https://github.com/PaddlePaddle/Paddle/pull/53886),[#53689](https://github.com/PaddlePaddle/Paddle/pull/53689),[#53679](https://github.com/PaddlePaddle/Paddle/pull/53679),[#53681](https://github.com/PaddlePaddle/Paddle/pull/53681),[#53532](https://github.com/PaddlePaddle/Paddle/pull/53532),[#47137](https://github.com/PaddlePaddle/Paddle/pull/47137),[#47045](https://github.com/PaddlePaddle/Paddle/pull/47045),[#52186](https://github.com/PaddlePaddle/Paddle/pull/52186),[#52490](https://github.com/PaddlePaddle/Paddle/pull/52490),[#53924](https://github.com/PaddlePaddle/Paddle/pull/53924),[#53938](https://github.com/PaddlePaddle/Paddle/pull/53938),[#53945](https://github.com/PaddlePaddle/Paddle/pull/53945),[#53851](https://github.com/PaddlePaddle/Paddle/pull/53851),[#53847](https://github.com/PaddlePaddle/Paddle/pull/53847),[#53818](https://github.com/PaddlePaddle/Paddle/pull/53818),[#53931](https://github.com/PaddlePaddle/Paddle/pull/53931) -- 支持 protobuf 升级。[#49875](https://github.com/PaddlePaddle/Paddle/pull/49875),[#48495](https://github.com/PaddlePaddle/Paddle/pull/48495),[#49673](https://github.com/PaddlePaddle/Paddle/pull/49673),[#52499](https://github.com/PaddlePaddle/Paddle/pull/52499),[#51161](https://github.com/PaddlePaddle/Paddle/pull/51161),[#49168](https://github.com/PaddlePaddle/Paddle/pull/49168) -- 支持第三方库离线编译。[#54326](https://github.com/PaddlePaddle/Paddle/pull/54326),[#54370](https://github.com/PaddlePaddle/Paddle/pull/54370),[#54335](https://github.com/PaddlePaddle/Paddle/pull/54335),[#54346](https://github.com/PaddlePaddle/Paddle/pull/54346),[#53744](https://github.com/PaddlePaddle/Paddle/pull/53744),[#54319](https://github.com/PaddlePaddle/Paddle/pull/54319),[#53915](https://github.com/PaddlePaddle/Paddle/pull/53915) -- phi 独立编译头文件依赖解耦。[#50456](https://github.com/PaddlePaddle/Paddle/pull/50456),[#47088](https://github.com/PaddlePaddle/Paddle/pull/47088),[#52573](https://github.com/PaddlePaddle/Paddle/pull/52573),[#52651](https://github.com/PaddlePaddle/Paddle/pull/52651) -- Python2.x 退场。[#48685](https://github.com/PaddlePaddle/Paddle/pull/48685) - -## 6. 安全 -- 修复了诸如空指针使用、非法地址访问、内存越界、除 0、Python IndexError 等问题。[PR49976](https://github.com/PaddlePaddle/Paddle/pull/49976), [ PR49993](https://github.com/PaddlePaddle/Paddle/pull/49993)[, PR49942](https://github.com/PaddlePaddle/Paddle/pull/49942), [PR49965](https://github.com/PaddlePaddle/Paddle/pull/49965)[, PR50000](https://github.com/PaddlePaddle/Paddle/pull/50000)[, PR50005](https://github.com/PaddlePaddle/Paddle/pull/50005)[, PR49953](https://github.com/PaddlePaddle/Paddle/pull/49953)[, PR49995](https://github.com/PaddlePaddle/Paddle/pull/49995)[, PR49974](https://github.com/PaddlePaddle/Paddle/pull/49974)[, PR50015](https://github.com/PaddlePaddle/Paddle/pull/50015)[, PR50010](https://github.com/PaddlePaddle/Paddle/pull/50010), [PR49979](https://github.com/PaddlePaddle/Paddle/pull/49979), [PR49994](https://github.com/PaddlePaddle/Paddle/pull/49994), [PR49977](https://github.com/PaddlePaddle/Paddle/pull/49977)[, PR49968](https://github.com/PaddlePaddle/Paddle/pull/49968), [PR49984](https://github.com/PaddlePaddle/Paddle/pull/49984)[, PR49958](https://github.com/PaddlePaddle/Paddle/pull/49958)[, PR50008](https://github.com/PaddlePaddle/Paddle/pull/50008)[, PR51714](https://github.com/PaddlePaddle/Paddle/pull/51714), [PR51847](https://github.com/PaddlePaddle/Paddle/pull/51847), [PR51034](https://github.com/PaddlePaddle/Paddle/pull/51034)[, PR51088](https://github.com/PaddlePaddle/Paddle/pull/51088)[, PR51091](https://github.com/PaddlePaddle/Paddle/pull/51091)[, PR51092](https://github.com/PaddlePaddle/Paddle/pull/51092), [PR49966](https://github.com/PaddlePaddle/Paddle/pull/49966), [PR49656](https://github.com/PaddlePaddle/Paddle/pull/49656), [PR52161](https://github.com/PaddlePaddle/Paddle/pull/52161), [PR49548](https://github.com/PaddlePaddle/Paddle/pull/49548), [PR49546](https://github.com/PaddlePaddle/Paddle/pull/49546), [PR49547](https://github.com/PaddlePaddle/Paddle/pull/49547), [PR49549](https://github.com/PaddlePaddle/Paddle/pull/49549), [PR51850](https://github.com/PaddlePaddle/Paddle/pull/51850) - -## Thanks to our Contributors -This release contains contributions from: -1want2sleep, 201716010711, 404988613, 5u13, 6clc, Ackeraa, Aganlengzi, ahahahahahaha, Ainavo, Allen Guo, andyj, Asthestarsfalll, Aurelius84, Ayuan, BellaZYL, Bjmw3, Bo Zhang, bukejiyu, caozhou, carryyu, Ccc, ccrrong, ceci3, chalsliu, Chang Xu, CHANGer, Charles-hit, Chen Weihang, chenjian, Chenxiao Niu, chenxiao120660, chenxujun, Chitsing KUI, cifar10, co63oc, CollaborativeFiltering, csy0225, cxxly, cyber-pioneer, cyberslack_lee, czr-gc, Dandelight, danleifeng, Danyang Zhang, dasen, denglianbin, Difer, dongfangshenzhu, DrowFish19, duanboqiang, duanyanhui, engineer, engineer1109, Epsilon Luoo, feifei-111, Feiyu Chan, Feng Ni, feng_shuai, Fisher, FlyingQianMM, Frank Lin, Galaxy1458, GaoYuYang, gaoziyuan, gem5, GGBond8488, Ghost Screaming, gongenlei, gouzil, Guanghua Yu, Guo Sheng, Guoxia Wang, Hamid Zare, Hanchiao, handiz, Haohongxiang, haosicheng, haozi, Happyd99, heliqi, hellockx, hellolllw, heyanru, hg-1099255210, hh-qiao, hjyp, hong, HongyuJia, houj04, hua-zi, Huang Jiyi, Huang Zhengjie, huangjiyi, huangjun12, Hui Zhang, Huihuang Zheng, Hulek, hwa, HydrogenSulfate, Ikko Eltociear Ashimine, iLeGend, Infinity_lee, Infrared1029, Jacek Czaja, jakpiase, james, jameszhang, Jiabin Yang, jiahongyu, jiangcheng, jiangfan06, Jianghai, jiaqianjing, jingsongliu, JingZhuangzhuang, jjyaoao, joanna.wozna.intel, junxiu777, Jx-qi, JYChen, JZ-LIANG, jzhang533, Kai Song, Kai Xing, Kaipeng Deng, Kang Zhao, kangguangli, Kevin 吴嘉文, Kim, Kim Yann, knamg, kuizhiqing, lanxianghit, Leding Li, Leo Chen, Leo Guo, levi131, Li Min, Li-fAngyU, Ligoml, lijialin03, lijin23, limingshu, Lin Manhui, LinearTemporalLogic, Linjie Chen, lishicheng1996, Little-chick, littleforest, liu zhengxi, liulinduo, liuruyan, liuzhenhai93, LiYuRio, lj970926, LokeZhou, LoneRanger, lubiu, Lucas, lugimzzz, Lux et Veritas, lxsbupt, LyndonKong, lzy, lzydev, Mahmoud Ashraf, Manan Goel, Maple Xie, Matsumoto Ruko, mayang002, MayYouBeProsperous, megemini, mengziheng, Meteor Liu, mhy, mhy-666, Ming-Xu Huang, ming1753, minghaoBD, mjxs, Moqim, Mountagha, Mr.Juice, mrcangye, NetPunk, Netpunk, nihao, niuliling123, Nyakku Shigure, OccupyMars2025, Ouyang Chao, pangengzheng, pangyoki, parap1uie-s, Paulina Gacek, Piotr Paturej, PommesPeter, PPGitub, PPPPzhang, PuQing, Qi Li, Qi Shao, QingshuChen, qipengh, qizhaoaoe, Rayman, RedContritio, RichardWooSJTU, risemeup1, Roc, ronnywang, Ruibiao Chen, Ruibin Cheung, RuohengMa, Ryan, SaltFish11, Sanbu, Scotty, scotty, seemingwang, Shaojie WANG, ShenLiang, shentanyue, Shijie, Shuangchi He, Siming Dai, Sing_chan, sneaxiy, Sonder, sprouteer, Sqhttwl, sunli, superwinner1, supplyout, SylarTiaNII, Sylwester Fraczek, Sławomir Siwek, taixiurong, Tao Luo, Taylor-Layrose, TeFeng Chen, Thomas Young, thunder95, Thunderbrook, Tian, Tian Zheng, tiancaishaonvjituizi, tianshuo78520a, tifa, Tinson Lai, Tomasz Socha, Tony Cao, ucsk, umiswing, ustiniankw, Vegetable dog, Vigi Zhang, Vvsmile, Wang Bojun, Wang Xin, Wang Xinyu, wangfengsheng1999, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, wangshengxiang, wangxiaoning, wangxinxin08, Wangzheee, WangZhen, wangzhen38, wasupandceacar, wawltor, Wei Shengyu, Weilong Wu, weishengying, Wen Sun, wenbin, wentao yu, wenzhe.wang, westfish, whisky-12, whs, Wilber, will-jl944, winter-wang, Winters Montagne, WJJ1995, wuhuachaocoding, wuyefeilin, wz1qqx, XiangGao, xiaoguoguo626807, xiaohemaikoo, xiaoluomi, xiaoting, xiaoxiaohehe001, Xiaoxu Chen, xiaoyuanzi914, Xinger, Xinyu Chen, xiongkun, xjmxyt, xu98bin, xysheng-baidu, yangguohao, yangjianfengo1, YangQun, YangZhou, yeliang2258, YepKong, Yichen Zhang, yikaikkk, Yiqun Liu, yjphhw, ykkk2333, Young-Flash, yu wentao, Yuang Liu, Yuanle Liu, YuanRisheng, yuchen202, yuehuayingxueluo, YuhangLi, Yulong Ao, YUNSHEN XIE, yunyaoXYY, YuRonan, zachary sun, ZeKai Zhou, Zenghui Yuan, zengshao0622, Zero Rains, Zhan Rongrui, Zhang Jun, Zhang Na, Zhang Ting, Zhang Zheng, zhangbo9674, ZhangDY-6483, zhangkaihuo, zhangxin81, zhangyikun02, zhangyingying520, zhangyuqin1998, zhaocaibei123, zhaoyingli, Zhen Wang, Zheng-Bicheng, Zhenghai Zhang, Zheng_Bicheng, zhenyun, Zhibao Li, zhiboniu, Zhong Hui, Zhou Wei, ZhouMengLei1999, zhoutianzi666, zhouzj, zhupengyang, zhurou603, zhuyipin, zhwesky2010, ziyoujiyi, zlsh80826, Zman, zmxdream, zqw_1997, Zuza Gawrysiak, zxcd, zyfncg, ZZK, zzk0, 丁一, 傅剑寒, 六个骨头, 卢林, 周周周, 姜永久, 学渣戊, 张春乔, 张正海, 柠檬味~, 王明冬, 石晓伟, 超级码牛, 陈沧夜, 骑马小猫 - -# 2.4.2 Release Note - - 版本修复了已知问题，并新增了少量功能。 - -## 训练框架（含分布式） - - * 修复 paddle.utils.dlpack.to_dlpack 在 for 循环里 API 多次创建 dlpack 对象的报错问题，修复引用对象计数错误导致 dlpack 实际指向内容被析构的问题。 [#50138](https://github.com/PaddlePaddle/Paddle/pull/50138) - * 修复 paddle.multiplex API 在多维 Input Tensor 场景下访存越界的问题并添加 check 机制。 [#49368](https://github.com/PaddlePaddle/Paddle/pull/49368) - * 引入 cutlass，实现 gemm+gather+scatter 的融合；优化 sparse conv 的训练和推理性能；优化 batch_norm 在 1D 输入数据下的推理性能。 [#50118](https://github.com/PaddlePaddle/Paddle/pull/50118) - * 修复因使用 constexpr 导致 gcc54 环境下编译失败的问题。 [#50421](https://github.com/PaddlePaddle/Paddle/pull/50421) - * 将 sum op 的 Kernel 迁移到 PHI 算子库，并且修复 infermeta 中 SelectedRows 无法获取正确 dim 的 bug。 [#49342](https://github.com/PaddlePaddle/Paddle/pull/49342) - * 修复 eigen 头文件错误引用导致的偶发编译错误。 [#48157](https://github.com/PaddlePaddle/Paddle/pull/48157) - * 修复 fold 算子在大 bs 输入下访存越界的问题。 [#49491](https://github.com/PaddlePaddle/Paddle/pull/49491) - * 通过增加类型判别，解决发送张量时，维度不统一，造成流水线并行 hang 住的问题。 [#50337](https://github.com/PaddlePaddle/Paddle/pull/50337) - * 修复了自定义算子输出梯度的参数顺序不连续时，反向算子的输出值可能为 None 的 bug。 [#48656](https://github.com/PaddlePaddle/Paddle/pull/48656) - * 修复 paddle.queeze_ API 在 inplace 操作时 shape 重复修改导致结果错误 bug。 [#49903](https://github.com/PaddlePaddle/Paddle/pull/49903) - * 修复动转静模式下无参数 Layer 无法调用 backward 的问题。 [#49812](https://github.com/PaddlePaddle/Paddle/pull/49812) - * 修复 CUDA11.8 在 windows 的编译问题。 [#50205](https://github.com/PaddlePaddle/Paddle/pull/50205) - * 修复 `FusedDropoutActBiasGrad` 在 H100 上不支持的错误。 [#47285](https://github.com/PaddlePaddle/Paddle/pull/47285) - * 新增 `debug_graphviz_path` 选项至 `build_strategy`。 [#46531](https://github.com/PaddlePaddle/Paddle/pull/46531) - * 修复未关闭的 `popen` 物件。 [#47053](https://github.com/PaddlePaddle/Paddle/pull/47053) - -## 部署方向（Paddle Inference） - - * 完善混合精度推理功能，提高混合精度推理稳定性。重构二阶段式 convert_to_mixed_precision 接口底层实现， enable_use_gpu 新增 precision 参数支持一阶段式。 [#49077](https://github.com/PaddlePaddle/Paddle/pull/49077)、[#49239](https://github.com/PaddlePaddle/Paddle/pull/49239)、[#49477](https://github.com/PaddlePaddle/Paddle/pull/49477) - * 支持 jetson ampere 架构下编译。 [#49364](https://github.com/PaddlePaddle/Paddle/pull/49364) - * 修复 fc kernel 低精度模式下的精度问题。 [#49781](https://github.com/PaddlePaddle/Paddle/pull/49781) - * 修复 CAPI 下， trt workspace 参数类型的错误。 [#48350](https://github.com/PaddlePaddle/Paddle/pull/48350) - * 修复 Paddle 1.x 版本下 arg_max arg_min 没有 flatten dtype 参数，推理时会报错的问题。 [#49771](https://github.com/PaddlePaddle/Paddle/pull/49771) - * 修复 split infermeta 重构后关于 lod 逻辑信息缺失问题。 [#49745](https://github.com/PaddlePaddle/Paddle/pull/49745) - * 修复常量折叠 pass 不正确设置，导致 conv2d 权重经折叠后为非 persistable 而没有进入 TensorRT engine 问题。 [#50105](https://github.com/PaddlePaddle/Paddle/pull/50105) - -# 2.4.1 Release Note - - -去除飞桨对 python.so 的依赖，修复在包括 conda 在内的特定的环境下，因无法找到 python.so 而造成运行失败的 Bug。 - - - -# 2.4.0 Release Note - -## 1. 重要更新 - -- **新动态图架构正式生效**：新动态图框架调大幅提升了调度性能，超 90%API 的调度性能提升超过 50%，超 50%套件模型性能提升超过 5%，功能架构更加清晰，二次开发能力和体验显著增强。 - -- **全面提升了飞桨的动静统一能力：** 动转静功能提供了更加丰富的 Python 语法支持，飞桨的 Python 语法覆盖率达到 90%，对语法转写逻辑进行了重点地优化，完备地支持了控制流语法，提供了更加流畅的一键转静态图体验；借助全新升级的静态图执行器，让动转静训练具有更优的加速能力，重点模型测试显示接近静态图最佳水平；提升了动转静的可扩展性，新增支持多函数合并导出和推理，支持用户使用 PHI 算子库进行二次开发和灵活部署，有效支撑语音领域 U2++特色模型的自定义解码。 - -- **新增稀疏计算类 API：** 新增 55 个稀疏 API `paddle.sparse.*`，支持稀疏计算主流场景，已应用于 3D 点云目标检测、Sparse Transformers 等任务的稀疏训练和推理部署，高稀疏度场景下相比使用 DenseTensor 提速 105.75%，相比同类产品稀疏计算提速 4.01%~58.55%；支持多种稀疏 Tensor(SparseCoo 和 SparseCsr 等)的计算，极致节省显存；同时保持了一致的使用体验，和稠密 Tensor 的 API 使用方式一致。 - -- **大规模图神经网络 GPU 训练引擎：** 通过 SSD、内存、显存的异构层次化存储技术，突破显存瓶颈,支持超大规模图的全 GPU 存储和训练；实现了游走、采样、训练的全 GPU 一体化解决方案，相比传统的分布式 CPU 解决方案，相同成本的情况下训练速度提升 10+倍。 - -- **环境适配：** 新增了适配 CUDA11.7 版本的预编译安装包，新增了支持在 Ubuntu 22.04 及以上版本中运行。 - -### 前瞻性预告 - -- 飞桨框架将在 2.5 版本废弃对 python 3.6 的支持。 -- 飞桨框架将会逐步废弃 python 端的`paddle.fluild`命名空间下的 API，在 2.5 版本时，部分该命名空间下的 API 将会被直接删除。 - -## 2. 不兼容升级 - -- 取消了适配 CUDA10.1 版本的预编译安装包。 -- Tensor.clear_gradient(bool set_to_zero)接口不再接收 kwargs 传入的值，只能通过 args 传入 set_to_zero 的 bool 变量。 -- 为了提高显存利用效率，动态图默认仅保留前向叶子结点变量的梯度如训练中网络参数的梯度，而不再支持默认保留非叶子结点的梯度。如果需要保留特定 Tensor 的梯度，可以在反向执行前调用 Tensor.retain_grads()接口。 -- paddle.autograd.PyLayer 将不再支持输入是 tuple 的情况，如果输入希望是一组 Tensor 的情况请传入 list of Tensor。 - -## 3. 训练框架（含分布式） - -### （1）新增 API 和增强 API 功能 -- **新增稀疏计算类 API**：paddle.sparse - - 新增 55 个稀疏 API，支持稀疏计算主流场景，已应用于 3D 点云目标检测、Sparse Transformers 等任务的稀疏训练和推理部署，高稀疏度场景下相比使用 DenseTensor 提速 105.75%，相比同类产品稀疏计算提速 4.01%~58.55%；支持多种稀疏 Tensor(SparseCoo 和 SparseCsr 等)的计算，极致节省显存；同时保持了一致的使用体验，和稠密 Tensor 的 API 使用方式一致。[#45849](https://github.com/PaddlePaddle/Paddle/pull/45849), [#46694](https://github.com/PaddlePaddle/Paddle/pull/46694), [#45086](https://github.com/PaddlePaddle/Paddle/pull/45086), [#41857](https://github.com/PaddlePaddle/Paddle/pull/41857), [#42935](https://github.com/PaddlePaddle/Paddle/pull/42935), [#43475](https://github.com/PaddlePaddle/Paddle/pull/43475), [#43668](https://github.com/PaddlePaddle/Paddle/pull/43668), [#43966](https://github.com/PaddlePaddle/Paddle/pull/43966), [#44022](https://github.com/PaddlePaddle/Paddle/pull/44022), [#44346](https://github.com/PaddlePaddle/Paddle/pull/44346), [#44432](https://github.com/PaddlePaddle/Paddle/pull/44432), [#44451](https://github.com/PaddlePaddle/Paddle/pull/44451), [#44743](https://github.com/PaddlePaddle/Paddle/pull/44743), [#42013](https://github.com/PaddlePaddle/Paddle/pull/42013), [#43520](https://github.com/PaddlePaddle/Paddle/pull/43520), [#41434](https://github.com/PaddlePaddle/Paddle/pull/41434), [#42130](https://github.com/PaddlePaddle/Paddle/pull/42130), [#41276](https://github.com/PaddlePaddle/Paddle/pull/41276), [#41857](https://github.com/PaddlePaddle/Paddle/pull/41857), [#41356](https://github.com/PaddlePaddle/Paddle/pull/41356) -- **新增语音领域 API：** paddle.audio - - 新增 MFCC、Spectrogram、LogMelSpectrogram 等特征提取 API，支持 GPU 计算，相比 CPU 实现处理性能提升 15x 倍以上，可大幅提升语音模型训练 GPU 利用率。[#45424](https://github.com/PaddlePaddle/Paddle/pull/45424) - - 新增窗函数、离散余弦变换等特征提取基础 API，方便用户自定义语音特征提取。[#45424](https://github.com/PaddlePaddle/Paddle/pull/45424) - - 新增语音 IO 模块，提供 2 种 音频 I/O backend，支持 6 种编解码，便捷地实现语音数据的加载。 [#45939](https://github.com/PaddlePaddle/Paddle/pull/45939) - - 新增 TESS，ESC50 语音分类数据集，方便用户完成经典语音分类模型。[#45939](https://github.com/PaddlePaddle/Paddle/pull/45939) -- **新增图学习领域 API：** paddle.geometric - - 图学习逐渐成为机器学习领域的关键技术，飞桨新增 paddle.geometric 模块提供更好的图学习建模和训练开发体验。 - - 消息传递：图学习消息传递机制是图建模的基础，因此新增 7 个图学习消息传递 API，更方便完成进行图学习建模。其中，新增的 3 个消息传递融合算子可大幅减少图模型训练显存占用，稠密图场景下 GCN 系列模型可节省 50%+显存，训练速度可提升 20%+。[#44848](https://github.com/PaddlePaddle/Paddle/pull/44848), [#44580](https://github.com/PaddlePaddle/Paddle/pull/44580), [#43174](https://github.com/PaddlePaddle/Paddle/pull/43174), [#44970](https://github.com/PaddlePaddle/Paddle/pull/44970) - - 图采样：图采样是图模型训练的性能瓶颈，此次新增了高性能图采样算子，支持高并发图采样，GraphSage 的采样速度可提升 32 倍以上，模型训练速度可提升 12 倍以上。[#44970](https://github.com/PaddlePaddle/Paddle/pull/44970) -- **新增视觉领域 API** - - paddle.vision 新增目标检测领域算子 paddle.vision.distribute_fpn_proposals([#43736](https://github.com/PaddlePaddle/Paddle/pull/43736)), paddle.vision.generate_proposals([#43611](https://github.com/PaddlePaddle/Paddle/pull/43611)), paddle.vision.matrix_nms([#44357](https://github.com/PaddlePaddle/Paddle/pull/44357)), paddle.vision.prior_box 和 paddle.vision.box_coder([#47282](https://github.com/PaddlePaddle/Paddle/pull/47282))。 - -- - **新增其他 API** - - 新增 iinfo([#45321](https://github.com/PaddlePaddle/Paddle/pull/45321)), count_nonzero([#44169](https://github.com/PaddlePaddle/Paddle/pull/44169)), nanmedian([#42385](https://github.com/PaddlePaddle/Paddle/pull/42385)), remainder\_ ([#45266](https://github.com/PaddlePaddle/Paddle/pull/45266)), take([#44741](https://github.com/PaddlePaddle/Paddle/pull/44741)), triu_indices([#45168](https://github.com/PaddlePaddle/Paddle/pull/45168)), sgn([#44568](https://github.com/PaddlePaddle/Paddle/pull/44568)), bucketize([#44195](https://github.com/PaddlePaddle/Paddle/pull/44195)), nanquantile([#41343](https://github.com/PaddlePaddle/Paddle/pull/41343)), frac([#41226](https://github.com/PaddlePaddle/Paddle/pull/41226)), logcumsumexp([#42267](https://github.com/PaddlePaddle/Paddle/pull/42267)), pairwise_distance([#44161](https://github.com/PaddlePaddle/Paddle/pull/44161)), heaviside([#41872](https://github.com/PaddlePaddle/Paddle/pull/41872)), logspace([#41261](https://github.com/PaddlePaddle/Paddle/pull/41261)), corrcoef([#40690](https://github.com/PaddlePaddle/Paddle/pull/40690)) - - 新增 RReLU([#41823](https://github.com/PaddlePaddle/Paddle/pull/41823)), CyclicLR([#40698](https://github.com/PaddlePaddle/Paddle/pull/40698)), OneCycleLR([#41825](https://github.com/PaddlePaddle/Paddle/pull/41825)), Softmax2D([#40910](https://github.com/PaddlePaddle/Paddle/pull/40910)), SoftMarginLoss([#42364](https://github.com/PaddlePaddle/Paddle/pull/42364)), MultiLabelSoftMarginLoss([#41183](https://github.com/PaddlePaddle/Paddle/pull/41183)), TripletMarginLoss([#40487](https://github.com/PaddlePaddle/Paddle/pull/40487)), TripletMarginWithDistanceLoss([#40545](https://github.com/PaddlePaddle/Paddle/pull/40545)), CosineEmbeddingLoss 和 cosine_embedding_loss([#41680](https://github.com/PaddlePaddle/Paddle/pull/41680)), PixelUnshuffle([#40728](https://github.com/PaddlePaddle/Paddle/pull/40728)), ChannelShuffle([#40743](https://github.com/PaddlePaddle/Paddle/pull/40743)) -- **增强 API 功能** - - 增加 BatchNorm1D 的大 batch_size 计算功能 [#43072](https://github.com/PaddlePaddle/Paddle/pull/43072) -- **完善集合通信分布式训练 API** - - 完善`fleet.init`函数，增加`log_level`参数，方便用户查看运行过程中的日志 [#45909](https://github.com/PaddlePaddle/Paddle/pull/45909) - - 新增`paddle.distributed.fleet.recompute_sequential paddle.distributed.fleet.recompute_hybrid`接口，方便用户使用 recompute 功能[#45348](https://github.com/PaddlePaddle/Paddle/pull/45348) - - 新增`paddle.distributed.fleet.layers.mpu` package，方便用户使用张量并行功能 [#45803](https://github.com/PaddlePaddle/Paddle/pull/45803) - - 新增通信 API `paddle.distributed.destroy_process_group paddle.distributed.isend paddle.distributed.irecv paddle.distributed.all_to_all_single`，提升了通信的功能完备性和易用性 [#43918](https://github.com/PaddlePaddle/Paddle/pull/43918) - - 新增`paddle.distributed.stream` 通信 package，性能比基础版本提升 5%到 10% [#46023](https://github.com/PaddlePaddle/Paddle/pull/46023) [#45282](https://github.com/PaddlePaddle/Paddle/pull/45282) - - 通信 API 新增多种数据类型`Char/Byte/Bool`等的支持，提升了通信的功能完备性和易用性 [#45574](https://github.com/PaddlePaddle/Paddle/pull/45574) [#45440](https://github.com/PaddlePaddle/Paddle/pull/45440) - - 通信 API 异步参数从`use_calc_stream`变成`sync_op`，增强了接口的语义可读性 [#46493](https://github.com/PaddlePaddle/Paddle/pull/46493) -- **增强高层 API** - - 高层 API 中视觉模型 ResNeXt 实现复用 ResNet 代码进行重构。 [#40588](https://github.com/PaddlePaddle/Paddle/pull/40588) - - 高层 API 中视觉模型 Inceptionv3、MobileNetv1、MobileNetv2、ShuffleNetv2 实现改进。[#40431](https://github.com/PaddlePaddle/Paddle/pull/40431) - -### （2）新功能及重要功能升级 - -- **新动态图架构正式上线**：新动态图框架调度性能大幅提升，相比原有架构大幅提升了调度性能，超 90%API 的调度性能提升超过 50%，超 50%套件模型性能提升超过 5%; 新动态图架构清晰，耦合度低，基于新架构实现 Hook、PyLayer 等扩展模块的学习与开发成本显著降低。[#37550](https://github.com/PaddlePaddle/Paddle/pull/37550)，[#37574](https://github.com/PaddlePaddle/Paddle/pull/37574)，[#37813](https://github.com/PaddlePaddle/Paddle/pull/37813)，[#37926](https://github.com/PaddlePaddle/Paddle/pull/37926)，[#39192](https://github.com/PaddlePaddle/Paddle/pull/39192)，[#37599](https://github.com/PaddlePaddle/Paddle/pull/37599)，[#37406](https://github.com/PaddlePaddle/Paddle/pull/37406)，[#37466](https://github.com/PaddlePaddle/Paddle/pull/37466)，[#37599](https://github.com/PaddlePaddle/Paddle/pull/37599)，[#40945](https://github.com/PaddlePaddle/Paddle/pull/40945)，[#39989](https://github.com/PaddlePaddle/Paddle/pull/39989) - -- **高阶自动微分机制**：为了更好支持科学计算等场景，飞桨框架针对高阶自动微分能力进一步完善优化。目前，已在`paddle.incubate.autograd` 目录下提供了支持前反向高阶自动微分相关试用功能及 API（当前处于孵化状态，相关功能及 API 签名可能会发生变化）。如果想自行实现相关模型、探索自动微分机制，请仔细阅读[高阶自动微分使用方法及限制](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/incubate/autograd/Overview_cn.html)。具体的升级包括： - 1. 静态图高阶微分机制升级，通过基础算子体系和程序变换，支持高阶前向及反向微分，并打通编译器、分布式功能。[#41919](https://github.com/PaddlePaddle/Paddle/pull/41919), [#41201](https://github.com/PaddlePaddle/Paddle/pull/41201) - 2. 新增前向和反向高阶自动微分 API， `paddle.incubate.autograd.forward_grad`, `paddle.incubate.autograd.grad`。[#43354](https://github.com/PaddlePaddle/Paddle/pull/43354) - 3. 新增 18 个高阶自动微分算子`sin`, `cos`, `exp`, `erf`, `abs`, `log`, `cast`, `where`, `equal`, `not_equal`, `greater_than`, `greater_equal`, `elementwise_pow` `square`, `elementwise_max`, `gelu`, `reduce_mean`, `size`。[#46184](https://github.com/PaddlePaddle/Paddle/pull/46184), [#46024](https://github.com/PaddlePaddle/Paddle/pull/46024), [#45888](https://github.com/PaddlePaddle/Paddle/pull/45888), [#45338](https://github.com/PaddlePaddle/Paddle/pull/45338), [#44345](https://github.com/PaddlePaddle/Paddle/pull/44345) - 4. 修复现有`elementwise_div`, `reduce_sum`, `p_norm`等算子缺陷。[#46514](https://github.com/PaddlePaddle/Paddle/pull/46514), [#46184](https://github.com/PaddlePaddle/Paddle/pull/46184) - -- **通用异构参数服务器架构**： - - 参数服务器 GPUGraph 基础架构升级，满足大规模应用落地：针对传统 CPU 存储和训练大规模图神经网络的成本高，稳定性低，性能不足的问题打造了纯 GPU 图训练引擎（PGLBox），通过 SSD、内存、显存的异构层次化存储技术，支持超大规模图模型训练，同等成本下训练性能相对 CPU 图训练引擎提升 10+倍，任务失败率下降到极低。[#44594](https://github.com/PaddlePaddle/Paddle/pull/44594) - - 大规模联邦参数服务器架构：针对大规模个性化推荐场景，基于异构 PS 基础架构，开发了大规模联邦参数服务器训练，支持千亿参数下的横向纵向联邦，它包括两个特性：用户私有参数本地更新，公共参数在远端更新，用户可灵活配置私有参数和公共参数的切分策略；新增中心调度节点 Coordinator，用户可从基类进行二次开发，自定义 Client 选择策略。[#42682](https://github.com/PaddlePaddle/Paddle/pull/42682)，[#44864](https://github.com/PaddlePaddle/Paddle/pull/44864)，[#44327](https://github.com/PaddlePaddle/Paddle/pull/44327) -- **自适应并行** - - 设计并推出了完善的自动并行接口体系，支持自动动转静分布式训练、自动分布式数据加载、自动分布式保存与加载、自动参数转换、自定义切分标记和自定义执行过程等。用户只需要基于单机组网就可以非常容易获得自动分布式训练能力，支持数据并行、模型并行、流水线并行和混合并行。[#45776](https://github.com/PaddlePaddle/Paddle/pull/45776) ，[#46552](https://github.com/PaddlePaddle/Paddle/pull/46552)，[#44202](https://github.com/PaddlePaddle/Paddle/pull/44202)，[#45840](https://github.com/PaddlePaddle/Paddle/pull/45840)，[#45518](https://github.com/PaddlePaddle/Paddle/pull/45518)，[#40528](https://github.com/PaddlePaddle/Paddle/pull/40528)，[#42838](https://github.com/PaddlePaddle/Paddle/pull/42838)，[#43093](https://github.com/PaddlePaddle/Paddle/pull/43093)，[#43312](https://github.com/PaddlePaddle/Paddle/pull/43312)，[#45053](https://github.com/PaddlePaddle/Paddle/pull/45053)。 - - 完善了自适应并行底层机制，包括升级分布式 cost model 设计和实现，为切分策略提供更好评价；为 Program IR 添加了原生分布式属性，丰富了 Cluster 功能。[#40457](https://github.com/PaddlePaddle/Paddle/pull/40457)，[#42601](https://github.com/PaddlePaddle/Paddle/pull/42601)，[#42727](https://github.com/PaddlePaddle/Paddle/pull/42727)，[#42874](https://github.com/PaddlePaddle/Paddle/pull/42784)，[#43114](https://github.com/PaddlePaddle/Paddle/pull/43114)，[#44095](https://github.com/PaddlePaddle/Paddle/pull/44095)，[#44146](https://github.com/PaddlePaddle/Paddle/pull/44146)，[#44701](https://github.com/PaddlePaddle/Paddle/pull/44701)，[#44973](https://github.com/PaddlePaddle/Paddle/pull/44973)，[#45002](https://github.com/PaddlePaddle/Paddle/pull/45002)，[#45118](https://github.com/PaddlePaddle/Paddle/pull/45118)，[#45237](https://github.com/PaddlePaddle/Paddle/pull/45237)，[#42576](https://github.com/PaddlePaddle/Paddle/pull/42576)，[#41722](https://github.com/PaddlePaddle/Paddle/pull/41722)，[#44150](https://github.com/PaddlePaddle/Paddle/pull/44150)， [#44989](https://github.com/PaddlePaddle/Paddle/pull/44989)， [#44951](https://github.com/PaddlePaddle/Paddle/pull/44951)， [#44963](https://github.com/PaddlePaddle/Paddle/pull/44963)。 - - 新增数据并行下 Sharding stage1/2/3 自动调优功能，在保证满足显存约束情况下，自动选择吞吐最高的 Sharding stage 策略。[#43782](https://github.com/PaddlePaddle/Paddle/pull/43782)。 - -- **训练硬件接入-插件式方案**：新增了自定义 Runtime/Kernel/CCL/Graph/Pass 等方案，硬件厂商可以根据硬件特性按需选择实现哪些模块。 - -- **ONNX 格式导出** - - 支持量化模型导出，导出后的 ONNX 模型使用 TensorRT 或 ONNXRuntime 加载推理，可获得 1.5~4 倍的推理加速 [#856](https://github.com/PaddlePaddle/Paddle2ONNX/pull/856)，[#782](https://github.com/PaddlePaddle/Paddle2ONNX/pull/782) - - 新增大于 2GB 的大模型导出 [#942](https://github.com/PaddlePaddle/Paddle2ONNX/pull/942) - -### （3）功能优化 -- **动转静分析转换 & 扩展能力全面提升** - - 为了提升模型动转静转换成功率和使用体验，重构了控制流语法的转写逻辑，升级核心语法为 JIT （just-in-time）范式，实现与 Python 代码的等价转写，并完善了 break、return、continue 等语法功能。[#43666](https://github.com/PaddlePaddle/Paddle/pull/43666)，[#43846](https://github.com/PaddlePaddle/Paddle/pull/43846)，[#43848](https://github.com/PaddlePaddle/Paddle/pull/43848)，[#43880](https://github.com/PaddlePaddle/Paddle/pull/43880)，[#43957](https://github.com/PaddlePaddle/Paddle/pull/43957)，[#43328](https://github.com/PaddlePaddle/Paddle/pull/43328)，[#43348](https://github.com/PaddlePaddle/Paddle/pull/43348)，[#43998](https://github.com/PaddlePaddle/Paddle/pull/43998)，[#44465](https://github.com/PaddlePaddle/Paddle/pull/44465)，[#44504](https://github.com/PaddlePaddle/Paddle/pull/44504)，[#43713](https://github.com/PaddlePaddle/Paddle/pull/43713)，[#43864](https://github.com/PaddlePaddle/Paddle/pull/43864)，[#43967](https://github.com/PaddlePaddle/Paddle/pull/43967)，[#44155](https://github.com/PaddlePaddle/Paddle/pull/44155)，[#44487](https://github.com/PaddlePaddle/Paddle/pull/44487)，[#44527](https://github.com/PaddlePaddle/Paddle/pull/44527)，[#45105](https://github.com/PaddlePaddle/Paddle/pull/45105)，[#45900](https://github.com/PaddlePaddle/Paddle/pull/45900) - - 为了支撑语音等场景自定义解码灵活部署场景，扩展了 jit.save/load 接口功能，支持用户多函数合并导出，并新增了 JITLayer 组件，支持类函数式调用，同时配合 PHI 算子库 C++ API 实现了自定义推理部署功能。[#44283](https://github.com/PaddlePaddle/Paddle/pull/44283)，[#41783](https://github.com/PaddlePaddle/Paddle/pull/41783)，[#43607](https://github.com/PaddlePaddle/Paddle/pull/43607)，[#43754](https://github.com/PaddlePaddle/Paddle/pull/43754)，[#43758](https://github.com/PaddlePaddle/Paddle/pull/43758)，[#43798](https://github.com/PaddlePaddle/Paddle/pull/43798)，[#44010](https://github.com/PaddlePaddle/Paddle/pull/44010)，[#44351](https://github.com/PaddlePaddle/Paddle/pull/44351)，[#44465](https://github.com/PaddlePaddle/Paddle/pull/44465)，[#44504](https://github.com/PaddlePaddle/Paddle/pull/44504)，[#44597](https://github.com/PaddlePaddle/Paddle/pull/44597)，[#44738](https://github.com/PaddlePaddle/Paddle/pull/44738)，[#44984](https://github.com/PaddlePaddle/Paddle/pull/44984)，[#46249](https://github.com/PaddlePaddle/Paddle/pull/46249) - - 为了统一 API 动静行为，升级了 20 个算子，支持在静态图中 Op 的 attribute 信息可变，保证动静行为一致，提升模型的动转静转换成功率。包括`pad2d`、`depthwise_conv2d_transpose`、`conv2d_transpose`、`adaptive_avg_pool2d`、`reverse`、`bincount`、`multinomial`、`reduce_sum`、`reduce_mean`、`reduce_prod`、`reduce_min`、`reduce_max`、`uniform`、`squeeze`、`max_unpool2d`、`dropout`、`cumsum`、`eye`、`argmin`、`argmax`，[#44737](https://github.com/PaddlePaddle/Paddle/pull/44737)，[#45084](https://github.com/PaddlePaddle/Paddle/pull/45084)，[#45189](https://github.com/PaddlePaddle/Paddle/pull/45189)，[#45391](https://github.com/PaddlePaddle/Paddle/pull/45391)，[#45417](https://github.com/PaddlePaddle/Paddle/pull/45417)，[#45427](https://github.com/PaddlePaddle/Paddle/pull/45427)、[#45514](https://github.com/PaddlePaddle/Paddle/pull/45514)、[#45525](https://github.com/PaddlePaddle/Paddle/pull/45525)、[#45543](https://github.com/PaddlePaddle/Paddle/pull/45543)、[#45660](https://github.com/PaddlePaddle/Paddle/pull/45660)、[#46352](https://github.com/PaddlePaddle/Paddle/pull/46352/)、[#46433](https://github.com/PaddlePaddle/Paddle/pull/46433)、[#45078](https://github.com/PaddlePaddle/Paddle/pull/45078)，[#45342](https://github.com/PaddlePaddle/Paddle/pull/45342)，[#45372](https://github.com/PaddlePaddle/Paddle/pull/45372)，[#45453](https://github.com/PaddlePaddle/Paddle/pull/45453)，[#45522](https://github.com/PaddlePaddle/Paddle/pull/45522)，[#45620](https://github.com/PaddlePaddle/Paddle/pull/45620) - - 为了解决用户动转静报错栈偶尔丢失问题，优化了报错模块的逻辑，提升了报错栈的可读性以及用户调试的使用体验。[#44054](https://github.com/PaddlePaddle/Paddle/pull/44054)，[#44083](https://github.com/PaddlePaddle/Paddle/pull/44083)，[#44781](https://github.com/PaddlePaddle/Paddle/pull/44781)，[#44996](https://github.com/PaddlePaddle/Paddle/pull/44996) - - 为了全面支持 Python 类型 Type Hint 语法，新增了 TypeHint 语法识别和转写模块。[#47121](https://github.com/PaddlePaddle/Paddle/pull/47121) - -- **PHI 算子库覆盖全量运算类算子**：继续建设高可复用算子库 PHI，将剩余的飞桨 2.x 运算类 PythonAPI 关联的算子以及相关内核均迁移到 PHI 算子库，并改写为函数式，新增了约 180 个前反向算子的 CPU&GPU 内核，以及 170 个 Kunlun 专用算子内核，进一步提升了新增算子时可复用的内核函数集。同时，新增了 100 余个 C++运算类 API，可支持在自定义算子中使用，进一步提升了基于飞桨进行外部扩展开发的易用性。[#44577](https://github.com/PaddlePaddle/Paddle/pull/44577)，[#44631](https://github.com/PaddlePaddle/Paddle/pull/44631)，[#44434](https://github.com/PaddlePaddle/Paddle/pull/44434)，[#44605](https://github.com/PaddlePaddle/Paddle/pull/44605)，[#44676](https://github.com/PaddlePaddle/Paddle/pull/44676)，[#44742](https://github.com/PaddlePaddle/Paddle/pull/44742)，[#44436](https://github.com/PaddlePaddle/Paddle/pull/44436)，[#45887](https://github.com/PaddlePaddle/Paddle/pull/45887)，[#45851](https://github.com/PaddlePaddle/Paddle/pull/45851)，[#45623](https://github.com/PaddlePaddle/Paddle/pull/45623)，[#45397](https://github.com/PaddlePaddle/Paddle/pull/45397)，[#45863](https://github.com/PaddlePaddle/Paddle/pull/45863) - -- **规范化算子定义，大幅提升模型简洁度**：针对飞桨 1.x 历史算子定义存在诸多冗余参数，理解适配成本高的问题，对约 150 个高频算子的冗余参数进行了集中清理，基本上将数学无关的参数清理完毕。这些冗余参数清理后，飞桨存储的推理模型中信息量明显减少，普遍裁减掉了约 40%的属性变量，显著提升了飞桨算子定义的清晰程度，提升了模型分析调试的体验；同时，也显著减小了飞桨存储推理模型的体积，普遍减小超过 70%，显著提升了飞桨模型的轻量化程度。[#44310](https://github.com/PaddlePaddle/Paddle/pull/44310) , [#45613](https://github.com/PaddlePaddle/Paddle/pull/45613) , [#45684](https://github.com/PaddlePaddle/Paddle/pull/45684) , [#45708](https://github.com/PaddlePaddle/Paddle/pull/45708) , [#45758](https://github.com/PaddlePaddle/Paddle/pull/45758) , [#45786](https://github.com/PaddlePaddle/Paddle/pull/45786) , [#45772](https://github.com/PaddlePaddle/Paddle/pull/45772) , [#45845](https://github.com/PaddlePaddle/Paddle/pull/45845) , [#45984](https://github.com/PaddlePaddle/Paddle/pull/45984) , [#46218](https://github.com/PaddlePaddle/Paddle/pull/46218) , [#46553](https://github.com/PaddlePaddle/Paddle/pull/46553) - -### （4）性能优化 - -- AMP 性能及精度优化 - - 更多算子增加 FP16 数据类型支持，包括 elementwise 系列算子, compare 系列算子, strided_slice, set_value, uniform_ramdom 等。（[#45504](https://github.com/PaddlePaddle/Paddle/pull/45504) [#44405](https://github.com/PaddlePaddle/Paddle/pull/44405) [#45496](https://github.com/PaddlePaddle/Paddle/pull/45496) [#46641](https://github.com/PaddlePaddle/Paddle/pull/46641) [#46906](https://github.com/PaddlePaddle/Paddle/pull/46906)） - - 优化 hard_swish 算子 FP16 Kernel 实现方案，保证精度无损。（ [35386](https://github.com/PaddlePaddle/Paddle/pull/35386) ） - - 更多算子增加 BF16 数据类型支持，包括 fused_linear、empty、selu、pow、adam、clip、embedding、gelu、pad3d、pixel_shuffle、tile、where 等。[#46364](https://github.com/PaddlePaddle/Paddle/pull/46364)，[#47177](https://github.com/PaddlePaddle/Paddle/pull/47177) -- 单机训练性能自动调优 - - Transpose OP 支持自动 Kernel 选择机制，可以针对不同模型配置自动搜索到性能最优的 Kernel 实现，提升模型性能。[#43310](https://github.com/PaddlePaddle/Paddle/pull/43310) (Transpose Op 接入自动调优功能) - - AMP Layout 自动切换支持新动态图模式，ResNet50、TSM、DeepLabV3 等模型在新动态图下通过 Layout 自动调整获得性能提升 9%~21%。([#45409](https://github.com/PaddlePaddle/Paddle/pull/45409), [#45751](https://github.com/PaddlePaddle/Paddle/pull/45751), [#45826](https://github.com/PaddlePaddle/Paddle/pull/45826), [#46880](https://github.com/PaddlePaddle/Paddle/pull/46880)) -- GPU 单机训练通用性能优化 - - 优化 Conv 类算子 cuDNN 算法的 Cache 方案，并 Cache 所有算法获取方式下的结果，大幅减少算子的 CPU 开销。（[#41891](https://github.com/PaddlePaddle/Paddle/pull/41891) [#47197](https://github.com/PaddlePaddle/Paddle/pull/47197)） - - 进一步优化多个算子的 GPU Kernel 和 Python 端性能，包括 dist, poisson, depthwise_conv2d、transpose, eigh, broadcast 类计算，reduce 类计算，layer_norm，cross_entropy 等，在更多配置场景下达到更优性能。（[#44946](https://github.com/PaddlePaddle/Paddle/pull/44946), [#45057](https://github.com/PaddlePaddle/Paddle/pull/45057), [#45160](https://github.com/PaddlePaddle/Paddle/pull/45160), [#42491](https://github.com/PaddlePaddle/Paddle/pull/42491), [#42704](https://github.com/PaddlePaddle/Paddle/pull/42704), [#42853](https://github.com/PaddlePaddle/Paddle/pull/42853), [#46287](https://github.com/PaddlePaddle/Paddle/pull/46287), [#46362](https://github.com/PaddlePaddle/Paddle/pull/46362), [#46490](https://github.com/PaddlePaddle/Paddle/pull/46490), [#46412](https://github.com/PaddlePaddle/Paddle/pull/46412), [#46623](https://github.com/PaddlePaddle/Paddle/pull/46623), [#40051](https://github.com/PaddlePaddle/Paddle/pull/40051)） -- 集合通信分布式训练性能优化 - - 为提高流水线并行调度效率，支持动态图 Interleaving 1F1B 调度策略，在 GPT-3 模型上性能提升 3%~4%。[#45797](https://github.com/PaddlePaddle/Paddle/pull/45797)，[#45869](https://github.com/PaddlePaddle/Paddle/pull/45869)，[#45922](https://github.com/PaddlePaddle/Paddle/pull/45922)，[#46209](https://github.com/PaddlePaddle/Paddle/pull/46209)，[#45402](https://github.com/PaddlePaddle/Paddle/pull/45402)，[#45444](https://github.com/PaddlePaddle/Paddle/pull/45444)，[#45497](https://github.com/PaddlePaddle/Paddle/pull/45497)，[#45797](https://github.com/PaddlePaddle/Paddle/pull/45797)，[#45869](https://github.com/PaddlePaddle/Paddle/pull/45869)，[#45922](https://github.com/PaddlePaddle/Paddle/pull/45922)，[#46209](https://github.com/PaddlePaddle/Paddle/pull/46209)，[#46399](https://github.com/PaddlePaddle/Paddle/pull/46399)，[#46483](https://github.com/PaddlePaddle/Paddle/pull/46483)，[#46876](https://github.com/PaddlePaddle/Paddle/pull/46876)，[#47242](https://github.com/PaddlePaddle/Paddle/pull/47242)，[#47249](https://github.com/PaddlePaddle/Paddle/pull/47249)，[#47497](https://github.com/PaddlePaddle/Paddle/pull/47497)，[#47517](https://github.com/PaddlePaddle/Paddle/pull/47517) - - 为提升 MLPerf BERT 模型的分布式训练性能，DistributedFusedLamb 分布式优化器支持分层 AllReduce，在 DCU 1024 卡上 MLPerf BERT 性能提升 17%。[#44821](https://github.com/PaddlePaddle/Paddle/pull/44821)，[#44843](https://github.com/PaddlePaddle/Paddle/pull/44843) - - 为优化使用数据并行 Data Parallel 时的显存占用，支持 Tensor Fusion 时的 Buffer Lazy 初始化策略，可降低等于模型参数量的显存占用量。[#45631](https://github.com/PaddlePaddle/Paddle/pull/45631)。 - - 分布式并行策略 Data Parallel 和 Sharding 支持 BF16 训练。[#46846](https://github.com/PaddlePaddle/Paddle/pull/46846)，[#47246](https://github.com/PaddlePaddle/Paddle/pull/47246) - - 为支持 Sequence Parallel 等策略，分布式流水线并行策略支持 enable_partial_send_recv 策略，支持传输 sequence parallel 切分后的 tensor。[#46992](https://github.com/PaddlePaddle/Paddle/pull/46992)，[#47083](https://github.com/PaddlePaddle/Paddle/pull/47083) - - 为提升 sharding stage 2 策略的性能，实现了 sharding stage 2 optimizer broadcast 参数与下一个 step forward 的 overlap，并使用多 CUDA Stream 进行通信，GPT 6.7B 模型 16 卡训练性能提升 11%。[#46495](https://github.com/PaddlePaddle/Paddle/pull/46495)，[#46656](https://github.com/PaddlePaddle/Paddle/pull/46656)，[#47061](https://github.com/PaddlePaddle/Paddle/pull/47061) - -### （5）问题修复 - -- 动转静 - - 修复了模型在多卡训练时 Parameter 无梯度场景下，动转静会报错的问题。[#44485](https://github.com/PaddlePaddle/Paddle/pull/44485) - - 修复了动转静时终端会有多余的框架日志误输出的问题。[#45754](https://github.com/PaddlePaddle/Paddle/pull/45754)，[#46800](https://github.com/PaddlePaddle/Paddle/pull/46800) - - 修复了模型中控制流中包含无需梯度的 Tensor 时，在动转静训练时会报错的问题。[#43034](https://github.com/PaddlePaddle/Paddle/pull/43034) - - 修复了动转静训练在梯度聚合时计算值错误的问题。[#44893](https://github.com/PaddlePaddle/Paddle/pull/44893) - - 修复了函数被@staticmethod 装饰时动转静会报错的问题。[#44983](https://github.com/PaddlePaddle/Paddle/pull/44983)，[#45268](https://github.com/PaddlePaddle/Paddle/pull/45268)，[#45277](https://github.com/PaddlePaddle/Paddle/pull/45277) - - 修复了部分场景下模型包含控制动转静训练时，显存占用过多的问题。[#45380](https://github.com/PaddlePaddle/Paddle/pull/45380) - - 修复了模型中包含复杂控制流时，动转静在组网阶段 shape 推导报错的问题。[#45916](https://github.com/PaddlePaddle/Paddle/pull/45916)，[#46020](https://github.com/PaddlePaddle/Paddle/pull/46020) -- 报错机制修复 - - 使用 np.testing.assert_allclose 替换 self.assertTrue(np.allclose(...))，获得更充分的报错信息 ([#44947)(https://github.com/PaddlePaddle/Paddle/pull/44947)， [#44988](https://github.com/PaddlePaddle/Paddle/pull/44988)，[#45213](https://github.com/PaddlePaddle/Paddle/pull/45213)) -- 集合通信分布式训练 - - 修复了通信库初始化、通信过程中的若干 bug，增强了系统运行稳定性 [#44964](https://github.com/PaddlePaddle/Paddle/pull/44964) [#45100](https://github.com/PaddlePaddle/Paddle/pull/45100) [#44758](https://github.com/PaddlePaddle/Paddle/pull/44758) - - 修复流水线并行容易 hang 的问题，增强策略的易用性 [#47201](https://github.com/PaddlePaddle/Paddle/pull/47201)；增强流水线功能支持不均衡的输入 [#47199](https://github.com/PaddlePaddle/Paddle/pull/47199) - - 修复新动态图 MP/PP 策略下性能低于老动态图的问题 [#47071](https://github.com/PaddlePaddle/Paddle/pull/47071) - - 修复 sharding stage2 策略错误维护参数 trainable 属性的 bug [#47240](https://github.com/PaddlePaddle/Paddle/pull/47240) - - 修复一系列 OP 在 tensor numel 大于 INT32_MAX 时的 bug。[#45711](https://github.com/PaddlePaddle/Paddle/pull/45711)，[#45741](https://github.com/PaddlePaddle/Paddle/pull/45741)，[#45897](https://github.com/PaddlePaddle/Paddle/pull/45897)，[#46158](https://github.com/PaddlePaddle/Paddle/pull/46158)，[#46767](https://github.com/PaddlePaddle/Paddle/pull/46767)，[#47191](https://github.com/PaddlePaddle/Paddle/pull/47191)，[#46045](https://github.com/PaddlePaddle/Paddle/pull/46045)，[#46160](https://github.com/PaddlePaddle/Paddle/pull/46160) - - 修复 FusedAttention 和 FusedFeedForward OP 显存占用过大的 bug。[#47236](https://github.com/PaddlePaddle/Paddle/pull/47236)，[#47235](https://github.com/PaddlePaddle/Paddle/pull/47235) - - 修复 multi_tensor_adam 和 multi_tensor_momentum OP 在传入的 parameters 是 list of dict 时参数更新错误的 bug。[#47352](https://github.com/PaddlePaddle/Paddle/pull/47352)，[#47372](https://github.com/PaddlePaddle/Paddle/pull/47372) - -## 4. 部署方向（Paddle Inference） - -### （1）新增特性 - -- 后端图引擎集成方案优化 - - 为了减少 Paddle-TensorRT 插件代码开发，以及减少 Paddle-TensorRT 子图数量从而降低资源占用率，开发了通用插件机制，可以自动对框架内丰富的 Phi 算子提供统一的 TensorRT 插件接口，在多数场景下可以有效减少显存占用。 [#46970](https://github.com/PaddlePaddle/Paddle/pull/46070)，[#46179](https://github.com/PaddlePaddle/Paddle/pull/46179)，[#46580](https://github.com/PaddlePaddle/Paddle/pull/46580) - - 为了方便用户在框架定制算子且能使得 Paddle-TensorRT 高效推理，进行功能升级支持升级框架自定义 Paddle-TensorRT 插件。[#46970](https://github.com/PaddlePaddle/Paddle/pull/46070) -- Inference 推理库构建系统优化，体积可按需裁剪 - - 预编译的安装包默认支持 TensorRT：训练用的预编译安装包与部署用的预编译安装包（Paddle Inference）统一为一个预编译安装包，且优化了构建系统，使得预编译的安装包默认支持 TensorRT，减少用户使用 PaddleTensorRT 时的切换成本。[#46008](https://github.com/PaddlePaddle/Paddle/pull/46008)，[#45824](https://github.com/PaddlePaddle/Paddle/pull/45824)，[#46058](https://github.com/PaddlePaddle/Paddle/pull/46058) - - 体积可按需裁剪：可依据模型算子进行裁剪。[#47033](https://github.com/PaddlePaddle/Paddle/pull/47033) , [#47049](https://github.com/PaddlePaddle/Paddle/pull/47049) , [#47047](https://github.com/PaddlePaddle/Paddle/pull/47047) -- Inference 支持原生 AMP - - 为了充分利用 GPU Tensor Core 计算能力，提升模型的推理性能，开发了模型精度转换工具，Inference GPU 原生支持了混合精度模型的推理。使用方式可参考[文档](https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/release/v2.4/docs-official/guides/nv_gpu_infer/gpu_mixed_precision.md)。[#43814](https://github.com/PaddlePaddle/Paddle/pull/43814)，[#43881](https://github.com/PaddlePaddle/Paddle/pull/43881)，[#44057](https://github.com/PaddlePaddle/Paddle/pull/44057)，[#44307](https://github.com/PaddlePaddle/Paddle/pull/44307)，[#44457](https://github.com/PaddlePaddle/Paddle/pull/44457)，[#44866](https://github.com/PaddlePaddle/Paddle/pull/44866)，[#45050](https://github.com/PaddlePaddle/Paddle/pull/45050)，[#45346](https://github.com/PaddlePaddle/Paddle/pull/45346)，[#45379](https://github.com/PaddlePaddle/Paddle/pull/45379)，[#45406](https://github.com/PaddlePaddle/Paddle/pull/45406)，[#45882](https://github.com/PaddlePaddle/Paddle/pull/45882) - - 为了提升混合精度下模型的推理性能，补充了未支持 FP16 计算的高频算子的 FP16 kernel，减少了由于输入精度不匹配插入 cast 算子的可能性，提升推理性能。[#44642](https://github.com/PaddlePaddle/Paddle/pull/44642)，[#45061](https://github.com/PaddlePaddle/Paddle/pull/45061)，[#44653](https://github.com/PaddlePaddle/Paddle/pull/44653)，[#45504](https://github.com/PaddlePaddle/Paddle/pull/45504)，[#45061](https://github.com/PaddlePaddle/Paddle/pull/45061)，[#44969](https://github.com/PaddlePaddle/Paddle/pull/44969)，[#44558](https://github.com/PaddlePaddle/Paddle/pull/44558)，[#44710](https://github.com/PaddlePaddle/Paddle/pull/44710)，[#43871](https://github.com/PaddlePaddle/Paddle/pull/43871)，[#44792](https://github.com/PaddlePaddle/Paddle/pull/44792) -- 压缩与推理引擎打通升级 - - 升级量化模型存储格式，新格式支持 Paddle Inference、PaddleLite 和 Paddle2ONNX 3 种部署方式，支持芯片类型包括 X86 CPU、NVIDIA GPU、Arm CPU。（[#46305](https://github.com/PaddlePaddle/Paddle/pull/46305) [#462832](https://github.com/PaddlePaddle/Paddle/pull/46283) [#46022](https://github.com/PaddlePaddle/Paddle/pull/46022)） - - 新增兼容 SoC/NPU 芯片的 INT8 全量化功能，可保证产出的 INT8 量化模型在 SoC/NPU 芯片上有最佳推理加速和精度。 -- 推理引擎与飞桨编译器（CINN）打通升级 - - 升级飞桨框架与编译器的接口模块，支持推理模型通过 Paddle Inference 接入编译器进行优化（[#44499](https://github.com/PaddlePaddle/Paddle/pull/44499) [#44708](https://github.com/PaddlePaddle/Paddle/pull/44708) ） - -### （2）底层优化 - -- **GPU 性能优化** - - 新增 matmul_v2、LSTM、reshape、fill_constant、swish、mulitclass_nms3、bilinear_interp_v2、split、silu、shuffle_channel 算子的 TensorRT 映射及完善动态 shape 的支持。多类重点模型性能提升 7%～90% 。([#46177](https://github.com/PaddlePaddle/Paddle/pull/46177)，[#44678](https://github.com/PaddlePaddle/Paddle/pull/44678)，[#44314](https://github.com/PaddlePaddle/Paddle/pull/44314)，[#44561](https://github.com/PaddlePaddle/Paddle/pull/44561)，[#45166](https://github.com/PaddlePaddle/Paddle/pull/45166), [#44411](https://github.com/PaddlePaddle/Paddle/pull/44411)，[#43424](https://github.com/PaddlePaddle/Paddle/pull/43424), [#44516](https://github.com/PaddlePaddle/Paddle/pull/44516)) - - 增加常量折叠 PASS 进行推理性能优化，提升 SwinTransformer、HifiGAN、FastSpeech2 等模型的性能。（[#45494](https://github.com/PaddlePaddle/Paddle/pull/45494)) - - 增加 conv_fusion workspacesize 的 cache，提升 conv_fusion 计算性能。([#45902](https://github.com/PaddlePaddle/Paddle/pull/45902)) -- **视觉 ViT 模型优化** - - 新增 ViT 模型 Attention 结构融合 PASS，并支持 OSS Plugin 和自动 padding，ViT 推理速度提升 30%-40% [#45019](https://github.com/PaddlePaddle/Paddle/pull/45019) [#45506](https://github.com/PaddlePaddle/Paddle/pull/45506) -- **大模型推理性能优化** - - 为提高超大生成模型推理速度以及显存节省，对多层 Transformer 融合算子(fused_multi_transformer_op)增加 INT8 实现（fused_multi_transformer_int8_op），支持生成模型的量化推理。结合矩阵乘算法选择、量化反量化 kernel 融合进行性能优化。 [#46169](https://github.com/PaddlePaddle/Paddle/pull/46169) - - 为了提升大模型推理使用 fused_multi_transformer 融合的易用性，增加 Pass 进行自动匹配融合。 -- **CPU 性能优化** - - 优化语音 U2++ 模型，FP32 模型推理速度提升 35%，INT8 模型推理速度提升 69% ([#47592](https://github.com/PaddlePaddle/Paddle/pull/47592) [#47127](https://github.com/PaddlePaddle/Paddle/pull/47127) [#47391](https://github.com/PaddlePaddle/Paddle/pull/47391) [#47234](https://github.com/PaddlePaddle/Paddle/pull/47234) [#47009](https://github.com/PaddlePaddle/Paddle/pull/47009) [#47080](https://github.com/PaddlePaddle/Paddle/pull/47080)) - - -### （3）问题修复 - -- TensorRT workspace size 大小设置支持 int64。（[#44469](https://github.com/PaddlePaddle/Paddle/pull/44469)） -- Paddle-TRT 中，全面支持 Op 的输入为权重。（[#45545](https://github.com/PaddlePaddle/Paddle/pull/45545)） -- Paddle-TRT 中，支持 conv2d_transpose/conv3d_transpose 含 output_padding 属性。（[#45004](https://github.com/PaddlePaddle/Paddle/pull/45004)） -- Paddle-TRT 中，增强 strided_slice 对动态 shape 的支持。（[#46819](https://github.com/PaddlePaddle/Paddle/pull/46819)） -- Paddle-TRT 中，优化了在多线程场景下运行时 context 的显存占用。（[#45468](https://github.com/PaddlePaddle/Paddle/pull/45468)） -- Paddle-TRT 中，修复了多个模型在同一进程中运行时，当初始化顺序变动时，反复生成序列化文件的问题。（[#43942](https://github.com/PaddlePaddle/Paddle/pull/43942)） -- 修复了同一进程中，多次初始化 Predictor 并运行时，偶发崩溃的问题。（[#45203](https://github.com/PaddlePaddle/Paddle/pull/45203)） -- 修复 MobileNetV3_large、ERNIE 3.0-Medium 和 bert 等量化模型推理精度异常问题 ([#45416](https://github.com/PaddlePaddle/Paddle/pull/45416) [#46283](https://github.com/PaddlePaddle/Paddle/pull/46283) [#45920](https://github.com/PaddlePaddle/Paddle/pull/45920) [#47573](https://github.com/PaddlePaddle/Paddle/pull/47574)) - -## 5. 环境适配 - -- 训练用的预编译安装包与部署用的预编译安装包（Paddle Inference）统一为一个预编译安装包，且优化了构建系统，使得预编译的安装包默认支持 TensorRT。 -- 取消了适配 CUDA10.1 版本的预编译安装包。 -- 新增了适配 CUDA11.7 版本的预编译安装包。 -- 源码编译时间缩短：减少模块间依赖，提升并行度，优化部分模块的编译速度，共同使的全量编译时间减少了约 20 分钟。 -- 支持在 windows 11、Centos 8、Ubuntu 22.04、Jetson 5.02 系统环境上运行飞桨，支持使用 WSL 2 工具在 windows 系统中运行飞桨 linux 安装包。 -- 修复飞桨在 glibc2.34+环境中运行错误的问题。 -- 优化了整个代码仓库中的 C++、Python、CMake 的代码风格，并引入或升级了以下的代码风格检查工具。 - - pre-commit 由 1.10.4 升级到 2.17.0： [#43103](https://github.com/PaddlePaddle/Paddle/pull/43103) - - pylint 由默认版本改为指定 2.12.0 版本： [#43103](https://github.com/PaddlePaddle/Paddle/pull/43103) - - remove-crlf 由 1.0.1 升级到 1.1.14： [#43103](https://github.com/PaddlePaddle/Paddle/pull/43103) - - cpplint 由默认版本改为指定 1.6.0 版本： [#43175](https://github.com/PaddlePaddle/Paddle/pull/43175)，[#43978](https://github.com/PaddlePaddle/Paddle/pull/43978)，[#43673](https://github.com/PaddlePaddle/Paddle/pull/43673)，[#43679](https://github.com/PaddlePaddle/Paddle/pull/43679)，[#43695](https://github.com/PaddlePaddle/Paddle/pull/43695)，[#43733](https://github.com/PaddlePaddle/Paddle/pull/43733)，[#43740](https://github.com/PaddlePaddle/Paddle/pull/43740) - - clang-format 由 3.8 升级到 13.0： [#42840](https://github.com/PaddlePaddle/Paddle/pull/42840)，[#43248](https://github.com/PaddlePaddle/Paddle/pull/43248)，[#43329](https://github.com/PaddlePaddle/Paddle/pull/43329)，[#43333](https://github.com/PaddlePaddle/Paddle/pull/43333)，[#43633](https://github.com/PaddlePaddle/Paddle/pull/43633)，[#43678](https://github.com/PaddlePaddle/Paddle/pull/43678) - - 引入 black 工具进行 python 代码的风格检查：[#46014](https://github.com/PaddlePaddle/Paddle/pull/46014) - - 引入 cmakelint 工具用于 cmake 文件代码检查，版本为 1.4.2： [#43222](https://github.com/PaddlePaddle/Paddle/pull/43222)，[#43406](https://github.com/PaddlePaddle/Paddle/pull/43406)，[#43414](https://github.com/PaddlePaddle/Paddle/pull/43414)，[#43428](https://github.com/PaddlePaddle/Paddle/pull/43428) - - 引入 cmake-format 用于 cmake 文件的自动格式化，版本为 0.6.13： [#43057](https://github.com/PaddlePaddle/Paddle/pull/43057) - -## 6. 硬件适配 ### 海光 DCU -- 增加在 DCU 上的 Profiler 功能，可以在 DCU 上对模型运行过程的性能数据进行收集、统计和展示，支持 kernel 层面的 DCU 占用率显示。 -### 昆仑芯 -- 增加在昆仑芯 2 代芯片上的 Profiler 功能，可以在昆仑芯 2 代芯片上对模型运行过程的性能数据进行收集、统计和展示，支持 kernel 层面的昆仑芯 2 代芯片占用率显示。 -- 昆仑芯 2 代芯片（昆仑芯 AI 加速卡 R200、R300、R200-8F、R200-8FS、RG800）训练/推理支持，已验证 PPYOLOE、PP-OCR、ERNIE 3.0、PP-TSM、PP-TTS、DLRM、PPO 等总计 51 个模型，支持静态图+动态图训练，支持混合精度训练，支持单机单卡、单机多卡训练，覆盖了智能视觉、自然语言处理、智能语音、智能推荐、强化学习 5 个领域。 -### 寒武纪 -- 寒武纪 MLU 芯片（MLU370 系列板卡）训练/推理支持，已验证 ResNet50、BERT、YoloV3、OCR-DB、Deeplabv3 等多个模型，支持静态图+动态图训练，支持混合精度训练，支持单机单卡、单机多卡训练。 -### Graphcore -- Graphcore IPU 芯片（包括 IPU Mk2 GC200 和 Bow IPU）训练/推理支持，支持 ResNet50、BERT 等模型，支持静态图和动转静模式训练，支持单芯片、单机、多机分布式训练。 -- 增加更多算子支持 -- 升级到 Poplar SDK v3.0.0 版本 [#46892](https://github.com/PaddlePaddle/Paddle/pull/46892) -* 支持使用动转静模式训练模型, 添加了一个新的 paddle.incubate.identity_loss op 用来辅助构图 [#43770](https://github.com/PaddlePaddle/Paddle/pull/43770) -* 支持 Paddle 原生的分布式训练 API paddle.distributed.launch [#43311](https://github.com/PaddlePaddle/Paddle/pull/43311) -* 支持使用混合精度训练模型 [#41733](https://github.com/PaddlePaddle/Paddle/pull/41733) -* Paddle Inference 支持使用 PopART 自定义算子 [#45235](https://github.com/PaddlePaddle/Paddle/pull/45235) - -### Intel -- 迁移 oneDNN 算子 transpose2_grad([#46139](https://github.com/PaddlePaddle/Paddle/pull/46139)), relu6_grad([#46501](https://github.com/PaddlePaddle/Paddle/pull/46501)), gaussian_random([#46747](https://github.com/PaddlePaddle/Paddle/pull/46747), [#45481](https://github.com/PaddlePaddle/Paddle/pull/45481)), sgd and stack([#46374](https://github.com/PaddlePaddle/Paddle/pull/46374)), concat+grad, expand+grad,fill_constant([#45863](https://github.com/PaddlePaddle/Paddle/pull/45863)), slice, slice_grad, split,pad and pad3d([#46101](https://github.com/PaddlePaddle/Paddle/pull/46101)), softmax_grad([#46257](https://github.com/PaddlePaddle/Paddle/pull/46257)), Shape([#46051](https://github.com/PaddlePaddle/Paddle/pull/46051)), Sum([#46239](https://github.com/PaddlePaddle/Paddle/pull/46239)), Transpose2_grad([#46139](https://github.com/PaddlePaddle/Paddle/pull/46139)), Cast, clip+grad andpool+grad([#45775](https://github.com/PaddlePaddle/Paddle/pull/45775)), Reduce sum+grad,mean+grad, min and max([#45536](https://github.com/PaddlePaddle/Paddle/pull/45536)), Relu and abs([#45397](https://github.com/PaddlePaddle/Paddle/pull/45397)), Gelu([#45596](https://github.com/PaddlePaddle/Paddle/pull/45596)), Scale([#45537](https://github.com/PaddlePaddle/Paddle/pull/45537)) -- 优化 fill_constant, fc, conv 等若干算子内核 -- 增加若干 Pass 融合优化 -- 优化 Adam-W CPU FP32 优化器 ([#42522](https://github.com/PaddlePaddle/Paddle/pull/42522)) -- 优化 pad3d fp32 onednn 算子内核实现 ([#43990](https://github.com/PaddlePaddle/Paddle/pull/43990)) -- 改进 matmul, FC andlookup_v2 内核的并发执行 ([#44023](https://github.com/PaddlePaddle/Paddle/pull/44023), [#44078](https://github.com/PaddlePaddle/Paddle/pull/444078), [#44640](https://github.com/PaddlePaddle/Paddle/pull/44640), [#44744](https://github.com/PaddlePaddle/Paddle/pull/44744), [#45249](https://github.com/PaddlePaddle/Paddle/pull/45249)) -- FC onednn 算子内核支持 bf16 ( [#42758](https://github.com/PaddlePaddle/Paddle/pull/42758), [#43154](https://github.com/PaddlePaddle/Paddle/pull/43154), [#43109](https://github.com/PaddlePaddle/Paddle/pull/43109)) -- 增加矩阵乘法和激活函数的融合([#43519](https://github.com/PaddlePaddle/Paddle/pull/43519), [#43198](https://github.com/PaddlePaddle/Paddle/pull/43198)) -- 支持卷积算子 int8 参数生产 IR passes ( [#44680](https://github.com/PaddlePaddle/Paddle/pull/44680), [#42625](https://github.com/PaddlePaddle/Paddle/pull/42625)) -- 增加 pool/avg 量化和 scales 修正 ([#44186](https://github.com/PaddlePaddle/Paddle/pull/44186)) -- 增加 matmul 和 elementwise onednn 算子内核融合([#45077](https://github.com/PaddlePaddle/Paddle/pull/45077)) -- 修复 QAT 精度问题 ([#43693](https://github.com/PaddlePaddle/Paddle/pull/43693), [#45936](https://github.com/PaddlePaddle/Paddle/pull/45936), [#46378](https://github.com/PaddlePaddle/Paddle/pull/46378)) -- 迁移 42 个 oneDNN 算子内核到 PHI 算子库 ([#46374](https://github.com/PaddlePaddle/Paddle/pull/46374), [#46101](https://github.com/PaddlePaddle/Paddle/pull/46101), [#45989](https://github.com/PaddlePaddle/Paddle/pull/45989), [#45863](https://github.com/PaddlePaddle/Paddle/pull/45863), [#45775](https://github.com/PaddlePaddle/Paddle/pull/45775), [#45626](https://github.com/PaddlePaddle/Paddle/pull/45626), [#45536](https://github.com/PaddlePaddle/Paddle/pull/45536), [#46501](https://github.com/PaddlePaddle/Paddle/pull/46501), [#46257](https://github.com/PaddlePaddle/Paddle/pull/46257), [#45596](https://github.com/PaddlePaddle/Paddle/pull/45596), [#45537](https://github.com/PaddlePaddle/Paddle/pull/45537), [#45481](https://github.com/PaddlePaddle/Paddle/pull/45481), [#45397](https://github.com/PaddlePaddle/Paddle/pull/45397), [#46239](https://github.com/PaddlePaddle/Paddle/pull/46239), [#46139](https://github.com/PaddlePaddle/Paddle/pull/46139), [#46051](https://github.com/PaddlePaddle/Paddle/pull/46051)) -- 量化 elementwise_sub 和 shape 算子内核 ([#42854](https://github.com/PaddlePaddle/Paddle/pull/42854), [#44124](https://github.com/PaddlePaddle/Paddle/pull/44124)) - -## Thanks to our Contributors - -This release contains contributions from: - -0x45f, Aganlengzi, Ainavo, Allen Guo, Asthestarsfalll, Aurelius84, Baibaifan, baoachun, BiynXu, Bo Zhang, BrilliantYuKaimin, cambriconhsq, caozhou, carryyu, ccrrong, ceci3, chalsliu, Chang Xu, Charles-hit, Chen Long, Chen Weihang, chenjian, chentianyu03, Chenxiao Niu, cifar10, crystal, csy0225, danleifeng, David Nicolas, dc-cheny, denglin-github, dongfangshenzhu, duanboqiang, duanyanhui, engineer, enzodechine, Fan Zhang, feifei-111, Feiyu Chan, Feng Ni, feng_shuai, FlyingQianMM, freeliuzc, furnace, fuyou765, fwenguang, Ghost Screaming, gongweibao, Guanghua Yu, guguguzi, Guoxia Wang, Haipeng Wang, handiz, Haohongxiang, haosicheng, helen88, heliqi, hong, HongyuJia, houj04, huangxu96, Hui Zhang, Huihuang Zheng, huzhiqiang, Jacek Czaja, Jack Zhou, jack603047588, Jackwaterveg, jakpiase, james, Jiabin Yang, jiangcheng, Jiaqi Liu, JingZhuangzhuang, joanna.wozna.intel, JYChen, JZ-LIANG, Kaipeng Deng, kangguangli, kuizhiqing, Leo Chen, Leo Guo, levi131, Li Min, Li-fAngyU, lidanqing, LielinJiang, Ligoml, Lijunhui, lilong12, limingshu, Lin Manhui, Linjie Chen, liqitong-a, littletomatodonkey, liu zhengxi, Liu-xiandong, liutiexing, Liyulingyue, LiYuRio, Lux et Veritas, lyq, Matsumoto Ruko, MayYouBeProsperous, mengqingchun02, Ming-Xu Huang, ming1753, minghaoBD, moyan, mrcangye, Netpunk, niuliling123, Nyakku Shigure, OccupyMars2025, onecatcn, pangyoki, parap1uie-s, peachlcy, piotrekobi, Qi Li, QingshuChen, qipengh, Rayman, Regan Yue, RichardWooSJTU, risemeup1, Roc, ronnywang, Rui Li, Ruibiao Chen, seemingwang, Shang Zhizhou, shangliang Xu, ShenLiang, shentanyue, Shijie, ShiningZhang, shixingbo, shiyutang, Shuangchi He, Siming Dai, Sing_chan, Skr Bang, SmirnovKol, sneaxiy, sprouteer, Sylwester Fraczek, Sławomir Siwek, taixiurong, Tao CHANG, TeFeng Chen, Thomas Young, thunder95, Thunderbrook, tiancaishaonvjituizi, tianshuo78520a, Tomasz Socha, TTerror, USTCKAY, Vigi Zhang, Walter, Wang Bojun, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, WangXi, wangxinxin08, Wangzheee, WangZhen, wangzhen38, wawltor, wbn, Wei Shengyu, Weilong Wu, weishengying, Wen Sun, wenbin, whs, Wilber, WJJ1995, wuhuachaocoding, wuhuanzhou, wuyefeilin, XiaoguangHu, xiaoguoguo626807, xiaohemaikoo, xiaoting, xiaoxiaohehe001, Xiaoxu Chen, xiayanming, Xingyuan Zhang, xiongkun, yang131313, yangguohao, YangZhou, Yanxing Shi, Yao Zihang, yaoxuefeng, yaozhixin, yeliang2258, Yilingyelu, Yiqun Liu, ykkk2333, Yuang Liu, Yuanle Liu, YuanRisheng, yuguo, Yulong Ao, Yulv-git, YUNSHEN XIE, Zhang Jun, Zhang Ting, Zhang Zheng, zhangbo9674, zhangbopd, zhangchunle, Zhangjingyu06, zhangkaihuo, zhangxiaoci, zhangyikun02, zhangzhenguo, Zhanlue Yang, zhaocaibei123, zhaoying9105, zhaoyingli, Zhen Wang, Zhengyang Song, zhiboniu, Zhong Hui, Zhou Wei, zhoutianzi666, zhupengyang, ziyoujiyi, zlsh80826, zmxdream, zn, Zuza Gawrysiak, zyfncg, 傅剑寒, 六个骨头, 津, 熊峻峰, 王明冬, 石晓伟 - - -# 2.3.1 Release Note - -## 1. 重要更新 - -- 2.3.1 版本是在 2.3 版本的基础上修复了已知问题，并且发布了支持 CUDA 11.6 的安装包。 - -## 2. 训练框架（含分布式） - -### （1）功能优化 - -#### API - -- 修改 `paddle.nn.initializer.KaimingUniform` 和 `paddle.nn.initializer.KaimingNormal` 两种初始化方式，使其支持多种类型的激活函数。([#43721](https://github.com/PaddlePaddle/Paddle/pull/43721), [#43827](https://github.com/PaddlePaddle/Paddle/pull/43827)) -- 优化 `paddle.io.DataLoader` 的数据预读取功能，使其支持设置了 `prefetch_factor` 设定的预读取数据的缓存数量，避免在读取大块数据时出现 IO 阻塞。([#43674](https://github.com/PaddlePaddle/Paddle/pull/43674)) - -#### 新动态图执行机制 - -- 修改新动态图 API 逻辑中 optional 类型 Tensor 的初始化方法，防止被提前析构导致数据异常。([#42561](https://github.com/PaddlePaddle/Paddle/pull/42561)) - -#### 全新静态图执行器 - -- 延迟初始化执行器中的线程池，避免只执行一轮的 `program`（如 `save、load、startup_program` 等）创建线程池。([#43768](https://github.com/PaddlePaddle/Paddle/pull/43768)) - -#### 混合精度训练 - -- 设置 `paddle.nn.Layer` 中 `set_state_dict` 中禁用 `state_dict` hook。([#43407](https://github.com/PaddlePaddle/Paddle/pull/43407)) - -#### 分布式训练 - -- 使 `paddle.incubate.nn.functional.fused_attention` 和 `paddle.incubate.nn.functional.fused_feedforward` 支持张量模型并行。([#43505](https://github.com/PaddlePaddle/Paddle/pull/43505)) - -#### 其他 - -- 调整框架算子内核打印字符串的格式，便于进行自动化拆分解析。([#42931](https://github.com/PaddlePaddle/Paddle/pull/42931)) -- 更新模型量化 API，支持 `rounding to nearest ties to even` 的四舍五入方式，支持量化取值范围 [-128, 127]。([#43829](https://github.com/PaddlePaddle/Paddle/pull/43829)) -- 量化感知训练适配支持 AMP 混合精度训练。([#43689](https://github.com/PaddlePaddle/Paddle/pull/43689)) -- 量化感知训练在启动时新增 `progress bar`，便于查看量化初始化进度，统计 out_threshold 时跳过 scale op，加速初始化过程。([#43454](https://github.com/PaddlePaddle/Paddle/pull/43454)) -- 动态图量化训练支持 `conv` 和 `bn` 融合，静态图离线量化支持设置 `skip_tensor_list` 来跳过某些层不做量化。([#43301](https://github.com/PaddlePaddle/Paddle/pull/43301)) - -### （2）性能优化 - -- 优化 `paddle.incubate.nn.functional.fused_attention` 和`paddle.incubate.nn.functional.fused_feedforward` 算子，增加 `add_residual` 属性，用以控制最后一步是否进行加 `residual` 操作，CAE 模型性能提升 7.7%。([#43719](https://github.com/PaddlePaddle/Paddle/pull/43719)) -- 优化 `linspace` 算子，将 `start`、`stop`、`num` 三个输入 Tensor 初始化在 CPU 上，避免在算子中进行 GPU -> CPU 拷贝，SOLOv2 模型性能提升 6%。([#43746](https://github.com/PaddlePaddle/Paddle/pull/43746)) - -### （3）问题修复 - -#### API - -- 修复 `paddle.io.DataLoader` 在 `return_list=True` 时因多线程冲突小概率报错问题。([#43691](https://github.com/PaddlePaddle/Paddle/pull/43691)) -- 修复 `paddle.nn.Layer` 的参数存在 `None` 类型参数时 `to` 方法报 NoneType 不存在 device 属性的错误。([#43597](https://github.com/PaddlePaddle/Paddle/pull/43597)) -- 修复 cumsum op 在某些 `shape`下计算结果出错的问题。([#42500](https://github.com/PaddlePaddle/Paddle/pull/42500), [#43777](https://github.com/PaddlePaddle/Paddle/pull/43777)) -- 修复静态图下 `Tensor.__getitem__`在使用 `bool`索引时组网阶段输出结果维度为 0 的问题。([#43246](https://github.com/PaddlePaddle/Paddle/pull/43246)) -- 修复 `paddle.slice` 和 `paddle.strided_slice` 处理参数为负数时出现异常的问题。([#43432](https://github.com/PaddlePaddle/Paddle/pull/43432)) -- 修复 set_value op 在处理切片 `step`为负数时赋值结果异常的问题。([#43694](https://github.com/PaddlePaddle/Paddle/pull/43694)) -- 修复 C++ 端 `copy` 接口不能在多卡设备间拷贝的问题。([#43728](https://github.com/PaddlePaddle/Paddle/pull/43728)) -- 修改 `paddle.incubate.nn.functional.fused_attention` 和 `paddle.incubate.nn.functional.fused_feedforward` 中属性命名引发的推理时的问题。([#43505](https://github.com/PaddlePaddle/Paddle/pull/43505)) -- 修复 ConditionalBlockGrad op 处理不需要 `grad`的 Tensor 时异常的问题。([#43034](https://github.com/PaddlePaddle/Paddle/pull/43034)) -- 解决 C++ 的 einsum op 反向速度优化引起的显存增加问题，并将反向优化默认打开。([#43397](https://github.com/PaddlePaddle/Paddle/pull/43397)) -- 修复单卡下 `paddle.io.DataLoader`多进程数据读取在固定随机种子时数据无法固定的问题。([#43702](https://github.com/PaddlePaddle/Paddle/pull/43702)) -- 修复 softmax op 在 Tensor 元素超过 2G 时，触发 CUDNN_STATUS_NOT_SUPPORT 的错误。([#43719](https://github.com/PaddlePaddle/Paddle/pull/43719)) -- 修复 trace op `Event` 字符串在不同算子无区分，导致性能分析不便利的问题。([#42789](https://github.com/PaddlePaddle/Paddle/pull/42789)) - -#### 其他 - -- 修复动转静多次 deepcopy 并保存导致的显存溢出问题。([#43141](https://github.com/PaddlePaddle/Paddle/pull/43141)) -- 修复自定义算子中使用的 PlaceType 类型升级引入的 device id 在多卡场景中出错的问题。([#43830](https://github.com/PaddlePaddle/Paddle/pull/43830)) -- 优化 `paddle.profiler.Profiler` timeline 可视化逻辑，将在 python 脚本中自定义的事件从 C++ 折叠层显示移动至 python 折叠层显示。([#42790](https://github.com/PaddlePaddle/Paddle/pull/42790)) - -## 3. 部署方向（Paddle Inference） - -### （1）新增特性 #### 新增功能 +- 新增对海光 DCU K100 支持。[#63535](https://github.com/PaddlePaddle/Paddle/pull/63535) +- 支持 complex64/128 数据类型，并支持 fused_bias_residual_layernorm、fused_bias_dropout_residual_layer_norm、rms_norm 等融合算子。 [#63217](https://github.com/PaddlePaddle/Paddle/pull/63217) -- CPU 上 ONNX Runtime 后端新增 PaddleSlim 量化模型支持。([#43774](https://github.com/PaddlePaddle/Paddle/pull/43774), [#43796](https://github.com/PaddlePaddle/Paddle/pull/43796)) - -### （2）底层优化 - -#### CPU 性能优化 - -- EnableMkldnn 配置中移除 `gpu_cpu_reshape2_matmul_fuse_pass`，修复 ResNet50 性能下降的问题。([#43750](https://github.com/PaddlePaddle/Paddle/pull/43750)) - -#### GPU 性能优化 - -- 添加 `bilinear_interp_v2` TensorRT convert 支持。([#43618](https://github.com/PaddlePaddle/Paddle/pull/43618)) -- 添加 `matmul_scale_fuse_pass`、`multihead_matmul_fuse_pass_v3`到 GPU pass，并添加单测。([#43765](https://github.com/PaddlePaddle/Paddle/pull/43765)) -- 添加 GPU handle 延迟初始化支持。([#43661](https://github.com/PaddlePaddle/Paddle/pull/43661)) - -### （3）问题修复 - -#### 框架及 API 修复 - -- 修复联编 Paddle-Lite XPU 时的编译报错问题。([#43178](https://github.com/PaddlePaddle/Paddle/pull/43178)) -- 修复 ERNIE 3.0 pass 误触发的问题。([#43948](https://github.com/PaddlePaddle/Paddle/pull/43948)) -- 修复 multihead op 中 int8 量化属性读不到的问题。([#43020](https://github.com/PaddlePaddle/Paddle/pull/43020)) - -#### 后端能力修复 - -- 修复 MKLDNN 中 elementwise_mul 和 matmul 两个 op 在运行量化推理过程中崩溃的问题。([#43725](https://github.com/PaddlePaddle/Paddle/pull/43725)) -- 修复同一模型在推理时 TensorRT 子图序列化文件反复生成的问题。([#42945](https://github.com/PaddlePaddle/Paddle/pull/43945), [#42633](https://github.com/PaddlePaddle/Paddle/pull/42633)) -- 修复 ONNX Runtime 后端与外部使用的 protobuf 冲突问题。([#43159](https://github.com/PaddlePaddle/Paddle/pull/43159), [#43742](https://github.com/PaddlePaddle/Paddle/pull/43742)) -- 修复 python 预测库 ONNX Runtime 后端在多输入情况下推理报错问题。([#43621](https://github.com/PaddlePaddle/Paddle/pull/43621)) - -## 4. 环境适配 - -### 编译安装 - -- 完成对 CUDA 11.6 的验证和适配，并在官网发布 CUDA 11.6 的安装包。([#43935](https://github.com/PaddlePaddle/Paddle/pull/43935), [#44005](https://github.com/PaddlePaddle/Paddle/pull/44005)) -- 修复在 Windows 上使用 CUDA 11.6 编译时的 cub 报错问题。([#43935](https://github.com/PaddlePaddle/Paddle/pull/43935), [#44005](https://github.com/PaddlePaddle/Paddle/pull/44005)) -- 修复 elementwise、reduce op 编译时间较长的问题。([#43202](https://github.com/PaddlePaddle/Paddle/pull/43202), [#42779](https://github.com/PaddlePaddle/Paddle/pull/42779), [#43205](https://github.com/PaddlePaddle/Paddle/pull/43205)) - -### 新硬件适配 - -- 寒武纪 MLU 支持飞桨 Profiler。([#42115](https://github.com/PaddlePaddle/Paddle/pull/42115)) -- GraphCore IPU 支持显示编译进度。([#42078](https://github.com/PaddlePaddle/Paddle/pull/42078)) - -# 2.3.0 Release Note - -## 1. 重要更新 - -我们很高兴地发布飞桨框架 2.3.0 版本，本版本包含如下重要更新。 - -### API - -- 新增 100 多个 API，覆盖自动微分、线性代数、概率分布、稀疏张量、框架性能分析、硬件设备管理、视觉领域等方面。 - -- 新增 4 个自动微分 API，11 个线性代数 API，21 个概率分布类 API，更好地支持科学计算、强化学习等场景。 - -- 新增 11 个 稀疏张量计算 API，支持创建 COO、CSR 格式的 Sparse Tensor 以及与 Tensor 互相转换等基础功能。 - -- 新增 9 个框架性能分析 API，以 `paddle.profiler.Profiler` 为核心，提供对训练、推理过程中性能数据的收集、导出和统计的功能。 - -- 新增 7 个硬件设备管理 API，更好支持硬件相关信息获取。 - -- 新增多个视觉、文本领域 API，方便复用 MobileNetV3, ResNeXt 等骨干网络，实现快速组网。 - -### 飞桨高可复用算子库 PHI - -- 发布飞桨高可复用算子库 PHI (Paddle HIgh reusability operator library)，支持组合式算子功能复用、Primitive 算子内核复用、插件式硬件加速库复用。针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 Phi，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 python 开发接口保持一致的 C++ 运算类 API，以及近 500 个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持 Primitive API 方式开发算子内核，可支持不同硬件（比如 GPU 和 XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如 NPU）的加速库，实现低成本复用硬件加速库。 - -### 分布式训练 - -- 全面升级自适应分布式训练架构，含弹性扩缩容、异步流水执行器、异构通信、自动并行等多个模块，支持了多种异构硬件下自动感知的分布式训练及分布式推理。 - -- 动态图混合并行下新增 MoE 并行策略、GroupSharded 并行策略、Pure FP16 等，进一步支持了动态图下大模型的高效并行训练。 - -- 全面升级优化了通用异构参数服务器架构，进行各模块的抽象简化，如通信、存储等，提升了参数服务器的二次开发体验；GPU 参数服务器在千亿参数百亿数据分钟级流式训练下性能提升 2.38 倍。 - -### 编译安装 - -- 从 2.3.0 版本开始，飞桨对框架支持的 GPU 架构种类进行了调整和升级。 - -### 推理部署 - -- 新增 Java API 和 ONNX Runtime CPU 后端。 - -- 支持 TensorRT 8.0 / 8.2 和结构化稀疏，针对 ERNIE 类结构模型性能深度优化。 - -### 硬件适配 - -- 新增自定义新硬件接入：提供一种插件式扩展 PaddlePaddle 硬件后端的方式。 - -- 新增对华为昇腾 910 / GraphCore IPU / 寒武纪 MLU / 昆仑芯 2 代多种异构芯片的训练/推理支持。 - -### 框架架构 - -- 这个版本中，我们在框架的执行器也做了大量工作，详情请见：[新动态图执行机制](#%E6%96%B0%E5%8A%A8%E6%80%81%E5%9B%BE%E6%89%A7%E8%A1%8C%E6%9C%BA%E5%88%B6) 与 [全新静态图执行器](#%E5%85%A8%E6%96%B0%E9%9D%99%E6%80%81%E5%9B%BE%E6%89%A7%E8%A1%8C%E5%99%A8)。 - -## 2. 不兼容升级 - -- 预编译安装包中移除 CUDA sm35 ARCH： 受到包体积大小的影响，在预编译的安装包中移除了 CUDA sm35 架构。([#41754](https://github.com/PaddlePaddle/Paddle/pull/41754)) - -- `paddle.to_tensor` 将一个 python int scalar 转换为 Tensor 时，在 Windows 上的默认数据类型由 int32 变为 int64，从而与 Linux/Mac 保持对齐。([#39662](https://github.com/PaddlePaddle/Paddle/pull/39662)) - -- 为了与 python3 下的除法行为保持一致，除法符号 `/` 从 rounding divide 变成 true divide，计算输出结果的数据类型从 int 切换成 float。([#40890](https://github.com/PaddlePaddle/Paddle/pull/40890)) - - - - - - - - - - - -
        -2.2 - -2.3.0 -
        -
        -
-```python
->>> import paddle
->>> a = paddle.to_tensor([327])
->>> b = paddle.to_tensor([80])
->>> a / b
-Tensor(shape=[1], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
-      [4])
-```
-
        -        		 -
        -
-```python
->>> import paddle
->>> a = paddle.to_tensor([327])
->>> b = paddle.to_tensor([80])
->>> a / b
-Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-      [4.08750010])
-```
-
        -
        - -- 修正 ELU 的公式，alpha < 0 时的计算方式与原论文对齐，从而修复小部分情况下的计算结果错误。同时，由于在 alpha < 0 无法在数学上仅从输出计算反向梯度，因此 elu_ 在 alpha < 0 时将报错。([#37316](https://github.com/PaddlePaddle/Paddle/pull/37316)) - - - - - - - - - - - -
        -2.2 - -2.3.0 -
        -
        -
-```python
-# elu(x) = max(0, x) + min(0, α ∗ (e^x − 1))
->>> import paddle
->>> x = paddle.to_tensor([-1., 6.])
->>> m = paddle.nn.ELU(-0.2)
->>> out = m(x)
->>> out
-Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-       [ 0.         , -74.48576355])
->>> out = paddle.nn.functional.elu_(x, alpha=-0.2, name=None)
->>> out
-Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-       [ 0.         , -74.48576355])
-```
-
        -        		 -
        -
-```python
-# elu(x) = x, if x > 0
-# elu(x) = α ∗ (e^x − 1), if x <= 0
->>> import paddle
->>> x = paddle.to_tensor([-1., 6.])
->>> m = paddle.nn.ELU(-0.2)
->>> out = m(x)
->>> out
-Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-       [0.12642412,  6.        ])
->>> out = paddle.nn.functional.elu_(x, alpha=-0.2, name=None)
-Traceback (most recent call last):
-  File "", line 1, in 
-  File "/usr/local/lib/python3.7/dist-packages/decorator.py", line 232, in fun
-    return caller(func, *(extras + args), **kw)
-  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/wrapped_decorator.py", line 25, in __impl__
-    return wrapped_func(*args, **kwargs)
-  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/inplace_utils.py", line 34, in __impl__
-    return func(*args, **kwargs)
-  File "/usr/local/lib/python3.7/dist-packages/paddle/nn/functional/activation.py", line 89, in elu_
-    assert alpha >= 0., "elu_ only support alpha >= 0, please use elu instead."
-AssertionError: elu_ only support alpha >= 0, please use elu instead.
-```
-
        -
        - -## 3. 训练框架（含分布式） - -### （1）新功能 - -#### API - -- 新增 4 个自动微分类 API，支持科学计算需求，具体列表如下：([#40692](https://github.com/PaddlePaddle/Paddle/pull/40692)) - - - `paddle.incubate.autograd.vjp`，计算向量-雅可比矩阵乘积。 - - - `paddle.incubate.autograd.jvp`，计算雅可比矩阵-向量乘积。 - - - `paddle.incubate.autograd.Jacobian`，计算雅可比矩阵。 - - - `paddle.incubate.autograd.Hessian`，计算海森矩阵。 - -- 新增线性代数类 API - - - 新增 `paddle.linalg.triangular_solve`，计算具有唯一解的三角系数线性方程组。([#36714](https://github.com/PaddlePaddle/Paddle/pull/36714)) - - - 新增 `paddle.linalg.eig`，计算一般方阵的特征分解。([#35764](https://github.com/PaddlePaddle/Paddle/pull/35764)) - - - 新增 `paddle.linalg.sovle`，计算线性方程组的解。([#35715](https://github.com/PaddlePaddle/Paddle/pull/35715)) - - - 新增 `paddle.linalg.lstsq`，计算线性方程组的最小二乘解。([#38585](https://github.com/PaddlePaddle/Paddle/pull/38585), [#38621](https://github.com/PaddlePaddle/Paddle/pull/38621)) - - - 新增 `paddle.linalg.qr`，计算矩阵的 QR 分解。([#35742](https://github.com/PaddlePaddle/Paddle/pull/35742), [#38824](https://github.com/PaddlePaddle/Paddle/pull/38824)) - - - 新增 `paddle.inner`，计算矩阵内积。([#37706](https://github.com/PaddlePaddle/Paddle/pull/37706)) - - - 新增 `paddle.outer`，计算矩阵外积。([#37706](https://github.com/PaddlePaddle/Paddle/pull/37706)) - - - 新增 `paddle.linalg.cov`，计算向量间协方差。([#38392](https://github.com/PaddlePaddle/Paddle/pull/38392)) - - - 新增 `paddle.linalg.cholesky_sovle`，计算方程 cholesky 解。([#38167](https://github.com/PaddlePaddle/Paddle/pull/38167)) - - - 新增 `paddle.linalg.lu`、 `paddle.linalg.lu_unpack`，计算矩阵 lu 分解、解压缩 lu 矩阵。([#38617](https://github.com/PaddlePaddle/Paddle/pull/38617), [#38559](https://github.com/PaddlePaddle/Paddle/pull/38559), [#38616](https://github.com/PaddlePaddle/Paddle/pull/38616)) - -- 新增 21 个概率分布类 API，包括 6 个随机变量分布，13 个随机变量变换，2 个 KL 散度计算，用于强化学习、变分推断、科学计算等场景，具体列表如下：([#40536](https://github.com/PaddlePaddle/Paddle/pull/40536), [#38820](https://github.com/PaddlePaddle/Paddle/pull/38820), [#38558](https://github.com/PaddlePaddle/Paddle/pull/38558/files), [#38445](https://github.com/PaddlePaddle/Paddle/pull/38445), [#38244](https://github.com/PaddlePaddle/Paddle/pull/38244), [#38047](https://github.com/PaddlePaddle/Paddle/pull/38047)) - - - `paddle.distribution.ExponentialFamily`，指数分布族基类。 - - - `paddle.distribution.Beta`，`Beta` 分布。 - - - `paddle.distribution.Dirichlet`，`Dirichlet` 分布。 - - - `paddle.distribution.Independent`，独立分布，用于创建高阶分布。 - - - `paddle.distribution.TransformedDistribution`，变换分布，用于通过基础分布及一系列变换生成高阶分布。 - - - `paddle.distribution.Multionmial`，多项分布。 - - - `paddle.distribution.Transform`，随机变量变换的基类。 - - - `paddle.distribution.AbsTransform`，取绝对值变换。 - - - `paddle.distribution.AffineTransform`，仿射变换。 - - - `paddle.distribution.ChainTransform`，变换的链式组合。 - - - `paddle.distribution.ExpTransform`，指数变换。 - - - `paddle.distribution.IndependentTransform`，独立变换，用于扩展变换定义域的 `event_dim`。 - - - `paddle.distribution.PowerTransform`，幂变换。 - - - `paddle.distribution.ReshapeTransform`，`reshape` 变换。 - - - `paddle.distribution.SigmoidTransform`，`sigmoid` 变换。 - - - `paddle.distribution.SoftmaxTransform`，`softmax` 变换。 - - - `paddle.distribution.StackTransform`，`stack` 变换，用于以 `stack` 方式组合多个变换。 - - - `paddle.distribution.StickBreakingTransform`, `stickbreaking` 变换。 - - - `paddle.distribution.TanhTransform`，`tanh` 变换。 - - - `paddle.distribution.kl_divergence`，计算 KL 散度。 - - - `paddle.distribution.register_kl`，注册用户自定义 KL 散度计算函数。 - -- 新增高层 API - - - 新增 `paddle.vision.models.AlexNet`、`paddle.vision.models.alexnet`，支持直接使用 AlexNet 模型。([#36058](https://github.com/PaddlePaddle/Paddle/pull/36058)) - - - 新增 `paddle.vision.models.DenseNet`、 `paddle.vision.models.densenet121`、 `paddle.vision.models.densenet161`、 `paddle.vision.models.densenet169`、 `paddle.vision.models.densenet201`、 `paddle.vision.models.densenet264`，支持直接使用 DenseNet 模型。([#36069](https://github.com/PaddlePaddle/Paddle/pull/36069)) - - - 新增 `paddle.vision.models.GoogLeNet`、`paddle.vision.models.googlenet`，支持直接使用 GoogLeNet 模型。([#36034](https://github.com/PaddlePaddle/Paddle/pull/36034)) - - - 新增 `paddle.vision.models.InceptionV3`、`paddle.vision.models.inception_v3`，支持直接使用 InceptionV3 模型。([#36064](https://github.com/PaddlePaddle/Paddle/pull/36064)) - - - 新增 `paddle.vision.models.MobileNetV3Small`、 `paddle.vision.models.MobileNetV3Large`、`paddle.vision.models.mobilenet_v3_small`、`paddle.vision.models.mobilenet_v3_large`，支持直接使用 MobileNetV3 模型。([#38653](https://github.com/PaddlePaddle/Paddle/pull/38653)) - - - 新增 `paddle.vision.models.resnext50_32x4d`、 `paddle.vision.models.resnext50_64x4d`、`paddle.vision.models.resnext101_32x4d`、`paddle.vision.models.resnext101_64x4d`、`paddle.vision.models.resnext152_32x4d`、`paddle.vision.models.resnext152_64x4d`，支持直接使用 ResNeXt 模型。([#36070](https://github.com/PaddlePaddle/Paddle/pull/36070)) - - - 新增 `paddle.vision.models.ShuffleNetV2`、 `paddle.vision.models.shufflenet_v2_x0_25`、`paddle.vision.models.shufflenet_v2_x0_33`、`paddle.vision.models.shufflenet_v2_x0_5`、`paddle.vision.models.shufflenet_v2_x1_0`、`paddle.vision.models.shufflenet_v2_x1_5`、`paddle.vision.models.shufflenet_v2_x2_0`、`paddle.vision.models.shufflenet_v2_swish`，支持直接使用 ShuffleNetV2 模型。([#36067](https://github.com/PaddlePaddle/Paddle/pull/36067)) - - - 新增 `paddle.vision.models.SqueezeNet`、 `paddle.vision.models.squeezenet1_0`、`paddle.vision.models.squeezenet1_1`，支持直接使用 SqueezeNet 模型。([#36066](https://github.com/PaddlePaddle/Paddle/pull/36066)) - - - 新增 `paddle.vision.models.wide_resnet50_2`、`paddle.vision.models.wide_resnet101_2`，支持直接使用 WideResNet 模型。([#36952](https://github.com/PaddlePaddle/Paddle/pull/36952)) - - - 新增`paddle.vision.ops.nms` API，支持单类别和多类别非极大抑制(non-maximum supression, nms)算法，用于目标检测预测任务加速。([#40962](https://github.com/PaddlePaddle/Paddle/pull/40962)) - - - 新增`paddle.vision.ops.roi_pool` 和 `paddle.vision.ops.RoIPool`，支持检测任务中 RoI 区域池化操作。([#36154](https://github.com/PaddlePaddle/Paddle/pull/36154)) - - - 新增`paddle.vision.ops.roi_align` 和 `paddle.vision.ops.RoIAlign`，支持检测任务中 RoI Align 操作。([#35102](https://github.com/PaddlePaddle/Paddle/pull/36154)) - - - 新增 `paddle.text.ViterbiDecoder`、`paddle.text.viterbi_decode` Viterbi 解码 API，主要用于序列标注模型的预测。([#35778](https://github.com/PaddlePaddle/Paddle/pull/35778)) - -- 新增 11 个 Sparse 类 API，支持创建 COO、CSR 格式的 Sparse Tensor，与 Tensor 互相转换等基础功能： - - - `paddle.sparse.sparse_coo_tensor`，创建 COO 格式的 Sparse Tensor。([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `paddle.sparse.sparse_csr_tensor`，创建 CSR 格式的 Sparse Tensor。([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `paddle.sparse.ReLU`，支持 SparseCooTensor 的 ReLU 激活层。([#40959](https://github.com/PaddlePaddle/Paddle/pull/40959)) - - - `paddle.sparse.functional.relu`，支持 SparseCooTensor 的 ReLU 函数。([#40959](https://github.com/PaddlePaddle/Paddle/pull/40959)) - - - `Tensor.values()`，获取 SparseCooTensor 或者 SparseCsrTensor 的非零元素方法。([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.indices()`，获取 SparseCooTensor 的坐标信息的方法。([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.crows()`，获取 SparseCsrTensor 的压缩行信息的方法。([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.cols()`，获取 SparseCsrTensor 的列信息的方法。([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.to_sparse_coo()`，将 DenseTensor 或者 SparseCsrTensor 转换为 SparseCooTensor。([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `Tensor.to_sparse_csr()`，将 DenseTensor 或者 SparseCooTensor 转换为 SparseCsrTensor。([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `Tensor.to_dense()`，将 SparseCooTensor 或者 SparseCsrTensor 转换为 DenseTensor。([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - -- 新增硬件相关 API - - - 新增 `paddle.device.cuda.max_memory_allocated`、`paddle.device.cuda.max_memory_reserved`、 `paddle.device.cuda.memory_allocated` 和 `paddle.device.cuda.memory_reserved` 四个 GPU 显存监测相关 API，方便实时查看和分析模型显存占用指标。([#38657](https://github.com/PaddlePaddle/Paddle/pull/38657)) - - - 新增 `paddle.device.cuda.get_device_properties`，支持返回 CUDA 设备属性信息。([#35661](https://github.com/PaddlePaddle/Paddle/pull/35661)) - - - 新增 `paddle.device.cuda.get_device_name` 和 `paddle.device.cuda.get_device_capability`，支持返回 GPU 设备名称信息和计算能力的主要和次要修订号。([#35672](https://github.com/PaddlePaddle/Paddle/pull/35672)) - -- 新增 Tensor 操作 API - - - 新增 `paddle.nansum`，沿 `axis` 对输入 Tensor 求和，且忽略掉 `NaNs` 值。([#38137](https://github.com/PaddlePaddle/Paddle/pull/38137)) - - - 新增 `paddle.nanmean`，沿 `axis`对输入 Tensor 求平均，且忽略掉 `NaNs` 值。([#40472](https://github.com/PaddlePaddle/Paddle/pull/40472)) - - - 新增 `paddle.clone`，返回输入 Tensor 的拷贝，并且提供梯度计算。([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - - - 新增 `paddle.Tensor.element_size`，返回 Tensor 中的单个元素在计算机中所分配的 bytes 数量。([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - - - 新增 `paddle.Tensor.to_uva_tensor`，支持将 numpy 对象转换为实际存储在 CPU，但可作为 CUDA 对象进行虚拟地址访问的功能。([#39146](https://github.com/PaddlePaddle/Paddle/pull/39146), [#38950](https://github.com/PaddlePaddle/Paddle/pull/38950)) - - - 新增`paddle.rot90`，沿 `axes` 指定的平面将 n 维 Tensor 旋转 90 度。([#37634](https://github.com/PaddlePaddle/Paddle/pull/37634)) - - - 新增`paddle.logit` 和 `paddle.Tensor.logit`，计算输入 Tensor 的 logit 函数值。([#37844](https://github.com/PaddlePaddle/Paddle/pull/37844)) - - - 新增 `paddle.repeat_interleave`，沿着指定轴对输入进行复制，创建并返回到一个新的 Tensor。([#37981](https://github.com/PaddlePaddle/Paddle/pull/37981)) - - - 新增 `paddle.renorm`，把 Tensor 在指定的 `axis` 切分成多块后分别进行 p norm 操作。([#38130](https://github.com/PaddlePaddle/Paddle/pull/38130), [#38459](https://github.com/PaddlePaddle/Paddle/pull/38459)) - - - 新增 `paddle.mode` 和 `paddle.Tensor.mode`，沿指定轴查找输入 Tensor 的众数及对应的索引。([#38446](https://github.com/PaddlePaddle/Paddle/pull/38446)) - - - 新增 `paddle.quantile` 和 `paddle.Tensor.quantile`，沿指定轴计算 Tensor 的 q 分位数。([#38567](https://github.com/PaddlePaddle/Paddle/pull/38567)) - - - 新增 `paddle.kthvalue` 和 `paddle.Tensor.kthvalue`，查找 Tensor 中指定轴上第 k 小的数及对应的索引。([#38386](https://github.com/PaddlePaddle/Paddle/pull/38386)) - - - 新增 `paddle.is_floating_point` 和 `paddle.Tensor.is_floating_point`，判断输入 Tensor 是否为浮点类型。([#37885](https://github.com/PaddlePaddle/Paddle/pull/37885)) - - - 新增 `paddle.erfinv` 和 `paddle.Tensor.erfinv`，计算输入 Tensor 的逆误差函数。([#38295](https://github.com/PaddlePaddle/Paddle/pull/38295)) - - - 新增 `paddle.lerp` 和 `paddle.Tensor.lerp`，根据给定权重计算输入 Tensor 间的线性插值。([#37253](https://github.com/PaddlePaddle/Paddle/pull/37253)) - - - 新增 `paddle.angle`，用于计算复数 Tensor 的相位角。([#37689](https://github.com/PaddlePaddle/Paddle/pull/37689)) - - - 新增`paddle.rad2deg`和`paddle.Tensor.rad2deg`，将元素从弧度的角度转换为度。([#37598](https://github.com/PaddlePaddle/Paddle/pull/37598)) - - - 新增`paddle.deg2rad`和`paddle.Tensor.deg2rad`，将元素从度的角度转换为弧度。([#37598](https://github.com/PaddlePaddle/Paddle/pull/37598)) - - - 新增`paddle.gcd`和`paddle.Tensor.gcd`，计算两个输入的按元素绝对值的最大公约数。([#37819](https://github.com/PaddlePaddle/Paddle/pull/37819)) - - - 新增`paddle.lcm`和`paddle.Tensor.lcm`，计算两个输入的按元素绝对值的最小公倍数。([#37819](https://github.com/PaddlePaddle/Paddle/pull/37819)) - - - 新增`paddle.amax`和`paddle.Tensor.amax`，对指定维度上的 Tensor 元素求最大值，正向结果和 max 一样，有多个相等的最大值时，反向的梯度平均分到这多个值的位置上。([#38417](https://github.com/PaddlePaddle/Paddle/pull/38417)) - - - 新增`paddle.amin`和`paddle.Tensor.amin`，对指定维度上的 Tensor 元素求最小值，正向结果和 min 一样，有多个相等的最小值时，反向的梯度平均分到这多个值的位置上。([#38417](https://github.com/PaddlePaddle/Paddle/pull/38417)) - - - 新增`paddle.isclose`，用于判断两个 Tensor 的每个元素是否接近。([#37135](https://github.com/PaddlePaddle/Paddle/pull/37135)) - - - 新增`paddle.put_along_axis` 和`paddle.take_along_axis`，用于提取或放置指定索引下标的元素。([#38608](https://github.com/PaddlePaddle/Paddle/pull/38608)) - - - 新增 `paddle.bincount` 和 `paddle.Tensor.bincount`，用于统计 Tensor 中每个元素出现的次数。([#36317](https://github.com/PaddlePaddle/Paddle/pull/36317)) - - - 新增 `paddle.fmax`、 `paddle.fmin`，扩展了 max/min 的功能，支持比较的两个 Tensor 中有 NaN 值的情况，即如果对应位置上有 1 个 NaN 值，则返回那个非 NaN 值；如果对应位置上有 2 个 NaN 值，则返回 NaN 值。([#37826](https://github.com/PaddlePaddle/Paddle/pull/37826)) - - - 新增 `paddle.diff`，用于计算沿给定维度的第 n 个前向差值，目前支持 n=1。([#37441](https://github.com/PaddlePaddle/Paddle/pull/37441)) - - - 新增 `paddle.asinh`、`paddle.acosh`、`paddle.atanh` 反双曲函数类 API。([#37076](https://github.com/PaddlePaddle/Paddle/pull/37076)) - - - 新增 `paddle.as_real`，`paddle.as_complex` 用于实数 Tensor 和复数 Tensor 之间的转换。([#37784](https://github.com/PaddlePaddle/Paddle/pull/37784)) - - - 新增 `paddle.complex` 用于给定实部和虚部构造复数 Tensor。([#37918](https://github.com/PaddlePaddle/Paddle/pull/37918), [#38272](https://github.com/PaddlePaddle/Paddle/pull/38272)) - - - 新增 `paddle.det` 与 `paddle.slogdet`，用于计算矩阵的行列式和行列式的自然对数。([#34992](https://github.com/PaddlePaddle/Paddle/pull/34992)) - - - 新增`paddle.nn.utils.parameters_to_vector`，可以将输入的多个 parameter 展平并连接为 1 个 1-D Tensor。([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - - - 新增`paddle.nn.utils.vector_to_parameters`，将 1 个 1-D Tensor 按顺序切分给输入的多个 parameter。([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - -- 新增组网类 API - - - 新增 `paddle.nn.Fold`、`paddle.nn.functional.fold`，支持将提取出的滑动局部区域块还原成 batch 的 Tensor。([#38613](https://github.com/PaddlePaddle/Paddle/pull/38613)) - - - 新增 `paddle.nn.CELU`、`paddle.nn.functional.celu`，支持 CELU 激活层。([#36088](https://github.com/PaddlePaddle/Paddle/pull/36088)) - - - 新增 `paddle.nn.HingeEmbeddingLoss`，增加计算 hinge embedding 损失的方式，通常用于学习 nonlinear embedding 或半监督学习。([#37540](https://github.com/PaddlePaddle/Paddle/pull/37540)) - - - 新增 `paddle.nn.ZeroPad2D` API，按照 padding 属性对输入进行零填充。([#37151](https://github.com/PaddlePaddle/Paddle/pull/37151)) - - - 新增 `paddle.nn.MaxUnPool3D` 和 `paddle.nn.MaxUnPool1D`，用于计算 3D 最大反池化和 1D 最大反池化。([#38716](https://github.com/PaddlePaddle/Paddle/pull/38716)) - - - 新增 `paddle.incubate.graph_khop_sampler`、`paddle.incubate.graph_sample_neighbors`、 `paddle.incubate.graph_reindex` API，支持图多阶邻居采样和图编号重索引操作，主要用于图神经网络模型训练。([#39146](https://github.com/PaddlePaddle/Paddle/pull/39146), [#40809](https://github.com/PaddlePaddle/Paddle/pull/40809)) - -- 新增随机数类 API - - - 新增 `paddle.poisson`，以输入 Tensor 为泊松分布的 lambda 参数，生成一个泊松分布的随机数 Tensor。([#38117](https://github.com/PaddlePaddle/Paddle/pull/38117)) - - - 新增 `paddle.randint_like` API，支持新建服从均匀分布的、范围在[low, high) 的随机 Tensor，输出的形状与输入的形状一致。([#36169](https://github.com/PaddlePaddle/Paddle/pull/36169)) - - - 新增 `paddle.Tensor.exponential_`，为 inplace 式 API，通过指数分布随机数来填充输入 Tensor。([#38256](https://github.com/PaddlePaddle/Paddle/pull/38256)) - -- 新增参数初始化类 API - - - 新增`paddle.nn.initializer.Dirac`，通过迪拉克 delta 函数来初始化 3D/4D/5D 参数，其常用于卷积层 Conv1D/Conv2D/Conv3D 的参数初始化。([#37389](https://github.com/PaddlePaddle/Paddle/pull/37389)) - - - 新增`paddle.nn.initializer.Orthogonal`，正交矩阵初始化，被初始化后的参数是（半）正交向量。([#37163](https://github.com/PaddlePaddle/Paddle/pull/37163)) - - - 新增`paddle.nn.initializer.calculate_gain`，获取激活函数的推荐增益值，增益值可用于设置某些初始化 API，以调整初始化范围。([#37163](https://github.com/PaddlePaddle/Paddle/pull/37163)) - -- 新增学习率类 API - - - 新增 `paddle.optimizer.lr.MultiplicativeDecay`，提供 `lambda` 函数设置学习率的策略。([#38250](https://github.com/PaddlePaddle/Paddle/pull/38250)) - -- 新增分布式相关 API - - - 新增 `paddle.incubate.optimizer.DistributedFusedLamb`，使得 Lamb 优化器可分布式更新参数。([#40011](https://github.com/PaddlePaddle/Paddle/pull/40011), [#39972](https://github.com/PaddlePaddle/Paddle/pull/39972), [#39900](https://github.com/PaddlePaddle/Paddle/pull/39900), [#39747](https://github.com/PaddlePaddle/Paddle/pull/39747), [#39148](https://github.com/PaddlePaddle/Paddle/pull/39148), [#39416](https://github.com/PaddlePaddle/Paddle/pull/39416)) - -- 新增优化器相关 API([#40710](https://github.com/PaddlePaddle/Paddle/pull/40710)) - - - `paddle.incubate.optimizer.functional.minimize_bfgs`，增加二阶优化器 BFGS。 - - - `paddle.incubate.optimizer.functional.minimize_lbfgs`，增加二阶优化器 L-BFGS。 - -- 新增 `paddle.incubate.multiprocessing`模块，支持 Tensor（CPU/GPU）在 python 进程间传输。([#37302](https://github.com/PaddlePaddle/Paddle/pull/37302), [#41339](https://github.com/PaddlePaddle/Paddle/pull/41339)) - -- 新增 `paddle.incubate.autotune.set_config` API，支持多版本 Kernel 自动选择、混合精度数据布局自动转换、DataLoader 的 num_workers 自动选择，以自动提升模型性能。([#42301](https://github.com/PaddlePaddle/Paddle/pull/42301)) - -- 新增 `paddle.incubate.nn.FusedMultiTransformer` 和 `paddle.incubate.nn.functional.fused_multi_transformer` API，可将多层 transformer 融合到一个 op 中，提升模型推理性能，注意：仅支持前向推理。([#42311](https://github.com/PaddlePaddle/Paddle/pull/42311)) - -- 新增动静统一的 einsum_v2 op，兼容原有 python 端 `paddle.einsum` 实现的同时支持动转静导出和更加完备的 Infershape 推导。([#42495](https://github.com/PaddlePaddle/Paddle/pull/42495), [#42327](https://github.com/PaddlePaddle/Paddle/pull/42327), [#42397](https://github.com/PaddlePaddle/Paddle/pull/42397), [#42105](https://github.com/PaddlePaddle/Paddle/pull/42105)) - -#### IR(Intermediate Representation) - -- 动态图转静态图 - - - 变量类型 StaticAnalysis 模块新增支持类似 `a, b = paddle.shape(x)` 的类型标记。([#39245](https://github.com/PaddlePaddle/Paddle/pull/39245)) - - - 新增支持 `InputSpec.name` 作为 Program 缓存 hash key 的计算字段。([#38273](https://github.com/PaddlePaddle/Paddle/pull/38273)) - - - 新增支持 `dict['key'] = x.shape` 语法。([#40611](https://github.com/PaddlePaddle/Paddle/pull/40611)) - - - 新增支持 Pure FP16 训练。([#36944](https://github.com/PaddlePaddle/Paddle/pull/36944)) - - - 新增支持 `for i in [x,y,z]` 语法。([#37259](https://github.com/PaddlePaddle/Paddle/pull/37259)) - - - 新增支持 python3 的 type hint 语法。([#36544](https://github.com/PaddlePaddle/Paddle/pull/36544)) - -- Pass 开发 - - - 新增基于 NVIDIA cuBlasLt Epilogue 的 FC + [relu|gelu] 的前向与反向融合。([#39437](https://github.com/PaddlePaddle/Paddle/pull/39437)) - -- Kernel Primitive API - - - 新增 GPU 平台 KP 算子，包括 cast、scale、clip、bce_loss、abs_grad、reduce_sum_grad、reduce_mean_grad、clip、bce_loss、full、full_like、distribution、 random、masked_select_kernel、where_index、masked_select_grad、dropout、sigmoid、where、abs_grad。([#36203](https://github.com/PaddlePaddle/Paddle/pull/36203), [#36423](https://github.com/PaddlePaddle/Paddle/pull/36423), [#39390](https://github.com/PaddlePaddle/Paddle/pull/39390), [#39734](https://github.com/PaddlePaddle/Paddle/pull/39734), [#38500](https://github.com/PaddlePaddle/Paddle/pull/38500), [#38959](https://github.com/PaddlePaddle/Paddle/pull/38959), [#39197](https://github.com/PaddlePaddle/Paddle/pull/39197/), [#39563](https://github.com/PaddlePaddle/Paddle/pull/39563), [#39666](https://github.com/PaddlePaddle/Paddle/pull/39666), [#40517](https://github.com/PaddlePaddle/Paddle/pull/40517), [#40617](https://github.com/PaddlePaddle/Paddle/pull/40617), [#40766](https://github.com/PaddlePaddle/Paddle/pull/40766), [#39898](https://github.com/PaddlePaddle/Paddle/pull/39898), [#39609](https://github.com/PaddlePaddle/Paddle/pull/39609)) - - - 新增支持 XPU2 源码编译模式。([#37254](https://github.com/PaddlePaddle/Paddle/pull/37254), [#40397](https://github.com/PaddlePaddle/Paddle/pull/40397), [#38455](https://github.com/PaddlePaddle/Paddle/pull/38455)) - - - 新增支持 KP 算子在 XPU2 和 GPU 中复用，包括 reduce、broadcast、elementwise_add、`exp、log、relu、sigmoid、leaky_relu、softplus、hard_swish、reciprocal`。([#36904](https://github.com/PaddlePaddle/Paddle/pull/36904), [#37226](https://github.com/PaddlePaddle/Paddle/pull/37226), [#38918](https://github.com/PaddlePaddle/Paddle/pull/38918), [#40560](https://github.com/PaddlePaddle/Paddle/pull/40560/), [#39787](https://github.com/PaddlePaddle/Paddle/pull/39787), [#39917](https://github.com/PaddlePaddle/Paddle/pull/39917), [#40002](https://github.com/PaddlePaddle/Paddle/pull/40002), [#40364](https://github.com/PaddlePaddle/Paddle/pull/40364)) - - - 新增 XPU2 平台 KP 算子单测，包括 `brelu、ceil、celu、elu、floor、hard_shrink、hard_sigmoid、log1p、logsigmoid、relu6、silu、soft_relu、softsign、sqrt、square、swish、thresholded_relu、softshrink`。([#40448](https://github.com/PaddlePaddle/Paddle/pull/40448), [#40524](https://github.com/PaddlePaddle/Paddle/pull/40524)) - - - 新增 XPU2 KP 模型支持，包括 resnet50、deepfm、wide_deep、yolov3-darknet53、det_mv3_db、bert、transformer、mobilenet_v3、GPT2。 - -#### 混合精度训练 - -- 从混合精度训练 `paddle.amp.GradScaler` 的 `minimize` 中拆分出 `paddle.amp.Gradscaler.unscale_` 方法，提供恢复 loss 的独立接口。([#35825](https://github.com/PaddlePaddle/Paddle/pull/35825)) - -- 为 `paddle.nn.ClipByGlobalNorm` 动态图模式添加 FP16 支持，为 clip op 添加 FP16 Kernel，使`clip`相关操作支持 FP16。([#36198](https://github.com/PaddlePaddle/Paddle/pull/36198), [#36577](https://github.com/PaddlePaddle/Paddle/pull/36577)) - -- 支持 `paddle.amp.decorate` 传入的`optimizer`参数为 None。([#37541](https://github.com/PaddlePaddle/Paddle/pull/37541)) - -- 为 merged_momentum op 添加支持输入多学习率、支持 use_nesterov 策略的计算、支持 regularization 计算。([#37527](https://github.com/PaddlePaddle/Paddle/pull/37527)) - -- 为`paddle.optimizer.Momentum`优化器添加 multi_tensor 策略、为`Optimzizer`类的`clear_grad`添加`set_to_zero`分支。([#37564](https://github.com/PaddlePaddle/Paddle/pull/37564)) - -- 为`paddle.optimizer.Adam`优化器添加 multi_tensor 策略。([#38010](https://github.com/PaddlePaddle/Paddle/pull/38010)) - -- 为`paddle.optimizer.SGD`优化器添加 multi_precision 策略。([#38231](https://github.com/PaddlePaddle/Paddle/pull/38231)) - -- 为优化器 `state_dict` 方法添加存储 `master weight` 参数。([#39121](https://github.com/PaddlePaddle/Paddle/pull/39121)) - -- 添加支持 op CUDA bfloat16 混合精度训练，支持 O1、O2 模式，通过 `paddle.amp.auto_cast` 可开启上述训练模式。([#39029](https://github.com/PaddlePaddle/Paddle/pull/39029), [#39815](https://github.com/PaddlePaddle/Paddle/pull/39815)) - -- 为如下 ops 添加 bfloat16 CUDA Kernel：matmul、concat、split、dropout、reshape、slice、squeeze、stack、transpose、unbind、elementwize_max、elementwize_add、elementwize_mul、elementwize_sub、scale、sum、layer_norm、p_norm、reduce_sum、softmax、log_softmax、sigmoid、sqrt、softplus、square、gaussian_random、fill_constant、fill_any_like。([#39485](https://github.com/PaddlePaddle/Paddle/pull/39485), [#39380](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39395](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39402](https://github.com/PaddlePaddle/Paddle/pull/39402), [#39457](https://github.com/PaddlePaddle/Paddle/pull/39457), [#39461](https://github.com/PaddlePaddle/Paddle/pull/39461), [#39602](https://github.com/PaddlePaddle/Paddle/pull/39602), [#39716](https://github.com/PaddlePaddle/Paddle/pull/39716), [#39683](https://github.com/PaddlePaddle/Paddle/pull/39683), [#39843](https://github.com/PaddlePaddle/Paddle/pull/39843), [#39999](https://github.com/PaddlePaddle/Paddle/pull/39999), [#40004](https://github.com/PaddlePaddle/Paddle/pull/40004), [#40027](https://github.com/PaddlePaddle/Paddle/pull/40027)) - -- 为如下 ops 添加 bfloat16 CPU Kernel：dropout、reshape、slice、squeeze、unsqueeze、stack、transpose、unbind、elementwize_max、elementwise_mul、elementwise_sub、gather。([#39380](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39395](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39402](https://github.com/PaddlePaddle/Paddle/pull/39402), [#39457](https://github.com/PaddlePaddle/Paddle/pull/39457), [#39461](https://github.com/PaddlePaddle/Paddle/pull/39461), [#39602](https://github.com/PaddlePaddle/Paddle/pull/39602), [#39716](https://github.com/PaddlePaddle/Paddle/pull/39716), [#39683](https://github.com/PaddlePaddle/Paddle/pull/39683)) - -- 支持打印 bfloat16 类型的 Tensor。([#39375](https://github.com/PaddlePaddle/Paddle/pull/39375), [#39370](https://github.com/PaddlePaddle/Paddle/pull/39370)) - -- 为`p_norm`、`elementwise_max` 、`fill_constant_batch_size_like``scatter`增加 FP16 计算支持。([#35888](https://github.com/PaddlePaddle/Paddle/pull/35888), [#39907](https://github.com/PaddlePaddle/Paddle/pull/39907), [#38136](https://github.com/PaddlePaddle/Paddle/pull/38136), [#38499](https://github.com/PaddlePaddle/Paddle/pull/38499)) - -- 为如下 ops 增加 int16_t 支持：cumsum、less_than、less_equal、greater_than、greater_equal、equal、not_equal、fill_any_like、grather_nd、reduce_sum、where_index、reshape、unsqueeze。([#39636](https://github.com/PaddlePaddle/Paddle/pull/39636)) - -- 为 cross_entropy op 增加 int16_t label 类型的支持。([#39409](https://github.com/PaddlePaddle/Paddle/pull/39409)) - -- 为 embedding op 增加 int16_t id 类型的支持。([#39381](https://github.com/PaddlePaddle/Paddle/pull/39381)) - -- 为 reduce_mean op 增加 FP16 类型的支持。([#38289](https://github.com/PaddlePaddle/Paddle/pull/38289)) - -- 为 elementwise_min op 增加 FP16 类型的支持。([#38123](https://github.com/PaddlePaddle/Paddle/pull/38123)) - -- 更新 bfloat16 AMP oneDNN 默认支持列表。([#39304](https://github.com/PaddlePaddle/Paddle/pull/39304)) - -#### 飞桨高可复用算子库 PHI - -针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 PHI，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 python 开发接口保持一致的 C++ 运算类 API，以及近 500 个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持 Primitive API 方式开发算子内核，可支持不同硬件（比如 GPU 和 XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如 NPU）的加速库，实现低成本复用硬件加速库。主要可分为以下几部分工作： - -- **算子库基础架构、核心组件与机制实现**：合理规划新算子库的目录结构，设计实现了新算子库的公共基础数据结构、新的函数式 InferMeta 和 Kernel 开发范式以及相应的注册和管理组件，并且支持 Kernel 文件的自动化编译对象生成及编译依赖关系生成，使开发者仅需关注函数式 Kernel 的实现，开发范式简洁清晰。([#34425](https://github.com/PaddlePaddle/Paddle/pull/34425), [#37107](https://github.com/PaddlePaddle/Paddle/pull/37107), [#36946](https://github.com/PaddlePaddle/Paddle/pull/36946), [#36948](https://github.com/PaddlePaddle/Paddle/pull/36948), [#37876](https://github.com/PaddlePaddle/Paddle/pull/37876), [#37916](https://github.com/PaddlePaddle/Paddle/pull/37916), [#37977](https://github.com/PaddlePaddle/Paddle/pull/37977), [#38078](https://github.com/PaddlePaddle/Paddle/pull/38078), [#38861](https://github.com/PaddlePaddle/Paddle/pull/38861), [#39123](https://github.com/PaddlePaddle/Paddle/pull/39123), [#39131](https://github.com/PaddlePaddle/Paddle/pull/39131), [#39748](https://github.com/PaddlePaddle/Paddle/pull/39748), [#39790](https://github.com/PaddlePaddle/Paddle/pull/39790), [#39941](https://github.com/PaddlePaddle/Paddle/pull/39941), [#40239](https://github.com/PaddlePaddle/Paddle/pull/40239), [#40635](https://github.com/PaddlePaddle/Paddle/pull/40635), [#41091](https://github.com/PaddlePaddle/Paddle/pull/41091), [#37409](https://github.com/PaddlePaddle/Paddle/pull/37409), [#37942](https://github.com/PaddlePaddle/Paddle/pull/37942), [#39002](https://github.com/PaddlePaddle/Paddle/pull/39002), [#38109](https://github.com/PaddlePaddle/Paddle/pull/38109), [#37881](https://github.com/PaddlePaddle/Paddle/pull/37881), [#37517](https://github.com/PaddlePaddle/Paddle/pull/37517), [#39870](https://github.com/PaddlePaddle/Paddle/pull/39870), [#40975](https://github.com/PaddlePaddle/Paddle/pull/40975), [#39475](https://github.com/PaddlePaddle/Paddle/pull/39475), [#37304](https://github.com/PaddlePaddle/Paddle/pull/37304), #36910, #37120, #37146, #37215, #37255, #37369, #38258, #38257, #38355, #38853, #38937, #38977, #38946, #39085, #39153, #39228, #38301, #38275, #38506, #38607, #38473, #38632, #38811, #38880, #38996, #38914, #39101) - -- **算子库 C++ API 体系建设**：设计实现了基于 yaml 配置文件的算子定义范式、自动生成了 200 余个 C++运算类 API，供内外部开发者复用，降低了基础运算的重复开发成本。([#37668](https://github.com/PaddlePaddle/Paddle/pull/37668), [#36938](https://github.com/PaddlePaddle/Paddle/pull/36938), [#38172](https://github.com/PaddlePaddle/Paddle/pull/38172), [#38182](https://github.com/PaddlePaddle/Paddle/pull/38182), [#38311](https://github.com/PaddlePaddle/Paddle/pull/38311), [#38438](https://github.com/PaddlePaddle/Paddle/pull/38438), [#39057](https://github.com/PaddlePaddle/Paddle/pull/39057), [#39229](https://github.com/PaddlePaddle/Paddle/pull/39229), [#39281](https://github.com/PaddlePaddle/Paddle/pull/39281), [#39263](https://github.com/PaddlePaddle/Paddle/pull/39263), [#39408](https://github.com/PaddlePaddle/Paddle/pull/39408), [#39436](https://github.com/PaddlePaddle/Paddle/pull/39436), [#39482](https://github.com/PaddlePaddle/Paddle/pull/39482), [#39497](https://github.com/PaddlePaddle/Paddle/pull/39497), [#39651](https://github.com/PaddlePaddle/Paddle/pull/39651), [#39521](https://github.com/PaddlePaddle/Paddle/pull/39521), [#39760](https://github.com/PaddlePaddle/Paddle/pull/39760), [#40060](https://github.com/PaddlePaddle/Paddle/pull/40060), [#40196](https://github.com/PaddlePaddle/Paddle/pull/40196), [#40218](https://github.com/PaddlePaddle/Paddle/pull/40218), [#40640](https://github.com/PaddlePaddle/Paddle/pull/40640), [#40732](https://github.com/PaddlePaddle/Paddle/pull/40732), [#40729](https://github.com/PaddlePaddle/Paddle/pull/40729), [#40840](https://github.com/PaddlePaddle/Paddle/pull/40840), [#40867](https://github.com/PaddlePaddle/Paddle/pull/40867), [#41025](https://github.com/PaddlePaddle/Paddle/pull/41025), [#41368](https://github.com/PaddlePaddle/Paddle/pull/41368)) - -- **算子库兼容各执行体系**：实现新的 InferMeta 及 Kernel 接入原动静态图执行体系、支持原 OpKernel 注册安全移除并迁移为新的 Kernel 形式。([#34425](https://github.com/PaddlePaddle/Paddle/pull/34425), [#38825](https://github.com/PaddlePaddle/Paddle/pull/38825), [#38837](https://github.com/PaddlePaddle/Paddle/pull/38837), [#38842](https://github.com/PaddlePaddle/Paddle/pull/38842), [#38976](https://github.com/PaddlePaddle/Paddle/pull/38976), [#39134](https://github.com/PaddlePaddle/Paddle/pull/39134), [#39140](https://github.com/PaddlePaddle/Paddle/pull/39140), [#39135](https://github.com/PaddlePaddle/Paddle/pull/39135), [#39252](https://github.com/PaddlePaddle/Paddle/pull/39252), [#39222](https://github.com/PaddlePaddle/Paddle/pull/39222), [#39351](https://github.com/PaddlePaddle/Paddle/pull/39351)) - -- **算子库底层数据结构及工具函数与框架解耦**：解除 Phi 在核心数据结构上对 框架的依赖，为后续 Phi 独立编译奠定基础，支持 infrt、自定义 Kernel 等一系列基于 Phi 的建设工作。([#38583](https://github.com/PaddlePaddle/Paddle/pull/38583), [#39188](https://github.com/PaddlePaddle/Paddle/pull/39188), [#39560](https://github.com/PaddlePaddle/Paddle/pull/39560), [#39931](https://github.com/PaddlePaddle/Paddle/pull/39931), [#39169](https://github.com/PaddlePaddle/Paddle/pull/39169), [#38951](https://github.com/PaddlePaddle/Paddle/pull/38951), [#38898](https://github.com/PaddlePaddle/Paddle/pull/38898), [#38873](https://github.com/PaddlePaddle/Paddle/pull/38873), [#38696](https://github.com/PaddlePaddle/Paddle/pull/38696), [#38651](https://github.com/PaddlePaddle/Paddle/pull/38651), [#39359](https://github.com/PaddlePaddle/Paddle/pull/39359), [#39305](https://github.com/PaddlePaddle/Paddle/pull/39305), [#39234](https://github.com/PaddlePaddle/Paddle/pull/39234), [#39098](https://github.com/PaddlePaddle/Paddle/pull/39098), [#39120](https://github.com/PaddlePaddle/Paddle/pull/39120), [#38979](https://github.com/PaddlePaddle/Paddle/pull/38979), [#38899](https://github.com/PaddlePaddle/Paddle/pull/38899), [#38844](https://github.com/PaddlePaddle/Paddle/pull/38844), [#39714](https://github.com/PaddlePaddle/Paddle/pull/39714), [#39729](https://github.com/PaddlePaddle/Paddle/pull/39729), [#39889](https://github.com/PaddlePaddle/Paddle/pull/39889), [#39587](https://github.com/PaddlePaddle/Paddle/pull/39587), [#39558](https://github.com/PaddlePaddle/Paddle/pull/39558), [#39514](https://github.com/PaddlePaddle/Paddle/pull/39514), [#39502](https://github.com/PaddlePaddle/Paddle/pull/39502), [#39300](https://github.com/PaddlePaddle/Paddle/pull/39300), [#39246](https://github.com/PaddlePaddle/Paddle/pull/39246), [#39124](https://github.com/PaddlePaddle/Paddle/pull/39124)) - -- **自定义算子机制与 Phi 整合并完善**：支持在自定义算子编写时调用 Phi 自动生成的 200 余个 C++运算类 API，降低自定义算子开发成本，并进行一系列问题修复。([#37122](https://github.com/PaddlePaddle/Paddle/pull/37122), [#37276](https://github.com/PaddlePaddle/Paddle/pull/37276), [#37281](https://github.com/PaddlePaddle/Paddle/pull/37281), [#37262](https://github.com/PaddlePaddle/Paddle/pull/37281), [#37415](https://github.com/PaddlePaddle/Paddle/pull/37415), [#37423](https://github.com/PaddlePaddle/Paddle/pull/37423), [#37583](https://github.com/PaddlePaddle/Paddle/pull/37683), [#38776](https://github.com/PaddlePaddle/Paddle/pull/38776), [#39353](https://github.com/PaddlePaddle/Paddle/pull/39353), [#41072](https://github.com/PaddlePaddle/Paddle/pull/41072)) - -- **算子规模化迁移改写**：迁移了约 250 个高频算子的前、反向算子内核 Kernel 至新算子库，改写为函数式，支持在 C++端通过调用多个基础 Kernel 函数封装，快速组合实现高性能算子；同时，添加相应的 yaml 算子定义，并接入新动态图执行体系，提升 python API 调度性能。迁移改写的算子包括： - - - sqrt ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - square ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - sin ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - sinh ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - elementwise_fmax ([#40140](https://github.com/PaddlePaddle/Paddle/pull/40140)) - - - elementwise_fmin ([#40140](https://github.com/PaddlePaddle/Paddle/pull/40140)) - - - pool2d ([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - max_pool2d_with_index ([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - pool3d ([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - max_pool3d_with_index ([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - fill_constant ([#36930](https://github.com/PaddlePaddle/Paddle/pull/36930), [#39465](https://github.com/PaddlePaddle/Paddle/pull/39465)) - - - p_norm ([#40819](https://github.com/PaddlePaddle/Paddle/pull/40819)) - - - fill_constant_batch_size_like ([#40784](https://github.com/PaddlePaddle/Paddle/pull/40784)) - - - conv2d ([#39354](https://github.com/PaddlePaddle/Paddle/pull/39354)) - - - conv2d_transpose ([#40675](https://github.com/PaddlePaddle/Paddle/pull/40675), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - conv3d ([#39354](https://github.com/PaddlePaddle/Paddle/pull/39354)) - - - conv3d_transpose ([#40675](https://github.com/PaddlePaddle/Paddle/pull/40675), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - mish ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - gather_nd ([#40090](https://github.com/PaddlePaddle/Paddle/pull/40090), [#40043](https://github.com/PaddlePaddle/Paddle/pull/40043)) - - - gather ([#40500](https://github.com/PaddlePaddle/Paddle/pull/40500)) - - - scatter ([#40090](https://github.com/PaddlePaddle/Paddle/pull/40090), [#40043](https://github.com/PaddlePaddle/Paddle/pull/40043)) - - - scatter_nd_add ([#40090](https://github.com/PaddlePaddle/Paddle/pull/40090), [#40043](https://github.com/PaddlePaddle/Paddle/pull/40043)) - - - sgd ([40045](https://github.com/PaddlePaddle/Paddle/pull/40045)) - - - momentum ([#41319](https://github.com/PaddlePaddle/Paddle/pull/41319)) - - - rmsprop ([#40994](https://github.com/PaddlePaddle/Paddle/pull/40994)) - - - index_sample ([#38130](https://github.com/PaddlePaddle/Paddle/pull/38130), [#38459](https://github.com/PaddlePaddle/Paddle/pull/38459),[#39905](https://github.com/PaddlePaddle/Paddle/pull/39905)) - - - adam ([#40351](https://github.com/PaddlePaddle/Paddle/pull/40351)) - - - layer_norm ([#40193](https://github.com/PaddlePaddle/Paddle/pull/40193)) - - - adagrad ([#40994](https://github.com/PaddlePaddle/Paddle/pull/40994/)) - - - adamax ([#40173](https://github.com/PaddlePaddle/Paddle/pull/40173)) - - - adadelta ([#40173](https://github.com/PaddlePaddle/Paddle/pull/40173)) - - - clip ([#40602](https://github.com/PaddlePaddle/Paddle/pull/40602), [#41661](https://github.com/PaddlePaddle/Paddle/pull/41661), [#41675](https://github.com/PaddlePaddle/Paddle/pull/41675)) - - - ceil ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - cos ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - atan ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - cosh ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - erf ([#40388](https://github.com/PaddlePaddle/Paddle/pull/40388)) - - - asin ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - acos ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - scale ([#39278](https://github.com/PaddlePaddle/Paddle/pull/39278)) - - - elementwise_pow ([#40993](https://github.com/PaddlePaddle/Paddle/pull/40993)) - - - elementwise_sub ([#39225](https://github.com/PaddlePaddle/Paddle/pull/39225), [#37260](https://github.com/PaddlePaddle/Paddle/pull/37260)) - - - round ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - floor ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - pow ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - elementwise_floordiv ([#40993](https://github.com/PaddlePaddle/Paddle/pull/40993)) - - - reciprocal ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - log1p ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - allclose ([#40469](https://github.com/PaddlePaddle/Paddle/pull/40469)) - - - mul ([#40833](https://github.com/PaddlePaddle/Paddle/pull/40833)) - - - elementwise_max ([#40590](https://github.com/PaddlePaddle/Paddle/pull/40590)) - - - elementwise_min ([#40590](https://github.com/PaddlePaddle/Paddle/pull/40590)) - - - elementwise_mod ([#40590](https://github.com/PaddlePaddle/Paddle/pull/40590)) - - - elementwise_add ([#39048](https://github.com/PaddlePaddle/Paddle/pull/39048), [#37043](https://github.com/PaddlePaddle/Paddle/pull/37043)) - - - matmul_v2 ([#36844](https://github.com/PaddlePaddle/Paddle/pull/36844), [#38713](https://github.com/PaddlePaddle/Paddle/pull/38713)) - - - elementwise_mul ([#41042](https://github.com/PaddlePaddle/Paddle/pull/41042), [#40252](https://github.com/PaddlePaddle/Paddle/pull/40252), [#37471](https://github.com/PaddlePaddle/Paddle/pull/37471)) - - - elementwise_div ([#40172](https://github.com/PaddlePaddle/Paddle/pull/40172), [#40039](https://github.com/PaddlePaddle/Paddle/pull/40039), [#37418](https://github.com/PaddlePaddle/Paddle/pull/37418)) - - - SelectedRows ([#39037](https://github.com/PaddlePaddle/Paddle/pull/39037), [#39087](https://github.com/PaddlePaddle/Paddle/pull/39087), [#39128](https://github.com/PaddlePaddle/Paddle/pull/39128), [#39162](https://github.com/PaddlePaddle/Paddle/pull/39162), [#39236](https://github.com/PaddlePaddle/Paddle/pull/39236)) - - - fill_any_like ([#39807](https://github.com/PaddlePaddle/Paddle/pull/39807)) - - - dot ([#38359](https://github.com/PaddlePaddle/Paddle/pull/38359)) - - - sum ([#40873](https://github.com/PaddlePaddle/Paddle/pull/40873)) - - - cumsum ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - diag_v2 ([#39914](https://github.com/PaddlePaddle/Paddle/pull/39914)) - - - auc ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - log_loss ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - one_hot_v2 ([39876](https://github.com/PaddlePaddle/Paddle/pull/39876)) - - - sigmoid_cross_entropy_with_logits ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - bce_loss ([#39868](https://github.com/PaddlePaddle/Paddle/pull/39868)) - - - argsort ([#40151](https://github.com/PaddlePaddle/Paddle/pull/40151)) - - - arg_max ([#40222](https://github.com/PaddlePaddle/Paddle/pull/40222)) - - - arg_min ([#40222](https://github.com/PaddlePaddle/Paddle/pull/40222)) - - - segment_pool ([#40099](https://github.com/PaddlePaddle/Paddle/pull/40099)) - - - frobenius_norm ([#40707](https://github.com/PaddlePaddle/Paddle/pull/40707), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - dist ([#40178](https://github.com/PaddlePaddle/Paddle/pull/40178)) - - - isnan_v2 ([#40076](https://github.com/PaddlePaddle/Paddle/pull/40076)) - - - logical_and ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - logical_not ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - isfinite_v2 ([#40076](https://github.com/PaddlePaddle/Paddle/pull/40076)) - - - logical_or ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - isinf_v2 ([#40076](https://github.com/PaddlePaddle/Paddle/pull/40076)) - - - is_empty ([#39919](https://github.com/PaddlePaddle/Paddle/pull/39919)) - - - logical_xor ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - less_than ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - not_equal ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - equal ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - less_equal ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - equal_all ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - uniform_random ([#39937](https://github.com/PaddlePaddle/Paddle/pull/39937)) - - - randint ([#39876](https://github.com/PaddlePaddle/Paddle/pull/39876), [#41375](https://github.com/PaddlePaddle/Paddle/pull/41375)) - - - randperm ([#41265](https://github.com/PaddlePaddle/Paddle/pull/41265)) - - - unbind ([#39789](https://github.com/PaddlePaddle/Paddle/pull/39789)) - - - bernoulli ([#39590](https://github.com/PaddlePaddle/Paddle/pull/39590)) - - - increment ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - multinomial ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - addmm ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - cholesky ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - where ([#39811](https://github.com/PaddlePaddle/Paddle/pull/39811)) - - - log10 ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - log2 ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - expm1 ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - atan2 ([#39806](https://github.com/PaddlePaddle/Paddle/pull/39806)) - - - gaussian_random ([#39932](https://github.com/PaddlePaddle/Paddle/pull/39932), [#40122](https://github.com/PaddlePaddle/Paddle/pull/40122), [#40191](https://github.com/PaddlePaddle/Paddle/pull/40191)) - - - empty ([#38334](https://github.com/PaddlePaddle/Paddle/pull/38334)) - - - truncated_gaussian_random ([#39971](https://github.com/PaddlePaddle/Paddle/pull/39971), [#40191](https://github.com/PaddlePaddle/Paddle/pull/40191)) - - - mv ([#39861](https://github.com/PaddlePaddle/Paddle/pull/39861), [#39954](https://github.com/PaddlePaddle/Paddle/pull/39954)) - - - tan ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - set_value ([#40195](https://github.com/PaddlePaddle/Paddle/pull/40195), [#40478](https://github.com/PaddlePaddle/Paddle/pull/40478), [#40636](https://github.com/PaddlePaddle/Paddle/pull/40636)) - - - bitwise_and ([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - bitwise_not ([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - bitwise_or ([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - poisson ([#39814](https://github.com/PaddlePaddle/Paddle/pull/39814)) - - - cholesky_solve ([#40387](https://github.com/PaddlePaddle/Paddle/pull/40387)) - - - bitwise_xor ([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - triangular_solve ([#40417](https://github.com/PaddlePaddle/Paddle/pull/40417)) - - - sigmoid ([#40626](https://github.com/PaddlePaddle/Paddle/pull/40626)) - - - atanh ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - softsign ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - thresholded_relu ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - tanh_shrink ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - stanh ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - reduce_mean ([#37559](https://github.com/PaddlePaddle/Paddle/pull/37559)) - - - reduce_max ([#40225](https://github.com/PaddlePaddle/Paddle/pull/40225)) - - - reduce_min ([#40374](https://github.com/PaddlePaddle/Paddle/pull/40374)) - - - mean ([#40872](https://github.com/PaddlePaddle/Paddle/pull/40872), [#41319](https://github.com/PaddlePaddle/Paddle/pull/41319)) - - - reduce_all ([#40374](https://github.com/PaddlePaddle/Paddle/pull/40374)) - - - reduce_any ([#40374](https://github.com/PaddlePaddle/Paddle/pull/40374)) - - - logsumexp ([#40790](https://github.com/PaddlePaddle/Paddle/pull/40790)) - - - softshrink ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - range ([#41265](https://github.com/PaddlePaddle/Paddle/pull/41265), [#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - stack ([#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - tile ([#40371](https://github.com/PaddlePaddle/Paddle/pull/40371)) - - - unique ([#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - unstack ([#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - slice ([#40736](https://github.com/PaddlePaddle/Paddle/pull/40736)) - - - transpose2 ([#39327](https://github.com/PaddlePaddle/Paddle/pull/39327)) - - - unsqueeze2 ([#40596](https://github.com/PaddlePaddle/Paddle/pull/40596)) - - - squeeze2 ([#40596](https://github.com/PaddlePaddle/Paddle/pull/40596)) - - - strided_slice ([#40708](https://github.com/PaddlePaddle/Paddle/pull/40708)) - - - softmax ([#39547](https://github.com/PaddlePaddle/Paddle/pull/39547)) - - - leaky_relu ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - gelu ([#40393](https://github.com/PaddlePaddle/Paddle/pull/40393)) - - - prelu ([#40393](https://github.com/PaddlePaddle/Paddle/pull/40393)) - - - log_softmax ([#40393](https://github.com/PaddlePaddle/Paddle/pull/40393)) - - - elu ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - logsigmoid ([#40626](https://github.com/PaddlePaddle/Paddle/pull/40626)) - - - psroi_pool ([#40353](https://github.com/PaddlePaddle/Paddle/pull/40353), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - kthvalue（[#40575](https://github.com/PaddlePaddle/Paddle/pull/40575)) - - - mode ([#40571](https://github.com/PaddlePaddle/Paddle/pull/40571)) - - - yolo_box ([#40112](https://github.com/PaddlePaddle/Paddle/pull/40112)) - - - yolov3_loss ([#40944](https://github.com/PaddlePaddle/Paddle/pull/40944)) - - - temporal_shift ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - depthwise_conv2d ([#39354](https://github.com/PaddlePaddle/Paddle/pull/39354)) - - - pad3d ([#40701](https://github.com/PaddlePaddle/Paddle/pull/40701)) - - - pad ([#40012](https://github.com/PaddlePaddle/Paddle/pull/40012)) - - - greater_equal ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - kldiv_loss ([#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - isclose ([#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - silu ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - unfold ([#39778](https://github.com/PaddlePaddle/Paddle/pull/39778)) - - - batch_norm ([39347](https://github.com/PaddlePaddle/Paddle/pull/39347)) - - - norm ([#39324](https://github.com/PaddlePaddle/Paddle/pull/39324)) - - - roi_pool ([#40574](https://github.com/PaddlePaddle/Paddle/pull/40574), [#40682](https://github.com/PaddlePaddle/Paddle/pull/40682), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - roi_align ([#40382](https://github.com/PaddlePaddle/Paddle/pull/40382), [#40556](https://github.com/PaddlePaddle/Paddle/pull/40556), [#41402](https://github.com/PaddlePaddle/Paddle/pull/41402)) - - - deformable_conv ([#40700](https://github.com/PaddlePaddle/Paddle/pull/40700), [#40794](https://github.com/PaddlePaddle/Paddle/pull/40794), [#41644](https://github.com/PaddlePaddle/Paddle/pull/41644)) - - - deformable_conv_v1 ([#40794](https://github.com/PaddlePaddle/Paddle/pull/40794), [#41644](https://github.com/PaddlePaddle/Paddle/pull/41644)) - - - label_smooth ([#39796](https://github.com/PaddlePaddle/Paddle/pull/39796)) - - - grid_sampler ([#40585](https://github.com/PaddlePaddle/Paddle/pull/40585)) - - - greater_than ([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - pixel_shuffle ([#39949](https://github.com/PaddlePaddle/Paddle/pull/39949), [#39712](https://github.com/PaddlePaddle/Paddle/pull/39712)) - - - nearest_interp_v2 ([#40855](https://github.com/PaddlePaddle/Paddle/pull/40855)) - - - bilinear_interp_v2 ([#40855](https://github.com/PaddlePaddle/Paddle/pull/40855)) - - - softmax_with_cross_entropy ([#40832](https://github.com/PaddlePaddle/Paddle/pull/40832)) - - - rnn ([#41007](https://github.com/PaddlePaddle/Paddle/pull/41007)) - - - reverse ([#40791](https://github.com/PaddlePaddle/Paddle/pull/40791)) - - - trace ([#39510](https://github.com/PaddlePaddle/Paddle/pull/39510)) - - - kron ([#40427](https://github.com/PaddlePaddle/Paddle/pull/40427)) - - - accuracy ([#39982](https://github.com/PaddlePaddle/Paddle/pull/39982)) - - - gather_tree ([#40082](https://github.com/PaddlePaddle/Paddle/pull/40082), [#39844](https://github.com/PaddlePaddle/Paddle/pull/39844)) - - - dropout ([#40148](https://github.com/PaddlePaddle/Paddle/pull/40148)) - - - bincount ([#39947](https://github.com/PaddlePaddle/Paddle/pull/39947)) - - - warpctc ([#41389](https://github.com/PaddlePaddle/Paddle/pull/41389), [#40023](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/40023)) - - - multiplex ([#40007](https://github.com/PaddlePaddle/Paddle/pull/40007), [#40102](https://github.com/PaddlePaddle/Paddle/pull/40102)) - - - qr ([#40007](https://github.com/PaddlePaddle/Paddle/pull/40007), [#40007](https://github.com/PaddlePaddle/Paddle/pull/40007)) - - - assign_value ([#40967](https://github.com/PaddlePaddle/Paddle/pull/40967)) - - - assign ([#40022](https://github.com/PaddlePaddle/Paddle/pull/40022)) - - - cast ([#37610](https://github.com/PaddlePaddle/Paddle/pull/37610)) - - - tril_triu ([#40007](https://github.com/PaddlePaddle/Paddle/pull/40007), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - where_index ([#40255](https://github.com/PaddlePaddle/Paddle/pull/40255)) - - - index_select ([#40260](https://github.com/PaddlePaddle/Paddle/pull/40260), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - roll ([#40257](https://github.com/PaddlePaddle/Paddle/pull/40257), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - cumprod (熊昆 [#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - shard_index ([#40254](https://github.com/PaddlePaddle/Paddle/pull/40254)) - - - reshape2 ([#40914](https://github.com/PaddlePaddle/Paddle/pull/40914), [#39631](https://github.com/PaddlePaddle/Paddle/pull/39631), [#38833](https://github.com/PaddlePaddle/Paddle/pull/38833), [#37164](https://github.com/PaddlePaddle/Paddle/pull/37164)) - - - flip ([#39822](https://github.com/PaddlePaddle/Paddle/pull/39822), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - eye ([#39712](https://github.com/PaddlePaddle/Paddle/pull/39712), [#40105](https://github.com/PaddlePaddle/Paddle/pull/40105), [#41476](https://github.com/PaddlePaddle/Paddle/pull/41476)) - - - lookup_table_v2 ([#39901](https://github.com/PaddlePaddle/Paddle/pull/39901)) - - - searchsorted ([#40520](https://github.com/PaddlePaddle/Paddle/pull/40520), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - adamw ([#40351](https://github.com/PaddlePaddle/Paddle/pull/40351)) - - - tanh ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - cross ([#39829](https://github.com/PaddlePaddle/Paddle/pull/39829)) - - - concat ([#38955](https://github.com/PaddlePaddle/Paddle/pull/38955), [#41112](https://github.com/PaddlePaddle/Paddle/pull/41112)) - - - split ([#39060](https://github.com/PaddlePaddle/Paddle/pull/39060)) - - - linspace ([#40124](https://github.com/PaddlePaddle/Paddle/pull/40124)) - - - huber_loss ([#39761](https://github.com/PaddlePaddle/Paddle/pull/39761)) - - - hierarchical_sigmoid ([#40553](https://github.com/PaddlePaddle/Paddle/pull/40553)) - - - nll_loss ([#39936](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/39936)) - - - graph_send_recv ([#40092](https://github.com/PaddlePaddle/Paddle/pull/40092), [#40320](https://github.com/PaddlePaddle/Paddle/pull/40320)) - - - abs ([#39492](https://github.com/PaddlePaddle/Paddle/pull/39492), [#39762](https://github.com/PaddlePaddle/Paddle/pull/39762)) - - - exp ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - rsqrt ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - viterbi_decode ([#40186](https://github.com/PaddlePaddle/Paddle/pull/40186)) - - - conj ([#38247](https://github.com/PaddlePaddle/Paddle/pull/38247)) - - - real ([#39777](https://github.com/PaddlePaddle/Paddle/pull/39777), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - imag ([#39777](https://github.com/PaddlePaddle/Paddle/pull/39777), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - take_along_axis ([#39959](https://github.com/PaddlePaddle/Paddle/pull/39959), [#40270](https://github.com/PaddlePaddle/Paddle/pull/40270), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - put_along_axis ([#39959](https://github.com/PaddlePaddle/Paddle/pull/39959), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - lgamma ([#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - relu ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - maxout ([#39959](https://github.com/PaddlePaddle/Paddle/pull/39959), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - log ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - bilinear_tensor_product ([#39903](https://github.com/PaddlePaddle/Paddle/pull/39903)) - - - flatten_contiguous_range ([#38712](https://github.com/PaddlePaddle/Paddle/pull/38712), [#36957](https://github.com/PaddlePaddle/Paddle/pull/36957), [#41345](https://github.com/PaddlePaddle/Paddle/pull/41345)) - - - matrix_rank ([#40074](https://github.com/PaddlePaddle/Paddle/pull/40074), [#40519](https://github.com/PaddlePaddle/Paddle/pull/40519), [#41466](https://github.com/PaddlePaddle/Paddle/pull/41466)) - - - logit ([#37844](https://github.com/PaddlePaddle/Paddle/pull/37844)) - - - lerp ([#40105](https://github.com/PaddlePaddle/Paddle/pull/40105), [#39524](https://github.com/PaddlePaddle/Paddle/pull/39524)) - - - erfinv ([#39949](https://github.com/PaddlePaddle/Paddle/pull/39949), [#39712](https://github.com/PaddlePaddle/Paddle/pull/39712)) - - - broadcast_tensors ([#40047](https://github.com/PaddlePaddle/Paddle/pull/40047)) - - - gumbel_softmax ([#39873](https://github.com/PaddlePaddle/Paddle/pull/39873)) - - - diagonal ([#39575](https://github.com/PaddlePaddle/Paddle/pull/39575)) - - - trunc ([#39543](https://github.com/PaddlePaddle/Paddle/pull/39543), [#39772](https://github.com/PaddlePaddle/Paddle/pull/39772)) - - - multi_dot ([#40038](https://github.com/PaddlePaddle/Paddle/pull/40038)) - - - matrix_power ([#40231](https://github.com/PaddlePaddle/Paddle/pull/40231)) - - - digamma ([#39240](https://github.com/PaddlePaddle/Paddle/pull/39240)) - - - masked_select ([#39193](https://github.com/PaddlePaddle/Paddle/pull/39193)) - - - determinant ([#40539](https://github.com/PaddlePaddle/Paddle/pull/40539)) - - - eigh ([#40213](https://github.com/PaddlePaddle/Paddle/pull/40213)) - - - size ([#39949](https://github.com/PaddlePaddle/Paddle/pull/39949), [#39712](https://github.com/PaddlePaddle/Paddle/pull/39712)) - - - shape ([#40248](https://github.com/PaddlePaddle/Paddle/pull/40248)) - - - reduce_sum ([#37559](https://github.com/PaddlePaddle/Paddle/pull/37559), [#41295](https://github.com/PaddlePaddle/Paddle/pull/41295)) - - - reduce_prod ([#39844](https://github.com/PaddlePaddle/Paddle/pull/39844)) - - - histogram ([#39496](https://github.com/PaddlePaddle/Paddle/pull/39496)) - - - meshgrid ([#41411](https://github.com/PaddlePaddle/Paddle/pull/41411)) - - - brelu ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - hard_swish ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - hard_shrink ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - selu (熊昆 [#39819](https://github.com/PaddlePaddle/Paddle/pull/39819)) - - - expand_v2 ([#39471](https://github.com/PaddlePaddle/Paddle/pull/39471)) - - - top_k_v2 ([#40064](https://github.com/PaddlePaddle/Paddle/pull/40064)) - - - expand_as_v2 ([#40373](https://github.com/PaddlePaddle/Paddle/pull/40373)) - - - swish ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - hard_sigmoid ([#40626](https://github.com/PaddlePaddle/Paddle/pull/40626)) - - - exp, det, assign, gaussian_random, matrix_rank, eye, deformable_conv。([#41755]exp, det, assign, gaussian_random, matrix_rank, eye, deformable_conv。([#41755](https://github.com/PaddlePaddle/Paddle/pull/41755), [#41737](https://github.com/PaddlePaddle/Paddle/pull/41737) - -#### 新动态图执行机制 - -针对飞桨原动态图执行机制的调度性能、二次开发能力差的问题，我们重构了动态图的底层执行机制。通过全新的调用执行方式，配合 Phi 算子库进行高效的运行时执行，对于 Phi 算子库支持的算子，切换到新动态图模式能体验到调度性能有较大幅度的提升。但是由于整体框架执行机制升级的工作量巨大，且该部分工作耦合了大量 Phi 算子库的工作， 因此在这个版本下我们仍未默认使用该执行方式。如果想要试用可以通过设置环境变量 `FLAGS_enable_eager_mode=1` 来切换使用。具体包括如下内容： - -- **新动态图执行机制基础架构、核心组件与机制实现**：静态化动态图相关执行代码，将原本的同质化的算子构建变成针对不同 Phi API 的特异化调用从而极大的优化了调度开销。([#36059](https://github.com/PaddlePaddle/Paddle/pull/36059), [#37323](https://github.com/PaddlePaddle/Paddle/pull/37323), [#37556](https://github.com/PaddlePaddle/Paddle/pull/37556), [#37555](https://github.com/PaddlePaddle/Paddle/pull/37555), [#37478](https://github.com/PaddlePaddle/Paddle/pull/37478), [#37458](https://github.com/PaddlePaddle/Paddle/pull/37458), [#37479](https://github.com/PaddlePaddle/Paddle/pull/37479), [#37599](https://github.com/PaddlePaddle/Paddle/pull/37599), [#37659](https://github.com/PaddlePaddle/Paddle/pull/37659), [#37654](https://github.com/PaddlePaddle/Paddle/pull/37654), [#39200](https://github.com/PaddlePaddle/Paddle/pull/39200), [#39309](https://github.com/PaddlePaddle/Paddle/pull/39309), [#39319](https://github.com/PaddlePaddle/Paddle/pull/39319), [#39414](https://github.com/PaddlePaddle/Paddle/pull/39414), [#39504](https://github.com/PaddlePaddle/Paddle/pull/39504), [#39526](https://github.com/PaddlePaddle/Paddle/pull/39526), [#39878](https://github.com/PaddlePaddle/Paddle/pull/39878), [#39963](https://github.com/PaddlePaddle/Paddle/pull/39963)) - -- **新动态图执行机制子功能开发、适配**：支持了更加灵活，更加完备的动态图子功能例如 hook，pylayer，double_grad, inplace，amp 等等。([#41396](https://github.com/PaddlePaddle/Paddle/pull/41396), [#40400](https://github.com/PaddlePaddle/Paddle/pull/40400), [#40695](https://github.com/PaddlePaddle/Paddle/pull/40695), [#41043](https://github.com/PaddlePaddle/Paddle/pull/41043), [#40915](https://github.com/PaddlePaddle/Paddle/pull/40915), [#41104](https://github.com/PaddlePaddle/Paddle/pull/41104), [#41350](https://github.com/PaddlePaddle/Paddle/pull/41350), [#41209](https://github.com/PaddlePaddle/Paddle/pull/41209), [#40830](https://github.com/PaddlePaddle/Paddle/pull/40830), [#40891](https://github.com/PaddlePaddle/Paddle/pull/40891), [#36814](https://github.com/PaddlePaddle/Paddle/pull/36814), [#37377](https://github.com/PaddlePaddle/Paddle/pull/37377), [#37193](https://github.com/PaddlePaddle/Paddle/pull/37193), [#36965](https://github.com/PaddlePaddle/Paddle/pull/36965), [#37810](https://github.com/PaddlePaddle/Paddle/pull/37810), [#36837](https://github.com/PaddlePaddle/Paddle/pull/36837), [#38488](https://github.com/PaddlePaddle/Paddle/pull/38488), [#39282](https://github.com/PaddlePaddle/Paddle/pull/39282), [#39449](https://github.com/PaddlePaddle/Paddle/pull/39449), [#39531](https://github.com/PaddlePaddle/Paddle/pull/39531), [#39638](https://github.com/PaddlePaddle/Paddle/pull/39638), [#39674](https://github.com/PaddlePaddle/Paddle/pull/39674), [#39893](https://github.com/PaddlePaddle/Paddle/pull/39893), [#40170](https://github.com/PaddlePaddle/Paddle/pull/40170), [#40693](https://github.com/PaddlePaddle/Paddle/pull/40693), [#40937](https://github.com/PaddlePaddle/Paddle/pull/40937), [#41016](https://github.com/PaddlePaddle/Paddle/pull/41016), [#41051](https://github.com/PaddlePaddle/Paddle/pull/41051), [#41121](https://github.com/PaddlePaddle/Paddle/pull/41121), [#41198](https://github.com/PaddlePaddle/Paddle/pull/41198), [#41287](https://github.com/PaddlePaddle/Paddle/pull/41287), [#41380](https://github.com/PaddlePaddle/Paddle/pull/41380), [#41306](https://github.com/PaddlePaddle/Paddle/pull/41306), [#41387](https://github.com/PaddlePaddle/Paddle/pull/41387), [#40623](https://github.com/PaddlePaddle/Paddle/pull/40623), [#40945](https://github.com/PaddlePaddle/Paddle/pull/40945), [#39282](https://github.com/PaddlePaddle/Paddle/pull/39282), [#39449](https://github.com/PaddlePaddle/Paddle/pull/39449), [#38488](https://github.com/PaddlePaddle/Paddle/pull/38488)) - -- **新动态图执行的自动代码生成机制**：当我们为了将大量的同质化算子的计算和调度逻辑分化成不同的特异化的调度逻辑时，我们发现这是一个非常庞大的工作，因此我们引入了全新的自动代码生成逻辑来生成代码从而简化动态图的运行时逻辑。同时，为了能够适配之前框架中的各类运行时逻辑，我们也利用了一些复杂的编译手段来运行时的获取信息从而生成更加准确的调度代码。([#37574](https://github.com/PaddlePaddle/Paddle/pull/37574), [#37575](https://github.com/PaddlePaddle/Paddle/pull/37575), [#37639](https://github.com/PaddlePaddle/Paddle/pull/37639), [#37723](https://github.com/PaddlePaddle/Paddle/pull/37723), [#37753](https://github.com/PaddlePaddle/Paddle/pull/37753), [#37812](https://github.com/PaddlePaddle/Paddle/pull/37812), [#37837](https://github.com/PaddlePaddle/Paddle/pull/37837), [#37910](https://github.com/PaddlePaddle/Paddle/pull/37910), [#37943](https://github.com/PaddlePaddle/Paddle/pull/37943), [#37992](https://github.com/PaddlePaddle/Paddle/pull/37992), [#37959](https://github.com/PaddlePaddle/Paddle/pull/37959), [#38017](https://github.com/PaddlePaddle/Paddle/pull/38017), [#37969](https://github.com/PaddlePaddle/Paddle/pull/37969), [#38160](https://github.com/PaddlePaddle/Paddle/pull/38160), [#38085](https://github.com/PaddlePaddle/Paddle/pull/38085), [#38562](https://github.com/PaddlePaddle/Paddle/pull/38562), [#38573](https://github.com/PaddlePaddle/Paddle/pull/38573), [#39192](https://github.com/PaddlePaddle/Paddle/pull/39192), [#39215](https://github.com/PaddlePaddle/Paddle/pull/39215), [#39355](https://github.com/PaddlePaddle/Paddle/pull/39355), [#39358](https://github.com/PaddlePaddle/Paddle/pull/39358), [#39328](https://github.com/PaddlePaddle/Paddle/pull/39328), [#39233](https://github.com/PaddlePaddle/Paddle/pull/39233), [#39628](https://github.com/PaddlePaddle/Paddle/pull/39628), [#39767](https://github.com/PaddlePaddle/Paddle/pull/39767), [#39743](https://github.com/PaddlePaddle/Paddle/pull/39743), [#39897](https://github.com/PaddlePaddle/Paddle/pull/39897), [#39797](https://github.com/PaddlePaddle/Paddle/pull/39797), [#39997](https://github.com/PaddlePaddle/Paddle/pull/39997), [#40058](https://github.com/PaddlePaddle/Paddle/pull/40058), [#40080](https://github.com/PaddlePaddle/Paddle/pull/40080), [#40107](https://github.com/PaddlePaddle/Paddle/pull/40107), [#39962](https://github.com/PaddlePaddle/Paddle/pull/39962), [#40132](https://github.com/PaddlePaddle/Paddle/pull/40132), [#40276](https://github.com/PaddlePaddle/Paddle/pull/40276), [#40266](https://github.com/PaddlePaddle/Paddle/pull/40266), [#40480](https://github.com/PaddlePaddle/Paddle/pull/40480), [#40482](https://github.com/PaddlePaddle/Paddle/pull/40482), [#40368](https://github.com/PaddlePaddle/Paddle/pull/40368), [#40650](https://github.com/PaddlePaddle/Paddle/pull/40650), [#40815](https://github.com/PaddlePaddle/Paddle/pull/40815), [#40907](https://github.com/PaddlePaddle/Paddle/pull/40907), [#40935](https://github.com/PaddlePaddle/Paddle/pull/40935), [#41089](https://github.com/PaddlePaddle/Paddle/pull/41089)) - -- **新动态图执行机制接入主框架，联合调试**：我们目前利用一些环境变量区分静态图模式和动态图模式（含新动态图和老动态图模式），这些模式下我们已经适配了大部分的动态图的逻辑，但是仍有大量问题正在修复中。([#37638](https://github.com/PaddlePaddle/Paddle/pull/37638), [#37643](https://github.com/PaddlePaddle/Paddle/pull/37643), [#37653](https://github.com/PaddlePaddle/Paddle/pull/37653), [#38314](https://github.com/PaddlePaddle/Paddle/pull/38314), [#38337](https://github.com/PaddlePaddle/Paddle/pull/38337), [#38338](https://github.com/PaddlePaddle/Paddle/pull/38338), [#39164](https://github.com/PaddlePaddle/Paddle/pull/39164), [#39326](https://github.com/PaddlePaddle/Paddle/pull/39326), [#40391](https://github.com/PaddlePaddle/Paddle/pull/40391), [#40201](https://github.com/PaddlePaddle/Paddle/pull/40201), [#40854](https://github.com/PaddlePaddle/Paddle/pull/40854), [#40887](https://github.com/PaddlePaddle/Paddle/pull/40887)) - -- **更新了动态图下的一些判断逻辑，支持兼容形态下的动态图快速执行路径**：([#40786](https://github.com/PaddlePaddle/Paddle/pull/40786)) - - - 非静态图模式（目前的过渡方案）：`_non_static_mode()`。 - - - 在动态图模式下且判断在新动态图（推荐的判断逻辑）：`_in_dygrah_mode()`。 - - - 在动态图模式下且判断在老动态图（不推荐的判断逻辑，在将来的版本中将废弃）：`_in_legacy_dygraph()`。 - - - 在动态图模式下开启老动态图并关闭新动态图：`_enable_legacy_dygraph()` 或者退出 `_test_eager_guard()`。 - - - 在动态图模式下开启新动态图并关闭老动态图：`_disable_legacy_dygraph()` 或者 `with _test_eager_guard()`。 - - - 在静态图或者动态图模式下判断在新动态图：`_in_eager_without_dygraph_check()`。 - -- **动态图重构后支持 inplace 策略**：输入与输出为同一个 Tensor。 - - - 为动态图重构中间态适配 inplace 策略。([#40400](https://github.com/PaddlePaddle/Paddle/pull/40400)) - - - 为动态图重构最终态适配 inplace 策略。([#40695](https://github.com/PaddlePaddle/Paddle/pull/40695)) - - - 动态图重构后，为 PyLayer 功能添加 inplace 策略。([#41043](https://github.com/PaddlePaddle/Paddle/pull/41043)) - - - 动态图重构后，为 Tensor 的 setitem 功能添加 inplace 策略。([#40915](https://github.com/PaddlePaddle/Paddle/pull/40915)) - - - 动态图重构后添加`_reset_grad_inplace_version`接口，将 Tensor 的梯度的 inplace version 置为 0。([#41101](https://github.com/PaddlePaddle/Paddle/pull/41101)) - - - 反向计算过程中如果不需要前向 Tensor 的值（no need buffer 属性），则不需要对该 Tensor 进行 inplace version 的检测操作。 为 no_need_buffer 的 Tensor 跳过 inplace version 的检查。([#41350](https://github.com/PaddlePaddle/Paddle/pull/41350)) - - - 统一动态图重构后与重构前对 inplace version 检查的报错信息。([#41209](https://github.com/PaddlePaddle/Paddle/pull/41209)) - -- **动态图重构后支持 view 策略**：输入与输出 Tensor 共享底层数据。 - - - 为动态图重构中间态适配 view 机制。包括`reshape`、`squeeze`、`unsqueeze`、`flatten` API。([#40830](https://github.com/PaddlePaddle/Paddle/pull/40830)) - - - 为动态图重构最终态适配 view 机制。包括`reshape` API。([#40891](https://github.com/PaddlePaddle/Paddle/pull/40891)) - -- **添加支持新动态图 eager Tensor 在 python 端的 weakref**。([#41797](https://github.com/PaddlePaddle/Paddle/pull/41797)) - -- **增强新动态图 DoubleGrad 功能**，支持基础的 DoubleGrad 功能。([#41893](https://github.com/PaddlePaddle/Paddle/pull/41893), [#41894](https://github.com/PaddlePaddle/Paddle/pull/41894), [#41895](https://github.com/PaddlePaddle/Paddle/pull/41895)) - -- **新增 `core.eager.StringTensor` 接口**，支持在 python 端构造 StringTensor 以及使用 StringTensor 相关 API。([#41039](https://github.com/PaddlePaddle/Paddle/pull/41039)) - -- **为 `core.eager.Tensor` 新增 `*grad_name` 和 `_grad_value` API**，返回梯度的名称和值。([#41990](https://github.com/PaddlePaddle/Paddle/pull/41990)) - -- **为动态图中间态添加对 no_need_buffer 属性的处理**。在 inplace 反向检查操作中，会跳过具有 no_need_buffer 属性的 Tensor 的检查。([#41720](https://github.com/PaddlePaddle/Paddle/pull/41720)) - - -#### 全新静态图执行器 -为了解决飞桨原静态图执行器在部分场景下调度性能不够理想，不便于扩展多 stream 等问题，我们实现了全新的性能优越，易于扩展的静态图执行器，充分利用了多 stream、多线程的异步调度能力。新执行器相当于原执行器是兼容升级，目前已在单机单卡场景下默认使用，用户不需要在训练代码中做任何修改即可自动使用。当然，我们也提供了接口来切换回原执行器，用户可以通过设置环境变量 `FLAGS_USE_STANDALONE_EXECUTOR=false` 来切换回原执行器。([#41179](https://github.com/PaddlePaddle/Paddle/pull/41179)) 主要内容如下： - -- 基础组件：用于执行器中多线程算子调度的高性能线程池 ([#35470](https://github.com/PaddlePaddle/Paddle/pull/35470), [#35930](https://github.com/PaddlePaddle/Paddle/pull/35930), [#36030](https://github.com/PaddlePaddle/Paddle/pull/36030), [#36480](https://github.com/PaddlePaddle/Paddle/pull/36480), [#36688](https://github.com/PaddlePaddle/Paddle/pull/36688), [#36740](https://github.com/PaddlePaddle/Paddle/pull/36740), [#38335](https://github.com/PaddlePaddle/Paddle/pull/38335), [#40770](https://github.com/PaddlePaddle/Paddle/pull/40770)) 及线程协同组件 ([#38779](https://github.com/PaddlePaddle/Paddle/pull/38779), [#40876](https://github.com/PaddlePaddle/Paddle/pull/40876), [#40912](https://github.com/PaddlePaddle/Paddle/pull/40912))，算子执行后及时地显存回收 ([#37642](https://github.com/PaddlePaddle/Paddle/pull/37642), [#39617](https://github.com/PaddlePaddle/Paddle/pull/39617), [#40859](https://github.com/PaddlePaddle/Paddle/pull/40859))，并行执行器新依赖分析算法 ([#37231](https://github.com/PaddlePaddle/Paddle/pull/37231)) 等。 - -- 调度逻辑：优化执行器中算子的调度方法，支持多 stream 的多线程异步调度机制，将数据类型、设备、布局等转换改为算子调度以提升性能，支持缓存算子 Kernel 选择，支持选择全新 Phi 算子等。([#35024](https://github.com/PaddlePaddle/Paddle/pull/35024), [#34922](https://github.com/PaddlePaddle/Paddle/pull/34922), [#35711](https://github.com/PaddlePaddle/Paddle/pull/35711), [#35928](https://github.com/PaddlePaddle/Paddle/pull/35928), [#39458](https://github.com/PaddlePaddle/Paddle/pull/39458)，[#36899](https://github.com/PaddlePaddle/Paddle/pull/36899))。 - -- 接口兼容：兼容原执行器的用户接口和功能，如对齐 python 端 Executor.run()、支持 Scope 中管理 Tensor 等，确保用户可以无感知地切换新执行器。([#37278](https://github.com/PaddlePaddle/Paddle/pull/37278), [#37379](https://github.com/PaddlePaddle/Paddle/pull/37379), [#37445](https://github.com/PaddlePaddle/Paddle/pull/37445), [#37510](https://github.com/PaddlePaddle/Paddle/pull/37510), [#40955](https://github.com/PaddlePaddle/Paddle/pull/40955), [#41778](https://github.com/PaddlePaddle/Paddle/pull/41178), [#41058](https://github.com/PaddlePaddle/Paddle/pull/41058), [#38584](https://github.com/PaddlePaddle/Paddle/pull/38584), [#37957](https://github.com/PaddlePaddle/Paddle/pull/37957), [#37672](https://github.com/PaddlePaddle/Paddle/pull/37672), [#37474](https://github.com/PaddlePaddle/Paddle/pull/37474), [#37085](https://github.com/PaddlePaddle/Paddle/pull/37085), [#37061](https://github.com/PaddlePaddle/Paddle/pull/37061), [#36945](https://github.com/PaddlePaddle/Paddle/pull/36945)) - -- 增强多线程场景下调试和报错功能，将子线程的报错捕获到主线程中统一抛出，以提升用户体验。([#36692](https://github.com/PaddlePaddle/Paddle/pull/36692)，[#36802](https://github.com/PaddlePaddle/Paddle/pull/36802)) - -- 修复新执行器通信流重置 Allocator 中 stream 缓存信息的问题，减少跨 stream 场景下的 RecordStream 开销，优化后 DeepFM 模型性能提升约 8%。([#42046](https://github.com/PaddlePaddle/Paddle/pull/42046)) - -- 优化新执行器算子间的依赖分析方法，提升运行性能；为 send/recv 通信算子建立正确依赖以支持流水线并行。([#42009](https://github.com/PaddlePaddle/Paddle/pull/42009)) - - -#### 分布式训练 - -- 集合通信多机多卡训练基础功能 - - - 新增弹性功能（含节点故障、扩容、缩容），提升分布式的容错能力。([#36684](https://github.com/PaddlePaddle/Paddle/pull/36684), [#37177](https://github.com/PaddlePaddle/Paddle/pull/37177), [#37781](https://github.com/PaddlePaddle/Paddle/pull/37781)) - - - Launch 启动模块，重构并新增 `master` 协同和节点个数 `nnodes` 定义，提升分布式启动易用性。([#40086](https://github.com/PaddlePaddle/Paddle/pull/40086), [#40568](https://github.com/PaddlePaddle/Paddle/pull/40568), [#40782](https://github.com/PaddlePaddle/Paddle/pull/40782), [#40844](https://github.com/PaddlePaddle/Paddle/pull/40844), [#40936](https://github.com/PaddlePaddle/Paddle/pull/40936), [#41190](https://github.com/PaddlePaddle/Paddle/pull/41190), [#41314](https://github.com/PaddlePaddle/Paddle/pull/41314)) - - - 新增对 GPU/NPU/XPU 多种硬件的异构训练的支持。([#37613](https://github.com/PaddlePaddle/Paddle/pull/37613), [#37998](https://github.com/PaddlePaddle/Paddle/pull/37998)) - - - 新增 fleet_executor 异步流水执行器。([#36966](https://github.com/PaddlePaddle/Paddle/pull/36966), [#37049](https://github.com/PaddlePaddle/Paddle/pull/37049), [#37087](https://github.com/PaddlePaddle/Paddle/pull/37087), [#37126](https://github.com/PaddlePaddle/Paddle/pull/37126), [#37150](https://github.com/PaddlePaddle/Paddle/pull/37150), [#37203](https://github.com/PaddlePaddle/Paddle/pull/37203), [#37167](https://github.com/PaddlePaddle/Paddle/pull/37167), [#37282](https://github.com/PaddlePaddle/Paddle/pull/37282), [#37319](https://github.com/PaddlePaddle/Paddle/pull/37319), [#37462](https://github.com/PaddlePaddle/Paddle/pull/37462), [#37507](https://github.com/PaddlePaddle/Paddle/pull/37507), [#37533](https://github.com/PaddlePaddle/Paddle/pull/37533), [#37576](https://github.com/PaddlePaddle/Paddle/pull/37576), [#37605](https://github.com/PaddlePaddle/Paddle/pull/37605), [#37691](https://github.com/PaddlePaddle/Paddle/pull/37691), [#37742](https://github.com/PaddlePaddle/Paddle/pull/37742), [#37783](https://github.com/PaddlePaddle/Paddle/pull/37783), [#37809](https://github.com/PaddlePaddle/Paddle/pull/37809), [#37862](https://github.com/PaddlePaddle/Paddle/pull/37862), [#37882](https://github.com/PaddlePaddle/Paddle/pull/37882), [#37934](https://github.com/PaddlePaddle/Paddle/pull/37934), [#38024](https://github.com/PaddlePaddle/Paddle/pull/38024), [#38083](https://github.com/PaddlePaddle/Paddle/pull/38083), [#38164](https://github.com/PaddlePaddle/Paddle/pull/38164), [#38261](https://github.com/PaddlePaddle/Paddle/pull/38261), [#38290](https://github.com/PaddlePaddle/Paddle/pull/38290), [#40607](https://github.com/PaddlePaddle/Paddle/pull/40607), [#37093](https://github.com/PaddlePaddle/Paddle/pull/37093), [#37106](https://github.com/PaddlePaddle/Paddle/pull/37106), [#37143](https://github.com/PaddlePaddle/Paddle/pull/37143), [#37338](https://github.com/PaddlePaddle/Paddle/pull/37338), [#37376](https://github.com/PaddlePaddle/Paddle/pull/37376), [#37485](https://github.com/PaddlePaddle/Paddle/pull/37485), [#37531](https://github.com/PaddlePaddle/Paddle/pull/37531), [#37623](https://github.com/PaddlePaddle/Paddle/pull/37623), [#37693](https://github.com/PaddlePaddle/Paddle/pull/37693), [#37755](https://github.com/PaddlePaddle/Paddle/pull/37755), [#37807](https://github.com/PaddlePaddle/Paddle/pull/37807), [#37889](https://github.com/PaddlePaddle/Paddle/pull/37889), [#38420](https://github.com/PaddlePaddle/Paddle/pull/38420), [#38539](https://github.com/PaddlePaddle/Paddle/pull/38539), [#36892](https://github.com/PaddlePaddle/Paddle/pull/36892), [#37084](https://github.com/PaddlePaddle/Paddle/pull/37084), [#37158](https://github.com/PaddlePaddle/Paddle/pull/37158), [#37361](https://github.com/PaddlePaddle/Paddle/pull/37361), [#37509](https://github.com/PaddlePaddle/Paddle/pull/37509), [#37603](https://github.com/PaddlePaddle/Paddle/pull/37603), [#37703](https://github.com/PaddlePaddle/Paddle/pull/37703), [#37824](https://github.com/PaddlePaddle/Paddle/pull/37824), [#38114](https://github.com/PaddlePaddle/Paddle/pull/38114), [#38322](https://github.com/PaddlePaddle/Paddle/pull/38322), [#38535](https://github.com/PaddlePaddle/Paddle/pull/38535), [#38650](https://github.com/PaddlePaddle/Paddle/pull/38650), [#38709](https://github.com/PaddlePaddle/Paddle/pull/38709), [#38799](https://github.com/PaddlePaddle/Paddle/pull/38799), [#38839](https://github.com/PaddlePaddle/Paddle/pull/38839), [#38904](https://github.com/PaddlePaddle/Paddle/pull/38904)) - - - 新增分布式大模型推理功能。([#38795](https://github.com/PaddlePaddle/Paddle/pull/38795), [#39012](https://github.com/PaddlePaddle/Paddle/pull/39012), [#39032](https://github.com/PaddlePaddle/Paddle/pull/39032), [#39076](https://github.com/PaddlePaddle/Paddle/pull/39076), [#39194](https://github.com/PaddlePaddle/Paddle/pull/39194), [#39207](https://github.com/PaddlePaddle/Paddle/pull/39207), [#39241](https://github.com/PaddlePaddle/Paddle/pull/39241), [#39603](https://github.com/PaddlePaddle/Paddle/pull/39603), [#39758](https://github.com/PaddlePaddle/Paddle/pull/39758), [#39992](https://github.com/PaddlePaddle/Paddle/pull/39992)) - -- 动态图混合并行 - - - 重构 `paddle.distributed.fleet.utils.recompute`，支持新动态图。([#41396](https://github.com/PaddlePaddle/Paddle/pull/41396)) - - - 支持 Pure FP16 训练。([#36420](https://github.com/PaddlePaddle/Paddle/pull/36420)) - - - 新增 MoE（Mixture of Experts）并行策略, 支持超大 MoE 模型训练。([#41092](https://github.com/PaddlePaddle/Paddle/pull/41092), [#40895](https://github.com/PaddlePaddle/Paddle/pull/40895), [#40850](https://github.com/PaddlePaddle/Paddle/pull/40580), [#39224](https://github.com/PaddlePaddle/Paddle/pull/39224)) - - - 新增 GroupSharded 并行策略，支持 stage1、stage2、stage3 三个阶段模型状态分组切片训练策略，支持同、异步通信，并可与 Recompute、AMP O1\O2、Offload、GroupShardedClipGrad、GroupShardedScaler 等基础功能组合使用。([#37489](https://github.com/PaddlePaddle/Paddle/pull/37489), [#37568](https://github.com/PaddlePaddle/Paddle/pull/37568), [#37707](https://github.com/PaddlePaddle/Paddle/pull/37707), [#37836](https://github.com/PaddlePaddle/Paddle/pull/37836), [#37947](https://github.com/PaddlePaddle/Paddle/pull/37947), [#38151](https://github.com/PaddlePaddle/Paddle/pull/38151), [#38407](https://github.com/PaddlePaddle/Paddle/pull/38407), [#38052](https://github.com/PaddlePaddle/Paddle/pull/38052), [#39112](https://github.com/PaddlePaddle/Paddle/pull/39112), [#38989](https://github.com/PaddlePaddle/Paddle/pull/38989), [#39171](https://github.com/PaddlePaddle/Paddle/pull/39171), [#39285](https://github.com/PaddlePaddle/Paddle/pull/39285), [#39334](https://github.com/PaddlePaddle/Paddle/pull/39334), [#39397](https://github.com/PaddlePaddle/Paddle/pull/39397), [#39581](https://github.com/PaddlePaddle/Paddle/pull/39581), [#39668](https://github.com/PaddlePaddle/Paddle/pull/39668), [#40129](https://github.com/PaddlePaddle/Paddle/pull/40129), [#40396](https://github.com/PaddlePaddle/Paddle/pull/40396), [#40488](https://github.com/PaddlePaddle/Paddle/pull/40488), [#40601](https://github.com/PaddlePaddle/Paddle/pull/40601)，[#37725](https://github.com/PaddlePaddle/Paddle/pull/37725)，[#37904](https://github.com/PaddlePaddle/Paddle/pull/37904), [#38064](https://github.com/PaddlePaddle/Paddle/pull/38064)) - -- 静态图混合并行 - - - 新增`scale_gradient`标志位至`gradient_scale_configs`，用于控制流水线并行下梯度聚合运算对梯度进行求平均运算的位置。([#36384](https://github.com/PaddlePaddle/Paddle/pull/36384)) - - - 张量模型并行下，dropout 支持设置确定性随机种子生成器，以确保非分布式变量的随机一致性和分布式变量的随机性。([#36228](https://github.com/PaddlePaddle/Paddle/pull/36228)) - - - NPU 混合并行支持 Offload，可节约 40%显存。([#37224](https://github.com/PaddlePaddle/Paddle/pull/37224)) - - - 为 seed op 增加 `force_cpu` 可选参数，使 dropout 可以直接从 CPU 读取 seed 的值。([#35820](https://github.com/PaddlePaddle/Paddle/pull/35820)) - - - 完善 Automatic Sparsity (ASP)sharding 策略，支持根据 program 选择 sharding 策略。(#[#40028](https://github.com/PaddlePaddle/Paddle/pull/40028)) - -- 自动并行 - - - 新增逻辑进程与物理设备自动映射后的进程重新启动（relaunch）。([#37523](https://github.com/PaddlePaddle/Paddle/pull/37523), [#37326](https://github.com/PaddlePaddle/Paddle/pull/37326)) - - - 完善自动并行底层机制和接口，利于各个模块统一和添加优化 pass。([#36617](https://github.com/PaddlePaddle/Paddle/pull/36617), [#38132](https://github.com/PaddlePaddle/Paddle/pull/38132)) - - - 新增统一资源表示，支持逻辑进程与物理设备自动映射功能。([#37091](https://github.com/PaddlePaddle/Paddle/pull/37091), [#37482](https://github.com/PaddlePaddle/Paddle/pull/37482), [#37094](https://github.com/PaddlePaddle/Paddle/pull/37094)) - - - 完善自动并行计算图反向和更新部分的分布式属性补全功能。([#36744](https://github.com/PaddlePaddle/Paddle/pull/36744)) - - - 新增数据切分功能。([#36055](https://github.com/PaddlePaddle/Paddle/pull/36055)) - - - 新增张量重切分功能，根据张量和算子的分布式属性对张量进行重新切分。([#40865](https://github.com/PaddlePaddle/Paddle/pull/40865), [#41106](https://github.com/PaddlePaddle/Paddle/pull/41106)) - - - 新增资源数量或并行策略变化时分布式参数的自动转换功能。([#40434](https://github.com/PaddlePaddle/Paddle/pull/40434)) - - - 新增梯度累加功能（GradientMerge），减少通信次数，提升训练效率。([#38259](https://github.com/PaddlePaddle/Paddle/pull/38259), [#40737](https://github.com/PaddlePaddle/Paddle/pull/40737)) - - - 新增重计算功能(Recompute)，优化显存。([#38920](https://github.com/PaddlePaddle/Paddle/pull/38920)) - - - 新增 Sharding 优化 pass， 支持 p-g-os 3 个 stage 的切分优化。([#38502](https://github.com/PaddlePaddle/Paddle/pull/38502)) - - - 新增 AMP + FP16 优化 pass。([#38764](https://github.com/PaddlePaddle/Paddle/pull/38764), [#40615](https://github.com/PaddlePaddle/Paddle/pull/40615)) - - - 新增 Transformer 类模型的 QKV fuse 切分。([#39080](https://github.com/PaddlePaddle/Paddle/pull/39080)) - - - 新增 while op 的分布式属性推导功能，确保迭代推导算法能收敛。([#39939](https://github.com/PaddlePaddle/Paddle/pull/39939), [#39086](https://github.com/PaddlePaddle/Paddle/pull/39086), [#39014](https://github.com/PaddlePaddle/Paddle/pull/39014)) - - - 支持子 block 和 while op 控制流的训练和推理。([#39612](https://github.com/PaddlePaddle/Paddle/pull/39612), [#39895](https://github.com/PaddlePaddle/Paddle/pull/39895), [#40077](https://github.com/PaddlePaddle/Paddle/pull/40077)) - -- 参数服务器 - - - GPUPS 下，新增 NAN/INF 值检查工具。([#38131](https://github.com/PaddlePaddle/Paddle/pull/38131)) - - - GPUPS 下，新增 set_date 接口，适配增量训练。([#36194](https://github.com/PaddlePaddle/Paddle/pull/36194)) - - - GPUPS 下，新增异步 release dataset 功能。([#37790](https://github.com/PaddlePaddle/Paddle/pull/37790)) - - - GPUPS 下，支持 Dump 参数和中间层 ([#36157](https://github.com/PaddlePaddle/Paddle/pull/36157))； - - - GPUPS 下，支持优化器参数配置。([#39783](https://github.com/PaddlePaddle/Paddle/pull/39783), [#39849](https://github.com/PaddlePaddle/Paddle/pull/39849)) - - - 统一参数服务器下，重构通信、存储等各个模块基类，提升各个模块的易二次开发性。([#41207](https://github.com/PaddlePaddle/Paddle/pull/41207), [#41022](https://github.com/PaddlePaddle/Paddle/pull/41022), [#40702](https://github.com/PaddlePaddle/Paddle/pull/40702), [#39341](https://github.com/PaddlePaddle/Paddle/pull/39341) [#39377](https://github.com/PaddlePaddle/Paddle/pull/39377), [#39191](https://github.com/PaddlePaddle/Paddle/pull/39191), [#39064](https://github.com/PaddlePaddle/Paddle/pull/39064)) - - - 统一参数服务器下，新增评估指标模块，支持 AUC/WuAUC/MaskAuc 等评估指标计算及可自定义扩展。([#38789](https://github.com/PaddlePaddle/Paddle/pull/38789)) - - - 支持在昆仑 2 芯片上的 XPU 参数服务器训练。([#41917](https://github.com/PaddlePaddle/Paddle/pull/41917), [#42266](https://github.com/PaddlePaddle/Paddle/pull/42266), [#41916](https://github.com/PaddlePaddle/Paddle/pull/41916)) - -#### Profiler - -- Python 层新增性能分析模块 `paddle.profiler`：提供对训推过程中性能数据的收集，导出和统计的功能。([#40065](https://github.com/PaddlePaddle/Paddle/pull/40065), [#40357](https://github.com/PaddlePaddle/Paddle/pull/40357), [#40888](https://github.com/PaddlePaddle/Paddle/pull/40888)) - - - `paddle.profiler.Profiler`，性能分析器，用户交互的接口。([#41029](https://github.com/PaddlePaddle/Paddle/pull/41029), [#41524](https://github.com/PaddlePaddle/Paddle/pull/41524), [#41157](https://github.com/PaddlePaddle/Paddle/pull/41157), [#40249](https://github.com/PaddlePaddle/Paddle/pull/40249), [#40111](https://github.com/PaddlePaddle/Paddle/pull/40111), [#39964](https://github.com/PaddlePaddle/Paddle/pull/39964), [#40133](https://github.com/PaddlePaddle/Paddle/pull/40133)) - - - `paddle.profiler.RecordEvent`，提供自定义打点来记录时间的功能。([#39693](https://github.com/PaddlePaddle/Paddle/pull/39693), [#39694](https://github.com/PaddlePaddle/Paddle/pull/39694), [#39695](https://github.com/PaddlePaddle/Paddle/pull/39695), [#39675](https://github.com/PaddlePaddle/Paddle/pull/39675),[#41445](https://github.com/PaddlePaddle/Paddle/pull/41445), [#41132](https://github.com/PaddlePaddle/Paddle/pull/41132)) - - - `paddle.profiler.ProfilerTarget`，指定性能分析的目标设备。 - - - `paddle.profiler.ProfilerState`，表示性能分析器的状态。 - - - `paddle.profiler.SortedKeys`，指定统计表单内数据的排序方式。 - - - `paddle.profiler.make_scheduler`，生成性能分析器状态的调度器，实现采集范围的周期性控制。 - - - `paddle.profiler.export_chrome_tracing`，将性能数据保存到可供 chrome://tracing 插件查看的 google chrome tracing 文件。([#39316](https://github.com/PaddlePaddle/Paddle/pull/39316), [#39984](https://github.com/PaddlePaddle/Paddle/pull/39984), [#41029](https://github.com/PaddlePaddle/Paddle/pull/41029)) - - - `paddle.profiler.export_protobuf`，将性能数据保存到内部结构表示的 protobuf 文件。([#39519](https://github.com/PaddlePaddle/Paddle/pull/39519), [#39109](https://github.com/PaddlePaddle/Paddle/pull/39109), [#39474](https://github.com/PaddlePaddle/Paddle/pull/39474)) - - - `paddle.profiler.load_profiler_result`，载入所保存到 protobuf 文件的性能数据。 - - - `paddle.profiler.Profiler`通过指定 `timer_only` 参数，对模型进行数据读取、step 开销和吞吐量的统计。([#40386](https://github.com/PaddlePaddle/Paddle/pull/40386)) - -- C++层重构 Profiler 底层基础设施 - - - 重构 Profiler 的控制器架构。([#38826](https://github.com/PaddlePaddle/Paddle/pull/38826), [#39230](https://github.com/PaddlePaddle/Paddle/pull/39230), [#39779](https://github.com/PaddlePaddle/Paddle/pull/39779)) - - - 新增 Host Tracer，收集主机侧性能指标。([#37629](https://github.com/PaddlePaddle/Paddle/pull/39629), [#37766](https://github.com/PaddlePaddle/Paddle/pull/37766), [#37944](https://github.com/PaddlePaddle/Paddle/pull/37944), [#38280](https://github.com/PaddlePaddle/Paddle/pull/38280), [#39975](https://github.com/PaddlePaddle/Paddle/pull/39975), [#40460](https://github.com/PaddlePaddle/Paddle/pull/40460)) - - - 新增 CUDA Tracer，收集设备侧性能指标。([#39488](https://github.com/PaddlePaddle/Paddle/pull/39488)) - - - Profiler 支持分级。([#39926](https://github.com/PaddlePaddle/Paddle/pull/39926)) - -- 修改新动态图下 op 的打点名称和类型。（[#41771](https://github.com/PaddlePaddle/Paddle/pull/41771/) - -- 添加 Kernel 表单，以及优化表单内容的展示方式。([#41989](https://github.com/PaddlePaddle/Paddle/pull/41989)) - -- 消除 Profiler 关闭情况下对模型前向计算造成性能下降的影响。([#42142](https://github.com/PaddlePaddle/Paddle/pull/42142)) - -#### CINN 编译器接入 - -飞桨的编译器功能在逐步丰富中，针对 CINN ([GitHub - PaddlePaddle/CINN: Compiler Infrastructure for Neural Networks](https://github.com/PaddlePaddle/CINN)) 的变更，Paddle 侧接入也进行了相对应的更改，以适配编译器 CINN 的功能。其中主要包括增加 Paddle-CINN 运行流程的子图管理相关功能，显存和速度性能的优化、开发过程发现的 bug 修复。 - -- 功能开发： - - - 子图 op 相关： - - - 添加从计算图中找到并生成 CINN 子图的功能。([#36345](https://github.com/PaddlePaddle/Paddle/pull/36345)) - - - 新增 cinn_launch op 作为运行时接入 CINN 的入口，负责调度 CINN 对子图进行编译、初始化数据空间、调度生成 Kernel 的执行。([#36600](https://github.com/PaddlePaddle/Paddle/pull/36600)) - - - 为 cinn_launch op 的 Kernel 实现添加辅助类 CinnLaunchContext 管理子图编译、运行的中间数据，提升可扩展性和代码可读性。([#37938](https://github.com/PaddlePaddle/Paddle/pull/37938)) - - - 为 CINN 子图添加额外的 fetch 结点，从而保证 CINN 外部结点能取到待 fetch 变量的值。([#37172](https://github.com/PaddlePaddle/Paddle/pull/37172), [#37190](https://github.com/PaddlePaddle/Paddle/pull/37190)) - - - 添加对 CINN 子图符号化的功能，符号化用于拓扑排序子图并返回 CINN 执行序列。([#36417](https://github.com/PaddlePaddle/Paddle/pull/36417)) - - - 新增 CinnCompiler 类，用于调用 CINN 编译模型中可使用 CINN 算子替换的子图。([#36562](https://github.com/PaddlePaddle/Paddle/pull/36562), [#36975](https://github.com/PaddlePaddle/Paddle/pull/36975)) - - - 为 CINN 符号化类新增获取子图 fetch 变量名的接口，防止编译优化中将 fetch 变量融合消除。([#37218](https://github.com/PaddlePaddle/Paddle/pull/37218)) - - - 程序开发检查、debug、API 变更相关： - - - 同步更新 CINN 中 NetBuilder API 名称的变化。([#40392](https://github.com/PaddlePaddle/Paddle/pull/40392)) - - - 为 Paddle-CINN 添加必要的用于 debug 的日志信息。([#36867](https://github.com/PaddlePaddle/Paddle/pull/36867)) - - - 添加 Paddle desc 与 CINN desc 互转函数。([#36100](https://github.com/PaddlePaddle/Paddle/pull/36100)) - - - 相比 Paddle，CINN 中实现的算子可能存在未使用到某些输入变量，因此在 cinn_launch op 中去除对输入变量必须被使用的检查。([#37119](https://github.com/PaddlePaddle/Paddle/pull/37119)) - - - 新增 cinn_instruction_run op 用于调用 CINN 执行单个生成指令，便于 Paddle 侧构建 Graph 调度运行子图。([#39435](https://github.com/PaddlePaddle/Paddle/pull/39435), [#39576](https://github.com/PaddlePaddle/Paddle/pull/39576)) - - - 在 Paddle 中添加编译 CINN 所需的 CUDA/CUBLAS/MKL/CINN pass 应用等控制宏。([#37066](https://github.com/PaddlePaddle/Paddle/pull/37066), [#36660](https://github.com/PaddlePaddle/Paddle/pull/36660)) - - - 增加 FLAGS_allow_cinn_ops 和 FLAGS_deny_cinn_ops 两个控制标记，用于控制 Paddle 训练中使用 CINN 算子代替原生算子的种类。([#36842](https://github.com/PaddlePaddle/Paddle/pull/36842)) - -- 性能优化： - - - 速度优化 - - - 优化 CinnCacheKey 的计算耗时。([#37786](https://github.com/PaddlePaddle/Paddle/pull/37786), [#37317](https://github.com/PaddlePaddle/Paddle/pull/37317)) - - - 缓存 CINN 编译子图的变量 scope，降低运行参数构造开销。([#37983](https://github.com/PaddlePaddle/Paddle/pull/37983)) - - - 子图编译时接入 CINN 自动调优，支持通过 flag 启用，便于后续进一步调优训练性能。([#41795](https://github.com/PaddlePaddle/Paddle/pull/41795)) - - - 重构子图编译时对编译结果的正确性校验，避免运行时重复检查，降低调度开销。([#41777](https://github.com/PaddlePaddle/Paddle/pull/41777)) - - - 在 Paddle-CINN 训练功能中默认启用 TransposeFolding 和 GemmRewriter 优化 pass。([#41084](https://github.com/PaddlePaddle/Paddle/pull/41084)) - - - 将 Paddle 中创建的 cuda stream 传入 CINN，使得 Paddle 和 CINN 执行计算时共用同一个 CUDA stream。([#37337](https://github.com/PaddlePaddle/Paddle/pull/37337)) - - - 将 CINN 优化 pass 应用逻辑从 Paddle 中移动到 CINN 中。([#42047](https://github.com/PaddlePaddle/Paddle/pull/42047), [#42070](https://github.com/PaddlePaddle/Paddle/pull/42070)) - - - 显存优化 - - - 为 cinn_launch op 添加 NoNeedBufferVars 声明无须 buffer 的输入变量列表，以便显存优化提前释放无效空间。([#38367](https://github.com/PaddlePaddle/Paddle/pull/38367)) - - - 传入子图外部变量的引用计数信息，便于 cinn_launch 内子图复用显存优化 pass，降低使用 CINN 的显存开销。([#39209](https://github.com/PaddlePaddle/Paddle/pull/39209), [#39622](https://github.com/PaddlePaddle/Paddle/pull/39622)) - - - 添加 CINN 编译生成的可执行指令集合转换为 Paddle Graph 的功能，支持复用 Paddle 调度器及显存优化 pass，进一步降低使用 CINN 的显存开销。([#39724](https://github.com/PaddlePaddle/Paddle/pull/39724), [#39911](https://github.com/PaddlePaddle/Paddle/pull/39911)) - - - 添加 cinn_instruction_run op 的 Kernel 支持根据编译结果推断的数据类型动态申请空间。([#40920](https://github.com/PaddlePaddle/Paddle/pull/40920)) - -- 问题修复： - - - 修复并优化 CINN 子图的生成逻辑。([#36503](https://github.com/PaddlePaddle/Paddle/pull/36503)) - - - 修复 Paddle-CINN 不支持无输入子图的问题。([#40814](https://github.com/PaddlePaddle/Paddle/pull/40814)) - - - 修复由于 CINN 无法处理 batch_norm 等算子中存在的无用输出而报错的问题。([#36996](https://github.com/PaddlePaddle/Paddle/pull/36996)) - - - 修复若干 CINN 子图划分以及符号化中存在的 bug，解决 Paddle 训练接入 CINN 全流程打通过程中遇到的问题。([#36739](https://github.com/PaddlePaddle/Paddle/pull/36739), [#36698](https://github.com/PaddlePaddle/Paddle/pull/36698) ) - - - CINN 尚不支持控制流，添加遇控制流跳过的逻辑。([#40812](https://github.com/PaddlePaddle/Paddle/pull/40812)) - -#### 其他 - -- 模型量化 - - - 升级量化存储格式，并统一动、静态图量化格式。([#41041](https://github.com/PaddlePaddle/Paddle/pull/41041)) - - - 新增离线量化方法：EMD、Adaround。([#40421](https://github.com/PaddlePaddle/Paddle/pull/40421), [#38460](https://github.com/PaddlePaddle/Paddle/pull/38460)) - - - 支持更多 op 适配模 op 量化。([#40083](https://github.com/PaddlePaddle/Paddle/pull/40083)) - - - 支持控制流中的 OP 量化。([#37498](https://github.com/PaddlePaddle/Paddle/pull/37498)) - - - 新增支持 matmul_v2 OP 的量化。([#36469](https://github.com/PaddlePaddle/Paddle/pull/36469)) - - - 新增支持量化后的 matmul_v2 在 TensorRT 上的推理。([#36594](https://github.com/PaddlePaddle/Paddle/pull/36594)) - -- 显存优化 - - - 实现多 stream 安全 Allocator，支持在多 stream 异步计算场景下安全高效地使用显存。([#37290](https://github.com/PaddlePaddle/Paddle/pull/37290)) - - - 新增运行时显存监控模块(paddle.device.cuda.max_memory_allocated, paddle.device.cuda.max_memory_reserved, paddle.device.cuda.memory_allocated and paddle.device.cuda.memory_reserved)，支持高性能地实时统计显存数据。([#38657](https://github.com/PaddlePaddle/Paddle/pull/38657)) - - - 实现 CPU-GPU 统一内存寻址（CUDA Managed Memory），支持在显存受限场景下训练超大模型。([#39075](https://github.com/PaddlePaddle/Paddle/pull/39075)) - - - C++底层新增 GetBasePtr 接口，用来获取设备接口 CUDAMalloc 创建的设备地址。([#37978](https://github.com/PaddlePaddle/Paddle/pull/37978)) - - - 减少 AutoGrowth Allocator 中 free blocks 的数量，提升显存分配性能。([#35732](https://github.com/PaddlePaddle/Paddle/pull/35732)) - - - 对于 `initializer.Normal` 和 `initializer.Constant` 数据类型是 FP16 的 Tensor 去除多余的 float32 临时 Tensor 以及 cast，节省 2 倍显存。([#38818](https://github.com/PaddlePaddle/Paddle/pull/38818)) - -- 动态图高阶导数组网测试 - - - 为动态图增加三阶导数组网测试，以及 Broadcast 情况的测试。([#36814](https://github.com/PaddlePaddle/Paddle/pull/36814), [#37377](https://github.com/PaddlePaddle/Paddle/pull/37377)) - -- 自定义 op：支持 ROCm(HIP) 平台进行自定义 op 注册。([#36771](https://github.com/PaddlePaddle/Paddle/pull/36771)) - -- Cost Model：增加基于运行 Profile 的 Cost Model。([#35774](https://github.com/PaddlePaddle/Paddle/pull/35774)) - -- 提供定制化层 (nn.Layer)的自动稀疏训练支持，让用戶可根据自定义的 Prune 函数来对其设计的层进行稀疏剪枝。([#40253](https://github.com/PaddlePaddle/Paddle/pull/40253)) - -- 新增字符串张量底层数据结构表示，使框架具备字符串张量表示和计算的能力。([#39830](https://github.com/PaddlePaddle/Paddle/pull/39830), [#40992](https://github.com/PaddlePaddle/Paddle/pull/40992)) - -- 新增或者升级 oneDNN FP32/int8/bfloat16 Kernel，包括： - - - ELU ([#37149](https://github.com/PaddlePaddle/Paddle/pull/37149)) - - - exp ([#38624](https://github.com/PaddlePaddle/Paddle/pull/38624)) - - - stack ([#37002](https://github.com/PaddlePaddle/Paddle/pull/37002)) - - - softplus ([#36382](https://github.com/PaddlePaddle/Paddle/pull/36382)) - - - round ([#39653](https://github.com/PaddlePaddle/Paddle/pull/39653)) - - - shape ([#36033](https://github.com/PaddlePaddle/Paddle/pull/36033)) - - - flatten and flatten2 ([#35892](https://github.com/PaddlePaddle/Paddle/pull/35892)) - - - slice ([#37630](https://github.com/PaddlePaddle/Paddle/pull/37630)) - - - elementwise_mul ([#40546](https://github.com/PaddlePaddle/Paddle/pull/40546)) - - - elementwise_add ([#38176](https://github.com/PaddlePaddle/Paddle/pull/38176)) - - - ementwise_div ([#36158](https://github.com/PaddlePaddle/Paddle/pull/36158)) - - - elementwise_sub ([#35662](https://github.com/PaddlePaddle/Paddle/pull/35662)) - - - roi_align ([#37848](https://github.com/PaddlePaddle/Paddle/pull/37848)) - - - nearest_interp and nearest_interp_v2 ([#37985](https://github.com/PaddlePaddle/Paddle/pull/37985)，[#38622](https://github.com/PaddlePaddle/Paddle/pull/38622)，[#39490](https://github.com/PaddlePaddle/Paddle/pull/39490)) - - - assembly optimized Adam ([#39158](https://github.com/PaddlePaddle/Paddle/pull/39158)) - - - logsoftmax ([#39793](https://github.com/PaddlePaddle/Paddle/pull/39793)) - - - activation ([#40721](https://github.com/PaddlePaddle/Paddle/pull/40721)) - - - mul ([#38552](https://github.com/PaddlePaddle/Paddle/pull/38552)) - - - mean ([#37104](https://github.com/PaddlePaddle/Paddle/pull/37104)) - - - relu ([#36265](https://github.com/PaddlePaddle/Paddle/pull/36265)) - - - pool2d ([#37081](https://github.com/PaddlePaddle/Paddle/pull/37081)) - - - concat ([#35889](https://github.com/PaddlePaddle/Paddle/pull/35889)) - - - conv2d ([#38507](https://github.com/PaddlePaddle/Paddle/pull/38507)，[#38938](https://github.com/PaddlePaddle/Paddle/pull/38938)，[#36284](https://github.com/PaddlePaddle/Paddle/pull/36284)) - - - LayerNorm ([#40418](https://github.com/PaddlePaddle/Paddle/pull/40418)) - -- 增加基于 SSD-内存-GPU 显存 的 3 级存储图检索引擎，支持大规模图神经网络训练。([#42472](https://github.com/PaddlePaddle/Paddle/pull/42472), [#42321](https://github.com/PaddlePaddle/Paddle/pull/42321), [#42027](https://github.com/PaddlePaddle/Paddle/pull/42027)) - -- 增加异构多云训练通信模块 switch，实现 Send/Recv 接口，支持多云异构通信。([#40965](https://github.com/PaddlePaddle/Paddle/pull/40965) [40911](https://github.com/PaddlePaddle/Paddle/pull/40911)) - -### （2）功能优化 - -#### API - -- 为 `paddle.Model`新增支持混合精度训练 O2 模式，即支持原来动/静态图的 Pure FP16 训练模式。([#36441](https://github.com/PaddlePaddle/Paddle/pull/40962441)) - -- 为 `paddle.nn.Layer` 支持 self chain 调用。([#36609](https://github.com/PaddlePaddle/Paddle/pull/36609)) - -- 为 `paddle.nn.Layer`的`to`方法添加`is_distributed`属性的设置，保证网络参数转换前后分布式属性保持一致。([#36221](https://github.com/PaddlePaddle/Paddle/pull/36221)) - -- 完善 `paddle.nn.Layer`的`to` 方法的参数转换逻辑，降低转换过程占用的峰值显存，提高转换成功率。([#36862](https://github.com/PaddlePaddle/Paddle/pull/36862)) - -- 为 `paddle.incubate.graph_send_recv`支持设置输出 Tensor 的 shape，有利于减少实际计算过程的显存占用。([#40509](https://github.com/PaddlePaddle/Paddle/pull/40509)) - -- 为 `paddle.incubate.segment_sum`、`segment_mean`、`segment_max`、`segment_min` 新增 int32、int64 数据类型支持。([#40577](https://github.com/PaddlePaddle/Paddle/pull/40577)) - -- 为 transpose op 新增 bool 类型支持。([#35886](https://github.com/PaddlePaddle/Paddle/pull/35886)) - -- 将 `paddle.mm` 底层算子从 matmul 切换到 matmul_v2。([#35770](https://github.com/PaddlePaddle/Paddle/pull/35770)) - -- 为 `paddle.einsum` 支持静态图模式调用，支持未知 shape。([#40360](https://github.com/PaddlePaddle/Paddle/pull/40360)) - -- 为 `paddle.nn.functional.margin_cross_entropy` 和 `paddle.nn.functional.class_center_sample` 支持数据并行。([#39852](https://github.com/PaddlePaddle/Paddle/pull/39852)) - -- 为 `paddle.nn.functional.grid_sample`支持形状为[1]的输入。([#36183](https://github.com/PaddlePaddle/Paddle/pull/36183)) - -- 为 `paddle.nn.PRelu` 支持 `NHWC` 数据格式。([#37019](https://github.com/PaddlePaddle/Paddle/pull/37019)) - -- 为 `paddle.nn.functional.class_center_sample` 支持使用 `paddle.seed` 固定随机状态。([#38248](https://github.com/PaddlePaddle/Paddle/pull/38248)) - -- 为 `paddle.fft` 下所有 API 新增 ROCM 后端支持，并优化 CUFFT 后端报错信息。([#36415](https://github.com/PaddlePaddle/Paddle/pull/36415), [#36114](https://github.com/PaddlePaddle/Paddle/pull/36114/files)) - -- 为 `Tensor.getitem` 增加对切片部分维度为 0 的功能支持，即允许切片索引结果为空。([#37313](https://github.com/PaddlePaddle/Paddle/pull/37313)) - -- 为 `Tensor.setitem` 支持 int 和 bool 类型 Tensor 使用 bool 索引。([#37761](https://github.com/PaddlePaddle/Paddle/pull/37761)) - -- 为 `paddle.nn.functional.interpolate` 支持 nearest 模式时输入 shape 为 5D。([#38868](https://github.com/PaddlePaddle/Paddle/pull/38868)) - -- 为 `paddle.nn.Embedding`、`paddle.gather` 增加 int16 支持。([#40964](https://github.com/PaddlePaddle/Paddle/pull/40964), [#40052](https://github.com/PaddlePaddle/Paddle/pull/40052)) - -- 为 `paddle.distributed.spawn`添加 CPU 单机数据并行。([#35745](https://github.com/PaddlePaddle/Paddle/pull/35745), [#36758](https://github.com/PaddlePaddle/Paddle/pull/36758), [#36637](https://github.com/PaddlePaddle/Paddle/pull/36637)) - -- 新增`depthwise_conv2d`MKLDNN 算子。([#38484](https://github.com/PaddlePaddle/Paddle/pull/38484)) - -- 为`paddle.abs`、`paddle.transpose`、`paddle.squeeze`、`paddle.unsqueeze`、 `paddle.matmul`、`paddle.full` 静态图数据类型检测中增加复数类型。([#40113](https://github.com/PaddlePaddle/Paddle/pull/40113)) - -- 为 `paddle.autograd.PyLayer` 支持 tuple/list 类型的参数。([#38146](https://github.com/PaddlePaddle/Paddle/pull/38146)) - -- 为 `paddle.autograd.PyLayer` 增加检查 inplace 策略下，输入叶子节点的 Tensor 的检查报错机制。([#37931](https://github.com/PaddlePaddle/Paddle/pull/37931)) - -- 为 `paddle.autograd.PyLayer` 支持 HIP 库。([#38184](https://github.com/PaddlePaddle/Paddle/pull/38184)) - -- 为 `paddle.take_along_axis`、`paddle.put_along_axis` 支持更多 size 的输入，允许 index 矩阵的 shape size 大于 arr 矩阵的 shape size。([#39072](https://github.com/PaddlePaddle/Paddle/pull/39072)) - -- 优化 API `paddle.nn.Pad2D`在 replicate 为 0 时的报错信息。([#36510](https://github.com/PaddlePaddle/Paddle/pull/36510/files)) - -- 支持 API `paddle.nn.Pad2D`在 tuple 格式的 pad 输入。([#35985](https://github.com/PaddlePaddle/Paddle/pull/35985/files)) - -- 新增 `paddle.distributed.InMemoryDataset` 中 tdm_sample API 以支持 TDM 算法中的采样操作。([#37044](https://github.com/PaddlePaddle/Paddle/pull/37044)) - -- 新增对于`paddle.jit.save`的 Pre-saving Hooks 机制。([#38186](https://github.com/PaddlePaddle/Paddle/pull/38186)) - -- 新增高阶微分相关 API： - - - `elementwise_add` 增加三阶 Kernel，支持三阶微分的计算。([#36508](https://github.com/PaddlePaddle/Paddle/pull/36508), [#36618](https://github.com/PaddlePaddle/Paddle/pull/36618)) - - - `matmul_v2` 增加三阶 Kernel，支持三阶微分的计算。([#36459](https://github.com/PaddlePaddle/Paddle/pull/36459)) - - - `elementwise_mul` 增加三阶 Kernel，支持三阶微分的计算。([#37152](https://github.com/PaddlePaddle/Paddle/pull/37547)) - -- 完善`paddle.amp.GradScaler`调用 check_finite_and_unscale op 的逻辑，消除该处创建 bool 变量所引入的 cudaMemcpy。([#37770](https://github.com/PaddlePaddle/Paddle/pull/37770)) - -- 新增对 unstack 和 unique op 元素个数为 0 的 Tensor 增加检查。([#36021](https://github.com/PaddlePaddle/Paddle/pull/36021)) - -- 新增支持昆仑 2 的多层、双向 LSTM 功能，完善 RNN 前反向 op，支持时序类模型训练使用。([#](https://github.com/PaddlePaddle/Paddle/pull/41781)[42076](https://github.com/PaddlePaddle/Paddle/pull/42076)) - -- 新增支持昆仑 2 的 bce_loss 前反向 op。([#41610](https://github.com/PaddlePaddle/Paddle/pull/41610)) - -- 添加 `paddle.linalg.det` 的反向实现。([#36013](https://github.com/PaddlePaddle/Paddle/pull/36013)) - -#### IR(Intermediate Representation) - -- 动态图转静态图 - - - 优化动转静下 `ProgramCache.last` 接口行为，使其返回最近使用的 Program，而非最后生成的 Program。([#39541](https://github.com/PaddlePaddle/Paddle/pull/39541)) - - - 优化动转静下 `paddle.reshape` API 的报错信息，新增推荐用法提示。([#40599](https://github.com/PaddlePaddle/Paddle/pull/40599)) - - - 优化动转静代码转写时 `is_api_in_module` 函数中异常捕获类型。([#40243](https://github.com/PaddlePaddle/Paddle/pull/40243)) - - - 优化动转静模块报错提示，默认隐藏 warning 信息。([#39730](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/39730)) - - - 增加动转静对于 type hint 语法的支持，提高变量类型分析的准确性。([#39572](https://github.com/PaddlePaddle/Paddle/pull/39572)) - - - 优化 `paddle.cond` 功能，允许 bool、int 等基本类型支持值相等。([#37888](https://github.com/PaddlePaddle/Paddle/pull/37888)) - - - 优化动转静`@to_static` 装饰普通函数时，允许切换 train/eval 模式。([#37383](https://github.com/PaddlePaddle/Paddle/pull/37383)) - - - 优化动转静报错栈，突出用户相关代码，减少框架冗余报错栈。([#36741](https://github.com/PaddlePaddle/Paddle/pull/36741)) - - - 移除`paddle.cond` 返回值中 `no_value` 占位符。([#36513](https://github.com/PaddlePaddle/Paddle/pull/36513)、[#36826](https://github.com/PaddlePaddle/Paddle/pull/36826)) - - - 为动转静 run_program op 适配新动态图模式。([#40198](https://github.com/PaddlePaddle/Paddle/pull/40198), [#40355](https://github.com/PaddlePaddle/Paddle/pull/40355)) - - - 新增对于 zip 语法的检查。([#37846](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/37846)) - - - 修复 `paddle.signal.frame`、`paddle.signal.stft`、`paddle.signal.istft` 因维度和类型判断错误导致的动转静失败问题。([#40113](https://github.com/PaddlePaddle/Paddle/pull/40113)) - - - 为 mean、pad3d ops 新增注册复数类型 Kernel。([#40113](https://github.com/PaddlePaddle/Paddle/pull/40113)) - -#### 混合精度训练 - -- 为 amp 添加 GPU Compute Capability 环境检查，对无法产生训练加速效果的 GPU 环境添加使用警告。([#38086](https://github.com/PaddlePaddle/Paddle/pull/38086)) - -- 添加`paddle.amp.decorate`与`paddle.DataParallel`同时使用时调用顺序的检查。([#38785](https://github.com/PaddlePaddle/Paddle/pull/38785)) - -#### 分布式训练 - -- 分布式训练基础功能 - - - 优化 Fleet API 和 DistributedStrategy 配置以使用动态图并行功能，提升动态图易用性。([#40408](https://github.com/PaddlePaddle/Paddle/pull/40408)) - - - 优化动态图混合并行 HybridParallelClipGrad 策略，支持 4D 混合并行 + Pure FP16 训练。([#36237](https://github.com/PaddlePaddle/Paddle/pull/36237), [#36555](https://github.com/PaddlePaddle/Paddle/pull/36555)) - - - 重构动态图数据并行策略，以支持新动态图和新通信库功能。([#40389](https://github.com/PaddlePaddle/Paddle/pull/40389), [#40593](https://github.com/PaddlePaddle/Paddle/pull/40593), [#40836](https://github.com/PaddlePaddle/Paddle/pull/40836), [#41119](https://github.com/PaddlePaddle/Paddle/pull/41119), [#41413](https://github.com/PaddlePaddle/Paddle/pull/41413), [#39987](https://github.com/PaddlePaddle/Paddle/pull/39987)) - - - 为 fused_attention op 支持分布式张量模型并行。([#40101](https://github.com/PaddlePaddle/Paddle/pull/40101)) - - - 为 fused_feedforward op 支持分布式张量模型并行。([#40160](https://github.com/PaddlePaddle/Paddle/pull/40160)) - -- 图检索引擎 - - - 优化图引擎的图采样接口返回的数据格式，采样速度提升 3 倍。([#37315](https://github.com/PaddlePaddle/Paddle/pull/37315)) - - - 减少图引擎线程量以提升性能。([#37098](https://github.com/PaddlePaddle/Paddle/pull/37098)) - - - 优化图引擎数据传输以提升性能。([#37341](https://github.com/PaddlePaddle/Paddle/pull/37341)) - - - 利用模型中 embedding op 的拓扑关系，优化 embedding op 的合并逻辑以提升性能。[(#35942)](https://github.com/PaddlePaddle/Paddle/pull/35942) - -- 通信库：重构通信库，提升通信库的易扩展性和二次开发性，支持异构通信。([#41398](https://github.com/PaddlePaddle/Paddle/pull/41398), [#39720](https://github.com/PaddlePaddle/Paddle/pull/39720), [#40911](https://github.com/PaddlePaddle/Paddle/pull/40911), [#40579](https://github.com/PaddlePaddle/Paddle/pull/40579), [#40629](https://github.com/PaddlePaddle/Paddle/pull/40629), [#40437](https://github.com/PaddlePaddle/Paddle/pull/40437), [#40430](https://github.com/PaddlePaddle/Paddle/pull/40430), [#40228](https://github.com/PaddlePaddle/Paddle/pull/40228), [#40181](https://github.com/PaddlePaddle/Paddle/pull/40181), [#40100](https://github.com/PaddlePaddle/Paddle/pull/40100), [#40097](https://github.com/PaddlePaddle/Paddle/pull/40097), [#39892](https://github.com/PaddlePaddle/Paddle/pull/39892), [#39384](https://github.com/PaddlePaddle/Paddle/pull/39384), [#39737](https://github.com/PaddlePaddle/Paddle/pull/39737), [#40040](https://github.com/PaddlePaddle/Paddle/pull/40040)) - -- 支持 `paddle.incubate.distributed.models.moe`中 MoE 相关接口(`moe.GShardGate`, `moe.BaseGate`, `moe.SwitchGate`, `moe.MoELayer`, `moe.ClipGradForMOEByGlobalNorm` )的公开。([#42300](https://github.com/PaddlePaddle/Paddle/pull/42300)) - -- 修复 `paddle.incubate.distributed.models.moe.MoELayer` 中使用 recomputing 可能报错的问题。([#42128](https://github.com/PaddlePaddle/Paddle/pull/42128)) - -- 修复新动态图流水线并行因为数据类型不同导致的报错 ([#41937](https://github.com/PaddlePaddle/Paddle/pull/41937) [#42053](https://github.com/PaddlePaddle/Paddle/pull/42053)) - -- 修复新动态图张量模型并行因为数据类型不同导致的报错 ([#41960](https://github.com/PaddlePaddle/Paddle/pull/41960)) - -#### 自定义算子 - -- 增强 C++自定义算子机制对二阶反向算子编写功能，支持为二阶反向算子的梯度输入变量添加后缀作为输出使用。([#41781](https://github.com/PaddlePaddle/Paddle/pull/41781)) - -- 移除 Tensor API 成员方法中对废弃的枚举类型 PlaceType 的使用，进行相应兼容处理，并添加 deprecated warning 提示。([#41882](https://github.com/PaddlePaddle/Paddle/pull/41882)) - -- 为原 Tensor API 的一系列废弃接口，包括不完整构造函数、reshape、mutable_data、copy_to 方法添加 deprecated warning 提示。([#41882](https://github.com/PaddlePaddle/Paddle/pull/41882)) - -#### 其他 - -- 报错调试优化 - - - 优化 cross_entropy op 对 `label` 的边界检查报错信息。([#40001](https://github.com/PaddlePaddle/Paddle/pull/40001)) - - - 为动态图添加 op 执行时`infer_shape`和`compute`方法的 profile record，用于在 timeline 中展示其开销。([#39023](https://github.com/PaddlePaddle/Paddle/pull/39023)) - - - 替换了 Windows 下容易出现未知异常的 `pybind::index_error` 报错提示。([#40538](https://github.com/PaddlePaddle/Paddle/pull/40538)) - - - 添加用户 scatter op 越界检查的报错信息。([#37429](https://github.com/PaddlePaddle/Paddle/pull/37429)) - -- 下载工具：针对`paddle.utils.download.get_path_from_url`中解压含多文件目录速度慢的问题，将原先循环遍历目录下文件逐一解压的方式替换为在目录上调用 extractall 一次解压的方式，解压速度大幅提升。([#37311](https://github.com/PaddlePaddle/Paddle/pull/37311)) - -- 加速 `fake_quantize_range_abs_max`、`fake_quantize_abs_max`、`fake_quantize_dequantize_abs_max`、 `fake_quantize_moving_average_abs_max` 等量化训练。([#40491](https://github.com/PaddlePaddle/Paddle/pull/40491)) - -### （3）性能优化 - -#### 分布式训练 - -- 混合并行优化器 sharding 支持 optimize_cast 优化，将前反向参数 cast 移到优化器阶段，性能提升 7%。([#35878](https://github.com/PaddlePaddle/Paddle/pull/35878)) - -- GPUPS 优化：支持梯度 fuse allreduce 训练，训练提升 20%。([#35131](https://github.com/PaddlePaddle/Paddle/pull/35131)) - -- GPUPS 优化：dump CPU 优化提速 3.21 倍。([#40068](https://github.com/PaddlePaddle/Paddle/pull/40068)) - -- CPU 参数服务器流式训练优化：支持稀疏参数统计量自动统计、稀疏参数增量保存等功能，训练性能提升 20%。([#36465](https://github.com/PaddlePaddle/Paddle/pull/36465), [#36601](https://github.com/PaddlePaddle/Paddle/pull/36601), [#36734](https://github.com/PaddlePaddle/Paddle/pull/36734), [#36909](https://github.com/PaddlePaddle/Paddle/pull/36909), [#36943](https://github.com/PaddlePaddle/Paddle/pull/36943), [#37181](https://github.com/PaddlePaddle/Paddle/pull/37181), [#37194](https://github.com/PaddlePaddle/Paddle/pull/37194), [#37515](https://github.com/PaddlePaddle/Paddle/pull/37515), [#37626](https://github.com/PaddlePaddle/Paddle/pull/37626), [#37995](https://github.com/PaddlePaddle/Paddle/pull/37995), [#38582](https://github.com/PaddlePaddle/Paddle/pull/38582), [#39250](https://github.com/PaddlePaddle/Paddle/pull/39250), [#40762](https://github.com/PaddlePaddle/Paddle/pull/40762), [#41234](https://github.com/PaddlePaddle/Paddle/pull/41234), [#41320](https://github.com/PaddlePaddle/Paddle/pull/41320), [#41400](https://github.com/PaddlePaddle/Paddle/pull/41400)) - -#### 算子优化 - -- 优化 `FasterTokenizer` 性能，性能与优化前相比提升 10%。([#36701](https://github.com/PaddlePaddle/Paddle/pull/36701)) - -- 优化 `index_select` 反向计算，性能较优化前有 3.7~25.2 倍提升。([#37055](https://github.com/PaddlePaddle/Paddle/pull/37055)) - -- 优化 `paddle.nn.ClipByGlobalNorm` 的性能，以 10*10 的 `paddle.nn.Linear` 为例，性能与优化前相比提升 30%左右。([#38209](https://github.com/PaddlePaddle/Paddle/pull/38209)) - -- 优化 `pnorm` 在 `axis` 维度极大或极小情况下的性能，前向速度提升 31~96 倍，反向速度提升 1.1~19 倍。([#37685](https://github.com/PaddlePaddle/Paddle/pull/37685), [#38215](https://github.com/PaddlePaddle/Paddle/pull/38215), [#39011](https://github.com/PaddlePaddle/Paddle/pull/39011)) - -- 优化 `softmax` 前、反向性能，对于 `axis!=-1` 的配置加速比为 2 倍左右。([#38602](https://github.com/PaddlePaddle/Paddle/pull/38602), [#38609](https://github.com/PaddlePaddle/Paddle/pull/38609), [#32387](https://github.com/PaddlePaddle/Paddle/pull/32387), [#37927](https://github.com/PaddlePaddle/Paddle/pull/37927/files)) - -- 优化 `log_softmax` 前、反向性能，对于 `axis!=-1`的配置加速比为 6~20 倍左右。([#38992](https://github.com/PaddlePaddle/Paddle/pull/38992), [#40612](https://github.com/PaddlePaddle/Paddle/pull/40612)) - -- 优化 `softmax_with_cross_entropy` 前、反向性能，对于 `hard_label` 的配置加速比为 1.3 倍左右。([#39553](https://github.com/PaddlePaddle/Paddle/pull/39553), [#40424](https://github.com/PaddlePaddle/Paddle/pull/40424), [#40643](https://github.com/PaddlePaddle/Paddle/pull/40643)) - -- 优化 `top_k` 性能，对于一维且 `k` 较大时(k=5000)的配置加速比为 22 倍以上。([#40941](https://github.com/PaddlePaddle/Paddle/pull/40941)) - -- 优化 `elementwise_mul` 反向计算，较优化前有 1.85~12.16 倍性能提升。([#37728](https://github.com/PaddlePaddle/Paddle/pull/37728)) - -- 优化 `elementwise_min` 反向和 `elementwise_max` 反向，较优化前打平或有 1.05~18.75 倍性能提升。([#38236](https://github.com/PaddlePaddle/Paddle/pull/38236), [#37906](https://github.com/PaddlePaddle/Paddle/pull/37906)) - -- 优化 `nearest_interp` 前向和反向计算，前向较优化前性能有 1.5~2.3 倍提升；反向性能较优化前有 60%~1.8 倍提升。([#38528](https://github.com/PaddlePaddle/Paddle/pull/38528), [#39067](https://github.com/PaddlePaddle/Paddle/pull/39067)) - -- 优化 `bilinear_interp` 前向和反向计算，前向较优化前性能有 0.4~2.3 倍提升；反向性能较优化前有 10%~30%提升。([#39243](https://github.com/PaddlePaddle/Paddle/pull/39243), [#39423](https://github.com/PaddlePaddle/Paddle/pull/39423)) - -- 优化 `dropout` 前向和反向计算，性能提升约 20%。([#39795](https://github.com/PaddlePaddle/Paddle/pull/39795), [#38859](https://github.com/PaddlePaddle/Paddle/pull/38859), [#38279](https://github.com/PaddlePaddle/Paddle/pull/38279), [#40053](https://github.com/PaddlePaddle/Paddle/pull/40053)) - -- 优化 `grid_sampler`前向和反向计算，前向较优化前性能有 10%~30%提升；反向性能较优化前有 10%~60%提升。([#39751](https://github.com/PaddlePaddle/Paddle/pull/39751)) - -- 优化 `group_norm` 前向和反向计算，前向性能提升 1.04~2.35 倍，反向性能提升 1.12~1.18 倍。([#39944](https://github.com/PaddlePaddle/Paddle/pull/39944), [#40657](https://github.com/PaddlePaddle/Paddle/pull/40657), [#39596](https://github.com/PaddlePaddle/Paddle/pull/39596)) - -- 优化 `conv1d` 前向和反向计算，前向性能提升 1.00~2.01 倍，反向性能提升 1.01~474.56 倍。([#38425](https://github.com/PaddlePaddle/Paddle/pull/38425)) - -- 优化 `elementwise_div` 反向计算，反向性能提升 1.02~29.25 倍。([#38044](https://github.com/PaddlePaddle/Paddle/pull/38044)) - -- 优化 `gelu` 前向和反向计算，前向性能提升 1.13~1.43 倍，反向性能提升 1.10～1.55 倍。([#38188](https://github.com/PaddlePaddle/Paddle/pull/38188), [#38263](https://github.com/PaddlePaddle/Paddle/pull/38263)) - -- 优化 `elementwise_sub` 反向计算，反向性能提升 1.04~15.64 倍。([#37754](https://github.com/PaddlePaddle/Paddle/pull/37754)) - -- 优化 `flip` 在输入一维数据时前向性能，性能提升 100%。([#37825](https://github.com/PaddlePaddle/Paddle/pull/37825)) - -- 优化 `layer_norm` 前向和反向计算，前向较优化前提升 2-5 倍，反向较优化前提升 20%~50%。([#39167](https://github.com/PaddlePaddle/Paddle/pull/39167), [#39247](https://github.com/PaddlePaddle/Paddle/pull/39247)) - -- 优化 `embedding` 前向和反向计算，前向较优化前最大提升 1.51 倍，反向较优化前提升 1.03~7.79 倍。([#39856](https://github.com/PaddlePaddle/Paddle/pull/39856), [#39886](https://github.com/PaddlePaddle/Paddle/pull/398866)) - -- 优化 `gelu` FP16 前向和反向计算，前向较优化前提升 9%~12%，反向较优化前提升 2%~9%。([#38980](https://github.com/PaddlePaddle/Paddle/pull/38980)) - -- 移除 `gather_nd`前反向算子中的 CPU -> GPU 显式数据传输操作，移除 `index_select` 前反向算子中的显式同步操作，将 `scatter_nd` 中的 GPU -> GPU 数据传输由同步操作改成异步操作。([#40933](https://github.com/PaddlePaddle/Paddle/pull/40933)) - -- 优化 `Lars optimzier` 计算，优化后 Resnet50 PF16 模型训练性能较优化前提升 5.1%。([#35652](https://github.com/PaddlePaddle/Paddle/pull/35652), [#35476](https://github.com/PaddlePaddle/Paddle/pull/35476)) - -- 优化 `AvgPool2dGrad` 计算，优化后性能较优化前提升 2.6 倍。([#35389](https://github.com/PaddlePaddle/Paddle/pull/35389)) - -- 优化 `Elementwise` 类计算对于多元输出的功能支持，优化后计算性能较优化前提升最多可达 15%。([#38329](https://github.com/PaddlePaddle/Paddle/pull/38329), [#38410](https://github.com/PaddlePaddle/Paddle/pull/38410)) - -- 优化 `Categorical`的 `probs`计算，简化计算逻辑，性能提升 4 ~ 5 倍。([#42178](https://github.com/PaddlePaddle/Paddle/pull/42178)) - -- `paddle.sum` 性能优化，性能相比优化前提升约 20%。([#42309](https://github.com/PaddlePaddle/Paddle/pull/42309)) - -#### 自动调优 - -新增训练全流程硬件感知性能自动调优功能，在图像分类、分割、检测和图像生成任务上与模型默认参数配置下的性能相比提升约 3%～50%以上。通过 `paddle.incubate.autotune.set_config` API 设置自动调优状态，当前默认关闭。自动调优具体包括三个层次： - -- `paddle.io.DataLoader` 新增自动调优功能，根据训练数据和设备资源选择最佳的模型 num_workers。([#42004](https://github.com/PaddlePaddle/Paddle/pull/42004)) - -- 新增混合精度训练数据布局自动调优功能，根据设备类型和数据类型选择最佳数据布局，并在运行时自动转换。([#41964](https://github.com/PaddlePaddle/Paddle/pull/41964)) - -- 新增 Conv 运行时所需 workspace size 阈值自动调整功能，根据 GPU 当前可申请显存资源情况来自动设置；基于通用的 AlgorithmCache 设计和 Kernel 计时组件，新增 Conv cuDNN 算法自动选择功能，支持数据变长模型。([#41833](https://github.com/PaddlePaddle/Paddle/pull/41833)) - -#### 调度优化 - -- 移除 `paddle.nn.ClipGradByGlobalNorm` 中的 CudaStreamSync 隐藏操作，减少执行时的调度开销，在 ptb 模型上有 5%的性能提升。([#42170](https://github.com/PaddlePaddle/Paddle/pull/42170)) - -- 优化一系列底层数据结构及原动态图执行体系中的细节实现，提升原动态图的调度性能。([#42010](https://github.com/PaddlePaddle/Paddle/pull/42010), [#42171](https://github.com/PaddlePaddle/Paddle/pull/42171), [#42224](https://github.com/PaddlePaddle/Paddle/pull/42224), [#42256](https://github.com/PaddlePaddle/Paddle/pull/42256), [#42306](https://github.com/PaddlePaddle/Paddle/pull/42306), [#42329](https://github.com/PaddlePaddle/Paddle/pull/42329)[, #42340](https://github.com/PaddlePaddle/Paddle/pull/42340), [#42368](https://github.com/PaddlePaddle/Paddle/pull/42368), [#42425](https://github.com/PaddlePaddle/Paddle/pull/42425)) - -- 简化 `paddle.distribution.Categorical`的 probs 计算逻辑，提升性能 4 到 5 倍。([#42178](https://github.com/PaddlePaddle/Paddle/pull/42178)) - -### （4）问题修复 - -#### API - -- 修复 `paddle.sum` 输入参数类型和输出参数类型不一致且 `axis` 轴对应的 reduce 元素个数为 1 时，输出类型错误问题。([#36123](https://github.com/PaddlePaddle/Paddle/pull/36123)) - -- 修复 `paddle.flops` 在 layer 输出类型为 tuple 时的 `AttributeError`。([#38850](https://github.com/PaddlePaddle/Paddle/pull/38850)) - -- 修复 `paddle.diag` 因为没有反向 Kernel 而无法传播梯度的问题。([#40447](https://github.com/PaddlePaddle/Paddle/pull/40447)) - -- 修复 `paddle.sort` 输入存在 NaN 值排序错误。([#41070](https://github.com/PaddlePaddle/Paddle/pull/41070)) - -- 修复 `paddle.full_like` 输入存在 Inf 值构建 Tensor 错误。([#40232](https://github.com/PaddlePaddle/Paddle/pull/40232)) - -- 修复 `paddle.strided_slice` 在输入 starts 中数据小于 -rank 时，strided_slice 结果与 slice 不一致的 bug。([#39066](https://github.com/PaddlePaddle/Paddle/pull/39066)) - -- 修复 `max_pool` 系列算子在返回 index 时 infer_shape 计算错误的问题，受影响的 API 有 `paddle.nn.functional.max_pool1d/2d/3d`, `paddle.nn.functional.adaptive_max_pool1d/2d/3d`, `paddle.nn.MaxPool1D/2D/3D`, `paddle.nn.AdaptiveMaxPool1D/2D/3D`。([#40139](https://github.com/PaddlePaddle/Paddle/pull/40139)) - -- 修复 `max_pool` 系列算子返回的 pooling_mask 的 dtype 错误的问题，现在 pooling_mask 的 dtype 为 int32，受影响的 API 有 `paddle.nn.functional.max_pool1d/2d/3d`, `paddle.nn.functional.adaptive_max_pool1d/2d/3d`, `paddle.nn.MaxPool1D/2D/3D`, `paddle.nn.AdaptiveMaxPool1D/2D/3D`。([#39314](https://github.com/PaddlePaddle/Paddle/pull/39314)) - -- 修复 `paddle.shape` 默认存在反向梯度导致计算错误的问题。([#37340](https://github.com/PaddlePaddle/Paddle/pull/37340)) - -- 修复 `paddle.nn.Layer` 的 `to` 方法同时转换 dtype 和 place 存在的 bug。([#37007](https://github.com/PaddlePaddle/Paddle/pull/38007)) - -- 修复 `paddle.amp.decorate` 无法对非叶子网络层的参数改写为 FP16 的 bug。([#38402](https://github.com/PaddlePaddle/Paddle/pull/38402)) - -- 修复 `paddle.amp.decorate` 将 `paddle.nn.BatchNorm1D`、`paddle.nn.BatchNorm2D`、`paddle.nn.BatchNorm3D` 非输入参数改写为 FP16 的 bug。([#38541](https://github.com/PaddlePaddle/Paddle/pull/38541)) - -- 修复 `paddle.amp.decorate` 将 `paddle.nn.SyncBatchNorm` 非输入参数改写为 FP16 的 bug。([#40943](https://github.com/PaddlePaddle/Paddle/pull/40943)) - -- 修复 `paddle.nn.Layer.to` 当中多余的 warning。([#36700](https://github.com/PaddlePaddle/Paddle/pull/36700)) - -- 修复 `paddle.nn.RNN` 在控制流下使用报错的问题。([#41162](https://github.com/PaddlePaddle/Paddle/pull/41162)) - -- 修复 `paddle.to_tensor` 无法指定 Tensor 的 CUDA Place 的问题。([#39662](https://github.com/PaddlePaddle/Paddle/pull/39662)) - -- 修复 `paddle.nn.Identity` 没有公开的问题。([#39615](https://github.com/PaddlePaddle/Paddle/pull/39615)) - -- 修复动态图重构后，`fill_` 和 `zero_` inplace API 的输入在 CUDAPinned Place 上时，输出值不正确的 bug。([#41229](https://github.com/PaddlePaddle/Paddle/pull/41229)) - -- 动态图重构后，修复使用 append op 的方式调用 assign op 导致输出 Tensor 的 inplace version 值不正确的 bug，修改为使用 `_C_ops` 的方式调用 assign op。([#41118](https://github.com/PaddlePaddle/Paddle/pull/41118)) - -- 移除 `elementwise_add` 三阶 Kernel 中不合理的代码，修复组网过程未初始化问题。([#36618](https://github.com/PaddlePaddle/Paddle/pull/36618)) - -- 修复 `conv2d` 执行 cuDNN Kernel 时属性缺失的问题。([#38827](https://github.com/PaddlePaddle/Paddle/pull/38827)) - -- 修复 `multiclass_nms3` 输出 shape 不正确的问题。([#40059](https://github.com/PaddlePaddle/Paddle/pull/40059)) - -- 修复 `yolo_box` 输出 shape 不正确的问题。([#40056](https://github.com/PaddlePaddle/Paddle/pull/40056)) - -- 修复高阶微分 `gradients` 接口在指定 target_grad 时未按预期生效的问题。([#40940](https://github.com/PaddlePaddle/Paddle/pull/40940/)) - -- 修复动态图 op`_BatchNormBase` 基类中修改了 default_dtype，导致后续组网参数类型错误的问题，受影响的 API 有 `paddle.nn.BatchNorm1D`，`paddle.nn.BatchNorm2D`，`paddle.nn.BatchNorm3D`，`paddle.nn.SyncBatchNorm`。具体原因是当 `get_default_dtype() == 'float16'` 时，通过 `set_default_dtype('float32')`修改默认参数数据类型，动态图组网的参数类型是通过 default_dtype 来创建的，因此当默认参数类型被修改后导致后续的组网参数类型错误。([#36376](https://github.com/PaddlePaddle/Paddle/pull/36376)) - -- 修复 batchnorm op 中，当数据类型为 FP32，且数据维度 `dims = 2，data_layout = NHWC` 时，反向 op 内中间变量未定义问题。([#37020](https://github.com/PaddlePaddle/Paddle/pull/37020)) - -- 修复静态图模式下，`paddle.static.nn.prelu` 对于 `NHWC` 输入格式且 `mode==channel` 权重的 shape 错误问题。([#38310](https://github.com/PaddlePaddle/Paddle/pull/38310)) - -- 修复多机情况下，`paddle.nn.functional.class_center_sample` CUDA 种子设置 bug。([#38815](https://github.com/PaddlePaddle/Paddle/pull/38815)) - -- 修复 `paddle.nn.functional.one_hot` 在输入不正确参数时，CUDA 版本无法正确报错的问题。([#41335](https://github.com/PaddlePaddle/Paddle/pull/41335)) - -- 修复 DCU 设备上回收显存的 callback 未及时触发导致显存 OOM 的问题。([#40445](https://github.com/PaddlePaddle/Paddle/pull/40445)) - -- 修复 `setitem` 索引赋值反向梯度传递异常以及动态图部分场景下 inplace 逻辑处理异常的问题。([#37023](https://github.com/PaddlePaddle/Paddle/pull/37023), [#38298](https://github.com/PaddlePaddle/Paddle/pull/38298)) - -- 修复动转静下 Tensor array 使用 Slice 索引异常的问题。([#39251](https://github.com/PaddlePaddle/Paddle/pull/39251)) - -- 修复 `paddle.Tensor.register_hook` 接口使用时临时变量未析构，从而导致内存或显存泄漏的问题。([#40716](https://github.com/PaddlePaddle/Paddle/pull/40716)) - -- 修复 `Tensor.getitem` 当索引是全为 False 的 bool Tensor 时无法取值的问题。([#41297](https://github.com/PaddlePaddle/Paddle/pull/41297)) - -- 修复 `Tensor.getitem` 当索引是 bool scalar Tensor 时无法取值的问题。([#40829](https://github.com/PaddlePaddle/Paddle/pull/40829)) - -- 修复 `paddle.index_select` 在 index 为 0-shape Tensor 时报错的问题。([#41383](https://github.com/PaddlePaddle/Paddle/pull/41383)) - -- 修复 `paddle.index_select`，`paddle.index_sample` 申请的 GPU 线程数超过有限机器资源时报错的问题。([#41127](https://github.com/PaddlePaddle/Paddle/pull/41127), [#37816](https://github.com/PaddlePaddle/Paddle/pull/37816), [#39736](https://github.com/PaddlePaddle/Paddle/pull/39736), [#41563](https://github.com/PaddlePaddle/Paddle/pull/41563)) - -- 修复 ReduceConfig、elemwise_grad、gather、gather_nd、scatter ops 申请 GPU 线程数超过有限机器资源时报错的问题。([#40813](https://github.com/PaddlePaddle/Paddle/pull/40813), [#41127](https://github.com/PaddlePaddle/Paddle/pull/41127)) - -- 修复 Kernel Primitive API 中 ReadData，ReadDataBc，ReadDataReduce 在 NX != 1 时访存越界的问题。([#36373](https://github.com/PaddlePaddle/Paddle/pull/36373)) - -- 修复 IndexRandom 数据类型错误导致数据溢出计算结果异常的问题。([#39867](https://github.com/PaddlePaddle/Paddle/pull/39867), [#39891](https://github.com/PaddlePaddle/Paddle/pull/39891)) - -- 修复 reduce op 在 reduce_num = 1 计算结果返回错误的问题。([#38771](https://github.com/PaddlePaddle/Paddle/pull/38771)) - -- 修复 reduce op 在 HIP 环境下 reduce 中间维度出现访存越界的问题。([#41273](https://github.com/PaddlePaddle/Paddle/pull/41273)) - -- 修复 matmul op 两个 FP16 一维向量计算时 Kernel 无法正常释放的问题。 - -- 修复部分算子在 CUDA 上因整型计算溢出导致的问题，包括：bernoulli、gaussian_random、gumbel_softmax、multinomial、truncated_gaussian_random、uniform_random_inplace、uniform_random ops。([#37670](https://github.com/PaddlePaddle/Paddle/pull/37670)) - -- 修复 `paddle.nn.Sequential` 在 for 循环遍历 sublayers 时会报 KeyError 错误的 bug。([#39372](https://github.com/PaddlePaddle/Paddle/pull/39372)) - -- 修复 `paddle.nn.functional.unfold` 在静态图下编译时检查 shape 错误的 bug。([#38907](https://github.com/PaddlePaddle/Paddle/pull/38907), [#38819](https://github.com/PaddlePaddle/Paddle/pull/38819)) - -- 修复静态图使用 dropout 时如果指定了 `axis` 后会报错的问题。([#37223](https://github.com/PaddlePaddle/Paddle/pull/37223)) - -- 迁移 `paddle.nn.MultiHeadAttention`中 matmul 算子到 matmul_v2 算子。([#36222](https://github.com/PaddlePaddle/Paddle/pull/36222)) - -- 修复 `paddle.nn.functional.label_smooth`在输入为空 Tensor 时抛出 FPE 的问题。([#35861](https://github.com/PaddlePaddle/Paddle/pull/35861)) - -- 修复 reshape op 空 Tensor 形变问题， 支持将空 Tensor rehape 成[-1]。([#36087](https://github.com/PaddlePaddle/Paddle/pull/36087)) - -- 修复 `fill_diagonal`参数 offset 非零时会造成修改值跨行问题。([#36212](https://github.com/PaddlePaddle/Paddle/pull/36212)) - -- 修改动态图模式下 range op 返回 stop gradient 设置成 True。([#37486](https://github.com/PaddlePaddle/Paddle/pull/37486)) - -- 修复 Lamb 优化器当 Beta1Pow 和 Beta2Pow 在 GPU 上时更新错误的 bug。([#38518](https://github.com/PaddlePaddle/Paddle/pull/38518)) - -- 修复 conv2d 算子 FLAGS_cudnn_deterministic 设置不生效的问题。([#37173](https://github.com/PaddlePaddle/Paddle/pull/37173)) - -- 修复因早期版本的 cufft 没有定义 CUFFT_VERSION 引发的问题。([#37312](https://github.com/PaddlePaddle/Paddle/pull/37312)) - -- 修复 `paddle.ifftshit`, `paddle.fftshift` 计算错误问题。([#36834](https://github.com/PaddlePaddle/Paddle/pull/36834), [#36748](https://github.com/PaddlePaddle/Paddle/pull/36748)) - -- 修复 `paddle.fft` 系列 API 中的 `axis` 计算错误。([#36321](https://github.com/PaddlePaddle/Paddle/pull/36321)) - -- 修复 batch_norm_grad op 在 FP16 数据类型时输出数据类型注册的 bug，该 bug 会导致部分场景下编译失败，并且对 FP16 计算精度会有一定影响。([#42461](https://github.com/PaddlePaddle/Paddle/pull/42461)) - -- 修复 `paddle.nn.functional.pad` API 在模型动转静时，padding 为 Tensor 条件下的 Infershape 信息错误问题。([#42414](https://github.com/PaddlePaddle/Paddle/pull/42414)) - -- 修复 `paddle.distribution.StickBreakingTransform` 输入维度超过 2 时异常的问题。([#41762](https://github.com/PaddlePaddle/Paddle/pull/41672)) - -- 修复 fused_attention op 中 QK^T 计算出 nan/inf 的问题。([#42032](https://github.com/PaddlePaddle/Paddle/pull/42032)) - -- 修复 fused_attention op 中 FusedResidualDropoutBias 在 V100 上计算出 nan/inf 问题。([#42398](https://github.com/PaddlePaddle/Paddle/pull/42398)) - -- 修复 full_like op 在执行时引入的多余的 data transform 问题。([#41973](https://github.com/PaddlePaddle/Paddle/pull/41973)) - -- 修复 p_norm op 在 GPU 环境上计算 nan 的问题。([#41804](https://github.com/PaddlePaddle/Paddle/pull/41804)) - -- 修复 split op 在参数 sections 存在为 0 的 size 情况下，段错误的问题。([#41755](https://github.com/PaddlePaddle/Paddle/pull/41755)) - -- 修复 6 个 elementwise op（pow、complex、divide_double、multiply_double、fmax、fmin）在需要 broadcast 的情况下，多卡训练时报 Place(gpu:0) 不支持的问题。([#42332](https://github.com/PaddlePaddle/Paddle/pull/42332)) - -- 修复 import paddle 时由于 PIL 版本升级导致的废弃接口报 warning 的问题。([#42307](https://github.com/PaddlePaddle/Paddle/pull/42307)) - -- 修复静态图下 `paddle.linalg.matrix_rank`不支持 tol 为 FP64 Tensor 的问题。([#42085](https://github.com/PaddlePaddle/Paddle/pull/42085)) - -#### IR(Intermediate Representation) - -- 动态图转静态图 - - - 修复 `tensor_array` 搭配控制流使用时，在反向梯度累加时存在的类型推导错误问题。([#39585](https://github.com/PaddlePaddle/Paddle/pull/39585), [#39689](https://github.com/PaddlePaddle/Paddle/pull/39689)) - - - 修复动转静 AMP 训练时参数梯度类型未被正确设置的问题。([#40938](https://github.com/PaddlePaddle/Paddle/pull/40938)) - - - 修复代码中存在错位注释时，动转静代码解析报错的问题。([#39035](https://github.com/PaddlePaddle/Paddle/pull/39035), [#38003](https://github.com/PaddlePaddle/Paddle/pull/38003)) - - - 修复动转静代码中调用非 forward 函数时，Tensor 未被正确转化为 Variable 的问题。([#37296](https://github.com/PaddlePaddle/Paddle/pull/37296), [#38540](https://github.com/PaddlePaddle/Paddle/pull/38540)) - - - 修复动转静代码转写时 `paddle` 被错误地作为变量传递的问题。([#37999](https://github.com/PaddlePaddle/Paddle/pull/37999)) - - - 修复模型动转静后调用 `paddle.flops` 时模型参数统计错误的问题。([#36852](https://github.com/PaddlePaddle/Paddle/pull/36852)) - - - 修复使用 `paddle.jit.save/load` 接口加载模型后，在 train 模式和 no_grad 上下文中，显存会一直增长的问题。([#36434](https://github.com/PaddlePaddle/Paddle/pull/36434)) - - - 添加在 convert_call 对 generator function 转换时的警告。([#35369](https://github.com/PaddlePaddle/Paddle/pull/35369)) - - - 修复 run_program op 依赖分析的问题。([#38470](https://github.com/PaddlePaddle/Paddle/pull/38470)) - - - 修复控制流 For 中返回单值时代码转换的问题。([#40683](https://github.com/PaddlePaddle/Paddle/pull/40683)) - - - 修复控制流 cond 的输入包含 LoDTensorArray 时，生成反向 op 会报错的问题。([#39585](https://github.com/PaddlePaddle/Paddle/pull/39585)) - - - 修复 `padddle.jit.save`在导出动转静模型时丢失顶层 Layer 的 forward_pre_hook 和 forward_post_hook 的问题。([#42273](https://github.com/PaddlePaddle/Paddle/pull/42273)) - - - 修复 `paddle.expand`中 shape 参数包含 Tensor 在动转静时会转换报错的问题。([#41973](https://github.com/PaddlePaddle/Paddle/pull/41973)) - -#### 分布式训练 - -- 分布式训练基础功能 - - - 修复分布式多机训练时，端口报错的问题。([#37274](https://github.com/PaddlePaddle/Paddle/pull/37274)) - - - 修复 brpc 编译依赖问题。([#37064](https://github.com/PaddlePaddle/Paddle/pull/37064)) - - - 修复 Fleet 启动时，由于 tcp 自连接产生的端口被占用的问题。([#38174](https://github.com/PaddlePaddle/Paddle/pull/38174)) - - - 修复数据并行下，由于 FP16 参数在多卡下初始化不一致，导致精度下降的问题。([#38838](https://github.com/PaddlePaddle/Paddle/pull/38838), [#38563](https://github.com/PaddlePaddle/Paddle/pull/38563), [#38405](https://github.com/PaddlePaddle/Paddle/pull/38405)) - - - 修复数据并行下，由于 FP16 梯度同步时，没有除以卡数，导致精度下降的问题。([#38378](https://github.com/PaddlePaddle/Paddle/pull/38378)) - -- 动态图混合并行 - - - 修复在混合并行下，通过使用新 update 接口，FP16 模式不更新参数的问题。([#36017](https://github.com/PaddlePaddle/Paddle/pull/36017)) - -- 静态图混合并行 - - - 修复分布式 dp 模式下 grad merge 与 ClipGradientByGlobalNorm 不兼容的问题。([#36334](https://github.com/PaddlePaddle/Paddle/pull/36334)) - - - 修复混合并行下，张量模型并行的非分布式参数在初始化阶段未被广播，导致各卡非分布式参数不一致的问题。([#36186](https://github.com/PaddlePaddle/Paddle/pull/36186)) - - - 修复 sharding 开启 offload 时，sharding 的 save_persistables 接口未保存 FP16 参数和 offload 持久化变量的问题。([#40477](https://github.com/PaddlePaddle/Paddle/pull/40477)) - - - 修复开启 sharding 训练时，ema 参数在非 0 号卡上无法保存的问题。([#39860](https://github.com/PaddlePaddle/Paddle/pull/39860)) - - - 修复 FC 按照列切分梯度计算错误的问题。([#38724](https://github.com/PaddlePaddle/Paddle/pull/38724)) - - - 修复 DistributedStrategy 设置为 without_graph_optimizer 时和 rnn 一起使用报错的问题。([#36176](https://github.com/PaddlePaddle/Paddle/pull/36176)) - -- GPUPS 参数服务器训练 - - - 修复 GPUPS 宏定义触发 CPU 分支编译问题。([#37248](https://github.com/PaddlePaddle/Paddle/pull/37248)) - - - 修复 GPUPS 流水线训练时在保存 delta 和 pullsparse 并发时引发的偶发报错问题。([#37233](https://github.com/PaddlePaddle/Paddle/pull/37233)) - - - 修复 HDFSClient 查询目录未返回全路径，引发下载报错问题。([#36590](https://github.com/PaddlePaddle/Paddle/pull/36590)) - - - 修复 GPUPS 流水线训练时拉取老参数问题。([#36512](https://github.com/PaddlePaddle/Paddle/pull/36512)) - - - 修复 GPUPS 多流 allocation 问题。([#37476](https://github.com/PaddlePaddle/Paddle/pull/37476)) - - - 修复 GPUPS pybind 出 core 的问题。([#37287](https://github.com/PaddlePaddle/Paddle/pull/37287)) - -#### 其他 - -- 修复动态图量化训练保存模型时 clip_extra 的问题。([#38323](https://github.com/PaddlePaddle/Paddle/pull/38323)) - -- 修复动态图量化训练 abs_max scale 初始化的问题。([#39307](https://github.com/PaddlePaddle/Paddle/pull/39307)) - -- 修复动态图量化训练保存模型节点异常的问题。([#38102](https://github.com/PaddlePaddle/Paddle/pull/38102), [#38012](https://github.com/PaddlePaddle/Paddle/pull/38012)) - -- 修复离线量化 flatten op 输出错误问题。([#37722](https://github.com/PaddlePaddle/Paddle/pull/37722)) - -- 修复了反量化 matmul op 时，维度对不上的问题。([#36982](https://github.com/PaddlePaddle/Paddle/pull/36982)) - -- 修复了量化无权重的 matmul_v2 时，错误添加量化 op 的问题。([#36593](https://github.com/PaddlePaddle/Paddle/pull/36593)) - -- 修复 conv op channel wise 量化在保存模型时 quant_axis 属性保存错误。([#39054](https://github.com/PaddlePaddle/Paddle/pull/39054)) - -- 修复 ChannelWise 量化训练速度慢的问题。([#40772](https://github.com/PaddlePaddle/Paddle/pull/40772)) - -- 修复量化训练初始化为 0 的 Tensor 出 NAN 的问题。([#36762](https://github.com/PaddlePaddle/Paddle/pull/36762)) - -- 修复多线程场景下混合精度 amp_level 设置错误问题。([#39198](https://github.com/PaddlePaddle/Paddle/pull/39198)) - -- 修复混合精度训练与 PyLayer，Recompute 等一起使用时，PyLayer 和 Recompute 中未正确设置混合精度的问题。([#39950](https://github.com/PaddlePaddle/Paddle/pull/39950), [#40042](https://github.com/PaddlePaddle/Paddle/pull/40042)) - -- 修复了 Mac 下编译自定义算子时 `D_GLIBCXX_USE_CXX11_ABI` 未生效的问题。([#37878](https://github.com/PaddlePaddle/Paddle/pull/37878)) - -- 修复 initializer 相关 API 在 block=None 时动静行为不统一的问题。([#37827](https://github.com/PaddlePaddle/Paddle/pull/37827)) - -- 修复 python3.6 环境下没有 fluid 模块的 bug。([#35862](https://github.com/PaddlePaddle/Paddle/pull/35862)) - -- 修复优化器 `paddle.optimizer.Adamw` 错误调用 adam op 的 bug。([#36028](https://github.com/PaddlePaddle/Paddle/pull/36028)) - -- 修复 multi tensor 策略下 `paddle.optimizer.Momentum` 优化器参数 `regularizer` 属性为 None 时的逻辑错误。([#38344](https://github.com/PaddlePaddle/Paddle/pull/38344)) - -- 修复 multi tensor 策略下 `paddle.optimizer.Momentum`、`paddle.optimizer.Adam` 优化器会对 `multi_precision` 属性进行修改的错误。([#38991](https://github.com/PaddlePaddle/Paddle/pull/38991)) - -- 修复最终态 API amp 与 optional 类型 Tensor 组合使用的代码编译错误。([#40980](https://github.com/PaddlePaddle/Paddle/pull/40980)) - -- 修复 paddle+lite+xpu 预测库调用 lite CPU 预测时会报错的 bug，修复 paddle+lite(without NNAdapter) 编译时会报错的 bug。([#37449](https://github.com/PaddlePaddle/Paddle/pull/37449)) - -- 修复 Debug 编译模式下 LoDTensorArray 因 Pybind11 绑定不一致导致 crash 的 bug。([#37954](https://github.com/PaddlePaddle/Paddle/pull/37954)) - -- 修复 shape 参数为 Tensor 和 int 构成列表的极端情况下，无法正确构建 Tensor 的 bug。([#38284](https://github.com/PaddlePaddle/Paddle/pull/38284)) - -- 修复 `paddle.optimizer.AdamW` API 兼容性问题。([#37905](https://github.com/PaddlePaddle/Paddle/pull/37905)) - -- 修复 _InstanceNormBase 中 extra_repr 的返回错误。([#38537](https://github.com/PaddlePaddle/Paddle/pull/38537)) - -- 修复联编开启 -DWITH_DISTRIBUTED 生成 Paddle Inference 缺少符号 `paddle::distributed::TensorTable` 的问题。([#41128](https://github.com/PaddlePaddle/Paddle/pull/41128)) - -- matmul_v2 op 新增 shape check，在 shape 中存在 0 值进行信息报错。([#35791](https://github.com/PaddlePaddle/Paddle/pull/35791)) - -- 修复动态图 recompute 对于没有梯度输入提示信息反复打印，改成用 warning 只打印一次的方式。([#38293](https://github.com/PaddlePaddle/Paddle/pull/38293)) - -- 修复 gelu op 在视觉模型中训练后期在验证集上精度低的问题。([#38450](https://github.com/PaddlePaddle/Paddle/pull/38450)) - -- 修复 adamw op 在数值计算上误差问题。([#37746](https://github.com/PaddlePaddle/Paddle/pull/37746)) - -- 补充 sparse_momentum `_C_ops` 接口 MasterParam 和 MasterParamOut 参数。([#39969](https://github.com/PaddlePaddle/Paddle/pull/39969)) - -- 修复 python3.6 环境下没有 `distributed` 模块的 bug。([#35848](https://github.com/PaddlePaddle/Paddle/pull/35848)) - -- 修复 eigh 单元测试数据初始化问题。([#39568](https://github.com/PaddlePaddle/Paddle/pull/39568)) - -- 修复 eigvalsh 单元测试数据初始化问题。([#39841](https://github.com/PaddlePaddle/Paddle/pull/39841)) - -- 修复 segment op 在 V100 上寄存器使用过多导致不能正常运行的问题。([#38113](https://github.com/PaddlePaddle/Paddle/pull/38113)) - -- 修复 conv 相关算子稀疏化维度错误的问题。([#36054](https://github.com/PaddlePaddle/Paddle/pull/36054)) - -- 提供自动稀疏训练（Automatic SParsity）静态图相关功能 Alias 至 `Paddle.static.sparsity`。([#36525](https://github.com/PaddlePaddle/Paddle/pull/36525)) - -- 修复 divide op 整数除法还是整数的 bug。([#40890](https://github.com/PaddlePaddle/Paddle/pull/40890)) - -- 修复 `paddle.multiplex` 候选 Tensor 大小为 0 崩溃问题。([#34972](https://github.com/PaddlePaddle/Paddle/pull/34972)) - -- 修复 `paddle.kl_div` 参数 `reduction` 给定情况下速度异常的问题。([#37283](https://github.com/PaddlePaddle/Paddle/pull/37283)) - -- 修复 Cifar 数据集加载 data source 无序的问题。([#37272](https://github.com/PaddlePaddle/Paddle/pull/37272)) - -- 修复 ProgressBar 类中 loss 从 uint16 到 float 的转换。([#39231](https://github.com/PaddlePaddle/Paddle/pull/39231)) - -- 修复 ShareBufferWith 共享数据类型的问题。([#37464](https://github.com/PaddlePaddle/Paddle/pull/37464), [#37247](https://github.com/PaddlePaddle/Paddle/pull/37247)) - -- 修复 `paddle.io.DataLoader` 使用 IterableDataset 并且 num_workers>0 时的性能问题。([#40541](https://github.com/PaddlePaddle/Paddle/pull/40541)) - -- 修复 `paddle.vision.ops.yolo_loss` 动态图返回值不全的问题。([#40185](https://github.com/PaddlePaddle/Paddle/pull/40185)) - -- 移出 `paddle.io.BatchSampler` 对输入参数 dataset 需要是 `paddle.io.Dataset` 类型的限制，扩大对用户自定义数据集的支持。([#40184](https://github.com/PaddlePaddle/Paddle/pull/40184)) - -- 修复 `paddle.summary` 报错 op_flops 不存在的问题。([#36489](https://github.com/PaddlePaddle/Paddle/pull/36489)) - -- 修复 lars_momentum op 在 lars_weight_decay=0 时公式错误的问题。([#40892](https://github.com/PaddlePaddle/Paddle/pull/40892)) - -- 修复 optimize-offload 无法保存 presistable var 的问题。([#36433](https://github.com/PaddlePaddle/Paddle/pull/36433)) - -- 修复 optimizer-offload 不支持 adamw op type 的问题。([#36432](https://github.com/PaddlePaddle/Paddle/pull/36432)) - -- 修复多线程场景下，Tracer 中 enable_program_desc_tracing_数据不安全的问题。([#39776](https://github.com/PaddlePaddle/Paddle/pull/39776)) - -- 修复模型读取时模型档案大小未初始化的问题。([#40518](https://github.com/PaddlePaddle/Paddle/pull/40518)) - -- 修复 Expand op 逻辑 bug，当输入 Tensor X 的维度，小于要拓展的 shape 时，可能导致取得 Out.Shape 是错误的。([#38677](https://github.com/PaddlePaddle/Paddle/pull/38677)) - -- 修复 Expand_As op 只取 y.shape，而没有 Y 变量输入时，导致的动转静报错。([#38677](https://github.com/PaddlePaddle/Paddle/pull/38677)) - -- 修复 Expand_As op 计算输出 shape 时逻辑的错误。([#38677](https://github.com/PaddlePaddle/Paddle/pull/38677)) - - -- 修复 `core.VarDesc.VarType.STRINGS` 类型的变量获取 `lod_level` 属性报错的问题，并且设置其 `lod_level` 为 None。([#39077](https://github.com/PaddlePaddle/Paddle/pull/39077)) - -- 修复框架功能 `PyLayer` 不支持不同 dtype 的问题。([#37974](https://github.com/PaddlePaddle/Paddle/pull/37974)) - -- 修复了学习率衰减 API `paddle.optimizer.lr.PolynomialDecay` 的零除问题。([#38782](https://github.com/PaddlePaddle/Paddle/pull/38782)) - -- 修复调用 DisableGlogInfo() 接口后依旧残留部分日志的问题。([#36356](https://github.com/PaddlePaddle/Paddle/pull/36356)) - -- 修复 SimpleRNN、GRU 和 LSTM API CPU 训练时多层 RNN（dropout 设置为 0 时）反向计算出错的问题。([#37080](https://github.com/PaddlePaddle/Paddle/pull/37080)) - -- 为 cufft 和 hipfft 后端的 fft 添加了 cache。([#36646](https://github.com/PaddlePaddle/Paddle/pull/36646)) - -- 使 `paddle.roll` 的 shifts 参数支持传入 Tensor。([#36727](https://github.com/PaddlePaddle/Paddle/pull/36727)) - -- 为 fft 添加 onemkl 作为可选的计算后端。([#36414](https://github.com/PaddlePaddle/Paddle/pull/36414)) - -- 修复 mamtul_v2 和 elementwise_div 两个 op 在 bfloat16 类型下的精度问题。([#42479](https://github.com/PaddlePaddle/Paddle/pull/42479)) - -- 修复显存回收时 LoDTensorArray 只清理内部 Tensor 而未清空 Array 导致的下个 step 可能出错的问题。([#42398](https://github.com/PaddlePaddle/Paddle/pull/42398)) - -## 4. 部署方向（Paddle Inference） - -### （1）新增特性 - -#### 新增 API - -- 增加 Java API，Java 开发者可以通过简单灵活的接口实现在服务端和云端的高性能推理。([#37162](https://github.com/PaddlePaddle/Paddle/pull/37162)) - -- 增加 `GetTrtCompileVersion` 和 `GetTrtRuntimeVersion` 接口，用于获取 TensorRT 版本信息。([#36429](https://github.com/PaddlePaddle/Paddle/pull/36429)) - -- 增加 `ShareExternalData` 接口，避免推理时对输入数据进行内存拷贝。([#39809](https://github.com/PaddlePaddle/Paddle/pull/39809)) - -#### 新增功能 - -- 新增 ONNX Runtime 后端支持，当前集成版本只支持 CPU。([#39988](https://github.com/PaddlePaddle/Paddle/pull/39988), [#40561](https://github.com/PaddlePaddle/Paddle/pull/40561)) - -- 基于 Paddle Lite 子图方式，新增昇腾 310 推理支持。([#35226](https://github.com/PaddlePaddle/Paddle/pull/35226)) - -- 新增原生 GPU FP16 推理功能。([#40531](https://github.com/PaddlePaddle/Paddle/pull/40531)) - -- switch_ir_debug 接口增加 dump 模型的功能。([#36581](https://github.com/PaddlePaddle/Paddle/pull/36581)) - -- 新增 TensorRT config 的配置接口：`void UpdateConfigInterleaved(paddle_infer::Config* c, bool with_interleaved)`，用于 int8 量化推理中特殊的数据排布。([#38884](https://github.com/PaddlePaddle/Paddle/pull/38884)) - -- log 中增加 TensorRT inspector 输出信息，仅在 TensorRT 8.2 及以上版本有效。([#38362](https://github.com/PaddlePaddle/Paddle/pull/38362)，[#38200](https://github.com/PaddlePaddle/Paddle/pull/38200))) - -- 增加 TensorRT ASP 稀疏推理支持。([#36413](https://github.com/PaddlePaddle/Paddle/pull/36413)) - -### （2）底层优化 - -#### CPU 性能优化 - -- 优化 MKLDNN 的缓存机制。([#38336](https://github.com/PaddlePaddle/Paddle/pull/38336), [#36980](https://github.com/PaddlePaddle/Paddle/pull/36980), [#36695](https://github.com/PaddlePaddle/Paddle/pull/36695)) - -- 新增 matmul_scale_fuse pass。([#37962](https://github.com/PaddlePaddle/Paddle/pull/37962)) - -- 新增 MKLDNN reshape_transpose_matmul_v2_mkldnn_fuse_pass。([#37847](https://github.com/PaddlePaddle/Paddle/pull/37847), [#40948](https://github.com/PaddlePaddle/Paddle/pull/40948)) - -- 新增 MKLDNN conv_hard_sigmoid_mkldnn_fuse_pass。([#36869](https://github.com/PaddlePaddle/Paddle/pull/36869)) - -- 新增 MKLDNN matmul_v2_transpose_reshape_fuse_pass。([#36481](https://github.com/PaddlePaddle/Paddle/pull/36481)) - -- 新增 MKLDNN softplus_activation_mkldnn_fuse_pass。([#36657](https://github.com/PaddlePaddle/Paddle/pull/36657)) - -- 新增 MKLDNN elt_act_mkldnn_fuse_pass。([#36541](https://github.com/PaddlePaddle/Paddle/pull/36541)) - -- 新增 MKLDNN mish 算子及 conv_mish_mkldnn_fuse_pass。([#38623](https://github.com/PaddlePaddle/Paddle/pull/38623)) - -#### GPU 性能优化 - -- 将推理默认的显存分配策略由 `naive_best_fit` 变更为 `auto_growth`，解决部分模型占满 GPU 显存问题。([#41491](https://github.com/PaddlePaddle/Paddle/pull/41491)) - -- 支持 gelu、FC+gelu ops 使用 TensorRT 推理。([#38399](https://github.com/PaddlePaddle/Paddle/pull/38399))合作团队 - -- 支持 `deformable_conv` 在静态 shape 下使用 TensorRT 推理。([#36612](https://github.com/PaddlePaddle/Paddle/pull/36612) [#36850](https://github.com/PaddlePaddle/Paddle/pull/36850) [#37345](https://github.com/PaddlePaddle/Paddle/pull/37345)) - -- 支持 nearest_interp_v2 op 使用 TensorRT 推理。([#34126](https://github.com/PaddlePaddle/Paddle/pull/34126)) - -- 增加 `yolo_box`TensorRT plugin，支持输入参数 `iou_aware` 和 `iou_aware_factor`，使推理计算得到的 IoU 作为置信度的因子。([#34128](https://github.com/PaddlePaddle/Paddle/pull/34128)) - -- 支持 `elementwise_sub` 和 `elementwise_div` 调用 TensorRT 推理。([#40806](https://github.com/PaddlePaddle/Paddle/pull/40806) [#41253](https://github.com/PaddlePaddle/Paddle/pull/41253)) - -- 支持 `multiclass_nms3` 使用 TensorRT 推理。([#41181](https://github.com/PaddlePaddle/Paddle/pull/41181) [#41344](https://github.com/PaddlePaddle/Paddle/pull/41344)) - -- 支持 flatten_contiguous_rang op 使用 TensorRT 推理。([#38922](https://github.com/PaddlePaddle/Paddle/pull/38922)) - -- 支持 `pool2d` 属性 `padding` 的维度为 4、`global_pooling` 和 `ceil_mode` 为 True 情况下使用 TensorRT 推理。([#39545](https://github.com/PaddlePaddle/Paddle/pull/39545)) - -- 支持 batch_norm 和 elementwise_add 为 5 维时使用 TensorRT 推理。([#36446](https://github.com/PaddlePaddle/Paddle/pull/36446)) - -- 新增 pool3d 使用 TensorRT 推理。([#36545](https://github.com/PaddlePaddle/Paddle/pull/36545), [#36783](https://github.com/PaddlePaddle/Paddle/pull/36783)) - -- 增加 `reduce` int32 和 float 类型使用 TensorRT 推理，增加 `reduce_mean` GPU 算子 int32、int64 注册。([#39088](https://github.com/PaddlePaddle/Paddle/pull/39088)) - -- 修改 MatmulV2ToMul pass，修改限定条件（不支持广播）和 op_teller 映射条件。([#36652](https://github.com/PaddlePaddle/Paddle/pull/36652)) - -- 增加 TenorRT plugin 接口 AddPluginV2IOExt 的支持。([#36493](https://github.com/PaddlePaddle/Paddle/pull/36493)) - -- 增加 roi_align op 中 aligned 属性并支持 TensorRT 推理。([#38905](https://github.com/PaddlePaddle/Paddle/pull/38905)) - -- 增加 concat 属性 `axis = -1` 时支持 TensorRT 推理。([#39096](https://github.com/PaddlePaddle/Paddle/pull/39096)) - -- 新增 TensorRT plugin ：preln_emb_eltwise_layernorm、 preln_skip_la、rnorm ops， 用于 ERNIE 类模型性能优化。([#39570](https://github.com/PaddlePaddle/Paddle/pull/39570)) - -- 新增 TensorRT fuse pass：preln_embedding_eltwise_layernorm_fuse_pass, preln_skip_layernorm_fuse_pass，用于 ERNIE 类模型性能优化。([#39508](https://github.com/PaddlePaddle/Paddle/pull/39508)) - -- 将 matmul 融合相关的 pass 基于不同的后端（GPU、CPU、TensorRT）拆开，支持 FC 权重的转置功能。([#39369](https://github.com/PaddlePaddle/Paddle/pull/39369)) - -- 新增 roll、strided_slice、slice op 在动态 shape 的情况下对 TensorRT 的支持。([#41913](https://github.com/PaddlePaddle/Paddle/pull/41913), [#41573](https://github.com/PaddlePaddle/Paddle/pull/41573), [#41467](https://github.com/PaddlePaddle/Paddle/pull/41467)) - -- 新增 div op 对 TensorRT 的支持。([#41243](https://github.com/PaddlePaddle/Paddle/pull/41243)) - -- 量化支持 - - - `PostTrainingQuantization` API 新增支持`paddle.io.DataLoader` 对象或者 `Python Generator`的输入。([#38686](https://github.com/PaddlePaddle/Paddle/pull/38686)) - - - ERNIE 全量化模型推理支持 interleaved 数据排布。([#39424](https://github.com/PaddlePaddle/Paddle/pull/39424)) - - - 支持 PaddleSlim 新量化模型格式推理。([#41049](https://github.com/PaddlePaddle/Paddle/pull/41049)) - - - 新增 matmul int8 量化的推理 op converter 和 plugin。([#37285](https://github.com/PaddlePaddle/Paddle/pull/37285)) - - - 新增判断模型所有 op 能否支持 int8 量化的 pass。([#36042](https://github.com/PaddlePaddle/Paddle/pull/36042)) - - - 支持 multihead attention 非变长分支中 FC 部分的量化推理。([#39660](https://github.com/PaddlePaddle/Paddle/pull/39660)) - -#### 昇腾 NPU 相关功能 - -- - 重构 shape 算子前向计算逻辑，支持在 NPU 上执行。([#39613](https://github.com/PaddlePaddle/Paddle/pull/39613)) - - - 重构 reshape 算子前向计算逻辑，支持 ShapeTensor 输入。([#38748](https://github.com/PaddlePaddle/Paddle/pull/38748)) - - - 模型权重加载时精度类型统一。([#39160](https://github.com/PaddlePaddle/Paddle/pull/39160)) - -### （3）问题修复 - -#### 框架及 API 修复 - -- 修复保存静态图时模型剪裁的问题。([#37579](https://github.com/PaddlePaddle/Paddle/pull/37579)) - -- C API 增加对的字符串的封装 PD_Cstr，并提供构造和析构的方式，避免用户直接使用 C 运行时库来析构字符串。([#38667](https://github.com/PaddlePaddle/Paddle/pull/38667)) - -- 修复预测时内存复用的逻辑问题。([#37324](https://github.com/PaddlePaddle/Paddle/pull/37324)) - -- 修复多线程下内存复用报错问题。([#37894](https://github.com/PaddlePaddle/Paddle/pull/37894)) - -- 在没有权重文件时，允许传递空字符串进行推理。([#38579](https://github.com/PaddlePaddle/Paddle/pull/38579)) - -- 修复开启 TensorRT dynamic shape 后不支持 clone 问题。([#38520](https://github.com/PaddlePaddle/Paddle/pull/38520)) - -- 修复开启 TensorRT dynamic shape 后多线程 clone 报错问题。([#40067](https://github.com/PaddlePaddle/Paddle/pull/40067)) - -- 修复 TensorRT engine 析构问题。([#35842](https://github.com/PaddlePaddle/Paddle/pull/35842), [#35938](https://github.com/PaddlePaddle/Paddle/pull/35938)) - -- lite xpu 接口修复无法选择 xpu 卡的问题。([#36610](https://github.com/PaddlePaddle/Paddle/pull/36610)) - -- TensorRT 动态 shape 参数自动生成接口增加文件存在性检查。([#36628](https://github.com/PaddlePaddle/Paddle/pull/36628)) - -- 修复 MKLDNN 不支持 conv3d 的问题。([#42055](https://github.com/PaddlePaddle/Paddle/pull/42055)) - -#### 后端能力修复 - -- 修复预测时 cuDNN 默认算法选择配置，使用非 deterministic 策略。([#41491](https://github.com/PaddlePaddle/Paddle/pull/41491)) - -- 修复 deformable_conv op 在 TensorRT plugin 资源回收处理错误的问题。([#38374](https://github.com/PaddlePaddle/Paddle/pull/38374)) - -- 修复 deformable_conv op 在 TensorRT plugin 序列化错误问题。([#38057](https://github.com/PaddlePaddle/Paddle/pull/38057)) - -- 适配 TensorRT 8.0 新的构建引擎和系列化 API。([#36769](https://github.com/PaddlePaddle/Paddle/pull/36769)) - -- 修复 Flatten2MatmulFusePass、Squeeze2MatmulFusePass、Reshape2MatmulFusePass 没有生效问题。([#37644](https://github.com/PaddlePaddle/Paddle/pull/37644)) - -- 修复 TensorRT 输入数据在上时报错的问题。([#37427](https://github.com/PaddlePaddle/Paddle/pull/37427)) - -- 增加输入维度错误时的报错信息。([#38962](https://github.com/PaddlePaddle/Paddle/pull/38962)) - -- 修复 EmbEltwiseLayernorm 输出类型错误的问题。([#40015](https://github.com/PaddlePaddle/Paddle/pull/40015)) - -- 删除 conv_affine_channel_fuse_pass 以及对应的单元测试。([#39817](https://github.com/PaddlePaddle/Paddle/pull/39817)) - -- 修复 adaptive_pool2d pass 错误替换 pool 属性的问题。([#39600](https://github.com/PaddlePaddle/Paddle/pull/39600)) - -- 修复 shuffle_channel_detect_pass 错误生成 shuffle_channel op 的问题。([#39242](https://github.com/PaddlePaddle/Paddle/pull/39242)) - -- 修复 transpose 参数错误。([#39006](https://github.com/PaddlePaddle/Paddle/pull/39006)) - -- 修复 nearest_interp_v2 输入 scale 维度小于 1 时崩溃的问题。([#38725](https://github.com/PaddlePaddle/Paddle/pull/38725)) - -- 修复 prelu 在 dynamic shape 时不支持一维输入的问题。([#39389](https://github.com/PaddlePaddle/Paddle/pull/39389)) - -- 修复 slice 的 special_slice_plugin 的核函数计算错误的问题。([#39875](https://github.com/PaddlePaddle/Paddle/pull/39875)) - -- 暂时禁用 skip_layernorm 变长下的 int8 分支，防止精度下降。([#39991](https://github.com/PaddlePaddle/Paddle/pull/39991)) - -- 修复关于支持 preln_ernie 模型的一些 bug。([#39733](https://github.com/PaddlePaddle/Paddle/pull/39733)) - -- 修复 slice 在 ERNIE 中 threads 可能超过限制的 bug，修复 spacial_slice 误触的 bug。([#39096](https://github.com/PaddlePaddle/Paddle/pull/39096)) - -- 修复 elementwise 在维度相同时不支持广播的问题。([#37908](https://github.com/PaddlePaddle/Paddle/pull/37908)) - -- 修复 nearest_interp op 当 align_corners 为 True 时，TensorRT layer 的结果和原生 op 的结果有 diff，底层实现不一样。([#37525](https://github.com/PaddlePaddle/Paddle/pull/37525)) - -- 修复 qkv_plugin：核函数计算错误。([#37096](https://github.com/PaddlePaddle/Paddle/pull/37096)) - -- 修复动态量化的推理 pass 的问题。([#35879](https://github.com/PaddlePaddle/Paddle/pull/35879)) - -- 当 Tensor 请求的内存容量低于已分配的 size 时直接复用。([#37880](https://github.com/PaddlePaddle/Paddle/pull/37880)) - -- 修复 ERNIE 定长模型开启 TensorRT 出现的 hang 问题。([#37839](https://github.com/PaddlePaddle/Paddle/pull/37839)) - -- 修复 TensorRT int8 时缺失 dynamic range 信息崩溃问题。([#36900](https://github.com/PaddlePaddle/Paddle/pull/36900)) - -- 修复 slice 反序列化代码问题。([#36588](https://github.com/PaddlePaddle/Paddle/pull/36588)) - -- 修复 yolo box 计算公式错误问题。([#36240](https://github.com/PaddlePaddle/Paddle/pull/36240)) - -- 修复老版本模型在使用新版本 roi_align 时崩溃问题。([#38788](https://github.com/PaddlePaddle/Paddle/pull/38788)) 外部开发者 - -- 修复 softmax 在 python 和 C++上性能差异较大的问题。([#37130](https://github.com/PaddlePaddle/Paddle/pull/37130)) - -- 修复 matmul 在静态 shape 2 维输入和动态 shape 3 维输入情况下推理失败问题。([#36849](https://github.com/PaddlePaddle/Paddle/pull/36849)) - -- 修复 reshape_transpose_matmul_mkldnn_fuse_pass 对 shape 处理不当问题。([#36731](https://github.com/PaddlePaddle/Paddle/pull/36731)) - -- 修复输入为 2 维，但 TensorRT 获取到 4 维的问题。([#36614](https://github.com/PaddlePaddle/Paddle/pull/36614)) - -- 修复 interpolate_v2 MKLDNN 算子在 scale 属性为空时报错问题。([#36623](https://github.com/PaddlePaddle/Paddle/pull/36623)) - -- 修复 recurrent 算子在多线程场景性能差问题。([#36052](https://github.com/PaddlePaddle/Paddle/pull/36052)) - -- 移除 relu、sigmoid、tanh、relu6、batch_norm、clip、concat、gelu、hard_sigmoid、prelu、softmax、split、swish 对 TensorRT 2 维输入的限制。([#37097](https://github.com/PaddlePaddle/Paddle/pull/37097)) - -- 修复 reshape op 使用 TensorRT 推理。([#41090](https://github.com/PaddlePaddle/Paddle/pull/41090)) - -- 修复 matmul 相关 pass，兼容 matmul_v2。([#36424](https://github.com/PaddlePaddle/Paddle/pull/36424)) - -- 开启 TensorRT 时，conv2d 算子中 padding 方式支持 VALID 及 SAME 属性。([#38999](https://github.com/PaddlePaddle/Paddle/pull/38999)) - -- 修复 MKLDNN 多输入算子量化问题。([#39593](https://github.com/PaddlePaddle/Paddle/pull/39593), [#39346](https://github.com/PaddlePaddle/Paddle/pull/39346), [#40717](https://github.com/PaddlePaddle/Paddle/pull/40717)) - -- 修复 MKLDNN 量化场景下 conv+activation 的 scale 错误问题。([#38331](https://github.com/PaddlePaddle/Paddle/pull/38331)) - -- 修复 MKLDNN 无参数算子量化中，根据后续算子量化情况不同需做不同处理的问题。([#39342](https://github.com/PaddlePaddle/Paddle/pull/39342)) - -- 修复 MKLDNN cpu_bfloat16_placement_pass 中的数据类型相关问题。([#38702](https://github.com/PaddlePaddle/Paddle/pull/38702)) - -- 修复 MKLDNN bfloat16 推理中 split 算子执行问题。([#39548](https://github.com/PaddlePaddle/Paddle/pull/39548)) - -- 修复 MKLDNN matmul_v2 算子不支持 6 维问题。([#36342](https://github.com/PaddlePaddle/Paddle/pull/36342), [#38665](https://github.com/PaddlePaddle/Paddle/pull/38665)) - -- 修复 MKLDNN matmul_v2_transpose_reshape 中的 MKLDNN DeviceContext 错误问题。([#38554](https://github.com/PaddlePaddle/Paddle/pull/38554)) - -- 修复分割模型在 MKLDNN 推理场景计算结果错误问题。([#37310](https://github.com/PaddlePaddle/Paddle/pull/37310)) - -- 修复 MKLDNN bfloat16 placement 算子列表并添加缺失算子。([#36291](https://github.com/PaddlePaddle/Paddle/pull/36291)) - -- 修复 MKLDNN 算子的格式问题，包括：FC、conv_transpose、6 维 Tensor 报错问题、conv 对 `NHWC` 输入的输出 format 错误问题。([#38890](https://github.com/PaddlePaddle/Paddle/pull/38890), [#37344](https://github.com/PaddlePaddle/Paddle/pull/37344), [#37175](https://github.com/PaddlePaddle/Paddle/pull/37175), [#38553](https://github.com/PaddlePaddle/Paddle/pull/38553), [#40049](https://github.com/PaddlePaddle/Paddle/pull/40049), [#39097](https://github.com/PaddlePaddle/Paddle/pull/39097)) - -- 修复 MKLDNN 多线程推理场景因 cache 机制报错问题。([#36290](https://github.com/PaddlePaddle/Paddle/pull/36290), [#35884](https://github.com/PaddlePaddle/Paddle/pull/35884)) - -- 修复 MKLDNN 因 matmul 及 FC 引起的量化模型精度异常问题。([#38023](https://github.com/PaddlePaddle/Paddle/pull/38023), [#37618](https://github.com/PaddlePaddle/Paddle/pull/37618)) - -- 修复 MKLDNN 量化转换脚本因 pass 缺少引起的量化模型精度异常问题。([#37619](https://github.com/PaddlePaddle/Paddle/pull/37619), [#40542](https://github.com/PaddlePaddle/Paddle/pull/40542), - [#38912](https://github.com/PaddlePaddle/Paddle/pull/38912)) - -- 修复 MKLDNN 开启量 op 因为数据类型不匹配崩溃的问题。([#38133](https://github.com/PaddlePaddle/Paddle/pull/38133)) - -- 修复 MKLDNN 某些 op 修改 layout 后需要改回原 layout 的问题。([#39422](https://github.com/PaddlePaddle/Paddle/pull/39422)) - -- 修复针对昇腾 910 推理场景下，由于未释放 GIL 锁，导致与昇腾软件栈冲突，python API 下报错的问题。([#38605](https://github.com/PaddlePaddle/Paddle/pull/38605)) - -## 5. 环境适配 - -### 编译安装 - -- 从 2.3.0 版本开始，飞桨对框架支持的 GPU 架构种类进行了调整和升级。(更多请参考：[飞桨支持的 GPU 架构](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.3rc/install/Tables.html#gpu)) - -备注： - -- PIP 源安装是指用 `pip install paddlepaddle` 或 `pip install paddlepaddle-gpu`从 PIP 官网下载安装包及依赖库的安装方式，支持架构种类少，安装包更轻量，下载源来自国外（相比 bos 源支持架构种类精简，安装包更轻量，只提供一种 CUDA 版本的安装包）。 - - - 2.3 版本之前，飞桨 PIP 源安装包（CUDA10.2）支持的 GPU 架构为：3.5, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5。 - - - 2.3 版本之后，飞桨 PIP 源安装包（CUDA11.0）支持的 GPU 架构为：6.0, 6.1, 7.0, 7.5, 8.0 - -- 飞桨官网 bos 源是指从飞桨官网下载安装包及依赖库的安装方式，支持的 GPU 架构更多，下载源来自国内，速度较快。（相比 PIP 源支持架构种类多，提供多个 CUDA 版本的安装包）： - - - 2.3 版本之前，飞桨官网 bos 源安装包支持的 GPU 架构： - - - CUDA10：3.5, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5； - - - CUDA11：5.2，6.0，6.1，7.0，7.5，8.0。 - - - 2.3 版本之后，飞桨官网 bos 源安装包支持的 GPU 架构 - - - CUDA10：3.5, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5； - - - CUDA11：3.5, 5.0, 6.0, 6.1, 7.0, 7.5, 8.0。 - -- 支持 Python 3.10，修复 Windows 下某些 PythonC API 变化导致的编译 bug。([#41180](https://github.com/PaddlePaddle/Paddle/pull/42180)) - -- Windows 平台支持 Visual Studio 2019 编译。([#38719](https://github.com/PaddlePaddle/Paddle/pull/38719)) - -- 消除 Windows 平台编译时出现的各种 warning。([#38034](https://github.com/PaddlePaddle/Paddle/pull/38034), [#37890](https://github.com/PaddlePaddle/Paddle/pull/37890), [#37442](https://github.com/PaddlePaddle/Paddle/pull/37442), [#37439](https://github.com/PaddlePaddle/Paddle/pull/37439), [#36857](https://github.com/PaddlePaddle/Paddle/pull/36857)) - -- 修复底层数据结构升级引入的 jetson 编译问题。([#39669](https://github.com/PaddlePaddle/Paddle/pull/39669), [#39441](https://github.com/PaddlePaddle/Paddle/pull/39441)) - - -### 新硬件适配 - -- 自定义新硬件接入：提供一种插件式扩展 PaddlePaddle 硬件后端的方式。通过该功能，开发者无需为特定硬件修改 PaddlePaddle 代码，只需实现标准接口，并编译成动态链接库，则可作为插件供 PaddlePaddle 调用。降低为 PaddlePaddle 添加新硬件后端的开发难度。当前支持自定义 Runtime 接入和自定义 Kernel 接入。 - -- 华为 NPU 芯片（Ascend910）训练/推理支持，支持 ResNet50、YoloV3、BERT、Transformer 等多个模型，支持静态图与混合精度训练，支持单卡、单机、多机分布式训练。 - -- Graphcore IPU 芯片（包括 IPU Mk2 GC200 和 Bow IPU）训练/推理支持，支持 ResNet50、BERT 等模型，支持静态图训练，支持单芯片、单机、多机分布式训练。 - -- 寒武纪 MLU 芯片（MLU370x4）训练/推理支持，支持 ResNet50 等模型，支持静态图+动态图训练，支持混合精度训练，支持单卡、单机、多机分布式训练。 - -- 昆仑芯 2 代芯片（昆仑芯 AI 加速卡 R200、R300）训练/推理支持，支持 ResNet50、YoloV3、OCR-DB、SSD、MobilnetV3、UNet、BERT、Transformer、GPT-2、Wide&Deep、DeepFM，支持静态图+动态图训练，支持混合精度训练，支持单机单卡、单机多卡训练。 - -## Thanks to our Contributors - -This release contains contributions from the project core team as well as: - -Adam Osewski, Allen Guo, arlesniak, chenenquan, chenyanlann, fengkuangxiaxia, fuqianya, fwenguang, guguguzi, helen88, houj04, Jacek Czaja, jakpiase, jianghaicheng, joanna.wozna.intel, joeqiao12, Leo Chen, Leo Guo, Li-fAngyU, lidanqing, Liyulingyue, Matsumoto GAO, maxhuiy, Ming-Xu Huang, Nyakku Shigure, piotrekobi, piotrekobiIntel, QingshuChen, qipengh, Skr Bang, Sylwester Fraczek, Sławomir Siwek, taixiurong, tanzhipeng, Tomasz Socha, TTerror, Webbley, yaozhixin, ykkk2333, yujun, Zhangjingyu06, zhangxiaoci, zhangyikun02, zhangyk0314, zlsh80826, zn, Zuza. +#### Bug 修复 +- 修复 DTK 和 ROCM 版本升级的编译错误问题。 [#62832](https://github.com/PaddlePaddle/Paddle/pull/62832),[#62931](https://github.com/PaddlePaddle/Paddle/pull/62931),[#61872](https://github.com/PaddlePaddle/Paddle/pull/61872),[#63738](https://github.com/PaddlePaddle/Paddle/pull/63738) + +## 10.环境更新 +此版本飞桨完成基础依赖库的发版和更新同步，移除了不再更新的老旧依赖库。完成了多项优化提升编译效率、兼容性，完善 CI 流水线监测功能以提升用户安装体验。修复了多个已知编译问题，完善 paddle 的编译系统，新增了一些特性支持。通过相关优化工作，飞桨框架的编译安装体验进一步提升，给开发者带来更好的使用和开发体验。 + +### 新增支持 +- 支持用户安装 paddle 不依赖本地的 cuda 和 cudnn，提升用户安装体验。[#60841](https://github.com/PaddlePaddle/Paddle/pull/60841)，[#61973](https://github.com/PaddlePaddle/Paddle/pull/61973),[#61862](https://github.com/PaddlePaddle/Paddle/pull/61862),[#61235](https://github.com/PaddlePaddle/Paddle/pull/61235),[#61209](https://github.com/PaddlePaddle/Paddle/pull/61209),[#61653](https://github.com/PaddlePaddle/Paddle/pull/61653),[#64083](https://github.com/PaddlePaddle/Paddle/pull/64083) +- 全面支持 CUDA 12.3，同时完成 cuda10.2 退场。[#63356](https://github.com/PaddlePaddle/Paddle/pull/63356),[#60299](https://github.com/PaddlePaddle/Paddle/pull/60299),[#64171](https://github.com/PaddlePaddle/Paddle/pull/64171),[#62189](https://github.com/PaddlePaddle/Paddle/pull/62189),[#63392](https://github.com/PaddlePaddle/Paddle/pull/63392),[#64228](https://github.com/PaddlePaddle/Paddle/pull/64228),[#62498](https://github.com/PaddlePaddle/Paddle/pull/62498),[#64298](https://github.com/PaddlePaddle/Paddle/pull/64298) +- 全面支持 Python 3.12，带来了更强大的语言特性和性能优化，同时完成 python3.7 退场。[#59875](https://github.com/PaddlePaddle/Paddle/pull/59875)，[#59877](https://github.com/PaddlePaddle/Paddle/pull/59877),[#59876](https://github.com/PaddlePaddle/Paddle/pull/59876) +- 其他 paddle 依赖的第三方库升级：[#63741](https://github.com/PaddlePaddle/Paddle/pull/63741),[#64447](https://github.com/PaddlePaddle/Paddle/pull/64447),[#60195](https://github.com/PaddlePaddle/Paddle/pull/60195),[#60110](https://github.com/PaddlePaddle/Paddle/pull/60110),[#61509](https://github.com/PaddlePaddle/Paddle/pull/61509) + +### 编译优化 +- 优化了 paddle 的 CMake 代码，显著提升了编译效率和编译体验。[##59995](https://github.com/PaddlePaddle/Paddle/pull/59995),[#60167](https://github.com/PaddlePaddle/Paddle/pull/60167)，[#61052](https://github.com/PaddlePaddle/Paddle/pull/61052)，[#59995](https://github.com/PaddlePaddle/Paddle/pull/59995)，[#59607](https://github.com/PaddlePaddle/Paddle/pull/59607),[#63093](https://github.com/PaddlePaddle/Paddle/pull/63093),[#63887](https://github.com/PaddlePaddle/Paddle/pull/63887),[#62969](https://github.com/PaddlePaddle/Paddle/pull/62969),[#64007](https://github.com/PaddlePaddle/Paddle/pull/64007),[#59811](https://github.com/PaddlePaddle/Paddle/pull/59811),[#63045](https://github.com/PaddlePaddle/Paddle/pull/63045),[#60235](https://github.com/PaddlePaddle/Paddle/pull/60235),[#60240](https://github.com/PaddlePaddle/Paddle/pull/60240),[#60235](https://github.com/PaddlePaddle/Paddle/pull/60235),[#61411](https://github.com/PaddlePaddle/Paddle/pull/61411),[#61944](https://github.com/PaddlePaddle/Paddle/pull/61944),[#61961](https://github.com/PaddlePaddle/Paddle/pull/61961),[#59990](https://github.com/PaddlePaddle/Paddle/pull/59990),[#59478](https://github.com/PaddlePaddle/Paddle/pull/59478),[#61501](https://github.com/PaddlePaddle/Paddle/pull/61501),[#60066](https://github.com/PaddlePaddle/Paddle/pull/60066),[#64133](https://github.com/PaddlePaddle/Paddle/pull/64133),[#64231](https://github.com/PaddlePaddle/Paddle/pull/64231),[#60087](https://github.com/PaddlePaddle/Paddle/pull/60087),[#60348](https://github.com/PaddlePaddle/Paddle/pull/60348),[#60737](https://github.com/PaddlePaddle/Paddle/pull/60737),[#61364](https://github.com/PaddlePaddle/Paddle/pull/61364),[#63214](https://github.com/PaddlePaddle/Paddle/pull/63214),[#62454](https://github.com/PaddlePaddle/Paddle/pull/62454),[#62473](https://github.com/PaddlePaddle/Paddle/pull/62473),[#63692](https://github.com/PaddlePaddle/Paddle/pull/63692),[#63950](https://github.com/PaddlePaddle/Paddle/pull/63950) +- 支持在 linux 和 windowx 下 C++单测链接动态库，大幅减少 C++单测的体积大小和整个 build 目录大小。[#60008](https://github.com/PaddlePaddle/Paddle/pull/60008),[#60960](https://github.com/PaddlePaddle/Paddle/pull/60960),[#60960](https://github.com/PaddlePaddle/Paddle/pull/60960),[#60961](https://github.com/PaddlePaddle/Paddle/pull/60961),[#60831](https://github.com/PaddlePaddle/Paddle/pull/60831),[#60832](https://github.com/PaddlePaddle/Paddle/pull/60832),[#60833](https://github.com/PaddlePaddle/Paddle/pull/60833),[#61372](https://github.com/PaddlePaddle/Paddle/pull/61372),[#60834](https://github.com/PaddlePaddle/Paddle/pull/60834),[#61374](https://github.com/PaddlePaddle/Paddle/pull/61374),[#61463](https://github.com/PaddlePaddle/Paddle/pull/61463),[#61376](https://github.com/PaddlePaddle/Paddle/pull/61376),[#60830](https://github.com/PaddlePaddle/Paddle/pull/60830),[#61373](https://github.com/PaddlePaddle/Paddle/pull/61373),[#61672](https://github.com/PaddlePaddle/Paddle/pull/61672),[#61375](https://github.com/PaddlePaddle/Paddle/pull/61375),[#61676](https://github.com/PaddlePaddle/Paddle/pull/61676),[#62036](https://github.com/PaddlePaddle/Paddle/pull/62036),[#61945](https://github.com/PaddlePaddle/Paddle/pull/61945),[#61675](https://github.com/PaddlePaddle/Paddle/pull/61675),[#61674](https://github.com/PaddlePaddle/Paddle/pull/61674),[#62773](https://github.com/PaddlePaddle/Paddle/pull/62773),[#61238](https://github.com/PaddlePaddle/Paddle/pull/61238),[#59988](https://github.com/PaddlePaddle/Paddle/pull/59988),[#60307](https://github.com/PaddlePaddle/Paddle/pull/60307),[#59612](https://github.com/PaddlePaddle/Paddle/pull/59612),[#59942](https://github.com/PaddlePaddle/Paddle/pull/59942),[#59968](https://github.com/PaddlePaddle/Paddle/pull/59968),[#59978](https://github.com/PaddlePaddle/Paddle/pull/59978),[#60121](https://github.com/PaddlePaddle/Paddle/pull/60121),[#60149](https://github.com/PaddlePaddle/Paddle/pull/60149),[#60161](https://github.com/PaddlePaddle/Paddle/pull/60161),[#60160](https://github.com/PaddlePaddle/Paddle/pull/60160),[#60230](https://github.com/PaddlePaddle/Paddle/pull/60230),[#60154](https://github.com/PaddlePaddle/Paddle/pull/60154),[#60356](https://github.com/PaddlePaddle/Paddle/pull/60356),[#60392](https://github.com/PaddlePaddle/Paddle/pull/60392),[#60517](https://github.com/PaddlePaddle/Paddle/pull/60517),[#61131](https://github.com/PaddlePaddle/Paddle/pull/61131),[#60959](https://github.com/PaddlePaddle/Paddle/pull/60959) +- 新增对 Clang 编译器的支持，用户现在可以使用 Clang 进行编译，享受更快的编译速度和更好的报错信息提示。[#63382](https://github.com/PaddlePaddle/Paddle/pull/63382),[#63133](https://github.com/PaddlePaddle/Paddle/pull/63133),[#61705](https://github.com/PaddlePaddle/Paddle/pull/61705),[#63152](https://github.com/PaddlePaddle/Paddle/pull/63152),[#63373](https://github.com/PaddlePaddle/Paddle/pull/63373) + +### CI 流水线改进 +- 对 CI 流水线中的合入代码监测机制进行了完善，确保更高的代码质量和稳定性。新增了功能监控模块，实时监控 CI 流水线的各项指标，确保每个阶段的顺利执行，及时发现和解决问题。[#61384](https://github.com/PaddlePaddle/Paddle/pull/61384),[#62190](https://github.com/PaddlePaddle/Paddle/pull/62190),[#60758](https://github.com/PaddlePaddle/Paddle/pull/60758),[#60399](https://github.com/PaddlePaddle/Paddle/pull/60399),[#58623](https://github.com/PaddlePaddle/Paddle/pull/58623),[#62177](https://github.com/PaddlePaddle/Paddle/pull/62177),[#62361](https://github.com/PaddlePaddle/Paddle/pull/62361),[#62893](https://github.com/PaddlePaddle/Paddle/pull/62893),[#63705](https://github.com/PaddlePaddle/Paddle/pull/63705),[#64476](https://github.com/PaddlePaddle/Paddle/pull/64476),[#64752](https://github.com/PaddlePaddle/Paddle/pull/64752),[#64733](https://github.com/PaddlePaddle/Paddle/pull/64733),[#61914](https://github.com/PaddlePaddle/Paddle/pull/61914) + +### 代码清理 +- 删除了一些老旧的代码。[#63580](https://github.com/PaddlePaddle/Paddle/pull/63580),[#62840](https://github.com/PaddlePaddle/Paddle/pull/62840),[#62886](https://github.com/PaddlePaddle/Paddle/pull/62886),[#63046](https://github.com/PaddlePaddle/Paddle/pull/63046),[#63004](https://github.com/PaddlePaddle/Paddle/pull/63004),[#63039](https://github.com/PaddlePaddle/Paddle/pull/63039),[#62733](https://github.com/PaddlePaddle/Paddle/pull/62733),[#62773](https://github.com/PaddlePaddle/Paddle/pull/62773),[#62768](https://github.com/PaddlePaddle/Paddle/pull/62768),[#62744](https://github.com/PaddlePaddle/Paddle/pull/62744),[#62861](https://github.com/PaddlePaddle/Paddle/pull/62861),[#62774](https://github.com/PaddlePaddle/Paddle/pull/62774),[#62851](https://github.com/PaddlePaddle/Paddle/pull/62851),[#62973](https://github.com/PaddlePaddle/Paddle/pull/62973),[#63273](https://github.com/PaddlePaddle/Paddle/pull/63273),[#62445](https://github.com/PaddlePaddle/Paddle/pull/62445),[#64382](https://github.com/PaddlePaddle/Paddle/pull/64382),[#64409](https://github.com/PaddlePaddle/Paddle/pull/64409),[#64391](https://github.com/PaddlePaddle/Paddle/pull/64391),[#64310](https://github.com/PaddlePaddle/Paddle/pull/64310),[#64348](https://github.com/PaddlePaddle/Paddle/pull/64348),[#64651](https://github.com/PaddlePaddle/Paddle/pull/64651),[#64709](https://github.com/PaddlePaddle/Paddle/pull/64709),[#61714](https://github.com/PaddlePaddle/Paddle/pull/61714),[#62109](https://github.com/PaddlePaddle/Paddle/pull/62109),[#61751](https://github.com/PaddlePaddle/Paddle/pull/61751),[#61691](https://github.com/PaddlePaddle/Paddle/pull/61691),[#61735](https://github.com/PaddlePaddle/Paddle/pull/61735) +### Bug 修复 +- 修复多个 paddle 框架的编译问题。[#63297](https://github.com/PaddlePaddle/Paddle/pull/63297)，[#62994](https://github.com/PaddlePaddle/Paddle/pull/62994)，[#62651](https://github.com/PaddlePaddle/Paddle/pull/62651)，[#64408](https://github.com/PaddlePaddle/Paddle/pull/64408)，[#60934](https://github.com/PaddlePaddle/Paddle/pull/60934)，[#62899](https://github.com/PaddlePaddle/Paddle/pull/62899)，[#60528](https://github.com/PaddlePaddle/Paddle/pull/60528)，[#63158](https://github.com/PaddlePaddle/Paddle/pull/63158)，[#64549](https://github.com/PaddlePaddle/Paddle/pull/64549)，[#62351](https://github.com/PaddlePaddle/Paddle/pull/62351)，[#61259](https://github.com/PaddlePaddle/Paddle/pull/61259),[#61281](https://github.com/PaddlePaddle/Paddle/pull/61281),[#62304](https://github.com/PaddlePaddle/Paddle/pull/62304),[#60736](https://github.com/PaddlePaddle/Paddle/pull/60736),[#60811](https://github.com/PaddlePaddle/Paddle/pull/60811),[#63949](https://github.com/PaddlePaddle/Paddle/pull/63949),[#59892](https://github.com/PaddlePaddle/Paddle/pull/59892),[#60767](https://github.com/PaddlePaddle/Paddle/pull/60767),[#60856](https://github.com/PaddlePaddle/Paddle/pull/60856),[#61286](https://github.com/PaddlePaddle/Paddle/pull/61286),[#61638](https://github.com/PaddlePaddle/Paddle/pull/61638),[#62079](https://github.com/PaddlePaddle/Paddle/pull/62079),[#62142](https://github.com/PaddlePaddle/Paddle/pull/62142),[#62823](https://github.com/PaddlePaddle/Paddle/pull/62823),[#62814](https://github.com/PaddlePaddle/Paddle/pull/62814),[#62425](https://github.com/PaddlePaddle/Paddle/pull/62425),[#62619](https://github.com/PaddlePaddle/Paddle/pull/62619),[#60207](https://github.com/PaddlePaddle/Paddle/pull/60207),[#60765](https://github.com/PaddlePaddle/Paddle/pull/60765),[#61870](https://github.com/PaddlePaddle/Paddle/pull/61870),[#61923](https://github.com/PaddlePaddle/Paddle/pull/61923),[#62144](https://github.com/PaddlePaddle/Paddle/pull/62144),[#62426](https://github.com/PaddlePaddle/Paddle/pull/62426),[#63848](https://github.com/PaddlePaddle/Paddle/pull/63848),[#60682](https://github.com/PaddlePaddle/Paddle/pull/60682),[#61369](https://github.com/PaddlePaddle/Paddle/pull/61369),[#62882](https://github.com/PaddlePaddle/Paddle/pull/62882),[#63944](https://github.com/PaddlePaddle/Paddle/pull/63944),[#64812](https://github.com/PaddlePaddle/Paddle/pull/64812),[#60654](https://github.com/PaddlePaddle/Paddle/pull/60654),[#60887](https://github.com/PaddlePaddle/Paddle/pull/60887),[#62058](https://github.com/PaddlePaddle/Paddle/pull/62058),[#64639](https://github.com/PaddlePaddle/Paddle/pull/64639),[#60115](https://github.com/PaddlePaddle/Paddle/pull/60115),[#61940](https://github.com/PaddlePaddle/Paddle/pull/61940),[#62614](https://github.com/PaddlePaddle/Paddle/pull/62614),[#59914](https://github.com/PaddlePaddle/Paddle/pull/59914),[#63762](https://github.com/PaddlePaddle/Paddle/pull/63762),[#60145](https://github.com/PaddlePaddle/Paddle/pull/60145),[#60285](https://github.com/PaddlePaddle/Paddle/pull/60285),[#60378](https://github.com/PaddlePaddle/Paddle/pull/60378),[#60393](https://github.com/PaddlePaddle/Paddle/pull/60393),[#61057](https://github.com/PaddlePaddle/Paddle/pull/61057),[#61058](https://github.com/PaddlePaddle/Paddle/pull/61058),[#61151](https://github.com/PaddlePaddle/Paddle/pull/61151),[#61347](https://github.com/PaddlePaddle/Paddle/pull/61347),[#61554](https://github.com/PaddlePaddle/Paddle/pull/61554),[#61844](https://github.com/PaddlePaddle/Paddle/pull/61844),[#62915](https://github.com/PaddlePaddle/Paddle/pull/62915),[#61852](https://github.com/PaddlePaddle/Paddle/pull/61852),[#61704](https://github.com/PaddlePaddle/Paddle/pull/61704),[#61991](https://github.com/PaddlePaddle/Paddle/pull/61991),[#62264](https://github.com/PaddlePaddle/Paddle/pull/62264),[#62762](https://github.com/PaddlePaddle/Paddle/pull/62762),[#63820](https://github.com/PaddlePaddle/Paddle/pull/63820),[#63864](https://github.com/PaddlePaddle/Paddle/pull/63864),[#65017](https://github.com/PaddlePaddle/Paddle/pull/65017),[#61183](https://github.com/PaddlePaddle/Paddle/pull/61183),[#59866](https://github.com/PaddlePaddle/Paddle/pull/59866),[#61171](https://github.com/PaddlePaddle/Paddle/pull/61171),[#61290](https://github.com/PaddlePaddle/Paddle/pull/61290),[#61725](https://github.com/PaddlePaddle/Paddle/pull/61725),[#61614](https://github.com/PaddlePaddle/Paddle/pull/61614),[#61721](https://github.com/PaddlePaddle/Paddle/pull/61721),[#61494](https://github.com/PaddlePaddle/Paddle/pull/61494),[#61556](https://github.com/PaddlePaddle/Paddle/pull/61556),[#61689](https://github.com/PaddlePaddle/Paddle/pull/61689) + +## 11.文档相关的问题修复 +- 随着 API 功能增强工作的开展，对部分 API 文档也同步进行了修正和增强。[#62875](https://github.com/PaddlePaddle/Paddle/pull/62875), [#59793](https://github.com/PaddlePaddle/Paddle/pull/59793), [#60002](https://github.com/PaddlePaddle/Paddle/pull/60002), [#59985](https://github.com/PaddlePaddle/Paddle/pull/59985), [#63365](https://github.com/PaddlePaddle/Paddle/pull/63365), [#60962](https://github.com/PaddlePaddle/Paddle/pull/60962), [#60942](https://github.com/PaddlePaddle/Paddle/pull/60942), [#64232](https://github.com/PaddlePaddle/Paddle/pull/64232), [#63255](https://github.com/PaddlePaddle/Paddle/pull/63255) +- 更新/补充 API 文档。bernoulli_ ([#64504](https://github.com/PaddlePaddle/Paddle/pull/64504))，paddle.static.ctr_metric_bundle ([#60912](https://github.com/PaddlePaddle/Paddle/pull/60912))，LayerNorm ([#62928](https://github.com/PaddlePaddle/Paddle/pull/62928))，Sequential ([#63128](https://github.com/PaddlePaddle/Paddle/pull/63128))，paddle.summary ([#63121](https://github.com/PaddlePaddle/Paddle/pull/63121))，AutoParallel 中的 ShardOptimizer ([#62933](https://github.com/PaddlePaddle/Paddle/pull/62933))，paddle.nccl.version ([#62480](https://github.com/PaddlePaddle/Paddle/pull/62480)) +- 更新 Readme 文件。[#59883](https://github.com/PaddlePaddle/Paddle/pull/59883),[#60691](https://github.com/PaddlePaddle/Paddle/pull/60691),[#60749](https://github.com/PaddlePaddle/Paddle/pull/60749) +- 将 mkldnn 更新为 onednn。[#63199](https://github.com/PaddlePaddle/Paddle/pull/63199),[#63202](https://github.com/PaddlePaddle/Paddle/pull/63202),[#63215](https://github.com/PaddlePaddle/Paddle/pull/63215),[#63209](https://github.com/PaddlePaddle/Paddle/pull/63209) +- 修复文档渲染错误。[#59725](https://github.com/PaddlePaddle/Paddle/pull/59725),[#60306](https://github.com/PaddlePaddle/Paddle/pull/60306) +- 修改了代码中大量的错别字，增强源码可读性。[#60093](https://github.com/PaddlePaddle/Paddle/pull/60093),[#60603](https://github.com/PaddlePaddle/Paddle/pull/60603),[#60631](https://github.com/PaddlePaddle/Paddle/pull/60631),[#60679](https://github.com/PaddlePaddle/Paddle/pull/60679),[#60741](https://github.com/PaddlePaddle/Paddle/pull/60741),[#60770](https://github.com/PaddlePaddle/Paddle/pull/60770),[#60784](https://github.com/PaddlePaddle/Paddle/pull/60784),[#60825](https://github.com/PaddlePaddle/Paddle/pull/60825),[#60857](https://github.com/PaddlePaddle/Paddle/pull/60857),[#60891](https://github.com/PaddlePaddle/Paddle/pull/60891),[#60921](https://github.com/PaddlePaddle/Paddle/pull/60921),[#60920](https://github.com/PaddlePaddle/Paddle/pull/60920),[#60923](https://github.com/PaddlePaddle/Paddle/pull/60923),[#60928](https://github.com/PaddlePaddle/Paddle/pull/60928),[#60940](https://github.com/PaddlePaddle/Paddle/pull/60940),[#60936](https://github.com/PaddlePaddle/Paddle/pull/60936),[#60932](https://github.com/PaddlePaddle/Paddle/pull/60932),[#60935](https://github.com/PaddlePaddle/Paddle/pull/60935),[#60931](https://github.com/PaddlePaddle/Paddle/pull/60931),[#60951](https://github.com/PaddlePaddle/Paddle/pull/60951),[#60964](https://github.com/PaddlePaddle/Paddle/pull/60964),[#60965](https://github.com/PaddlePaddle/Paddle/pull/60965),[#60967](https://github.com/PaddlePaddle/Paddle/pull/60967),[#60972](https://github.com/PaddlePaddle/Paddle/pull/60972),[#60971](https://github.com/PaddlePaddle/Paddle/pull/60971),[#60980](https://github.com/PaddlePaddle/Paddle/pull/60980),[#60984](https://github.com/PaddlePaddle/Paddle/pull/60984),[#60985](https://github.com/PaddlePaddle/Paddle/pull/60985),[#60989](https://github.com/PaddlePaddle/Paddle/pull/60989),[#60990](https://github.com/PaddlePaddle/Paddle/pull/60990),[#60991](https://github.com/PaddlePaddle/Paddle/pull/60991),[#60992](https://github.com/PaddlePaddle/Paddle/pull/60992),[#60994](https://github.com/PaddlePaddle/Paddle/pull/60994),[#60995](https://github.com/PaddlePaddle/Paddle/pull/60995),[#60996](https://github.com/PaddlePaddle/Paddle/pull/60996),[#61001](https://github.com/PaddlePaddle/Paddle/pull/61001),[#61000](https://github.com/PaddlePaddle/Paddle/pull/61000),[#60999](https://github.com/PaddlePaddle/Paddle/pull/60999),[#60998](https://github.com/PaddlePaddle/Paddle/pull/60998),[#61026](https://github.com/PaddlePaddle/Paddle/pull/61026),[#61009](https://github.com/PaddlePaddle/Paddle/pull/61009),[#61034](https://github.com/PaddlePaddle/Paddle/pull/61034),[#61033](https://github.com/PaddlePaddle/Paddle/pull/61033),[#61020](https://github.com/PaddlePaddle/Paddle/pull/61020),[#61092](https://github.com/PaddlePaddle/Paddle/pull/61092),[#61066](https://github.com/PaddlePaddle/Paddle/pull/61066),[#61063](https://github.com/PaddlePaddle/Paddle/pull/61063),[#61089](https://github.com/PaddlePaddle/Paddle/pull/61089),[#61071](https://github.com/PaddlePaddle/Paddle/pull/61071),[#61129](https://github.com/PaddlePaddle/Paddle/pull/61129),[#61128](https://github.com/PaddlePaddle/Paddle/pull/61128),[#61126](https://github.com/PaddlePaddle/Paddle/pull/61126),[#61123](https://github.com/PaddlePaddle/Paddle/pull/61123),[#61113](https://github.com/PaddlePaddle/Paddle/pull/61113),[#61189](https://github.com/PaddlePaddle/Paddle/pull/61189),[#61175](https://github.com/PaddlePaddle/Paddle/pull/61175),[#61153](https://github.com/PaddlePaddle/Paddle/pull/61153),[#61198](https://github.com/PaddlePaddle/Paddle/pull/61198),[#61206](https://github.com/PaddlePaddle/Paddle/pull/61206),[#61256](https://github.com/PaddlePaddle/Paddle/pull/61256),[#61255](https://github.com/PaddlePaddle/Paddle/pull/61255),[#61251](https://github.com/PaddlePaddle/Paddle/pull/61251),[#61246](https://github.com/PaddlePaddle/Paddle/pull/61246),[#61245](https://github.com/PaddlePaddle/Paddle/pull/61245),[#61231](https://github.com/PaddlePaddle/Paddle/pull/61231),[#61247](https://github.com/PaddlePaddle/Paddle/pull/61247),[#61265](https://github.com/PaddlePaddle/Paddle/pull/61265),[#61264](https://github.com/PaddlePaddle/Paddle/pull/61264),[#61266](https://github.com/PaddlePaddle/Paddle/pull/61266),[#61267](https://github.com/PaddlePaddle/Paddle/pull/61267),[#61268](https://github.com/PaddlePaddle/Paddle/pull/61268),[#61270](https://github.com/PaddlePaddle/Paddle/pull/61270),[#61334](https://github.com/PaddlePaddle/Paddle/pull/61334),[#61392](https://github.com/PaddlePaddle/Paddle/pull/61392),[#61404](https://github.com/PaddlePaddle/Paddle/pull/61404),[#61318](https://github.com/PaddlePaddle/Paddle/pull/61318),[#61383](https://github.com/PaddlePaddle/Paddle/pull/61383),[#61306](https://github.com/PaddlePaddle/Paddle/pull/61306),[#61324](https://github.com/PaddlePaddle/Paddle/pull/61324),[#61426](https://github.com/PaddlePaddle/Paddle/pull/61426),[#61390](https://github.com/PaddlePaddle/Paddle/pull/61390),[#61419](https://github.com/PaddlePaddle/Paddle/pull/61419),[#61420](https://github.com/PaddlePaddle/Paddle/pull/61420),[#61408](https://github.com/PaddlePaddle/Paddle/pull/61408),[#61425](https://github.com/PaddlePaddle/Paddle/pull/61425),[#61557](https://github.com/PaddlePaddle/Paddle/pull/61557),[#61628](https://github.com/PaddlePaddle/Paddle/pull/61628),[#61652](https://github.com/PaddlePaddle/Paddle/pull/61652),[#61602](https://github.com/PaddlePaddle/Paddle/pull/61602),[#61558](https://github.com/PaddlePaddle/Paddle/pull/61558),[#61660](https://github.com/PaddlePaddle/Paddle/pull/61660),[#61423](https://github.com/PaddlePaddle/Paddle/pull/61423),[#61627](https://github.com/PaddlePaddle/Paddle/pull/61627),[#61685](https://github.com/PaddlePaddle/Paddle/pull/61685),[#61690](https://github.com/PaddlePaddle/Paddle/pull/61690),[#61727](https://github.com/PaddlePaddle/Paddle/pull/61727),[#61738](https://github.com/PaddlePaddle/Paddle/pull/61738),[#61740](https://github.com/PaddlePaddle/Paddle/pull/61740),[#61741](https://github.com/PaddlePaddle/Paddle/pull/61741),[#61743](https://github.com/PaddlePaddle/Paddle/pull/61743),[#61744](https://github.com/PaddlePaddle/Paddle/pull/61744),[#61745](https://github.com/PaddlePaddle/Paddle/pull/61745),[#61761](https://github.com/PaddlePaddle/Paddle/pull/61761),[#61762](https://github.com/PaddlePaddle/Paddle/pull/61762),[#61764](https://github.com/PaddlePaddle/Paddle/pull/61764),[#61767](https://github.com/PaddlePaddle/Paddle/pull/61767),[#61768](https://github.com/PaddlePaddle/Paddle/pull/61768),[#61774](https://github.com/PaddlePaddle/Paddle/pull/61774),[#61781](https://github.com/PaddlePaddle/Paddle/pull/61781),[#61783](https://github.com/PaddlePaddle/Paddle/pull/61783),[#61757](https://github.com/PaddlePaddle/Paddle/pull/61757),[#61732](https://github.com/PaddlePaddle/Paddle/pull/61732),[#61776](https://github.com/PaddlePaddle/Paddle/pull/61776),[#61780](https://github.com/PaddlePaddle/Paddle/pull/61780),[#61730](https://github.com/PaddlePaddle/Paddle/pull/61730),[#61728](https://github.com/PaddlePaddle/Paddle/pull/61728),[#61633](https://github.com/PaddlePaddle/Paddle/pull/61633),[#61720](https://github.com/PaddlePaddle/Paddle/pull/61720),[#61734](https://github.com/PaddlePaddle/Paddle/pull/61734),[#61779](https://github.com/PaddlePaddle/Paddle/pull/61779),[#61775](https://github.com/PaddlePaddle/Paddle/pull/61775),[#61773](https://github.com/PaddlePaddle/Paddle/pull/61773),[#61787](https://github.com/PaddlePaddle/Paddle/pull/61787),[#61687](https://github.com/PaddlePaddle/Paddle/pull/61687),[#61747](https://github.com/PaddlePaddle/Paddle/pull/61747),[#61760](https://github.com/PaddlePaddle/Paddle/pull/61760),[#61782](https://github.com/PaddlePaddle/Paddle/pull/61782),[#61800](https://github.com/PaddlePaddle/Paddle/pull/61800),[#61748](https://github.com/PaddlePaddle/Paddle/pull/61748),[#61772](https://github.com/PaddlePaddle/Paddle/pull/61772),[#61786](https://github.com/PaddlePaddle/Paddle/pull/61786),[#61880](https://github.com/PaddlePaddle/Paddle/pull/61880),[#61718](https://github.com/PaddlePaddle/Paddle/pull/61718),[#61742](https://github.com/PaddlePaddle/Paddle/pull/61742),[#61766](https://github.com/PaddlePaddle/Paddle/pull/61766),[#61835](https://github.com/PaddlePaddle/Paddle/pull/61835),[#61838](https://github.com/PaddlePaddle/Paddle/pull/61838),[#61754](https://github.com/PaddlePaddle/Paddle/pull/61754),[#61833](https://github.com/PaddlePaddle/Paddle/pull/61833),[#61749](https://github.com/PaddlePaddle/Paddle/pull/61749),[#61938](https://github.com/PaddlePaddle/Paddle/pull/61938),[#61919](https://github.com/PaddlePaddle/Paddle/pull/61919),[#61924](https://github.com/PaddlePaddle/Paddle/pull/61924),[#61778](https://github.com/PaddlePaddle/Paddle/pull/61778),[#61839](https://github.com/PaddlePaddle/Paddle/pull/61839),[#61879](https://github.com/PaddlePaddle/Paddle/pull/61879),[#61929](https://github.com/PaddlePaddle/Paddle/pull/61929),[#61801](https://github.com/PaddlePaddle/Paddle/pull/61801),[#61788](https://github.com/PaddlePaddle/Paddle/pull/61788),[#61999](https://github.com/PaddlePaddle/Paddle/pull/61999),[#61928](https://github.com/PaddlePaddle/Paddle/pull/61928),[#61958](https://github.com/PaddlePaddle/Paddle/pull/61958),[#61982](https://github.com/PaddlePaddle/Paddle/pull/61982),[#61996](https://github.com/PaddlePaddle/Paddle/pull/61996),[#61953](https://github.com/PaddlePaddle/Paddle/pull/61953),[#61998](https://github.com/PaddlePaddle/Paddle/pull/61998),[#62003](https://github.com/PaddlePaddle/Paddle/pull/62003),[#61921](https://github.com/PaddlePaddle/Paddle/pull/61921),[#61881](https://github.com/PaddlePaddle/Paddle/pull/61881),[#61746](https://github.com/PaddlePaddle/Paddle/pull/61746),[#61955](https://github.com/PaddlePaddle/Paddle/pull/61955),[#62002](https://github.com/PaddlePaddle/Paddle/pull/62002),[#62001](https://github.com/PaddlePaddle/Paddle/pull/62001),[#61997](https://github.com/PaddlePaddle/Paddle/pull/61997),[#61765](https://github.com/PaddlePaddle/Paddle/pull/61765),[#61956](https://github.com/PaddlePaddle/Paddle/pull/61956),[#62004](https://github.com/PaddlePaddle/Paddle/pull/62004),[#62044](https://github.com/PaddlePaddle/Paddle/pull/62044),[#62040](https://github.com/PaddlePaddle/Paddle/pull/62040),[#62043](https://github.com/PaddlePaddle/Paddle/pull/62043),[#62042](https://github.com/PaddlePaddle/Paddle/pull/62042),[#62041](https://github.com/PaddlePaddle/Paddle/pull/62041),[#62039](https://github.com/PaddlePaddle/Paddle/pull/62039),[#62019](https://github.com/PaddlePaddle/Paddle/pull/62019),[#61910](https://github.com/PaddlePaddle/Paddle/pull/61910),[#61882](https://github.com/PaddlePaddle/Paddle/pull/61882),[#61836](https://github.com/PaddlePaddle/Paddle/pull/61836),[#62013](https://github.com/PaddlePaddle/Paddle/pull/62013),[#62055](https://github.com/PaddlePaddle/Paddle/pull/62055),[#62047](https://github.com/PaddlePaddle/Paddle/pull/62047),[#62000](https://github.com/PaddlePaddle/Paddle/pull/62000),[#62048](https://github.com/PaddlePaddle/Paddle/pull/62048),[#62075](https://github.com/PaddlePaddle/Paddle/pull/62075),[#62038](https://github.com/PaddlePaddle/Paddle/pull/62038),[#62045](https://github.com/PaddlePaddle/Paddle/pull/62045),[#62105](https://github.com/PaddlePaddle/Paddle/pull/62105),[#62214](https://github.com/PaddlePaddle/Paddle/pull/62214),[#62212](https://github.com/PaddlePaddle/Paddle/pull/62212),[#62183](https://github.com/PaddlePaddle/Paddle/pull/62183)，[#62182](https://github.com/PaddlePaddle/Paddle/pull/62182),[#62181](https://github.com/PaddlePaddle/Paddle/pull/62181),[#62179](https://github.com/PaddlePaddle/Paddle/pull/62179),[#62178](https://github.com/PaddlePaddle/Paddle/pull/62178),[#62172](https://github.com/PaddlePaddle/Paddle/pull/62172),[#62168](https://github.com/PaddlePaddle/Paddle/pull/62168),[#62163](https://github.com/PaddlePaddle/Paddle/pull/62163),[#62162](https://github.com/PaddlePaddle/Paddle/pull/62162),[#62161](https://github.com/PaddlePaddle/Paddle/pull/62161),[#62160](https://github.com/PaddlePaddle/Paddle/pull/62160),[#62046](https://github.com/PaddlePaddle/Paddle/pull/62046),[#62175](https://github.com/PaddlePaddle/Paddle/pull/62175),[#62259](https://github.com/PaddlePaddle/Paddle/pull/62259),[#62258](https://github.com/PaddlePaddle/Paddle/pull/62258),[#62213](https://github.com/PaddlePaddle/Paddle/pull/62213),[#62260](https://github.com/PaddlePaddle/Paddle/pull/62260),[#62290](https://github.com/PaddlePaddle/Paddle/pull/62290),[#62288](https://github.com/PaddlePaddle/Paddle/pull/62288),[#62323](https://github.com/PaddlePaddle/Paddle/pull/62323),[#62319](https://github.com/PaddlePaddle/Paddle/pull/62319),[#62331](https://github.com/PaddlePaddle/Paddle/pull/62331),[#62330](https://github.com/PaddlePaddle/Paddle/pull/62330),[#62329](https://github.com/PaddlePaddle/Paddle/pull/62329),[#62324](https://github.com/PaddlePaddle/Paddle/pull/62324),[#62317](https://github.com/PaddlePaddle/Paddle/pull/62317),[#62311](https://github.com/PaddlePaddle/Paddle/pull/62311),[#62310](https://github.com/PaddlePaddle/Paddle/pull/62310),[#62308](https://github.com/PaddlePaddle/Paddle/pull/62308),[#62289](https://github.com/PaddlePaddle/Paddle/pull/62289),[#62307](https://github.com/PaddlePaddle/Paddle/pull/62307),[#62315](https://github.com/PaddlePaddle/Paddle/pull/62315),[#62406](https://github.com/PaddlePaddle/Paddle/pull/62406),[#62458](https://github.com/PaddlePaddle/Paddle/pull/62458),[#62459](https://github.com/PaddlePaddle/Paddle/pull/62459),[#62481](https://github.com/PaddlePaddle/Paddle/pull/62481),[#62465](https://github.com/PaddlePaddle/Paddle/pull/62465),[#62462](https://github.com/PaddlePaddle/Paddle/pull/62462),[#62453](https://github.com/PaddlePaddle/Paddle/pull/62453),[#62496](https://github.com/PaddlePaddle/Paddle/pull/62496),[#62457](https://github.com/PaddlePaddle/Paddle/pull/62457),[#62537](https://github.com/PaddlePaddle/Paddle/pull/62537),[#62514](https://github.com/PaddlePaddle/Paddle/pull/62514),[#62548](https://github.com/PaddlePaddle/Paddle/pull/62548),[#62544](https://github.com/PaddlePaddle/Paddle/pull/62544),[#62575](https://github.com/PaddlePaddle/Paddle/pull/62575),[#62463](https://github.com/PaddlePaddle/Paddle/pull/62463),[#62643](https://github.com/PaddlePaddle/Paddle/pull/62643),[#62803](https://github.com/PaddlePaddle/Paddle/pull/62803),[#62924](https://github.com/PaddlePaddle/Paddle/pull/62924),[#63037](https://github.com/PaddlePaddle/Paddle/pull/63037),[#63102](https://github.com/PaddlePaddle/Paddle/pull/63102),[#63139](https://github.com/PaddlePaddle/Paddle/pull/63139),[#63092](https://github.com/PaddlePaddle/Paddle/pull/63092),[#63147](https://github.com/PaddlePaddle/Paddle/pull/63147),[#60518](https://github.com/PaddlePaddle/Paddle/pull/60518),[#60485](https://github.com/PaddlePaddle/Paddle/pull/60485),[#61273](https://github.com/PaddlePaddle/Paddle/pull/61273),[#63429](https://github.com/PaddlePaddle/Paddle/pull/63429),[#61954](https://github.com/PaddlePaddle/Paddle/pull/61954) + +## 12.其他升级内容 +与用户使用无关的改动，包括废弃代码清理、无用单测清理、调试或者监控机制升级等。[#63377](https://github.com/PaddlePaddle/Paddle/pull/63377),[#64106](https://github.com/PaddlePaddle/Paddle/pull/64106),[#64220](https://github.com/PaddlePaddle/Paddle/pull/64220),[#64293](https://github.com/PaddlePaddle/Paddle/pull/64293),[#64464](https://github.com/PaddlePaddle/Paddle/pull/64464),[#64944](https://github.com/PaddlePaddle/Paddle/pull/64944),[#63638](https://github.com/PaddlePaddle/Paddle/pull/63638),[#63732](https://github.com/PaddlePaddle/Paddle/pull/63732),[#63735](https://github.com/PaddlePaddle/Paddle/pull/63735),[#63826](https://github.com/PaddlePaddle/Paddle/pull/63826),[#63982](https://github.com/PaddlePaddle/Paddle/pull/63982),[#63737](https://github.com/PaddlePaddle/Paddle/pull/63737),[#64471](https://github.com/PaddlePaddle/Paddle/pull/64471),[#64574](https://github.com/PaddlePaddle/Paddle/pull/64574),[#64494](https://github.com/PaddlePaddle/Paddle/pull/64494),[#62775](https://github.com/PaddlePaddle/Paddle/pull/62775),[#63601](https://github.com/PaddlePaddle/Paddle/pull/63601),[#62564](https://github.com/PaddlePaddle/Paddle/pull/62564),[#63772](https://github.com/PaddlePaddle/Paddle/pull/63772),[#64719](https://github.com/PaddlePaddle/Paddle/pull/64719),[#61640](https://github.com/PaddlePaddle/Paddle/pull/61640),[#63459](https://github.com/PaddlePaddle/Paddle/pull/63459),[#64062](https://github.com/PaddlePaddle/Paddle/pull/64062),[#63480](https://github.com/PaddlePaddle/Paddle/pull/63480),[#63833](https://github.com/PaddlePaddle/Paddle/pull/63833)[#63673](https://github.com/PaddlePaddle/Paddle/pull/63673),[#63672](https://github.com/PaddlePaddle/Paddle/pull/63672),[#64131](https://github.com/PaddlePaddle/Paddle/pull/64131),[#64156](https://github.com/PaddlePaddle/Paddle/pull/64156),[#64155](https://github.com/PaddlePaddle/Paddle/pull/64155),[#64159](https://github.com/PaddlePaddle/Paddle/pull/64159),[#63902](https://github.com/PaddlePaddle/Paddle/pull/63902),[#64230](https://github.com/PaddlePaddle/Paddle/pull/64230),[#64229](https://github.com/PaddlePaddle/Paddle/pull/64229),[#64236](https://github.com/PaddlePaddle/Paddle/pull/64236),[#64260](https://github.com/PaddlePaddle/Paddle/pull/64260),[#64175](https://github.com/PaddlePaddle/Paddle/pull/64175),[#64250](https://github.com/PaddlePaddle/Paddle/pull/64250),[#64269](https://github.com/PaddlePaddle/Paddle/pull/64269),[#64238](https://github.com/PaddlePaddle/Paddle/pull/64238),[#64349](https://github.com/PaddlePaddle/Paddle/pull/64349),[#64394](https://github.com/PaddlePaddle/Paddle/pull/64394),[#64402](https://github.com/PaddlePaddle/Paddle/pull/64402),[#64401](https://github.com/PaddlePaddle/Paddle/pull/64401),[#64388](https://github.com/PaddlePaddle/Paddle/pull/64388),[#64329](https://github.com/PaddlePaddle/Paddle/pull/64329),[#64502](https://github.com/PaddlePaddle/Paddle/pull/64502),[#64501](https://github.com/PaddlePaddle/Paddle/pull/64501),[#64515](https://github.com/PaddlePaddle/Paddle/pull/64515),[#64503](https://github.com/PaddlePaddle/Paddle/pull/64503),[#64514](https://github.com/PaddlePaddle/Paddle/pull/64514),[#64601](https://github.com/PaddlePaddle/Paddle/pull/64601),[#64564](https://github.com/PaddlePaddle/Paddle/pull/64564),[#64012](https://github.com/PaddlePaddle/Paddle/pull/64012),[#64697](https://github.com/PaddlePaddle/Paddle/pull/64697),[#64682](https://github.com/PaddlePaddle/Paddle/pull/64682),[#64051](https://github.com/PaddlePaddle/Paddle/pull/64051),[#63267](https://github.com/PaddlePaddle/Paddle/pull/63267),[#63426](https://github.com/PaddlePaddle/Paddle/pull/63426),[#63626](https://github.com/PaddlePaddle/Paddle/pull/63626),[#63257](https://github.com/PaddlePaddle/Paddle/pull/63257),[#63266](https://github.com/PaddlePaddle/Paddle/pull/63266),[#63468](https://github.com/PaddlePaddle/Paddle/pull/63468),[#63262](https://github.com/PaddlePaddle/Paddle/pull/63262),[#63248](https://github.com/PaddlePaddle/Paddle/pull/63248),[#63241](https://github.com/PaddlePaddle/Paddle/pull/63241),[#63252](https://github.com/PaddlePaddle/Paddle/pull/63252),[#63258](https://github.com/PaddlePaddle/Paddle/pull/63258),[#63235](https://github.com/PaddlePaddle/Paddle/pull/63235),[#63399](https://github.com/PaddlePaddle/Paddle/pull/63399),[#63488](https://github.com/PaddlePaddle/Paddle/pull/63488),[#63487](https://github.com/PaddlePaddle/Paddle/pull/63487),[#63466](https://github.com/PaddlePaddle/Paddle/pull/63466),[#63464](https://github.com/PaddlePaddle/Paddle/pull/63464),[#63483](https://github.com/PaddlePaddle/Paddle/pull/63483),[#63486](https://github.com/PaddlePaddle/Paddle/pull/63486),[#63475](https://github.com/PaddlePaddle/Paddle/pull/63475),[#63489](https://github.com/PaddlePaddle/Paddle/pull/63489),[#63470](https://github.com/PaddlePaddle/Paddle/pull/63470),[#63457](https://github.com/PaddlePaddle/Paddle/pull/63457),[#63493](https://github.com/PaddlePaddle/Paddle/pull/63493),[#63561](https://github.com/PaddlePaddle/Paddle/pull/63561),[#63584](https://github.com/PaddlePaddle/Paddle/pull/63584),[#63587](https://github.com/PaddlePaddle/Paddle/pull/63587),[#63586](https://github.com/PaddlePaddle/Paddle/pull/63586),[#63569](https://github.com/PaddlePaddle/Paddle/pull/63569),[#63559](https://github.com/PaddlePaddle/Paddle/pull/63559),[#63558](https://github.com/PaddlePaddle/Paddle/pull/63558),[#63555](https://github.com/PaddlePaddle/Paddle/pull/63555),[#63543](https://github.com/PaddlePaddle/Paddle/pull/63543),[#63589](https://github.com/PaddlePaddle/Paddle/pull/63589),[#63583](https://github.com/PaddlePaddle/Paddle/pull/63583),[#63565](https://github.com/PaddlePaddle/Paddle/pull/63565),[#63564](https://github.com/PaddlePaddle/Paddle/pull/63564),[#63265](https://github.com/PaddlePaddle/Paddle/pull/63265),[#63562](https://github.com/PaddlePaddle/Paddle/pull/63562),[#63591](https://github.com/PaddlePaddle/Paddle/pull/63591),[#63460](https://github.com/PaddlePaddle/Paddle/pull/63460),[#63238](https://github.com/PaddlePaddle/Paddle/pull/63238),[#63631](https://github.com/PaddlePaddle/Paddle/pull/63631),[#63707](https://github.com/PaddlePaddle/Paddle/pull/63707),[#63714](https://github.com/PaddlePaddle/Paddle/pull/63714),[#63854](https://github.com/PaddlePaddle/Paddle/pull/63854),[#63929](https://github.com/PaddlePaddle/Paddle/pull/63929),[#63532](https://github.com/PaddlePaddle/Paddle/pull/63532),[#59628](https://github.com/PaddlePaddle/Paddle/pull/59628),[#62209](https://github.com/PaddlePaddle/Paddle/pull/62209),[#63742](https://github.com/PaddlePaddle/Paddle/pull/63742),[#60518](https://github.com/PaddlePaddle/Paddle/pull/60518),[#62078](https://github.com/PaddlePaddle/Paddle/pull/62078),[#62684](https://github.com/PaddlePaddle/Paddle/pull/62684),[#62723](https://github.com/PaddlePaddle/Paddle/pull/62723),[#64141](https://github.com/PaddlePaddle/Paddle/pull/64141),[#60404](https://github.com/PaddlePaddle/Paddle/pull/60404),[#64212](https://github.com/PaddlePaddle/Paddle/pull/64212),[#60652](https://github.com/PaddlePaddle/Paddle/pull/60652),[#64545](https://github.com/PaddlePaddle/Paddle/pull/64545),[#64477](https://github.com/PaddlePaddle/Paddle/pull/64477),[#64556](https://github.com/PaddlePaddle/Paddle/pull/64556),[#63160](https://github.com/PaddlePaddle/Paddle/pull/63160),[#63796](https://github.com/PaddlePaddle/Paddle/pull/63796),[#64693](https://github.com/PaddlePaddle/Paddle/pull/64693),[#64484](https://github.com/PaddlePaddle/Paddle/pull/64484),[#64677](https://github.com/PaddlePaddle/Paddle/pull/64677),[#64461](https://github.com/PaddlePaddle/Paddle/pull/64461),[#63189](https://github.com/PaddlePaddle/Paddle/pull/63189),[#63855](https://github.com/PaddlePaddle/Paddle/pull/63855),[#63896](https://github.com/PaddlePaddle/Paddle/pull/63896),[#63193](https://github.com/PaddlePaddle/Paddle/pull/63193),[#63200](https://github.com/PaddlePaddle/Paddle/pull/63200),[#63406](https://github.com/PaddlePaddle/Paddle/pull/63406),[#61283](https://github.com/PaddlePaddle/Paddle/pull/61283),[#63607](https://github.com/PaddlePaddle/Paddle/pull/63607),[#64486](https://github.com/PaddlePaddle/Paddle/pull/64486),[#64004](https://github.com/PaddlePaddle/Paddle/pull/64004),[#63132](https://github.com/PaddlePaddle/Paddle/pull/63132),[#63553](https://github.com/PaddlePaddle/Paddle/pull/63553),[#63572](https://github.com/PaddlePaddle/Paddle/pull/63572),[#63794](https://github.com/PaddlePaddle/Paddle/pull/63794),[#63919](https://github.com/PaddlePaddle/Paddle/pull/63919),[#63980](https://github.com/PaddlePaddle/Paddle/pull/63980),[#62917](https://github.com/PaddlePaddle/Paddle/pull/62917),[#64451](https://github.com/PaddlePaddle/Paddle/pull/64451),[#63541](https://github.com/PaddlePaddle/Paddle/pull/63541),[#63703](https://github.com/PaddlePaddle/Paddle/pull/63703),[#64536](https://github.com/PaddlePaddle/Paddle/pull/64536),[#63264](https://github.com/PaddlePaddle/Paddle/pull/63264),[#63335](https://github.com/PaddlePaddle/Paddle/pull/63335),[#63841](https://github.com/PaddlePaddle/Paddle/pull/63841),[#64628](https://github.com/PaddlePaddle/Paddle/pull/64628),[#63419](https://github.com/PaddlePaddle/Paddle/pull/63419),[#62210](https://github.com/PaddlePaddle/Paddle/pull/62210),[#63557](https://github.com/PaddlePaddle/Paddle/pull/63557),[#63064](https://github.com/PaddlePaddle/Paddle/pull/63064),[#61442](https://github.com/PaddlePaddle/Paddle/pull/61442),[#63537](https://github.com/PaddlePaddle/Paddle/pull/63537),[#63839](https://github.com/PaddlePaddle/Paddle/pull/63839),[#60927](https://github.com/PaddlePaddle/Paddle/pull/60927),[#60566](https://github.com/PaddlePaddle/Paddle/pull/60566),[#60842](https://github.com/PaddlePaddle/Paddle/pull/60842),[#64612](https://github.com/PaddlePaddle/Paddle/pull/64612),[#60047](https://github.com/PaddlePaddle/Paddle/pull/60047),[#63898](https://github.com/PaddlePaddle/Paddle/pull/63898),[#60415](https://github.com/PaddlePaddle/Paddle/pull/60415),[#60474](https://github.com/PaddlePaddle/Paddle/pull/60474),[#60439](https://github.com/PaddlePaddle/Paddle/pull/60439),[#60565](https://github.com/PaddlePaddle/Paddle/pull/60565),[#64414](https://github.com/PaddlePaddle/Paddle/pull/64414),[#62526](https://github.com/PaddlePaddle/Paddle/pull/62526),[#54183](https://github.com/PaddlePaddle/Paddle/pull/54183),[#64096](https://github.com/PaddlePaddle/Paddle/pull/64096),[#61325](https://github.com/PaddlePaddle/Paddle/pull/61325),[#60629](https://github.com/PaddlePaddle/Paddle/pull/60629),[#61051](https://github.com/PaddlePaddle/Paddle/pull/61051),[#62103](https://github.com/PaddlePaddle/Paddle/pull/62103),[#63594](https://github.com/PaddlePaddle/Paddle/pull/63594),[#60968](https://github.com/PaddlePaddle/Paddle/pull/60968),[#64613](https://github.com/PaddlePaddle/Paddle/pull/64613),[#64073](https://github.com/PaddlePaddle/Paddle/pull/64073),[#63816](https://github.com/PaddlePaddle/Paddle/pull/63816),[#64416](https://github.com/PaddlePaddle/Paddle/pull/64416),[#62499](https://github.com/PaddlePaddle/Paddle/pull/62499),[#64531](https://github.com/PaddlePaddle/Paddle/pull/64531),[#63827](https://github.com/PaddlePaddle/Paddle/pull/63827),[#59885](https://github.com/PaddlePaddle/Paddle/pull/59885),[#59949](https://github.com/PaddlePaddle/Paddle/pull/59949),[#63428](https://github.com/PaddlePaddle/Paddle/pull/63428),[#63218](https://github.com/PaddlePaddle/Paddle/pull/63218),[#63538](https://github.com/PaddlePaddle/Paddle/pull/63538),[#64497](https://github.com/PaddlePaddle/Paddle/pull/64497),[#63082](https://github.com/PaddlePaddle/Paddle/pull/63082),[#64395](https://github.com/PaddlePaddle/Paddle/pull/64395),[#60183](https://github.com/PaddlePaddle/Paddle/pull/60183),[#63691](https://github.com/PaddlePaddle/Paddle/pull/63691),[#64428](https://github.com/PaddlePaddle/Paddle/pull/64428),[#64648](https://github.com/PaddlePaddle/Paddle/pull/64648),[#64650](https://github.com/PaddlePaddle/Paddle/pull/64650),[#59926](https://github.com/PaddlePaddle/Paddle/pull/59926),[#59750](https://github.com/PaddlePaddle/Paddle/pull/59750),[#60080](https://github.com/PaddlePaddle/Paddle/pull/60080),[#60208](https://github.com/PaddlePaddle/Paddle/pull/60208),[#64124](https://github.com/PaddlePaddle/Paddle/pull/64124),[#64187](https://github.com/PaddlePaddle/Paddle/pull/64187),[#64166](https://github.com/PaddlePaddle/Paddle/pull/64166),[#64284](https://github.com/PaddlePaddle/Paddle/pull/64284),[#64253](https://github.com/PaddlePaddle/Paddle/pull/64253),[#64555](https://github.com/PaddlePaddle/Paddle/pull/64555),[#59878](https://github.com/PaddlePaddle/Paddle/pull/59878),[#64081](https://github.com/PaddlePaddle/Paddle/pull/64081) + +## 13.贡献者名单 +6clc, Android zhang, Asthestarsfalll, Ataf Fazledin Ahamed, Aurelius84, AyaseNana, Baizhou Zhang, bapijun, BiynXu, Botao Zhou, Bo Zhang, bukejiyu, caozhou, chalsliu, Chang Xu, Charles-hit, chen2016013, Chen Zhiyang, C.J.0_0, cmcamdy, co63oc, coco, cyber-pioneer, cyberslack_lee, danleifeng, diadestiny, Difer, Dmovic, Eddie-Wang, Eddie Zhang, engineer1109, enzodechine, fanhaoxuee, feifei-111, flying-forever, Frank Lin, freeliuzc, fsczz, Galaxy1458, GGBond8488, Ghost Screaming, gongweibao, gouzil, Guoxia Wang, handiz, HankYang, Haohongxiang, haosicheng, hess, hjyp, hong, Hongqing-work, Hongwen Xin, HongyuJia, houj04, huangjiyi, Huihuang Zheng, hxzd5568, hyDONG, HydrogenSulfate, idontkonwher, iLeGend, Jeng Bai-Cheng, Jianbang Yang, Jia Wenxuan, JYChen, jzhang533, JZ-LIANG, Kai Song, kangguangli, kevin, Kunbo Ding, lanxianghit, Leo Chen, Leo Guo, lijialin03, lijin23, linkk08, Liujie0926, Liuyinfeng, liu zhengxi, liuzhenhai93, liym27, LiYuRio, lizexu123, LoneRanger, Longzhi Wang, Lucas, Lu Qi, lzy, lzydev, MayYouBeProsperous, megemini, Meiyim, ming1753, Mingdong Wang, ndren, NeroLoh, NetPunk, Nguyen Cong Vinh, Nyakku Shigure, Omri Alon, onepick, ooo oo, pangengzheng, PommesPeter, Qi Li, QingshuChen, Qi Shao, RedContritio, Reese Wang, RichardWooSJTU, risemeup1, Roc, ronnywang, Ruibiao Chen, Ruibin Cheung, RuohengMa, Ryan, Shaopeng Ling, ShenLiang, Shijie, Shuhao Liang, Siming Dai, skywalker2012, smallpoxscattered, sneaxiy, Sonder, Sunny-bot1, Tao Luo, tc20042008, Terry, Tian, tianhaodongbd, tianshuo78520a, Tianyu Feng, Tian Zheng, Tongkai, Travis-Lee, unseenme, Vigi Zhang, walkalone20, Wang Bojun, wanghuancoder, wangna11BD, Wang Xin, Wangzheee, WangZhen, wanly young, wawltor, wendaxiao, Wen Sun, wentao yu, Wenyu, wenzhe.wang, Winters Montagne, winter-wang, WoWYoYLoL, Wu Chencan, Wu Fei, wuhuachaocoding, Xianduo Li, XiangGao, XiaociZhang, xiaoguoguo626807, xiaoxiaohehe001, Xiao Xiyuan, Xiaoxu Chen, xiaoyao0115, xiaoye, xingmingyyj, Xinyi_LI, Xinyu Yang, xiongkun, xuxinyi389, xysheng-baidu, yangguohao, YibLiu, Yichen Zhang, yinfan98, yinwei, Yiqun Liu, YKTian, Yuang Liu, Yuanle Liu, YuanRisheng, yuguo, yujun, yulangz, YUNSHEN XIE, zbt78, ZelinMa557, Zero Rains, Zeyu Chen, zhangbo9674, Zhang,Lirong, Zhang Ting, zhangyikun02, zhangyuqin1998, Zhan Rongrui, zhaohaixu, zhaoyingli, Zhenghai Zhang, zhengzhonghui, zhink, ZhouMengLei1999, zhouzj, zhupengyang, zhurou603, zhuyipin, zhwesky2010, Zichao, zxcd, zyfncg, zyt1024, 东百月, 傅剑寒, 周周周, 周波涛, 张春乔, 萧 diff --git a/docs/release_note_en.md b/docs/release_note_en.md index 3e6ea0cd50f..0052edd54df 100644 --- a/docs/release_note_en.md +++ b/docs/release_note_en.md @@ -1,3495 +1,537 @@ -# 2.6.0 Release Note +# 3.0 Beta Release Note -## 1. Important Updates +# Overview of PaddlePaddle 3.0 Beta -- **Paddle New generation IR(PIR)** : In order to further improve scalability of the PaddlePaddle framework, we have developed a new generation intermediate representaion. It abstracts underlying core concepts of the PaddlePaddle framework, such as Operation, Attribute and Type, providing developers with flexible and efficient basic components. By introducing Dialect mechanism, PIR can comprehensively and hierarchically satisfy needs of each module for intermediate representations to greatly enhancing scalability of the framework. PIR strictly follows Static Single Assignment (SSA) principle, ensuring unity of top-level structure and harmonious coexistence of "operator sequentiality" and "computational graph semantics". In addition, PIR provides a more concise and low-cost Pass development process, with a series of built-in rich and functional Pass optimization strategies. It provides technical support for the ultimate performance optimization of large-scale models. -- **Static graph construction and compiler Optimization Architecture**: In order to further improve performance of the framework, PaddlePaddle's dynamic to static training capability has been comprehensively upgraded to support adaptive graph construction capability. This has been tested on more than 700 PaddlePaddle industry-level models, with 100% success rate of one line code converter to start static training. Meanwhile, Compiler Infrastructure for Neural Networks (CINN) of PaddlePaddle framework is integrated into PaddlePaddle main Repo, making the compiler and PaddlePaddle more integrated. CINN completes architectural optimization and improvement of expansion capability, increasing system stability. Based on PIR framework, it is much more easied to bind dynamic to static, primitive operator, executor and compiler together, to providing more space for boosting overall performance of PaddlePaddle framework. -- **Enhanced dynamic graph distributed capability**: Large models pose higher demands on the distributed training performance of framework. PaddlePaddle has comprehensive optimizations in dimensions of communication library, graph analysis, distributed strategy and task enable/disable, enhancing distributed computing capability of PaddlePaddle's dynamic graph and providing support for efficient training of large models. In terms of performance, training performance is further improved by reducing pipelined GPU memory occupation, adopting TensorFusion technology, implementing communication computation overlap, and reducing non-essential data synchronization copies. Meanwhile, flexibility of hybrid-parallel debugging is improved through environment variable control Optimizer. In addition, stability of system is significantly improved by fixing related Bugs. -- **Auto parallel architecture with dynamic-static unification**: In order to further reduce difficulty of programming and optimizing large models, PaddlePaddle has fully optimized the Semi-Auto Parallel programming paradigm with dynamic-static unification, simplifying programming complexity for developers. Developers do not need to deeply understand complex concepts and APIs under the manual parallel programming paradigm, such as row-parallel, and column-parallel. They only need a small amount of tensor distribution annotations to implement the hybrid parallelism. The distribution specification will be propagated to all tensors and operators automatically, and the framework would handle the communication and synchronization needed by distributed training appropriately. Meanwhile, it supports dynamic-to-static distributed training by adding one extra code only, allowing developers to efficiently implement any mixed parallelism strategy and deeply simplify the development process of hybrid-parallel training paradigm. -- **Hardware Integration Solution (CustomDevice)**: With increased demand for parallel training on new hardware in large model scenarios, PaddlePaddle has added support for distributed advanced policies, custom operators, and custom fusion policies. Distributed communication library is upgraded, with newly added support for many advanced distributed policies such as MP, GroupShared, PP, SP and MOE. Moreover, it supports vendors to flexibly access Transformer operator libraries of different granularities and modify the computation graph through Fusion Pass for performance acceleration. -- **Installation and development experience**: use of modular compilation optimizes logics of CMake codes, and improves efficiency of PaddlePaddle full compilation and incremental compilation. In addition, this can increase efficiency of RD development. It supports Python3.12, CUDA12, Hopper architecture compilation, with introduction of Clang and other tools to fully optimize code formats. In addition, C++ is changed from linking static libraries to linking dynamic libraries to reduce compilation volume. These optimizations provide users with a smoother and more efficient installation and development experience. +The core features of this version mainly include new technologies such as dynamic-static unity auto parallel and automatic optimization of neural network compiler, to aim to address the new challenges in the current deep learning field.PaddlePaddle Framework 3.0 Beta extends the design concepts of 2.x such as dynamic-static unity and integrated training and inference. The development interface is fully compatible with 2.x version. This means that codes developed in version 2.x can run directly on version 3.x without modification in most cases. Several key features are detailed as follows: -## 2. Incompatible Upgrade +- Dynamic-static graph unified auto parallel: To make the parallel training programming of large models easier, PaddlePaddle has also optimized the semi-auto parallel programming paradigm with dynamic-static graph unified. Developers do not need to delve into the complex concepts and APIs need in manual parallel programming; developers only need to perform a small amount of tensor sharding annotation to complete the construction of hybrid parallelism for large models. The framework is able to automatically derive distributed sharding states and add communication operators, and also supports one-key dynamic-to-static distributed training, thus dramatically simplifying the development of hybrid parallel training codes. In terms of dynamic-static unity, PaddlePaddle has comprehensively upgraded its dynamic-to-static training capability by adopting bytecode-based dynamic-static conversion technology, to support adaptive graph construction functions. It has been verified on more than 700 PaddlePaddle industrial-grade models, achieving a 100% success rate of one-key dynamic-to-static training. +- Automatic optimization of neural network compiler: PaddlePaddle Compiler Infrastructure for Neural Networks (CINN) adopts the design of integration with the framework, supporting the efficient training and dynamic shape inference of generative models, scientific computing models and other models. This provides a good balance between computational flexibility and high performance. The inference performance of Llama2 and Stable Diffusion models has been improved by 30% through automatic fusion of operators and code generation technology. +- High-order automatic differentiation: In order to better support scientific computing scenarios, PaddlePaddle Framework designs and implements high-order automatic differentiation technology based on combinatorial operator mechanism, combined with automatic optimization technology of neural network compiler. We have tested more than 40 differential equations in scientific computing scenarios, and its solution speed is 70% ahead of similar products in the industry. +- Highly scalable intermediate representation: In order to improve the scalability of the PaddlePaddle framework, we have developed a highly scalable Paddle Intermediate Representation (PIR).This representation systematically abstracts the underlying core concepts and provides flexible and efficient components. PIR serves as the infrastructure to support a number of technologies such as dynamic-to-static, automatic differentiation, auto parallel, combinatorial operators, and graph optimization; it is widely used in scenarios such as distributed training, model compression, and inference deployment. With the Declarative Rewrite Rule (DRR) mechanism provided by PIR, the development cost of Pass can be reduced by 60%.We have tested over 900 model configurations and the results show that the overall performance of inference improves by more than 10% after using PIR. +- Multi-Hardware adaptation: PaddlePaddle provides a well-functioning and low-cost solution for large model hardware adaptation. The new hardware only needs to be adapted with more than 30 interfaces to support training, compression and inference of large models. Meanwhile, PaddlePaddle provides compiler-based hardware access mode, and hardware vendors only need to implement the compiler's code generation back-end in the form of plug-ins to achieve efficient adaptation with the PaddlePaddle framework.PaddlePaddle hardware access this time has additional support for the daily release of four hardware units: Kunlun XPU, Ascend NPU, Hygon DCU and Cambricon MLU. -- In order to avoid misuse, we removed the 0-dimensional Tensor compatibility state switch, to achieve the same API behaviors as industry's mainstream habits. In the previous version, we already supported 0-dimensional Tensor, but we added a compatibility state switch in order to avoid error reporting of some models, as much as possible. That is, in some scenarios where model suite is used frequently and modification is not completed, we still used 1-dimensional Tensor with only 1 element to replace the 0-dimensional Tensor by default. In this version, compatibility state switch is removed, so the 1-dimensional Tensor with only 1 element will no longer be used, to replace 0-dimensional Tensor in any scenario. Behaviors of 376 APIs that should support the 0-dimensional Tensor have been corrected and unified, to thoroughly complete support for the 0-dimensional Tensor.[#57036](https://github.com/PaddlePaddle/Paddle/pull/57036), [#54581](https://github.com/PaddlePaddle/Paddle/pull/54581), [#54500](https://github.com/PaddlePaddle/Paddle/pull/54500) -- To improve API usability, paddle.nn.functional.diag_embed has been streamlined to paddle.diag_embed, with support of use of Tensor.diag_embed. [#58223](https://github.com/PaddlePaddle/Paddle/pull/58223) -- In order to solve the problem of differential computation error caused by Tensor index writing (e.g., tensor[0] = 10) under static graphs, and to comply with static graph specifications, this version introduces paddle.static.setitem API. In static graph environments, this API is recommended to support indexed write operations for tensor, instead of subscript operators. This change does not affect dynamic graph environments, where index write using subscript operators are still allowed. [#53682](https://github.com/PaddlePaddle/Paddle/pull/53682) -- paddle.fluid API is completely retired in this version. In this update, we completely removed all paddle.fluid APIs and deleted the fluid directory. Meanwhile, a small number of PaddlePaddle underlying public components have been consolidated into the paddle.base directory. It is unnecessary for PaddlePaddle users to pay attention to fluid-related concepts and APIs, further simplifying PaddlePaddle API system and improving readability.[#56576](https://github.com/PaddlePaddle/Paddle/pull/56576), [#54424](https://github.com/PaddlePaddle/Paddle/pull/54424), [#54829](https://github.com/PaddlePaddle/Paddle/pull/54829), [#53992](https://github.com/PaddlePaddle/Paddle/pull/53992), [#54806](https://github.com/PaddlePaddle/Paddle/pull/54806), [#55754](https://github.com/PaddlePaddle/Paddle/pull/55754), [#55986](https://github.com/PaddlePaddle/Paddle/pull/55986), [#55345](https://github.com/PaddlePaddle/Paddle/pull/55345), [#56099](https://github.com/PaddlePaddle/Paddle/pull/56099), [#51717](https://github.com/PaddlePaddle/Paddle/pull/51717), [#54152](https://github.com/PaddlePaddle/Paddle/pull/54152), [#55522](https://github.com/PaddlePaddle/Paddle/pull/55522), [#55757](https://github.com/PaddlePaddle/Paddle/pull/55757), [#58521](https://github.com/PaddlePaddle/Paddle/pull/58521), [#54936](https://github.com/PaddlePaddle/Paddle/pull/54936), [#55007](https://github.com/PaddlePaddle/Paddle/pull/55007), [#55661](https://github.com/PaddlePaddle/Paddle/pull/55661), [#55970](https://github.com/PaddlePaddle/Paddle/pull/55970) +This version includes the continuous improvement of some of the existing features of the framework 2.x. Meanwhile, the new features of this version bring significant improvements in terms of user experience, performance, ease of secondary development and hardware adaptability. In addition to the above core features, this version continues to enrich and enhance the API functions to meet more scenarios at the user experience level, optimizes and improves the distributed parallel strategy optimization and reasoning function enhancement for the large model scenarios, makes thorough improvement in terms of ease-of-use in compilation and installation, makes a new synchronous upgrade to the installation method and version of the dependency packages, strengthens the security of the system comprehensively, and makes comprehensive error-correction checks to the product documentation. We have also carried out a cleanup of some deprecated codes to ensure architectural simplicity. The performance of PaddlePaddle 3.0 Beta is still mature and stable without the use of new features, and each new feature provides a switch for flexible control, which makes it easy for users to quickly understand the related product features and experience comparison. -## 3. Training Framework (including Distributed) +## User Experience Upgrade -### Python API +### Incompatibility Upgrade -#### Upgrade Tensor indexing mechanism +- PaddlePaddle API supports type promotion.In the most common calculations such as addition, subtraction, multiplication, and division, if the two inputs are of different data types, it is necessary to determine the data type of the output. Historically, PaddlePaddle partially supported this and the actual rules were not clear. Objectively, there were dynamic-static inconsistency, inconsistent API and operator overloading, and inconsistent interchange rates, and unexpected problems (hard to fix) especially in the case of large models using a mix of bf16/fp16 and fp32 for a wide range of calculations. Starting from the 3.0 beta, PaddlePaddle has clarified the [type promotion rules](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/advanced/auto_type_promotion_cn.html), and defined in detail the types of Tensor vs Tensor and Tensor vs. 1 number (Scalar) computation results, ensuring that the computation conforms to the exchange law, the operator overloading is consistent with the results of the binary API, and the results of the dynamic graph are consistent with those of the static graph. This is more in line with user understanding and industry practice. [#60638](https://github.com/PaddlePaddle/Paddle/pull/60638), [#63842](https://github.com/PaddlePaddle/Paddle/pull/63842), [#60011](https://github.com/PaddlePaddle/Paddle/pull/60011) -This version comprehensively optimizes basic index, advanced index and joint index functions of Tensor, to better comply with industry standards and user habits. Specifically, we added support for view in basic index, fixed some wrong behaviors in advanced index, and implemented read function of joint index. In addition, we have sunk index parsing to C++ level, improved performance of high-level indexing operators, and removed redundant computations in bool indexing. With these optimizations, performance of Tensor's basic, advanced and joint index has been improved comprehensively. [#56893](https://github.com/PaddlePaddle/Paddle/pull/56893), [#58643](https://github.com/PaddlePaddle/Paddle/pull/58643), [#57986](https://github.com/PaddlePaddle/Paddle/pull/57986), [#56272](https://github.com/PaddlePaddle/Paddle/pull/56272), [#58856](https://github.com/PaddlePaddle/Paddle/pull/58856), [#55211](https://github.com/PaddlePaddle/Paddle/pull/55211), [#57023](https://github.com/PaddlePaddle/Paddle/pull/57023), [#56613](https://github.com/PaddlePaddle/Paddle/pull/56613), [#55602](https://github.com/PaddlePaddle/Paddle/pull/55602), [#59281](https://github.com/PaddlePaddle/Paddle/pull/59281), [#57737](https://github.com/PaddlePaddle/Paddle/pull/57737) +### Deprecated Features -#### Upgrade Inplace mechanism +- There have been two versions stably supporting 0-dimensional Tensor. This version removes the switch `FLAGS_set_to_1d` that converts a 0-dimensional Tensor to a 1-dimensional Tensor with only 1 element in some cases. This switch is for compatibility with the incorrect way of writing a 1-element 1-dimensional Tensor to represent a 0-dimensional Tensor in some kits. That is, the current PaddlePaddle fully distinguish between the semantics of a 0-dimensional Tensor and a 1-dimensional Tensor with only 1 element, both are not equivalent. [#61227](https://github.com/PaddlePaddle/Paddle/pull/61227) -In earlier versions, in order to ensure correctness of inverse differentiation calculations, when reverse calculation of an API depends on its forward input data, PaddlePaddle avoids using Inplace operation method, with possibly overwriting original input data. This mechanism simplifies implementation process, and also limits the ability of many APIs to implement Inplace functionality. As a result, user experience may be affected. -In this version, PaddlePaddle has fully upgraded the Inplace mechanism. It implements automatic detection of the dependency of reverse computation on forward inputs, to save input data when needed. Therefore, more Inplace operations are supported. This improvement not only improves memory usage efficiency, but also enhances functionality of the API. -In addition, we have added 109 new APIs that support Inplace operations, including paddle.abs\_, paddle.sin\_/cos\_/tan\_, comparison operations such as paddle.greater_than\_/less_than\_/equal\_, logical operations such as paddle.logical_and\_/logical_or\_/logical_not\_, paddle.neg\_ and paddle.log\_. While enriching the feature set of PaddlePaddle, it improves users' efficiency and convenience in numerical computation and deep learning tasks. [#54683](https://github.com/PaddlePaddle/Paddle/pull/54683), [#55078](https://github.com/PaddlePaddle/Paddle/pull/55078), [#55576](https://github.com/PaddlePaddle/Paddle/pull/55576), [#56888](https://github.com/PaddlePaddle/Paddle/pull/56888), [#55509](https://github.com/PaddlePaddle/Paddle/pull/55509), [#57093](https://github.com/PaddlePaddle/Paddle/pull/57093) +### New API Features -#### Other new APIs +Compared with the previous version, this version is added with 126 new APIs, richer API functions to better support the needs of large models, and scientific computation. The details are as follows: -- Added paddle.nn.functional.scaled_dot_product_attention. This significantly improves computational efficiency of the attention mechanism in large models, and meets demand for high-performance computation in large-scale deep learning models. [#55242](https://github.com/PaddlePaddle/Paddle/pull/55242) -- Added a series of new scientific computing-related APIs, including paddle.cummax and paddle.cummin for cumulative maximum and minimum computation, paddle.index_fill and paddle.masked_fill for filling tensor by index or mask, paddle.linalg.pca_lowrank for low-rank principal component analysis, paddle.hypot for calculating length of the hypotenuses of right triangles, and paddle.atleast_1d, paddle.atleast_2d, and paddle.atleast_3d to ensure the tensor is at least one, two, or three dimensional. We also provide paddle.select_scatter and paddle.diagonal_scatter for more flexible selection and hashing of tensor data, and paddle.multigammaln for choosing the natural logarithm of multigamma function. In addition, new optimizer-related APIs are added in this version, including: paddle.optimizer.lr.LinearLR and paddle.optimizer.lr.CosineAnnealingWarmRestarts for learning rate scheduling strategies; introduction of paddle.io.SubsetRandomSampler to support random sampling from a subset of data. These added APIs will further enhance flexibility and efficiency of PaddlePaddle in various application scenarios. [#57416](https://github.com/PaddlePaddle/Paddle/pull/57416), [#53546](https://github.com/PaddlePaddle/Paddle/pull/53546), [#53743](https://github.com/PaddlePaddle/Paddle/pull/53743), [#57295](https://github.com/PaddlePaddle/Paddle/pull/57295), [#57726](https://github.com/PaddlePaddle/Paddle/pull/57726), [#58764](https://github.com/PaddlePaddle/Paddle/pull/58764), [#58323](https://github.com/PaddlePaddle/Paddle/pull/58323), [#57720](https://github.com/PaddlePaddle/Paddle/pull/57720), [#58209](https://github.com/PaddlePaddle/Paddle/pull/58209), [#58214](https://github.com/PaddlePaddle/Paddle/pull/58214), [#57792](https://github.com/PaddlePaddle/Paddle/pull/57792), [#51395](https://github.com/PaddlePaddle/Paddle/pull/51395), [#57724](https://github.com/PaddlePaddle/Paddle/pull/57724), [#57355](https://github.com/PaddlePaddle/Paddle/pull/57355), [#57744](https://github.com/PaddlePaddle/Paddle/pull/57744), [#58244](https://github.com/PaddlePaddle/Paddle/pull/58244), [#57599](https://github.com/PaddlePaddle/Paddle/pull/57599), [#59343](https://github.com/PaddlePaddle/Paddle/pull/59343), [#57879](https://github.com/PaddlePaddle/Paddle/pull/57879) +- Add Tensor computation API. `paddle.gammaln`, `paddle.gammainc`, `paddle.gammaincc`, `paddle.sinc`, `paddle.pdist`, `paddle.histogramdd`,`paddle.signbit`, `paddle.copysign`, `paddle.bitwise_right_shift/bitwise_left_shift`, `paddle.isposinf/isneginf/isreal`, `paddle.isin`, `paddle.hsplit/dsplit`, `paddle.column_stack/row_stack/dstack/hstack/vstack`, `paddle.slice_scatter`, `paddle.masked_scatter` [#60553](https://github.com/PaddlePaddle/Paddle/pull/60553), [#59311](https://github.com/PaddlePaddle/Paddle/pull/59311), [#59357](https://github.com/PaddlePaddle/Paddle/pull/59357), [#63521](https://github.com/PaddlePaddle/Paddle/pull/63521), [#57869](https://github.com/PaddlePaddle/Paddle/pull/57869), [#57880](https://github.com/PaddlePaddle/Paddle/pull/57880), [#57882](https://github.com/PaddlePaddle/Paddle/pull/57882), [#60150](https://github.com/PaddlePaddle/Paddle/pull/60150), [#57785](https://github.com/PaddlePaddle/Paddle/pull/57785), [#58092](https://github.com/PaddlePaddle/Paddle/pull/58092), [#63523](https://github.com/PaddlePaddle/Paddle/pull/63523), [#64001](https://github.com/PaddlePaddle/Paddle/pull/64001), [#58917](https://github.com/PaddlePaddle/Paddle/pull/58917), [#59127](https://github.com/PaddlePaddle/Paddle/pull/59127), [#59973](https://github.com/PaddlePaddle/Paddle/pull/59973), [#59383](https://github.com/PaddlePaddle/Paddle/pull/59383) +- Add probability distribution API. `paddle.distribution.ContinuousBernoulli`, `paddle.distribution.MultivariateNormal`, `paddle.distribution.Exponential`, `paddle.distribution.Gamma`, `paddle.distribution.Binomial`, `paddle.distribution.Poisson` [#58004](https://github.com/PaddlePaddle/Paddle/pull/58004), [#57899](https://github.com/PaddlePaddle/Paddle/pull/57899), [#57856](https://github.com/PaddlePaddle/Paddle/pull/57856) +- Add optimizer API. `paddle.optimizer.ASGD`, `paddle.optimizer.NAdam`, `paddle.optimizer.RAdam`, `paddle.optimizer.Rprop` [#58834](https://github.com/PaddlePaddle/Paddle/pull/58834), [#63671](https://github.com/PaddlePaddle/Paddle/pull/63671), [#58851](https://github.com/PaddlePaddle/Paddle/pull/58851) +- Add Linear Algebra API. `paddle.linalg.matrix_exp` [#59715](https://github.com/PaddlePaddle/Paddle/pull/59715) +- Add other APIs. `paddle.bernoulli_`, `paddle.nn.ZeroPad1D/ZeroPad3D`, `paddle.nn.AdaptiveLogSoftmaxWithLoss`, `paddle.Tensor.apply` [#64252](https://github.com/PaddlePaddle/Paddle/pull/64252), [#59690](https://github.com/PaddlePaddle/Paddle/pull/59690), [#63728](https://github.com/PaddlePaddle/Paddle/pull/63728), [#63302](https://github.com/PaddlePaddle/Paddle/pull/63302), [#59374](https://github.com/PaddlePaddle/Paddle/pull/59374),[#63227](https://github.com/PaddlePaddle/Paddle/pull/63227) -### New Generation of Paddle Intermediate Representation (PIR) +### Some API Enhancements -PIR systematically abstracts underlying core concepts such as Operation, Attribute and Type, to build a set of flexible and powerful base components for developers. In addition, PaddlePaddle can comprehensively and hierarchically manage requirements of each module on Intermediate Representation (IR) by introducing the concept of Dialect, and support developers to customize extension of Dialect according to specific needs to significantly improving scalability and adaptability of framework. In terms of designs, PIR strictly follows the Static Single Assignment (SSA) principle, unifies top-level structure, realizes compatibility of "Operator sequentiality" and "computational graph semantics". This provides a clear and consistent view of the complex computation process. In order to further optimize performance of large models, PIR also provides a set of more concise and low-cost Pass development processes, including Declarative Rewrite Rule (DRR) and Pattern Rewriter. In addition, a series of rich and full-featured Pass optimization strategies are built-in, to deeply optimize application according to characteristics of large models, thus providing strong support for ultimate performance of large models. Through these innovative designs and optimization methods, PIR lays a solid foundation for efficient operation and continuous expansion of the PaddlePaddle framework. +- Enhance about 30 APIs to support complex number computation, such as `paddle.log`, `paddle.log1p`, `paddle.square`, and `paddle.reciprocal`, to extend the support for more scientific computing scenarios. [#62448](https://github.com/PaddlePaddle/Paddle/pull/62448), [#60821](https://github.com/PaddlePaddle/Paddle/pull/60821), [#60897](https://github.com/PaddlePaddle/Paddle/pull/60897), [#62764](https://github.com/PaddlePaddle/Paddle/pull/62764), [#59536](https://github.com/PaddlePaddle/Paddle/pull/59536), [#59529](https://github.com/PaddlePaddle/Paddle/pull/59529), [#63207](https://github.com/PaddlePaddle/Paddle/pull/63207), [#62237](https://github.com/PaddlePaddle/Paddle/pull/62237), [#64684](https://github.com/PaddlePaddle/Paddle/pull/64684) +- Enhance 46 APIs, to make existing APIs easier to use and easier to convert to codes,including but not limited to, adding API parameters, extending the data types supported by the APIs, and fixing the existing unreasonable designs. [#59890](https://github.com/PaddlePaddle/Paddle/pull/59890), [#63513](https://github.com/PaddlePaddle/Paddle/pull/63513), [#59674](https://github.com/PaddlePaddle/Paddle/pull/59674), [#62778](https://github.com/PaddlePaddle/Paddle/pull/62778), [#64110](https://github.com/PaddlePaddle/Paddle/pull/64110), [#63222](https://github.com/PaddlePaddle/Paddle/pull/63222), [#64331](https://github.com/PaddlePaddle/Paddle/pull/64331), [#64715](https://github.com/PaddlePaddle/Paddle/pull/64715), [#61155](https://github.com/PaddlePaddle/Paddle/pull/61155), [#60070](https://github.com/PaddlePaddle/Paddle/pull/60070), [#61974](https://github.com/PaddlePaddle/Paddle/pull/61974), [#62407](https://github.com/PaddlePaddle/Paddle/pull/62407), [#62672](https://github.com/PaddlePaddle/Paddle/pull/62672),[#62722](https://github.com/PaddlePaddle/Paddle/pull/62722)， [#62876](https://github.com/PaddlePaddle/Paddle/pull/62876), [#63284](https://github.com/PaddlePaddle/Paddle/pull/63284), [#63860](https://github.com/PaddlePaddle/Paddle/pull/63860), [#60466](https://github.com/PaddlePaddle/Paddle/pull/60466), [#63690](https://github.com/PaddlePaddle/Paddle/pull/63690), [#63953](https://github.com/PaddlePaddle/Paddle/pull/63953), [#63901](https://github.com/PaddlePaddle/Paddle/pull/63901), [#62624](https://github.com/PaddlePaddle/Paddle/pull/62624), [#59857](https://github.com/PaddlePaddle/Paddle/pull/59857), [#60084](https://github.com/PaddlePaddle/Paddle/pull/60084), [#60766](https://github.com/PaddlePaddle/Paddle/pull/60766), [#62788](https://github.com/PaddlePaddle/Paddle/pull/62788), [#62937](https://github.com/PaddlePaddle/Paddle/pull/62937), [#63134](https://github.com/PaddlePaddle/Paddle/pull/63134), [#62966](https://github.com/PaddlePaddle/Paddle/pull/62966), [#63648](https://github.com/PaddlePaddle/Paddle/pull/63648), [#63881](https://github.com/PaddlePaddle/Paddle/pull/63881), [#64358](https://github.com/PaddlePaddle/Paddle/pull/64358), [#60503](https://github.com/PaddlePaddle/Paddle/pull/60503), [#63604](https://github.com/PaddlePaddle/Paddle/pull/63604), [#62338](https://github.com/PaddlePaddle/Paddle/pull/62338) +- Enhance single-test infrastructure for higher-order differentiation, making it easier to add single-test use cases for higher-order differentiation. [#62074](https://github.com/PaddlePaddle/Paddle/pull/62074) -#### New features +### API Performance Improvements -- Abstracted core concepts of IR bottom layer and provided developers with flexible base components, such as Operation, Attribute, Value, Type, Trait, and Interface. [#56354](https://github.com/PaddlePaddle/Paddle/pull/56354),[#57106](https://github.com/PaddlePaddle/Paddle/pull/57106),[#57349](https://github.com/PaddlePaddle/Paddle/pull/57349),[#54844](https://github.com/PaddlePaddle/Paddle/pull/54844),[#54984](https://github.com/PaddlePaddle/Paddle/pull/54984),[#54565](https://github.com/PaddlePaddle/Paddle/pull/54565),[#54562](https://github.com/PaddlePaddle/Paddle/pull/54562),[#57249](https://github.com/PaddlePaddle/Paddle/pull/57249),[#57550](https://github.com/PaddlePaddle/Paddle/pull/57550),[#59278](https://github.com/PaddlePaddle/Paddle/pull/59278),[#54875](https://github.com/PaddlePaddle/Paddle/pull/54875),[#55041](https://github.com/PaddlePaddle/Paddle/pull/55041),[#54987](https://github.com/PaddlePaddle/Paddle/pull/54987),[#55903](https://github.com/PaddlePaddle/Paddle/pull/55903),[#57582](https://github.com/PaddlePaddle/Paddle/pull/57582),[#57580](https://github.com/PaddlePaddle/Paddle/pull/57580),[#58052](https://github.com/PaddlePaddle/Paddle/pull/58052),[#55322](https://github.com/PaddlePaddle/Paddle/pull/55322),[#57418](https://github.com/PaddlePaddle/Paddle/pull/57418),[#57635](https://github.com/PaddlePaddle/Paddle/pull/57635),[#55328](https://github.com/PaddlePaddle/Paddle/pull/55328),[#57463](https://github.com/PaddlePaddle/Paddle/pull/57463),[#59791](https://github.com/PaddlePaddle/Paddle/pull/59791),[#59821](https://github.com/PaddlePaddle/Paddle/pull/59821),[#59115](https://github.com/PaddlePaddle/Paddle/pull/59115),[#57461](https://github.com/PaddlePaddle/Paddle/pull/57461),[#59392](https://github.com/PaddlePaddle/Paddle/pull/59392),[#57373](https://github.com/PaddlePaddle/Paddle/pull/57373),[#59118](https://github.com/PaddlePaddle/Paddle/pull/59118) -- Added Dialect mechanism to support comprehensive and hierarchical management of intermediate representation requirements of each module of framework. Through built-in Builtin Dialect, it supports developers to customize and extend Dialect according to their needs. [#56325](https://github.com/PaddlePaddle/Paddle/pull/56325),[#57539](https://github.com/PaddlePaddle/Paddle/pull/57539),[#54682](https://github.com/PaddlePaddle/Paddle/pull/54682),[#55381](https://github.com/PaddlePaddle/Paddle/pull/55381),[#56156](https://github.com/PaddlePaddle/Paddle/pull/56156),[#56431](https://github.com/PaddlePaddle/Paddle/pull/56431),[#56615](https://github.com/PaddlePaddle/Paddle/pull/56615),[#57103](https://github.com/PaddlePaddle/Paddle/pull/57103),[#57209](https://github.com/PaddlePaddle/Paddle/pull/57209) -- Normalized PaddlePaddle static graph operator system. Added OperatorDialect and KernelDialect. Managed conceptual differences of operators in the form of Dialect during compilation and execution, making Architecture clearer. [#56284](https://github.com/PaddlePaddle/Paddle/pull/56284),[#54469](https://github.com/PaddlePaddle/Paddle/pull/54469),[#58660](https://github.com/PaddlePaddle/Paddle/pull/58660),[#58975](https://github.com/PaddlePaddle/Paddle/pull/58975),[#56680](https://github.com/PaddlePaddle/Paddle/pull/56680),[#54790](https://github.com/PaddlePaddle/Paddle/pull/54790),[#54826](https://github.com/PaddlePaddle/Paddle/pull/54826),[#54840](https://github.com/PaddlePaddle/Paddle/pull/54840),[#55699](https://github.com/PaddlePaddle/Paddle/pull/55699),[#55648](https://github.com/PaddlePaddle/Paddle/pull/55648),[#55880](https://github.com/PaddlePaddle/Paddle/pull/55880),[#56101](https://github.com/PaddlePaddle/Paddle/pull/56101),[#56754](https://github.com/PaddlePaddle/Paddle/pull/56754),[#54944](https://github.com/PaddlePaddle/Paddle/pull/54944),[#56836](https://github.com/PaddlePaddle/Paddle/pull/56836),[#57185](https://github.com/PaddlePaddle/Paddle/pull/57185),[#58757](https://github.com/PaddlePaddle/Paddle/pull/58757),[#56243](https://github.com/PaddlePaddle/Paddle/pull/56243),[#56436](https://github.com/PaddlePaddle/Paddle/pull/56436),[#57741](https://github.com/PaddlePaddle/Paddle/pull/57741),[#59124](https://github.com/PaddlePaddle/Paddle/pull/59124),[#57054](https://github.com/PaddlePaddle/Paddle/pull/57054),[#56984](https://github.com/PaddlePaddle/Paddle/pull/56984),[#57403](https://github.com/PaddlePaddle/Paddle/pull/57403),[#57904](https://github.com/PaddlePaddle/Paddle/pull/57904),[#58031](https://github.com/PaddlePaddle/Paddle/pull/58031),[#56924](https://github.com/PaddlePaddle/Paddle/pull/56924),[#59270](https://github.com/PaddlePaddle/Paddle/pull/59270),[#55343](https://github.com/PaddlePaddle/Paddle/pull/55343),[#56557](https://github.com/PaddlePaddle/Paddle/pull/56557),[#55693](https://github.com/PaddlePaddle/Paddle/pull/55693),[#54428](https://github.com/PaddlePaddle/Paddle/pull/54428) -- Added ShapeDialect with built-in rich shape operation operators for constructing dynamic shape constraints and expressions for AI compilers. [#56727](https://github.com/PaddlePaddle/Paddle/pull/56727),[#59254](https://github.com/PaddlePaddle/Paddle/pull/59254),[#58368](https://github.com/PaddlePaddle/Paddle/pull/58368),[#57069](https://github.com/PaddlePaddle/Paddle/pull/57069),[#57337](https://github.com/PaddlePaddle/Paddle/pull/57337),[#56351](https://github.com/PaddlePaddle/Paddle/pull/56351),[#57029](https://github.com/PaddlePaddle/Paddle/pull/57029),[#58036](https://github.com/PaddlePaddle/Paddle/pull/58036),[#59032](https://github.com/PaddlePaddle/Paddle/pull/59032),[#57961](https://github.com/PaddlePaddle/Paddle/pull/57961),[#56427](https://github.com/PaddlePaddle/Paddle/pull/56427),[#57459](https://github.com/PaddlePaddle/Paddle/pull/57459) -- Unified top-level structure of Framework Program, supporting compatible representation of "operator sequentiality" and "computational graph semantics", decoupling dependency on ir::Graph, and strictly following the principle of Static Single Assignment (SSA). [#59369](https://github.com/PaddlePaddle/Paddle/pull/59369),[#54563](https://github.com/PaddlePaddle/Paddle/pull/54563),[#57051](https://github.com/PaddlePaddle/Paddle/pull/57051),[#57306](https://github.com/PaddlePaddle/Paddle/pull/57306),[#57857](https://github.com/PaddlePaddle/Paddle/pull/57857) -- Added IrPrinter and IrPaser components to support serialization and deserialization of PIR Programs, providing a friendly debugging experience for PIR development. [#55695](https://github.com/PaddlePaddle/Paddle/pull/55695),[#59449](https://github.com/PaddlePaddle/Paddle/pull/59449),[#54369](https://github.com/PaddlePaddle/Paddle/pull/54369),[#54499](https://github.com/PaddlePaddle/Paddle/pull/54499),[#55518](https://github.com/PaddlePaddle/Paddle/pull/55518),[#55784](https://github.com/PaddlePaddle/Paddle/pull/55784),[#57180](https://github.com/PaddlePaddle/Paddle/pull/57180),[#57471](https://github.com/PaddlePaddle/Paddle/pull/57471),[#54859](https://github.com/PaddlePaddle/Paddle/pull/54859),[#54968](https://github.com/PaddlePaddle/Paddle/pull/54968),[#55209](https://github.com/PaddlePaddle/Paddle/pull/55209),[#57314](https://github.com/PaddlePaddle/Paddle/pull/57314),[#57969](https://github.com/PaddlePaddle/Paddle/pull/57969) -- Built a new, simple and low-cost Pass development system based on Declarative Rewrite Rule (DDR) and Pattern Rewriter, with built-in a series of rich and full-featured Pass Optimization strategies, to accelerate training and inference execution process. [#54385](https://github.com/PaddlePaddle/Paddle/pull/54385),[#54738](https://github.com/PaddlePaddle/Paddle/pull/54738),[#55859](https://github.com/PaddlePaddle/Paddle/pull/55859),[#56638](https://github.com/PaddlePaddle/Paddle/pull/56638),[#57090](https://github.com/PaddlePaddle/Paddle/pull/57090),[#58673](https://github.com/PaddlePaddle/Paddle/pull/58673),[#59415](https://github.com/PaddlePaddle/Paddle/pull/59415),[#56729](https://github.com/PaddlePaddle/Paddle/pull/56729),[#58655](https://github.com/PaddlePaddle/Paddle/pull/58655) -- Added ProgramTranslator component, to support conversion from ProgramDesc to new generation of IR representations of PaddlePaddle by pressing one key, with provision of easy-to-use C++ and Python interfaces. [#55433](https://github.com/PaddlePaddle/Paddle/pull/55433),[#54470](https://github.com/PaddlePaddle/Paddle/pull/54470),[#58044](https://github.com/PaddlePaddle/Paddle/pull/58044),[#58390](https://github.com/PaddlePaddle/Paddle/pull/58390),[#58100](https://github.com/PaddlePaddle/Paddle/pull/58100),[#55403](https://github.com/PaddlePaddle/Paddle/pull/55403),[#55406](https://github.com/PaddlePaddle/Paddle/pull/55406),[#54719](https://github.com/PaddlePaddle/Paddle/pull/54719),[#56550](https://github.com/PaddlePaddle/Paddle/pull/56550),[#55448](https://github.com/PaddlePaddle/Paddle/pull/55448),[#55453](https://github.com/PaddlePaddle/Paddle/pull/55453),[#56294](https://github.com/PaddlePaddle/Paddle/pull/56294),[#56308](https://github.com/PaddlePaddle/Paddle/pull/56308),[#56842](https://github.com/PaddlePaddle/Paddle/pull/56842),[#58517](https://github.com/PaddlePaddle/Paddle/pull/58517) -- With help of automatic code generation technology, it can generate the full amount of static graph operator representations for PaddlePaddle framework by pressing one key. Sank static graph networking logic to C++ side and bind it to \_C_ops module. This can greatly streamline API code on Python side, realize ultimate dynamic-static unification of APIs of PaddlePaddle Framework, and upgrade a lot of Python APIs to support static graph networking of the new IR. [#56570](https://github.com/PaddlePaddle/Paddle/pull/56570),[#55745](https://github.com/PaddlePaddle/Paddle/pull/55745),[#56955](https://github.com/PaddlePaddle/Paddle/pull/56955),[#57298](https://github.com/PaddlePaddle/Paddle/pull/57298),[#57946](https://github.com/PaddlePaddle/Paddle/pull/57946),[#57248](https://github.com/PaddlePaddle/Paddle/pull/57248),[#56080](https://github.com/PaddlePaddle/Paddle/pull/56080),[#54396](https://github.com/PaddlePaddle/Paddle/pull/54396),[#54551](https://github.com/PaddlePaddle/Paddle/pull/54551),[#56520](https://github.com/PaddlePaddle/Paddle/pull/56520),[#55002](https://github.com/PaddlePaddle/Paddle/pull/55002),[#57067](https://github.com/PaddlePaddle/Paddle/pull/57067),[#59320](https://github.com/PaddlePaddle/Paddle/pull/59320),[#59348](https://github.com/PaddlePaddle/Paddle/pull/59348),[#57164](https://github.com/PaddlePaddle/Paddle/pull/57164),[#57267](https://github.com/PaddlePaddle/Paddle/pull/57267),[#59064](https://github.com/PaddlePaddle/Paddle/pull/59064),[#54340](https://github.com/PaddlePaddle/Paddle/pull/54340),[#54895](https://github.com/PaddlePaddle/Paddle/pull/54895),[#55004](https://github.com/PaddlePaddle/Paddle/pull/55004),[#56196](https://github.com/PaddlePaddle/Paddle/pull/56196),[#56862](https://github.com/PaddlePaddle/Paddle/pull/56862),[#58991](https://github.com/PaddlePaddle/Paddle/pull/58991),[#55428](https://github.com/PaddlePaddle/Paddle/pull/55428),[#55909](https://github.com/PaddlePaddle/Paddle/pull/55909),[#56241](https://github.com/PaddlePaddle/Paddle/pull/56241),[#56526](https://github.com/PaddlePaddle/Paddle/pull/56526),[#56571](https://github.com/PaddlePaddle/Paddle/pull/56571),[#56518](https://github.com/PaddlePaddle/Paddle/pull/56518),[#57016](https://github.com/PaddlePaddle/Paddle/pull/57016),[#56653](https://github.com/PaddlePaddle/Paddle/pull/56653),[#56809](https://github.com/PaddlePaddle/Paddle/pull/56809),[#57158](https://github.com/PaddlePaddle/Paddle/pull/57158),[#55422](https://github.com/PaddlePaddle/Paddle/pull/55422),[#55458](https://github.com/PaddlePaddle/Paddle/pull/55458),[#55432](https://github.com/PaddlePaddle/Paddle/pull/55432),[#55467](https://github.com/PaddlePaddle/Paddle/pull/55467),[#55483](https://github.com/PaddlePaddle/Paddle/pull/55483),[#55419](https://github.com/PaddlePaddle/Paddle/pull/55419),[#55517](https://github.com/PaddlePaddle/Paddle/pull/55517),[#55500](https://github.com/PaddlePaddle/Paddle/pull/55500),[#56674](https://github.com/PaddlePaddle/Paddle/pull/56674),[#57693](https://github.com/PaddlePaddle/Paddle/pull/57693),[#55008](https://github.com/PaddlePaddle/Paddle/pull/55008),[#57166](https://github.com/PaddlePaddle/Paddle/pull/57166),[#57157](https://github.com/PaddlePaddle/Paddle/pull/57157),[#57159](https://github.com/PaddlePaddle/Paddle/pull/57159),[#57175](https://github.com/PaddlePaddle/Paddle/pull/57175),[#57325](https://github.com/PaddlePaddle/Paddle/pull/57325),[#57330](https://github.com/PaddlePaddle/Paddle/pull/57330),[#57415](https://github.com/PaddlePaddle/Paddle/pull/57415),[#57122](https://github.com/PaddlePaddle/Paddle/pull/57122),[#57393](https://github.com/PaddlePaddle/Paddle/pull/57393),[#57344](https://github.com/PaddlePaddle/Paddle/pull/57344),[#57667](https://github.com/PaddlePaddle/Paddle/pull/57667),[#57348](https://github.com/PaddlePaddle/Paddle/pull/57348),[#57700](https://github.com/PaddlePaddle/Paddle/pull/57700),[#58093](https://github.com/PaddlePaddle/Paddle/pull/58093),[#58005](https://github.com/PaddlePaddle/Paddle/pull/58005),[#58081](https://github.com/PaddlePaddle/Paddle/pull/58081),[#58094](https://github.com/PaddlePaddle/Paddle/pull/58094),[#58137](https://github.com/PaddlePaddle/Paddle/pull/58137),[#58287](https://github.com/PaddlePaddle/Paddle/pull/58287),[#58352](https://github.com/PaddlePaddle/Paddle/pull/58352),[#58340](https://github.com/PaddlePaddle/Paddle/pull/58340),[#58363](https://github.com/PaddlePaddle/Paddle/pull/58363),[#58331](https://github.com/PaddlePaddle/Paddle/pull/58331),[#58343](https://github.com/PaddlePaddle/Paddle/pull/58343),[#58317](https://github.com/PaddlePaddle/Paddle/pull/58317),[#58450](https://github.com/PaddlePaddle/Paddle/pull/58450),[#58377](https://github.com/PaddlePaddle/Paddle/pull/58377),[#58466](https://github.com/PaddlePaddle/Paddle/pull/58466),[#58470](https://github.com/PaddlePaddle/Paddle/pull/58470),[#58491](https://github.com/PaddlePaddle/Paddle/pull/58491),[#58546](https://github.com/PaddlePaddle/Paddle/pull/58546),[#58587](https://github.com/PaddlePaddle/Paddle/pull/58587),[#58453](https://github.com/PaddlePaddle/Paddle/pull/58453),[#58634](https://github.com/PaddlePaddle/Paddle/pull/58634),[#58604](https://github.com/PaddlePaddle/Paddle/pull/58604),[#58605](https://github.com/PaddlePaddle/Paddle/pull/58605),[#58593](https://github.com/PaddlePaddle/Paddle/pull/58593),[#58675](https://github.com/PaddlePaddle/Paddle/pull/58675),[#58699](https://github.com/PaddlePaddle/Paddle/pull/58699),[#58384](https://github.com/PaddlePaddle/Paddle/pull/58384),[#58629](https://github.com/PaddlePaddle/Paddle/pull/58629),[#58579](https://github.com/PaddlePaddle/Paddle/pull/58579),[#58695](https://github.com/PaddlePaddle/Paddle/pull/58695),[#58548](https://github.com/PaddlePaddle/Paddle/pull/58548),[#58688](https://github.com/PaddlePaddle/Paddle/pull/58688),[#58792](https://github.com/PaddlePaddle/Paddle/pull/58792),[#58843](https://github.com/PaddlePaddle/Paddle/pull/58843),[#58840](https://github.com/PaddlePaddle/Paddle/pull/58840),[#58718](https://github.com/PaddlePaddle/Paddle/pull/58718),[#58883](https://github.com/PaddlePaddle/Paddle/pull/58883),[#58785](https://github.com/PaddlePaddle/Paddle/pull/58785),[#58608](https://github.com/PaddlePaddle/Paddle/pull/58608),[#58781](https://github.com/PaddlePaddle/Paddle/pull/58781),[#58783](https://github.com/PaddlePaddle/Paddle/pull/58783),[#58429](https://github.com/PaddlePaddle/Paddle/pull/58429),[#58685](https://github.com/PaddlePaddle/Paddle/pull/58685),[#58696](https://github.com/PaddlePaddle/Paddle/pull/58696),[#58690](https://github.com/PaddlePaddle/Paddle/pull/58690),[#58831](https://github.com/PaddlePaddle/Paddle/pull/58831),[#58929](https://github.com/PaddlePaddle/Paddle/pull/58929),[#58740](https://github.com/PaddlePaddle/Paddle/pull/58740),[#58937](https://github.com/PaddlePaddle/Paddle/pull/58937),[#58782](https://github.com/PaddlePaddle/Paddle/pull/58782),[#58833](https://github.com/PaddlePaddle/Paddle/pull/58833),[#58882](https://github.com/PaddlePaddle/Paddle/pull/58882),[#58935](https://github.com/PaddlePaddle/Paddle/pull/58935),[#58931](https://github.com/PaddlePaddle/Paddle/pull/58931),[#59041](https://github.com/PaddlePaddle/Paddle/pull/59041),[#59040](https://github.com/PaddlePaddle/Paddle/pull/59040),[#58877](https://github.com/PaddlePaddle/Paddle/pull/58877),[#58888](https://github.com/PaddlePaddle/Paddle/pull/58888),[#59042](https://github.com/PaddlePaddle/Paddle/pull/59042),[#58780](https://github.com/PaddlePaddle/Paddle/pull/58780),[#58682](https://github.com/PaddlePaddle/Paddle/pull/58682),[#58815](https://github.com/PaddlePaddle/Paddle/pull/58815),[#58676](https://github.com/PaddlePaddle/Paddle/pull/58676),[#58678](https://github.com/PaddlePaddle/Paddle/pull/58678),[#58446](https://github.com/PaddlePaddle/Paddle/pull/58446),[#59077](https://github.com/PaddlePaddle/Paddle/pull/59077),[#59091](https://github.com/PaddlePaddle/Paddle/pull/59091),[#58661](https://github.com/PaddlePaddle/Paddle/pull/58661),[#58832](https://github.com/PaddlePaddle/Paddle/pull/58832),[#58642](https://github.com/PaddlePaddle/Paddle/pull/58642),[#58698](https://github.com/PaddlePaddle/Paddle/pull/58698),[#59313](https://github.com/PaddlePaddle/Paddle/pull/59313),[#59371](https://github.com/PaddlePaddle/Paddle/pull/59371),[#58700](https://github.com/PaddlePaddle/Paddle/pull/58700),[#58953](https://github.com/PaddlePaddle/Paddle/pull/58953),[#58879](https://github.com/PaddlePaddle/Paddle/pull/58879),[#59469](https://github.com/PaddlePaddle/Paddle/pull/59469),[#59573](https://github.com/PaddlePaddle/Paddle/pull/59573),[#59481](https://github.com/PaddlePaddle/Paddle/pull/59481),[#59419](https://github.com/PaddlePaddle/Paddle/pull/59419),[#59509](https://github.com/PaddlePaddle/Paddle/pull/59509),[#58735](https://github.com/PaddlePaddle/Paddle/pull/58735),[#59616](https://github.com/PaddlePaddle/Paddle/pull/59616),[#59582](https://github.com/PaddlePaddle/Paddle/pull/59582),[#59420](https://github.com/PaddlePaddle/Paddle/pull/59420),[#59500](https://github.com/PaddlePaddle/Paddle/pull/59500),[#58911](https://github.com/PaddlePaddle/Paddle/pull/58911),[#59535](https://github.com/PaddlePaddle/Paddle/pull/59535),[#54891](https://github.com/PaddlePaddle/Paddle/pull/54891),[#56794](https://github.com/PaddlePaddle/Paddle/pull/56794),[#57477](https://github.com/PaddlePaddle/Paddle/pull/57477),[#57929](https://github.com/PaddlePaddle/Paddle/pull/57929),[#57765](https://github.com/PaddlePaddle/Paddle/pull/57765),[#58693](https://github.com/PaddlePaddle/Paddle/pull/58693),[#58603](https://github.com/PaddlePaddle/Paddle/pull/58603),[#56291](https://github.com/PaddlePaddle/Paddle/pull/56291),[#57123](https://github.com/PaddlePaddle/Paddle/pull/57123),[#57317](https://github.com/PaddlePaddle/Paddle/pull/57317),[#57341](https://github.com/PaddlePaddle/Paddle/pull/57341),[#57020](https://github.com/PaddlePaddle/Paddle/pull/57020),[#57324](https://github.com/PaddlePaddle/Paddle/pull/57324),[#57761](https://github.com/PaddlePaddle/Paddle/pull/57761),[#57762](https://github.com/PaddlePaddle/Paddle/pull/57762),[#57907](https://github.com/PaddlePaddle/Paddle/pull/57907),[#57909](https://github.com/PaddlePaddle/Paddle/pull/57909),[#58099](https://github.com/PaddlePaddle/Paddle/pull/58099),[#58110](https://github.com/PaddlePaddle/Paddle/pull/58110),[#58114](https://github.com/PaddlePaddle/Paddle/pull/58114),[#58139](https://github.com/PaddlePaddle/Paddle/pull/58139),[#58144](https://github.com/PaddlePaddle/Paddle/pull/58144),[#58165](https://github.com/PaddlePaddle/Paddle/pull/58165),[#58194](https://github.com/PaddlePaddle/Paddle/pull/58194),[#58138](https://github.com/PaddlePaddle/Paddle/pull/58138),[#58113](https://github.com/PaddlePaddle/Paddle/pull/58113),[#58245](https://github.com/PaddlePaddle/Paddle/pull/58245),[#58318](https://github.com/PaddlePaddle/Paddle/pull/58318),[#58105](https://github.com/PaddlePaddle/Paddle/pull/58105),[#58348](https://github.com/PaddlePaddle/Paddle/pull/58348),[#58235](https://github.com/PaddlePaddle/Paddle/pull/58235),[#58354](https://github.com/PaddlePaddle/Paddle/pull/58354),[#58341](https://github.com/PaddlePaddle/Paddle/pull/58341),[#58445](https://github.com/PaddlePaddle/Paddle/pull/58445),[#58418](https://github.com/PaddlePaddle/Paddle/pull/58418),[#58239](https://github.com/PaddlePaddle/Paddle/pull/58239),[#58473](https://github.com/PaddlePaddle/Paddle/pull/58473),[#58239](https://github.com/PaddlePaddle/Paddle/pull/58239),[#58391](https://github.com/PaddlePaddle/Paddle/pull/58391),[#58501](https://github.com/PaddlePaddle/Paddle/pull/58501),[#58519](https://github.com/PaddlePaddle/Paddle/pull/58519),[#58416](https://github.com/PaddlePaddle/Paddle/pull/58416),[#58588](https://github.com/PaddlePaddle/Paddle/pull/58588),[#58531](https://github.com/PaddlePaddle/Paddle/pull/58531),[#58730](https://github.com/PaddlePaddle/Paddle/pull/58730),[#58773](https://github.com/PaddlePaddle/Paddle/pull/58773),[#58862](https://github.com/PaddlePaddle/Paddle/pull/58862),[#58946](https://github.com/PaddlePaddle/Paddle/pull/58946),[#58500](https://github.com/PaddlePaddle/Paddle/pull/58500),[#56585](https://github.com/PaddlePaddle/Paddle/pull/56585),[#57480](https://github.com/PaddlePaddle/Paddle/pull/57480),[#57433](https://github.com/PaddlePaddle/Paddle/pull/57433),[#58498](https://github.com/PaddlePaddle/Paddle/pull/58498) +- Focus on optimizing the performance of Tensor basic index, advanced index, and combined index, improving computational performance by 2X to 31X on GPUs and 1.8X to 1004X on CPUs. [#60254](https://github.com/PaddlePaddle/Paddle/pull/60254), [#60276](https://github.com/PaddlePaddle/Paddle/pull/60276), [#60452](https://github.com/PaddlePaddle/Paddle/pull/60452), [#60771](https://github.com/PaddlePaddle/Paddle/pull/60771), [#61021](https://github.com/PaddlePaddle/Paddle/pull/61021), [#60983](https://github.com/PaddlePaddle/Paddle/pull/60983), [#61060](https://github.com/PaddlePaddle/Paddle/pull/61060), [#60618](https://github.com/PaddlePaddle/Paddle/pull/60618) -#### Function optimization +### Bug Fixing -- Upgraded static graph executor to extend more Kernel Instruction types, and supported loading of PIR with efficiently scheduling execution. This has significant video memory and performance gains in training and inference. [#54570](https://github.com/PaddlePaddle/Paddle/pull/54570),[#58665](https://github.com/PaddlePaddle/Paddle/pull/58665),[#57291](https://github.com/PaddlePaddle/Paddle/pull/57291),[#54452](https://github.com/PaddlePaddle/Paddle/pull/54452),[#57431](https://github.com/PaddlePaddle/Paddle/pull/57431),[#54692](https://github.com/PaddlePaddle/Paddle/pull/54692),[#55112](https://github.com/PaddlePaddle/Paddle/pull/55112),[#55210](https://github.com/PaddlePaddle/Paddle/pull/55210),[#55401](https://github.com/PaddlePaddle/Paddle/pull/55401),[#55772](https://github.com/PaddlePaddle/Paddle/pull/55772),[#55828](https://github.com/PaddlePaddle/Paddle/pull/55828),[#56148](https://github.com/PaddlePaddle/Paddle/pull/56148),[#54763](https://github.com/PaddlePaddle/Paddle/pull/54763),[#56886](https://github.com/PaddlePaddle/Paddle/pull/56886),[#57284](https://github.com/PaddlePaddle/Paddle/pull/57284),[#57268](https://github.com/PaddlePaddle/Paddle/pull/57268),[#57791](https://github.com/PaddlePaddle/Paddle/pull/57791),[#56789](https://github.com/PaddlePaddle/Paddle/pull/56789),[#56704](https://github.com/PaddlePaddle/Paddle/pull/56704),[#57594](https://github.com/PaddlePaddle/Paddle/pull/57594),[#58397](https://github.com/PaddlePaddle/Paddle/pull/58397),[#58337](https://github.com/PaddlePaddle/Paddle/pull/58337),[#58756](https://github.com/PaddlePaddle/Paddle/pull/58756),[#58371](https://github.com/PaddlePaddle/Paddle/pull/58371) -- Reconstructed auto-differentiation module for PIR, migrate and adapted the high-order auto-differentiation function. Optimized Stop Gradient transfer mechanism, so logic is clearer and function is more robust. [#55660](https://github.com/PaddlePaddle/Paddle/pull/55660),[#57084](https://github.com/PaddlePaddle/Paddle/pull/57084),[#56890](https://github.com/PaddlePaddle/Paddle/pull/56890),[#58942](https://github.com/PaddlePaddle/Paddle/pull/58942),[#59373](https://github.com/PaddlePaddle/Paddle/pull/59373),[#57206](https://github.com/PaddlePaddle/Paddle/pull/57206),[#58145](https://github.com/PaddlePaddle/Paddle/pull/58145),[#55235](https://github.com/PaddlePaddle/Paddle/pull/55235),[#57255](https://github.com/PaddlePaddle/Paddle/pull/57255),[#56925](https://github.com/PaddlePaddle/Paddle/pull/56925),[#55957](https://github.com/PaddlePaddle/Paddle/pull/55957),[#56163](https://github.com/PaddlePaddle/Paddle/pull/56163),[#56316](https://github.com/PaddlePaddle/Paddle/pull/56316),[#57294](https://github.com/PaddlePaddle/Paddle/pull/57294),[#57449](https://github.com/PaddlePaddle/Paddle/pull/57449),[#59520](https://github.com/PaddlePaddle/Paddle/pull/59520),[#59565](https://github.com/PaddlePaddle/Paddle/pull/59565),[#56265](https://github.com/PaddlePaddle/Paddle/pull/56265),[#56512](https://github.com/PaddlePaddle/Paddle/pull/56512),[#56650](https://github.com/PaddlePaddle/Paddle/pull/56650),[#57183](https://github.com/PaddlePaddle/Paddle/pull/57183),[#57956](https://github.com/PaddlePaddle/Paddle/pull/57956),[#59100](https://github.com/PaddlePaddle/Paddle/pull/59100) -- Optimized design and representation of control flow forward and reverse operators, introduced ControlFlow Dialect, and supported conversion and execution from control flow operators to PIR under ProgramDesc. [#58729](https://github.com/PaddlePaddle/Paddle/pull/58729),[#57364](https://github.com/PaddlePaddle/Paddle/pull/57364),[#58625](https://github.com/PaddlePaddle/Paddle/pull/58625),[#57475](https://github.com/PaddlePaddle/Paddle/pull/57475),[#57265](https://github.com/PaddlePaddle/Paddle/pull/57265),[#56799](https://github.com/PaddlePaddle/Paddle/pull/56799),[#59033](https://github.com/PaddlePaddle/Paddle/pull/59033),[#57342](https://github.com/PaddlePaddle/Paddle/pull/57342),[#57801](https://github.com/PaddlePaddle/Paddle/pull/57801),[#57958](https://github.com/PaddlePaddle/Paddle/pull/57958),[#57949](https://github.com/PaddlePaddle/Paddle/pull/57949),[#57937](https://github.com/PaddlePaddle/Paddle/pull/57937),[#59231](https://github.com/PaddlePaddle/Paddle/pull/59231),[#59496](https://github.com/PaddlePaddle/Paddle/pull/59496),[#59321](https://github.com/PaddlePaddle/Paddle/pull/59321),[#58088](https://github.com/PaddlePaddle/Paddle/pull/58088),[#58198](https://github.com/PaddlePaddle/Paddle/pull/58198),[#58024](https://github.com/PaddlePaddle/Paddle/pull/58024),[#58089](https://github.com/PaddlePaddle/Paddle/pull/58089),[#58086](https://github.com/PaddlePaddle/Paddle/pull/58086),[#59175](https://github.com/PaddlePaddle/Paddle/pull/59175),[#59423](https://github.com/PaddlePaddle/Paddle/pull/59423),[#59567](https://github.com/PaddlePaddle/Paddle/pull/59567),[#58098](https://github.com/PaddlePaddle/Paddle/pull/58098),[#58163](https://github.com/PaddlePaddle/Paddle/pull/58163),[#58250](https://github.com/PaddlePaddle/Paddle/pull/58250),[#58277](https://github.com/PaddlePaddle/Paddle/pull/58277),[#58355](https://github.com/PaddlePaddle/Paddle/pull/58355),[#59020](https://github.com/PaddlePaddle/Paddle/pull/59020),[#59200](https://github.com/PaddlePaddle/Paddle/pull/59200),[#59585](https://github.com/PaddlePaddle/Paddle/pull/59585),[#58109](https://github.com/PaddlePaddle/Paddle/pull/58109) -- Upgraded dynamic to static execution flow to support PIR, optimized dynamic to static subgraph Pass mechanism, and supported users to try and use functions in the PIR system under the @to_static function. [#57566](https://github.com/PaddlePaddle/Paddle/pull/57566),[#55620](https://github.com/PaddlePaddle/Paddle/pull/55620),[#56791](https://github.com/PaddlePaddle/Paddle/pull/56791),[#57357](https://github.com/PaddlePaddle/Paddle/pull/57357),[#59152](https://github.com/PaddlePaddle/Paddle/pull/59152),[#59312](https://github.com/PaddlePaddle/Paddle/pull/59312),[#58630](https://github.com/PaddlePaddle/Paddle/pull/58630),[#56035](https://github.com/PaddlePaddle/Paddle/pull/56035),[#59447](https://github.com/PaddlePaddle/Paddle/pull/59447),[#57361](https://github.com/PaddlePaddle/Paddle/pull/57361),[#59261](https://github.com/PaddlePaddle/Paddle/pull/59261),[#59774](https://github.com/PaddlePaddle/Paddle/pull/59774) -- Upgraded combination operator function with introducing the concept of Backend to manage logic of combination operator module of dynamic and static graphs in a hierarchical way. Sank necessary components and operator splitting rules into C++, to dramatically reduce maintenance costs. [#58153](https://github.com/PaddlePaddle/Paddle/pull/58153),[#56391](https://github.com/PaddlePaddle/Paddle/pull/56391),[#56614](https://github.com/PaddlePaddle/Paddle/pull/56614),[#57030](https://github.com/PaddlePaddle/Paddle/pull/57030),[#57554](https://github.com/PaddlePaddle/Paddle/pull/57554),[#58018](https://github.com/PaddlePaddle/Paddle/pull/58018),[#58130](https://github.com/PaddlePaddle/Paddle/pull/58130),[#58581](https://github.com/PaddlePaddle/Paddle/pull/58581),[#58679](https://github.com/PaddlePaddle/Paddle/pull/58679),[#59054](https://github.com/PaddlePaddle/Paddle/pull/59054),[#55480](https://github.com/PaddlePaddle/Paddle/pull/55480),[#58451](https://github.com/PaddlePaddle/Paddle/pull/58451),[#55647](https://github.com/PaddlePaddle/Paddle/pull/55647),[#56342](https://github.com/PaddlePaddle/Paddle/pull/56342),[#56798](https://github.com/PaddlePaddle/Paddle/pull/56798),[#57561](https://github.com/PaddlePaddle/Paddle/pull/57561),[#58023](https://github.com/PaddlePaddle/Paddle/pull/58023),[#57722](https://github.com/PaddlePaddle/Paddle/pull/57722) +- Fix errors in `paddle.optimizer.LBFGS` caused by using non-Tensor computations [#60219](https://github.com/PaddlePaddle/Paddle/pull/60219) +- Fix the problem of random numbers not being fixed in `paddle.optimizer.LBFGS` [#60591](https://github.com/PaddlePaddle/Paddle/pull/60591) +- Fix the incorrect calculation of gradient of `set_value` operator [#59034](https://github.com/PaddlePaddle/Paddle/pull/59034) +- Fix the problem of Tensor basic index adapting to PIR [#60259](https://github.com/PaddlePaddle/Paddle/pull/60259), [#61103](https://github.com/PaddlePaddle/Paddle/pull/61103) +- Fix the problem of Tensor combined index assignment [problem](https://github.com/PaddlePaddle/Paddle/issues/60376) [#60447](https://github.com/PaddlePaddle/Paddle/pull/60447) +- Fix the problem when Tensor combined index takes values [problem] [#61922](https://github.com/PaddlePaddle/Paddle/pull/61922) +- Fix `paddle.flatten` stride calculation error issue, with being able to add `paddle.flatten_` [#63084](https://github.com/PaddlePaddle/Paddle/pull/63084) +- Fix the result inconsistency problem between `paddle.index_fill` and `paddle.index_fill_` [#59863](https://github.com/PaddlePaddle/Paddle/pull/59863) +- Fix the `paddle.masked_scatter` error report issue [#60835](https://github.com/PaddlePaddle/Paddle/pull/60835) +- Fix the `paddle.histogramdd` cpu error report issue [#61891](https://github.com/PaddlePaddle/Paddle/pull/61891) +- Fix the bug that `paddle.cast_` continuous use on cpu leads to incorrect result [#60054](https://github.com/PaddlePaddle/Paddle/pull/60054) +- Fix `paddle.put_along_axis` bug when input size is very large [#60551](https://github.com/PaddlePaddle/Paddle/pull/60551) +- Fix `paddle.nanmedian` cpu error report issue [#63221](https://github.com/PaddlePaddle/Paddle/pull/63221) +- Fix the bug that `paddle.median` does not support inputs other than floating-point types in the min branch. [#64444](https://github.com/PaddlePaddle/Paddle/pull/64444) +- Fix the dataloader issue in distributed scenarios. [#62696](https://github.com/PaddlePaddle/Paddle/pull/62696), [#63378](https://github.com/PaddlePaddle/Paddle/pull/63378) +- Fix the formatting issue in error prompt [#63106](https://github.com/PaddlePaddle/Paddle/pull/63106), [#63144](https://github.com/PaddlePaddle/Paddle/pull/63144) +- Fix the format issue under GLOG_v>=6. [#63345](https://github.com/PaddlePaddle/Paddle/pull/63345) -#### Performance optimization +### Security Improvements -- Added PIR Program operators such as DCE and constant_folding_pass, and structure-optimized Pass. [#54935](https://github.com/PaddlePaddle/Paddle/pull/54935),[#59430](https://github.com/PaddlePaddle/Paddle/pull/59430),[#58753](https://github.com/PaddlePaddle/Paddle/pull/58753),[#58732](https://github.com/PaddlePaddle/Paddle/pull/58732) +- Enhance the checking of parent_ids [#62826](https://github.com/PaddlePaddle/Paddle/pull/62826) -2. Added optimization operators fusing class Pass, such as fused_attention, fused_dropout_add, fused_gemm_epilogue_pass, fused_linear_param_grad_add_pass, fused_weight_only_linear_pass, and fused_softmax_mask_upper_triangle, to improve training and inference performance. [#57557](https://github.com/PaddlePaddle/Paddle/pull/57557),[#58272](https://github.com/PaddlePaddle/Paddle/pull/58272),[#58188](https://github.com/PaddlePaddle/Paddle/pull/58188),[#58401](https://github.com/PaddlePaddle/Paddle/pull/58401),[#59366](https://github.com/PaddlePaddle/Paddle/pull/59366),[#57655](https://github.com/PaddlePaddle/Paddle/pull/57655),[#57360](https://github.com/PaddlePaddle/Paddle/pull/57360),[#56672](https://github.com/PaddlePaddle/Paddle/pull/56672),[#58537](https://github.com/PaddlePaddle/Paddle/pull/58537),[#56247](https://github.com/PaddlePaddle/Paddle/pull/56247),[#59391](https://github.com/PaddlePaddle/Paddle/pull/59391),[#58897](https://github.com/PaddlePaddle/Paddle/pull/58897),[#54933](https://github.com/PaddlePaddle/Paddle/pull/54933) +## Basic Execution Architecture -### Dynamic to static capability enhancement +PIR basic functions have been upgraded and improved comprehensively, and the maturity level has been greatly improved. Based on PIR, the design of the PaddlePaddle infrastructure is more reasonable, ensuring the excellent performance and good scalability of the framework. In this version, we have completed the inference verification of PIR in multiple scenarios: For the single-machine scenario, complete the PIR back-end switching in the dynamic-to-static scenarios; For inference scenario, complete the verification of all the stock models, and 84.2% of the models have a gain of 10%+; we have completed the verification of distributed scenarios based on PIR. Meanwhile, based on PIR, we have completed the development and validation of core modules such as control flow, backward logic, save/load, and OneDNN adaptation, which lays a solid foundation for the switching of the PaddlePaddle PIR to the default mode. The functional completeness, execution efficiency and stability of the PaddlePaddle framework operator system are further improved, bringing better use and development experience to the developers. -Dynamic to static graph conversion is a key technology in deep learning frameworks. It allows developers to find the best balance between flexibility and training efficiency. This version of PaddlePaddle has fully upgraded core Dynamic to Static functionality. Success rate of dynamic to static training is up to 100% among 700+ models in PaddlePaddle industry-grade model library. +### Function Optimization -#### New features +- Improve the basic functions of PIR, including basic type system enhancement, debugging, printing, Pass development, and AMP support, to enhance the development efficiency of PIR. [#60723](https://github.com/PaddlePaddle/Paddle/pull/60723), [#60677](https://github.com/PaddlePaddle/Paddle/pull/60677), [#60783](https://github.com/PaddlePaddle/Paddle/pull/60783), [#60798](https://github.com/PaddlePaddle/Paddle/pull/60798), [#61053](https://github.com/PaddlePaddle/Paddle/pull/61053), [#61366](https://github.com/PaddlePaddle/Paddle/pull/61366), [#61446](https://github.com/PaddlePaddle/Paddle/pull/61446), [#60024](https://github.com/PaddlePaddle/Paddle/pull/60024), [#59939](https://github.com/PaddlePaddle/Paddle/pull/59939), [#63376](https://github.com/PaddlePaddle/Paddle/pull/63376), [#61853](https://github.com/PaddlePaddle/Paddle/pull/61853), [#63914](https://github.com/PaddlePaddle/Paddle/pull/63914), [#60170](https://github.com/PaddlePaddle/Paddle/pull/60170), [#60678](https://github.com/PaddlePaddle/Paddle/pull/60678), [#64093](https://github.com/PaddlePaddle/Paddle/pull/64093), [#64065](https://github.com/PaddlePaddle/Paddle/pull/64065), [#62451](https://github.com/PaddlePaddle/Paddle/pull/62451), [#59784](https://github.com/PaddlePaddle/Paddle/pull/59784), [#60136](https://github.com/PaddlePaddle/Paddle/pull/60136), [#63336](https://github.com/PaddlePaddle/Paddle/pull/63336), [#62108](https://github.com/PaddlePaddle/Paddle/pull/62108), [#60860](https://github.com/PaddlePaddle/Paddle/pull/60860), [#60536](https://github.com/PaddlePaddle/Paddle/pull/60536), [#60590](https://github.com/PaddlePaddle/Paddle/pull/60590), [#60752](https://github.com/PaddlePaddle/Paddle/pull/60752), [#61435](https://github.com/PaddlePaddle/Paddle/pull/61435), [#62977](https://github.com/PaddlePaddle/Paddle/pull/62977), [#62139](https://github.com/PaddlePaddle/Paddle/pull/62139), [#60432](https://github.com/PaddlePaddle/Paddle/pull/60432), [#61452](https://github.com/PaddlePaddle/Paddle/pull/61452), [#61978](https://github.com/PaddlePaddle/Paddle/pull/61978), [#62262](https://github.com/PaddlePaddle/Paddle/pull/62262), [#62422](https://github.com/PaddlePaddle/Paddle/pull/62422), [#60359](https://github.com/PaddlePaddle/Paddle/pull/60359), [#62989](https://github.com/PaddlePaddle/Paddle/pull/62989), [#61297](https://github.com/PaddlePaddle/Paddle/pull/61297), [#61399](https://github.com/PaddlePaddle/Paddle/pull/61399), [#61871](https://github.com/PaddlePaddle/Paddle/pull/61871), [#61496](https://github.com/PaddlePaddle/Paddle/pull/61496), [#62413](https://github.com/PaddlePaddle/Paddle/pull/62413) +- Optimize the execution logic of the PaddlePaddle actuator, improve the Pass system, enhance the performance of training and inference, to better support distributed parallel logic operation. [#60182](https://github.com/PaddlePaddle/Paddle/pull/60182), [#60516](https://github.com/PaddlePaddle/Paddle/pull/60516), [#63573](https://github.com/PaddlePaddle/Paddle/pull/63573), [#60181](https://github.com/PaddlePaddle/Paddle/pull/60181), [#59792](https://github.com/PaddlePaddle/Paddle/pull/59792), [#62025](https://github.com/PaddlePaddle/Paddle/pull/62025), [#61160](https://github.com/PaddlePaddle/Paddle/pull/61160), [#61188](https://github.com/PaddlePaddle/Paddle/pull/61188), [#61277](https://github.com/PaddlePaddle/Paddle/pull/61277), [#61669](https://github.com/PaddlePaddle/Paddle/pull/61669), [#60823](https://github.com/PaddlePaddle/Paddle/pull/60823), [#61310](https://github.com/PaddlePaddle/Paddle/pull/61310), [#60892](https://github.com/PaddlePaddle/Paddle/pull/60892), [#60578](https://github.com/PaddlePaddle/Paddle/pull/60578), [#61657](https://github.com/PaddlePaddle/Paddle/pull/61657), [#62638](https://github.com/PaddlePaddle/Paddle/pull/62638), [#63960](https://github.com/PaddlePaddle/Paddle/pull/63960), [#64234](https://github.com/PaddlePaddle/Paddle/pull/64234) -- Adopted Python Eval Frame and VM simulation execution technology to innovatively implement an adaptive Graph Break mechanism. This mechanism is especially designed for control flow scenarios. By introducing the CallLayer mechanism, it makes full use of the advantage of PaddlePaddle dynamic-static unification motion. Support hybrid mode of Abstract Syntax Tree (AST) and bytecode simulation. Efficiently captures control flow operators, thus dramatically improving ability of computational graph to be static. At cache optimization level, fuse advanced optimization technologies such as common sub-expression elimination, to significantly improve execution efficiency of Guard. These optimizations not only reduce redundant computations, but also improve overall system operation speed. To enhance robustness of the system, a simple and efficient data intermediate layer structure is designed. Structure supports correctness recovery of SideEffects, ensuring stability and reliability of system in complex environments. In addition, it is widely compatible with mainstream interpreter versions from Python 3.8 to 3.11, providing users with a wide range of applicability. [#57824](https://github.com/PaddlePaddle/Paddle/pull/57824),[#55887](https://github.com/PaddlePaddle/Paddle/pull/55887),[#58155](https://github.com/PaddlePaddle/Paddle/pull/58155),[#56107](https://github.com/PaddlePaddle/Paddle/pull/56107),[#57490](https://github.com/PaddlePaddle/Paddle/pull/57490),[#58829](https://github.com/PaddlePaddle/Paddle/pull/58829),[#57240](https://github.com/PaddlePaddle/Paddle/pull/57240),[#57588](https://github.com/PaddlePaddle/Paddle/pull/57588),[#58117](https://github.com/PaddlePaddle/Paddle/pull/58117),[#59823](https://github.com/PaddlePaddle/Paddle/pull/59823),[#56077](https://github.com/PaddlePaddle/Paddle/pull/56077),[#58956](https://github.com/PaddlePaddle/Paddle/pull/58956),[#57653](https://github.com/PaddlePaddle/Paddle/pull/57653),[#59855](https://github.com/PaddlePaddle/Paddle/pull/59855),[#59017](https://github.com/PaddlePaddle/Paddle/pull/59017),[#58424](https://github.com/PaddlePaddle/Paddle/pull/58424),[#58187](https://github.com/PaddlePaddle/Paddle/pull/58187),[#57793](https://github.com/PaddlePaddle/Paddle/pull/57793),[#59698](https://github.com/PaddlePaddle/Paddle/pull/59698),[#59747](https://github.com/PaddlePaddle/Paddle/pull/59747),[#59710](https://github.com/PaddlePaddle/Paddle/pull/59710),[#59297](https://github.com/PaddlePaddle/Paddle/pull/59297),[#58423](https://github.com/PaddlePaddle/Paddle/pull/58423),[#56262](https://github.com/PaddlePaddle/Paddle/pull/56262),[#58103](https://github.com/PaddlePaddle/Paddle/pull/58103),[#58538](https://github.com/PaddlePaddle/Paddle/pull/58538),[#58771](https://github.com/PaddlePaddle/Paddle/pull/58771),[#59191](https://github.com/PaddlePaddle/Paddle/pull/59191),[#57754](https://github.com/PaddlePaddle/Paddle/pull/57754),[#59439](https://github.com/PaddlePaddle/Paddle/pull/59439),[#59816](https://github.com/PaddlePaddle/Paddle/pull/59816),[#59035](https://github.com/PaddlePaddle/Paddle/pull/59035) -- Added dynamic to static syntax transcription parsing for PyLayer functions, making PyLayer's conversion between dynamic and static graphs smoother. Users can now seamlessly carry out dynamic to static training on PyLayer, to easily export inference models. [#56108](https://github.com/PaddlePaddle/Paddle/pull/56108),[#56531](https://github.com/PaddlePaddle/Paddle/pull/56531),[#57066](https://github.com/PaddlePaddle/Paddle/pull/57066),[#57633](https://github.com/PaddlePaddle/Paddle/pull/57633) +### PIR New Features -#### Bug Fix +- Realize reverse logic based on PIR, generate reverse computation graph directly, and support higher-order differentiation at the same time. [#60174](https://github.com/PaddlePaddle/Paddle/pull/60174), [#60328](https://github.com/PaddlePaddle/Paddle/pull/60328), [#60818](https://github.com/PaddlePaddle/Paddle/pull/60818), [#61352](https://github.com/PaddlePaddle/Paddle/pull/61352), [#61661](https://github.com/PaddlePaddle/Paddle/pull/61661), [#61927](https://github.com/PaddlePaddle/Paddle/pull/61927), [#62772](https://github.com/PaddlePaddle/Paddle/pull/62772), [#60360](https://github.com/PaddlePaddle/Paddle/pull/60360), [#60866](https://github.com/PaddlePaddle/Paddle/pull/60866), [#60970](https://github.com/PaddlePaddle/Paddle/pull/60970), [#60810](https://github.com/PaddlePaddle/Paddle/pull/60810), [#64696](https://github.com/PaddlePaddle/Paddle/pull/64696), [#59844](https://github.com/PaddlePaddle/Paddle/pull/59844), [#59999](https://github.com/PaddlePaddle/Paddle/pull/59999), [#60262](https://github.com/PaddlePaddle/Paddle/pull/60262), [#60338](https://github.com/PaddlePaddle/Paddle/pull/60338), [#59935](https://github.com/PaddlePaddle/Paddle/pull/59935), [#59982](https://github.com/PaddlePaddle/Paddle/pull/59982), [#60221](https://github.com/PaddlePaddle/Paddle/pull/60221), [#62621](https://github.com/PaddlePaddle/Paddle/pull/62621), [#60044](https://github.com/PaddlePaddle/Paddle/pull/60044), [#59790](https://github.com/PaddlePaddle/Paddle/pull/59790), [#60529](https://github.com/PaddlePaddle/Paddle/pull/60529), [#61378](https://github.com/PaddlePaddle/Paddle/pull/61378), [#61584](https://github.com/PaddlePaddle/Paddle/pull/61584) +- Implement control flow logic based on PIR to improve the expressive ability of PIR and better support multi-scenario services such as training and inference. [#61396](https://github.com/PaddlePaddle/Paddle/pull/61396), [#64045](https://github.com/PaddlePaddle/Paddle/pull/64045), [#60953](https://github.com/PaddlePaddle/Paddle/pull/60953), [#61091](https://github.com/PaddlePaddle/Paddle/pull/61091), [#61304](https://github.com/PaddlePaddle/Paddle/pull/61304), [#62093](https://github.com/PaddlePaddle/Paddle/pull/62093), [#64710](https://github.com/PaddlePaddle/Paddle/pull/64710), [#60668](https://github.com/PaddlePaddle/Paddle/pull/60668), [#60433](https://github.com/PaddlePaddle/Paddle/pull/60433), [#60963](https://github.com/PaddlePaddle/Paddle/pull/60963), [#61192](https://github.com/PaddlePaddle/Paddle/pull/61192), [#60895](https://github.com/PaddlePaddle/Paddle/pull/60895), [#60017](https://github.com/PaddlePaddle/Paddle/pull/60017), [#60369](https://github.com/PaddlePaddle/Paddle/pull/60369), [#60330](https://github.com/PaddlePaddle/Paddle/pull/60330), [#60364](https://github.com/PaddlePaddle/Paddle/pull/60364), [#61416](https://github.com/PaddlePaddle/Paddle/pull/61416), [#60460](https://github.com/PaddlePaddle/Paddle/pull/60460), [#60703](https://github.com/PaddlePaddle/Paddle/pull/60703), [#61027](https://github.com/PaddlePaddle/Paddle/pull/61027) +- Realize save/load logic based on PIR, to carry out the process of PIR and upstream/downstream training and inference services. [#63438](https://github.com/PaddlePaddle/Paddle/pull/63438), [#63574](https://github.com/PaddlePaddle/Paddle/pull/63574), [#64281](https://github.com/PaddlePaddle/Paddle/pull/64281), [#64327](https://github.com/PaddlePaddle/Paddle/pull/64327), [#63622](https://github.com/PaddlePaddle/Paddle/pull/63622), [#64507](https://github.com/PaddlePaddle/Paddle/pull/64507), [#63389](https://github.com/PaddlePaddle/Paddle/pull/63389), [#63539](https://github.com/PaddlePaddle/Paddle/pull/63539), [#63749](https://github.com/PaddlePaddle/Paddle/pull/63749), [#63957](https://github.com/PaddlePaddle/Paddle/pull/63957), [#64044](https://github.com/PaddlePaddle/Paddle/pull/64044), [#64121](https://github.com/PaddlePaddle/Paddle/pull/64121), [#64239](https://github.com/PaddlePaddle/Paddle/pull/64239), [#63818](https://github.com/PaddlePaddle/Paddle/pull/63818), [#63910](https://github.com/PaddlePaddle/Paddle/pull/63910),[#63380](https://github.com/PaddlePaddle/Paddle/pull/63380)[#63380](https://github.com/PaddlePaddle/Paddle/pull/63380),[#63275](https://github.com/PaddlePaddle/Paddle/pull/63275),[#63663](https://github.com/PaddlePaddle/Paddle/pull/63663),[#64692](https://github.com/PaddlePaddle/Paddle/pull/64692),[#63958](https://github.com/PaddlePaddle/Paddle/pull/63958) +- Completed the development and validation of OneDNN related basic functions to prepare for the full-scale switch of OneDNN. [#60680](https://github.com/PaddlePaddle/Paddle/pull/60680), [#60665](https://github.com/PaddlePaddle/Paddle/pull/60665), [#63162](https://github.com/PaddlePaddle/Paddle/pull/63162), [#59917](https://github.com/PaddlePaddle/Paddle/pull/59917), [#62901](https://github.com/PaddlePaddle/Paddle/pull/62901), [#59918](https://github.com/PaddlePaddle/Paddle/pull/59918), [#60257](https://github.com/PaddlePaddle/Paddle/pull/60257), [#60502](https://github.com/PaddlePaddle/Paddle/pull/60502), [#61062](https://github.com/PaddlePaddle/Paddle/pull/61062), [#61170](https://github.com/PaddlePaddle/Paddle/pull/61170), [#61474](https://github.com/PaddlePaddle/Paddle/pull/61474), [#60874](https://github.com/PaddlePaddle/Paddle/pull/60874), [#61495](https://github.com/PaddlePaddle/Paddle/pull/61495), [#61664](https://github.com/PaddlePaddle/Paddle/pull/61664), [#61649](https://github.com/PaddlePaddle/Paddle/pull/61649), [#61592](https://github.com/PaddlePaddle/Paddle/pull/61592), [#61667](https://github.com/PaddlePaddle/Paddle/pull/61667), [#61137](https://github.com/PaddlePaddle/Paddle/pull/61137), [#60952](https://github.com/PaddlePaddle/Paddle/pull/60952), [#61651](https://github.com/PaddlePaddle/Paddle/pull/61651), [#62126](https://github.com/PaddlePaddle/Paddle/pull/62126), [#62187](https://github.com/PaddlePaddle/Paddle/pull/62187), [#61307](https://github.com/PaddlePaddle/Paddle/pull/61307), [#62734](https://github.com/PaddlePaddle/Paddle/pull/62734), [#60974](https://github.com/PaddlePaddle/Paddle/pull/60974), [#61451](https://github.com/PaddlePaddle/Paddle/pull/61451), [#61011](https://github.com/PaddlePaddle/Paddle/pull/61011), [#61218](https://github.com/PaddlePaddle/Paddle/pull/61218), [#61623](https://github.com/PaddlePaddle/Paddle/pull/61623), [#61893](https://github.com/PaddlePaddle/Paddle/pull/61893), [#61876](https://github.com/PaddlePaddle/Paddle/pull/61876), [#61892](https://github.com/PaddlePaddle/Paddle/pull/61892), [#62085](https://github.com/PaddlePaddle/Paddle/pull/62085), [#62220](https://github.com/PaddlePaddle/Paddle/pull/62220), [#62244](https://github.com/PaddlePaddle/Paddle/pull/62244), [#62265](https://github.com/PaddlePaddle/Paddle/pull/62265), [#60754](https://github.com/PaddlePaddle/Paddle/pull/60754), [#60896](https://github.com/PaddlePaddle/Paddle/pull/60896), [#61868](https://github.com/PaddlePaddle/Paddle/pull/61868), [#61659](https://github.com/PaddlePaddle/Paddle/pull/61659), [#62241](https://github.com/PaddlePaddle/Paddle/pull/62241), [#62471](https://github.com/PaddlePaddle/Paddle/pull/62471), [#61165](https://github.com/PaddlePaddle/Paddle/pull/61165),[#64441](https://github.com/PaddlePaddle/Paddle/pull/64441),[#63141](https://github.com/PaddlePaddle/Paddle/pull/63141),[#63145](https://github.com/PaddlePaddle/Paddle/pull/63145),[#63592](https://github.com/PaddlePaddle/Paddle/pull/63592),[#63617](https://github.com/PaddlePaddle/Paddle/pull/63617),[#63518](https://github.com/PaddlePaddle/Paddle/pull/63518),[#63726](https://github.com/PaddlePaddle/Paddle/pull/63726),[#63853](https://github.com/PaddlePaddle/Paddle/pull/63853),[#63812](https://github.com/PaddlePaddle/Paddle/pull/63812),[#63811](https://github.com/PaddlePaddle/Paddle/pull/63811),[#64524](https://github.com/PaddlePaddle/Paddle/pull/64524),[#62993](https://github.com/PaddlePaddle/Paddle/pull/62993),[#63516](https://github.com/PaddlePaddle/Paddle/pull/63516),[#62998](https://github.com/PaddlePaddle/Paddle/pull/62998),[#63151](https://github.com/PaddlePaddle/Paddle/pull/63151),[#64661](https://github.com/PaddlePaddle/Paddle/pull/64661),[#64433](https://github.com/PaddlePaddle/Paddle/pull/64433),[#64448](https://github.com/PaddlePaddle/Paddle/pull/64448),[#63201](https://github.com/PaddlePaddle/Paddle/pull/63201),[#63230](https://github.com/PaddlePaddle/Paddle/pull/63230),[#63233](https://github.com/PaddlePaddle/Paddle/pull/63233),[#63281](https://github.com/PaddlePaddle/Paddle/pull/63281),[#64671](https://github.com/PaddlePaddle/Paddle/pull/64671),[#63274](https://github.com/PaddlePaddle/Paddle/pull/63274) +- Implement Sparse related logic based on PIR, including basic Type and operator expression, and complete the verification of Sparse key functions. [#62868](https://github.com/PaddlePaddle/Paddle/pull/62868), [#63015](https://github.com/PaddlePaddle/Paddle/pull/63015), [#62894](https://github.com/PaddlePaddle/Paddle/pull/62894) -- Fixed the issue that video memory is abnormal in some scenarios of dynamic to static in is_test=True mode. [#58350](https://github.com/PaddlePaddle/Paddle/pull/58350) -- Fixed the issue that function decorated by @to_static is exported to jit.save model in scenarios like foo(x,x,y). [#55963](https://github.com/PaddlePaddle/Paddle/pull/55963) -- Fixed the issue that dynamic and static logic of some API behaviors is not uniform. This improves success rate and user experience of dynamic to static graph conversion. [#56092](https://github.com/PaddlePaddle/Paddle/pull/56092) +### Dynamic-to-static Function Optimization -#### Fixed vulnerability +Optimize the dynamic-to-static basic capability, adapt to the dynamic dimension in SOT training scenarios, and support Python 3.12. -- Fixed a potential security vulnerability in use of eval() in dynamic to static syntax transcription module. [#60100](https://github.com/PaddlePaddle/Paddle/pull/60100) +- Complete the PIR adaptation in dynamic-to-static scenarios. [#60988](https://github.com/PaddlePaddle/Paddle/pull/60988), [#61936](https://github.com/PaddlePaddle/Paddle/pull/61936), [#59929](https://github.com/PaddlePaddle/Paddle/pull/59929), [#61790](https://github.com/PaddlePaddle/Paddle/pull/61790), [#64323](https://github.com/PaddlePaddle/Paddle/pull/64323), [#62030](https://github.com/PaddlePaddle/Paddle/pull/62030), [#61143](https://github.com/PaddlePaddle/Paddle/pull/61143), [#62680](https://github.com/PaddlePaddle/Paddle/pull/62680), [#63309](https://github.com/PaddlePaddle/Paddle/pull/63309), [#63311](https://github.com/PaddlePaddle/Paddle/pull/63311), [#62199](https://github.com/PaddlePaddle/Paddle/pull/62199) +- SOT adapts to Python 3.12 bytecode, and the dynamic-to-static SOT function can be used in Python 3.12. [#61414](https://github.com/PaddlePaddle/Paddle/pull/61414), [#59562](https://github.com/PaddlePaddle/Paddle/pull/59562), [#61031](https://github.com/PaddlePaddle/Paddle/pull/61031), [#61272](https://github.com/PaddlePaddle/Paddle/pull/61272), [#61412](https://github.com/PaddlePaddle/Paddle/pull/61412), [#61305](https://github.com/PaddlePaddle/Paddle/pull/61305), [#61964](https://github.com/PaddlePaddle/Paddle/pull/61964), [#62008](https://github.com/PaddlePaddle/Paddle/pull/62008), [#62028](https://github.com/PaddlePaddle/Paddle/pull/62028), [#61995](https://github.com/PaddlePaddle/Paddle/pull/61995), [#62073](https://github.com/PaddlePaddle/Paddle/pull/62073), [#62120](https://github.com/PaddlePaddle/Paddle/pull/62120), [#62218](https://github.com/PaddlePaddle/Paddle/pull/62218), [#62155](https://github.com/PaddlePaddle/Paddle/pull/62155) +- SOT completes the adaptation of the dynamic dimension of the training scenario, avoiding triggering duplicate graph compositions in dimension changes, and improving the operation efficiency. [#64278](https://github.com/PaddlePaddle/Paddle/pull/64278), [#64435](https://github.com/PaddlePaddle/Paddle/pull/64435), [#64499](https://github.com/PaddlePaddle/Paddle/pull/64499), [#64500](https://github.com/PaddlePaddle/Paddle/pull/64500), [#62080](https://github.com/PaddlePaddle/Paddle/pull/62080) -### Enhanced distributed dynamic graph capability +### Operator Mechanisms -In order to meet the needs of large models, this version focuses on improving the distributed computing capability of the dynamic graph of the PaddlePaddle. Various improvements have been made in communication library, graph analysis, distributed policies and task enable/disable, to provide comprehensive support for large model training. In terms of performance, we further improved training performance by reducing streaming parallel GPU memory occupation, adopting TensorFusion technology, implementing communication computation overlap, and reducing non-essential data synchronization copies. Meanwhile, flexibility of hybrid-parallel debugging is improved through environment variable control Optimizer. In addition, stability of system is further improved by fixing related Bugs. +For the problems of incomplete implementation of some kernels and inefficient calculation logic, we have improved and optimized some of the operator implementation and internal mechanisms of framework, fixed some known problems, and supported some new features. -#### New features +- For XPU kernel, we have optimized the data type support of `numel`, `concat`, and `slice`, and the mixed-precision training support for `AdamW` optimizer. [#63715](https://github.com/PaddlePaddle/Paddle/pull/63715), [#61617](https://github.com/PaddlePaddle/Paddle/pull/61617), [#61694](https://github.com/PaddlePaddle/Paddle/pull/61694), [#64542](https://github.com/PaddlePaddle/Paddle/pull/64542), [#63644](https://github.com/PaddlePaddle/Paddle/pull/63644), [#61340](https://github.com/PaddlePaddle/Paddle/pull/61340), [#63108](https://github.com/PaddlePaddle/Paddle/pull/63108) +- Improve the function and performance of some operators. [#59413](https://github.com/PaddlePaddle/Paddle/pull/59413), [#60295](https://github.com/PaddlePaddle/Paddle/pull/60295), [#64304](https://github.com/PaddlePaddle/Paddle/pull/64304), [#60979](https://github.com/PaddlePaddle/Paddle/pull/60979), [#63556](https://github.com/PaddlePaddle/Paddle/pull/63556), [#63061](https://github.com/PaddlePaddle/Paddle/pull/63061), [#62533](https://github.com/PaddlePaddle/Paddle/pull/62533) +- Improve the mechanism of composite operators, and optimize composite logic for some operators. [#59448](https://github.com/PaddlePaddle/Paddle/pull/59448), [#60505](https://github.com/PaddlePaddle/Paddle/pull/60505), [#59891](https://github.com/PaddlePaddle/Paddle/pull/59891), [#63161](https://github.com/PaddlePaddle/Paddle/pull/63161), [#63245](https://github.com/PaddlePaddle/Paddle/pull/63245), [#63782](https://github.com/PaddlePaddle/Paddle/pull/63782), [#64346](https://github.com/PaddlePaddle/Paddle/pull/64346), [#63156](https://github.com/PaddlePaddle/Paddle/pull/63156), [#63171](https://github.com/PaddlePaddle/Paddle/pull/63171), [#61315](https://github.com/PaddlePaddle/Paddle/pull/61315), [#61701](https://github.com/PaddlePaddle/Paddle/pull/61701), [#61874](https://github.com/PaddlePaddle/Paddle/pull/61874), [#61873](https://github.com/PaddlePaddle/Paddle/pull/61873), [#62059](https://github.com/PaddlePaddle/Paddle/pull/62059), [#61912](https://github.com/PaddlePaddle/Paddle/pull/61912), [#62112](https://github.com/PaddlePaddle/Paddle/pull/62112), [#63011](https://github.com/PaddlePaddle/Paddle/pull/63011), [#63009](https://github.com/PaddlePaddle/Paddle/pull/63009), [#64714](https://github.com/PaddlePaddle/Paddle/pull/64714) -- Added TraceHang function in communication library, to quickly locate the faulty node when cluster training has Hang problem. [#59217](https://github.com/PaddlePaddle/Paddle/pull/59217) -- In order to improve training efficiency and reduce memory, dynamic graph supports stride mechanism. [#55156](https://github.com/PaddlePaddle/Paddle/pull/55156),[#54762](https://github.com/PaddlePaddle/Paddle/pull/54762),[#55850](https://github.com/PaddlePaddle/Paddle/pull/55850),[#59190](https://github.com/PaddlePaddle/Paddle/pull/59190),[#57005](https://github.com/PaddlePaddle/Paddle/pull/57005),[#57005](https://github.com/PaddlePaddle/Paddle/pull/57005),[#57331](https://github.com/PaddlePaddle/Paddle/pull/57331),[#58033](https://github.com/PaddlePaddle/Paddle/pull/58033),[#58033](https://github.com/PaddlePaddle/Paddle/pull/58033),[#58303](https://github.com/PaddlePaddle/Paddle/pull/58303),[#57835](https://github.com/PaddlePaddle/Paddle/pull/57835),[#57189](https://github.com/PaddlePaddle/Paddle/pull/57189) -- Enhanced paddleviz function to facilitate analysis of computational graphs. [#56837](https://github.com/PaddlePaddle/Paddle/pull/56837),[#57626](https://github.com/PaddlePaddle/Paddle/pull/57626) -- In distributed Sharding strategies (Stage1,2,3), added main_grad function to support higher precision gradient accumulation, and reduce precision loss caused by low precision accumulation. [#57972](https://github.com/PaddlePaddle/Paddle/pull/57972),[#57934](https://github.com/PaddlePaddle/Paddle/pull/57934),[#57473](https://github.com/PaddlePaddle/Paddle/pull/57473),[#57537](https://github.com/PaddlePaddle/Paddle/pull/57537),[#59611](https://github.com/PaddlePaddle/Paddle/pull/59611),[#57960](https://github.com/PaddlePaddle/Paddle/pull/57960) -- In Sharding Stage1 strategy, added a switch variable to control whether to perform fusion calculation on Optimizer. [#58790](https://github.com/PaddlePaddle/Paddle/pull/58790) -- In Recompute function, added support for Tuple input parameters, enhancing calling ability of Recompute interface. [#56793](https://github.com/PaddlePaddle/Paddle/pull/56793) -- Enhanced Launch function, allowing distributed training without specifying endpoints in dynamic graphs. [#54636](https://github.com/PaddlePaddle/Paddle/pull/54636) +### Bug Fixing -#### Function optimization +- Fix the bugs related to PIR, actuator, and dynamic-to-static. [#64442](https://github.com/PaddlePaddle/Paddle/pull/64442), [#60443](https://github.com/PaddlePaddle/Paddle/pull/60443), [#60122](https://github.com/PaddlePaddle/Paddle/pull/60122), [#60625](https://github.com/PaddlePaddle/Paddle/pull/60625), [#60607](https://github.com/PaddlePaddle/Paddle/pull/60607), [#60705](https://github.com/PaddlePaddle/Paddle/pull/60705), [#61110](https://github.com/PaddlePaddle/Paddle/pull/61110), [#61278](https://github.com/PaddlePaddle/Paddle/pull/61278), [#61448](https://github.com/PaddlePaddle/Paddle/pull/61448), [#61491](https://github.com/PaddlePaddle/Paddle/pull/61491), [#61692](https://github.com/PaddlePaddle/Paddle/pull/61692), [#62100](https://github.com/PaddlePaddle/Paddle/pull/62100), [#62239](https://github.com/PaddlePaddle/Paddle/pull/62239), [#62365](https://github.com/PaddlePaddle/Paddle/pull/62365), [#62758](https://github.com/PaddlePaddle/Paddle/pull/62758), [#63395](https://github.com/PaddlePaddle/Paddle/pull/63395), [#64272](https://github.com/PaddlePaddle/Paddle/pull/64272), [#62165](https://github.com/PaddlePaddle/Paddle/pull/62165), [#64151](https://github.com/PaddlePaddle/Paddle/pull/64151), [#64204](https://github.com/PaddlePaddle/Paddle/pull/64204), [#64815](https://github.com/PaddlePaddle/Paddle/pull/64815), [#63757](https://github.com/PaddlePaddle/Paddle/pull/63757), [#61972](https://github.com/PaddlePaddle/Paddle/pull/61972), [#64806](https://github.com/PaddlePaddle/Paddle/pull/64806), [#60010](https://github.com/PaddlePaddle/Paddle/pull/60010), [#60461](https://github.com/PaddlePaddle/Paddle/pull/60461), [#60310](https://github.com/PaddlePaddle/Paddle/pull/60310), [#62006](https://github.com/PaddlePaddle/Paddle/pull/62006), [#61591](https://github.com/PaddlePaddle/Paddle/pull/61591), [#60327](https://github.com/PaddlePaddle/Paddle/pull/60327), [#60720](https://github.com/PaddlePaddle/Paddle/pull/60720), [#64656](https://github.com/PaddlePaddle/Paddle/pull/64656), [#60236](https://github.com/PaddlePaddle/Paddle/pull/60236), [#60684](https://github.com/PaddlePaddle/Paddle/pull/60684), [#60790](https://github.com/PaddlePaddle/Paddle/pull/60790), [#60944](https://github.com/PaddlePaddle/Paddle/pull/60944), [#62056](https://github.com/PaddlePaddle/Paddle/pull/62056), [#62891](https://github.com/PaddlePaddle/Paddle/pull/62891), [#64676](https://github.com/PaddlePaddle/Paddle/pull/64676), [#60271](https://github.com/PaddlePaddle/Paddle/pull/60271), [#60634](https://github.com/PaddlePaddle/Paddle/pull/60634), [#60663](https://github.com/PaddlePaddle/Paddle/pull/60663), [#60827](https://github.com/PaddlePaddle/Paddle/pull/60827), [#60845](https://github.com/PaddlePaddle/Paddle/pull/60845), [#60905](https://github.com/PaddlePaddle/Paddle/pull/60905), [#60945](https://github.com/PaddlePaddle/Paddle/pull/60945), [#60949](https://github.com/PaddlePaddle/Paddle/pull/60949), [#61107](https://github.com/PaddlePaddle/Paddle/pull/61107), [#61111](https://github.com/PaddlePaddle/Paddle/pull/61111), [#61117](https://github.com/PaddlePaddle/Paddle/pull/61117), [#61158](https://github.com/PaddlePaddle/Paddle/pull/61158), [#61177](https://github.com/PaddlePaddle/Paddle/pull/61177), [#61355](https://github.com/PaddlePaddle/Paddle/pull/61355), [#61593](https://github.com/PaddlePaddle/Paddle/pull/61593), [#61666](https://github.com/PaddlePaddle/Paddle/pull/61666), [#61934](https://github.com/PaddlePaddle/Paddle/pull/61934), [#62216](https://github.com/PaddlePaddle/Paddle/pull/62216), [#62491](https://github.com/PaddlePaddle/Paddle/pull/62491), [#62515](https://github.com/PaddlePaddle/Paddle/pull/62515), [#62594](https://github.com/PaddlePaddle/Paddle/pull/62594), [#62605](https://github.com/PaddlePaddle/Paddle/pull/62605), [#62895](https://github.com/PaddlePaddle/Paddle/pull/62895), [#62913](https://github.com/PaddlePaddle/Paddle/pull/62913), [#64413](https://github.com/PaddlePaddle/Paddle/pull/64413), [#59947](https://github.com/PaddlePaddle/Paddle/pull/59947), [#60264](https://github.com/PaddlePaddle/Paddle/pull/60264), [#60721](https://github.com/PaddlePaddle/Paddle/pull/60721), [#63113](https://github.com/PaddlePaddle/Paddle/pull/63113), [#63629](https://github.com/PaddlePaddle/Paddle/pull/63629), [#64300](https://github.com/PaddlePaddle/Paddle/pull/64300), [#64450](https://github.com/PaddlePaddle/Paddle/pull/64450), [#64532](https://github.com/PaddlePaddle/Paddle/pull/64532), [#64561](https://github.com/PaddlePaddle/Paddle/pull/64561), [#64625](https://github.com/PaddlePaddle/Paddle/pull/64625), [#64731](https://github.com/PaddlePaddle/Paddle/pull/64731), [#60059](https://github.com/PaddlePaddle/Paddle/pull/60059), [#60487](https://github.com/PaddlePaddle/Paddle/pull/60487), [#60423](https://github.com/PaddlePaddle/Paddle/pull/60423), [#61599](https://github.com/PaddlePaddle/Paddle/pull/61599), [#62032](https://github.com/PaddlePaddle/Paddle/pull/62032), [#62686](https://github.com/PaddlePaddle/Paddle/pull/62686), [#64055](https://github.com/PaddlePaddle/Paddle/pull/64055), [#60751](https://github.com/PaddlePaddle/Paddle/pull/60751), [#61646](https://github.com/PaddlePaddle/Paddle/pull/61646), [#60454](https://github.com/PaddlePaddle/Paddle/pull/60454), [#62530](https://github.com/PaddlePaddle/Paddle/pull/62530), [#62821](https://github.com/PaddlePaddle/Paddle/pull/62821), [#64454](https://github.com/PaddlePaddle/Paddle/pull/64454), [#64754](https://github.com/PaddlePaddle/Paddle/pull/64754), [#59860](https://github.com/PaddlePaddle/Paddle/pull/59860), [#60280](https://github.com/PaddlePaddle/Paddle/pull/60280), [#60357](https://github.com/PaddlePaddle/Paddle/pull/60357), [#60363](https://github.com/PaddlePaddle/Paddle/pull/60363), [#60900](https://github.com/PaddlePaddle/Paddle/pull/60900), [#61185](https://github.com/PaddlePaddle/Paddle/pull/61185), [#61505](https://github.com/PaddlePaddle/Paddle/pull/61505), [#61644](https://github.com/PaddlePaddle/Paddle/pull/61644), [#62256](https://github.com/PaddlePaddle/Paddle/pull/62256), [#62396](https://github.com/PaddlePaddle/Paddle/pull/62396), [#63040](https://github.com/PaddlePaddle/Paddle/pull/63040), [#63409](https://github.com/PaddlePaddle/Paddle/pull/63409), [#63764](https://github.com/PaddlePaddle/Paddle/pull/63764), [#59571](https://github.com/PaddlePaddle/Paddle/pull/59571), [#59894](https://github.com/PaddlePaddle/Paddle/pull/59894), [#59569](https://github.com/PaddlePaddle/Paddle/pull/59569), [#59896](https://github.com/PaddlePaddle/Paddle/pull/59896), [#60015](https://github.com/PaddlePaddle/Paddle/pull/60015), [#60081](https://github.com/PaddlePaddle/Paddle/pull/60081), [#60164](https://github.com/PaddlePaddle/Paddle/pull/60164), [#60200](https://github.com/PaddlePaddle/Paddle/pull/60200), [#60211](https://github.com/PaddlePaddle/Paddle/pull/60211), [#60267](https://github.com/PaddlePaddle/Paddle/pull/60267), [#60458](https://github.com/PaddlePaddle/Paddle/pull/60458), [#60395](https://github.com/PaddlePaddle/Paddle/pull/60395), [#60907](https://github.com/PaddlePaddle/Paddle/pull/60907), [#60707](https://github.com/PaddlePaddle/Paddle/pull/60707), [#60993](https://github.com/PaddlePaddle/Paddle/pull/60993), [#61401](https://github.com/PaddlePaddle/Paddle/pull/61401), [#61433](https://github.com/PaddlePaddle/Paddle/pull/61433), [#61450](https://github.com/PaddlePaddle/Paddle/pull/61450), [#61577](https://github.com/PaddlePaddle/Paddle/pull/61577), [#61575](https://github.com/PaddlePaddle/Paddle/pull/61575), [#61703](https://github.com/PaddlePaddle/Paddle/pull/61703), [#61711](https://github.com/PaddlePaddle/Paddle/pull/61711), [#61883](https://github.com/PaddlePaddle/Paddle/pull/61883), [#61822](https://github.com/PaddlePaddle/Paddle/pull/61822), [#62012](https://github.com/PaddlePaddle/Paddle/pull/62012), [#61858](https://github.com/PaddlePaddle/Paddle/pull/61858), [#62176](https://github.com/PaddlePaddle/Paddle/pull/62176), [#62257](https://github.com/PaddlePaddle/Paddle/pull/62257), [#62470](https://github.com/PaddlePaddle/Paddle/pull/62470), [#62536](https://github.com/PaddlePaddle/Paddle/pull/62536), [#62606](https://github.com/PaddlePaddle/Paddle/pull/62606), [#62808](https://github.com/PaddlePaddle/Paddle/pull/62808), [#62854](https://github.com/PaddlePaddle/Paddle/pull/62854), [#62879](https://github.com/PaddlePaddle/Paddle/pull/62879), [#62864](https://github.com/PaddlePaddle/Paddle/pull/62864), [#63063](https://github.com/PaddlePaddle/Paddle/pull/63063), [#62958](https://github.com/PaddlePaddle/Paddle/pull/62958), [#63397](https://github.com/PaddlePaddle/Paddle/pull/63397), [#63805](https://github.com/PaddlePaddle/Paddle/pull/63805), [#63694](https://github.com/PaddlePaddle/Paddle/pull/63694), [#64168](https://github.com/PaddlePaddle/Paddle/pull/64168), [#64184](https://github.com/PaddlePaddle/Paddle/pull/64184), [#64174](https://github.com/PaddlePaddle/Paddle/pull/64174), [#64315](https://github.com/PaddlePaddle/Paddle/pull/64315), [#64362](https://github.com/PaddlePaddle/Paddle/pull/64362), [#64400](https://github.com/PaddlePaddle/Paddle/pull/64400), [#64475](https://github.com/PaddlePaddle/Paddle/pull/64475), [#64458](https://github.com/PaddlePaddle/Paddle/pull/64458), [#64548](https://github.com/PaddlePaddle/Paddle/pull/64548), [#59858](https://github.com/PaddlePaddle/Paddle/pull/59858), [#61132](https://github.com/PaddlePaddle/Paddle/pull/61132), [#62010](https://github.com/PaddlePaddle/Paddle/pull/62010), [#62069](https://github.com/PaddlePaddle/Paddle/pull/62069), [#62707](https://github.com/PaddlePaddle/Paddle/pull/62707), [#62921](https://github.com/PaddlePaddle/Paddle/pull/62921), [#63085](https://github.com/PaddlePaddle/Paddle/pull/63085), [#63321](https://github.com/PaddlePaddle/Paddle/pull/63321), [#63351](https://github.com/PaddlePaddle/Paddle/pull/63351), [#63549](https://github.com/PaddlePaddle/Paddle/pull/63549), [#64567](https://github.com/PaddlePaddle/Paddle/pull/64567), [#59936](https://github.com/PaddlePaddle/Paddle/pull/59936), [#60269](https://github.com/PaddlePaddle/Paddle/pull/60269), [#60879](https://github.com/PaddlePaddle/Paddle/pull/60879), [#61314](https://github.com/PaddlePaddle/Paddle/pull/61314), [#61391](https://github.com/PaddlePaddle/Paddle/pull/61391), [#61479](https://github.com/PaddlePaddle/Paddle/pull/61479), [#61789](https://github.com/PaddlePaddle/Paddle/pull/61789), [#61832](https://github.com/PaddlePaddle/Paddle/pull/61832), [#61864](https://github.com/PaddlePaddle/Paddle/pull/61864), [#61917](https://github.com/PaddlePaddle/Paddle/pull/61917), [#62052](https://github.com/PaddlePaddle/Paddle/pull/62052), [#62068](https://github.com/PaddlePaddle/Paddle/pull/62068), [#62293](https://github.com/PaddlePaddle/Paddle/pull/62293), [#62479](https://github.com/PaddlePaddle/Paddle/pull/62479), [#62506](https://github.com/PaddlePaddle/Paddle/pull/62506), [#59948](https://github.com/PaddlePaddle/Paddle/pull/59948), [#64118](https://github.com/PaddlePaddle/Paddle/pull/64118), [#64126](https://github.com/PaddlePaddle/Paddle/pull/64126), [#64195](https://github.com/PaddlePaddle/Paddle/pull/64195), [#64307](https://github.com/PaddlePaddle/Paddle/pull/64307), [#64314](https://github.com/PaddlePaddle/Paddle/pull/64314), [#64276](https://github.com/PaddlePaddle/Paddle/pull/64276), [#64312](https://github.com/PaddlePaddle/Paddle/pull/64312), [#64350](https://github.com/PaddlePaddle/Paddle/pull/64350), [#64319](https://github.com/PaddlePaddle/Paddle/pull/64319), [#64463](https://github.com/PaddlePaddle/Paddle/pull/64463), [#64457](https://github.com/PaddlePaddle/Paddle/pull/64457), [#64455](https://github.com/PaddlePaddle/Paddle/pull/64455), [#64487](https://github.com/PaddlePaddle/Paddle/pull/64487), [#64645](https://github.com/PaddlePaddle/Paddle/pull/64645), [#63155](https://github.com/PaddlePaddle/Paddle/pull/63155), [#59893](https://github.com/PaddlePaddle/Paddle/pull/59893), [#63332](https://github.com/PaddlePaddle/Paddle/pull/63332), [#63332](https://github.com/PaddlePaddle/Paddle/pull/63332), [#64786](https://github.com/PaddlePaddle/Paddle/pull/64786), [#60515](https://github.com/PaddlePaddle/Paddle/pull/60515), [#60627](https://github.com/PaddlePaddle/Paddle/pull/60627), [#60863](https://github.com/PaddlePaddle/Paddle/pull/60863), [#60854](https://github.com/PaddlePaddle/Paddle/pull/60854), [#61447](https://github.com/PaddlePaddle/Paddle/pull/61447), [#61440](https://github.com/PaddlePaddle/Paddle/pull/61440), [#61932](https://github.com/PaddlePaddle/Paddle/pull/61932), [#62131](https://github.com/PaddlePaddle/Paddle/pull/62131), [#62252](https://github.com/PaddlePaddle/Paddle/pull/62252), [#62283](https://github.com/PaddlePaddle/Paddle/pull/62283), [#62358](https://github.com/PaddlePaddle/Paddle/pull/62358), [#62411](https://github.com/PaddlePaddle/Paddle/pull/62411), [#62424](https://github.com/PaddlePaddle/Paddle/pull/62424), [#62810](https://github.com/PaddlePaddle/Paddle/pull/62810), [#62811](https://github.com/PaddlePaddle/Paddle/pull/62811), [#62896](https://github.com/PaddlePaddle/Paddle/pull/62896), [#62947](https://github.com/PaddlePaddle/Paddle/pull/62947), [#63182](https://github.com/PaddlePaddle/Paddle/pull/63182), [#63190](https://github.com/PaddlePaddle/Paddle/pull/63190), [#63294](https://github.com/PaddlePaddle/Paddle/pull/63294), [#63306](https://github.com/PaddlePaddle/Paddle/pull/63306), [#63352](https://github.com/PaddlePaddle/Paddle/pull/63352), [#63404](https://github.com/PaddlePaddle/Paddle/pull/63404), [#63474](https://github.com/PaddlePaddle/Paddle/pull/63474), [#64013](https://github.com/PaddlePaddle/Paddle/pull/64013), [#64674](https://github.com/PaddlePaddle/Paddle/pull/64674),[#60055](https://github.com/PaddlePaddle/Paddle/pull/60055),[#62050](https://github.com/PaddlePaddle/Paddle/pull/62050),[#62770](https://github.com/PaddlePaddle/Paddle/pull/62770),[#63234](https://github.com/PaddlePaddle/Paddle/pull/63234),[#63374](https://github.com/PaddlePaddle/Paddle/pull/63374),[#64277](https://github.com/PaddlePaddle/Paddle/pull/64277), [#63420](https://github.com/PaddlePaddle/Paddle/pull/63420), [#60312](https://github.com/PaddlePaddle/Paddle/pull/60312), [#63810](https://github.com/PaddlePaddle/Paddle/pull/63810), [#64631](https://github.com/PaddlePaddle/Paddle/pull/64631), [#63970](https://github.com/PaddlePaddle/Paddle/pull/63970), [#63708](https://github.com/PaddlePaddle/Paddle/pull/63708), [#62062](https://github.com/PaddlePaddle/Paddle/pull/62062), [#60898](https://github.com/PaddlePaddle/Paddle/pull/60898), [#62373](https://github.com/PaddlePaddle/Paddle/pull/62373), [#59878](https://github.com/PaddlePaddle/Paddle/pull/59878) +- Fix some bugs in operator mechanism, operator implementation logic and related unit tests. [#63792](https://github.com/PaddlePaddle/Paddle/pull/63792), [#60570](https://github.com/PaddlePaddle/Paddle/pull/60570), [#61572](https://github.com/PaddlePaddle/Paddle/pull/61572), [#59971](https://github.com/PaddlePaddle/Paddle/pull/59971), [#61336](https://github.com/PaddlePaddle/Paddle/pull/61336), [#63276](https://github.com/PaddlePaddle/Paddle/pull/63276), [#63251](https://github.com/PaddlePaddle/Paddle/pull/63251), [#63697](https://github.com/PaddlePaddle/Paddle/pull/63697), [#63706](https://github.com/PaddlePaddle/Paddle/pull/63706), [#64685](https://github.com/PaddlePaddle/Paddle/pull/64685), [#64009](https://github.com/PaddlePaddle/Paddle/pull/64009), [#62461](https://github.com/PaddlePaddle/Paddle/pull/62461), [#61568](https://github.com/PaddlePaddle/Paddle/pull/61568), [#63912](https://github.com/PaddlePaddle/Paddle/pull/63912), [#60475](https://github.com/PaddlePaddle/Paddle/pull/60475), [#60222](https://github.com/PaddlePaddle/Paddle/pull/60222), [#63961](https://github.com/PaddlePaddle/Paddle/pull/63961), [#63593](https://github.com/PaddlePaddle/Paddle/pull/63593) -- Implemented new communication library with dynamic-static unification. Communication operators are fully adapted to PHI operator system, reducing development and maintenance costs to better support dynamic graphs and auto parallel architecture upgrade. [#54417](https://github.com/PaddlePaddle/Paddle/pull/54417),[#57768](https://github.com/PaddlePaddle/Paddle/pull/57768),[#57897](https://github.com/PaddlePaddle/Paddle/pull/57897),[#55537](https://github.com/PaddlePaddle/Paddle/pull/55537),[#56604](https://github.com/PaddlePaddle/Paddle/pull/56604),[#57519](https://github.com/PaddlePaddle/Paddle/pull/57519),[#56088](https://github.com/PaddlePaddle/Paddle/pull/56088),[#57153](https://github.com/PaddlePaddle/Paddle/pull/57153),[#57161](https://github.com/PaddlePaddle/Paddle/pull/57161),[#57252](https://github.com/PaddlePaddle/Paddle/pull/57252),[#57251](https://github.com/PaddlePaddle/Paddle/pull/57251),[#57208](https://github.com/PaddlePaddle/Paddle/pull/57208),[#57305](https://github.com/PaddlePaddle/Paddle/pull/57305),[#57424](https://github.com/PaddlePaddle/Paddle/pull/57424),[#57548](https://github.com/PaddlePaddle/Paddle/pull/57548),[#57560](https://github.com/PaddlePaddle/Paddle/pull/57560),[#57564](https://github.com/PaddlePaddle/Paddle/pull/57564),[#57233](https://github.com/PaddlePaddle/Paddle/pull/57233),[#55726](https://github.com/PaddlePaddle/Paddle/pull/55726),[#58073](https://github.com/PaddlePaddle/Paddle/pull/58073) -- TCPStore is changed to a single instance to support dynamic graphs and auto parallel more flexibly. [#55956](https://github.com/PaddlePaddle/Paddle/pull/55956) -- Improved maintainability and flexibility of distributed policies such as MP/PP/SP, including addition of printing warning and error messages, structural cleanup of code files, and optimization of PP restrictions on inputs. [#54448](https://github.com/PaddlePaddle/Paddle/pull/54448),[#59762](https://github.com/PaddlePaddle/Paddle/pull/59762),[#55462](https://github.com/PaddlePaddle/Paddle/pull/55462),[#54788](https://github.com/PaddlePaddle/Paddle/pull/54788),[#54664](https://github.com/PaddlePaddle/Paddle/pull/54664),[#56456](https://github.com/PaddlePaddle/Paddle/pull/56456),[#55540](https://github.com/PaddlePaddle/Paddle/pull/55540) -- In PP strategy, added support for P2P communication in computation flow, making communication mode more flexible. [#54747](https://github.com/PaddlePaddle/Paddle/pull/54747) -- Sharding strategy supports reduce Operation on gradient. [#58842](https://github.com/PaddlePaddle/Paddle/pull/58842),[#57967](https://github.com/PaddlePaddle/Paddle/pull/57967),[#55495](https://github.com/PaddlePaddle/Paddle/pull/55495) +### Developer Content -#### Performance optimization +- Developer related contents include PIR switching, unit test start, function verification and other PR. [#60621](https://github.com/PaddlePaddle/Paddle/pull/60621), [#59703](https://github.com/PaddlePaddle/Paddle/pull/59703), [#59694](https://github.com/PaddlePaddle/Paddle/pull/59694), [#59717](https://github.com/PaddlePaddle/Paddle/pull/59717), [#59729](https://github.com/PaddlePaddle/Paddle/pull/59729), [#59730](https://github.com/PaddlePaddle/Paddle/pull/59730), [#60216](https://github.com/PaddlePaddle/Paddle/pull/60216), [#60238](https://github.com/PaddlePaddle/Paddle/pull/60238), [#60246](https://github.com/PaddlePaddle/Paddle/pull/60246), [#60343](https://github.com/PaddlePaddle/Paddle/pull/60343), [#60302](https://github.com/PaddlePaddle/Paddle/pull/60302), [#60870](https://github.com/PaddlePaddle/Paddle/pull/60870), [#59956](https://github.com/PaddlePaddle/Paddle/pull/59956), [#60795](https://github.com/PaddlePaddle/Paddle/pull/60795), [#62528](https://github.com/PaddlePaddle/Paddle/pull/62528), [#59932](https://github.com/PaddlePaddle/Paddle/pull/59932), [#59636](https://github.com/PaddlePaddle/Paddle/pull/59636), [#59959](https://github.com/PaddlePaddle/Paddle/pull/59959), [#59734](https://github.com/PaddlePaddle/Paddle/pull/59734), [#60287](https://github.com/PaddlePaddle/Paddle/pull/60287), [#60347](https://github.com/PaddlePaddle/Paddle/pull/60347), [#60335](https://github.com/PaddlePaddle/Paddle/pull/60335), [#60332](https://github.com/PaddlePaddle/Paddle/pull/60332), [#59631](https://github.com/PaddlePaddle/Paddle/pull/59631), [#60255](https://github.com/PaddlePaddle/Paddle/pull/60255), [#60329](https://github.com/PaddlePaddle/Paddle/pull/60329), [#60401](https://github.com/PaddlePaddle/Paddle/pull/60401), [#60522](https://github.com/PaddlePaddle/Paddle/pull/60522), [#60792](https://github.com/PaddlePaddle/Paddle/pull/60792), [#59617](https://github.com/PaddlePaddle/Paddle/pull/59617), [#60277](https://github.com/PaddlePaddle/Paddle/pull/60277), [#60584](https://github.com/PaddlePaddle/Paddle/pull/60584), [#60911](https://github.com/PaddlePaddle/Paddle/pull/60911), [#61322](https://github.com/PaddlePaddle/Paddle/pull/61322), [#60838](https://github.com/PaddlePaddle/Paddle/pull/60838), [#60602](https://github.com/PaddlePaddle/Paddle/pull/60602), [#61458](https://github.com/PaddlePaddle/Paddle/pull/61458), [#61607](https://github.com/PaddlePaddle/Paddle/pull/61607), [#61960](https://github.com/PaddlePaddle/Paddle/pull/61960), [#60484](https://github.com/PaddlePaddle/Paddle/pull/60484), [#61662](https://github.com/PaddlePaddle/Paddle/pull/61662), [#62263](https://github.com/PaddlePaddle/Paddle/pull/62263), [#62270](https://github.com/PaddlePaddle/Paddle/pull/62270), [#62469](https://github.com/PaddlePaddle/Paddle/pull/62469), [#62416](https://github.com/PaddlePaddle/Paddle/pull/62416), [#62443](https://github.com/PaddlePaddle/Paddle/pull/62443), [#62412](https://github.com/PaddlePaddle/Paddle/pull/62412), [#62541](https://github.com/PaddlePaddle/Paddle/pull/62541), [#62634](https://github.com/PaddlePaddle/Paddle/pull/62634), [#62369](https://github.com/PaddlePaddle/Paddle/pull/62369), [#60805](https://github.com/PaddlePaddle/Paddle/pull/60805), [#62644](https://github.com/PaddlePaddle/Paddle/pull/62644), [#62494](https://github.com/PaddlePaddle/Paddle/pull/62494), [#62767](https://github.com/PaddlePaddle/Paddle/pull/62767), [#62735](https://github.com/PaddlePaddle/Paddle/pull/62735), [#62802](https://github.com/PaddlePaddle/Paddle/pull/62802), [#62801](https://github.com/PaddlePaddle/Paddle/pull/62801), [#62783](https://github.com/PaddlePaddle/Paddle/pull/62783), [#62579](https://github.com/PaddlePaddle/Paddle/pull/62579), [#62833](https://github.com/PaddlePaddle/Paddle/pull/62833), [#62668](https://github.com/PaddlePaddle/Paddle/pull/62668), [#62972](https://github.com/PaddlePaddle/Paddle/pull/62972), [#62505](https://github.com/PaddlePaddle/Paddle/pull/62505), [#63005](https://github.com/PaddlePaddle/Paddle/pull/63005), [#62900](https://github.com/PaddlePaddle/Paddle/pull/62900), [#60577](https://github.com/PaddlePaddle/Paddle/pull/60577), [#60877](https://github.com/PaddlePaddle/Paddle/pull/60877), [#61076](https://github.com/PaddlePaddle/Paddle/pull/61076), [#61038](https://github.com/PaddlePaddle/Paddle/pull/61038), [#61112](https://github.com/PaddlePaddle/Paddle/pull/61112), [#61120](https://github.com/PaddlePaddle/Paddle/pull/61120), [#61582](https://github.com/PaddlePaddle/Paddle/pull/61582), [#61119](https://github.com/PaddlePaddle/Paddle/pull/61119), [#61036](https://github.com/PaddlePaddle/Paddle/pull/61036), [#61289](https://github.com/PaddlePaddle/Paddle/pull/61289), [#60695](https://github.com/PaddlePaddle/Paddle/pull/60695), [#61039](https://github.com/PaddlePaddle/Paddle/pull/61039), [#61963](https://github.com/PaddlePaddle/Paddle/pull/61963), [#62118](https://github.com/PaddlePaddle/Paddle/pull/62118), [#62797](https://github.com/PaddlePaddle/Paddle/pull/62797), [#62807](https://github.com/PaddlePaddle/Paddle/pull/62807), [#62887](https://github.com/PaddlePaddle/Paddle/pull/62887), [#62830](https://github.com/PaddlePaddle/Paddle/pull/62830), [#62849](https://github.com/PaddlePaddle/Paddle/pull/62849), [#62750](https://github.com/PaddlePaddle/Paddle/pull/62750), [#62965](https://github.com/PaddlePaddle/Paddle/pull/62965), [#59742](https://github.com/PaddlePaddle/Paddle/pull/59742), [#59867](https://github.com/PaddlePaddle/Paddle/pull/59867), [#60836](https://github.com/PaddlePaddle/Paddle/pull/60836), [#60902](https://github.com/PaddlePaddle/Paddle/pull/60902), [#61228](https://github.com/PaddlePaddle/Paddle/pull/61228), [#60037](https://github.com/PaddlePaddle/Paddle/pull/60037), [#60079](https://github.com/PaddlePaddle/Paddle/pull/60079), [#60173](https://github.com/PaddlePaddle/Paddle/pull/60173), [#60373](https://github.com/PaddlePaddle/Paddle/pull/60373), [#60380](https://github.com/PaddlePaddle/Paddle/pull/60380), [#60381](https://github.com/PaddlePaddle/Paddle/pull/60381), [#60750](https://github.com/PaddlePaddle/Paddle/pull/60750), [#61065](https://github.com/PaddlePaddle/Paddle/pull/61065), [#61122](https://github.com/PaddlePaddle/Paddle/pull/61122), [#61074](https://github.com/PaddlePaddle/Paddle/pull/61074), [#61204](https://github.com/PaddlePaddle/Paddle/pull/61204), [#61191](https://github.com/PaddlePaddle/Paddle/pull/61191), [#61182](https://github.com/PaddlePaddle/Paddle/pull/61182), [#61219](https://github.com/PaddlePaddle/Paddle/pull/61219), [#61296](https://github.com/PaddlePaddle/Paddle/pull/61296), [#61503](https://github.com/PaddlePaddle/Paddle/pull/61503), [#61484](https://github.com/PaddlePaddle/Paddle/pull/61484), [#61513](https://github.com/PaddlePaddle/Paddle/pull/61513), [#61476](https://github.com/PaddlePaddle/Paddle/pull/61476), [#61510](https://github.com/PaddlePaddle/Paddle/pull/61510), [#61511](https://github.com/PaddlePaddle/Paddle/pull/61511), [#61526](https://github.com/PaddlePaddle/Paddle/pull/61526), [#61524](https://github.com/PaddlePaddle/Paddle/pull/61524), [#61525](https://github.com/PaddlePaddle/Paddle/pull/61525), [#61466](https://github.com/PaddlePaddle/Paddle/pull/61466), [#61497](https://github.com/PaddlePaddle/Paddle/pull/61497), [#61538](https://github.com/PaddlePaddle/Paddle/pull/61538), [#61533](https://github.com/PaddlePaddle/Paddle/pull/61533), [#61530](https://github.com/PaddlePaddle/Paddle/pull/61530), [#61468](https://github.com/PaddlePaddle/Paddle/pull/61468), [#61527](https://github.com/PaddlePaddle/Paddle/pull/61527), [#61535](https://github.com/PaddlePaddle/Paddle/pull/61535), [#61512](https://github.com/PaddlePaddle/Paddle/pull/61512), [#61531](https://github.com/PaddlePaddle/Paddle/pull/61531), [#61539](https://github.com/PaddlePaddle/Paddle/pull/61539), [#61532](https://github.com/PaddlePaddle/Paddle/pull/61532), [#61521](https://github.com/PaddlePaddle/Paddle/pull/61521), [#61517](https://github.com/PaddlePaddle/Paddle/pull/61517), [#61518](https://github.com/PaddlePaddle/Paddle/pull/61518), [#61550](https://github.com/PaddlePaddle/Paddle/pull/61550), [#61545](https://github.com/PaddlePaddle/Paddle/pull/61545), [#61548](https://github.com/PaddlePaddle/Paddle/pull/61548), [#61519](https://github.com/PaddlePaddle/Paddle/pull/61519), [#61549](https://github.com/PaddlePaddle/Paddle/pull/61549), [#61574](https://github.com/PaddlePaddle/Paddle/pull/61574), [#61585](https://github.com/PaddlePaddle/Paddle/pull/61585), [#61581](https://github.com/PaddlePaddle/Paddle/pull/61581), [#61553](https://github.com/PaddlePaddle/Paddle/pull/61553), [#61504](https://github.com/PaddlePaddle/Paddle/pull/61504), [#61603](https://github.com/PaddlePaddle/Paddle/pull/61603), [#61534](https://github.com/PaddlePaddle/Paddle/pull/61534), [#61567](https://github.com/PaddlePaddle/Paddle/pull/61567), [#61523](https://github.com/PaddlePaddle/Paddle/pull/61523), [#61565](https://github.com/PaddlePaddle/Paddle/pull/61565), [#61564](https://github.com/PaddlePaddle/Paddle/pull/61564), [#61707](https://github.com/PaddlePaddle/Paddle/pull/61707), [#61560](https://github.com/PaddlePaddle/Paddle/pull/61560), [#61684](https://github.com/PaddlePaddle/Paddle/pull/61684), [#61706](https://github.com/PaddlePaddle/Paddle/pull/61706), [#61724](https://github.com/PaddlePaddle/Paddle/pull/61724), [#61719](https://github.com/PaddlePaddle/Paddle/pull/61719), [#61729](https://github.com/PaddlePaddle/Paddle/pull/61729), [#61763](https://github.com/PaddlePaddle/Paddle/pull/61763), [#61755](https://github.com/PaddlePaddle/Paddle/pull/61755), [#61737](https://github.com/PaddlePaddle/Paddle/pull/61737), [#61750](https://github.com/PaddlePaddle/Paddle/pull/61750), [#61753](https://github.com/PaddlePaddle/Paddle/pull/61753), [#61756](https://github.com/PaddlePaddle/Paddle/pull/61756), [#61777](https://github.com/PaddlePaddle/Paddle/pull/61777), [#61758](https://github.com/PaddlePaddle/Paddle/pull/61758), [#61731](https://github.com/PaddlePaddle/Paddle/pull/61731), [#61771](https://github.com/PaddlePaddle/Paddle/pull/61771), [#61739](https://github.com/PaddlePaddle/Paddle/pull/61739), [#61559](https://github.com/PaddlePaddle/Paddle/pull/61559), [#61717](https://github.com/PaddlePaddle/Paddle/pull/61717), [#61733](https://github.com/PaddlePaddle/Paddle/pull/61733), [#61563](https://github.com/PaddlePaddle/Paddle/pull/61563), [#61546](https://github.com/PaddlePaddle/Paddle/pull/61546), [#61566](https://github.com/PaddlePaddle/Paddle/pull/61566), [#61562](https://github.com/PaddlePaddle/Paddle/pull/61562), [#61793](https://github.com/PaddlePaddle/Paddle/pull/61793), [#61902](https://github.com/PaddlePaddle/Paddle/pull/61902), [#61905](https://github.com/PaddlePaddle/Paddle/pull/61905), [#61904](https://github.com/PaddlePaddle/Paddle/pull/61904), [#62227](https://github.com/PaddlePaddle/Paddle/pull/62227), 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[#62637](https://github.com/PaddlePaddle/Paddle/pull/62637), [#62777](https://github.com/PaddlePaddle/Paddle/pull/62777), [#62779](https://github.com/PaddlePaddle/Paddle/pull/62779), [#63226](https://github.com/PaddlePaddle/Paddle/pull/63226), [#63287](https://github.com/PaddlePaddle/Paddle/pull/63287), [#63398](https://github.com/PaddlePaddle/Paddle/pull/63398), [#63431](https://github.com/PaddlePaddle/Paddle/pull/63431), [#64000](https://github.com/PaddlePaddle/Paddle/pull/64000), [#64058](https://github.com/PaddlePaddle/Paddle/pull/64058), [#64059](https://github.com/PaddlePaddle/Paddle/pull/64059), [#64063](https://github.com/PaddlePaddle/Paddle/pull/64063), [#64066](https://github.com/PaddlePaddle/Paddle/pull/64066), [#64089](https://github.com/PaddlePaddle/Paddle/pull/64089), [#64170](https://github.com/PaddlePaddle/Paddle/pull/64170), [#64235](https://github.com/PaddlePaddle/Paddle/pull/64235), [#64237](https://github.com/PaddlePaddle/Paddle/pull/64237), [#64243](https://github.com/PaddlePaddle/Paddle/pull/64243), [#64242](https://github.com/PaddlePaddle/Paddle/pull/64242), [#64286](https://github.com/PaddlePaddle/Paddle/pull/64286), [#64322](https://github.com/PaddlePaddle/Paddle/pull/64322), [#64317](https://github.com/PaddlePaddle/Paddle/pull/64317), [#64490](https://github.com/PaddlePaddle/Paddle/pull/64490), [#60138](https://github.com/PaddlePaddle/Paddle/pull/60138), [#62384](https://github.com/PaddlePaddle/Paddle/pull/62384), [#59702](https://github.com/PaddlePaddle/Paddle/pull/59702), [#60341](https://github.com/PaddlePaddle/Paddle/pull/60341), [#60636](https://github.com/PaddlePaddle/Paddle/pull/60636), [#60714](https://github.com/PaddlePaddle/Paddle/pull/60714), [#60716](https://github.com/PaddlePaddle/Paddle/pull/60716), [#60700](https://github.com/PaddlePaddle/Paddle/pull/60700), [#60702](https://github.com/PaddlePaddle/Paddle/pull/60702), [#60704](https://github.com/PaddlePaddle/Paddle/pull/60704), [#60715](https://github.com/PaddlePaddle/Paddle/pull/60715), [#60713](https://github.com/PaddlePaddle/Paddle/pull/60713), [#60711](https://github.com/PaddlePaddle/Paddle/pull/60711), [#60724](https://github.com/PaddlePaddle/Paddle/pull/60724), [#60803](https://github.com/PaddlePaddle/Paddle/pull/60803), [#61331](https://github.com/PaddlePaddle/Paddle/pull/61331), [#63286](https://github.com/PaddlePaddle/Paddle/pull/63286), [#60473](https://github.com/PaddlePaddle/Paddle/pull/60473), [#61046](https://github.com/PaddlePaddle/Paddle/pull/61046), [#61859](https://github.com/PaddlePaddle/Paddle/pull/61859), [#60675](https://github.com/PaddlePaddle/Paddle/pull/60675), [#60719](https://github.com/PaddlePaddle/Paddle/pull/60719), [#62863](https://github.com/PaddlePaddle/Paddle/pull/62863), [#63013](https://github.com/PaddlePaddle/Paddle/pull/63013), [#61293](https://github.com/PaddlePaddle/Paddle/pull/61293), [#62781](https://github.com/PaddlePaddle/Paddle/pull/62781), 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[#63472](https://github.com/PaddlePaddle/Paddle/pull/63472), [#60193](https://github.com/PaddlePaddle/Paddle/pull/60193), [#60197](https://github.com/PaddlePaddle/Paddle/pull/60197), [#60198](https://github.com/PaddlePaddle/Paddle/pull/60198), [#60346](https://github.com/PaddlePaddle/Paddle/pull/60346), [#60318](https://github.com/PaddlePaddle/Paddle/pull/60318), [#60645](https://github.com/PaddlePaddle/Paddle/pull/60645), [#60650](https://github.com/PaddlePaddle/Paddle/pull/60650), [#60660](https://github.com/PaddlePaddle/Paddle/pull/60660), [#60706](https://github.com/PaddlePaddle/Paddle/pull/60706), [#60799](https://github.com/PaddlePaddle/Paddle/pull/60799), [#60837](https://github.com/PaddlePaddle/Paddle/pull/60837), [#60817](https://github.com/PaddlePaddle/Paddle/pull/60817), [#60820](https://github.com/PaddlePaddle/Paddle/pull/60820), [#60894](https://github.com/PaddlePaddle/Paddle/pull/60894), [#61079](https://github.com/PaddlePaddle/Paddle/pull/61079), [#61087](https://github.com/PaddlePaddle/Paddle/pull/61087), [#61073](https://github.com/PaddlePaddle/Paddle/pull/61073), [#61072](https://github.com/PaddlePaddle/Paddle/pull/61072), [#61127](https://github.com/PaddlePaddle/Paddle/pull/61127), [#61097](https://github.com/PaddlePaddle/Paddle/pull/61097), [#61365](https://github.com/PaddlePaddle/Paddle/pull/61365), [#61456](https://github.com/PaddlePaddle/Paddle/pull/61456), [#61846](https://github.com/PaddlePaddle/Paddle/pull/61846), [#62217](https://github.com/PaddlePaddle/Paddle/pull/62217), [#62519](https://github.com/PaddlePaddle/Paddle/pull/62519), [#62881](https://github.com/PaddlePaddle/Paddle/pull/62881), [#62880](https://github.com/PaddlePaddle/Paddle/pull/62880), [#59723](https://github.com/PaddlePaddle/Paddle/pull/59723), [#59722](https://github.com/PaddlePaddle/Paddle/pull/59722), [#59797](https://github.com/PaddlePaddle/Paddle/pull/59797), [#59960](https://github.com/PaddlePaddle/Paddle/pull/59960), [#59761](https://github.com/PaddlePaddle/Paddle/pull/59761), [#59996](https://github.com/PaddlePaddle/Paddle/pull/59996), [#60009](https://github.com/PaddlePaddle/Paddle/pull/60009), [#58896](https://github.com/PaddlePaddle/Paddle/pull/58896), [#60051](https://github.com/PaddlePaddle/Paddle/pull/60051), [#60410](https://github.com/PaddlePaddle/Paddle/pull/60410), [#60420](https://github.com/PaddlePaddle/Paddle/pull/60420), [#60548](https://github.com/PaddlePaddle/Paddle/pull/60548), [#60575](https://github.com/PaddlePaddle/Paddle/pull/60575), [#60726](https://github.com/PaddlePaddle/Paddle/pull/60726), [#60809](https://github.com/PaddlePaddle/Paddle/pull/60809), [#61346](https://github.com/PaddlePaddle/Paddle/pull/61346), [#61222](https://github.com/PaddlePaddle/Paddle/pull/61222), [#61099](https://github.com/PaddlePaddle/Paddle/pull/61099), [#62254](https://github.com/PaddlePaddle/Paddle/pull/62254), [#62269](https://github.com/PaddlePaddle/Paddle/pull/62269), [#62362](https://github.com/PaddlePaddle/Paddle/pull/62362) +- Improve the underlying error checking mechanism of PaddlePaddle to facilitate developers' debugging. [#62571](https://github.com/PaddlePaddle/Paddle/pull/62571), [#62602](https://github.com/PaddlePaddle/Paddle/pull/62602), [#60903](https://github.com/PaddlePaddle/Paddle/pull/60903), [#64695](https://github.com/PaddlePaddle/Paddle/pull/64695), [#59907](https://github.com/PaddlePaddle/Paddle/pull/59907), [#62018](https://github.com/PaddlePaddle/Paddle/pull/62018), [#62839](https://github.com/PaddlePaddle/Paddle/pull/62839), [#60651](https://github.com/PaddlePaddle/Paddle/pull/60651), [#61488](https://github.com/PaddlePaddle/Paddle/pull/61488), [#64064](https://github.com/PaddlePaddle/Paddle/pull/64064), [#63192](https://github.com/PaddlePaddle/Paddle/pull/63192), [#63525](https://github.com/PaddlePaddle/Paddle/pull/63525)。 -- Implemented timely release of last layer of PP strategy, to save video memory. [#54505](https://github.com/PaddlePaddle/Paddle/pull/54505) -- In MP strategy Tensor fusion, supported incoming params group to enhance Tensor fusion function. Improved allreduce asynchronous communication performance, and enhanced training performance through overlap of computation and communication. [#57690](https://github.com/PaddlePaddle/Paddle/pull/57690),[#55662](https://github.com/PaddlePaddle/Paddle/pull/55662) -- In Sharding strategy, carried out overlap for reverse computation and gradient communication, to improve training performance. For Sharding stage1, added Tensor fusion and fuse grad clip, and optimizer, to improve computational efficiency. Supported overlap between VPP and DP/Sharding Stage1, to improve communication and computation parallelism. Optimized performance of Sharding Stage1 under FP16. Check only gradient responsible for this sharding rank in the check finite stage, to reduce computation overhead; added environment variables to control whether Optimize is performed to save video memory, to achieve use of fewer resources for model training debugging. [#55598](https://github.com/PaddlePaddle/Paddle/pull/55598),[#55427](https://github.com/PaddlePaddle/Paddle/pull/55427),[#56063](https://github.com/PaddlePaddle/Paddle/pull/56063),[#55766](https://github.com/PaddlePaddle/Paddle/pull/55766),[#59848](https://github.com/PaddlePaddle/Paddle/pull/59848) -- In Hybrid Parallel strategy, arranged Tensor fusion under PP/VPP to pre-run, to solve the problem of extra overhead of runtime fuse on video memory. Improved model training performance by reducing non-essential synchronous memcpy. [#54403](https://github.com/PaddlePaddle/Paddle/pull/54403),[#57215](https://github.com/PaddlePaddle/Paddle/pull/57215) +### Vulnerability Fixing -#### Bug Fix +- Fix potential security vulnerabilities. [#59957](https://github.com/PaddlePaddle/Paddle/pull/59957), [#61032](https://github.com/PaddlePaddle/Paddle/pull/61032), [#61356](https://github.com/PaddlePaddle/Paddle/pull/61356), [#61573](https://github.com/PaddlePaddle/Paddle/pull/61573), [#61671](https://github.com/PaddlePaddle/Paddle/pull/61671), [#62345](https://github.com/PaddlePaddle/Paddle/pull/62345), [#60097](https://github.com/PaddlePaddle/Paddle/pull/60097), [#61161](https://github.com/PaddlePaddle/Paddle/pull/61161), [#61294](https://github.com/PaddlePaddle/Paddle/pull/61294), [#61349](https://github.com/PaddlePaddle/Paddle/pull/61349), [#61344](https://github.com/PaddlePaddle/Paddle/pull/61344), [#61162](https://github.com/PaddlePaddle/Paddle/pull/61162), [#61285](https://github.com/PaddlePaddle/Paddle/pull/61285), [#61826](https://github.com/PaddlePaddle/Paddle/pull/61826), [#59967](https://github.com/PaddlePaddle/Paddle/pull/59967), [#59976](https://github.com/PaddlePaddle/Paddle/pull/59976), [#59979](https://github.com/PaddlePaddle/Paddle/pull/59979)[#60527](https://github.com/PaddlePaddle/Paddle/pull/60527),[#60646](https://github.com/PaddlePaddle/Paddle/pull/60646),[#61827](https://github.com/PaddlePaddle/Paddle/pull/61827) -- Fixed 13 bugs in PP, Launch function, MP strategy, and fuse_rope, to enhance stability of distributed strategies. At mechanism level, fixed errors of inplace and tensor reference to improve stability. [#55116](https://github.com/PaddlePaddle/Paddle/pull/55116),[#55782](https://github.com/PaddlePaddle/Paddle/pull/55782),[#59609](https://github.com/PaddlePaddle/Paddle/pull/59609),[#57394](https://github.com/PaddlePaddle/Paddle/pull/57394),[#55864](https://github.com/PaddlePaddle/Paddle/pull/55864),[#58482](https://github.com/PaddlePaddle/Paddle/pull/58482),[#54571](https://github.com/PaddlePaddle/Paddle/pull/54571),[#55896](https://github.com/PaddlePaddle/Paddle/pull/55896),[#54648](https://github.com/PaddlePaddle/Paddle/pull/54648),[#58307](https://github.com/PaddlePaddle/Paddle/pull/58307),[#55679](https://github.com/PaddlePaddle/Paddle/pull/55679),[#58133](https://github.com/PaddlePaddle/Paddle/pull/58133),[#58408](https://github.com/PaddlePaddle/Paddle/pull/58408),[#59707](https://github.com/PaddlePaddle/Paddle/pull/59707),[#55342](https://github.com/PaddlePaddle/Paddle/pull/55342),[#54703](https://github.com/PaddlePaddle/Paddle/pull/54703),[#54869](https://github.com/PaddlePaddle/Paddle/pull/54869),[#55568](https://github.com/PaddlePaddle/Paddle/pull/55568),[#55233](https://github.com/PaddlePaddle/Paddle/pull/55233),[#56418](https://github.com/PaddlePaddle/Paddle/pull/56418),[#56428](https://github.com/PaddlePaddle/Paddle/pull/56428),[#56892](https://github.com/PaddlePaddle/Paddle/pull/56892),[#57192](https://github.com/PaddlePaddle/Paddle/pull/57192),[#59161](https://github.com/PaddlePaddle/Paddle/pull/59161),[#59340](https://github.com/PaddlePaddle/Paddle/pull/59340),[#57006](https://github.com/PaddlePaddle/Paddle/pull/57006),[#57353](https://github.com/PaddlePaddle/Paddle/pull/57353),[#57352](https://github.com/PaddlePaddle/Paddle/pull/57352),[#59088](https://github.com/PaddlePaddle/Paddle/pull/59088) -- Fixed bug that PP strategy can't release single-layer output in time. Fixed the bug that initialization process may Hang. [#54624](https://github.com/PaddlePaddle/Paddle/pull/54624),[#58844](https://github.com/PaddlePaddle/Paddle/pull/58844),[#54673](https://github.com/PaddlePaddle/Paddle/pull/54673),[#58376](https://github.com/PaddlePaddle/Paddle/pull/58376) -- Fixed the bug calculation is wrong when input data type is not uniform under MP strategy. Fixed the bug of parameter synchronization under MP strategy. Fixed the bug user input config is not used correctly. [#58858](https://github.com/PaddlePaddle/Paddle/pull/58858),[#57918](https://github.com/PaddlePaddle/Paddle/pull/57918),[#58037](https://github.com/PaddlePaddle/Paddle/pull/58037) -- Unified judgment method of dygraph and dynamic mode. [#54633](https://github.com/PaddlePaddle/Paddle/pull/54633) -- Fixed the bug shape of sin and cos in fuse_rope is not correct. [#56132](https://github.com/PaddlePaddle/Paddle/pull/56132) -- Fixed the bug task fails to due to long endpoints in Luanch distributed scenarios. Fixed the bug endpoints may be out of order. [#55011](https://github.com/PaddlePaddle/Paddle/pull/55011),[#55478](https://github.com/PaddlePaddle/Paddle/pull/55478) -- Fixed the bug MEA function may cause segmentation fault error. [#55408](https://github.com/PaddlePaddle/Paddle/pull/55408) +### Deprecated Features -### Auto parallel +- Clean up deprecated actuators and other logic to reduce redundant codes. [#64822](https://github.com/PaddlePaddle/Paddle/pull/64822), [#60941](https://github.com/PaddlePaddle/Paddle/pull/60941) -This release fully optimizes Auto Parallel programming paradigm with dynamic-static unification to simplify programming complexity for developers. Developers do not need to understand complex concepts and APIs in manual parallel programming paradigm, such as row-parallel, column-parallel, and so on. A small amount of tensor distribution annotations is required to build a hybrid parallel model. Framework will handle the derivation of distribution states of all tensors and operators, and adding appropriate communication operators. Meanwhile, it supports the dynamic to static distributed training by just one extra code changed, enabling developers to efficiently and easily implement any hybrid parallel strategy. This can significantly reduce development costs of hybrid parallel training codes. +## Compiler Infrastructure for Neural Networks (CINN) -#### Improved auto parallel core functions +In version 3.0, the compiler architecture has been significantly upgraded. Based on Shape Dialect, build a symbolic automatic derivation and simplification system, support symbolic expression and constraint construction, and support end-to-end execution under the dynamic shape of the compiler. Meanwhile, CINN has upgraded the automatic fusion of subgraphs and Pass Pipline mechanism, merged the core modules of dynamic and static shapes, and merged the iteration paths, so that the architecture is clear and unified. In this version, the compiler has been refactored in important back-end modules such as AST Compute, Schedule strategy, and Tiling, improving the general optimization capability of the compiler, and verifies the training, inference correctness and speedup performance of the dynamic shapes on the subgraphs of PaddlePaddle Industry Suite models and typical large models Llama2-7B and Stable Diffusion models. -- Implemented auto parallel core APIs such as process_mesh, placement, shard_tensor, reshard, dtensor_from_fn, unshard_dtensor, shard_layer, to_static, and so on. [#55494](https://github.com/PaddlePaddle/Paddle/pull/55494),[#59059](https://github.com/PaddlePaddle/Paddle/pull/59059),[#56561](https://github.com/PaddlePaddle/Paddle/pull/56561),[#54425](https://github.com/PaddlePaddle/Paddle/pull/54425),[#59557](https://github.com/PaddlePaddle/Paddle/pull/59557),[#59682](https://github.com/PaddlePaddle/Paddle/pull/59682),[#56565](https://github.com/PaddlePaddle/Paddle/pull/56565),[#59862](https://github.com/PaddlePaddle/Paddle/pull/59862),[#59856](https://github.com/PaddlePaddle/Paddle/pull/59856),[#59342](https://github.com/PaddlePaddle/Paddle/pull/59342),[#59575](https://github.com/PaddlePaddle/Paddle/pull/59575),[#57604](https://github.com/PaddlePaddle/Paddle/pull/57604),[#57293](https://github.com/PaddlePaddle/Paddle/pull/57293),[#57278](https://github.com/PaddlePaddle/Paddle/pull/57278) -- Implemented Sharding derivation rules based on Enisum expressions, and completed 20+ classes of operator Sharding derivation rules, which covers LLaMA, GPT and other transformer-like large language models. [#55196](https://github.com/PaddlePaddle/Paddle/pull/55196),[#53863](https://github.com/PaddlePaddle/Paddle/pull/53863),[#56257](https://github.com/PaddlePaddle/Paddle/pull/56257),[#55394](https://github.com/PaddlePaddle/Paddle/pull/55394),[#54810](https://github.com/PaddlePaddle/Paddle/pull/54810),[#55508](https://github.com/PaddlePaddle/Paddle/pull/55508),[#56257](https://github.com/PaddlePaddle/Paddle/pull/56257),[#57813](https://github.com/PaddlePaddle/Paddle/pull/57813),[#58149](https://github.com/PaddlePaddle/Paddle/pull/58149),[#58506](https://github.com/PaddlePaddle/Paddle/pull/58506),[#58563](https://github.com/PaddlePaddle/Paddle/pull/58563),[#58360](https://github.com/PaddlePaddle/Paddle/pull/58360),[#58920](https://github.com/PaddlePaddle/Paddle/pull/58920),[#59050](https://github.com/PaddlePaddle/Paddle/pull/59050),[#58760](https://github.com/PaddlePaddle/Paddle/pull/58760),[#59083](https://github.com/PaddlePaddle/Paddle/pull/59083),[#59236](https://github.com/PaddlePaddle/Paddle/pull/59236),[#59350](https://github.com/PaddlePaddle/Paddle/pull/59350),[#59411](https://github.com/PaddlePaddle/Paddle/pull/59411),[#59260](https://github.com/PaddlePaddle/Paddle/pull/59260),[#54373](https://github.com/PaddlePaddle/Paddle/pull/54373),[#54991](https://github.com/PaddlePaddle/Paddle/pull/54991),[#55397](https://github.com/PaddlePaddle/Paddle/pull/55397),[#55350](https://github.com/PaddlePaddle/Paddle/pull/55350),[#55177](https://github.com/PaddlePaddle/Paddle/pull/55177),[#56443](https://github.com/PaddlePaddle/Paddle/pull/56443),[#58097](https://github.com/PaddlePaddle/Paddle/pull/58097),[#56509](https://github.com/PaddlePaddle/Paddle/pull/56509),[#56502](https://github.com/PaddlePaddle/Paddle/pull/56502),[#56504](https://github.com/PaddlePaddle/Paddle/pull/56504),[#56506](https://github.com/PaddlePaddle/Paddle/pull/56506),[#56507](https://github.com/PaddlePaddle/Paddle/pull/56507),[#56505](https://github.com/PaddlePaddle/Paddle/pull/56505),[#57176](https://github.com/PaddlePaddle/Paddle/pull/57176),[#57374](https://github.com/PaddlePaddle/Paddle/pull/57374),[#57573](https://github.com/PaddlePaddle/Paddle/pull/57573),[#57545](https://github.com/PaddlePaddle/Paddle/pull/57545),[#57875](https://github.com/PaddlePaddle/Paddle/pull/57875),[#57866](https://github.com/PaddlePaddle/Paddle/pull/57866),[#58854](https://github.com/PaddlePaddle/Paddle/pull/58854),[#59109](https://github.com/PaddlePaddle/Paddle/pull/59109),[#59185](https://github.com/PaddlePaddle/Paddle/pull/59185),[#58913](https://github.com/PaddlePaddle/Paddle/pull/58913),[#59547](https://github.com/PaddlePaddle/Paddle/pull/59547),[#58296](https://github.com/PaddlePaddle/Paddle/pull/58296),[#59545](https://github.com/PaddlePaddle/Paddle/pull/59545),[#59039](https://github.com/PaddlePaddle/Paddle/pull/59039),[#59002](https://github.com/PaddlePaddle/Paddle/pull/59002),[#58087](https://github.com/PaddlePaddle/Paddle/pull/58087),[#56367](https://github.com/PaddlePaddle/Paddle/pull/56367),[#57877](https://github.com/PaddlePaddle/Paddle/pull/57877),[#56839](https://github.com/PaddlePaddle/Paddle/pull/56839),[#59003](https://github.com/PaddlePaddle/Paddle/pull/59003),[#57269](https://github.com/PaddlePaddle/Paddle/pull/57269),[#55130](https://github.com/PaddlePaddle/Paddle/pull/55130),[#58474](https://github.com/PaddlePaddle/Paddle/pull/58474),[#57197](https://github.com/PaddlePaddle/Paddle/pull/57197),[#57467](https://github.com/PaddlePaddle/Paddle/pull/57467),[#57259](https://github.com/PaddlePaddle/Paddle/pull/57259),[#57280](https://github.com/PaddlePaddle/Paddle/pull/57280),[#56508](https://github.com/PaddlePaddle/Paddle/pull/56508) -- Implemented distributed checkpoint storage and loading with dynamic-static unification. Supports ReShard upon arbitrary Sharding of storage and loading in a Sharding state. [#59659](https://github.com/PaddlePaddle/Paddle/pull/59659),[#59843](https://github.com/PaddlePaddle/Paddle/pull/59843),[#60033](https://github.com/PaddlePaddle/Paddle/pull/60033),[#60034](https://github.com/PaddlePaddle/Paddle/pull/60034) +### New Features -#### Enhanced semi-auto parallel capability of dynamic graph +1. Upgrade the new automatic subgraph fusion mechanism, and innovatively propose the TrivialOp and ReduceOp fusion theory, supporting a wider range of vertical fusion and horizontal fusion, ensuring the correctness and robustness of subgraph fusion, and giving full play to the fusion potential of the neural network compiler.（[#63340](https://github.com/PaddlePaddle/Paddle/pull/63340)、[#63913](https://github.com/PaddlePaddle/Paddle/pull/63913)、[#63579](https://github.com/PaddlePaddle/Paddle/pull/63579)、[#63605](https://github.com/PaddlePaddle/Paddle/pull/63605)、[#60769](https://github.com/PaddlePaddle/Paddle/pull/60769)、[#62088](https://github.com/PaddlePaddle/Paddle/pull/62088)、[#63124](https://github.com/PaddlePaddle/Paddle/pull/63124)、[#63658](https://github.com/PaddlePaddle/Paddle/pull/63658)、[#64557](https://github.com/PaddlePaddle/Paddle/pull/64557)、[#63318](https://github.com/PaddlePaddle/Paddle/pull/63318)、[#62545](https://github.com/PaddlePaddle/Paddle/pull/62545)） +2. Add the symbol derivation function of dynamic shapes. Based on the Shape Dialect, realize the dynamic symbol construction, automatic derivation, constraint expression, symbol simplification and other mechanisms, introduce the DimExpr concept, upgrade the support for the PaddlePaddle framework of the InferSymbolicShape logic of the 150 + typical primitive operators, and provide more information for training and inference with compiler support for dynamic shapes.（[#60843](https://github.com/PaddlePaddle/Paddle/pull/60843)、[#62662](https://github.com/PaddlePaddle/Paddle/pull/62662)、[#63790](https://github.com/PaddlePaddle/Paddle/pull/63790)、[#60098](https://github.com/PaddlePaddle/Paddle/pull/60098)、[#60511](https://github.com/PaddlePaddle/Paddle/pull/60511)、[#61232](https://github.com/PaddlePaddle/Paddle/pull/61232)、[#61939](https://github.com/PaddlePaddle/Paddle/pull/61939)、[#62798](https://github.com/PaddlePaddle/Paddle/pull/62798)、[#62955](https://github.com/PaddlePaddle/Paddle/pull/62955)、[#63029](https://github.com/PaddlePaddle/Paddle/pull/63029)、[#60572](https://github.com/PaddlePaddle/Paddle/pull/60572)、[#61035](https://github.com/PaddlePaddle/Paddle/pull/61035)、[#61224](https://github.com/PaddlePaddle/Paddle/pull/61224)、[#61587](https://github.com/PaddlePaddle/Paddle/pull/61587)、[#61937](https://github.com/PaddlePaddle/Paddle/pull/61937)、[#62314](https://github.com/PaddlePaddle/Paddle/pull/62314)、[#62394](https://github.com/PaddlePaddle/Paddle/pull/62394)、[#62569](https://github.com/PaddlePaddle/Paddle/pull/62569)、[#62495](https://github.com/PaddlePaddle/Paddle/pull/62495)、[#62844](https://github.com/PaddlePaddle/Paddle/pull/62844)、[#63000](https://github.com/PaddlePaddle/Paddle/pull/63000)、[#63016](https://github.com/PaddlePaddle/Paddle/pull/63016)、[#64222](https://github.com/PaddlePaddle/Paddle/pull/64222)、[#60129](https://github.com/PaddlePaddle/Paddle/pull/60129)、[#60899](https://github.com/PaddlePaddle/Paddle/pull/60899)、[#61342](https://github.com/PaddlePaddle/Paddle/pull/61342)、[#61439](https://github.com/PaddlePaddle/Paddle/pull/61439)、[#62766](https://github.com/PaddlePaddle/Paddle/pull/62766)、[#61133](https://github.com/PaddlePaddle/Paddle/pull/61133)、[#61430](https://github.com/PaddlePaddle/Paddle/pull/61430)、[#61498](https://github.com/PaddlePaddle/Paddle/pull/61498)、[#61680](https://github.com/PaddlePaddle/Paddle/pull/61680)、[#63367](https://github.com/PaddlePaddle/Paddle/pull/63367)、[#62151](https://github.com/PaddlePaddle/Paddle/pull/62151)、[#62665](https://github.com/PaddlePaddle/Paddle/pull/62665)、[#61407](https://github.com/PaddlePaddle/Paddle/pull/61407)、[#61502](https://github.com/PaddlePaddle/Paddle/pull/61502)、[#61655](https://github.com/PaddlePaddle/Paddle/pull/61655)、[#64115](https://github.com/PaddlePaddle/Paddle/pull/64115)、[#61791](https://github.com/PaddlePaddle/Paddle/pull/61791)、[#62141](https://github.com/PaddlePaddle/Paddle/pull/62141)、[#63422](https://github.com/PaddlePaddle/Paddle/pull/63422)、[#63577](https://github.com/PaddlePaddle/Paddle/pull/63577)、[#63978](https://github.com/PaddlePaddle/Paddle/pull/63978)、[#63576](https://github.com/PaddlePaddle/Paddle/pull/63576)、[#63947](https://github.com/PaddlePaddle/Paddle/pull/63947)、[#64332](https://github.com/PaddlePaddle/Paddle/pull/64332)、[#63990](https://github.com/PaddlePaddle/Paddle/pull/63990)） +3. Add the Pass Pipline function, including PdToCinn, CinnPreprocess, BuildGroupOp, GroupClusterOp, CinnLowering, Accuracy Check and other Pass strategies, to support the Lowering and execution of subgraphs in dynamic and static shapes, with a clear architecture.（[#61611](https://github.com/PaddlePaddle/Paddle/pull/61611)、[#62612](https://github.com/PaddlePaddle/Paddle/pull/62612)、[#64354](https://github.com/PaddlePaddle/Paddle/pull/64354)、[#61848](https://github.com/PaddlePaddle/Paddle/pull/61848)、[#62316](https://github.com/PaddlePaddle/Paddle/pull/62316)、[#64152](https://github.com/PaddlePaddle/Paddle/pull/64152)、[#61619](https://github.com/PaddlePaddle/Paddle/pull/61619)、[#62318](https://github.com/PaddlePaddle/Paddle/pull/62318)、[#61977](https://github.com/PaddlePaddle/Paddle/pull/61977)、[#62211](https://github.com/PaddlePaddle/Paddle/pull/62211)、[#63972](https://github.com/PaddlePaddle/Paddle/pull/63972)、[#63686](https://github.com/PaddlePaddle/Paddle/pull/63686)、[#64505](https://github.com/PaddlePaddle/Paddle/pull/64505)） +4. Add the support for BuketLower and DyShapeSchdule functions, to realize automatic bucket compilation and optimization according to the range of dynamic shapes; and adapt and upgrade the logic of CodeGen module to support the generation of InferShape function and the distribution of conditional branching function of Host function, so as to support the acceleration of training inference under the dynamic Shape of large models.（[#62730](https://github.com/PaddlePaddle/Paddle/pull/62730)、[#61115](https://github.com/PaddlePaddle/Paddle/pull/61115)、[#59941](https://github.com/PaddlePaddle/Paddle/pull/59941)、[#62207](https://github.com/PaddlePaddle/Paddle/pull/62207)、[#64318](https://github.com/PaddlePaddle/Paddle/pull/64318)、[#64345](https://github.com/PaddlePaddle/Paddle/pull/64345)、[#60519](https://github.com/PaddlePaddle/Paddle/pull/60519)、[#62584](https://github.com/PaddlePaddle/Paddle/pull/62584)、[#60828](https://github.com/PaddlePaddle/Paddle/pull/60828)、[#60533](https://github.com/PaddlePaddle/Paddle/pull/60533)、[#61436](https://github.com/PaddlePaddle/Paddle/pull/61436)、[#62071](https://github.com/PaddlePaddle/Paddle/pull/62071)、[#63971](https://github.com/PaddlePaddle/Paddle/pull/63971)、[#61656](https://github.com/PaddlePaddle/Paddle/pull/61656)、[#63083](https://github.com/PaddlePaddle/Paddle/pull/63083)、[#64405](https://github.com/PaddlePaddle/Paddle/pull/64405)、[#63047](https://github.com/PaddlePaddle/Paddle/pull/63047)、[#64655](https://github.com/PaddlePaddle/Paddle/pull/64655)、[#63095](https://github.com/PaddlePaddle/Paddle/pull/63095)、[#63829](https://github.com/PaddlePaddle/Paddle/pull/63829)、[#63572](https://github.com/PaddlePaddle/Paddle/pull/63572)） +5. Add support for compilation caching strategy, to automatically recognize, merge and reuse compilation results of the same subgraph structure, improve compilation efficiency by using multi-threading, so as to enhance the user experience.（[#62952](https://github.com/PaddlePaddle/Paddle/pull/62952)、[#63269](https://github.com/PaddlePaddle/Paddle/pull/63269)、[#64718](https://github.com/PaddlePaddle/Paddle/pull/64718)、[#61367](https://github.com/PaddlePaddle/Paddle/pull/61367)、[#63305](https://github.com/PaddlePaddle/Paddle/pull/63305)、[#63750](https://github.com/PaddlePaddle/Paddle/pull/63750)、[#63871](https://github.com/PaddlePaddle/Paddle/pull/63871)、[#64893](https://github.com/PaddlePaddle/Paddle/pull/64893)） +6. Add support for GenerateShape mechanism, add corresponding AST Compute operator definitions, support automatic resolution of dynamic symbols, and automatic generation of ShapeOp in the Lowering stage.（[#64167](https://github.com/PaddlePaddle/Paddle/pull/64167)、[#64636](https://github.com/PaddlePaddle/Paddle/pull/64636)、[#61993](https://github.com/PaddlePaddle/Paddle/pull/61993)、[#64843](https://github.com/PaddlePaddle/Paddle/pull/64843)、[#62587](https://github.com/PaddlePaddle/Paddle/pull/62587)） -- Basic data structure supplementation: Added DistTensor, Placements and other distributed specific basic data structures on C++ end, and exposed to Python end. Supports debugging and printing of related attributes and values. [#58930](https://github.com/PaddlePaddle/Paddle/pull/58930),[#59068](https://github.com/PaddlePaddle/Paddle/pull/59068),[#55436](https://github.com/PaddlePaddle/Paddle/pull/55436),[#56449](https://github.com/PaddlePaddle/Paddle/pull/56449),[#59683](https://github.com/PaddlePaddle/Paddle/pull/59683),[#55593](https://github.com/PaddlePaddle/Paddle/pull/55593),[#58032](https://github.com/PaddlePaddle/Paddle/pull/58032),[#56368](https://github.com/PaddlePaddle/Paddle/pull/56368),[#59086](https://github.com/PaddlePaddle/Paddle/pull/59086) -- Added SPMD derivation and Reshard generation logic in execution flow for all operators, and adapted to multiple types of inputs and outputs such as vector and optional, as well as special mechanisms such as cpu fallback and multi-kernel selection. [#56602](https://github.com/PaddlePaddle/Paddle/pull/56602),[#57321](https://github.com/PaddlePaddle/Paddle/pull/57321),[#57092](https://github.com/PaddlePaddle/Paddle/pull/57092),[#56831](https://github.com/PaddlePaddle/Paddle/pull/56831),[#57119](https://github.com/PaddlePaddle/Paddle/pull/57119),[#58819](https://github.com/PaddlePaddle/Paddle/pull/58819),[#58254](https://github.com/PaddlePaddle/Paddle/pull/58254),[#55698](https://github.com/PaddlePaddle/Paddle/pull/55698),[#59241](https://github.com/PaddlePaddle/Paddle/pull/59241),[#59328](https://github.com/PaddlePaddle/Paddle/pull/59328),[#58644](https://github.com/PaddlePaddle/Paddle/pull/58644),[#56202](https://github.com/PaddlePaddle/Paddle/pull/56202),[#59159](https://github.com/PaddlePaddle/Paddle/pull/59159),[#58573](https://github.com/PaddlePaddle/Paddle/pull/58573),[#59246](https://github.com/PaddlePaddle/Paddle/pull/59246),[#59133](https://github.com/PaddlePaddle/Paddle/pull/59133),[#59186](https://github.com/PaddlePaddle/Paddle/pull/59186),[#57505](https://github.com/PaddlePaddle/Paddle/pull/57505),[#57241](https://github.com/PaddlePaddle/Paddle/pull/57241),[#58928](https://github.com/PaddlePaddle/Paddle/pull/58928) +### Function Optimization -- Adapted auto parallel execution logic for special types of operators, such as custom operators. Supports automatic conversion of DistTensor and DenseTensor as mixed inputs. [#57774](https://github.com/PaddlePaddle/Paddle/pull/57774),[#59108](https://github.com/PaddlePaddle/Paddle/pull/59108),[#58436](https://github.com/PaddlePaddle/Paddle/pull/58436),[#59523](https://github.com/PaddlePaddle/Paddle/pull/59523),[#59136](https://github.com/PaddlePaddle/Paddle/pull/59136),[#59352](https://github.com/PaddlePaddle/Paddle/pull/59352),[#59062](https://github.com/PaddlePaddle/Paddle/pull/59062),[#58434](https://github.com/PaddlePaddle/Paddle/pull/58434),[#59148](https://github.com/PaddlePaddle/Paddle/pull/59148),[#58553](https://github.com/PaddlePaddle/Paddle/pull/58553),[#58716](https://github.com/PaddlePaddle/Paddle/pull/58716),[#58369](https://github.com/PaddlePaddle/Paddle/pull/58369),[#59061](https://github.com/PaddlePaddle/Paddle/pull/59061),[#58841](https://github.com/PaddlePaddle/Paddle/pull/58841),[#59139](https://github.com/PaddlePaddle/Paddle/pull/59139),[#59141](https://github.com/PaddlePaddle/Paddle/pull/59141),[#58837](https://github.com/PaddlePaddle/Paddle/pull/58837),[#59137](https://github.com/PaddlePaddle/Paddle/pull/59137),[#59143](https://github.com/PaddlePaddle/Paddle/pull/59143) +1. Optimize BuildCinnPass logic, upgrade the compiler's perception strategy for black and white list operators, and improve the robustness of Pass logic.（[#62372](https://github.com/PaddlePaddle/Paddle/pull/62372)、[#61081](https://github.com/PaddlePaddle/Paddle/pull/61081)、[#61225](https://github.com/PaddlePaddle/Paddle/pull/61225)、[#58863](https://github.com/PaddlePaddle/Paddle/pull/58863)） +2. Optimize the OpLoweringGroup data structure, remove unnecessary interfaces and members, and reduce the coupling between upstream and downstream modules.（[#62339](https://github.com/PaddlePaddle/Paddle/pull/62339)） +3. Optimize the component design of the compiler on the architecture Arch, to abstract the concept of hardware, and reduce the cost of adapting to domestic hardware.（[#63530](https://github.com/PaddlePaddle/Paddle/pull/63530)、[#64347](https://github.com/PaddlePaddle/Paddle/pull/64347)、[#64506](https://github.com/PaddlePaddle/Paddle/pull/64506)、[#64587](https://github.com/PaddlePaddle/Paddle/pull/64587)） +4. Upgrade the AST Compute module of the compiler's back-end operator, to adapt to support the computing logic of dynamic Shape.（[#62488](https://github.com/PaddlePaddle/Paddle/pull/62488)、[#63581](https://github.com/PaddlePaddle/Paddle/pull/63581)、[#63687](https://github.com/PaddlePaddle/Paddle/pull/63687)、[#63654](https://github.com/PaddlePaddle/Paddle/pull/63654)、[#64217](https://github.com/PaddlePaddle/Paddle/pull/64217)） -- Optimized dynamic graph execution system: Adapted Autograd execution process. Supports dynamic graph's inverse gradient aggregation, AMP, Hook, PyLayer, View, custom operators, and other surrounding mechanisms. [#58437](https://github.com/PaddlePaddle/Paddle/pull/58437),[#58769](https://github.com/PaddlePaddle/Paddle/pull/58769),[#58796](https://github.com/PaddlePaddle/Paddle/pull/58796),[#58339](https://github.com/PaddlePaddle/Paddle/pull/58339),[#58409](https://github.com/PaddlePaddle/Paddle/pull/58409),[#58772](https://github.com/PaddlePaddle/Paddle/pull/58772),[#58380](https://github.com/PaddlePaddle/Paddle/pull/58380),[#58447](https://github.com/PaddlePaddle/Paddle/pull/58447),[#58706](https://github.com/PaddlePaddle/Paddle/pull/58706),[#58656](https://github.com/PaddlePaddle/Paddle/pull/58656),[#58172](https://github.com/PaddlePaddle/Paddle/pull/58172),[#59401](https://github.com/PaddlePaddle/Paddle/pull/59401),[#58727](https://github.com/PaddlePaddle/Paddle/pull/58727),[#58238](https://github.com/PaddlePaddle/Paddle/pull/58238),[#59243](https://github.com/PaddlePaddle/Paddle/pull/59243),[#58469](https://github.com/PaddlePaddle/Paddle/pull/58469),[#58442](https://github.com/PaddlePaddle/Paddle/pull/58442),[#58487](https://github.com/PaddlePaddle/Paddle/pull/58487),[#58476](https://github.com/PaddlePaddle/Paddle/pull/58476),[#59706](https://github.com/PaddlePaddle/Paddle/pull/59706) +### Performance Optimization -- Added support for Pipeline Parallelism, Sequence Parallelism and other distributed parallelism. [#58126](https://github.com/PaddlePaddle/Paddle/pull/58126),[#59766](https://github.com/PaddlePaddle/Paddle/pull/59766),[#59060](https://github.com/PaddlePaddle/Paddle/pull/59060),[#59841](https://github.com/PaddlePaddle/Paddle/pull/59841),[#58609](https://github.com/PaddlePaddle/Paddle/pull/58609),[#59688](https://github.com/PaddlePaddle/Paddle/pull/59688),[#58449](https://github.com/PaddlePaddle/Paddle/pull/58449)、[#59598](https://github.com/PaddlePaddle/Paddle/pull/59598) -- Added various Reshard strategies and support tensor conversions between different distributed states. [#58592](https://github.com/PaddlePaddle/Paddle/pull/58592),[#59138](https://github.com/PaddlePaddle/Paddle/pull/59138),[#59367](https://github.com/PaddlePaddle/Paddle/pull/59367),[#59621](https://github.com/PaddlePaddle/Paddle/pull/59621),[#59758](https://github.com/PaddlePaddle/Paddle/pull/59758),[#59777](https://github.com/PaddlePaddle/Paddle/pull/59777),[#56975](https://github.com/PaddlePaddle/Paddle/pull/56975),[#58550](https://github.com/PaddlePaddle/Paddle/pull/58550),[#58703](https://github.com/PaddlePaddle/Paddle/pull/58703),[#57210](https://github.com/PaddlePaddle/Paddle/pull/57210),[#58734](https://github.com/PaddlePaddle/Paddle/pull/58734),[#56833](https://github.com/PaddlePaddle/Paddle/pull/56833),[#59292](https://github.com/PaddlePaddle/Paddle/pull/59292),[#57432](https://github.com/PaddlePaddle/Paddle/pull/57432),[#57568](https://github.com/PaddlePaddle/Paddle/pull/57568),[#56553](https://github.com/PaddlePaddle/Paddle/pull/56553),[#58284](https://github.com/PaddlePaddle/Paddle/pull/58284),[#56039](https://github.com/PaddlePaddle/Paddle/pull/56039),[#55552](https://github.com/PaddlePaddle/Paddle/pull/55552),[#56149](https://github.com/PaddlePaddle/Paddle/pull/56149) +1. Optimize the Schedule logic of AST IR, restructure the core modules such as Vectorize, Unroll, AxisBind, and ComputeAt, and merged the iterative paths of dynamic and static shapes, so as to reduce the development and maintenance costs.（[#60449](https://github.com/PaddlePaddle/Paddle/pull/60449)、[#60155](https://github.com/PaddlePaddle/Paddle/pull/60155)、[#60342](https://github.com/PaddlePaddle/Paddle/pull/60342)、[#60498](https://github.com/PaddlePaddle/Paddle/pull/60498)、[#60538](https://github.com/PaddlePaddle/Paddle/pull/60538)、[#60190](https://github.com/PaddlePaddle/Paddle/pull/60190)、[#61197](https://github.com/PaddlePaddle/Paddle/pull/61197)、[#63140](https://github.com/PaddlePaddle/Paddle/pull/63140)、[#61156](https://github.com/PaddlePaddle/Paddle/pull/61156)） +2. Optimize the Tiling strategy and temp Buffer function, support warp-level memory continuous Read and cache_read cache_write function, and improve the subgraph execution performance.（[#64240](https://github.com/PaddlePaddle/Paddle/pull/64240)、[#60562](https://github.com/PaddlePaddle/Paddle/pull/60562)、[#64711](https://github.com/PaddlePaddle/Paddle/pull/64711)、[#62856](https://github.com/PaddlePaddle/Paddle/pull/62856)、[#61576](https://github.com/PaddlePaddle/Paddle/pull/61576)、[#61901](https://github.com/PaddlePaddle/Paddle/pull/61901)、[#62581](https://github.com/PaddlePaddle/Paddle/pull/62581)、[#61987](https://github.com/PaddlePaddle/Paddle/pull/61987)、[#60190](https://github.com/PaddlePaddle/Paddle/pull/60190)、[#63138](https://github.com/PaddlePaddle/Paddle/pull/63138)、[#62517](https://github.com/PaddlePaddle/Paddle/pull/62517)） +3. Support automatic search function of Schedule configuration and AOT offline saving mechanism to accelerate the performance of subgraph Kernel.（[#64271](https://github.com/PaddlePaddle/Paddle/pull/64271)、[#64588](https://github.com/PaddlePaddle/Paddle/pull/64588)、[#64694](https://github.com/PaddlePaddle/Paddle/pull/64694)、[#64620](https://github.com/PaddlePaddle/Paddle/pull/64620)、[#64702](https://github.com/PaddlePaddle/Paddle/pull/64702)、[#63086](https://github.com/PaddlePaddle/Paddle/pull/63086)） +4. Support OptimizeReductionTactic optimization strategy to improve kernel performance in Reduce scenarios.（[#6066](https://github.com/PaddlePaddle/Paddle/pull/60661)、[#61363](https://github.com/PaddlePaddle/Paddle/pull/61363)、[#60881](https://github.com/PaddlePaddle/Paddle/pull/60881)、[#63859](https://github.com/PaddlePaddle/Paddle/pull/63859)） +5. Enhance DCE Pass function, remove redundant If/For branch codes and improve execution efficiency.（[#61682](https://github.com/PaddlePaddle/Paddle/pull/61682)） +6. Add support for FuseParallelMatmulPass Pass, integrate multiple Matmul operators to achieve acceleration.（[#63623](https://github.com/PaddlePaddle/Paddle/pull/63623)） -#### Enhanced semi-auto parallel for static graphs +### Bug Fixing -- Added Sequence Parallel Parallelism; added FThenB, Interleaved 1F1B, Eager 1F1B, VPP and other scheduling modes for Pipeline Parallel, and supported the hybrid parallel between the above new parallelism and original parallelism. Supported visualization of pipeline scheduling. Upgraded gradient synchronization mechanism which supports gradient synchronization when data is sharded on any broadcast dimension. [#57605](https://github.com/PaddlePaddle/Paddle/pull/57605),[#54727](https://github.com/PaddlePaddle/Paddle/pull/54727),[#54409](https://github.com/PaddlePaddle/Paddle/pull/54409),[#54787](https://github.com/PaddlePaddle/Paddle/pull/54787),[#58313](https://github.com/PaddlePaddle/Paddle/pull/58313),[#59179](https://github.com/PaddlePaddle/Paddle/pull/59179),[#59416](https://github.com/PaddlePaddle/Paddle/pull/59416),[#59719](https://github.com/PaddlePaddle/Paddle/pull/59719),[#59822](https://github.com/PaddlePaddle/Paddle/pull/59822),[#59057](https://github.com/PaddlePaddle/Paddle/pull/59057),[#59522](https://github.com/PaddlePaddle/Paddle/pull/59522),[#57061](https://github.com/PaddlePaddle/Paddle/pull/57061) -- Adapted the executor to PIR, and supported PIR optimization Pass. In distributed scenarios, supports fuse_linear fuse, and etc., to improve performance. [#58459](https://github.com/PaddlePaddle/Paddle/pull/58459),[#58528](https://github.com/PaddlePaddle/Paddle/pull/58528),[#55555](https://github.com/PaddlePaddle/Paddle/pull/55555),[#59757](https://github.com/PaddlePaddle/Paddle/pull/59757),[#59102](https://github.com/PaddlePaddle/Paddle/pull/59102),[#57917](https://github.com/PaddlePaddle/Paddle/pull/57917) -- Upgraded underlying architecture: upgraded the executor to reuse the results of data-flow dependency analysis and static kernel selection; upgraded entire graph based sharding completion mechanism, to switch to new sharding derivation rules and support some long-tailed cases; optimized the support of control flow under distributed static graph to adapt to more scenarios; reduced the graph compilation time and refined error message format to improve user experience. [#55389](https://github.com/PaddlePaddle/Paddle/pull/55389),[#55650](https://github.com/PaddlePaddle/Paddle/pull/55650),[#54938](https://github.com/PaddlePaddle/Paddle/pull/54938),[#57447](https://github.com/PaddlePaddle/Paddle/pull/57447),[#57751](https://github.com/PaddlePaddle/Paddle/pull/57751),[#57742](https://github.com/PaddlePaddle/Paddle/pull/57742),[#59524](https://github.com/PaddlePaddle/Paddle/pull/59524),[#59526](https://github.com/PaddlePaddle/Paddle/pull/59526),[#58669](https://github.com/PaddlePaddle/Paddle/pull/58669),[#57616](https://github.com/PaddlePaddle/Paddle/pull/57616),[#56511](https://github.com/PaddlePaddle/Paddle/pull/56511),[#55727](https://github.com/PaddlePaddle/Paddle/pull/55727),[#58906](https://github.com/PaddlePaddle/Paddle/pull/58906),[#56016](https://github.com/PaddlePaddle/Paddle/pull/56016),[#54897](https://github.com/PaddlePaddle/Paddle/pull/54897) -- Optimized the gpu memory usage in static graph mode, and added refined recomputing strategy; optimized auto mixed precision pass, and allows users to manually specify auto-cast region and fixed some bugs; supports parallel computation of cross-entropy; supports fusion operators such as scaled_dot_product_attention, fuse_rope, etc.; performs scheduling optimization to support better overlap between communication and computation in tensor parallelism and pipeline parallelsim. [#58421](https://github.com/PaddlePaddle/Paddle/pull/58421),[#58533](https://github.com/PaddlePaddle/Paddle/pull/58533),[#59498](https://github.com/PaddlePaddle/Paddle/pull/59498),[#59498](https://github.com/PaddlePaddle/Paddle/pull/59498),[#59187](https://github.com/PaddlePaddle/Paddle/pull/59187),[#59188](https://github.com/PaddlePaddle/Paddle/pull/59188),[#58172](https://github.com/PaddlePaddle/Paddle/pull/58172),[#58628](https://github.com/PaddlePaddle/Paddle/pull/58628),[#56185](https://github.com/PaddlePaddle/Paddle/pull/56185),[#56696](https://github.com/PaddlePaddle/Paddle/pull/56696),[#59497](https://github.com/PaddlePaddle/Paddle/pull/59497),[#58304](https://github.com/PaddlePaddle/Paddle/pull/58304),[#58977](https://github.com/PaddlePaddle/Paddle/pull/58977) +1. Fix the bug when Lowering some special operators to the compiler, to improve the end-to-end user experience.（[#60800](https://github.com/PaddlePaddle/Paddle/pull/60800)、[#64720](https://github.com/PaddlePaddle/Paddle/pull/64720)、[#62593](https://github.com/PaddlePaddle/Paddle/pull/62593)、[#62661](https://github.com/PaddlePaddle/Paddle/pull/62661)、[#64626](https://github.com/PaddlePaddle/Paddle/pull/64626)、[#63320](https://github.com/PaddlePaddle/Paddle/pull/63320)、[#64581](https://github.com/PaddlePaddle/Paddle/pull/64581)、[#61608](https://github.com/PaddlePaddle/Paddle/pull/61608)、[#64135](https://github.com/PaddlePaddle/Paddle/pull/64135)、[#64659](https://github.com/PaddlePaddle/Paddle/pull/64659)、[#62391](https://github.com/PaddlePaddle/Paddle/pull/62391)、[#62490](https://github.com/PaddlePaddle/Paddle/pull/62490)、[#63891](https://github.com/PaddlePaddle/Paddle/pull/63891)、[#64529](https://github.com/PaddlePaddle/Paddle/pull/64529)） +2. Fix a bug in the symbolic derivation logic of some operators.（[#62141](https://github.com/PaddlePaddle/Paddle/pull/62141)、[#62376](https://github.com/PaddlePaddle/Paddle/pull/62376)、[#62941](https://github.com/PaddlePaddle/Paddle/pull/62941)、[#63322](https://github.com/PaddlePaddle/Paddle/pull/63322)、[#64672](https://github.com/PaddlePaddle/Paddle/pull/64672)、[#64407](https://github.com/PaddlePaddle/Paddle/pull/64407)、[#60241](https://github.com/PaddlePaddle/Paddle/pull/60241)、[#60440](https://github.com/PaddlePaddle/Paddle/pull/60440)、[#62503](https://github.com/PaddlePaddle/Paddle/pull/62503)、[#62997](https://github.com/PaddlePaddle/Paddle/pull/62997)、[#63169](https://github.com/PaddlePaddle/Paddle/pull/63169)、[#61098](https://github.com/PaddlePaddle/Paddle/pull/61098)、[#63973](https://github.com/PaddlePaddle/Paddle/pull/63973)、[#62248](https://github.com/PaddlePaddle/Paddle/pull/62248)、[#62321](https://github.com/PaddlePaddle/Paddle/pull/62321)、[#63755](https://github.com/PaddlePaddle/Paddle/pull/63755)、[#63917](https://github.com/PaddlePaddle/Paddle/pull/63917)、[#63903](https://github.com/PaddlePaddle/Paddle/pull/63903)、[#64173](https://github.com/PaddlePaddle/Paddle/pull/64173)、[#64525](https://github.com/PaddlePaddle/Paddle/pull/64525)、[#64615](https://github.com/PaddlePaddle/Paddle/pull/64615)、[#62247](https://github.com/PaddlePaddle/Paddle/pull/62247)、[#62455](https://github.com/PaddlePaddle/Paddle/pull/62455)、[#62898](https://github.com/PaddlePaddle/Paddle/pull/62898)、[#62867](https://github.com/PaddlePaddle/Paddle/pull/62867)、[#63608](https://github.com/PaddlePaddle/Paddle/pull/63608)、[#63789](https://github.com/PaddlePaddle/Paddle/pull/63789)、[#64085](https://github.com/PaddlePaddle/Paddle/pull/64085)、[#64136](https://github.com/PaddlePaddle/Paddle/pull/64136)、[#64181](https://github.com/PaddlePaddle/Paddle/pull/64181)） +3. Fix the problems of compiler execution errors under dynamic and static shapes, to improve the robustness of the framework mechanism.（[#60813](https://github.com/PaddlePaddle/Paddle/pull/60813)、[#61877](https://github.com/PaddlePaddle/Paddle/pull/61877)、[#61909](https://github.com/PaddlePaddle/Paddle/pull/61909)、[#62954](https://github.com/PaddlePaddle/Paddle/pull/62954)、[#63614](https://github.com/PaddlePaddle/Paddle/pull/63614)、[#60339](https://github.com/PaddlePaddle/Paddle/pull/60339)、[#60623](https://github.com/PaddlePaddle/Paddle/pull/60623)、[#60658](https://github.com/PaddlePaddle/Paddle/pull/60658)、[#60669](https://github.com/PaddlePaddle/Paddle/pull/60669)、[#58823](https://github.com/PaddlePaddle/Paddle/pull/58823)、[#62483](https://github.com/PaddlePaddle/Paddle/pull/62483)、[#62742](https://github.com/PaddlePaddle/Paddle/pull/62742)、[#61797](https://github.com/PaddlePaddle/Paddle/pull/61797)、[#63411](https://github.com/PaddlePaddle/Paddle/pull/63411)、[#64077](https://github.com/PaddlePaddle/Paddle/pull/64077)、[#62736](https://github.com/PaddlePaddle/Paddle/pull/62736)、[#62390](https://github.com/PaddlePaddle/Paddle/pull/62390)、[#63689](https://github.com/PaddlePaddle/Paddle/pull/63689)） -#### AutoTuner +### Deprecated Features -This release implements a profiling based automatic search and tuning tool named AutoTuner for parallel strategies, to automatically combine parallel and optimization strategies. Users can select effective combination configurations for experiments, and AutoTuner will search for the optimal configuration for large model training and inference given the model and hardware specification. In addition, AutoTuner implements a variety of pruning methods, including gpu memory modelling based pruning, so the search space and search time can be significantly reduced. [#54460](https://github.com/PaddlePaddle/Paddle/pull/54460),[#54668](https://github.com/PaddlePaddle/Paddle/pull/54668),[#59794](https://github.com/PaddlePaddle/Paddle/pull/59794),[#59727](https://github.com/PaddlePaddle/Paddle/pull/59727),[#59782](https://github.com/PaddlePaddle/Paddle/pull/59782),[#54834](https://github.com/PaddlePaddle/Paddle/pull/54834),[#58127](https://github.com/PaddlePaddle/Paddle/pull/58127),[#56968](https://github.com/PaddlePaddle/Paddle/pull/56968),[#55466](https://github.com/PaddlePaddle/Paddle/pull/55466),[#56939](https://github.com/PaddlePaddle/Paddle/pull/56939),[#58183](https://github.com/PaddlePaddle/Paddle/pull/58183),[#58314](https://github.com/PaddlePaddle/Paddle/pull/58314),[#55499](https://github.com/PaddlePaddle/Paddle/pull/55499),[#59748](https://github.com/PaddlePaddle/Paddle/pull/59748) +1. Remove useless symbol-related components such as adt DimExpr, SymbolicDimExpr and ShapedTypeInterface.（[#60901](https://github.com/PaddlePaddle/Paddle/pull/60901)、[#60933](https://github.com/PaddlePaddle/Paddle/pull/60933)、[#60744](https://github.com/PaddlePaddle/Paddle/pull/60744)、[#64176](https://github.com/PaddlePaddle/Paddle/pull/64176)、[#64140](https://github.com/PaddlePaddle/Paddle/pull/64140)） +2. Remove the old Group Cluster, and the front-end representation under the old IR, to improve the simplicity of the architecture.（[#63683](https://github.com/PaddlePaddle/Paddle/pull/63683)、[#64630](https://github.com/PaddlePaddle/Paddle/pull/64630)、[#61380](https://github.com/PaddlePaddle/Paddle/pull/61380)） -### Operator library +## Auto-Parallel Architecture -#### Incompatible upgrade +In order to further enhance the usability of the Auto Parallel architecture in large model training scenarios, PaddlePaddle has improved the Auto Parallel functionality in dynamic-static graphs, including the newly added parallel strategies such as sharding parallelism and interleaved pipeline parallelism, including support of lazy initialization parameters. Add and enhance the SPMD derivation rules for some of the operators. The auto-parallel architecture has been comprehensively verified in a number of mainstream large language models. Meanwhile, in order to build the new 3.0 architecture of PaddlePaddle, the static graph auto parallel architecture has been comprehensively upgraded based on PIR, the new generation intermediate representation of Paddlepaddle. It introduces DistDialect for distributed related components, and natively support DistAttr and DistTensor in the computation graph representation, and smooth the transfom from static to dynmaic graph, further enhance the unity of auto parallel usage in dynamic and static graph mode. Finally, a number of performance optimization technologies have been added and improved, including zero bubble pipeline scheduling strategy, achieving the same or even better end-to-end training performance compared to the manual parallelism on typical large models such as Llama-2 13B/70B. -In order to improve maintainability of PaddlePaddle framework, some deprecated operators in the framework (e.g. diag_v1, isfinite_v1, pad2d_v1, etc.) have been removed, and models using these operators saved through the PaddlePaddle 1.x training will not be able to infer on new version of PaddlePaddle. [#57895](https://github.com/PaddlePaddle/Paddle/pull/57895),[#57892](https://github.com/PaddlePaddle/Paddle/pull/57892),[#57898](https://github.com/PaddlePaddle/Paddle/pull/57898),[#57730](https://github.com/PaddlePaddle/Paddle/pull/57730),[#57732](https://github.com/PaddlePaddle/Paddle/pull/57732),[#57810](https://github.com/PaddlePaddle/Paddle/pull/57810),[#57884](https://github.com/PaddlePaddle/Paddle/pull/57884),[#57794](https://github.com/PaddlePaddle/Paddle/pull/57794),[#57926](https://github.com/PaddlePaddle/Paddle/pull/57926),[#57925](https://github.com/PaddlePaddle/Paddle/pull/57925),[#57807](https://github.com/PaddlePaddle/Paddle/pull/57807),[#57808](https://github.com/PaddlePaddle/Paddle/pull/57808) +### Function Improvements -#### Operator library enhancements +- Add the dtensor_from_local interface for creating DistTensor from local tensor after sharding (correspondingly, shard_tensor is the created DistTensor from global tensor before sharding). [#60206](https://github.com/PaddlePaddle/Paddle/pull/60206) +- Add the unshard_tensor interface to convert DistTensor to global tensor, which is reciprocal operation to shard_tensor. [#60272](https://github.com/PaddlePaddle/Paddle/pull/60272) +- To reduce the GPU memory usage during training, add Sharding parallelism, and support stage1, stage2 and stage3 modes. [#61926](https://github.com/PaddlePaddle/Paddle/pull/61926), [#62711](https://github.com/PaddlePaddle/Paddle/pull/62711), [#62486](https://github.com/PaddlePaddle/Paddle/pull/62486), [#62230](https://github.com/PaddlePaddle/Paddle/pull/62230) +- To solve the problem of insufficient GPU memory when initializing parameters first and then sharding them, add the LazyInit function, to support slicing parameters first and then initializing them. [#60316](https://github.com/PaddlePaddle/Paddle/pull/60316), [#60441](https://github.com/PaddlePaddle/Paddle/pull/60441), [#60563](https://github.com/PaddlePaddle/Paddle/pull/60563), [#61792](https://github.com/PaddlePaddle/Paddle/pull/61792) +- In order to reduce the bubble of pipeline parallel, add the interleaved pipeline parallel parallelism has been added, and support automatically converting the pipeline parallel of the user's networking to interleaved pipeline parallel through configuration, so that the user doesn't need to perform complicated marking in the networking. [#59751](https://github.com/PaddlePaddle/Paddle/pull/59751), [#60050](https://github.com/PaddlePaddle/Paddle/pull/60050), [#60467](https://github.com/PaddlePaddle/Paddle/pull/60467), [#60868](https://github.com/PaddlePaddle/Paddle/pull/60868), [#60187](https://github.com/PaddlePaddle/Paddle/pull/60187), [#62884](https://github.com/PaddlePaddle/Paddle/pull/62884), [#60560](https://github.com/PaddlePaddle/Paddle/pull/60560), [#61541](https://github.com/PaddlePaddle/Paddle/pull/61541) +- Add the SPMD derivation rules for stack, gather, scatter_grad, cumsum, unbind, swiglu, and fused_linear_param_grad. Improve and optimize the implementation of fused_rope, reshape, flatten, fused_rms_norm, slice, tile, flash_attn, cross_entropy and other operator slice derivation rules, to solve the problem of incompatibility in some of the model networking scenarios. [#62720](https://github.com/PaddlePaddle/Paddle/pull/62720), [#64202](https://github.com/PaddlePaddle/Paddle/pull/64202), [#63361](https://github.com/PaddlePaddle/Paddle/pull/63361), [#63290](https://github.com/PaddlePaddle/Paddle/pull/63290), [#61460](https://github.com/PaddlePaddle/Paddle/pull/61460), [#59986](https://github.com/PaddlePaddle/Paddle/pull/59986), [#61184](https://github.com/PaddlePaddle/Paddle/pull/61184), [#60144](https://github.com/PaddlePaddle/Paddle/pull/60144), [#62525](https://github.com/PaddlePaddle/Paddle/pull/62525), [#62053](https://github.com/PaddlePaddle/Paddle/pull/62053), [#60709](https://github.com/PaddlePaddle/Paddle/pull/60709), [#60111](https://github.com/PaddlePaddle/Paddle/pull/60111), [#63681](https://github.com/PaddlePaddle/Paddle/pull/63681), [#62180](https://github.com/PaddlePaddle/Paddle/pull/62180), [#60794](https://github.com/PaddlePaddle/Paddle/pull/60794), [#60632](https://github.com/PaddlePaddle/Paddle/pull/60632), [#62439](https://github.com/PaddlePaddle/Paddle/pull/62439) +- Improve the distributed checkpoint storage and loading function, support master_weights strategy, and fix the random hanging problem. [#60027](https://github.com/PaddlePaddle/Paddle/pull/60027), [#59872](https://github.com/PaddlePaddle/Paddle/pull/59872) +- In order to support the auto parallel of arbitrary shape tensor, add the non-uniform tensor sharding feature. [#62611](https://github.com/PaddlePaddle/Paddle/pull/62611), [#61432](https://github.com/PaddlePaddle/Paddle/pull/61432) +- In order to support users to use customized operators in the auto parallel networking, support user registration outside the framework to customize the SPMD derivation rules for this class of operators. [#60509](https://github.com/PaddlePaddle/Paddle/pull/60509) +- Improve the slice SPMD rule, and support the transition from any state to replicate and from replicate state to any state. [#60281](https://github.com/PaddlePaddle/Paddle/pull/60281), [#59869](https://github.com/PaddlePaddle/Paddle/pull/59869) +- Add MoE expert parallelism (experimental). Currently, only dynamic graph auto parallel is supported. [#63904](https://github.com/PaddlePaddle/Paddle/pull/63904) +- Fix some process adaptation problems of auto parallel and dynamic diagram execution, and dynamic to static. [#60214](https://github.com/PaddlePaddle/Paddle/pull/60214), [#60546](https://github.com/PaddlePaddle/Paddle/pull/60546), [#62082](https://github.com/PaddlePaddle/Paddle/pull/62082), [#61313](https://github.com/PaddlePaddle/Paddle/pull/61313), [#61840](https://github.com/PaddlePaddle/Paddle/pull/61840), [#60614](https://github.com/PaddlePaddle/Paddle/pull/60614), [#60234](https://github.com/PaddlePaddle/Paddle/pull/60234), [#64813](https://github.com/PaddlePaddle/Paddle/pull/64813), [#61606](https://github.com/PaddlePaddle/Paddle/pull/61606), [#63405](https://github.com/PaddlePaddle/Paddle/pull/63405), [#64334](https://github.com/PaddlePaddle/Paddle/pull/64334), [#60504](https://github.com/PaddlePaddle/Paddle/pull/60504) -- The complex kernels of PaddlePaddle PHI operator library have been further enhanced, and a total of 40+ complex kernels have been added. [#55380](https://github.com/PaddlePaddle/Paddle/pull/55380), [#56349](https://github.com/PaddlePaddle/Paddle/pull/56349), [#56412](https://github.com/PaddlePaddle/Paddle/pull/56412), [#56323](https://github.com/PaddlePaddle/Paddle/pull/56323), [#56723](https://github.com/PaddlePaddle/Paddle/pull/56723), [#56457](https://github.com/PaddlePaddle/Paddle/pull/56457), [#56903](https://github.com/PaddlePaddle/Paddle/pull/56903)[#56914](https://github.com/PaddlePaddle/Paddle/pull/56914), [#57116](https://github.com/PaddlePaddle/Paddle/pull/57116), [#56048](https://github.com/PaddlePaddle/Paddle/pull/56048), [#57244](https://github.com/PaddlePaddle/Paddle/pull/57244), [#57639](https://github.com/PaddlePaddle/Paddle/pull/57639), [#57638](https://github.com/PaddlePaddle/Paddle/pull/57638), [#57540](https://github.com/PaddlePaddle/Paddle/pull/57540), [#58545](https://github.com/PaddlePaddle/Paddle/pull/58545), [#58336](https://github.com/PaddlePaddle/Paddle/pull/58336), [#58532](https://github.com/PaddlePaddle/Paddle/pull/58532), [#58839](https://github.com/PaddlePaddle/Paddle/pull/58839), [#59079](https://github.com/PaddlePaddle/Paddle/pull/59079), [#59277](https://github.com/PaddlePaddle/Paddle/pull/59277), [#59122](https://github.com/PaddlePaddle/Paddle/pull/59122), [#57058](https://github.com/PaddlePaddle/Paddle/pull/57058) +### Performance Optimization -- Optimized and added XPU kernels for some operators, and enhanced the support for data types such as bfloat16 on XPU kernel. [#54478](https://github.com/PaddlePaddle/Paddle/pull/54478), [#57740](https://github.com/PaddlePaddle/Paddle/pull/57740), [#58346](https://github.com/PaddlePaddle/Paddle/pull/58346), [#58456](https://github.com/PaddlePaddle/Paddle/pull/58456), [#58662](https://github.com/PaddlePaddle/Paddle/pull/58662), [#59066](https://github.com/PaddlePaddle/Paddle/pull/59066), [#59263](https://github.com/PaddlePaddle/Paddle/pull/59263)), [#59375](https://github.com/PaddlePaddle/Paddle/pull/59375), [#59505](https://github.com/PaddlePaddle/Paddle/pull/59505), [#59653](https://github.com/PaddlePaddle/Paddle/pull/59653), [#55001](https://github.com/PaddlePaddle/Paddle/pull/55001), [#57272](https://github.com/PaddlePaddle/Paddle/pull/57272), [#56169](https://github.com/PaddlePaddle/Paddle/pull/56169), [#59454](https://github.com/PaddlePaddle/Paddle/pull/59454), [#59480](https://github.com/PaddlePaddle/Paddle/pull/59480), [#55914](https://github.com/PaddlePaddle/Paddle/pull/55914), [#54758](https://github.com/PaddlePaddle/Paddle/pull/54758), [#54827](https://github.com/PaddlePaddle/Paddle/pull/54827), [#58364](https://github.com/PaddlePaddle/Paddle/pull/58364), [#58419](https://github.com/PaddlePaddle/Paddle/pull/58419), [#58982](https://github.com/PaddlePaddle/Paddle/pull/58982), [#57216](https://github.com/PaddlePaddle/Paddle/pull/57216), [#59166](https://github.com/PaddlePaddle/Paddle/pull/59166), [#55033](https://github.com/PaddlePaddle/Paddle/pull/55033), [#55375](https://github.com/PaddlePaddle/Paddle/pull/55375), [#58805](https://github.com/PaddlePaddle/Paddle/pull/58805), [#59389](https://github.com/PaddlePaddle/Paddle/pull/59389), [#57077](https://github.com/PaddlePaddle/Paddle/pull/57077), [#55166](https://github.com/PaddlePaddle/Paddle/pull/55166), [#56773](https://github.com/PaddlePaddle/Paddle/pull/56773) +- In order to reduce the bubble in pipeline parallel, support the reverse computation of parameter and activation splitting in backward, and add zero bubble pipeline scheduling strategy to improve the training performance. [#62865](https://github.com/PaddlePaddle/Paddle/pull/62865), [#62737](https://github.com/PaddlePaddle/Paddle/pull/62737), [#64534](https://github.com/PaddlePaddle/Paddle/pull/64534), +- To improve the performance of sequence parallel, perform fusion on related communication operations and computation operations, and optimize redundant transopse operations. [#64807](https://github.com/PaddlePaddle/Paddle/pull/64807), [#63948](https://github.com/PaddlePaddle/Paddle/pull/63948), [#64316](https://github.com/PaddlePaddle/Paddle/pull/64316), [#64119](https://github.com/PaddlePaddle/Paddle/pull/64119) +- Optimize the time consumption of auto parallel graph optimization for static graphs, to reduce the delay from the start of training to the completion of the first step. [#59912](https://github.com/PaddlePaddle/Paddle/pull/59912), [#61817](https://github.com/PaddlePaddle/Paddle/pull/61817), [#60022](https://github.com/PaddlePaddle/Paddle/pull/60022), [#60125](https://github.com/PaddlePaddle/Paddle/pull/60125) +- Optimize the time consumption of related communication operations in hybrid parallel scenarios. [#62157](https://github.com/PaddlePaddle/Paddle/pull/62157), [#61622](https://github.com/PaddlePaddle/Paddle/pull/61622) +- Optimize the redundant video memory consumption of parameters under the auto parallel dynamic-to-static. [#62746](https://github.com/PaddlePaddle/Paddle/pull/62746) +- Improve the hybrid precision training function of auto parallel, support the setting of local auto_cast and black/white list, support master grad function, and adapt to different parallel strategies. [60158](https://github.com/PaddlePaddle/Paddle/pull/60158), [#59987](https://github.com/PaddlePaddle/Paddle/pull/59987), [#62629](https://github.com/PaddlePaddle/Paddle/pull/62629), [#60385](https://github.com/PaddlePaddle/Paddle/pull/60385), [#62015](https://github.com/PaddlePaddle/Paddle/pull/62015), [#60514](https://github.com/PaddlePaddle/Paddle/pull/60514), [#61221](https://github.com/PaddlePaddle/Paddle/pull/61221), [#60779](https://github.com/PaddlePaddle/Paddle/pull/60779), [#63228](https://github.com/PaddlePaddle/Paddle/pull/63228) +- Optimize non-essential casts caused by type promotion and amp to improve performance. [#63293](https://github.com/PaddlePaddle/Paddle/pull/63293), [#63228](https://github.com/PaddlePaddle/Paddle/pull/63228) -- Added some operators for optimizing large model training and inference performance. [#55758](https://github.com/PaddlePaddle/Paddle/pull/55758), [#54998](https://github.com/PaddlePaddle/Paddle/pull/54998), [#55400](https://github.com/PaddlePaddle/Paddle/pull/55400), [#54630](https://github.com/PaddlePaddle/Paddle/pull/54630), [#55969](https://github.com/PaddlePaddle/Paddle/pull/55969), [#55026](https://github.com/PaddlePaddle/Paddle/pull/55026), [#58986](https://github.com/PaddlePaddle/Paddle/pull/58986) +### Upgrade Static Graph Auto Parallel Architecture -- Improved mechanism of Tensor Strided in the operator library. [#59422](https://github.com/PaddlePaddle/Paddle/pull/59422), [#59325](https://github.com/PaddlePaddle/Paddle/pull/59325), [#56863](https://github.com/PaddlePaddle/Paddle/pull/56863), [#56882](https://github.com/PaddlePaddle/Paddle/pull/56882), [#56947](https://github.com/PaddlePaddle/Paddle/pull/56947) +- Based on the new generation of Intermediate Representation(PIR), add the new DistDialect, natively supporting DistAttr and DistTensor in computation graph representation, and realizing the direct binding of distributed attributes between tensor or operator, which making the auto-parallel architecture more simple and unified. [#63828](https://github.com/PaddlePaddle/Paddle/pull/63828), [#64299](https://github.com/PaddlePaddle/Paddle/pull/64299), [#63870](https://github.com/PaddlePaddle/Paddle/pull/63870), [#64144](https://github.com/PaddlePaddle/Paddle/pull/64144), [#62524](https://github.com/PaddlePaddle/Paddle/pull/62524), [#62630](https://github.com/PaddlePaddle/Paddle/pull/62630), [#62897](https://github.com/PaddlePaddle/Paddle/pull/62897), [#60478](https://github.com/PaddlePaddle/Paddle/pull/60478), [#60574](https://github.com/PaddlePaddle/Paddle/pull/60574), [#63876](https://github.com/PaddlePaddle/Paddle/pull/63876), [#63798](https://github.com/PaddlePaddle/Paddle/pull/63798), [#62560](https://github.com/PaddlePaddle/Paddle/pull/62560), [#63676](https://github.com/PaddlePaddle/Paddle/pull/63676) +- Improve APIs such as shard_tensor, reshard, and to_static, to support users to convert the dynamic graph model networking directly into PIR static computation graph for better performance. [#62945](https://github.com/PaddlePaddle/Paddle/pull/62945), [#62356](https://github.com/PaddlePaddle/Paddle/pull/62356), [#60175](https://github.com/PaddlePaddle/Paddle/pull/60175), [#62654](https://github.com/PaddlePaddle/Paddle/pull/62654), [#63347](https://github.com/PaddlePaddle/Paddle/pull/63347) +- Optimize the auto-parallel graph optimization compilation process, and reduce the compilation and optimization time of static graphs by refactoring and optimizing the procedure of computation graph parallelization and communication resolution. [#64137](https://github.com/PaddlePaddle/Paddle/pull/64137), [#62201](https://github.com/PaddlePaddle/Paddle/pull/62201), [#64143](https://github.com/PaddlePaddle/Paddle/pull/64143), [#62560](https://github.com/PaddlePaddle/Paddle/pull/62560) +- Optimize the procedure of the SPMD derivation in static graphs to achieve the consistency results under dynamic-static graphs, which improves the unity and stability of the architecture. [#62659](https://github.com/PaddlePaddle/Paddle/pull/62659), [#62547](https://github.com/PaddlePaddle/Paddle/pull/62547), [#63117](https://github.com/PaddlePaddle/Paddle/pull/63117), [#63434](https://github.com/PaddlePaddle/Paddle/pull/63434), [#63770](https://github.com/PaddlePaddle/Paddle/pull/63770), [#64361](https://github.com/PaddlePaddle/Paddle/pull/64361), [#63073](https://github.com/PaddlePaddle/Paddle/pull/63073) +- Upgrade the implementation of Reshard conversion in static graphs, and use consistent conversion rules under dynamic-static graphs to ensure the consistency of the execution logic and results of tensor reshard conversion in dynamic-static graphs, so as to improve user experience. [#62718](https://github.com/PaddlePaddle/Paddle/pull/62718), [#62694](https://github.com/PaddlePaddle/Paddle/pull/62694), [#60215](https://github.com/PaddlePaddle/Paddle/pull/60215), [#63362](https://github.com/PaddlePaddle/Paddle/pull/63362), [#63072](https://github.com/PaddlePaddle/Paddle/pull/63072), [#63962](https://github.com/PaddlePaddle/Paddle/pull/63962), [#64223](https://github.com/PaddlePaddle/Paddle/pull/64223), [#61796](https://github.com/PaddlePaddle/Paddle/pull/61796), [#64465](https://github.com/PaddlePaddle/Paddle/pull/64465), [#64623](https://github.com/PaddlePaddle/Paddle/pull/64623), [#64418](https://github.com/PaddlePaddle/Paddle/pull/64418) -- Optimized function implementation and template function in some kernels to reduce size of complied library package. [#57083](https://github.com/PaddlePaddle/Paddle/pull/57083), [#57299](https://github.com/PaddlePaddle/Paddle/pull/57299), [#57261](https://github.com/PaddlePaddle/Paddle/pull/57261), [#57290](https://github.com/PaddlePaddle/Paddle/pull/57290), [#57118](https://github.com/PaddlePaddle/Paddle/pull/57118), [#57551](https://github.com/PaddlePaddle/Paddle/pull/57551), [#57509](https://github.com/PaddlePaddle/Paddle/pull/57509), [#57558](https://github.com/PaddlePaddle/Paddle/pull/57558), [#57064](https://github.com/PaddlePaddle/Paddle/pull/57064), [#57365](https://github.com/PaddlePaddle/Paddle/pull/57365), [#57327](https://github.com/PaddlePaddle/Paddle/pull/57327), [#57603](https://github.com/PaddlePaddle/Paddle/pull/57603), [#57671](https://github.com/PaddlePaddle/Paddle/pull/57671), [#57672](https://github.com/PaddlePaddle/Paddle/pull/57672), [#57631](https://github.com/PaddlePaddle/Paddle/pull/57631), [#57082](https://github.com/PaddlePaddle/Paddle/pull/57082), [#57721](https://github.com/PaddlePaddle/Paddle/pull/57721), [#57823](https://github.com/PaddlePaddle/Paddle/pull/57823), [#57821](https://github.com/PaddlePaddle/Paddle/pull/57821), [#57815](https://github.com/PaddlePaddle/Paddle/pull/57815), [#57822](https://github.com/PaddlePaddle/Paddle/pull/57822), [#57541](https://github.com/PaddlePaddle/Paddle/pull/57541), [#57817](https://github.com/PaddlePaddle/Paddle/pull/57817), [#57838](https://github.com/PaddlePaddle/Paddle/pull/57838) +### Automatic Search and Tuning of Training Strategies -#### Fixed bug +In order to improve the ease of use of the training strategy automatic search and tuning tool (AutoTuner), support user-defined search items, support for setting the priority of search items, and support for user-configured illegal strategy combinations, to comprehensively enhance the error reporting information in the runtime and post-run logs, and support for AutoTuner on NPU devices. [#60101](https://github.com/PaddlePaddle/Paddle/pull/60101), [#60294](https://github.com/PaddlePaddle/Paddle/pull/60294), [#61898](https://github.com/PaddlePaddle/Paddle/pull/61898), [#60248](https://github.com/PaddlePaddle/Paddle/pull/60248), [#60417](https://github.com/PaddlePaddle/Paddle/pull/60417), [#60954](https://github.com/PaddlePaddle/Paddle/pull/60954), [#61499](https://github.com/PaddlePaddle/Paddle/pull/61499), [#62724](https://github.com/PaddlePaddle/Paddle/pull/62724), [#60954](https://github.com/PaddlePaddle/Paddle/pull/60954), [#63693](https://github.com/PaddlePaddle/Paddle/pull/63693), [#62853](https://github.com/PaddlePaddle/Paddle/pull/62853), [#62984](https://github.com/PaddlePaddle/Paddle/pull/62984) -- Fixed some bugs with CUDA 12 adaptation of the PaddlePaddle framework. [#54640](https://github.com/PaddlePaddle/Paddle/pull/54640), [#57820](https://github.com/PaddlePaddle/Paddle/pull/57820), [#58958](https://github.com/PaddlePaddle/Paddle/pull/58958), [#58179](https://github.com/PaddlePaddle/Paddle/pull/58179), [#55594](https://github.com/PaddlePaddle/Paddle/pull/55594) +## Cuda Training Performance Optimization -### CUDA +This upgrade achieves the improvement of large model training efficiency from multiple perspectives, such as operator computation efficiency, distributed communication optimization, and video memory optimization. -#### New features +### Function Improvements -- Added debugging class API paddle.amp.debugging.check_check_numerics. Calculated and returned number of outliers (NaN, Inf) and zero elements in this Tensor value. [#54301](https://github.com/PaddlePaddle/Paddle/pull/54301) -- Added fused_rope fusion operator to accelerate LLaMA class large model training.[#54351](https://github.com/PaddlePaddle/Paddle/pull/54351) -- Updated CUDNN Frontend API version to v0.9.1 and added fused_scale_bias_add_relu fusion operator to accelerate ResNet networks. Note this feature is in experimental period and is disabled by default. [#58367](https://github.com/PaddlePaddle/Paddle/pull/58367), [#54949](https://github.com/PaddlePaddle/Paddle/pull/54949), [#58504](https://github.com/PaddlePaddle/Paddle/pull/58504) -- Based on Flash-Attention v2, added Tensor-like Mask function support. Inverse operator supports deterministic computation for debugging. [#57276](https://github.com/PaddlePaddle/Paddle/pull/57276), [#56363](https://github.com/PaddlePaddle/Paddle/pull/56363) -- Modified sparse conv3d backend implementation to support 2d shapes, avoiding front-end reshape overhead. [#54707](https://github.com/PaddlePaddle/Paddle/pull/54707) -- Added matmul_int8 operator. ([#55228](https://github.com/PaddlePaddle/Paddle/pull/55228)) +- Enhance the FlashAttention operator function, including support for NVIDIA SM90 GPU compilation, support for Group Query Attention, support for cuDNN access, support for QKV-packed form inputs, and so on. [#59820](https://github.com/PaddlePaddle/Paddle/pull/59820)，[#60776](https://github.com/PaddlePaddle/Paddle/pull/60776)，[#58680](https://github.com/PaddlePaddle/Paddle/pull/58680)，[#63289](https://github.com/PaddlePaddle/Paddle/pull/63289) +- In the Repeat_interleave operator, add support for BFloat16 data type. [#61854](https://github.com/PaddlePaddle/Paddle/pull/61854) +- For the issues of many interface parameters of ResNet-like models such as fused_scale_bias_add_relu, fused_scale_bias_relu_conv_bn, and fused_dconv_drelu_dbn, and the ease of use of operators, add the fuse_resunit pass, to support automatic fusion of the abovementioned operators, to achieve generic performance optimization. ([#59771](https://github.com/PaddlePaddle/Paddle/pull/59771)) -#### Function optimization +### Performance Improvement -- Optimized CUDA Graph’s support for random number operators.[#58310](https://github.com/PaddlePaddle/Paddle/pull/58310) -- Enhanced automatic mixed-precision training default functionality, including: - - Optimizing the experience of using automatic mixed precision training interface.[#58152](https://github.com/PaddlePaddle/Paddle/pull/58152),[#55364](https://github.com/PaddlePaddle/Paddle/pull/55364),[#57903](https://github.com/PaddlePaddle/Paddle/pull/57903) - - Added matrix computation class operators such as fused_attention, fused_feedforward, and fused_gemm_epilogue to framework's default whitelist, and unified default black and white list settings for dynamic and static graphs. [#55373](https://github.com/PaddlePaddle/Paddle/pull/55373), [#55713](https://github.com/PaddlePaddle/Paddle/pull/55713) - - The argsort, dist, erfinv, nanmedian, poisson operators and lamb optimizer operators support FP16 and BF16 low precision computing. [#51662](https://github.com/PaddlePaddle/Paddle/pull/51662), [#55105](https://github.com/PaddlePaddle/Paddle/pull/55105), [#55287](https://github.com/PaddlePaddle/Paddle/pull/55287), [#55824](https://github.com/PaddlePaddle/Paddle/pull/55824), [#56056](https://github.com/PaddlePaddle/Paddle/pull/56056), [#56184](https://github.com/PaddlePaddle/Paddle/pull/56184), [#55641](https://github.com/PaddlePaddle/Paddle/pull/55641) - - Fixed elementwise_max operator low-precision implementation. Changed to use FP32 type for numerical computing, and reduce precision loss. [#54799](https://github.com/PaddlePaddle/Paddle/pull/54799) - - Changed temporary result Tensor needed for Reduce class operator to FP32 type, to avoid precision loss caused by converting intermediate result to low precision. [#55709](https://github.com/PaddlePaddle/Paddle/pull/55709)) -- Optimized GPU codes for flip, roll & roll_grad, index_put & index_put_grad, etc. Removed unnecessary C++ templates to optimize compilation time and reduce compiled binary size without performance degradation. [#57309](https://github.com/PaddlePaddle/Paddle/pull/57309), [#57525](https://github.com/PaddlePaddle/Paddle/pull/57525) -- For the bernoulli operator, added a check on legitimacy of input probabilities. [#59174](https://github.com/PaddlePaddle/Paddle/pull/59174) +- To address the problem of large GPU memory consumption during the computation of SwiGLU activation module of the Llama models, add the SwiGLU fusion operator to save the memory consumption of intermediate variables, thus reducing the memory overhead during the training process of the large model, and reducing the recomputation to improve the performance. The performance of the Llama-70B model is improved by 9%. [#61508](https://github.com/PaddlePaddle/Paddle/pull/61508) +- To address the problem of higher percentage of communications in Sequence Parallel, realize the overlap between Sequence Parallel reverse process communication and Matmul computation, saving the end-to-end time consumption and improving the end-to-end performance of large model training scenarios by 1%~2%. [#62284](https://github.com/PaddlePaddle/Paddle/pull/62284)，[#63531](https://github.com/PaddlePaddle/Paddle/pull/63531) +- For the problem of slow training speed due to the need to divide by nranks after sharding reverse communications, support the fusion of reverse communication and division by nranks operation, and support the mode of ReduceScatter Average, to improve the performance of large model training. [#62623](https://github.com/PaddlePaddle/Paddle/pull/62623) +- For the problem of jitter training speed caused by the input data broadcasting process of the tensor model parallel process, fix the unnecessary synchronization between CPU and GPU in the data broadcasting, to ensure the stability of the training speed. [#60816](https://github.com/PaddlePaddle/Paddle/pull/60816) +- For the problem of low training speed due to the long parallel P2P communication time of pipelined models, realize the overlap of P2P communication and forward-backward computation. The end-to-end training performance of large models is improved by 2%~3%. [#61935](https://github.com/PaddlePaddle/Paddle/pull/61935),[#62051](https://github.com/PaddlePaddle/Paddle/pull/62051，[#62051](https://github.com/PaddlePaddle/Paddle/pull/62051)) +- For the problem of low inefficiency of bias gradient computation of fused_linear_param_grad_add operator, optimize the computation efficiency of bias gradient computation, and improve the end-to-end training performance of large model by 0.2%. [#63114](https://github.com/PaddlePaddle/Paddle/pull/63114) +- For the problem of long time-consuming parameter broadcasting process after the end of sharding reverse computation, implement the overlap between parameter broadcasting and next step computation. As a result, the end-to-end training performance of large model is improved by more than 2%. [#63945](https://github.com/PaddlePaddle/Paddle/pull/63945) +- To address the problem that the gradient occupies too much video memory during the pipelined parallel training, as a result of slow training speed due to the introduction of multiple computations, we have implemented the gradient dynamic release technique, to improve the end-to-end training performance of large models by 3.4%. [#59739](https://github.com/PaddlePaddle/Paddle/pull/59739) -#### Performance optimization +### Bug Fixing -- Optimized BroadcastKernel's support for large Tensor. Change to call INT32 version implementation for multiple times for large Tensor Sharding, improving operator performance by 7.27x. [#57313](https://github.com/PaddlePaddle/Paddle/pull/57313), [#57996](https://github.com/PaddlePaddle/Paddle/pull/57996) -- Optimized performance of Tensor save interface by copying the Tensor to CPU and then converting to numpy, to avoid overhead of automatically converting the Tensor to a continuous Tensor when Tensor is not continuous. [#57040](https://github.com/PaddlePaddle/Paddle/pull/57040) +- Fix the problem of StreamSafeCUDAAllocator CUDA Event resource leakage, as a result of slowdown of large model training. [#64621](https://github.com/PaddlePaddle/Paddle/pull/64621) +- Fix the bug of reverse calculation error of fused_rotary_position_embedding operator. [#60217](https://github.com/PaddlePaddle/Paddle/pull/60217) +- Fix the bug that customized operators cannot control the calculation accuracy by black and white lists in AMP scenarios. [#60052](https://github.com/PaddlePaddle/Paddle/pull/60052) +- Fix the bug that operators such as add_, and divide_ natively supporting operations with different data types have unanticipated type boosting when type boosting occurs. [#64302](https://github.com/PaddlePaddle/Paddle/pull/64302) -#### Bug Fix +## Distributed Strategy Enhancements -- Fixed bug of memmory_efficient_attention operator supporting the sm_90. [#58070](https://github.com/PaddlePaddle/Paddle/pull/58070) -- Fixed the NaN problem of softmax operator when axis=-1 and length is greater than 100000. [#57851](https://github.com/PaddlePaddle/Paddle/pull/57851) -- Fixed bug of GPU access error in some cases for set_constant operator. [#59905](https://github.com/PaddlePaddle/Paddle/pull/59905) -- Fixed GPU storage read/write contention issue in fast implementation version of layer_norm operator. [#56435](https://github.com/PaddlePaddle/Paddle/pull/56435) +Focus on strengthening the functional experience of PaddlePaddle dynamic graph distributed computing, and make various functional improvements to parallel strategies such as AutoTuner, pipeline parallel, and sharding, and enhance the flexibility of large model training. Add the features such as Flash Attention Mask, which significantly reduce the video memory usage of large model training, especially long-sequence training, improve training performance, and provide stronger capability support for large model training. In addition, several bugs and potential security risks have been fixed, which has significantly improved the overall stability of the system. -### Expanded Compiler Infrastructure for Neural Networks (CINN) +### Function Optimization -In this update, PaddlePaddle CINN focuses on optimization of architecture and comprehensive expansion of its capabilities. In view of increasing demand for dynamic shapes for large models, effective operation and optimization strategies of compiler under dynamic shapes are initially explored and implemented. -At the architectural level, Python DSL is introduced, significantly improving CINN's development convenience and Debug capability and enabling developers to write and debug codes more efficiently. Meanwhile, logic of Schedule has been refactored to be dominated by GroupSchedule, enabling more general and stable optimization strategies at operator Group level. In order to enhance stability of CINN, a strong constraint component is explored and introduced. This can effectively reduce uncertainties and potential errors in the system. In addition, historical tool classes and software structure of CINN are systematically organized, optimized and improved, to further enhance readability and maintainability of codes. In terms of integration with other PaddlePaddle components, tight integration of CINN with PIR and Paddle has been further strengthened, making compiler more coherent with overall PaddlePaddle framework. This improvement not only enhances performance of the compiler, but also provides developers with a smoother and more unified development experience. +- Optimize the search space of Autotuner, which significantly improves the performance of search. [#62608](https://github.com/PaddlePaddle/Paddle/pull/62608) +- For the problem of pipeline parallel that the training may be wrong due to the checking of sending type in the eval process, add the training configuration, to skip the redundant receiving check of pipelined sending, featuring higher flexibility and better performance. [#63001](https://github.com/PaddlePaddle/Paddle/pull/63001) +- In the dynamic graph pipeline parallel, add the checking of the size and type of the sent and received data, and add the error message, making the robustness and debuggability better. [#59405](https://github.com/PaddlePaddle/Paddle/pull/59405) +- Support the settings of multiple loss functions with returning multiple losses in dynamic graph pipeline, which improves the flexibility of dynamic graph pipeline. [#63167](https://github.com/PaddlePaddle/Paddle/pull/63167) +- In the dynamic graph pipeline, add the pipeline cache clearing configuration option, to clear the cache sent and received in the pipeline in time to better support dynamic batchsize training. [#62277](https://github.com/PaddlePaddle/Paddle/pull/62277) +- For the problem that the sharding stage3 strategy cannot be aligned bit by bit, replace the unordered set with OrderedSet to avoid the error caused by the accumulation order, as a result of alignment bit by bit after fixing. [#60085](https://github.com/PaddlePaddle/Paddle/pull/60085) +- In order to further reduce the video memory usage in sequence parallel, add a new method of recalculating allgather, to reduce the video memory size of the activation of allgather. [#64244](https://github.com/PaddlePaddle/Paddle/pull/64244) -#### Compatibility upgrade +### New Features for Dynamic Graphs -- Updated storage read interface to be compatible with Paddle 2.0. [#55836](https://github.com/PaddlePaddle/Paddle/pull/55836) -- Updated relu6 Op Mapper compatibility. [#55611](https://github.com/PaddlePaddle/Paddle/pull/55611) +- For the search space of autotuner, add a new search dimension of refined recompute, which makes the search result more accurate and the threshold of model tuning lower. [#62430](https://github.com/PaddlePaddle/Paddle/pull/62430) +- For the problem of limiting the training batch size in virtual pipeline parallel, modify the pipeline scheduling method, to flexibly set the batch size, so as to support more flexible batch size. [#61561](https://github.com/PaddlePaddle/Paddle/pull/61561),[#60314](https://github.com/PaddlePaddle/Paddle/pull/60134) +- In order to solve the problem that the video memory occupation of the mask is a quadratic complexity with low performance in sequence length when using flash attention with a mask, the memory complexity of the mask is reduced from the quadrature of the sequence length to the first square by using the sparse mask, to optimize the memory of the mask. This reduces the number of storage accesses. Meanwhile, use share memory to accelerate memory access, greatly improving the performance. [#62029](https://github.com/PaddlePaddle/Paddle/pull/62029) +- Add the dynamic graph sharding parallel strategy, to improve the communications and computation overlap function, to improve the performance of the training process. [#60455](https://github.com/PaddlePaddle/Paddle/pull/60455) -#### Modification deprecation +### Communication Library Function Optimization -- Removed old Schedule form. [#55566](https://github.com/PaddlePaddle/Paddle/pull/55566),[#55391](https://github.com/PaddlePaddle/Paddle/pull/55391) -- Removed some obsolete tests. [#56245](https://github.com/PaddlePaddle/Paddle/pull/56245),[#57987](https://github.com/PaddlePaddle/Paddle/pull/57987) -- Removed the remove_nested_block Visitor tool that no longer works. [#56972](https://github.com/PaddlePaddle/Paddle/pull/56972) -- Removed other useless codes. [#55413](https://github.com/PaddlePaddle/Paddle/pull/55413) +- Enhance the functionality of the NCCL communication library to support the initialization of customized NCCL libraries by passing additional initialization parameters during initialization. [#62193](https://github.com/PaddlePaddle/Paddle/pull/62193) +- Add the NCCL library path search function to support more flexible NCCL library search methods. [#62492](https://github.com/PaddlePaddle/Paddle/pull/62492) -#### New features +### Bug Fixing -- Added CINN paddle.framework.core.is_run_with_cinn() API on the PaddlePaddle side. [#54355](https://github.com/PaddlePaddle/Paddle/pull/54355) -- Added CINN related operator logics, including various combinatorial operator’s disassembly logic. [#56072](https://github.com/PaddlePaddle/Paddle/pull/56072),[#58210](https://github.com/PaddlePaddle/Paddle/pull/58210),[#58502](https://github.com/PaddlePaddle/Paddle/pull/58502), [#58591](https://github.com/PaddlePaddle/Paddle/pull/58591), [#58981](https://github.com/PaddlePaddle/Paddle/pull/58981), [#59135](https://github.com/PaddlePaddle/Paddle/pull/59135), [#59274](https://github.com/PaddlePaddle/Paddle/pull/59274), [#59306](https://github.com/PaddlePaddle/Paddle/pull/59306), [#59202](https://github.com/PaddlePaddle/Paddle/pull/59202), [#59176](https://github.com/PaddlePaddle/Paddle/pull/59176), [#59534](https://github.com/PaddlePaddle/Paddle/pull/59534), [#59713](https://github.com/PaddlePaddle/Paddle/pull/59713), [#59798](https://github.com/PaddlePaddle/Paddle/pull/59798); Supports bf16, amp and other forms [#54399](https://github.com/PaddlePaddle/Paddle/pull/54399), [#54368](https://github.com/PaddlePaddle/Paddle/pull/54368), [#54608](https://github.com/PaddlePaddle/Paddle/pull/54608); Supports operator zero-dimensional capability [#54892](https://github.com/PaddlePaddle/Paddle/pull/54892), [#54919](https://github.com/PaddlePaddle/Paddle/pull/54919), [#54907](https://github.com/PaddlePaddle/Paddle/pull/54907), [#54966](https://github.com/PaddlePaddle/Paddle/pull/54966) -- Supports CINN and PaddlePaddle PIR, and combinator operator junction operation mode, so new PIR and CINN operation is integrated. [#54732](https://github.com/PaddlePaddle/Paddle/pull/54732), [#56074](https://github.com/PaddlePaddle/Paddle/pull/56074), [#58216](https://github.com/PaddlePaddle/Paddle/pull/58216), [#55680](https://github.com/PaddlePaddle/Paddle/pull/55680), [#56302](https://github.com/PaddlePaddle/Paddle/pull/56302), [#59037](https://github.com/PaddlePaddle/Paddle/pull/59037), [#55186](https://github.com/PaddlePaddle/Paddle/pull/55186), [#58641](https://github.com/PaddlePaddle/Paddle/pull/58641) -- There are strongly constrained components to stabilize CINN changes. [#58719](https://github.com/PaddlePaddle/Paddle/pull/58719), [#59309](https://github.com/PaddlePaddle/Paddle/pull/59309), [#58993](https://github.com/PaddlePaddle/Paddle/pull/58993) -- Added Group Schedule related CINN architecture process. [#58399](https://github.com/PaddlePaddle/Paddle/pull/58399), [#56444](https://github.com/PaddlePaddle/Paddle/pull/56444) -- Added CUTLASS, error handling, and NVRTC Cubin Fmad options to CINN architecture functions preliminarily. [#58079](https://github.com/PaddlePaddle/Paddle/pull/58079), [#57198](https://github.com/PaddlePaddle/Paddle/pull/57198), [#58794](https://github.com/PaddlePaddle/Paddle/pull/58794) -- Added Python interface language for CINN. [#57731](https://github.com/PaddlePaddle/Paddle/pull/57731), [#57515](https://github.com/PaddlePaddle/Paddle/pull/57515), [#57644](https://github.com/PaddlePaddle/Paddle/pull/57644), [#57981](https://github.com/PaddlePaddle/Paddle/pull/57981), [#58009](https://github.com/PaddlePaddle/Paddle/pull/58009) -- Added dynamic shape functionality for CINN to cover ASTGen to generate dynamic shape symbols, to replace the ISL to generate dynamic shape signals [#56360](https://github.com/PaddlePaddle/Paddle/pull/56360), [#57207](https://github.com/PaddlePaddle/Paddle/pull/57207), [#57454](https://github.com/PaddlePaddle/Paddle/pull/57454); Added Bucket Conditional Compilation functionality [#59165](https://github.com/PaddlePaddle/Paddle/pull/59165); Added Schedule, Device, and IR level support for dynamic shape [#58988](https://github.com/PaddlePaddle/Paddle/pull/58988), [#59493](https://github.com/PaddlePaddle/Paddle/pull/59493), [#58717](https://github.com/PaddlePaddle/Paddle/pull/58717), [#58602](https://github.com/PaddlePaddle/Paddle/pull/58602), [#59196](https://github.com/PaddlePaddle/Paddle/pull/59196) -- Supports CINN Group Schedule operator – at Group level, perform more general and stable Schedule optimization. [#56122](https://github.com/PaddlePaddle/Paddle/pull/56122), [#57777](https://github.com/PaddlePaddle/Paddle/pull/57777), [#57569](https://github.com/PaddlePaddle/Paddle/pull/57569) +- Fix the problem of dbias_out space application of fused_linear_param_grad_add_kernel operator, and add the gradient address checking logic to make the error message easier to debug. [#363433](https://github.com/PaddlePaddle/Paddle/pull/63433),[#64460](https://github.com/PaddlePaddle/Paddle/pull/64460) +- Fix the problem that the sharding policy does not scale the gradient when comm_overlap is turned off in the support of reduce_avg operation. [#62702](https://github.com/PaddlePaddle/Paddle/pull/62702) +- Fix the bug related to fusion in the calculation order of main grad in Stage2. [#59142](https://github.com/PaddlePaddle/Paddle/pull/59142) +- Fix the bug that the switch attribute cannot be found when reduce_avg communication operation is turned on under the sharding strategy. [#62502](https://github.com/PaddlePaddle/Paddle/pull/62502) +- Fix the problem of setting stop_gradient=True for some parameters when Sharding stage1 training supports non-training parameter training. [#62616](https://github.com/PaddlePaddle/Paddle/pull/62616) +- Fix the bug of message printing when TCP is turned off, to prevent misleading users. [#62631](https://github.com/PaddlePaddle/Paddle/pull/62631) +- Fix the DataParallel training problem and solve multi-card training error when some gradients are not initialized and segmentation fault error occurs in data parallel training. [#62299](https://github.com/PaddlePaddle/Paddle/pull/62299) +- For the scenario of turning on sequence parallel, fix the bug caused by weight freezing in some models. [#63596](https://github.com/PaddlePaddle/Paddle/pull/63596) +- Fix some bugs for autotuner scenarios with single dp. [#60757](https://github.com/PaddlePaddle/Paddle/pull/60757) +- Fix aadiff bug of streaming parallel strategy. ([#64716](https://github.com/PaddlePaddle/Paddle/pull/64716)) +- Remove some distributed unit tests. ([#62762](https://github.com/PaddlePaddle/Paddle/pull/62762)) -#### Function optimization +### Security Risk Fixing -- Enriched or improved operator functionality, including improvements to various operator processes such as Repair Reverse, FP16, Infershape, Operator Single Test, etc. [#56320](https://github.com/PaddlePaddle/Paddle/pull/56320), [#56845](https://github.com/PaddlePaddle/Paddle/pull/56845), [#54939](https://github.com/PaddlePaddle/Paddle/pull/54939),[#54378](https://github.com/PaddlePaddle/Paddle/pull/54378),[#55321](https://github.com/PaddlePaddle/Paddle/pull/55321),[#55336](https://github.com/PaddlePaddle/Paddle/pull/55336),[#55337](https://github.com/PaddlePaddle/Paddle/pull/55337),[#55442](https://github.com/PaddlePaddle/Paddle/pull/55442),[#55470](https://github.com/PaddlePaddle/Paddle/pull/55470),[#55489](https://github.com/PaddlePaddle/Paddle/pull/55489),[#55510](https://github.com/PaddlePaddle/Paddle/pull/55510),[#55547](https://github.com/PaddlePaddle/Paddle/pull/55547),[#55505](https://github.com/PaddlePaddle/Paddle/pull/55505),[#55563](https://github.com/PaddlePaddle/Paddle/pull/55563),[#54280](https://github.com/PaddlePaddle/Paddle/pull/54280),[#59650](https://github.com/PaddlePaddle/Paddle/pull/59650),[#54862](https://github.com/PaddlePaddle/Paddle/pull/54862),[#55135](https://github.com/PaddlePaddle/Paddle/pull/55135),[#55292](https://github.com/PaddlePaddle/Paddle/pull/55292),[#55333](https://github.com/PaddlePaddle/Paddle/pull/55333),[#55316](https://github.com/PaddlePaddle/Paddle/pull/55316),[#55379](https://github.com/PaddlePaddle/Paddle/pull/55379),[#55326](https://github.com/PaddlePaddle/Paddle/pull/55326) -- Improved CINN, PaddlePaddle, PIR, combinator operator junction operation, including various and PIR and its actuator interface and CINN mutual support. [#59170](https://github.com/PaddlePaddle/Paddle/pull/59170),[#58766](https://github.com/PaddlePaddle/Paddle/pull/58766),[#59255](https://github.com/PaddlePaddle/Paddle/pull/59255),[#59203](https://github.com/PaddlePaddle/Paddle/pull/59203),[#59024](https://github.com/PaddlePaddle/Paddle/pull/59024),[#57829](https://github.com/PaddlePaddle/Paddle/pull/57829),[#58135](https://github.com/PaddlePaddle/Paddle/pull/58135),[#58193](https://github.com/PaddlePaddle/Paddle/pull/58193),[#58207](https://github.com/PaddlePaddle/Paddle/pull/58207),[#58606](https://github.com/PaddlePaddle/Paddle/pull/58606),[#59437](https://github.com/PaddlePaddle/Paddle/pull/59437),[#59759](https://github.com/PaddlePaddle/Paddle/pull/59759),[#55075](https://github.com/PaddlePaddle/Paddle/pull/55075),[#56805](https://github.com/PaddlePaddle/Paddle/pull/56805),[#57764](https://github.com/PaddlePaddle/Paddle/pull/57764),[#58620](https://github.com/PaddlePaddle/Paddle/pull/58620),[#59769](https://github.com/PaddlePaddle/Paddle/pull/59769),[#58702](https://github.com/PaddlePaddle/Paddle/pull/58702),[#58749](https://github.com/PaddlePaddle/Paddle/pull/58749),[#59025](https://github.com/PaddlePaddle/Paddle/pull/59025),[#58820](https://github.com/PaddlePaddle/Paddle/pull/58820),[#58908](https://github.com/PaddlePaddle/Paddle/pull/58908),[#58169](https://github.com/PaddlePaddle/Paddle/pull/58169) -- There are strongly constrained components to stabilize CINN changes. [#55090](https://github.com/PaddlePaddle/Paddle/pull/55090),[#55705](https://github.com/PaddlePaddle/Paddle/pull/55705),[#57587](https://github.com/PaddlePaddle/Paddle/pull/57587),[#59501](https://github.com/PaddlePaddle/Paddle/pull/59501) -- Improved CINN IR and related tool codes. [#55145](https://github.com/PaddlePaddle/Paddle/pull/55145),[#55955](https://github.com/PaddlePaddle/Paddle/pull/55955),[#56307](https://github.com/PaddlePaddle/Paddle/pull/56307),[#55519](https://github.com/PaddlePaddle/Paddle/pull/55519),[#56958](https://github.com/PaddlePaddle/Paddle/pull/56958),[#57019](https://github.com/PaddlePaddle/Paddle/pull/57019),[#57230](https://github.com/PaddlePaddle/Paddle/pull/57230),[#57531](https://github.com/PaddlePaddle/Paddle/pull/57531),[#57532](https://github.com/PaddlePaddle/Paddle/pull/57532),[#57524](https://github.com/PaddlePaddle/Paddle/pull/57524),[#58770](https://github.com/PaddlePaddle/Paddle/pull/58770),[#59337](https://github.com/PaddlePaddle/Paddle/pull/59337),[#59096](https://github.com/PaddlePaddle/Paddle/pull/59096),[#56274](https://github.com/PaddlePaddle/Paddle/pull/56274),[#56350](https://github.com/PaddlePaddle/Paddle/pull/56350),[#57312](https://github.com/PaddlePaddle/Paddle/pull/57312),[#55171](https://github.com/PaddlePaddle/Paddle/pull/55171) -- Supports CINN Group Schedule operator – at Group level, perform more general and stable Schedule optimization. [#54982](https://github.com/PaddlePaddle/Paddle/pull/54982),[#57963](https://github.com/PaddlePaddle/Paddle/pull/57963),[#58220](https://github.com/PaddlePaddle/Paddle/pull/58220),[#55484](https://github.com/PaddlePaddle/Paddle/pull/55484),[#55935](https://github.com/PaddlePaddle/Paddle/pull/55935),[#55590](https://github.com/PaddlePaddle/Paddle/pull/55590),[#56530](https://github.com/PaddlePaddle/Paddle/pull/56530),[#58344](https://github.com/PaddlePaddle/Paddle/pull/58344),[#59810](https://github.com/PaddlePaddle/Paddle/pull/59810) -- CINN architectural improvements, including parallel compilation, low-level storage allocation method, print information, Group structure, Pass structure, etc. [#56282](https://github.com/PaddlePaddle/Paddle/pull/56282), [#59014](https://github.com/PaddlePaddle/Paddle/pull/59014),[#59209](https://github.com/PaddlePaddle/Paddle/pull/59209),[#52660](https://github.com/PaddlePaddle/Paddle/pull/52660),[#54749](https://github.com/PaddlePaddle/Paddle/pull/54749),[#58694](https://github.com/PaddlePaddle/Paddle/pull/58694),[#58940](https://github.com/PaddlePaddle/Paddle/pull/58940),[#59504](https://github.com/PaddlePaddle/Paddle/pull/59504),[#56123](https://github.com/PaddlePaddle/Paddle/pull/56123) -- Improved CINN codegen, jit instruction, dim args, and host kernel to support dynamic shape. [#58825](https://github.com/PaddlePaddle/Paddle/pull/58825),[#59395](https://github.com/PaddlePaddle/Paddle/pull/59395),[#59398](https://github.com/PaddlePaddle/Paddle/pull/59398),[#59540](https://github.com/PaddlePaddle/Paddle/pull/59540),[#59470](https://github.com/PaddlePaddle/Paddle/pull/59470),[#59640](https://github.com/PaddlePaddle/Paddle/pull/59640) -- CINN error reporting optimization. [#54983](https://github.com/PaddlePaddle/Paddle/pull/54983),[#55544](https://github.com/PaddlePaddle/Paddle/pull/55544) -- Improved cleanup of CINN codes, including CI, file paths, C++17, Flags, third-party libraries, Docker, etc. [#55018](https://github.com/PaddlePaddle/Paddle/pull/55018),[#55121](https://github.com/PaddlePaddle/Paddle/pull/55121),[#55009](https://github.com/PaddlePaddle/Paddle/pull/55009),[#55888](https://github.com/PaddlePaddle/Paddle/pull/55888),[#56168](https://github.com/PaddlePaddle/Paddle/pull/56168),[#56192](https://github.com/PaddlePaddle/Paddle/pull/56192),[#56896](https://github.com/PaddlePaddle/Paddle/pull/56896),[#53861](https://github.com/PaddlePaddle/Paddle/pull/53861),[#55208](https://github.com/PaddlePaddle/Paddle/pull/55208) +- Fix security vulnerability against security leakage risk in prune_by_memory_estimation operator. [#61320](https://github.com/PaddlePaddle/Paddle/pull/61320) -#### Performance optimization +## Parameter Server -- Fusion of vit attention. [#54139](https://github.com/PaddlePaddle/Paddle/pull/54139) -- Optimized block reduce. [#58196](https://github.com/PaddlePaddle/Paddle/pull/58196) +This update mainly fixes several bugs in the process of using the parameter server as well as compilation and installation issues. -#### Fixed bug +### Bug Fixing -- Fixed operator-related bugs. [#56280](https://github.com/PaddlePaddle/Paddle/pull/56280),[#57767](https://github.com/PaddlePaddle/Paddle/pull/57767),[#58406](https://github.com/PaddlePaddle/Paddle/pull/58406),[#54406](https://github.com/PaddlePaddle/Paddle/pull/54406),[#54494](https://github.com/PaddlePaddle/Paddle/pull/54494),[#54751](https://github.com/PaddlePaddle/Paddle/pull/54751),[#55674](https://github.com/PaddlePaddle/Paddle/pull/55674),[#55684](https://github.com/PaddlePaddle/Paddle/pull/55684),[#55683](https://github.com/PaddlePaddle/Paddle/pull/55683),[#57798](https://github.com/PaddlePaddle/Paddle/pull/57798),[#57816](https://github.com/PaddlePaddle/Paddle/pull/57816),[#57687](https://github.com/PaddlePaddle/Paddle/pull/57687),[#56719](https://github.com/PaddlePaddle/Paddle/pull/56719),[#59756](https://github.com/PaddlePaddle/Paddle/pull/59756),[#59770](https://github.com/PaddlePaddle/Paddle/pull/59770),[#58811](https://github.com/PaddlePaddle/Paddle/pull/58811) -- Fixed process architecture-related bugs. [#54899](https://github.com/PaddlePaddle/Paddle/pull/54899),[#59737](https://github.com/PaddlePaddle/Paddle/pull/59737),[#59356](https://github.com/PaddlePaddle/Paddle/pull/59356),[#56105](https://github.com/PaddlePaddle/Paddle/pull/56105),[#56662](https://github.com/PaddlePaddle/Paddle/pull/56662),[#58146](https://github.com/PaddlePaddle/Paddle/pull/58146),[#58910](https://github.com/PaddlePaddle/Paddle/pull/58910),[#58121](https://github.com/PaddlePaddle/Paddle/pull/58121),[#58943](https://github.com/PaddlePaddle/Paddle/pull/58943),[#58886](https://github.com/PaddlePaddle/Paddle/pull/58886),[#59642](https://github.com/PaddlePaddle/Paddle/pull/59642),[#56164](https://github.com/PaddlePaddle/Paddle/pull/56164),[#56338](https://github.com/PaddlePaddle/Paddle/pull/56338),[#56966](https://github.com/PaddlePaddle/Paddle/pull/56966),[#59112](https://github.com/PaddlePaddle/Paddle/pull/59112),[#55820](https://github.com/PaddlePaddle/Paddle/pull/55820),[#56660](https://github.com/PaddlePaddle/Paddle/pull/56660),[#57307](https://github.com/PaddlePaddle/Paddle/pull/57307),[#57530](https://github.com/PaddlePaddle/Paddle/pull/57530),[#58236](https://github.com/PaddlePaddle/Paddle/pull/58236),[#55190](https://github.com/PaddlePaddle/Paddle/pull/55190),[#55043](https://github.com/PaddlePaddle/Paddle/pull/55043),[#55667](https://github.com/PaddlePaddle/Paddle/pull/55667) -- Other bugs. [#57239](https://github.com/PaddlePaddle/Paddle/pull/57239),[#55530](https://github.com/PaddlePaddle/Paddle/pull/55530),[#56605](https://github.com/PaddlePaddle/Paddle/pull/56605),[#58243](https://github.com/PaddlePaddle/Paddle/pull/58243),[#58197](https://github.com/PaddlePaddle/Paddle/pull/58197),[#58197](https://github.com/PaddlePaddle/Paddle/pull/58197),[#56086](https://github.com/PaddlePaddle/Paddle/pull/56086),[#56065](https://github.com/PaddlePaddle/Paddle/pull/56065),[#58775](https://github.com/PaddlePaddle/Paddle/pull/58775),[#54750](https://github.com/PaddlePaddle/Paddle/pull/54750),[#58595](https://github.com/PaddlePaddle/Paddle/pull/58595),[#58873](https://github.com/PaddlePaddle/Paddle/pull/58873) +- For the problem of reading and writing out of bounds of the unique operator, fix the problem of setting the wrong length in the calculation process of the unique operator to ensure the correctness of the operation of the unique operator. [#60840](https://github.com/PaddlePaddle/Paddle/pull/60840) +- Fixed some bugs in PGLBox save/load and compilation process to ensure the correctness of PGLBox function in response to the lack of save/load function and compilation error in PGLBox training process. [#63905](https://github.com/PaddlePaddle/Paddle/pull/63905) +- Fix the setting value of use_ps_gpu in CPUPS to ensure the correctness of the CPUPS training process, in response to the problem that the CPUPS training process triggers the GPUPS logic and causes the training to crash. [#61406](https://github.com/PaddlePaddle/Paddle/pull/61406) +- For the problem that the cudaErrorInvalidResourceHandle error occurs in GPUPS training in CUDA 12.3, add the device id switching mechanism, to ensure that the corresponding resource operation is carried out on the correct device. [#63391](https://github.com/PaddlePaddle/Paddle/pull/63391) +- For the problem of garbled codes in PGLBox Embedding Dump process, fix the bug of improper use of C++ std::string, to ensure the correctness of Embedding Dump results. [#65179](https://github.com/PaddlePaddle/Paddle/pull/65179) -#### Documentation +### Documentation Improvement -- Added README file. [#58349](https://github.com/PaddlePaddle/Paddle/pull/58349) +- Access security warnings in the RPC interface documentation, to remind users that they need to use this interface under secure network conditions. [#64100](https://github.com/PaddlePaddle/Paddle/pull/64100) -## 4. Deployment Direction (Paddle Inference) +### Security Enhancement -### General inference optimization +- Fix several code security issues to prevent malicious code injection. [#60023](https://github.com/PaddlePaddle/Paddle/pull/60023)，[#60544](https://github.com/PaddlePaddle/Paddle/pull/60544)，[#60615](https://github.com/PaddlePaddle/Paddle/pull/60615) + +## Inference Deployment + +The inference framework is based on PIR upgraded PASS under GPU, XPU, CPU hardware, to significantly reduce the number of lines of codes compared with the previous version, and improve development efficiency. The underlying executor is upgraded to a new version of asynchronous executor, improving inference performance on most models. Complete the adaptive interconnection for inference acceleration based on CINN compiler. Add the switches for these features. Users can turn on the features through settings. In addition, Paddle Inference supports direct loading of optimized serialized models under mixed inference with TensorRT subgraphs natively, to reduce startup time consumption. For Paddle-TensorRT, add the interfaces to flexibly control node computation precision and whether the subgraph enters TensorRT computation. It is convenient for debugging. For performance optimization, GPU, XPU, CPU are added with more Transformer and LLM computing acceleration fusion operator, such as group attention mechanism fusion operator, GQA structure, and WINT4, and support for automatic matching by PASS. -This version of the upgrade improves performance and ease-of-use of the inference engine on GPU and CPU, reducing user cost and application cost of online inference. On GPU: A high-performance multi-threaded asynchronous executor is supported, and inference performance of each model is improved by 5%~10%. The new version of TensorRT and BF16 inference capabilities are also supported, and TensorRT inference performance and ease of use are further improved. On CPU: The latest version of OneDNN high-performance inference is supported. SwinTransformer, FastRCNN and other series of models have greatly improved performance. +### New Features -- matmul supports transpose and broadcast operations. [#56827](https://github.com/PaddlePaddle/Paddle/pull/56827) -- TruncatedNormal and Assign supports FP64 data types. [#57507](https://github.com/PaddlePaddle/Paddle/pull/57507) -- Supports conv2d explicit quantized inference. [#57160](https://github.com/PaddlePaddle/Paddle/pull/57160),[#58015](https://github.com/PaddlePaddle/Paddle/pull/58015) -- Added conv_fuse_pass. Support conv + bn fusion. The conv2d_fusion is renamed fused_conv2d_add_act. [#58724](https://github.com/PaddlePaddle/Paddle/pull/58724),[#55374](https://github.com/PaddlePaddle/Paddle/pull/55374),[#54477](https://github.com/PaddlePaddle/Paddle/pull/54477),[#59431](https://github.com/PaddlePaddle/Paddle/pull/59431) -- Mixed precision inference supports OP whitelisting. [#56535](https://github.com/PaddlePaddle/Paddle/pull/56535) -- OneDNN optimization is enabled by default. Supports SwinTransformer, FastRCNNd and other inference optimizations. [#58560](https://github.com/PaddlePaddle/Paddle/pull/58560),[#59394](https://github.com/PaddlePaddle/Paddle/pull/59394),[#59421](https://github.com/PaddlePaddle/Paddle/pull/59421),[#58435](https://github.com/PaddlePaddle/Paddle/pull/58435),[#58488](https://github.com/PaddlePaddle/Paddle/pull/58488),[#59259](https://github.com/PaddlePaddle/Paddle/pull/59259),[#56303](https://github.com/PaddlePaddle/Paddle/pull/56303),[#56782](https://github.com/PaddlePaddle/Paddle/pull/56782),[#57598](https://github.com/PaddlePaddle/Paddle/pull/57598),[#58361](https://github.com/PaddlePaddle/Paddle/pull/58361),[#59641](https://github.com/PaddlePaddle/Paddle/pull/59641),[#59527](https://github.com/PaddlePaddle/Paddle/pull/59527),[#59663](https://github.com/PaddlePaddle/Paddle/pull/59663),[#59744](https://github.com/PaddlePaddle/Paddle/pull/59744) -- Added share_data and support for pass in specified data. [#57933](https://github.com/PaddlePaddle/Paddle/pull/57933) - -### Large model inference optimized - -The fine-grained fusion inference optimization of generative large models is realized. Optimization solution ensures high-performance inference capability and excellent expandability. Users can flexibly utilize various fine-grained fusion operators and PaddlePaddle native operators to build a network structure of generative large models in free combinations as required, thus achieving efficient and low-cost inference. In addition, our solution also supports mainstream generative large model structure, significantly reducing deployment cost of inference for such models and strongly supports efficient and low-cost implementation of generative large models. +- Paddle-TensorRT + - The API called at the underlying of Paddle-TensorRT is upgraded. When the version of TensorRT is later than 8.5, the EnqueueV2 API called (which will be deprecated in the future) is upgraded to the EnqueueV3 API. [#60807](https://github.com/PaddlePaddle/Paddle/pull/60807) + - Add the config.exp_disable_tensorrt_subgraph() to set some subgraphs not to enter TensorRT. [#61967](https://github.com/PaddlePaddle/Paddle/pull/61967) + - Add the config.exp_disable_tensorrt_dynamic_shape_ops() to set dynamic shape input operators not to enter TensorRT. The default value is False. [#62352](https://github.com/PaddlePaddle/Paddle/pull/62352) + - Add the config.exp_specify_tensorrt_subgraph_precision() to set nodes to run different precision types. [#62402](https://github.com/PaddlePaddle/Paddle/pull/62402) +- In the Inference, add switch to turn on CINN compiler. When configuring inference config, turn on CINN through config.enable_cinn(). [#61949](https://github.com/PaddlePaddle/Paddle/pull/61949) +- PIR use mechanism in the Inference upgrade + - In the config, add enable_new_ir() interface to enable PIR. [#61968](https://github.com/PaddlePaddle/Paddle/pull/61968) + - In the config, add set_optimization_level() interface to set different optimization levels. [#61968](https://github.com/PaddlePaddle/Paddle/pull/61968) + - In the PIR mechanism, the PASS function supports custom C++PASS. [#62468](https://github.com/PaddlePaddle/Paddle/pull/62468) + - The inference library exposes PIR-related implementation header files to the outside world. Support users' secondary development based on PIR, such as custom Pass development. [#61863](https://github.com/PaddlePaddle/Paddle/pull/61863),[#62293](https://github.com/PaddlePaddle/Paddle/pull/62293) + - The PIR mechanism supports input and output of the Hook operator by registering the Predictor. [#63101](https://github.com/PaddlePaddle/Paddle/pull/63101) +- The multi-layer Transformer fusion operator fused_multi_transformer_op supports GQA calculation. [#64125](https://github.com/PaddlePaddle/Paddle/pull/64125) + +### Function Improvements + +- The inference supports loading optimized models directly, making it possible to skip IR optimization altogether. The deployment in this way can minimize framework overhead. [#61598](https://github.com/PaddlePaddle/Paddle/pull/61598) +- Re-specify the shape range information file when loading the saved IR PASS optimized model inference. [#60457](https://github.com/PaddlePaddle/Paddle/pull/60457) +- Collect the Shape information within the subgraph of the control flow operator, supporting the use of Paddle-TensorRT inference acceleration. [#60451](https://github.com/PaddlePaddle/Paddle/pull/60451) ,[#59588](https://github.com/PaddlePaddle/Paddle/pull/59588) +- The mixed-precision PASS (auto_mixed_precision_pass) for GPU-native inference supports the handling of sparse Tensor. [#62656](https://github.com/PaddlePaddle/Paddle/pull/62656) +- XPU hardware related function + - XPU's fused PASS for Conv and FC supports conversion from Float to INT31 type. [#59981](https://github.com/PaddlePaddle/Paddle/pull/59981) + - XPU's strided slice operator supports the setting of strides non-negative. [#62268](https://github.com/PaddlePaddle/Paddle/pull/62268) + - XPU's multi-layer Encoder fusion PASS is adaptive to sequence length and supports variable length. [#63825](https://github.com/PaddlePaddle/Paddle/pull/63825) +- Paddle TensorRT INT8 computation mode supports tile operator into TensorRT computation, to improve INT8 performance of some models. [#60189](https://github.com/PaddlePaddle/Paddle/pull/60189) + +### Model Compression + +Fix bugs and optimize functions mainly for Post Training Quantization (PTQ) and Quantization Aware Training (QAT). + +- Support the simulation quantization grouped by channel. [#61828](https://github.com/PaddlePaddle/Paddle/pull/61828) +- Support automatic saving of quantization scale to model parameter file under dynamic graphs. [#59441](https://github.com/PaddlePaddle/Paddle/pull/59441) +- Remove the restriction that the dataloader must be a DataLoader instance. [#61798](https://github.com/PaddlePaddle/Paddle/pull/61798) + +### Performance Optimization + +- Upgrade the inference executor to reduce the video memory usage at runtime while keeping the performance unchanged. This can be used through config.enable_use_executor(True). [#57920](https://github.com/PaddlePaddle/Paddle/pull/57920),[#58452](https://github.com/PaddlePaddle/Paddle/pull/58452),[#63350](https://github.com/PaddlePaddle/Paddle/pull/63350),[#64466](https://github.com/PaddlePaddle/Paddle/pull/64466) +- Upgrade oneDNN version of paddle inference to v3.4. Its overall performance has been improved compared with v3.3. [#64661](https://github.com/PaddlePaddle/Paddle/pull/64661) +- Upgrade the CUTLASS-based support for matrix multiplication and activation fusion calculation. ([#61925](https://github.com/PaddlePaddle/Paddle/pull/61925)) + +#### Add generic PASS in PIR mechanism + +- Add identity_op_clean_pass and matmul_scale_fuse_pass. [#59840](https://github.com/PaddlePaddle/Paddle/pull/59840) +- Add fused_flash_attn_pass. The pass can call flash_attention to replace the original attentions computation. [#64213](https://github.com/PaddlePaddle/Paddle/pull/64213),[#64707](https://github.com/PaddlePaddle/Paddle/pull/64707),[#63304](https://github.com/PaddlePaddle/Paddle/pull/63304) +- In the inference PIR new architecture, upgrade layout adjustment algorithm, support the NHWC inference of conv class and norm class. The performance tested on SD models is significantly improved. [#63628](https://github.com/PaddlePaddle/Paddle/pull/63628),[#64634](https://github.com/PaddlePaddle/Paddle/pull/64634),[#64658](https://github.com/PaddlePaddle/Paddle/pull/64658),[#64708](https://github.com/PaddlePaddle/Paddle/pull/64708),[#64830](https://github.com/PaddlePaddle/Paddle/pull/64830),[#64896](https://github.com/PaddlePaddle/Paddle/pull/64896) +- Add remove_redundant_transpose PASS. [#63357](https://github.com/PaddlePaddle/Paddle/pull/63357) +- Enable CSE PASS in inference to improve inference performance. [#64523](https://github.com/PaddlePaddle/Paddle/pull/64523) + +#### GPU Performance Optimizations + +Include new fusion operators and new PASS under PIR mechanism. + +- Optimize the performance of sparse convolution operator (sparse conv) to improve the inference performance of BEV and other models. [#63067](https://github.com/PaddlePaddle/Paddle/pull/63067) +- Add the fusion PASS based on flash attention. [#63220](https://github.com/PaddlePaddle/Paddle/pull/63220) +- The inference supports elementwise_add+group_norm+silu activated operator fusion pattern and its corresponding fusion kernel. [#64199](https://github.com/PaddlePaddle/Paddle/pull/64199) +- The Matrix multiplication calculation supports groupwise's Weight only INT4 calculation. [#60422](https://github.com/PaddlePaddle/Paddle/pull/60422) 、[#63212](https://github.com/PaddlePaddle/Paddle/pull/63212) 、[#60204](https://github.com/PaddlePaddle/Paddle/pull/60204)) +- The implementation of the group attention mechanism fusion operator block_multi_head_attention supports KV Cache quantization. [#59951](https://github.com/PaddlePaddle/Paddle/pull/59951)) +- The Inference uses CUTLASS upgraded conv fusion operator to implement and support PASS automatic fusion. Support bias and activation. Compared to the original cuDNN, the new operator has significant performance acceleration. It is used through config.exp_enable_use_cutlass(True). [#64201](https://github.com/PaddlePaddle/Paddle/pull/64201)、[#64641](https://github.com/PaddlePaddle/Paddle/pull/64641) +- Add the blha_get_max_len operator and remove every call to get_max_len in block_multihead_attention. The function application is used for large model dynamic inference acceleration. [#64246](https://github.com/PaddlePaddle/Paddle/pull/64246) +- Data layout optimization: PASS prohibits using NHWC mode calculation in the conv fusion operator FP32 precision type, because cuDNN will cause performance degradation under this condition. [#63400](https://github.com/PaddlePaddle/Paddle/pull/63400) +- GPU peak video memory optimization: upgrade the underlying interface TryShrinkMemory, and upgrade to support GPU place under the support for the release of the idle video memory in the pool. In certain scenarios, peak video memory can be significantly cut. [#61319](https://github.com/PaddlePaddle/Paddle/pull/61319) + +#### CPU performance optimization + +Include new fusion operator. Add PASS under PIR mechanism and optimize part of Kernel. + +- Add scale_matmul_fuse_pass. [#63313](https://github.com/PaddlePaddle/Paddle/pull/63313) +- Add CPU implementation in fused_bias_residual_layernorm and fused_rms_norm to improve inference speed. [#63196](https://github.com/PaddlePaddle/Paddle/pull/63196)、[#63165](https://github.com/PaddlePaddle/Paddle/pull/63165) +- Add the cache optimization for Deconvolution kernel, to greatly improve the execution speed of this operator. [#60922](https://github.com/PaddlePaddle/Paddle/pull/60922) +- In PIR, add depthwise_conv fusion PASS, to convert the depthwise_conv operator to conv2d, thus using the onednn conv2d kernel optimization to improve the inference speed of this operator. [#63051](https://github.com/PaddlePaddle/Paddle/pull/63051) +- In PIR, add Conv and Activation Fusion PASS (conv_activation_mkldnn_fuse_pass), to support the fusion of conv and 13 kinds of activation functions, thus greatly improving the inference speed of conv-related operators. [#63145](https://github.com/PaddlePaddle/Paddle/pull/63145) +- In PIR, add the fusion PASS (operator_unsqueeze_onednn_fuse_pass) between multiple operators and unsqueeze, to improve inference speed. [#63592](https://github.com/PaddlePaddle/Paddle/pull/63592) +- In PIR, add PASS (operator_reshape_onednn_fuse_pass) to fuse reshape into multiple operators. [#63812](https://github.com/PaddlePaddle/Paddle/pull/63812) +- In PIR, add scale fusion PASS (operator_scale_onednn_fuse_pass). [#63811](https://github.com/PaddlePaddle/Paddle/pull/63811) +- In PIR, add PASS (conv2d_transpose_bias operator) that fuses conv and bias. [#62241](https://github.com/PaddlePaddle/Paddle/pull/62241) +- In PIR, add onednn_placement_pass, which supports 151 operators to convert from Phi operators to oneDNN operators, so that the oneDNN high-performance library can be used for optimization, to improve the inference speed. [#63982](https://github.com/PaddlePaddle/Paddle/pull/63982) +- In PIR, add the fusion between Elementwise type operators and 13 activation functions, to greatly improve the inference speed of enabling Onednn on the CPU. [#63516](https://github.com/PaddlePaddle/Paddle/pull/63516) +- In PIR, add the fusion of multiple conv + concat + activation functions and fused_conv + concat + activation functions, to greatly improve the inference speed when there are concat and activation functions in conv. [#62993](https://github.com/PaddlePaddle/Paddle/pull/62993)、 [#62713](https://github.com/PaddlePaddle/Paddle/pull/62713) +- In PIR, add matmul+add operator fusion PASS (matmul_elementwise_add_fuse_pass). [#62715](https://github.com/PaddlePaddle/Paddle/pull/62715) +- In PIR, add the scale parameter to fold PASS (scale_matmul_fuse_pass). [#63313](https://github.com/PaddlePaddle/Paddle/pull/63313) +- In PIR, add the fusion PASS (softplus_activation_fuse_pass) between softplus and 12 activation functions. [#63617](https://github.com/PaddlePaddle/Paddle/pull/63617) +- In PIR, add fc operator conversion PASS (fc_onednn_enable_pass). [#63518](https://github.com/PaddlePaddle/Paddle/pull/63518) +- In PIR, add self-attention operator fusion PASS (self_attention_fuse_pass). [#63726](https://github.com/PaddlePaddle/Paddle/pull/63726) +- In PIR, add fusion PASS (fc_activation_fuse_pass) between fc and 12 activation functions. [#63853](https://github.com/PaddlePaddle/Paddle/pull/63853) +- In PIR, add BatchNorm folded PASS (conv2d_bn_onednn_fuse_pass) to amplify the fusion probability of subsequent PASS. [#64524](https://github.com/PaddlePaddle/Paddle/pull/64524) +- In PIR, add the fusion PASS (matmul_activation_fuse_pass) between matmul and 12 activation functions. [#62901](https://github.com/PaddlePaddle/Paddle/pull/62901) +- In PIR, add reshape + transpose + reshape fusion PASS (shuffle_channel_detect_pass), which is fused into a shuffle_channel operator under specific conditions. [#64053](https://github.com/PaddlePaddle/Paddle/pull/64053) +- In PIR, add reshape + transpose + matmul fusion PASS (reshape_transpose_matmul_fuse_pass). [#62998](https://github.com/PaddlePaddle/Paddle/pull/62998) +- In PIR, add matmul + transpose + reshape fusion PASS (matmul_transpose_reshape_fuse_pass) to PIR to significantly improve performance in some scenarios. [#63151](https://github.com/PaddlePaddle/Paddle/pull/63151)(https://github.com/PaddlePaddle/Paddle/pull/63151) +- XPU hardware new fusion PASS optimization: + - Add qk_qkv_attention_xpu_fuse_pass and qkv_attention_xpu_kernel in XPU hardware. [#60089](https://github.com/PaddlePaddle/Paddle/pull/60089) + - Add rotary position encoded fusion operator, to support elementwise_mul + strided_slice + sin/cos+ stack fusion to 1 operator in XPU hardware. [#60025](https://github.com/PaddlePaddle/Paddle/pull/60025) + - Add group_norm_silu_xpu_fuse_pass. [#62689](https://github.com/PaddlePaddle/Paddle/pull/62689) + - Add weight_only_linear_xpu_pass. [#64185](https://github.com/PaddlePaddle/Paddle/pull/64185) + - Add block_multihead_attention operator and PASS, to support large model inference for LLaMA2 models in XPU devices. [#65036](https://github.com/PaddlePaddle/Paddle/pull/65036) + - Support float16 type for squeeze_excitation_block_xpu_kernel. [#61023](https://github.com/PaddlePaddle/Paddle/pull/61023) + +### Bug Fixing + +- Fix mixed-precision conversions in models such as faster_rcnn_swin_tiny_fpn_1x_coco, and solve the mixed_precision_pass error. [#64673](https://github.com/PaddlePaddle/Paddle/pull/64673) +- Block fused_conv2d_add_act pass from being validated in activation functions that are sigmoid (fused conv2d and sigmoid cause performance degradation between cudnn versions 8.0 and 8.7). [#64717](https://github.com/PaddlePaddle/Paddle/pull/64717) +- Fix compilation issues with self_dp_attention and fused_layer_norm_avx_kernel in Clang12. [#63414](https://github.com/PaddlePaddle/Paddle/pull/63414) +- Fix the issue that scale and zeroPoints in the qdq operator of some models are deleted prematurely in the IR/Pass stage. [#62225](https://github.com/PaddlePaddle/Paddle/pull/62225) +- Fix the issue that causes an error to be reported when both Config.UseOptimizedModel() and config.EnableMemoryOptim() are turned on. [#62501](https://github.com/PaddlePaddle/Paddle/pull/62501) +- Add constraint on matmul_scale_fuse_pass, where input w must be a weight or the pass will not be matched. [#62850](https://github.com/PaddlePaddle/Paddle/pull/62850) +- Keep inference model output key ordering guaranteed to be the same as when dynamic graph models are exported. [#63791](https://github.com/PaddlePaddle/Paddle/pull/63791) +- Fix the error in subgraph when the constant fold PASS is in "the folded op and its input and output are not in the same subgraph." [#62148](https://github.com/PaddlePaddle/Paddle/pull/62148) +- Fix several runtime problems in PaddleTRT mode. Include the failure of quantization calibration table generation caused by yolo_box operator in int8 mode, and the error caused by incorrect handling of dim attribute data type in reduce operator. [#61596](https://github.com/PaddlePaddle/Paddle/pull/61596) +- Fix some runtime error problems in mixed-precision inference mode.Include the errors caused by sharing weights among fused conv2d operators without correctly converting weight layout, fused conv2d operator backend not properly selected as cuDNN, fused conv2d operator incorrectly handling bias dimension under NHWC, incorrectly handling input data type of norm class operator. [#60955](https://github.com/PaddlePaddle/Paddle/pull/60955)、[#60076](https://github.com/PaddlePaddle/Paddle/pull/60076)、[#63007](https://github.com/PaddlePaddle/Paddle/pull/63007)、[#63988](https://github.com/PaddlePaddle/Paddle/pull/63988) +- Fix the problem that config.delete_pass function does not take effect. [#61056](https://github.com/PaddlePaddle/Paddle/pull/61056) +- Fix the GC mechanism of While control flow in PIR to recycle unwanted inputs in advance and reduce the peak memory, for example, 2GB memory reduction in LLaMA 7B model. [#63062](https://github.com/PaddlePaddle/Paddle/pull/63062) +- Fix the OneDNN mean kernel rollback error. [#64676](https://github.com/PaddlePaddle/Paddle/pull/64676) +- Fix the conv_bias_fuse_pass strong constraints newly added, e.g., the shape of the bias cannot be 1, so as to ensure the stability of the pass inference result. [#64412](https://github.com/PaddlePaddle/Paddle/pull/64412) +- Fix the conv_elementwise_add_onednn_fuse_pass strong constraints newly added, e.g., conv2d_out and residual_param must have the same size, so that the pass inference is stable. [#64448](https://github.com/PaddlePaddle/Paddle/pull/64448) +- Fix the problem of repeatedly inserting quantized inverse-quantization operators under certain circumstances [#63082](https://github.com/PaddlePaddle/Paddle/pull/63082) + +## Hardware Adaptation + +### Adaptation Scheme (Custom Device) + +For PaddlePaddle hardware access, add the daily release supports for 4 hardware Kunlun XPU, Ascend NPU, Hygon DCU and Cambricon MLU this time. Meanwhile, the problems in distributed communications have been fixed through large model training and inference deployment, and performance is optimized through functions such as video memory optimization, and overlap of computation and communication. Furthermore, each hardware is also added to support a large number of BFloat16 data type operators this time, as well as many operator fusion Pass and fusion operators on each hardware. Through the hardware and software together, hardware large Transformer operator library is accessed to fully improve the performance of large models. + +#### New Features + +- Add the support for distributed policy sharding stage1 v2. [#61500](https://github.com/PaddlePaddle/Paddle/pull/61500) +- Support the distributed communication module in BF16 data type.Add some operators to support for BF16 data types such as empty, shape, etc. [#60768](https://github.com/PaddlePaddle/Paddle/pull/60768),[#62140](https://github.com/PaddlePaddle/Paddle/pull/62140),[#62604](https://github.com/PaddlePaddle/Paddle/pull/62604) +- Add the support for get_comm_name interface, support for memory stat function, and support for Profiler to record memory time. [#62556](https://github.com/PaddlePaddle/Paddle/pull/62556),[#61030](https://github.com/PaddlePaddle/Paddle/pull/61030),[#62292](https://github.com/PaddlePaddle/Paddle/pull/62292) +- Add support for some fusion strategies and operators, including silu_fuse_pass, conv_elementwise_add_act_fuse_pass, and generator offset. [#60595](https://github.com/PaddlePaddle/Paddle/pull/60595),[#60708](https://github.com/PaddlePaddle/Paddle/pull/60708),[#60616](https://github.com/PaddlePaddle/Paddle/pull/60616) + +#### Performance Optimization + +- The distributed communication strategy Sharing uses asynchronous strategy in Broadcast parameter, to improve the overlap between computation and communication. [#59745](https://github.com/PaddlePaddle/Paddle/pull/59745) +- Add the support for STRIDED Layout operator to improve the performance of the operator. [#62532](https://github.com/PaddlePaddle/Paddle/pull/62532),[#62697](https://github.com/PaddlePaddle/Paddle/pull/62697),[#62649](https://github.com/PaddlePaddle/Paddle/pull/62649) +- Optimize the memory usage of elementwise_mul operator.[#62377](https://github.com/PaddlePaddle/Paddle/pull/62377) + +#### Bug Fixing + +- Fix the bug under the distributed strategy Sharing. [#61942](https://github.com/PaddlePaddle/Paddle/pull/61942),[#62236](https://github.com/PaddlePaddle/Paddle/pull/62236),[#62305](https://github.com/PaddlePaddle/Paddle/pull/62305),[#62535](https://github.com/PaddlePaddle/Paddle/pull/62535),[#62572](https://github.com/PaddlePaddle/Paddle/pull/62572),[#61601](https://github.com/PaddlePaddle/Paddle/pull/61601) +- Fix the problem that the operator cannot be registered due to c_embedding operator is not under PHI namespace. [#60774](https://github.com/PaddlePaddle/Paddle/pull/60774) +- Fix the xccl_comm release issue. [#60465](https://github.com/PaddlePaddle/Paddle/pull/60465) +- Fix data address error caused by index_put operator fallbacking cpu. [#61842](https://github.com/PaddlePaddle/Paddle/pull/61842) +- Fix stream_safe_custom_device_allocator issue. [#63369](https://github.com/PaddlePaddle/Paddle/pull/63369) +- Fix the distributed worker port conflict issue. [#61409](https://github.com/PaddlePaddle/Paddle/pull/61409) +- Fix comm data type to improve device compatibility. [#62306](https://github.com/PaddlePaddle/Paddle/pull/62306) +- Unify the use of comm data type to phi::DataType. [#62464](https://github.com/PaddlePaddle/Paddle/pull/62464),[#62562](https://github.com/PaddlePaddle/Paddle/pull/62562) +- Fix the problem of missing precision parameter in PD_ConfigEnableCustomDevice. [#63702](https://github.com/PaddlePaddle/Paddle/pull/63702) + +### Kunlun XPU + +#### New Features + +- Add the support for BF16 data types for some operators, including compare_kernel and add reduce_all_kernel ([#63602](https://github.com/PaddlePaddle/Paddle/pull/63602)), empty([#60212](https://github.com/PaddlePaddle/Paddle/pull/60212)), hybrid_parallel_optimizer([#60213](https://github.com/PaddlePaddle/Paddle/pull/60213)), reduce_max/reduce_min([#60453](https://github.com/PaddlePaddle/Paddle/pull/60453)), all_reduce/concat/split([#62364](https://github.com/PaddlePaddle/Paddle/pull/62364)), tile/tile_grad([#63075](https://github.com/PaddlePaddle/Paddle/pull/63075)), accuracy([#63863](https://github.com/PaddlePaddle/Paddle/pull/63863)), swiglu/set_value([#64070](https://github.com/PaddlePaddle/Paddle/pull/64070)), amp_master_grad([#63865](https://github.com/PaddlePaddle/Paddle/pull/63865)), c_concat ([#63403](https://github.com/PaddlePaddle/Paddle/pull/63403)), flatten ([#63997](https://github.com/PaddlePaddle/Paddle/pull/63997)), compare_op ([#64473](https://github.com/PaddlePaddle/Paddle/pull/64473)), moment1/moment2 ([#62688](https://github.com/PaddlePaddle/Paddle/pull/62688)), fused_rope ([#60064](https://github.com/PaddlePaddle/Paddle/pull/60064)), c_softmax_with_cross_entropy ([#60472](https://github.com/PaddlePaddle/Paddle/pull/60472)), elementwise_pow/square/sin/cos ([#60402](https://github.com/PaddlePaddle/Paddle/pull/60402)), strided_slice ([#60382](https://github.com/PaddlePaddle/Paddle/pull/60382)), tile/sigmoid_grad ([#60119](https://github.com/PaddlePaddle/Paddle/pull/60119)), elementwise_sub/elementwise_div ([#60386](https://github.com/PaddlePaddle/Paddle/pull/60386)), softmax_with_cross_entropy ([#63759](https://github.com/PaddlePaddle/Paddle/pull/63759)) +- Add the support for INT8 data types for some operators, including multi_encoder_xpu ([#61212](https://github.com/PaddlePaddle/Paddle/pull/61212)), qkv_attention ([#63105](https://github.com/PaddlePaddle/Paddle/pull/63105)) +- Update Kunlun SDK versions including BKCL, XHPC, XCCL, etc. [#59895](https://github.com/PaddlePaddle/Paddle/pull/59895)、[#59888](https://github.com/PaddlePaddle/Paddle/pull/59888)、[#63624](https://github.com/PaddlePaddle/Paddle/pull/63624), [#60305](https://github.com/PaddlePaddle/Paddle/pull/60305), [#62076](https://github.com/PaddlePaddle/Paddle/pull/62076), [#62646](https://github.com/PaddlePaddle/Paddle/pull/62646), [#63520](https://github.com/PaddlePaddle/Paddle/pull/63520), [#64163](https://github.com/PaddlePaddle/Paddle/pull/64163), [#64326](https://github.com/PaddlePaddle/Paddle/pull/64326), [#60617](https://github.com/PaddlePaddle/Paddle/pull/60617), [#60377](https://github.com/PaddlePaddle/Paddle/pull/60377), [#60421](https://github.com/PaddlePaddle/Paddle/pull/60421), [#60598](https://github.com/PaddlePaddle/Paddle/pull/60598), [#61199](https://github.com/PaddlePaddle/Paddle/pull/61199) +- Add the support for memory stat function. [#61116](https://github.com/PaddlePaddle/Paddle/pull/61116) +- Add multi-stream support, to assign default l3/gm buffer size to each stream. [#62729](https://github.com/PaddlePaddle/Paddle/pull/62729) +- Add nonzero operator, to support simulator XPUSIM_SKIP_RUN mode. [#60224](https://github.com/PaddlePaddle/Paddle/pull/60224)。[#60388](https://github.com/PaddlePaddle/Paddle/pull/60388) +- Add stride_slice and stride_slice_grad operators, to support strides < 0. [#62749](https://github.com/PaddlePaddle/Paddle/pull/62749) +- Add rotary_embedding, to support use_neox_rotary_style == True. [#64090](https://github.com/PaddlePaddle/Paddle/pull/64090) +- Add fusion Pass and fusion operators including cross_attention ([#63203](https://github.com/PaddlePaddle/Paddle/pull/63203)), fused_bias_act ([#62232](https://github.com/PaddlePaddle/Paddle/pull/62232)), fused_layernorm ([#62228](https://github.com/PaddlePaddle/Paddle/pull/62228)), group_norm_silu_xpu_fuse_pass ([#63342](https://github.com/PaddlePaddle/Paddle/pull/63342)) +- Add the support for distributed policy sharding stage3. [#57457](https://github.com/PaddlePaddle/Paddle/pull/57457) +- Add the support for tf32 fc quantization mode. [#62273](https://github.com/PaddlePaddle/Paddle/pull/62273) +- Add the flash attention operator. [#60065](https://github.com/PaddlePaddle/Paddle/pull/60065) +- Add the roformer relative embedding pass & kernel and support multi_encoder_xpu. [#62089](https://github.com/PaddlePaddle/Paddle/pull/62089) +- Add the support for pp + sharding strategy. [#63640](https://github.com/PaddlePaddle/Paddle/pull/63640) +- Upgrade the XPU communication library architecture to support dynamic-static unified communication library function. [#63817](https://github.com/PaddlePaddle/Paddle/pull/63817) + +#### Performance Optimization + +- Add XHPC buffer manager to improve the performance of Paddle and XHPC memory collaboration. [#63924](https://github.com/PaddlePaddle/Paddle/pull/63924) +- Enhance TensorSetConstantXPU performance and support BF16 data type. [#63920](https://github.com/PaddlePaddle/Paddle/pull/63920),[#61818](https://github.com/PaddlePaddle/Paddle/pull/61818) +- Fusion multiple group norm + silu + conv modules and compress the video memory. [#62892](https://github.com/PaddlePaddle/Paddle/pull/62892) +- Optimize XPU memory allocation in comm manager. [#64139](https://github.com/PaddlePaddle/Paddle/pull/64139) +- Optimize operator performance, including mean_all_grad ([#61148](https://github.com/PaddlePaddle/Paddle/pull/61148)), dropout_v2 ([#61029](https://github.com/PaddlePaddle/Paddle/pull/61029)), fused_rotary_position_embedding ([#62846](https://github.com/PaddlePaddle/Paddle/pull/62846)), cross_entropy ([#63159](https://github.com/PaddlePaddle/Paddle/pull/63159)), elementwise_add ([#64289](https://github.com/PaddlePaddle/Paddle/pull/64289)), fused_gemm_epilogue ([#61350](https://github.com/PaddlePaddle/Paddle/pull/61350), check_nan_or_inf ([#60853](https://github.com/PaddlePaddle/Paddle/pull/60853)) + +#### Bug Fixing + +- Fix the tile operator support for 0-dimensional Tensor. [#64279](https://github.com/PaddlePaddle/Paddle/pull/64279) +- Fix the group_norm_silu_fuse_pass. [#63449](https://github.com/PaddlePaddle/Paddle/pull/63449) +- Fix the XPU API GM memory issue. [#60260](https://github.com/PaddlePaddle/Paddle/pull/60260),[#60387](https://github.com/PaddlePaddle/Paddle/pull/60387),[#62940](https://github.com/PaddlePaddle/Paddle/pull/62940) +- Fix the distributed strategy Sharing stage1 v2 bug. [#64209](https://github.com/PaddlePaddle/Paddle/pull/64209) +- Fix the XPU constant issue. [#60763](https://github.com/PaddlePaddle/Paddle/pull/60763) +- Fix some operator issues, including AdamW ([#62251](https://github.com/PaddlePaddle/Paddle/pull/62251)), dropout_v3 ([#62726](https://github.com/PaddlePaddle/Paddle/pull/62726)), softmax([#63780](https://github.com/PaddlePaddle/Paddle/pull/63780)) , fused rope embedding ([#62143](https://github.com/PaddlePaddle/Paddle/pull/62143)), elementwise_add ([#60252](https://github.com/PaddlePaddle/Paddle/pull/60252)), resnet_basic_block ([#62914](https://github.com/PaddlePaddle/Paddle/pull/62914)) +- Fix XPU runtime and installation related issues. [#60028](https://github.com/PaddlePaddle/Paddle/pull/60028),[#61970](https://github.com/PaddlePaddle/Paddle/pull/61970) +- Fix XPU compilation bugs. [#63307](https://github.com/PaddlePaddle/Paddle/pull/63307) +- Fix end-side memory related bugs when initializing XPU communication library. [#64396](https://github.com/PaddlePaddle/Paddle/pull/64396) -- Supports the FMHA/MMHA for CacheKV division block scheduling. [#59462](https://github.com/PaddlePaddle/Paddle/pull/59462) -- RoPE encoding fusion operator supports input sin/cos values. [#55415](https://github.com/PaddlePaddle/Paddle/pull/55415) -- Added fine-grained fusion operators. Supports high-performance inference optimization of generative large models. Added operators such as quant_linear, weight_quantize, and linear_compress for support of large model quantitative inference. [#57852](https://github.com/PaddlePaddle/Paddle/pull/57852),[#55128](https://github.com/PaddlePaddle/Paddle/pull/55128),[#59090](https://github.com/PaddlePaddle/Paddle/pull/59090),[#56706](https://github.com/PaddlePaddle/Paddle/pull/56706),[#59951](https://github.com/PaddlePaddle/Paddle/pull/59951),[#55490](https://github.com/PaddlePaddle/Paddle/pull/55490),[#59291](https://github.com/PaddlePaddle/Paddle/pull/59291),[#59441](https://github.com/PaddlePaddle/Paddle/pull/59441),[#59778](https://github.com/PaddlePaddle/Paddle/pull/59778),[#59651](https://github.com/PaddlePaddle/Paddle/pull/59651)[#55301](https://github.com/PaddlePaddle/Paddle/pull/55301),[#58637](https://github.com/PaddlePaddle/Paddle/pull/58637),[#56673](https://github.com/PaddlePaddle/Paddle/pull/56673),[#56401](https://github.com/PaddlePaddle/Paddle/pull/56401) -- Supports variable length inference series API. [#57948](https://github.com/PaddlePaddle/Paddle/pull/57948) -- Supports the GQA inference. [#58472](https://github.com/PaddlePaddle/Paddle/pull/58472),[#58836](https://github.com/PaddlePaddle/Paddle/pull/58836) -- Added masked multihead attention. Supports high performance MMHA inference. [#55344](https://github.com/PaddlePaddle/Paddle/pull/55344),[#56411](https://github.com/PaddlePaddle/Paddle/pull/56411),[#58134](https://github.com/PaddlePaddle/Paddle/pull/58134),[#57936](https://github.com/PaddlePaddle/Paddle/pull/57936) -- weight_quantize/weight_only_linear supports the Volta architecture. [#58082](https://github.com/PaddlePaddle/Paddle/pull/58082) -- Added weight_only_linear_grad for support of large model weight only quantization gradient transfer-back. [#57685](https://github.com/PaddlePaddle/Paddle/pull/57685) -- Fixed large model dynamic to static bug. Optimized communication initialization logic between static graph cards. [#56390](https://github.com/PaddlePaddle/Paddle/pull/56390),[#57169](https://github.com/PaddlePaddle/Paddle/pull/57169),[#56688](https://github.com/PaddlePaddle/Paddle/pull/56688),[#56592](https://github.com/PaddlePaddle/Paddle/pull/56592),[#58868](https://github.com/PaddlePaddle/Paddle/pull/58868) -- Optimized top_p_sampling random number generation logic. [#59494](https://github.com/PaddlePaddle/Paddle/pull/59494) - -### Paddle-TensorRT Inference Optimization - -- elementwise_add fusion supports NHWC format. [#56795](https://github.com/PaddlePaddle/Paddle/pull/56795) -- conv2d supports filter as input. [#55246](https://github.com/PaddlePaddle/Paddle/pull/55246)。 -- Supports BF16 and FP64 inference. [#59765](https://github.com/PaddlePaddle/Paddle/pull/59765),[#55520](https://github.com/PaddlePaddle/Paddle/pull/55520) -- Added MarkTrtEngineOutputs API. Users can specify TensorRT Engine outputs. [#56858](https://github.com/PaddlePaddle/Paddle/pull/56858),[#56188](https://github.com/PaddlePaddle/Paddle/pull/56188),[#57407](https://github.com/PaddlePaddle/Paddle/pull/57407) -- Customized OP can generate TensorRT Plugin automatically. [#58976](https://github.com/PaddlePaddle/Paddle/pull/58976),[#56037](https://github.com/PaddlePaddle/Paddle/pull/56037) -- TensorRT inference allows users to specify input hook to optimize shape collection process. [#59466](https://github.com/PaddlePaddle/Paddle/pull/59466),[#54841](https://github.com/PaddlePaddle/Paddle/pull/54841),[#57498](https://github.com/PaddlePaddle/Paddle/pull/57498),[#54861](https://github.com/PaddlePaddle/Paddle/pull/54861),[#54432](https://github.com/PaddlePaddle/Paddle/pull/54432),[#55503](https://github.com/PaddlePaddle/Paddle/pull/55503) -- TensorRT Inference supports inference model after saving Tuning. [#55893](https://github.com/PaddlePaddle/Paddle/pull/55893),[#56952](https://github.com/PaddlePaddle/Paddle/pull/56952),[#57031](https://github.com/PaddlePaddle/Paddle/pull/57031) -- Supports variable length Transformer model PromptTuning. [#57034](https://github.com/PaddlePaddle/Paddle/pull/57034) -- Added operators such as bitwise_and, bitwise_or, bitwise_not, cumsum, einsum, lookup_table, assign, flip, size, scatter, solve, unbind, reduce, and argsort. Optimized support of existing operators. [#59214](https://github.com/PaddlePaddle/Paddle/pull/59214),[#59293](https://github.com/PaddlePaddle/Paddle/pull/59293),[#54882](https://github.com/PaddlePaddle/Paddle/pull/54882),[#54097](https://github.com/PaddlePaddle/Paddle/pull/54097),[#54860](https://github.com/PaddlePaddle/Paddle/pull/54860),[#55426](https://github.com/PaddlePaddle/Paddle/pull/55426),[#54372](https://github.com/PaddlePaddle/Paddle/pull/54372),[#55688](https://github.com/PaddlePaddle/Paddle/pull/55688),[#56069](https://github.com/PaddlePaddle/Paddle/pull/56069),[#59563](https://github.com/PaddlePaddle/Paddle/pull/59563),[#59317](https://github.com/PaddlePaddle/Paddle/pull/59317),[#59424](https://github.com/PaddlePaddle/Paddle/pull/59424),[#55476](https://github.com/PaddlePaddle/Paddle/pull/55476),[#56043](https://github.com/PaddlePaddle/Paddle/pull/56043),[#58549](https://github.com/PaddlePaddle/Paddle/pull/58549),[#57326](https://github.com/PaddlePaddle/Paddle/pull/57326),[#59409](https://github.com/PaddlePaddle/Paddle/pull/59409)) -- TensorRT enables video memory sharing by default. [#59495](https://github.com/PaddlePaddle/Paddle/pull/59495),[#58251](https://github.com/PaddlePaddle/Paddle/pull/58251) -- PrelnResidualBiasPluginDynamic supports 4D input. [#56304](https://github.com/PaddlePaddle/Paddle/pull/56304) -- Added support for FlashAttention for Paddle-TRT inference for architectures below SM80.[#56492](https://github.com/PaddlePaddle/Paddle/pull/56492) - -### Modification deprecation - -- Removed fc_elementwise_add fusion from OneDNN. [#55504](https://github.com/PaddlePaddle/Paddle/pull/55504) -- Removed redunant op. [#54442](https://github.com/PaddlePaddle/Paddle/pull/54442) - -### Bug Fix - -- Fixed “Inference so” link flags conflict issue. [#59755](https://github.com/PaddlePaddle/Paddle/pull/59755) -- Fixed constant_folding pass execution error. [#55556](https://github.com/PaddlePaddle/Paddle/pull/55556) -- Fixed softmax forward speed bug and reverse accuracy bug. [#56036](https://github.com/PaddlePaddle/Paddle/pull/56036),[#57858](https://github.com/PaddlePaddle/Paddle/pull/57858)[#57538](https://github.com/PaddlePaddle/Paddle/pull/57538) -- Fixed customized OP while error and export bug. [#58898](https://github.com/PaddlePaddle/Paddle/pull/58898),[#59318](https://github.com/PaddlePaddle/Paddle/pull/59318) -- Fixed CUDA 12.0 compilation problem on Windows platform. [#59852](https://github.com/PaddlePaddle/Paddle/pull/59852) -- Fixed bug of inference partial operator error when TensorRT version is later than 8.6. [#54379](https://github.com/PaddlePaddle/Paddle/pull/54379),[#54679](https://github.com/PaddlePaddle/Paddle/pull/54679),[#54251](https://github.com/PaddlePaddle/Paddle/pull/54251) -- Fixed and removed inference fusion Pass. [#54846](https://github.com/PaddlePaddle/Paddle/pull/54846),[#54887](https://github.com/PaddlePaddle/Paddle/pull/54887),[#55573](https://github.com/PaddlePaddle/Paddle/pull/55573),[#56434](https://github.com/PaddlePaddle/Paddle/pull/56434),[#56326](https://github.com/PaddlePaddle/Paddle/pull/56326),[#56753](https://github.com/PaddlePaddle/Paddle/pull/56753),[#57491](https://github.com/PaddlePaddle/Paddle/pull/57491),[#56909](https://github.com/PaddlePaddle/Paddle/pull/56909),[#54536](https://github.com/PaddlePaddle/Paddle/pull/54536),[#55073](https://github.com/PaddlePaddle/Paddle/pull/55073),[#55081](https://github.com/PaddlePaddle/Paddle/pull/55081),[#55240](https://github.com/PaddlePaddle/Paddle/pull/55240),[#56439](https://github.com/PaddlePaddle/Paddle/pull/56439),[#59009](https://github.com/PaddlePaddle/Paddle/pull/59009) -- Fixed error of multi-stream inference context switching. [#57629](https://github.com/PaddlePaddle/Paddle/pull/57629),[#58048](https://github.com/PaddlePaddle/Paddle/pull/58048),[#54994](https://github.com/PaddlePaddle/Paddle/pull/54994) - -## 5. Hardware Support - -### Hardware Integration Solution (Custom Device) - -In this update, added support for distributed advanced strategy, custom operator and custom fusion strategy. By upgrading distributed communication library, supports MP, GroupShared, PP, SP, MOE and other advanced distributed strategies. Meanwhile, enables vendors to flexibly access Transformer operator libraries of different granularities, and modify computation graph through Fusion Pass for performance acceleration. - -#### New features - -- Upgraded CustomDevice to support for Paddle's latest distributed communication library CommContext. Added a variety of advanced distributed strategies such as GroupShared and MOE. [#56301](https://github.com/PaddlePaddle/Paddle/pull/56301),[#54671](https://github.com/PaddlePaddle/Paddle/pull/54671),[#57957](https://github.com/PaddlePaddle/Paddle/pull/57957),[#56669](https://github.com/PaddlePaddle/Paddle/pull/56669),[#54384](https://github.com/PaddlePaddle/Paddle/pull/54384),[#54572](https://github.com/PaddlePaddle/Paddle/pull/54572),[#54573](https://github.com/PaddlePaddle/Paddle/pull/54573),[#54676](https://github.com/PaddlePaddle/Paddle/pull/54676) -- Upgraded CustomDevice to support CustomOP. Users can register undefined operators in Paddle PHI operator library. CustomDevice can support CustomOP via CAPI. [#57038](https://github.com/PaddlePaddle/Paddle/pull/57038),[#55532](https://github.com/PaddlePaddle/Paddle/pull/55532),[#56755](https://github.com/PaddlePaddle/Paddle/pull/56755),[#55532](https://github.com/PaddlePaddle/Paddle/pull/55532),[#55533](https://github.com/PaddlePaddle/Paddle/pull/55533),[#55659](https://github.com/PaddlePaddle/Paddle/pull/55659) -- Added CustomDevice's support for CustomPass function. Modified the computation graph IR through Python API. [#55511](https://github.com/PaddlePaddle/Paddle/pull/55511),[#55728](https://github.com/PaddlePaddle/Paddle/pull/55728) -- Added CustomDevice’s support for Paddle run_check. [#56318](https://github.com/PaddlePaddle/Paddle/pull/56318) -- Added CustomDevice’s support for StreamSafeAllocator. [#55393](https://github.com/PaddlePaddle/Paddle/pull/55393),[#56380](https://github.com/PaddlePaddle/Paddle/pull/56380),[#56536](https://github.com/PaddlePaddle/Paddle/pull/56536),[#58035](https://github.com/PaddlePaddle/Paddle/pull/58035) -- Added CustomDevice’s support for DataTransform. [#56627](https://github.com/PaddlePaddle/Paddle/pull/56627) - -#### Function optimization - -- Added CustomDevice’s support for more PaddlePaddle APIs such as Variable.set_value, adamw, share_external_data, mp_allreduce_sum, tensor.numpy, get_paddle_place, and GeneratorState. [#55272](https://github.com/PaddlePaddle/Paddle/pull/55272), [#56386](https://github.com/PaddlePaddle/Paddle/pull/56386), [#57253](https://github.com/PaddlePaddle/Paddle/pull/57253), [#56927](https://github.com/PaddlePaddle/Paddle/pull/56927),[#56189](https://github.com/PaddlePaddle/Paddle/pull/56189),[#55225](https://github.com/PaddlePaddle/Paddle/pull/55225),[#55247](https://github.com/PaddlePaddle/Paddle/pull/55247) -- Modified CustomDevice dynamic library loading method from RTLD_NOW to RTLD_LAZY, to facilitate subsequent checking of compatibility of CustomDevice related software stack version. [#57544](https://github.com/PaddlePaddle/Paddle/pull/57544) -- Added CustomDevice's detection function for FP16 operator under mixed precision training. [#56053](https://github.com/PaddlePaddle/Paddle/pull/56053),[#56176](https://github.com/PaddlePaddle/Paddle/pull/56176) - -#### Bug Fix - -- Fixed some problems in CustomDevice's support for distributed communication libraries. [#55293](https://github.com/PaddlePaddle/Paddle/pull/55293),[#58038](https://github.com/PaddlePaddle/Paddle/pull/58038),[#59800](https://github.com/PaddlePaddle/Paddle/pull/59800) -- Fixed some problems in CustomDevice on some operators, including c_softmax_with_cross_entropy,data loader,SplitDenseTensor,grad accumulation,atan2 grad.[#56486](https://github.com/PaddlePaddle/Paddle/pull/56486),[#55541](https://github.com/PaddlePaddle/Paddle/pull/55541),[#55615](https://github.com/PaddlePaddle/Paddle/pull/55615),[#56052](https://github.com/PaddlePaddle/Paddle/pull/56052),[#56067](https://github.com/PaddlePaddle/Paddle/pull/56067) -- Fixed some problems of device management in CustomDevice, including device exceptions ([#56556](https://github.com/PaddlePaddle/Paddle/pull/56556),[#58639](https://github.com/PaddlePaddle/Paddle/pull/58639),[#55173](https://github.com/PaddlePaddle/Paddle/pull/55173)), exception events ([#56745](https://github.com/PaddlePaddle/Paddle/pull/56745),[#58059](https://github.com/PaddlePaddle/Paddle/pull/58059)), video memory exception ([#56977](https://github.com/PaddlePaddle/Paddle/pull/56977),[#59247](https://github.com/PaddlePaddle/Paddle/pull/59247),[#54606](https://github.com/PaddlePaddle/Paddle/pull/54606)), device initialization ([#57099](https://github.com/PaddlePaddle/Paddle/pull/57099),[#57994](https://github.com/PaddlePaddle/Paddle/pull/57994)), device release ([#54932](https://github.com/PaddlePaddle/Paddle/pull/54932),[#55351](https://github.com/PaddlePaddle/Paddle/pull/55351),[#55783](https://github.com/PaddlePaddle/Paddle/pull/55783)), and device resource pooling, etc.([#55229](https://github.com/PaddlePaddle/Paddle/pull/55229),[#56580](https://github.com/PaddlePaddle/Paddle/pull/56580)) -- Fixed CustomDevice compilation-related issues. [#56760](https://github.com/PaddlePaddle/Paddle/pull/56760),[#56766](https://github.com/PaddlePaddle/Paddle/pull/56766) - -### Kunlunxin XPU - -#### New features - -- Added XPTI (XPU Profiling Tool Interface) to support collection and analysis function of runtime performance data. [#54685](https://github.com/PaddlePaddle/Paddle/pull/54685),[#54690](https://github.com/PaddlePaddle/Paddle/pull/54690),[#54800](https://github.com/PaddlePaddle/Paddle/pull/54800) -- Supports Paddle's latest distributed communication library CommContext. [#59418](https://github.com/PaddlePaddle/Paddle/pull/59418) -- Added XPU fusion operators, for example, fast_where. [#55628](https://github.com/PaddlePaddle/Paddle/pull/55628) -- Added support for XPU Pluign function, facilitating users to develop XPU customized operators through XTDK programming. [#55101](https://github.com/PaddlePaddle/Paddle/pull/55101),[#59326](https://github.com/PaddlePaddle/Paddle/pull/59326) -- Added XPU’s support for AutoGrowthAllocator. [#54121](https://github.com/PaddlePaddle/Paddle/pull/54121) -- Added operator support list of Kunlun3. [#57683](https://github.com/PaddlePaddle/Paddle/pull/57683) - -#### Function optimization - -- Upgraded XPU Inference API. [#54342](https://github.com/PaddlePaddle/Paddle/pull/54342) -- Optimized performance of some XPU operators. Added support for bf16 in some XPU operators, including unique/index_put,squeeze/unsqueeze kernels,swish/swish_grad,scatter_nd_add_grad/slice,rsqrt/bitwise_or/arange_tensor,where,collective. [#56582](https://github.com/PaddlePaddle/Paddle/pull/56582),[#58161](https://github.com/PaddlePaddle/Paddle/pull/58161),[#58440](https://github.com/PaddlePaddle/Paddle/pull/58440),[#58580](https://github.com/PaddlePaddle/Paddle/pull/58580),[#58950](https://github.com/PaddlePaddle/Paddle/pull/58950),[#58616](https://github.com/PaddlePaddle/Paddle/pull/58616),[#59273](https://github.com/PaddlePaddle/Paddle/pull/59273) -- Optimized XPU memory management to avoid memory leakage. [#59334](https://github.com/PaddlePaddle/Paddle/pull/59334),[#54847](https://github.com/PaddlePaddle/Paddle/pull/54847) -- Supports INT8 inference. [#57258](https://github.com/PaddlePaddle/Paddle/pull/57258) -- Added support for FP16 series inference operators. [#55642](https://github.com/PaddlePaddle/Paddle/pull/55642),[#54410](https://github.com/PaddlePaddle/Paddle/pull/54410) -- Supports share_external_memory interface to pass input and output. [#55170](https://github.com/PaddlePaddle/Paddle/pull/55170) -- Supports open source quantization model XPU inference. [#58568](https://github.com/PaddlePaddle/Paddle/pull/58568) -- Added context_gm_size configuration, instead of allocating global memory in Pass. [#54674](https://github.com/PaddlePaddle/Paddle/pull/54674) -- Added embedding and fast_gather_nd plugin. [#56488](https://github.com/PaddlePaddle/Paddle/pull/56488),[#56103](https://github.com/PaddlePaddle/Paddle/pull/56103) -- Supports fusion of fast_layternorm + leaky_relu. [#57113](https://github.com/PaddlePaddle/Paddle/pull/57113) -- Supports elementwise_min/max/floordiv/where inference in KL1 and KL2 precision. [#58422](https://github.com/PaddlePaddle/Paddle/pull/58422) -- Supports autotune configuration of fc and conv2d operator. [#58801](https://github.com/PaddlePaddle/Paddle/pull/58801) -- Supports conv and fc dynamic quantization. [#59307](https://github.com/PaddlePaddle/Paddle/pull/59307) -- fc + act fusion support for sigmoid, swish and relu6. [#54486](https://github.com/PaddlePaddle/Paddle/pull/54486) -- elementwise_sub/elementwise_div supports int data type. [#55920](https://github.com/PaddlePaddle/Paddle/pull/55920) - -#### Bug Fix - -- Fixed XPU communication library issues and some operator issues including rnn, layer_norm_grad, yolo_box. ([#55475](https://github.com/PaddlePaddle/Paddle/pull/55475),[#55515](https://github.com/PaddlePaddle/Paddle/pull/55515)) ([#55656](https://github.com/PaddlePaddle/Paddle/pull/55656),[#54669](https://github.com/PaddlePaddle/Paddle/pull/54669),[#55310](https://github.com/PaddlePaddle/Paddle/pull/55310) - -### Hygon DCU - -#### Bug Fix - -- Fixed some operator bugs of Hygon DCU, including rnn, concat/split, fft, and so on.[#59402](https://github.com/PaddlePaddle/Paddle/pull/59402),[#55821](https://github.com/PaddlePaddle/Paddle/pull/55821),[#56340](https://github.com/PaddlePaddle/Paddle/pull/56340)) -- Fixed issues related to communication library of Hygon DCU. [#57110](https://github.com/PaddlePaddle/Paddle/pull/57110) -- Fixed compilation-related problems of Hygon DCU. [#59775](https://github.com/PaddlePaddle/Paddle/pull/59775),[#55507](https://github.com/PaddlePaddle/Paddle/pull/55507),[#55612](https://github.com/PaddlePaddle/Paddle/pull/55612),[#54952](https://github.com/PaddlePaddle/Paddle/pull/54952),[#55076](https://github.com/PaddlePaddle/Paddle/pull/55076),[#56079](https://github.com/PaddlePaddle/Paddle/pull/56079),[#54874](https://github.com/PaddlePaddle/Paddle/pull/54874)) -- Fixed support issue of Hygon DCU for BF16 data type. [#56517](https://github.com/PaddlePaddle/Paddle/pull/56517) - -## 6. Environment Adaptation - -Adopted modular compilation to optimize CMake codes, improving efficiency of compilation of PaddlePaddle. This can increase efficiency of RD local development. Meanwhile, supports compilation in Python3.12, CUDA12, and Hopper architecture, and using Clang tool to comprehensively optimize code formats. In addition, C++ unitest is changed from linking static libraries to linking dynamic libraries to reduce compilation size. These improvements provide users with a smoother and more efficient installation and development experience. - -- CMake code optimization: stratify directories into independent static libraries, to improve incremental compilation efficiency. [#59095](https://github.com/PaddlePaddle/Paddle/pull/59095), [#58960](https://github.com/PaddlePaddle/Paddle/pull/58960),[#56591](https://github.com/PaddlePaddle/Paddle/pull/56591),[#58484](https://github.com/PaddlePaddle/Paddle/pull/58484) -- CMake compilation stratification: to realize compilation layering of PaddlePaddle architecture from bottom-up and improve compilation efficiency. [#56442](https://github.com/PaddlePaddle/Paddle/pull/56442),[#54729](https://github.com/PaddlePaddle/Paddle/pull/54729),[#55733](https://github.com/PaddlePaddle/Paddle/pull/55733),[#56352](https://github.com/PaddlePaddle/Paddle/pull/56352),[#55109](https://github.com/PaddlePaddle/Paddle/pull/55109),[#54992](https://github.com/PaddlePaddle/Paddle/pull/54992),[#57698](https://github.com/PaddlePaddle/Paddle/pull/57698),[#55147](https://github.com/PaddlePaddle/Paddle/pull/55147),[#55113](https://github.com/PaddlePaddle/Paddle/pull/55113),[#56691](https://github.com/PaddlePaddle/Paddle/pull/56691),[#58618](https://github.com/PaddlePaddle/Paddle/pull/58618),[#58899](https://github.com/PaddlePaddle/Paddle/pull/58899),[#59140](https://github.com/PaddlePaddle/Paddle/pull/59140),[#59129](https://github.com/PaddlePaddle/Paddle/pull/59129),[#59222](https://github.com/PaddlePaddle/Paddle/pull/59222),[#59105](https://github.com/PaddlePaddle/Paddle/pull/59105),[#59711](https://github.com/PaddlePaddle/Paddle/pull/59711) -- Offline compilation of third-party libraries: Third-party dependent libraries are compiled offline, so CI/CE system does not need to download third-party libraries repeatedly in every compilation, improving operation efficiency of the CI/CE system. [#54344](https://github.com/PaddlePaddle/Paddle/pull/54344),[#54370](https://github.com/PaddlePaddle/Paddle/pull/54370),[#54466](https://github.com/PaddlePaddle/Paddle/pull/54466),[#54438](https://github.com/PaddlePaddle/Paddle/pull/54438),[#54388](https://github.com/PaddlePaddle/Paddle/pull/54388),[#54436](https://github.com/PaddlePaddle/Paddle/pull/54436),[#54392](https://github.com/PaddlePaddle/Paddle/pull/54392),[#54646](https://github.com/PaddlePaddle/Paddle/pull/54646),[#54380](https://github.com/PaddlePaddle/Paddle/pull/54380),[#55501](https://github.com/PaddlePaddle/Paddle/pull/55501),[#55136](https://github.com/PaddlePaddle/Paddle/pull/55136),[#54451](https://github.com/PaddlePaddle/Paddle/pull/54451),[#55631](https://github.com/PaddlePaddle/Paddle/pull/55631),[#55549](https://github.com/PaddlePaddle/Paddle/pull/55549),[#56165](https://github.com/PaddlePaddle/Paddle/pull/56165),[#54391](https://github.com/PaddlePaddle/Paddle/pull/54391),[#54614](https://github.com/PaddlePaddle/Paddle/pull/54614),[#54522](https://github.com/PaddlePaddle/Paddle/pull/54522),[#54764](https://github.com/PaddlePaddle/Paddle/pull/54764),[#54400](https://github.com/PaddlePaddle/Paddle/pull/54400),[#54322](https://github.com/PaddlePaddle/Paddle/pull/54322) -- PaddlePaddle supports Python 3.12. [#59396](https://github.com/PaddlePaddle/Paddle/pull/59396),[#58069](https://github.com/PaddlePaddle/Paddle/pull/58069) -- Using Clang tool to optimize source codes and improve code quality. [#59626](https://github.com/PaddlePaddle/Paddle/pull/59626),[#55895](https://github.com/PaddlePaddle/Paddle/pull/55895),[#56632](https://github.com/PaddlePaddle/Paddle/pull/56632),[#54449](https://github.com/PaddlePaddle/Paddle/pull/54449),[#54523](https://github.com/PaddlePaddle/Paddle/pull/54523),[#54796](https://github.com/PaddlePaddle/Paddle/pull/54796),[#55847](https://github.com/PaddlePaddle/Paddle/pull/55847),[#55807](https://github.com/PaddlePaddle/Paddle/pull/55807),[#56261](https://github.com/PaddlePaddle/Paddle/pull/56261),[#57522](https://github.com/PaddlePaddle/Paddle/pull/57522),[#57868](https://github.com/PaddlePaddle/Paddle/pull/57868),[#57809](https://github.com/PaddlePaddle/Paddle/pull/57809),[#55658](https://github.com/PaddlePaddle/Paddle/pull/55658),[#58285](https://github.com/PaddlePaddle/Paddle/pull/58285),[#55491](https://github.com/PaddlePaddle/Paddle/pull/55491),[#55506](https://github.com/PaddlePaddle/Paddle/pull/55506),[#55279](https://github.com/PaddlePaddle/Paddle/pull/55279),[#55741](https://github.com/PaddlePaddle/Paddle/pull/55741),[#55894](https://github.com/PaddlePaddle/Paddle/pull/55894),[#55704](https://github.com/PaddlePaddle/Paddle/pull/55704),[#55800](https://github.com/PaddlePaddle/Paddle/pull/55800),[#55799](https://github.com/PaddlePaddle/Paddle/pull/55799),[#55983](https://github.com/PaddlePaddle/Paddle/pull/55983),[#55954](https://github.com/PaddlePaddle/Paddle/pull/55954),[#55764](https://github.com/PaddlePaddle/Paddle/pull/55764),[#56246](https://github.com/PaddlePaddle/Paddle/pull/56246),[#56219](https://github.com/PaddlePaddle/Paddle/pull/56219),[#56217](https://github.com/PaddlePaddle/Paddle/pull/56217),[#56216](https://github.com/PaddlePaddle/Paddle/pull/56216),[#56208](https://github.com/PaddlePaddle/Paddle/pull/56208),[#56134](https://github.com/PaddlePaddle/Paddle/pull/56134),[#56253](https://github.com/PaddlePaddle/Paddle/pull/56253),[#56255](https://github.com/PaddlePaddle/Paddle/pull/56255),[#56693](https://github.com/PaddlePaddle/Paddle/pull/56693),[#56692](https://github.com/PaddlePaddle/Paddle/pull/56692),[#56637](https://github.com/PaddlePaddle/Paddle/pull/56637),[#56636](https://github.com/PaddlePaddle/Paddle/pull/56636),[#56647](https://github.com/PaddlePaddle/Paddle/pull/56647),[#56218](https://github.com/PaddlePaddle/Paddle/pull/56218),[#56640](https://github.com/PaddlePaddle/Paddle/pull/56640),[#56635](https://github.com/PaddlePaddle/Paddle/pull/56635),[#55675](https://github.com/PaddlePaddle/Paddle/pull/55675),[#56601](https://github.com/PaddlePaddle/Paddle/pull/56601),[#56485](https://github.com/PaddlePaddle/Paddle/pull/56485),[#56648](https://github.com/PaddlePaddle/Paddle/pull/56648),[#56747](https://github.com/PaddlePaddle/Paddle/pull/56747),[#56676](https://github.com/PaddlePaddle/Paddle/pull/56676),[#56649](https://github.com/PaddlePaddle/Paddle/pull/56649),[#56895](https://github.com/PaddlePaddle/Paddle/pull/56895),[#56994](https://github.com/PaddlePaddle/Paddle/pull/56994),[#56904](https://github.com/PaddlePaddle/Paddle/pull/56904),[#56744](https://github.com/PaddlePaddle/Paddle/pull/56744),[#56954](https://github.com/PaddlePaddle/Paddle/pull/56954),[#57114](https://github.com/PaddlePaddle/Paddle/pull/57114),[#57343](https://github.com/PaddlePaddle/Paddle/pull/57343),[#57483](https://github.com/PaddlePaddle/Paddle/pull/57483),[#57871](https://github.com/PaddlePaddle/Paddle/pull/57871),[#57861](https://github.com/PaddlePaddle/Paddle/pull/57861),[#58028](https://github.com/PaddlePaddle/Paddle/pull/58028),[#57627](https://github.com/PaddlePaddle/Paddle/pull/57627),[#59072](https://github.com/PaddlePaddle/Paddle/pull/59072) -- C++ unitest has changed from linking static libraries to linking dynamic libraries, reducing compilation size and improving compilation efficiency. [#59477](https://github.com/PaddlePaddle/Paddle/pull/59477),[#56630](https://github.com/PaddlePaddle/Paddle/pull/56630),[#57789](https://github.com/PaddlePaddle/Paddle/pull/57789),[#54257](https://github.com/PaddlePaddle/Paddle/pull/54257),[#59620](https://github.com/PaddlePaddle/Paddle/pull/59620),[#59384](https://github.com/PaddlePaddle/Paddle/pull/59384),[#59619](https://github.com/PaddlePaddle/Paddle/pull/59619),[#58583](https://github.com/PaddlePaddle/Paddle/pull/58583),[#58821](https://github.com/PaddlePaddle/Paddle/pull/58821),[#58710](https://github.com/PaddlePaddle/Paddle/pull/58710)，[#58619](https://github.com/PaddlePaddle/Paddle/pull/58619) -- Fixed bug related to source code compilation, improving compilation efficiency. [#56617](https://github.com/PaddlePaddle/Paddle/pull/56617),[#58195](https://github.com/PaddlePaddle/Paddle/pull/58195),[#56136](https://github.com/PaddlePaddle/Paddle/pull/56136),[#54540](https://github.com/PaddlePaddle/Paddle/pull/54540),[#57172](https://github.com/PaddlePaddle/Paddle/pull/57172),[#54429](https://github.com/PaddlePaddle/Paddle/pull/54429),[#55603](https://github.com/PaddlePaddle/Paddle/pull/55603),[#54807](https://github.com/PaddlePaddle/Paddle/pull/54807),[#56102](https://github.com/PaddlePaddle/Paddle/pull/56102),[#56829](https://github.com/PaddlePaddle/Paddle/pull/56829),[#56951](https://github.com/PaddlePaddle/Paddle/pull/56951),[#56555](https://github.com/PaddlePaddle/Paddle/pull/56555),[#57781](https://github.com/PaddlePaddle/Paddle/pull/57781),[#57836](https://github.com/PaddlePaddle/Paddle/pull/57836),[#58807](https://github.com/PaddlePaddle/Paddle/pull/58807),[#54535](https://github.com/PaddlePaddle/Paddle/pull/54535),[#54946](https://github.com/PaddlePaddle/Paddle/pull/54946),[#54437](https://github.com/PaddlePaddle/Paddle/pull/54437),[#54411](https://github.com/PaddlePaddle/Paddle/pull/54411),[#54411](https://github.com/PaddlePaddle/Paddle/pull/54411),[#54391](https://github.com/PaddlePaddle/Paddle/pull/54391),[#54466](https://github.com/PaddlePaddle/Paddle/pull/54466),[#54480](https://github.com/PaddlePaddle/Paddle/pull/54480),[#54480](https://github.com/PaddlePaddle/Paddle/pull/54480),[#54724](https://github.com/PaddlePaddle/Paddle/pull/54724),[#59193](https://github.com/PaddlePaddle/Paddle/pull/59193),[#54735](https://github.com/PaddlePaddle/Paddle/pull/54735),[#54812](https://github.com/PaddlePaddle/Paddle/pull/54812),[#56430](https://github.com/PaddlePaddle/Paddle/pull/56430),[#56655](https://github.com/PaddlePaddle/Paddle/pull/56655),[#56684](https://github.com/PaddlePaddle/Paddle/pull/56684),[#56774](https://github.com/PaddlePaddle/Paddle/pull/56774),[#56936](https://github.com/PaddlePaddle/Paddle/pull/56936),[#56949](https://github.com/PaddlePaddle/Paddle/pull/56949),[#56974](https://github.com/PaddlePaddle/Paddle/pull/56974),[#57171](https://github.com/PaddlePaddle/Paddle/pull/57171),[#57712](https://github.com/PaddlePaddle/Paddle/pull/57712),[#56617](https://github.com/PaddlePaddle/Paddle/pull/56617),[#58181](https://github.com/PaddlePaddle/Paddle/pull/58181),[#58253](https://github.com/PaddlePaddle/Paddle/pull/58253),[#58268](https://github.com/PaddlePaddle/Paddle/pull/58268),[#59051](https://github.com/PaddlePaddle/Paddle/pull/59051),[#59048](https://github.com/PaddlePaddle/Paddle/pull/59048),[#59081](https://github.com/PaddlePaddle/Paddle/pull/59081),[#59076](https://github.com/PaddlePaddle/Paddle/pull/59076),[#59155](https://github.com/PaddlePaddle/Paddle/pull/59155),[#59253](https://github.com/PaddlePaddle/Paddle/pull/59253),[#59347](https://github.com/PaddlePaddle/Paddle/pull/59347),[#58957](https://github.com/PaddlePaddle/Paddle/pull/58957),[#59443](https://github.com/PaddlePaddle/Paddle/pull/59443),[#58998](https://github.com/PaddlePaddle/Paddle/pull/58998),[#57574](https://github.com/PaddlePaddle/Paddle/pull/57574),[#55889](https://github.com/PaddlePaddle/Paddle/pull/55889),[#59078](https://github.com/PaddlePaddle/Paddle/pull/59078),[#55762](https://github.com/PaddlePaddle/Paddle/pull/55762),[#56252](https://github.com/PaddlePaddle/Paddle/pull/56252),[#56715](https://github.com/PaddlePaddle/Paddle/pull/56715),[#54905](https://github.com/PaddlePaddle/Paddle/pull/54905),[#56978](https://github.com/PaddlePaddle/Paddle/pull/56978),[#57032](https://github.com/PaddlePaddle/Paddle/pull/57032),[#57179](https://github.com/PaddlePaddle/Paddle/pull/57179),[#57179](https://github.com/PaddlePaddle/Paddle/pull/57179),[#58996](https://github.com/PaddlePaddle/Paddle/pull/58996),[#59915](https://github.com/PaddlePaddle/Paddle/pull/59915),[#54883](https://github.com/PaddlePaddle/Paddle/pull/54883),[#56746](https://github.com/PaddlePaddle/Paddle/pull/56746),[#57674](https://github.com/PaddlePaddle/Paddle/pull/57674),[#60117](https://github.com/PaddlePaddle/Paddle/pull/60117),[#55627](https://github.com/PaddlePaddle/Paddle/pull/55627),[#54568](https://github.com/PaddlePaddle/Paddle/pull/54568),[#54450](https://github.com/PaddlePaddle/Paddle/pull/54450),[#54513](https://github.com/PaddlePaddle/Paddle/pull/54513),[#54615](https://github.com/PaddlePaddle/Paddle/pull/54615),[#54913](https://github.com/PaddlePaddle/Paddle/pull/54913),[#54916](https://github.com/PaddlePaddle/Paddle/pull/54916),[#55148](https://github.com/PaddlePaddle/Paddle/pull/55148),[#55125](https://github.com/PaddlePaddle/Paddle/pull/55125),[#55479](https://github.com/PaddlePaddle/Paddle/pull/55479),[#55723](https://github.com/PaddlePaddle/Paddle/pull/55723),[#55831](https://github.com/PaddlePaddle/Paddle/pull/55831),[#55904](https://github.com/PaddlePaddle/Paddle/pull/55904),[#56085](https://github.com/PaddlePaddle/Paddle/pull/56085),[#56259](https://github.com/PaddlePaddle/Paddle/pull/56259),[#56366](https://github.com/PaddlePaddle/Paddle/pull/56366),[#56366](https://github.com/PaddlePaddle/Paddle/pull/56366),[#56546](https://github.com/PaddlePaddle/Paddle/pull/56546),[#56679](https://github.com/PaddlePaddle/Paddle/pull/56679),[#57222](https://github.com/PaddlePaddle/Paddle/pull/57222),[#57387](https://github.com/PaddlePaddle/Paddle/pull/57387),[#57993](https://github.com/PaddlePaddle/Paddle/pull/57993),[#59556](https://github.com/PaddlePaddle/Paddle/pull/59556),[#57931](https://github.com/PaddlePaddle/Paddle/pull/57931),[#58112](https://github.com/PaddlePaddle/Paddle/pull/58112),[#54228](https://github.com/PaddlePaddle/Paddle/pull/54228),[#56913](https://github.com/PaddlePaddle/Paddle/pull/56913),[#56993](https://github.com/PaddlePaddle/Paddle/pull/56993),[#55042](https://github.com/PaddlePaddle/Paddle/pull/55042),[#55305](https://github.com/PaddlePaddle/Paddle/pull/55305),[#55286](https://github.com/PaddlePaddle/Paddle/pull/55286),[#56634](https://github.com/PaddlePaddle/Paddle/pull/56634),[#57778](https://github.com/PaddlePaddle/Paddle/pull/57778),[#58374](https://github.com/PaddlePaddle/Paddle/pull/58374),[#58640](https://github.com/PaddlePaddle/Paddle/pull/58640),[#58822](https://github.com/PaddlePaddle/Paddle/pull/58822),[#59055](https://github.com/PaddlePaddle/Paddle/pull/59055),[#59303](https://github.com/PaddlePaddle/Paddle/pull/59303),[#59487](https://github.com/PaddlePaddle/Paddle/pull/59487),[#58400](https://github.com/PaddlePaddle/Paddle/pull/58400),[#59283](https://github.com/PaddlePaddle/Paddle/pull/59283),[#54791](https://github.com/PaddlePaddle/Paddle/pull/54791),[#59134](https://github.com/PaddlePaddle/Paddle/pull/59134),[#56206](https://github.com/PaddlePaddle/Paddle/pull/56206),[#56199](https://github.com/PaddlePaddle/Paddle/pull/56199),[#56670](https://github.com/PaddlePaddle/Paddle/pull/56670),[#58923](https://github.com/PaddlePaddle/Paddle/pull/58923) -- Fixed bug related to Paddle ARM compilation. [#55416](https://github.com/PaddlePaddle/Paddle/pull/55416),[#55548](https://github.com/PaddlePaddle/Paddle/pull/55548) - -## Thanks to Our Contributors - -Azure-Tang, zhaoyinglia, From00, JZ-LIANG, xysheng-baidu, SylarTiaNII, kuizhiqing, zhiqiu, FeixLiu, liuzhenhai93, GhostScreaming, pangengzheng, xiaoyewww, wanghuancoder, ForFishes, hitywt, danleifeng, tianshuo78520a, ykkk2333, houj04, lj970926, XiaociZhang, HarperCy, cqulilujia, runzhech, RuohengMa, Caozhou1995, kangguangli, heavyrain-lzy, zyfncg, SigureMo, YuanRisheng, lchdl, LiYuRio, AndSonder, Wennie396, zhangbo9674, liudongxue01, risemeup1, phlrain, winter-wang, yuanlehome, NALLEIN, Liujie0926, yuguo-Jack, gitliuyf, zh794390558, Aurelius84, 6clc, GGBond8488, xiaoguoguo626807, Wong4j, iosmers, xiaoxiaohehe001, LielinJiang, carryyu, Difers, yangxiaoyu14, xuxinyi389, cxxly, gongshaotian, jjyaoao, lijialin03, lxd-cumt, cyber-pioneer, HydrogenSulfate, MayYouBeProsperous, Charles-hit, Patrick-Star125, ScottWong98, huangjiyi, DrRyanHuang, jinyouzhi, BeingGod, Wanglongzhi2001, yangguohao, zyt1024, longranger2, 2742195759, megemini, thisjiang, kevincheng2, zhoutianzi666, Wangzheee, ming1753, tianhaodongbd, freeliuzc, zhenyun-li, MARD1NO, RichardWooSJTU, eee4017, leo0519, csy0225, wwbitejotunn, bukejiyu, jiweibo, iamsonderr, ckl117, ronny1996, zhanglirong1999, LLee233, ZHUI, wangxn12138, zhwesky2010, Courtesy-Xs, zoooo0820, llyyxx0413, Asthestarsfalll, zxcd, pkuzyc, idontkonwher, sneaxiy, hong19860320, ZibinGuo, leolishaohao, MuShangCC, zhupengyang, shentanyue, Travis-Lee, wz1qqx, frank-oops, newway, QingshuChen, zhangyk0314, HandSomeLEEw, Shixiaowei02, zhangyuqin1998, Xing-lil, zhhsplendid, jiahy0825, xinyu-intel, MarioLulab, 0x45f, Tom-Zheng, xingmingyyj, zhangbopd, gouzil, zeroRains, BiynXu, WintersMontagne10335, wuhuachaocoding, GreatV, chenwhql, deepllz, parap1uie-s, ozogxyz, FisherWY, changeyoung98, zhiboniu, YangQun1 dynamicheart, Xreki, liugddx, Lylinnnnn, YSF-A, zzjjay, YanhuiDua, lishicheng1996, USTCKAY, abenmao, cocoshe, HermitSun, ccsuzzh, sanbuphy, enkilee, RedContritio, Liyulingyue, zrr1999, chen2016013, Galaxy1458, chalsliu, mrcangye, XieYunshen, zhiheng-liu, haohongxiang, ZzSean, JamesLim-sy, yuehuayingxueluo, niuliling123, umiswing, sijunhe, littsk, SecretXV, zhurou603, zhangjun, caizejun, yangjianfengo1, vivienfanghuagood, Xinyu302, lizexu123, yghstill, Li-fAngyU, VigiZhang, co63oc, dhanush-2501, ooooo-create, PommesPeter, zeus2x7, akshatvishu, jzhang533, Sekiro-x, gumblex, BernieHuang2008, YibinLiu666, qiuwenbogdut, XavierZXY, MqLeet, zhangting2020, mingxu1067, Ainavo, SSKlearns, yuchen202, silverling, zade23, wenxiaohahaha, NKNaN, Tsaiyue, fsczz, Tomoko-hjf, rhmaaa, zbt78, Hhankyangg, wangzhen38, zhengqiwen1997, engineer1109, onepick, qili93, Rane2021, nemonameless, DesmonDay, RachelXu7, ceci3, lyuwenyu, liuruyan, LokeZhou, shiyutang, lanxianghit, feifei-111, Sahala08, sunzhongkai588, Kaedeharai, Candy2Tang, liyongchao911, whisky-12, InsaneOnion, yoyoIcy, KongAKun, linzeyang, MuhammadNizamani, eltociear, Ligoml, LUZY0726, Windfarer, FlyingQianMM, jeng1220, junelotus, zlsh80826, Vvsmile, Frida-a, TonibMw, guoshengCS, zhink, ZhangYulongg, AlbertVan, fengxin-hello, mjp9527, entired, DanGuge. - -# 2.5.0 Release Note - -## 1. Highlights -- **New dynamic-static unification architecture**: Implement a new dynamic-to-static plus compiler execution model in combination with the basic operator, and complete the whole dynamic-to-static, combinator and neural network compiler optimization and acceleration process on the ResNet50&Bert model. For the dynamic-to-static, complete the whole graph fallback core function development, and support the fallback to dynamic graph training execution in case of dynamic-to-static failure. For the combinator, design a set of basic operator systems containing more than 150 basic operators, to achieve the python layer forward operator splitting mechanism and the reverse operator splitting mechanism of static graphs, to realize splitting of more than 70 commonly used forward and reverse operators. For the CINN compiler, fix the correctness bug, develop the key Pass, add manual schedule rules, achieve automatic generation of kernel codes, and improve performance of ResNet50 model by 12% and Bert model by 10%. -- **Operator architecture unification of PHI operator library**: Unify all remaining 350+ operator kernels under the original operator system into PHI operator Library. Unify the way of defining operator in the original operator system into the operator definition form of PHI operator library (configuration of operator definition based on YAML), enhancing unity of the architecture, and reducing comprehension cost of framework development. Decouple all the Fluid header files that the PHI operator library depends on and compile them independently as dynamic link libraries to provide a lighter reuse of the operator library for secondary development of the framework. Continue to standardize and adjust unspecified operators, as well as operator kernels in the PaddlePaddle framework. It is easy for developers to understand and reduce the cost of accessing the hardware. -- **Full go-live of new actuator for static graph**: The new actuator for static graph implements a number of functions and performance optimization, and completes unification and replacement of the original multiple sets of old actuators. The new actuator becomes the back-end default execution engine for the static graph single card and distributed training python side entrance, as well as dynamic-to-static, control flow, CINN, etc. This significantly improves scheduling performance of the framework, and the functional architecture is clearer. Secondary development capability is significantly enhanced. -- **Python API supporting 0-dimensional tensor**: clear semantics are defined between tensor of shape [1,] and tensor of shape [], and fixed many API behaviors to support tensor of shape [], such as `paddle.sum` etc. -- **New environment adaptation**: Adapt to CUDA 12. Compilation with gcc12 is supported. - -## **2. Incompatibility Upgrade** -- PaddlePaddle API supports 0-dimensional tensor.PaddlePaddle previously used a 1-dimensional tensor with a shape of [1] instead of a 0-dimensional tensor, which is different from current mainstream habits. It increases development and debugging cost of the model, and sometimes leads to unintended errors. This release fixes 376 APIs that need to support 0-dimensional tensor, and implements tools widely used by the community such as EinOps. For example, in previous cases, output loss in model training was a 1-dimensional tensor. To take out or print the loss, it was often necessary to use codes like `loss.numpy()[0]`.After this modification, output loss in model training is a 0-dimensional tensor. When using `loss.numpy()`, users can take out or print the loss. The codes are short, easy to understand, and in line with the industry's habit. -- `paddle.fluid ` API is fully decommissioned. According to the plan that has been previewed in the last version, 1116 `paddle.fluid ` APIs and related internal interfaces have been decommissioned, and the remaining few related internal interfaces will be cleaned up in the next version.fluid API belongs to the historical APIs that PaddlePaddle 2.0 had planned to remove, but delayed the cleanup in consideration of compatibility and other factors. This decommissioning cleanup will not affect programs developed based on PaddlePaddle 2.0, and the PaddlePaddle API system will be more concise and easier to understand. -- Complete code cleanup at the old version of the dynamic graph Python side.So far, the Python side only uses the new version of dynamic graph to call the C++ core logic. -- In order to unify the training method of data parallel for static graph model, original single-process multi-card training method is abandoned, including `paddle.static.ParallelExecutor ` and `paddle.static. CompiledProgram(). with_data_parallel( )` APIs, because this set of APIs only supports single-computer multi-card, does not support multi-computer multi-card, and the underlying execution performance is poor.It is recommended to use the multi-process multi-card training method uniformly, i.e., `paddle.distributed.launch ` API for distributed training with data parallel. This upgrade affects only static graphs, and does not affect dynamic graphs and dynamic-to-static training. If you use the decommissioned API, please refer to the documentation on [data parallel](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/06_distributed_training/cluster_quick_start_collective_cn.html) to modify model code. [#50351](https://github.com/PaddlePaddle/Paddle/pull/50351)，[#50501](https://github.com/PaddlePaddle/Paddle/pull/50501)，[#51240](https://github.com/PaddlePaddle/Paddle/pull/51240)，[#51701](https://github.com/PaddlePaddle/Paddle/pull/51701)，[#51616](https://github.com/PaddlePaddle/Paddle/pull/51616)，[#51369](https://github.com/PaddlePaddle/Paddle/pull/51369)，[#52671](https://github.com/PaddlePaddle/Paddle/pull/52671) -- Remove the original adaptation code of Ascend NPU and Cambricon MLU in the framework, upgrade all to CustomDevice plug-in adaptation, and migrate the adaptation code of Ascend NPU and Cambricon MLU to PaddleCustomDevice warehouse. - -## 3. Training Framework (Including Distributed) -### Python API -#### API supporting 0-dimensional tensor -- API input supports 0-dimensional tensor, involving `paddle.reshape `, `paddle.trace `, `paddle.linalg.norm ` and other 286 APIs. [#53208](https://github.com/PaddlePaddle/Paddle/pull/53208), [#53592](https://github.com/PaddlePaddle/Paddle/pull/53592), [#47074](https://github.com/PaddlePaddle/Paddle/pull/47074), [#53186](https://github.com/PaddlePaddle/Paddle/pull/53186), [#47677](https://github.com/PaddlePaddle/Paddle/pull/47677), [#49357](https://github.com/PaddlePaddle/Paddle/pull/49357), [#50237](https://github.com/PaddlePaddle/Paddle/pull/50237), [#46555](https://github.com/PaddlePaddle/Paddle/pull/46555), [#47219](https://github.com/PaddlePaddle/Paddle/pull/47219), [#47501](https://github.com/PaddlePaddle/Paddle/pull/47501), [#47858](https://github.com/PaddlePaddle/Paddle/pull/47858), [#47961](https://github.com/PaddlePaddle/Paddle/pull/47961), [#48058](https://github.com/PaddlePaddle/Paddle/pull/48058), [#48007](https://github.com/PaddlePaddle/Paddle/pull/48007), [#49755](https://github.com/PaddlePaddle/Paddle/pull/49755), [#51024](https://github.com/PaddlePaddle/Paddle/pull/51024), [#51566](https://github.com/PaddlePaddle/Paddle/pull/51566), [#51899](https://github.com/PaddlePaddle/Paddle/pull/51899), [#49813](https://github.com/PaddlePaddle/Paddle/pull/49813), [#47812](https://github.com/PaddlePaddle/Paddle/pull/47812), [#47849](https://github.com/PaddlePaddle/Paddle/pull/47849), [#47251](https://github.com/PaddlePaddle/Paddle/pull/47251), [#53125](https://github.com/PaddlePaddle/Paddle/pull/53125), [#53828](https://github.com/PaddlePaddle/Paddle/pull/53828), [#51265](https://github.com/PaddlePaddle/Paddle/pull/51265), [#47689](https://github.com/PaddlePaddle/Paddle/pull/47689), [#48452](https://github.com/PaddlePaddle/Paddle/pull/48452), [#49072](https://github.com/PaddlePaddle/Paddle/pull/49072), [#48638](https://github.com/PaddlePaddle/Paddle/pull/48638), [#49175](https://github.com/PaddlePaddle/Paddle/pull/49175), [#49279](https://github.com/PaddlePaddle/Paddle/pull/49279), [#50857](https://github.com/PaddlePaddle/Paddle/pull/50857), [#49805](https://github.com/PaddlePaddle/Paddle/pull/49805), [#47734](https://github.com/PaddlePaddle/Paddle/pull/47734), [#45992](https://github.com/PaddlePaddle/Paddle/pull/45992), [#49616](https://github.com/PaddlePaddle/Paddle/pull/49616), [#49959](https://github.com/PaddlePaddle/Paddle/pull/49959), [#50536](https://github.com/PaddlePaddle/Paddle/pull/50536), [#49544](https://github.com/PaddlePaddle/Paddle/pull/49544), [#49842](https://github.com/PaddlePaddle/Paddle/pull/49842), [#46909](https://github.com/PaddlePaddle/Paddle/pull/46909), [#49361](https://github.com/PaddlePaddle/Paddle/pull/49361), [#50169](https://github.com/PaddlePaddle/Paddle/pull/50169), [#48314](https://github.com/PaddlePaddle/Paddle/pull/48314), [#48735](https://github.com/PaddlePaddle/Paddle/pull/48735), [#49122](https://github.com/PaddlePaddle/Paddle/pull/49122), [#49122](https://github.com/PaddlePaddle/Paddle/pull/49122), [#49177](https://github.com/PaddlePaddle/Paddle/pull/49177), [#49501](https://github.com/PaddlePaddle/Paddle/pull/49501), [#49562](https://github.com/PaddlePaddle/Paddle/pull/49562), [#49340](https://github.com/PaddlePaddle/Paddle/pull/49340), [#49550](https://github.com/PaddlePaddle/Paddle/pull/49550), [#49596](https://github.com/PaddlePaddle/Paddle/pull/49596), [#49730](https://github.com/PaddlePaddle/Paddle/pull/49730), [#49667](https://github.com/PaddlePaddle/Paddle/pull/49667), [#49692](https://github.com/PaddlePaddle/Paddle/pull/49692), [#49854](https://github.com/PaddlePaddle/Paddle/pull/49854), [#49845](https://github.com/PaddlePaddle/Paddle/pull/49845), [#49803](https://github.com/PaddlePaddle/Paddle/pull/49803), [#49889](https://github.com/PaddlePaddle/Paddle/pull/49889), [#49904](https://github.com/PaddlePaddle/Paddle/pull/49904), [#49518](https://github.com/PaddlePaddle/Paddle/pull/49518), [#49884](https://github.com/PaddlePaddle/Paddle/pull/49884), [#49880](https://github.com/PaddlePaddle/Paddle/pull/49880), [#49862](https://github.com/PaddlePaddle/Paddle/pull/49862), [#49921](https://github.com/PaddlePaddle/Paddle/pull/49921), [#49260](https://github.com/PaddlePaddle/Paddle/pull/49260), [#49929](https://github.com/PaddlePaddle/Paddle/pull/49929), [#49570](https://github.com/PaddlePaddle/Paddle/pull/49570), [#49882](https://github.com/PaddlePaddle/Paddle/pull/49882), [#50213](https://github.com/PaddlePaddle/Paddle/pull/50213), [#49780](https://github.com/PaddlePaddle/Paddle/pull/49780), [#50271](https://github.com/PaddlePaddle/Paddle/pull/50271), [#50289](https://github.com/PaddlePaddle/Paddle/pull/50289), [#50293](https://github.com/PaddlePaddle/Paddle/pull/50293), [#49735](https://github.com/PaddlePaddle/Paddle/pull/49735), [#50433](https://github.com/PaddlePaddle/Paddle/pull/50433), [#49847](https://github.com/PaddlePaddle/Paddle/pull/49847), [#50635](https://github.com/PaddlePaddle/Paddle/pull/50635), [#50950](https://github.com/PaddlePaddle/Paddle/pull/50950), [#50947](https://github.com/PaddlePaddle/Paddle/pull/50947), [#49460](https://github.com/PaddlePaddle/Paddle/pull/49460), [#53087](https://github.com/PaddlePaddle/Paddle/pull/53087), [#51687](https://github.com/PaddlePaddle/Paddle/pull/51687), [#52185](https://github.com/PaddlePaddle/Paddle/pull/52185), [#54649](https://github.com/PaddlePaddle/Paddle/pull/54649) -- API output supports 0-dimensional tensor, involving `paddle.sum `, `paddle.min/max `, `paddle.any/all ` and other 90 APIs. [#52891](https://github.com/PaddlePaddle/Paddle/pull/52891), [#52861](https://github.com/PaddlePaddle/Paddle/pull/52861), [#52775](https://github.com/PaddlePaddle/Paddle/pull/52775), [#52850](https://github.com/PaddlePaddle/Paddle/pull/52850), [#52843](https://github.com/PaddlePaddle/Paddle/pull/52843), [#52857](https://github.com/PaddlePaddle/Paddle/pull/52857), [#51721](https://github.com/PaddlePaddle/Paddle/pull/51721), [#53051](https://github.com/PaddlePaddle/Paddle/pull/53051), [#53192](https://github.com/PaddlePaddle/Paddle/pull/53192), [#52739](https://github.com/PaddlePaddle/Paddle/pull/52739), [#52741](https://github.com/PaddlePaddle/Paddle/pull/52741), [#53175](https://github.com/PaddlePaddle/Paddle/pull/53175), [#51889](https://github.com/PaddlePaddle/Paddle/pull/51889), [#53199](https://github.com/PaddlePaddle/Paddle/pull/53199), [#53242](https://github.com/PaddlePaddle/Paddle/pull/53242), [#53421](https://github.com/PaddlePaddle/Paddle/pull/53421) -- In addition to the support of 0-dimensional tensor, fix the original non-standard codes, and provide hints and compatibility for non-standard usage in the model codes. [#51562](https://github.com/PaddlePaddle/Paddle/pull/51562), [#51586](https://github.com/PaddlePaddle/Paddle/pull/51586), [#51757](https://github.com/PaddlePaddle/Paddle/pull/51757), [#52197](https://github.com/PaddlePaddle/Paddle/pull/52197), [#54117](https://github.com/PaddlePaddle/Paddle/pull/54117)。 - -#### new API -- Add `paddle.autograd.jacobian` and `paddle.autograd.hessian` APIs for scientific computing. [#53331](https://github.com/PaddlePaddle/Paddle/pull/53331) -- Add sparse computing API. For example, `paddle.sparse.reshape `, `paddle.sparse.sum ` and `paddle.sparse.slice `. [#46694](https://github.com/PaddlePaddle/Paddle/pull/46694), [#51513](https://github.com/PaddlePaddle/Paddle/pull/51513), [#53794](https://github.com/PaddlePaddle/Paddle/pull/53794), [#51406](https://github.com/PaddlePaddle/Paddle/pull/51406) -- Add APIsFor example, `paddle.optimizer.LBFGS `, `paddle.index_put ` and `paddle.logaddexp `. [#53314](https://github.com/PaddlePaddle/Paddle/pull/53314), [#51912](https://github.com/PaddlePaddle/Paddle/pull/51912), [#52886](https://github.com/PaddlePaddle/Paddle/pull/52886), [#50843](https://github.com/PaddlePaddle/Paddle/pull/50843), [#47282](https://github.com/PaddlePaddle/Paddle/pull/47282), [#52284](https://github.com/PaddlePaddle/Paddle/pull/52284) - -### Dynamic graphs -#### New features -- Add paddle.nn.utils.clip_grad_norm_ for gradient clipping support and paddle.Tensor.data_ptr for getting the address of the Tensor data's memory/GPU memory. [PR49935](https://github.com/PaddlePaddle/Paddle/pull/49935)[, PR48235](https://github.com/PaddlePaddle/Paddle/pull/48235), [PR49173](https://github.com/PaddlePaddle/Paddle/pull/49173) -- Add the saved_tensors_hooks mechanism, for temporary storage and retrieval of forward Tensor used in backward computation. [PR45763](https://github.com/PaddlePaddle/Paddle/pull/45763), [PR46215](https://github.com/PaddlePaddle/Paddle/pull/46215), [PR48124](https://github.com/PaddlePaddle/Paddle/pull/48124) -- Tensor supports pickler, for serialization of Tensor. [PR47025](https://github.com/PaddlePaddle/Paddle/pull/47025), [PR48179](https://github.com/PaddlePaddle/Paddle/pull/48179) -- Add debug logs, to print forward Python stacks when nan/inf appears in reverse. [PR53217](https://github.com/PaddlePaddle/Paddle/pull/53217) [PR52639](https://github.com/PaddlePaddle/Paddle/pull/52639) [PR52729](https://github.com/PaddlePaddle/Paddle/pull/52729) -- Add the support for expand_v2, tile, concat, assign, slice higher-order differentiation. [PR45941](https://github.com/PaddlePaddle/Paddle/pull/45941), [PR45942](https://github.com/PaddlePaddle/Paddle/pull/45942), [PR45940](https://github.com/PaddlePaddle/Paddle/pull/45940), [PR45879](https://github.com/PaddlePaddle/Paddle/pull/45879), [PR45960](https://github.com/PaddlePaddle/Paddle/pull/45960) - -#### Improvements -- Optimize log printing for dynamic graphs, including log content, VLog level, and error reporting content. [PR45783](https://github.com/PaddlePaddle/Paddle/pull/45783), [PR46349](https://github.com/PaddlePaddle/Paddle/pull/46349), [PR46934](https://github.com/PaddlePaddle/Paddle/pull/46934), [PR47724](https://github.com/PaddlePaddle/Paddle/pull/47724) -- Add FLAGS_auto_growth_chunk_size_in_mb for minimum chunk size settings of auto_growth_allocator. [PR52204](https://github.com/PaddlePaddle/Paddle/pull/52204) - -#### bug fix -- Fix bugs in some operators, including batch_norm, slice, set_value, scale, multinomial, adam, conv, transpose2_grad, conv2d_transpose_double_grad. [PR47802](https://github.com/PaddlePaddle/Paddle/pull/47802), [PR47634](https://github.com/PaddlePaddle/Paddle/pull/47634), [PR47349](https://github.com/PaddlePaddle/Paddle/pull/47349), [PR46124](https://github.com/PaddlePaddle/Paddle/pull/46124), [PR46147](https://github.com/PaddlePaddle/Paddle/pull/46147), [PR50388](https://github.com/PaddlePaddle/Paddle/pull/50388), [PR48626](https://github.com/PaddlePaddle/Paddle/pull/48626), [PR48519](https://github.com/PaddlePaddle/Paddle/pull/48519), [PR50386](https://github.com/PaddlePaddle/Paddle/pull/50386), [PR48432](https://github.com/PaddlePaddle/Paddle/pull/48432), [PR51851](https://github.com/PaddlePaddle/Paddle/pull/51851) -- Fix some PyLayer bugs. [PR51740](https://github.com/PaddlePaddle/Paddle/pull/51740), [PR47154](https://github.com/PaddlePaddle/Paddle/pull/47154), [PR47323](https://github.com/PaddlePaddle/Paddle/pull/47323), [PR54041](https://github.com/PaddlePaddle/Paddle/pull/54041), [PR48533](https://github.com/PaddlePaddle/Paddle/pull/48533) -- Makes sure sync_batch_norm is sequential in reverse to avoid hang or precision errors due to misordering. [PR52268](https://github.com/PaddlePaddle/Paddle/pull/52268), [PR52860](https://github.com/PaddlePaddle/Paddle/pull/52860), [PR52779](https://github.com/PaddlePaddle/Paddle/pull/52779) -- Fix a bug of linspace under AMP. [PR46088](https://github.com/PaddlePaddle/Paddle/pull/46088) -- Fix Python C API’s incorrect call that causes Windows to crash. [PR46833](https://github.com/PaddlePaddle/Paddle/pull/46833) -- Fix the bug that DataLoader may miss deleting/dev/shm. [PR48511](https://github.com/PaddlePaddle/Paddle/pull/48511) -- Fix some bugs of paddle.grad. [PR47151](https://github.com/PaddlePaddle/Paddle/pull/47151) -- Add error message for operators that do not support higher order differentiation. [PR47231](https://github.com/PaddlePaddle/Paddle/pull/47231) -- Add numpyarray support for python operators. [PR48229](https://github.com/PaddlePaddle/Paddle/pull/48229) -- Delete either of element_size APIs. [PR49631](https://github.com/PaddlePaddle/Paddle/pull/49631) -- Fix the bug of crash when opening old dynamic graph VLOG. [PR47115](https://github.com/PaddlePaddle/Paddle/pull/47115) -- For XPU, change to d2h+h2d in case of d2d, to solve the multi-threading problem. [PR48373](https://github.com/PaddlePaddle/Paddle/pull/48373) - -#### Performance optimization -- Python operators sink to C++ implementation, to improve API performance. There is a 3x to 6x performance improvement in this class of APIs after sinking. [PR45811](https://github.com/PaddlePaddle/Paddle/pull/45811), [PR46326](https://github.com/PaddlePaddle/Paddle/pull/46326), [PR46329](https://github.com/PaddlePaddle/Paddle/pull/46329), [PR46520](https://github.com/PaddlePaddle/Paddle/pull/46520), [PR46542](https://github.com/PaddlePaddle/Paddle/pull/46542), [PR46565](https://github.com/PaddlePaddle/Paddle/pull/46565), [PR47060](https://github.com/PaddlePaddle/Paddle/pull/47060), [PR47077](https://github.com/PaddlePaddle/Paddle/pull/47077), [PR47174](https://github.com/PaddlePaddle/Paddle/pull/47174), [PR47315](https://github.com/PaddlePaddle/Paddle/pull/47315) -- Optimize the Optimizer CPU scheduling performance to reduce GPU Gap caused by Optimizer phase. [PR49787](https://github.com/PaddlePaddle/Paddle/pull/49787), [PR50188](https://github.com/PaddlePaddle/Paddle/pull/50188)[, PR51340](https://github.com/PaddlePaddle/Paddle/pull/51340), [PR49864](https://github.com/PaddlePaddle/Paddle/pull/49864), [PR50158](https://github.com/PaddlePaddle/Paddle/pull/50158), [PR50335](https://github.com/PaddlePaddle/Paddle/pull/50335) -- According to the logic that API can be sunk to C++, API is sunk to C++ to improve API performance. [PR46412](https://github.com/PaddlePaddle/Paddle/pull/46412), [PR46190](https://github.com/PaddlePaddle/Paddle/pull/46190) -- Optimize unnecessary call logic on Python side under dynamic graph, to improve API performance. [PR46221](https://github.com/PaddlePaddle/Paddle/pull/46221), [PR49473](https://github.com/PaddlePaddle/Paddle/pull/49473), [PR49574](https://github.com/PaddlePaddle/Paddle/pull/49574), [PR49589](https://github.com/PaddlePaddle/Paddle/pull/49589), [PR49612](https://github.com/PaddlePaddle/Paddle/pull/49612), [PR49717](https://github.com/PaddlePaddle/Paddle/pull/49717)[, PR49733](https://github.com/PaddlePaddle/Paddle/pull/49733), [PR49823](https://github.com/PaddlePaddle/Paddle/pull/49823)[, PR49508](https://github.com/PaddlePaddle/Paddle/pull/49508), [PR46840](https://github.com/PaddlePaddle/Paddle/pull/46840) -- Optimize use of Allocator to improve dynamic graph API scheduling performance. [PR47125](https://github.com/PaddlePaddle/Paddle/pull/47125), [PR48548](https://github.com/PaddlePaddle/Paddle/pull/48548), [PR50995](https://github.com/PaddlePaddle/Paddle/pull/50995), [PR47731](https://github.com/PaddlePaddle/Paddle/pull/47731) -- Optimize fused_attention operator performance. [PR48902](https://github.com/PaddlePaddle/Paddle/pull/48902) -- For optimizer's _add_accumulator, if device is CPU and under dynamic graphs, use full to initialize var directly. [PR48189](https://github.com/PaddlePaddle/Paddle/pull/48189) -- Prune unnecessarily executed subgraphs for inverse graphs to improve performance. [PR47827](https://github.com/PaddlePaddle/Paddle/pull/47827) -- Optimize performance of initalizers. [PR46033](https://github.com/PaddlePaddle/Paddle/pull/46033) -- Add fused dropout add operator to improve computation performance when dropout and add are used together. [#52903](https://github.com/PaddlePaddle/Paddle/pull/52903) - -### Static graphs -#### The new static graph executor is now fully go-live. -The new actuator for static graph implements a number of functions and performance optimizations, and completes unification and replacement of the original multiple sets of old actuators. The new actuator becomes the back-end default execution engine for the static graph single card and distributed training python side entrance, as well as dynamic-to-static, control flow, CINN, etc. This significantly improves scheduling performance of the framework, and the functional architecture is clearer. Secondary development capability is significantly enhanced. [#45913](https://github.com/PaddlePaddle/Paddle/pull/45913)，[#46025](https://github.com/PaddlePaddle/Paddle/pull/46025)，[#48911](https://github.com/PaddlePaddle/Paddle/pull/48911)，[#50239](https://github.com/PaddlePaddle/Paddle/pull/50239)，[#45696](https://github.com/PaddlePaddle/Paddle/pull/45696)，[#46092](https://github.com/PaddlePaddle/Paddle/pull/46092)，[#48158](https://github.com/PaddlePaddle/Paddle/pull/48158),[#51389](https://github.com/PaddlePaddle/Paddle/pull/51389)，[#49708](https://github.com/PaddlePaddle/Paddle/pull/49708)，[#49275](https://github.com/PaddlePaddle/Paddle/pull/49275),[#48789](https://github.com/PaddlePaddle/Paddle/pull/48789)，[#49939](https://github.com/PaddlePaddle/Paddle/pull/49939)，[#51149](https://github.com/PaddlePaddle/Paddle/pull/51149)，[#52652](https://github.com/PaddlePaddle/Paddle/pull/52652) - -### Operator library -#### Enhance functions of customized operators -New function support for custom extension mechanism to achieve the C++ extension of the arithmetic function binding to the Python side, to further enhance the framework's secondary development capabilities. The extension supports custom hardware to use a custom operator mechanism to meet the needs of hardware manufacturers to implement non-Paddle existing operations. The extension supports custom operators in the implementation of the `inplace `, `vector < Tensor> ` output, `optional < Tnesor> ` input and other high-level mechanisms in custom operators. Optimized scheduling performance of custom operators in dynamic graph mode, with a 25.4% performance improvement for operators with multiple input parameters. Add new commonly used operators and APIs for custom operator Tensor extensions. Support chaining calls and simplify code writing. Optimize the operator kernel selection mechanism. Improve the logic of some operator kernels, enhance supported data types and optimize performance. Add and improve XPU kernels 100+. Fix 170+ bugs. -[#49222](https://github.com/PaddlePaddle/Paddle/pull/49222), [#51773](https://github.com/PaddlePaddle/Paddle/pull/51773), [#51923](https://github.com/PaddlePaddle/Paddle/pull/51923), [#53080](https://github.com/PaddlePaddle/Paddle/pull/53080), [#50731](https://github.com/PaddlePaddle/Paddle/pull/50731), [#50563](https://github.com/PaddlePaddle/Paddle/pull/50563), [#50840](https://github.com/PaddlePaddle/Paddle/pull/50840), [#50983](https://github.com/PaddlePaddle/Paddle/pull/50983), [#51713](https://github.com/PaddlePaddle/Paddle/pull/51713), [#48733](https://github.com/PaddlePaddle/Paddle/pull/48733), [#50558](https://github.com/PaddlePaddle/Paddle/pull/50558), [#50764](https://github.com/PaddlePaddle/Paddle/pull/50764), [#51973](https://github.com/PaddlePaddle/Paddle/pull/51973), [#52216](https://github.com/PaddlePaddle/Paddle/pull/52216), [#51027](https://github.com/PaddlePaddle/Paddle/pull/51027), [#50745](https://github.com/PaddlePaddle/Paddle/pull/50745), [#50756](https://github.com/PaddlePaddle/Paddle/pull/50756), [#50886](https://github.com/PaddlePaddle/Paddle/pull/50886), [#50813](https://github.com/PaddlePaddle/Paddle/pull/50813), [#50869](https://github.com/PaddlePaddle/Paddle/pull/50869), [#51085](https://github.com/PaddlePaddle/Paddle/pull/51085), [#51646](https://github.com/PaddlePaddle/Paddle/pull/51646), [#51620](https://github.com/PaddlePaddle/Paddle/pull/51620), [#51844](https://github.com/PaddlePaddle/Paddle/pull/51844), [#52421](https://github.com/PaddlePaddle/Paddle/pull/52421), [#52872](https://github.com/PaddlePaddle/Paddle/pull/52872), [#52597](https://github.com/PaddlePaddle/Paddle/pull/52597), [#50582](https://github.com/PaddlePaddle/Paddle/pull/50582), [#52114](https://github.com/PaddlePaddle/Paddle/pull/52114), [#52915](https://github.com/PaddlePaddle/Paddle/pull/52915), [#50928](https://github.com/PaddlePaddle/Paddle/pull/50928), [#48272](https://github.com/PaddlePaddle/Paddle/pull/48272), [#48702](https://github.com/PaddlePaddle/Paddle/pull/48702), [#52191](https://github.com/PaddlePaddle/Paddle/pull/52191), [#52191](https://github.com/PaddlePaddle/Paddle/pull/52191), [#47374](https://github.com/PaddlePaddle/Paddle/pull/47374), [#47375](https://github.com/PaddlePaddle/Paddle/pull/47375), [#47378](https://github.com/PaddlePaddle/Paddle/pull/47378), [#54126](https://github.com/PaddlePaddle/Paddle/pull/54126), [#47638](https://github.com/PaddlePaddle/Paddle/pull/47638), [#47661](https://github.com/PaddlePaddle/Paddle/pull/47661), [#50606](https://github.com/PaddlePaddle/Paddle/pull/50606), [#53528](https://github.com/PaddlePaddle/Paddle/pull/53528), [#50599](https://github.com/PaddlePaddle/Paddle/pull/50599), [#51727](https://github.com/PaddlePaddle/Paddle/pull/51727), [#50825](https://github.com/PaddlePaddle/Paddle/pull/50825), [#50773](https://github.com/PaddlePaddle/Paddle/pull/50773), [#50979](https://github.com/PaddlePaddle/Paddle/pull/50979), [#53336](https://github.com/PaddlePaddle/Paddle/pull/53336), [#53555](https://github.com/PaddlePaddle/Paddle/pull/53555), [#53716](https://github.com/PaddlePaddle/Paddle/pull/53716), [#53753](https://github.com/PaddlePaddle/Paddle/pull/53753), [#53981](https://github.com/PaddlePaddle/Paddle/pull/53981), [#53977](https://github.com/PaddlePaddle/Paddle/pull/53977), [#53980](https://github.com/PaddlePaddle/Paddle/pull/53980), [#54043](https://github.com/PaddlePaddle/Paddle/pull/54043), [#54066](https://github.com/PaddlePaddle/Paddle/pull/54066), [#52866](https://github.com/PaddlePaddle/Paddle/pull/52866), [#53043](https://github.com/PaddlePaddle/Paddle/pull/53043), [#53325](https://github.com/PaddlePaddle/Paddle/pull/53325), [#54323](https://github.com/PaddlePaddle/Paddle/pull/54323), [#54367](https://github.com/PaddlePaddle/Paddle/pull/54367), [#51353](https://github.com/PaddlePaddle/Paddle/pull/51353), [#53749](https://github.com/PaddlePaddle/Paddle/pull/53749), [#50013](https://github.com/PaddlePaddle/Paddle/pull/50013), [#47570](https://github.com/PaddlePaddle/Paddle/pull/47570), [#50997](https://github.com/PaddlePaddle/Paddle/pull/50997), [#51241](https://github.com/PaddlePaddle/Paddle/pull/51241), [#49537](https://github.com/PaddlePaddle/Paddle/pull/49537) - -#### Unification of operator architecture -Unify all remaining 350+ operator kernels under the original operator system into PHI operator library. Unify the way of defining operator in the original operator system into the operator definition form of PHI operator library (configuration of operator definition based on YAML), enhancing unity of the architecture, and reducing comprehension cost of framework development. Decouple all Fluid header files the PHI operator library depends on and compile them independently as dynamic link libraries to provide a lighter reuse of the operator library for secondary development of the framework. Continue to standardize and adjust unspecified operators, as well as operator kernels in the PaddlePaddle framework. It is easy for developers to understand and reduce cost of accessing hardware. -[#47856](https://github.com/PaddlePaddle/Paddle/pull/47856), [#49328](https://github.com/PaddlePaddle/Paddle/pull/49328), [#49138](https://github.com/PaddlePaddle/Paddle/pull/49138), [#52014](https://github.com/PaddlePaddle/Paddle/pull/52014), [#52044](https://github.com/PaddlePaddle/Paddle/pull/52044), [#52116](https://github.com/PaddlePaddle/Paddle/pull/52116), [#52486](https://github.com/PaddlePaddle/Paddle/pull/52486), [#52101](https://github.com/PaddlePaddle/Paddle/pull/52101), [#52882](https://github.com/PaddlePaddle/Paddle/pull/52882), [#53003](https://github.com/PaddlePaddle/Paddle/pull/53003), [#53034](https://github.com/PaddlePaddle/Paddle/pull/53034), [#51914](https://github.com/PaddlePaddle/Paddle/pull/51914), [#49116](https://github.com/PaddlePaddle/Paddle/pull/49116), [#52626](https://github.com/PaddlePaddle/Paddle/pull/52626), [#52878](https://github.com/PaddlePaddle/Paddle/pull/52878), [#52879](https://github.com/PaddlePaddle/Paddle/pull/52879), [#52880](https://github.com/PaddlePaddle/Paddle/pull/52880), [#52875](https://github.com/PaddlePaddle/Paddle/pull/52875), [#51600](https://github.com/PaddlePaddle/Paddle/pull/51600), [#51601](https://github.com/PaddlePaddle/Paddle/pull/51601), [#51590](https://github.com/PaddlePaddle/Paddle/pull/51590), [#51887](https://github.com/PaddlePaddle/Paddle/pull/51887), [#51891](https://github.com/PaddlePaddle/Paddle/pull/51891), [#52036](https://github.com/PaddlePaddle/Paddle/pull/52036), [#52130](https://github.com/PaddlePaddle/Paddle/pull/52130), [#52134](https://github.com/PaddlePaddle/Paddle/pull/52134), [#51951](https://github.com/PaddlePaddle/Paddle/pull/51951), [#51886](https://github.com/PaddlePaddle/Paddle/pull/51886), [#52274](https://github.com/PaddlePaddle/Paddle/pull/52274), [#52263](https://github.com/PaddlePaddle/Paddle/pull/52263), [#51913](https://github.com/PaddlePaddle/Paddle/pull/51913), [#52145](https://github.com/PaddlePaddle/Paddle/pull/52145), [#52347](https://github.com/PaddlePaddle/Paddle/pull/52347), [#52370](https://github.com/PaddlePaddle/Paddle/pull/52370), [#52437](https://github.com/PaddlePaddle/Paddle/pull/52437), [#52424](https://github.com/PaddlePaddle/Paddle/pull/52424), [#52231](https://github.com/PaddlePaddle/Paddle/pull/52231), [#52522](https://github.com/PaddlePaddle/Paddle/pull/52522), [#52529](https://github.com/PaddlePaddle/Paddle/pull/52529), [#52802](https://github.com/PaddlePaddle/Paddle/pull/52802), [#52799](https://github.com/PaddlePaddle/Paddle/pull/52799), [#52855](https://github.com/PaddlePaddle/Paddle/pull/52855), [#52711](https://github.com/PaddlePaddle/Paddle/pull/52711), [#52940](https://github.com/PaddlePaddle/Paddle/pull/52940), [#53309](https://github.com/PaddlePaddle/Paddle/pull/53309), [#47817](https://github.com/PaddlePaddle/Paddle/pull/47817), [#48001](https://github.com/PaddlePaddle/Paddle/pull/48001), [#48063](https://github.com/PaddlePaddle/Paddle/pull/48063), [#48049](https://github.com/PaddlePaddle/Paddle/pull/48049), [#48168](https://github.com/PaddlePaddle/Paddle/pull/48168), [#48415](https://github.com/PaddlePaddle/Paddle/pull/48415), [#48696](https://github.com/PaddlePaddle/Paddle/pull/48696), [#48970](https://github.com/PaddlePaddle/Paddle/pull/48970), [#50183](https://github.com/PaddlePaddle/Paddle/pull/50183), [#50407](https://github.com/PaddlePaddle/Paddle/pull/50407), [#50498](https://github.com/PaddlePaddle/Paddle/pull/50498), [#50419](https://github.com/PaddlePaddle/Paddle/pull/50419), [#50282](https://github.com/PaddlePaddle/Paddle/pull/50282), [#50870](https://github.com/PaddlePaddle/Paddle/pull/50870), [#50911](https://github.com/PaddlePaddle/Paddle/pull/50911), [#50865](https://github.com/PaddlePaddle/Paddle/pull/50865), [#51288](https://github.com/PaddlePaddle/Paddle/pull/51288), [#53735](https://github.com/PaddlePaddle/Paddle/pull/53735), [#47248](https://github.com/PaddlePaddle/Paddle/pull/47248), [#47787](https://github.com/PaddlePaddle/Paddle/pull/47787), [#52202](https://github.com/PaddlePaddle/Paddle/pull/52202), -[#47579](https://github.com/PaddlePaddle/Paddle/pull/47579), [#49444](https://github.com/PaddlePaddle/Paddle/pull/49444), [#45772](https://github.com/PaddlePaddle/Paddle/pull/45772), [#51264](https://github.com/PaddlePaddle/Paddle/pull/51264), [#51634](https://github.com/PaddlePaddle/Paddle/pull/51634), [#51631](https://github.com/PaddlePaddle/Paddle/pull/51631), [#47385](https://github.com/PaddlePaddle/Paddle/pull/47385), [#46342](https://github.com/PaddlePaddle/Paddle/pull/46342), [#47510](https://github.com/PaddlePaddle/Paddle/pull/47510), [#47532](https://github.com/PaddlePaddle/Paddle/pull/47532), [#47702](https://github.com/PaddlePaddle/Paddle/pull/47702), [#47860](https://github.com/PaddlePaddle/Paddle/pull/47860), [#49470](https://github.com/PaddlePaddle/Paddle/pull/49470), [#50358](https://github.com/PaddlePaddle/Paddle/pull/50358), [#49121](https://github.com/PaddlePaddle/Paddle/pull/49121), [#50190](https://github.com/PaddlePaddle/Paddle/pull/50190), [#52374](https://github.com/PaddlePaddle/Paddle/pull/52374), [#52372](https://github.com/PaddlePaddle/Paddle/pull/52372), [#52375](https://github.com/PaddlePaddle/Paddle/pull/52375), [#52371](https://github.com/PaddlePaddle/Paddle/pull/52371) - -### Dynamic-to-static plus combinator -#### New features -- Add the combination rules for combinators such as dropout, silu, stack, relu, expand, unsqueeze, pow, squeeze, meshgrid, batch_norm, layer_norm, group_norm, instance_norm, full_like, split, split_with_num, gelu, mean, flatten, rsqrt, hadswish [#50497](https://github.com/PaddlePaddle/Paddle/pull/50497), [#50838](https://github.com/PaddlePaddle/Paddle/pull/50838), [#50861](https://github.com/PaddlePaddle/Paddle/pull/50861), [#50819](https://github.com/PaddlePaddle/Paddle/pull/50819), [#50810](https://github.com/PaddlePaddle/Paddle/pull/50810), [#51527](https://github.com/PaddlePaddle/Paddle/pull/51527), [#51070](https://github.com/PaddlePaddle/Paddle/pull/51070), [#51539](https://github.com/PaddlePaddle/Paddle/pull/51539), [#51061](https://github.com/PaddlePaddle/Paddle/pull/51061), [#49894](https://github.com/PaddlePaddle/Paddle/pull/49894), [#50422](https://github.com/PaddlePaddle/Paddle/pull/50422), [#51874](https://github.com/PaddlePaddle/Paddle/pull/51874), [#51341](https://github.com/PaddlePaddle/Paddle/pull/51341), [#50295](https://github.com/PaddlePaddle/Paddle/pull/50295), [#50298](https://github.com/PaddlePaddle/Paddle/pull/50298), [#50672](https://github.com/PaddlePaddle/Paddle/pull/50672), [#51432](https://github.com/PaddlePaddle/Paddle/pull/51432), [#51003](https://github.com/PaddlePaddle/Paddle/pull/51003) -- Add the vjp rule for combinators such as gather_nd, reduce_max, group_norm, relu, reduce_max, gather, topk, sqrt, elementwise_pow, softmax, batch_norm, prod, multiply, expand, div, relu, slice, cumsum, sigmoid, layer_norm, sin, cos, roll, instance_norm, abs, assign, tile, scatter_nd_add, erf, floor, log, silu, leaky_relu, pad [#50966](https://github.com/PaddlePaddle/Paddle/pull/50966), [#51653](https://github.com/PaddlePaddle/Paddle/pull/51653), [#52663](https://github.com/PaddlePaddle/Paddle/pull/52663), [#51742](https://github.com/PaddlePaddle/Paddle/pull/51742), [#52203](https://github.com/PaddlePaddle/Paddle/pull/52203), [#50794](https://github.com/PaddlePaddle/Paddle/pull/50794), [#50305](https://github.com/PaddlePaddle/Paddle/pull/50305), [#50786](https://github.com/PaddlePaddle/Paddle/pull/50786), [#50679](https://github.com/PaddlePaddle/Paddle/pull/50679), [#51045](https://github.com/PaddlePaddle/Paddle/pull/51045), [#51230](https://github.com/PaddlePaddle/Paddle/pull/51230), [#51474](https://github.com/PaddlePaddle/Paddle/pull/51474), [#51283](https://github.com/PaddlePaddle/Paddle/pull/51283), [#51238](https://github.com/PaddlePaddle/Paddle/pull/51238), [#49831](https://github.com/PaddlePaddle/Paddle/pull/49831), [#51838](https://github.com/PaddlePaddle/Paddle/pull/51838), [#50771](https://github.com/PaddlePaddle/Paddle/pull/50771), [#50565](https://github.com/PaddlePaddle/Paddle/pull/50565), [#51768](https://github.com/PaddlePaddle/Paddle/pull/51768), [#51750](https://github.com/PaddlePaddle/Paddle/pull/51750), [#51748](https://github.com/PaddlePaddle/Paddle/pull/51748), [#52532](https://github.com/PaddlePaddle/Paddle/pull/52532), [#52935](https://github.com/PaddlePaddle/Paddle/pull/52935), [#50963](https://github.com/PaddlePaddle/Paddle/pull/50963), [#51430](https://github.com/PaddlePaddle/Paddle/pull/51430), [#53141](https://github.com/PaddlePaddle/Paddle/pull/53141), [#52469](https://github.com/PaddlePaddle/Paddle/pull/52469), [#50436](https://github.com/PaddlePaddle/Paddle/pull/50436), [#51059](https://github.com/PaddlePaddle/Paddle/pull/51059), [#51296](https://github.com/PaddlePaddle/Paddle/pull/51296), [#52533](https://github.com/PaddlePaddle/Paddle/pull/52533), [#53374](https://github.com/PaddlePaddle/Paddle/pull/53374) -- Add the second-order differentiation rule for combinators such as matmul, tanh, and elementwise [#50452](https://github.com/PaddlePaddle/Paddle/pull/50452), [#52192](https://github.com/PaddlePaddle/Paddle/pull/52192), [#53014](https://github.com/PaddlePaddle/Paddle/pull/53014) -- Add the bf16 datatype support for combinators such as exp, reduce_mean, softmax, divide, cast, layer_norm, prod, meshgrid, expand_as, dropout, concat, gather_nd, elementwise_max, elementwise_pow, reduce_max [#54263](https://github.com/PaddlePaddle/Paddle/pull/54263)， [#54236](https://github.com/PaddlePaddle/Paddle/pull/54236), [#53865](https://github.com/PaddlePaddle/Paddle/pull/53865), [#54175](https://github.com/PaddlePaddle/Paddle/pull/54175), [#54399](https://github.com/PaddlePaddle/Paddle/pull/54399) -- Add support for assigning semantics to containers in control flow in dynamic-to-static. [#51248](https://github.com/PaddlePaddle/Paddle/pull/51248) -- For to_static, add full graph fallback function. When dynamic-to-static conversion fails, the whole graph can fall back to the dynamic graph mode of execution. For the fallback mechanism, add the set_eval_frame API. [#50111](https://github.com/PaddlePaddle/Paddle/pull/50111), [#52006](https://github.com/PaddlePaddle/Paddle/pull/52006) -- For to_static, support the combinator mechanism. Support the scenario of using register_hook under to_static decoration; [#49836](https://github.com/PaddlePaddle/Paddle/pull/49836), [#52948](https://github.com/PaddlePaddle/Paddle/pull/52948), [#53572](https://github.com/PaddlePaddle/Paddle/pull/53572) -- Add a backend parameter to the to_static API. It can be specified as `CINN` or None. When the parameter is specified as CINN, the CINN compiler will be used to accelerate training and inference. [#52596](https://github.com/PaddlePaddle/Paddle/pull/52596) -- Add the code automatic generation function for the primitive API. Based on operator definitions in ops.yaml and legacy_ops.yaml, automatically generate code for the primitive API. Automatically generate the Tensor computation API. [#50315](https://github.com/PaddlePaddle/Paddle/pull/50315), [#49654](https://github.com/PaddlePaddle/Paddle/pull/49654), [#50642](https://github.com/PaddlePaddle/Paddle/pull/50642) -- Add the function of forward combination of operators. By registering the combination rules of forward operators, it can split forward operators into base operators. [#49605](https://github.com/PaddlePaddle/Paddle/pull/49605) -- Add the combinator switch. You can set environmental variables in shell to split operators in different ways. [#50309](https://github.com/PaddlePaddle/Paddle/pull/50309) -- Add `OpTest ` combination test function to guarantee accuracy of operators. Add elementwise class base operator unit test. Add batch_norm CINN unit test. [#50509](https://github.com/PaddlePaddle/Paddle/pull/50509), [#50807](https://github.com/PaddlePaddle/Paddle/pull/50807), [#52815](https://github.com/PaddlePaddle/Paddle/pull/52815) - -#### Improvements -- Add combinator to support FP16 operation and AMP O1 operation. Add AMP logic for softmax and layer_norm operators. [#52397](https://github.com/PaddlePaddle/Paddle/pull/52397), [#52598](https://github.com/PaddlePaddle/Paddle/pull/52598), [#51473](https://github.com/PaddlePaddle/Paddle/pull/51473) -- Simplify combination rules and vjp rules of the combinator batch_norm. [#54012](https://github.com/PaddlePaddle/Paddle/pull/54012), [#51827](https://github.com/PaddlePaddle/Paddle/pull/51827), [#51933](https://github.com/PaddlePaddle/Paddle/pull/51933), -- Optimize combination rules for combinators, and improve performance of combination rules with containing scalar. Optimize log printing for combinators. [#51960](https://github.com/PaddlePaddle/Paddle/pull/51960), [#50160](https://github.com/PaddlePaddle/Paddle/pull/50160) -- Combinator supports the jit.save API. Add custom VJP rule API. [#52344](https://github.com/PaddlePaddle/Paddle/pull/52344), [#50885](https://github.com/PaddlePaddle/Paddle/pull/50885) -- Remove the overwrite parameter from combinator gather_grad. [#52707](https://github.com/PaddlePaddle/Paddle/pull/52707) -- Clean up dynamic-to-static code style, optimize error message, and standardize logs. [#48637](https://github.com/PaddlePaddle/Paddle/pull/48637), [#46128](https://github.com/PaddlePaddle/Paddle/pull/46128), [#52527](https://github.com/PaddlePaddle/Paddle/pull/52527), [#46800](https://github.com/PaddlePaddle/Paddle/pull/46800),[#46415](https://github.com/PaddlePaddle/Paddle/pull/46415) -- For dynamic-to-static, call the append backward to get `grad var name ` to fix the error in the high order gradient computation. [#53250](https://github.com/PaddlePaddle/Paddle/pull/53250) -- Upgrade the dynamic-to-static function, and clean up the temporary directory of to_static to speed up code conversion. Enhance to_static to automatically skip internal API. Support use of to_static decorator in the program. [#47102](https://github.com/PaddlePaddle/Paddle/pull/47102), [#50596](https://github.com/PaddlePaddle/Paddle/pull/50596), [#45768](https://github.com/PaddlePaddle/Paddle/pull/45768) -- For dynamic-to-static, optimize `print ` function conversion to support printing Tensor parameters at the networking stage. Upgrade the parameter collection mechanism. [#48672](https://github.com/PaddlePaddle/Paddle/pull/48672), [#50336](https://github.com/PaddlePaddle/Paddle/pull/50336) - -#### bug fix -- For the combinator, fix cmake compilation errors. Fix cuda 12 test errors. Fix bugs of operators such as meshgird, expand_as, concat, conv, and arrange. [#49643](https://github.com/PaddlePaddle/Paddle/pull/49643), [#54622](https://github.com/PaddlePaddle/Paddle/pull/54622), [#53951](https://github.com/PaddlePaddle/Paddle/pull/53951), [#53951](https://github.com/PaddlePaddle/Paddle/pull/53951), [#53350](https://github.com/PaddlePaddle/Paddle/pull/53350), [#51486](https://github.com/PaddlePaddle/Paddle/pull/51486), [#52764](https://github.com/PaddlePaddle/Paddle/pull/52764) -- For the combinator, fix the bug in a number of scenarios such as rank=1, shape=-1, amp, and multi-process. [#51413](https://github.com/PaddlePaddle/Paddle/pull/51413), [#51435](https://github.com/PaddlePaddle/Paddle/pull/51435), [#50518](https://github.com/PaddlePaddle/Paddle/pull/50518), [#47301](https://github.com/PaddlePaddle/Paddle/pull/47301), -- For the combinator, fix bugs in automatic code generation of composite grad maker and static prim api. Fix bugs that op creation attributes are missing, and some combination rules do not take effect. [#50854](https://github.com/PaddlePaddle/Paddle/pull/50854), [#51445](https://github.com/PaddlePaddle/Paddle/pull/51445), [#50780](https://github.com/PaddlePaddle/Paddle/pull/50780), [#52120](https://github.com/PaddlePaddle/Paddle/pull/52120) -- Fix some other bugs for combinators [#50086](https://github.com/PaddlePaddle/Paddle/pull/50086), [#51208](https://github.com/PaddlePaddle/Paddle/pull/51208), [#51577](https://github.com/PaddlePaddle/Paddle/pull/51577), [#53598](https://github.com/PaddlePaddle/Paddle/pull/53598), [#47500](https://github.com/PaddlePaddle/Paddle/pull/47500), [#52119](https://github.com/PaddlePaddle/Paddle/pull/52119), [#50397](https://github.com/PaddlePaddle/Paddle/pull/50397), [#50527](https://github.com/PaddlePaddle/Paddle/pull/50527), [#50788](https://github.com/PaddlePaddle/Paddle/pull/50788), [#51014](https://github.com/PaddlePaddle/Paddle/pull/51014), [#52154](https://github.com/PaddlePaddle/Paddle/pull/52154), [#52752](https://github.com/PaddlePaddle/Paddle/pull/52752) -- For dynamic-to-static, fix the bugs of dataloader, cond input dict, transformer import, T5 model memory leak, and grad var name parsing error. [#49821](https://github.com/PaddlePaddle/Paddle/pull/49821)， [#47299](https://github.com/PaddlePaddle/Paddle/pull/47299), [#50776](https://github.com/PaddlePaddle/Paddle/pull/50776), [#50883](https://github.com/PaddlePaddle/Paddle/pull/50883), [#51100](https://github.com/PaddlePaddle/Paddle/pull/51100), [#51464](https://github.com/PaddlePaddle/Paddle/pull/51464), [#51966](https://github.com/PaddlePaddle/Paddle/pull/51966), [#52110](https://github.com/PaddlePaddle/Paddle/pull/52110), [#52821](https://github.com/PaddlePaddle/Paddle/pull/52821) -- For dynamic-to-static, fix the bugs of Lazy initialization, Windows training, is_paddle_func failure, and recurrent op failure to delete pass. [#50785](https://github.com/PaddlePaddle/Paddle/pull/50785), [#52580](https://github.com/PaddlePaddle/Paddle/pull/52580), [#51585](https://github.com/PaddlePaddle/Paddle/pull/51585), [#51763](https://github.com/PaddlePaddle/Paddle/pull/51763), [#51763](https://github.com/PaddlePaddle/Paddle/pull/51763) - -#### Performance optimization -- Add scope caching and reuse mechanism during execution of run_program_op in dynamic-to-static, to avoid passing new scope for each step. [#45813](https://github.com/PaddlePaddle/Paddle/pull/45813) - -### Distributed training -#### Dynamic graph distributed training -- Remove the distributed sharding API in the old dynamic graphs. [#49334](https://github.com/PaddlePaddle/Paddle/pull/49334) -- Upgrade fleet to distributed directory. [#50834](https://github.com/PaddlePaddle/Paddle/pull/50834) -- Optimize log printing for distributed strategies. [#47761](https://github.com/PaddlePaddle/Paddle/pull/47761) -- For re-computation, support hook mode, inplace function, and stop_gradient mode. Support more flexible use. [#48471](https://github.com/PaddlePaddle/Paddle/pull/48471), [#47985](https://github.com/PaddlePaddle/Paddle/pull/47985) -- Data parallel - - For data parallel, support no_sync API for blocking parameter gradient communications. Support the parameter synchronization function. Add scale API to scale parameters. [#47536](https://github.com/PaddlePaddle/Paddle/pull/47536),[#51895](https://github.com/PaddlePaddle/Paddle/pull/51895),[#47519](https://github.com/PaddlePaddle/Paddle/pull/47519) - - Fix the problem of video memory leakage under data parallel. [#47369](https://github.com/PaddlePaddle/Paddle/pull/47369),[#47444](https://github.com/PaddlePaddle/Paddle/pull/47444),[#48668](https://github.com/PaddlePaddle/Paddle/pull/48668) - - Support sparse parameter gradient synchronization. [#52785](https://github.com/PaddlePaddle/Paddle/pull/52785) -- Pipeline parallel - - Optimize pipeline performance, and remove communication wait. Optimize scheduling and communication overlap. [#46209](https://github.com/PaddlePaddle/Paddle/pull/46209),[#54003](https://github.com/PaddlePaddle/Paddle/pull/54003),[#54312](https://github.com/PaddlePaddle/Paddle/pull/54312),[#53384](https://github.com/PaddlePaddle/Paddle/pull/53384),[#54310](https://github.com/PaddlePaddle/Paddle/pull/54310),[#46399](https://github.com/PaddlePaddle/Paddle/pull/46399),[#46483](https://github.com/PaddlePaddle/Paddle/pull/46483),[#46780](https://github.com/PaddlePaddle/Paddle/pull/46780),[#46116](https://github.com/PaddlePaddle/Paddle/pull/46116) - - Support custom sharding, log printing, random seed setting, and timer elapsed time printing. [#53344](https://github.com/PaddlePaddle/Paddle/pull/53344), [#47670](https://github.com/PaddlePaddle/Paddle/pull/47670),[#47336](https://github.com/PaddlePaddle/Paddle/pull/47336),[#52656](https://github.com/PaddlePaddle/Paddle/pull/52656),[#53831](https://github.com/PaddlePaddle/Paddle/pull/53831) - - Optimize video memory release logic in pipeline scheduling, and release intermediate variables and data in advance. [#54557](https://github.com/PaddlePaddle/Paddle/pull/54557), [#47199](https://github.com/PaddlePaddle/Paddle/pull/47199),[#47497](https://github.com/PaddlePaddle/Paddle/pull/47497),[#48045](https://github.com/PaddlePaddle/Paddle/pull/48045),[#54672](https://github.com/PaddlePaddle/Paddle/pull/54672) - - Support VPP mode and model saving for pipeline parallel. [#54196](https://github.com/PaddlePaddle/Paddle/pull/54196), [#52927](https://github.com/PaddlePaddle/Paddle/pull/52927),[#47801](https://github.com/PaddlePaddle/Paddle/pull/47801),[#45922](https://github.com/PaddlePaddle/Paddle/pull/45922),[#47242](https://github.com/PaddlePaddle/Paddle/pull/47242) -- Grouping sharding parallel - - sharding stage2 parallel supports the quantization function, hybrid parallel training, gradient accumulation, XPU hardware, BF16 low precision computation, optimizer learning rate setting, offload function, and data parallel. [#47169](https://github.com/PaddlePaddle/Paddle/pull/47169),[#47535](https://github.com/PaddlePaddle/Paddle/pull/47535), [#46795](https://github.com/PaddlePaddle/Paddle/pull/46795),[#47711](https://github.com/PaddlePaddle/Paddle/pull/47711),[#48310](https://github.com/PaddlePaddle/Paddle/pull/48310),[#46846](https://github.com/PaddlePaddle/Paddle/pull/46846),[#48857](https://github.com/PaddlePaddle/Paddle/pull/48857),[#49196](https://github.com/PaddlePaddle/Paddle/pull/49196),[#49931](https://github.com/PaddlePaddle/Paddle/pull/49931),[#47114](https://github.com/PaddlePaddle/Paddle/pull/47114),[#49767](https://github.com/PaddlePaddle/Paddle/pull/49767) - - Optimize sharing stage2 performance. Support the communication computation overlap. [#46495](https://github.com/PaddlePaddle/Paddle/pull/46495),[#46894](https://github.com/PaddlePaddle/Paddle/pull/46894) - - sharding stage3 support shared parameters, and untrainable parameters. [#48695](https://github.com/PaddlePaddle/Paddle/pull/48695),[#48577](https://github.com/PaddlePaddle/Paddle/pull/48577) -- Tensor model parallel - - Optimize tensor model parallel performance to reduce performance impact of stream sharding. [#47715](https://github.com/PaddlePaddle/Paddle/pull/47715),[#51617](https://github.com/PaddlePaddle/Paddle/pull/51617) - - Support parameter, optimizer shapes, gradient synchronization. [#51428](https://github.com/PaddlePaddle/Paddle/pull/51428),[#53254](https://github.com/PaddlePaddle/Paddle/pull/53254), [#53335](https://github.com/PaddlePaddle/Paddle/pull/53335),[#45803](https://github.com/PaddlePaddle/Paddle/pull/45803),[#46303](https://github.com/PaddlePaddle/Paddle/pull/46303),[#52293](https://github.com/PaddlePaddle/Paddle/pull/52293) - - Optimize tensor model parallel operators such as c_embedding, softmax_with_corss_entropy. [#53197](https://github.com/PaddlePaddle/Paddle/pull/53197),[#53547](https://github.com/PaddlePaddle/Paddle/pull/53547),[#53541](https://github.com/PaddlePaddle/Paddle/pull/53541),[#52789](https://github.com/PaddlePaddle/Paddle/pull/52789),[#46491](https://github.com/PaddlePaddle/Paddle/pull/46491),[#52742](https://github.com/PaddlePaddle/Paddle/pull/52742),[#53419](https://github.com/PaddlePaddle/Paddle/pull/53419) -- Launch - - Support distributed Launch function, with keeping independent logs. [#53207](https://github.com/PaddlePaddle/Paddle/pull/53207),[#50405](https://github.com/PaddlePaddle/Paddle/pull/50405) - - Add framework print environment variable function, log overwrite function, log return, and environment check. It is easy to change the debug environment variable. [#53243](https://github.com/PaddlePaddle/Paddle/pull/53243),[#53243](https://github.com/PaddlePaddle/Paddle/pull/53243), [#51803](https://github.com/PaddlePaddle/Paddle/pull/51803), [#53990](https://github.com/PaddlePaddle/Paddle/pull/53990) -- Communication library - - Add custom mixed parallel communication groups, topology information printing, and custom communication topology order. [#47021](https://github.com/PaddlePaddle/Paddle/pull/47021),[#54000](https://github.com/PaddlePaddle/Paddle/pull/54000),[#51781](https://github.com/PaddlePaddle/Paddle/pull/51781) - - Remove communication library dependency on Place information [#47857](https://github.com/PaddlePaddle/Paddle/pull/47857) - - Add communications library to support GLOO operator. Support send/recv/gather. [#52221](https://github.com/PaddlePaddle/Paddle/pull/52221), [#52334](https://github.com/PaddlePaddle/Paddle/pull/52334),[#49084](https://github.com/PaddlePaddle/Paddle/pull/49084) - - Disable reverse computation of communication operator. [#47636](https://github.com/PaddlePaddle/Paddle/pull/47636) - - Add communication library static shape check, to help determine whether communication volume is matched. [#48256](https://github.com/PaddlePaddle/Paddle/pull/48256),[#48915](https://github.com/PaddlePaddle/Paddle/pull/48915),[#48646](https://github.com/PaddlePaddle/Paddle/pull/48646) - - Support communication python object type, BF16 type, alltoall, reduce, allgather, group call, global gather, broadcast, and scatter communication methods. Support XPU device communications. [#51765](https://github.com/PaddlePaddle/Paddle/pull/51765),[#45844](https://github.com/PaddlePaddle/Paddle/pull/45844),[#48059](https://github.com/PaddlePaddle/Paddle/pull/48059),[#48115](https://github.com/PaddlePaddle/Paddle/pull/48115), [#48339](https://github.com/PaddlePaddle/Paddle/pull/48339),[#49252](https://github.com/PaddlePaddle/Paddle/pull/49252),[#49451](https://github.com/PaddlePaddle/Paddle/pull/49451),[#50085](https://github.com/PaddlePaddle/Paddle/pull/50085),[#50701](https://github.com/PaddlePaddle/Paddle/pull/50701),[#48208](https://github.com/PaddlePaddle/Paddle/pull/48208),[#48736](https://github.com/PaddlePaddle/Paddle/pull/48736),[#51762](https://github.com/PaddlePaddle/Paddle/pull/51762),[#52495](https://github.com/PaddlePaddle/Paddle/pull/52495),[#53514](https://github.com/PaddlePaddle/Paddle/pull/53514),[#48232](https://github.com/PaddlePaddle/Paddle/pull/48232),[#49896](https://github.com/PaddlePaddle/Paddle/pull/49896),[#49941](https://github.com/PaddlePaddle/Paddle/pull/49941),[#45584](https://github.com/PaddlePaddle/Paddle/pull/45584) - - Add support for communications between computational streams. [#46182](https://github.com/PaddlePaddle/Paddle/pull/46182),[#46023](https://github.com/PaddlePaddle/Paddle/pull/46023),[#46295](https://github.com/PaddlePaddle/Paddle/pull/46295),[#46761](https://github.com/PaddlePaddle/Paddle/pull/46761),[#47481](https://github.com/PaddlePaddle/Paddle/pull/47481),[#47740](https://github.com/PaddlePaddle/Paddle/pull/47740),[#47976](https://github.com/PaddlePaddle/Paddle/pull/47976),[#48163](https://github.com/PaddlePaddle/Paddle/pull/48163),[#48396](https://github.com/PaddlePaddle/Paddle/pull/48396),[#48308](https://github.com/PaddlePaddle/Paddle/pull/48308),[#47110](https://github.com/PaddlePaddle/Paddle/pull/47110),[#53089](https://github.com/PaddlePaddle/Paddle/pull/53089) - - Optimize communication library TCP linking time. [#49810](https://github.com/PaddlePaddle/Paddle/pull/49810),[#47184](https://github.com/PaddlePaddle/Paddle/pull/47184) - -#### Automatic parallel -- Improve semi-automatic parallel for static graphs: - - Add FLOPs computation function for multiple operators, and add computation Cost modelling based on FLOPs. [#48083](https://github.com/PaddlePaddle/Paddle/pull/48083),[#47978](https://github.com/PaddlePaddle/Paddle/pull/47978),[#47595](https://github.com/PaddlePaddle/Paddle/pull/47595),[#48083](https://github.com/PaddlePaddle/Paddle/pull/48083),[#48084](https://github.com/PaddlePaddle/Paddle/pull/48084),[#47816](https://github.com/PaddlePaddle/Paddle/pull/47816) - - Improve API ease-of-use. Perfect the DistAttr, Process Mesh, Engine API, information printing, input and output modules. Implement the Engine new cost API. It can be used to theoretically analyze model running time and video memory overhead. [#47503](https://github.com/PaddlePaddle/Paddle/pull/47503),[#46416](https://github.com/PaddlePaddle/Paddle/pull/46416),[#46554](https://github.com/PaddlePaddle/Paddle/pull/46554), [#46633](https://github.com/PaddlePaddle/Paddle/pull/46633),[#49214](https://github.com/PaddlePaddle/Paddle/pull/49214),[#53848](https://github.com/PaddlePaddle/Paddle/pull/53848),[#46552](https://github.com/PaddlePaddle/Paddle/pull/46552), [#47043](https://github.com/PaddlePaddle/Paddle/pull/47043), [#49665](https://github.com/PaddlePaddle/Paddle/pull/49665), [#52912](https://github.com/PaddlePaddle/Paddle/pull/52912), [#45776](https://github.com/PaddlePaddle/Paddle/pull/45776), [#47263](https://github.com/PaddlePaddle/Paddle/pull/47263) - - Optimize the generality and ease of use of Pass. Support more scenarios, and reduce time spent on Pass pre-analysis. [#46519](https://github.com/PaddlePaddle/Paddle/pull/46519),[#47358](https://github.com/PaddlePaddle/Paddle/pull/47358),[#46391](https://github.com/PaddlePaddle/Paddle/pull/46391), [#51035](https://github.com/PaddlePaddle/Paddle/pull/51035) - - Enhance debugging capabilities with distributed randomness control mechanisms and hybrid parallel precision alignment tools. [#52903](https://github.com/PaddlePaddle/Paddle/pull/52903),[#49865](https://github.com/PaddlePaddle/Paddle/pull/49865) - - Support automatic sharding of inference generation task networking. Adapt special usage of control flow and conditional block in the generation model. [#46771](https://github.com/PaddlePaddle/Paddle/pull/46771), [#54067](https://github.com/PaddlePaddle/Paddle/pull/54067) - - Improve grad_clip to support load balancing in data parallel scenarios. [#49510](https://github.com/PaddlePaddle/Paddle/pull/49510), [#49249](https://github.com/PaddlePaddle/Paddle/pull/49249) -- Semi-automatic parallel performance improvement for static graphs: - - Add the Sharding Pass automated communication Fuse and multi-streams communication functions, with throughput performance improved by 26% on two machines for GPT 6.7B model. [#48604](https://github.com/PaddlePaddle/Paddle/pull/48604), [#47180](https://github.com/PaddlePaddle/Paddle/pull/47180),[#46180](https://github.com/PaddlePaddle/Paddle/pull/46180) - - Add Recompute optimization strategy tuning function. Select optimal recompute checkpoint settings based on video memory and model size. [#48608](https://github.com/PaddlePaddle/Paddle/pull/48608),[#47846](https://github.com/PaddlePaddle/Paddle/pull/47846),[#49010](https://github.com/PaddlePaddle/Paddle/pull/49010) - - For the pipeline parallel, add 1F1B scheduling optimization Pass [#54260](https://github.com/PaddlePaddle/Paddle/pull/54260), [#45915](https://github.com/PaddlePaddle/Paddle/pull/45915) - - Optimize data parallel. Support optimizations such as converged communication and communication computation Overlap, with performance improved by 5% in GPT 1.3B model. [#48092](https://github.com/PaddlePaddle/Paddle/pull/48092),[#45643](https://github.com/PaddlePaddle/Paddle/pull/45643),[#49744](https://github.com/PaddlePaddle/Paddle/pull/49744), [#47578](https://github.com/PaddlePaddle/Paddle/pull/47578) - - Optimize Reshard module concate performance. Reduce number of concates in some scenarios. [#47809](https://github.com/PaddlePaddle/Paddle/pull/47809) - - Optimize mixing accuracy, upgrade Pass performance, support BF16 low accuracy, and adapt the auto mixing parallel of the while loop control flow. [#51285](https://github.com/PaddlePaddle/Paddle/pull/51285),[#51147](https://github.com/PaddlePaddle/Paddle/pull/51147), [#49219](https://github.com/PaddlePaddle/Paddle/pull/49219), [#49079](https://github.com/PaddlePaddle/Paddle/pull/49079) -- Improve function of fully automatic parallel for static graphs: - - Add new rule-based fully automated search strategy. [#51859](https://github.com/PaddlePaddle/Paddle/pull/51859),[#51908](https://github.com/PaddlePaddle/Paddle/pull/51908),[#52053](https://github.com/PaddlePaddle/Paddle/pull/52053),[#48316](https://github.com/PaddlePaddle/Paddle/pull/48316),[#48464](https://github.com/PaddlePaddle/Paddle/pull/48464), [#52041](https://github.com/PaddlePaddle/Paddle/pull/52041) - - Improve automatic parallel modelling capability, enriching single-node topology modelling and communication volume modelling. [#52723](https://github.com/PaddlePaddle/Paddle/pull/52723),[#46387](https://github.com/PaddlePaddle/Paddle/pull/46387),[#47043](https://github.com/PaddlePaddle/Paddle/pull/47043) - -#### Parameter server -- Clean up the all list in ps directory, in which API is not exposed [#51289](https://github.com/PaddlePaddle/Paddle/pull/51289) -- Clean up cvm operator [#48989](https://github.com/PaddlePaddle/Paddle/pull/48989) -- For GPUPS, add support for AFS. [#46611](https://github.com/PaddlePaddle/Paddle/pull/46611) -- Degrade PGLBOX2.0 log, fix stuck issue of dense parameter, fix the bug that barrier does not take effect, and add get_epoch_finish python side interface [#49946](https://github.com/PaddlePaddle/Paddle/pull/49946),[#50166](https://github.com/PaddlePaddle/Paddle/pull/50166),[#50349](https://github.com/PaddlePaddle/Paddle/pull/50349) -- GPUPs run to switch to specified mode. [#51115](https://github.com/PaddlePaddle/Paddle/pull/51115) -- GPUPS is added to benchmark. [#49587](https://github.com/PaddlePaddle/Paddle/pull/49587),[#49649](https://github.com/PaddlePaddle/Paddle/pull/49649) -- Fix the GPUPS optimizer selection bug, fix reader reading problem, and fix RPC compilation problem. [#47026](https://github.com/PaddlePaddle/Paddle/pull/47026),[#47192](https://github.com/PaddlePaddle/Paddle/pull/47192),[#49878](https://github.com/PaddlePaddle/Paddle/pull/49878), [#46356](https://github.com/PaddlePaddle/Paddle/pull/46356),[#46575](https://github.com/PaddlePaddle/Paddle/pull/46575),[#49389](https://github.com/PaddlePaddle/Paddle/pull/49389),[#46258](https://github.com/PaddlePaddle/Paddle/pull/46258),[#50136](https://github.com/PaddlePaddle/Paddle/pull/50136) -- Add rocksdb compilation method. [#46074](https://github.com/PaddlePaddle/Paddle/pull/46074) - -### CUDA -#### New features -- Add compilation support for CUDA 12.0. Fix related unit test. ([#49539](https://github.com/PaddlePaddle/Paddle/pull/49539), [#54542](https://github.com/PaddlePaddle/Paddle/pull/54542)) -- Add CUDNN Frontend API compilation support and related unit test. You can use `WITH_CUDNN_FRONTEND=ON ` compilation option for start. ([#47524](https://github.com/PaddlePaddle/Paddle/pull/47524), [#47612](https://github.com/PaddlePaddle/Paddle/pull/47612)) - -#### Improvements -- Add mixed precision strategy and optimize precision: - - Add and optimize FP16 and BF16 data type support for more than 200 operators in the framework, including logsumexp, reduce_max, cumprod, sync_batch_norm, compare class OP, etc. Carry out precision optimization and unit test for all FP16 and BF16 operators. Improve the unit test framework function for low-precision operators, to ensure there is no loss of accuracy in the process of large-model training. ([#51193](https://github.com/PaddlePaddle/Paddle/pull/51193), [#51114](https://github.com/PaddlePaddle/Paddle/pull/51114), [#45817](https://github.com/PaddlePaddle/Paddle/pull/45817), [#52862](https://github.com/PaddlePaddle/Paddle/pull/52862), [#52919](https://github.com/PaddlePaddle/Paddle/pull/52919), [#52921](https://github.com/PaddlePaddle/Paddle/pull/52921), [#46413](https://github.com/PaddlePaddle/Paddle/pull/46413), [#48205](https://github.com/PaddlePaddle/Paddle/pull/48205), [#54193](https://github.com/PaddlePaddle/Paddle/pull/54193), [#48041](https://github.com/PaddlePaddle/Paddle/pull/48041), [#48121](https://github.com/PaddlePaddle/Paddle/pull/48121), [#46364](https://github.com/PaddlePaddle/Paddle/pull/46364), [#51153](https://github.com/PaddlePaddle/Paddle/pull/51153), [#53023](https://github.com/PaddlePaddle/Paddle/pull/53023), [#53079](https://github.com/PaddlePaddle/Paddle/pull/53079), [#53137](https://github.com/PaddlePaddle/Paddle/pull/53137), [#46212](https://github.com/PaddlePaddle/Paddle/pull/46212), [#50908](https://github.com/PaddlePaddle/Paddle/pull/50908), [#52555](https://github.com/PaddlePaddle/Paddle/pull/52555), [#51582](https://github.com/PaddlePaddle/Paddle/pull/51582), [#47897](https://github.com/PaddlePaddle/Paddle/pull/47897), [#45601](https://github.com/PaddlePaddle/Paddle/pull/45601), [#53522](https://github.com/PaddlePaddle/Paddle/pull/53522), [#52666](https://github.com/PaddlePaddle/Paddle/pull/52666), [#50101](https://github.com/PaddlePaddle/Paddle/pull/50101), [#48315](https://github.com/PaddlePaddle/Paddle/pull/48315), [#50847](https://github.com/PaddlePaddle/Paddle/pull/50847), [#50905](https://github.com/PaddlePaddle/Paddle/pull/50905), [#50906](https://github.com/PaddlePaddle/Paddle/pull/50906), [#50909](https://github.com/PaddlePaddle/Paddle/pull/50909), [#50916](https://github.com/PaddlePaddle/Paddle/pull/50916), [#50917](https://github.com/PaddlePaddle/Paddle/pull/50917), [#50920](https://github.com/PaddlePaddle/Paddle/pull/50920), [#50919](https://github.com/PaddlePaddle/Paddle/pull/50919), [#50904](https://github.com/PaddlePaddle/Paddle/pull/50904), [#50918](https://github.com/PaddlePaddle/Paddle/pull/50918), [#50938](https://github.com/PaddlePaddle/Paddle/pull/50938), [#50858](https://github.com/PaddlePaddle/Paddle/pull/50858), [#50933](https://github.com/PaddlePaddle/Paddle/pull/50933), [#50945](https://github.com/PaddlePaddle/Paddle/pull/50945), [#50936](https://github.com/PaddlePaddle/Paddle/pull/50936), [#51168](https://github.com/PaddlePaddle/Paddle/pull/51168), [#51493](https://github.com/PaddlePaddle/Paddle/pull/51493), [#50924](https://github.com/PaddlePaddle/Paddle/pull/50924), [#50923](https://github.com/PaddlePaddle/Paddle/pull/50923), [#50926](https://github.com/PaddlePaddle/Paddle/pull/50926), [#50925](https://github.com/PaddlePaddle/Paddle/pull/50925), [#50930](https://github.com/PaddlePaddle/Paddle/pull/50930), [#53284](https://github.com/PaddlePaddle/Paddle/pull/53284), [#53286](https://github.com/PaddlePaddle/Paddle/pull/53286), [#53285](https://github.com/PaddlePaddle/Paddle/pull/53285), [#50976](https://github.com/PaddlePaddle/Paddle/pull/50976), [#50915](https://github.com/PaddlePaddle/Paddle/pull/50915), [#50915](https://github.com/PaddlePaddle/Paddle/pull/50915), [#48192](https://github.com/PaddlePaddle/Paddle/pull/48192), [#50993](https://github.com/PaddlePaddle/Paddle/pull/50993)， [#50998](https://github.com/PaddlePaddle/Paddle/pull/50998), [#51380](https://github.com/PaddlePaddle/Paddle/pull/51380), [#51137](https://github.com/PaddlePaddle/Paddle/pull/51137), [#51106](https://github.com/PaddlePaddle/Paddle/pull/51106), [#51197](https://github.com/PaddlePaddle/Paddle/pull/51197), [#51159](https://github.com/PaddlePaddle/Paddle/pull/51159), [#51552](https://github.com/PaddlePaddle/Paddle/pull/51552), [#51151](https://github.com/PaddlePaddle/Paddle/pull/51151), [#51005](https://github.com/PaddlePaddle/Paddle/pull/51005), [#51565](https://github.com/PaddlePaddle/Paddle/pull/51565), [#51036](https://github.com/PaddlePaddle/Paddle/pull/51036), [#51185](https://github.com/PaddlePaddle/Paddle/pull/51185), [#51791](https://github.com/PaddlePaddle/Paddle/pull/51791), [#51083](https://github.com/PaddlePaddle/Paddle/pull/51083), [#51694](https://github.com/PaddlePaddle/Paddle/pull/51694), [#51689](https://github.com/PaddlePaddle/Paddle/pull/51689), [#51009](https://github.com/PaddlePaddle/Paddle/pull/51009), [#51051](https://github.com/PaddlePaddle/Paddle/pull/51051), [#51532](https://github.com/PaddlePaddle/Paddle/pull/51532), [#51978](https://github.com/PaddlePaddle/Paddle/pull/51978), [#51903](https://github.com/PaddlePaddle/Paddle/pull/51903), [#51888](https://github.com/PaddlePaddle/Paddle/pull/51888), [#52016](https://github.com/PaddlePaddle/Paddle/pull/52016), [#52035](https://github.com/PaddlePaddle/Paddle/pull/52035), [#52184](https://github.com/PaddlePaddle/Paddle/pull/52184), [#52018](https://github.com/PaddlePaddle/Paddle/pull/52018), [#51787](https://github.com/PaddlePaddle/Paddle/pull/51787), [#51640](https://github.com/PaddlePaddle/Paddle/pull/51640), [#52172](https://github.com/PaddlePaddle/Paddle/pull/52172), [#52193](https://github.com/PaddlePaddle/Paddle/pull/52193), [#51160](https://github.com/PaddlePaddle/Paddle/pull/51160), [#51809](https://github.com/PaddlePaddle/Paddle/pull/51809), [#51678](https://github.com/PaddlePaddle/Paddle/pull/51678), [#52158](https://github.com/PaddlePaddle/Paddle/pull/52158), [#51015](https://github.com/PaddlePaddle/Paddle/pull/51015), [#52240](https://github.com/PaddlePaddle/Paddle/pull/52240), [#52276](https://github.com/PaddlePaddle/Paddle/pull/52276), [#52233](https://github.com/PaddlePaddle/Paddle/pull/52233), [#52220](https://github.com/PaddlePaddle/Paddle/pull/52220), [#52107](https://github.com/PaddlePaddle/Paddle/pull/52107), [#52282](https://github.com/PaddlePaddle/Paddle/pull/52282), [#52311](https://github.com/PaddlePaddle/Paddle/pull/52311), [#52315](https://github.com/PaddlePaddle/Paddle/pull/52315), [#52357](https://github.com/PaddlePaddle/Paddle/pull/52357), [#52256](https://github.com/PaddlePaddle/Paddle/pull/52256), [#51649](https://github.com/PaddlePaddle/Paddle/pull/51649), [#52413](https://github.com/PaddlePaddle/Paddle/pull/52413), [#52369](https://github.com/PaddlePaddle/Paddle/pull/52369), [#51837](https://github.com/PaddlePaddle/Paddle/pull/51837), [#52112](https://github.com/PaddlePaddle/Paddle/pull/52112), [#51819](https://github.com/PaddlePaddle/Paddle/pull/51819), [#52388](https://github.com/PaddlePaddle/Paddle/pull/52388), [#52411](https://github.com/PaddlePaddle/Paddle/pull/52411), [#52521](https://github.com/PaddlePaddle/Paddle/pull/52521), [#51300](https://github.com/PaddlePaddle/Paddle/pull/51300), [#51117](https://github.com/PaddlePaddle/Paddle/pull/51117), [#52380](https://github.com/PaddlePaddle/Paddle/pull/52380), [#52317](https://github.com/PaddlePaddle/Paddle/pull/52317), [#51263](https://github.com/PaddlePaddle/Paddle/pull/51263), [#52668](https://github.com/PaddlePaddle/Paddle/pull/52668), [#52259](https://github.com/PaddlePaddle/Paddle/pull/52259), [#50999](https://github.com/PaddlePaddle/Paddle/pull/50999), [#52407](https://github.com/PaddlePaddle/Paddle/pull/52407), [#52288](https://github.com/PaddlePaddle/Paddle/pull/52288), [#52845](https://github.com/PaddlePaddle/Paddle/pull/52845), [#50953](https://github.com/PaddlePaddle/Paddle/pull/50953), [#52667](https://github.com/PaddlePaddle/Paddle/pull/52667), [#52582](https://github.com/PaddlePaddle/Paddle/pull/52582), [#52426](https://github.com/PaddlePaddle/Paddle/pull/52426), [#51884](https://github.com/PaddlePaddle/Paddle/pull/51884), [#52630](https://github.com/PaddlePaddle/Paddle/pull/52630), [#52136](https://github.com/PaddlePaddle/Paddle/pull/52136), [#52604](https://github.com/PaddlePaddle/Paddle/pull/52604), [#51615](https://github.com/PaddlePaddle/Paddle/pull/51615), [#51275](https://github.com/PaddlePaddle/Paddle/pull/51275), [#52898](https://github.com/PaddlePaddle/Paddle/pull/52898), [#52918](https://github.com/PaddlePaddle/Paddle/pull/52918), [#52572](https://github.com/PaddlePaddle/Paddle/pull/52572), [#52683](https://github.com/PaddlePaddle/Paddle/pull/52683), [#52956](https://github.com/PaddlePaddle/Paddle/pull/52956), [#52963](https://github.com/PaddlePaddle/Paddle/pull/52963), [#52954](https://github.com/PaddlePaddle/Paddle/pull/52954), [#52444](https://github.com/PaddlePaddle/Paddle/pull/52444), [#52314](https://github.com/PaddlePaddle/Paddle/pull/52314), [#52887](https://github.com/PaddlePaddle/Paddle/pull/52887), [#52195](https://github.com/PaddlePaddle/Paddle/pull/52195), [#53100](https://github.com/PaddlePaddle/Paddle/pull/53100), [#52961](https://github.com/PaddlePaddle/Paddle/pull/52961), [#52953](https://github.com/PaddlePaddle/Paddle/pull/52953), [#53111](https://github.com/PaddlePaddle/Paddle/pull/53111), [#53549](https://github.com/PaddlePaddle/Paddle/pull/53549), [#53736](https://github.com/PaddlePaddle/Paddle/pull/53736), [#52920](https://github.com/PaddlePaddle/Paddle/pull/52920), [#53195](https://github.com/PaddlePaddle/Paddle/pull/53195), [#53535](https://github.com/PaddlePaddle/Paddle/pull/53535), [#53876](https://github.com/PaddlePaddle/Paddle/pull/53876), [#53785](https://github.com/PaddlePaddle/Paddle/pull/53785), [#53722](https://github.com/PaddlePaddle/Paddle/pull/53722), [#54285](https://github.com/PaddlePaddle/Paddle/pull/54285), [#54232](https://github.com/PaddlePaddle/Paddle/pull/54232), [#53922](https://github.com/PaddlePaddle/Paddle/pull/53922), [#47277](https://github.com/PaddlePaddle/Paddle/pull/47277), [#50811](https://github.com/PaddlePaddle/Paddle/pull/50811), [#54571](https://github.com/PaddlePaddle/Paddle/pull/54571), [#50129](https://github.com/PaddlePaddle/Paddle/pull/50129), [#50340](https://github.com/PaddlePaddle/Paddle/pull/50340), [#50848](https://github.com/PaddlePaddle/Paddle/pull/50848), [#50849](https://github.com/PaddlePaddle/Paddle/pull/50849), [#50868](https://github.com/PaddlePaddle/Paddle/pull/50868), [#50878](https://github.com/PaddlePaddle/Paddle/pull/50878), [#50929](https://github.com/PaddlePaddle/Paddle/pull/50929), [#50939](https://github.com/PaddlePaddle/Paddle/pull/50939), [#50973](https://github.com/PaddlePaddle/Paddle/pull/50973), [#50913](https://github.com/PaddlePaddle/Paddle/pull/50913), [#51145](https://github.com/PaddlePaddle/Paddle/pull/51145), [#51090](https://github.com/PaddlePaddle/Paddle/pull/51090), [#51098](https://github.com/PaddlePaddle/Paddle/pull/51098), [#51094](https://github.com/PaddlePaddle/Paddle/pull/51094), [#51216](https://github.com/PaddlePaddle/Paddle/pull/51216), [#51736](https://github.com/PaddlePaddle/Paddle/pull/51736), [#51684](https://github.com/PaddlePaddle/Paddle/pull/51684), [#51925](https://github.com/PaddlePaddle/Paddle/pull/51925), [#54030](https://github.com/PaddlePaddle/Paddle/pull/54030), [#50700](https://github.com/PaddlePaddle/Paddle/pull/50700), [#52264](https://github.com/PaddlePaddle/Paddle/pull/52264), [#51069](https://github.com/PaddlePaddle/Paddle/pull/51069), [#51101](https://github.com/PaddlePaddle/Paddle/pull/51101), [#51286](https://github.com/PaddlePaddle/Paddle/pull/51286), [#53582](https://github.com/PaddlePaddle/Paddle/pull/53582),[#49869](https://github.com/PaddlePaddle/Paddle/pull/49869))) -- AMP optimization: Comprehensively upgrade and optimize ease of use, accuracy stability and debuggability of AMP training, to better support acceleration of large model training. In terms of ease of use, unify the API for dynamic and static graphs. Add new conversion interfaces such as model.float(), model.float16() and model.bfloat16(). In terms of accuracy stability, enhance automatic adjustment of the strategy for BF16 type. Optimize blacklist settings. Enhance support of the multi_precision function by optimizer operators Adagrad, Adamax, Adadelta, and RMSProp. In the O2 mode, improve master grad mechanism, add type promotion mechanism and a new parameter for the specific module to use float32 computation to guarantee accuracy. In terms of debuggability, add the paddle.amp.debugging module to provide operator statistics, outlier detection, and accuracy comparison. ( [#50132](https://github.com/PaddlePaddle/Paddle/pull/50132), [#50078](https://github.com/PaddlePaddle/Paddle/pull/50078), [#50131](https://github.com/PaddlePaddle/Paddle/pull/50131), [#49705](https://github.com/PaddlePaddle/Paddle/pull/49705), [#52936](https://github.com/PaddlePaddle/Paddle/pull/52936), [#52871](https://github.com/PaddlePaddle/Paddle/pull/52871), [#53289](https://github.com/PaddlePaddle/Paddle/pull/53289), [#53362](https://github.com/PaddlePaddle/Paddle/pull/53362), [#54240](https://github.com/PaddlePaddle/Paddle/pull/54240), [#53768](https://github.com/PaddlePaddle/Paddle/pull/53768), [#48041](https://github.com/PaddlePaddle/Paddle/pull/48041), [#47672](https://github.com/PaddlePaddle/Paddle/pull/47672), [#48843](https://github.com/PaddlePaddle/Paddle/pull/48843), [#49391](https://github.com/PaddlePaddle/Paddle/pull/49391), [#51635](https://github.com/PaddlePaddle/Paddle/pull/51635), [#45541](https://github.com/PaddlePaddle/Paddle/pull/45541), [#53742](https://github.com/PaddlePaddle/Paddle/pull/53742), [#51020](https://github.com/PaddlePaddle/Paddle/pull/51020), [#51063](https://github.com/PaddlePaddle/Paddle/pull/51063), [#52514](https://github.com/PaddlePaddle/Paddle/pull/52514), [#50940](https://github.com/PaddlePaddle/Paddle/pull/50940), [#52936](https://github.com/PaddlePaddle/Paddle/pull/52936), [#53439](https://github.com/PaddlePaddle/Paddle/pull/53439), [#53712](https://github.com/PaddlePaddle/Paddle/pull/53712), [#48238](https://github.com/PaddlePaddle/Paddle/pull/48238), [#52215](https://github.com/PaddlePaddle/Paddle/pull/52215), [#53012](https://github.com/PaddlePaddle/Paddle/pull/53012), [#52918](https://github.com/PaddlePaddle/Paddle/pull/52918), [#54571](https://github.com/PaddlePaddle/Paddle/pull/54571)) -- For GroupNorm operator, add support for NHWC data format. ([#47533](https://github.com/PaddlePaddle/Paddle/pull/47533)) -- For index_put operator, add support for mixed data types of bool and int. ([#54195](https://github.com/PaddlePaddle/Paddle/pull/54195)) -- Add sparse.is_nan API for determining whether a sparse tensor contains a NaN element. ([#51513](https://github.com/PaddlePaddle/Paddle/pull/51513)) - -#### bug fix -- Fix bugs of computation errors of several operators such as trace, roll, dropout_nd, and log_softmax, stack overflow, and some unit test error. ([#50243](https://github.com/PaddlePaddle/Paddle/pull/50243), [#52012](https://github.com/PaddlePaddle/Paddle/pull/52012), [#53795](https://github.com/PaddlePaddle/Paddle/pull/53795), [#53149](https://github.com/PaddlePaddle/Paddle/pull/53149), [#53654](https://github.com/PaddlePaddle/Paddle/pull/53654), [#51054](https://github.com/PaddlePaddle/Paddle/pull/51054), [#49373](https://github.com/PaddlePaddle/Paddle/pull/49373), [#53038](https://github.com/PaddlePaddle/Paddle/pull/53038)) -- Fix the problem that conv operator exhaustive search does not work in some scenarios. ([#47065](https://github.com/PaddlePaddle/Paddle/pull/47065)) -- Fix timeout problem of collective_reduce_scatter and other operators on A100. ([#54513](https://github.com/PaddlePaddle/Paddle/pull/54513)) -- Fix the problem of attribute error in FusedLinear unit test. ([#50359](https://github.com/PaddlePaddle/Paddle/pull/50359)) -- Fix the OOM problem that may occur when using Profiler. ([#46089](https://github.com/PaddlePaddle/Paddle/pull/46089)) - -#### Performance optimization -- Further optimize GPU Kernel and eigen implementations of the framework's large number of operators, including max_pool3d, dropout, adaptive_pooling, depthwise_conv2d, transpose, eigh, broadcast class computations, reduce class computations, prelu, logsumexp, and sparse, to achieve better performance in more configuration scenarios. ([#45820](https://github.com/PaddlePaddle/Paddle/pull/45820), [#45959](https://github.com/PaddlePaddle/Paddle/pull/45959), [#45934](https://github.com/PaddlePaddle/Paddle/pull/45934), [#46332](https://github.com/PaddlePaddle/Paddle/pull/46332), [#46287](https://github.com/PaddlePaddle/Paddle/pull/46287), [#47233](https://github.com/PaddlePaddle/Paddle/pull/47233), [#48855](https://github.com/PaddlePaddle/Paddle/pull/48855), [#48560](https://github.com/PaddlePaddle/Paddle/pull/48560), [#49419](https://github.com/PaddlePaddle/Paddle/pull/49419), [#49748](https://github.com/PaddlePaddle/Paddle/pull/49748), [#50348](https://github.com/PaddlePaddle/Paddle/pull/50348), [#52401](https://github.com/PaddlePaddle/Paddle/pull/52401), [#51131](https://github.com/PaddlePaddle/Paddle/pull/51131), [#51141](https://github.com/PaddlePaddle/Paddle/pull/51141), [#51479](https://github.com/PaddlePaddle/Paddle/pull/51479), [#51835](https://github.com/PaddlePaddle/Paddle/pull/51835), [#52509](https://github.com/PaddlePaddle/Paddle/pull/52509), [#52482](https://github.com/PaddlePaddle/Paddle/pull/52482), [#52700](https://github.com/PaddlePaddle/Paddle/pull/52700), [#53112](https://github.com/PaddlePaddle/Paddle/pull/53112), [#53659](https://github.com/PaddlePaddle/Paddle/pull/53659), [#53658](https://github.com/PaddlePaddle/Paddle/pull/53658), [#53154](https://github.com/PaddlePaddle/Paddle/pull/53154), [#54071](https://github.com/PaddlePaddle/Paddle/pull/54071), [#53622](https://github.com/PaddlePaddle/Paddle/pull/53622), [#52952](https://github.com/PaddlePaddle/Paddle/pull/52952), [#46046](https://github.com/PaddlePaddle/Paddle/pull/46046), [#46119](https://github.com/PaddlePaddle/Paddle/pull/46119), [#45946](https://github.com/PaddlePaddle/Paddle/pull/45946), [#47212](https://github.com/PaddlePaddle/Paddle/pull/47212), [#47791](https://github.com/PaddlePaddle/Paddle/pull/47791), [#47454](https://github.com/PaddlePaddle/Paddle/pull/47454), [#45230](https://github.com/PaddlePaddle/Paddle/pull/45230), [#48899](https://github.com/PaddlePaddle/Paddle/pull/48899), [#33051](https://github.com/PaddlePaddle/Paddle/pull/33051), [#49040](https://github.com/PaddlePaddle/Paddle/pull/49040), [#48992](https://github.com/PaddlePaddle/Paddle/pull/48992), [#49086](https://github.com/PaddlePaddle/Paddle/pull/49086), [#50808](https://github.com/PaddlePaddle/Paddle/pull/50808), [#46431](https://github.com/PaddlePaddle/Paddle/pull/46431), [#50931](https://github.com/PaddlePaddle/Paddle/pull/50931), [#48056](https://github.com/PaddlePaddle/Paddle/pull/48056), [#46071](https://github.com/PaddlePaddle/Paddle/pull/46071), [#49231](https://github.com/PaddlePaddle/Paddle/pull/49231), [#38660](https://github.com/PaddlePaddle/Paddle/pull/38660), [#50287](https://github.com/PaddlePaddle/Paddle/pull/50287), [#46111](https://github.com/PaddlePaddle/Paddle/pull/46111), [#46997](https://github.com/PaddlePaddle/Paddle/pull/46997), [#45854](https://github.com/PaddlePaddle/Paddle/pull/45854), [#47738](https://github.com/PaddlePaddle/Paddle/pull/47738), [#48635](https://github.com/PaddlePaddle/Paddle/pull/48635), [#50353](https://github.com/PaddlePaddle/Paddle/pull/50353), [#50362](https://github.com/PaddlePaddle/Paddle/pull/50362), [#51934](https://github.com/PaddlePaddle/Paddle/pull/51934), [#54045](https://github.com/PaddlePaddle/Paddle/pull/54045), [#46679](https://github.com/PaddlePaddle/Paddle/pull/46679), [#52093](https://github.com/PaddlePaddle/Paddle/pull/52093), [#52969](https://github.com/PaddlePaddle/Paddle/pull/52969)) -- Provide more fusion implementations and related fusion pass, such as fused_feed_forward, gather-gemm-scatter, matmul + bias, layernorm_shift_partition + element_add, and elementwise class fusion, to further improve performance of models that use the mode. ( [#50423](https://github.com/PaddlePaddle/Paddle/pull/50423), [#50091](https://github.com/PaddlePaddle/Paddle/pull/50091), [#50364](https://github.com/PaddlePaddle/Paddle/pull/50364), [#53017](https://github.com/PaddlePaddle/Paddle/pull/53017), [#50755](https://github.com/PaddlePaddle/Paddle/pull/50755), [#50050](https://github.com/PaddlePaddle/Paddle/pull/50050), [#47099](https://github.com/PaddlePaddle/Paddle/pull/47099), [#48848](https://github.com/PaddlePaddle/Paddle/pull/48848), [#49383](https://github.com/PaddlePaddle/Paddle/pull/49383), [#50809](https://github.com/PaddlePaddle/Paddle/pull/50809), [#52361](https://github.com/PaddlePaddle/Paddle/pull/52361), [#52028](https://github.com/PaddlePaddle/Paddle/pull/52028), [#48439](https://github.com/PaddlePaddle/Paddle/pull/48439), [#49009](https://github.com/PaddlePaddle/Paddle/pull/49009), [#51427](https://github.com/PaddlePaddle/Paddle/pull/51427), [#52731](https://github.com/PaddlePaddle/Paddle/pull/52731), [#51805](https://github.com/PaddlePaddle/Paddle/pull/51805)) - -### Intermediate Representation -In order to guarantee stability and reduce R&D cost of the IR system, we have developed a new IR system for PaddlePaddle. Complete basic data structure definition, operator definition generation, and execution system adaptation. In order to better support higher-order requirements of scientific computing scenarios, complete higher-order adaptation of operators such as silu and cast. -- Complete the definition of IR data structure, including type system and operator definition. Implement execution adaptation with phi kernel. [#51112](https://github.com/PaddlePaddle/Paddle/pull/51112)， [#51992](https://github.com/PaddlePaddle/Paddle/pull/51992), [#50412](https://github.com/PaddlePaddle/Paddle/pull/50412), [#53557](https://github.com/PaddlePaddle/Paddle/pull/53557), [#53953](https://github.com/PaddlePaddle/Paddle/pull/53953), [#50959](https://github.com/PaddlePaddle/Paddle/pull/50959), [#54250](https://github.com/PaddlePaddle/Paddle/pull/54250), [#54197](https://github.com/PaddlePaddle/Paddle/pull/54197), [#54289](https://github.com/PaddlePaddle/Paddle/pull/54289), [#51636](https://github.com/PaddlePaddle/Paddle/pull/51636), [#52846](https://github.com/PaddlePaddle/Paddle/pull/52846), [#53988](https://github.com/PaddlePaddle/Paddle/pull/53988), [#54143](https://github.com/PaddlePaddle/Paddle/pull/54143), [#54035](https://github.com/PaddlePaddle/Paddle/pull/54035), [#54052](https://github.com/PaddlePaddle/Paddle/pull/54052), [#54340](https://github.com/PaddlePaddle/Paddle/pull/54340), [#54356](https://github.com/PaddlePaddle/Paddle/pull/54356), [#54068](https://github.com/PaddlePaddle/Paddle/pull/54068), [#53894](https://github.com/PaddlePaddle/Paddle/pull/53894), [#53707](https://github.com/PaddlePaddle/Paddle/pull/53707), [#54185](https://github.com/PaddlePaddle/Paddle/pull/54185), [#54031](https://github.com/PaddlePaddle/Paddle/pull/54031), [#54220](https://github.com/PaddlePaddle/Paddle/pull/54220), [#54275](https://github.com/PaddlePaddle/Paddle/pull/54275), [#54281](https://github.com/PaddlePaddle/Paddle/pull/54281), [#54186](https://github.com/PaddlePaddle/Paddle/pull/54186), [#54259](https://github.com/PaddlePaddle/Paddle/pull/54259), [#54124](https://github.com/PaddlePaddle/Paddle/pull/54124), [#54292](https://github.com/PaddlePaddle/Paddle/pull/54292), [#48068](https://github.com/PaddlePaddle/Paddle/pull/48068), [#53978](https://github.com/PaddlePaddle/Paddle/pull/53978) -- Improve the basic pass setup, including basic pass definition, pass registration management. [#54023](https://github.com/PaddlePaddle/Paddle/pull/54023),[#54170](https://github.com/PaddlePaddle/Paddle/pull/54170), [#54170](https://github.com/PaddlePaddle/Paddle/pull/54170), [#54308](https://github.com/PaddlePaddle/Paddle/pull/54308), [#54348](https://github.com/PaddlePaddle/Paddle/pull/54348), [#54385](https://github.com/PaddlePaddle/Paddle/pull/54385) -- Improve adaptation of high-level arithmetic, including modification of the basic module and adaptation of silu and cast arithmetic. [#52005](https://github.com/PaddlePaddle/Paddle/pull/52005), [#53425](https://github.com/PaddlePaddle/Paddle/pull/53425), [#53417](https://github.com/PaddlePaddle/Paddle/pull/53417), [#53417](https://github.com/PaddlePaddle/Paddle/pull/53417), [#53498](https://github.com/PaddlePaddle/Paddle/pull/53498), [#53171](https://github.com/PaddlePaddle/Paddle/pull/53171), [#53632](https://github.com/PaddlePaddle/Paddle/pull/53632), [#53605](https://github.com/PaddlePaddle/Paddle/pull/53605), [#53746](https://github.com/PaddlePaddle/Paddle/pull/53746), [#53874](https://github.com/PaddlePaddle/Paddle/pull/53874), [#54164](https://github.com/PaddlePaddle/Paddle/pull/54164), [#45888](https://github.com/PaddlePaddle/Paddle/pull/45888), [#46024](https://github.com/PaddlePaddle/Paddle/pull/46024), [#46446](https://github.com/PaddlePaddle/Paddle/pull/46446), [#46960](https://github.com/PaddlePaddle/Paddle/pull/46960) - -### CINN compiler -#### New features -- Add CINN support for 0D-Tensor. At present, in order to cooperate with the upgrade of the main framework, it is supported by adding pass temporarily. We will replace and upgrade the solution later. ([#53382](https://github.com/PaddlePaddle/Paddle/pull/53382), [#53955](https://github.com/PaddlePaddle/Paddle/pull/53955), [#54064](https://github.com/PaddlePaddle/Paddle/pull/54064), [#54118](https://github.com/PaddlePaddle/Paddle/pull/54118), [#54216](https://github.com/PaddlePaddle/Paddle/pull/54216), [#53454](https://github.com/PaddlePaddle/Paddle/pull/53454)) -- Add CINN support for int8/uint8/int16/uint16/bf16 data types. ([#50566](https://github.com/PaddlePaddle/Paddle/pull/50566), [#53637](https://github.com/PaddlePaddle/Paddle/pull/53637)) -- Add support for the CINN expand operator. ([#46776](https://github.com/PaddlePaddle/Paddle/pull/46776)) -- Add CINN support for PaddleInference. ([#45009](https://github.com/PaddlePaddle/Paddle/pull/45009)) - -#### Improvements -- For CINN compiler, pass skip_gc_vars attribute to CINN subgraph. CINN adds fetch operator for skip_gc_vars. [#49471](https://github.com/PaddlePaddle/Paddle/pull/49471), [#49553](https://github.com/PaddlePaddle/Paddle/pull/49553) -- For CINN compiler, conv2d and conv2d_grad do not use cinn operator by default. [#51645](https://github.com/PaddlePaddle/Paddle/pull/51645) -- Add build_cinn_pass to BuildStrategy for use in dynamic-to-static ([#49496](https://github.com/PaddlePaddle/Paddle/pull/49496)) -- Add reshape operator to perform unit test under combinator mechanism. ([#51276](https://github.com/PaddlePaddle/Paddle/pull/51276)) -- Change version of the main framework binding CINN from fixed commit to develop. ([#49775](https://github.com/PaddlePaddle/Paddle/pull/49775)) -- Set default Target parameter for CINN. ([#50182](https://github.com/PaddlePaddle/Paddle/pull/50182)) - -#### bug fix -- Fix the problem of inconsistent operator order after topology sorting during CINN symbolization. ([#52556](https://github.com/PaddlePaddle/Paddle/pull/52556)) -- Fix some operator computation errors, accuracy degradation, and unit test related problems. ([#53859](https://github.com/PaddlePaddle/Paddle/pull/53859), [#54261](https://github.com/PaddlePaddle/Paddle/pull/54261), [#46801](https://github.com/PaddlePaddle/Paddle/pull/46801), [#53676](https://github.com/PaddlePaddle/Paddle/pull/53676), [#53772](https://github.com/PaddlePaddle/Paddle/pull/53772)) -- Fix the problem of CINN support for float16 type. ([#48249](https://github.com/PaddlePaddle/Paddle/pull/48249)) -- Fix the problem in build_cinn_pass. ([#46843](https://github.com/PaddlePaddle/Paddle/pull/46843)) -- Fix the problem of no data area due to incorrect GC when CINN is turned on during combinator + dynamic-to-static. ([#50116](https://github.com/PaddlePaddle/Paddle/pull/50116)) -- Fix the problems of compiler dropout amp error, combinator resnet error, and inplace variable not found [#51688](https://github.com/PaddlePaddle/Paddle/pull/51688), [#52813](https://github.com/PaddlePaddle/Paddle/pull/52813), [#51769](https://github.com/PaddlePaddle/Paddle/pull/51769) - -#### Performance optimization -- Optimize reshape related fusion strategy ([#53066](https://github.com/PaddlePaddle/Paddle/pull/53066)) -- Optimize performance of BuildCINNPass. ([#49696](https://github.com/PaddlePaddle/Paddle/pull/49696)) -- Optimize performance of subgraph detection module. ([#45040](https://github.com/PaddlePaddle/Paddle/pull/45040), [#46937](https://github.com/PaddlePaddle/Paddle/pull/46937)) - -### Hardware support -#### CustomDevice -- Add support for the distributed strategy MP/Sharding/PP/MoE and recompute on the training side. Add support for the distributed strategy MP on the inference side. Support for hardware Ascend NPU and Cambricon MLU accessed through CustomDevice, without changing any codes, to automatically inherit all new distributed strategies added by CustomDevice. [#52872](https://github.com/PaddlePaddle/Paddle/pull/52872), [#54384](https://github.com/PaddlePaddle/Paddle/pull/54384), [#53220](https://github.com/PaddlePaddle/Paddle/pull/53220), [#54572](https://github.com/PaddlePaddle/Paddle/pull/54572), [#54573](https://github.com/PaddlePaddle/Paddle/pull/54573), [#54676](https://github.com/PaddlePaddle/Paddle/pull/54676), [#53044](https://github.com/PaddlePaddle/Paddle/pull/53044), [#53719](https://github.com/PaddlePaddle/Paddle/pull/53719), [#53701](https://github.com/PaddlePaddle/Paddle/pull/53701), [#53702](https://github.com/PaddlePaddle/Paddle/pull/53702), [#53703](https://github.com/PaddlePaddle/Paddle/pull/53703) -- Add API paddle.device.is_compiled_with_custom_device. It is convenient for users to judge whether the current environment supports the plug-in device backend of a certain hardware. [#49271](https://github.com/PaddlePaddle/Paddle/pull/49721) -- Add environment variable CUSTOM_DEVICE_BLACK_LIST setting, to support automatic heterogeneous operation on CPU of blacklisted operators. [#50409](https://github.com/PaddlePaddle/Paddle/pull/50409), [#50666](https://github.com/PaddlePaddle/Paddle/pull/50666) -- Optimize CustomDevice performance by reducing number of calls to get_device_count interface in runtime. [#46963](https://github.com/PaddlePaddle/Paddle/pull/46963) - -#### KUNLUNXIN XPU -- For the training side, use a new version of dynamic graph, with adding support for distributed strategy MP/Sharding/PP and recompute function, and communication library. For the inference side, add support for distributed strategy MP and support for XPU FasterTransformer operator acceleration library. [#49531](https://github.com/PaddlePaddle/Paddle/pull/49531), [#49815](https://github.com/PaddlePaddle/Paddle/pull/49815), [#48897](https://github.com/PaddlePaddle/Paddle/pull/48897), [#50717](https://github.com/PaddlePaddle/Paddle/pull/50717), [#51082](https://github.com/PaddlePaddle/Paddle/pull/51082), [#49757](https://github.com/PaddlePaddle/Paddle/pull/49757), [#51399](https://github.com/PaddlePaddle/Paddle/pull/51399), [#50329](https://github.com/PaddlePaddle/Paddle/pull/50329), [#48369](https://github.com/PaddlePaddle/Paddle/pull/48369), [#47838](https://github.com/PaddlePaddle/Paddle/pull/47838),[#48076](https://github.com/PaddlePaddle/Paddle/pull/48076),[#47882](https://github.com/PaddlePaddle/Paddle/pull/47882),[#48961](https://github.com/PaddlePaddle/Paddle/pull/48961),[#49043](https://github.com/PaddlePaddle/Paddle/pull/49043),[#49749](https://github.com/PaddlePaddle/Paddle/pull/49749),[#49806](https://github.com/PaddlePaddle/Paddle/pull/49806),[#53427](https://github.com/PaddlePaddle/Paddle/pull/53427),[#48470](https://github.com/PaddlePaddle/Paddle/pull/48470),[#49207](https://github.com/PaddlePaddle/Paddle/pull/49207),[#52296](https://github.com/PaddlePaddle/Paddle/pull/52296),[#51785](https://github.com/PaddlePaddle/Paddle/pull/51785),[#47168](https://github.com/PaddlePaddle/Paddle/pull/47168),[#47445](https://github.com/PaddlePaddle/Paddle/pull/47445),[#50200](https://github.com/PaddlePaddle/Paddle/pull/50200),[#49934](https://github.com/PaddlePaddle/Paddle/pull/49934),[#50792](https://github.com/PaddlePaddle/Paddle/pull/50792),[#52228](https://github.com/PaddlePaddle/Paddle/pull/52228),[#53337](https://github.com/PaddlePaddle/Paddle/pull/53337),[#53389](https://github.com/PaddlePaddle/Paddle/pull/53389),[#53496](https://github.com/PaddlePaddle/Paddle/pull/53496),[#53609](https://github.com/PaddlePaddle/Paddle/pull/53609),[#53697](https://github.com/PaddlePaddle/Paddle/pull/53697),[#53496](https://github.com/PaddlePaddle/Paddle/pull/53496),[#53720](https://github.com/PaddlePaddle/Paddle/pull/53720),[#53734](https://github.com/PaddlePaddle/Paddle/pull/53734),[#54172](https://github.com/PaddlePaddle/Paddle/pull/54172),[PR46227](https://github.com/PaddlePaddle/Paddle/pull/46227) - -## 4. Deployment Direction（Paddle Inference） -### New features -- Support Paddle TensorRT multiple subgraph TensorRT engine or TensorRT engine between different Predictors to share video memory in order to save video memory. [#45842](https://github.com/PaddlePaddle/Paddle/pull/45842) [#47631](https://github.com/PaddlePaddle/Paddle/pull/47631) -- For the C++ API, add Shape and data type API to obtain the input Tensor, and add Shape and data type API to obtain the output Tensor. For the C API, add SetExecStream, EnableMkldnnInt8 and other C++ existing APIs for serviced deployment. [#49758](https://github.com/PaddlePaddle/Paddle/pull/49758) -- Add paddle.inference.Predictor.register_output_hook() API. Support printing of the output of each layer under GPU inference in case of debugging. Support use in control flow models such as While. It should be noted the API does not support Paddle-TensorRT. [#54433](https://github.com/PaddlePaddle/Paddle/pull/54433) ，[#47050](https://github.com/PaddlePaddle/Paddle/pull/47050) ， [#54254](https://github.com/PaddlePaddle/Paddle/pull/54254) 。 -- Paddle Inference Predictor API supports paddle::Tensor as input and output, so users can directly reuse the PaddlePaddle dynamics graph for pre-inference and post-inference processing. ([#50445](https://github.com/PaddlePaddle/Paddle/pull/50445)) -- Enhance Paddle TensorRT dynamic shape running ability, config.enable_tuned_tensorrt_dynamic_shape() API to build TensorRT Engine at runtime without passing any parameters. It is unnecessary to collect shape information before running. To avoid rebuilding at runtime, it is necessary to overwrite minimum and maximum Shape in first operations for several times. [#52162](https://github.com/PaddlePaddle/Paddle/pull/52162) 。 -- Paddle-TensorRT supports model input in NHWC format. [#49633](https://github.com/PaddlePaddle/Paddle/pull/49633) 。 -- Extend config.Exp_DisableTensorRtOPs API to disable access to TensorRT by specifying the name of the Tensor variable. [#49497](https://github.com/PaddlePaddle/Paddle/pull/49497) 。 - -### Improvements -- Enhance GPU mixed-precision inference (non-Paddle TensorRT scenarios). For the Config.enable_use_gpu enhancement, you can set precision type. [#47993](https://github.com/PaddlePaddle/Paddle/pull/47993) -- Support double type input for inference. [#51786](https://github.com/PaddlePaddle/Paddle/pull/51786) 。 -- Since the TensorRT operator does not support the INT64 type, leading to running failure of INT64 data type in the model. Paddle-TensorRT has been enhanced to automatically convert, with reducing the model to run in the INT32 type when model contains INT64 data type. [#45547](https://github.com/PaddlePaddle/Paddle/pull/45547) -- Paddle-TensorRT supports more operators into TensorRT inference, including: - - expand_v2，gather_nd，rsqrt，sign，not，onehot，arg_min，temporal_shift，expend_as_v2，setvalue，index_select，round，acosh，square，reduce_max，not_equal，reduce_min，reduce_prod，grid_sampler，elementwise_mod，pad3d ，greater_equal，bitwise，cumsum，matmul_v2，reciprocal，where，bmm，take_along_axis，less_than，greater_than， logical_or， logical_xor， logical_and， less_equal，range，reduce_all，reduce_any ，fill_any_like ，pow - - [#47002](https://github.com/PaddlePaddle/Paddle/pull/47002) , [#47589](https://github.com/PaddlePaddle/Paddle/pull/47589) ，[#48223](https://github.com/PaddlePaddle/Paddle/pull/48223) ，[#48557](https://github.com/PaddlePaddle/Paddle/pull/48557) ， [#48655](https://github.com/PaddlePaddle/Paddle/pull/48655) ， [#49113](https://github.com/PaddlePaddle/Paddle/pull/49113) ， [#51207](https://github.com/PaddlePaddle/Paddle/pull/51207) ，[#51028](https://github.com/PaddlePaddle/Paddle/pull/51028) ，[#50341](https://github.com/PaddlePaddle/Paddle/pull/50341) ，[#51498](https://github.com/PaddlePaddle/Paddle/pull/51498) ，[#48534](https://github.com/PaddlePaddle/Paddle/pull/48534) ，[#48684](https://github.com/PaddlePaddle/Paddle/pull/48684) ， [#49393](https://github.com/PaddlePaddle/Paddle/pull/49393) ， [#49615](https://github.com/PaddlePaddle/Paddle/pull/49615) ，[#50934](https://github.com/PaddlePaddle/Paddle/pull/50934) ，[#50974](https://github.com/PaddlePaddle/Paddle/pull/50974)，[#50986](https://github.com/PaddlePaddle/Paddle/pull/50986) ， [#52000](https://github.com/PaddlePaddle/Paddle/pull/52000) ，[#51971](https://github.com/PaddlePaddle/Paddle/pull/51971) ， [#52518](https://github.com/PaddlePaddle/Paddle/pull/52518) ，[#44918](https://github.com/PaddlePaddle/Paddle/pull/44918) ，[#48230](https://github.com/PaddlePaddle/Paddle/pull/48230) ，[#47820](https://github.com/PaddlePaddle/Paddle/pull/47820) ， [#46877](https://github.com/PaddlePaddle/Paddle/pull/46877) ， [#48358](https://github.com/PaddlePaddle/Paddle/pull/48358) ， [#48592](https://github.com/PaddlePaddle/Paddle/pull/48592) ，[#48697](https://github.com/PaddlePaddle/Paddle/pull/48697) , [#53088](https://github.com/PaddlePaddle/Paddle/pull/53088) ， [#47974](https://github.com/PaddlePaddle/Paddle/pull/47974) ， [#53462](https://github.com/PaddlePaddle/Paddle/pull/53462) -- Enhance Paddle-TensorRT mapping operators strided_slice, instance_norm, prelu, argmax, cast, nearest_interp_v2, elementwise, bilinear. [#46819](https://github.com/PaddlePaddle/Paddle/pull/46819) ，[#47998](https://github.com/PaddlePaddle/Paddle/pull/47998) ，[#48043](https://github.com/PaddlePaddle/Paddle/pull/48043) ，[#48998](https://github.com/PaddlePaddle/Paddle/pull/48998) ， [#49675](https://github.com/PaddlePaddle/Paddle/pull/49675) , [#47495](https://github.com/PaddlePaddle/Paddle/pull/47495) -- Paddle-TensorRT partial operators (scale, square, sum, swish, expand_as_v2, prelu, gelu, hard_swish, hard_sigmoid, leaky_relu,softmax, stack, clip, cast, flatten_contiguous_range, unary, equal, elementwise_op). Support 0-dimensional Tensor. [#53660](https://github.com/PaddlePaddle/Paddle/pull/53660) ，[#53627](https://github.com/PaddlePaddle/Paddle/pull/53627) ， [#53634](https://github.com/PaddlePaddle/Paddle/pull/53634) ， [#53714](https://github.com/PaddlePaddle/Paddle/pull/53714) ， [#53729](https://github.com/PaddlePaddle/Paddle/pull/53729) ，[#53769](https://github.com/PaddlePaddle/Paddle/pull/53769) ，[#53506](https://github.com/PaddlePaddle/Paddle/pull/53506) ，[#53704](https://github.com/PaddlePaddle/Paddle/pull/53704) -- Support compilation for versions earlier than GCC12 + CUDA 12.0. [#50106](https://github.com/PaddlePaddle/Paddle/pull/50106) -- Paddle-TensorRT's DeformableConv plugin supports dynamic Shape input. [#50698](https://github.com/PaddlePaddle/Paddle/pull/50698) -- For Paddle-TensorRT, add plugin support for lookup_table operator. [#46613](https://github.com/PaddlePaddle/Paddle/pull/46613) -- Add config.enable_low_precision_io() API to support low-precision type input in Paddle-TensorRT scenario. [#52485](https://github.com/PaddlePaddle/Paddle/pull/52485) -- Paddle-TensorRT's LayerNorm plugin supports FP16 computation. [#45043](https://github.com/PaddlePaddle/Paddle/pull/45043) -- Predictor's input data paddle_infer::Tensor supports bool type. [#49388](https://github.com/PaddlePaddle/Paddle/pull/49388) -- Paddle-TensorRT enhanced Convolution implementation uses ConvolutionNd. [#47653](https://github.com/PaddlePaddle/Paddle/pull/47653) -- conv2d_fusion operator supports NHWC format. [#49047](https://github.com/PaddlePaddle/Paddle/pull/49047) -- Adjust the directory structure related to Phi operators under C++ inference library. [#53091](https://github.com/PaddlePaddle/Paddle/pull/53091) -- Support rebuilding TensorRT Engine instead of reporting errors when TensorRT serialization and loading versions do not match. [#50775](https://github.com/PaddlePaddle/Paddle/pull/50775) 。 -- Optimize Paddle-TensorRT runtime to print log messages. [#50181](https://github.com/PaddlePaddle/Paddle/pull/50181) -- Support elementwise 0-dimensional Tensor inputs for oneDNN-based CPU inference. [#51656](https://github.com/PaddlePaddle/Paddle/pull/51656) -- Clean up and normalize support for Paddle-TensorRT's FC, matmul, matmul_v2 operators, and unify and upgrade to use TensorRT's IMatrixMultiplyLayer for support. [#52222](https://github.com/PaddlePaddle/Paddle/pull/52222) - -### Performance optimization -- Support multiple lookup_tables into Paddle-TensorRT's Embedding+Eltwise+LayerNorm fusion. [#46243](https://github.com/PaddlePaddle/Paddle/pull/46243) ，[#46230](https://github.com/PaddlePaddle/Paddle/pull/46230) -- Add MoE fusion Phi operator to improve inference performance of MoE model. [#48703](https://github.com/PaddlePaddle/Paddle/pull/48703) -- In the scenario of INT8 quantized inference, Paddle-TensorRT plugin can fall back to FP16 computation, instead of FP32 computation. [#50554](https://github.com/PaddlePaddle/Paddle/pull/50554) -- Optimize memory and video memory in case of inference. [#49051](https://github.com/PaddlePaddle/Paddle/pull/49051) ， [#49046](https://github.com/PaddlePaddle/Paddle/pull/49046) ，[#53930](https://github.com/PaddlePaddle/Paddle/pull/53930) -- Optimize Layout and enhance Pass. [#52997](https://github.com/PaddlePaddle/Paddle/pull/52997) -- Support caching of operator Shape inferences to improve model inference performance. [#48312](https://github.com/PaddlePaddle/Paddle/pull/48312) -- Optimize bias+add+relu fusion using half2 instructions. [#49048](https://github.com/PaddlePaddle/Paddle/pull/49048) -- Optimize Concat Kernel for multiple inputs using vectorization operations. [#49540](https://github.com/PaddlePaddle/Paddle/pull/49540) -- Implement Convolution, Depthwise Convolution and related fusion operators based on CUTLASS to improve inference speed. [#47989](https://github.com/PaddlePaddle/Paddle/pull/47989) ，[#50603](https://github.com/PaddlePaddle/Paddle/pull/50603) ，[#51792](https://github.com/PaddlePaddle/Paddle/pull/51792) ，[#50603](https://github.com/PaddlePaddle/Paddle/pull/50603) -- Paddle-TensorRT supports FlashAttention’s plugin, to improve inference speed of models such as StableDiffusion. [#49438](https://github.com/PaddlePaddle/Paddle/pull/49438) 。 -- Add Transpose+LayerNorm fusion PASS, to improve inference speed of models such as StableDiffusion. [#50082](https://github.com/PaddlePaddle/Paddle/pull/50082) 。 -- Add Elementwise+Transpose fusion. [#50081](https://github.com/PaddlePaddle/Paddle/pull/50081) -- Optimize Paddle-TensorRT Group Norm plugin implementation. [#49160](https://github.com/PaddlePaddle/Paddle/pull/49160) -- For Config.EnableTensorRtEngine() API, add use_cuda_graph parameter. You can enable CUDA Graph. It should be noted you need to ensure the model input shape remains unchanged during usage, to reduce runtime consumption. [#53406](https://github.com/PaddlePaddle/Paddle/pull/53406) -- Support inplace operation of Reshape, to reduce copying time of the model at runtime. [#49146](https://github.com/PaddlePaddle/Paddle/pull/49146) -- Optimize LayerNorm kernel implementation based on oneDNN. [#47782](https://github.com/PaddlePaddle/Paddle/pull/47782) -- Support fusion of quantize+transpose and transpose+dequantize based on oneDNN. [#49509](https://github.com/PaddlePaddle/Paddle/pull/49509) -- When MKLDNN is turned on in CPU inference, FC-related fusion pass is enabled by default, to improve performance. [#45704](https://github.com/PaddlePaddle/Paddle/pull/45704) -- CPU OneDNN inference supports suqeeze2 + transpose2 fusion. [#47592](https://github.com/PaddlePaddle/Paddle/pull/47592) - -### XPU inference enhancement and performance optimization -- Add ExpRunWithRuntimeConfig API and XpuRuntimeConfig, to allow settings of parameters such as external streams, and L3 cache during inference. GetExecStream API supports obtaining Kunlun external stream objects. Input and output support Kunlun device memory, to reduce D2H and H2D overheads. [#53334](https://github.com/PaddlePaddle/Paddle/pull/53334)、 [#52466](https://github.com/PaddlePaddle/Paddle/pull/52466)、 [#53240](https://github.com/PaddlePaddle/Paddle/pull/53240) -- Add multi-encoder, fused_multi_transformer and fusion pass, to improve performance of ERNIE and Transformer class models. [#50570](https://github.com/PaddlePaddle/Paddle/pull/50570)、[#51346](https://github.com/PaddlePaddle/Paddle/pull/51346)、 [#50499](https://github.com/PaddlePaddle/Paddle/pull/50499)、[#53982](https://github.com/PaddlePaddle/Paddle/pull/53982)、[#50759](https://github.com/PaddlePaddle/Paddle/pull/50759)、[#51571](https://github.com/PaddlePaddle/Paddle/pull/51571)、 [#53144](https://github.com/PaddlePaddle/Paddle/pull/53144)、[#53306](https://github.com/PaddlePaddle/Paddle/pull/53306) -- Optimize BeamSearch performance. Transform, remove and fuse fine-grained operators such as write_read_array and gather, to improve model performance when beam_size=1. [#53130](https://github.com/PaddlePaddle/Paddle/pull/53130) -- Transform multiple stack operators with the same input into unsqueeze operators that support broadcast. Unsquee/squeeze supports inplace computation. [#52099](https://github.com/PaddlePaddle/Paddle/pull/52099) -- Add support for exporting multi-card inference models for Kunlunxin. [#50490](https://github.com/PaddlePaddle/Paddle/pull/50490) -- Add embedding_with_eltwise_add fusion pass and operator phi kernel, to reduce video memory usage and improve inference performance. [#50590](https://github.com/PaddlePaddle/Paddle/pull/50590) -- interpolate class operator phi kernel supports FP16. [#52358](https://github.com/PaddlePaddle/Paddle/pull/52358) -- argmax operator supports INT32 type output. [#51303](https://github.com/PaddlePaddle/Paddle/pull/51303) -- Fix the error of only model file when saving serialized model after turning on mixed-precision inference mode. [#52994](https://github.com/PaddlePaddle/Paddle/pull/52994) -- Fix segment error of instance_norm when scale and bias are empty. [#52627](https://github.com/PaddlePaddle/Paddle/pull/52627) -- conv_transpose operator supports FP16. [#53626](https://github.com/PaddlePaddle/Paddle/pull/53626) -- Add yolo_box_xpu fusion pass and operator phi kernel, to optimize YOLO model generic substructure. [#54163](https://github.com/PaddlePaddle/Paddle/pull/54163) -- Add conv2d_xpu fusion pass and operator phi kernel, and support FP16 inference, to optimize convolution operation inference consumption time. [#52247](https://github.com/PaddlePaddle/Paddle/pull/52247) ，[#53626](https://github.com/PaddlePaddle/Paddle/pull/53626) -- Add sigmoid_elementmul generic fusion pass, to fuse to swish operator to match conv2d_fusion pass to improve YOLO model inference performance. [#53580](https://github.com/PaddlePaddle/Paddle/pull/53580) -- Add act_add fusion pass and operator phi kernel to improve inference performance. [#53965](https://github.com/PaddlePaddle/Paddle/pull/53965) -- Add fold_interp_outsize fusion pass, to improve inference performance. [#54245](https://github.com/PaddlePaddle/Paddle/pull/54245) -- Solve the problem of incorrect results due to duplicate fusion when there is shared weight in FC. [#51108](https://github.com/PaddlePaddle/Paddle/pull/51108)、[#51039](https://github.com/PaddlePaddle/Paddle/pull/51039) -- Remove op_device attribute where operator is only used for training, to prevent wrong choice of place for training during inference. [#51029](https://github.com/PaddlePaddle/Paddle/pull/51029) -- Support saving of optimized models, allowing PASS optimization to be skipped in case of re-inference, to reduce first time inference time. [#53696](https://github.com/PaddlePaddle/Paddle/pull/53696) -- Solve the problem of computation error caused by the CPUPlace input of operator Kernel being forced to copy to XPU. [#51306](https://github.com/PaddlePaddle/Paddle/pull/51306) -- subblock supports early copying of H2D parameters to improve inference performance. [#51876](https://github.com/PaddlePaddle/Paddle/pull/51876) -- Fix scale memory size of the output activation of Kunlunxin 2nd generation chip. [#53505](https://github.com/PaddlePaddle/Paddle/pull/53505) -- In new executor Kunlunxin D2D copy, support asynchronous execution. [#51876](https://github.com/PaddlePaddle/Paddle/pull/51876) -- Remove concat operator with only one input. [#52304](https://github.com/PaddlePaddle/Paddle/pull/52304) -- lookup_table_v2 supports FP16 to remove redundant cast operator. [#52888](https://github.com/PaddlePaddle/Paddle/pull/52888) -- Control flow While operator supports caching scope, to reduce overhead of creating new scope every time. [#52628](https://github.com/PaddlePaddle/Paddle/pull/52628) -- Scatter newly supports FP16, to remove redundant cast operators and elementwise_mul operators with an input of 1. [#52831](https://github.com/PaddlePaddle/Paddle/pull/52831) - -### Model quantization -- Upgrade of dynamic graph quantization function. - - Add a new API for quantization training of dynamic graph models: ```paddle.quantization.QAT ```. Support passing quantization-related parameters through configuration, simplifying quantization training process and difficulty of secondary development. ([#49398](https://github.com/PaddlePaddle/Paddle/pull/49398)) - - Add a new offline quantization API: ```paddle.quantization.PTQ ```. Support exporting quantization model to model format supported by inference. ([#50107](https://github.com/PaddlePaddle/Paddle/pull/50107)) - - Add STUB operator to simulate actual quantization operation during training process. ([#50510](https://github.com/PaddlePaddle/Paddle/pull/50510)) -- Support quantization training model to load parameters of offline quantization model. Support more operators for quantization, including matmul, scale, and conv1d. [#47892](https://github.com/PaddlePaddle/Paddle/pull/47892)， [#45911](https://github.com/PaddlePaddle/Paddle/pull/45911)，[#48912](https://github.com/PaddlePaddle/Paddle/pull/48912) -- Support hybrid parallel training of static graph quantization training. [#52219](https://github.com/PaddlePaddle/Paddle/pull/52219) -- Fix the problem in the process of dynamic graph quantization: - - Repeat insertion of quantization nodes when exporting quantization training models. [#48751](https://github.com/PaddlePaddle/Paddle/pull/48751) - - Fix the problem of inserting quantization nodes into model input. [#49926](https://github.com/PaddlePaddle/Paddle/pull/49926) - -## 5. Environment Adaptation -Improve efficiency of source code compilation, and promote setuptools + ninja compilation method to increase development efficiency: In CPU scenarios, full amount of compilation time is reduced by 20 min, and compilation speed is increased by 24.52%. In GPU scenario, full amount of compilation time is reduced by 22 min, and compilation speed is increased by 29.31%. In order to adapt to mainstream development environments, PaddlePaddle supports gcc12 compilation and C++17 in the source code, and adapts to the latest CUDA12. In terms of code quality, complete cleanup of compilation warnings, to improve compilation experience. At the third-party dependency level, we have upgraded the version of underlying protobuf to reduce dependency, cleaned up deprecated attributes of some earlier versions of dependency libraries and old code formats, and removed support for Python 2.x. -- ninja compilation adaptation to improve compilation speed. [#52433](https://github.com/PaddlePaddle/Paddle/pull/52433),[#48932](https://github.com/PaddlePaddle/Paddle/pull/48932),[#49420](https://github.com/PaddlePaddle/Paddle/pull/49420),[#48435](https://github.com/PaddlePaddle/Paddle/pull/48435),[#49303](https://github.com/PaddlePaddle/Paddle/pull/49303),[#49448](https://github.com/PaddlePaddle/Paddle/pull/49448),[#49838](https://github.com/PaddlePaddle/Paddle/pull/49838),[#50067](https://github.com/PaddlePaddle/Paddle/pull/50067),[#52796](https://github.com/PaddlePaddle/Paddle/pull/52796),[#50431](https://github.com/PaddlePaddle/Paddle/pull/50431),[#49181](https://github.com/PaddlePaddle/Paddle/pull/49181),[#48867](https://github.com/PaddlePaddle/Paddle/pull/48867),[#48490](https://github.com/PaddlePaddle/Paddle/pull/48490),[#48211](https://github.com/PaddlePaddle/Paddle/pull/48211),[#49499](https://github.com/PaddlePaddle/Paddle/pull/49499),[#53076](https://github.com/PaddlePaddle/Paddle/pull/53076) -- setuptools compilation and package all-in-one adaptation. [#48770](https://github.com/PaddlePaddle/Paddle/pull/48770),[#46957](https://github.com/PaddlePaddle/Paddle/pull/46957),[#49583](https://github.com/PaddlePaddle/Paddle/pull/49583),[#47602](https://github.com/PaddlePaddle/Paddle/pull/47602),[#48301](https://github.com/PaddlePaddle/Paddle/pull/48301),[#50800](https://github.com/PaddlePaddle/Paddle/pull/50800),[#42575](https://github.com/PaddlePaddle/Paddle/pull/42575)),[#49826](https://github.com/PaddlePaddle/Paddle/pull/49826),[#49002](https://github.com/PaddlePaddle/Paddle/pull/49002),[#51443](https://github.com/PaddlePaddle/Paddle/pull/51443),[#51528](https://github.com/PaddlePaddle/Paddle/pull/51528),[#52621](https://github.com/PaddlePaddle/Paddle/pull/52621),[#52465](https://github.com/PaddlePaddle/Paddle/pull/52465) -- gcc12 support. [#52960](https://github.com/PaddlePaddle/Paddle/pull/52960),[#52265](https://github.com/PaddlePaddle/Paddle/pull/52265),[#46546](https://github.com/PaddlePaddle/Paddle/pull/46546),[#52318](https://github.com/PaddlePaddle/Paddle/pull/52318),[#46808](https://github.com/PaddlePaddle/Paddle/pull/46808),[#47466](https://github.com/PaddlePaddle/Paddle/pull/47466),[#52083](https://github.com/PaddlePaddle/Paddle/pull/52083),[#48176](https://github.com/PaddlePaddle/Paddle/pull/48176),[#49423](https://github.com/PaddlePaddle/Paddle/pull/49423),[#49452](https://github.com/PaddlePaddle/Paddle/pull/49452),[#51037](https://github.com/PaddlePaddle/Paddle/pull/51037),[#52007](https://github.com/PaddlePaddle/Paddle/pull/52007),[#52441](https://github.com/PaddlePaddle/Paddle/pull/52441),[#52085](https://github.com/PaddlePaddle/Paddle/pull/52085),[#50817](https://github.com/PaddlePaddle/Paddle/pull/50817),[#52646](https://github.com/PaddlePaddle/Paddle/pull/52646),[#50777](https://github.com/PaddlePaddle/Paddle/pull/50777),[#53288](https://github.com/PaddlePaddle/Paddle/pull/53288),[#54009](https://github.com/PaddlePaddle/Paddle/pull/54009) -- c++17 standard support. [#53345](https://github.com/PaddlePaddle/Paddle/pull/53345),[#53892](https://github.com/PaddlePaddle/Paddle/pull/53892),[#54282](https://github.com/PaddlePaddle/Paddle/pull/54282),[#49017](https://github.com/PaddlePaddle/Paddle/pull/49017),[#47635](https://github.com/PaddlePaddle/Paddle/pull/47635),[#54258](https://github.com/PaddlePaddle/Paddle/pull/54258) -- cuda12 support. [#52285](https://github.com/PaddlePaddle/Paddle/pull/52285),[#49592](https://github.com/PaddlePaddle/Paddle/pull/49592),[#52232](https://github.com/PaddlePaddle/Paddle/pull/52232),[#52654](https://github.com/PaddlePaddle/Paddle/pull/52654),[#54641](https://github.com/PaddlePaddle/Paddle/pull/54641) -- CodeStyle。[#45909](https://github.com/PaddlePaddle/Paddle/pull/45909),[#47772](https://github.com/PaddlePaddle/Paddle/pull/47772),[#48538](https://github.com/PaddlePaddle/Paddle/pull/48538),[#49522](https://github.com/PaddlePaddle/Paddle/pull/49522),[#47264](https://github.com/PaddlePaddle/Paddle/pull/47264),[#49558](https://github.com/PaddlePaddle/Paddle/pull/49558) -- Compilation Warning is removed. [#47163](https://github.com/PaddlePaddle/Paddle/pull/47163),[#47216](https://github.com/PaddlePaddle/Paddle/pull/47216),[#47309](https://github.com/PaddlePaddle/Paddle/pull/47309)，[#47252](https://github.com/PaddlePaddle/Paddle/pull/47252)，[#47341](https://github.com/PaddlePaddle/Paddle/pull/47341)，[#47399](https://github.com/PaddlePaddle/Paddle/pull/47399)，[#47513](https://github.com/PaddlePaddle/Paddle/pull/47513)，[#47558](https://github.com/PaddlePaddle/Paddle/pull/47558)，[#47706](https://github.com/PaddlePaddle/Paddle/pull/47706)，[#52717](https://github.com/PaddlePaddle/Paddle/pull/52717)，[#51203](https://github.com/PaddlePaddle/Paddle/pull/51203)，[#51336](https://github.com/PaddlePaddle/Paddle/pull/51336)，[#51608](https://github.com/PaddlePaddle/Paddle/pull/51608)，[#51633](https://github.com/PaddlePaddle/Paddle/pull/51633),[#46644](https://github.com/PaddlePaddle/Paddle/pull/46644),[#53092](https://github.com/PaddlePaddle/Paddle/pull/53092),[#53185](https://github.com/PaddlePaddle/Paddle/pull/53185),[#53246](https://github.com/PaddlePaddle/Paddle/pull/53246),[#53650](https://github.com/PaddlePaddle/Paddle/pull/53650),[#53683](https://github.com/PaddlePaddle/Paddle/pull/53683),[#53687](https://github.com/PaddlePaddle/Paddle/pull/53687),[#53886](https://github.com/PaddlePaddle/Paddle/pull/53886),[#53689](https://github.com/PaddlePaddle/Paddle/pull/53689),[#53679](https://github.com/PaddlePaddle/Paddle/pull/53679),[#53681](https://github.com/PaddlePaddle/Paddle/pull/53681),[#53532](https://github.com/PaddlePaddle/Paddle/pull/53532),[#47137](https://github.com/PaddlePaddle/Paddle/pull/47137),[#47045](https://github.com/PaddlePaddle/Paddle/pull/47045),[#52186](https://github.com/PaddlePaddle/Paddle/pull/52186),[#52490](https://github.com/PaddlePaddle/Paddle/pull/52490),[#53924](https://github.com/PaddlePaddle/Paddle/pull/53924),[#53938](https://github.com/PaddlePaddle/Paddle/pull/53938),[#53945](https://github.com/PaddlePaddle/Paddle/pull/53945),[#53851](https://github.com/PaddlePaddle/Paddle/pull/53851),[#53847](https://github.com/PaddlePaddle/Paddle/pull/53847),[#53818](https://github.com/PaddlePaddle/Paddle/pull/53818),[#53931](https://github.com/PaddlePaddle/Paddle/pull/53931) -- Support protobuf upgrade. [#49875](https://github.com/PaddlePaddle/Paddle/pull/49875),[#48495](https://github.com/PaddlePaddle/Paddle/pull/48495),[#49673](https://github.com/PaddlePaddle/Paddle/pull/49673),[#52499](https://github.com/PaddlePaddle/Paddle/pull/52499),[#51161](https://github.com/PaddlePaddle/Paddle/pull/51161),[#49168](https://github.com/PaddlePaddle/Paddle/pull/49168) -- Support offline compilation of third-party libraries. [#54326](https://github.com/PaddlePaddle/Paddle/pull/54326),[#54370](https://github.com/PaddlePaddle/Paddle/pull/54370),[#54335](https://github.com/PaddlePaddle/Paddle/pull/54335),[#54346](https://github.com/PaddlePaddle/Paddle/pull/54346),[#53744](https://github.com/PaddlePaddle/Paddle/pull/53744),[#54319](https://github.com/PaddlePaddle/Paddle/pull/54319),[#53915](https://github.com/PaddlePaddle/Paddle/pull/53915) -- Phi independent compilation header file dependency decoupling. [#50456](https://github.com/PaddlePaddle/Paddle/pull/50456),[#47088](https://github.com/PaddlePaddle/Paddle/pull/47088),[#52573](https://github.com/PaddlePaddle/Paddle/pull/52573),[#52651](https://github.com/PaddlePaddle/Paddle/pull/52651) -- Python2.x decommissioning. [#48685](https://github.com/PaddlePaddle/Paddle/pull/48685) - -## 6. Security -- Fix bugs such as null pointer usage, illegal address access, memory out of bounds, divide by 0, and Python IndexError [PR49976](https://github.com/PaddlePaddle/Paddle/pull/49976), [ PR49993](https://github.com/PaddlePaddle/Paddle/pull/49993)[, PR49942](https://github.com/PaddlePaddle/Paddle/pull/49942), [PR49965](https://github.com/PaddlePaddle/Paddle/pull/49965)[, PR50000](https://github.com/PaddlePaddle/Paddle/pull/50000)[, PR50005](https://github.com/PaddlePaddle/Paddle/pull/50005)[, PR49953](https://github.com/PaddlePaddle/Paddle/pull/49953)[, PR49995](https://github.com/PaddlePaddle/Paddle/pull/49995)[, PR49974](https://github.com/PaddlePaddle/Paddle/pull/49974)[, PR50015](https://github.com/PaddlePaddle/Paddle/pull/50015)[, PR50010](https://github.com/PaddlePaddle/Paddle/pull/50010), [PR49979](https://github.com/PaddlePaddle/Paddle/pull/49979), [PR49994](https://github.com/PaddlePaddle/Paddle/pull/49994), [PR49977](https://github.com/PaddlePaddle/Paddle/pull/49977)[, PR49968](https://github.com/PaddlePaddle/Paddle/pull/49968), [PR49984](https://github.com/PaddlePaddle/Paddle/pull/49984)[, PR49958](https://github.com/PaddlePaddle/Paddle/pull/49958)[, PR50008](https://github.com/PaddlePaddle/Paddle/pull/50008)[, PR51714](https://github.com/PaddlePaddle/Paddle/pull/51714), [PR51847](https://github.com/PaddlePaddle/Paddle/pull/51847), [PR51034](https://github.com/PaddlePaddle/Paddle/pull/51034)[, PR51088](https://github.com/PaddlePaddle/Paddle/pull/51088)[, PR51091](https://github.com/PaddlePaddle/Paddle/pull/51091)[, PR51092](https://github.com/PaddlePaddle/Paddle/pull/51092), [PR49966](https://github.com/PaddlePaddle/Paddle/pull/49966), [PR49656](https://github.com/PaddlePaddle/Paddle/pull/49656), [PR52161](https://github.com/PaddlePaddle/Paddle/pull/52161), [PR49548](https://github.com/PaddlePaddle/Paddle/pull/49548), [PR49546](https://github.com/PaddlePaddle/Paddle/pull/49546), [PR49547](https://github.com/PaddlePaddle/Paddle/pull/49547), [PR49549](https://github.com/PaddlePaddle/Paddle/pull/49549), [PR51850](https://github.com/PaddlePaddle/Paddle/pull/51850) - -## Thanks to our Contributors -This release contains contributions from: -1want2sleep, 201716010711, 404988613, 5u13, 6clc, Ackeraa, Aganlengzi, ahahahahahaha, Ainavo, Allen Guo, andyj, Asthestarsfalll, Aurelius84, Ayuan, BellaZYL, Bjmw3, Bo Zhang, bukejiyu, caozhou, carryyu, Ccc, ccrrong, ceci3, chalsliu, Chang Xu, CHANGer, Charles-hit, Chen Weihang, chenjian, Chenxiao Niu, chenxiao120660, chenxujun, Chitsing KUI, cifar10, co63oc, CollaborativeFiltering, csy0225, cxxly, cyber-pioneer, cyberslack_lee, czr-gc, Dandelight, danleifeng, Danyang Zhang, dasen, denglianbin, Difer, dongfangshenzhu, DrowFish19, duanboqiang, duanyanhui, engineer, engineer1109, Epsilon Luoo, feifei-111, Feiyu Chan, Feng Ni, feng_shuai, Fisher, FlyingQianMM, Frank Lin, Galaxy1458, GaoYuYang, gaoziyuan, gem5, GGBond8488, Ghost Screaming, gongenlei, gouzil, Guanghua Yu, Guo Sheng, Guoxia Wang, Hamid Zare, Hanchiao, handiz, Haohongxiang, haosicheng, haozi, Happyd99, heliqi, hellockx, hellolllw, heyanru, hg-1099255210, hh-qiao, hjyp, hong, HongyuJia, houj04, hua-zi, Huang Jiyi, Huang Zhengjie, huangjiyi, huangjun12, Hui Zhang, Huihuang Zheng, Hulek, hwa, HydrogenSulfate, Ikko Eltociear Ashimine, iLeGend, Infinity_lee, Infrared1029, Jacek Czaja, jakpiase, james, jameszhang, Jiabin Yang, jiahongyu, jiangcheng, jiangfan06, Jianghai, jiaqianjing, jingsongliu, JingZhuangzhuang, jjyaoao, joanna.wozna.intel, junxiu777, Jx-qi, JYChen, JZ-LIANG, jzhang533, Kai Song, Kai Xing, Kaipeng Deng, Kang Zhao, kangguangli, Kevin Wu Jiawen , Kim, Kim Yann, knamg, kuizhiqing, lanxianghit, Leding Li, Leo Chen, Leo Guo, levi131, Li Min, Li-fAngyU, Ligoml, lijialin03, lijin23, limingshu, Lin Manhui, LinearTemporalLogic, Linjie Chen, lishicheng1996, Little-chick, littleforest, liu zhengxi, liulinduo, liuruyan, liuzhenhai93, LiYuRio, lj970926, LokeZhou, LoneRanger, lubiu, Lucas, lugimzzz, Lux et Veritas, lxsbupt, LyndonKong, lzy, lzydev, Mahmoud Ashraf, Manan Goel, Maple Xie, Matsumoto Ruko, mayang002, MayYouBeProsperous, megemini, mengziheng, Meteor Liu, mhy, mhy-666, Ming-Xu Huang, ming1753, minghaoBD, mjxs, Moqim, Mountagha, Mr.Juice, mrcangye, NetPunk, Netpunk, nihao, niuliling123, Nyakku Shigure, OccupyMars2025, Ouyang Chao, pangengzheng, pangyoki, parap1uie-s, Paulina Gacek, Piotr Paturej, PommesPeter, PPGitub, PPPPzhang, PuQing, Qi Li, Qi Shao, QingshuChen, qipengh, qizhaoaoe, Rayman, RedContritio, RichardWooSJTU, risemeup1, Roc, ronnywang, Ruibiao Chen, Ruibin Cheung, RuohengMa, Ryan, SaltFish11, Sanbu, Scotty, scotty, seemingwang, Shaojie WANG, ShenLiang, shentanyue, Shijie, Shuangchi He, Siming Dai, Sing_chan, sneaxiy, Sonder, sprouteer, Sqhttwl, sunli, superwinner1, supplyout, SylarTiaNII, Sylwester Fraczek, Sławomir Siwek, taixiurong, Tao Luo, Taylor-Layrose, TeFeng Chen, Thomas Young, thunder95, Thunderbrook, Tian, Tian Zheng, tiancaishaonvjituizi, tianshuo78520a, tifa, Tinson Lai, Tomasz Socha, Tony Cao, ucsk, umiswing, ustiniankw, Vegetable dog, Vigi Zhang, Vvsmile, Wang Bojun, Wang Xin, Wang Xinyu, wangfengsheng1999, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, wangshengxiang, wangxiaoning, wangxinxin08, Wangzheee, WangZhen, wangzhen38, wasupandceacar, wawltor, Wei Shengyu, Weilong Wu, weishengying, Wen Sun, wenbin, wentao yu, wenzhe.wang, westfish, whisky-12, whs, Wilber, will-jl944, winter-wang, Winters Montagne, WJJ1995, wuhuachaocoding, wuyefeilin, wz1qqx, XiangGao, xiaoguoguo626807, xiaohemaikoo, xiaoluomi, xiaoting, xiaoxiaohehe001, Xiaoxu Chen, xiaoyuanzi914, Xinger, Xinyu Chen, xiongkun, xjmxyt, xu98bin, xysheng-baidu, yangguohao, yangjianfengo1, YangQun, YangZhou, yeliang2258, YepKong, Yichen Zhang, yikaikkk, Yiqun Liu, yjphhw, ykkk2333, Young-Flash, yu wentao, Yuang Liu, Yuanle Liu, YuanRisheng, yuchen202, yuehuayingxueluo, YuhangLi, Yulong Ao, YUNSHEN XIE, yunyaoXYY, YuRonan, zachary sun, ZeKai Zhou, Zenghui Yuan, zengshao0622, Zero Rains, Zhan Rongrui, Zhang Jun, Zhang Na, Zhang Ting, Zhang Zheng, zhangbo9674, ZhangDY-6483, zhangkaihuo, zhangxin81, zhangyikun02, zhangyingying520, zhangyuqin1998, zhaocaibei123, zhaoyingli, Zhen Wang, Zheng-Bicheng, Zhenghai Zhang, Zheng_Bicheng, zhenyun, Zhibao Li, zhiboniu, Zhong Hui, Zhou Wei, ZhouMengLei1999, zhoutianzi666, zhouzj, zhupengyang, zhurou603, zhuyipin, zhwesky2010, ziyoujiyi, zlsh80826, Zman, zmxdream, zqw_1997, Zuza Gawrysiak, zxcd, zyfncg, ZZK, zzk0, Ding Yi, Fu Jianhan, Liu Ge Gu Tou, Lu Lin, Zhou Zhouzhou, Jiang Yongyong, Xue Zhawu, Zhang Chunqiao, Zhang Zhenghai, Ning Meng Wei, Wang Mingdong, Shi Xiaowei, Chao Ji Ma Niu, Chen Cangye, Qi Ma Xiao Mao - -# 2.4.2 Release Note - - V2.4.2 fixed known bugs, and added a tiny set of features. - -## Training Framework (distributed included) - - - Fix the problem while using paddle.utils.dlpack.to_dlpack API to create dlpack objects multiple times in the for loop, and fix the bug that the reference counting error causes the memory actually pointed by dlpack to be destructed unexpectedly. [#50138](https://github.com/PaddlePaddle/Paddle/pull/50138) - - Fixed the issue of out-of-bounds memory access when the input tensor is multi-dimensional in paddle.multiplex API. [#49368](https://github.com/PaddlePaddle/Paddle/pull/49368) - - Fix the occasional compilation error caused by incorrect referencing of the Eigen header file. [#48157](https://github.com/PaddlePaddle/Paddle/pull/48157) - - Fixed the bug that the output value of the backward operator may be None when the output gradient parameter order of the custom operator is not continuous.[#48656](https://github.com/PaddlePaddle/Paddle/pull/48656) - - Add cutlass and implement the fusion kernel of gather+gemm+scatter; Optimize training and inference performance of sparse convolution; Optimize inference performance of batch_norm under 1D input data.[#50118](https://github.com/PaddlePaddle/Paddle/pull/50118) - - Fix compilation failure in gcc54 environment caused by using constexpr. [#50421](https://github.com/PaddlePaddle/Paddle/pull/50421) - - Move sum op kernel to PHI and fix bug that can't get correct SelectedRows' dims when run infermeta.[#49342](https://github.com/PaddlePaddle/Paddle/pull/49342) - - Fixed the issue that the fold operator accesses memory out of bounds under large bs input.[#49491](https://github.com/PaddlePaddle/Paddle/pull/49491) - - Fix the problem that no parameter Layer cannot call backward under dynamic to static mode.[#49812](https://github.com/PaddlePaddle/Paddle/pull/49812) - - Fix the compile problem of CUDA11.8 on windows platform.[#50205](https://github.com/PaddlePaddle/Paddle/pull/50205) - - Fix the unsupported error for `FusedDropoutActBiasGrad` on H100.[#47285](https://github.com/PaddlePaddle/Paddle/pull/47285) - - Add `debug_graphviz_path` option into `build_strategy`.[#46531](https://github.com/PaddlePaddle/Paddle/pull/46531) - - Fix the not closed `popen` object.[#47053](https://github.com/PaddlePaddle/Paddle/pull/47053) - -## Deployment Direction (Paddle Inference) - - - Improve the functionality and stability of mixed-precision inference. Reconstruct the implementation of interface convert_to_mixed_precision and add parameter precision to interface enable_use_gpu.[#49077](https://github.com/PaddlePaddle/Paddle/pull/49077)、[#49239](https://github.com/PaddlePaddle/Paddle/pull/49239)、[#49477](https://github.com/PaddlePaddle/Paddle/pull/49477) - - Support compilation under jetson ampere architecture.[#49364](https://github.com/PaddlePaddle/Paddle/pull/49364) - - Fixed fc kernel diff.[#49781](https://github.com/PaddlePaddle/Paddle/pull/49781) - - Fixed the error of trt workspace parameter type under CAPI. [#48350](https://github.com/PaddlePaddle/Paddle/pull/48350) - - Fixed the error caused by arg_max/arg_min without flatten dtype parameter in Paddle 1.x version. [#49771](https://github.com/PaddlePaddle/Paddle/pull/49771) - - Fixed the bug of missing information about lod logic after split infermeta's refactoring. [#49745](https://github.com/PaddlePaddle/Paddle/pull/49745) - - Fixed the bug of the constant-folding pass, which causes the conv2d weight to be non-persistent after folding and not enter the TensorRT engine. [#50105](https://github.com/PaddlePaddle/Paddle/pull/50105) - -# 2.4.1 Release Note - - -Remove the dependence of the Paddle on python.so, and fix the bug that fails to execute due to the inability to find python.so in specific environments, including conda. - - -# 2.4.0 Release Note - -## 1. Important Updates - -- **New dynamic graph architecture is officially effective**: The new dynamic graph framework has significantly improved the scheduling performance. The scheduling performance of more than 90% APIs is improved by over 50%, and the model performance of more than 50% kits is improved by over 5%. The functional architecture is clearer, and the secondary development capability and experience are significantly enhanced. - -- **Comprehensive improvement of the dynamic-static unification ability of the PaddlePaddle**: The dynamic-to-static function is provided with richer Python syntax support. The Python syntax coverage of the PaddlePaddle reaches 90%. The syntax transcription logic is mainly optimized to completely support the control flow syntax, with providing smooth dynamic-to-static graph experiences by pressing one key. With the newly upgraded static graph executor, the dynamic-to-static training has better acceleration capability, and the key model test shows that it is close to the best level of the static graph. The dynamic-to-static scalability is improved, with newly supporting multi-function merge export and inference. Users can use the PHI operator library for secondary development and flexible deployment. This can effectively support the custom decoding of U2++ featured models in the speech domain. - -- **Add sparse computing APIs**: Add 55 sparse APIs `paddle.sparse.*` and support mainstream sparse computing scenarios. The APIs have been applied to sparse training and inference deployment for 3D point cloud target detection, Sparse Transformers, and other tasks, with a speedup of 105.75% compared to DenseTensor in high sparse scenarios. In contrast to similar products, the speed of sparse computing is increased by 4.01%-58.55%. Support the computing of a variety of sparse Tensors (SparseCoo and SparseCsr). This is the ultimate saving of video memory. Meanwhile, it maintains a consistent usage experience, with the same usage method of the dense Tensor API. - -- **Large-scale graph neural network GPU training engine**: Through the heterogeneous hierarchical storage technology of SSD, memory, and video memory, it breaks through the video memory bottleneck and supports all-GPU storage and training of super-large-scale graphs. It realizes the all-GPU integrated solution of walk, sampling and training. This can increase the training speed by more than 10x under the same costs, compared to the traditional distributed CPU solution. - -- **Environment adaptation**: Add pre-compiled installer adapted to CUDA version 11.7. It newly supports the running in Ubuntu 22.04 or later. - -### Forward-looking forecast - -- PaddlePaddle Framework will deprecate support for python 3.6 in version 2.5. -- The PaddlePaddle framework will gradually deprecate the API under the `paddle.fluild` namespace on the python side, and some of the APIs under this namespace will be directly removed in version 2.5. - -## 2. Incompatibility upgrade - -- The pre-compiled installer for CUDA version 10.1 is cancelled. -- The -Tensor.clear_gradient(bool set_to_zero) interface will not take the value passed by kwargs, and will have to pass the bool variable of set_to_zero through args. -- In order to improve the utilization efficiency of video memory, only the gradients of forward leaf node variables, such as the gradients of network parameters in training, are retained in the dynamic graph by default, instead of the gradients of non-leaf nodes. If you need to preserve a specific Tensor gradient, you can call the Tensor.retain_grads() interface before reverse execution. -- paddle.autograd. PyLayer will no longer support the case where the input is tuple, pass in a list of Tensor if you want a group of them. - -## 3. Training framework (including the distributed feature) - -### （1）New APIs and enhanced API functions -- **Add the sparse computing class API**：paddle.sparse - - Add 55 sparse APIs and support mainstream sparse computing scenarios. The APIs have been applied to sparse training and inference deployment for 3D point cloud target detection, Sparse Transformers, and other tasks, with a speedup of 105.75% compared to DenseTensor in high sparse scenarios. In contrast to similar products, the speed of sparse computing is increased by 4.01%-58.55%. Support the computing of a variety of sparse Tensors (SparseCoo and SparseCsr). This is the ultimate saving of video memory. Meanwhile, it maintains a consistent usage experience, with the same usage method of the dense Tensor API.[#45849](https://github.com/PaddlePaddle/Paddle/pull/45849), [#46694](https://github.com/PaddlePaddle/Paddle/pull/46694), [#45086](https://github.com/PaddlePaddle/Paddle/pull/45086), [#41857](https://github.com/PaddlePaddle/Paddle/pull/41857), [#42935](https://github.com/PaddlePaddle/Paddle/pull/42935), [#43475](https://github.com/PaddlePaddle/Paddle/pull/43475), [#43668](https://github.com/PaddlePaddle/Paddle/pull/43668), [#43966](https://github.com/PaddlePaddle/Paddle/pull/43966), [#44022](https://github.com/PaddlePaddle/Paddle/pull/44022), [#44346](https://github.com/PaddlePaddle/Paddle/pull/44346), [#44432](https://github.com/PaddlePaddle/Paddle/pull/44432), [#44451](https://github.com/PaddlePaddle/Paddle/pull/44451), [#44743](https://github.com/PaddlePaddle/Paddle/pull/44743), [#42013](https://github.com/PaddlePaddle/Paddle/pull/42013), [#43520](https://github.com/PaddlePaddle/Paddle/pull/43520), [#41434](https://github.com/PaddlePaddle/Paddle/pull/41434), [#42130](https://github.com/PaddlePaddle/Paddle/pull/42130), [#41276](https://github.com/PaddlePaddle/Paddle/pull/41276), [#41857](https://github.com/PaddlePaddle/Paddle/pull/41857), [#41356](https://github.com/PaddlePaddle/Paddle/pull/41356) -- **Add the audio field API：** paddle.audio - - Add the feature extraction APIs such as MFCC, Spectrogram, and LogMelSpectrogram. Support the GPU computing. The performance increases by more than 15x compared to the CPU. This can significantly improve the GPU utilization in speech model training.[#45424](https://github.com/PaddlePaddle/Paddle/pull/45424) - - Add the feature extraction basic APIs such as Window Function and Discrete Cosine Transform. This can facilitate users to customize the speech feature extraction.[#45424](https://github.com/PaddlePaddle/Paddle/pull/45424) - - Add the speech I/O module. It provides 2 types of audio I/O backend and supports 6 types of codecs for convenient loading of speech data. [#45939](https://github.com/PaddlePaddle/Paddle/pull/45939) - - Add TESS and ESC50 speech classification datasets. It is convenient for users to complete the classical speech classification model.[#45939](https://github.com/PaddlePaddle/Paddle/pull/45939) -- **Add the graph learning domain API:** paddle.geometric - - Graph learning is gradually becoming a key technology in the field of machine learning. The new paddle.geometric module of PaddlePaddle provides a better modeling and training development experience of graph learning. - - Message passing: The message passing mechanism of the graph learning is the basis of graph modeling. We add 7 graph learning message passing APIs to make it more convenient to complete the modeling of the graph learning. Among them, 3 newly added message passing fusion operators can significantly reduce the GPU memory consumption in the GNN model training. In the dense graph scenarios, more than 50% of GPU memory can be saved in the models of GCN series, and the training speed can increase by more than 20%.[#44848](https://github.com/PaddlePaddle/Paddle/pull/44848), [#44580](https://github.com/PaddlePaddle/Paddle/pull/44580), [#43174](https://github.com/PaddlePaddle/Paddle/pull/43174), [#44970](https://github.com/PaddlePaddle/Paddle/pull/44970) - - Graph sampling: Graph sampling is the performance bottleneck of GNN model training. This newly added high-performance graph sampling operator supports high concurrent graph sampling. It can increase the sampling speed of GraphSage by more than 32 times and the model training speed by more than 12 times.[#44970](https://github.com/PaddlePaddle/Paddle/pull/44970) -- **Add the vision domain API** - - The paddle.vision is added with target detection domain operators.([#43736](https://github.com/PaddlePaddle/Paddle/pull/43736)), paddle.vision.generate_proposals([#43611](https://github.com/PaddlePaddle/Paddle/pull/43611)), paddle.vision.matrix_nms([#44357](https://github.com/PaddlePaddle/Paddle/pull/44357)), paddle.vision.prior_box 和 paddle.vision.box_coder( [#47282](https://github.com/PaddlePaddle/Paddle/pull/47282) ). - -- - **Add other API** - - Add the iinfo([#45321](https://github.com/PaddlePaddle/Paddle/pull/45321)), count_nonzero([#44169](https://github.com/PaddlePaddle/Paddle/pull/44169)), nanmedian([#42385](https://github.com/PaddlePaddle/Paddle/pull/42385)), remainder\_ ([#45266](https://github.com/PaddlePaddle/Paddle/pull/45266)), take([#44741](https://github.com/PaddlePaddle/Paddle/pull/44741)), triu_indices([#45168](https://github.com/PaddlePaddle/Paddle/pull/45168)), sgn([#44568](https://github.com/PaddlePaddle/Paddle/pull/44568)), bucketize([#44195](https://github.com/PaddlePaddle/Paddle/pull/44195)), nanquantile([#41343](https://github.com/PaddlePaddle/Paddle/pull/41343)), frac([#41226](https://github.com/PaddlePaddle/Paddle/pull/41226)), logcumsumexp([#42267](https://github.com/PaddlePaddle/Paddle/pull/42267)), pairwise_distance([#44161](https://github.com/PaddlePaddle/Paddle/pull/44161)), heaviside([#41872](https://github.com/PaddlePaddle/Paddle/pull/41872)), logspace([#41261](https://github.com/PaddlePaddle/Paddle/pull/41261)), corrcoef([#40690](https://github.com/PaddlePaddle/Paddle/pull/40690)) - - Add the RReLU([#41823](https://github.com/PaddlePaddle/Paddle/pull/41823)), CyclicLR([#40698](https://github.com/PaddlePaddle/Paddle/pull/40698)), OneCycleLR([#41825](https://github.com/PaddlePaddle/Paddle/pull/41825)), Softmax2D([#40910](https://github.com/PaddlePaddle/Paddle/pull/40910)), SoftMarginLoss([#42364](https://github.com/PaddlePaddle/Paddle/pull/42364)), MultiLabelSoftMarginLoss([#41183](https://github.com/PaddlePaddle/Paddle/pull/41183)), TripletMarginLoss([#40487](https://github.com/PaddlePaddle/Paddle/pull/40487)), TripletMarginWithDistanceLoss([#40545](https://github.com/PaddlePaddle/Paddle/pull/40545)), CosineEmbeddingLoss 和 cosine_embedding_loss([#41680](https://github.com/PaddlePaddle/Paddle/pull/41680)), PixelUnshuffle([#40728](https://github.com/PaddlePaddle/Paddle/pull/40728)), ChannelShuffle([#40743](https://github.com/PaddlePaddle/Paddle/pull/40743)) -- **Enhanced API functions** - - Add the large batch_size calculation function of BatchNorm1D [#43072](https://github.com/PaddlePaddle/Paddle/pull/43072) -- **Optimize the collective communications distributed training API** - - Optimize the `fleet.init` function, and add the `log_level` parameter to facilitate users to view logs during operation [#45909](https://github.com/PaddlePaddle/Paddle/pull/45909) - - Add the `paddle.distributed.fleet.recompute_sequential paddle.distributed.fleet.recompute_hybrid` interface. It is convenient for users to use the recompute function [#45348](https://github.com/PaddlePaddle/Paddle/pull/45348) - - Add the `paddle.distributed.fleet.layers.mpu` package. It is convenient for users to use tensor parallel function [#45803](https://github.com/PaddlePaddle/Paddle/pull/45803) - - Add the communication API `paddle.distributed.destroy_process_group paddle.distributed.isend paddle.distributed.irecv paddle.distributed.all_to_all_single`. It improves the completeness and ease of use of communication [#43918](https://github.com/PaddlePaddle/Paddle/pull/43918) - - Add the `paddle.distributed.stream` package. The performance is increased by 5% to 10% compared to the base version[#46023](https://github.com/PaddlePaddle/Paddle/pull/46023) [#45282](https://github.com/PaddlePaddle/Paddle/pull/45282) - - The communication API is added with the support of multiple data types such as `Char/Byte/Bool`. It improves the completeness and ease of use of communication [#45574](https://github.com/PaddlePaddle/Paddle/pull/45574) [#45440](https://github.com/PaddlePaddle/Paddle/pull/45440) - - The communication API asynchronous parameter is changed from`use_calc_stream` to `sync_op`, It enhances the semantic readability of the interface [#46493](https://github.com/PaddlePaddle/Paddle/pull/46493) -- **Enhanced high-level API** - - The visual model ResNeXt in the high-level API implements the reuse of the ResNet code for refactoring. [#40588](https://github.com/PaddlePaddle/Paddle/pull/40588) - - The visual models Inceptionv3, MobileNetv1, MobileNetv2, and ShuffleNetv2 in the high level API are improved.[#40431](https://github.com/PaddlePaddle/Paddle/pull/40431) - -### （2）New functions and important upgrades - -- **The new dynamic graph architecture is officially launched**：The scheduling performance of the new dynamic graph framework is greatly improved. Compared with the original architecture, the scheduling performance is significantly enhanced. The scheduling performance of more than 90% APIs is improved by over 50%, and the model performance of more than 50% of kits is improved by over 5%. The new dynamic graph architecture is clear, and the coupling is low. The learning and development costs of extension modules such as Hook and PyLayer are significantly reduced based on the new architecture. [#37550](https://github.com/PaddlePaddle/Paddle/pull/37550) , [#37574](https://github.com/PaddlePaddle/Paddle/pull/37574) , [#37813](https://github.com/PaddlePaddle/Paddle/pull/37813) , [#37926](https://github.com/PaddlePaddle/Paddle/pull/37926) , [#39192](https://github.com/PaddlePaddle/Paddle/pull/39192) , [#37599](https://github.com/PaddlePaddle/Paddle/pull/37599) , [#37406](https://github.com/PaddlePaddle/Paddle/pull/37406) , [#37466](https://github.com/PaddlePaddle/Paddle/pull/37466) , [#37599](https://github.com/PaddlePaddle/Paddle/pull/37599) , [#40945](https://github.com/PaddlePaddle/Paddle/pull/40945) , [#39989](https://github.com/PaddlePaddle/Paddle/pull/39989) - -- **High-order auto-differentiation mechanism**：In order to better support scientific computing and other scenarios, the PaddlePaddle framework has been further improved and optimized for higher-order auto-differentiation capabilities. At present, the `paddle.incubate.autograd` directory has provided relevant trial functions and APIs for forward/reverse higher-order auto-differentiation (Currently they are in incubation, and related functions and API signatures may change).If you intend to implement related models and explore the auto-differentiation mechanism by yourself, please read the [usage and limitations of higher-order auto-differentiation](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/incubate/autograd/Overview_cn.html) carefully. Specific upgrades include： - 1. Static graph higher-order differentiation mechanism upgrade. Through the base operator system and program transformation, it supports higher-order forward and reverse differentiation, with the availability of the compiler and distributed functions.[#41919](https://github.com/PaddlePaddle/Paddle/pull/41919), [#41201](https://github.com/PaddlePaddle/Paddle/pull/41201) - 2. Add the forward and reverse higher-order auto-differentiation API, `paddle.incubate.autograd.forward_grad`, `paddle.incubate.autograd.grad`. [#43354](https://github.com/PaddlePaddle/Paddle/pull/43354) - 3. Add 18 higher-order auto-differentiation operators:`sin`, `cos`, `exp`, `erf`, `abs`, `log`, `cast`, `where`, `equal`, `not_equal`, `greater_than`, `greater_equal`, `elementwise_pow` `square`, `elementwise_max`, `gelu`, `reduce_mean`, `size`. [#46184](https://github.com/PaddlePaddle/Paddle/pull/46184), [#46024](https://github.com/PaddlePaddle/Paddle/pull/46024), [#45888](https://github.com/PaddlePaddle/Paddle/pull/45888), [#45338](https://github.com/PaddlePaddle/Paddle/pull/45338), [#44345](https://github.com/PaddlePaddle/Paddle/pull/44345) - 4. Fix the existing bugs of the operators such as`elementwise_div`, `reduce_sum`, `p_norm`. [#46514](https://github.com/PaddlePaddle/Paddle/pull/46514), [#46184](https://github.com/PaddlePaddle/Paddle/pull/46184) -- **Generic heterogeneous parameter server architecture**： - - Parameter server GPUGraph infrastructure upgraded to meet the implementation needs of large-scale applications: The storage and training of large-scale graph neural networks based on the traditional CPU feature high cost, low stability, and less performance. To overcome these problems, we have built a pure GPU graph training engine (PGLBox). Through the heterogeneous hierarchical storage technology of SSD, memory and video memory, it supports the training of ultra-large scale graph models. The training performance is improved by more than 10x compared with CPU graph training engine on the premise of equal cost. The task failure rate is extremely low.[#44594](https://github.com/PaddlePaddle/Paddle/pull/44594) - - Large-scale federation parameter server architecture: For large-scale personalized recommendation scenarios, the large-scale federation parameter server training is developed based on the heterogeneous PS infrastructure, to support horizontal and vertical federation under hundreds of billions of parameters. It includes two features: User private parameters updated locally and public parameters updated remotely. Users can flexibly configure the slicing policy for private and public parameters. A new central scheduling node Coordinator is added. Users can perform secondary development from the base class to customize the Client selection policy. [#42682](https://github.com/PaddlePaddle/Paddle/pull/42682) , [#44864](https://github.com/PaddlePaddle/Paddle/pull/44864) , [#44327](https://github.com/PaddlePaddle/Paddle/pull/44327) -- **Adaptive parallel** - - Design and launch a complete automatic parallelism interface system: Support automatic dynamic-to-static distributed training, automatic distributed data loading, automatic distributed saving and loading, automatic parameter conversion, custom slice marker and custom execution process. Users can easily obtain the automatic distributed training capability based on a single machine networking. It supports data parallel, model parallel, pipeline parallel, and hybrid parallel. [#45776](https://github.com/PaddlePaddle/Paddle/pull/45776) ，[#46552](https://github.com/PaddlePaddle/Paddle/pull/46552) , [#44202](https://github.com/PaddlePaddle/Paddle/pull/44202) , [#45840](https://github.com/PaddlePaddle/Paddle/pull/45840) , [#45518](https://github.com/PaddlePaddle/Paddle/pull/45518) , [#40528](https://github.com/PaddlePaddle/Paddle/pull/40528), [#42838](https://github.com/PaddlePaddle/Paddle/pull/42838), [#43093](https://github.com/PaddlePaddle/Paddle/pull/43093), [#43312](https://github.com/PaddlePaddle/Paddle/pull/43312), [#45053](https://github.com/PaddlePaddle/Paddle/pull/45053). - - Improve the underlying adaptive parallel mechanism, including the upgrade of the distributed costmodel design and implementation, to provide better evaluation of the slice policy. Add the native distributed properties to ProgramIR and enrich the Cluster functions. [#40457](https://github.com/PaddlePaddle/Paddle/pull/40457) , [#42601](https://github.com/PaddlePaddle/Paddle/pull/42601) , [#42727](https://github.com/PaddlePaddle/Paddle/pull/42727) , [#42874](https://github.com/PaddlePaddle/Paddle/pull/42784) , [#43114](https://github.com/PaddlePaddle/Paddle/pull/43114) , [#44095](https://github.com/PaddlePaddle/Paddle/pull/44095) , [#44146](https://github.com/PaddlePaddle/Paddle/pull/44146) , [#44701](https://github.com/PaddlePaddle/Paddle/pull/44701) , [#44973](https://github.com/PaddlePaddle/Paddle/pull/44973) , [#45002](https://github.com/PaddlePaddle/Paddle/pull/45002) , [#45118](https://github.com/PaddlePaddle/Paddle/pull/45118) , [#45237](https://github.com/PaddlePaddle/Paddle/pull/45237) , [#42576](https://github.com/PaddlePaddle/Paddle/pull/42576) , [#41722](https://github.com/PaddlePaddle/Paddle/pull/41722) , [#44150](https://github.com/PaddlePaddle/Paddle/pull/44150) , [#44989](https://github.com/PaddlePaddle/Paddle/pull/44989), [#44951](https://github.com/PaddlePaddle/Paddle/pull/44951), [#44963](https://github.com/PaddlePaddle/Paddle/pull/44963) . - - Add the Shardingstage1/2/3 AutoTuning feature under data parallel. This allows to automatically select the highest throughput Shardingstage policy while ensuring that the video memory constraints are met. [#43782](https://github.com/PaddlePaddle/Paddle/pull/43782) . - -- **Training hardware access - Plug-in solutions**：Add custom Runtime/Kernel/CCL/Graph/Pass solutions. The hardware vendors can choose which modules to implement on-demand based on hardware characteristics. - -- **ONNX format export** - - Support the quantized model export. The exported ONNX model uses TensorRT or ONNXRuntime to load inference. About 1.5~4 times inference acceleration can be obtained [#856](https://github.com/PaddlePaddle/Paddle2ONNX/pull/856), [#782](https://github.com/PaddlePaddle/Paddle2ONNX/pull/782) - - Add the export of a large model greater than 2GB [#942](https://github.com/PaddlePaddle/Paddle2ONNX/pull/942) - -### （3）Function optimization -- **Comprehensive increase of dynamic-to-static analysis conversion & extension capabilities** - - In order to improve the success rate and experience of model dynamic-to-static conversion, the transcription logic of control flow syntax is reconstructed. The core syntax has been upgraded to JIT (just-in-time) paradigm to achieve equivalent transcription with Python codes. The syntax functions such as break, return and continue are improved.[#43666](https://github.com/PaddlePaddle/Paddle/pull/43666) , [#43846](https://github.com/PaddlePaddle/Paddle/pull/43846) , [#43848](https://github.com/PaddlePaddle/Paddle/pull/43848) , [#43880](https://github.com/PaddlePaddle/Paddle/pull/43880) , [#43957](https://github.com/PaddlePaddle/Paddle/pull/43957) , [#43328](https://github.com/PaddlePaddle/Paddle/pull/43328) , [#43348](https://github.com/PaddlePaddle/Paddle/pull/43348) , [#43998](https://github.com/PaddlePaddle/Paddle/pull/43998) , [#44465](https://github.com/PaddlePaddle/Paddle/pull/44465) , [#44504](https://github.com/PaddlePaddle/Paddle/pull/44504) , [#43713](https://github.com/PaddlePaddle/Paddle/pull/43713) , [#43864](https://github.com/PaddlePaddle/Paddle/pull/43864) , [#43967](https://github.com/PaddlePaddle/Paddle/pull/43967) , [#44155](https://github.com/PaddlePaddle/Paddle/pull/44155) , [#44487](https://github.com/PaddlePaddle/Paddle/pull/44487) , [#44527](https://github.com/PaddlePaddle/Paddle/pull/44527) , [#45105](https://github.com/PaddlePaddle/Paddle/pull/45105) , [#45900](https://github.com/PaddlePaddle/Paddle/pull/45900) - - In order to support the voice custom decoding flexible deployment scenarios, the jit.save/load interface function is extended to support user multi-function merge and export. A new JITLayer component is added to support the invocation of class functions. Meanwhile, the custom inference deployment function is implemented with the PHI operator library C++ API. [#44283](https://github.com/PaddlePaddle/Paddle/pull/44283), [#41783](https://github.com/PaddlePaddle/Paddle/pull/41783), [#43607](https://github.com/PaddlePaddle/Paddle/pull/43607), [#43754](https://github.com/PaddlePaddle/Paddle/pull/43754), [#43758](https://github.com/PaddlePaddle/Paddle/pull/43758), [#43798](https://github.com/PaddlePaddle/Paddle/pull/43798), [#44010](https://github.com/PaddlePaddle/Paddle/pull/44010), [#44351](https://github.com/PaddlePaddle/Paddle/pull/44351), [#44465](https://github.com/PaddlePaddle/Paddle/pull/44465), [#44504](https://github.com/PaddlePaddle/Paddle/pull/44504), [#44597](https://github.com/PaddlePaddle/Paddle/pull/44597), [#44738](https://github.com/PaddlePaddle/Paddle/pull/44738), [#44984](https://github.com/PaddlePaddle/Paddle/pull/44984), [#46249](https://github.com/PaddlePaddle/Paddle/pull/46249) - - In order to unify API dynamic and static behaviors, 20 operators are upgraded to support variable attribute information of Op in static graphs, to ensure consistent dynamic and static behaviors and improve the success rate of dynamic-to-static conversion of models. Include `pad2d`,`depthwise_conv2d_transpose`,`conv2d_transpose`,`adaptive_avg_pool2d`,`reverse`,`bincount`,`multinomial`,`reduce_sum`,`reduce_mean`,`reduce_prod`,`reduce_min`,`reduce_max`,`uniform`,`squeeze`,`max_unpool2d`,`dropout`,`cumsum`,`eye`,`argmin`,`argmax`. [#44737](https://github.com/PaddlePaddle/Paddle/pull/44737), [#45084](https://github.com/PaddlePaddle/Paddle/pull/45084), [#45189](https://github.com/PaddlePaddle/Paddle/pull/45189), [#45391](https://github.com/PaddlePaddle/Paddle/pull/45391), [#45417](https://github.com/PaddlePaddle/Paddle/pull/45417), [#45427](https://github.com/PaddlePaddle/Paddle/pull/45427), [#45514](https://github.com/PaddlePaddle/Paddle/pull/45514), [#45525](https://github.com/PaddlePaddle/Paddle/pull/45525), [#45543](https://github.com/PaddlePaddle/Paddle/pull/45543), [#45660](https://github.com/PaddlePaddle/Paddle/pull/45660), [#46352](https://github.com/PaddlePaddle/Paddle/pull/46352/), [#46433](https://github.com/PaddlePaddle/Paddle/pull/46433), [#45078](https://github.com/PaddlePaddle/Paddle/pull/45078), [#45342](https://github.com/PaddlePaddle/Paddle/pull/45342), [#45372](https://github.com/PaddlePaddle/Paddle/pull/45372), [#45453](https://github.com/PaddlePaddle/Paddle/pull/45453), [#45522](https://github.com/PaddlePaddle/Paddle/pull/45522), [#45620](https://github.com/PaddlePaddle/Paddle/pull/45620) - - In order to solve the problem of occasional loss of error reporting stack for user dynamic-to-static, the logic of the error reporting module is optimized to improve the readability of the error reporting stack and the user debugging experience. [#44054](https://github.com/PaddlePaddle/Paddle/pull/44054), [#44083](https://github.com/PaddlePaddle/Paddle/pull/44083), [#44781](https://github.com/PaddlePaddle/Paddle/pull/44781), [#44996](https://github.com/PaddlePaddle/Paddle/pull/44996) - - Add the TypeHint syntax recognition and transcription module to fully support Python Type Hint syntax. [#47121](https://github.com/PaddlePaddle/Paddle/pull/47121) - -- **PHI operator library covers the full amount of arithmetic class operators**：Continuously build the highly reusable operator library PHI. The remaining PaddlePaddle 2.x arithmetic class PythonAPI-associated operators and related kernels are migrated to the PHI operators library and rewritten as functional expression. Add about 180 forward/reverse operator CPU&GPU kernels, and 170 Kunlun-specific arithmetic kernels. This further enhances the kernel function sets that can be reused when new operators are added. In addition, add more than 100 C++ arithmetic class APIs. These APIs can be used in the custom operators, further enhancing the ease of use for external extension development based on the PaddlePaddle. [#44577](https://github.com/PaddlePaddle/Paddle/pull/44577), [#44631](https://github.com/PaddlePaddle/Paddle/pull/44631), [#44434](https://github.com/PaddlePaddle/Paddle/pull/44434), [#44605](https://github.com/PaddlePaddle/Paddle/pull/44605), [#44676](https://github.com/PaddlePaddle/Paddle/pull/44676), [#44742](https://github.com/PaddlePaddle/Paddle/pull/44742), [#44436](https://github.com/PaddlePaddle/Paddle/pull/44436) , [#45887](https://github.com/PaddlePaddle/Paddle/pull/45887), [#45851](https://github.com/PaddlePaddle/Paddle/pull/45851), [#45623](https://github.com/PaddlePaddle/Paddle/pull/45623), [#45397](https://github.com/PaddlePaddle/Paddle/pull/45397), [#45863](https://github.com/PaddlePaddle/Paddle/pull/45863) - -- **Normalized operator definitions with significantly improving the model simplicity**：For the problems of many redundant parameters in the historical operator definitions of PaddlePaddle 1.x and the high cost of understanding the adaptation, the redundant parameters of about 150 high-frequency operators are cleaned up centrally. Basically, the mathematically irrelevant parameters are removed. After these redundant parameters are cleaned up, the amount of information in the inference model stored in the PaddlePaddle is significantly reduced. Generally, about 40% of the attribute variables are removed, significantly improving the clarity of the PaddlePaddle operator definition, and improving the experience of model analysis and debugging. Meanwhile, the size of the inference model stored in the PaddlePaddle is also significantly reduced by more than 70%. As a result, this can significantly improve the lightweight of the PaddlePaddle model. [#44310](https://github.com/PaddlePaddle/Paddle/pull/44310) , [#45613](https://github.com/PaddlePaddle/Paddle/pull/45613) , [#45684](https://github.com/PaddlePaddle/Paddle/pull/45684) , [#45708](https://github.com/PaddlePaddle/Paddle/pull/45708) , [#45758](https://github.com/PaddlePaddle/Paddle/pull/45758) , [#45786](https://github.com/PaddlePaddle/Paddle/pull/45786) , [#45772](https://github.com/PaddlePaddle/Paddle/pull/45772) , [#45845](https://github.com/PaddlePaddle/Paddle/pull/45845) , [#45984](https://github.com/PaddlePaddle/Paddle/pull/45984) , [#46218](https://github.com/PaddlePaddle/Paddle/pull/46218) , [#46553](https://github.com/PaddlePaddle/Paddle/pull/46553) - -### （4）Performance optimization - -- AMP performance and accuracy optimization - - More operators are added with the support of FP16 data types, including elementwise series operators, compare series operators, strided_slice, set_value, uniform_ramdom, etc.（[#45504](https://github.com/PaddlePaddle/Paddle/pull/45504) [#44405](https://github.com/PaddlePaddle/Paddle/pull/44405) [#45496](https://github.com/PaddlePaddle/Paddle/pull/45496) [#46641](https://github.com/PaddlePaddle/Paddle/pull/46641), [#46906](https://github.com/PaddlePaddle/Paddle/pull/46906) ） - - Optimize the implementation scheme of the hard_swish operator FP16 Kernel to guarantee the accuracy without loss. （ [35386](https://github.com/PaddlePaddle/Paddle/pull/35386) ） - - More operators are added with the support of BF16 data types, including fused_linear, empty, selu, pow, adam, clip, embedding, gelu, pad3d, pixel_shuffle, tile, where, etc. [#46364](https://github.com/PaddlePaddle/Paddle/pull/46364), [#47177](https://github.com/PaddlePaddle/Paddle/pull/47177) -- AutoTuning of single machine training performance - - Transpose OP supports automatic Kernel selection mechanism. This allows the automatic search for the best Kernel implementation for different model configurations, improving the model performance. [#43310](https://github.com/PaddlePaddle/Paddle/pull/43310) (Transpose Op access AutoTuning function) - - AMP Layout auto-switching supports the new dynamic graph mode. For the ResNet50, TSM, and DeepLabV3 models, the performance increases by 9%-21% by Layout AutoTuning in the new dynamic graph. ([#45409](https://github.com/PaddlePaddle/Paddle/pull/45409), [#45751](https://github.com/PaddlePaddle/Paddle/pull/45751), [#45826](https://github.com/PaddlePaddle/Paddle/pull/45826), [#46880](https://github.com/PaddlePaddle/Paddle/pull/46880)) -- Generic performance optimization of GPU single machine training - - Optimize the Cache scheme of the Conv operator cuDNN algorithm and Cache the results in all algorithm acquisition methods. This can significantly reduce the CPU overhead of the operator.（[#41891](https://github.com/PaddlePaddle/Paddle/pull/41891) [#47197](https://github.com/PaddlePaddle/Paddle/pull/47197) ） - - Further optimize the GPU Kernel and Python side performance of multiple operators, including dist, poisson, depthwise_conv2d, transpose, eigh, broadcast computation, reduce computation, layer_norm, cross_entropy, etc. This can achieve better performance in more configuration scenarios. （[#44946](https://github.com/PaddlePaddle/Paddle/pull/44946), [#45057](https://github.com/PaddlePaddle/Paddle/pull/45057), [#45160](https://github.com/PaddlePaddle/Paddle/pull/45160), [#42491](https://github.com/PaddlePaddle/Paddle/pull/42491), [#42704](https://github.com/PaddlePaddle/Paddle/pull/42704), [#42853](https://github.com/PaddlePaddle/Paddle/pull/42853), [#46287](https://github.com/PaddlePaddle/Paddle/pull/46287), [#46362](https://github.com/PaddlePaddle/Paddle/pull/46362), [#46490](https://github.com/PaddlePaddle/Paddle/pull/46490), [#46412](https://github.com/PaddlePaddle/Paddle/pull/46412), [#46623](https://github.com/PaddlePaddle/Paddle/pull/46623), [#40051](https://github.com/PaddlePaddle/Paddle/pull/40051) ） -- Performance optimization of distributed training for collective communications - - To improve pipeline parallel scheduling efficiency, support the dynamic graph Interleaving1F1B scheduling policy. In the GPT-3 model, the performance is improved by 3%-4%. [#45797](https://github.com/PaddlePaddle/Paddle/pull/45797) , [#45869](https://github.com/PaddlePaddle/Paddle/pull/45869) , [#45922](https://github.com/PaddlePaddle/Paddle/pull/45922) , [#46209](https://github.com/PaddlePaddle/Paddle/pull/46209) , [#45402](https://github.com/PaddlePaddle/Paddle/pull/45402) , [#45444](https://github.com/PaddlePaddle/Paddle/pull/45444) , [#45497](https://github.com/PaddlePaddle/Paddle/pull/45497) , [#45797](https://github.com/PaddlePaddle/Paddle/pull/45797) , [#45869](https://github.com/PaddlePaddle/Paddle/pull/45869) , [#45922](https://github.com/PaddlePaddle/Paddle/pull/45922), [#46209](https://github.com/PaddlePaddle/Paddle/pull/46209), [#46399](https://github.com/PaddlePaddle/Paddle/pull/46399) , [#46483](https://github.com/PaddlePaddle/Paddle/pull/46483) , [#46876](https://github.com/PaddlePaddle/Paddle/pull/46876) , [#47242](https://github.com/PaddlePaddle/Paddle/pull/47242) , [#47249](https://github.com/PaddlePaddle/Paddle/pull/47249) , [#47497](https://github.com/PaddlePaddle/Paddle/pull/47497) , [#47517](https://github.com/PaddlePaddle/Paddle/pull/47517) - - To improve the distributed training performance of the MLPerfBERT model, the DistributedFusedLamb distributed optimizer supports hierarchical AllReduce. It improves MLPerfBERT performance by 17% on the DCU1024 card. [#44821](https://github.com/PaddlePaddle/Paddle/pull/44821) , [#44843](https://github.com/PaddlePaddle/Paddle/pull/44843) - - To optimize the video memory footprint when using DataParallel, the Buffer Lazy initialization policy for Tensor Fusion is supported, thus reducing the video memory footprint by an amount equal to the number of model parameters. [#45631](https://github.com/PaddlePaddle/Paddle/pull/45631). - - Distributed parallel policies DataParallel and Sharding support BF16 training. [#46846](https://github.com/PaddlePaddle/Paddle/pull/46846) , [#47246](https://github.com/PaddlePaddle/Paddle/pull/47246) - - To support the Sequence Parallel policy, the Distributed Pipeline Parallel supports enable_partial_send_recv policy, and supports the tensor after slice of the transmission sequence parallel. [#46992](https://github.com/PaddlePaddle/Paddle/pull/46992) , [#47083](https://github.com/PaddlePaddle/Paddle/pull/47083) - - To improve the performance of sharding stage 2 policy, implement the overlap of sharding stage 2 optimizer broadcast parameters with next step forward and use multi-CUDA Stream for communication. In the GPT 6.7B model, the 16-card training performance is improved by 11%. [#46495](https://github.com/PaddlePaddle/Paddle/pull/46495) , [#46656](https://github.com/PaddlePaddle/Paddle/pull/46656) , [#47061](https://github.com/PaddlePaddle/Paddle/pull/47061) - -### （5）Bug fix - -- Dynamic-to-static - - Fix the bug of reporting an error in dynamic-to-static of the model in a Parameter no-gradient scenario during multi-card training. [#44485](https://github.com/PaddlePaddle/Paddle/pull/44485) - - Fix the bug of where redundant frame logs are mistakenly output by the terminal in the dynamic-to-static. [#45754](https://github.com/PaddlePaddle/Paddle/pull/45754), [#46800](https://github.com/PaddlePaddle/Paddle/pull/46800) - - Fix the bug of reporting an error in the dynamic-to-static training when the control flow in the model contains a Tensor that does not require a gradient. [#43034](https://github.com/PaddlePaddle/Paddle/pull/43034) - - Fix the bug of incorrect computation value during gradient aggregation in the dynamic-to-static training. [#44893](https://github.com/PaddlePaddle/Paddle/pull/44893) - - Fix the bug of reporting an error in the dynamic-to-static when the function is decorated with @staticmethod. [#44983](https://github.com/PaddlePaddle/Paddle/pull/44983), [#45268](https://github.com/PaddlePaddle/Paddle/pull/45268), [#45277](https://github.com/PaddlePaddle/Paddle/pull/45277) - - Fix the bug of too much video memory footprint in some scenarios where the model contains the dynamic-to-static training. [#45380](https://github.com/PaddlePaddle/Paddle/pull/45380) - - Fix the bug of reporting an error of dynamic-to-static shape derivation in the networking phase when the model contains a complex control flow. [#45916](https://github.com/PaddlePaddle/Paddle/pull/45916), [#46020](https://github.com/PaddlePaddle/Paddle/pull/46020) -- Fix the error report mechanism - - Replace self.assertTrue(np.allclose(...)) with np.testing.assert_allclose to get fuller error reporting information ( [#44947](https://github.com/PaddlePaddle/Paddle/pull/44947), [#44988](https://github.com/PaddlePaddle/Paddle/pull/44988), [#45213](https://github.com/PaddlePaddle/Paddle/pull/45213)) -- Distributed training in collective communications - - Fix several bugs in communication library initialization and communication process, and enhance the system operation stability. [#44964](https://github.com/PaddlePaddle/Paddle/pull/44964) [#45100](https://github.com/PaddlePaddle/Paddle/pull/45100) [#44758](https://github.com/PaddlePaddle/Paddle/pull/44758) - - Fix the bug of frequent occurrences of hang in pipeline parallel, and enhance the ease of use of the policy [#47201](https://github.com/PaddlePaddle/Paddle/pull/47201); enhance the pipeline function to support unbalanced input. [#47199](https://github.com/PaddlePaddle/Paddle/pull/47199) - - Fix the bug that the performance of the new dynamic graph MP/PP policy is lower than the old dynamic graph. [#47071](https://github.com/PaddlePaddle/Paddle/pull/47071) - - Fix the bug that the shardingstage2 policy incorrectly maintains the parameter trainable property. [#47240](https://github.com/PaddlePaddle/Paddle/pull/47240) - - Fix the bug that tensornumel is greater than INT32_MAX in series of OPs. [#45711](https://github.com/PaddlePaddle/Paddle/pull/45711), [#45741](https://github.com/PaddlePaddle/Paddle/pull/45741), [#45897](https://github.com/PaddlePaddle/Paddle/pull/45897), [#46158](https://github.com/PaddlePaddle/Paddle/pull/46158), [#46767](https://github.com/PaddlePaddle/Paddle/pull/46767), [#47191](https://github.com/PaddlePaddle/Paddle/pull/47191), [#46045](https://github.com/PaddlePaddle/Paddle/pull/46045), [#46160](https://github.com/PaddlePaddle/Paddle/pull/46160) - - Fix the bug of too much video memory footprint in FusedAttention and Fused FeedForward OP.[#47236](https://github.com/PaddlePaddle/Paddle/pull/47236), [#47235](https://github.com/PaddlePaddle/Paddle/pull/47235) - - Fix the bug of incorrect parameter update in multi_tensor_adam and multi_tensor_momentumOP when the parameters passed in are listofdict. [#47352](https://github.com/PaddlePaddle/Paddle/pull/47352), [#47372](https://github.com/PaddlePaddle/Paddle/pull/47372) - -## 4. Deployment direction (Paddle Inference) - -### （1）New features - -- Optimize the back-end graph engine integration scheme - - In order to reduce Paddle-TensorRT plugin code development and reduce the number of Paddle-TensorRT subgraphs and thus reducing resource usage, a generic plugin mechanism has been developed, to automatically provide a unified TensorRT plugin interface for rich Phi operators in the framework. As a result, the video memory footprint can be effectively reduced in most scenarios. [#46970](https://github.com/PaddlePaddle/Paddle/pull/46070), [#46179](https://github.com/PaddlePaddle/Paddle/pull/46179), [#46580](https://github.com/PaddlePaddle/Paddle/pull/46580) - - In order to facilitate users to customize operators in the framework and make Paddle-TensorRT perform efficient inference, the function is upgraded to support the framework custom Paddle-TensorRT plugin. [#46970](https://github.com/PaddlePaddle/Paddle/pull/46070) -- Optimize the Inference library build system. The size can be pruned on demand - - Pre-compiled installer supports TensorRT by default: The pre-compiled installer for training and the pre-compiled installer for deployment (Paddle Inference) are unified into one pre-compiled installer. The build system is optimized so that the pre-compiled installer supports TensorRT by default, reducing the switching cost for users using PaddleTensorRT. [#46008](https://github.com/PaddlePaddle/Paddle/pull/46008), [#45824](https://github.com/PaddlePaddle/Paddle/pull/45824), [#46058](https://github.com/PaddlePaddle/Paddle/pull/46058) - - The size can be pruned on demand: Pruned according to the model operator. [#47033](https://github.com/PaddlePaddle/Paddle/pull/47033) , [#47049](https://github.com/PaddlePaddle/Paddle/pull/47049) , [#47047](https://github.com/PaddlePaddle/Paddle/pull/47047) -- Inference supports native AMP - - In order to make full use of GPUTensorCore computation capability and improve the model inference performance, a model accuracy conversion tool has been developed. The InferenceGPU natively supports the inference of the mixed precision model. For the usages, refer to the documentation. [documentation](https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/release/v2.4/docs-official/guides/nv_gpu_infer/gpu_mixed_precision.md), [#43814](https://github.com/PaddlePaddle/Paddle/pull/43814), [#43881](https://github.com/PaddlePaddle/Paddle/pull/43881), [#44057](https://github.com/PaddlePaddle/Paddle/pull/44057), [#44307](https://github.com/PaddlePaddle/Paddle/pull/44307), [#44457](https://github.com/PaddlePaddle/Paddle/pull/44457), [#44866](https://github.com/PaddlePaddle/Paddle/pull/44866), [#45050](https://github.com/PaddlePaddle/Paddle/pull/45050), [#45346](https://github.com/PaddlePaddle/Paddle/pull/45346), [#45379](https://github.com/PaddlePaddle/Paddle/pull/45379), [#45406](https://github.com/PaddlePaddle/Paddle/pull/45406), [#45882](https://github.com/PaddlePaddle/Paddle/pull/45882) - - In order to improve the inference performance of the mixed precision model, the FP16kernel of high-frequency operators that do not support FP16 computation is supplemented, thus reducing the possibility of inserting the cast operator due to input precision mismatch. The inference performance is improved. [#44642](https://github.com/PaddlePaddle/Paddle/pull/44642), [#45061](https://github.com/PaddlePaddle/Paddle/pull/45061), [#44653](https://github.com/PaddlePaddle/Paddle/pull/44653), [#45504](https://github.com/PaddlePaddle/Paddle/pull/45504), [#45061](https://github.com/PaddlePaddle/Paddle/pull/45061), [#44969](https://github.com/PaddlePaddle/Paddle/pull/44969), [#44558](https://github.com/PaddlePaddle/Paddle/pull/44558), [#44710](https://github.com/PaddlePaddle/Paddle/pull/44710), [#43871](https://github.com/PaddlePaddle/Paddle/pull/43871), [#44792](https://github.com/PaddlePaddle/Paddle/pull/44792) -- Upgrade the compression and inference engine - - Upgrade the quantization model storage format. The new format supports PaddleInference, PaddleLite and Paddle2ONNX 3 deployment methods. The supported chips include X86 CPU, NVIDIA GPU, and Arm CPU. （[#46305](https://github.com/PaddlePaddle/Paddle/pull/46305), [#462832](https://github.com/PaddlePaddle/Paddle/pull/46283), [#46022](https://github.com/PaddlePaddle/Paddle/pull/46022) ） - - Add the INT8 full quantization function compatible with SoC/NPU chips. This can ensure the output INT8 quantization model has the best inference acceleration and precision on SoC/NPU chips. -- Add the INT8 full quantization function compatible with SoC/NPU chips. This can ensure the output INT8 quantization model has the best inference acceleration and precision on SoC/NPU chips. - - Upgrade the interface module between the PaddlePaddle framework and compiler, to support inference models to access the compiler for optimization via Paddle Inference. （[#44499](https://github.com/PaddlePaddle/Paddle/pull/44499) [#44708](https://github.com/PaddlePaddle/Paddle/pull/44708) ） - -### （2）Underlying optimization - -- **GPU performance optimization** - - Add the TensorRT mapping for operators such as matmul_v2, LSTM, reshape, fill_constant, swish, mulitclass_nms3, bilinear_interp_v2, split, silu, shuffle_channel operators. Optimize the support for the dynamic shape. Performance improved by 7% to 90% for multi-class focused models. ([#46177](https://github.com/PaddlePaddle/Paddle/pull/46177), [#44678](https://github.com/PaddlePaddle/Paddle/pull/44678), [#44314](https://github.com/PaddlePaddle/Paddle/pull/44314), [#44561](https://github.com/PaddlePaddle/Paddle/pull/44561), [#45166](https://github.com/PaddlePaddle/Paddle/pull/45166), [#44411](https://github.com/PaddlePaddle/Paddle/pull/44411), [#43424](https://github.com/PaddlePaddle/Paddle/pull/43424), [#44516](https://github.com/PaddlePaddle/Paddle/pull/44516)) - - Add constant folding PASS for inference performance optimization, to improve the performance of SwinTransformer, HifiGAN, FastSpeech2, and other models.（[#45494](https://github.com/PaddlePaddle/Paddle/pull/45494)) - - Add cache of conv_fusionworkspacesize, to improve the computation performance of conv_fusion. ([#45902](https://github.com/PaddlePaddle/Paddle/pull/45902)) -- **Vision ViT model optimization** - - Add the ViT model Attention structure fusion PASS, and support OSSPlugin and auto padding. The ViT inference speed increases by 30%-40%. [#45019](https://github.com/PaddlePaddle/Paddle/pull/45019) [#45506](https://github.com/PaddlePaddle/Paddle/pull/45506) -- **Inference performance optimization of large model** - - To improve the inference speed of very large generative models and save the video memory, add INT8 implementation (fused_multi_transformer_int8_op) to the multi-layer Transformer fusion operator (fused_multi_transformer_op), and support quantized inference of generative models. Use the matrix multiplication algorithm to select, quantize/de-quantize the kernel fusion for performance optimization. [#46169](https://github.com/PaddlePaddle/Paddle/pull/46169) - - Add Pass for automatic matching fusion in order to improve the ease of use of fused_multi_transformer fusion for large model inference. -- **CPU performance optimization** - - Optimize the speech U2++ model. The FP32 model inference speed is improved by 35%. The INT8 model inference speed is improved by 69%. ([#47592](https://github.com/PaddlePaddle/Paddle/pull/47592), [#47127](https://github.com/PaddlePaddle/Paddle/pull/47127), [#47391](https://github.com/PaddlePaddle/Paddle/pull/47391), [#47234](https://github.com/PaddlePaddle/Paddle/pull/47234), [#47009](https://github.com/PaddlePaddle/Paddle/pull/47009), [#47080](https://github.com/PaddlePaddle/Paddle/pull/47080)) - - -### （3）Bug fix - -- TensorRT workspace size supports int64. （[#44469](https://github.com/PaddlePaddle/Paddle/pull/44469) ） -- In Paddle-TRT, fully support Op's input as weight.（[#45545](https://github.com/PaddlePaddle/Paddle/pull/45545) ） -- In Paddle-TRT, support conv2d_transpose/conv3d_transpose to have the output_padding attribute.（[#45004](https://github.com/PaddlePaddle/Paddle/pull/45004) ） -- In Paddle-TRT, enhance the strided_slice support for dynamic shape. （[#46819](https://github.com/PaddlePaddle/Paddle/pull/46819) ） -- In Paddle-TRT, optimize the video memory footprint of context when running in multi-thread scenarios.（[#45468](https://github.com/PaddlePaddle/Paddle/pull/45468) ） -- In Paddle-TRT, fix the bug of repeatedly generating serialization files in case of change of initialization sequences when multiple models run in the same process.（[#43942](https://github.com/PaddlePaddle/Paddle/pull/43942) ） -- Fix the bug of occasional crash when Predictor is initialized to run for multiple times in the same process.（[#45203](https://github.com/PaddlePaddle/Paddle/pull/45203) ） -- Fix the bug of abnormal inference accuracy of quantization models such as MobileNetV3_large, ERNIE 3.0-Medium and bert ([#45416](https://github.com/PaddlePaddle/Paddle/pull/45416), [#46283](https://github.com/PaddlePaddle/Paddle/pull/46283), [#45920](https://github.com/PaddlePaddle/Paddle/pull/45920) [#47573](https://github.com/PaddlePaddle/Paddle/pull/47574)) - -## 5. Environment adaptation - -- The pre-compiled installer for training and the pre-compiled installer for deployment (Paddle Inference) are unified into one pre-compiled installer. The build system is optimized so that the pre-compiled installer supports TensorRT by default. -- The pre-compiled installer for CUDA version 10.1 is cancelled. -- Add the pre-compiled installer for CUDA 11.7. -- Decrease of source code compilation time: Reduce inter-module dependencies, improve the parallel, and optimize the compilation speed of some modules. The full compilation time is reduced by about 20 minutes in total. -- Support the running of PaddlePaddle on windows 11, Centos 8, Ubuntu 22.04, Jetson 5.02 system environment. Support to run PaddlePaddle linux installer in windows system by using the WSL 2 tool. -- Fix the running error bug of the PaddlePaddle in glibc2.34+ environment. -- Optimize the code style of C++, Python, CMake in the whole code repository. Introduce or upgrade the following code style checking tools. - - pre-commit is upgraded from 1.10.4 to 2.17.0： [#43103](https://github.com/PaddlePaddle/Paddle/pull/43103) - - pylint is changed from default version to specify as： [#43103](https://github.com/PaddlePaddle/Paddle/pull/43103) - - remove-crlf is upgraded from 1.0.1 to 1.1.14 ： [#43103](https://github.com/PaddlePaddle/Paddle/pull/43103) - - cpplint is changed from default version to specify as 1.6.0 ： [#43175](https://github.com/PaddlePaddle/Paddle/pull/43175), [#43978](https://github.com/PaddlePaddle/Paddle/pull/43978), [#43673](https://github.com/PaddlePaddle/Paddle/pull/43673), [#43679](https://github.com/PaddlePaddle/Paddle/pull/43679), [#43695](https://github.com/PaddlePaddle/Paddle/pull/43695), [#43733](https://github.com/PaddlePaddle/Paddle/pull/43733), [#43740](https://github.com/PaddlePaddle/Paddle/pull/43740) - - clang-format is upgrade from 3.8 to 13.0 ： [#42840](https://github.com/PaddlePaddle/Paddle/pull/42840), [#43248](https://github.com/PaddlePaddle/Paddle/pull/43248), [#43329](https://github.com/PaddlePaddle/Paddle/pull/43329), [#43333](https://github.com/PaddlePaddle/Paddle/pull/43333), [#43633](https://github.com/PaddlePaddle/Paddle/pull/43633), [#43678](https://github.com/PaddlePaddle/Paddle/pull/43678) - - Introduce the black tool for python code style checking ：[#46014](https://github.com/PaddlePaddle/Paddle/pull/46014) - - Introduce the cmakelint tool for cmake file code checking. Version is 1.4.2 ： [#43222](https://github.com/PaddlePaddle/Paddle/pull/43222), [#43406](https://github.com/PaddlePaddle/Paddle/pull/43406), [#43414](https://github.com/PaddlePaddle/Paddle/pull/43414), [#43428](https://github.com/PaddlePaddle/Paddle/pull/43428) - - Introduce cmake-format for automatic formatting of cmake files. Version is 0.6.13 ： [#43057](https://github.com/PaddlePaddle/Paddle/pull/43057) - -## 6. Hardware adaptation ### Hygon DCU -- Add the Profiler function on DCU, to collect, count and display performance data of model running process on DCU, and support DCU occupancy display at kernel level. -### Kunlunxin Chip -- Add Profiler function on Kunlunxin 2 generation chip, which can collect, count and display the performance data of model running process on Kunlunxin 2 generation chip, and support occupancy display of Kunlunxin 2 generation chip at kernel level. -- Training/reasoning support for Kunlunxin 2 generation chips (Kunlunxin AI accelerator cards R200, R300, R200-8F, R200-8FS, RG800), a total of 51 models such as PPYOLOE, PP-OCR, ERNIE3.0, PP-TSM, PP-TTS, DLRM, PPO, etc. have been verified, supporting static graph + dynamic graph training, supporting mixed precision training, support single machine single card and single machine multi-card training, covering 5 fields of intelligent vision, natural language processing, intelligent speech, intelligent recommendation, reinforcement learning. -### Cambricon -- Support the training/inference of Cambricon MLU chip (MLU370 series of boards): The ResNet50, BERT, YoloV3, OCR-DB, Deeplabv3 and many other models are verified. Support the static graph + dynamic graph training. Support mixed precision training. Support the single machine single card and single machine multi-card training. -### Graphcore -- Support the training/inference of Graphcore IPU chip (including IPU Mk2 GC200 and Bow IPU). Support ResNet50, BERT and other models. Support the static graph and dynamic-to-static graph mode training. Support the single chip, single machine, and multi-machine distributed training. -- Add the support of more operators -- Upgrade to Poplar SDK v3.0.0 [#46892](https://github.com/PaddlePaddle/Paddle/pull/46892) -* Support the training models by using the dynamic-to-static graph mode. Add a new paddle.incubate.identity_loss op to assist with composition [#43770](https://github.com/PaddlePaddle/Paddle/pull/43770) -* Support the Paddle native distributed training API: paddle.distributed.launch [#43311](https://github.com/PaddlePaddle/Paddle/pull/43311) -* Support the training models with the mixed precision [#41733](https://github.com/PaddlePaddle/Paddle/pull/41733) -* Paddle Inference supports custom operators by using PopART [#45235](https://github.com/PaddlePaddle/Paddle/pull/45235) - -### Intel -- Migrate oneDNN operators : transpose2_grad([#46139](https://github.com/PaddlePaddle/Paddle/pull/46139)), relu6_grad([#46501](https://github.com/PaddlePaddle/Paddle/pull/46501)), gaussian_random([#46747](https://github.com/PaddlePaddle/Paddle/pull/46747), [#45481](https://github.com/PaddlePaddle/Paddle/pull/45481)), sgd and stack([#46374](https://github.com/PaddlePaddle/Paddle/pull/46374)), concat+grad, expand+grad,fill_constant([#45863](https://github.com/PaddlePaddle/Paddle/pull/45863)), slice, slice_grad, split,pad and pad3d([#46101](https://github.com/PaddlePaddle/Paddle/pull/46101)), softmax_grad([#46257](https://github.com/PaddlePaddle/Paddle/pull/46257)), Shape([#46051](https://github.com/PaddlePaddle/Paddle/pull/46051)), Sum([#46239](https://github.com/PaddlePaddle/Paddle/pull/46239)), Transpose2_grad([#46139](https://github.com/PaddlePaddle/Paddle/pull/46139)), Cast, clip+grad andpool+grad([#45775](https://github.com/PaddlePaddle/Paddle/pull/45775)), Reduce sum+grad,mean+grad, min and max([#45536](https://github.com/PaddlePaddle/Paddle/pull/45536)), Relu and abs([#45397](https://github.com/PaddlePaddle/Paddle/pull/45397)), Gelu([#45596](https://github.com/PaddlePaddle/Paddle/pull/45596)), Scale([#45537](https://github.com/PaddlePaddle/Paddle/pull/45537)) -- Optimize kernels of fill_constant, fc, conv, and a number of operators -- Add several Pass fusion optimizations -- Optimize the Adam-W CPU FP32 optimizer ([#42522](https://github.com/PaddlePaddle/Paddle/pull/42522)) -- Optimize pad3d fp32 onednn operator kernel implementation ([#43990](https://github.com/PaddlePaddle/Paddle/pull/43990)) -- Optimize the concurrent execution of matmul, FC andlookup_v2 kernels ([#44023](https://github.com/PaddlePaddle/Paddle/pull/44023), [#44078](https://github.com/PaddlePaddle/Paddle/pull/444078), [#44640](https://github.com/PaddlePaddle/Paddle/pull/44640), [#44744](https://github.com/PaddlePaddle/Paddle/pull/44744), [#45249](https://github.com/PaddlePaddle/Paddle/pull/45249)) -- FC onednn operator kernel supports bf16 ( [#42758](https://github.com/PaddlePaddle/Paddle/pull/42758), [#43154](https://github.com/PaddlePaddle/Paddle/pull/43154), [#43109](https://github.com/PaddlePaddle/Paddle/pull/43109)) -- Add the fusion of matrix multiplication and activation functions ([#43519](https://github.com/PaddlePaddle/Paddle/pull/43519), [#43198](https://github.com/PaddlePaddle/Paddle/pull/43198)) -- Support convolution operator int8 parameter production IR passes ( [#44680](https://github.com/PaddlePaddle/Paddle/pull/44680), [#42625](https://github.com/PaddlePaddle/Paddle/pull/42625)) -- Add pool/avg quantization and scales correction ([#44186](https://github.com/PaddlePaddle/Paddle/pull/44186)) -- Add the matmul and elementwise onednn operator kernel fusion ([#45077](https://github.com/PaddlePaddle/Paddle/pull/45077)) -- Fix the QAT precision bug ([#43693](https://github.com/PaddlePaddle/Paddle/pull/43693), [#45936](https://github.com/PaddlePaddle/Paddle/pull/45936), [#46378](https://github.com/PaddlePaddle/Paddle/pull/46378)) -- Migrate 42 oneDNN operator kernels to PHI operator library ([#46374](https://github.com/PaddlePaddle/Paddle/pull/46374), [#46101](https://github.com/PaddlePaddle/Paddle/pull/46101), [#45989](https://github.com/PaddlePaddle/Paddle/pull/45989), [#45863](https://github.com/PaddlePaddle/Paddle/pull/45863), [#45775](https://github.com/PaddlePaddle/Paddle/pull/45775), [#45626](https://github.com/PaddlePaddle/Paddle/pull/45626), [#45536](https://github.com/PaddlePaddle/Paddle/pull/45536), [#46501](https://github.com/PaddlePaddle/Paddle/pull/46501), [#46257](https://github.com/PaddlePaddle/Paddle/pull/46257), [#45596](https://github.com/PaddlePaddle/Paddle/pull/45596), [#45537](https://github.com/PaddlePaddle/Paddle/pull/45537), [#45481](https://github.com/PaddlePaddle/Paddle/pull/45481), [#45397](https://github.com/PaddlePaddle/Paddle/pull/45397), [#46239](https://github.com/PaddlePaddle/Paddle/pull/46239), [#46139](https://github.com/PaddlePaddle/Paddle/pull/46139), [#46051](https://github.com/PaddlePaddle/Paddle/pull/46051)) -- Quantize the elementwise_sub and shape operator kernels ([#42854](https://github.com/PaddlePaddle/Paddle/pull/42854), [#44124](https://github.com/PaddlePaddle/Paddle/pull/44124)) - -## Thanks to our Contributors - -This release contains contributions from: - -0x45f, Aganlengzi, Ainavo, Allen Guo, Asthestarsfalll, Aurelius84, Baibaifan, baoachun, BiynXu, Bo Zhang, BrilliantYuKaimin, cambriconhsq, caozhou, carryyu, ccrrong, ceci3, chalsliu, Chang Xu, Charles-hit, Chen Long, Chen Weihang, chenjian, chentianyu03, Chenxiao Niu, cifar10, crystal, csy0225, danleifeng, David Nicolas, dc-cheny, denglin-github, dongfangshenzhu, duanboqiang, duanyanhui, engineer, enzodechine, Fan Zhang, feifei-111, Feiyu Chan, Feng Ni, feng_shuai, FlyingQianMM, freeliuzc, furnace, fuyou765, fwenguang, Ghost Screaming, gongweibao, Guanghua Yu, guguguzi, Guoxia Wang, Haipeng Wang, handiz, Haohongxiang, haosicheng, helen88, heliqi, hong, HongyuJia, houj04, huangxu96, Hui Zhang, Huihuang Zheng, huzhiqiang, Jacek Czaja, Jack Zhou, jack603047588, Jackwaterveg, jakpiase, james, Jiabin Yang, jiangcheng, Jiaqi Liu, JingZhuangzhuang, joanna.wozna.intel, JYChen, JZ-LIANG, Kaipeng Deng, kangguangli, kuizhiqing, Leo Chen, Leo Guo, levi131, Li Min, Li-fAngyU, lidanqing, LielinJiang, Ligoml, Lijunhui, lilong12, limingshu, Lin Manhui, Linjie Chen, liqitong-a, littletomatodonkey, liu zhengxi, Liu-xiandong, liutiexing, Liyulingyue, LiYuRio, Lux et Veritas, lyq, Matsumoto Ruko, MayYouBeProsperous, mengqingchun02, Ming-Xu Huang, ming1753, minghaoBD, moyan, mrcangye, Netpunk, niuliling123, Nyakku Shigure, OccupyMars2025, onecatcn, pangyoki, parap1uie-s, peachlcy, piotrekobi, Qi Li, QingshuChen, qipengh, Rayman, Regan Yue, RichardWooSJTU, risemeup1, Roc, ronnywang, Rui Li, Ruibiao Chen, seemingwang, Shang Zhizhou, shangliang Xu, ShenLiang, shentanyue, Shijie, ShiningZhang, shixingbo, shiyutang, Shuangchi He, Siming Dai, Sing_chan, Skr Bang, SmirnovKol, sneaxiy, sprouteer, Sylwester Fraczek, Sławomir Siwek, taixiurong, Tao CHANG, TeFeng Chen, Thomas Young, thunder95, Thunderbrook, tiancaishaonvjituizi, tianshuo78520a, Tomasz Socha, TTerror, USTCKAY, Vigi Zhang, Walter, Wang Bojun, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, WangXi, wangxinxin08, Wangzheee, WangZhen, wangzhen38, wawltor, wbn, Wei Shengyu, Weilong Wu, weishengying, Wen Sun, wenbin, whs, Wilber, WJJ1995, wuhuachaocoding, wuhuanzhou, wuyefeilin, XiaoguangHu, xiaoguoguo626807, xiaohemaikoo, xiaoting, xiaoxiaohehe001, Xiaoxu Chen, xiayanming, Xingyuan Zhang, xiongkun, yang131313, yangguohao, YangZhou, Yanxing Shi, Yao Zihang, yaoxuefeng, yaozhixin, yeliang2258, Yilingyelu, Yiqun Liu, ykkk2333, Yuang Liu, Yuanle Liu, YuanRisheng, yuguo, Yulong Ao, Yulv-git, YUNSHEN XIE, Zhang Jun, Zhang Ting, Zhang Zheng, zhangbo9674, zhangbopd, zhangchunle, Zhangjingyu06, zhangkaihuo, zhangxiaoci, zhangyikun02, zhangzhenguo, Zhanlue Yang, zhaocaibei123, zhaoying9105, zhaoyingli, Zhen Wang, Zhengyang Song, zhiboniu, Zhong Hui, Zhou Wei, zhoutianzi666, zhupengyang, ziyoujiyi, zlsh80826, zmxdream, zn, Zuza Gawrysiak, zyfncg, 傅剑寒, 六个骨头, 津, 熊峻峰, 王明冬, 石晓伟 - -# 2.3.1 Release Note - -## **1. Important Updates** - -- V2.3.1 is built on V2.3 by fixing known issues and releasing precompiled binary that supports CUDA 11.6. - -## **2. Training Framework (distributed included)** - -### **(1) Function Optimization** - -#### API - -- Modify two initialization modes of `paddle.nn.initializer.KaimingUniform` and `paddle.nn.initializer.KaimingNormal`, to support multiple types of activation functions. ([#43721](https://github.com/PaddlePaddle/Paddle/pull/43721), [#43827](https://github.com/PaddlePaddle/Paddle/pull/43827)) -- Optimize the data pre-fetching function of `paddle.io.DataLoader`, so that it can support the setting of the `prefetch_factor` to set the cache size of pre-fetched data. This can avoid IO blocking when reading large blocks of data. ([#43674](https://github.com/PaddlePaddle/Paddle/pull/43674)) - -#### **New dynamic graph execution mechanism** - -- Modify the initialization method of optional type Tensor in the new dynamic graph API logic to prevent data exceptions caused by early destruction. ([#42561](https://github.com/PaddlePaddle/Paddle/pull/42561)) - -#### **New static graph executor** - -- Defer initialization of the thread pools in the executor, to avoid creating thread pools for `programs` that execute only once (e.g.,`save, load, startup_program`, etc.). ([#43768](https://github.com/PaddlePaddle/Paddle/pull/43768)) - -#### **Mixed precision training** - -- Disabling `state_dict` hook in `set_state_dict` in `paddle.nn.Layer`. ([#43407](https://github.com/PaddlePaddle/Paddle/pull/43407)) - -#### **Distributed training** - -- Enabling tensor parallelism in `paddle.incubate.nn.functional.fused_attention` and `paddle.incubate.nn.functional.fused_feedforward`. ([#43505](https://github.com/PaddlePaddle/Paddle/pull/43505)) - -#### **Others** - -- Adjust print format of the framework operator kernels to facilitate automated splitting and parsing. ([#42931](https://github.com/PaddlePaddle/Paddle/pull/42931)) -- Update the model quantization API to support the round-off in `rounding to nearest ties to even`, and support quantization in the range [-128, 127]. ([#43829](https://github.com/PaddlePaddle/Paddle/pull/43829)) -- Support AMP mixed precision training in quantization-aware training. ([#43689](https://github.com/PaddlePaddle/Paddle/pull/43689)) -- Add the `progress bar` at the beginning of quantization-aware training, so that it is easy to check the progress of quantization initialization. Skip the scale op when counting out_threshold to speed up the initialization process. ([#43454](https://github.com/PaddlePaddle/Paddle/pull/43454)) -- Support `conv` and `bn` fusion in the dynamic graph quantization training. Support the settings of skip_tensor_list in the static graph offline quantization, to skip some layers without quantization. ([#43301](https://github.com/PaddlePaddle/Paddle/pull/43301)) - -### **(2) Performance Optimization** - -- Optimize`paddle.incubate.nn.functional.fused_attention` and `paddle.incubate.nn.functional.fused_feedforward`operators. Add `add_residual` property to control whether to perform add-`residual` operation in the last step. The performance of CAE model is improved by 7.7%. ([#43719](https://github.com/PaddlePaddle/Paddle/pull/43719)) -- Optimize `linspace` operator. Initialize three input Tensor of `start`,`stop` and `num` on CPU, to avoid GPU->CPU copy in the operator. This can speed up SOLOv2 model performance by 6%. ([#43746](https://github.com/PaddlePaddle/Paddle/pull/43746)) - -### **(3) Bug Fix** - -#### API - -- Fix the error reported by `paddle.io.DataLoader` when `return_list=True` due to multi-thread conflict. ([#43691](https://github.com/PaddlePaddle/Paddle/pull/43691)) -- Fix the error that the `to` method reports NoneType does not have the device attribute when the `paddle.nn.Layer` parameter has the `None` type parameter. ([#43597](https://github.com/PaddlePaddle/Paddle/pull/43597)) -- Fix the bug that the calculation result of cumsum op is wrong in some `shape` settings. ([#42500](https://github.com/PaddlePaddle/Paddle/pull/42500), [#43777](https://github.com/PaddlePaddle/Paddle/pull/43777)) -- Fix the bug that the output result dimension of `Tensor.__getitem__` is 0 in the networking stage when using `bool` index in the static graph. ([#43246](https://github.com/PaddlePaddle/Paddle/pull/43246)) -- Fix the bug occurred when `paddle.slice` and `paddle.strided_slice` handle negative parameters. ([#43432](https://github.com/PaddlePaddle/Paddle/pull/43432)) -- Fix the bug that the assignment result of set_value op is abnormal when the processing slice `step` is negative. ([#43694](https://github.com/PaddlePaddle/Paddle/pull/43694)) -- Fix the bug that the `copy` interface in C++ cannot copy between multiple cards. ([#43728](https://github.com/PaddlePaddle/Paddle/pull/43728)) -- Fix the bug in inference stage caused by attribute naming in `paddle.incubate.nn.functional.fused_attention`and `paddle.incubate.nn.functional.fused_feedforward`. ([#43505](https://github.com/PaddlePaddle/Paddle/pull/43505)) -- Fix an exception in ConditionalBlockGrad op when processing Tensor that does not require `grad`. ([#43034](https://github.com/PaddlePaddle/Paddle/pull/43034)) -- Fix the bug of device memory increase caused by einsum op in the speed optimization of backward computation. By default, this optimization is enabled. ([#43397](https://github.com/PaddlePaddle/Paddle/pull/43397)) -- Fix the bug that data fails to be fixed when `paddle.io.DataLoader` multi-process data reads the fixing random seeds under a single card. ([#43702](https://github.com/PaddlePaddle/Paddle/pull/43702)) -- Fix the bug that softmax op triggers CUDNN_STATUS_NOT_SUPPORT when the Tensor exceeds 2G. ([#43719](https://github.com/PaddlePaddle/Paddle/pull/43719)) -- Fix the bug that the trace op `Event` string is indistinguishable among different operators that cause the inconvenient performance analysis. ([#42789](https://github.com/PaddlePaddle/Paddle/pull/42789)) - -#### **Others** - -- Fix the bug of overflowing device memory caused by multiple deepcopy and saving in case of dynamic-to-static. ([#43141](https://github.com/PaddlePaddle/Paddle/pull/43141)) -- Fix the bug that the device id introduced by the upgrade of PlaceType used in the custom operator is wrong in the multi-card scenario. ([#43830](https://github.com/PaddlePaddle/Paddle/pull/43830)) -- Optimize the `paddle.profiler.Profiler` timeline visualization logic, move events customized in python scripts from C++ folding display to python folding display. ([#42790](https://github.com/PaddlePaddle/Paddle/pull/42790)) - -## **3.** Deployment Direction (Paddle Inference) - -### **(1) New Features** - -#### **New functions** - -- Add the support of the PaddleSlim quantization model for ONNX Runtime backends on CPUs. ([#43774](https://github.com/PaddlePaddle/Paddle/pull/43774), [#43796](https://github.com/PaddlePaddle/Paddle/pull/43796)) - -### **(2) Underlying Optimization** - -#### **CPU performance optimization** - -- Remove `gpu_cpu_reshape2_matmul_fuse_pass` from EnableMkldnn configuration to fix the bug of ResNet50 performance degradation. ([#43750](https://github.com/PaddlePaddle/Paddle/pull/43750)) - -#### **GPU performance optimization** - -- Add the support of `bilinear_interp_v2` TensorRT convert. ([#43618](https://github.com/PaddlePaddle/Paddle/pull/43618)) -- Add `matmul_scale_fuse_pass` and `multihead_matmul_fuse_pass_v3` to GPU pass. ([#43765](https://github.com/PaddlePaddle/Paddle/pull/43765)) -- Add the support of the GPU handle deferred initialization. ([#43661](https://github.com/PaddlePaddle/Paddle/pull/43661)) - -### **(3) Bug Fixing** - -#### **Framework and API fixing** - -- Fix the compile error problem when binding Paddle-Lite XPU. ([#43178](https://github.com/PaddlePaddle/Paddle/pull/43178)) -- Fix the bug of false trigger of ERNIE 3.0 pass. ([#43948](https://github.com/PaddlePaddle/Paddle/pull/43948)) -- Fix the bug that int8 quantization attribute in multihead op cannot be read. ([#43020](https://github.com/PaddlePaddle/Paddle/pull/43020)) - -#### **Backend capability fixing** - -- Fix the bug that two ops of elementwise_mul and matmul in MKLDNN are crashed during quantitative inference. ([#43725](https://github.com/PaddlePaddle/Paddle/pull/43725)) -- Fix a bug where TensorRT subgraph serialization files are repeatedly generated for the same model during inference. ([#42945](https://github.com/PaddlePaddle/Paddle/pull/43945), [#42633](https://github.com/PaddlePaddle/Paddle/pull/42633)) -- Fix a conflict between the ONNX Runtime backend and the externally use of protobuf. ([#43159](https://github.com/PaddlePaddle/Paddle/pull/43159), [#43742](https://github.com/PaddlePaddle/Paddle/pull/43742)) -- Fix an error reported by python prediction library when using ONNX Runtime backend in case of multiple inputs. ([#43621](https://github.com/PaddlePaddle/Paddle/pull/43621)) - -## **4. Environment Adaptation** - -### **Compile and install** - -- Complete verification and adaptation of CUDA 11.6, and release CUDA 11.6 precompiled binary. ([#43935](https://github.com/PaddlePaddle/Paddle/pull/43935), [#44005](https://github.com/PaddlePaddle/Paddle/pull/44005)) -- Fix a cub error when compiling with CUDA 11.6 on Windows. ([#43935](https://github.com/PaddlePaddle/Paddle/pull/43935), [#44005](https://github.com/PaddlePaddle/Paddle/pull/44005)) -- Fix the bug of long compilation time for elementwise and reduce op. ([#43202](https://github.com/PaddlePaddle/Paddle/pull/43202), [#42779](https://github.com/PaddlePaddle/Paddle/pull/42779), [#43205](https://github.com/PaddlePaddle/Paddle/pull/43205)) - -### **New hardware adaptation** - -- Cambricon MLU supports PaddlePaddle Profiler. ([#42115](https://github.com/PaddlePaddle/Paddle/pull/42115)) -- GraphCore IPU supports visualization of compilation progress. ([#42078](https://github.com/PaddlePaddle/Paddle/pull/42078)) - -# 2.3.0 Release Note - -## 1. **Important Updates** - -We are excited to release the PaddlePaddle Framework V2.3.0. This version contains the following highlights. - -### API - -- Added more than 100 new APIs, covering automatic differentiation, linear algebra, probability distribution, sparse tensor, framework performance analysis, hardware device management, vision domain, etc. - -- Added 4 new automatic differentiation APIs, 11 new linear algebra APIs, and 21 new probability distribution APIs to better support use cases in scientific computing, reinforcement learning, xand other application areas. - -- Added 11 new Sparse Tensor APIs including basic functions of sparse tensor construction and conversion. The COO and CSR formats are supported. - -- Added 9 new framework performance analysis APIs. The new performance profiling APIs, centered around Paddle.Profiler.Profiler, help users collect and analyze performance statistics during training and inference. - -- Added 7 APIs for device management, facilitating hardware information acquistion. - -- Added several visual and text domain APIs to facilitate ~~the~~ reusability of MobileNetV3, ResNeXt and other backbone networks, to achieve the fast networking. - - -### **Paddle** HIgh reusability operator l**ibrary** - -- We announce PHI as the new Paddle HIgh reusability operator library. PHI provides Primitive API, enabling kernel reuse for operator development. As a refactored functional operator library, PHI aims to solve legacy problems that harm the framework's performance and reusability, in particular on the operator development. Such problems include inefficient ways of cross using operators, unclear operator interfaces and lacking direct calls to the operator library in C++. With PHI, new operators can be easily implemented by composing functions available in the functional library. The library provides over 200 C++ operator class APIs and nearly 500 kernels. Composing new operators through these built-in functions can greatly reduce the user's development effort. PHI supports different types of hardware (e.g., GPU and XPU). In addition, PHI is extensible with plugins for accommodating third party accelerators (such as NPU) in a low cost and reusable fashion. In short, PHI supports low level operator composability, the reuse of kernels through Primitives, and accelerators through plugins. - -### **Distributed Training** - -- Fully upgrade the adaptive distributed training architecture, including multiple modules such as elastic resource management, asynchronous pipelined executor, heterogeneous communication, and automatic parallelism, and support the hard-aware distributed training and inference under a variety of heterogeneous hardware. - -- Add MoE parallel strategy, GroupSharded parallel strategy, and Pure FP16 under dynamic graph hybrid Parallelism, which further supports the efficient distributed training of large models under the dynamic graph. - -- Comprehensively upgrade and optimize the architecture of general heterogeneous parameter server, and simplify each module, such as communication and storage, to improve the secondary development experience of parameter server. The performance of GPU parameter server is improved by 2.38 times under 100 billion parameters and 10 billion data. - - -### **Compile and Install** - -- From version 2.3.0, PaddlePaddle upgrades GPU architectures supported. - - -### **Inference Deployment** - -- Add the Java API and ONNX Runtime CPU backend. - -- Support the TensorRT 8.0 / 8.2 and structured sparsity, with deep performance optimization for ERNIE-like structural models. - - -### **Hardware Backend Extention** - -- Add custom device support: provide a plug-in way to extend PaddlePaddle hardware backend. - -- Add training/inference support for multiple heterogeneous chips such as HUAWEI Ascend 910 / GraphCore IPU / Cambricon MLU / KUNLUNXIN 2. - - -### **Framework Architecture** - -- In this version, we did a lot of work on the framework executor. For details, please see [New Dynamic Graph Execution Mechanism](#new-dynamic-graph-execution-mechanism) and [New Static Graph Executor](#new-static-graph-executor). - -## **2. Incompatibility Upgrade** - -- Due to limitation of the binary size, sm35 CUDA ARCH is dropped in pre-compiled binaries. ([#41754](https://github.com/PaddlePaddle/Paddle/pull/41754)) - -- When `paddle.to_tensor` converts a python int scalar to a Tensor, the default data type on Windows changes from int32 to int64, thus alignment with Linux/Mac. ([#39662](https://github.com/PaddlePaddle/Paddle/pull/39662)) - -- To keep consistency with division behavior under python3, the division symbol `/` has been changed from “rounding divide” to “true divide”, and the data type of the computed output has been switched from int to float. ([#40890](https://github.com/PaddlePaddle/Paddle/pull/40890)) - - - - - - - - - - - - -
        -2.2 - -2.3.0 -
        -
        -
-```python
->>> import paddle
->>> a = paddle.to_tensor([327])
->>> b = paddle.to_tensor([80])
->>> a / b
-Tensor(shape=[1], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
-      [4])
-```
-
        -        		 -
        -
-```python
->>> import paddle
->>> a = paddle.to_tensor([327])
->>> b = paddle.to_tensor([80])
->>> a / b
-Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-      [4.08750010])
-```
-
        -
        - -- Revise the ELU's formula. The computing method in case of alpha <0 aligns with the original paper, thus fixing a small number of cases where the results are incorrectly calculated. Meanwhile, elu_ will report an error in case of alpha <0, because it is not mathematically possible to compute the inverse gradient from the output only at alpha <0. ([#37316](https://github.com/PaddlePaddle/Paddle/pull/37316)) - - - - - - - - - - - -
        -2.2 - -2.3.0 -
        -
        -
-```python
-# elu(x) = max(0, x) + min(0, α ∗ (e^x − 1))
->>> import paddle
->>> x = paddle.to_tensor([-1., 6.])
->>> m = paddle.nn.ELU(-0.2)
->>> out = m(x)
->>> out
-Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-       [ 0.        , -74.48576355])
->>> out = paddle.nn.functional.elu_(x, alpha=-0.2, name=None)
->>> out
-Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-       [ 0.        , -74.48576355])
-```
-
        -        		 -
        -
-```python
-# elu(x) = x, if x > 0
-# elu(x) = α ∗ (e^x − 1), if x <= 0
->>> import paddle
->>> x = paddle.to_tensor([-1., 6.])
->>> m = paddle.nn.ELU(-0.2)
->>> out = m(x)
->>> out
-Tensor(shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-       [0.12642412,  6.        ])
->>> out = paddle.nn.functional.elu_(x, alpha=-0.2, name=None)
-Traceback (most recent call last):
-  File "", line 1, in 
-  File "/usr/local/lib/python3.7/dist-packages/decorator.py", line 232, in fun
-    return caller(func, *(extras + args), **kw)
-  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/wrapped_decorator.py", line 25, in __impl__
-    return wrapped_func(*args, **kwargs)
-  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/inplace_utils.py", line 34, in __impl__
-    return func(*args, **kwargs)
-  File "/usr/local/lib/python3.7/dist-packages/paddle/nn/functional/activation.py", line 89, in elu_
-    assert alpha >= 0., "elu_ only support alpha >= 0, please use elu instead."
-AssertionError: elu_ only support alpha >= 0, please use elu instead.
-```
-
        -
        - -## **3. Training Framework (with the distributed function)** - -### **(1) New functions** - -#### API - -- Add 4 new automatic differentiation APIs to support scientific computing, as listed below: ([#40692](https://github.com/PaddlePaddle/Paddle/pull/40692)) - - - `paddle.incubate.autograd.vjp`, compute vector-Jacobi matrix product. - - - `paddle.incubate.autograd.jvp`, compute Jacobi matrix-vector product. - - - `paddle.incubate.autograd.Jacobian`, compute Jacobi matrix. - - - `paddle.incubate.autograd.Hessian`, compute Hessian matrix. - -- Add linear algebra class API - - - Add `paddle.linalg.triangular_solve`, to compute a system of linear equations with unique solutions through a triangular coefficient. ([#36714](https://github.com/PaddlePaddle/Paddle/pull/36714)) - - - Add `paddle.linalg.eig`, to compute the characteristic decomposition of the general square matrix. ([#35764](https://github.com/PaddlePaddle/Paddle/pull/35764)) - - - Add `paddle.linalg.sovle`, to compute solutions to systems of linear equations. ([#35715](https://github.com/PaddlePaddle/Paddle/pull/35715)) - - - Add `paddle.linalg.lstsq`, to compute least-squares solutions to systems of linear equations. ([#38585](https://github.com/PaddlePaddle/Paddle/pull/38585), [#38621](https://github.com/PaddlePaddle/Paddle/pull/38621)) - - - Add `paddle.linalg.qr`, compute QR decomposition of matrix. ([#35742](https://github.com/PaddlePaddle/Paddle/pull/35742), [#38824](https://github.com/PaddlePaddle/Paddle/pull/38824)) - - - Add `paddle.inner`, to compute inner product of a matrix. ([#37706](https://github.com/PaddlePaddle/Paddle/pull/37706)) - - - Add `paddle.outer`, to compute outer product of a matrix. ([#37706](https://github.com/PaddlePaddle/Paddle/pull/37706)) - - - Add `paddle.linalg.cov`, to compute covariance between vectors. ([#38392](https://github.com/PaddlePaddle/Paddle/pull/38392)) - - - Add `paddle.linalg.cholesky_sovle`, to compute the cholesky solution of the equation. ([#38167](https://github.com/PaddlePaddle/Paddle/pull/38167)) - - - Add `paddle.linalg.lu` and `paddle.linalg.lu_unpack`, to compute matrix lu decomposition, and decompress lu matrix. ([#38617](https://github.com/PaddlePaddle/Paddle/pull/38617), [#38559](https://github.com/PaddlePaddle/Paddle/pull/38559), [#38616](https://github.com/PaddlePaddle/Paddle/pull/38616)) - -- Add 21 new probability distribution class APIs for reinforcement learning, variation inference, scientific computing, and other scenarios. Including 6 random variable distributions, 13 random variable transformations, and 2 KL divergence computing. as listed below: ([#40536](https://github.com/PaddlePaddle/Paddle/pull/40536), [#38820](https://github.com/PaddlePaddle/Paddle/pull/38820), [#38558](https://github.com/PaddlePaddle/Paddle/pull/38558/files), [#38445](https://github.com/PaddlePaddle/Paddle/pull/38445), [#38244](https://github.com/PaddlePaddle/Paddle/pull/38244), [#38047](https://github.com/PaddlePaddle/Paddle/pull/38047)) - - - `paddle.distribution.ExponentialFamily`, exponential distribution family base class. - - - `paddle.distribution.Beta`, `Beta` distribution. - - - `paddle.distribution.Dirichlet`, `Dirichlet` distribution. - - - `paddle.distribution.Independent`, Independent distribution, used to create higher order distributions. - - - `paddle.distribution.TransformedDistribution`, Transform distribution, used to generate higher-order distributions through the base distribution and a series of transformations. - - - `paddle.distribution.Multionmial`, a multinomial distribution. - - - `paddle.distribution.Transform`, base class for transforming random variables. - - - `paddle.distribution.AbsTransform`, take absolute value transform. - - - `paddle.distribution.AffineTransform`, affine transform. - - - `paddle.distribution.ChainTransform`, chain combination of the transform. - - - `paddle.distribution.ExpTransform`, exponential transform. - - - `paddle.distribution.IndependentTransform`, independent transform, used to extend the `event_dim` of the transform definition field. - - - `paddle.distribution.PowerTransform`, power transform. - - - `paddle.distribution.ReshapeTransform`, `reshape` transform. - - - `paddle.distribution.SigmoidTransform`, `sigmoid` transform. - - - `paddle.distribution.SoftmaxTransform`, `softmax` transform. - - - `paddle.distribution.StackTransform`, `stack` transform, used to combine multiple transforms in a `stack` method. - - - `paddle.distribution.StickBreakingTransform`, `stickbreaking` transform. - - - `paddle.distribution.TanhTransform`, `tanh` transform. - - - `paddle.distribution.kl_divergence`, compute KL divergence. - - - `paddle.distribution.register_kl`, register user-defined KL divergence calculation function. - -- Add high-level API - - - Add `paddle.vision.models.AlexNet` and `paddle.vision.models.alexnet`, to use AlexNet models directly. ([#36058](https://github.com/PaddlePaddle/Paddle/pull/36058)) - - - Add `paddle.vision.models.DenseNet`, `paddle.vision.models.densenet121`, `paddle.vision.models.densenet161`, `paddle.vision.models. densenet169`, `paddle.vision.models.densenet201`, and `paddle.vision.models.densenet264`, to use DenseNet models directly. ([#36069](https://github.com/PaddlePaddle/Paddle/pull/36069)) - - - Add `paddle.vision.models.GoogLeNet` and `paddle.vision.models.googlenet`, to use GoogLeNet models directly. ([#36034](https://github.com/PaddlePaddle/Paddle/pull/36034)) - - - Add `paddle.vision.models.InceptionV3`, `paddle.vision.models.inception_v3`, to use InceptionV3 models directly. ([#36064](https://github.com/PaddlePaddle/Paddle/pull/36064)) - - - Add `paddle.vision.models.MobileNetV3Small`, `paddle.vision.models.MobileNetV3Large`, `paddle.vision.models.mobilenet_v3_small`, and `paddle.vision.models.mobilenet_v3_large`, to use MobileNetV3 models directly. ([#38653](https://github.com/PaddlePaddle/Paddle/pull/38653)) - - - Add `paddle.vision.models.resnext50_32x4d`, `paddle.vision.models.resnext50_64x4d`, `paddle.vision.models. paddle.vision.models.resnext101_32x4d`, `paddle.vision.models.resnext101_64x4d`, `paddle.vision.models.resnext152_32x4d`, and `paddle.vision.models.resnext152_64x4d`, to use ResNeXt models directly. ([#36070](https://github.com/PaddlePaddle/Paddle/pull/36070)) - - - Add `paddle.vision.models.ShuffleNetV2`, `paddle.vision.models.shufflenet_v2_x0_25`, `paddle.vision.models.shufflenet_v2_x0_33`, `paddle.vision.models.shufflenet_v2_x0_5`, `paddle.vision.models.shufflenet_v2_x1_0`, `paddle.vision.models.shufflenet_v2_x1_5`, `paddle.vision.models.shufflenet_v2_x2_0`, and `paddle.vision.models.shufflenet_v2_swish`, to use ShuffleNetV2 models directly ([#36067](https://github.com/PaddlePaddle/Paddle/pull/36067)) - - - Add `paddle.vision.models.SqueezeNet`, `paddle.vision.models.squeezenet1_0`, and `paddle.vision.models.squeezenet1_1`, to use SqueezeNet models directly. ([#36066](https://github.com/PaddlePaddle/Paddle/pull/36066)) - - - Add `paddle.vision.models.wide_resnet50_2`, and `paddle.vision.models.wide_resnet101_2`, to use WideResNet models directly. ([#36952](https://github.com/PaddlePaddle/Paddle/pull/36952)) - - - Add `paddle.vision.ops.nms` API, to support single-category and multi-category non-maximum suppression (NMS) algorithms for target detection and prediction task acceleration ([#40962](https://github.com/PaddlePaddle/Paddle/pull/40962)) - - - Add `paddle.vision.ops.roi_pool` and `paddle.vision.ops.RoIPool`, to support RoI region pooling operations in detection tasks. ([#36154](https://github.com/PaddlePaddle/Paddle/pull/36154)) - - - Add `paddle.vision.ops.roi_align` and `paddle.vision.ops.RoIAlign`, to support RoI Align operations in detection tasks. ([#35102](https://github.com/PaddlePaddle/Paddle/pull/36154)) - - - Add `paddle.text.ViterbiDecoder`, and `paddle.text.viterbi_decode` Viterbi decoding API, mainly for sequence tagging model prediction. ([#35778](https://github.com/PaddlePaddle/Paddle/pull/35778)) - -- Add 11 Sparse class APIs, to support basic functions, such as creating Sparse Tensor in COO and CSR formats, and add C++ inter-converting with Tensor. - - - `paddle.sparse.sparse_coo_tensor`，create Sparse Tensor in COO format. ([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `paddle.sparse.sparse_csr_tensor`，create Sparse Tensor in CSR format. ([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `paddle.sparse.ReLU`，support ReLU activation layer for SparseCooTensor. ([#40959](https://github.com/PaddlePaddle/Paddle/pull/40959)) - - - `paddle.sparse.functional.relu`，support ReLU function of SparseCooTensor. ([#40959](https://github.com/PaddlePaddle/Paddle/pull/40959)) - - - `Tensor.values()`，c++ method to get non-zero elements of a SparseCooTensor or SparseCsrTensor. ([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.indices()`，c++ method to get the coordinate information of a SparseCooTensor. ([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.crows()`，c++ method to get information about the compressed row information of the SparseCsrTensor. ([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.cols()`，c++ method to get the column information of the SparseCsrTensor ([#40608](https://github.com/PaddlePaddle/Paddle/pull/40608)) - - - `Tensor.to_sparse_coo()`，c++ method to convert a DenseTensor or SparseCsrTensor to a SparseCooTensor. ([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `Tensor.to_sparse_csr()`，c++ convert a DenseTensor or SparseCooTensor to a SparseCsrTensor. ([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - - - `Tensor.to_dense()`，c++ convert a SparseCooTensor or SparseCsrTensor to a DenseTensor. ([#40780](https://github.com/PaddlePaddle/Paddle/pull/40780)) - -- Add hardware related APIs - - - Add four GPU memory monitoring related APIs: `paddle.device.cuda.max_memory_allocated`, `paddle.device.cuda.max_memory_reserved`, `paddle.device.cuda.memory_allocated`, and `paddle.device.cuda.memory_reserved`, to view and analyze the GPU memory usage in real-time. ([#38657](https://github.com/PaddlePaddle/Paddle/pull/38657)) - - - Add `paddle.device.cuda.get_device_properties`, to return the properties of the GPU device. ([#35661](https://github.com/PaddlePaddle/Paddle/pull/35661)) - - - Add `paddle.device.cuda.get_device_name` and `paddle.device.cuda.get_device_capability`, to return the name and compute capability of the GPU device. ([#35672](https://github.com/PaddlePaddle/Paddle/pull/35672)) - -- Add Tensor operation API - - - Add `paddle.nansum`, to sum input Tensor along `axis` with ignoring the `NaNs` values. ([#38137](https://github.com/PaddlePaddle/Paddle/pull/38137)) - - - Add `paddle.nanmean`,to average input Tensor along `axis` with ignoring the `NaNs` values. ([#40472](https://github.com/PaddlePaddle/Paddle/pull/40472)) - - - Add `paddle.clone`, to return a copy of the input Tensor and provide gradient calculation. ([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - - - Add `paddle.Tensor.element_size`, to return the number of bytes allocated for a single element in a Tensor. ([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - - - Add `paddle.Tensor.to_uva_tensor`, to convert the numpy objects to be accessed by CUDA objects with virtual addresses, which are stored in CPU memory physically. ([#39146](https://github.com/PaddlePaddle/Paddle/pull/39146), [#38950](https://github.com/PaddlePaddle/Paddle/pull/38950)) - - - Add `paddle.rot90`, to rotate the n-dimensional Tensor by 90 degrees along the plane specified by `axes`. ([#37634](https://github.com/PaddlePaddle/Paddle/pull/37634)) - - - Add `paddle.logit` and `paddle.Tensor.logit`, to compute the logit function values for input Tensor. ([#37844](https://github.com/PaddlePaddle/Paddle/pull/37844)) - - - Add `paddle.repeat_interleave`, to copy the input along the specified axis, and return a new Tensor. ([#37981](https://github.com/PaddlePaddle/Paddle/pull/37981)) - - - Add `paddle.renorm`, to split the Tensor into multiple pieces at the specified `axis` and then perform p norm operations separately. ([#38130](https://github.com/PaddlePaddle/Paddle/pull/38130), [#38459](https://github.com/PaddlePaddle/Paddle/pull/38459)) - - - Add `paddle.mode` and `paddle.Tensor.mode`, to search the values and indices of the input Tensor along the specified axis. ([#38446](https://github.com/PaddlePaddle/Paddle/pull/38446)) - - - Add `paddle.quantile` and `paddle.Tensor.quantile`, to compute the q-quantile of a Tensor along the specified axis. ([#38567](https://github.com/PaddlePaddle/Paddle/pull/38567)) - - - Add `paddle.kthvalue` and `paddle.Tensor.kthvalue`, to find the values and indices of the kth smallest at the specified axis. ([#38386](https://github.com/PaddlePaddle/Paddle/pull/38386)) - - - Add `paddle.is_floating_point` and `paddle.Tensor.is_floating_point`, to determine if the input Tensor is the floating point type. ([#37885](https://github.com/PaddlePaddle/Paddle/pull/37885)) - - - Add `paddle.erfinv` and `paddle.Tensor.erfinv`, to compute the inverse error function of the input Tensor. ([#38295](https://github.com/PaddlePaddle/Paddle/pull/38295)) - - - Add `paddle.lerp` and `paddle.Tensor.lerp`, to compute linear interpolation among the input Tensors based on the given weights. ([#37253](https://github.com/PaddlePaddle/Paddle/pull/37253)) - - - Add `paddle.angle`, to compute the phase angle of a complex Tensor. ([#37689](https://github.com/PaddlePaddle/Paddle/pull/37689)) - - - Add `paddle.rad2deg` and `paddle.Tensor.rad2deg`, to convert each of the elements of input from the angles in radians to the degrees. ([#37598](https://github.com/PaddlePaddle/Paddle/pull/37598)) - - - Add `paddle.deg2rad` and `paddle.Tensor.deg2rad`, to convert each of the elements of input from the degrees in radians to the angles. ([#37598](https://github.com/PaddlePaddle/Paddle/pull/37598)) - - - Add `paddle.gcd` and `paddle.Tensor.gcd`, to compute the greatest common divisors of the absolute values of two inputs by element. ([#37819](https://github.com/PaddlePaddle/Paddle/pull/37819)) - - - Add `paddle.lcm` and `paddle.Tensor.lcm`, to compute the least common multiple of the absolute value of two inputs by element. ([#37819](https://github.com/PaddlePaddle/Paddle/pull/37819)) - - - Add `paddle.amax` and `paddle.Tensor.amax`, to get the maximum value of Tensor elements along the specified dimension. ([#38417](https://github.com/PaddlePaddle/Paddle/pull/38417)) - - - Add `paddle.amin` and `paddle.Tensor.amin`, to get the minimum value of Tensor elements along the specified dimension. ([#38417](https://github.com/PaddlePaddle/Paddle/pull/38417)) - - - Add `paddle.isclose`, to determine if each element of two Tensors is close to each other. ([#37135](https://github.com/PaddlePaddle/Paddle/pull/37135)) - - - Add `paddle.put_along_axis` and `paddle.take_along_axis`, for extracting or placing elements with specified index subscripts. ([#38608](https://github.com/PaddlePaddle/Paddle/pull/38608)) - - - Add `paddle.bincount` and `paddle.Tensor.bincount`, for counting the number of occurrences of each element in a Tensor. ([#36317](https://github.com/PaddlePaddle/Paddle/pull/36317)) - - - Add `paddle.fmax` and `paddle.fmin`, to extend the max/min function to support the case of NaN values in the two Tensors. If there is one NaN value in the corresponding position, return that non-NaN value; if there are two NaN values in the corresponding position, return the NaN value. ([#37826](https://github.com/PaddlePaddle/Paddle/pull/37826)) - - - Add `paddle.diff`, for computing the nth forward difference along a given dimension. It currently supports n=1. ([#37441](https://github.com/PaddlePaddle/Paddle/pull/37441)) - - - Add inverse hyperbolic functions: `paddle.asinh`, `paddle.acosh`, and `paddle.atanh`. ([#37076](https://github.com/PaddlePaddle/Paddle/pull/37076)) - - - Add `paddle.as_real` and `paddle.as_complex` for conversion between real Tensor and complex Tensor. ([#37784](https://github.com/PaddlePaddle/Paddle/pull/37784)) - - - Add `paddle.complex`, for constructing a complex Tensor with the given real and imaginary parts. ([#37918](https://github.com/PaddlePaddle/Paddle/pull/37918), [#38272](https://github.com/PaddlePaddle/Paddle/pull/38272)) - - - Add `paddle.det` and `paddle.slogdet`, to compute the determinant of a matrix and the natural logarithm of the determinant. ([#34992](https://github.com/PaddlePaddle/Paddle/pull/34992)) - - - Add `paddle.nn.utils.parameters_to_vector`, to flatten parameters to a 1-D Tensor. ([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - - - Add `paddle.nn.utils.vector_to_parameters`, to transform a Tensor with 1-D shape to the parameters. ([#38020](https://github.com/PaddlePaddle/Paddle/pull/38020)) - -- Add networking class APIs - - - Add `paddle.nn.Fold` and `paddle.nn.functional.fold`, to extract sliding local area blocks for the Tensors of a batch. ([#38613](https://github.com/PaddlePaddle/Paddle/pull/38613)) - - - Add `paddle.nn.CELU` and `paddle.nn.functional.celu`, to support the CELU activation layer. ([#36088](https://github.com/PaddlePaddle/Paddle/pull/36088)) - - - Add `paddle.nn.HingeEmbeddingLoss`. Add a way to compute hinge embedding loss. It is usually used for nonlinear embedding or semi-supervised learning. ([#37540](https://github.com/PaddlePaddle/Paddle/pull/37540)) - - - Add `paddle.nn.ZeroPad2D` API, for zero-padding according to the padding property. ([#37151](https://github.com/PaddlePaddle/Paddle/pull/37151)) - - - Add `paddle.nn.MaxUnPool3D` and `paddle.nn.MaxUnPool1D`, for computing 3D maximum inverse pooling and 1D maximum inverse pooling. ([#38716](https://github.com/PaddlePaddle/Paddle/pull/38716)) - - - Add `paddle.incubate.graph_khop_sampler`, `paddle.incubate.graph_sample_neighbors`, and `paddle.incubate.graph_reindex` APIs, to support graph multi-order neighbor sampling and graph reindexing operations. They are mainly used for graph neural network model training. ([#39146](https://github.com/PaddlePaddle/Paddle/pull/39146), [#40809](https://github.com/PaddlePaddle/Paddle/pull/40809)) - -- Add random number class APIs - - - Add `paddle.poisson`, to generate a Tensor that obeys Poisson distributed with the lambda parameter. ([#38117](https://github.com/PaddlePaddle/Paddle/pull/38117)) - - - Add `paddle.randint_like` API, to generate a new Tensor that obeys uniform distribution in the range [low, high), with the shape of the output matching the shape of the input. ([#36169](https://github.com/PaddlePaddle/Paddle/pull/36169)) - - - Add `paddle.Tensor.exponential_`. It is an inplace style API that populates the input Tensor with exponentially distributed random numbers. ([#38256](https://github.com/PaddlePaddle/Paddle/pull/38256)) - -- Add parameter initialization class APIs - - - Add `paddle.nn.initializer.Dirac`, to initialize 3D/4D/5D parameters with Dirac delta functions. It is commonly used for initialization of Conv1D/Conv2D/Conv3D parameters in the convolution layer. ([#37389](https://github.com/PaddlePaddle/Paddle/pull/37389)) - - - Add `paddle.nn.initializer.Orthogonal` for orthogonal matrix initialization. The initialized parameter is the (semi-) orthogonal vector. ([#37163](https://github.com/PaddlePaddle/Paddle/pull/37163)) - - - Add `paddle.nn.initializer.calculate_gain`, to get the recommended gain value for the activation function. The gain value can be used to set certain initialization APIs to adjust the initialization range. ([#37163](https://github.com/PaddlePaddle/Paddle/pull/37163)) - -- Add learning rate class API - - - Add `paddle.optimizer.lr.MultiplicativeDecay`, to provide the `lambda` function to set the learning rate. ([#38250](https://github.com/PaddlePaddle/Paddle/pull/38250)) -- Add distributed-related APIs - - - Add `paddle.incubate.optimizer.DistributedFusedLamb`, to allow the Lamb optimizer to update parameters distributedly. ([#40011](https://github.com/PaddlePaddle/Paddle/pull/40011), [#39972](https://github.com/PaddlePaddle/Paddle/pull/39972), [#39900](https://github.com/PaddlePaddle/Paddle/pull/39900), [#39747](https://github.com/PaddlePaddle/Paddle/pull/39747), [#39148](https://github.com/PaddlePaddle/Paddle/pull/39148), [#39416](https://github.com/PaddlePaddle/Paddle/pull/39416)) -- Add new optimizer-related APIs([#40710](https://github.com/PaddlePaddle/Paddle/pull/40710)) - - - `paddle.incubate.optimizer.functional.minimize_bfgs`，add second-order optimizer BFGS. - - - `paddle.incubate.optimizer.functional.minimize_lbfgs`，add second-order optimizer L-BFGS. - -- Add `paddle.incubate.multiprocessing` module, to provide Tensor (CPU/GPU) data transfer between python processes. ([#37302](https://github.com/PaddlePaddle/Paddle/pull/37302), [#41339](https://github.com/PaddlePaddle/Paddle/pull/41339)) - -- Add `paddle.incubate.autotune.set_config` API, to support multi-version Kernel auto-selection, mixed precision data layout auto-conversion, and num_workers auto-selection for DataLoader to automatically improve model performance. ([#42301](https://github.com/PaddlePaddle/Paddle/pull/42301)) - -- Add `paddle.incubate.nn.FusedMultiTransformer` and `paddle.incubate.nn.functional.fused_multi_transformer` API, to fuse multiple layers of transformers into a single op to improve model inference performance. It should be noted that only forward is supported. ([#42311](https://github.com/PaddlePaddle/Paddle/pull/42311)) - -- Add einsum_v2 operators for consistent interface between dynamic graph mode and static graph mode. It is compatible with the `paddle.einsum` implementation at the original python side, while supporting dynamic to static export and more complete Infershape inference. ([#42495](https://github.com/PaddlePaddle/Paddle/pull/42495), [#42327](https://github.com/PaddlePaddle/Paddle/pull/42327), [#42397](https://github.com/PaddlePaddle/Paddle/pull/42397), [#42105](https://github.com/PaddlePaddle/Paddle/pull/42105)) - - -#### IR(Intermediate Representation) - -- Dynamic graph to static graph - - - For the variable type StaticAnalysis module, add support for type tag similar to `a, b = paddle.shape(x)`. ([#39245](https://github.com/PaddlePaddle/Paddle/pull/39245)) - - - Add a computed field, supporting `InputSpec.name` as the Program cache hash key. ([#38273](https://github.com/PaddlePaddle/Paddle/pull/38273)) - - - Add syntax for supporting `dict['key'] = x.shape`. ([#40611](https://github.com/PaddlePaddle/Paddle/pull/40611)) - - - Add the support for Pure FP16 training. ([#36944](https://github.com/PaddlePaddle/Paddle/pull/36944)) - - - Add the support `for i in [x,y,z]` syntax. ([#37259](https://github.com/PaddlePaddle/Paddle/pull/37259)) - - - Add the support for type hint syntax of python3. ([#36544](https://github.com/PaddlePaddle/Paddle/pull/36544)) - -- Pass development - - - Add forward and backward fusion for FC + [relu|gelu] based on NVIDIA cuBlasLt Epilogue. ([#39437](https://github.com/PaddlePaddle/Paddle/pull/39437)) -- Kernel Primitive API - - - Add KP operators on GPU platform, including cast, scale, clip, bce_loss, abs_grad, reduce_sum_grad, reduce_mean_grad, clip, bce_loss, full, full_like, distribution, random, masked_select_kernel, where_index, masked_select_grad, dropout, sigmoid, where, and abs_grad. ([#36203](https://github.com/PaddlePaddle/Paddle/pull/36203), [#36423](https://github.com/PaddlePaddle/Paddle/pull/36423), [#39390](https://github.com/PaddlePaddle/Paddle/pull/39390), [#39734](https://github.com/PaddlePaddle/Paddle/pull/39734), [#38500](https://github.com/PaddlePaddle/Paddle/pull/38500), [#38959](https://github.com/PaddlePaddle/Paddle/pull/38959), [#39197](https://github.com/PaddlePaddle/Paddle/pull/39197/), [#39563](https://github.com/PaddlePaddle/Paddle/pull/39563), [#39666](https://github.com/PaddlePaddle/Paddle/pull/39666), [#40517](https://github.com/PaddlePaddle/Paddle/pull/40517), [#40617](https://github.com/PaddlePaddle/Paddle/pull/40617), [#40766](https://github.com/PaddlePaddle/Paddle/pull/40766), [#39898](https://github.com/PaddlePaddle/Paddle/pull/39898), [#39609](https://github.com/PaddlePaddle/Paddle/pull/39609)) - - - Add the support for XPU2 source code compilation mode. ([#37254](https://github.com/PaddlePaddle/Paddle/pull/37254), [#40397](https://github.com/PaddlePaddle/Paddle/pull/40397), [#38455](https://github.com/PaddlePaddle/Paddle/pull/38455)) - - - Add the support for KP operator reuse on XPU2 and GPU, including reduce, broadcast, elementwise_add, `exp、log、relu、sigmoid、leaky_relu、softplus、hard_swish、reciprocal`。([#36904](https://github.com/PaddlePaddle/Paddle/pull/36904), [#37226](https://github.com/PaddlePaddle/Paddle/pull/37226), [#38918](https://github.com/PaddlePaddle/Paddle/pull/38918), [#40560](https://github.com/PaddlePaddle/Paddle/pull/40560/), [#39787](https://github.com/PaddlePaddle/Paddle/pull/39787), [#39917](https://github.com/PaddlePaddle/Paddle/pull/39917), [#40002](https://github.com/PaddlePaddle/Paddle/pull/40002), [#40364](https://github.com/PaddlePaddle/Paddle/pull/40364)) - - - Add unit tests of KP operators on the XPU2 platform, including `brelu、ceil、celu、elu、floor、hard_shrink、hard_sigmoid、log1p、logsigmoid、relu6、silu、soft_relu、softsign、sqrt、square、swish、thresholded_relu、softshrink`。([#40448](https://github.com/PaddlePaddle/Paddle/pull/40448), [#40524](https://github.com/PaddlePaddle/Paddle/pull/40524)) - - - Add the support for XPU2 KP models, including resnet50, deepfm, wide_deep, yolov3-darknet53, det_mv3_db, bert, transformer, mobilenet_v3, and GPT2. - - -#### **Mixed Precision Training** - -- Split the `paddle.amp.GradScaler.unscale_` method from the `minimize` of the mixed precision training `paddle.amp.GradScaler`, to provide a separate interface for recovering the loss. ([#35825](https://github.com/PaddlePaddle/Paddle/pull/35825)) - -- Add the FP16 support for `paddle.nn.ClipByGlobalNorm` dynamic graph mode. Add FP16 Kernel for clip op to enable clip-related operations to support FP16 compute. ([#36198](https://github.com/PaddlePaddle/Paddle/pull/36198), [#36577](https://github.com/PaddlePaddle/Paddle/pull/36577)) - -- Support the case that the `optimizer` parameter transferred from `paddle.amp.decorate` is Nan. ([#37541](https://github.com/PaddlePaddle/Paddle/pull/37541)) - -- For the merged_momentum op，add the support of input multiple learning rates, the computing for use_nesterov policy and the regularization computing. ([#37527](https://github.com/PaddlePaddle/Paddle/pull/37527)) - -- Add multi_tensor policy to `paddle.optimizer.Momentum` optimizer. Add `set_to_zero` branch to `clear_grad` of `Optimzizer` class. ([#37564](https://github.com/PaddlePaddle/Paddle/pull/37564)) - -- Add multi_tensor policy to `paddle.optimizer.Adam`. ([#38010](https://github.com/PaddlePaddle/Paddle/pull/38010)) - -- Add multi_precision policy to `paddle.optimizer.SGD` optimizer. ([#38231](https://github.com/PaddlePaddle/Paddle/pull/38231)) - -- Add the storage `master weight` parameter to the optimizer `state_dict` method. ([#39121](https://github.com/PaddlePaddle/Paddle/pull/39121)) - -- Add support for op CUDA bfloat16 mixed precision training. Support for O1 and O2 modes. Enable the above training modes via `paddle.amp.auto_cast`. ([#39029](https://github.com/PaddlePaddle/Paddle/pull/39029), [#39815](https://github.com/PaddlePaddle/Paddle/pull/39815)) - -- Add bfloat16 CUDA Kernel for the following ops: matmul, concat, split, dropout, reshape, slice, squeeze, stack, transpose, unbind, elementwize_max, elementwize_add, elementwize_mul, elementwize_sub, scale, sum, layer_norm, p_norm, reduce_sum, softmax, log_softmax, sigmoid, sqrt, softplus, square, gaussian_random, fill_constant, and fill_any_like. ([#39485](https://github.com/PaddlePaddle/Paddle/pull/39485), [#39380](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39395](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39402](https://github.com/PaddlePaddle/Paddle/pull/39402), [#39457](https://github.com/PaddlePaddle/Paddle/pull/39457), [#39461](https://github.com/PaddlePaddle/Paddle/pull/39461), [#39602](https://github.com/PaddlePaddle/Paddle/pull/39602), [#39716](https://github.com/PaddlePaddle/Paddle/pull/39716), [#39683](https://github.com/PaddlePaddle/Paddle/pull/39683), [#39843](https://github.com/PaddlePaddle/Paddle/pull/39843), [#39999](https://github.com/PaddlePaddle/Paddle/pull/39999), [#40004](https://github.com/PaddlePaddle/Paddle/pull/40004), [#40027](https://github.com/PaddlePaddle/Paddle/pull/40027)) - -- Add bfloat16 CPU Kernel for the following ops: dropout, reshape, slice, squeeze, unsqueeze, stack, transpose, unbind, elementwize_max, elementwise_mul, elementwise_sub, and gather. ([#39380](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39395](https://github.com/PaddlePaddle/Paddle/pull/39380), [#39402](https://github.com/PaddlePaddle/Paddle/pull/39402), [#39457](https://github.com/PaddlePaddle/Paddle/pull/39457), [#39461](https://github.com/PaddlePaddle/Paddle/pull/39461), [#39602](https://github.com/PaddlePaddle/Paddle/pull/39602), [#39716](https://github.com/PaddlePaddle/Paddle/pull/39716), [#39683](https://github.com/PaddlePaddle/Paddle/pull/39683)) - -- Support printing of Tensor with data of bfloat16. ([#39375](https://github.com/PaddlePaddle/Paddle/pull/39375), [#39370](https://github.com/PaddlePaddle/Paddle/pull/39370)) - -- Add support for FP16 computation for `p_norm`, `elementwise_max`, and `fill_constant_batch_size_like ``scatter`. ([#35888](https://github.com/PaddlePaddle/Paddle/pull/35888), [#39907](https://github.com/PaddlePaddle/Paddle/pull/39907), [#38136](https://github.com/PaddlePaddle/Paddle/pull/38136), [#38499](https://github.com/PaddlePaddle/Paddle/pull/38499)) - -- Add support for int16_t for the following ops: cumsum, less_than, less_equal, greater_than, greater_equal, equal, not_equal, fill_any_like, grather_nd reduce_sum, where_index, reshape, and unsqueeze. ([#39636](https://github.com/PaddlePaddle/Paddle/pull/39636)) - -- Add support for int16_t label type for cross_entropy op. ([#39409](https://github.com/PaddlePaddle/Paddle/pull/39409)) - -- Add support for int16_t id type for embedding op. ([#39381](https://github.com/PaddlePaddle/Paddle/pull/39381)) - -- Add support for FP16 type for reduce_mean op. ([#38289](https://github.com/PaddlePaddle/Paddle/pull/38289)) - -- Add support for FP16 type for elementwise_min op. ([#38123](https://github.com/PaddlePaddle/Paddle/pull/38123)) - -- Update bfloat16 AMP oneDNN default support list. ([#39304](https://github.com/PaddlePaddle/Paddle/pull/39304)) - - -#### **Paddle HIgh reusability operator library** - -We announce PHI as the new Paddle HIgh reusability operator library. PHI provides Primitive API, enabling kernel reuse for operator development. As a refactored functional operator library, PHI aims to solve legacy problems that harm the framework's performance and reusability, in particular on the operator development. Such problems include inefficient ways of cross using operators, unclear operator interfaces and lacking direct calls to the operator library in C++. With PHI, new operators can be easily implemented by composing functions available in the functional library. The library provides over 200 C++ operator class APIs and nearly 500 kernels. Composing new operators through these built-in functions can greatly reduce the user's development effort. PHI supports different types of hardware (e.g., GPU and XPU). In addition, PHI is extensible with plugins for accommodating third party accelerators (such as NPU) in a low cost and reusable fashion. In short, PHI supports low level operator composabilty, the reuse of kernels through Primitives, and accelerators through plugins.The main contents include six parts as below: - -- **The implementation of the operator library infrastructure, core components and mechanisms**: The directory structure of the new operator library is reasonably planned, design and implement the common base data structure of the new operator library, the new functional InferMeta and Kernel development paradigm and the corresponding registration and management components. Support the automated compilation object generation and compilation dependency generation of Kernel files, allowing developers to focus only on the functional Kernel implementation, and making the development paradigm clear and concise. ([#34425](https://github.com/PaddlePaddle/Paddle/pull/34425), [#37107](https://github.com/PaddlePaddle/Paddle/pull/37107), [#36946](https://github.com/PaddlePaddle/Paddle/pull/36946), [#36948](https://github.com/PaddlePaddle/Paddle/pull/36948), [#37876](https://github.com/PaddlePaddle/Paddle/pull/37876), [#37916](https://github.com/PaddlePaddle/Paddle/pull/37916), [#37977](https://github.com/PaddlePaddle/Paddle/pull/37977), [38078](https://github.com/PaddlePaddle/Paddle/pull/38078), [#38861](https://github.com/PaddlePaddle/Paddle/pull/38861), [#39123](https://github.com/PaddlePaddle/Paddle/pull/39123), [#39131](https://github.com/PaddlePaddle/Paddle/pull/39131), [#39748](https://github.com/PaddlePaddle/Paddle/pull/39748), [#39790](https://github.com/PaddlePaddle/Paddle/pull/39790), [#39941](https://github.com/PaddlePaddle/Paddle/pull/39941), [#40239](https://github.com/PaddlePaddle/Paddle/pull/40239), [#40635](https://github.com/PaddlePaddle/Paddle/pull/40635), [#41091](https://github.com/PaddlePaddle/Paddle/pull/41091), [#37409](https://github.com/PaddlePaddle/Paddle/pull/37409), [#37942](https://github.com/PaddlePaddle/Paddle/pull/37942), [#39002](https://github.com/PaddlePaddle/Paddle/pull/39002), [#38109](https://github.com/PaddlePaddle/Paddle/pull/38109), [#37881](https://github.com/PaddlePaddle/Paddle/pull/37881), [#37517](https://github.com/PaddlePaddle/Paddle/pull/37517), [#39870](https://github.com/PaddlePaddle/Paddle/pull/39870), [#40975](https://github.com/PaddlePaddle/Paddle/pull/40975), [#39475](https://github.com/PaddlePaddle/Paddle/pull/39475), [#37304](https://github.com/PaddlePaddle/Paddle/pull/37304), #36910, #37120, #37146, #37215, #37255, #37369, #38258, #38257, #38355, #38853, #38937, #38977, #38946, #39085, #39153, #39228, #38301, #38275, #38506, #38607, #38473, #38632, #38811, #38880, #38996, #38914, #39101) - -- **Operator library C++ API system construction**: design and implement yaml configuration file-based operator definition paradigm, to automatically generate more than 200 C++ operator class APIs for internal and external developers to reuse. This reduces the cost of repeated development of basic operators. ([#37668](https://github.com/PaddlePaddle/Paddle/pull/37668), [#36938](https://github.com/PaddlePaddle/Paddle/pull/36938), [#38172](https://github.com/PaddlePaddle/Paddle/pull/38172), [#38182](https://github.com/PaddlePaddle/Paddle/pull/38182), [#38311](https://github.com/PaddlePaddle/Paddle/pull/38311), [#38438](https://github.com/PaddlePaddle/Paddle/pull/38438), [#39057](https://github.com/PaddlePaddle/Paddle/pull/39057), [#39229](https://github.com/PaddlePaddle/Paddle/pull/39229), [#39281](https://github.com/PaddlePaddle/Paddle/pull/39281), [#39263](https://github.com/PaddlePaddle/Paddle/pull/39263), [#39408](https://github.com/PaddlePaddle/Paddle/pull/39408), [#39436](https://github.com/PaddlePaddle/Paddle/pull/39436), [#39482](https://github.com/PaddlePaddle/Paddle/pull/39482), [#39497](https://github.com/PaddlePaddle/Paddle/pull/39497), [#39651](https://github.com/PaddlePaddle/Paddle/pull/39651), [#39521](https://github.com/PaddlePaddle/Paddle/pull/39521), [#39760](https://github.com/PaddlePaddle/Paddle/pull/39760), [#40060](https://github.com/PaddlePaddle/Paddle/pull/40060), [#40196](https://github.com/PaddlePaddle/Paddle/pull/40196), [#40218](https://github.com/PaddlePaddle/Paddle/pull/40218), [#40640](https://github.com/PaddlePaddle/Paddle/pull/40640), [#40732](https://github.com/PaddlePaddle/Paddle/pull/40732), [#40729](https://github.com/PaddlePaddle/Paddle/pull/40729), [#40840](https://github.com/PaddlePaddle/Paddle/pull/40840), [#40867](https://github.com/PaddlePaddle/Paddle/pull/40867), [#41025](https://github.com/PaddlePaddle/Paddle/pull/41025), [#41368](https://github.com/PaddlePaddle/Paddle/pull/41368)) - -- **Operator library compatible with various execution systems**: Implement new InferMeta and Kernel to access the original dynamic and static graph execution system. Support the safe removal of the original OpKernel registration and migration to the new Kernel form. ([#34425](https://github.com/PaddlePaddle/Paddle/pull/34425), [#38825](https://github.com/PaddlePaddle/Paddle/pull/38825), [#38837](https://github.com/PaddlePaddle/Paddle/pull/38837), [#38842](https://github.com/PaddlePaddle/Paddle/pull/38842), [#38976](https://github.com/PaddlePaddle/Paddle/pull/38976), [#39134](https://github.com/PaddlePaddle/Paddle/pull/39134), [#39140](https://github.com/PaddlePaddle/Paddle/pull/39140), [#39135](https://github.com/PaddlePaddle/Paddle/pull/39135), [#39252](https://github.com/PaddlePaddle/Paddle/pull/39252), [#39222](https://github.com/PaddlePaddle/Paddle/pull/39222), [#39351](https://github.com/PaddlePaddle/Paddle/pull/39351)) - -- **Decouple the underlying data structures and tool functions of the operator library from the framework**: Relieve PHI's dependence on the framework for core data structures, lay the foundation for subsequent independent compilation of PHI, and support infrt, custom Kernel, and a series of Phi-based construction work ([#38583](https://github.com/PaddlePaddle/Paddle/pull/38583), [#39188](https://github.com/PaddlePaddle/Paddle/pull/39188), [#39560](https://github.com/PaddlePaddle/Paddle/pull/39560), [#39931](https://github.com/PaddlePaddle/Paddle/pull/39931), [#39169](https://github.com/PaddlePaddle/Paddle/pull/39169), [#38951](https://github.com/PaddlePaddle/Paddle/pull/38951), [#38898](https://github.com/PaddlePaddle/Paddle/pull/38898), [#38873](https://github.com/PaddlePaddle/Paddle/pull/38873), [#38696](https://github.com/PaddlePaddle/Paddle/pull/38696), [#38651](https://github.com/PaddlePaddle/Paddle/pull/38651), [#39359](https://github.com/PaddlePaddle/Paddle/pull/39359), [#39305](https://github.com/PaddlePaddle/Paddle/pull/39305), [#39234](https://github.com/PaddlePaddle/Paddle/pull/39234), [#39098](https://github.com/PaddlePaddle/Paddle/pull/39098), [#39120](https://github.com/PaddlePaddle/Paddle/pull/39120), [#38979](https://github.com/PaddlePaddle/Paddle/pull/38979), [#38899](https://github.com/PaddlePaddle/Paddle/pull/38899), [#38844](https://github.com/PaddlePaddle/Paddle/pull/38844), [#39714](https://github.com/PaddlePaddle/Paddle/pull/39714), [#39729](https://github.com/PaddlePaddle/Paddle/pull/39729), [#39889](https://github.com/PaddlePaddle/Paddle/pull/39889), [#39587](https://github.com/PaddlePaddle/Paddle/pull/39587), [#39558](https://github.com/PaddlePaddle/Paddle/pull/39558), [#39514](https://github.com/PaddlePaddle/Paddle/pull/39514), [#39502](https://github.com/PaddlePaddle/Paddle/pull/39502), [#39300](https://github.com/PaddlePaddle/Paddle/pull/39300), [#39246](https://github.com/PaddlePaddle/Paddle/pull/39246), [#39124](https://github.com/PaddlePaddle/Paddle/pull/39124)) - -- **Integration between custom operator mechanism and Phi with improvement**: support for calling over 200 C++ operator class APIs automatically generated by PHI when writing custom operators. This reduces custom operator development costs. A series of bugs are fixed. ([#37122](https://github.com/PaddlePaddle/Paddle/pull/37122), [#37276](https://github.com/PaddlePaddle/Paddle/pull/37276), [#37281](https://github.com/PaddlePaddle/Paddle/pull/37281), [#37262](https://github.com/PaddlePaddle/Paddle/pull/37281), [#37415](https://github.com/PaddlePaddle/Paddle/pull/37415), [#37423](https://github.com/PaddlePaddle/Paddle/pull/37423), [#37583](https://github.com/PaddlePaddle/Paddle/pull/37683), [#38776](https://github.com/PaddlePaddle/Paddle/pull/38776), [#39353](https://github.com/PaddlePaddle/Paddle/pull/39353), [#41072](https://github.com/PaddlePaddle/Paddle/pull/41072)) - -- **Operator scale migration and refactoring**: migrate about 250 high-frequency forward and backward operator Kernel to the new operator library and refactor them as a single function. Achieve the high-performance operator by encapsulating multiple base Kernel functions on the C++ side for the fast combination. Meanwhile, add the corresponding yaml operator definition, and access to the new dynamic graph execution system to improve the python API scheduling performance. The migrated and refactored operators include: - - - sqrt ([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - square([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - sin ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - sinh ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - elementwise_fmax([#40140](https://github.com/PaddlePaddle/Paddle/pull/40140)) - - - elementwise_fmin([#40140](https://github.com/PaddlePaddle/Paddle/pull/40140)) - - - pool2d([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - max_pool2d_with_index([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - pool3d([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - max_pool3d_with_index([#40208](https://github.com/PaddlePaddle/Paddle/pull/40208), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - fill_constant ([#36930](https://github.com/PaddlePaddle/Paddle/pull/36930), [#39465](https://github.com/PaddlePaddle/Paddle/pull/39465)) - - - p_norm ([#40819](https://github.com/PaddlePaddle/Paddle/pull/40819)) - - - fill_constant_batch_size_like ([#40784](https://github.com/PaddlePaddle/Paddle/pull/40784)) - - - conv2d([#39354](https://github.com/PaddlePaddle/Paddle/pull/39354)) - - - conv2d_transpose([#40675](https://github.com/PaddlePaddle/Paddle/pull/40675), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - conv3d([#39354](https://github.com/PaddlePaddle/Paddle/pull/39354)) - - - conv3d_transpose([#40675](https://github.com/PaddlePaddle/Paddle/pull/40675), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - mish([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - gather_nd ([#40090](https://github.com/PaddlePaddle/Paddle/pull/40090), [#40043](https://github.com/PaddlePaddle/Paddle/pull/40043)) - - - gather ([#40500](https://github.com/PaddlePaddle/Paddle/pull/40500)) - - - scatter ([#40090](https://github.com/PaddlePaddle/Paddle/pull/40090), [#40043](https://github.com/PaddlePaddle/Paddle/pull/40043)) - - - scatter_nd_add ([#40090](https://github.com/PaddlePaddle/Paddle/pull/40090), [#40043](https://github.com/PaddlePaddle/Paddle/pull/40043)) - - - sgd([40045](https://github.com/PaddlePaddle/Paddle/pull/40045)) - - - momentum ([#41319](https://github.com/PaddlePaddle/Paddle/pull/41319)) - - - rmsprop([#40994](https://github.com/PaddlePaddle/Paddle/pull/40994)) - - - index_sample([#38130](https://github.com/PaddlePaddle/Paddle/pull/38130), [#38459](https://github.com/PaddlePaddle/Paddle/pull/38459),[#39905](https://github.com/PaddlePaddle/Paddle/pull/39905)) - - - adam ([#40351](https://github.com/PaddlePaddle/Paddle/pull/40351)) - - - layer_norm([#40193](https://github.com/PaddlePaddle/Paddle/pull/40193)) - - - adagrad([#40994](https://github.com/PaddlePaddle/Paddle/pull/40994/)) - - - adamax ([#40173](https://github.com/PaddlePaddle/Paddle/pull/40173)) - - - adadelta ([#40173](https://github.com/PaddlePaddle/Paddle/pull/40173)) - - - clip([#40602](https://github.com/PaddlePaddle/Paddle/pull/40602), [#41661](https://github.com/PaddlePaddle/Paddle/pull/41661), [#41675](https://github.com/PaddlePaddle/Paddle/pull/41675)) - - - ceil ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - cos ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - atan ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - cosh ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - erf([#40388](https://github.com/PaddlePaddle/Paddle/pull/40388)) - - - asin ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - acos ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - scale ([#39278](https://github.com/PaddlePaddle/Paddle/pull/39278)) - - - elementwise_pow ([#40993](https://github.com/PaddlePaddle/Paddle/pull/40993)) - - - elementwise_sub ([#39225](https://github.com/PaddlePaddle/Paddle/pull/39225), [#37260](https://github.com/PaddlePaddle/Paddle/pull/37260)) - - - round ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - floor ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - pow ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - elementwise_floordiv ([#40993](https://github.com/PaddlePaddle/Paddle/pull/40993)) - - - reciprocal([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - log1p ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - allclose ([#40469](https://github.com/PaddlePaddle/Paddle/pull/40469)) - - - mul ([#40833](https://github.com/PaddlePaddle/Paddle/pull/40833)) - - - elementwise_max ([#40590](https://github.com/PaddlePaddle/Paddle/pull/40590)) - - - elementwise_min ([#40590](https://github.com/PaddlePaddle/Paddle/pull/40590)) - - - elementwise_mod ([#40590](https://github.com/PaddlePaddle/Paddle/pull/40590)) - - - elementwise_add ([#39048](https://github.com/PaddlePaddle/Paddle/pull/39048), [#37043](https://github.com/PaddlePaddle/Paddle/pull/37043)) - - - matmul_v2 ([#36844](https://github.com/PaddlePaddle/Paddle/pull/36844), [#38713](https://github.com/PaddlePaddle/Paddle/pull/38713)) - - - elementwise_mul ([#41042](https://github.com/PaddlePaddle/Paddle/pull/41042), [#40252](https://github.com/PaddlePaddle/Paddle/pull/40252), [#37471](https://github.com/PaddlePaddle/Paddle/pull/37471)) - - - elementwise_div ([#40172](https://github.com/PaddlePaddle/Paddle/pull/40172), [#40039](https://github.com/PaddlePaddle/Paddle/pull/40039), [#37418](https://github.com/PaddlePaddle/Paddle/pull/37418)) - - - SelectedRows ([#39037](https://github.com/PaddlePaddle/Paddle/pull/39037), [#39087](https://github.com/PaddlePaddle/Paddle/pull/39087), [#39128](https://github.com/PaddlePaddle/Paddle/pull/39128), [#39162](https://github.com/PaddlePaddle/Paddle/pull/39162), [#39236](https://github.com/PaddlePaddle/Paddle/pull/39236)) - - - fill_any_like ([#39807](https://github.com/PaddlePaddle/Paddle/pull/39807)) - - - dot([#38359](https://github.com/PaddlePaddle/Paddle/pull/38359)) - - - sum ([#40873](https://github.com/PaddlePaddle/Paddle/pull/40873)) - - - cumsum ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - diag_v2 ([#39914](https://github.com/PaddlePaddle/Paddle/pull/39914)) - - - auc ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - log_loss ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - one_hot_v2([39876](https://github.com/PaddlePaddle/Paddle/pull/39876)) - - - sigmoid_cross_entropy_with_logits ([#39976](https://github.com/PaddlePaddle/Paddle/pull/39976), [#40200](https://github.com/PaddlePaddle/Paddle/pull/40200)) - - - bce_loss ([#39868](https://github.com/PaddlePaddle/Paddle/pull/39868)) - - - argsort ([#40151](https://github.com/PaddlePaddle/Paddle/pull/40151)) - - - arg_max ([#40222](https://github.com/PaddlePaddle/Paddle/pull/40222)) - - - arg_min ([#40222](https://github.com/PaddlePaddle/Paddle/pull/40222)) - - - segment_pool ([#40099](https://github.com/PaddlePaddle/Paddle/pull/40099)) - - - frobenius_norm([#40707](https://github.com/PaddlePaddle/Paddle/pull/40707), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - dist ([#40178](https://github.com/PaddlePaddle/Paddle/pull/40178)) - - - isnan_v2 ([#40076](https://github.com/PaddlePaddle/Paddle/pull/40076)) - - - logical_and ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - logical_not ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - isfinite_v2 ([#40076](https://github.com/PaddlePaddle/Paddle/pull/40076)) - - - logical_or ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - isinf_v2 ([#40076](https://github.com/PaddlePaddle/Paddle/pull/40076)) - - - is_empty ([#39919](https://github.com/PaddlePaddle/Paddle/pull/39919)) - - - logical_xor ([#39942](https://github.com/PaddlePaddle/Paddle/pull/39942)) - - - less_than([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - not_equal([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - equal([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - less_equal([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - equal_all([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - uniform_random ([#39937](https://github.com/PaddlePaddle/Paddle/pull/39937)) - - - randint ([#39876](https://github.com/PaddlePaddle/Paddle/pull/39876), [#41375](https://github.com/PaddlePaddle/Paddle/pull/41375)) - - - randperm ([#41265](https://github.com/PaddlePaddle/Paddle/pull/41265)) - - - unbind ([#39789](https://github.com/PaddlePaddle/Paddle/pull/39789)) - - - bernoulli ([#39590](https://github.com/PaddlePaddle/Paddle/pull/39590)) - - - increment ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - multinomial ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - addmm ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - cholesky ([#39858](https://github.com/PaddlePaddle/Paddle/pull/39858), [#39913](https://github.com/PaddlePaddle/Paddle/pull/39913)) - - - where ([#39811](https://github.com/PaddlePaddle/Paddle/pull/39811)) - - - log10 ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - log2 ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - expm1([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - atan2 ([#39806](https://github.com/PaddlePaddle/Paddle/pull/39806)) - - - gaussian_random ([#39932](https://github.com/PaddlePaddle/Paddle/pull/39932), [#40122](https://github.com/PaddlePaddle/Paddle/pull/40122), [#40191](https://github.com/PaddlePaddle/Paddle/pull/40191)) - - - empty ([#38334](https://github.com/PaddlePaddle/Paddle/pull/38334)) - - - truncated_gaussian_random ([#39971](https://github.com/PaddlePaddle/Paddle/pull/39971), [#40191](https://github.com/PaddlePaddle/Paddle/pull/40191)) - - - mv ([#39861](https://github.com/PaddlePaddle/Paddle/pull/39861), [#39954](https://github.com/PaddlePaddle/Paddle/pull/39954)) - - - tan ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - set_value ([#40195](https://github.com/PaddlePaddle/Paddle/pull/40195), [#40478](https://github.com/PaddlePaddle/Paddle/pull/40478), [#40636](https://github.com/PaddlePaddle/Paddle/pull/40636)) - - - bitwise_and ([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - bitwise_not([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - bitwise_or([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - poisson([#39814](https://github.com/PaddlePaddle/Paddle/pull/39814)) - - - cholesky_solve([#40387](https://github.com/PaddlePaddle/Paddle/pull/40387)) - - - bitwise_xor([#40031](https://github.com/PaddlePaddle/Paddle/pull/40031)) - - - triangular_solve([#40417](https://github.com/PaddlePaddle/Paddle/pull/40417)) - - - sigmoid ([#40626](https://github.com/PaddlePaddle/Paddle/pull/40626)) - - - atanh ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - softsign([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - thresholded_relu ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - tanh_shrink ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - stanh([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - reduce_mean ([#37559](https://github.com/PaddlePaddle/Paddle/pull/37559)) - - - reduce_max([#40225](https://github.com/PaddlePaddle/Paddle/pull/40225)) - - - reduce_min ([#40374](https://github.com/PaddlePaddle/Paddle/pull/40374)) - - - mean ([#40872](https://github.com/PaddlePaddle/Paddle/pull/40872), [#41319](https://github.com/PaddlePaddle/Paddle/pull/41319)) - - - reduce_all ([#40374](https://github.com/PaddlePaddle/Paddle/pull/40374)) - - - reduce_any ([#40374](https://github.com/PaddlePaddle/Paddle/pull/40374)) - - - logsumexp ([#40790](https://github.com/PaddlePaddle/Paddle/pull/40790)) - - - softshrink([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - range ([#41265](https://github.com/PaddlePaddle/Paddle/pull/41265), [#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - stack([#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - tile ([#40371](https://github.com/PaddlePaddle/Paddle/pull/40371)) - - - unique([#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - unstack([#40581](https://github.com/PaddlePaddle/Paddle/pull/40851)) - - - slice([#40736](https://github.com/PaddlePaddle/Paddle/pull/40736)) - - - transpose2([#39327](https://github.com/PaddlePaddle/Paddle/pull/39327)) - - - unsqueeze2( [#40596](https://github.com/PaddlePaddle/Paddle/pull/40596)) - - - squeeze2( [#40596](https://github.com/PaddlePaddle/Paddle/pull/40596)) - - - strided_slice ([#40708](https://github.com/PaddlePaddle/Paddle/pull/40708)) - - - softmax ([#39547](https://github.com/PaddlePaddle/Paddle/pull/39547)) - - - leaky_relu ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - gelu ([#40393](https://github.com/PaddlePaddle/Paddle/pull/40393)) - - - prelu ([#40393](https://github.com/PaddlePaddle/Paddle/pull/40393)) - - - log_softmax ([#40393](https://github.com/PaddlePaddle/Paddle/pull/40393)) - - - elu ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - logsigmoid ([#40626](https://github.com/PaddlePaddle/Paddle/pull/40626)) - - - psroi_pool ([#40353](https://github.com/PaddlePaddle/Paddle/pull/40353), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - kthvalue([#40575](https://github.com/PaddlePaddle/Paddle/pull/40575)) - - - mode ([#40571](https://github.com/PaddlePaddle/Paddle/pull/40571)) - - - yolo_box([#40112](https://github.com/PaddlePaddle/Paddle/pull/40112)) - - - yolov3_loss ([#40944](https://github.com/PaddlePaddle/Paddle/pull/40944)) - - - temporal_shift([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - depthwise_conv2d([#39354](https://github.com/PaddlePaddle/Paddle/pull/39354)) - - - pad3d ([#40701](https://github.com/PaddlePaddle/Paddle/pull/40701)) - - - pad( [#40012](https://github.com/PaddlePaddle/Paddle/pull/40012)) - - - greater_equal([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - kldiv_loss ([#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - isclose ([#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - silu ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - unfold ([#39778](https://github.com/PaddlePaddle/Paddle/pull/39778)) - - - batch_norm([39347](https://github.com/PaddlePaddle/Paddle/pull/39347)) - - - norm([#39324](https://github.com/PaddlePaddle/Paddle/pull/39324)) - - - roi_pool ([#40574](https://github.com/PaddlePaddle/Paddle/pull/40574), [#40682](https://github.com/PaddlePaddle/Paddle/pull/40682), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - roi_align ([#40382](https://github.com/PaddlePaddle/Paddle/pull/40382), [#40556](https://github.com/PaddlePaddle/Paddle/pull/40556), [#41402](https://github.com/PaddlePaddle/Paddle/pull/41402)) - - - deformable_conv ([#40700](https://github.com/PaddlePaddle/Paddle/pull/40700), [#40794](https://github.com/PaddlePaddle/Paddle/pull/40794), [#41644](https://github.com/PaddlePaddle/Paddle/pull/41644)) - - - deformable_conv_v1 ([#40794](https://github.com/PaddlePaddle/Paddle/pull/40794), [#41644](https://github.com/PaddlePaddle/Paddle/pull/41644)) - - - label_smooth ([#39796](https://github.com/PaddlePaddle/Paddle/pull/39796)) - - - grid_sampler ([#40585](https://github.com/PaddlePaddle/Paddle/pull/40585)) - - - greater_than([#39970](https://github.com/PaddlePaddle/Paddle/pull/39970)) - - - pixel_shuffle ([#39949](https://github.com/PaddlePaddle/Paddle/pull/39949), [#39712](https://github.com/PaddlePaddle/Paddle/pull/39712)) - - - nearest_interp_v2 ([#40855](https://github.com/PaddlePaddle/Paddle/pull/40855)) - - - bilinear_interp_v2 ([#40855](https://github.com/PaddlePaddle/Paddle/pull/40855)) - - - softmax_with_cross_entropy ([#40832](https://github.com/PaddlePaddle/Paddle/pull/40832)) - - - rnn ([#41007](https://github.com/PaddlePaddle/Paddle/pull/41007)) - - - reverse ([#40791](https://github.com/PaddlePaddle/Paddle/pull/40791)) - - - trace ([#39510](https://github.com/PaddlePaddle/Paddle/pull/39510)) - - - kron([#40427](https://github.com/PaddlePaddle/Paddle/pull/40427)) - - - accuracy([#39982](https://github.com/PaddlePaddle/Paddle/pull/39982)) - - - gather_tree ([#40082](https://github.com/PaddlePaddle/Paddle/pull/40082), [#39844](https://github.com/PaddlePaddle/Paddle/pull/39844)) - - - dropout([#40148](https://github.com/PaddlePaddle/Paddle/pull/40148)) - - - bincount ([#39947](https://github.com/PaddlePaddle/Paddle/pull/39947)) - - - warpctc ([#41389](https://github.com/PaddlePaddle/Paddle/pull/41389), [#40023](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/40023)) - - - multiplex([#40007](https://github.com/PaddlePaddle/Paddle/pull/40007), [#40102](https://github.com/PaddlePaddle/Paddle/pull/40102)) - - - qr([#40007](https://github.com/PaddlePaddle/Paddle/pull/40007), [#40007](https://github.com/PaddlePaddle/Paddle/pull/40007)) - - - assign_value ([#40967](https://github.com/PaddlePaddle/Paddle/pull/40967)) - - - assign ([#40022](https://github.com/PaddlePaddle/Paddle/pull/40022)) - - - cast ([#37610](https://github.com/PaddlePaddle/Paddle/pull/37610)) - - - tril_triu([#40007](https://github.com/PaddlePaddle/Paddle/pull/40007), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - where_index ([#40255](https://github.com/PaddlePaddle/Paddle/pull/40255)) - - - index_select ([#40260](https://github.com/PaddlePaddle/Paddle/pull/40260), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - roll ([#40257](https://github.com/PaddlePaddle/Paddle/pull/40257), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - cumprod (Xiong Kun [#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - shard_index ([#40254](https://github.com/PaddlePaddle/Paddle/pull/40254)) - - - reshape2 ([#40914](https://github.com/PaddlePaddle/Paddle/pull/40914), [#39631](https://github.com/PaddlePaddle/Paddle/pull/39631), [#38833](https://github.com/PaddlePaddle/Paddle/pull/38833), [#37164](https://github.com/PaddlePaddle/Paddle/pull/37164)) - - - flip ([#39822](https://github.com/PaddlePaddle/Paddle/pull/39822), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - eye ([#39712](https://github.com/PaddlePaddle/Paddle/pull/39712), [#40105](https://github.com/PaddlePaddle/Paddle/pull/40105), [#41476](https://github.com/PaddlePaddle/Paddle/pull/41476)) - - - lookup_table_v2([#39901](https://github.com/PaddlePaddle/Paddle/pull/39901)) - - - searchsorted([#40520](https://github.com/PaddlePaddle/Paddle/pull/40520), [#41053](https://github.com/PaddlePaddle/Paddle/pull/41053)) - - - adamw ([#40351](https://github.com/PaddlePaddle/Paddle/pull/40351)) - - - tanh ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - cross ([#39829](https://github.com/PaddlePaddle/Paddle/pull/39829)) - - - concat ([#38955](https://github.com/PaddlePaddle/Paddle/pull/38955), [#41112](https://github.com/PaddlePaddle/Paddle/pull/41112)) - - - split ([#39060](https://github.com/PaddlePaddle/Paddle/pull/39060)) - - - linspace ([#40124](https://github.com/PaddlePaddle/Paddle/pull/40124)) - - - huber_loss ([#39761](https://github.com/PaddlePaddle/Paddle/pull/39761)) - - - hierarchical_sigmoid([#40553](https://github.com/PaddlePaddle/Paddle/pull/40553)) - - - nll_loss ([#39936](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/39936)) - - - graph_send_recv ([#40092](https://github.com/PaddlePaddle/Paddle/pull/40092), [#40320](https://github.com/PaddlePaddle/Paddle/pull/40320)) - - - abs([#39492](https://github.com/PaddlePaddle/Paddle/pull/39492), [#39762](https://github.com/PaddlePaddle/Paddle/pull/39762)) - - - exp([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - rsqrt([#40727](https://github.com/PaddlePaddle/Paddle/pull/40727)) - - - viterbi_decode ([#40186](https://github.com/PaddlePaddle/Paddle/pull/40186)) - - - conj ([#38247](https://github.com/PaddlePaddle/Paddle/pull/38247)) - - - real ([#39777](https://github.com/PaddlePaddle/Paddle/pull/39777), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - imag ([#39777](https://github.com/PaddlePaddle/Paddle/pull/39777), [#41173](https://github.com/PaddlePaddle/Paddle/pull/41173)) - - - take_along_axis ([#39959](https://github.com/PaddlePaddle/Paddle/pull/39959), [#40270](https://github.com/PaddlePaddle/Paddle/pull/40270), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - put_along_axis ([#39959](https://github.com/PaddlePaddle/Paddle/pull/39959), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - lgamma ([#39770](https://github.com/PaddlePaddle/Paddle/pull/39770)) - - - relu ([#40175](https://github.com/PaddlePaddle/Paddle/pull/40175)) - - - maxout ([#39959](https://github.com/PaddlePaddle/Paddle/pull/39959), [#40974](https://github.com/PaddlePaddle/Paddle/pull/40974)) - - - log ([#40785](https://github.com/PaddlePaddle/Paddle/pull/40785)) - - - bilinear_tensor_product([#39903](https://github.com/PaddlePaddle/Paddle/pull/39903)) - - - flatten_contiguous_range ([#38712](https://github.com/PaddlePaddle/Paddle/pull/38712), [#36957](https://github.com/PaddlePaddle/Paddle/pull/36957), [#41345](https://github.com/PaddlePaddle/Paddle/pull/41345)) - - - matrix_rank ([#40074](https://github.com/PaddlePaddle/Paddle/pull/40074), [#40519](https://github.com/PaddlePaddle/Paddle/pull/40519), [#41466](https://github.com/PaddlePaddle/Paddle/pull/41466)) - - - logit ([#37844](https://github.com/PaddlePaddle/Paddle/pull/37844)) - - - lerp ([#40105](https://github.com/PaddlePaddle/Paddle/pull/40105), [#39524](https://github.com/PaddlePaddle/Paddle/pull/39524)) - - - erfinv ([#39949](https://github.com/PaddlePaddle/Paddle/pull/39949), [#39712](https://github.com/PaddlePaddle/Paddle/pull/39712)) - - - broadcast_tensors([#40047](https://github.com/PaddlePaddle/Paddle/pull/40047)) - - - gumbel_softmax([#39873](https://github.com/PaddlePaddle/Paddle/pull/39873)) - - - diagonal ([#39575](https://github.com/PaddlePaddle/Paddle/pull/39575)) - - - trunc ([#39543](https://github.com/PaddlePaddle/Paddle/pull/39543), [#39772](https://github.com/PaddlePaddle/Paddle/pull/39772)) - - - multi_dot ([#40038](https://github.com/PaddlePaddle/Paddle/pull/40038)) - - - matrix_power ([#40231](https://github.com/PaddlePaddle/Paddle/pull/40231)) - - - digamma([#39240](https://github.com/PaddlePaddle/Paddle/pull/39240)) - - - masked_select([#39193](https://github.com/PaddlePaddle/Paddle/pull/39193)) - - - determinant ([#40539](https://github.com/PaddlePaddle/Paddle/pull/40539)) - - - eigh ([#40213](https://github.com/PaddlePaddle/Paddle/pull/40213)) - - - size ([#39949](https://github.com/PaddlePaddle/Paddle/pull/39949), [#39712](https://github.com/PaddlePaddle/Paddle/pull/39712)) - - - shape ([#40248](https://github.com/PaddlePaddle/Paddle/pull/40248)) - - - reduce_sum([#37559](https://github.com/PaddlePaddle/Paddle/pull/37559), [#41295](https://github.com/PaddlePaddle/Paddle/pull/41295)) - - - reduce_prod ([#39844](https://github.com/PaddlePaddle/Paddle/pull/39844)) - - - histogram([#39496](https://github.com/PaddlePaddle/Paddle/pull/39496)) - - - meshgrid ([#41411](https://github.com/PaddlePaddle/Paddle/pull/41411)) - - - brelu ([#40385](https://github.com/PaddlePaddle/Paddle/pull/40385)) - - - hard_swish ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - hard_shrink ([#40565](https://github.com/PaddlePaddle/Paddle/pull/40565)) - - - selu ([#39819](https://github.com/PaddlePaddle/Paddle/pull/39819)) - - - expand_v2 ([#39471](https://github.com/PaddlePaddle/Paddle/pull/39471)) - - - top_k_v2([#40064](https://github.com/PaddlePaddle/Paddle/pull/40064)) - - - expand_as_v2([#40373](https://github.com/PaddlePaddle/Paddle/pull/40373)) - - - swish ([#40913](https://github.com/PaddlePaddle/Paddle/pull/40913)) - - - hard_sigmoid ([#40626](https://github.com/PaddlePaddle/Paddle/pull/40626)) - - - exp, det, assign, gaussian_random, matrix_rank, eye, and deformable_conv. ([#41755](https://github.com/PaddlePaddle/Paddle/pull/41755), [#41737](https://github.com/PaddlePaddle/Paddle/pull/41737)) - -#### **New Dynamic Graph Execution Mechanism** - -To improve scheduling performance and custom development capability of the dynamic graph execution mechanism of the PaddlePaddle, we have reconstructed the underlying execution mechanism of the dynamic graph. With the new execution method, the PHI operator library can be used for efficient runtime execution. For the operators supported by the PHI operator library, switching to the new dynamic graph mode will get a significant improvement in scheduling performance. However, due to the huge workload required in the upgrade of the overall framework execution mechanism and this part of the work is coupled with a lot on the PHI operator library, we still do not use this execution method by default in this version. If you want to try it, you can switch to it by setting the environment variable `FLAGS_enable_eager_mode=1`.The details are as follows: - -- **Implementation of dynamic graph execution infrastructure, core components and mechanism**: By staticizing dynamic graph-related execution codes, the original homogeneous operators constructing converted to specific calling for different PHI APIs, thus greatly optimizing the scheduling overhead. ([#36059](https://github.com/PaddlePaddle/Paddle/pull/36059), [#37323](https://github.com/PaddlePaddle/Paddle/pull/37323), [#37556](https://github.com/PaddlePaddle/Paddle/pull/37556), [#37555](https://github.com/PaddlePaddle/Paddle/pull/37555), [#37478](https://github.com/PaddlePaddle/Paddle/pull/37478), [#37458](https://github.com/PaddlePaddle/Paddle/pull/37458), [#37479](https://github.com/PaddlePaddle/Paddle/pull/37479), [#37599](https://github.com/PaddlePaddle/Paddle/pull/37599), [#37659](https://github.com/PaddlePaddle/Paddle/pull/37659), [#37654](https://github.com/PaddlePaddle/Paddle/pull/37654), [#39200](https://github.com/PaddlePaddle/Paddle/pull/39200), [#39309](https://github.com/PaddlePaddle/Paddle/pull/39309), [#39319](https://github.com/PaddlePaddle/Paddle/pull/39319), [#39414](https://github.com/PaddlePaddle/Paddle/pull/39414), [#39504](https://github.com/PaddlePaddle/Paddle/pull/39504), [#39526](https://github.com/PaddlePaddle/Paddle/pull/39526), [#39878](https://github.com/PaddlePaddle/Paddle/pull/39878), [#39963](https://github.com/PaddlePaddle/Paddle/pull/39963)) - -- **New dynamic graph execution mechanism sub-function development and adaptation**: support more flexible and complete dynamic graph sub-functions such as hook, pylayer, double_grad, inplace, amp, etc. ([#41396](https://github.com/PaddlePaddle/Paddle/pull/41396), [#40400](https://github.com/PaddlePaddle/Paddle/pull/40400), [#40695](https://github.com/PaddlePaddle/Paddle/pull/40695), [#41043](https://github.com/PaddlePaddle/Paddle/pull/41043), [#40915](https://github.com/PaddlePaddle/Paddle/pull/40915), [#41104](https://github.com/PaddlePaddle/Paddle/pull/41104), [#41350](https://github.com/PaddlePaddle/Paddle/pull/41350), [#41209](https://github.com/PaddlePaddle/Paddle/pull/41209), [#40830](https://github.com/PaddlePaddle/Paddle/pull/40830), [#40891](https://github.com/PaddlePaddle/Paddle/pull/40891), [#36814](https://github.com/PaddlePaddle/Paddle/pull/36814), [#37377](https://github.com/PaddlePaddle/Paddle/pull/37377), [#37193](https://github.com/PaddlePaddle/Paddle/pull/37193), [#36965](https://github.com/PaddlePaddle/Paddle/pull/36965), [#37810](https://github.com/PaddlePaddle/Paddle/pull/37810), [#36837](https://github.com/PaddlePaddle/Paddle/pull/36837), [#38488](https://github.com/PaddlePaddle/Paddle/pull/38488), [#39282](https://github.com/PaddlePaddle/Paddle/pull/39282), [#39449](https://github.com/PaddlePaddle/Paddle/pull/39449), [#39531](https://github.com/PaddlePaddle/Paddle/pull/39531), [#39638](https://github.com/PaddlePaddle/Paddle/pull/39638), [#39674](https://github.com/PaddlePaddle/Paddle/pull/39674), [#39893](https://github.com/PaddlePaddle/Paddle/pull/39893), [#40170](https://github.com/PaddlePaddle/Paddle/pull/40170), [#40693](https://github.com/PaddlePaddle/Paddle/pull/40693), [#40937](https://github.com/PaddlePaddle/Paddle/pull/40937), [#41016](https://github.com/PaddlePaddle/Paddle/pull/41016), [#41051](https://github.com/PaddlePaddle/Paddle/pull/41051), [#41121](https://github.com/PaddlePaddle/Paddle/pull/41121), [#41198](https://github.com/PaddlePaddle/Paddle/pull/41198), [#41287](https://github.com/PaddlePaddle/Paddle/pull/41287), [#41380](https://github.com/PaddlePaddle/Paddle/pull/41380), [#41306](https://github.com/PaddlePaddle/Paddle/pull/41306), [#41387](https://github.com/PaddlePaddle/Paddle/pull/41387), [#40623](https://github.com/PaddlePaddle/Paddle/pull/40623), [#40945](https://github.com/PaddlePaddle/Paddle/pull/40945), [#39282](https://github.com/PaddlePaddle/Paddle/pull/39282), [#39449](https://github.com/PaddlePaddle/Paddle/pull/39449), [#38488](https://github.com/PaddlePaddle/Paddle/pull/38488)) - -- **Automatic code generation mechanism for new dynamic graph execution**: When we are trying to split the computation and scheduling logic of a large number of homogeneous operators into different specific scheduling logics, we find that it is a huge workload. So we introduce a new automatic code generation logic to generate code and thus simplify the runtime logic of dynamic graphs. Meanwhile, in order to adapt to the various types of runtime logic in the previous framework, we also use some complicated compilation techniques to obtain information at runtime to generate more accurate scheduling code. ([#37574](https://github.com/PaddlePaddle/Paddle/pull/37574), [#37575](https://github.com/PaddlePaddle/Paddle/pull/37575), [#37639](https://github.com/PaddlePaddle/Paddle/pull/37639), [#37723](https://github.com/PaddlePaddle/Paddle/pull/37723), [#37753](https://github.com/PaddlePaddle/Paddle/pull/37753), [#37812](https://github.com/PaddlePaddle/Paddle/pull/37812), [#37837](https://github.com/PaddlePaddle/Paddle/pull/37837), [#37910](https://github.com/PaddlePaddle/Paddle/pull/37910), [#37943](https://github.com/PaddlePaddle/Paddle/pull/37943), [#37992](https://github.com/PaddlePaddle/Paddle/pull/37992), [#37959](https://github.com/PaddlePaddle/Paddle/pull/37959), [#38017](https://github.com/PaddlePaddle/Paddle/pull/38017), [#37969](https://github.com/PaddlePaddle/Paddle/pull/37969), [#38160](https://github.com/PaddlePaddle/Paddle/pull/38160), [#38085](https://github.com/PaddlePaddle/Paddle/pull/38085), [#38562](https://github.com/PaddlePaddle/Paddle/pull/38562), [#38573](https://github.com/PaddlePaddle/Paddle/pull/38573), [#39192](https://github.com/PaddlePaddle/Paddle/pull/39192), [#39215](https://github.com/PaddlePaddle/Paddle/pull/39215), [#39355](https://github.com/PaddlePaddle/Paddle/pull/39355), [#39358](https://github.com/PaddlePaddle/Paddle/pull/39358), [#39328](https://github.com/PaddlePaddle/Paddle/pull/39328), [#39233](https://github.com/PaddlePaddle/Paddle/pull/39233), [#39628](https://github.com/PaddlePaddle/Paddle/pull/39628), [#39767](https://github.com/PaddlePaddle/Paddle/pull/39767), [#39743](https://github.com/PaddlePaddle/Paddle/pull/39743), [#39897](https://github.com/PaddlePaddle/Paddle/pull/39897), [#39797](https://github.com/PaddlePaddle/Paddle/pull/39797), [#39997](https://github.com/PaddlePaddle/Paddle/pull/39997), [#40058](https://github.com/PaddlePaddle/Paddle/pull/40058), [#40080](https://github.com/PaddlePaddle/Paddle/pull/40080), [#40107](https://github.com/PaddlePaddle/Paddle/pull/40107), [#39962](https://github.com/PaddlePaddle/Paddle/pull/39962), [#40132](https://github.com/PaddlePaddle/Paddle/pull/40132), [#40276](https://github.com/PaddlePaddle/Paddle/pull/40276), [#40266](https://github.com/PaddlePaddle/Paddle/pull/40266), [#40480](https://github.com/PaddlePaddle/Paddle/pull/40480), [#40482](https://github.com/PaddlePaddle/Paddle/pull/40482), [#40368](https://github.com/PaddlePaddle/Paddle/pull/40368), [#40650](https://github.com/PaddlePaddle/Paddle/pull/40650), [#40815](https://github.com/PaddlePaddle/Paddle/pull/40815), [#40907](https://github.com/PaddlePaddle/Paddle/pull/40907), [#40935](https://github.com/PaddlePaddle/Paddle/pull/40935), [#41089](https://github.com/PaddlePaddle/Paddle/pull/41089)) - -- **New dynamic graph execution mechanism accessed into the main framework and Integration test**: we currently use some environment variables to distinguish between static graph mode and dynamic graph mode (including new dynamic graph and old dynamic graph mode). We have adapted most logics of dynamic graphs in these modes. However, there are still a lot of problems being fixed. ([#37638](https://github.com/PaddlePaddle/Paddle/pull/37638), [#37643](https://github.com/PaddlePaddle/Paddle/pull/37643), [#37653](https://github.com/PaddlePaddle/Paddle/pull/37653), [#38314](https://github.com/PaddlePaddle/Paddle/pull/38314), [#38337](https://github.com/PaddlePaddle/Paddle/pull/38337), [#38338](https://github.com/PaddlePaddle/Paddle/pull/38338), [#39164](https://github.com/PaddlePaddle/Paddle/pull/39164), [#39326](https://github.com/PaddlePaddle/Paddle/pull/39326), [#40391](https://github.com/PaddlePaddle/Paddle/pull/40391), [#40201](https://github.com/PaddlePaddle/Paddle/pull/40201), [#40854](https://github.com/PaddlePaddle/Paddle/pull/40854), [#40887](https://github.com/PaddlePaddle/Paddle/pull/40887)) - -- **Update some judgment logics under dynamic graphs, to support fast execution paths for dynamic graphs in compatible forms**：([#40786](https://github.com/PaddlePaddle/Paddle/pull/40786)) - - - Non-static graph mode (current transition scheme): `_non_static_mode()`。 - - - Determined as new dynamic graph in dynamic graph mode (recommended judgment logic): `_in_dygrah_mode()`。 - - - Determined as old dynamic graph in dynamic graph mode (Not recommended. It will be deprecated in future versions): `_in_legacy_dygraph()`。 - - - Enable old dynamic graph and disable new dynamic graph in dynamic graph mode: `_enable_legacy_dygraph()` or exit `_test_eager_guard()`。 - - - Enable new dynamic graph and disable old dynamic graph in dynamic graph mode: `_disable_legacy_dygraph()` or with `with _test_eager_guard()`。 - - - Determine in new dynamic graph in static or dynamic graph mode: `_in_eager_without_dygraph_check()`。 - -- **Support inplace after dynamic graph reconstruction**: input and output are the same Tensor. - - - Adapt the inplace strategy for dynamic graph reconstruction intermediate states. ([#40400](https://github.com/PaddlePaddle/Paddle/pull/40400)) - - - Adapt the inplace strategy to the final state of the dynamic graph reconstruction. ([#40695](https://github.com/PaddlePaddle/Paddle/pull/40695)) - - - Add inplace strategy to PyLayer function after dynamical graph reconstruction. ([#41043](https://github.com/PaddlePaddle/Paddle/pull/41043)) - - - Add inplace strategy for Tensor's setitem function after dynamical graph reconstruction. ([#40915](https://github.com/PaddlePaddle/Paddle/pull/40915)) - - - Add `_reset_grad_inplace_version` interface after dynamic graph reconstruction, to set the inplace version of the Tensor's gradient to 0. ([#41101](https://github.com/PaddlePaddle/Paddle/pull/41101)) - - - If the value of the forward Tensor is not needed during the inverse computation (no need buffer property), the inplace version detection operation is not needed for that Tensor. For Tensor with no_need_buffer, skip the inplace version check. ([#41350](https://github.com/PaddlePaddle/Paddle/pull/41350)) - - - Unify error messages for inplace version checks after and before reconstruction of dynamic graphs. ([#41209](https://github.com/PaddlePaddle/Paddle/pull/41209)) - -- **Support view strategy after dynamical graph reconstruction**: input and output Tensor share underlying data. - - - Adapt the view strategy for dynamic graph reconstruction intermediate states. Include `reshape`, `squeeze`, `unsqueeze`, and `flatten` APIs. ([#40830](https://github.com/PaddlePaddle/Paddle/pull/40830)) - - - Adapt the view strategy for dynamic graph reconstruction final state. Include `reshape` API. ([#40891](https://github.com/PaddlePaddle/Paddle/pull/40891)) - -- **Add support for weakref on the python side of the new dynamic graph eager Tensor.** ([#41797](https://github.com/PaddlePaddle/Paddle/pull/41797)) - -- **Enhance the new dynamic graph DoubleGrad function** to support the basic DoubleGrad feature. ([#41893](https://github.com/PaddlePaddle/Paddle/pull/41893), [#41894](https://github.com/PaddlePaddle/Paddle/pull/41894), [#41895](https://github.com/PaddlePaddle/Paddle/pull/41895)) - -- **Add `core.eager.StringTensor` interface**, to support the construction of StringTensor on python side and the use of the StringTensor related APIs. ([#41039](https://github.com/PaddlePaddle/Paddle/pull/41039)) - -- **Add `_grad_name` and `_grad_value`*to `core.eager.Tensor` to return the name and value of a gradient. ([#41990](https://github.com/PaddlePaddle/Paddle/pull/41990)) - -- **Add the processing of the no_need_buffer attribute for dynamic graph intermediate state.** The Tensor with the no_need_buffer attribute is skipped in the inplace backward check operation. ([#41720](https://github.com/PaddlePaddle/Paddle/pull/41720)) - - -#### **New Static Graph Executor** - -In order to solve the problem that the original static graph executor of the PaddlePaddle is not good enough for scheduling in some scenarios and it is not easy to use multiple streams, we have implemented a new static graph executor with superior performance. It is easy to take advantage of the asynchronous scheduling capabilities of multi-streams and multi-threads. The new executor is a compatible upgrade of the original executor. At present, it is used by default in single-card scenarios. Users do not need to make any changes in the training codes. It can be used automatically. Of course, we also provide an interface to switch back to the original executor. Users can switch back to the original executor by setting the environment variable: `FLAGS_USE_STANDALONE_EXECUTOR=false`. ([#41179](https://github.com/PaddlePaddle/Paddle/pull/41179)) The main contents are as follows. - -- Basic components: High-performance thread pool for multi-threaded scheduling in the executor ([#35470](https://github.com/PaddlePaddle/Paddle/pull/35470), [#35930](https://github.com/PaddlePaddle/Paddle/pull/35930), [#36030](https://github.com/PaddlePaddle/Paddle/pull/36030), [#36480](https://github.com/PaddlePaddle/Paddle/pull/36480), [#36688](https://github.com/PaddlePaddle/Paddle/pull/36688), [#36740](https://github.com/PaddlePaddle/Paddle/pull/36740), [#38335](https://github.com/PaddlePaddle/Paddle/pull/38335), [#40770](https://github.com/PaddlePaddle/Paddle/pull/40770)) and thread co-op component ([#38779](https://github.com/PaddlePaddle/Paddle/pull/38779), [#40876](https://github.com/PaddlePaddle/Paddle/pull/40876), [#40912](https://github.com/PaddlePaddle/Paddle/pull/40912)). There is the timely memory recovery after operator execution ([#37642](https://github.com/PaddlePaddle/Paddle/pull/37642), [#39617](https://github.com/PaddlePaddle/Paddle/pull/39617), [#40859](https://github.com/PaddlePaddle/Paddle/pull/40859)). There is the new dependency analysis algorithm for parallel executor ([#37231](https://github.com/PaddlePaddle/Paddle/pull/37231)) etc. - -- Scheduling logic: Optimize the scheduling method of operator in the executor. Support multi-stream multi-threaded asynchronous scheduling mechanism. Change transforms such as data type, device, and layout to the operator scheduling to improve performance. Support caching the selection of operator Kernel. Support the selection of new PHI operator. ([#35024](https://github.com/PaddlePaddle/Paddle/pull/35024), [#34922](https://github.com/PaddlePaddle/Paddle/pull/34922), [#35711](https://github.com/PaddlePaddle/Paddle/pull/35711), [#35928](https://github.com/PaddlePaddle/Paddle/pull/35928), [#39458](https://github.com/PaddlePaddle/Paddle/pull/39458)，[#36899](https://github.com/PaddlePaddle/Paddle/pull/36899))。 - -- Interface compatibility: Compatible with the user interface and functionality of the original executor, such as alignment with python interface Executor.run(), support for managing Tensor in Scope, etc. This ensures that users can switch to the new executor without perception. ([#37278](https://github.com/PaddlePaddle/Paddle/pull/37278), [#37379](https://github.com/PaddlePaddle/Paddle/pull/37379), [#37445](https://github.com/PaddlePaddle/Paddle/pull/37445), [#37510](https://github.com/PaddlePaddle/Paddle/pull/37510), [#40955](https://github.com/PaddlePaddle/Paddle/pull/40955), [#41778](https://github.com/PaddlePaddle/Paddle/pull/41178), [#41058](https://github.com/PaddlePaddle/Paddle/pull/41058), [#38584](https://github.com/PaddlePaddle/Paddle/pull/38584), [#37957](https://github.com/PaddlePaddle/Paddle/pull/37957), [#37672](https://github.com/PaddlePaddle/Paddle/pull/37672), [#37474](https://github.com/PaddlePaddle/Paddle/pull/37474), [#37085](https://github.com/PaddlePaddle/Paddle/pull/37085), [#37061](https://github.com/PaddlePaddle/Paddle/pull/37061), [#36945](https://github.com/PaddlePaddle/Paddle/pull/36945)) - -- Enhance debugging and error reporting in multi-threaded scenarios by capturing error reports from sub-threads and throwing them uniformly in the main thread. This can improve user experience. ([#36692](https://github.com/PaddlePaddle/Paddle/pull/36692)，[#36802](https://github.com/PaddlePaddle/Paddle/pull/36802)) - -- Fix the bug with the new executor communication flow resetting stream cache information in the allocator, to reduce RecordStream overhead in cross-stream scenarios. This improves performance of DeepFM models by about 8% after optimization. ([#42046](https://github.com/PaddlePaddle/Paddle/pull/42046)) - -- Optimize the dependency analysis method between new executor operators to improve runtime performance. Establish correct dependencies for send/recv communication operators to support pipeline parallel. ([#42009](https://github.com/PaddlePaddle/Paddle/pull/42009)) - - - -#### **Distributed Training** - -- Basic functions of multi-machine multi-card parallel training based on collective communication - - - Add support for elastic training, enables scaling up and down the number of workers, enables training process resuming when node failure，to improve the fault tolerance of distributed training. ([#36684](https://github.com/PaddlePaddle/Paddle/pull/36684), [#37177](https://github.com/PaddlePaddle/Paddle/pull/37177), [#37781](https://github.com/PaddlePaddle/Paddle/pull/37781)) - - - Refactor launch startup module, add `master` collaboration and node number `nnodes` definition, to improve the ease of using the distributed startup. ([#40086](https://github.com/PaddlePaddle/Paddle/pull/40086), [#40568](https://github.com/PaddlePaddle/Paddle/pull/40568), [#40782](https://github.com/PaddlePaddle/Paddle/pull/40782), [#40844](https://github.com/PaddlePaddle/Paddle/pull/40844), [#40936](https://github.com/PaddlePaddle/Paddle/pull/40936), [#41190](https://github.com/PaddlePaddle/Paddle/pull/41190), [#41314](https://github.com/PaddlePaddle/Paddle/pull/41314)) - - - Add support for GPU/NPU/XPU multi-hardware heterogeneous training. ([#37613](https://github.com/PaddlePaddle/Paddle/pull/37613), [#37998](https://github.com/PaddlePaddle/Paddle/pull/37998)) - - - Add fleet_executor asynchronous pipeline executor. ([#36966](https://github.com/PaddlePaddle/Paddle/pull/36966), [#37049](https://github.com/PaddlePaddle/Paddle/pull/37049), [#37087](https://github.com/PaddlePaddle/Paddle/pull/37087), [#37126](https://github.com/PaddlePaddle/Paddle/pull/37126), [#37150](https://github.com/PaddlePaddle/Paddle/pull/37150), [#37203](https://github.com/PaddlePaddle/Paddle/pull/37203), [#37167](https://github.com/PaddlePaddle/Paddle/pull/37167), [#37282](https://github.com/PaddlePaddle/Paddle/pull/37282), [#37319](https://github.com/PaddlePaddle/Paddle/pull/37319), [#37462](https://github.com/PaddlePaddle/Paddle/pull/37462), [#37507](https://github.com/PaddlePaddle/Paddle/pull/37507), [#37533](https://github.com/PaddlePaddle/Paddle/pull/37533), [#37576](https://github.com/PaddlePaddle/Paddle/pull/37576), [#37605](https://github.com/PaddlePaddle/Paddle/pull/37605), [#37691](https://github.com/PaddlePaddle/Paddle/pull/37691), [#37742](https://github.com/PaddlePaddle/Paddle/pull/37742), [#37783](https://github.com/PaddlePaddle/Paddle/pull/37783), [#37809](https://github.com/PaddlePaddle/Paddle/pull/37809), [#37862](https://github.com/PaddlePaddle/Paddle/pull/37862), [#37882](https://github.com/PaddlePaddle/Paddle/pull/37882), [#37934](https://github.com/PaddlePaddle/Paddle/pull/37934), [#38024](https://github.com/PaddlePaddle/Paddle/pull/38024), [#38083](https://github.com/PaddlePaddle/Paddle/pull/38083), [#38164](https://github.com/PaddlePaddle/Paddle/pull/38164), [#38261](https://github.com/PaddlePaddle/Paddle/pull/38261), [#38290](https://github.com/PaddlePaddle/Paddle/pull/38290), [#40607](https://github.com/PaddlePaddle/Paddle/pull/40607), [#37093](https://github.com/PaddlePaddle/Paddle/pull/37093), [#37106](https://github.com/PaddlePaddle/Paddle/pull/37106), [#37143](https://github.com/PaddlePaddle/Paddle/pull/37143), [#37338](https://github.com/PaddlePaddle/Paddle/pull/37338), [#37376](https://github.com/PaddlePaddle/Paddle/pull/37376), [#37485](https://github.com/PaddlePaddle/Paddle/pull/37485), [#37531](https://github.com/PaddlePaddle/Paddle/pull/37531), [#37623](https://github.com/PaddlePaddle/Paddle/pull/37623), [#37693](https://github.com/PaddlePaddle/Paddle/pull/37693), [#37755](https://github.com/PaddlePaddle/Paddle/pull/37755), [#37807](https://github.com/PaddlePaddle/Paddle/pull/37807), [#37889](https://github.com/PaddlePaddle/Paddle/pull/37889), [#38420](https://github.com/PaddlePaddle/Paddle/pull/38420), [#38539](https://github.com/PaddlePaddle/Paddle/pull/38539), [#36892](https://github.com/PaddlePaddle/Paddle/pull/36892), [#37084](https://github.com/PaddlePaddle/Paddle/pull/37084), [#37158](https://github.com/PaddlePaddle/Paddle/pull/37158), [#37361](https://github.com/PaddlePaddle/Paddle/pull/37361), [#37509](https://github.com/PaddlePaddle/Paddle/pull/37509), [#37603](https://github.com/PaddlePaddle/Paddle/pull/37603), [#37703](https://github.com/PaddlePaddle/Paddle/pull/37703), [#37824](https://github.com/PaddlePaddle/Paddle/pull/37824), [#38114](https://github.com/PaddlePaddle/Paddle/pull/38114), [#38322](https://github.com/PaddlePaddle/Paddle/pull/38322), [#38535](https://github.com/PaddlePaddle/Paddle/pull/38535), [#38650](https://github.com/PaddlePaddle/Paddle/pull/38650), [#38709](https://github.com/PaddlePaddle/Paddle/pull/38709), [#38799](https://github.com/PaddlePaddle/Paddle/pull/38799), [#38839](https://github.com/PaddlePaddle/Paddle/pull/38839), [#38904](https://github.com/PaddlePaddle/Paddle/pull/38904)) - - - Add distributed inference function for large-scale model. ([#38795](https://github.com/PaddlePaddle/Paddle/pull/38795), [#39012](https://github.com/PaddlePaddle/Paddle/pull/39012), [#39032](https://github.com/PaddlePaddle/Paddle/pull/39032), [#39076](https://github.com/PaddlePaddle/Paddle/pull/39076), [#39194](https://github.com/PaddlePaddle/Paddle/pull/39194), [#39207](https://github.com/PaddlePaddle/Paddle/pull/39207), [#39241](https://github.com/PaddlePaddle/Paddle/pull/39241), [#39603](https://github.com/PaddlePaddle/Paddle/pull/39603), [#39758](https://github.com/PaddlePaddle/Paddle/pull/39758), [#39992](https://github.com/PaddlePaddle/Paddle/pull/39992)). - -- Dynamic graph hybrid parallelism - - - Reconstruct `paddle.distributed.fleet.utils.recompute`, to support new dynamic computational graph. ([#41396](https://github.com/PaddlePaddle/Paddle/pull/41396)) - - - Add pure FP16 training to support data parallelism. ([#36420](https://github.com/PaddlePaddle/Paddle/pull/36420)) - - - Add MoE (Mixture of Experts) parallel strategy, to support large-scale MoE model training. ([#41092](https://github.com/PaddlePaddle/Paddle/pull/41092), [#40895](https://github.com/PaddlePaddle/Paddle/pull/40895), [#40850](https://github.com/PaddlePaddle/Paddle/pull/40580), [#39224](https://github.com/PaddlePaddle/Paddle/pull/39224)) - - - Add GroupSharded parallel strategy. Support stage1, stage2, stage3, and it supports synchronous and asynchronous communication. It can be used together with the basic function combinations such as Recompute, AMP O1\O2, Offload, GroupShardedClipGrad, and GroupShardedScaler. ([#37489](https://github.com/PaddlePaddle/Paddle/pull/37489), [#37568](https://github.com/PaddlePaddle/Paddle/pull/37568), [#37707](https://github.com/PaddlePaddle/Paddle/pull/37707), [#37836](https://github.com/PaddlePaddle/Paddle/pull/37836), [#37947](https://github.com/PaddlePaddle/Paddle/pull/37947), [#38151](https://github.com/PaddlePaddle/Paddle/pull/38151), [#38407](https://github.com/PaddlePaddle/Paddle/pull/38407), [#38052](https://github.com/PaddlePaddle/Paddle/pull/38052), [#39112](https://github.com/PaddlePaddle/Paddle/pull/39112), [#38989](https://github.com/PaddlePaddle/Paddle/pull/38989), [#39171](https://github.com/PaddlePaddle/Paddle/pull/39171), [#39285](https://github.com/PaddlePaddle/Paddle/pull/39285), [#39334](https://github.com/PaddlePaddle/Paddle/pull/39334), [#39397](https://github.com/PaddlePaddle/Paddle/pull/39397), [#39581](https://github.com/PaddlePaddle/Paddle/pull/39581), [#39668](https://github.com/PaddlePaddle/Paddle/pull/39668), [#40129](https://github.com/PaddlePaddle/Paddle/pull/40129), [#40396](https://github.com/PaddlePaddle/Paddle/pull/40396), [#40488](https://github.com/PaddlePaddle/Paddle/pull/40488), [#40601](https://github.com/PaddlePaddle/Paddle/pull/40601)，[#37725](https://github.com/PaddlePaddle/Paddle/pull/37725)，[#37904](https://github.com/PaddlePaddle/Paddle/pull/37904), [#38064](https://github.com/PaddlePaddle/Paddle/pull/38064)) - -- Static graph hybrid parallelism - - - Add `scale_gradient` flag bit to `gradient_scale_configs` to control the position where the gradient aggregation operation averages the gradients under pipeline parallelism. ([#36384](https://github.com/PaddlePaddle/Paddle/pull/36384)) - - - Under tensor parallelism, the dropout op supports the settings of deterministic random seed generators, to ensure random consistency for non-distributed variables and randomness of distributed variables. ([#36228](https://github.com/PaddlePaddle/Paddle/pull/36228)) - - - NPU hybrid parallelism supports Offload, with saving 40% of NPU memory. ([#37224](https://github.com/PaddlePaddle/Paddle/pull/37224)) - - - Add `force_cpu` optional parameter to the seed op, to allow dropout to read seed values directly from CPU. ([#35820](https://github.com/PaddlePaddle/Paddle/pull/35820)) - - - Improve the Automatic Sparsity (ASP) sharding strategy and support the selection of sharding strategy according to the program. ([#40028](https://github.com/PaddlePaddle/Paddle/pull/40028)) - -- Automatic parallel - - - Add the process restart (relaunch) after automatic mapping between logical processes and physical devices. ([#37523](https://github.com/PaddlePaddle/Paddle/pull/37523), [#37326](https://github.com/PaddlePaddle/Paddle/pull/37326)) - - - Improve the underlying mechanism and interface for automatic parallel to facilitate the unification of modules and add the optimized pass. ([#36617](https://github.com/PaddlePaddle/Paddle/pull/36617), [#38132](https://github.com/PaddlePaddle/Paddle/pull/38132)) - - - Add unified resource representation, to support for automatic mapping between logical processes and physical devices. ([#37091](https://github.com/PaddlePaddle/Paddle/pull/37091), [#37482](https://github.com/PaddlePaddle/Paddle/pull/37482), [#37094](https://github.com/PaddlePaddle/Paddle/pull/37094)) - - - Improve the distributed attribute complementation for the backward and update parts of the computation graph. ([#36744](https://github.com/PaddlePaddle/Paddle/pull/36744)) - - - Add data slicing function. ([#36055](https://github.com/PaddlePaddle/Paddle/pull/36055)) - - - Add tensor resharding function to reshard the tensor according to the distributed properties of the tensor and operator. ([#40865](https://github.com/PaddlePaddle/Paddle/pull/40865), [#41106](https://github.com/PaddlePaddle/Paddle/pull/41106)) - - - Add the automatic conversion pass of distributed parameters when the number of resources or parallel policy changes. ([#40434](https://github.com/PaddlePaddle/Paddle/pull/40434)) - - - Add GradientMerge pass to reduce the number of communications and improve training efficiency. ([#38259](https://github.com/PaddlePaddle/Paddle/pull/38259), [#40737](https://github.com/PaddlePaddle/Paddle/pull/40737)) - - - Add Recompute pass to reduce the activation memory storage. ([#38920](https://github.com/PaddlePaddle/Paddle/pull/38920)) - - - Add Sharding optimization pass, to support p-g-os 3 stage optimization. ([#38502](https://github.com/PaddlePaddle/Paddle/pull/38502)) - - - Add AMP + FP16 optimization pass. ([#38764](https://github.com/PaddlePaddle/Paddle/pull/38764), [#40615](https://github.com/PaddlePaddle/Paddle/pull/40615)) - - - Add fused QKV parallelization for Transformer class model. ([#39080](https://github.com/PaddlePaddle/Paddle/pull/39080)) - - - Improve the sharding propagation for while op to ensure convergence of the fix-point algorithm. ([#39939](https://github.com/PaddlePaddle/Paddle/pull/39939), [#39086](https://github.com/PaddlePaddle/Paddle/pull/39086), [#39014](https://github.com/PaddlePaddle/Paddle/pull/39014)) - - - Support training and inference for sub-block and while op control flow. ([#39612](https://github.com/PaddlePaddle/Paddle/pull/39612), [#39895](https://github.com/PaddlePaddle/Paddle/pull/39895), [#40077](https://github.com/PaddlePaddle/Paddle/pull/40077)) - -- Parameter Server - - - Add NaN/Inf value checking tool under GPUPS. ([#38131](https://github.com/PaddlePaddle/Paddle/pull/38131)) - - - Under GPUPS, add set_date interface to adapt incremental training. ([#36194](https://github.com/PaddlePaddle/Paddle/pull/36194)) - - - Under GPUPS, add asynchronous release dataset function. ([#37790](https://github.com/PaddlePaddle/Paddle/pull/37790)) - - - Under GPUPS, support the Dump parameters and intermediate layers([#36157](https://github.com/PaddlePaddle/Paddle/pull/36157))； - - - Under GPUPS, support the optimizer parameter configuration. ([#39783](https://github.com/PaddlePaddle/Paddle/pull/39783), [#39849](https://github.com/PaddlePaddle/Paddle/pull/39849)) - - - Under the Unified Parameter Server, refactor the base classes of each module such as communication and storage, to improve the ease of secondary development of each module. ([#41207](https://github.com/PaddlePaddle/Paddle/pull/41207), [#41022](https://github.com/PaddlePaddle/Paddle/pull/41022), [#40702](https://github.com/PaddlePaddle/Paddle/pull/40702), [#39341](https://github.com/PaddlePaddle/Paddle/pull/39341) [#39377](https://github.com/PaddlePaddle/Paddle/pull/39377), [#39191](https://github.com/PaddlePaddle/Paddle/pull/39191), [#39064](https://github.com/PaddlePaddle/Paddle/pull/39064)) - - - Add evaluation metrics module under the Unified Parameter Server, to support AUC/WuAUC/MaskAUC and other evaluation metrics calculation and customizable extensions. ([#38789](https://github.com/PaddlePaddle/Paddle/pull/38789)) - - - Supports XPU parameter server training on KUNLUNXIN 2. ([#41917](https://github.com/PaddlePaddle/Paddle/pull/41917), [#42266](https://github.com/PaddlePaddle/Paddle/pull/42266), [#41916](https://github.com/PaddlePaddle/Paddle/pull/41916)) - -#### Profiler - -- Add the performance analysis module `paddle.profiler` in the Python layer: Provide the ability to collect, export, and count performance data during the training push. ([#40065](https://github.com/PaddlePaddle/Paddle/pull/40065), [#40357](https://github.com/PaddlePaddle/Paddle/pull/40357), [#40888](https://github.com/PaddlePaddle/Paddle/pull/40888)) - - - `paddle.profiler.Profiler`: performance analyzer, interface for user interaction. ([#41029](https://github.com/PaddlePaddle/Paddle/pull/41029), [#41524](https://github.com/PaddlePaddle/Paddle/pull/41524), [#41157](https://github.com/PaddlePaddle/Paddle/pull/41157), [#40249](https://github.com/PaddlePaddle/Paddle/pull/40249), [#40111](https://github.com/PaddlePaddle/Paddle/pull/40111), [#39964](https://github.com/PaddlePaddle/Paddle/pull/39964), [#40133](https://github.com/PaddlePaddle/Paddle/pull/40133)) - - - `paddle.profiler.RecordEvent`: provide custom punches to record time. ([#39693](https://github.com/PaddlePaddle/Paddle/pull/39693), [#39694](https://github.com/PaddlePaddle/Paddle/pull/39694), [#39695](https://github.com/PaddlePaddle/Paddle/pull/39695), [#39675](https://github.com/PaddlePaddle/Paddle/pull/39675),[#41445](https://github.com/PaddlePaddle/Paddle/pull/41445), [#41132](https://github.com/PaddlePaddle/Paddle/pull/41132)) - - - `paddle.profiler.ProfilerTarget`: specify the target device for performance analysis. - - - `paddle.profiler.ProfilerState`: indicate the state of the performance analyzer. - - - `paddle.profiler.SortedKeys`: specify the sorting method of the data within the statistics form. - - - `paddle.profiler.make_scheduler`: the scheduler generating the performance analyzer state and implement the periodic control of the collection scope. - - - `paddle.profiler.export_chrome_tracing`: save performance data to a google chrome tracing file viewable by the chrome://tracing plugin. ([#39316](https://github.com/PaddlePaddle/Paddle/pull/39316), [#39984](https://github.com/PaddlePaddle/Paddle/pull/39984), [#41029](https://github.com/PaddlePaddle/Paddle/pull/41029)) - - - `paddle.profiler.export_protobuf`: save performance data to a protobuf file represented by internal structure. ([#39519](https://github.com/PaddlePaddle/Paddle/pull/39519), [#39109](https://github.com/PaddlePaddle/Paddle/pull/39109), [#39474](https://github.com/PaddlePaddle/Paddle/pull/39474)) - - - `paddle.profiler.load_profiler_result`: load the performance data saved to a protobuf file. - - - `paddle.profiler.Profiler` generate statistics for data reading, step overhead and throughput for the model training by specifying the `timer_only` parameter. ([#40386](https://github.com/PaddlePaddle/Paddle/pull/40386)) - -- Refactor Profiler underlying infrastructure in C++ layer - - - Refactor the Profiler's controller architecture. ([#38826](https://github.com/PaddlePaddle/Paddle/pull/38826), [#39230](https://github.com/PaddlePaddle/Paddle/pull/39230), [#39779](https://github.com/PaddlePaddle/Paddle/pull/39779) ) - - - Add Host Tracer to collect host-side performance metrics. ([#37629](https://github.com/PaddlePaddle/Paddle/pull/39629), [#37766](https://github.com/PaddlePaddle/Paddle/pull/37766), [#37944](https://github.com/PaddlePaddle/Paddle/pull/37944), [#38280](https://github.com/PaddlePaddle/Paddle/pull/38280), [#39975](https://github.com/PaddlePaddle/Paddle/pull/39975), [#40460](https://github.com/PaddlePaddle/Paddle/pull/40460)) - - - Add CUDA Tracer to collect device-side performance metrics. ([#39488](https://github.com/PaddlePaddle/Paddle/pull/39488)) - - - Profiler support for grading. ([#39926](https://github.com/PaddlePaddle/Paddle/pull/39926)) - -- Modify the name and type of logging for op under new dynamic graph. ([#41771](https://github.com/PaddlePaddle/Paddle/pull/41771/) - -- Add Kernel running statistics into profilers' summarization and optimize the summarization. ([#41989](https://github.com/PaddlePaddle/Paddle/pull/41989) - -- Remove side-effect to performance in forward computing forward when Profiler is off. ([#42142](https://github.com/PaddlePaddle/Paddle/pull/42142)) - -#### **CINN compiler adoption** - -With the recent development of PaddlePaddle's compiler, a.k.a, CINN([GitHub - PaddlePaddle/CINN: Compiler Infrastructure for Neural Networks](https://github.com/PaddlePaddle/CINN)), paddle framework has also been changed to adapt the compiler CINN features. These include the subgraph management related functions for the Paddle-CINN runtime, optimization of memory and speed performance, and bug fixing during development. - -- Functions developed: - - - Subgraph op related functions: - - - Add the function to find and generate CINN subgraphs from computational graphs. ([#36345](https://github.com/PaddlePaddle/Paddle/pull/36345)) - - - Add cinn_launch op as a runtime entry point to CINN. It is responsible for scheduling CINN to compile the subgraph, to initialize the data, and to execute the generated kernels. ([#36600](https://github.com/PaddlePaddle/Paddle/pull/36600)) - - - Add a helper class `CinnLaunchContext` to the kernel implementation of cinn_launch op to manage the intermediate data for compiling and running subgraphs, to improve scalability and code readability. ([#37938](https://github.com/PaddlePaddle/Paddle/pull/37938)) - - - Add additional fetch nodes to CINN subgraphs, thus ensuring that CINN external nodes can fetch the values of variables. ([#37172](https://github.com/PaddlePaddle/Paddle/pull/37172), [#37190](https://github.com/PaddlePaddle/Paddle/pull/37190)) - - - Add the function to symbolize a CINN subgraph, which is used to topologically sort the subgraphs and return the CINN execution sequence. ([#36417](https://github.com/PaddlePaddle/Paddle/pull/36417) - - - Add `CinnCompiler` class for involking subgraphs in the CINN compiled graph that can be replaced by using CINN operators. ([#36562](https://github.com/PaddlePaddle/Paddle/pull/36562), [#36975](https://github.com/PaddlePaddle/Paddle/pull/36975)) - - - Add the interface to CINN symbolization class to get the names of subgraph fetched variables to prevent fetched variables from being eliminated in compilation optimizations. ([#37218](https://github.com/PaddlePaddle/Paddle/pull/37218)) - - - Checking, debugging, and PI changes related: - - - Synchronize the update of NetBuilder API name changes in CINN. ([#40392](https://github.com/PaddlePaddle/Paddle/pull/40392)) - - - Add necessary log information to Paddle-CINN for better debugging. ([#36867](https://github.com/PaddlePaddle/Paddle/pull/36867)) - - - Add the bidirectional conversion function between Paddle desc and CINN desc. ([#36100](https://github.com/PaddlePaddle/Paddle/pull/36100)) - - - The operator implemented in CINN may not use some input variables compared to Paddle. Therefore, remove the check that the input variables must be used in the cinn_launch op. ([#37119](https://github.com/PaddlePaddle/Paddle/pull/37119)) - - - Added cinn_instruction_run op for invoking CINN to execute a single generation instruction, facilitating the construction of scheduling run subgraphs on the Paddle side. ([#39435](https://github.com/PaddlePaddle/Paddle/pull/39435), [#39576](https://github.com/PaddlePaddle/Paddle/pull/39576)) - - - Add control macros to Paddle for CUDA/CUBLAS/MKL/CINN pass application required to compile CINN. ([#37066](https://github.com/PaddlePaddle/Paddle/pull/37066), [#36660](https://github.com/PaddlePaddle/Paddle/pull/36660)) - - - Add two control flags FLAGS_allow_cinn_ops and FLAGS_deny_cinn_ops to control the categories of CINN operators used to replace native operators during Paddle training. ([#36842](https://github.com/PaddlePaddle/Paddle/pull/36842)) - -- Performance optimization: - - - Speed optimization - - - Optimize the computational time consumed by CinnCacheKey. ([#37786](https://github.com/PaddlePaddle/Paddle/pull/37786), [#37317](https://github.com/PaddlePaddle/Paddle/pull/37317)) - - - Cache variable scope for CINN compiled subgraphs to reduce runtime parameter construction overhead. ([#37983](https://github.com/PaddlePaddle/Paddle/pull/37983)) - - - Utilize CINN's auto-tuning in case of subgraph compilation, could be enabled by flag, for further tuning of training performance. ([#41795](https://github.com/PaddlePaddle/Paddle/pull/41795)) - - - Refactor the correctness check of compilation results in case of subgraph compilation to avoid repeated checks at runtime and reduce the scheduling overhead. ([#41777](https://github.com/PaddlePaddle/Paddle/pull/41777)) - - - Enable TransposeFolding and GemmRewriter optimization passes by default in Paddle-CINN training. ([#41084](https://github.com/PaddlePaddle/Paddle/pull/41084)) - - - Pass the cuda stream created in Paddle into CINN so that Paddle and CINN can use the same CUDA stream in cuda computing. ([#37337](https://github.com/PaddlePaddle/Paddle/pull/37337)) - - - Move CINN optimization pass application logic from Paddle to CINN. ([#42047](https://github.com/PaddlePaddle/Paddle/pull/42047), [#42070](https://github.com/PaddlePaddle/Paddle/pull/42070)) - - - Device memory optimization - - - Add NoNeedBufferVars to cinn_launch op to declare a list of input variables that do not require a buffer, so that the memory can be freed in advance. ([#38367](https://github.com/PaddlePaddle/Paddle/pull/38367)) - - - Pass in reference count information for external variables to the subgraph, so that subgraphs within cinn_launch can reuse memory optimization passes and reduce the memory overhead in using CINN. ([#39209](https://github.com/PaddlePaddle/Paddle/pull/39209), [#39622](https://github.com/PaddlePaddle/Paddle/pull/39622)) - - - Add the function to convert a collection of executable instructions generated by CINN compilation to a Paddle Graph, supporting reuse of the Paddle scheduler and memory optimization pass, further reducing the memory overhead in using CINN. ([#39724](https://github.com/PaddlePaddle/Paddle/pull/39724), [#39911](https://github.com/PaddlePaddle/Paddle/pull/39911)) - - - Add Kernel of cinn_instruction_run op, to support dynamic device memory requests based on data types inferred from compilation results. ([#40920](https://github.com/PaddlePaddle/Paddle/pull/40920)) - -- Bug fixing: - - - Fix and optimize the generation logic of CINN subgraphs. ([#36503](https://github.com/PaddlePaddle/Paddle/pull/36503)) - - - Fix the bug that Paddle-CINN does not support no-input subgraphs. ([#40814](https://github.com/PaddlePaddle/Paddle/pull/40814)) - - - Fix an error reported due to CINN not being able to handle useless outputs in operators such as batch_norm. ([#36996](https://github.com/PaddlePaddle/Paddle/pull/36996)) - - - Fix several bugs in CINN subgraph partitioning and symbolization, and solve problems with Paddle training accessing the CINN. ([#36739](https://github.com/PaddlePaddle/Paddle/pull/36739), [#36698](https://github.com/PaddlePaddle/Paddle/pull/36698) ) - - - CINN does not yet support the control flow yet. Add logic to skip control flow when encountered. ([#40812](https://github.com/PaddlePaddle/Paddle/pull/40812)) - -#### **Other** - -- Model quantization - - - Upgrade quantization storage format to unify quantization formats for dynamic and static graphs. ([#41041](https://github.com/PaddlePaddle/Paddle/pull/41041)) - - - Add new post training quantization (PTQ): EMD and Adaround. ([#40421](https://github.com/PaddlePaddle/Paddle/pull/40421), [#38460](https://github.com/PaddlePaddle/Paddle/pull/38460)) - - - Support to quantize more operations in PTQ and QAT, such as crop, split, ab, unsqueeze etc. ([#40083](https://github.com/PaddlePaddle/Paddle/pull/40083)) - - - Support to quantize operators in control flow. ([#37498](https://github.com/PaddlePaddle/Paddle/pull/37498)) - - - Support quantization of matmul_v2 operator. ([#36469](https://github.com/PaddlePaddle/Paddle/pull/36469)) - - - Add support for quantized matmul_v2 inference on TensorRT. ([#36594](https://github.com/PaddlePaddle/Paddle/pull/36594)) - -- CUDA memory optimization - - - Implement multi-stream safe Allocator to support safe and efficient use of CUDA memory in asynchronous computing scenarios. ([#37290](https://github.com/PaddlePaddle/Paddle/pull/37290)) - - - Add new APIs (paddle.device.cuda.max_memory_allocated, paddle.device.cuda.max_memory_reserved, paddle.device.cuda.memory_allocated and paddle.device.cuda.memory_reserved) for GPU memory monitoring in runtime. ([#38657](https://github.com/PaddlePaddle/Paddle/pull/38657)) - - - Support allocate CUDA Managed Memory to train super large models in memory-constrained scenarios. ([#39075](https://github.com/PaddlePaddle/Paddle/pull/39075)) - - - Add GetBasePtr interface in C++ to get device address created with *cudaMalloc*. ([#37978](https://github.com/PaddlePaddle/Paddle/pull/37978)) - - - Reduce the number of free blocks in AutoGrowth Allocator to improve memory allocation performance. ([#35732](https://github.com/PaddlePaddle/Paddle/pull/35732)) - - - Remove redundant Float32 temporary tensor and cast operation for tensor with data type FP16 in`initializer.Normal` and `initializer.Constant`to save 2x memory. ([#38818](https://github.com/PaddlePaddle/Paddle/pull/38818)) - -- High-order derivative testing for models in dynamic graphs. - - - Add third-order derivative testing for network in dynamic graphs. ([#36814](https://github.com/PaddlePaddle/Paddle/pull/36814), [#37377](https://github.com/PaddlePaddle/Paddle/pull/37377)) -- Custom op: Support to custom op in ROCm(HIP) platform. ([#36771](https://github.com/PaddlePaddle/Paddle/pull/36771)) - -- Cost Model: Add basic Cost Model based on profiling infomation. ([#35774](https://github.com/PaddlePaddle/Paddle/pull/35774)) - -- Added a function to allow user to add their own layer and correspond pruning way to ASP support. ([#40253](https://github.com/PaddlePaddle/Paddle/pull/40253)) - -- Add string tensor data structure, allowing the framework to have the ability to represent and process string. ([#39830](https://github.com/PaddlePaddle/Paddle/pull/39830), [#40992](https://github.com/PaddlePaddle/Paddle/pull/40992)) - -- Add or upgrade oneDNN FP32/int8/bfloat16 Kernel, including: - - - ELU ([#37149](https://github.com/PaddlePaddle/Paddle/pull/37149)) - - - exp ([#38624](https://github.com/PaddlePaddle/Paddle/pull/38624)) - - - stack ([#37002](https://github.com/PaddlePaddle/Paddle/pull/37002)) - - - softplus ([#36382](https://github.com/PaddlePaddle/Paddle/pull/36382)) - - - round ([#39653](https://github.com/PaddlePaddle/Paddle/pull/39653)) - - - shape ([#36033](https://github.com/PaddlePaddle/Paddle/pull/36033)) - - - flatten and flatten2 ([#35892](https://github.com/PaddlePaddle/Paddle/pull/35892)) - - - slice ([#37630](https://github.com/PaddlePaddle/Paddle/pull/37630)) - - - elementwise_mul ([#40546](https://github.com/PaddlePaddle/Paddle/pull/40546)) - - - elementwise_add ([#38176](https://github.com/PaddlePaddle/Paddle/pull/38176)) - - - ementwise_div ([#36158](https://github.com/PaddlePaddle/Paddle/pull/36158)) - - - elementwise_sub ([#35662](https://github.com/PaddlePaddle/Paddle/pull/35662)) - - - roi_align ([#37848](https://github.com/PaddlePaddle/Paddle/pull/37848)) - - - nearest_interp and nearest_interp_v2 ([#37985](https://github.com/PaddlePaddle/Paddle/pull/37985)，[#38622](https://github.com/PaddlePaddle/Paddle/pull/38622)，[#39490](https://github.com/PaddlePaddle/Paddle/pull/39490)) - - - assembly optimized Adam ([#39158](https://github.com/PaddlePaddle/Paddle/pull/39158)) - - - logsoftmax ([#39793](https://github.com/PaddlePaddle/Paddle/pull/39793)) - - - activation ([#40721](https://github.com/PaddlePaddle/Paddle/pull/40721)) - - - mul ([#38552](https://github.com/PaddlePaddle/Paddle/pull/38552)) - - - mean ([#37104](https://github.com/PaddlePaddle/Paddle/pull/37104)) - - - relu ([#36265](https://github.com/PaddlePaddle/Paddle/pull/36265)) - - - pool2d ([#37081](https://github.com/PaddlePaddle/Paddle/pull/37081)) - - - concat ([#35889](https://github.com/PaddlePaddle/Paddle/pull/35889)) - - - conv2d ([#38507](https://github.com/PaddlePaddle/Paddle/pull/38507)，[#38938](https://github.com/PaddlePaddle/Paddle/pull/38938)，[#36284](https://github.com/PaddlePaddle/Paddle/pull/36284)) - - - LayerNorm ([#40418](https://github.com/PaddlePaddle/Paddle/pull/40418)) - -- Add the 3-stage storage graph retrieval engine based on SSD - host memory - GPU device memory, to support large-scale graph neural network training. ([#42472](https://github.com/PaddlePaddle/Paddle/pull/42472), [#42321](https://github.com/PaddlePaddle/Paddle/pull/42321), [#42027](https://github.com/PaddlePaddle/Paddle/pull/42027)) - -- Add heterogeneous multi-cloud training communication module switch, implement the Send/Recv interface function, and support multiple heterogeneous cloud communication. ([#40965](https://github.com/PaddlePaddle/Paddle/pull/40965) [40911](https://github.com/PaddlePaddle/Paddle/pull/40911)) - -### **(2) Function optimization** - -#### API - -- Add backward implementation of `paddle.linalg.det `. ([#36013](https://github.com/PaddlePaddle/Paddle/pull/36013)) - -- Add support for mixed precision training O2 mode for `paddle.Model`, i.e., support for Pure FP16 training mode of the original dynamic/static graphs. ([#36441](https://github.com/PaddlePaddle/Paddle/pull/40962441)) - -- Support for self chain calls for `paddle.nn.Layer`. ([#36609](https://github.com/PaddlePaddle/Paddle/pull/36609)) - -- Add settings of `is_distributed` property for the `to` method of `paddle.nn.Layer` to ensure that the distributed properties remain consistent before and after network parameter transform. ([#36221](https://github.com/PaddlePaddle/Paddle/pull/36221)) - -- Improve the parameter conversion logic of the `to` method of `paddle.nn.Layer`, to reduce the peak memory consumption of the conversion process and improve the conversion success rate. ([#36862](https://github.com/PaddlePaddle/Paddle/pull/36862)) - -- Support settings of the shape of the output Tensor for `paddle.incubate.graph_send_recv` to reduce the memory usage during the actual computation. ([#40509](https://github.com/PaddlePaddle/Paddle/pull/40509)) - -- Add the support of int32 and int64 data types for `paddle.incubate.segment_sum`, `segment_mean`, `segment_max`, and `segment_min`. ([#40577](https://github.com/PaddlePaddle/Paddle/pull/40577)) - -- Add the support of the bool type for transpose op. ([#35886](https://github.com/PaddlePaddle/Paddle/pull/35886)) - -- Switch the `paddle.mm` underlying operator from matmul to matmul_v2. ([#35770](https://github.com/PaddlePaddle/Paddle/pull/35770)) - -- Support static graph mode and support the unknown shape for `paddle.einsum`. ([#40360](https://github.com/PaddlePaddle/Paddle/pull/40360)) - -- Support data`parallelism for paddle.nn.functional.margin_cross_entropy` and `paddle.nn.functional.class_center_sample`. ([#39852](https://github.com/PaddlePaddle/Paddle/pull/39852)) - -- Support input of shape [1] for `paddle.nn.functional.grid_sample`. ([#36183](https://github.com/PaddlePaddle/Paddle/pull/36183)) - -- Support NHWC data format for `paddle.nn.PRelu`. ([#37019](https://github.com/PaddlePaddle/Paddle/pull/37019)) - -- Support the fixed random state using `paddle.seed` for `paddle.nn.functional.class_center_sample`. ([#38248](https://github.com/PaddlePaddle/Paddle/pull/38248)) - -- Add ROCM backend support for all APIs under `paddle.fft`, and optimize CUFFT backend error messages. ([#36415](https://github.com/PaddlePaddle/Paddle/pull/36415), [#36114](https://github.com/PaddlePaddle/Paddle/pull/36114/files)) - -- Support the function that the slicing dimension i 0, that is, allow slicing index results to be empty. ([#37313](https://github.com/PaddlePaddle/Paddle/pull/37313)) - -- Support int and bool type Tensor with using bool index for `Tensor.setitem`. ([#37761](https://github.com/PaddlePaddle/Paddle/pull/37761)) - -- Support nearest mode for `paddle.nn.functional.interpolate` when the input shape is 5D. ([#38868](https://github.com/PaddlePaddle/Paddle/pull/38868)) - -- Add the support of int16 for `paddle.nn.Embedding`and`paddle.gather`. ([#40964](https://github.com/PaddlePaddle/Paddle/pull/40964), [#40052](https://github.com/PaddlePaddle/Paddle/pull/40052)) - -- Support data`parallelism on single machine on``CPU platform``in paddle.distributed.spawn`. ([#35745](https://github.com/PaddlePaddle/Paddle/pull/35745), [#36758](https://github.com/PaddlePaddle/Paddle/pull/36758), [#36637](https://github.com/PaddlePaddle/Paddle/pull/36637)) - -- Add `depthwise_conv2d` MKLDNN operator. ([#38484](https://github.com/PaddlePaddle/Paddle/pull/38484)) - -- Add complex types check in the static graph model for API`paddle.abs`, `paddle.transpose`, `paddle.squeeze`, `paddle.unsqueeze`, `paddle.matmul`, and `paddle.full`. ([#40113](https://github.com/PaddlePaddle/Paddle/pull/40113)) - -- Support tuple and list type arguments for `paddle.autograd.PyLayer`. ([#38146](https://github.com/PaddlePaddle/Paddle/pull/38146)) - -- Add check whether tensor is inplace and leaf when calculate gradient. ([#37931](https://github.com/PaddlePaddle/Paddle/pull/37931)) - -- Support HIP library for `paddle.autograd.PyLayer`. ([#38184](https://github.com/PaddlePaddle/Paddle/pull/38184)) - -- Support more size inputs for `paddle.take_along_axis` and `paddle.put_along_axis`, and allow index matrix shape size to be larger than array matrix shape size. ([#39072](https://github.com/PaddlePaddle/Paddle/pull/39072)) - -- Optimize the error report message of API `paddle.nn.Pad2D` when replicate is 0. ([#36510](https://github.com/PaddlePaddle/Paddle/pull/36510/files)) - -- Support pad input in tuple format for API `paddle.nn.Pad2D`. ([#35985](https://github.com/PaddlePaddle/Paddle/pull/35985/files)) - -- Add tdm_sample API in `paddle.distributed.InMemoryDataset` to support sampling operations in TDM algorithms. ([#37044](https://github.com/PaddlePaddle/Paddle/pull/37044)) - -- Add Pre-saving Hooks mechanism for `paddle.jit.save`. ([#38186](https://github.com/PaddlePaddle/Paddle/pull/38186)) - -- Add new higher-order differentiation-related APIs. - - - `elementwise_add`: add third-order Kernel, to support computation of third-order differentiation. ([#36508](https://github.com/PaddlePaddle/Paddle/pull/36508), [#36618](https://github.com/PaddlePaddle/Paddle/pull/36618)) - - - `matmul_v2`: add third-order Kernel, to support computation of third-order differentiation. ([#36459](https://github.com/PaddlePaddle/Paddle/pull/36459)) - - - `elementwise_mul`: Add third-order Kernel, to support computation of third-order differentiation. ([#37152](https://github.com/PaddlePaddle/Paddle/pull/37547)) - -- Improve the logic of the `paddle.amp.GradScaler` to call check_finite_and_unscale op, to eliminate the cudaMemcpy introduced by the creation of the bool variable. ([#37770](https://github.com/PaddlePaddle/Paddle/pull/37770)) - -- Add check for unstack and unique op in case of input Tensor with 0 elements. ([#36021](https://github.com/PaddlePaddle/Paddle/pull/36021)) - -- Add new multi-layer, bi-directional LSTM function that supports KUNLUNXIN 2, to improve RNN forward/backward ops, and support the use of temporal model training. ([#](https://github.com/PaddlePaddle/Paddle/pull/41781)[42076](https://github.com/PaddlePaddle/Paddle/pull/42076)) - -- Add bce_loss forward/backward ops for KUNLUNXIN 2. ([#41610](https://github.com/PaddlePaddle/Paddle/pull/41610)) - -- Add backward implementation of `paddle.linalg.det `. ([#36013](https://github.com/PaddlePaddle/Paddle/pull/36013)) - -#### IR(Intermediate Representation) - -- Dynamic Graphs to Static Graphs - - - Optimize the behavior of the `ProgramCache.last` interface for dynamic graph to static graph so that it returns the most recently used Program instead of the final generated Program. ([#39541](https://github.com/PaddlePaddle/Paddle/pull/39541)) - - - Optimize the error report message for the `paddle.reshape` API for dynamic graph to static graph, and add a new recommended usage hint. ([#40599](https://github.com/PaddlePaddle/Paddle/pull/40599)) - - - Optimize the type of exception catch in the `is_api_in_module` function when transcribing dynamic code to static code. ([#40243](https://github.com/PaddlePaddle/Paddle/pull/40243)) - - - Optimize the hint of error message for dynamic graph to static graph，hide warning information by default. ([#39730](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/39730)) - - - Add the support of type hint syntax for dynamic graph to static graph to improve the accuracy of variable type analysis. ([#39572](https://github.com/PaddlePaddle/Paddle/pull/39572)) - - - Optimize the `paddle.cond` function to allow values are equal for basic types such as bool and int. ([#37888](https://github.com/PaddlePaddle/Paddle/pull/37888)) - - - Optimize the decorate function `@to_static` to allow the switch of the train/eval mode. ([#37383](https://github.com/PaddlePaddle/Paddle/pull/37383)) - - - Optimize the stack of error report for dynamic graph to static graph, to highlight user-related codes and reduce the framework redundant error stack. ([#36741](https://github.com/PaddlePaddle/Paddle/pull/36741)) - - - Remove `no_value` placeholder from the return value of `paddle.cond`. ([#36513](https://github.com/PaddlePaddle/Paddle/pull/36513)、[#36826](https://github.com/PaddlePaddle/Paddle/pull/36826)) - - - Adapt the run_program op to the new dynamic graph mode. ([#40198](https://github.com/PaddlePaddle/Paddle/pull/40198), [#40355](https://github.com/PaddlePaddle/Paddle/pull/40355)) - - - Add check for zip syntax. ([#37846](https://github.com/PaddlePaddle/Paddle/pull/https://github.com/PaddlePaddle/Paddle/pull/37846)) - - - Fix the dynamic graph to static graph failure due to the error of dimension and type judgment in the `paddle.signal.frame`, `paddle.signal.stft` and `paddle.signal.istft`. ([#40113](https://github.com/PaddlePaddle/Paddle/pull/40113)) - - - Add registration of plural type Kernel for mean, pad3d ops. ([#40113](https://github.com/PaddlePaddle/Paddle/pull/40113)) - - -#### **Mixed Precision Training** - -- Add GPU Compute Capability environment check for amp. Add the usage warning for GPU environments that the fail acceleration for training. ([#38086](https://github.com/PaddlePaddle/Paddle/pull/38086)) - -- Add check of calling order when using `paddle.amp.decorate` and `paddle.DataParallel` at the same time. ([#38785](https://github.com/PaddlePaddle/Paddle/pull/38785)) - - -#### **Distributed Training** - -- Basic functions of the distributed training - - - Optimize Fleet API and DistributedStrategy configuration to use dynamic graph parallel function conveniently. ([#40408](https://github.com/PaddlePaddle/Paddle/pull/40408)) - - - Optimize Dynamic Graph mixed parallel HybridParallelClipGrad strategy, support 4D hybrid parallel and Pure FP16 training. ([#36237](https://github.com/PaddlePaddle/Paddle/pull/36237), [#36555](https://github.com/PaddlePaddle/Paddle/pull/36555)) - - - Restructure dynamic graph data parallel strategy, to support new dynamic graph and communication. ([#40389](https://github.com/PaddlePaddle/Paddle/pull/40389), [#40593](https://github.com/PaddlePaddle/Paddle/pull/40593), [#40836](https://github.com/PaddlePaddle/Paddle/pull/40836), [#41119](https://github.com/PaddlePaddle/Paddle/pull/41119), [#41413](https://github.com/PaddlePaddle/Paddle/pull/41413), [#39987](https://github.com/PaddlePaddle/Paddle/pull/39987)) - - - Support distributed tensor model parallel for fused_attention op. ([#40101](https://github.com/PaddlePaddle/Paddle/pull/40101)) - - - Support the distributed tensor model parallel for fused_feedforward op. ([#40160](https://github.com/PaddlePaddle/Paddle/pull/40160)) - -- Graph retrieval engine - - - Optimize the data format returned by the graph sampling interface of the graph engine, with a 3x improvement of the sampling speed. ([#37315](https://github.com/PaddlePaddle/Paddle/pull/37315)) - - - Reduce the amount of graph engine threads to improve performance. ([#37098](https://github.com/PaddlePaddle/Paddle/pull/37098)) - - - Optimize graph engine data transfer to improve performance. ([#37341](https://github.com/PaddlePaddle/Paddle/pull/37341)) - - - Optimize the merge logic of embedding op to improve performance by exploiting the topological relationship of embedding op in the model. [(#35942)](https://github.com/PaddlePaddle/Paddle/pull/35942) - -- Communication library: restructure the communication library to improve the scalability and development of the communication library, and support heterogeneous communication. ([#41398](https://github.com/PaddlePaddle/Paddle/pull/41398), [#39720](https://github.com/PaddlePaddle/Paddle/pull/39720), [#40911](https://github.com/PaddlePaddle/Paddle/pull/40911), [#40579](https://github.com/PaddlePaddle/Paddle/pull/40579), [#40629](https://github.com/PaddlePaddle/Paddle/pull/40629), [#40437](https://github.com/PaddlePaddle/Paddle/pull/40437), [#40430](https://github.com/PaddlePaddle/Paddle/pull/40430), [#40228](https://github.com/PaddlePaddle/Paddle/pull/40228), [#40181](https://github.com/PaddlePaddle/Paddle/pull/40181), [#40100](https://github.com/PaddlePaddle/Paddle/pull/40100), [#40097](https://github.com/PaddlePaddle/Paddle/pull/40097), [#39892](https://github.com/PaddlePaddle/Paddle/pull/39892), [#39384](https://github.com/PaddlePaddle/Paddle/pull/39384), [#39737](https://github.com/PaddlePaddle/Paddle/pull/39737), [#40040](https://github.com/PaddlePaddle/Paddle/pull/40040)) - -- Support the publication of MoE-related interfaces in `paddle.incubate.distributed.models.moe ` (`moe.GShardGate `, `moe.BaseGate `, `moe.SwitchGate `, `moe.MoELayer `, and `moe. ClipGradForMOEByGlobalNorm `). ([#42300](https://github.com/PaddlePaddle/Paddle/pull/42300)) - -- Fix the error report in the use of recomputing in `paddle.incubate.distributed.models.moe.MoELayer `. ([#42128](https://github.com/PaddlePaddle/Paddle/pull/42128)) - -- Fix the error report in the new dynamic graph pipeline parallel caused by different data types ([#41937](https://github.com/PaddlePaddle/Paddle/pull/41937) [#42053](https://github.com/PaddlePaddle/Paddle/pull/42053)) - -- Fix the error report in the new dynamic graph tensor model parallel due to different data types([#41960](https://github.com/PaddlePaddle/Paddle/pull/41960)) - -#### **Custom operator** - -- Enhance the C++ custom operator mechanism for writing second-order gradient operators, to support adding suffixes to the gradient input variables of second-order gradient operators for use as outputs. ([#41781](https://github.com/PaddlePaddle/Paddle/pull/41781)) - -- Remove the use of the deprecated enumeration type `PlaceType` from the Tensor API member methods, make it compatible, and add a deprecation warning. ([#41882](https://github.com/PaddlePaddle/Paddle/pull/41882)) - -- Add deprecated warning for a number of deprecated interfaces of the original Tensor API, including the incomplete constructor, reshape, mutable_data, and copy_to methods. ([#41882](https://github.com/PaddlePaddle/Paddle/pull/41882)) - -#### **Other** - -- Error report and debugging optimization - - - Optimize `the error message of the label` boundary check for the cross_entropy op. ([#40001](https://github.com/PaddlePaddle/Paddle/pull/40001)) - - - Add profile record for `infer_shape` and `compute` methods of op execution of dynamic graphs, show their cost in timeline. ([#39023](https://github.com/PaddlePaddle/Paddle/pull/39023)) - - - Replace `pybind::index_error` error hint on Windows for unknown exceptions. ([#40538](https://github.com/PaddlePaddle/Paddle/pull/40538)) - - - Add the error message in the out-of-bounds checks for user scatter op. ([#37429](https://github.com/PaddlePaddle/Paddle/pull/37429)) - -- Download tool: For the problem of slow decompression of directories with multiple files in `paddle.utils.download.get_path_from_url`, replace the original way (traverse directory in loop) of decompressing files in directories one by one by calling extractall on the directory, which greatly improves the decompression speed. ([#37311](https://github.com/PaddlePaddle/Paddle/pull/37311)) - -- Speed up the quantization training for`fake_quantize_range_abs_max`、`fake_quantize_abs_max`、`fake_quantize_dequantize_abs_max`、 `fake_quantize_moving_average_abs_max`, etc. ([#40491](https://github.com/PaddlePaddle/Paddle/pull/40491)) - - -### **(3) Performance optimization** - -#### **Distributed Training** - -- Hybrid parallel optimizer `sharding_optimizer` supports `optimize_cast` optimization, which move the parameter cast during forward and backwark stage to the optimizer stage. This improves performance by 7%. ([#35878](https://github.com/PaddlePaddle/Paddle/pull/35878)) - -- GPUPS optimization: support for gradient fuse allreduce training. This improves training performance by 20%. ([#35131](https://github.com/PaddlePaddle/Paddle/pull/35131)) - -- GPUPS optimization: dump CPU optimization speed improves by 3.21x. ([#40068](https://github.com/PaddlePaddle/Paddle/pull/40068)) - -- CPU parameter server streaming training optimization: support for automatic statistics of sparse parameter statistics, incremental saving of sparse parameters, etc. The training performance improves by 20%. ([#36465](https://github.com/PaddlePaddle/Paddle/pull/36465), [#36601](https://github.com/PaddlePaddle/Paddle/pull/36601), [#36734](https://github.com/PaddlePaddle/Paddle/pull/36734), [#36909](https://github.com/PaddlePaddle/Paddle/pull/36909), [#36943](https://github.com/PaddlePaddle/Paddle/pull/36943), [#37181](https://github.com/PaddlePaddle/Paddle/pull/37181), [#37194](https://github.com/PaddlePaddle/Paddle/pull/37194), [#37515](https://github.com/PaddlePaddle/Paddle/pull/37515), [#37626](https://github.com/PaddlePaddle/Paddle/pull/37626), [#37995](https://github.com/PaddlePaddle/Paddle/pull/37995), [#38582](https://github.com/PaddlePaddle/Paddle/pull/38582), [#39250](https://github.com/PaddlePaddle/Paddle/pull/39250), [#40762](https://github.com/PaddlePaddle/Paddle/pull/40762), [#41234](https://github.com/PaddlePaddle/Paddle/pull/41234), [#41320](https://github.com/PaddlePaddle/Paddle/pull/41320), [#41400](https://github.com/PaddlePaddle/Paddle/pull/41400)) - -#### **Auto-tuning** - -Add hardware-aware automatic performance tuning for the full training process, with performance improvements of about 3% to 50% or more on image classification, segmentation, detection, and image generation tasks compared to the model's default configuration. The auto-tuning status is set via the `paddle.incubate.autotune.set_config ` API. By default, it is currently disabled. Auto-tuning has three specific levels: - -- Add the auto-tuning function to `paddle.io.DataLoader `, to select the best num_workers based on training data and device resources. ([#42004](https://github.com/PaddlePaddle/Paddle/pull/42004)) - -- Add mixed-precision training data layout auto-tuning feature, to select the best data layout based on device type and data type, and automatically convert it at runtime. ([#41964](https://github.com/PaddlePaddle/Paddle/pull/41964)) - -- Add the automatic tuning of the required workspace size threshold for Conv, which is automatically set based on the GPU's currently available requested device memory resources. Add the automatic selection of Conv cuDNN algorithms based on the generic AlgorithmCache design and Kernel timing component, which supports data variation length models. ([#41833](https://github.com/PaddlePaddle/Paddle/pull/41833)) - -#### **Operator Optimization** - -- Optimize `FasterTokenizer` performance, with a 10% performance improvement compared to pre-optimization. ([#36701](https://github.com/PaddlePaddle/Paddle/pull/36701)) - -- Optimize `index_select` inverse computation, with 3.7~25.2x performance improvement over pre-optimization. ([#37055](https://github.com/PaddlePaddle/Paddle/pull/37055)) - -- Optimize the performance of `paddle.nn.ClipByGlobalNorm`. Take 10*10 `paddle.nn.Linear` as an example. In contrast to pre-optimization, the performance improves by about 30%. ([#38209](https://github.com/PaddlePaddle/Paddle/pull/38209)) - -- Optimize the performance of `pnorm` with very large or very small `axis` dimensions, with 31-96x improvement in forward speed and 1.1-19x improvement in backward speed. ([#37685](https://github.com/PaddlePaddle/Paddle/pull/37685), [#38215](https://github.com/PaddlePaddle/Paddle/pull/38215), [#39011](https://github.com/PaddlePaddle/Paddle/pull/39011)) - -- Optimize `softmax` forward and backward performance, with a speedup ratio of about 2x for the `axis!=-1` configuration. ([#38602](https://github.com/PaddlePaddle/Paddle/pull/38602), [#38609](https://github.com/PaddlePaddle/Paddle/pull/38609), [#32387](https://github.com/PaddlePaddle/Paddle/pull/32387), [#37927](https://github.com/PaddlePaddle/Paddle/pull/37927/files)) - -- Optimize `log_softmax` forward and backward performance, with a speedup ratio of about 6x to 20x for `axis!=-1` configurations. ([#38992](https://github.com/PaddlePaddle/Paddle/pull/38992), [#40612](https://github.com/PaddlePaddle/Paddle/pull/40612)) - -- Optimize `softmax_with_cross_entropy` forward and backward performance, with a speedup ratio of about 1.3x for the `hard_label` configuration. ([#39553](https://github.com/PaddlePaddle/Paddle/pull/39553), [#40424](https://github.com/PaddlePaddle/Paddle/pull/40424), [#40643](https://github.com/PaddlePaddle/Paddle/pull/40643)) - -- Optimize `top_k` performance, with a speedup ratio of more than 22x for one-dimension and larger `k` (k=5000) configuration. ([#40941](https://github.com/PaddlePaddle/Paddle/pull/40941)) - -- Optimize `elementwise_mul` backward computation, with 1.85~12.16x performance improvement over pre-optimization. ([#37728](https://github.com/PaddlePaddle/Paddle/pull/37728)) - -- Optimize `elementwise_min` and `elementwise_max` backward computation, to equalize or improve performance by 1.05x to 18.75x over pre-optimization. ([#38236](https://github.com/PaddlePaddle/Paddle/pull/38236), [#37906](https://github.com/PaddlePaddle/Paddle/pull/37906)) - -- Optimize `nearest_interp` forward and backward computation, with forward performance improvement by 1.5x to 2.3x over pre-optimization, and backward performance improvement by 60% to 1.8x over pre-optimization. ([#38528](https://github.com/PaddlePaddle/Paddle/pull/38528), [#39067](https://github.com/PaddlePaddle/Paddle/pull/39067)) - -- Optimize `bilinear_interp` forward and backward computation, with forward performance improvement by 0.4x to 2.3x over pre-optimization, and backward performance improvement by 10%-30% over pre-optimization. ([#39243](https://github.com/PaddlePaddle/Paddle/pull/39243), [#39423](https://github.com/PaddlePaddle/Paddle/pull/39423)) - -- Optimize `dropout` forward and backward computation, with performance improvement by about 20%. ([#39795](https://github.com/PaddlePaddle/Paddle/pull/39795), [#38859](https://github.com/PaddlePaddle/Paddle/pull/38859), [#38279](https://github.com/PaddlePaddle/Paddle/pull/38279), [#40053](https://github.com/PaddlePaddle/Paddle/pull/40053)) - -- Optimize `grid_sampler` forward and backward computation, with forward performance improvement by 10% to 30% over pre-optimization, and backward performance improvement by 10% to 60% over pre-optimization. ([#39751](https://github.com/PaddlePaddle/Paddle/pull/39751)) - -- Optimize `group_norm` forward and backward computation, with the forward performance improvement by 1.04x to 2.35x, and backward performance improvement by 1.12x to 1.18x. ([#39944](https://github.com/PaddlePaddle/Paddle/pull/39944), [#40657](https://github.com/PaddlePaddle/Paddle/pull/40657), [#39596](https://github.com/PaddlePaddle/Paddle/pull/39596)) - -- Optimize `conv1d` forward and backward computation, with the forward performance improvement by 1.00x to 2.01x, and backward performance improvement by 1.01x to 474.56x. ([#38425](https://github.com/PaddlePaddle/Paddle/pull/38425)) - -- Optimize `elementwise_div` backward computation, with the backward performance improvement by 1.02x to 29.25x. ([#38044](https://github.com/PaddlePaddle/Paddle/pull/38044)) - -- Optimize `gelu` forward and backward computation, with the backward performance improvement by 1.13x to 1.43x, and reverse performance improvement by 1.10x to 1.55x. ([#38188](https://github.com/PaddlePaddle/Paddle/pull/38188), [#38263](https://github.com/PaddlePaddle/Paddle/pull/38263)) - -- Optimize `elementwise_sub` backward computation, with the backward performance improvement by 1.04x to 15.64x. ([#37754](https://github.com/PaddlePaddle/Paddle/pull/37754)) - -- Optimize `flip's` forward performance on one-dimensional data input, with the performance improvement by 100%. ([#37825](https://github.com/PaddlePaddle/Paddle/pull/37825)) - -- Optimize `layer_norm` forward and backward computation, with the forward performance improvement by 2x to 5x over pre-optimization, and backward performance improvement by 20% to 50% over pre-optimization. ([#39167](https://github.com/PaddlePaddle/Paddle/pull/39167), [#39247](https://github.com/PaddlePaddle/Paddle/pull/39247)) - -- Optimize `embedding` forward and backward computation, with a maximum improvement of 1.51x in forward performance and 1.03x to 7.79x in backward performance. ([#39856](https://github.com/PaddlePaddle/Paddle/pull/39856), [#39886](https://github.com/PaddlePaddle/Paddle/pull/398866)) - -- Optimize `gelu` FP16 forward and backward calculations, with forward performance improvement by 9% to 12% over pre-optimization, and backward performance improvement by 2% to 9% over pre-optimization. ([#38980](https://github.com/PaddlePaddle/Paddle/pull/38980)) - -- Remove CPU -> GPU explicit data transfer operation in `gather_nd` forward and backward operators, and remove the explicit synchronous operation in `index_select` forward and backward operators. Change GPU -> GPU data transfer in `scatter_nd` from synchronous operation to asynchronous operation. ([#40933](https://github.com/PaddlePaddle/Paddle/pull/40933)) - -- Optimize `Lars optimzier` computation, with the training performance improvement of Resnet50 PF16 model by 5.1% over pre-optimization. ([#35652](https://github.com/PaddlePaddle/Paddle/pull/35652), [#35476](https://github.com/PaddlePaddle/Paddle/pull/35476)) - -- Optimize `AvgPool2dGrad` computation, with the performance improvement by 2.6x over pre-optimization. ([#35389](https://github.com/PaddlePaddle/Paddle/pull/35389)) - -- Optimize `Elementwise` computation for multivariate output, improving performance by up to 15% over pre-optimization. ([#38329](https://github.com/PaddlePaddle/Paddle/pull/38329), [#38410](https://github.com/PaddlePaddle/Paddle/pull/38410)) - -- Optimize `Categorical`the probs computation, simplify the computation logic, and improve the performance by 4x to 5x. ([#42178](https://github.com/PaddlePaddle/Paddle/pull/42178)) - -- Optimize the `paddle.sum ` performance, with performance improvement by about 20%. ([#42309](https://github.com/PaddlePaddle/Paddle/pull/42309)) - -- Remove CudaStreamSync operation from `paddle.nn.ClipGradByGlobalNorm ` to reduce scheduling overhead during execution, with 5% performance improvement on ptb models. ([#42170](https://github.com/PaddlePaddle/Paddle/pull/42170)) - -- Optimize a series of underlying data structures and detailed implementations in the original dynamic graph execution system to improve the scheduling performance of the original dynamic graph. ([#42010](https://github.com/PaddlePaddle/Paddle/pull/42010), [#42171](https://github.com/PaddlePaddle/Paddle/pull/42171), [#42224](https://github.com/PaddlePaddle/Paddle/pull/42224), [#42256](https://github.com/PaddlePaddle/Paddle/pull/42256), [#42306](https://github.com/PaddlePaddle/Paddle/pull/42306), [#42329](https://github.com/PaddlePaddle/Paddle/pull/42329)[, #42340](https://github.com/PaddlePaddle/Paddle/pull/42340), [#42368](https://github.com/PaddlePaddle/Paddle/pull/42368), [#42425](https://github.com/PaddlePaddle/Paddle/pull/42425)) - -- Simplify the probs calculation logics of `paddle.distribution.Categorical `, to improve performance by 4x to 5x. ([#42178](https://github.com/PaddlePaddle/Paddle/pull/42178)) - -### **(4) Bug fixing** - -#### API - -- Fix the output type error with `paddle.sum` when the input parameter type and output parameter type do not match and the number of reduce elements on the `axis` is 1. ([#36123](https://github.com/PaddlePaddle/Paddle/pull/36123)) - -- Fix an `AttributeError` in `paddle.flops` when the layer output type is tuple. ([#38850](https://github.com/PaddlePaddle/Paddle/pull/38850)) - -- Fix the `paddle.diag` failing to propagate gradients because there is no backward kernel. ([#40447](https://github.com/PaddlePaddle/Paddle/pull/40447)) - -- Fix an error in sorting `paddle.sort` input with NaN values. ([#41070](https://github.com/PaddlePaddle/Paddle/pull/41070)) - -- Fix the error when`paddle.full_like`'s input contains INF value. ([#40232](https://github.com/PaddlePaddle/Paddle/pull/40232)) - -- Fix the bug in `paddle.strided_slice`: strided_slice result does not consistent with slice when the data in the input of starts is less than -rank. ([#39066](https://github.com/PaddlePaddle/Paddle/pull/39066)) - -- Fix the bug in the `max_pool` family of operators where infer_shape is calculated incorrectly when index is returned. This affects the APIs: `paddle.nn.functional.max_pool1d/2d/3d`, `paddle.nn.functional.adaptive_max_pool1d/2d/3d`, `paddle.nn.MaxPool1D/2D/3D`, `paddle.nn.AdaptiveMaxPool1D/2D/3D`. ([#40139](https://github.com/PaddlePaddle/Paddle/pull/40139)) - -- Fix an issue where the dtype of pooling_mask returned by the `max_pool` family of operators is incorrect. Now the dtype of pooling_mask is int32. The affected APIs are `paddle.nn.functional.max_pool1d/2d/3d`, `paddle.nn.functional.adaptive_max_pool1d/2d/3d`, `paddle.nn.MaxPool1D/2D/3D`, `paddle.nn.AdaptiveMaxPool1D/2D/3D`. ([#39314](https://github.com/PaddlePaddle/Paddle/pull/39314) ) - -- Fix the bug with `paddle.shape` where the backward gradient by default causes a computation error. ([#37340](https://github.com/PaddlePaddle/Paddle/pull/37340)) - -- Fix the bug in `paddle.nn.Layer's` `to` method when converting both dtype and place at the same time. ([#37007](https://github.com/PaddlePaddle/Paddle/pull/38007)) - -- Fix the bug that `paddle.amp.decorate` fails to rewrite the parameters of non-leaf network layers to FP16. ([#38402](https://github.com/PaddlePaddle/Paddle/pull/38402)) - -- Fix the bug that the `paddle.amp.decorate` rewrites the non-input parameter in `paddle.nn.BatchNorm1D`, `paddle.nn.BatchNorm2D`, and `paddle.nn.BatchNorm3D` to FP16. ([#38541](https://github.com/PaddlePaddle/Paddle/pull/38541)) - -- Fix the bug that the `paddle.amp.decorate` rewrites the non-input parameter in `paddle.nn.SyncBatchNorm` to FP16. ([#40943](https://github.com/PaddlePaddle/Paddle/pull/40943)) - -- Fix redundant warnings in `paddle.nn.Layer.to`. ([#36700](https://github.com/PaddlePaddle/Paddle/pull/36700)) - -- Fix the bug in `paddle.nn.RNN` when being used inside control flow. ([#41162](https://github.com/PaddlePaddle/Paddle/pull/41162)) - -- Fix the bug that the `paddle.to_tensor` fails to specify the CUDAPlace of the Tensor. ([#39662](https://github.com/PaddlePaddle/Paddle/pull/39662)) - -- Fix the issue that`paddle.nn.Identity` is not exposed. ([#39615](https://github.com/PaddlePaddle/Paddle/pull/39615)) - -- Fix the bug where the output values of the `fill_` and `zero_` inplace APIs are incorrect when the input is on a CUDAPinned Place after dynamic graph reconstruction. ([#41229](https://github.com/PaddlePaddle/Paddle/pull/41229)) - -- After refactoring the dynamic graph, fix the bug of incorrect inplace version value of the output Tensor when calling assign op using the append op. Change it to call assign op using the `_C_ops`. ([#41118](https://github.com/PaddlePaddle/Paddle/pull/41118)) - -- Remove unreasonable codes in the `elementwise_add` 's third-order kernel, and fix an uninitialized issue in the network creation process. ([#36618](https://github.com/PaddlePaddle/Paddle/pull/36618)) - -- Fix the missing attribute bug in `conv2d` execution of cuDNN Kernel. ([#38827](https://github.com/PaddlePaddle/Paddle/pull/38827)) - -- Fix an issue where `multiclass_nms3` output shape is incorrect. ([#40059](https://github.com/PaddlePaddle/Paddle/pull/40059)) - -- Fix an issue with `yolo_box` outputting incorrect shape. ([#40056](https://github.com/PaddlePaddle/Paddle/pull/40056)) - -- Fix an issue where the higher-order differentiation `gradients` interface does not take effect as expected when target_grad is specified. ([#40940](https://github.com/PaddlePaddle/Paddle/pull/40940/)) - -- Fix an issue that the network parameter type is incorrect when the default_dtype is modified in the op`_BatchNormBase` base class in the dynamic graph mode. The affected APIs are `paddle.nn.BatchNorm1D`，`paddle.nn.BatchNorm2D`，`paddle.nn.BatchNorm3D`， and `paddle.nn.SyncBatchNorm`. Specific reason: when `get_default_dtype() == 'float16'`, the default parameter data type is modified by `set_default_dtype('float32')`. The parameter type in dynamic graph mode is created by default_dtype; therefore, the change of the default parameter type causes the subsequent networking Parameter type error. ([#36376](https://github.com/PaddlePaddle/Paddle/pull/36376)) - -- Fix the bug of the undefined intermediate variable in the backward op in batchnorm op in case that the data type is FP32 and the data dimension is `dims = 2 and data_layout = NHWC`. ([#37020](https://github.com/PaddlePaddle/Paddle/pull/37020)) - -- Fix the bug that shape of weights is incorrect, when using`paddle.static.nn.prelu` in static graph mode, and input format is`NHWC`, `mode==channel`. ([#38310](https://github.com/PaddlePaddle/Paddle/pull/38310)) - -- Fix the bug of `paddle.nn.functional.class_center_sample`: CUDA seed setting issue in multi-machine case. ([#38815](https://github.com/PaddlePaddle/Paddle/pull/38815)) - -- Fix the bug of failing to report error when the input of`paddle.nn.functional.one_hot`is incorrect. ([#41335](https://github.com/PaddlePaddle/Paddle/pull/41335)) - -- Fix an issue where a callback to reclaim device memory on a DCU device is not triggered in time, resulting in an OOM of the device memory. ([#40445](https://github.com/PaddlePaddle/Paddle/pull/40445)) - -- Fix the bugs of `setitem` backward gradient abnormal and inplace logic handling abnormal in some dynamic graph scenarios. ([#37023](https://github.com/PaddlePaddle/Paddle/pull/37023), [#38298](https://github.com/PaddlePaddle/Paddle/pull/38298)) - -- Fix the bug of index abnormal when Tensor array uses the Slice to index in the dynamic to static scenarios. ([#39251](https://github.com/PaddlePaddle/Paddle/pull/39251)) - -- Fix the bug of memory or device memory leaks caused by some temporary variables not being correctly destructed when `paddle.Tensor.register_hook` interface is used. ([#40716](https://github.com/PaddlePaddle/Paddle/pull/40716)) - -- Fix the bug that `Tensor.getitem` cannot get the value when the index is a bool Tensor with all False. ([#41297](https://github.com/PaddlePaddle/Paddle/pull/41297)) - -- Fix the bug that `Tensor.getitem` cannot get the value when the index is a bool scalar Tensor. ([#40829](https://github.com/PaddlePaddle/Paddle/pull/40829)) - -- Fix the bug in `paddle.index_select` when index is a 0-shape Tensor. ([#41383](https://github.com/PaddlePaddle/Paddle/pull/41383)) - -- Fix the bug when the number of GPU threads requested by `paddle.index_select` and `paddle.index_sample` exceeds the limited machine resources. ([#41127](https://github.com/PaddlePaddle/Paddle/pull/41127), [#37816](https://github.com/PaddlePaddle/Paddle/pull/37816), [#39736](https://github.com/PaddlePaddle/Paddle/pull/39736), [#41563](https://github.com/PaddlePaddle/Paddle/pull/41563)) - -- Fix the bug when ReduceConfig, elemwise_grad, gather, gather_nd, and scatter ops request more GPU threads than the limited machine resources. ([#40813](https://github.com/PaddlePaddle/Paddle/pull/40813), [#41127](https://github.com/PaddlePaddle/Paddle/pull/41127)) - -- Fix the bug that the memory access is out of boundary when NX ! = 1 in ReadData, ReadDataBc, and ReadDataReduce in Kernel Primitive API. ([#36373](https://github.com/PaddlePaddle/Paddle/pull/36373)) - -- Fix the bug of the computation result abnormal due to data overflow caused by the IndexRandom data type error. ([#39867](https://github.com/PaddlePaddle/Paddle/pull/39867), [#39891](https://github.com/PaddlePaddle/Paddle/pull/39891)) - -- Fix the bug of the returned computing result error of reduce op when reduce_num = 1. ([#38771](https://github.com/PaddlePaddle/Paddle/pull/38771)) - -- Fix the bug of the memory access out-of-bound of reduce op in the middle dimension of reduce in HIP environments. ([#41273](https://github.com/PaddlePaddle/Paddle/pull/41273)) - -- Fix the bug of Kernel failed to properly release in the computation of two FP16 one-dimensional vectors of matmul op. - -- Fix the bug caused by CUDA integer computation overflow for some operators, including: bernoulli, gaussian_random, gumbel_softmax, multinomial, truncated_gaussian_random, uniform_ random_inplace, and uniform_random ops. ([#37670](https://github.com/PaddlePaddle/Paddle/pull/37670)) - -- Fix the bug where `paddle.nn.Sequential` reports a KeyError error when traversing sublayers in a for loop. ([#39372](https://github.com/PaddlePaddle/Paddle/pull/39372)) - -- Fix the bug of the check shape error in `paddle.nn.functional.unfold` when compiling in static graphs. ([#38907](https://github.com/PaddlePaddle/Paddle/pull/38907), [#38819](https://github.com/PaddlePaddle/Paddle/pull/38819)) - -- Fix the bug of reporting an error if `axis` is specified when using dropout for static graphs. ([#37223](https://github.com/PaddlePaddle/Paddle/pull/37223)) - -- Migrate the matmul operator in the `paddle.nn.MultiHeadAttention` to the matmul_v2 operator. ([#36222](https://github.com/PaddlePaddle/Paddle/pull/36222)) - -- Fix the bug occurred in throwing FPE when the empty Tensor is used in `paddle.nn.functional.label_smooth`. ([#35861](https://github.com/PaddlePaddle/Paddle/pull/35861)) - -- Fix the deformation bug of reshape op when input is an empty Tensor. Support the empty Tensor rehape to [-1]. ([#36087](https://github.com/PaddlePaddle/Paddle/pull/36087)) - -- Fix the bug of the modified values will incorrectly override other rows when the `fill_diagonal` 's input parameter offset is non-zero. ([#36212](https://github.com/PaddlePaddle/Paddle/pull/36212)) - -- Modify stop_gradient returned by the range op bing set to True in dynamic graph mode. ([#37486](https://github.com/PaddlePaddle/Paddle/pull/37486)) - -- Fix the bug where Lamb optimizer is updated incorrectly when Beta1Pow and Beta2Pow are on the GPU. ([#38518](https://github.com/PaddlePaddle/Paddle/pull/38518)) - -- Fix the bug where the conv2d operator doesn't respect to FLAGS_cudnn_deterministic. ([#37173](https://github.com/PaddlePaddle/Paddle/pull/37173)) - -- Fix the bug caused by an earlier version of cufft that does not define CUFFT_VERSION. ([#37312](https://github.com/PaddlePaddle/Paddle/pull/37312)) - -- Fix the computing error of `paddle.ifftshit` and `paddle.fftshift`. ([#36834](https://github.com/PaddlePaddle/Paddle/pull/36834), [#36748](https://github.com/PaddlePaddle/Paddle/pull/36748)) - -- Fix the `axis` computation error in `paddle.fft` series of APIs. ([#36321](https://github.com/PaddlePaddle/Paddle/pull/36321)) - -- Fix an output data type registration bug of batch_norm_grad op in case of FP16 data type. This bug causes the compilation failure in some scenarios. There is also the impact on FP16 computational precision. ([#42461](https://github.com/PaddlePaddle/Paddle/pull/42461)) - -- Fix the incorrect Infershape information bug in the `paddle.nn.functional.pad ` API when the padding is Tensor in dynamic to static conversion. ([#42414](https://github.com/PaddlePaddle/Paddle/pull/42414)) - -- Fix an exception in `paddle.distribution.StickBreakingTransform ` when the input dimension exceeds 2. ([#41762](https://github.com/PaddlePaddle/Paddle/pull/41672)) - -- Fix a nan/inf bug calculated with QK^T in fused_attention op. ([#42032](https://github.com/PaddlePaddle/Paddle/pull/42032)) - -- Fix a nan/inf bug calculated in fused_attention op with FusedResidualDropoutBias on V100. ([#42398](https://github.com/PaddlePaddle/Paddle/pull/42398)) - -- Fix a redundant data transform bug introduced by the full_like op during execution. ([#41973](https://github.com/PaddlePaddle/Paddle/pull/41973)) - -- Fix a problem with p_norm op calculating nan on GPU environments. ([#41804](https://github.com/PaddlePaddle/Paddle/pull/41804)) - -- Fix a section error of split op when the sections parameter has a size of 0. ([#41755](https://github.com/PaddlePaddle/Paddle/pull/41755)) - -- Fix the bug of reporting not supporting Place (gpu:0) in multi-card training when broadcast is required in 6 elementwise ops (pow, complex, divide_double, multiply_double, fmax, and fmin). ([#42332](https://github.com/PaddlePaddle/Paddle/pull/42332)) - -- Fix the bug that the deprecated interface reports a warning in case of `import paddle` due to a PIL version update. ([#42307](https://github.com/PaddlePaddle/Paddle/pull/42307)) - -- Fix the bug that `paddle.linalg.matrix_rank ` does not support tol as FP64 Tensor under static graph. ([#42085](https://github.com/PaddlePaddle/Paddle/pull/42085)) - -#### IR(Intermediate Representation) - -- Dynamic to static graphs - - - Fix a type derivation error in reverse gradient accumulation when the `tensor_array` is used with the control flow. ([#39585](https://github.com/PaddlePaddle/Paddle/pull/39585), [#39689](https://github.com/PaddlePaddle/Paddle/pull/39689)) - - - Fix an issue where the parameter gradient type is not set correctly during dynamic to static AMP training. ([#40938](https://github.com/PaddlePaddle/Paddle/pull/40938)) - - - Fix an issue of reporting an error in the dynamic to static transcription when there are misplaced annotations in the codes. ([#39035](https://github.com/PaddlePaddle/Paddle/pull/39035), [#38003](https://github.com/PaddlePaddle/Paddle/pull/38003)) - - - Fix an issue where Tensor is not properly converted to Variable when calling a non-forward function in dynamic to static codes. ([#37296](https://github.com/PaddlePaddle/Paddle/pull/37296), [#38540](https://github.com/PaddlePaddle/Paddle/pull/38540)) - - - Fix an issue where `paddle` is incorrectly passed as a variable when dynamic to static transcription. ([#37999](https://github.com/PaddlePaddle/Paddle/pull/37999)) - - - Fix an issue where model parameters are incorrectly counted when calling `paddle.flops` after model dynamic to static conversion. ([#36852](https://github.com/PaddlePaddle/Paddle/pull/36852)) - - - Fix an issue where GPU memory will keep growing in train mode and no_grad contexts after loading models using the `paddle.jit.save/load` interface. ([#36434](https://github.com/PaddlePaddle/Paddle/pull/36434)) - - - Add warning in function of convert_call when converting the generator function. ([#35369](https://github.com/PaddlePaddle/Paddle/pull/35369)) - - - Fix the run_program op dependency analysis bug. ([#38470](https://github.com/PaddlePaddle/Paddle/pull/38470)) - - - Fix the code conversion bug when returning a single value in control flow For. ([#40683](https://github.com/PaddlePaddle/Paddle/pull/40683)) - - - Fix the bug when generating a reverse op when the input to conditional_block op contains LoDTensorArray. ([#39585](https://github.com/PaddlePaddle/Paddle/pull/39585)) - - - Fix the bug that `padddle.jit.save ` loses the forward_pre_hook and forward_post_hook of the top Layer in case of the export of a dynamic-to-static graph mode. ([#42273](https://github.com/PaddlePaddle/Paddle/pull/42273)) - - - Fix the dynamic to static conversion error report where the shape parameter in `paddle.expand ` contains a Tensor. ([#41973](https://github.com/PaddlePaddle/Paddle/pull/41973)) - - -#### **Distributed Training** - -- Distributed training basic functions - - - Fix the bug of a port reporting error in the distributed multi-machine training. ([#37274](https://github.com/PaddlePaddle/Paddle/pull/37274)) - - - Fix the brpc compilation dependency bug. ([#37064](https://github.com/PaddlePaddle/Paddle/pull/37064)) - - - Fix an occupied port issue due to tcp self-connections when Fleet starts. ([#38174](https://github.com/PaddlePaddle/Paddle/pull/38174)) - - - Fix the precision degradation bug under data parallel due to inconsistent initialization of FP16 parameters under multiple cards. ([#38838](https://github.com/PaddlePaddle/Paddle/pull/38838), [#38563](https://github.com/PaddlePaddle/Paddle/pull/38563), [#38405](https://github.com/PaddlePaddle/Paddle/pull/38405)) - - - Fix the precision degradation under data parallel due to FP16 gradient synchronization without dividing by the number of cards. ([#38378](https://github.com/PaddlePaddle/Paddle/pull/38378)) - -- Dynamic graph mixing parallel - - - Fix the bug where parameters are not updated in FP16 mode under mixed parallel by using the new update interface. ([#36017](https://github.com/PaddlePaddle/Paddle/pull/36017)) -- Static graph mixing parallel - - - Fix an issue where grad merge is not compatible with ClipGradientByGlobalNorm in distributed dp mode. ([#36334](https://github.com/PaddlePaddle/Paddle/pull/36334)) - - - Fix an issue under hybrid parallelism where the non-distributed parameters of tensor model parallelism are not broadcast during the initialization phase, resulting in inconsistent non-distributed parameters across cards. ([#36186](https://github.com/PaddlePaddle/Paddle/pull/36186)) - - - Fix the issue that sharding's save_persistables interface does not save FP16 parameters and offload persistent variables when sharding is enabled with offload. ([#40477](https://github.com/PaddlePaddle/Paddle/pull/40477)) - - - Fix the bug where ema parameters are not saved on non-0 cards when sharding is enabled for training. ([#39860](https://github.com/PaddlePaddle/Paddle/pull/39860)) - - - Fix an issue where FC incorrectly calculates gradients according to column cuts. ([#38724](https://github.com/PaddlePaddle/Paddle/pull/38724)) - - - Fix the bug reported when DistributedStrategy is set to without_graph_optimizer when used with rnn. ([#36176](https://github.com/PaddlePaddle/Paddle/pull/36176)) - -- GPUPS Parameter Server Training - - - Fix the CPU branch compilation bug triggered by the GPUPS macro definition. ([#37248](https://github.com/PaddlePaddle/Paddle/pull/37248)) - - - Fix an occasional error raised when saving delta and pullsparse concurrency during GPUPS streamline training. ([#37233](https://github.com/PaddlePaddle/Paddle/pull/37233)) - - - Fix a download error issue caused by HDFSClient querying a directory without returning the full path. ([#36590](https://github.com/PaddlePaddle/Paddle/pull/36590)) - - - Fix the bug with pulling old parameters in GPUPS streamline training. ([#36512](https://github.com/PaddlePaddle/Paddle/pull/36512)) - - - Fix a GPUPS multi-stream allocation issue. ([#37476](https://github.com/PaddlePaddle/Paddle/pull/37476)) - - - Fix the bug of the GPUPS pybind out of core. ([#37287](https://github.com/PaddlePaddle/Paddle/pull/37287)) - - -#### **Other** - -- Fix the clip_extra issue when saving models for dynamic graph quantization training. ([#38323](https://github.com/PaddlePaddle/Paddle/pull/38323)) - -- Fix an issue with abs_max scale initialization for dynamic graph quantization training. ([#39307](https://github.com/PaddlePaddle/Paddle/pull/39307)) - -- Fix an issue of exceptions in saving model in dynamic graph quantization training. ([#38102](https://github.com/PaddlePaddle/Paddle/pull/38102), [#38012](https://github.com/PaddlePaddle/Paddle/pull/38012)) - -- Fix the offline quantization flatten op output error. ([#37722](https://github.com/PaddlePaddle/Paddle/pull/37722)) - -- Fix the non-matching dimension bug in case of inverse quantization matmul op. ([#36982](https://github.com/PaddlePaddle/Paddle/pull/36982)) - -- Fix the bug of adding quantization op when quantizing matmul_v2 without weights. ([#36593](https://github.com/PaddlePaddle/Paddle/pull/36593)) - -- Fix the error of saving the quant_axis attribute in the conv op channel-wise quantization when saving the models. ([#39054](https://github.com/PaddlePaddle/Paddle/pull/39054)) - -- Fix the slow training of channel-wise quantization. ([#40772](https://github.com/PaddlePaddle/Paddle/pull/40772)) - -- Fix the bug of quantization training when dividing by tensor(initialized as 0) leads to nan. ([#36762](https://github.com/PaddlePaddle/Paddle/pull/36762)) - -- Fix incorrect settings of amp_level for mixed precision in multi-threaded scenarios. ([#39198](https://github.com/PaddlePaddle/Paddle/pull/39198)) - -- Fix an issue where PyLayer and Recompute is not set mixed precision correctly when mixed precision training is used with PyLayer and Recompute. ([#39950](https://github.com/PaddlePaddle/Paddle/pull/39950), [#40042](https://github.com/PaddlePaddle/Paddle/pull/40042)) - -- Fix an issue where `D_GLIBCXX_USE_CXX11_ABI` does not take effect when compiling custom operators under Mac. ([#37878](https://github.com/PaddlePaddle/Paddle/pull/37878)) - -- Fix the bug of inconsistent dynamic and static behaviors in case of block=None the initializer-related API. ([#37827](https://github.com/PaddlePaddle/Paddle/pull/37827)) - -- Fix the bug in python 3.6 where there is no fluid module. ([#35862](https://github.com/PaddlePaddle/Paddle/pull/35862)) - -- Fix the bug where optimizer `paddle.optimizer.Adamw` incorrectly calls adam op. ([#36028](https://github.com/PaddlePaddle/Paddle/pull/36028)) - -- Fix a logic error when the `paddle.optimizer.Momentum` optimizer parameter `regularizer` property is None under the multi tensor policy. ([#38344](https://github.com/PaddlePaddle/Paddle/pull/38344)) - -- Fix the bug that the `paddle.optimizer.Momentum` and `paddle.optimizer.Adam` optimizers modify the `multi_precision` property under the multi tensor policy. ([#38991](https://github.com/PaddlePaddle/Paddle/pull/38991)) - -- Fix the code compilation error when using final-state API amp in combination with optional Tensor. ([#40980](https://github.com/PaddlePaddle/Paddle/pull/40980)) - -- Fix the bug where paddle+lite+xpu prediction library would report an error when calling lite CPU prediction, and fix the bug where paddle+lite(without NNAdapter) would report an error when compiling. ([#37449](https://github.com/PaddlePaddle/Paddle/pull/37449)) - -- Fix the bug in Debug compile mode where LoDTensorArray crashes due to inconsistent Pybind11 bindings. ([#37954](https://github.com/PaddlePaddle/Paddle/pull/37954)) - -- Fix the bug that prevents correct construction of Tensor in the extreme case where the shape parameter is a list of Tensor mix with int. ([#38284](https://github.com/PaddlePaddle/Paddle/pull/38284)) - -- Fix a compatibility issue with the `paddle.optimizer.AdamW` API. ([#37905](https://github.com/PaddlePaddle/Paddle/pull/37905)) - -- Fix the bug in _InstanceNormBase where the returne value of extra_repr is incorrect. ([#38537](https://github.com/PaddlePaddle/Paddle/pull/38537)) - -- Fix the bug that the Paddle Inference lacks of the symbol `paddle::distributed::TensorTable` when the -DWITH_DISTRIBUTED is uesd. ([#41128](https://github.com/PaddlePaddle/Paddle/pull/41128)) - -- matmul_v2 op reports error when there is a 0 value in the shape. ([#35791](https://github.com/PaddlePaddle/Paddle/pull/35791)) - -- Fix the problem of the repeated printing for no gradient input hint message of the recomputed in dynamic graphs. Change it to the printing only once with using warning. ([#38293](https://github.com/PaddlePaddle/Paddle/pull/38293)) - -- Fix the low accuracy bug on the validation set in later epoch training in visual models in the gelu op. ([#38450](https://github.com/PaddlePaddle/Paddle/pull/38450)) - -- Fix adamw op error in numerical computation. ([#37746](https://github.com/PaddlePaddle/Paddle/pull/37746)) - -- Add the parameters in the sparse_momentum `_C_ops` interface. ([#39969](https://github.com/PaddlePaddle/Paddle/pull/39969)) - -- Fix the bug where there is no `distributed` module in python 3.6. ([#35848](https://github.com/PaddlePaddle/Paddle/pull/35848)) - -- Fix the eigh unit test data initialization problem. ([#39568](https://github.com/PaddlePaddle/Paddle/pull/39568)) - -- Fix the eigvalsh unit test data initialization problem. ([#39841](https://github.com/PaddlePaddle/Paddle/pull/39841)) - -- Fix the bug of not working properly due to excessive register usage on V100 by segment op. ([#38113](https://github.com/PaddlePaddle/Paddle/pull/38113)) - -- Fix the bug with conv-related op sparsification incorrectly set dimension. ([#36054](https://github.com/PaddlePaddle/Paddle/pull/36054)) - -- Provide Automatic SParsity training for static graph-related function Alias to `Paddle.static.sparsity`. ([#36525](https://github.com/PaddlePaddle/Paddle/pull/36525)) - -- Fix the bug where divide op’s integer division is still an integer. ([#40890](https://github.com/PaddlePaddle/Paddle/pull/40890)) - -- Fix the crash bug of`paddle.multiplex` when input Tensor value is 0. ([#34972](https://github.com/PaddlePaddle/Paddle/pull/34972)) - -- Fix a speed exception for set `reduction` parameter in `paddlpaddle.nn.functional.kl_div`. ([#37283](https://github.com/PaddlePaddle/Paddle/pull/37283)) - -- Fix the data source unsorted bug in loading the Cifar dataset. ([#37272](https://github.com/PaddlePaddle/Paddle/pull/37272)) - -- Fix the conversion of loss from uint16 to float in the ProgressBar class. ([#39231](https://github.com/PaddlePaddle/Paddle/pull/39231)) - -- Fix the ShareBufferWith shared data type problem. ([#37464](https://github.com/PaddlePaddle/Paddle/pull/37464), [#37247](https://github.com/PaddlePaddle/Paddle/pull/37247)) - -- Fix the performance issue when `paddle.io.DataLoader` uses IterableDataset and num_workers>0. ([#40541](https://github.com/PaddlePaddle/Paddle/pull/40541)) - -- Fix the bug with `paddle.vision.ops.yolo_loss` returns incomplete values in dynamic graph. ([#40185](https://github.com/PaddlePaddle/Paddle/pull/40185)) - -- Remove the restriction that the input parameter dataset of `paddle.io.BatchSampler` needs to be the `paddle.io.Dataset` type, to expand the support for user-defined datasets. ([#40184](https://github.com/PaddlePaddle/Paddle/pull/40184)) - -- Fix the bug of `paddle.summary` reporting that op_flops does not exist. ([#36489](https://github.com/PaddlePaddle/Paddle/pull/36489)) - -- Fix the formula error of lars_momentum op when lars_weight_decay=0. ([#40892](https://github.com/PaddlePaddle/Paddle/pull/40892)) - -- Fix the bug that the optimize-offload cannot save presistable var. ([#36433](https://github.com/PaddlePaddle/Paddle/pull/36433)) - -- Fix an issue where optimizer-offload does not support adamw op type. ([#36432](https://github.com/PaddlePaddle/Paddle/pull/36432)) - -- Fix an issue where enable_program_desc_tracing_data in Tracer is not safe in multi-threaded scenarios. ([#39776](https://github.com/PaddlePaddle/Paddle/pull/39776)) - -- Fix an issue where the model file size is not initialized when the model is read. ([#40518](https://github.com/PaddlePaddle/Paddle/pull/40518)) - -- Fix the logic bug of the Expand op. When the dimension of the input Tensor X is smaller than the shape to be expanded, it may result in the incorrect Out.Shape. ([#38677](https://github.com/PaddlePaddle/Paddle/pull/38677)) - -- Fix the dynamic to static transcription error when the Expand_As op takes only y.shape without Y variable entered. ([#38677](https://github.com/PaddlePaddle/Paddle/pull/38677)) - -- Fix the logic error when Expand_As op computes the output shape. ([#38677](https://github.com/PaddlePaddle/Paddle/pull/38677)) - -- Fix the bug that the variables of the `core.VarDesc.VarType.STRINGS` type report error when getting the `lod_level` property and setting its `lod_level` to None. ([#39077](https://github.com/PaddlePaddle/Paddle/pull/39077)) - -- Fix an issue where the framework function `Pylayer` does not support different dtypes. ([#37974](https://github.com/PaddlePaddle/Paddle/pull/37974)) - -- Fix the bug of division by zero of the learning rate decay API `paddle.optimizer.lr.PolynomialDecay`. ([#38782](https://github.com/PaddlePaddle/Paddle/pull/38782)) - -- Fix the issue where some logs remained after calling the DisableGlogInfo() interface. ([#36356](https://github.com/PaddlePaddle/Paddle/pull/36356)) - -- Fix an error in backward of multi-layer RNN (when dropout is set to 0) in the training of SimpleRNN, GRU and LSTM API CPU. ([#37080](https://github.com/PaddlePaddle/Paddle/pull/37080)) - -- Add cache for fft on the backend of cufft and hipfft. ([#36646](https://github.com/PaddlePaddle/Paddle/pull/36646)) - -- Enable the shifts parameter of `paddle.roll` to support transfer in Tensor. ([#36727](https://github.com/PaddlePaddle/Paddle/pull/36727)) - -- Add onemkl to fft as an optional computation backend. ([#36414](https://github.com/PaddlePaddle/Paddle/pull/36414)) - -- Fix the precision bug in the bfloat16 type under two mamtul_v2 and elementwise_div ops. ([#42479](https://github.com/PaddlePaddle/Paddle/pull/42479)) - -- Fix a possible error in the next step caused by LoDTensorArray clearing only the internal Tensor and not clearing the Array during device memory recycling. ([#42398](https://github.com/PaddlePaddle/Paddle/pull/42398)) - - -## **4. Deployment Direction (Paddle Inference)** - -### **(1) New features** - -#### **New APIs** - -- Add the Java API so that Java developers can implement high performance inference on the server and in the cloud through a simple and flexible interface. ([#37162](https://github.com/PaddlePaddle/Paddle/pull/37162)) - -- Add `GetTrtCompileVersion` and `GetTrtRuntimeVersion` interfaces for getting TensorRT version information. ([#36429](https://github.com/PaddlePaddle/Paddle/pull/36429)) - -- Add the `ShareExternalData` interface to avoid memory copy of input data during inference. ([#39809](https://github.com/PaddlePaddle/Paddle/pull/39809)) - - -#### **New functions** - -- Add ONNX Runtime backend support. Currently it supports only CPU in the integrated version. ([#39988](https://github.com/PaddlePaddle/Paddle/pull/39988), [#40561](https://github.com/PaddlePaddle/Paddle/pull/40561)) - -- Add support for Ascend 310 inference based on the Paddle Lite subgraph approach. ([#35226](https://github.com/PaddlePaddle/Paddle/pull/35226)) - -- Add the native GPU FP16 inference. ([#40531](https://github.com/PaddlePaddle/Paddle/pull/40531)) - -- For the switch_ir_debug interface, add the dump model function. ([#36581](https://github.com/PaddlePaddle/Paddle/pull/36581)) - -- Add the configuration interface for TensorRT config: `void UpdateConfigInterleaved(paddle_infer::Config* c, bool with_interleaved)` for special data layout in int8 quantization inference. ([#38884](https://github.com/PaddlePaddle/Paddle/pull/38884)) - -- Add TensorRT inspector output information to the log. It is valid only for TensorRT 8.2 or later. ([#38362](https://github.com/PaddlePaddle/Paddle/pull/38362)，[#38200](https://github.com/PaddlePaddle/Paddle/pull/38200))) - -- Add the support of the TensorRT ASP sparse inference. ([#36413](https://github.com/PaddlePaddle/Paddle/pull/36413)) - - -### **(2) Underlying optimization** - -#### **CPU performance optimization** - -- Optimize the caching mechanism of MKLDNN. ([#38336](https://github.com/PaddlePaddle/Paddle/pull/38336), [#36980](https://github.com/PaddlePaddle/Paddle/pull/36980), [#36695](https://github.com/PaddlePaddle/Paddle/pull/36695)) - -- Add matmul_scale_fuse pass. ([#37962](https://github.com/PaddlePaddle/Paddle/pull/37962)) - -- Add MKLDNN reshape_transpose_matmul_v2_mkldnn_fuse_pass. ([#37847](https://github.com/PaddlePaddle/Paddle/pull/37847), [#40948](https://github.com/PaddlePaddle/Paddle/pull/40948)) - -- Add MKLDNN conv_hard_sigmoid_mkldnn_fuse_pass. ([#36869](https://github.com/PaddlePaddle/Paddle/pull/36869)) - -- Add MKLDNN matmul_v2_transpose_reshape_fuse_pass. ([#36481](https://github.com/PaddlePaddle/Paddle/pull/36481)) - -- Add MKLDNN softplus_activation_mkldnn_fuse_pass. ([#36657](https://github.com/PaddlePaddle/Paddle/pull/36657)) - -- Add MKLDNN elt_act_mkldnn_fuse_pass. ([#36541](https://github.com/PaddlePaddle/Paddle/pull/36541)) - -- Add MKLDNN mish operator and conv_mish_mkldnn_fuse_pass. ([#38623](https://github.com/PaddlePaddle/Paddle/pull/38623)) - - -#### **GPU performance optimization** - -- Change the inference default video memory allocation policy from `naive_best_fit` to `auto_growth`, to solve the problem of some models filled up with the GPU video memory. ([#41491](https://github.com/PaddlePaddle/Paddle/pull/41491)) - -- Support gelu and FC+gelu ops using TensorRT inference. ([#38399](https://github.com/PaddlePaddle/Paddle/pull/38399)) - -- Support `deformable_conv` inference using TensorRT under static shape. ([#36612](https://github.com/PaddlePaddle/Paddle/pull/36612) [#36850](https://github.com/PaddlePaddle/Paddle/pull/36850) [#37345](https://github.com/PaddlePaddle/Paddle/pull/37345)) - -- Support nearest_interp_v2 op using TensorRT inference. ([#34126](https://github.com/PaddlePaddle/Paddle/pull/34126)) - -- Add `yolo_box` TensorRT plugin to support input parameters `iou_aware` and `iou_aware_factor` so that the IoU computed by inference is used as a factor for confidence. ([#34128](https://github.com/PaddlePaddle/Paddle/pull/34128)) - -- Support `elementwise_sub` and `elementwise_div` calling for TensorRT inference. ([#40806](https://github.com/PaddlePaddle/Paddle/pull/40806) [#41253](https://github.com/PaddlePaddle/Paddle/pull/41253)) - -- Support `multiclass_nms3` using TensorRT inference. ([#41181](https://github.com/PaddlePaddle/Paddle/pull/41181) [#41344](https://github.com/PaddlePaddle/Paddle/pull/41344)) - -- Support flatten_contiguous_rang op using TensorRT inference. ([#38922](https://github.com/PaddlePaddle/Paddle/pull/38922)) - -- Support for `pool2d` attribute `padding` using TensorRT inference when dimension is 4, and `global_pooling` and `ceil_mode` are True. ([#39545](https://github.com/PaddlePaddle/Paddle/pull/39545)) - -- Support batch_norm and elementwise_add using TensorRT inference when dimension is 5. ([#36446](https://github.com/PaddlePaddle/Paddle/pull/36446)) - -- Add pool3d to use TensorRT inference. ([#36545](https://github.com/PaddlePaddle/Paddle/pull/36545), [#36783](https://github.com/PaddlePaddle/Paddle/pull/36783)) - -- Add the `reduce` int32 and float types to use TensorRT inference. Add `reduce_mean` GPU operator int32 and int64 registration. ([#39088](https://github.com/PaddlePaddle/Paddle/pull/39088)) - -- Modify MatmulV2ToMul pass. Modify the qualifier (not support of broadcast) and op_teller mapping condition. ([#36652](https://github.com/PaddlePaddle/Paddle/pull/36652)) - -- Add the support for TenorRT plugin interface AddPluginV2IOExt. ([#36493](https://github.com/PaddlePaddle/Paddle/pull/36493)) - -- Add the aligned attribute in roi_align op and support for TensorRT inference. ([#38905](https://github.com/PaddlePaddle/Paddle/pull/38905)) - -- Add the support for TensorRT inference with concat attribute `axis = -1`. ([#39096](https://github.com/PaddlePaddle/Paddle/pull/39096)) - -- Add TensorRT plugin: preln_emb_eltwise_layernorm, preln_skip_la, and rnorm ops, for ERNIE-like model performance optimization. ([#39570](https://github.com/PaddlePaddle/Paddle/pull/39570)) - -- Add TensorRT fuse pass: preln_embedding_eltwise_layernorm_fuse_pass, preln_skip_layernorm_fuse_pass, for ERNIE-like model performance optimization. ([#39508](https://github.com/PaddlePaddle/Paddle/pull/39508)) - -- Split matmul fusion-related passes based on different backends (GPU, CPU, TensorRT), to support transpose function for FC weights. ([#39369](https://github.com/PaddlePaddle/Paddle/pull/39369)) - -- Add the support to TensorRT by roll, strided_slice, and slice op in case of dynamic shapes. ([#41913](https://github.com/PaddlePaddle/Paddle/pull/41913), [#41573](https://github.com/PaddlePaddle/Paddle/pull/41573), [#41467](https://github.com/PaddlePaddle/Paddle/pull/41467)) - -- Add div op support for TensorRT. ([#41243](https://github.com/PaddlePaddle/Paddle/pull/41243)) - -- Quantization support - - - For the `PostTrainingQuantization` API, add the support for `paddle.io.DataLoader` object or `Python Generator` input. ([#38686](https://github.com/PaddlePaddle/Paddle/pull/38686)) - - - ERNIE full quantization model inference supports for interleaved data layout. ([#39424](https://github.com/PaddlePaddle/Paddle/pull/39424)) - - - Support for PaddleSlim new quantile model format inference. ([#41049](https://github.com/PaddlePaddle/Paddle/pull/41049)) - - - Add matmul int8 quantization inference op converter and plugin. ([#37285](https://github.com/PaddlePaddle/Paddle/pull/37285)) - - - Add pass to determine if all ops in the model can support int8 quantization. ([#36042](https://github.com/PaddlePaddle/Paddle/pull/36042)) - - - Support quantization inference for the FC part of the multihead attention of the non-variable-length branch. ([#39660](https://github.com/PaddlePaddle/Paddle/pull/39660)) - - -#### **Ascend NPU Related Features** - -- - Refactor shape operator forward computation logic to support execution on NPU. ([#39613](https://github.com/PaddlePaddle/Paddle/pull/39613)) - - - Refactor reshape operator forward computation logic to support ShapeTensor input. ([#38748](https://github.com/PaddlePaddle/Paddle/pull/38748)) - - - Uniform accuracy type when loading model weights. ([#39160](https://github.com/PaddlePaddle/Paddle/pull/39160)) - - -### **(3) Bug fixing** - -#### **Framework and API fixing** - -- Fix the bug of model clipping when saving static graphs. ([#37579](https://github.com/PaddlePaddle/Paddle/pull/37579)) - -- For the C API, add wrapper PD_Cstr for strings, and provide construction and destructing methods to avoid users to use C runtime library to destruct strings directly. ([#38667](https://github.com/PaddlePaddle/Paddle/pull/38667)) - -- Fix the logic bug with memory reuse at prediction time. ([#37324](https://github.com/PaddlePaddle/Paddle/pull/37324)) - -- Fix memory reuse error reporting in multi-threading. ([#37894](https://github.com/PaddlePaddle/Paddle/pull/37894)) - -- Allow passing empty strings for inference when no weight file is available. ([#38579](https://github.com/PaddlePaddle/Paddle/pull/38579)) - -- Fix an issue of clone not being supported when TensorRT dynamic shape is enabled. ([#38520](https://github.com/PaddlePaddle/Paddle/pull/38520)) - -- Fix multi-threaded clone error after TensorRT dynamic shape is enabled. ([#40067](https://github.com/PaddlePaddle/Paddle/pull/40067)) - -- Fix a TensorRT engine destructing issue. ([#35842](https://github.com/PaddlePaddle/Paddle/pull/35842), [#35938](https://github.com/PaddlePaddle/Paddle/pull/35938)) - -- For the lite xpu interface, fix an issue where the xpu card cannot be selected. ([#36610](https://github.com/PaddlePaddle/Paddle/pull/36610)) - -- The TensorRT dynamic shape parameter automatically generate the interface, to add the file existence check. ([#36628](https://github.com/PaddlePaddle/Paddle/pull/36628)) - -- Fix the bug that the MKLDNN does not support conv3d. ([#42055](https://github.com/PaddlePaddle/Paddle/pull/42055)) - -#### **Backend Capability Fixing** - -- Fix cuDNN default algorithm selection configuration for prediction, with using non-deterministic policies. ([#41491](https://github.com/PaddlePaddle/Paddle/pull/41491)) - -- Fix the bug with deformable_conv op in TensorRT plugin resource recovery handling error. ([#38374](https://github.com/PaddlePaddle/Paddle/pull/38374)) - -- Fix a serialization error in the TensorRT plugin for deformable_conv op. ([#38057](https://github.com/PaddlePaddle/Paddle/pull/38057)) - -- Adapt the new refactor engine and serialization API of TensorRT 8.0. ([#36769](https://github.com/PaddlePaddle/Paddle/pull/36769)) - -- Fix the bug that the Flatten2MatmulFusePass, Squeeze2MatmulFusePass, and Reshape2MatmulFusePass do not take effect. ([#37644](https://github.com/PaddlePaddle/Paddle/pull/37644)) - -- Fix the bug with TensorRT input data reporting errors. ([#37427](https://github.com/PaddlePaddle/Paddle/pull/37427)) - -- Add error message when input dimension is wrong. ([#38962](https://github.com/PaddlePaddle/Paddle/pull/38962)) - -- Fix the bug with EmbEltwiseLayernorm output type error. ([#40015](https://github.com/PaddlePaddle/Paddle/pull/40015)) - -- Remove conv_affine_channel_fuse_pass and the corresponding unit test. ([#39817](https://github.com/PaddlePaddle/Paddle/pull/39817)) - -- Fix an issue where the adaptive_pool2d pass incorrectly replaces the pool attribute. ([#39600](https://github.com/PaddlePaddle/Paddle/pull/39600)) - -- Fix the bug that shuffle_channel_detect_pass incorrectly generates shuffle_channel op. ([#39242](https://github.com/PaddlePaddle/Paddle/pull/39242)) - -- Fix transpose parameter error. ([#39006](https://github.com/PaddlePaddle/Paddle/pull/39006)) - -- Fix the crash bug when nearest_interp_v2 input scale dimension is less than 1. ([#38725](https://github.com/PaddlePaddle/Paddle/pull/38725)) - -- Fix the bug that the prelu does not support one-dimensional input in dynamic shape. ([#39389](https://github.com/PaddlePaddle/Paddle/pull/39389)) - -- Fix the bug in the kernel function of slice's special_slice_plugin. ([#39875](https://github.com/PaddlePaddle/Paddle/pull/39875)) - -- Temporarily disable int8 branch under skip_layernorm variable length to prevent accuracy degradation. ([#39991](https://github.com/PaddlePaddle/Paddle/pull/39991)) - -- Fix some bugs regarding support for preln_ernie models. ([#39733](https://github.com/PaddlePaddle/Paddle/pull/39733)) - -- Fix the bug that slice may exceed threads limit in ERNIE. Fix the bug that the spacial_slice is incorrectly triggered. ([#39096](https://github.com/PaddlePaddle/Paddle/pull/39096)) - -- Fix the bug that the elementwise does not support broadcast when the dimension is the same. ([#37908](https://github.com/PaddlePaddle/Paddle/pull/37908)) - -- Fix the problem that the underlying implementation is different in the nearest_interp op when align_corners is True and TensorRT layer results and native op have diff. ([#37525](https://github.com/PaddlePaddle/Paddle/pull/37525)) - -- Fix qkv_plugin: Kernel function computation error. ([#37096](https://github.com/PaddlePaddle/Paddle/pull/37096)) - -- Fix the bug with inference pass for dynamic quantization. ([#35879](https://github.com/PaddlePaddle/Paddle/pull/35879)) - -- Reuse directly when Tensor requests less memory than the allocated size. ([#37880](https://github.com/PaddlePaddle/Paddle/pull/37880)) - -- Fix the hang bug when ERNIE fixed-length model is enabled with TensorRT. ([#37839](https://github.com/PaddlePaddle/Paddle/pull/37839)) - -- Fix the crash bug when TensorRT int8 lacks of dynamic range information. ([#36900](https://github.com/PaddlePaddle/Paddle/pull/36900)) - -- Fix the bug with slice deserialization code. ([#36588](https://github.com/PaddlePaddle/Paddle/pull/36588)) - -- Fix yolo box calculation formula error. ([#36240](https://github.com/PaddlePaddle/Paddle/pull/36240)) - -- Fix the crash bug when the earlier version model uses a later version of roi_align. ([#38788](https://github.com/PaddlePaddle/Paddle/pull/38788)) External Developers - -- Fix the bug of a large performance difference of softmax between python and C++. ([#37130](https://github.com/PaddlePaddle/Paddle/pull/37130)) - -- Fix matmul inference failure on static shape 2-dimensional input and dynamic shape 3-dimensional input. ([#36849](https://github.com/PaddlePaddle/Paddle/pull/36849)) - -- Fix reshape_transpose_matmul_mkldnn_fuse_pass mishandling of shapes. ([#36731](https://github.com/PaddlePaddle/Paddle/pull/36731)) - -- Fix an issue where TensorRT gets 4 dimensions when the input is 2 dimensions. ([#36614](https://github.com/PaddlePaddle/Paddle/pull/36614)) - -- Fix the bug report when the interpolate_v2 MKLDNN operator is null in the scale attribute. ([#36623](https://github.com/PaddlePaddle/Paddle/pull/36623)) - -- Fix poor performance of the recurrent operator in multi-threaded scenarios. ([#36052](https://github.com/PaddlePaddle/Paddle/pull/36052)) - -- Remove restrictions of relu, sigmoid, tanh, relu6, batch_norm, clip, concat, gelu, hard_sigmoid, prelu, softmax, split, and swish on TensorRT 2-dimensional inputs. ([#37097](https://github.com/PaddlePaddle/Paddle/pull/37097)) - -- Fix reshape op to use TensorRT inference. ([#41090](https://github.com/PaddlePaddle/Paddle/pull/41090)) - -- Fix matmul related pass, which is compatible with matmul_v2. ([#36424](https://github.com/PaddlePaddle/Paddle/pull/36424)) - -- Support VALID and SAME attributes in the padding method of the conv2d operator when TensorRT is enabled. ([#38999](https://github.com/PaddlePaddle/Paddle/pull/38999)) - -- Fix MKLDNN multi-input operator quantization problem. ([#39593](https://github.com/PaddlePaddle/Paddle/pull/39593), [#39346](https://github.com/PaddlePaddle/Paddle/pull/39346), [#40717](https://github.com/PaddlePaddle/Paddle/pull/40717)) - -- Fix scale error of conv+activation in MKLDNN quantization scenarios. ([#38331](https://github.com/PaddlePaddle/Paddle/pull/38331)) - -- Fix the bug in MKLDNN quantization without parameters where the quantization of subsequent operators is handled differently. ([#39342](https://github.com/PaddlePaddle/Paddle/pull/39342)) - -- Fix a data type related issue in MKLDNN cpu_bfloat16_placement_pass. ([#38702](https://github.com/PaddlePaddle/Paddle/pull/38702)) - -- Fix a split operator execution issue in MKLDNN bfloat16 inference. ([#39548](https://github.com/PaddlePaddle/Paddle/pull/39548)) - -- Fix the bug with MKLDNN matmul_v2 operator not supporting 6 dimensions. ([#36342](https://github.com/PaddlePaddle/Paddle/pull/36342), [#38665](https://github.com/PaddlePaddle/Paddle/pull/38665)) - -- Fix MKLDNN DeviceContext error in MKLDNN matmul_v2_transpose_reshape. ([#38554](https://github.com/PaddlePaddle/Paddle/pull/38554)) - -- Fix incorrectly calculated results for segmentation models in MKLDNN inference scenarios. ([#37310](https://github.com/PaddlePaddle/Paddle/pull/37310)) - -- Fix MKLDNN bfloat16 placement operator list and add the missing operator. ([#36291](https://github.com/PaddlePaddle/Paddle/pull/36291)) - -- Fix the format bug of MKLDNN operators, including: FC, conv_transpose, 6-dimensional Tensor error reporting, and wrong output format of conv to NHWC input. ([#38890](https://github.com/PaddlePaddle/Paddle/pull/38890), [#37344](https://github.com/PaddlePaddle/Paddle/pull/37344), [#37175](https://github.com/PaddlePaddle/Paddle/pull/37175), [#38553](https://github.com/PaddlePaddle/Paddle/pull/38553), [#40049](https://github.com/PaddlePaddle/Paddle/pull/40049), [#39097](https://github.com/PaddlePaddle/Paddle/pull/39097)) - -- Fix MKLDNN multi-threaded reasoning scenario error due to cache mechanism. ([#36290](https://github.com/PaddlePaddle/Paddle/pull/36290), [#35884](https://github.com/PaddlePaddle/Paddle/pull/35884)) - -- Fix MKLDNN quantization model accuracy anomaly caused by matmul and FC. ([#38023](https://github.com/PaddlePaddle/Paddle/pull/38023), [#37618](https://github.com/PaddlePaddle/Paddle/pull/37618)) - -- Fix the abnormal quantization model accuracy issue in MKLDNN quantization conversion scripts caused by missing passes. ([#37619](https://github.com/PaddlePaddle/Paddle/pull/37619), [#40542](https://github.com/PaddlePaddle/Paddle/pull/40542),[#38912](https://github.com/PaddlePaddle/Paddle/pull/38912)) - -- Fix the crash bug in MKLDNN enabling volume op due to data type mismatch. ([#38133](https://github.com/PaddlePaddle/Paddle/pull/38133)) - -- Fix an issue where some MKLDNN ops need to change back to the original layout after modifying the layout. ([#39422](https://github.com/PaddlePaddle/Paddle/pull/39422)) - -- Fix the bug of Python API error report due to conflict with Ascend software stack, because the GIL lock is not released in the Ascend 910 inference scenario. ([#38605](https://github.com/PaddlePaddle/Paddle/pull/38605)) - - -## **5. Environment Adaptation** - -### **Compile and Install** - -- From version 2.3.0, PaddlePaddle has adjusted and upgraded the types of GPU architectures supported by the framework. (For more information, please refer to: [GPU architectures supported by PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.3rc/install/Tables.html#gpu)) - - -Notes: - -- PIP source installation means downloading the installation package and dependency libraries from PIP official website with using `pip install paddlepaddle` or `pip install paddlepaddle-gpu`. This supports less architecture types, and lighter installation package,and only one CUDA version of the installation package is provided(compared with BOS source). - - - Prior to version 2.3, the PIP source installer (CUDA10.2) supports the following GPU architectures: 3.5, 5.0, 5.2, 6.0, 6.1, 7.0, and 7.5. - - - Later than version 2.3, the PIP source installer (CUDA11.0) supports the following GPU architectures: 6.0, 6.1, 7.0, 7.5, 8.0 - -- The BOS source is a way to download the installation package and dependency libraries from the official website of PaddlePaddle, which supports more GPU architectures. The download source is from China and it is much faster. (compared with PIP source, it supports more kinds of architectures and provides multiple CUDA versions of installation packages). - - - Prior to version 2.3, the GPU architectures supported by the bos source installer on the PaddlePaddle website: - - CUDA10: 3.5, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5； +#### New Features - - CUDA11: 5.2，6.0，6.1，7.0，7.5，8.0。 +- Add the support for Hygon DCU K100. [#63535](https://github.com/PaddlePaddle/Paddle/pull/63535) +- Support the complex64/128 data type and fusion operators such as fused_bias_residual_layernorm, fused_bias_dropout_residual_layer_norm, and rms_norm. [#63217](https://github.com/PaddlePaddle/Paddle/pull/63217) - - Later than version 2.3, the GPU architectures supported by the bos source installer on the PaddlePaddle website: +#### Bug Fixing - - CUDA10: 3.5, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5； +- Fix compilation error issues in DTK and ROCM version upgrades. [#62832](https://github.com/PaddlePaddle/Paddle/pull/62832),[#62931](https://github.com/PaddlePaddle/Paddle/pull/62931),[#61872](https://github.com/PaddlePaddle/Paddle/pull/61872),[#63738](https://github.com/PaddlePaddle/Paddle/pull/63738) - - CUDA11: 3.5, 5.0, 6.0, 6.1, 7.0, 7.5, 8.0。 +## Environment Updates -- Support Python 3.10. Fix compilation bugs caused by some PythonC API changes on Windows. ([#41180](https://github.com/PaddlePaddle/Paddle/pull/42180)) +In this PaddlePaddle version, we complete the release and update synchronization of the basic dependency libraries, and remove the old dependency libraries that are no longer updated. Complete a number of optimizations to improve compilation efficiency and compatibility, and improve the CI pipeline monitoring function to enhance the user installation experience. Fixe the several known compilation problems, improved the compilation system of paddle, and add some new features. Through the optimizations, the compilation and installation experience of the PaddlePaddle framework is further improved to bring developers a better use and development experience. -- The Windows platform supports the compilation through Visual Studio 2019. ([#38719](https://github.com/PaddlePaddle/Paddle/pull/38719)) +### New Support -- Eliminate various warnings when compiling on the Windows platform. ([#38034](https://github.com/PaddlePaddle/Paddle/pull/38034), [#37890](https://github.com/PaddlePaddle/Paddle/pull/37890), [#37442](https://github.com/PaddlePaddle/Paddle/pull/37442), [#37439](https://github.com/PaddlePaddle/Paddle/pull/37439), [#36857](https://github.com/PaddlePaddle/Paddle/pull/36857)) +- Support users to install paddle without relying on local cuda and cudnn, thus improving the user installation experience. [#60841](https://github.com/PaddlePaddle/Paddle/pull/60841)，[#61973](https://github.com/PaddlePaddle/Paddle/pull/61973),[#61862](https://github.com/PaddlePaddle/Paddle/pull/61862),[#61235](https://github.com/PaddlePaddle/Paddle/pull/61235),[#61209](https://github.com/PaddlePaddle/Paddle/pull/61209),[#61653](https://github.com/PaddlePaddle/Paddle/pull/61653),[#64083](https://github.com/PaddlePaddle/Paddle/pull/64083) +- Support CUDA 12.3 completely. Complete the retirement of cuda10.2. [#63356](https://github.com/PaddlePaddle/Paddle/pull/63356),[#60299](https://github.com/PaddlePaddle/Paddle/pull/60299),[#64171](https://github.com/PaddlePaddle/Paddle/pull/64171),[#62189](https://github.com/PaddlePaddle/Paddle/pull/62189),[#63392](https://github.com/PaddlePaddle/Paddle/pull/63392),[#64228](https://github.com/PaddlePaddle/Paddle/pull/64228),[#62498](https://github.com/PaddlePaddle/Paddle/pull/62498),[#64298](https://github.com/PaddlePaddle/Paddle/pull/64298) +- Support Python 3.12 completely, bringing more powerful language features and performance optimizations. Complete the retirement of python3.7. [#59875](https://github.com/PaddlePaddle/Paddle/pull/59875)，[#59877](https://github.com/PaddlePaddle/Paddle/pull/59877),[#59876](https://github.com/PaddlePaddle/Paddle/pull/59876) +- Upgrade of other paddle-dependent third-party libraries: [#63741](https://github.com/PaddlePaddle/Paddle/pull/63741),[#64447](https://github.com/PaddlePaddle/Paddle/pull/64447),[#60195](https://github.com/PaddlePaddle/Paddle/pull/60195),[#60110](https://github.com/PaddlePaddle/Paddle/pull/60110),[#61509](https://github.com/PaddlePaddle/Paddle/pull/61509) -- Fix jetson compilation issues introduced by the underlying data structure upgrade. ([#39669](https://github.com/PaddlePaddle/Paddle/pull/39669), [#39441](https://github.com/PaddlePaddle/Paddle/pull/39441)) +### Compilation Optimizations +- Optimize paddle's CMake codes, significantly improving compilation efficiency and experience. [##59995](https://github.com/PaddlePaddle/Paddle/pull/59995),[#60167](https://github.com/PaddlePaddle/Paddle/pull/60167)，[#61052](https://github.com/PaddlePaddle/Paddle/pull/61052)，[#59995](https://github.com/PaddlePaddle/Paddle/pull/59995)，[#59607](https://github.com/PaddlePaddle/Paddle/pull/59607),[#63093](https://github.com/PaddlePaddle/Paddle/pull/63093),[#63887](https://github.com/PaddlePaddle/Paddle/pull/63887),[#62969](https://github.com/PaddlePaddle/Paddle/pull/62969),[#64007](https://github.com/PaddlePaddle/Paddle/pull/64007),[#59811](https://github.com/PaddlePaddle/Paddle/pull/59811),[#63045](https://github.com/PaddlePaddle/Paddle/pull/63045),[#60235](https://github.com/PaddlePaddle/Paddle/pull/60235),[#60240](https://github.com/PaddlePaddle/Paddle/pull/60240),[#60235](https://github.com/PaddlePaddle/Paddle/pull/60235),[#61411](https://github.com/PaddlePaddle/Paddle/pull/61411),[#61944](https://github.com/PaddlePaddle/Paddle/pull/61944),[#61961](https://github.com/PaddlePaddle/Paddle/pull/61961),[#59990](https://github.com/PaddlePaddle/Paddle/pull/59990),[#59478](https://github.com/PaddlePaddle/Paddle/pull/59478),[#61501](https://github.com/PaddlePaddle/Paddle/pull/61501),[#60066](https://github.com/PaddlePaddle/Paddle/pull/60066),[#64133](https://github.com/PaddlePaddle/Paddle/pull/64133),[#64231](https://github.com/PaddlePaddle/Paddle/pull/64231),[#60087](https://github.com/PaddlePaddle/Paddle/pull/60087),[#60348](https://github.com/PaddlePaddle/Paddle/pull/60348),[#60737](https://github.com/PaddlePaddle/Paddle/pull/60737),[#61364](https://github.com/PaddlePaddle/Paddle/pull/61364),[#63214](https://github.com/PaddlePaddle/Paddle/pull/63214),[#62454](https://github.com/PaddlePaddle/Paddle/pull/62454),[#62473](https://github.com/PaddlePaddle/Paddle/pull/62473),[#63692](https://github.com/PaddlePaddle/Paddle/pull/63692),[#63950](https://github.com/PaddlePaddle/Paddle/pull/63950) +- Support C++ unit test link dynamic library under linux and windowx, greatly reducing the size of C++ unit test and the size of the entire build directory. [#60008](https://github.com/PaddlePaddle/Paddle/pull/60008),[#60960](https://github.com/PaddlePaddle/Paddle/pull/60960),[#60960](https://github.com/PaddlePaddle/Paddle/pull/60960),[#60961](https://github.com/PaddlePaddle/Paddle/pull/60961),[#60831](https://github.com/PaddlePaddle/Paddle/pull/60831),[#60832](https://github.com/PaddlePaddle/Paddle/pull/60832),[#60833](https://github.com/PaddlePaddle/Paddle/pull/60833),[#61372](https://github.com/PaddlePaddle/Paddle/pull/61372),[#60834](https://github.com/PaddlePaddle/Paddle/pull/60834),[#61374](https://github.com/PaddlePaddle/Paddle/pull/61374),[#61463](https://github.com/PaddlePaddle/Paddle/pull/61463),[#61376](https://github.com/PaddlePaddle/Paddle/pull/61376),[#60830](https://github.com/PaddlePaddle/Paddle/pull/60830),[#61373](https://github.com/PaddlePaddle/Paddle/pull/61373),[#61672](https://github.com/PaddlePaddle/Paddle/pull/61672),[#61375](https://github.com/PaddlePaddle/Paddle/pull/61375),[#61676](https://github.com/PaddlePaddle/Paddle/pull/61676),[#62036](https://github.com/PaddlePaddle/Paddle/pull/62036),[#61945](https://github.com/PaddlePaddle/Paddle/pull/61945),[#61675](https://github.com/PaddlePaddle/Paddle/pull/61675),[#61674](https://github.com/PaddlePaddle/Paddle/pull/61674),[#62773](https://github.com/PaddlePaddle/Paddle/pull/62773),[#61238](https://github.com/PaddlePaddle/Paddle/pull/61238),[#59988](https://github.com/PaddlePaddle/Paddle/pull/59988),[#60307](https://github.com/PaddlePaddle/Paddle/pull/60307),[#59612](https://github.com/PaddlePaddle/Paddle/pull/59612),[#59942](https://github.com/PaddlePaddle/Paddle/pull/59942),[#59968](https://github.com/PaddlePaddle/Paddle/pull/59968),[#59978](https://github.com/PaddlePaddle/Paddle/pull/59978),[#60121](https://github.com/PaddlePaddle/Paddle/pull/60121),[#60149](https://github.com/PaddlePaddle/Paddle/pull/60149),[#60161](https://github.com/PaddlePaddle/Paddle/pull/60161),[#60160](https://github.com/PaddlePaddle/Paddle/pull/60160),[#60230](https://github.com/PaddlePaddle/Paddle/pull/60230),[#60154](https://github.com/PaddlePaddle/Paddle/pull/60154),[#60356](https://github.com/PaddlePaddle/Paddle/pull/60356),[#60392](https://github.com/PaddlePaddle/Paddle/pull/60392),[#60517](https://github.com/PaddlePaddle/Paddle/pull/60517),[#61131](https://github.com/PaddlePaddle/Paddle/pull/61131),[#60959](https://github.com/PaddlePaddle/Paddle/pull/60959) +- Add the support for Clang compiler. Users can now use Clang to compile, enjoying faster compilation speed and better error message prompts. [#63382](https://github.com/PaddlePaddle/Paddle/pull/63382),[#63133](https://github.com/PaddlePaddle/Paddle/pull/63133),[#61705](https://github.com/PaddlePaddle/Paddle/pull/61705),[#63152](https://github.com/PaddlePaddle/Paddle/pull/63152),[#63373](https://github.com/PaddlePaddle/Paddle/pull/63373) -### **New Hardware Backend Extention** +### CI Pipeline Improvements -- Custom device support: provide a plug-in way to extend PaddlePaddle hardware backend. With this function, developers do not need to modify PaddlePaddle codes for specific hardware, but simply implement the standard interface and compile it into a dynamic link library that can be called by PaddlePaddle as a plug-in.This reduces the development effort of adding a new hardware backend to PaddlePaddle. Currently it supports custom Runtime and custom Kernel. +- Improve the merge-in code monitoring mechanism in the CI pipeline, to ensure higher code quality and stability. Add a function monitoring module, to monitor various indicators of the CI pipeline in real time, ensuring smooth execution of each stage, to identify and resolve issues in a timely manner. [#61384](https://github.com/PaddlePaddle/Paddle/pull/61384),[#62190](https://github.com/PaddlePaddle/Paddle/pull/62190),[#60758](https://github.com/PaddlePaddle/Paddle/pull/60758),[#60399](https://github.com/PaddlePaddle/Paddle/pull/60399),[#58623](https://github.com/PaddlePaddle/Paddle/pull/58623),[#62177](https://github.com/PaddlePaddle/Paddle/pull/62177),[#62361](https://github.com/PaddlePaddle/Paddle/pull/62361),[#62893](https://github.com/PaddlePaddle/Paddle/pull/62893),[#63705](https://github.com/PaddlePaddle/Paddle/pull/63705),[#64476](https://github.com/PaddlePaddle/Paddle/pull/64476),[#64752](https://github.com/PaddlePaddle/Paddle/pull/64752),[#64733](https://github.com/PaddlePaddle/Paddle/pull/64733),[#61914](https://github.com/PaddlePaddle/Paddle/pull/61914) -- Support Huawei NPU chip (Ascend910) training/inference. Support ResNet50, YoloV3, BERT, Transformer and many other models. Support static + dynamic graph and auto-mixed precision training. Support single card, and distribute training across multiple cards, multiple machines. +### Code Cleanup -- Support Graphcore IPU chip (including IPU Mk2 GC200 and Bow IPU) training/inference. Support ResNet50, BERT and other models. Support static graph training. Support single card, and distribute training across multiple cards, multiple machines. +- Remove some old codes. [#63580](https://github.com/PaddlePaddle/Paddle/pull/63580),[#62840](https://github.com/PaddlePaddle/Paddle/pull/62840),[#62886](https://github.com/PaddlePaddle/Paddle/pull/62886),[#63046](https://github.com/PaddlePaddle/Paddle/pull/63046),[#63004](https://github.com/PaddlePaddle/Paddle/pull/63004),[#63039](https://github.com/PaddlePaddle/Paddle/pull/63039),[#62733](https://github.com/PaddlePaddle/Paddle/pull/62733),[#62773](https://github.com/PaddlePaddle/Paddle/pull/62773),[#62768](https://github.com/PaddlePaddle/Paddle/pull/62768),[#62744](https://github.com/PaddlePaddle/Paddle/pull/62744),[#62861](https://github.com/PaddlePaddle/Paddle/pull/62861),[#62774](https://github.com/PaddlePaddle/Paddle/pull/62774),[#62851](https://github.com/PaddlePaddle/Paddle/pull/62851),[#62973](https://github.com/PaddlePaddle/Paddle/pull/62973),[#63273](https://github.com/PaddlePaddle/Paddle/pull/63273),[#62445](https://github.com/PaddlePaddle/Paddle/pull/62445),[#64382](https://github.com/PaddlePaddle/Paddle/pull/64382),[#64409](https://github.com/PaddlePaddle/Paddle/pull/64409),[#64391](https://github.com/PaddlePaddle/Paddle/pull/64391),[#64310](https://github.com/PaddlePaddle/Paddle/pull/64310),[#64348](https://github.com/PaddlePaddle/Paddle/pull/64348),[#64651](https://github.com/PaddlePaddle/Paddle/pull/64651),[#64709](https://github.com/PaddlePaddle/Paddle/pull/64709),[#61714](https://github.com/PaddlePaddle/Paddle/pull/61714),[#62109](https://github.com/PaddlePaddle/Paddle/pull/62109),[#61751](https://github.com/PaddlePaddle/Paddle/pull/61751),[#61691](https://github.com/PaddlePaddle/Paddle/pull/61691),[#61735](https://github.com/PaddlePaddle/Paddle/pull/61735) -- Support cambricon MLU chip (MLU370x4) training/inference. Support models such as ResNet50. Support static graph + dynamic graph training. Support auto-mixed precision training. Support single card, and distribute training across multiple cards, multiple machines. +### Bug Fixing -- Support KUNLUNXIN 2 chips (KUNLUNXIN AI acceleration cards R200, R300) training/inference. Support ResNet50, YoloV3, OCR-DB, SSD, MobilnetV3, UNet, BERT, Transformer, GPT-2, Wide&Deep, and DeepFM. Support static graph + dynamic graph training. Support auto-mixed precision training. Support single card, and distribute training across multiple cards, multiple machines. +- Fix several compilation issues of paddle framework. [#63297](https://github.com/PaddlePaddle/Paddle/pull/63297)，[#62994](https://github.com/PaddlePaddle/Paddle/pull/62994)，[#62651](https://github.com/PaddlePaddle/Paddle/pull/62651)，[#64408](https://github.com/PaddlePaddle/Paddle/pull/64408)，[#60934](https://github.com/PaddlePaddle/Paddle/pull/60934)，[#62899](https://github.com/PaddlePaddle/Paddle/pull/62899)，[#60528](https://github.com/PaddlePaddle/Paddle/pull/60528)，[#63158](https://github.com/PaddlePaddle/Paddle/pull/63158)，[#64549](https://github.com/PaddlePaddle/Paddle/pull/64549)，[#62351](https://github.com/PaddlePaddle/Paddle/pull/62351)，[#61259](https://github.com/PaddlePaddle/Paddle/pull/61259),[#61281](https://github.com/PaddlePaddle/Paddle/pull/61281),[#62304](https://github.com/PaddlePaddle/Paddle/pull/62304),[#60736](https://github.com/PaddlePaddle/Paddle/pull/60736),[#60811](https://github.com/PaddlePaddle/Paddle/pull/60811),[#63949](https://github.com/PaddlePaddle/Paddle/pull/63949),[#59892](https://github.com/PaddlePaddle/Paddle/pull/59892),[#60767](https://github.com/PaddlePaddle/Paddle/pull/60767),[#60856](https://github.com/PaddlePaddle/Paddle/pull/60856),[#61286](https://github.com/PaddlePaddle/Paddle/pull/61286),[#61638](https://github.com/PaddlePaddle/Paddle/pull/61638),[#62079](https://github.com/PaddlePaddle/Paddle/pull/62079),[#62142](https://github.com/PaddlePaddle/Paddle/pull/62142),[#62823](https://github.com/PaddlePaddle/Paddle/pull/62823),[#62814](https://github.com/PaddlePaddle/Paddle/pull/62814),[#62425](https://github.com/PaddlePaddle/Paddle/pull/62425),[#62619](https://github.com/PaddlePaddle/Paddle/pull/62619),[#60207](https://github.com/PaddlePaddle/Paddle/pull/60207),[#60765](https://github.com/PaddlePaddle/Paddle/pull/60765),[#61870](https://github.com/PaddlePaddle/Paddle/pull/61870),[#61923](https://github.com/PaddlePaddle/Paddle/pull/61923),[#62144](https://github.com/PaddlePaddle/Paddle/pull/62144),[#62426](https://github.com/PaddlePaddle/Paddle/pull/62426),[#63848](https://github.com/PaddlePaddle/Paddle/pull/63848),[#60682](https://github.com/PaddlePaddle/Paddle/pull/60682),[#61369](https://github.com/PaddlePaddle/Paddle/pull/61369),[#62882](https://github.com/PaddlePaddle/Paddle/pull/62882),[#63944](https://github.com/PaddlePaddle/Paddle/pull/63944),[#64812](https://github.com/PaddlePaddle/Paddle/pull/64812),[#60654](https://github.com/PaddlePaddle/Paddle/pull/60654),[#60887](https://github.com/PaddlePaddle/Paddle/pull/60887),[#62058](https://github.com/PaddlePaddle/Paddle/pull/62058),[#64639](https://github.com/PaddlePaddle/Paddle/pull/64639),[#60115](https://github.com/PaddlePaddle/Paddle/pull/60115),[#61940](https://github.com/PaddlePaddle/Paddle/pull/61940),[#62614](https://github.com/PaddlePaddle/Paddle/pull/62614),[#59914](https://github.com/PaddlePaddle/Paddle/pull/59914),[#63762](https://github.com/PaddlePaddle/Paddle/pull/63762),[#60145](https://github.com/PaddlePaddle/Paddle/pull/60145),[#60285](https://github.com/PaddlePaddle/Paddle/pull/60285),[#60378](https://github.com/PaddlePaddle/Paddle/pull/60378),[#60393](https://github.com/PaddlePaddle/Paddle/pull/60393),[#61057](https://github.com/PaddlePaddle/Paddle/pull/61057),[#61058](https://github.com/PaddlePaddle/Paddle/pull/61058),[#61151](https://github.com/PaddlePaddle/Paddle/pull/61151),[#61347](https://github.com/PaddlePaddle/Paddle/pull/61347),[#61554](https://github.com/PaddlePaddle/Paddle/pull/61554),[#61844](https://github.com/PaddlePaddle/Paddle/pull/61844),[#62915](https://github.com/PaddlePaddle/Paddle/pull/62915),[#61852](https://github.com/PaddlePaddle/Paddle/pull/61852),[#61704](https://github.com/PaddlePaddle/Paddle/pull/61704),[#61991](https://github.com/PaddlePaddle/Paddle/pull/61991),[#62264](https://github.com/PaddlePaddle/Paddle/pull/62264),[#62762](https://github.com/PaddlePaddle/Paddle/pull/62762),[#63820](https://github.com/PaddlePaddle/Paddle/pull/63820),[#63864](https://github.com/PaddlePaddle/Paddle/pull/63864),[#65017](https://github.com/PaddlePaddle/Paddle/pull/65017),[#61183](https://github.com/PaddlePaddle/Paddle/pull/61183),[#59866](https://github.com/PaddlePaddle/Paddle/pull/59866),[#61171](https://github.com/PaddlePaddle/Paddle/pull/61171),[#61290](https://github.com/PaddlePaddle/Paddle/pull/61290),[#61725](https://github.com/PaddlePaddle/Paddle/pull/61725),[#61614](https://github.com/PaddlePaddle/Paddle/pull/61614),[#61721](https://github.com/PaddlePaddle/Paddle/pull/61721),[#61494](https://github.com/PaddlePaddle/Paddle/pull/61494),[#61556](https://github.com/PaddlePaddle/Paddle/pull/61556),[#61689](https://github.com/PaddlePaddle/Paddle/pull/61689) +## Documentation-related Bug Fixing -## Thanks to our Contributors +- With the enhancement of API feature, some API documentations have been fixed and enhanced simultaneously. [#62875](https://github.com/PaddlePaddle/Paddle/pull/62875), [#59793](https://github.com/PaddlePaddle/Paddle/pull/59793), [#60002](https://github.com/PaddlePaddle/Paddle/pull/60002), [#59985](https://github.com/PaddlePaddle/Paddle/pull/59985), [#63365](https://github.com/PaddlePaddle/Paddle/pull/63365), [#60962](https://github.com/PaddlePaddle/Paddle/pull/60962), [#60942](https://github.com/PaddlePaddle/Paddle/pull/60942), [#64232](https://github.com/PaddlePaddle/Paddle/pull/64232), [#63255](https://github.com/PaddlePaddle/Paddle/pull/63255) +- Update/supplement API documentation. bernoulli_ ([#64504](https://github.com/PaddlePaddle/Paddle/pull/64504)), paddle.static.ctr_metric_bundle ([#60912](https://github.com/PaddlePaddle/Paddle/pull/60912)), LayerNorm ([#62928](https://github.com/PaddlePaddle/Paddle/pull/62928)), Sequential ([#63128](https://github.com/PaddlePaddle/Paddle/pull/63128)), paddle.summary ([#63121](https://github.com/PaddlePaddle/Paddle/pull/63121)), ShardOptimizer in AutoParallel ([#62933](https://github.com/PaddlePaddle/Paddle/pull/62933)), paddle.nccl.version ([#62480](https://github.com/PaddlePaddle/Paddle/pull/62480)) +- Update the Readme file. [#59883](https://github.com/PaddlePaddle/Paddle/pull/59883),[#60691](https://github.com/PaddlePaddle/Paddle/pull/60691),[#60749](https://github.com/PaddlePaddle/Paddle/pull/60749) +- Update mkldnn to onednn. [#63199](https://github.com/PaddlePaddle/Paddle/pull/63199),[#63202](https://github.com/PaddlePaddle/Paddle/pull/63202),[#63215](https://github.com/PaddlePaddle/Paddle/pull/63215),[#63209](https://github.com/PaddlePaddle/Paddle/pull/63209) +- Fix document rendering bugs. [#59725](https://github.com/PaddlePaddle/Paddle/pull/59725),[#60306](https://github.com/PaddlePaddle/Paddle/pull/60306) +- Fix a lot of typos in the codes to enhance source readability. [#60093](https://github.com/PaddlePaddle/Paddle/pull/60093),[#60603](https://github.com/PaddlePaddle/Paddle/pull/60603),[#60631](https://github.com/PaddlePaddle/Paddle/pull/60631),[#60679](https://github.com/PaddlePaddle/Paddle/pull/60679),[#60741](https://github.com/PaddlePaddle/Paddle/pull/60741),[#60770](https://github.com/PaddlePaddle/Paddle/pull/60770),[#60784](https://github.com/PaddlePaddle/Paddle/pull/60784),[#60825](https://github.com/PaddlePaddle/Paddle/pull/60825),[#60857](https://github.com/PaddlePaddle/Paddle/pull/60857),[#60891](https://github.com/PaddlePaddle/Paddle/pull/60891),[#60921](https://github.com/PaddlePaddle/Paddle/pull/60921),[#60920](https://github.com/PaddlePaddle/Paddle/pull/60920),[#60923](https://github.com/PaddlePaddle/Paddle/pull/60923),[#60928](https://github.com/PaddlePaddle/Paddle/pull/60928),[#60940](https://github.com/PaddlePaddle/Paddle/pull/60940),[#60936](https://github.com/PaddlePaddle/Paddle/pull/60936),[#60932](https://github.com/PaddlePaddle/Paddle/pull/60932),[#60935](https://github.com/PaddlePaddle/Paddle/pull/60935),[#60931](https://github.com/PaddlePaddle/Paddle/pull/60931),[#60951](https://github.com/PaddlePaddle/Paddle/pull/60951),[#60964](https://github.com/PaddlePaddle/Paddle/pull/60964),[#60965](https://github.com/PaddlePaddle/Paddle/pull/60965),[#60967](https://github.com/PaddlePaddle/Paddle/pull/60967),[#60972](https://github.com/PaddlePaddle/Paddle/pull/60972),[#60971](https://github.com/PaddlePaddle/Paddle/pull/60971),[#60980](https://github.com/PaddlePaddle/Paddle/pull/60980),[#60984](https://github.com/PaddlePaddle/Paddle/pull/60984),[#60985](https://github.com/PaddlePaddle/Paddle/pull/60985),[#60989](https://github.com/PaddlePaddle/Paddle/pull/60989),[#60990](https://github.com/PaddlePaddle/Paddle/pull/60990),[#60991](https://github.com/PaddlePaddle/Paddle/pull/60991),[#60992](https://github.com/PaddlePaddle/Paddle/pull/60992),[#60994](https://github.com/PaddlePaddle/Paddle/pull/60994),[#60995](https://github.com/PaddlePaddle/Paddle/pull/60995),[#60996](https://github.com/PaddlePaddle/Paddle/pull/60996),[#61001](https://github.com/PaddlePaddle/Paddle/pull/61001),[#61000](https://github.com/PaddlePaddle/Paddle/pull/61000),[#60999](https://github.com/PaddlePaddle/Paddle/pull/60999),[#60998](https://github.com/PaddlePaddle/Paddle/pull/60998),[#61026](https://github.com/PaddlePaddle/Paddle/pull/61026),[#61009](https://github.com/PaddlePaddle/Paddle/pull/61009),[#61034](https://github.com/PaddlePaddle/Paddle/pull/61034),[#61033](https://github.com/PaddlePaddle/Paddle/pull/61033),[#61020](https://github.com/PaddlePaddle/Paddle/pull/61020),[#61092](https://github.com/PaddlePaddle/Paddle/pull/61092),[#61066](https://github.com/PaddlePaddle/Paddle/pull/61066),[#61063](https://github.com/PaddlePaddle/Paddle/pull/61063),[#61089](https://github.com/PaddlePaddle/Paddle/pull/61089),[#61071](https://github.com/PaddlePaddle/Paddle/pull/61071),[#61129](https://github.com/PaddlePaddle/Paddle/pull/61129),[#61128](https://github.com/PaddlePaddle/Paddle/pull/61128),[#61126](https://github.com/PaddlePaddle/Paddle/pull/61126),[#61123](https://github.com/PaddlePaddle/Paddle/pull/61123),[#61113](https://github.com/PaddlePaddle/Paddle/pull/61113),[#61189](https://github.com/PaddlePaddle/Paddle/pull/61189),[#61175](https://github.com/PaddlePaddle/Paddle/pull/61175),[#61153](https://github.com/PaddlePaddle/Paddle/pull/61153),[#61198](https://github.com/PaddlePaddle/Paddle/pull/61198),[#61206](https://github.com/PaddlePaddle/Paddle/pull/61206),[#61256](https://github.com/PaddlePaddle/Paddle/pull/61256),[#61255](https://github.com/PaddlePaddle/Paddle/pull/61255),[#61251](https://github.com/PaddlePaddle/Paddle/pull/61251),[#61246](https://github.com/PaddlePaddle/Paddle/pull/61246),[#61245](https://github.com/PaddlePaddle/Paddle/pull/61245),[#61231](https://github.com/PaddlePaddle/Paddle/pull/61231),[#61247](https://github.com/PaddlePaddle/Paddle/pull/61247),[#61265](https://github.com/PaddlePaddle/Paddle/pull/61265),[#61264](https://github.com/PaddlePaddle/Paddle/pull/61264),[#61266](https://github.com/PaddlePaddle/Paddle/pull/61266),[#61267](https://github.com/PaddlePaddle/Paddle/pull/61267),[#61268](https://github.com/PaddlePaddle/Paddle/pull/61268),[#61270](https://github.com/PaddlePaddle/Paddle/pull/61270),[#61334](https://github.com/PaddlePaddle/Paddle/pull/61334),[#61392](https://github.com/PaddlePaddle/Paddle/pull/61392),[#61404](https://github.com/PaddlePaddle/Paddle/pull/61404),[#61318](https://github.com/PaddlePaddle/Paddle/pull/61318),[#61383](https://github.com/PaddlePaddle/Paddle/pull/61383),[#61306](https://github.com/PaddlePaddle/Paddle/pull/61306),[#61324](https://github.com/PaddlePaddle/Paddle/pull/61324),[#61426](https://github.com/PaddlePaddle/Paddle/pull/61426),[#61390](https://github.com/PaddlePaddle/Paddle/pull/61390),[#61419](https://github.com/PaddlePaddle/Paddle/pull/61419),[#61420](https://github.com/PaddlePaddle/Paddle/pull/61420),[#61408](https://github.com/PaddlePaddle/Paddle/pull/61408),[#61425](https://github.com/PaddlePaddle/Paddle/pull/61425),[#61557](https://github.com/PaddlePaddle/Paddle/pull/61557),[#61628](https://github.com/PaddlePaddle/Paddle/pull/61628),[#61652](https://github.com/PaddlePaddle/Paddle/pull/61652),[#61602](https://github.com/PaddlePaddle/Paddle/pull/61602),[#61558](https://github.com/PaddlePaddle/Paddle/pull/61558),[#61660](https://github.com/PaddlePaddle/Paddle/pull/61660),[#61423](https://github.com/PaddlePaddle/Paddle/pull/61423),[#61627](https://github.com/PaddlePaddle/Paddle/pull/61627),[#61685](https://github.com/PaddlePaddle/Paddle/pull/61685),[#61690](https://github.com/PaddlePaddle/Paddle/pull/61690),[#61727](https://github.com/PaddlePaddle/Paddle/pull/61727),[#61738](https://github.com/PaddlePaddle/Paddle/pull/61738),[#61740](https://github.com/PaddlePaddle/Paddle/pull/61740),[#61741](https://github.com/PaddlePaddle/Paddle/pull/61741),[#61743](https://github.com/PaddlePaddle/Paddle/pull/61743),[#61744](https://github.com/PaddlePaddle/Paddle/pull/61744),[#61745](https://github.com/PaddlePaddle/Paddle/pull/61745),[#61761](https://github.com/PaddlePaddle/Paddle/pull/61761),[#61762](https://github.com/PaddlePaddle/Paddle/pull/61762),[#61764](https://github.com/PaddlePaddle/Paddle/pull/61764),[#61767](https://github.com/PaddlePaddle/Paddle/pull/61767),[#61768](https://github.com/PaddlePaddle/Paddle/pull/61768),[#61774](https://github.com/PaddlePaddle/Paddle/pull/61774),[#61781](https://github.com/PaddlePaddle/Paddle/pull/61781),[#61783](https://github.com/PaddlePaddle/Paddle/pull/61783),[#61757](https://github.com/PaddlePaddle/Paddle/pull/61757),[#61732](https://github.com/PaddlePaddle/Paddle/pull/61732),[#61776](https://github.com/PaddlePaddle/Paddle/pull/61776),[#61780](https://github.com/PaddlePaddle/Paddle/pull/61780),[#61730](https://github.com/PaddlePaddle/Paddle/pull/61730),[#61728](https://github.com/PaddlePaddle/Paddle/pull/61728),[#61633](https://github.com/PaddlePaddle/Paddle/pull/61633),[#61720](https://github.com/PaddlePaddle/Paddle/pull/61720),[#61734](https://github.com/PaddlePaddle/Paddle/pull/61734),[#61779](https://github.com/PaddlePaddle/Paddle/pull/61779),[#61775](https://github.com/PaddlePaddle/Paddle/pull/61775),[#61773](https://github.com/PaddlePaddle/Paddle/pull/61773),[#61787](https://github.com/PaddlePaddle/Paddle/pull/61787),[#61687](https://github.com/PaddlePaddle/Paddle/pull/61687),[#61747](https://github.com/PaddlePaddle/Paddle/pull/61747),[#61760](https://github.com/PaddlePaddle/Paddle/pull/61760),[#61782](https://github.com/PaddlePaddle/Paddle/pull/61782),[#61800](https://github.com/PaddlePaddle/Paddle/pull/61800),[#61748](https://github.com/PaddlePaddle/Paddle/pull/61748),[#61772](https://github.com/PaddlePaddle/Paddle/pull/61772),[#61786](https://github.com/PaddlePaddle/Paddle/pull/61786),[#61880](https://github.com/PaddlePaddle/Paddle/pull/61880),[#61718](https://github.com/PaddlePaddle/Paddle/pull/61718),[#61742](https://github.com/PaddlePaddle/Paddle/pull/61742),[#61766](https://github.com/PaddlePaddle/Paddle/pull/61766),[#61835](https://github.com/PaddlePaddle/Paddle/pull/61835),[#61838](https://github.com/PaddlePaddle/Paddle/pull/61838),[#61754](https://github.com/PaddlePaddle/Paddle/pull/61754),[#61833](https://github.com/PaddlePaddle/Paddle/pull/61833),[#61749](https://github.com/PaddlePaddle/Paddle/pull/61749),[#61938](https://github.com/PaddlePaddle/Paddle/pull/61938),[#61919](https://github.com/PaddlePaddle/Paddle/pull/61919),[#61924](https://github.com/PaddlePaddle/Paddle/pull/61924),[#61778](https://github.com/PaddlePaddle/Paddle/pull/61778),[#61839](https://github.com/PaddlePaddle/Paddle/pull/61839),[#61879](https://github.com/PaddlePaddle/Paddle/pull/61879),[#61929](https://github.com/PaddlePaddle/Paddle/pull/61929),[#61801](https://github.com/PaddlePaddle/Paddle/pull/61801),[#61788](https://github.com/PaddlePaddle/Paddle/pull/61788),[#61999](https://github.com/PaddlePaddle/Paddle/pull/61999),[#61928](https://github.com/PaddlePaddle/Paddle/pull/61928),[#61958](https://github.com/PaddlePaddle/Paddle/pull/61958),[#61982](https://github.com/PaddlePaddle/Paddle/pull/61982),[#61996](https://github.com/PaddlePaddle/Paddle/pull/61996),[#61953](https://github.com/PaddlePaddle/Paddle/pull/61953),[#61998](https://github.com/PaddlePaddle/Paddle/pull/61998),[#62003](https://github.com/PaddlePaddle/Paddle/pull/62003),[#61921](https://github.com/PaddlePaddle/Paddle/pull/61921),[#61881](https://github.com/PaddlePaddle/Paddle/pull/61881),[#61746](https://github.com/PaddlePaddle/Paddle/pull/61746),[#61955](https://github.com/PaddlePaddle/Paddle/pull/61955),[#62002](https://github.com/PaddlePaddle/Paddle/pull/62002),[#62001](https://github.com/PaddlePaddle/Paddle/pull/62001),[#61997](https://github.com/PaddlePaddle/Paddle/pull/61997),[#61765](https://github.com/PaddlePaddle/Paddle/pull/61765),[#61956](https://github.com/PaddlePaddle/Paddle/pull/61956),[#62004](https://github.com/PaddlePaddle/Paddle/pull/62004),[#62044](https://github.com/PaddlePaddle/Paddle/pull/62044),[#62040](https://github.com/PaddlePaddle/Paddle/pull/62040),[#62043](https://github.com/PaddlePaddle/Paddle/pull/62043),[#62042](https://github.com/PaddlePaddle/Paddle/pull/62042),[#62041](https://github.com/PaddlePaddle/Paddle/pull/62041),[#62039](https://github.com/PaddlePaddle/Paddle/pull/62039),[#62019](https://github.com/PaddlePaddle/Paddle/pull/62019),[#61910](https://github.com/PaddlePaddle/Paddle/pull/61910),[#61882](https://github.com/PaddlePaddle/Paddle/pull/61882),[#61836](https://github.com/PaddlePaddle/Paddle/pull/61836),[#62013](https://github.com/PaddlePaddle/Paddle/pull/62013),[#62055](https://github.com/PaddlePaddle/Paddle/pull/62055),[#62047](https://github.com/PaddlePaddle/Paddle/pull/62047),[#62000](https://github.com/PaddlePaddle/Paddle/pull/62000),[#62048](https://github.com/PaddlePaddle/Paddle/pull/62048),[#62075](https://github.com/PaddlePaddle/Paddle/pull/62075),[#62038](https://github.com/PaddlePaddle/Paddle/pull/62038),[#62045](https://github.com/PaddlePaddle/Paddle/pull/62045),[#62105](https://github.com/PaddlePaddle/Paddle/pull/62105),[#62214](https://github.com/PaddlePaddle/Paddle/pull/62214),[#62212](https://github.com/PaddlePaddle/Paddle/pull/62212),[#62183](https://github.com/PaddlePaddle/Paddle/pull/62183)，[#62182](https://github.com/PaddlePaddle/Paddle/pull/62182),[#62181](https://github.com/PaddlePaddle/Paddle/pull/62181),[#62179](https://github.com/PaddlePaddle/Paddle/pull/62179),[#62178](https://github.com/PaddlePaddle/Paddle/pull/62178),[#62172](https://github.com/PaddlePaddle/Paddle/pull/62172),[#62168](https://github.com/PaddlePaddle/Paddle/pull/62168),[#62163](https://github.com/PaddlePaddle/Paddle/pull/62163),[#62162](https://github.com/PaddlePaddle/Paddle/pull/62162),[#62161](https://github.com/PaddlePaddle/Paddle/pull/62161),[#62160](https://github.com/PaddlePaddle/Paddle/pull/62160),[#62046](https://github.com/PaddlePaddle/Paddle/pull/62046),[#62175](https://github.com/PaddlePaddle/Paddle/pull/62175),[#62259](https://github.com/PaddlePaddle/Paddle/pull/62259),[#62258](https://github.com/PaddlePaddle/Paddle/pull/62258),[#62213](https://github.com/PaddlePaddle/Paddle/pull/62213),[#62260](https://github.com/PaddlePaddle/Paddle/pull/62260),[#62290](https://github.com/PaddlePaddle/Paddle/pull/62290),[#62288](https://github.com/PaddlePaddle/Paddle/pull/62288),[#62323](https://github.com/PaddlePaddle/Paddle/pull/62323),[#62319](https://github.com/PaddlePaddle/Paddle/pull/62319),[#62331](https://github.com/PaddlePaddle/Paddle/pull/62331),[#62330](https://github.com/PaddlePaddle/Paddle/pull/62330),[#62329](https://github.com/PaddlePaddle/Paddle/pull/62329),[#62324](https://github.com/PaddlePaddle/Paddle/pull/62324),[#62317](https://github.com/PaddlePaddle/Paddle/pull/62317),[#62311](https://github.com/PaddlePaddle/Paddle/pull/62311),[#62310](https://github.com/PaddlePaddle/Paddle/pull/62310),[#62308](https://github.com/PaddlePaddle/Paddle/pull/62308),[#62289](https://github.com/PaddlePaddle/Paddle/pull/62289),[#62307](https://github.com/PaddlePaddle/Paddle/pull/62307),[#62315](https://github.com/PaddlePaddle/Paddle/pull/62315),[#62406](https://github.com/PaddlePaddle/Paddle/pull/62406),[#62458](https://github.com/PaddlePaddle/Paddle/pull/62458),[#62459](https://github.com/PaddlePaddle/Paddle/pull/62459),[#62481](https://github.com/PaddlePaddle/Paddle/pull/62481),[#62465](https://github.com/PaddlePaddle/Paddle/pull/62465),[#62462](https://github.com/PaddlePaddle/Paddle/pull/62462),[#62453](https://github.com/PaddlePaddle/Paddle/pull/62453),[#62496](https://github.com/PaddlePaddle/Paddle/pull/62496),[#62457](https://github.com/PaddlePaddle/Paddle/pull/62457),[#62537](https://github.com/PaddlePaddle/Paddle/pull/62537),[#62514](https://github.com/PaddlePaddle/Paddle/pull/62514),[#62548](https://github.com/PaddlePaddle/Paddle/pull/62548),[#62544](https://github.com/PaddlePaddle/Paddle/pull/62544),[#62575](https://github.com/PaddlePaddle/Paddle/pull/62575),[#62463](https://github.com/PaddlePaddle/Paddle/pull/62463),[#62643](https://github.com/PaddlePaddle/Paddle/pull/62643),[#62803](https://github.com/PaddlePaddle/Paddle/pull/62803),[#62924](https://github.com/PaddlePaddle/Paddle/pull/62924),[#63037](https://github.com/PaddlePaddle/Paddle/pull/63037),[#63102](https://github.com/PaddlePaddle/Paddle/pull/63102),[#63139](https://github.com/PaddlePaddle/Paddle/pull/63139),[#63092](https://github.com/PaddlePaddle/Paddle/pull/63092),[#63147](https://github.com/PaddlePaddle/Paddle/pull/63147),[#60518](https://github.com/PaddlePaddle/Paddle/pull/60518),[#60485](https://github.com/PaddlePaddle/Paddle/pull/60485),[#61273](https://github.com/PaddlePaddle/Paddle/pull/61273),[#63429](https://github.com/PaddlePaddle/Paddle/pull/63429),[#61954](https://github.com/PaddlePaddle/Paddle/pull/61954) -This release contains contributions from the project core team as well as: +## Others -Adam Osewski, Allen Guo, arlesniak, chenenquan, chenyanlann, fengkuangxiaxia, fuqianya, fwenguang, guguguzi, helen88, houj04, Jacek Czaja, jakpiase, jianghaicheng, joanna.wozna.intel, joeqiao12, Leo Chen, Leo Guo, Li-fAngyU, lidanqing, Liyulingyue, Matsumoto GAO, maxhuiy, Ming-Xu Huang, Nyakku Shigure, piotrekobi, piotrekobiIntel, QingshuChen, qipengh, Skr Bang, Sylwester Fraczek, Sławomir Siwek, taixiurong, tanzhipeng, Tomasz Socha, TTerror, Webbley, yaozhixin, ykkk2333, yujun, Zhangjingyu06, zhangxiaoci, zhangyikun02, zhangyk0314, zlsh80826, zn, Zuza. +Non-user related changes, including deprecated code cleanup, useless unit test cleanup, debugging or upgrade of monitoring mechanism. [#63377](https://github.com/PaddlePaddle/Paddle/pull/63377),[#64106](https://github.com/PaddlePaddle/Paddle/pull/64106),[#64220](https://github.com/PaddlePaddle/Paddle/pull/64220),[#64293](https://github.com/PaddlePaddle/Paddle/pull/64293),[#64464](https://github.com/PaddlePaddle/Paddle/pull/64464),[#64944](https://github.com/PaddlePaddle/Paddle/pull/64944),[#63638](https://github.com/PaddlePaddle/Paddle/pull/63638),[#63732](https://github.com/PaddlePaddle/Paddle/pull/63732),[#63735](https://github.com/PaddlePaddle/Paddle/pull/63735),[#63826](https://github.com/PaddlePaddle/Paddle/pull/63826),[#63982](https://github.com/PaddlePaddle/Paddle/pull/63982),[#63737](https://github.com/PaddlePaddle/Paddle/pull/63737),[#64471](https://github.com/PaddlePaddle/Paddle/pull/64471),[#64574](https://github.com/PaddlePaddle/Paddle/pull/64574),[#64494](https://github.com/PaddlePaddle/Paddle/pull/64494),[#62775](https://github.com/PaddlePaddle/Paddle/pull/62775),[#63601](https://github.com/PaddlePaddle/Paddle/pull/63601),[#62564](https://github.com/PaddlePaddle/Paddle/pull/62564),[#63772](https://github.com/PaddlePaddle/Paddle/pull/63772),[#64719](https://github.com/PaddlePaddle/Paddle/pull/64719),[#61640](https://github.com/PaddlePaddle/Paddle/pull/61640),[#63459](https://github.com/PaddlePaddle/Paddle/pull/63459),[#64062](https://github.com/PaddlePaddle/Paddle/pull/64062),[#63480](https://github.com/PaddlePaddle/Paddle/pull/63480),[#63833](https://github.com/PaddlePaddle/Paddle/pull/63833)[#63673](https://github.com/PaddlePaddle/Paddle/pull/63673),[#63672](https://github.com/PaddlePaddle/Paddle/pull/63672),[#64131](https://github.com/PaddlePaddle/Paddle/pull/64131),[#64156](https://github.com/PaddlePaddle/Paddle/pull/64156),[#64155](https://github.com/PaddlePaddle/Paddle/pull/64155),[#64159](https://github.com/PaddlePaddle/Paddle/pull/64159),[#63902](https://github.com/PaddlePaddle/Paddle/pull/63902),[#64230](https://github.com/PaddlePaddle/Paddle/pull/64230),[#64229](https://github.com/PaddlePaddle/Paddle/pull/64229),[#64236](https://github.com/PaddlePaddle/Paddle/pull/64236),[#64260](https://github.com/PaddlePaddle/Paddle/pull/64260),[#64175](https://github.com/PaddlePaddle/Paddle/pull/64175),[#64250](https://github.com/PaddlePaddle/Paddle/pull/64250),[#64269](https://github.com/PaddlePaddle/Paddle/pull/64269),[#64238](https://github.com/PaddlePaddle/Paddle/pull/64238),[#64349](https://github.com/PaddlePaddle/Paddle/pull/64349),[#64394](https://github.com/PaddlePaddle/Paddle/pull/64394),[#64402](https://github.com/PaddlePaddle/Paddle/pull/64402),[#64401](https://github.com/PaddlePaddle/Paddle/pull/64401),[#64388](https://github.com/PaddlePaddle/Paddle/pull/64388),[#64329](https://github.com/PaddlePaddle/Paddle/pull/64329),[#64502](https://github.com/PaddlePaddle/Paddle/pull/64502),[#64501](https://github.com/PaddlePaddle/Paddle/pull/64501),[#64515](https://github.com/PaddlePaddle/Paddle/pull/64515),[#64503](https://github.com/PaddlePaddle/Paddle/pull/64503),[#64514](https://github.com/PaddlePaddle/Paddle/pull/64514),[#64601](https://github.com/PaddlePaddle/Paddle/pull/64601),[#64564](https://github.com/PaddlePaddle/Paddle/pull/64564),[#64012](https://github.com/PaddlePaddle/Paddle/pull/64012),[#64697](https://github.com/PaddlePaddle/Paddle/pull/64697),[#64682](https://github.com/PaddlePaddle/Paddle/pull/64682),[#64051](https://github.com/PaddlePaddle/Paddle/pull/64051),[#63267](https://github.com/PaddlePaddle/Paddle/pull/63267),[#63426](https://github.com/PaddlePaddle/Paddle/pull/63426),[#63626](https://github.com/PaddlePaddle/Paddle/pull/63626),[#63257](https://github.com/PaddlePaddle/Paddle/pull/63257),[#63266](https://github.com/PaddlePaddle/Paddle/pull/63266),[#63468](https://github.com/PaddlePaddle/Paddle/pull/63468),[#63262](https://github.com/PaddlePaddle/Paddle/pull/63262),[#63248](https://github.com/PaddlePaddle/Paddle/pull/63248),[#63241](https://github.com/PaddlePaddle/Paddle/pull/63241),[#63252](https://github.com/PaddlePaddle/Paddle/pull/63252),[#63258](https://github.com/PaddlePaddle/Paddle/pull/63258),[#63235](https://github.com/PaddlePaddle/Paddle/pull/63235),[#63399](https://github.com/PaddlePaddle/Paddle/pull/63399),[#63488](https://github.com/PaddlePaddle/Paddle/pull/63488),[#63487](https://github.com/PaddlePaddle/Paddle/pull/63487),[#63466](https://github.com/PaddlePaddle/Paddle/pull/63466),[#63464](https://github.com/PaddlePaddle/Paddle/pull/63464),[#63483](https://github.com/PaddlePaddle/Paddle/pull/63483),[#63486](https://github.com/PaddlePaddle/Paddle/pull/63486),[#63475](https://github.com/PaddlePaddle/Paddle/pull/63475),[#63489](https://github.com/PaddlePaddle/Paddle/pull/63489),[#63470](https://github.com/PaddlePaddle/Paddle/pull/63470),[#63457](https://github.com/PaddlePaddle/Paddle/pull/63457),[#63493](https://github.com/PaddlePaddle/Paddle/pull/63493),[#63561](https://github.com/PaddlePaddle/Paddle/pull/63561),[#63584](https://github.com/PaddlePaddle/Paddle/pull/63584),[#63587](https://gith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Paddle/pull/60565),[#64414](https://github.com/PaddlePaddle/Paddle/pull/64414),[#62526](https://github.com/PaddlePaddle/Paddle/pull/62526),[#54183](https://github.com/PaddlePaddle/Paddle/pull/54183),[#64096](https://github.com/PaddlePaddle/Paddle/pull/64096),[#61325](https://github.com/PaddlePaddle/Paddle/pull/61325),[#60629](https://github.com/PaddlePaddle/Paddle/pull/60629),[#61051](https://github.com/PaddlePaddle/Paddle/pull/61051),[#62103](https://github.com/PaddlePaddle/Paddle/pull/62103),[#63594](https://github.com/PaddlePaddle/Paddle/pull/63594),[#60968](https://github.com/PaddlePaddle/Paddle/pull/60968),[#64613](https://github.com/PaddlePaddle/Paddle/pull/64613),[#64073](https://github.com/PaddlePaddle/Paddle/pull/64073),[#63816](https://github.com/PaddlePaddle/Paddle/pull/63816),[#64416](https://github.com/PaddlePaddle/Paddle/pull/64416),[#62499](https://github.com/PaddlePaddle/Paddle/pull/62499),[#64531](https://github.com/PaddlePaddle/Paddle/pull/64531),[#63827](https://github.com/PaddlePaddle/Paddle/pull/63827),[#59885](https://github.com/PaddlePaddle/Paddle/pull/59885),[#59949](https://github.com/PaddlePaddle/Paddle/pull/59949),[#63428](https://github.com/PaddlePaddle/Paddle/pull/63428),[#63218](https://github.com/PaddlePaddle/Paddle/pull/63218),[#63538](https://github.com/PaddlePaddle/Paddle/pull/63538),[#64497](https://github.com/PaddlePaddle/Paddle/pull/64497),[#63082](https://github.com/PaddlePaddle/Paddle/pull/63082),[#64395](https://github.com/PaddlePaddle/Paddle/pull/64395),[#60183](https://github.com/PaddlePaddle/Paddle/pull/60183),[#63691](https://github.com/PaddlePaddle/Paddle/pull/63691),[#64428](https://github.com/PaddlePaddle/Paddle/pull/64428),[#64648](https://github.com/PaddlePaddle/Paddle/pull/64648),[#64650](https://github.com/PaddlePaddle/Paddle/pull/64650),[#59926](https://github.com/PaddlePaddle/Paddle/pull/59926),[#59750](https://github.com/PaddlePaddle/Paddle/pull/59750),[#60080](https://github.com/PaddlePaddle/Paddle/pull/60080),[#60208](https://github.com/PaddlePaddle/Paddle/pull/60208),[#64124](https://github.com/PaddlePaddle/Paddle/pull/64124),[#64187](https://github.com/PaddlePaddle/Paddle/pull/64187),[#64166](https://github.com/PaddlePaddle/Paddle/pull/64166),[#64284](https://github.com/PaddlePaddle/Paddle/pull/64284),[#64253](https://github.com/PaddlePaddle/Paddle/pull/64253),[#64555](https://github.com/PaddlePaddle/Paddle/pull/64555),[#59878](https://github.com/PaddlePaddle/Paddle/pull/59878),[#64081](https://github.com/PaddlePaddle/Paddle/pull/64081) diff --git a/pyproject.toml b/pyproject.toml index 138e1385de0..3b270c0e1cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,9 @@ select = [ # Pyupgrade "UP", + # Flake8-pyi + "PYI", + # NumPy-specific rules "NPY001", "NPY003", @@ -52,19 +55,24 @@ select = [ # Pylint "PLE", - "PLC0414", "PLC3002", "PLR0206", "PLR0402", - "PLR1701", "PLR1711", "PLR1722", "PLW3301", + # Flake8-simplify + "SIM101", + # Pygrep-hooks "PGH004", + # Flake8-type-checking + "TCH", + # Ruff-specific rules + "RUF010", "RUF100", ] unfixable = [ From 9f9769f4a1e19f4f7488eca965ca962e0d2d8d72 Mon Sep 17 00:00:00 2001 From: ooooo <3164076421@qq.com> Date: Fri, 12 Jul 2024 11:03:51 +0800 Subject: [PATCH 5/7] update --- .../api_difference/ops/torch.tensor.md | 58 +++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md diff --git a/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md new file mode 100644 index 00000000000..dffc762fadb --- /dev/null +++ b/docs/guides/model_convert/convert_from_pytorch/api_difference/ops/torch.tensor.md @@ -0,0 +1,58 @@ +## [torch 参数更多 ]torch.tensor +### [torch.tensor](https://pytorch.org/docs/stable/generated/torch.tensor.html?highlight=tensor#torch.tensor) + +```python +torch.tensor(data, + dtype=None, + device=None, + requires_grad=False, + pin_memory=False) +``` + +### [paddle.to_tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/to_tensor_cn.html#to-tensor) + +```python +paddle.to_tensor(data, + dtype=None, + place=None, + stop_gradient=True) +``` + +PyTorch 相比 Paddle 支持更多其他参数，具体如下： +### 参数映射 +| PyTorch | PaddlePaddle | 备注 | +| ------------- | ------------ | ------------------------------------------------------ | +| data | data | 表示输入的数据。 | +| dtype | dtype | 表示数据的类型。 | +| device | place | 表示 Tensor 存放设备位置，两者参数类型不相同，需要转写。 | +| requires_grad | stop_gradient | 表示是否计算梯度，两者参数功能相反，需要转写。 | +| pin_memeory | - | 表示是否使用锁页内存， Paddle 无此参数，需要转写。 | + +### 转写示例 + +#### device: Tensor 的设备 +```python +# PyTorch 写法 +torch.tensor(3, device=torch.device('cpu')) + +# Paddle 写法 +y = paddle.to_tensor(3, place=paddle.CPUPlace()) +``` + +#### requires_grad：是否不阻断梯度传导 +```python +# PyTorch 写法 +x = torch.tensor(3, requires_grad=True) + +# Paddle 写法 +x = paddle.to_tensor(3, stop_gradient=False) +``` + +#### pin_memory：是否分配到固定内存上 +```python +# PyTorch 写法 +x = torch.tensor(3, pin_memory=True) + +# Paddle 写法 +x = paddle.to_tensor(3).pin_memory() +``` From 50d93d169971043b3e8be3bd5a31435a39f77491 Mon Sep 17 00:00:00 2001 From: zachary sun <70642955+sunzhongkai588@users.noreply.github.com> Date: Wed, 11 Sep 2024 16:28:21 +0800 Subject: [PATCH 6/7] Update docs/guides/10_contribution/hackathon_cn.md --- docs/guides/10_contribution/hackathon_cn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/guides/10_contribution/hackathon_cn.md b/docs/guides/10_contribution/hackathon_cn.md index 9555eb0cdc7..aee9638c559 100644 --- a/docs/guides/10_contribution/hackathon_cn.md +++ b/docs/guides/10_contribution/hackathon_cn.md @@ -169,7 +169,7 @@ RFC 通过并合入后形成 Tracking Issue。原则上完成 RFC 的开发者 > [!NOTE] > -> 1. 优秀 RFC 案例：[将 xdoctest 引入到飞桨框架工作流中](https://github.com/PaddlePaddle/community/blob/master/rfcs/Docs/将%20xdoctest%20 引入到飞桨框架工作流中.md) +> 1. 优秀 RFC 案例：[将 xdoctest 引入到飞桨框架工作流中](https://github.com/PaddlePaddle/community/blob/master/rfcs/Docs/将%20xdoctest%20引入到飞桨框架工作流中.md) > 2. 优秀 Tracking Issue 案例：[将 xdoctest 引入到飞桨框架工作流中 Tracking Issue](https://github.com/PaddlePaddle/Paddle/issues/54705) **(6) 任务开发、提交作品** From d6ef0488d31d29ffc1f43c76f3c8630e90a9011a Mon Sep 17 00:00:00 2001 From: zachary sun <70642955+sunzhongkai588@users.noreply.github.com> Date: Wed, 11 Sep 2024 16:28:27 +0800 Subject: [PATCH 7/7] Update docs/api/paddle/sysconfig/Overview_cn.rst --- docs/api/paddle/sysconfig/Overview_cn.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/api/paddle/sysconfig/Overview_cn.rst b/docs/api/paddle/sysconfig/Overview_cn.rst index 04e2d541373..703d1ce05fb 100644 --- a/docs/api/paddle/sysconfig/Overview_cn.rst +++ b/docs/api/paddle/sysconfig/Overview_cn.rst @@ -1,4 +1,3 @@ - .. _cn_overview_sysconfig: paddle.sysconfig

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b9d52690770..00000000000 --- a/docs/dev_guides/api_contributing_guides/new_cpp_op_en.md +++ /dev/null @@ -1,478 +0,0 @@ -# How to write a new operator - - -## Background - -Here are the base types needed. For details, please refer to the design docs. - -- `class OpProtoAndCheckerMaker`: Describes an Operator's input, output, attributes and description, mainly used to interface with Python API. -- `framework::OperatorBase`: Operator (Op)base class. -- `framework::OpKernel`: Base class for Op computation kernel. -- `framework::OperatorWithKernel`: Inherited from OperatorBase, describing an operator with computation kernels. - - -Operators can be categorized into two groups: operator with kernel(s) and operator without kernel(s). An operator with kernel(s) inherits from `OperatorWithKernel` while the one without kernel(s) inherits from `OperatorBase`. This tutorial focuses on implementing operators with kernels. In short, an operator includes the following information: - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Information Where is it defined
    OpProtoMake definition `.cc`files, Backward Op does not need an OpProtoMake interface.
    Op definition `.cc` files
    Kernel implementation The kernel methods shared between CPU and CUDA are defined in `.h` files. CPU-specific kernels live in `.cc` files, while CUDA-specific kernels are implemented in `.cu`files.
    Registering the Op Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation.
    - - -New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions.** - - -Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc), as an example to introduce the writing of an Operator with Kernel. - - -## Implementing C++ Types - - -### Defining ProtoMaker - -Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and one output. - -First, define `ProtoMaker` to describe the Operator's input, output, and additional comments: - -```cpp -class MulOpMaker : public framework::OpProtoAndCheckerMaker { - public: - MulOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "(Tensor), 2D tensor of size (M x K)"); - AddInput("Y", "(Tensor), 2D tensor of size (K x N)"); - AddOutput("Out", "(Tensor), 2D tensor of size (M x N)"); - AddComment(R"DOC( -Two Element Mul Operator. -The equation is: Out = X * Y -)DOC"); - } -}; -``` - -[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc#L76-L127)is inherited from`framework::OpProtoAndCheckerMaker`, consisting of 2 variables in the constructor： - - - `framework::OpProto` stores Operator input and variable attribute, used for generating Python API interfaces. - - `framework::OpAttrChecker` is used to validate variable attributes. - -The constructor utilizes `AddInput` to add input parameter, `AddOutput` to add output parameter, and `AddComment` to add comments for the Op, so that the corresponding information will be added to `OpProto`. - -The code above adds two inputs `X` and `Y` to `MulOp`, an output `Out`, and their corresponding descriptions. Names are given in accordance to Paddle's [naming convention](https://github.com/PaddlePaddle/FluidDoc/blob/release/1.2/doc/fluid/dev/name_convention.md). - - -An additional example [`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/scale_op.cc#L38-L55) is implemented as follows: - - -```cpp -template -class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { - public: - ScaleOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "(Tensor) Input tensor of scale operator."); - AddOutput("Out", "(Tensor) Output tensor of scale operator."); - AddComment(R"DOC( -Scale operator -$$Out = scale*X$$ -)DOC"); - AddAttr("scale", - "(float, default 1.0)" - "The scaling factor of the scale operator.") - .SetDefault(1.0); - } -}; -``` - -Note `AddAttr("scale", "...").SetDefault(1.0);` adds `scale`constant as an attribute, and sets the default value to 1.0. - - -### Defining the GradProtoMaker class - -Each Op must have a corresponding GradProtoMaker. If GradProtoMaker corresponding to the forward Op is not customized, Fluid provides DefaultGradProtoMaker. The default registration will use all input and output, including Input, Output, Output@Grad and so on. Using unnecessary variables will cause waste of memory. -The following example defines ScaleOp's GradProtoMaker. - -```cpp -class ScaleGradMaker : public framework::SingleGradOpDescMaker { - public: - using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; - - std::unique_ptr Apply() const override { - auto *grad_op = new framework::OpDesc(); - grad_op->SetType("scale"); - grad_op->SetInput("X", OutputGrad("Out")); - grad_op->SetOutput("Out", InputGrad("X")); - grad_op->SetAttr("scale", GetAttr("scale")); - return std::unique_ptr(grad_op); - } -}; -``` - -### Defining Operator - -The following code defines the interface for MulOp: - -```cpp -class MulOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(const framework::InferShapeContext &ctx) const override { - //never use Input or Output if you want a to get a LoDTensor. - auto dim0 = ctx.Input("X")->dims(); - auto dim1 = ctx.Input("Y")->dims(); - PADDLE_ENFORCE_EQ(dim0.size(), 2, - "input X(%s) should be a tensor with 2 dims, a matrix", - ctx.op_.Input("X")); - PADDLE_ENFORCE_EQ(dim1.size(), 2, - "input Y(%s) should be a tensor with 2 dims, a matrix", - ctx.op_.Input("Y")); - PADDLE_ENFORCE_EQ( - dim0[1], dim1[0], - "First matrix's width must be equal with second matrix's height."); - ctx.Output("Out")->Resize({dim0[0], dim1[1]}); - } -}; -``` - -[`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc#L24) is inherited from `OperatorWithKernel`. Its `public` member - -```cpp -using framework::OperatorWithKernel::OperatorWithKernel; -``` - -expresses an operator constructor using base class `OperatorWithKernel`, alternatively written as - -```cpp -MulOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorWithKernel(type, inputs, outputs, attrs) {} -``` - -`InferShape` interface needs to be re-written.`InferShape` is a const method and cannot modify Op's member variables. Its constant member `const framework::InferShapeContext &ctx` can be used to extract input, output, and attributes. Its functions are - - - 1). validate and error out early: it checks input data dimensions and types. - - 2). configures the tensor shape in the output. - -Usually `OpProtoMaker` and `Op` definitions are written in `.cc` files, which also include the registration methods introduced later. - - -### Defining OpKernel - -`MulKernel` is derived from `framework::OpKernel`, which includes the following templates: - -- `typename DeviceContext` denotes device context type. When different devices, namely the CPU and the CUDA, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/cross_entropy_op.h#L43). - -- `typename T` denotes data type, such as `float` or `double`. - -`MulKernel` types need to rewrite the interface for `Compute`. - -- `Compute` takes one input parameter: `const framework::ExecutionContext& context`. -- Compared with `InferShapeContext`, `ExecutionContext` includes device types, and can similarly extract input, output, and attribute variables. -- `Compute` function implements the computation logics of an `OpKernel`. - -The input and output of Op can be obtained by `ExecutionContext::Input()` and `ExecutionContext::Output()` respectively. - -**Note:** If the input/output variable type of op is `LoDTensor` (In Fluid, all Tensors are LoDTensor type by default), please write `ExecutionContext::Input()` and `ExecutionContext:: Output()`, do not write `ExecutionContext::Input()` and `ExecutionContext::Output()`. Because if the actual variable type is `SelectedRows`, the `Input()` and `Output()` methods will specialize the `SelectedRows` type to `Tensor`, causing a potential error. - - -`MulKernel`'s implementation of `Compute` is as follows: - -```cpp -template -class MulKernel : public framework::OpKernel { -public: -void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Input("Y"); - auto* Z = context.Output("Out"); - Z->mutable_data(context.GetPlace()); - auto& device_context = context.template device_context(); - math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); -} -}; -``` - -Note that **different devices (CPU, CUDA)share one Op definition; whether or not they share the same `OpKernel` depends on whether functions called by `Compute`can support both devices.** - -`MulOp`'s CPU and CUDA share the same `Kernel`. A non-sharing `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/cross_entropy_op.cc). - -To ease the writing of `OpKernel` compute, and for reusing code cross-device, [`Eigen-unsupported Tensor`](https://bitbucket.org) module is used to implement `Compute` interface. To learn about how the Eigen library is used in PaddlePaddle, please see [usage document](https://github.com/PaddlePaddle/FluidDoc/blob/release/1.2/doc/fluid/dev/use_eigen_cn.md). - - -This concludes the forward implementation of an operator. Next its operation and kernel need to be registered in a `.cc` file. - -The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**. - - - -### Registering Operator and OpKernel - -- In `.cc` files, register forward and backward operator classes and the CPU kernel. - - ```cpp - namespace ops = paddle::operators; - REGISTER_OPERATOR(mul, ops::MulOp, ops::MulOpMaker, - paddle::framework::DefaultGradOpDescMaker) - REGISTER_OPERATOR(mul_grad, ops::MulGradOp) - REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); - REGISTER_OP_CPU_KERNEL(mul_grad, - ops::MulGradKernel); - ``` - - In that code block, - - - `REGISTER_OPERATOR` registers the `ops::MulOp` class, with the type named `mul`. Its `ProtoMaker` is `ops::MulOpMaker`. Register `ops::MulOpGrad` as type named `mul_grad`. - - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specializes template parameters as type `paddle::platform::CPUPlace` and `float`, and also registers `ops::MulGradKernel`. - - -- Registering CUDA Kernel in `.cu` files - - Note that if CUDA Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as - - ```cpp - // if use Eigen unsupported module before include head files - #define EIGEN_USE_GPU - - namespace ops = paddle::operators; - REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel); - REGISTER_OP_CUDA_KERNEL(mul_grad, - ops::MulGradKernel); - - ``` - - -### Compilation - -In folder `build/paddle/fluid/operators`, run the following commands to compile. - -``` -make mul_op -``` - - -## Python Binding - -The system will automatically bind the new op to Python and link it to a generated library. - - -## Unit Tests - -Unit tests for an operator include - -1. comparing a forward operator's implementations on different devices (CPU, CUDA) - -2. comparing a backward operator's implementation on different devices (CPU, CUDA) - -3. a gradient test for the backward operator. - -Here, we introduce the [unit tests for `MulOp`](https://github.com/PaddlePaddle/Paddle/tree/develop/test/legacy_test/test_mul_op.py). - - - -### Unit Test for Forward Operators - -The Op unit test is inherited from `OpTest`. More specific unit tests are done in `TestMulOp`. To test the Operator, you need to: - -1. Define input, output, and related property parameters in the `setUp` function. -2. Generate random input data. -3. Implement the same calculation logic as the forward operator in the Python script to get the output, which is to be compared with the output of the forward operator calculation. -4. The backward calculation has been automatically integrated into the test framework and the corresponding interface can be called directly. - - ```python - import unittest - import numpy as np - from op_test import OpTest - - - class TestMulOp(OpTest): - def setUp(self): - self.op_type = "mul" - self.inputs = { - 'X': np.random.random((32, 84)).astype("float32"), - 'Y': np.random.random((84, 100)).astype("float32") - } - self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} - - def test_check_output(self): - self.check_output() - - def test_check_grad_normal(self): - self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - - def test_check_grad_ingore_x(self): - self.check_grad( - ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - - def test_check_grad_ingore_y(self): - self.check_grad( - ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) - ``` - - -The code above first loads required packages. In addition, we have - -- `self.op_type = "mul" ` defines the type that is identical to what the operator's registered type. -- `self.inputs` defines input, with type `numpy.array` and initializes it. -- `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script. - - -### Unit Test for Backward Operators - -In the backward operator test: - -- `check_grad` is called in `test_check_grad_normal` to use numerical methods to detect gradient correctness and stability. -- The first parameter `["X", "Y"]` : specifies gradient check for the input variables `X`, `Y`. -- The second parameter `"Out"` : specifies the final output target variable `Out` of the forward network. -- The third parameter `max_relative_error`: specifies the maximum error value that can be tolerated when checking gradients. -- The `test_check_grad_ingore_x` and `test_check_grad_ingore_y` branches are used to test cases where only one input gradient needs to be calculated. - - - -### Compiling and Running - - -Any new unit testing file of the format `test_*.py` added to the directory `test/legacy_test` is automatically added to the project to compile. - -Note that **running unit tests requires compiling the entire project** and requires compiling with flag `WITH_TESTING` on i.e. `cmake paddle_dir -DWITH_TESTING=ON`. - -After successfully compiling the project, run the following command to run unit tests: - -```bash -make test ARGS="-R test_mul_op -V" -``` - -Or, - -```bash -ctest -R test_mul_op -``` - - - -## Remarks - -- The type with which an operator is registered needs to be identical to the Op's name. Registering `REGISTER_OPERATOR(B, ...)` in `A_op.cc` will cause unit testing failures. -- If the operator does not implement a CUDA kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail. -- If multiple operators rely on some shared methods, a file NOT named `*_op.*` can be created to store them, such as `gather.h`. - - - - - -### PADDLE_ENFORCE Usage Note - -To check the validity of data when implementing Op, you need to use macro definitions such as PADDLE_ENFORCE and PADDLE_ENFORCE_EQ. The basic format is as follows: - -``` -PADDLE_ENFORCE (expression, error message) -PADDLE_ENFORCE_EQ (comparison object A, comparison object B, error message) -``` - -If the expression is true, or the comparison object A=B, the check will be passed, otherwise the program will be terminated and the corresponding error message will be fed back to the user. -In order to ensure that the feedbacks are user-friendly and easy to understand, developers need to pay attention to how to use them. - - - -#### General Principles - -Any place where PADDLE_ENFORCE and PADDLE_ENFORCE_EQ are used must have a properly detailed explanation of the comments! **Error message** can't be empty! - - - -#### Error Message Standard - -1. [required] Where does it go wrong? Why is it wrong? - - - For example: `ValueError: Mismatched label shape` - -2. [optional] What is the expected input? What is the actual input? - - - For example: `Expected labels dimension=1. Received 4.` - -3. [optional] Can you come up with a suggestion? - - - For example: `Suggested Fix: If your classifier expects one-hot encoding label, check your n_classes argument to the estimatorand/or the shape of your label.Otherwise, check the shape of your label.` - -If it is not necessary or concise description is enough to clearly express the above points, just write based on actual needs. - - - -#### Typical Problems - - -1.No error message exists or error message is too short to provide effective notification to the user. - - Problem example 1: Absent message - ``` - PADDLE_ENFORCE(ctx->HasInput("X"), ""); - ``` - Problem example 2: The prompt message is too short - ``` - PADDLE_ENFORCE(i != nullptr, "i must be set"); // What is i? - ``` - -2.Using developer-defined variable abbreviations in error messages is not easy to understand. - - Example of the problem: - ``` - PADDLE_ENFORCE(forward_pd != nullptr, - "Fail to find eltwise_fwd_pd in device context"); //eltwise_fwd_pduser may not be understood - ``` - -3.The OP internally calls the illegal interface: If Op appears inside Output = ShareDataWith(Input) - Example of the problem: - ```cpp - auto *out = ctx.Output("Out"); - auto *in = ctx.Input("X"); - out->ShareDataWith(*in); - ``` - - If there is Output = ShareDataWith(Input) inside Op, it will equivalently indicate a hidden edge in the operator graph, which connects Input and Output. This edge cannot be expressed in graph analysis, causing error based on graph optimization. - -4.Performance of OP implementation. It called eigen's broadcast, chop and other operations, the performance will be over several times worse than the handwritten cuda kernel. At this point, the implementation of cpu can reuse eigen, and the gpu implementation can implement cuda kernel. - - - - -#### Special Instructions for OP InferShape Check Message - -- Check input and output variables, please follow the following format -`Input(variable name) of OP name operator should not be null.` - - The correct example: - ``` - PADDLE_ENFORCE(ctx->HasInput("Input"), - "Input(Input) of LSTMP operator should not be null."); - ``` - -- Backward Op input and output check, to write the name of the backward Op - - The correct example: - ``` - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of LoDResetGrad opreator should not be null."); - ``` diff --git a/docs/dev_guides/api_contributing_guides/new_cpp_op_notes_en.md b/docs/dev_guides/api_contributing_guides/new_cpp_op_notes_en.md deleted file mode 100644 index d2a12943d7a..00000000000 --- a/docs/dev_guides/api_contributing_guides/new_cpp_op_notes_en.md +++ /dev/null @@ -1,183 +0,0 @@ -# Notes on operator development - -## Building logic of Fluid's op -### 1.Building logic of Fluid's op -All Ops in Fluid are derived from `OperatorBase` , and all Ops are stateless. Each Op contains only four variable members: type, inputs, outputs, and attribute. - -The core method of Op is Run. The Run method requires two resources: data resources and computing resources. These two resources are obtained respectively from `Scope` and `Place`. Inside the framework, there is a global `DeviceContextPool`, which is used to record the mapping relationship between `Place` and `DeviceContext`, which means each `Place` has only one `DeviceContext` corresponding to it, and `DeviceContext` stores the computing resources of the current device. For example, for GPU, these resources include `cudnn_handle`, `cublas_handle`, `stream`, and so on. All the internal calculations (data copy and CUDA Kernel, etc.) of Op must be done in `DeviceContext`. - -The Fluid framework is designed to run on a variety of devices and third-party libraries, and some Op implementations may vary on different the devices or third-party libraries. Therefore, Fluid introduced the OpKernel's approach, which means an Op can have multiple OpKernels. Such Ops are derived from `OperatorWithKernel`, and the representative of such Ops is conv, the OpKernels of conv_op are: `GemmConvKernel`, `CUDNNConvOpKernel`, `ConvMKLDNNOpKernel`, and each OpKernel has two data types, double and float. Ops that do not need OpKernel inclue `WhileOp` and so on. - -Operator inheritance diagram: -![op_inheritance_relation_diagram](./op_inheritance_relation_diagram.png) - -For further information, please refer to: [multi_devices](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/multi_devices) , [scope](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/concepts/scope.md) , [Developer's_Guide_to_Paddle_Fluid](https://github.com/PaddlePaddle/FluidDoc/blob/release/1.2/doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md) - -### 2.Op's registration logic -The registration entries for each Operator include: - ```C++ - OpCreator creator_; - GradOpMakerFN grad_op_maker_; - proto::OpProto* proto_{nullptr}; - OpAttrChecker* checker_{nullptr}; - InferVarTypeFN infer_var_type_; - InferShapeFN infer_shape_; - ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Registration EntryTypeDescriptionUsage
    proto::OpProto Class Store the input/output/properties/Op type of Op Call at compile time
    GradOpMakerFN Functor Return a set of OpDescs of the reverse Op corresponding to the current Op, because the reverse ones of the forward Op may consist of multiple Ops Call at compile time
    OpAttrChecker Class Check the Op's attr Call at compile time
    InferVarTypeFN Functor Used to infer the type of the output Var, such as LoDTensor, SelectedRows, or others Call at compile time
    InferShapeFN Functor Used to infer the Shape of the Output The usage is different at compile time and runtime. At compile time, it is called in Python side; If the Op is derived from OperatorWithKernel, at the runtime it will be called at op.run
    OpCreator Functor Create a new OperatorBase for each call Call at runtime
    - -Usually you need to call REGISTER_OPERATOR when you make comments on Op, which is: - ``` - REGISTER_OPERATOR(op_type, - OperatorBase - Op_maker_and_checker_maker, - Op_grad_opmaker, - Op_infer_var_shape, - Op_infer_var_type) - ``` - -**Note:** - -1. For all Op, the first three parameters are required, op_type specifies the name of op, OperatorBase is the object instance of this Op, op_maker_and_checker_maker is the maker of op and the checker of attr in op. -2. If the Op has a reverse, it must have op_grad_opmaker, because in backward, the reverse Op's Maker will be obtained from the forward Op. -3. The framework provides a default op_grad_opmaker:`DefaultGradOpDescMaker`, which will use the input and output of the forward Op as the input of the reverse Op, and the gradients of the input to forward Op's as the output of the reverse Op, and copy the attributes of the forward Op to it. **Note:** DefaultGradOpDescMaker will take all the input and output of the forward Op as the reverse Op input. Even if this input is not necessary, the absence of this will prevent us from doing memory optimization for the unused variables. -4. The framework does not provide a default op_infer_var_shape method. If the Op has no OpKernel, you usually need to add the corresponding op_infer_var_shape method. If the Op has OpKernel, you need to implement the `InferShape` method of `OperatorWithKernel`. You don't need to provide the op_infer_var_shape method. For details, refer to [while_op.cc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/controlflow/while_op.cc), [conv_op.cc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/conv_op.cc). -5. The framework does not provide a default op_infer_var_type method, the user needs to add op_infer_var_type according to the actual situation. Strictly speaking, every Op should register an InferVarType, and op_infer_var_type infers the type and dtype of the output Var according to the type and dtype of the input Var. **Note:** In the Python-side LayerHelper, the create_variable_for_type_inference operation returns a Variable which is a LoDTensor. The C++-side InferVarType can modify the type and dtype of the `Variable`. - - -For more details, please refer to: [How to write a new Op](new_op_en.html) - -## Notes on Writing an Op -### 1. input and output types supported by Op -The input and output of Fluid's Ops are `Variable`. In design, `Variable` can store any type. Op's input and output `Variable` may be of any type, and usually the `Variable` stores `LoDTensor` and `SelectedRows` . - -**Note:** - -- `context.Input("Input")` often appears in the code. It does not mean that the `Variable` of "Input" is `Tensor`, but indicates that the `Tensor` is obtained from `LoDTensor` in the `Variable` of the "Input". If the `Variable` of "Input" is `SelectedRows`, an error will be reported. -- If "Input" is `SelectedRows`, `context->GetInputDim("Input")` will return `var->Get().GetCompleteDims()` instead of Dim of `Tensor` in `SelectedRows` . - -### 2. Do not modify the input data inside Op. -Never make any modification of the input data inside Op, as there may be other Ops that need to read this input. - -### 3. The data type needs to be registered for OpKernel -Currently all OpKernel are required to register double and float data types. - -### 4.Op compatibility issue -The modification of Op needs to consider the compatibility problem. Please ensure that the previous model can be loaded and run normally after the modification of Op which means that the model trained by the old version can be loaded and run with Paddle inference library of new version. **So developers should ensure that the Input, Output and Attribute of OPs cannot be modified (except for documents) or deleted. And developers can add Input, Output and Attribute, but the added Input and Output must be set to be dispensable, and the default value of added Attribute must be set. For more details, please refer to [OP Input/Output/Attribute Compatibility Modification](https://github.com/PaddlePaddle/Paddle/wiki/OP-Input-Output-Attribute-Compatibility-Modification(English-Version))**. - -### 5.Call ShareDataWith -The function of ShareDataWith is to make the two Tensors share the underlying buffer. When calling this operation, special attention should be paid. In the Op, the ShareDataWith cannot be applied to the output of Op. In other words, the Tensor of the Op output must be from Malloc. - -### 6. Sparse gradient parameter's update method -At present, the sparse gradient will first merge the gradient when updating, which is to add up the gradients of the same parameter, and then update the parameters and additional parameters (such as velocity). - -### 7. (Video) Memory optimization -If the reverse of Op does not require all of the input and output of the forward op as its input, please do not use `DefaultGradOpDescMaker`, which will prevent Memory/Video Memory optimization for unused variables. - -### 8. Calls made on Hybrid device -Since the GPU is executed asynchronously, the GPU side may not be actually executed after the CPU call returns. Therefore, if you create a temporary variable in Op that you need to use at the GPU runtime, when the GPU starts running, the temporary variable may have been released on the CPU side, which may cause GPU calculation errors. - -Some of the synchronous and asynchronous operations in the GPU: -``` -The following device operations are asynchronous with respect to the host: - Kernel launches; - Memory copies within a single device's memory; - Memory copies from host to device of a memory block of 64 KB or less; - Memory copies performed by functions that are suffixed with Async; - Memory set function calls. -``` - -Note on cudaMemCpy and cudaMemCpyAsync: - -- If the data transfer is from the GPU side to the CPU side with non-pinned memory , the data transfer will be synchronous, even if an asynchronous copy operation is called. -- If the data is transferred from the CPU side to the CPU side, the data transfer will be synchronous, even if an asynchronous copy operation is called. - -For more information, please refer to: [Asynchronous Concurrent Execution](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#asynchronous-concurrent-execution) , [API synchronization behavior](https://Docs.nvidia.com/cuda/cuda-runtime-api/api-sync-behavior.html#api-sync-behavior) - -## Op Performance Optimization -### 1. Selection of third-party libraries -In the process of writing Op, the operations provided by high-performance libraries (such as cudnn, mkldnn, mklml, eigen, etc.) are preferred, but the benchmark must be done. Some operations in the library may be slower in deep learning tasks. Because the operations provided in high-performance libraries (such as eigen, etc.) are more generalized and in terms of performance, they may not be sufficient. Usually the amount of data in the deep learning model is small, so in some cases some of the high-performance libraries may be compromised to a slower speed. For example, all Op (forward and reverse) of the Elementwise set. The Elementwise operation is called relatively frequently in the model. Especially Elementwise_add, which is used to add offset to many operations. In the previous implementation, Elementwise_op directly calls the Eigen library. Since the Elementwise operation needs to broadcast the data in many cases, and the experiment finds that the Eigen library is slower to broadcast, whose reason is in this PR[#6229](https://github.com/PaddlePaddle/Paddle/pull/6229). - -### 2.Op performance optimization -The calculation speed of Op is related to the amount of data input. For some Op, different calculation methods can be selected according to the attribute parameters in Op and Shape of the input data. For example, concat_op, when axis>=1, in the process of concatenating multiple tensors, you need to make many copies for each tensor. If it is on GPU, you need to call cudaMemCopy. Relative to the CPU, the GPU is an external device. So each time the GPU is called, there will a certain overhead. And when more times of copying are required, the overhead is more prominent. At present, the implementation of concat_op will select different calling methods according to the Shape and axis values of the input data. If there are a relatively large number of input tensors, and the axis is not equal to 0, the multiple copy operations will be converted into a CUDA Kernel to complete the process; if input tensor are less, and the axis is equal to 0, direct copy will be used. The relevant experiment is described in this PR ([#8669](https://github.com/PaddlePaddle/Paddle/pull/8669)) . - -Since the call of CUDA Kernel has a certain overhead, multiple calls of the CUDA Kernel in Op may affect the execution speed of Op. For example, the previous sequence_expand_op contains many CUDA Kernels. Usually, these CUDA Kernels process a small amount of data, so frequent calls to such Kernels will affect the calculation speed of Op. In this case, it is better to combine these small CUDA Kernels into one. This idea is used in the optimization of the sequence_expand_op procedure (related PR[#9289](https://github.com/PaddlePaddle/Paddle/pull/9289)). The optimized sequence_expand_op is about twice as fast as the previous implementation, the relevant experiments are introduced in the PR ([#9289](https://github.com/PaddlePaddle/Paddle/pull/9289)). - -Reduce the number of copy and sync operations between the CPU and the GPU. For example, the fetch operation will update the model parameters and get a loss after each iteration, and the copy of the data from the GPU to the Non-Pinned-Memory CPU is synchronous, so frequent fetching for multiple parameters will reduce the model training speed. - -## Op numerical stability -### 1. Some Ops have numerical stability problems -The main reason for numerical stability is that when the program is run multiple times, the order in which the floating-point data is processed may be different, resulting in different final calculation results. The GPU is accelerated by multi-threaded parallel computing, so it is commonplace that the order of operations on floating-point numbers is not fixed. - -At present, it is found that the result of the convolution operation in cudnn, MaxPooling in cudnn, CudaAtomicXX in CUDA, and aggregation of parameter gradients in Reduce mode of ParallelExecutor are not certain. - -For this purpose, some FLAGS is added to the Fluid. For example, FLAGS_cudnn_deterministic is used to force cudnn to use the deterministic algorithm, and FLAGS_cpu_deterministic to force the CPU-side calculation to use the deterministic method. - -## Other -### 1. Error message -The Enforce prompt message cannot be empty and needs to be written, because the error message can analyze the cause of the error more quickly and conveniently. - -### 2.Op's mathematical formula -If Op has a mathematical formula, be sure to write the mathematical formula in the code and display it in the Doc of the Python API, because the user may need to understand how Paddle implements Op when comparing the calculation results among different frameworks. - -**Note:** The formula preview must be done before the merge to the develop branch. Example: [dynamic_lstmp](../../../api/layers/nn.html#dynamic-lstmp). - -### 3. The order of parameters in the Python-side Op interface -The order of the parameters in the Python API is generally ranked by importance, taking fc as an example: -``` -def fc(input, - size, - num_flatten_dims=1, - param_attr=None, - bias_attr=None, - act=None, - is_test=False, - name=None) -``` diff --git a/docs/dev_guides/api_contributing_guides/new_python_api_cn.md b/docs/dev_guides/api_contributing_guides/new_python_api_cn.md index c7c7406093e..96ddd2e9074 100644 --- a/docs/dev_guides/api_contributing_guides/new_python_api_cn.md +++ b/docs/dev_guides/api_contributing_guides/new_python_api_cn.md @@ -17,7 +17,7 @@ API 作为用户使用飞桨框架的接口，承接着实现用户模型开发 - 已签署 [贡献者许可协议（Contributor License Agreement，CLA）](https://cla-assistant.io/PaddlePaddle/Paddle)； -- 已阅读 [代码贡献流程](..\code_contributing_path_cn.html)、[贡献前阅读](read_before_contributing_cn.html) 和相关代码规范； +- 已阅读 [代码贡献流程](../code_contributing_path_cn.html)、[贡献前阅读](read_before_contributing_cn.html) 和相关代码规范； - 已根据 [API 设计和命名规范](api_design_guidelines_standard_cn.html) 确定了新增 API 的名称和存放位置； @@ -56,12 +56,12 @@ API 作为用户使用飞桨框架的接口，承接着实现用户模型开发 先看一个简单的 Python API 的代码样例，如图 1 所示，可以看到主要包括以下几部分： -- **函数定义**：定义 Python 接口函数。 +- **函数定义**：定义 Python 接口函数与参数类型。 - **英文文档**：API 的英文文档直接写在 `.py` 代码文件中，如下图所示；API 的中文文档则写到 [PaddlePaddle/docs](https://github.com/PaddlePaddle/docs) 仓库中。 - **代码示例**：该 API 的使用示例代码。 - **函数主体代码**：包括输入参数的检查、调用算子的执行逻辑等内容。 -