Bump tensorflow from 2.11.0 to 2.13.1

[dependabot]

Bumps tensorflow from 2.11.0 to 2.13.1.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.13.1

Release 2.13.1

Bug Fixes and Other Changes

• Refactor CpuExecutable to propagate LLVM errors.

TensorFlow 2.13.0

Release 2.13.0

TensorFlow

Breaking Changes

• The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure.

Major Features and Improvements

• tf.lite

  • Added 16-bit and 64-bit float type support for built-in op cast.
  • The Python TF Lite Interpreter bindings now have an option experimental_disable_delegate_clustering to turn-off delegate clustering.
  • Added int16x8 support for the built-in op exp
  • Added int16x8 support for the built-in op mirror_pad
  • Added int16x8 support for the built-in ops space_to_batch_nd and batch_to_space_nd
  • Added 16-bit int type support for built-in op less, greater_than, equal
  • Added 8-bit and 16-bit support for floor_div and floor_mod.
  • Added 16-bit and 32-bit int support for the built-in op bitcast.
  • Added 8-bit/16-bit/32-bit int/uint support for the built-in op bitwise_xor
  • Added int16 indices support for built-in op gather and gather_nd.
  • Added 8-bit/16-bit/32-bit int/uint support for the built-in op right_shift
  • Added reference implementation for 16-bit int unquantized add.
  • Added reference implementation for 16-bit int and 32-bit unsigned int unquantized mul.
  • add_op supports broadcasting up to 6 dimensions.
  • Added 16-bit support for top_k.

• tf.function

  • ConcreteFunction (tf.types.experimental.ConcreteFunction) as generated through get_concrete_function now performs holistic input validation similar to calling tf.function directly. This can cause breakages where existing calls pass Tensors with the wrong shape or omit certain non-Tensor arguments (including default values).

• tf.nn

  • tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse now support ids and weights described by tf.RaggedTensors.
  • Added a new boolean argument allow_fast_lookup to tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse, which enables a simplified and typically faster lookup procedure.

• tf.data

  • tf.data.Dataset.zip now supports Python-style zipping, i.e. Dataset.zip(a, b, c).
  • tf.data.Dataset.shuffle now supports tf.data.UNKNOWN_CARDINALITY When doing a "full shuffle" using dataset = dataset.shuffle(dataset.cardinality()). But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.15.0

TensorFlow

Breaking Changes

• tf.types.experimental.GenericFunction has been renamed to tf.types.experimental.PolymorphicFunction.

Known Caveats

Major Features and Improvements

• oneDNN CPU performance optimizations Windows x64 & x86.

  • Windows x64 & x86 packages:
    • oneDNN optimizations are enabled by default on X86 CPUs
  • To explicitly enable or disable oneDNN optimizations, set the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, unset the environment variable.
  • oneDNN optimizations can yield slightly different numerical results from without oneDNN optimizations due to floating-point round-off errors from different computation approaches and orders.
  • To verify if oneDNN 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.

• Making the tf.function type system fully available:

  • tf.types.experimental.TraceType now allows custom tf.function inputs to declare Tensor decomposition and type casting support.
  • Introducing tf.types.experimental.FunctionType as the comprehensive representation of the signature of tf.function callables. It can be accessed through the function_type property of tf.functions and ConcreteFunctions. See the tf.types.experimental.FunctionType documentation for more details.

... (truncated)

Commits

• f841394 Merge pull request #61846 from elfringham/fix_r213
• f415883 [Linaro:ARM_CI] Exclude unit tests that are failing
• e1a5419 Merge pull request #61835 from tensorflow-jenkins/version-numbers-2.13.1-20520
• ba0b5bd Update version numbers to 2.13.1
• 0f7c4ff Merge pull request #61795 from tensorflow-jenkins/relnotes-2.13.1-30774
• e99e29b Merge pull request #61829 from elfringham/fix_tf_io
• fa13788 Need to be more precise about version of TensorFlow IO
• 8729ad3 Update RELEASE.md
• e637fd7 Merge pull request #61818 from rtg0795/r2.13
• f2c704b [XLA:CPU] Refactor CpuExecutable so LLVM errors can be propagated
• Additional commits viewable in compare view

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