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When it is not found, a full rebuild will be done. +config: ae968def50d1148969a65908bf8471bc +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/html/_images/profiler.png b/docs/html/_images/profiler.png new file mode 100644 index 0000000..cbed276 Binary files /dev/null and b/docs/html/_images/profiler.png differ diff --git a/docs/html/_sources/advanced.rst.txt b/docs/html/_sources/advanced.rst.txt new file mode 100644 index 0000000..08af202 --- /dev/null +++ b/docs/html/_sources/advanced.rst.txt @@ -0,0 +1,143 @@ +:orphan: + +.. _ipu_advanced: + +Accelerator: IPU training +========================= +**Audience:** Users looking to customize IPU training for massive models. + + +---- + +Advanced IPU options +-------------------- + +IPUs provide further optimizations to speed up training. By using the ``IPUStrategy`` we can set the ``device_iterations``, which controls the number of iterations run directly on the IPU devices before returning to the host. Increasing the number of on-device iterations will improve throughput, as there is less device to host communication required. + +.. note:: + + When using model parallelism, it is a hard requirement to increase the number of device iterations to ensure we fully saturate the devices via micro-batching. see :ref:`ipu-model-parallelism` for more information. + +.. code-block:: python + + import lightning.pytorch as pl + from lightning.pytorch.strategies import IPUStrategy + + model = MyLightningModule() + trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=32)) + trainer.fit(model) + +Note that by default we return the last device iteration loss. You can override this by passing in your own ``poptorch.Options`` and setting the AnchorMode as described in the `PopTorch documentation `__. + +.. code-block:: python + + import poptorch + import lightning.pytorch as pl + from lightning.pytorch.strategies import IPUStrategy + + model = MyLightningModule() + inference_opts = poptorch.Options() + inference_opts.deviceIterations(32) + + training_opts = poptorch.Options() + training_opts.anchorMode(poptorch.AnchorMode.All) + training_opts.deviceIterations(32) + + trainer = Trainer( + accelerator="ipu", devices=8, strategy=IPUStrategy(inference_opts=inference_opts, training_opts=training_opts) + ) + trainer.fit(model) + +You can also override all options by passing the ``poptorch.Options`` to the plugin. See `PopTorch options documentation `__ for more information. + +---- + +.. _ipu-model-parallelism: + +Model parallelism +----------------- + +Due to the IPU architecture, larger models should be parallelized across IPUs by design. Currently PopTorch provides the capabilities via annotations as described in `parallel execution strategies `__. + +Below is an example using the block annotation in a LightningModule. + +.. note:: + + Currently, when using model parallelism we do not infer the number of IPUs required for you. This is done via the annotations themselves. If you specify 4 different IDs when defining Blocks, this means your model will be split onto 4 different IPUs. + + This is also mutually exclusive with the Trainer flag. In other words, if your model is split onto 2 IPUs and you set ``Trainer(accelerator="ipu", devices=4)`` this will require 8 IPUs in total: data parallelism will be used to replicate the two-IPU model 4 times. + + When pipelining the model you must also increase the `device_iterations` to ensure full data saturation of the devices data, i.e whilst one device in the model pipeline processes a batch of data, the other device can start on the next batch. For example if the model is split onto 4 IPUs, we require `device_iterations` to be at-least 4. + + +.. code-block:: python + + import lightning.pytorch as pl + import poptorch + + + class MyLightningModule(pl.LightningModule): + def __init__(self): + super().__init__() + # This will place layer1, layer2+layer3, layer4, softmax on different IPUs at runtime. + # BeginBlock will start a new id for all layers within this block + self.layer1 = poptorch.BeginBlock(torch.nn.Linear(5, 10), ipu_id=0) + + # This layer starts a new block, + # adding subsequent layers to this current block at runtime + # till the next block has been declared + self.layer2 = poptorch.BeginBlock(torch.nn.Linear(10, 5), ipu_id=1) + self.layer3 = torch.nn.Linear(5, 5) + + # Create new blocks + self.layer4 = poptorch.BeginBlock(torch.nn.Linear(5, 5), ipu_id=2) + self.softmax = poptorch.BeginBlock(torch.nn.Softmax(dim=1), ipu_id=3) + + ... + + + model = MyLightningModule() + trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=20)) + trainer.fit(model) + + +You can also use the block context manager within the forward function, or any of the step functions. + +.. code-block:: python + + import lightning.pytorch as pl + import poptorch + + + class MyLightningModule(pl.LightningModule): + def __init__(self): + super().__init__() + self.layer1 = torch.nn.Linear(5, 10) + self.layer2 = torch.nn.Linear(10, 5) + self.layer3 = torch.nn.Linear(5, 5) + self.layer4 = torch.nn.Linear(5, 5) + + self.act = torch.nn.ReLU() + self.softmax = torch.nn.Softmax(dim=1) + + def forward(self, x): + with poptorch.Block(ipu_id=0): + x = self.act(self.layer1(x)) + + with poptorch.Block(ipu_id=1): + x = self.act(self.layer2(x)) + + with poptorch.Block(ipu_id=2): + x = self.act(self.layer3(x)) + x = self.act(self.layer4(x)) + + with poptorch.Block(ipu_id=3): + x = self.softmax(x) + return x + + ... + + + model = MyLightningModule() + trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=20)) + trainer.fit(model) diff --git a/docs/html/_sources/basic.rst.txt b/docs/html/_sources/basic.rst.txt new file mode 100644 index 0000000..8dfc245 --- /dev/null +++ b/docs/html/_sources/basic.rst.txt @@ -0,0 +1,71 @@ +:orphan: + +.. _ipu_basic: + +Accelerator: IPU training +========================= +**Audience:** Users looking to save money and run large models faster using single or multiple IPU devices. + + +---- + +What is an IPU? +--------------- + +The Graphcore `Intelligence Processing Unit (IPU) `__, built for Artificial Intelligence and Machine Learning, consists of many individual cores, called *tiles*, allowing highly parallel computation. Due to the high bandwidth between tiles, IPUs facilitate machine learning loads where parallelization is essential. Because computation is heavily parallelized, + +IPUs operate in a different way to conventional accelerators such as CPU/GPUs. IPUs do not require large batch sizes for maximum parallelization, can provide optimizations across the compiled graph and rely on model parallelism to fully utilize tiles for larger models. + +IPUs are used to build IPU-PODs, rack-based systems of IPU-Machines for larger workloads. See the `IPU Architecture `__ for more information. + +See the `Graphcore Glossary `__ for the definitions of other IPU-specific terminology. + +---- + +Run on IPU +---------- + +To enable PyTorch Lightning to utilize the IPU accelerator, simply provide ``accelerator="ipu"`` parameter to the Trainer class. + +To use multiple IPUs set the devices to a number that is a power of 2 (i.e: 2, 4, 8, 16, ...) + +.. code-block:: python + + # run on as many IPUs as available by default + trainer = Trainer(accelerator="auto", devices="auto", strategy="auto") + # equivalent to + trainer = Trainer() + + # run on one IPU + trainer = Trainer(accelerator="ipu", devices=1) + # run on multiple IPUs + trainer = Trainer(accelerator="ipu", devices=8) + # choose the number of devices automatically + trainer = Trainer(accelerator="ipu", devices="auto") + +---- + +How to access IPUs +------------------ + +To use IPUs you must have access to a system with IPU devices. To get access see `get started `__. + +You must ensure that the IPU system has enabled the PopART and Poplar packages from the SDK. Instructions are in the Get Started guide for your IPU system, on the Graphcore `documents portal `__. + +---- + +.. _known-limitations: + +Known limitations +----------------- + +Currently there are some known limitations that are being addressed in the near future to make the experience seamless when moving from different devices. + +Please see the `MNIST example `__ which displays most of the limitations and how to overcome them till they are resolved. + +* ``self.log`` is not supported in the ``training_step``, ``validation_step``, ``test_step`` or ``predict_step``. This is due to the step function being traced and sent to the IPU devices. +* Since the step functions are traced, branching logic or any form of primitive values are traced into constants. Be mindful as this could lead to errors in your custom code. +* Clipping gradients is not supported. +* It is not possible to use :class:`torch.utils.data.BatchSampler` in your dataloaders if you are using multiple IPUs. +* IPUs handle the data transfer to the device on the host, hence the hooks :meth:`~lightning.pytorch.core.hooks.ModelHooks.transfer_batch_to_device` and + :meth:`~lightning.pytorch.core.hooks.ModelHooks.on_after_batch_transfer` do not apply here and if you have overridden any of them, an exception will be raised. diff --git a/docs/html/_sources/index.rst.txt b/docs/html/_sources/index.rst.txt new file mode 100644 index 0000000..34f20b0 --- /dev/null +++ b/docs/html/_sources/index.rst.txt @@ -0,0 +1,67 @@ +.. Lightning-AI-Graphcore documentation master file, created by + sphinx-quickstart on Wed Mar 25 21:34:07 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Accelerator: IPU training +========================= + +.. raw:: html + +
+
+ +.. Add callout items below this line + +.. displayitem:: + :header: Prepare your code (Optional) + :description: Prepare your code to run on any hardware + :col_css: col-md-6 + :button_link: prepare.html + :height: 150 + :tag: basic + +.. displayitem:: + :header: Basic + :description: Learn the basics of single and multi-IPU training. + :col_css: col-md-6 + :button_link: basic.html + :height: 150 + :tag: basic + +.. displayitem:: + :header: Intermediate + :description: Tune model performance with mix-precision settings and the performance analyser. + :col_css: col-md-6 + :button_link: intermediate.html + :height: 150 + :tag: intermediate + +.. displayitem:: + :header: Advanced + :description: Learn advanced techniques to customize IPU training for massive models. + :col_css: col-md-6 + :button_link: advanced.html + :height: 150 + :tag: advanced + +.. raw:: html + +
+
+ + +.. toctree:: + :maxdepth: 1 + :name: start + :caption: Start here + + readme + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/html/_sources/intermediate.rst.txt b/docs/html/_sources/intermediate.rst.txt new file mode 100644 index 0000000..6416355 --- /dev/null +++ b/docs/html/_sources/intermediate.rst.txt @@ -0,0 +1,64 @@ +:orphan: + +.. _ipu_intermediate: + +Accelerator: IPU training +========================= +**Audience:** IPU users looking to increase performance via mixed precision and analysis tools. + + +---- + +Mixed precision & 16 bit precision +---------------------------------- + +Lightning also supports training in mixed precision with IPUs. +By default, IPU training will use 32-bit precision. To enable mixed precision, +set the precision flag. + +.. note:: + Currently there is no dynamic scaling of the loss with mixed precision training. + +.. code-block:: python + + import lightning.pytorch as pl + + model = MyLightningModule() + trainer = pl.Trainer(accelerator="ipu", devices=8, precision=16) + trainer.fit(model) + +You can also use pure 16-bit training, where the weights are also in 16-bit precision. + +.. code-block:: python + + import lightning.pytorch as pl + from lightning.pytorch.strategies import IPUStrategy + + model = MyLightningModule() + model = model.half() + trainer = pl.Trainer(accelerator="ipu", devices=8, precision=16) + trainer.fit(model) + +---- + +PopVision Graph Analyser +------------------------ + +.. figure:: _static/images/ipu/profiler.png + :alt: PopVision Graph Analyser + :width: 500 + +Lightning supports integration with the `PopVision Graph Analyser Tool `__. This helps to look at utilization of IPU devices and provides helpful metrics during the lifecycle of your trainer. Once you have gained access, The PopVision Graph Analyser Tool can be downloaded via the `GraphCore download website `__. + +Lightning supports dumping all reports to a directory to open using the tool. + +.. code-block:: python + + import lightning.pytorch as pl + from lightning.pytorch.strategies import IPUStrategy + + model = MyLightningModule() + trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(autoreport_dir="report_dir/")) + trainer.fit(model) + +This will dump all reports to ``report_dir/`` which can then be opened using the Graph Analyser Tool, see `Opening Reports `__. diff --git a/docs/html/_sources/prepare.rst.txt b/docs/html/_sources/prepare.rst.txt new file mode 100644 index 0000000..ca765e7 --- /dev/null +++ b/docs/html/_sources/prepare.rst.txt @@ -0,0 +1,171 @@ +:orphan: + +.. _ipu_prepare: + +######################################## +Hardware agnostic training (preparation) +######################################## + +To train on CPU/GPU/TPU without changing your code, we need to build a few good habits :) + +---- + +***************************** +Delete .cuda() or .to() calls +***************************** + +Delete any calls to .cuda() or .to(device). + +.. testcode:: + + # before lightning + def forward(self, x): + x = x.cuda(0) + layer_1.cuda(0) + x_hat = layer_1(x) + + + # after lightning + def forward(self, x): + x_hat = layer_1(x) + +---- + +************************************************ +Init tensors using Tensor.to and register_buffer +************************************************ +When you need to create a new tensor, use ``Tensor.to``. +This will make your code scale to any arbitrary number of GPUs or TPUs with Lightning. + +.. testcode:: + + # before lightning + def forward(self, x): + z = torch.Tensor(2, 3) + z = z.cuda(0) + + + # with lightning + def forward(self, x): + z = torch.Tensor(2, 3) + z = z.to(x) + +The :class:`~lightning.pytorch.core.module.LightningModule` knows what device it is on. You can access the reference via ``self.device``. +Sometimes it is necessary to store tensors as module attributes. However, if they are not parameters they will +remain on the CPU even if the module gets moved to a new device. To prevent that and remain device agnostic, +register the tensor as a buffer in your modules' ``__init__`` method with :meth:`~torch.nn.Module.register_buffer`. + +.. testcode:: + + class LitModel(LightningModule): + def __init__(self): + ... + self.register_buffer("sigma", torch.eye(3)) + # you can now access self.sigma anywhere in your module + +---- + +*************** +Remove samplers +*************** + +:class:`~torch.utils.data.distributed.DistributedSampler` is automatically handled by Lightning. + +---- + +*************************************** +Synchronize validation and test logging +*************************************** + +When running in distributed mode, we have to ensure that the validation and test step logging calls are synchronized across processes. +This is done by adding ``sync_dist=True`` to all ``self.log`` calls in the validation and test step. +This ensures that each GPU worker has the same behaviour when tracking model checkpoints, which is important for later downstream tasks such as testing the best checkpoint across all workers. +The ``sync_dist`` option can also be used in logging calls during the step methods, but be aware that this can lead to significant communication overhead and slow down your training. + +Note if you use any built in metrics or custom metrics that use `TorchMetrics `_, these do not need to be updated and are automatically handled for you. + +.. testcode:: + + def validation_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = self.loss(logits, y) + # Add sync_dist=True to sync logging across all GPU workers (may have performance impact) + self.log("validation_loss", loss, on_step=True, on_epoch=True, sync_dist=True) + + + def test_step(self, batch, batch_idx): + x, y = batch + logits = self(x) + loss = self.loss(logits, y) + # Add sync_dist=True to sync logging across all GPU workers (may have performance impact) + self.log("test_loss", loss, on_step=True, on_epoch=True, sync_dist=True) + +It is possible to perform some computation manually and log the reduced result on rank 0 as follows: + +.. code-block:: python + + def __init__(self): + super().__init__() + self.outputs = [] + + + def test_step(self, batch, batch_idx): + x, y = batch + tensors = self(x) + self.outputs.append(tensors) + return tensors + + + def on_test_epoch_end(self): + mean = torch.mean(self.all_gather(self.outputs)) + self.outputs.clear() # free memory + + # When logging only on rank 0, don't forget to add + # `rank_zero_only=True` to avoid deadlocks on synchronization. + # caveat: monitoring this is unimplemented. see https://github.com/Lightning-AI/lightning/issues/15852 + if self.trainer.is_global_zero: + self.log("my_reduced_metric", mean, rank_zero_only=True) + +---- + +********************** +Make models pickleable +********************** +It's very likely your code is already `pickleable `_, +in that case no change in necessary. +However, if you run a distributed model and get the following error: + +.. code-block:: + + self._launch(process_obj) + File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47, + in _launch reduction.dump(process_obj, fp) + File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/reduction.py", line 60, in dump + ForkingPickler(file, protocol).dump(obj) + _pickle.PicklingError: Can't pickle at 0x2b599e088ae8>: + attribute lookup on __main__ failed + +This means something in your model definition, transforms, optimizer, dataloader or callbacks cannot be pickled, and the following code will fail: + +.. code-block:: python + + import pickle + + pickle.dump(some_object) + +This is a limitation of using multiple processes for distributed training within PyTorch. +To fix this issue, find your piece of code that cannot be pickled. The end of the stacktrace +is usually helpful. +ie: in the stacktrace example here, there seems to be a lambda function somewhere in the code +which cannot be pickled. + +.. code-block:: + + self._launch(process_obj) + File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47, + in _launch reduction.dump(process_obj, fp) + File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/reduction.py", line 60, in dump + ForkingPickler(file, protocol).dump(obj) + _pickle.PicklingError: Can't pickle [THIS IS THE THING TO FIND AND DELETE]: + attribute lookup on __main__ failed diff --git a/docs/html/_sources/readme.md.txt b/docs/html/_sources/readme.md.txt new file mode 100644 index 0000000..771a805 --- /dev/null +++ b/docs/html/_sources/readme.md.txt @@ -0,0 +1,66 @@ +# Lightning ⚡ GraphCore + +**Audience:** Users looking to save money and run large models faster using single or multiple IPU devices. + + + + + + + +## What is an IPU? + +The Graphcore [Intelligence Processing Unit (IPU)](https://www.graphcore.ai/products/ipu), built for Artificial Intelligence and Machine Learning, consists of many individual cores, called *tiles*, allowing highly parallel computation. Due to the high bandwidth between tiles, IPUs facilitate machine learning loads where parallelization is essential. Because computation is heavily parallelized, + +IPUs operate in a different way to conventional accelerators such as CPU/GPUs. IPUs do not require large batch sizes for maximum parallelization, can provide optimizations across the compiled graph and rely on model parallelism to fully utilize tiles for larger models. + +IPUs are used to build IPU-PODs, rack-based systems of IPU-Machines for larger workloads. See the [IPU Architecture](https://www.graphcore.ai/products/ipu) for more information. + +See the [Graphcore Glossary](https://docs.graphcore.ai/projects/graphcore-glossary/) for the definitions of other IPU-specific terminology. + +______________________________________________________________________ + +## Installation + +```bash +pip install -U lightning-graphcore[lightning] +``` + +## Run on IPU + +To enable PyTorch Lightning to utilize the IPU accelerator, simply provide `accelerator="ipu"` parameter to the Trainer class. + +To use multiple IPUs set the devices to a number that is a power of 2 (i.e: 2, 4, 8, 16, ...) + +```py +from lightning import Trainer +# run on as many IPUs as available by default +trainer = Trainer(accelerator="auto", devices="auto", strategy="auto") +# equivalent to +trainer = Trainer() + +# run on one IPU +trainer = Trainer(accelerator="ipu", devices=1) +# run on multiple IPUs +trainer = Trainer(accelerator="ipu", devices=8) +# choose the number of devices automatically +trainer = Trainer(accelerator="ipu", devices="auto") +``` + +## How to access IPUs + +To use IPUs you must have access to a system with IPU devices. To get access see [get started](https://www.graphcore.ai/getstarted). + +You must ensure that the IPU system has enabled the PopART and Poplar packages from the SDK. Instructions are in the Get Started guide for your IPU system, on the Graphcore [documents portal](https://docs.graphcore.ai/page/getting-started.html). + +## Known limitations + +Currently there are some known limitations that are being addressed in the near future to make the experience seamless when moving from different devices. + +Please see the [MNIST example](https://github.com/Lightning-AI/lightning/blob/master/examples/pytorch/ipu/mnist_sample.py) which displays most of the limitations and how to overcome them till they are resolved. + +- `self.log` is not supported in the `training_step`, `validation_step`, `test_step` or `predict_step`. This is due to the step function being traced and sent to the IPU devices. +- Since the step functions are traced, branching logic or any form of primitive values are traced into constants. Be mindful as this could lead to errors in your custom code. +- Clipping gradients is not supported. +- It is not possible to use `BatchSampler` in your dataloaders if you are using multiple IPUs. +- IPUs handle the data transfer to the device on the host, hence the hooks `ModelHooks.transfer_batch_to_device` and `ModelHooks.on_after_batch_transfer` do not apply here and if you have overridden any of them, an exception will be raised. diff --git a/docs/html/_static/basic.css b/docs/html/_static/basic.css new file mode 100644 index 0000000..7577acb --- /dev/null +++ b/docs/html/_static/basic.css @@ -0,0 +1,903 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 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border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp 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overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + 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---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/docs/html/_static/copybutton.js b/docs/html/_static/copybutton.js new file mode 100644 index 0000000..aef241a --- /dev/null +++ b/docs/html/_static/copybutton.js @@ -0,0 +1,78 @@ +/* Copied from the official Python docs: https://docs.python.org/3/_static/copybutton.js */ +$(document).ready(function () { + /* Add a [>>>] button on the top-right corner of code samples to hide + * the >>> and ... prompts and the output and thus make the code + * copyable. */ + var div = $( + ".highlight-python .highlight," + + ".highlight-python3 .highlight," + + ".highlight-pycon .highlight," + + ".highlight-default .highlight", + ); + var pre = div.find("pre"); + + // get the styles from the current theme + pre.parent().parent().css("position", "relative"); + var hide_text = "Hide the prompts and output"; + var show_text = "Show the prompts and output"; + var border_width = pre.css("border-top-width"); + var border_style = pre.css("border-top-style"); + var border_color = pre.css("border-top-color"); + var button_styles = { + cursor: "pointer", + position: "absolute", + top: "0", + right: "0", + "border-color": border_color, + "border-style": border_style, + "border-width": border_width, + color: border_color, + "text-size": "75%", + "font-family": "monospace", + "padding-left": "0.2em", + "padding-right": "0.2em", + "border-radius": "0 3px 0 0", + }; + + // create and add the button to all the code blocks that contain >>> + div.each(function (index) { + var jthis = $(this); + if (jthis.find(".gp").length > 0) { + var button = $('>>>'); + button.css(button_styles); + button.attr("title", hide_text); + button.data("hidden", "false"); + jthis.prepend(button); + } + // tracebacks (.gt) contain bare text elements that need to be + // wrapped in a span to work with .nextUntil() (see later) + jthis + .find("pre:has(.gt)") + .contents() + .filter(function () { + return this.nodeType == 3 && this.data.trim().length > 0; + }) + .wrap(""); + }); + + // define the behavior of the button when it's clicked + $(".copybutton").click(function (e) { + e.preventDefault(); + var button = $(this); + if (button.data("hidden") === "false") { + // hide the code output + button.parent().find(".go, .gp, .gt").hide(); + button.next("pre").find(".gt").nextUntil(".gp, .go").css("visibility", "hidden"); + button.css("text-decoration", "line-through"); 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s=this.getTipElement(),l=d.getUID(this.constructor.NAME);s.setAttribute("id",l),this.element.setAttribute("aria-describedby",l),this.setContent(),this.config.animation&&o.default(s).addClass(Lt);var r="function"==typeof this.config.placement?this.config.placement.call(this,s,this.element):this.config.placement,u=this._getAttachment(r);this.addAttachmentClass(u);var f=this._getContainer();o.default(s).data(this.constructor.DATA_KEY,this),o.default.contains(this.element.ownerDocument.documentElement,this.tip)||o.default(s).appendTo(f),o.default(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new a.default(this.element,s,this._getPopperConfig(u)),o.default(s).addClass(Rt),o.default(s).addClass(this.config.customClass),"ontouchstart"in document.documentElement&&o.default(document.body).children().on("mouseover",null,o.default.noop);var c=function(){t.config.animation&&t._fixTransition();var 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t=this,e=o.default.Event("show.bs.toast");if(o.default(this._element).trigger(e),!e.isDefaultPrevented()){this._clearTimeout(),this._config.animation&&this._element.classList.add("fade");var n=function(){t._element.classList.remove(ye),t._element.classList.add(ve),o.default(t._element).trigger("shown.bs.toast"),t._config.autohide&&(t._timeout=setTimeout((function(){t.hide()}),t._config.delay))};if(this._element.classList.remove(_e),d.reflow(this._element),this._element.classList.add(ye),this._config.animation){var i=d.getTransitionDurationFromElement(this._element);o.default(this._element).one(d.TRANSITION_END,n).emulateTransitionEnd(i)}else n()}},e.hide=function(){if(this._element.classList.contains(ve)){var t=o.default.Event("hide.bs.toast");o.default(this._element).trigger(t),t.isDefaultPrevented()||this._close()}},e.dispose=function(){this._clearTimeout(),this._element.classList.contains(ve)&&this._element.classList.remove(ve),o.default(this._element).off(be),o.default.removeData(this._element,me),this._element=null,this._config=null},e._getConfig=function(t){return t=r({},Ee,o.default(this._element).data(),"object"==typeof t&&t?t:{}),d.typeCheckConfig("toast",t,this.constructor.DefaultType),t},e._setListeners=function(){var t=this;o.default(this._element).on(be,'[data-dismiss="toast"]',(function(){return t.hide()}))},e._close=function(){var t=this,e=function(){t._element.classList.add(_e),o.default(t._element).trigger("hidden.bs.toast")};if(this._element.classList.remove(ve),this._config.animation){var n=d.getTransitionDurationFromElement(this._element);o.default(this._element).one(d.TRANSITION_END,e).emulateTransitionEnd(n)}else 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namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // 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query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/html/_static/sphinx_highlight.js b/docs/html/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/docs/html/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/html/_static/sphinx_paramlinks.css b/docs/html/_static/sphinx_paramlinks.css new file mode 100644 index 0000000..1d320a0 --- /dev/null +++ b/docs/html/_static/sphinx_paramlinks.css @@ -0,0 +1,13 @@ +li > a.paramlink { + visibility: hidden; +} + + +p:hover > a.headerlink, p:hover > a.paramlink, li:hover > a.paramlink, li:hover > a.headerlink { + visibility: visible; +} + +a.paramname { + color:inherit; +} + diff --git a/docs/html/_static/theme_variables.jinja b/docs/html/_static/theme_variables.jinja new file mode 100644 index 0000000..f6b8c75 --- /dev/null +++ b/docs/html/_static/theme_variables.jinja @@ -0,0 +1,18 @@ +{%- set external_urls = { + 'github': 'https://github.com/Lightning-AI/lightning-graphcore', + 'github_issues': 'https://github.com/Lightning-AI/lightning-graphcore/issues', + 'contributing': 'https://github.com/Lightning-AI/lightning/blob/master/CONTRIBUTING.md', + 'governance': 'https://github.com/Lightning-AI/lightning/blob/master/governance.md', + 'docs': 'https://lightning-graphcore.rtfd.io/en/latest', + 'twitter': 'https://twitter.com/PyTorchLightnin', + 'discuss': 'https://pytorch-lightning.slack.com', + 'tutorials': 'https://lightning-graphcore.readthedocs.io/en/latest/#tutorials', + 'previous_pytorch_versions': 'https://lightning-graphcore.rtfd.io/en/latest/', + 'home': 'https://lightning-graphcore.rtfd.io/en/latest/', + 'get_started': 'https://lightning-graphcore.readthedocs.io/en/latest/introduction_guide.html', + 'features': 'https://lightning-graphcore.rtfd.io/en/latest/', + 'blog': 'https://www.Lightning-AI.ai/blog', + 'resources': 'https://lightning-graphcore.readthedocs.io/en/latest/#community-examples', + 'support': 'https://lightning-graphcore.rtfd.io/en/latest/', +} +-%} diff --git a/docs/html/advanced.html b/docs/html/advanced.html new file mode 100644 index 0000000..7bdd026 --- /dev/null +++ b/docs/html/advanced.html @@ -0,0 +1,713 @@ + + + + + + + + + + + + + + + + + + Accelerator: IPU training — Lightning-Graphcore 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Accelerator: IPU training

+

Audience: Users looking to customize IPU training for massive models.

+
+
+

Advanced IPU options

+

IPUs provide further optimizations to speed up training. By using the IPUStrategy we can set the device_iterations, which controls the number of iterations run directly on the IPU devices before returning to the host. Increasing the number of on-device iterations will improve throughput, as there is less device to host communication required.

+
+

Note

+

When using model parallelism, it is a hard requirement to increase the number of device iterations to ensure we fully saturate the devices via micro-batching. see Model parallelism for more information.

+
+
import lightning.pytorch as pl
+from lightning.pytorch.strategies import IPUStrategy
+
+model = MyLightningModule()
+trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=32))
+trainer.fit(model)
+
+
+

Note that by default we return the last device iteration loss. You can override this by passing in your own poptorch.Options and setting the AnchorMode as described in the PopTorch documentation.

+
import poptorch
+import lightning.pytorch as pl
+from lightning.pytorch.strategies import IPUStrategy
+
+model = MyLightningModule()
+inference_opts = poptorch.Options()
+inference_opts.deviceIterations(32)
+
+training_opts = poptorch.Options()
+training_opts.anchorMode(poptorch.AnchorMode.All)
+training_opts.deviceIterations(32)
+
+trainer = Trainer(
+    accelerator="ipu", devices=8, strategy=IPUStrategy(inference_opts=inference_opts, training_opts=training_opts)
+)
+trainer.fit(model)
+
+
+

You can also override all options by passing the poptorch.Options to the plugin. See PopTorch options documentation for more information.

+
+
+
+

Model parallelism

+

Due to the IPU architecture, larger models should be parallelized across IPUs by design. Currently PopTorch provides the capabilities via annotations as described in parallel execution strategies.

+

Below is an example using the block annotation in a LightningModule.

+
+

Note

+

Currently, when using model parallelism we do not infer the number of IPUs required for you. This is done via the annotations themselves. If you specify 4 different IDs when defining Blocks, this means your model will be split onto 4 different IPUs.

+

This is also mutually exclusive with the Trainer flag. In other words, if your model is split onto 2 IPUs and you set Trainer(accelerator="ipu", devices=4) this will require 8 IPUs in total: data parallelism will be used to replicate the two-IPU model 4 times.

+

When pipelining the model you must also increase the device_iterations to ensure full data saturation of the devices data, i.e whilst one device in the model pipeline processes a batch of data, the other device can start on the next batch. For example if the model is split onto 4 IPUs, we require device_iterations to be at-least 4.

+
+
import lightning.pytorch as pl
+import poptorch
+
+
+class MyLightningModule(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+        # This will place layer1, layer2+layer3, layer4, softmax on different IPUs at runtime.
+        # BeginBlock will start a new id for all layers within this block
+        self.layer1 = poptorch.BeginBlock(torch.nn.Linear(5, 10), ipu_id=0)
+
+        # This layer starts a new block,
+        # adding subsequent layers to this current block at runtime
+        # till the next block has been declared
+        self.layer2 = poptorch.BeginBlock(torch.nn.Linear(10, 5), ipu_id=1)
+        self.layer3 = torch.nn.Linear(5, 5)
+
+        # Create new blocks
+        self.layer4 = poptorch.BeginBlock(torch.nn.Linear(5, 5), ipu_id=2)
+        self.softmax = poptorch.BeginBlock(torch.nn.Softmax(dim=1), ipu_id=3)
+
+    ...
+
+
+model = MyLightningModule()
+trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=20))
+trainer.fit(model)
+
+
+

You can also use the block context manager within the forward function, or any of the step functions.

+
import lightning.pytorch as pl
+import poptorch
+
+
+class MyLightningModule(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.layer1 = torch.nn.Linear(5, 10)
+        self.layer2 = torch.nn.Linear(10, 5)
+        self.layer3 = torch.nn.Linear(5, 5)
+        self.layer4 = torch.nn.Linear(5, 5)
+
+        self.act = torch.nn.ReLU()
+        self.softmax = torch.nn.Softmax(dim=1)
+
+    def forward(self, x):
+        with poptorch.Block(ipu_id=0):
+            x = self.act(self.layer1(x))
+
+        with poptorch.Block(ipu_id=1):
+            x = self.act(self.layer2(x))
+
+        with poptorch.Block(ipu_id=2):
+            x = self.act(self.layer3(x))
+            x = self.act(self.layer4(x))
+
+        with poptorch.Block(ipu_id=3):
+            x = self.softmax(x)
+        return x
+
+    ...
+
+
+model = MyLightningModule()
+trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=20))
+trainer.fit(model)
+
+
+
+
+ + +
+ +
+
+ + + + +
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+ © Copyright Copyright (c) 2020-2023, Lightning-AI et al... + +

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Accelerator: IPU training

+

Audience: Users looking to save money and run large models faster using single or multiple IPU devices.

+
+
+

What is an IPU?

+

The Graphcore Intelligence Processing Unit (IPU), built for Artificial Intelligence and Machine Learning, consists of many individual cores, called tiles, allowing highly parallel computation. Due to the high bandwidth between tiles, IPUs facilitate machine learning loads where parallelization is essential. Because computation is heavily parallelized,

+

IPUs operate in a different way to conventional accelerators such as CPU/GPUs. IPUs do not require large batch sizes for maximum parallelization, can provide optimizations across the compiled graph and rely on model parallelism to fully utilize tiles for larger models.

+

IPUs are used to build IPU-PODs, rack-based systems of IPU-Machines for larger workloads. See the IPU Architecture for more information.

+

See the Graphcore Glossary for the definitions of other IPU-specific terminology.

+
+
+
+

Run on IPU

+

To enable PyTorch Lightning to utilize the IPU accelerator, simply provide accelerator="ipu" parameter to the Trainer class.

+

To use multiple IPUs set the devices to a number that is a power of 2 (i.e: 2, 4, 8, 16, …)

+
# run on as many IPUs as available by default
+trainer = Trainer(accelerator="auto", devices="auto", strategy="auto")
+# equivalent to
+trainer = Trainer()
+
+# run on one IPU
+trainer = Trainer(accelerator="ipu", devices=1)
+# run on multiple IPUs
+trainer = Trainer(accelerator="ipu", devices=8)
+# choose the number of devices automatically
+trainer = Trainer(accelerator="ipu", devices="auto")
+
+
+
+
+
+

How to access IPUs

+

To use IPUs you must have access to a system with IPU devices. To get access see get started.

+

You must ensure that the IPU system has enabled the PopART and Poplar packages from the SDK. Instructions are in the Get Started guide for your IPU system, on the Graphcore documents portal.

+
+
+
+

Known limitations

+

Currently there are some known limitations that are being addressed in the near future to make the experience seamless when moving from different devices.

+

Please see the MNIST example which displays most of the limitations and how to overcome them till they are resolved.

+
    +

  • self.log is not supported in the training_step, validation_step, test_step or predict_step. This is due to the step function being traced and sent to the IPU devices.

    • +

  • Since the step functions are traced, branching logic or any form of primitive values are traced into constants. Be mindful as this could lead to errors in your custom code.

    • +

  • Clipping gradients is not supported.

    • +

  • It is not possible to use torch.utils.data.BatchSampler in your dataloaders if you are using multiple IPUs.

    • +

  • IPUs handle the data transfer to the device on the host, hence the hooks transfer_batch_to_device() and +on_after_batch_transfer() do not apply here and if you have overridden any of them, an exception will be raised.

    • +
+
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Prepare your code to run on any hardware

+
+ +
basic
+

+

+ +
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+

Basic

+

Learn the basics of single and multi-IPU training.

+
+ +
basic
+

+

+ +
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+

Intermediate

+

Tune model performance with mix-precision settings and the performance analyser.

+
+ +
intermediate
+

+

+ +
+
+

Advanced

+

Learn advanced techniques to customize IPU training for massive models.

+
+ +
advanced
+

+
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+

Start here

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Indices and tables

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Accelerator: IPU training

+

Audience: IPU users looking to increase performance via mixed precision and analysis tools.

+
+
+

Mixed precision & 16 bit precision

+

Lightning also supports training in mixed precision with IPUs. +By default, IPU training will use 32-bit precision. To enable mixed precision, +set the precision flag.

+
+

Note

+

Currently there is no dynamic scaling of the loss with mixed precision training.

+
+
import lightning.pytorch as pl
+
+model = MyLightningModule()
+trainer = pl.Trainer(accelerator="ipu", devices=8, precision=16)
+trainer.fit(model)
+
+
+

You can also use pure 16-bit training, where the weights are also in 16-bit precision.

+
import lightning.pytorch as pl
+from lightning.pytorch.strategies import IPUStrategy
+
+model = MyLightningModule()
+model = model.half()
+trainer = pl.Trainer(accelerator="ipu", devices=8, precision=16)
+trainer.fit(model)
+
+
+
+
+
+

PopVision Graph Analyser

+
+PopVision Graph Analyser +
+

Lightning supports integration with the PopVision Graph Analyser Tool. This helps to look at utilization of IPU devices and provides helpful metrics during the lifecycle of your trainer. Once you have gained access, The PopVision Graph Analyser Tool can be downloaded via the GraphCore download website.

+

Lightning supports dumping all reports to a directory to open using the tool.

+
import lightning.pytorch as pl
+from lightning.pytorch.strategies import IPUStrategy
+
+model = MyLightningModule()
+trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(autoreport_dir="report_dir/"))
+trainer.fit(model)
+
+
+

This will dump all reports to report_dir/ which can then be opened using the Graph Analyser Tool, see Opening Reports.

+
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Hardware agnostic training (preparation)

+

To train on CPU/GPU/TPU without changing your code, we need to build a few good habits :)

+
+
+

Delete .cuda() or .to() calls

+

Delete any calls to .cuda() or .to(device).

+
# before lightning
+def forward(self, x):
+    x = x.cuda(0)
+    layer_1.cuda(0)
+    x_hat = layer_1(x)
+
+
+# after lightning
+def forward(self, x):
+    x_hat = layer_1(x)
+
+
+
+
+
+

Init tensors using Tensor.to and register_buffer

+

When you need to create a new tensor, use Tensor.to. +This will make your code scale to any arbitrary number of GPUs or TPUs with Lightning.

+
# before lightning
+def forward(self, x):
+    z = torch.Tensor(2, 3)
+    z = z.cuda(0)
+
+
+# with lightning
+def forward(self, x):
+    z = torch.Tensor(2, 3)
+    z = z.to(x)
+
+
+

The LightningModule knows what device it is on. You can access the reference via self.device. +Sometimes it is necessary to store tensors as module attributes. However, if they are not parameters they will +remain on the CPU even if the module gets moved to a new device. To prevent that and remain device agnostic, +register the tensor as a buffer in your modules’ __init__ method with register_buffer().

+
class LitModel(LightningModule):
+    def __init__(self):
+        ...
+        self.register_buffer("sigma", torch.eye(3))
+        # you can now access self.sigma anywhere in your module
+
+
+
+
+
+

Remove samplers

+

DistributedSampler is automatically handled by Lightning.

+
+
+
+

Synchronize validation and test logging

+

When running in distributed mode, we have to ensure that the validation and test step logging calls are synchronized across processes. +This is done by adding sync_dist=True to all self.log calls in the validation and test step. +This ensures that each GPU worker has the same behaviour when tracking model checkpoints, which is important for later downstream tasks such as testing the best checkpoint across all workers. +The sync_dist option can also be used in logging calls during the step methods, but be aware that this can lead to significant communication overhead and slow down your training.

+

Note if you use any built in metrics or custom metrics that use TorchMetrics, these do not need to be updated and are automatically handled for you.

+
def validation_step(self, batch, batch_idx):
+    x, y = batch
+    logits = self(x)
+    loss = self.loss(logits, y)
+    # Add sync_dist=True to sync logging across all GPU workers (may have performance impact)
+    self.log("validation_loss", loss, on_step=True, on_epoch=True, sync_dist=True)
+
+
+def test_step(self, batch, batch_idx):
+    x, y = batch
+    logits = self(x)
+    loss = self.loss(logits, y)
+    # Add sync_dist=True to sync logging across all GPU workers (may have performance impact)
+    self.log("test_loss", loss, on_step=True, on_epoch=True, sync_dist=True)
+
+
+

It is possible to perform some computation manually and log the reduced result on rank 0 as follows:

+
def __init__(self):
+    super().__init__()
+    self.outputs = []
+
+
+def test_step(self, batch, batch_idx):
+    x, y = batch
+    tensors = self(x)
+    self.outputs.append(tensors)
+    return tensors
+
+
+def on_test_epoch_end(self):
+    mean = torch.mean(self.all_gather(self.outputs))
+    self.outputs.clear()  # free memory
+
+    # When logging only on rank 0, don't forget to add
+    # `rank_zero_only=True` to avoid deadlocks on synchronization.
+    # caveat: monitoring this is unimplemented. see https://github.com/Lightning-AI/lightning/issues/15852
+    if self.trainer.is_global_zero:
+        self.log("my_reduced_metric", mean, rank_zero_only=True)
+
+
+
+
+
+

Make models pickleable

+

It’s very likely your code is already pickleable, +in that case no change in necessary. +However, if you run a distributed model and get the following error:

+
self._launch(process_obj)
+File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47,
+in _launch reduction.dump(process_obj, fp)
+File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
+ForkingPickler(file, protocol).dump(obj)
+_pickle.PicklingError: Can't pickle <function <lambda> at 0x2b599e088ae8>:
+attribute lookup <lambda> on __main__ failed
+
+
+

This means something in your model definition, transforms, optimizer, dataloader or callbacks cannot be pickled, and the following code will fail:

+
import pickle
+
+pickle.dump(some_object)
+
+
+

This is a limitation of using multiple processes for distributed training within PyTorch. +To fix this issue, find your piece of code that cannot be pickled. The end of the stacktrace +is usually helpful. +ie: in the stacktrace example here, there seems to be a lambda function somewhere in the code +which cannot be pickled.

+
self._launch(process_obj)
+File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/popen_spawn_posix.py", line 47,
+in _launch reduction.dump(process_obj, fp)
+File "/net/software/local/python/3.6.5/lib/python3.6/multiprocessing/reduction.py", line 60, in dump
+ForkingPickler(file, protocol).dump(obj)
+_pickle.PicklingError: Can't pickle [THIS IS THE THING TO FIND AND DELETE]:
+attribute lookup <lambda> on __main__ failed
+
+
+
+
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Lightning ⚡ GraphCore

+

Audience: Users looking to save money and run large models faster using single or multiple IPU devices.

+
+

What is an IPU?

+

The Graphcore Intelligence Processing Unit (IPU), built for Artificial Intelligence and Machine Learning, consists of many individual cores, called tiles, allowing highly parallel computation. Due to the high bandwidth between tiles, IPUs facilitate machine learning loads where parallelization is essential. Because computation is heavily parallelized,

+

IPUs operate in a different way to conventional accelerators such as CPU/GPUs. IPUs do not require large batch sizes for maximum parallelization, can provide optimizations across the compiled graph and rely on model parallelism to fully utilize tiles for larger models.

+

IPUs are used to build IPU-PODs, rack-based systems of IPU-Machines for larger workloads. See the IPU Architecture for more information.

+

See the Graphcore Glossary for the definitions of other IPU-specific terminology.

+
+
+
+

Installation

+
pip install -U lightning-graphcore[lightning]
+
+
+
+
+

Run on IPU

+

To enable PyTorch Lightning to utilize the IPU accelerator, simply provide accelerator="ipu" parameter to the Trainer class.

+

To use multiple IPUs set the devices to a number that is a power of 2 (i.e: 2, 4, 8, 16, …)

+
from lightning import Trainer
+# run on as many IPUs as available by default
+trainer = Trainer(accelerator="auto", devices="auto", strategy="auto")
+# equivalent to
+trainer = Trainer()
+
+# run on one IPU
+trainer = Trainer(accelerator="ipu", devices=1)
+# run on multiple IPUs
+trainer = Trainer(accelerator="ipu", devices=8)
+# choose the number of devices automatically
+trainer = Trainer(accelerator="ipu", devices="auto")
+
+
+
+
+

How to access IPUs

+

To use IPUs you must have access to a system with IPU devices. To get access see get started.

+

You must ensure that the IPU system has enabled the PopART and Poplar packages from the SDK. Instructions are in the Get Started guide for your IPU system, on the Graphcore documents portal.

+
+
+

Known limitations

+

Currently there are some known limitations that are being addressed in the near future to make the experience seamless when moving from different devices.

+

Please see the MNIST example which displays most of the limitations and how to overcome them till they are resolved.

+
    +

  • self.log is not supported in the training_step, validation_step, test_step or predict_step. This is due to the step function being traced and sent to the IPU devices.

    • +

  • Since the step functions are traced, branching logic or any form of primitive values are traced into constants. Be mindful as this could lead to errors in your custom code.

    • +

  • Clipping gradients is not supported.

    • +

  • It is not possible to use BatchSampler in your dataloaders if you are using multiple IPUs.

    • +

  • IPUs handle the data transfer to the device on the host, hence the hooks ModelHooks.transfer_batch_to_device and ModelHooks.on_after_batch_transfer do not apply here and if you have overridden any of them, an exception will be raised.

    • +
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