Release 2.11.0

Breaking Changes

• The tf.keras.optimizers.Optimizer base class now points to the new Keras optimizer, while the old optimizers have been moved to the tf.keras.optimizers.legacy namespace.

  If you find your workflow failing due to this change, you may be facing one of the following issues:

  • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
  • TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend migrating your workflow to TF2 for stable support and new features.
  • Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
  • Learning rate schedule access. When using a tf.keras.optimizers.schedules.LearningRateSchedule, the new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
  • If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
  • Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
  • Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.

  The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on the new tf.keras.optimizers.Optimizer base class.

• tensorflow/python/keras code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. Please remove any import of tensorflow.python.keras and use the public API with from tensorflow import keras or import tensorflow as tf; tf.keras.

Major Features and Improvements

• tf.lite:

  • New operations supported: tf.math.unsorted_segment_sum, tf.atan2 and tf.sign.
  • Updates to existing operations:
    • tfl.mul now supports complex32 inputs.

• tf.experimental.StructuredTensor:

  • Introduced tf.experimental.StructuredTensor, which provides a flexible and TensorFlow-native way to encode structured data such as protocol buffers or pandas dataframes.

• tf.keras:

  • Added a new get_metrics_result() method to tf.keras.models.Model.
    • Returns the current metrics values of the model as a dict.
  • Added a new group normalization layer - tf.keras.layers.GroupNormalization.
  • Added weight decay support for all Keras optimizers via the weight_decay argument.
  • Added the Adafactor optimizer - tf.keras.optimizers.Adafactor.
  • Added warmstart_embedding_matrix to tf.keras.utils.
    • This utility can be used to warmstart an embedding matrix, so you reuse previously-learned word embeddings when working with a new set of words which may include previously unseen words (the embedding vectors for unseen words will be randomly initialized).

• tf.Variable:

  • Added CompositeTensor as a base class to ResourceVariable.
    • This allows tf.Variables to be nested in tf.experimental.ExtensionTypes.
  • Added a new constructor argument experimental_enable_variable_lifting to tf.Variable, defaulting to True.
    • When it's set to False, the variable won't be lifted out of tf.function; thus it can be used as a tf.function-local variable: during each execution of the tf.function, the variable will be created and then disposed, similar to a local (that is, stack-allocated) variable in C/C++. Currently, experimental_enable_variable_lifting=False only works on non-XLA devices (for example, under @tf.function(jit_compile=False)).

• TF SavedModel:

  • Added fingerprint.pb to the SavedModel directory. The fingerprint.pb file is a protobuf containing the "fingerprint" of the SavedModel. See the RFC for more details regarding its design and properties.

• TF pip:

  • Windows CPU-builds for x86/x64 processors are now built, maintained, tested and released by a third party: Intel. Installing the Windows-native pip packages for tensorflow or tensorflow-cpu would install Intel's tensorflow-intel package. These packages are provided on an as-is basis. TensorFlow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. For using TensorFlow GPU on Windows, you will need to install TensorFlow in WSL2.

Bug Fixes and Other Changes

• tf.image:

  • Added an optional parameter return_index_map to tf.image.ssim, which causes the returned value to be the local SSIM map instead of the global mean.

• TF Core:

  • tf.custom_gradient can now be applied to functions that accept "composite" tensors, such as tf.RaggedTensor, as inputs.
  • Fix device placement issues related to datasets with ragged tensors of strings (i.e. variant encoded data with types not supported on GPU).
  • experimental_follow_type_hints for tf.function has been deprecated. Please use input_signature or reduce_retracing to minimize retracing.

• tf.SparseTensor:

  • Introduced set_shape, which sets the static dense shape of the sparse tensor and has the same semantics as tf.Tensor.set_shape.

Security

• TF is currently using giflib 5.2.1 which has CVE-2022-28506. TF is not affected by the CVE as it does not use DumpScreen2RGB at all.
• Fixes an OOB seg fault in DynamicStitch due to missing validation (CVE-2022-41883)
• Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)
• Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)
• Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)
• Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)
• Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)
• Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)
• Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)
• Fixes a CHECK fail in BCast (CVE-2022-41890)
• Fixes a segfault in TensorListConcat (CVE-2022-41891)
• Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)
• Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)
• Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)
• Fixes a crash in Mfcc (CVE-2022-41896)
• Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)
• Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)
• Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)
• Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)
• Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)
• Fixes an OOB write in grappler (CVE-2022-41902)
• Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)
• Fixes a CHECK fail in PyFunc (CVE-2022-41908)
• Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)
• Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)
• Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)
• Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar Dwivedi, Alexander Grund, alif_elham, Aman Agarwal, amoitra, Andrei Ivanov, andreii, Andrew Goodbody, angerson, Ashay Rane, Azeem Shaikh, Ben Barsdell, bhack, Bhavani Subramanian, Cedric Nugteren, Chandra Kumar Ramasamy, Christopher Bate, CohenAriel, Cotarou, cramasam, Enrico Minack, Francisco Unda, Frederic Bastien, gadagashwini, Gauri1 Deshpande, george, Jake, Jeff, Jerry Ge, Jingxuan He, Jojimon Varghese, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, kcoul, Keith Smiley, Kevin Hu, Kun Lu, kushanam, Lianmin Zheng, liuyuanqiang, Louis Sugy, Mahmoud Abuzaina, Marius Brehler, mdfaijul, Meenakshi Venkataraman, Milos Puzovic, mohantym, Namrata-Ibm, Nathan John Sircombe, Nathan Luehr, Olaf Lipinski, Om Thakkar, Osman F Bayram, Patrice Vignola, Pavani Majety, Philipp Hack, Prianka Liz Kariat, Rahul Batra, RajeshT, Renato Golin, riestere, Roger Iyengar, Rohit Santhanam, Rsanthanam-Amd, Sadeed Pv, Samuel Marks, Shimokawa, Naoaki, Siddhesh Kothadi, Simengliu-Nv, Sindre Seppola, snadampal, Srinivasan Narayanamoorthy, sushreebarsa, syedshahbaaz, Tamas Bela Feher, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tom Anderson, Tomohiro Endo, Trevor Morris, vibhutisawant, Victor Zhang, Vremold, Xavier Bonaventura, Yanming Wang, Yasir Modak, Yimei Sun, Yong Tang, Yulv-Git, zhuoran.liu, zotanika

Release 2.10.1

This release introduces several vulnerability fixes:

• Fixes an OOB seg fault in DynamicStitch due to missing validation (CVE-2022-41883)
• Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)
• Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)
• Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)
• Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)
• Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)
• Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)
• Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)
• Fixes a CHECK fail in BCast (CVE-2022-41890)
• Fixes a segfault in TensorListConcat (CVE-2022-41891)
• Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)
• Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)
• Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)
• Fixes a crash in Mfcc (CVE-2022-41896)
• Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)
• Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)
• Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)
• Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)
• Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)
• Fixes an OOB write in grappler (CVE-2022-41902)
• Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)
• Fixes a CHECK fail in PyFunc (CVE-2022-41908)
• Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)
• Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)
• Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)
• Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.9.3

This release introduces several vulnerability fixes:

• Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)
• Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)
• Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)
• Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)
• Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)
• Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)
• Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)
• Fixes a CHECK fail in BCast (CVE-2022-41890)
• Fixes a segfault in TensorListConcat (CVE-2022-41891)
• Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)
• Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)
• Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)
• Fixes a crash in Mfcc (CVE-2022-41896)
• Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)
• Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)
• Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)
• Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)
• Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)
• Fixes an OOB write in grappler (CVE-2022-41902)
• Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)
• Fixes a CHECK fail in PyFunc (CVE-2022-41908)
• Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)
• Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)
• Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)
• Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.8.4

This release introduces several vulnerability fixes:

• Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)
• Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)
• Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)
• Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)
• Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)
• Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)
• Fixes a CHECK fail in BCast (CVE-2022-41890)
• Fixes a segfault in TensorListConcat (CVE-2022-41891)
• Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)
• Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)
• Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)
• Fixes a crash in Mfcc (CVE-2022-41896)
• Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)
• Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)
• Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)
• Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)
• Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)
• Fixes an OOB write in grappler (CVE-2022-41902)
• Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)
• Fixes a CHECK fail in PyFunc (CVE-2022-41908)
• Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)
• Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)
• Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)
• Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.11.0

Breaking Changes

• tf.keras.optimizers.Optimizer now points to the new Keras optimizer, and old optimizers have moved to the tf.keras.optimizers.legacy namespace.
  If you find your workflow failing due to this change, you may be facing one of the following issues:

  • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
  • TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend to migrate your workflow to TF2 for stable support and new features.
  • Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
  • Learning rate schedule access. When using a LearningRateSchedule, The new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
  • If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
  • Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
  • Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.

  The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on tf.keras.optimizers.Optimizer, the new base class.

• tensorflow/python/keras code is a legacy copy of Keras since 2.7 release, and will be deleted in 2.12 release. Please remove any import of tensorflow.python.keras and use public API with from tensorflow import keras or import tensorflow as tf; tf.keras.

Major Features and Improvements

• tf.lite:

  • New operations supported: tf.unsortedsegmentmin, tf.atan2 and tf.sign.
  • Updates to existing operations:
    • tfl.mul now supports complex32 inputs.

• tf.experimental.StructuredTensor

  • Introduced tf.experimental.StructuredTensor, which provides a flexible and TensorFlow-native way to encode structured data such as protocol buffers or pandas dataframes.

• tf.keras:

  • Added a new get_metrics_result() method to tf.keras.models.Model.
    • Returns the current metrics values of the model as a dict.
  • Added a new group normalization layer - tf.keras.layers.GroupNormalization.
  • Added weight decay support for all Keras optimizers.
  • Added Adafactor optimizer tf.keras.optimizers.Adafactor.
  • Added warmstart_embedding_matrix to tf.keras.utils.
    • This utility can be used to warmstart an embeddings matrix, so you reuse previously-learned word embeddings when working with a new set of words which may include previously unseen words (the embedding vectors for unseen words will be randomly initialized).

• tf.Variable:

  • Added CompositeTensor as a baseclass to ResourceVariable.
    • This allows tf.Variables to be nested in tf.experimental.ExtensionTypes.
  • Added a new constructor argument experimental_enable_variable_lifting to tf.Variable, defaulting to True.
    • When it's False, the variable won't be lifted out of tf.function, thus it can be used as a tf.function-local variable: during each execution of the tf.function, the variable will be created and then disposed, similar to a local (that is, stack-allocated) variable in C/C++. Currently, experimental_enable_variable_lifting=False only works on non-XLA devices (for example, under @tf.function(jit_compile=False)).

• TF SavedModel:

  • Added fingerprint.pb to the SavedModel directory. The fingerprint.pb file is a protobuf containing the "fingerprint" of the SavedModel. See the RFC for more details regarding its design and properties.

• TF pip:

  • Windows CPU-builds for x86/x64 processors are now built, maintained, tested and released by a third party: Intel. Installing the windows-native pip packages for tensorflow or tensorflow-cpu would install Intel's tensorflow-intel package. These packages are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. For using TensorFlow GPU on Windows, you will need to install TensorFlow in WSL2.

Bug Fixes and Other Changes

• tf.image

  • Added an optional parameter return_index_map to tf.image.ssim which causes the returned value to be the local SSIM map instead of the global mean.

• TF Core:

  • tf.custom_gradient can now be applied to functions that accept "composite" tensors, such as tf.RaggedTensor, as inputs.
  • Fix device placement issues related to datasets with ragged tensors of strings (i.e. variant encoded data with types not supported on GPU).
  • experimental_follow_type_hints for tf.function has been deprecated. Please use input_signature or reduce_retracing to minimize retracing.

• tf.SparseTensor:

  • Introduced set_shape, which sets the static dense shape of the sparse tensor and has the same semantics as tf.Tensor.set_shape.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar Dwivedi, Alexander Grund, alif_elham, Aman Agarwal, amoitra, Andrei Ivanov, andreii, Andrew Goodbody, angerson, Ashay Rane, Azeem Shaikh, Ben Barsdell, bhack, Bhavani Subramanian, Cedric Nugteren, Chandra Kumar Ramasamy, Christopher Bate, CohenAriel, Cotarou, cramasam, Enrico Minack, Francisco Unda, Frederic Bastien, gadagashwini, Gauri1 Deshpande, george, Jake, Jeff, Jerry Ge, Jingxuan He, Jojimon Varghese, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, kcoul, Keith Smiley, Kevin Hu, Kun Lu, kushanam, Lianmin Zheng, liuyuanqiang, Louis Sugy, Mahmoud Abuzaina, Marius Brehler, mdfaijul, Meenakshi Venkataraman, Milos Puzovic, mohantym, Namrata-Ibm, Nathan John Sircombe, Nathan Luehr, Olaf Lipinski, Om Thakkar, Osman F Bayram, Patrice Vignola, Pavani Majety, Philipp Hack, Prianka Liz Kariat, Rahul Batra, RajeshT, Renato Golin, riestere, Roger Iyengar, Rohit Santhanam, Rsanthanam-Amd, Sadeed Pv, Samuel Marks, Shimokawa, Naoaki, Siddhesh Kothadi, Simengliu-Nv, Sindre Seppola, snadampal, Srinivasan Narayanamoorthy, sushreebarsa, syedshahbaaz, Tamas Bela Feher, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tom Anderson, Tomohiro Endo, Trevor Morris, vibhutisawant, Victor Zhang, Vremold, Xavier Bonaventura, Yanming Wang, Yasir Modak, Yimei Sun, Yong Tang, Yulv-Git, zhuoran.liu, zotanika

Release 2.11.0

Breaking Changes

• tf.keras.optimizers.Optimizer now points to the new Keras optimizer, and old optimizers have moved to the tf.keras.optimizers.legacy namespace. If you find your workflow failing due to this change, you may be facing one of the following issues:

  • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
  • TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend to migrate your workflow to TF2 for stable support and new features.
  • Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
  • Learning rate schedule access. When using a LearningRateSchedule, The new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
  • If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
  • Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls optimizer to update different parts of model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
  • Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.

  The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on tf.keras.optimizers.Optimizer, the new base class.

Major Features and Improvements

• tf.lite:

  • New operations supported: tf.unsortedsegmentmin, tf.atan2 and tf.sign.
  • Updates to existing operations:
    • tfl.mul now supports complex32 inputs.

• tf.experimental.StructuredTensor

  • Introduced tf.experimental.StructuredTensor, which provides a flexible and TensorFlow-native way to encode structured data such as protocol buffers or pandas dataframes.

• tf.keras:

  • Added a new get_metrics_result() method to tf.keras.models.Model.
    • Returns the current metrics values of the model as a dict.
  • Added a new group normalization layer - tf.keras.layers.GroupNormalization.
  • Added weight decay support for all Keras optimizers.
  • Added Adafactor optimizer tf.keras.optimizers.Adafactor.
  • Added warmstart_embedding_matrix to tf.keras.utils.
    • This utility can be used to warmstart an embeddings matrix, so you reuse previously-learned word embeddings when working with a new set of words which may include previously unseen words (the embedding vectors for unseen words will be randomly initialized).

• tf.Variable:

  • Added CompositeTensor as a baseclass to ResourceVariable.
    • This allows tf.Variables to be nested in tf.experimental.ExtensionTypes.
  • Added a new constructor argument experimental_enable_variable_lifting to tf.Variable, defaulting to True.
    • When it's False, the variable won't be lifted out of tf.function, thus it can be used as a tf.function-local variable: during each execution of the tf.function, the variable will be created and then disposed, similar to a local (that is, stack-allocated) variable in C/C++. Currently, experimental_enable_variable_lifting=False only works on non-XLA devices (for example, under @tf.function(jit_compile=False)).

• TF SavedModel:

  • Added fingerprint.pb to the SavedModel directory. The fingerprint.pb file is a protobuf containing the "fingerprint" of the SavedModel. See the RFC for more details regarding its design and properties.

• TF pip:

  • Windows CPU-builds for x86/x64 processors are now built, maintained, tested and released by a third party: Intel. Installing the windows-native pip packages for tensorflow or tensorflow-cpu would install Intel's tensorflow-intel package. These packages are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. For using TensorFlow GPU on Windows, you will need to install TensorFlow in WSL2.

Bug Fixes and Other Changes

• tf.image

  • Added an optional parameter return_index_map to tf.image.ssim which causes the returned value to be the local SSIM map instead of the global mean.

• TF Core:

  • tf.custom_gradient can now be applied to functions that accept "composite" tensors, such as tf.RaggedTensor, as inputs.
  • Fix device placement issues related to datasets with ragged tensors of strings (i.e. variant encoded data with types not supported on GPU).
  • experimental_follow_type_hints for tf.function has been deprecated. Please use input_signature or reduce_retracing to minimize retracing.

• tf.SparseTensor:

  • Introduced set_shape, which sets the static dense shape of the sparse tensor and has the same semantics as tf.Tensor.set_shape.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar Dwivedi, Alexander Grund, alif_elham, Aman Agarwal, amoitra, Andrei Ivanov, andreii, Andrew Goodbody, angerson, Ashay Rane, Azeem Shaikh, Ben Barsdell, bhack, Bhavani Subramanian, Cedric Nugteren, Chandra Kumar Ramasamy, Christopher Bate, CohenAriel, Cotarou, cramasam, Enrico Minack, Francisco Unda, Frederic Bastien, gadagashwini, Gauri1 Deshpande, george, Jake, Jeff, Jerry Ge, Jingxuan He, Jojimon Varghese, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, kcoul, Keith Smiley, Kevin Hu, Kun Lu, kushanam, Lianmin Zheng, liuyuanqiang, Louis Sugy, Mahmoud Abuzaina, Marius Brehler, mdfaijul, Meenakshi Venkataraman, Milos Puzovic, mohantym, Namrata-Ibm, Nathan John Sircombe, Nathan Luehr, Olaf Lipinski, Om Thakkar, Osman F Bayram, Patrice Vignola, Pavani Majety, Philipp Hack, Prianka Liz Kariat, Rahul Batra, RajeshT, Renato Golin, riestere, Roger Iyengar, Rohit Santhanam, Rsanthanam-Amd, Sadeed Pv, Samuel Marks, Shimokawa, Naoaki, Siddhesh Kothadi, Simengliu-Nv, Sindre Seppola, snadampal, Srinivasan Narayanamoorthy, sushreebarsa, syedshahbaaz, Tamas Bela Feher, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tom Anderson, Tomohiro Endo, Trevor Morris, vibhutisawant, Victor Zhang, Vremold, Xavier Bonaventura, Yanming Wang, Yasir Modak, Yimei Sun, Yong Tang, Yulv-Git, zhuoran.liu, zotanika

Release 2.11.0

Breaking Changes

• tf.keras.optimizers.Optimizer now points to the new Keras optimizer, and old optimizers have moved to the tf.keras.optimizers.legacy namespace. If you find your workflow failing due to this change, you may be facing one of the following issues:

  • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
  • TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend to migrate your workflow to TF2 for stable support and new features.
  • Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
  • Learning rate schedule access. When using a LearningRateSchedule, The new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
  • If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
  • Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls optimizer to update different parts of model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
  • Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.

  The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on tf.keras.optimizers.Optimizer, the new base class.

Major Features and Improvements

• tf.lite:

  • New operations supported: tf.unsortedsegmentmin, tf.atan2 and tf.sign.
  • Updates to existing operations:
    • tfl.mul now supports complex32 inputs.

• tf.experimental.StructuredTensor

  • Introduced tf.experimental.StructuredTensor, which provides a flexible and TensorFlow-native way to encode structured data such as protocol buffers or pandas dataframes.

• tf.keras:

  • Added a new get_metrics_result() method to tf.keras.models.Model.
    • Returns the current metrics values of the model as a dict.
  • Added a new group normalization layer - tf.keras.layers.GroupNormalization.
  • Added weight decay support for all Keras optimizers.
  • Added Adafactor optimizer tf.keras.optimizers.Adafactor.
  • Added warmstart_embedding_matrix to tf.keras.utils.
    • This utility can be used to warmstart an embeddings matrix, so you reuse previously-learned word embeddings when working with a new set of words which may include previously unseen words (the embedding vectors for unseen words will be randomly initialized).

• tf.Variable:

  • Added CompositeTensor as a baseclass to ResourceVariable.
    • This allows tf.Variables to be nested in tf.experimental.ExtensionTypes.
  • Added a new constructor argument experimental_enable_variable_lifting to tf.Variable, defaulting to True.
    • When it's False, the variable won't be lifted out of tf.function, thus it can be used as a tf.function-local variable: during each execution of the tf.function, the variable will be created and then disposed, similar to a local (that is, stack-allocated) variable in C/C++. Currently, experimental_enable_variable_lifting=False only works on non-XLA devices (for example, under @tf.function(jit_compile=False)).

• TF SavedModel:

  • Added fingerprint.pb to the SavedModel directory. The fingerprint.pb file is a protobuf containing the "fingerprint" of the SavedModel. See the RFC for more details regarding its design and properties.

• TF pip:

  • Windows CPU-builds for x86/x64 processors are now built, maintained, tested and released by a third party: Intel. Installing the windows-native pip packages for tensorflow or tensorflow-cpu would install Intel's tensorflow-intel package. These packages are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. For using TensorFlow GPU on Windows, you will need to install TensorFlow in WSL2.

Bug Fixes and Other Changes

• tf.image

  • Added an optional parameter return_index_map to tf.image.ssim which causes the returned value to be the local SSIM map instead of the global mean.

• TF Core:

  • tf.custom_gradient can now be applied to functions that accept "composite" tensors, such as tf.RaggedTensor, as inputs.
  • Fix device placement issues related to datasets with ragged tensors of strings (i.e. variant encoded data with types not supported on GPU).
  • experimental_follow_type_hints for tf.function has been deprecated. Please use input_signature or reduce_retracing to minimize retracing.

• tf.SparseTensor:

  • Introduced set_shape, which sets the static dense shape of the sparse tensor and has the same semantics as tf.Tensor.set_shape.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar Dwivedi, Alexander Grund, alif_elham, Aman Agarwal, amoitra, Andrei Ivanov, andreii, Andrew Goodbody, angerson, Ashay Rane, Azeem Shaikh, Ben Barsdell, bhack, Bhavani Subramanian, Cedric Nugteren, Chandra Kumar Ramasamy, Christopher Bate, CohenAriel, Cotarou, cramasam, Enrico Minack, Francisco Unda, Frederic Bastien, gadagashwini, Gauri1 Deshpande, george, Jake, Jeff, Jerry Ge, Jingxuan He, Jojimon Varghese, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, kcoul, Keith Smiley, Kevin Hu, Kun Lu, kushanam, Lianmin Zheng, liuyuanqiang, Louis Sugy, Mahmoud Abuzaina, Marius Brehler, mdfaijul, Meenakshi Venkataraman, Milos Puzovic, mohantym, Namrata-Ibm, Nathan John Sircombe, Nathan Luehr, Olaf Lipinski, Om Thakkar, Osman F Bayram, Patrice Vignola, Pavani Majety, Philipp Hack, Prianka Liz Kariat, Rahul Batra, RajeshT, Renato Golin, riestere, Roger Iyengar, Rohit Santhanam, Rsanthanam-Amd, Sadeed Pv, Samuel Marks, Shimokawa, Naoaki, Siddhesh Kothadi, Simengliu-Nv, Sindre Seppola, snadampal, Srinivasan Narayanamoorthy, sushreebarsa, syedshahbaaz, Tamas Bela Feher, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tom Anderson, Tomohiro Endo, Trevor Morris, vibhutisawant, Victor Zhang, Vremold, Xavier Bonaventura, Yanming Wang, Yasir Modak, Yimei Sun, Yong Tang, Yulv-Git, zhuoran.liu, zotanika

Release 2.10.0

Breaking Changes

• Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
• Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
• tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
• RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
  • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
  • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Deprecations

• The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
  • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
  • Stop constructing Status objects from tensorflow::error::Code.
  • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
    • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
    • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
    • In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
• tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Major Features and Improvements

• tf.lite:

  • New operations supported:
    • tflite SelectV2 now supports 5D.
    • tf.einsum is supported with multiple unknown shapes.
    • tf.unsortedsegmentprod op is supported.
    • tf.unsortedsegmentmax op is supported.
    • tf.unsortedsegmentsum op is supported.
  • Updates to existing operations:
    • tfl.scatter_nd now supports I1 for update arg.
  • Upgrade Flatbuffers v2.0.5 from v1.12.0

• tf.keras:

  • EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
  • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
  • Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
  • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
  • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
  • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
  • Improved masking support for tf.keras.layers.MultiHeadAttention.
    • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
    • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
  • Added ignore_class argument in the loss SparseCategoricalCrossentropy and metrics IoU and MeanIoU, to specify a class index to be ignored during loss/metric computation (e.g. a background/void class).
  • Added tf.keras.models.experimental.SharpnessAwareMinimization. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.

• tf.data:

  • Added support for cross-trainer data caching in tf.data service. This saves computation resources when concurrent training jobs train from the same dataset. See (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers) for more details.
  • Added dataset_id to tf.data.experimental.service.register_dataset. If provided, tf.data service will use the provided ID for the dataset. If the dataset ID already exists, no new dataset will be registered. This is useful if multiple training jobs need to use the same dataset for training. In this case, users should call register_dataset with the same dataset_id.
  • Added a new field, inject_prefetch, to tf.data.experimental.OptimizationOptions. If it is set to True,tf.data will now automatically add a prefetch transformation to datasets that end in synchronous transformations. This enables data generation to be overlapped with data consumption. This may cause a small increase in memory usage due to buffering. To enable this behavior, set inject_prefetch=True in tf.data.experimental.OptimizationOptions.
  • Added a new value to tf.data.Options.autotune.autotune_algorithm: STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.
  • Added tf.data.experimental.from_list, a new API for creating Datasets from lists of elements.

• tf.distribute:

  • Added tf.distribute.experimental.PreemptionCheckpointHandler to handle worker preemption/maintenance and cluster-wise consistent error reporting for tf.distribute.MultiWorkerMirroredStrategy. Specifically, for the type of interruption with advance notice, it automatically saves a checkpoint, exits the program without raising an unrecoverable error, and restores the progress when training restarts.

• tf.math:

  • Added tf.math.approx_max_k and tf.math.approx_min_k which are the optimized alternatives to tf.math.top_k on TPU. The performance difference range from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XLA kernel is used.

• tf.train:

  • Added tf.train.TrackableView which allows users to inspect the TensorFlow Trackable object (e.g. tf.Module, Keras Layers and models).

• tf.vectorized_map:

  • Added an optional parameter: warn. This parameter controls whether or not warnings will be printed when operations in the provided fn fall back to a while loop.

• XLA:

  • MWMS is now compilable with XLA.
  • Compute Library for the Arm® Architecture (ACL) is supported for aarch64 CPU XLA runtime

• CPU performance optimizations:

  • x86 CPUs: oneDNN bfloat16 auto-mixed precision grappler graph optimization pass has been renamed from auto_mixed_precision_mkl to auto_mixed_precision_onednn_bfloat16. See example usage here.
  • aarch64 CPUs: Experimental performance optimizations from Compute Library for the Arm® Architecture (ACL) are available through oneDNN in the default Linux aarch64 package (pip install tensorflow).
    • The optimizations are disabled by default.
    • Set the environment variable TF_ENABLE_ONEDNN_OPTS=1 to enable the optimizations. Setting the variable to 0 or unsetting it will disable the optimizations.
    • These optimizations can yield slightly different numerical results from when they are off due to floating-point round-off errors from different computation approaches and orders.
    • To verify that the optimizations are on, look for a message with "oneDNN custom operations are on" in the log. If the exact phrase is not there, it means they are off.

Bug Fixes and Other Changes

• New argument experimental_device_ordinal in LogicalDeviceConfiguration to control the order of logical devices. (GPU only)

• tf.keras:

  • Changed the TensorBoard tag names produced by the tf.keras.callbacks.TensorBoard callback, so that summaries logged automatically for model weights now include either a /histogram or /image suffix in their tag names, in order to prevent tag name collisions across summary types.

• When running on GPU (with cuDNN version 7.6.3 or later),tf.nn.depthwise_conv2d backprop to filter (and therefore also tf.keras.layers.DepthwiseConv2D) now operate deterministically (and tf.errors.UnimplementedError is no longer thrown) when op-determinism has been enabled via tf.config.experimental.enable_op_determinism. This closes issue 47174.

• tf.random

  • Added tf.random.experimental.stateless_shuffle, a stateless version of tf.random.shuffle.

Security

• Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)
• Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)
• Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)
• Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)
• Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)
• Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)
• Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)
• Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)
• Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)
• Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)
• Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)
• Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)
• Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)
• Fixes a segfault in QuantizedAvgPool (CVE-2022-35966)
• Fixes a segfault in QuantizedAdd (CVE-2022-35967)
• Fixes a CHECK fail in AvgPoolGrad (CVE-2022-35968)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35969)
• Fixes a segfault in QuantizedInstanceNorm (CVE-2022-35970)
• Fixes a CHECK fail in FakeQuantWithMinMaxVars (CVE-2022-35971)
• Fixes a segfault in Requantize (CVE-2022-36017)
• Fixes a segfault in QuantizedBiasAdd (CVE-2022-35972)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannel (CVE-2022-36019)
• Fixes a segfault in QuantizedMatMul (CVE-2022-35973)
• Fixes a segfault in QuantizeDownAndShrinkRange (CVE-2022-35974)
• Fixes segfaults in QuantizedRelu and QuantizedRelu6 (CVE-2022-35979)
• Fixes a CHECK fail in FractionalMaxPoolGrad (CVE-2022-35981)
• Fixes a CHECK fail in RaggedTensorToVariant (CVE-2022-36018)
• Fixes a CHECK fail in QuantizeAndDequantizeV3 (CVE-2022-36026)
• Fixes a segfault in SparseBincount (CVE-2022-35982)
• Fixes a CHECK fail in Save and SaveSlices (CVE-2022-35983)
• Fixes a CHECK fail in ParameterizedTruncatedNormal (CVE-2022-35984)
• Fixes a CHECK fail in LRNGrad (CVE-2022-35985)
• Fixes a segfault in RaggedBincount (CVE-2022-35986)
• Fixes a CHECK fail in DenseBincount (CVE-2022-35987)
• Fixes a CHECK fail in tf.linalg.matrix_rank (CVE-2022-35988)
• Fixes a CHECK fail in MaxPool (CVE-2022-35989)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35999)
• Fixes a CHECK fail in EmptyTensorList (CVE-2022-35998)
• Fixes a CHECK fail in tf.sparse.cross (CVE-2022-35997)
• Fixes a floating point exception in Conv2D (CVE-2022-35996)
• Fixes a CHECK fail in AudioSummaryV2 (CVE-2022-35995)
• Fixes a CHECK fail in CollectiveGather (CVE-2022-35994)
• Fixes a CHECK fail in SetSize (CVE-2022-35993)
• Fixes a CHECK fail in TensorListFromTensor (CVE-2022-35992)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 (CVE-2022-35991)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannelGradient (CVE-2022-35990)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsGradient (CVE-2022-36005)
• Fixes a CHECK fail in tf.random.gamma (CVE-2022-36004)
• Fixes a CHECK fail in RandomPoissonV2 (CVE-2022-36003)
• Fixes a CHECK fail in Unbatch (CVE-2022-36002)
• Fixes a CHECK fail in DrawBoundingBoxes (CVE-2022-36001)
• Fixes a CHECK fail in Eig (CVE-2022-36000)
• Fixes a null dereference on MLIR on empty function attributes (CVE-2022-36011)
• Fixes an assertion failure on MLIR empty edge names (CVE-2022-36012)
• Fixes a null-dereference in mlir::tfg::GraphDefImporter::ConvertNodeDef (CVE-2022-36013)
• Fixes a null-dereference in mlir::tfg::TFOp::nameAttr (CVE-2022-36014)
• Fixes an integer overflow in math ops (CVE-2022-36015)
• Fixes a CHECK-fail in tensorflow::full_type::SubstituteFromAttrs (CVE-2022-36016)
• Fixes an OOB read in Gather_nd op in TF Lite Micro (CVE-2022-35938)

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abolfazl Shahbazi, Adam Lanicek, Amin Benarieb, andreii, Andrew Fitzgibbon, Andrew Goodbody, angerson, Ashiq Imran, Aurélien Geron, Banikumar Maiti (Intel Aipg), Ben Barsdell, Ben Mares, bhack, Bhavani Subramanian, Bill Schnurr, Byungsoo Oh, Chandra Sr Potula, Chengji Yao, Chris Carpita, Christopher Bate, chunduriv, Cliff Woolley, Cliffs Dover, Cloud Han, Code-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, guozhong.zhuang, Hui Peng, James Gerity, Jason Furmanek, Jonathan Dekhtiar, Jueon Park, Kaixi Hou, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, kushanam, Learning-To-Play, Li-Wen Chang, lipracer, liuyuanqiang, Louis Sugy, Lucas David, Lukas Geiger, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, Meenakshi Venkataraman, Michal Szutenberg, Michele Di Giorgio, Mickaël Salamin, Nathan John Sircombe, Nathan Luehr, Neil Girdhar, Nils Reichardt, Nishidha Panpaliya, Nobuo Tsukamoto, Om Thakkar, Patrice Vignola, Philipp Hack, Pooya Jannaty, Prianka Liz Kariat, pshiko, Rajeshwar Reddy T, rdl4199, Rohit Santhanam, Rsanthanam-Amd, Sachin Muradi, Saoirse Stewart, Serge Panev, Shu Wang, Srinivasan Narayanamoorthy, Stella Stamenova, Stephan Hartmann, Sunita Nadampalli, synandi, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Trevor Morris, Xiaoming (Jason) Cui, Yimei Sun, Yong Tang, Yuanqiang Liu, Yulv-Git, Zhoulong Jiang, ZihengJiang

Release 2.9.2

This releases introduces several vulnerability fixes:

• Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)
• Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)
• Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)
• Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)
• Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)
• Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)
• Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)
• Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)
• Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)
• Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)
• Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)
• Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)
• Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)
• Fixes a segfault in QuantizedAvgPool (CVE-2022-35966)
• Fixes a segfault in QuantizedAdd (CVE-2022-35967)
• Fixes a CHECK fail in AvgPoolGrad (CVE-2022-35968)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35969)
• Fixes a segfault in QuantizedInstanceNorm (CVE-2022-35970)
• Fixes a CHECK fail in FakeQuantWithMinMaxVars (CVE-2022-35971)
• Fixes a segfault in Requantize (CVE-2022-36017)
• Fixes a segfault in QuantizedBiasAdd (CVE-2022-35972)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannel (CVE-2022-36019)
• Fixes a segfault in QuantizedMatMul (CVE-2022-35973)
• Fixes a segfault in QuantizeDownAndShrinkRange (CVE-2022-35974)
• Fixes segfaults in QuantizedRelu and QuantizedRelu6 (CVE-2022-35979)
• Fixes a CHECK fail in FractionalMaxPoolGrad (CVE-2022-35981)
• Fixes a CHECK fail in RaggedTensorToVariant (CVE-2022-36018)
• Fixes a CHECK fail in QuantizeAndDequantizeV3 (CVE-2022-36026)
• Fixes a segfault in SparseBincount (CVE-2022-35982)
• Fixes a CHECK fail in Save and SaveSlices (CVE-2022-35983)
• Fixes a CHECK fail in ParameterizedTruncatedNormal (CVE-2022-35984)
• Fixes a CHECK fail in LRNGrad (CVE-2022-35985)
• Fixes a segfault in RaggedBincount (CVE-2022-35986)
• Fixes a CHECK fail in DenseBincount (CVE-2022-35987)
• Fixes a CHECK fail in tf.linalg.matrix_rank (CVE-2022-35988)
• Fixes a CHECK fail in MaxPool (CVE-2022-35989)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35999)
• Fixes a CHECK fail in EmptyTensorList (CVE-2022-35998)
• Fixes a CHECK fail in tf.sparse.cross (CVE-2022-35997)
• Fixes a floating point exception in Conv2D (CVE-2022-35996)
• Fixes a CHECK fail in AudioSummaryV2 (CVE-2022-35995)
• Fixes a CHECK fail in CollectiveGather (CVE-2022-35994)
• Fixes a CHECK fail in SetSize (CVE-2022-35993)
• Fixes a CHECK fail in TensorListFromTensor (CVE-2022-35992)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 (CVE-2022-35991)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannelGradient (CVE-2022-35990)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsGradient (CVE-2022-36005)
• Fixes a CHECK fail in tf.random.gamma (CVE-2022-36004)
• Fixes a CHECK fail in RandomPoissonV2 (CVE-2022-36003)
• Fixes a CHECK fail in Unbatch (CVE-2022-36002)
• Fixes a CHECK fail in DrawBoundingBoxes (CVE-2022-36001)
• Fixes a CHECK fail in Eig (CVE-2022-36000)
• Fixes a null dereference on MLIR on empty function attributes (CVE-2022-36011)
• Fixes an assertion failure on MLIR empty edge names (CVE-2022-36012)
• Fixes a null-dereference in mlir::tfg::GraphDefImporter::ConvertNodeDef (CVE-2022-36013)
• Fixes a null-dereference in mlir::tfg::TFOp::nameAttr (CVE-2022-36014)
• Fixes an integer overflow in math ops (CVE-2022-36015)
• Fixes a CHECK-fail in tensorflow::full_type::SubstituteFromAttrs (CVE-2022-36016)
• Fixes an OOB read in Gather_nd op in TF Lite Micro (CVE-2022-35938)

Release 2.8.3

This releases introduces several vulnerability fixes:

• Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)
• Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)
• Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)
• Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)
• Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)
• Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)
• Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)
• Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)
• Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)
• Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)
• Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)
• Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)
• Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)
• Fixes a segfault in QuantizedAvgPool (CVE-2022-35966)
• Fixes a segfault in QuantizedAdd (CVE-2022-35967)
• Fixes a CHECK fail in AvgPoolGrad (CVE-2022-35968)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35969)
• Fixes a segfault in QuantizedInstanceNorm (CVE-2022-35970)
• Fixes a CHECK fail in FakeQuantWithMinMaxVars (CVE-2022-35971)
• Fixes a segfault in Requantize (CVE-2022-36017)
• Fixes a segfault in QuantizedBiasAdd (CVE-2022-35972)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannel (CVE-2022-36019)
• Fixes a segfault in QuantizedMatMul (CVE-2022-35973)
• Fixes a segfault in QuantizeDownAndShrinkRange (CVE-2022-35974)
• Fixes segfaults in QuantizedRelu and QuantizedRelu6 (CVE-2022-35979)
• Fixes a CHECK fail in FractionalMaxPoolGrad (CVE-2022-35981)
• Fixes a CHECK fail in RaggedTensorToVariant (CVE-2022-36018)
• Fixes a CHECK fail in QuantizeAndDequantizeV3 (CVE-2022-36026)
• Fixes a segfault in SparseBincount (CVE-2022-35982)
• Fixes a CHECK fail in Save and SaveSlices (CVE-2022-35983)
• Fixes a CHECK fail in ParameterizedTruncatedNormal (CVE-2022-35984)
• Fixes a CHECK fail in LRNGrad (CVE-2022-35985)
• Fixes a segfault in RaggedBincount (CVE-2022-35986)
• Fixes a CHECK fail in DenseBincount (CVE-2022-35987)
• Fixes a CHECK fail in tf.linalg.matrix_rank (CVE-2022-35988)
• Fixes a CHECK fail in MaxPool (CVE-2022-35989)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35999)
• Fixes a CHECK fail in EmptyTensorList (CVE-2022-35998)
• Fixes a CHECK fail in tf.sparse.cross (CVE-2022-35997)
• Fixes a floating point exception in Conv2D (CVE-2022-35996)
• Fixes a CHECK fail in AudioSummaryV2 (CVE-2022-35995)
• Fixes a CHECK fail in CollectiveGather (CVE-2022-35994)
• Fixes a CHECK fail in SetSize (CVE-2022-35993)
• Fixes a CHECK fail in TensorListFromTensor (CVE-2022-35992)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 (CVE-2022-35991)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannelGradient (CVE-2022-35990)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsGradient (CVE-2022-36005)
• Fixes a CHECK fail in tf.random.gamma (CVE-2022-36004)
• Fixes a CHECK fail in RandomPoissonV2 (CVE-2022-36003)
• Fixes a CHECK fail in Unbatch (CVE-2022-36002)
• Fixes a CHECK fail in DrawBoundingBoxes (CVE-2022-36001)
• Fixes a CHECK fail in Eig (CVE-2022-36000)
• Fixes a null dereference on MLIR on empty function attributes (CVE-2022-36011)
• Fixes an assertion failure on MLIR empty edge names (CVE-2022-36012)
• Fixes a null-dereference in mlir::tfg::GraphDefImporter::ConvertNodeDef (CVE-2022-36013)
• Fixes a null-dereference in mlir::tfg::TFOp::nameAttr (CVE-2022-36014)
• Fixes an integer overflow in math ops (CVE-2022-36015)
• Fixes a CHECK-fail in tensorflow::full_type::SubstituteFromAttrs (CVE-2022-36016)
• Fixes an OOB read in Gather_nd op in TF Lite Micro (CVE-2022-35938)

Release 2.7.4

Note: This is the last release in the 2.7.x series

This releases introduces several vulnerability fixes:

• Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)
• Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)
• Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)
• Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)
• Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)
• Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)
• Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)
• Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)
• Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)
• Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)
• Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)
• Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)
• Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)
• Fixes a segfault in QuantizedAvgPool (CVE-2022-35966)
• Fixes a segfault in QuantizedAdd (CVE-2022-35967)
• Fixes a CHECK fail in AvgPoolGrad (CVE-2022-35968)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35969)
• Fixes a segfault in QuantizedInstanceNorm (CVE-2022-35970)
• Fixes a CHECK fail in FakeQuantWithMinMaxVars (CVE-2022-35971)
• Fixes a segfault in Requantize (CVE-2022-36017)
• Fixes a segfault in QuantizedBiasAdd (CVE-2022-35972)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannel (CVE-2022-36019)
• Fixes a segfault in QuantizedMatMul (CVE-2022-35973)
• Fixes a segfault in QuantizeDownAndShrinkRange (CVE-2022-35974)
• Fixes segfaults in QuantizedRelu and QuantizedRelu6 (CVE-2022-35979)
• Fixes a CHECK fail in FractionalMaxPoolGrad (CVE-2022-35981)
• Fixes a CHECK fail in RaggedTensorToVariant (CVE-2022-36018)
• Fixes a CHECK fail in QuantizeAndDequantizeV3 (CVE-2022-36026)
• Fixes a segfault in SparseBincount (CVE-2022-35982)
• Fixes a CHECK fail in Save and SaveSlices (CVE-2022-35983)
• Fixes a CHECK fail in ParameterizedTruncatedNormal (CVE-2022-35984)
• Fixes a CHECK fail in LRNGrad (CVE-2022-35985)
• Fixes a segfault in RaggedBincount (CVE-2022-35986)
• Fixes a CHECK fail in DenseBincount (CVE-2022-35987)
• Fixes a CHECK fail in tf.linalg.matrix_rank (CVE-2022-35988)
• Fixes a CHECK fail in MaxPool (CVE-2022-35989)
• Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35999)
• Fixes a CHECK fail in EmptyTensorList (CVE-2022-35998)
• Fixes a CHECK fail in tf.sparse.cross (CVE-2022-35997)
• Fixes a floating point exception in Conv2D (CVE-2022-35996)
• Fixes a CHECK fail in AudioSummaryV2 (CVE-2022-35995)
• Fixes a CHECK fail in CollectiveGather (CVE-2022-35994)
• Fixes a CHECK fail in SetSize (CVE-2022-35993)
• Fixes a CHECK fail in TensorListFromTensor (CVE-2022-35992)
• Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 (CVE-2022-35991)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannelGradient (CVE-2022-35990)
• Fixes a CHECK fail in FakeQuantWithMinMaxVarsGradient (CVE-2022-36005)
• Fixes a CHECK fail in tf.random.gamma (CVE-2022-36004)
• Fixes a CHECK fail in RandomPoissonV2 (CVE-2022-36003)
• Fixes a CHECK fail in Unbatch (CVE-2022-36002)
• Fixes a CHECK fail in DrawBoundingBoxes (CVE-2022-36001)
• Fixes a CHECK fail in Eig (CVE-2022-36000)
• Fixes a null dereference on MLIR on empty function attributes (CVE-2022-36011)
• Fixes an assertion failure on MLIR empty edge names (CVE-2022-36012)
• Fixes a null-dereference in mlir::tfg::GraphDefImporter::ConvertNodeDef (CVE-2022-36013)
• Fixes a null-dereference in mlir::tfg::TFOp::nameAttr (CVE-2022-36014)
• Fixes an integer overflow in math ops (CVE-2022-36015)
• Fixes a CHECK-fail in tensorflow::full_type::SubstituteFromAttrs (CVE-2022-36016)
• Fixes an OOB read in Gather_nd op in TF Lite Micro (CVE-2022-35938)

Release 2.10.0

Breaking Changes

• Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
• Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
• tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
• RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
  • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
  • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Deprecations

• The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
  • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
  • Stop constructing Status objects from tensorflow::error::Code.
  • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
    • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
    • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
    • In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
• tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Major Features and Improvements

• tf.lite:

  • New operations supported:
    • tflite SelectV2 now supports 5D.
    • tf.einsum is supported with multiple unknown shapes.
    • tf.unsortedsegmentprod op is supported.
    • tf.unsortedsegmentmax op is supported.
    • tf.unsortedsegmentsum op is supported.
  • Updates to existing operations:
    • tfl.scatter_nd now supports I1 for update arg.
  • Upgrade Flatbuffers v2.0.5 from v1.12.0

• tf.keras:

  • EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
  • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
  • Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
  • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
  • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
  • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
  • Improved masking support for tf.keras.layers.MultiHeadAttention.
    • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
    • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
  • Added ignore_class argument in the loss SparseCategoricalCrossentropy and metrics IoU and MeanIoU, to specify a class index to be ignored during loss/metric computation (e.g. a background/void class).
  • Added tf.keras.models.experimental.SharpnessAwareMinimization. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.

• tf.data:

  • Added support for cross-trainer data caching in tf.data service. This saves computation resources when concurrent training jobs train from the same dataset. See (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers) for more details.
  • Added dataset_id to tf.data.experimental.service.register_dataset. If provided, tf.data service will use the provided ID for the dataset. If the dataset ID already exists, no new dataset will be registered. This is useful if multiple training jobs need to use the same dataset for training. In this case, users should call register_dataset with the same dataset_id.
  • Added a new field, inject_prefetch, to tf.data.experimental.OptimizationOptions. If it is set to True,tf.data will now automatically add a prefetch transformation to datasets that end in synchronous transformations. This enables data generation to be overlapped with data consumption. This may cause a small increase in memory usage due to buffering. To enable this behavior, set inject_prefetch=True in tf.data.experimental.OptimizationOptions.
  • Added a new value to tf.data.Options.autotune.autotune_algorithm: STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.
  • Added tf.data.experimental.from_list, a new API for creating Datasets from lists of elements.

• tf.distribute:

  • Added tf.distribute.experimental.PreemptionCheckpointHandler to handle worker preemption/maintenance and cluster-wise consistent error reporting for tf.distribute.MultiWorkerMirroredStrategy. Specifically, for the type of interruption with advance notice, it automatically saves a checkpoint, exits the program without raising an unrecoverable error, and restores the progress when training restarts.

• tf.math:

  • Added tf.math.approx_max_k and tf.math.approx_min_k which are the optimized alternatives to tf.math.top_k on TPU. The performance difference range from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XLA kernel is used.

• tf.train:

  • Added tf.train.TrackableView which allows users to inspect the TensorFlow Trackable object (e.g. tf.Module, Keras Layers and models).

• tf.vectorized_map:

  • Added an optional parameter: warn. This parameter controls whether or not warnings will be printed when operations in the provided fn fall back to a while loop.

• XLA:

  • MWMS is now compilable with XLA.
  • Compute Library for the Arm® Architecture (ACL) is supported for aarch64 CPU XLA runtime

• CPU performance optimizations:

  • x86 CPUs: oneDNN bfloat16 auto-mixed precision grappler graph optimization pass has been renamed from auto_mixed_precision_mkl to auto_mixed_precision_onednn_bfloat16. See example usage here.
  • aarch64 CPUs: Experimental performance optimizations from Compute Library for the Arm® Architecture (ACL) are available through oneDNN in the default Linux aarch64 package (pip install tensorflow).
    • The optimizations are disabled by default.
    • Set the environment variable TF_ENABLE_ONEDNN_OPTS=1 to enable the optimizations. Setting the variable to 0 or unsetting it will disable the optimizations.
    • These optimizations can yield slightly different numerical results from when they are off due to floating-point round-off errors from different computation approaches and orders.
    • To verify that the optimizations are on, look for a message with "oneDNN custom operations are on" in the log. If the exact phrase is not there, it means they are off.

Bug Fixes and Other Changes

• New argument experimental_device_ordinal in LogicalDeviceConfiguration to control the order of logical devices. (GPU only)

• tf.keras:

  • Changed the TensorBoard tag names produced by the tf.keras.callbacks.TensorBoard callback, so that summaries logged automatically for model weights now include either a /histogram or /image suffix in their tag names, in order to prevent tag name collisions across summary types.

• When running on GPU (with cuDNN version 7.6.3 or later),tf.nn.depthwise_conv2d backprop to filter (and therefore also tf.keras.layers.DepthwiseConv2D) now operate deterministically (and tf.errors.UnimplementedError is no longer thrown) when op-determinism has been enabled via tf.config.experimental.enable_op_determinism. This closes issue 47174.

• tf.random

  • Added tf.random.experimental.stateless_shuffle, a stateless version of tf.random.shuffle.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abolfazl Shahbazi, Adam Lanicek, Amin Benarieb, andreii, Andrew Fitzgibbon, Andrew Goodbody, angerson, Ashiq Imran, Aurélien Geron, Banikumar Maiti (Intel Aipg), Ben Barsdell, Ben Mares, bhack, Bhavani Subramanian, Bill Schnurr, Byungsoo Oh, Chandra Sr Potula, Chengji Yao, Chris Carpita, Christopher Bate, chunduriv, Cliff Woolley, Cliffs Dover, Cloud Han, Code-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, guozhong.zhuang, Hui Peng, James Gerity, Jason Furmanek, Jonathan Dekhtiar, Jueon Park, Kaixi Hou, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, kushanam, Learning-To-Play, Li-Wen Chang, lipracer, liuyuanqiang, Louis Sugy, Lucas David, Lukas Geiger, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, Meenakshi Venkataraman, Michal Szutenberg, Michele Di Giorgio, Mickaël Salamin, Nathan John Sircombe, Nathan Luehr, Neil Girdhar, Nils Reichardt, Nishidha Panpaliya, Nobuo Tsukamoto, Om Thakkar, Patrice Vignola, Philipp Hack, Pooya Jannaty, Prianka Liz Kariat, pshiko, Rajeshwar Reddy T, rdl4199, Rohit Santhanam, Rsanthanam-Amd, Sachin Muradi, Saoirse Stewart, Serge Panev, Shu Wang, Srinivasan Narayanamoorthy, Stella Stamenova, Stephan Hartmann, Sunita Nadampalli, synandi, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Trevor Morris, Xiaoming (Jason) Cui, Yimei Sun, Yong Tang, Yuanqiang Liu, Yulv-Git, Zhoulong Jiang, ZihengJiang

Release 2.10.0

Breaking Changes

• Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
• Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
• tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
• RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
  • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
  • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Major Features and Improvements

• tf.lite:

  • New operations supported:
    • tflite SelectV2 now supports 5D.
    • tf.einsum is supported with multiple unknown shapes.
    • tf.unsortedsegmentprod op is supported.
    • tf.unsortedsegmentmax op is supported.
    • tf.unsortedsegmentsum op is supported.
  • Updates to existing operations:
    • tfl.scatter_nd now supports I1 for update arg.
  • Upgrade Flatbuffers v2.0.5 from v1.12.0

• tf.keras:

  • EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
  • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
  • Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
  • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
  • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
  • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
  • Improved masking support for tf.keras.layers.MultiHeadAttention.
    • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
    • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
  • Added ignore_class argument in the loss SparseCategoricalCrossentropy and metrics IoU and MeanIoU, to specify a class index to be ignored during loss/metric computation (e.g. a background/void class).
  • Added tf.keras.models.experimental.SharpnessAwareMinimization. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.

• tf.data:

  • Added support for cross-trainer data caching in tf.data service. This saves computation resources when concurrent training jobs train from the same dataset. See (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers) for more details.
  • Added dataset_id to tf.data.experimental.service.register_dataset. If provided, tf.data service will use the provided ID for the dataset. If the dataset ID already exists, no new dataset will be registered. This is useful if multiple training jobs need to use the same dataset for training. In this case, users should call register_dataset with the same dataset_id.
  • Added a new field, inject_prefetch, to tf.data.experimental.OptimizationOptions. If it is set to True,tf.data will now automatically add a prefetch transformation to datasets that end in synchronous transformations. This enables data generation to be overlapped with data consumption. This may cause a small increase in memory usage due to buffering. To enable this behavior, set inject_prefetch=True in tf.data.experimental.OptimizationOptions.
  • Added a new value to tf.data.Options.autotune.autotune_algorithm: STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.
  • Added tf.data.experimental.from_list, a new API for creating Datasets from lists of elements.

• tf.distribute:

  • Added tf.distribute.experimental.PreemptionCheckpointHandler to handle worker preemption/maintenance and cluster-wise consistent error reporting for tf.distribute.MultiWorkerMirroredStrategy. Specifically, for the type of interruption with advance notice, it automatically saves a checkpoint, exits the program without raising an unrecoverable error, and restores the progress when training restarts.

• tf.math:

  • Added tf.math.approx_max_k and tf.math.approx_min_k which are the optimized alternatives to tf.math.top_k on TPU. The performance difference range from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XLA kernel is used.

• tf.train:

  • Added tf.train.TrackableView which allows users to inspect the TensorFlow Trackable object (e.g. tf.Module, Keras Layers and models).

• tf.vectorized_map:

  • Added an optional parameter: warn. This parameter controls whether or not warnings will be printed when operations in the provided fn fall back to a while loop.

• XLA:

  • MWMS is now compilable with XLA.

• oneDNN CPU performance optimizations:

  • x86 CPUs: oneDNN bfloat16 auto-mixed precision grappler graph optimization pass has been renamed from auto_mixed_precision_mkl to auto_mixed_precision_onednn_bfloat16. See example usage here.
  • aarch64 CPUs: Experimental Arm Compute Library (ACL) CPU performance optimizations through oneDNN are available in the default Linux aarch64 package (pip install tensorflow).
    • The optimizations are disabled by default.
    • Set the environment variable TF_ENABLE_ONEDNN_OPTS=1 to enable the optimizations. Setting the variable to 0 or unsetting it will disable the optimizations.
    • These optimizations can yield slightly different numerical results from when they are off due to floating-point round-off errors from different computation approaches and orders.
    • To verify that the optimizations are on, look for a message with "oneDNN custom operations are on" in the log. If the exact phrase is not there, it means they are off.

Bug Fixes and Other Changes

• New argument experimental_device_ordinal in LogicalDeviceConfiguration to control the order of logical devices. (GPU only)

• tf.keras:

  • Changed the TensorBoard tag names produced by the tf.keras.callbacks.TensorBoard callback, so that summaries logged automatically for model weights now include either a /histogram or /image suffix in their tag names, in order to prevent tag name collisions across summary types.

• When running on GPU (with cuDNN version 7.6.3 or later),tf.nn.depthwise_conv2d backprop to filter (and therefore also tf.keras.layers.DepthwiseConv2D) now operate deterministically (and tf.errors.UnimplementedError is no longer thrown) when op-determinism has been enabled via tf.config.experimental.enable_op_determinism. This closes issue 47174.

• tf.random

  • Added tf.random.experimental.stateless_shuffle, a stateless version of tf.random.shuffle.

Deprecations

• The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
  • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
  • Stop constructing Status objects from tensorflow::error::Code.
  • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
    • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
    • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
    • In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
• tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abolfazl Shahbazi, Adam Lanicek, Amin Benarieb, andreii, Andrew Fitzgibbon, Andrew Goodbody, angerson, Ashiq Imran, Aurélien Geron, Banikumar Maiti (Intel Aipg), Ben Barsdell, Ben Mares, bhack, Bhavani Subramanian, Bill Schnurr, Byungsoo Oh, Chandra Sr Potula, Chengji Yao, Chris Carpita, Christopher Bate, chunduriv, Cliff Woolley, Cliffs Dover, Cloud Han, Code-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, guozhong.zhuang, Hui Peng, James Gerity, Jason Furmanek, Jonathan Dekhtiar, Jueon Park, Kaixi Hou, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, kushanam, Learning-To-Play, Li-Wen Chang, lipracer, liuyuanqiang, Louis Sugy, Lucas David, Lukas Geiger, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, Meenakshi Venkataraman, Michal Szutenberg, Michele Di Giorgio, Mickaël Salamin, Nathan John Sircombe, Nathan Luehr, Neil Girdhar, Nils Reichardt, Nishidha Panpaliya, Nobuo Tsukamoto, Om Thakkar, Patrice Vignola, Philipp Hack, Pooya Jannaty, Prianka Liz Kariat, pshiko, Rajeshwar Reddy T, rdl4199, Rohit Santhanam, Rsanthanam-Amd, Sachin Muradi, Saoirse Stewart, Serge Panev, Shu Wang, Srinivasan Narayanamoorthy, Stella Stamenova, Stephan Hartmann, Sunita Nadampalli, synandi, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Trevor Morris, Xiaoming (Jason) Cui, Yimei Sun, Yong Tang, Yuanqiang Liu, Yulv-Git, Zhoulong Jiang, ZihengJiang

Release 2.10.0

Breaking Changes

• Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
• Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
• tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
• RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
  • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
  • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Major Features and Improvements

• tf.lite:

  • New operations supported:
    • tflite SelectV2 now supports 5D.
    • tf.einsum is supported with multiple unknown shapes.
    • tf.unsortedsegmentprod op is supported.
    • tf.unsortedsegmentmax op is supported.
    • tf.unsortedsegmentsum op is supported.
  • Updates to existing operations:
    • tfl.scatter_nd now supports I1 for update arg.
  • Upgrade Flatbuffers v2.0.5 from v1.12.0

• tf.keras:

  • EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
  • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
  • Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
  • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
  • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
  • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
  • Improved masking support for tf.keras.layers.MultiHeadAttention.
    • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
    • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
  • Added ignore_class argument in the loss SparseCategoricalCrossentropy and metrics IoU and MeanIoU, to specify a class index to be ignored during loss/metric computation (e.g. a background/void class).
  • Added tf.keras.models.experimental.SharpnessAwareMinimization. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.

• tf.data:

  • Added support for cross-trainer data caching in tf.data service. This saves computation resources when concurrent training jobs train from the same dataset. See (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers) for more details.
  • Added dataset_id to tf.data.experimental.service.register_dataset. If provided, tf.data service will use the provided ID for the dataset. If the dataset ID already exists, no new dataset will be registered. This is useful if multiple training jobs need to use the same dataset for training. In this case, users should call register_dataset with the same dataset_id.
  • Added a new field, inject_prefetch, to tf.data.experimental.OptimizationOptions. If it is set to True,tf.data will now automatically add a prefetch transformation to datasets that end in synchronous transformations. This enables data generation to be overlapped with data consumption. This may cause a small increase in memory usage due to buffering. To enable this behavior, set inject_prefetch=True in tf.data.experimental.OptimizationOptions.
  • Added a new value to tf.data.Options.autotune.autotune_algorithm: STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.
  • Added tf.data.experimental.from_list, a new API for creating Datasets from lists of elements.

• tf.distribute:

  • Added tf.distribute.experimental.PreemptionCheckpointHandler to handle worker preemption/maintenance and cluster-wise consistent error reporting for tf.distribute.MultiWorkerMirroredStrategy. Specifically, for the type of interruption with advance notice, it automatically saves a checkpoint, exits the program without raising an unrecoverable error, and restores the progress when training restarts.

• tf.math:

  • Added tf.math.approx_max_k and tf.math.approx_min_k which are the optimized alternatives to tf.math.top_k on TPU. The performance difference range from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XLA kernel is used.

• tf.train:

  • Added tf.train.TrackableView which allows users to inspect the TensorFlow Trackable object (e.g. tf.Module, Keras Layers and models).

• tf.vectorized_map:

  • Added an optional parameter: warn. This parameter controls whether or not warnings will be printed when operations in the provided fn fall back to a while loop.

• XLA:

  • MWMS is now compilable with XLA.

• oneDNN CPU performance optimizations:

  • x86 CPUs: oneDNN bfloat16 auto-mixed precision grappler graph optimization pass has been renamed from auto_mixed_precision_mkl to auto_mixed_precision_onednn_bfloat16. See example usage here.
  • aarch64 CPUs: Experimental oneDNN optimizations are available in the default Linux aarch64 package (pip install tensorflow).
    • The optimizations are disabled by default.
    • Set the environment variable TF_ENABLE_ONEDNN_OPTS=1 to enable the optimizations. Setting the variable to 0 or unsetting it will disable the optimizations.
    • These optimizations can yield slightly different numerical results from when they are off due to floating-point round-off errors from different computation approaches and orders.
    • To verify that the optimizations are on, look for a message with "oneDNN custom operations are on" in the log. If the exact phrase is not there, it means they are off.

Bug Fixes and Other Changes

• New argument experimental_device_ordinal in LogicalDeviceConfiguration to control the order of logical devices. (GPU only)

• tf.keras:

  • Changed the TensorBoard tag names produced by the tf.keras.callbacks.TensorBoard callback, so that summaries logged automatically for model weights now include either a /histogram or /image suffix in their tag names, in order to prevent tag name collisions across summary types.

• When running on GPU (with cuDNN version 7.6.3 or later),tf.nn.depthwise_conv2d backprop to filter (and therefore also tf.keras.layers.DepthwiseConv2D) now operate deterministically (and tf.errors.UnimplementedError is no longer thrown) when op-determinism has been enabled via tf.config.experimental.enable_op_determinism. This closes issue 47174.

• tf.random

  • Added tf.random.experimental.stateless_shuffle, a stateless version of tf.random.shuffle.

Deprecations

• The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
  • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
  • Stop constructing Status objects from tensorflow::error::Code.
  • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
    • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
    • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
    • In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
• tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abolfazl Shahbazi, Adam Lanicek, Amin Benarieb, andreii, Andrew Fitzgibbon, Andrew Goodbody, angerson, Ashiq Imran, Aurélien Geron, Banikumar Maiti (Intel Aipg), Ben Barsdell, Ben Mares, bhack, Bhavani Subramanian, Bill Schnurr, Byungsoo Oh, Chandra Sr Potula, Chengji Yao, Chris Carpita, Christopher Bate, chunduriv, Cliff Woolley, Cliffs Dover, Cloud Han, Code-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, guozhong.zhuang, Hui Peng, James Gerity, Jason Furmanek, Jonathan Dekhtiar, Jueon Park, Kaixi Hou, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, kushanam, Learning-To-Play, Li-Wen Chang, lipracer, liuyuanqiang, Louis Sugy, Lucas David, Lukas Geiger, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, Meenakshi Venkataraman, Michal Szutenberg, Michele Di Giorgio, Mickaël Salamin, Nathan John Sircombe, Nathan Luehr, Neil Girdhar, Nils Reichardt, Nishidha Panpaliya, Nobuo Tsukamoto, Om Thakkar, Patrice Vignola, Philipp Hack, Pooya Jannaty, Prianka Liz Kariat, pshiko, Rajeshwar Reddy T, rdl4199, Rohit Santhanam, Rsanthanam-Amd, Sachin Muradi, Saoirse Stewart, Serge Panev, Shu Wang, Srinivasan Narayanamoorthy, Stella Stamenova, Stephan Hartmann, Sunita Nadampalli, synandi, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Trevor Morris, Xiaoming (Jason) Cui, Yimei Sun, Yong Tang, Yuanqiang Liu, Yulv-Git, Zhoulong Jiang, ZihengJiang

Release 2.10.0

Breaking Changes

• Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
• Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
• tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g., tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
• RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
  • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
  • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Major Features and Improvements

• tf.lite:

  • New operations supported:
    • tflite SelectV2 now supports 5D.
    • tf.einsum is supported with multiple unknown shapes.
    • tf.unsortedsegmentprod op is supported.
    • tf.unsortedsegmentmax op is supported.
    • tf.unsortedsegmentsum op is supported.
  • Updates to existing operations:
    • tfl.scatter_nd now supports I1 for update arg.
  • Upgrade Flatbuffers v2.0.5 from v1.12.0

• tf.keras:

  • EinsumDense layer moved from experimental to core. Its import path moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
  • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
  • Added subset="both" support in tf.keras.utils.image_dataset_from_directory, tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
  • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
  • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
  • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
  • Improved masking support for tf.keras.layers.MultiHeadAttention.
    • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
    • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
  • Added ignore_class argument in the loss SparseCategoricalCrossentropy and metrics IoU and MeanIoU, to specify a class index to be ignored during loss/metric computation (e.g. a background/void class).
  • Added tf.keras.models.experimental.SharpnessAwareMinimization. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.

• tf.data:

  • Added support for cross-trainer data caching in tf.data service. This saves computation resources when concurrent training jobs train from the same dataset. See https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers for more details.
  • Added dataset_id to tf.data.experimental.service.register_dataset. If provided, tf.data service will use the provided ID for the dataset. If the dataset ID already exists, no new dataset will be registered. This is useful if multiple training jobs need to use the same dataset for training. In this case, users should call register_dataset with the same dataset_id.
  • Added a new field, inject_prefetch, to tf.data.experimental.OptimizationOptions. If it is set to True, tf.data will now automatically add a prefetch transformation to datasets that end in synchronous transformations. This enables data generation to be overlapped with data consumption. This may cause a small increase in memory usage due to buffering. To enable this behavior, set inject_prefetch=True in tf.data.experimental.OptimizationOptions.
  • Added a new value to tf.data.Options.autotune.autotune_algorithm: STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.
  • Added tf.data.experimental.from_list, a new API for creating Datasets from lists of elements.

• tf.distribute:

  • Added tf.distribute.experimental.PreemptionCheckpointHandler to handle worker preemption/maintenance and cluster-wise consistent error reporting for tf.distribute.MultiWorkerMirroredStrategy. Specifically, for the type of interruption with advance notice, it automatically saves a checkpoint, exits the program without raising an unrecoverable error, and restores the progress when training restarts.

• tf.math:

  • Added tf.math.approx_max_k and tf.math.approx_min_k which are the optimized alternatives to tf.math.top_k on TPU. The performance difference range from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XLA kernel is used.

• tf.train:

  • Added tf.train.TrackableView which allows users to inspect the TensorFlow Trackable object (e.g. tf.Module, Keras Layers and models).

• tf.vectorized_map:

  • Added an optional parameter: warn. This parameter controls whether or not warnings will be printed when operations in the provided fn fall back to a while loop.

• XLA:

  • MWMS is now compilable with XLA.

Bug Fixes and Other Changes

• New argument experimental_device_ordinal in LogicalDeviceConfiguration to control the order of logical devices. (GPU only)

• tf.keras:

  • Changed the TensorBoard tag names produced by the tf.keras.callbacks.TensorBoard callback, so that summaries logged automatically for model weights now include either a /histogram or /image suffix in their tag names, in order to prevent tag name collisions across summary types.

• When running on GPU (with cuDNN version 7.6.3 or later), tf.nn.depthwise_conv2d backprop to filter (and therefore also tf.keras.layers.DepthwiseConv2D) now operate deterministically (and tf.errors.UnimplementedError is no longer thrown) when op-determinism has been enabled via tf.config.experimental.enable_op_determinism. This closes issue 47174.

• tf.random

  • Added tf.random.experimental.stateless_shuffle, a stateless version of tf.random.shuffle.

Deprecations

• The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
  • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
  • Stop constructing Status objects from tensorflow::error::Code.
  • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
    • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
    • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
    • In the last resort, use e.g. static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
• tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abolfazl Shahbazi, Adam Lanicek, Amin Benarieb, andreii, Andrew Fitzgibbon, Andrew Goodbody, angerson, Ashiq Imran, Aurélien Geron, Banikumar Maiti (Intel Aipg), Ben Barsdell, Ben Mares, bhack, Bhavani Subramanian, Bill Schnurr, Byungsoo Oh, Chandra Sr Potula, Chengji Yao, Chris Carpita, Christopher Bate, chunduriv, Cliff Woolley, Cliffs Dover, Cloud Han, Code-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, guozhong.zhuang, Hui Peng, James Gerity, Jason Furmanek, Jonathan Dekhtiar, Jueon Park, Kaixi Hou, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, kushanam, Learning-To-Play, Li-Wen Chang, lipracer, liuyuanqiang, Louis Sugy, Lucas David, Lukas Geiger, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, Meenakshi Venkataraman, Michal Szutenberg, Michele Di Giorgio, Mickaël Salamin, Nathan John Sircombe, Nathan Luehr, Neil Girdhar, Nils Reichardt, Nishidha Panpaliya, Nobuo Tsukamoto, Om Thakkar, Patrice Vignola, Philipp Hack, Pooya Jannaty, Prianka Liz Kariat, pshiko, Rajeshwar Reddy T, rdl4199, Rohit Santhanam, Rsanthanam-Amd, Sachin Muradi, Saoirse Stewart, Serge Panev, Shu Wang, Srinivasan Narayanamoorthy, Stella Stamenova, Stephan Hartmann, Sunita Nadampalli, synandi, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Trevor Morris, Xiaoming (Jason) Cui, Yimei Sun, Yong Tang, Yuanqiang Liu, Yulv-Git, Zhoulong Jiang, ZihengJiang

Release 2.9.1

Add an upper bound for protobuf in setup.py since protobuf after version 3.20 is currently incompatible with TensorFlow. See https://github.com/tensorflow/tensorflow/issues/53234, https://github.com/protocolbuffers/protobuf/issues/9954 and https://github.com/tensorflow/tensorflow/issues/56077.

Release 2.8.2

Add an upper bound for protobuf in setup.py since protobuf after version 3.20 is currently incompatible with TensorFlow. See https://github.com/tensorflow/tensorflow/issues/53234, https://github.com/protocolbuffers/protobuf/issues/9954 and https://github.com/tensorflow/tensorflow/issues/56077.

Release 2.6.5

Add an upper bound for protobuf in setup.py since protobuf after version 3.20 is currently incompatible with TensorFlow. See https://github.com/tensorflow/tensorflow/issues/53234, https://github.com/protocolbuffers/protobuf/issues/9954 and https://github.com/tensorflow/tensorflow/issues/56077.

This is the final release in the 2.6.x series.

Release 2.7.3

Add an upper bound for protobuf in setup.py since protobuf after version 3.20 is currently incompatible with TensorFlow. See https://github.com/tensorflow/tensorflow/issues/53234, https://github.com/protocolbuffers/protobuf/issues/9954 and https://github.com/tensorflow/tensorflow/issues/56077.

Release 2.9.0

Breaking Changes

• Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow Decision Forests.
• Build, Compilation and Packaging
  • TensorFlow is now compiled with _GLIBCXX_USE_CXX11_ABI=1. Downstream projects that encounter std::__cxx11 or [abi:cxx11] linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.
  • TensorFlow Python wheels now specifically conform to manylinux2014, an upgrade from manylinux2010. The minimum Pip version supporting manylinux2014 is Pip 19.3 (see pypa/manylinux. This change may affect you if you have been using TensorFlow on a very old platform equivalent to CentOS 6, as manylinux2014 targets CentOS 7 as a compatibility base. Note that TensorFlow does not officially support either platform.
  • Discussion for these changes can be found on SIG Build's TensorFlow Community Forum thread
• The tf.keras.mixed_precision.experimental API has been removed. The non-experimental symbols under tf.keras.mixed_precision have been available since TensorFlow 2.4 and should be used instead.
  • The non-experimental API has some minor differences from the experimental API. In most cases, you only need to make three minor changes:
    • Remove the word "experimental" from tf.keras.mixed_precision symbols. E.g., replace tf.keras.mixed_precision.experimental.global_policy with tf.keras.mixed_precision.global_policy.
    • Replace tf.keras.mixed_precision.experimental.set_policy with tf.keras.mixed_precision.set_global_policy. The experimental symbol set_policy was renamed to set_global_policy in the non-experimental API.
    • Replace LossScaleOptimizer(opt, "dynamic") with LossScaleOptimizer(opt). If you pass anything other than "dynamic" to the second argument, see (1) of the next section.
  • In the following rare cases, you need to make more changes when switching to the non-experimental API:
    • If you passed anything other than "dynamic" to the loss_scale argument (the second argument) of LossScaleOptimizer:
      • The LossScaleOptimizer constructor takes in different arguments. See the TF 2.7 documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer for details on the differences, which has examples on how to convert to the non-experimental LossScaleOptimizer.
    • If you passed a value to the loss_scale argument (the second argument) of Policy:
      • The experimental version of Policy optionally took in a tf.compat.v1.mixed_precision.LossScale in the constructor, which defaulted to a dynamic loss scale for the "mixed_float16" policy and no loss scale for other policies. In Model.compile, if the model's policy had a loss scale, the optimizer would be wrapped with a LossScaleOptimizer. With the non-experimental Policy, there is no loss scale associated with the Policy, and Model.compile wraps the optimizer with a LossScaleOptimizer if and only if the policy is a "mixed_float16" policy. If you previously passed a LossScale to the experimental Policy, consider just removing it, as the default loss scaling behavior is usually what you want. If you really want to customize the loss scaling behavior, you can wrap your optimizer with a LossScaleOptimizer before passing it to Model.compile.
    • If you use the very rarely-used function tf.keras.mixed_precision.experimental.get_layer_policy:
      • Replace tf.keras.mixed_precision.experimental.get_layer_policy(layer) with layer.dtype_policy.
• tf.mixed_precision.experimental.LossScale and its subclasses have been removed from the TF2 namespace. This symbols were very rarely used and were only useful in TF2 for use in the now-removed tf.keras.mixed_precision.experimental API. The symbols are still available under tf.compat.v1.mixed_precision.
• The experimental_relax_shapes heuristic for tf.function has been deprecated and replaced with reduce_retracing which encompasses broader heuristics to reduce the number of retraces (see below)

Major Features and Improvements

• tf.keras:

  • Added tf.keras.applications.resnet_rs models. This includes the ResNetRS50, ResNetRS101, ResNetRS152, ResNetRS200, ResNetRS270, ResNetRS350 and ResNetRS420 model architectures. The ResNetRS models are based on the architecture described in Revisiting ResNets: Improved Training and Scaling Strategies
  • Added tf.keras.optimizers.experimental.Optimizer. The reworked optimizer gives more control over different phases of optimizer calls, and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and RMSprop optimizers based on tf.keras.optimizers.experimental.Optimizer. Generally the new optimizers work in the same way as the old ones, but support new constructor arguments. In the future, the symbols tf.keras.optimizers.Optimizer/Adam/etc will point to the new optimizers, and the previous generation of optimizers will be moved to tf.keras.optimizers.legacy.Optimizer/Adam/etc.
  • Added L2 unit normalization layer tf.keras.layers.UnitNormalization.
  • Added tf.keras.regularizers.OrthogonalRegularizer, a new regularizer that encourages orthogonality between the rows (or columns) or a weight matrix.
  • Added tf.keras.layers.RandomBrightness layer for image preprocessing.
  • Added APIs for switching between interactive logging and absl logging. By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use tf.keras.utils.disable_interactive_logging() to write the logs to ABSL logging. You can also use tf.keras.utils.enable_interactive_logging() to change it back to stdout, or tf.keras.utils.is_interactive_logging_enabled() to check if interactive logging is enabled.
  • Changed default value for the verbose argument of Model.evaluate() and Model.predict() to "auto", which defaults to verbose=1 for most cases and defaults to verbose=2 when used with ParameterServerStrategy or with interactive logging disabled.
  • Argument jit_compile in Model.compile() now applies to Model.evaluate() and Model.predict(). Setting jit_compile=True in compile() compiles the model's training, evaluation, and inference steps to XLA. Note that jit_compile=True may not necessarily work for all models.
  • Added DTensor-related Keras APIs under tf.keras.dtensor namespace. The APIs are still classified as experimental. You are welcome to try it out. Please check the tutoral and guide on https://www.tensorflow.org/ for more details about DTensor.

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.math.argmin/tf.math.argmax for input data type tf.bool on CPU.
    • tf.nn.gelu op for output data type tf.float32 and quantization on CPU.
  • Add nominal support for unsigned 16-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.
  • Add support for unsigned 16-bit integer tensor types in cast op.
  • Experimental support for lowering list_ops.tensor_list_set_item with DynamicUpdateSlice.
  • Enabled a new MLIR-based dynamic range quantization backend by default
    • The new backend is used for post-training int8 dynamic range quantization and post-training float16 quantization.
    • Set experimental_new_dynamic_range_quantizer in tf.lite.TFLiteConverter to False to disable this change
  • Native TF Lite variables are now enabled during conversion by default on all v2 TfLiteConverter entry points. experimental_enable_resource_variables on tf.lite.TFLiteConverter is now True by default and will be removed in the future.

• tf.function:

  • Custom classes used as arguments for tf.function can now specify rules regarding when retracing needs to occur by implementing the Tracing Protocol available through tf.types.experimental.SupportsTracingProtocol.
  • TypeSpec classes (as associated with ExtensionTypes) also implement the Tracing Protocol which can be overriden if necessary.
  • The newly introduced reduce_retracing option also uses the Tracing Protocol to proactively generate generalized traces similar to experimental_relax_shapes (which has now been deprecated).

• Unified eager and tf.function execution:

  • Eager mode can now execute each op as a tf.function, allowing for more consistent feature support in future releases.
  • It is available for immediate use.
    • See the TF_RUN_EAGER_OP_AS_FUNCTION environment variable in eager context.
    • Eager performance should be similar with this feature enabled.
      • A roughly 5us per-op overhead may be observed when running many small functions.
      • Note a known issue with GPU performance.
    • The behavior of tf.function itself is unaffected.
  • Note: This feature will be enabled by default in an upcoming version of TensorFlow.

• tf.experimental.dtensor: Added DTensor, an extension to TensorFlow for large-scale modeling with minimal changes to user code. You are welcome to try it out, though be aware that the DTensor API is experimental and up-to backward-incompatible changes. DTensor and Keras integration is published under tf.keras.dtensor in this release (refer to the tf.keras entry). The tutoral and guide for DTensor will be published on https://www.tensorflow.org/. Please stay tuned.

• oneDNN CPU performance optimizations are available in Linux x86, Windows x86, and Linux aarch64 packages.

  • Linux x86 packages:
    • oneDNN optimizations are enabled by default on CPUs with neural-network-focused hardware features such as AVX512_VNNI, AVX512_BF16, AMX, etc. (Intel Cascade Lake and newer CPUs.)
      • Example performance speedups.
    • For older CPUs, oneDNN optimizations are disabled by default.
  • Windows x86 package: oneDNN optimizations are disabled by default.
  • Linux aach64 (--config=mkl_aarch64) package:
    • Experimental oneDNN optimizations are disabled by default.
    • If you experience issues with oneDNN optimizations on, we recommend turning them off.
  • To explicitly enable or disable oneDNN optimizations, set the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. (The variable is checked during import tensorflow.) To fall back to default settings, unset the environment variable.
  • These optimizations can yield slightly different numerical results from when they are off due to floating-point round-off errors from different computation approaches and orders.
  • To verify that the optimizations are on, look for a message with "oneDNN custom operations are on" in the log. If the exact phrase is not there, it means they are off.

Bug Fixes and Other Changes

• tf.data:

  • Fixed bug in tf.data.experimental.parse_example_dataset when tf.io.RaggedFeatures would specify value_key but no partitions. Before the fix, setting value_key but no partitions would result in the feature key being replaced by the value key, e.g. {'value_key': <RaggedTensor>} instead of {'key': <RaggedTensor>}. Now the correct feature key will be used. This aligns the behavior of tf.data.experimental.parse_example_dataset to match the behavior of tf.io.parse_example.
  • Added a new field, filter_parallelization, to tf.data.experimental.OptimizationOptions. If it is set to True, tf.data will run Filter transformation with multiple threads. Its default value is False if not specified.

• tf.keras:

  • Fixed bug in optimizers that prevented them from properly checkpointing slot variables when they are ShardedVariables (used for training with tf.distribute.experimental.ParameterServerStrategy).

• tf.random:

  • Added tf.random.experimental.index_shuffle, for shuffling a sequence without materializing the sequence in memory.

• tf.RaggedTensor:

  • Introduced tf.experimental.RowPartition, which encodes how one dimension in a RaggedTensor relates to another, into the public API.
  • Introduced tf.experimental.DynamicRaggedShape, which represents the shape of a RaggedTensor.

Security

• Fixes a code injection in saved_model_cli (CVE-2022-29216)
• Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
• Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
• Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
• Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
• Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
• Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
• Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
• Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
• Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
• Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
• Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
• Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
• Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)
• Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)
• Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)
• Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)
• Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)
• Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)
• Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)
• Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)
• Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)
• Fixes a heap buffer overflow due to incorrect hash function (CVE-2022-29210)
• Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115
• Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Debattista, Abel Soares Siqueira, Abhishek Varma, Andrei Ivanov, andreii, Andrew Goodbody, apeltop, Arnab Dutta, Ashiq Imran, Banikumar Maiti (Intel Aipg), Ben Greiner, Benjamin Peterson, bhack, Christopher Bate, chunduriv, Copybara-Service, DEKHTIARJonathan, Deven Desai, Duncan Riach, Eric Kunze, Everton Constantino, Faruk D, Fredrik Knutsson, gadagashwini, Gauri1 Deshpande, gtiHibGele, Guozhong Zhuang, Islem-Esi, Ivanov Viktor, Jason Furmanek, Jason Zaman, Jim, Jinzhe Zeng, John Laxson, Jonas Eschle, Jonas Eschle 'Mayou36, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, KaurkerDevourer, Koan-Sin Tan, kushanam, Laramie Leavitt, Li-Wen Chang, lipracer, Louis Sugy, Lu Teng, Mahmoud Abuzaina, Malcolm Slaney, Malik Shahzad Muzaffar, Marek Šuppa, Matt Conley, Michael Melesse, Milos Puzovic, mohantym, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Patrice Vignola, peterjc123, Philip Turner, Rajeshwar Reddy T, Robert Kalmar, Rodrigo Formigone, Rohit Santhanam, rui, Sachin Muradi, Saduf2019, sandip, Scott Leishman, Serge Panev, Shi,Guangyong, Srinivasan Narayanamoorthy, stanley, Steven I Reeves, stevenireeves, sushreebarsa, Tamas Bela Feher, Tao He, Thomas Schmeyer, Tiago Almeida, Trevor Morris, Uday Bondhugula, Uwe L. Korn, Varghese, Jojimon, Vishnuvardhan Janapati, William Muir, William Raveane, xutianming, Yasuhiro Matsumoto, Yimei Sun, Yong Tang, Yu Feng, Yuriy Chernyshov, zhaozheng09

Release 2.6.4

This releases introduces several vulnerability fixes:

• Fixes a code injection in saved_model_cli (CVE-2022-29216)
• Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
• Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
• Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
• Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
• Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
• Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
• Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
• Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
• Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
• Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
• Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
• Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
• Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)
• Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)
• Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)
• Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)
• Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)
• Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)
• Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)
• Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)
• Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)
• Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115
• Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

Release 2.8.1

This releases introduces several vulnerability fixes:

• Fixes a code injection in saved_model_cli (CVE-2022-29216)
• Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
• Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
• Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
• Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
• Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
• Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
• Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
• Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
• Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
• Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
• Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
• Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
• Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)
• Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)
• Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)
• Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)
• Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)
• Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)
• Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)
• Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)
• Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)
• Fixes a heap buffer overflow due to incorrect hash function (CVE-2022-29210)
• Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115
• Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

Release 2.7.2

This releases introduces several vulnerability fixes:

• Fixes a code injection in saved_model_cli (CVE-2022-29216)
• Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
• Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
• Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
• Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
• Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
• Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
• Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
• Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
• Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
• Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
• Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
• Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
• Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)
• Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)
• Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)
• Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)
• Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)
• Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)
• Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)
• Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)
• Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)
• Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)
• Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115
• Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

Release 2.9.0

Breaking Changes

• Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow Decision Forests.
• Build, Compilation and Packaging
  • TensorFlow is now compiled with _GLIBCXX_USE_CXX11_ABI=1. Downstream projects that encounter std::__cxx11 or [abi:cxx11] linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.
  • TensorFlow Python wheels now specifically conform to manylinux2014, an upgrade from manylinux2010. The minimum Pip version supporting manylinux2014 is Pip 19.3 (see pypa/manylinux. This change may affect you if you have been using TensorFlow on a very old platform equivalent to CentOS 6, as manylinux2014 targets CentOS 7 as a compatibility base. Note that TensorFlow does not officially support either platform.
  • Discussion for these changes can be found on SIG Build's TensorFlow Community Forum thread
• The tf.keras.mixed_precision.experimental API has been removed. The non-experimental symbols under tf.keras.mixed_precision have been available since TensorFlow 2.4 and should be used instead.
  • The non-experimental API has some minor differences from the experimental API. In most cases, you only need to make three minor changes:
    • Remove the word "experimental" from tf.keras.mixed_precision symbols. E.g., replace tf.keras.mixed_precision.experimental.global_policy with tf.keras.mixed_precision.global_policy.
    • Replace tf.keras.mixed_precision.experimental.set_policy with tf.keras.mixed_precision.set_global_policy. The experimental symbol set_policy was renamed to set_global_policy in the non-experimental API.
    • Replace LossScaleOptimizer(opt, "dynamic") with LossScaleOptimizer(opt). If you pass anything other than "dynamic" to the second argument, see (1) of the next section.
  • In the following rare cases, you need to make more changes when switching to the non-experimental API:
    • If you passed anything other than "dynamic" to the loss_scale argument (the second argument) of LossScaleOptimizer:
      • The LossScaleOptimizer constructor takes in different arguments. See the TF 2.7 documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer for details on the differences, which has examples on how to convert to the non-experimental LossScaleOptimizer.
    • If you passed a value to the loss_scale argument (the second argument) of Policy:
      • The experimental version of Policy optionally took in a tf.compat.v1.mixed_precision.LossScale in the constructor, which defaulted to a dynamic loss scale for the "mixed_float16" policy and no loss scale for other policies. In Model.compile, if the model's policy had a loss scale, the optimizer would be wrapped with a LossScaleOptimizer. With the non-experimental Policy, there is no loss scale associated with the Policy, and Model.compile wraps the optimizer with a LossScaleOptimizer if and only if the policy is a "mixed_float16" policy. If you previously passed a LossScale to the experimental Policy, consider just removing it, as the default loss scaling behavior is usually what you want. If you really want to customize the loss scaling behavior, you can wrap your optimizer with a LossScaleOptimizer before passing it to Model.compile.
    • If you use the very rarely-used function tf.keras.mixed_precision.experimental.get_layer_policy:
      • Replace tf.keras.mixed_precision.experimental.get_layer_policy(layer) with layer.dtype_policy.
• tf.mixed_precision.experimental.LossScale and its subclasses have been removed from the TF2 namespace. This symbols were very rarely used and were only useful in TF2 for use in the now-removed tf.keras.mixed_precision.experimental API. The symbols are still available under tf.compat.v1.mixed_precision.
• The experimental_relax_shapes heuristic for tf.function has been deprecated and replaced with reduce_retracing which encompasses broader heuristics to reduce the number of retraces (see below)

Major Features and Improvements

• tf.keras:

  • Added tf.keras.applications.resnet_rs models. This includes the ResNetRS50, ResNetRS101, ResNetRS152, ResNetRS200, ResNetRS270, ResNetRS350 and ResNetRS420 model architectures. The ResNetRS models are based on the architecture described in Revisiting ResNets: Improved Training and Scaling Strategies
  • Added tf.keras.optimizers.experimental.Optimizer. The reworked optimizer gives more control over different phases of optimizer calls, and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and RMSprop optimizers based on tf.keras.optimizers.experimental.Optimizer. Generally the new optimizers work in the same way as the old ones, but support new constructor arguments. In the future, the symbols tf.keras.optimizers.Optimizer/Adam/etc will point to the new optimizers, and the previous generation of optimizers will be moved to tf.keras.optimizers.legacy.Optimizer/Adam/etc.
  • Added L2 unit normalization layer tf.keras.layers.UnitNormalization.
  • Added tf.keras.regularizers.OrthogonalRegularizer, a new regularizer that encourages orthogonality between the rows (or columns) or a weight matrix.
  • Added tf.keras.layers.RandomBrightness layer for image preprocessing.
  • Added APIs for switching between interactive logging and absl logging. By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use tf.keras.utils.disable_interactive_logging() to write the logs to ABSL logging. You can also use tf.keras.utils.enable_interactive_logging() to change it back to stdout, or tf.keras.utils.is_interactive_logging_enabled() to check if interactive logging is enabled.
  • Changed default value for the verbose argument of Model.evaluate() and Model.predict() to "auto", which defaults to verbose=1 for most cases and defaults to verbose=2 when used with ParameterServerStrategy or with interactive logging disabled.
  • Argument jit_compile in Model.compile() now applies to Model.evaluate() and Model.predict(). Setting jit_compile=True in compile() compiles the model's training, evaluation, and inference steps to XLA. Note that jit_compile=True may not necessarily work for all models.
  • Added DTensor-related Keras APIs under tf.keras.dtensor namespace. The APIs are still classified as experimental. You are welcome to try it out. Please check the tutoral and guide on https://www.tensorflow.org/ for more details about DTensor.

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.math.argmin/tf.math.argmax for input data type tf.bool on CPU.
    • tf.nn.gelu op for output data type tf.float32 and quantization on CPU.
  • Add nominal support for unsigned 16-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.
  • Add support for unsigned 16-bit integer tensor types in cast op.
  • Experimental support for lowering list_ops.tensor_list_set_item with DynamicUpdateSlice.
  • Enabled a new MLIR-based dynamic range quantization backend by default
    • The new backend is used for post-training int8 dynamic range quantization and post-training float16 quantization.
    • Set experimental_new_dynamic_range_quantizer in tf.lite.TFLiteConverter to False to disable this change
  • Native TF Lite variables are now enabled during conversion by default on all v2 TfLiteConverter entry points. experimental_enable_resource_variables on tf.lite.TFLiteConverter is now True by default and will be removed in the future.

• tf.function:

  • Custom classes used as arguments for tf.function can now specify rules regarding when retracing needs to occur by implementing the Tracing Protocol available through tf.types.experimental.SupportsTracingProtocol.
  • TypeSpec classes (as associated with ExtensionTypes) also implement the Tracing Protocol which can be overriden if necessary.
  • The newly introduced reduce_retracing option also uses the Tracing Protocol to proactively generate generalized traces similar to experimental_relax_shapes (which has now been deprecated).

• Unified eager and tf.function execution:

  • Eager mode can now execute each op as a tf.function, allowing for more consistent feature support in future releases.
  • It is available for immediate use.
    • See the TF_RUN_EAGER_OP_AS_FUNCTION environment variable in eager context.
    • Eager performance should be similar with this feature enabled.
      • A roughly 5us per-op overhead may be observed when running many small functions.
      • Note a known issue with GPU performance.
    • The behavior of tf.function itself is unaffected.
  • Note: This feature will be enabled by default in an upcoming version of TensorFlow.

• tf.experimental.dtensor: Added DTensor, an extension to TensorFlow for large-scale modeling with minimal changes to user code. You are welcome to try it out, though be aware that the DTensor API is experimental and up-to backward-incompatible changes. DTensor and Keras integration is published under tf.keras.dtensor in this release (refer to the tf.keras entry). The tutoral and guide for DTensor will be published on https://www.tensorflow.org/. Please stay tuned.

Bug Fixes and Other Changes

• tf.data:

  • Fixed bug in tf.data.experimental.parse_example_dataset when tf.io.RaggedFeatures would specify value_key but no partitions. Before the fix, setting value_key but no partitions would result in the feature key being replaced by the value key, e.g. {'value_key': <RaggedTensor>} instead of {'key': <RaggedTensor>}. Now the correct feature key will be used. This aligns the behavior of tf.data.experimental.parse_example_dataset to match the behavior of tf.io.parse_example.
  • Added a new field, filter_parallelization, to tf.data.experimental.OptimizationOptions. If it is set to True, tf.data will run Filter transformation with multiple threads. Its default value is False if not specified.

• tf.keras:

  • Fixed bug in optimizers that prevented them from properly checkpointing slot variables when they are ShardedVariables (used for training with tf.distribute.experimental.ParameterServerStrategy).

• tf.random:

  • Added tf.random.experimental.index_shuffle, for shuffling a sequence without materializing the sequence in memory.

• tf.RaggedTensor:

  • Introduced tf.experimental.RowPartition, which encodes how one dimension in a RaggedTensor relates to another, into the public API.
  • Introduced tf.experimental.DynamicRaggedShape, which represents the shape of a RaggedTensor.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Debattista, Abel Soares Siqueira, Abhishek Varma, Andrei Ivanov, andreii, Andrew Goodbody, apeltop, Arnab Dutta, Ashiq Imran, Banikumar Maiti (Intel Aipg), Ben Greiner, Benjamin Peterson, bhack, Christopher Bate, chunduriv, Copybara-Service, DEKHTIARJonathan, Deven Desai, Duncan Riach, Eric Kunze, Everton Constantino, Faruk D, Fredrik Knutsson, gadagashwini, Gauri1 Deshpande, gtiHibGele, Guozhong Zhuang, Islem-Esi, Ivanov Viktor, Jason Furmanek, Jason Zaman, Jim, Jinzhe Zeng, John Laxson, Jonas Eschle, Jonas Eschle 'Mayou36, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, KaurkerDevourer, Koan-Sin Tan, kushanam, Laramie Leavitt, Li-Wen Chang, lipracer, Louis Sugy, Lu Teng, Mahmoud Abuzaina, Malcolm Slaney, Malik Shahzad Muzaffar, Marek Šuppa, Matt Conley, Michael Melesse, Milos Puzovic, mohantym, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Patrice Vignola, peterjc123, Philip Turner, Rajeshwar Reddy T, Robert Kalmar, Rodrigo Formigone, Rohit Santhanam, rui, Sachin Muradi, Saduf2019, sandip, Scott Leishman, Serge Panev, Shi,Guangyong, Srinivasan Narayanamoorthy, stanley, Steven I Reeves, stevenireeves, sushreebarsa, Tamas Bela Feher, Tao He, Thomas Schmeyer, Tiago Almeida, Trevor Morris, Uday Bondhugula, Uwe L. Korn, Varghese, Jojimon, Vishnuvardhan Janapati, William Muir, William Raveane, xutianming, Yasuhiro Matsumoto, Yimei Sun, Yong Tang, Yu Feng, Yuriy Chernyshov, zhaozheng09

Release 2.9.0

Breaking Changes

• Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow Decision Forests.
• Build, Compilation and Packaging
  • TensorFlow is now compiled with _GLIBCXX_USE_CXX11_ABI=1. Downstream projects that encounter std::__cxx11 or [abi:cxx11] linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.
  • TensorFlow Python wheels now specifically conform to manylinux2014, an upgrade from manylinux2010. The minimum Pip version supporting manylinux2014 is Pip 19.3 (see pypa/manylinux. This change may affect you if you have been using TensorFlow on a very old platform equivalent to CentOS 6, as manylinux2014 targets CentOS 7 as a compatibility base. Note that TensorFlow does not officially support either platform.
  • Discussion for these changes can be found on SIG Build's TensorFlow Community Forum thread
• The tf.keras.mixed_precision.experimental API has been removed. The non-experimental symbols under tf.keras.mixed_precision have been available since TensorFlow 2.4 and should be used instead.
  • The non-experimental API has some minor differences from the experimental API. In most cases, you only need to make three minor changes:
    • Remove the word "experimental" from tf.keras.mixed_precision symbols. E.g., replace tf.keras.mixed_precision.experimental.global_policy with tf.keras.mixed_precision.global_policy.
    • Replace tf.keras.mixed_precision.experimental.set_policy with tf.keras.mixed_precision.set_global_policy. The experimental symbol set_policy was renamed to set_global_policy in the non-experimental API.
    • Replace LossScaleOptimizer(opt, "dynamic") with LossScaleOptimizer(opt). If you pass anything other than "dynamic" to the second argument, see (1) of the next section.
  • In the following rare cases, you need to make more changes when switching to the non-experimental API:
    • If you passed anything other than "dynamic" to the loss_scale argument (the second argument) of LossScaleOptimizer:
      • The LossScaleOptimizer constructor takes in different arguments. See the TF 2.7 documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer for details on the differences, which has examples on how to convert to the non-experimental LossScaleOptimizer.
    • If you passed a value to the loss_scale argument (the second argument) of Policy:
      • The experimental version of Policy optionally took in a tf.compat.v1.mixed_precision.LossScale in the constructor, which defaulted to a dynamic loss scale for the "mixed_float16" policy and no loss scale for other policies. In Model.compile, if the model's policy had a loss scale, the optimizer would be wrapped with a LossScaleOptimizer. With the non-experimental Policy, there is no loss scale associated with the Policy, and Model.compile wraps the optimizer with a LossScaleOptimizer if and only if the policy is a "mixed_float16" policy. If you previously passed a LossScale to the experimental Policy, consider just removing it, as the default loss scaling behavior is usually what you want. If you really want to customize the loss scaling behavior, you can wrap your optimizer with a LossScaleOptimizer before passing it to Model.compile.
    • If you use the very rarely-used function tf.keras.mixed_precision.experimental.get_layer_policy:
      • Replace tf.keras.mixed_precision.experimental.get_layer_policy(layer) with layer.dtype_policy.
• tf.mixed_precision.experimental.LossScale and its subclasses have been removed from the TF2 namespace. This symbols were very rarely used and were only useful in TF2 for use in the now-removed tf.keras.mixed_precision.experimental API. The symbols are still available under tf.compat.v1.mixed_precision.
• The experimental_relax_shapes heuristic for tf.function has been deprecated and replaced with reduce_retracing which encompasses broader heuristics to reduce the number of retraces (see below)

Major Features and Improvements

• tf.keras:

  • Added tf.keras.applications.resnet_rs models. This includes the ResNetRS50, ResNetRS101, ResNetRS152, ResNetRS200, ResNetRS270, ResNetRS350 and ResNetRS420 model architectures. The ResNetRS models are based on the architecture described in Revisiting ResNets: Improved Training and Scaling Strategies
  • Added tf.keras.optimizers.experimental.Optimizer. The reworked optimizer gives more control over different phases of optimizer calls, and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and RMSprop optimizers based on tf.keras.optimizers.experimental.Optimizer. Generally the new optimizers work in the same way as the old ones, but support new constructor arguments. In the future, the symbols tf.keras.optimizers.Optimizer/Adam/etc will point to the new optimizers, and the previous generation of optimizers will be moved to tf.keras.optimizers.legacy.Optimizer/Adam/etc.
  • Added L2 unit normalization layer tf.keras.layers.UnitNormalization.
  • Added tf.keras.regularizers.OrthogonalRegularizer, a new regularizer that encourages orthogonality between the rows (or columns) or a weight matrix.
  • Added tf.keras.layers.RandomBrightness layer for image preprocessing.
  • Added APIs for switching between interactive logging and absl logging. By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use tf.keras.utils.disable_interactive_logging() to write the logs to ABSL logging. You can also use tf.keras.utils.enable_interactive_logging() to change it back to stdout, or tf.keras.utils.is_interactive_logging_enabled() to check if interactive logging is enabled.
  • Changed default value for the verbose argument of Model.evaluate() and Model.predict() to "auto", which defaults to verbose=1 for most cases and defaults to verbose=2 when used with ParameterServerStrategy or with interactive logging disabled.
  • Argument jit_compile in Model.compile() now applies to Model.evaluate() and Model.predict(). Setting jit_compile=True in compile() compiles the model's training, evaluation, and inference steps to XLA. Note that jit_compile=True may not necessarily work for all models.
  • Added DTensor-related Keras APIs under tf.keras.dtensor namespace. The APIs are still classified as experimental. You are welcome to try it out. Please check the tutoral and guide on https://www.tensorflow.org/ for more details about DTensor.

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.math.argmin/tf.math.argmax for input data type tf.bool on CPU.
    • tf.nn.gelu op for output data type tf.float32 and quantization on CPU.
  • Add nominal support for unsigned 16-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.
  • Add support for unsigned 16-bit integer tensor types in cast op.
  • Experimental support for lowering list_ops.tensor_list_set_item with DynamicUpdateSlice.
  • Enabled a new MLIR-based dynamic range quantization backend by default
    • The new backend is used for post-training int8 dynamic range quantization and post-training float16 quantization.
    • Set experimental_new_dynamic_range_quantizer in tf.lite.TFLiteConverter to False to disable this change
  • Native TF Lite variables are now enabled during conversion by default on all v2 TfLiteConverter entry points. experimental_enable_resource_variables on tf.lite.TFLiteConverter is now True by default and will be removed in the future.

• tf.function:

  • Custom classes used as arguments for tf.function can now specify rules regarding when retracing needs to occur by implementing the Tracing Protocol available through tf.types.experimental.SupportsTracingProtocol.
  • TypeSpec classes (as associated with ExtensionTypes) also implement the Tracing Protocol which can be overriden if necessary.
  • The newly introduced reduce_retracing option also uses the Tracing Protocol to proactively generate generalized traces similar to experimental_relax_shapes (which has now been deprecated).

• Unified eager and tf.function execution:

  • Eager mode can now execute each op as a tf.function, allowing for more consistent feature support in future releases.
  • It is available for immediate use.
    • See the TF_RUN_EAGER_OP_AS_FUNCTION environment variable in eager context.
    • Eager performance should be similar with this feature enabled.
      • A roughly 5us per-op overhead may be observed when running many small functions.
      • Note a known issue with GPU performance.
    • The behavior of tf.function itself is unaffected.
  • Note: This feature will be enabled by default in an upcoming version of TensorFlow.

• tf.experimental.dtensor: Added DTensor, an extension to TensorFlow for large-scale modeling with minimal changes to user code. You are welcome to try it out, though be aware that the DTensor API is experimental and up-to backward-incompatible changes. DTensor and Keras integration is published under tf.keras.dtensor in this release (refer to the tf.keras entry). The tutoral and guide for DTensor will be published on https://www.tensorflow.org/. Please stay tuned.

Bug Fixes and Other Changes

• tf.data:

  • Fixed bug in tf.data.experimental.parse_example_dataset when tf.io.RaggedFeatures would specify value_key but no partitions. Before the fix, setting value_key but no partitions would result in the feature key being replaced by the value key, e.g. {'value_key': <RaggedTensor>} instead of {'key': <RaggedTensor>}. Now the correct feature key will be used. This aligns the behavior of tf.data.experimental.parse_example_dataset to match the behavior of tf.io.parse_example.
  • Added a new field, filter_parallelization, to tf.data.experimental.OptimizationOptions. If it is set to True, tf.data will run Filter transformation with multiple threads. Its default value is False if not specified.

• tf.keras:

  • Fixed bug in optimizers that prevented them from properly checkpointing slot variables when they are ShardedVariables (used for training with tf.distribute.experimental.ParameterServerStrategy).

• tf.random:

  • Added tf.random.experimental.index_shuffle, for shuffling a sequence without materializing the sequence in memory.

• tf.RaggedTensor:

  • Introduced tf.experimental.RowPartition, which encodes how one dimension in a RaggedTensor relates to another, into the public API.
  • Introduced tf.experimental.DynamicRaggedShape, which represents the shape of a RaggedTensor.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Debattista, Abel Soares Siqueira, Abhishek Varma, Andrei Ivanov, andreii, Andrew Goodbody, apeltop, Arnab Dutta, Ashiq Imran, Banikumar Maiti (Intel Aipg), Ben Greiner, Benjamin Peterson, bhack, Christopher Bate, chunduriv, Copybara-Service, DEKHTIARJonathan, Deven Desai, Duncan Riach, Eric Kunze, Everton Constantino, Faruk D, Fredrik Knutsson, gadagashwini, Gauri1 Deshpande, gtiHibGele, Guozhong Zhuang, Islem-Esi, Ivanov Viktor, Jason Furmanek, Jason Zaman, Jim, Jinzhe Zeng, John Laxson, Jonas Eschle, Jonas Eschle 'Mayou36, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, KaurkerDevourer, Koan-Sin Tan, kushanam, Laramie Leavitt, Li-Wen Chang, lipracer, Louis Sugy, Lu Teng, Mahmoud Abuzaina, Malcolm Slaney, Malik Shahzad Muzaffar, Marek Šuppa, Matt Conley, Michael Melesse, Milos Puzovic, mohantym, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Patrice Vignola, peterjc123, Philip Turner, Rajeshwar Reddy T, Robert Kalmar, Rodrigo Formigone, Rohit Santhanam, rui, Sachin Muradi, Saduf2019, sandip, Scott Leishman, Serge Panev, Shi,Guangyong, Srinivasan Narayanamoorthy, stanley, Steven I Reeves, stevenireeves, sushreebarsa, Tamas Bela Feher, Tao He, Thomas Schmeyer, Tiago Almeida, Trevor Morris, Uday Bondhugula, Uwe L. Korn, Varghese, Jojimon, Vishnuvardhan Janapati, William Muir, William Raveane, xutianming, Yasuhiro Matsumoto, Yimei Sun, Yong Tang, Yu Feng, Yuriy Chernyshov, zhaozheng09

Release 2.9.0

Breaking Changes

• Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow Decision Forests.
• Build, Compilation and Packaging
  • TensorFlow is now compiled with _GLIBCXX_USE_CXX11_ABI=1. Downstream projects that encounter std::__cxx11 or [abi:cxx11] linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.
  • TensorFlow Python wheels now specifically conform to manylinux2014, an upgrade from manylinux2010. The minimum Pip version supporting manylinux2014 is Pip 19.3 (see pypa/manylinux. This change may affect you if you have been using TensorFlow on a very old platform equivalent to CentOS 6, as manylinux2014 targets CentOS 7 as a compatibility base. Note that TensorFlow does not officially support either platform.
  • Discussion for these changes can be found on SIG Build's TensorFlow Community Forum thread
• The tf.keras.mixed_precision.experimental API has been removed. The non-experimental symbols under tf.keras.mixed_precision have been available since TensorFlow 2.4 and should be used instead.
  • The non-experimental API has some minor differences from the experimental API. In most cases, you only need to make three minor changes:
    • Remove the word "experimental" from tf.keras.mixed_precision symbols. E.g., replace tf.keras.mixed_precision.experimental.global_policy with tf.keras.mixed_precision.global_policy.
    • Replace tf.keras.mixed_precision.experimental.set_policy with tf.keras.mixed_precision.set_global_policy. The experimental symbol set_policy was renamed to set_global_policy in the non-experimental API.
    • Replace LossScaleOptimizer(opt, "dynamic") with LossScaleOptimizer(opt). If you pass anything other than "dynamic" to the second argument, see (1) of the next section.
  • In the following rare cases, you need to make more changes when switching to the non-experimental API:
    • If you passed anything other than "dynamic" to the loss_scale argument (the second argument) of LossScaleOptimizer:
      • The LossScaleOptimizer constructor takes in different arguments. See the TF 2.7 documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer for details on the differences, which has examples on how to convert to the non-experimental LossScaleOptimizer.
    • If you passed a value to the loss_scale argument (the second argument) of Policy:
      • The experimental version of Policy optionally took in a tf.compat.v1.mixed_precision.LossScale in the constructor, which defaulted to a dynamic loss scale for the "mixed_float16" policy and no loss scale for other policies. In Model.compile, if the model's policy had a loss scale, the optimizer would be wrapped with a LossScaleOptimizer. With the non-experimental Policy, there is no loss scale associated with the Policy, and Model.compile wraps the optimizer with a LossScaleOptimizer if and only if the policy is a "mixed_float16" policy. If you previously passed a LossScale to the experimental Policy, consider just removing it, as the default loss scaling behavior is usually what you want. If you really want to customize the loss scaling behavior, you can wrap your optimizer with a LossScaleOptimizer before passing it to Model.compile.
    • If you use the very rarely-used function tf.keras.mixed_precision.experimental.get_layer_policy:
      • Replace tf.keras.mixed_precision.experimental.get_layer_policy(layer) with layer.dtype_policy.
• tf.mixed_precision.experimental.LossScale and its subclasses have been removed from the TF2 namespace. This symbols were very rarely used and were only useful in TF2 for use in the now-removed tf.keras.mixed_precision.experimental API. The symbols are still available under tf.compat.v1.mixed_precision.
• The experimental_relax_shapes heuristic for tf.function has been deprecated and replaced with reduce_retracing which encompasses broader heuristics to reduce the number of retraces (see below)

Major Features and Improvements

• tf.keras:

  • Added tf.keras.applications.resnet_rs models. This includes the ResNetRS50, ResNetRS101, ResNetRS152, ResNetRS200, ResNetRS270, ResNetRS350 and ResNetRS420 model architectures. The ResNetRS models are based on the architecture described in Revisiting ResNets: Improved Training and Scaling Strategies
  • Added tf.keras.optimizers.experimental.Optimizer. The reworked optimizer gives more control over different phases of optimizer calls, and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and RMSprop optimizers based on tf.keras.optimizers.experimental.Optimizer. Generally the new optimizers work in the same way as the old ones, but support new constructor arguments. In the future, the symbols tf.keras.optimizers.Optimizer/Adam/etc will point to the new optimizers, and the previous generation of optimizers will be moved to tf.keras.optimizers.legacy.Optimizer/Adam/etc.
  • Added L2 unit normalization layer tf.keras.layers.UnitNormalization.
  • Added tf.keras.regularizers.OrthogonalRegularizer, a new regularizer that encourages orthogonality between the rows (or columns) or a weight matrix.
  • Added tf.keras.layers.RandomBrightness layer for image preprocessing.
  • Added APIs for switching between interactive logging and absl logging. By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use tf.keras.utils.disable_interactive_logging() to write the logs to ABSL logging. You can also use tf.keras.utils.enable_interactive_logging() to change it back to stdout, or tf.keras.utils.is_interactive_logging_enabled() to check if interactive logging is enabled.
  • Changed default value for the verbose argument of Model.evaluate() and Model.predict() to "auto", which defaults to verbose=1 for most cases and defaults to verbose=2 when used with ParameterServerStrategy or with interactive logging disabled.
  • Argument jit_compile in Model.compile() now applies to Model.evaluate() and Model.predict(). Setting jit_compile=True in compile() compiles the model's training, evaluation, and inference steps to XLA. Note that jit_compile=True may not necessarily work for all models.
  • Added DTensor-related Keras APIs under tf.keras.dtensor namespace. The APIs are still classified as experimental. You are welcome to try it out. Please check the tutoral and guide on https://www.tensorflow.org/ for more details about DTensor.

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.math.argmin/tf.math.argmax for input data type tf.bool on CPU.
    • tf.nn.gelu op for output data type tf.float32 and quantization on CPU.
  • Add nominal support for unsigned 16-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.
  • Add support for unsigned 16-bit integer tensor types in cast op.
  • Experimental support for lowering list_ops.tensor_list_set_item with DynamicUpdateSlice.
  • Enabled a new MLIR-based dynamic range quantization backend by default
    • The new backend is used for post-training int8 dynamic range quantization and post-training float16 quantization.
    • Set experimental_new_dynamic_range_quantizer in tf.lite.TFLiteConverter to False to disable this change
  • Native TF Lite variables are now enabled during conversion by default on all v2 TfLiteConverter entry points. experimental_enable_resource_variables on tf.lite.TFLiteConverter is now True by default and will be removed in the future.

• tf.function:

  • Custom classes used as arguments for tf.function can now specify rules regarding when retracing needs to occur by implementing the Tracing Protocol available through tf.types.experimental.SupportsTracingProtocol.
  • TypeSpec classes (as associated with ExtensionTypes) also implement the Tracing Protocol which can be overriden if necessary.
  • The newly introduced reduce_retracing option also uses the Tracing Protocol to proactively generate generalized traces similar to experimental_relax_shapes (which has now been deprecated).

• Unified eager and tf.function execution:

  • Eager mode can now execute each op as a tf.function, allowing for more consistent feature support in future releases.
  • It is available for immediate use.
    • See the TF_RUN_EAGER_OP_AS_FUNCTION environment variable in eager context.
    • Eager performance should be similar with this feature enabled.
      • A roughly 5us per-op overhead may be observed when running many small functions.
      • Note a known issue with GPU performance.
    • The behavior of tf.function itself is unaffected.
  • Note: This feature will be enabled by default in an upcoming version of TensorFlow.

Bug Fixes and Other Changes

• tf.data:

  • Fixed bug in tf.data.experimental.parse_example_dataset when tf.io.RaggedFeatures would specify value_key but no partitions. Before the fix, setting value_key but no partitions would result in the feature key being replaced by the value key, e.g. {'value_key': <RaggedTensor>} instead of {'key': <RaggedTensor>}. Now the correct feature key will be used. This aligns the behavior of tf.data.experimental.parse_example_dataset to match the behavior of tf.io.parse_example.
  • Added a new field, filter_parallelization, to tf.data.experimental.OptimizationOptions. If it is set to True, tf.data will run Filter transformation with multiple threads. Its default value is False if not specified.

• tf.keras:

  • Fixed bug in optimizers that prevented them from properly checkpointing slot variables when they are ShardedVariables (used for training with tf.distribute.experimental.ParameterServerStrategy).

• tf.random:

  • Added tf.random.experimental.index_shuffle, for shuffling a sequence without materializing the sequence in memory.

• tf.RaggedTensor:

  • Introduced tf.experimental.RowPartition, which encodes how one dimension in a RaggedTensor relates to another, into the public API.
  • Introduced tf.experimental.DynamicRaggedShape, which represents the shape of a RaggedTensor.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Debattista, Abel Soares Siqueira, Abhishek Varma, Andrei Ivanov, andreii, Andrew Goodbody, apeltop, Arnab Dutta, Ashiq Imran, Banikumar Maiti (Intel Aipg), Ben Greiner, Benjamin Peterson, bhack, Christopher Bate, chunduriv, Copybara-Service, DEKHTIARJonathan, Deven Desai, Duncan Riach, Eric Kunze, Everton Constantino, Faruk D, Fredrik Knutsson, gadagashwini, Gauri1 Deshpande, gtiHibGele, Guozhong Zhuang, Islem-Esi, Ivanov Viktor, Jason Furmanek, Jason Zaman, Jim, Jinzhe Zeng, John Laxson, Jonas Eschle, Jonas Eschle 'Mayou36, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, KaurkerDevourer, Koan-Sin Tan, kushanam, Laramie Leavitt, Li-Wen Chang, lipracer, Louis Sugy, Lu Teng, Mahmoud Abuzaina, Malcolm Slaney, Malik Shahzad Muzaffar, Marek Šuppa, Matt Conley, Michael Melesse, Milos Puzovic, mohantym, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Patrice Vignola, peterjc123, Philip Turner, Rajeshwar Reddy T, Robert Kalmar, Rodrigo Formigone, Rohit Santhanam, rui, Sachin Muradi, Saduf2019, sandip, Scott Leishman, Serge Panev, Shi,Guangyong, Srinivasan Narayanamoorthy, stanley, Steven I Reeves, stevenireeves, sushreebarsa, Tamas Bela Feher, Tao He, Thomas Schmeyer, Tiago Almeida, Trevor Morris, Uday Bondhugula, Uwe L. Korn, Varghese, Jojimon, Vishnuvardhan Janapati, William Muir, William Raveane, xutianming, Yasuhiro Matsumoto, Yimei Sun, Yong Tang, Yu Feng, Yuriy Chernyshov, zhaozheng09

Release 2.8.0

Major Features and Improvements

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.raw_ops.Bucketize op on CPU.
    • tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.
    • tf.random.normal op for output data type tf.float32 on CPU.
    • tf.random.uniform op for output data type tf.float32 on CPU.
    • tf.random.categorical op for output data type tf.int64 on CPU.

• tensorflow.experimental.tensorrt:

  • conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and allow_build_at_runtime.
  • Added a new parameter called save_gpu_specific_engines to the .save() function inside TrtGraphConverterV2. When False, the .save() function won't save any TRT engines that have been built. When True (default), the original behavior is preserved.
  • TrtGraphConverterV2 provides a new API called .summary() which outputs a summary of the inference converted by TF-TRT. It namely shows each TRTEngineOp with their input(s)' and output(s)' shape and dtype. A detailed version of the summary is available which prints additionally all the TensorFlow OPs included in each of the TRTEngineOps.

• tf.tpu.experimental.embedding:

  • tf.tpu.experimental.embedding.FeatureConfig now takes an additional argument output_shape which can specify the shape of the output activation for the feature.
  • tf.tpu.experimental.embedding.TPUEmbedding now has the same behavior as tf.tpu.experimental.embedding.serving_embedding_lookup which can take arbitrary rank of dense and sparse tensor. For ragged tensor, though the input tensor remains to be rank 2, the activations now can be rank 2 or above by specifying the output shape in the feature config or via the build method.

• Add tf.config.experimental.enable_op_determinism, which makes TensorFlow ops run deterministically at the cost of performance. Replaces the TF_DETERMINISTIC_OPS environmental variable, which is now deprecated. The "Bug Fixes and Other Changes" section lists more determinism-related changes.

• (Since TF 2.7) Add PluggableDevice support to TensorFlow Profiler.

Bug Fixes and Other Changes

• tf.data:

  • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
  • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.
    • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
    • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.

• tf.lite:

  • Adds GPU Delegation support for serialization to Java API. This boosts initialization time up to 90% when OpenCL is available.
  • Deprecated Interpreter::SetNumThreads, in favor of InterpreterBuilder::SetNumThreads.

• tf.keras:

  • Adds tf.compat.v1.keras.utils.get_or_create_layer to aid migration to TF2 by enabling tracking of nested keras models created in TF1-style, when used with the tf.compat.v1.keras.utils.track_tf1_style_variables decorator.
  • Added a tf.keras.layers.experimental.preprocessing.HashedCrossing layer which applies the hashing trick to the concatenation of crossed scalar inputs. This provides a stateless way to try adding feature crosses of integer or string data to a model.
  • Removed keras.layers.experimental.preprocessing.CategoryCrossing. Users should migrate to the HashedCrossing layer or use tf.sparse.cross/tf.ragged.cross directly.
  • Added additional standardize and split modes to TextVectorization:
    • standardize="lower" will lowercase inputs.
    • standardize="string_punctuation" will remove all puncuation.
    • split="character" will split on every unicode character.
  • Added an output_mode argument to the Discretization and Hashing layers with the same semantics as other preprocessing layers. All categorical preprocessing layers now support output_mode.
  • All preprocessing layer output will follow the compute dtype of a tf.keras.mixed_precision.Policy, unless constructed with output_mode="int" in which case output will be tf.int64. The output type of any preprocessing layer can be controlled individually by passing a dtype argument to the layer.
  • tf.random.Generator for keras initializers and all RNG code.
  • Added 3 new APIs for enable/disable/check the usage of tf.random.Generator in keras backend, which will be the new backend for all the RNG in Keras. We plan to switch on the new code path by default in tf 2.8, and the behavior change will likely to cause some breakage on user side (eg if the test is checking against some golden nubmer). These 3 APIs will allow user to disable and switch back to legacy behavior if they prefer. In future (eg TF 2.10), we expect to totally remove the legacy code path (stateful random Ops), and these 3 APIs will be removed as well.
  • tf.keras.callbacks.experimental.BackupAndRestore is now available as tf.keras.callbacks.BackupAndRestore. The experimental endpoint is deprecated and will be removed in a future release.
  • tf.keras.experimental.SidecarEvaluator is now available as tf.keras.utils.SidecarEvaluator. The experimental endpoint is deprecated and will be removed in a future release.
  • Metrics update and collection logic in default Model.train_step() is now customizable via overriding Model.compute_metrics().
  • Losses computation logic in default Model.train_step() is now customizable via overriding Model.compute_loss().
  • jit_compile added to Model.compile() on an opt-in basis to compile the model's training step with XLA. Note that jit_compile=True may not necessarily work for all models.

• Deterministic Op Functionality:

  • Fix regression in deterministic selection of deterministic cuDNN convolution algorithms, a regression that was introduced in v2.5. Note that nondeterministic out-of-memory events while selecting algorithms could still lead to nondeterminism, although this is very unlikely. This additional, unlikely source will be eliminated in a later version.
  • Add determinsitic GPU implementations of:
    • tf.function(jit_compile=True)'s that use Scatter.
    • (since v2.7) Stateful ops used in tf.data.Dataset
    • (since v2.7) tf.convert_to_tensor when fed with (sparse) tf.IndexedSlices (because it uses tf.math.unsorted_segment_sum)
    • (since v2.7) tf.gather backprop (because tf.convert_to_tensor reduces tf.gather's (sparse) tf.IndexedSlices gradients into its dense params input)
    • (since v2.7) tf.math.segment_mean
    • (since v2.7) tf.math.segment_prod
    • (since v2.7) tf.math.segment_sum
    • (since v2.7) tf.math.unsorted_segment_mean
    • (since v2.7) tf.math.unsorted_segment_prod
    • (since v2.7) tf.math.unsorted_segment_sum
    • (since v2.7) tf.math.unsorted_segment_sqrt
    • (since v2.7) tf.nn.ctc_loss (resolved, possibly in prior release, and confirmed with tests)
    • (since v2.7)tf.nn.sparse_softmax_crossentropy_with_logits
  • (since v2.7) Run tf.scatter_nd and other related scatter functions, such as tf.tensor_scatter_nd_update, on CPU (with significant performance penalty).
  • Add determinism-unimplemented exception-throwing to the following ops. When op-determinism is expected (i.e. after tf.config.experimental.enable_op_determinism has been called), an attempt to use the specified paths through the following ops on a GPU will cause tf.errors.UnimplementedError (with an understandable message), unless otherwise specified, to be thrown.
    • FakeQuantWithMinMaxVarsGradient and FakeQuantWithMinMaxVarsPerChannelGradient
    • (since v2.7) tf.compat.v1.get_seed if the global random seed has not yet been set (via tf.random.set_seed). Throws RuntimeError from Python or InvalidArgument from C++
    • (since v2.7) tf.compat.v1.nn.fused_batch_norm backprop to offset when is_training=False
    • (since v2.7) tf.image.adjust_contrast forward
    • (since v2.7) tf.image.resize with method=ResizeMethod.NEAREST backprop
    • (since v2.7) tf.linalg.svd
    • (since v2.7) tf.math.bincount
    • (since v2.7) tf.nn.depthwise_conv2d backprop to filter when not using cuDNN convolution
    • (since v2.7) tf.nn.dilation2d gradient
    • (since v2.7) tf.nn.max_pool_with_argmax gradient
    • (since v2.7) tf.raw_ops.DebugNumericSummary and tf.raw_ops.DebugNumericSummaryV2
    • (since v2.7) tf.timestamp. Throws FailedPrecondition
    • (since v2.7) tf.Variable.scatter_add (and other scatter methods, both on ref and resource variables)
    • (since v2.7) The random-number-generating ops in the tf.random module when the global random seed has not yet been set (via tf.random.set_seed). Throws RuntimeError from Python or InvalidArgument from C++

• TensorFlow-oneDNN no longer supports explicit use of oneDNN blocked tensor format, e.g., setting the environment variable TF_ENABLE_MKL_NATIVE_FORMAT will not have any effect.

• TensorFlow has been validated on Windows Subsystem for Linux 2 (aka WSL 2) for both GPUs and CPUs.

• Due to security issues (see section below), all boosted trees code has been deprecated. Users should switch to TensorFlow Decision Forests. TF's boosted trees code will be eliminated before the branch cut for TF 2.9 and will no longer be present since that release.

Security

• Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
• Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
• Fixes a heap OOB access in Dequantize (CVE-2022-21726)
• Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
• Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
• Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
• Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
• Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
• Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
• Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
• Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
• Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
• Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
• Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
• Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
• Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
• Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
• Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
• Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
• Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
• Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
• Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
• Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
• Fixes an integer overflow in TFLite (CVE-2022-23559)
• Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
• Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
• Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
• Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
• Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
• Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
• Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
• Fixes a heap OOB write in Grappler (CVE-2022-23566)
• Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
• Fixes a null-dereference when specializing tensor type (CVE-2022-23570)
• Fixes a crash when type cannot be specialized (CVE-2022-23572)
• Fixes a heap OOB read/write in SpecializeType (CVE-2022-23574)
• Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
• Fixes a null dereference in GetInitOp (CVE-2022-23577)
• Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
• Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
• Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
• Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
• Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
• Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)
• Fixes a use after free in DecodePng kernel (CVE-2022-23584)
• Fixes a memory leak in decoding PNG images (CVE-2022-23585)
• Fixes multiple CHECK-fails in function.cc (CVE-2022-23586)
• Fixes multiple CHECK-fails due to attempting to build a reference tensor (CVE-2022-23588)
• Fixes an integer overflow in Grappler cost estimation of crop and resize operation (CVE-2022-23587)
• Fixes a null pointer dereference in Grappler's IsConstant (CVE-2022-23589)
• Fixes a CHECK failure in constant folding (CVE-2021-41197)
• Fixes a stack overflow due to self-recursive function in GraphDef (CVE-2022-23591)
• Fixes a heap OOB access in RunForwardTypeInference (CVE-2022-23592)
• Fixes a crash due to erroneous StatusOr (CVE-2022-23590)
• Fixes multiple crashes and heap OOB accesses in TFG dialect (MLIR) (CVE-2022-23594)
• Fixes a segfault in simplifyBroadcast (MLIR) (CVE-2022-23593)
• Fixes a null pointer dereference in BuildXlaCompilationCache (XLA) (CVE-2022-23595)
• Updates icu to 69.1 to handle CVE-2020-10531

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

8bitmp3, Adam Lanicek, ag.ramesh, alesapin, Andrew Goodbody, annasuheyla, Ariel Elkin, Arnab Dutta, Ben Barsdell, bhack, cfRod, Chengji Yao, Christopher Bate, dan, Dan F-M, David Korczynski, DEKHTIARJonathan, dengzhiyuan, Deven Desai, Duncan Riach, Eli Osherovich, Ewout Ter Hoeven, ez2take, Faijul Amin, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, Georgiy Manuilov, Guilherme De Lázari, Guozhong Zhuang, H1Gdev, homuler, Hongxu Jia, Jacky_Yin, jayfurmanek, jgehw, Jhalak Patel, Jinzhe Zeng, Johan Gunnarsson, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, Kevin Cheng, Koan-Sin Tan, Kruglov-Dmitry, Kun Lu, Lemo, Lequn Chen, long.chen, Louis Sugy, Mahmoud Abuzaina, Mao, Marius Brehler, Mark Harfouche, Martin Patz, Maxiwell S. Garcia, Meenakshi Venkataraman, Michael Melesse, Mrinal Tyagi, Måns Nilsson, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Oktay Ozturk, Patrice Vignola, Pawel-Polyai, Rama Ketineni, Ramesh Sampath, Reza Rahimi, Rob Suderman, Robert Kalmar, Rohit Santhanam, Sachin Muradi, Saduf2019, Samuel Marks, Shi,Guangyong, Sidong-Wei, Srinivasan Narayanamoorthy, Srishti Srivastava, Steven I Reeves, stevenireeves, Supernovae, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Thomas Schmeyer, tilakrayal, Valery Mironov, Victor Guo, Vignesh Kothapalli, Vishnuvardhan Janapati, wamuir, Wang,Quintin, William Muir, William Raveane, Yash Goel, Yimei Sun, Yong Tang, Yuduo Wu

Release 2.7.1

This releases introduces several vulnerability fixes:

• Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
• Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
• Fixes a heap OOB access in Dequantize (CVE-2022-21726)
• Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
• Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
• Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
• Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
• Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
• Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
• Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
• Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
• Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
• Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
• Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
• Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
• Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
• Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
• Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
• Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
• Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
• Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
• Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
• Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
• Fixes an integer overflow in TFLite (CVE-2022-23559)
• Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
• Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
• Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
• Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
• Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
• Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
• Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
• Fixes a heap OOB write in Grappler (CVE-2022-23566)
• Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
• Fixes a null-dereference when specializing tensor type (CVE-2022-23570)
• Fixes a crash when type cannot be specialized (CVE-2022-23572)
• Fixes a heap OOB read/write in SpecializeType (CVE-2022-23574)
• Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
• Fixes a null dereference in GetInitOp (CVE-2022-23577)
• Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
• Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
• Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
• Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
• Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
• Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)
• Fixes a use after free in DecodePng kernel (CVE-2022-23584)
• Fixes a memory leak in decoding PNG images (CVE-2022-23585)
• Fixes multiple CHECK-fails in function.cc (CVE-2022-23586)
• Fixes multiple CHECK-fails due to attempting to build a reference tensor (CVE-2022-23588)
• Fixes an integer overflow in Grappler cost estimation of crop and resize operation (CVE-2022-23587)
• Fixes a null pointer dereference in Grappler's IsConstant (CVE-2022-23589)
• Fixes a CHECK failure in constant folding (CVE-2021-41197)
• Fixes a stack overflow due to self-recursive function in GraphDef (CVE-2022-23591)
• Fixes a crash due to erroneous StatusOr (CVE-2022-23590)
• Fixes multiple crashes and heap OOB accesses in TFG dialect (MLIR) (CVE-2022-23594)
• Fixes a null pointer dereference in BuildXlaCompilationCache (XLA) (CVE-2022-23595)
• Updates icu to 69.1 to handle CVE-2020-10531

Release 2.6.3

This releases introduces several vulnerability fixes:

• Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
• Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
• Fixes a heap OOB access in Dequantize (CVE-2022-21726)
• Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
• Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
• Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
• Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
• Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
• Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
• Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
• Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
• Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
• Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
• Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
• Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
• Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
• Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
• Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
• Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
• Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
• Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
• Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
• Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
• Fixes an integer overflow in TFLite (CVE-2022-23559)
• Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
• Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
• Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
• Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
• Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
• Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
• Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
• Fixes a heap OOB write in Grappler (CVE-2022-23566)
• Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
• Fixes a null-dereference when specializing tensor type (CVE-2022-23570)
• Fixes a crash when type cannot be specialized (CVE-2022-23572)
• Fixes a heap OOB read/write in SpecializeType (CVE-2022-23574)
• Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
• Fixes a null dereference in GetInitOp (CVE-2022-23577)
• Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
• Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
• Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
• Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
• Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
• Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)
• Fixes a use after free in DecodePng kernel (CVE-2022-23584)
• Fixes a memory leak in decoding PNG images (CVE-2022-23585)
• Fixes multiple CHECK-fails in function.cc (CVE-2022-23586)
• Fixes multiple CHECK-fails due to attempting to build a reference tensor (CVE-2022-23588)
• Fixes an integer overflow in Grappler cost estimation of crop and resize operation (CVE-2022-23587)
• Fixes a null pointer dereference in Grappler's IsConstant (CVE-2022-23589)
• Fixes a CHECK failure in constant folding (CVE-2021-41197)
• Fixes a stack overflow due to self-recursive function in GraphDef (CVE-2022-23591)
• Fixes a null pointer dereference in BuildXlaCompilationCache (XLA) (CVE-2022-23595)
• Updates icu to 69.1 to handle CVE-2020-10531

Release 2.5.3

Note: This is the last release in the 2.5 series.

This releases introduces several vulnerability fixes:

• Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
• Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
• Fixes a heap OOB access in Dequantize (CVE-2022-21726)
• Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
• Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
• Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
• Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
• Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
• Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
• Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
• Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
• Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
• Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
• Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
• Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
• Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
• Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
• Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
• Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
• Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
• Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
• Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
• Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
• Fixes an integer overflow in TFLite (CVE-2022-23559)
• Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
• Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
• Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
• Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
• Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
• Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
• Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
• Fixes a heap OOB write in Grappler (CVE-2022-23566)
• Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
• Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
• Fixes a null dereference in GetInitOp (CVE-2022-23577)
• Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
• Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
• Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
• Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
• Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
• Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)
• Fixes a use after free in DecodePng kernel (CVE-2022-23584)
• Fixes a memory leak in decoding PNG images (CVE-2022-23585)
• Fixes multiple CHECK-fails in function.cc (CVE-2022-23586)
• Fixes multiple CHECK-fails due to attempting to build a reference tensor (CVE-2022-23588)
• Fixes an integer overflow in Grappler cost estimation of crop and resize operation (CVE-2022-23587)
• Fixes a null pointer dereference in Grappler's IsConstant (CVE-2022-23589)
• Fixes a CHECK failure in constant folding (CVE-2021-41197)
• Fixes a stack overflow due to self-recursive function in GraphDef (CVE-2022-23591)
• Updates icu to 69.1 to handle CVE-2020-10531