Sourced from tensorflow's releases.

TensorFlow 2.5.0

Release 2.5.0

Major Features and Improvements

• Support for Python3.9 has been added.
• tf.data:
  • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
  • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
  • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
  • Options returned by tf.data.Dataset.options() are no longer mutable.
  • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
• tf.lite
  • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
    • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
• tf.keras
  • tf.keras.metrics.AUC now support logit predictions.
  • Enabled a new supported input type in Model.fit, tf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fn. DatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
• tf.distribute
  • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
  • Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
• TPU embedding support
  • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
• PluggableDevice
  • Third-party devices can now connect to TensorFlow as plug-ins through StreamExecutor C API. and PluggableDevice interface.
    • Add custom ops and kernels through kernel and op registration C API.
    • Register custom graph optimization passes with graph optimization C API.
• oneAPI Deep Neural Network Library (oneDNN) CPU performance optimizations from Intel-optimized TensorFlow are now available in the official x86-64 Linux and Windows builds.
  • They are off by default. Enable them by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
  • We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.
• TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0

Breaking Changes

• The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

Bug Fixes and Other Changes

• tf.keras:
  • Preprocessing layers API consistency changes:
    • StringLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization.
    • IntegerLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization. Renamed max_values, oov_value and mask_value to max_tokens, oov_token and mask_token to align with StringLookup and TextVectorization.
    • TextVectorization default for pad_to_max_tokens switched to False.
    • CategoryEncoding no longer supports adapt, IntegerLookup now supports equivalent functionality. max_tokens argument renamed to num_tokens.
    • Discretization added num_bins argument for learning bins boundaries through calling adapt on a dataset. Renamed bins argument to bin_boundaries for specifying bins without adapt.
  • Improvements to model saving/loading:
    • model.load_weights now accepts paths to saved models.

... (truncated)

Sourced from tensorflow's changelog.

Release 2.5.0

Breaking Changes

• The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

Known Caveats

Major Features and Improvements

• TPU embedding support

  • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.

• tf.keras.metrics.AUC now support logit predictions.

• Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.

• tf.data:

  • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
  • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
  • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
  • Options returned by tf.data.Dataset.options() are no longer mutable.
  • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().

• tf.lite

  • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)

... (truncated)

• a4dfb8d Merge pull request #49124 from tensorflow/mm-cherrypick-tf-data-segfault-fix-...
• 2107b1d Merge pull request #49116 from tensorflow-jenkins/version-numbers-2.5.0-17609
• 16b8139 Update snapshot_dataset_op.cc
• 86a0d86 Merge pull request #49126 from geetachavan1/cherrypicks_X9ZNY
• 9436ae6 Merge pull request #49128 from geetachavan1/cherrypicks_D73J5
• 6b2bf99 Validate that a and b are proper sparse tensors
• c03ad1a Ensure validation sticks in banded_triangular_solve_op
• 12a6ead Merge pull request #49120 from geetachavan1/cherrypicks_KJ5M9
• b67f5b8 Merge pull request #49118 from geetachavan1/cherrypicks_BIDTR
• a13c0ad [tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecth
• Additional commits viewable in compare view

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