Sourced fromtensorflow-cpu's releases.

TensorFlow 2.12.1

Release 2.12.1

Bug Fixes and Other Changes

• The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

• Build, Compilation and Packaging

  • Removed redundant packagestensorflow-gpu andtf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch totensorflow ortf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. Seehttps://pypi.org/project/tensorflow-gpu for more details.

• tf.function:

  • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
    • Usingfunctools.wraps on a function with different signature
    • Usingfunctools.partial with an invalidtf.function input
  • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
  • Parameterlesstf.functions are assumed to have an emptyinput_signature instead of an undefined one even if theinput_signature is unspecified.
  • tf.types.experimental.TraceType now requires an additionalplaceholder_value method to be defined.
  • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

• Experimental APIstf.config.experimental.enable_mlir_graph_optimization andtf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

• Support for Python 3.11 has been added.

• Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

• tf.lite:

  • Add 16-bit float type support for built-in opfill.
  • Transpose now supports 6D tensors.
  • Float LSTM now supports diagonal recurrent tensors:https://arxiv.org/abs/1903.08023

• tf.experimental.dtensor:

  • Coordination service now works withdtensor.initialize_accelerator_system, and enabled by default.
  • Addtf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

• tf.data:

  • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through theexperimental_symbolic_checkpoint option oftf.data.Options().
  • Added a newrerandomize_each_iteration argument for thetf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). Ifseed is set andrerandomize_each_iteration=True, therandom() operation will produce a different (deterministic) sequence of numbers every epoch.

... (truncated)

Sourced fromtensorflow-cpu's changelog.

Release 2.12.1

Bug Fixes and Other Changes

• The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

Release 2.12.0

Breaking Changes

• Build, Compilation and Packaging

  • Removed redundant packagestensorflow-gpu andtf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch totensorflow ortf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. Seehttps://pypi.org/project/tensorflow-gpu for more details.

• tf.function:

  • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
    • Usingfunctools.wraps on a function with different signature
    • Usingfunctools.partial with an invalidtf.function input
  • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
  • Parameterlesstf.functions are assumed to have an emptyinput_signature instead of an undefined one even if theinput_signature is unspecified.
  • tf.types.experimental.TraceType now requires an additionalplaceholder_value method to be defined.
  • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

• Experimental APIstf.config.experimental.enable_mlir_graph_optimization andtf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

• Support for Python 3.11 has been added.

• Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

• tf.lite:

  • Add 16-bit float type support for built-in opfill.
  • Transpose now supports 6D tensors.
  • Float LSTM now supports diagonal recurrent tensors:https://arxiv.org/abs/1903.08023

• tf.experimental.dtensor:

  • Coordination service now works withdtensor.initialize_accelerator_system, and enabled by default.
  • Addtf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

• tf.data:

  • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through theexperimental_symbolic_checkpoint option oftf.data.Options().
  • Added a newrerandomize_each_iteration argument for thetf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). Ifseed is set andrerandomize_each_iteration=True, therandom() operation will produce a different (deterministic) sequence of numbers every epoch.
  • Added a newrerandomize_each_iteration argument for thetf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. Ifseed is set andrerandomize_each_iteration=True, thesample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

• tf.test:

... (truncated)

• 8e2b665 Merge pull request#61094 from tensorflow/venkat-patch-444
• 02478f0 Fix unit test failure caused by numpy update
• 2cd9b41 Merge pull request#61082 from tensorflow/venkat-patch-333
• 7995c95 Updating Simplified retry logic to DNS cache
• 29479ed Merge pull request#60872 from tensorflow/r2.12-c45a6c0b1cb
• e76a933 Simplified retry logic to DNS cache
• 76addf7 Merge pull request#60850 from elfringham/non_pip_fix
• 05987a8 [Linaro:ARM_CI] Fix permissions for running nonpip tests
• 23724d2 Merge pull request#60842 from elfringham/r2.12
• 496730b Limit typing_extensions to less than 4.6.0 until it works
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