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[RLlib](deps): Bump tensorflow from 2.5.0 to 2.7.0 in /python/requirements/rllib #7

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Bumps tensorflow from 2.5.0 to 2.7.0.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.7.0

Release 2.7.0

Breaking Changes

  • tf.keras:

    • The methods Model.fit(), Model.predict(), and Model.evaluate() will no longer uprank input data of shape (batch_size,) to become (batch_size, 1). This enables Model subclasses to process scalar data in their train_step()/test_step()/predict_step() methods.
      Note that this change may break certain subclassed models. You can revert back to the previous behavior by adding upranking yourself in the train_step()/test_step()/predict_step() methods, e.g. if x.shape.rank == 1: x = tf.expand_dims(x, axis=-1). Functional models as well as Sequential models built with an explicit input shape are not affected.
    • The methods Model.to_yaml() and keras.models.model_from_yaml have been replaced to raise a RuntimeError as they can be abused to cause arbitrary code execution. It is recommended to use JSON serialization instead of YAML, or, a better alternative, serialize to H5.
    • LinearModel and WideDeepModel are moved to the tf.compat.v1.keras.models. namespace (tf.compat.v1.keras.models.LinearModel and tf.compat.v1.keras.models.WideDeepModel), and their experimental endpoints (tf.keras.experimental.models.LinearModel and tf.keras.experimental.models.WideDeepModel) are being deprecated.
    • RNG behavior change for all tf.keras.initializers classes. For any class constructed with a fixed seed, it will no longer generate same value when invoked multiple times. Instead, it will return different value, but a determinisitic sequence. This change will make the initialize behavior align between v1 and v2.

  • tf.lite:

    • Rename fields SignatureDef table in schema to maximize the parity with TF SavedModel's Signature concept.
    • Deprecate Makefile builds. Makefile users need to migrate their builds to CMake or Bazel. Please refer to the Build TensorFlow Lite with CMake and Build TensorFlow Lite for ARM boards for the migration.
    • Deprecate tflite::OpResolver::GetDelegates. The list returned by TfLite's BuiltinOpResolver::GetDelegates is now always empty. Instead, recommend using new method tflite::OpResolver::GetDelegateCreators in order to achieve lazy initialization on TfLite delegate instances.

  • TF Core:

    • tf.Graph.get_name_scope() now always returns a string, as documented. Previously, when called within name_scope("") or name_scope(None) contexts, it returned None; now it returns the empty string.
    • tensorflow/core/ir/ contains a new MLIR-based Graph dialect that is isomorphic to GraphDef and will be used to replace GraphDef-based (e.g., Grappler) optimizations.
    • Deprecated and removed attrs() function in shape inference. All attributes should be queried by name now (rather than range returned) to enable changing the underlying storage there.
    • The following Python symbols were accidentally added in earlier versions of TensorFlow and now are removed. Each symbol has a replacement that should be used instead, but note the replacement's argument names are different.
      • tf.quantize_and_dequantize_v4 (accidentally introduced in TensorFlow 2.4): Use tf.quantization.quantize_and_dequantize_v2 instead.
      • tf.batch_mat_mul_v3 (accidentally introduced in TensorFlow 2.6): Use tf.linalg.matmul instead.
      • tf.sparse_segment_sum_grad (accidentally introduced in TensorFlow 2.6): Use tf.raw_ops.SparseSegmentSumGrad instead. Directly calling this op is typically not necessary, as it is automatically used when computing the gradient of tf.sparse.segment_sum.
    • Renaming of tensorflow::int64 to int_64_t in numerous places (the former is an alias for the latter) which could result in needing to regenerate selective op registration headers else execution would fail with unregistered kernels error.

  • Modular File System Migration:

    • Support for S3 and HDFS file systems has been migrated to a modular file systems based approach and is now available in https://github.com/tensorflow/io. The tensorflow-io python package should be installed for S3 and HDFS support with tensorflow.

Major Features and Improvements

  • Improvements to the TensorFlow debugging experience:

    • Previously, TensorFlow error stack traces involved many internal frames, which could be challenging to read through, while not being actionable for end users. As of TF 2.7, TensorFlow filters internal frames in most errors that it raises, to keep stack traces short, readable, and focused on what's actionable for end users (their own code).

      This behavior can be disabled by calling tf.debugging.disable_traceback_filtering(), and can be re-enabled via tf.debugging.enable_traceback_filtering(). If you are debugging a TensorFlow-internal issue (e.g. to prepare a TensorFlow PR), make sure to disable traceback filtering. You can check whether this feature is currently enabled by calling tf.debugging.is_traceback_filtering_enabled().

      Note that this feature is only available with Python 3.7 or higher.

    • Improve the informativeness of error messages raised by Keras Layer.__call__(), by adding the full list of argument values passed to the layer in every exception.

  • Introduce the tf.compat.v1.keras.utils.track_tf1_style_variables decorator, which enables using large classes of tf1-style variable_scope, get_variable, and compat.v1.layer-based components from within TF2 models running with TF2 behavior enabled.

  • tf.data:

    • tf.data service now supports auto-sharding. Users specify the sharding policy with tf.data.experimental.service.ShardingPolicy enum. It can be one of OFF (equivalent to today's "parallel_epochs" mode), DYNAMIC (equivalent to today's "distributed_epoch" mode), or one of the static sharding policies: FILE, DATA, FILE_OR_DATA, or HINT (corresponding to values of tf.data.experimental.AutoShardPolicy).

      Static sharding (auto-sharding) requires the number of tf.data service workers be fixed. Users need to specify the worker addresses in tensorflow.data.experimental.DispatcherConfig.

    • tf.data.experimental.service.register_dataset now accepts optional compression argument.

  • Keras:

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.7.0

Breaking Changes

  • tf.keras:

    • The methods Model.fit(), Model.predict(), and Model.evaluate() will no longer uprank input data of shape (batch_size,) to become (batch_size, 1). This enables Model subclasses to process scalar data in their train_step()/test_step()/predict_step() methods.
      Note that this change may break certain subclassed models. You can revert back to the previous behavior by adding upranking yourself in the train_step()/test_step()/predict_step() methods, e.g. if x.shape.rank == 1: x = tf.expand_dims(x, axis=-1). Functional models as well as Sequential models built with an explicit input shape are not affected.
    • The methods Model.to_yaml() and keras.models.model_from_yaml have been replaced to raise a RuntimeError as they can be abused to cause arbitrary code execution. It is recommended to use JSON serialization instead of YAML, or, a better alternative, serialize to H5.
    • LinearModel and WideDeepModel are moved to the tf.compat.v1.keras.models. namespace (tf.compat.v1.keras.models.LinearModel and tf.compat.v1.keras.models.WideDeepModel), and their experimental endpoints (tf.keras.experimental.models.LinearModel and tf.keras.experimental.models.WideDeepModel) are being deprecated.
    • RNG behavior change for all tf.keras.initializers classes. For any class constructed with a fixed seed, it will no longer generate same value when invoked multiple times. Instead, it will return different value, but a determinisitic sequence. This change will make the initialize behavior align between v1 and v2.

  • tf.lite:

    • Rename fields SignatureDef table in schema to maximize the parity with TF SavedModel's Signature concept.
    • Deprecate Makefile builds. Makefile users need to migrate their builds to CMake or Bazel. Please refer to the Build TensorFlow Lite with CMake and Build TensorFlow Lite for ARM boards for the migration.
    • Deprecate tflite::OpResolver::GetDelegates. The list returned by TfLite's BuiltinOpResolver::GetDelegates is now always empty. Instead, recommend using new method tflite::OpResolver::GetDelegateCreators in order to achieve lazy initialization on TfLite delegate instances.

  • TF Core:

    • tf.Graph.get_name_scope() now always returns a string, as documented. Previously, when called within name_scope("") or name_scope(None) contexts, it returned None; now it returns the empty string.
    • tensorflow/core/ir/ contains a new MLIR-based Graph dialect that is isomorphic to GraphDef and will be used to replace GraphDef-based (e.g., Grappler) optimizations.
    • Deprecated and removed attrs() function in shape inference. All attributes should be queried by name now (rather than range returned) to enable changing the underlying storage there.
    • The following Python symbols were accidentally added in earlier versions of TensorFlow and now are removed. Each symbol has a replacement that should be used instead, but note the replacement's argument names are different.
      • tf.quantize_and_dequantize_v4 (accidentally introduced in TensorFlow 2.4): Use tf.quantization.quantize_and_dequantize_v2 instead.
      • tf.batch_mat_mul_v3 (accidentally introduced in TensorFlow 2.6): Use tf.linalg.matmul instead.
      • tf.sparse_segment_sum_grad (accidentally introduced in TensorFlow 2.6): Use tf.raw_ops.SparseSegmentSumGrad instead. Directly calling this op is typically not necessary, as it is automatically used when computing the gradient of tf.sparse.segment_sum.
    • Renaming of tensorflow::int64 to int_64_t in numerous places (the former is an alias for the latter) which could result in needing to regenerate selective op registration headers else execution would fail with unregistered kernels error.

  • Modular File System Migration:

    • Support for S3 and HDFS file systems has been migrated to a modular file systems based approach and is now available in https://github.com/tensorflow/io. The tensorflow-io python package should be installed for S3 and HDFS support with tensorflow.

Major Features and Improvements

  • Improvements to the TensorFlow debugging experience:

    • Previously, TensorFlow error stack traces involved many internal frames, which could be challenging to read through, while not being actionable for end users. As of TF 2.7, TensorFlow filters internal frames in most errors that it raises, to keep stack traces short, readable, and focused on what's actionable for end users (their own code).

      This behavior can be disabled by calling tf.debugging.disable_traceback_filtering(), and can be re-enabled via tf.debugging.enable_traceback_filtering(). If you are debugging a TensorFlow-internal issue (e.g. to prepare a TensorFlow PR), make sure to disable traceback filtering. You can check whether this feature is currently enabled by calling tf.debugging.is_traceback_filtering_enabled().

      Note that this feature is only available with Python 3.7 or higher.

    • Improve the informativeness of error messages raised by Keras Layer.__call__(), by adding the full list of argument values passed to the layer in every exception.

  • Introduce the tf.compat.v1.keras.utils.track_tf1_style_variables decorator, which enables using large classes of tf1-style variable_scope, get_variable, and compat.v1.layer-based components from within TF2 models running with TF2 behavior enabled.

  • tf.data:

    • tf.data service now supports auto-sharding. Users specify the sharding policy with tf.data.experimental.service.ShardingPolicy enum. It can be one of OFF (equivalent to today's "parallel_epochs" mode), DYNAMIC (equivalent to today's "distributed_epoch" mode), or one of the static sharding policies: FILE, DATA, FILE_OR_DATA, or HINT (corresponding to values of tf.data.experimental.AutoShardPolicy).

      Static sharding (auto-sharding) requires the number of tf.data service workers be fixed. Users need to specify the worker addresses in tensorflow.data.experimental.DispatcherConfig.

    • tf.data.experimental.service.register_dataset now accepts optional compression argument.

  • Keras:

    • tf.keras.layers.Conv now includes a public convolution_op method. This method can be used to simplify the implementation of Conv subclasses. There are two primary ways to use this new method. The first is to use the method directly in your own call method:

... (truncated)

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[RLlib](deps): Bump tensorflow in /python/requirements/rllib
31ec6c9 
Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.0 to 2.7.0.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v2.5.0...v2.7.0)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Nov 18, 2021
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ArturNiederfahrenhorst pushed a commit that referenced this pull request Jul 28, 2022
@kfstorm
[Core] No RAY_LOG in the constructor of DelayManager (ray-project#26958)
34d0233 
We encountered SIGSEGV when running Python test `python/ray/tests/test_failure_2.py::test_list_named_actors_timeout`. The stack is:

```
#0  0x00007fffed30f393 in std::basic_string<char, std::char_traits<char>, std::allocator<char> >::basic_string(std::string const&) ()
   from /lib64/libstdc++.so.6
#1  0x00007fffee707649 in ray::RayLog::GetLoggerName() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#2  0x00007fffee70aa90 in ray::SpdLogMessage::Flush() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#3  0x00007fffee70af28 in ray::RayLog::~RayLog() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#4  0x00007fffee2b570d in ray::asio::testing::(anonymous namespace)::DelayManager::Init() [clone .constprop.0] ()
   from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#5  0x00007fffedd0d95a in _GLOBAL__sub_I_asio_chaos.cc () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#6  0x00007ffff7fe282a in call_init.part () from /lib64/ld-linux-x86-64.so.2
#7  0x00007ffff7fe2931 in _dl_init () from /lib64/ld-linux-x86-64.so.2
#8  0x00007ffff7fe674c in dl_open_worker () from /lib64/ld-linux-x86-64.so.2
#9  0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#10 0x00007ffff7fe5ffe in _dl_open () from /lib64/ld-linux-x86-64.so.2
#11 0x00007ffff7d5f39c in dlopen_doit () from /lib64/libdl.so.2
#12 0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#13 0x00007ffff7b82f13 in _dl_catch_error () from /lib64/libc.so.6
#14 0x00007ffff7d5fb09 in _dlerror_run () from /lib64/libdl.so.2
#15 0x00007ffff7d5f42a in dlopen@@GLIBC_2.2.5 () from /lib64/libdl.so.2
#16 0x00007fffef04d330 in py_dl_open (self=<optimized out>, args=<optimized out>)
    at /tmp/python-build.20220507135524.257789/Python-3.7.11/Modules/_ctypes/callproc.c:1369
```

The root cause is that when loading `_raylet.so`, `static DelayManager _delay_manager` is initialized and `RAY_LOG(ERROR) << "RAY_testing_asio_delay_us is set to " << delay_env;` is executed. However, the static variables declared in `logging.cc` are not initialized yet (in this case, `std::string RayLog::logger_name_ = "ray_log_sink"`).

It's better not to rely on the initialization order of static variables in different compilation units because it's not guaranteed. I propose to change all `RAY_LOG`s to `std::cerr` in `DelayManager::Init()`.

The crash happens in Ant's internal codebase. Not sure why this test case passes in the community version though.

BTW, I've tried different approaches:

1. Using a static local variable in `get_delay_us` and remove the global variable. This doesn't work because `init()` needs to access the variable as well.
2. Defining the global variable as type `std::unique_ptr<DelayManager>` and initialize it in `get_delay_us`. This works but it requires a lock to be thread-safe.
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Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Oct 31, 2022

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Nov 4, 2022

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Nov 6, 2022

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Nov 11, 2022

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Nov 17, 2022

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dependabot bot commented on behalf of github Dec 1, 2022

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Dec 16, 2022

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dependabot bot commented on behalf of github Dec 27, 2022

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Jan 3, 2023

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Jan 13, 2023

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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dependabot bot commented on behalf of github Jan 20, 2023

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

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