Release 2.6.0-rc2

Breaking Changes

• tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead.
• tf.lite:
  • Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they’re no longersupported. It’s recommended to just use keras lstm instead.
• Keras been split into a separate PIP package (keras), and its code has been moved to the GitHub repositorykeras-team/keras. The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. The existing code in tensorflow/python/keras is a staled copy and will be removed in future release (2.7). Please remove any imports to tensorflow.python.keras and replace them with public tf.keras API instead.

Known Caveats

• TF Core:
  • A longstanding bug in tf.while_loop, which caused it to execute sequentially, even when parallel_iterations>1, has now been fixed. However, the increased parallelism may result in increased memory use. Users who experience unwanted regressions should reset their while_loop’s parallel_iterations value to 1, which is consistent with prior behavior.

Major Features and Improvements

• tf.keras:
  • Keras has been split into a separate PIP package (keras), and its code has been moved to the GitHub repository keras-team/keras. The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. All Keras-related PRs and issues should now be directed to the GitHub repository keras-team/keras.
  • tf.keras.utils.experimental.DatasetCreator now takes an optional tf.distribute.InputOptions for specific options when used with distribution.
  • tf.keras.experimental.SidecarEvaluator is now available for a program intended to be run on an evaluator task, which is commonly used to supplement a training cluster running with tf.distribute.experimental.ParameterServerStrategy (see `https://www.tensorflow.org/tutorials/distribute/parameter_server_training). It can also be used with single-worker training or other strategies. See docstring for more info.
  • Preprocessing layers moved from experimental to core.
    • Import paths moved from tf.keras.layers.preprocessing.experimental to tf.keras.layers.
  • Updates to Preprocessing layers API for consistency and clarity:
    • StringLookup and IntegerLookup default for mask_token changed to None. This matches the default masking behavior of Hashing and Embedding layers. To keep existing behavior, pass mask_token="" during layer creation.
    • Renamed "binary" output mode to "multi_hot" for CategoryEncoding, StringLookup, IntegerLookup, and TextVectorization. Multi-hot encoding will no longer automatically uprank rank 1 inputs, so these layers can now multi-hot encode unbatched multi-dimensional samples.
    • Added a new output mode "one_hot" for CategoryEncoding, StringLookup, IntegerLookup, which will encode each element in an input batch individually, and automatically append a new output dimension if necessary. Use this mode on rank 1 inputs for the old "binary" behavior of one-hot encoding a batch of scalars.
    • Normalization will no longer automatically uprank rank 1 inputs, allowing normalization of unbatched multi-dimensional samples.
• tf.lite:
  • The recommended Android NDK version for building TensorFlow Lite has been changed from r18b to r19c.
  • Supports int64 for mul.
  • Supports native variable builtin ops - ReadVariable, AssignVariable.
  • Converter:
    • Experimental support for variables in TFLite. To enable through conversion, users need to set experimental_enable_resource_variables on tf.lite.TFLiteConverter to True. Note: mutable variables is only available using from_saved_model in this release, support for other methods is coming soon.
    • Old Converter (TOCO) is getting removed from next release. It’s been deprecated for few releases already.
• tf.saved_model:
  • SavedModels can now save custom gradients. Use the option tf.saved_model.SaveOption(experimental_custom_gradients=True) to enable this feature. The documentation in Advanced autodiff has been updated.
  • Object metadata has now been deprecated and no longer saved to the SavedModel.
• TF Core:
  • Added tf.config.experimental.reset_memory_stats to reset the tracked peak memory returned by tf.config.experimental.get_memory_info.
• tf.data:
  • Added target_workers param to data_service_ops.from_dataset_id and data_service_ops.distribute. Users can specify "AUTO", "ANY", or "LOCAL" (case insensitive). If "AUTO", tf.data service runtime decides which workers to read from. If "ANY", TF workers read from any tf.data service workers. If "LOCAL", TF workers will only read from local in-processs tf.data service workers. "AUTO" works well for most cases, while users can specify other targets. For example, "LOCAL" would help avoid RPCs and data copy if every TF worker colocates with a tf.data service worker. Currently, "AUTO" reads from any tf.data service workers to preserve existing behavior. The default value is "AUTO".

Bug Fixes and Other Changes

• TF Core:
  • Added tf.lookup.experimental.MutableHashTable, which provides a generic mutable hash table implementation.
    • Compared to tf.lookup.experimental.DenseHashTable this offers lower overall memory usage, and a cleaner API. It does not require specifying a delete_key and empty_key that cannot be inserted into the table.
  • Added support for specifying number of subdivisions in all reduce host collective. This parallelizes work on CPU and speeds up the collective performance. Default behavior is unchanged.
  • Add an option perturb_singular to tf.linalg.tridiagonal_solve that allows solving linear systems with a numerically singular tridiagonal matrix, e.g. for use in inverse iteration.
  • Added tf.linalg.eigh_tridiagonal that computes the eigenvalues of a Hermitian tridiagonal matrix.
  • tf.constant now places its output on the current default device.
  • SavedModel
    • Added tf.saved_model.experimental.TrackableResource, which allows the creation of custom wrapper objects for resource tensors.
    • Added a SavedModel load option to allow restoring partial checkpoints into the SavedModel. See [tf.saved_model.LoadOptions] (https://www.tensorflow.org/api_docs/python/tf/saved_model/LoadOptions) for details.
  • Added a new op SparseSegmentSumGrad to match the other sparse segment gradient ops and avoid an extra gather operation that was in the previous gradient implementation.
  • Added a new session config setting internal_fragmentation_fraction, which controls when the BFC Allocator needs to split an oversized chunk to satisfy an allocation request.
  • Added tf.get_current_name_scope() which returns the current full name scope string that will be prepended to op names.
• tf.data:
  • Promoting tf.data.experimental.bucket_by_sequence_length API to tf.data.Dataset.bucket_by_sequence_length and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.get_single_element API to tf.data.Dataset.get_single_element and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.group_by_window API to tf.data.Dataset.group_by_window and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.RandomDataset API to tf.data.Dataset.random and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.scan API to tf.data.Dataset.scan and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.snapshot API to tf.data.Dataset.shapshot and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.take_while API to tf.data.Dataset.take_while and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.ThreadingOptions API to tf.data.ThreadingOptions and deprecating the experimental endpoint.
  • Promoting tf.data.experimental.unique API to tf.data.Dataset.unique and deprecating the experimental endpoint.
  • Added stop_on_empty_dataset parameter to sample_from_datasets and choose_from_datasets. Setting stop_on_empty_dataset=True will stop sampling if it encounters an empty dataset. This preserves the sampling ratio throughout training. The prior behavior was to continue sampling, skipping over exhausted datasets, until all datasets are exhausted. By default, the original behavior (stop_on_empty_dataset=False) is preserved.
  • Removed previously deprecated tf.data statistics related APIs:
    • tf.data.Options.experimental_stats
    • tf.data.experimental.StatsAggregator
    • tf.data.experimental.StatsOptions.*
    • tf.data.experimental.bytes_produced_stats
    • tf.data.experimental.latency_stats
  • Removed the following experimental tf.data optimization APIs:
    • tf.data.experimental.MapVectorizationOptions.*
    • tf.data.experimental.OptimizationOptions.filter_with_random_uniform_fusion
    • tf.data.experimental.OptimizationOptions.hoist_random_uniform
    • tf.data.experimental.OptimizationOptions.map_vectorization * tf.data.experimental.OptimizationOptions.reorder_data_discarding_ops
• tf.keras:
  • Fix usage of __getitem__ slicing in Keras Functional APIs when the inputs are RaggedTensor objects.
  • Add keepdims argument to all GlobalPooling layers.
  • Add include_preprocessing argument to MobileNetV3 architectures to control the inclusion of Rescaling layer in the model.
  • Add optional argument (force) to make_(train|test|predict)_funtion methods to skip the cached function and generate a new one. This is useful to regenerate in a single call the compiled training function when any .trainable attribute of any model’s layer has changed.
  • Models now have a save_spec property which contains the TensorSpec specs for calling the model. This spec is automatically saved when the model is called for the first time.
• tf.linalg:
  • Add CompositeTensor as a base class to LinearOperator.
• tf.lite:
  • Fix mean op reference quantization rounding issue.
  • Added framework_stable BUILD target, which links in only the non-experimental TF Lite APIs.
  • Remove deprecated Java Interpreter methods:
    • modifyGraphWithDelegate - Use Interpreter.Options.addDelegate
    • setNumThreads - Use Interpreter.Options.setNumThreads
  • Add Conv3DTranspose as a builtin op.
• tf.summary:
  • Fix tf.summary.should_record_summaries() so it correctly reflects when summaries will be written, even when tf.summary.record_if() is not n effect, by returning True tensor if default writer is present.
• Grappler:
  • Disable default Grappler optimization timeout to make the optimization pipeline deterministic. This may lead to increased model loading time, because time spent in graph optimizations is now unbounded (was 20 minutes).
• Deterministic Op Functionality (enabled by setting TF_DETERMINISTIC_OPS to "true" or "1"):
  • Add a deterministic GPU implementation of tf.nn.softmax_cross_entropy_with_logits. See PR 49178.
  • Add a deterministic CPU implementation of tf.image.crop_and_resize. See PR 48905.
  • Add determinism-unimplemented exception-throwing to the following ops. When op-determinism is expected, an attempt to use the specified paths through the following ops on a GPU will cause tf.errors.UnimplementedError (with an understandable message) to be thrown.
    • tf.nn.sparse_softmax_cross_entropy_with_logits forwards and/or backwards. See PR 47925.
    • tf.image.crop_and_resize gradient w.r.t. either image or boxes. See PR 48905.
    • tf.sparse.sparse_dense_matmul forwards. See PR 50355.

Thanks to our Contributors

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

Aadhitya A, Abhilash Mahendrakar, Abhishek Varma, Abin Shahab, Adam Hillier, Aditya Kane, AdityaKane2001, ag.ramesh, Amogh Joshi, Armen Poghosov, armkevincheng, Avrosh K, Ayan Moitra, azazhu, Banikumar Maiti, Bas Aarts, bhack, Bhanu Prakash Bandaru Venkata, Billy Cao, Bohumir Zamecnik, Bradley Reece, CyanXu, Daniel Situnayake, David Pal, Ddavis-2015, DEKHTIARJonathan, Deven Desai, Duncan Riach, Edward, Eli Osherovich, Eugene Kuznetsov, europeanplaice, evelynmitchell, Evgeniy Polyakov, Felix Vollmer, Florentin Hennecker, François Chollet, Frederic Bastien, Fredrik Knutsson, Gabriele Macchi, Gaurav Shukla, Gauri1 Deshpande, geetachavan1, Georgiy Manuilov, H, Hengwen Tong, Henri Woodcock, Hiran Sarkar, Ilya Arzhannikov, Janghoo Lee, jdematos, Jens Meder, Jerry Shih, jgehw, Jim Fisher, Jingbei Li, Jiri Podivin, Joachim Gehweiler, Johannes Lade, Jonas I. Liechti, Jonas Liechti, Jonas Ohlsson, Jonathan Dekhtiar, Julian Gross, Kaixi Hou, Kevin Cheng, Koan-Sin Tan, Kulin Seth, linzewen, Liubov Batanina, luisleee, Lukas Geiger, Mahmoud Abuzaina, mathgaming, Matt Conley, Max H. Gerlach, mdfaijul, Mh Kwon, Michael Martis, Michal Szutenberg, Måns Nilsson, nammbash, Neil Girdhar, Nicholas Vadivelu, Nick Kreeger, Nirjas Jakilim, okyanusoz, Patrice Vignola, Patrik Laurell, Pedro Marques, Philipp Hack, Phillip Cloud, Piergiacomo De Marchi, Prashant Kumar, puneeshkhanna, pvarouktsis, QQ喵, Rajeshwar Reddy T, Rama Ketineni, Reza Rahimi, Robert Kalmar, rsun, Ryan Kuester, Saduf2019, Sean Morgan, Sean Moriarity, Shaochen Shi, Sheng, Yang, Shu Wang, Shuai Zhang, Soojeong, Stanley-Nod, Steven I Reeves, stevenireeves, Suraj Sudhir, Sven Mayer, Tamas Bela Feher, tashuang.zk, tcervi, Teng Lu, Thales Elero Cervi, Thibaut Goetghebuer-Planchon, Thomas Walther, Till Brychcy, Trent Lo, Uday Bondhugula, vishakha.agrawal, Vishnuvardhan Janapati, wamuir, Wenwen Ouyang, wenwu, Williard Joshua Jose, xiaohong1031, Xiaoming (Jason) Cui, Xinan Jiang, Yasir Modak, Yi Li, Yong Tang, zilinzhu, 박상준, 이장