Release 2.7.0-rc0

Breaking Changes

• tf.keras:
  • The methods Model.fit(), Model.predict(), and Model.evaluate() will no longer uprank input data of shape (batch_size,) to become (batch_size, 1). This enables Model subclasses to process scalar data in their train_step()/@test_step()@/@predict_step()@ methods.
    Note that this change may break certain subclassed models. You can revert back to the previous behavior by adding upranking yourself in the train_step()/@test_step()@/@predict_step()@ methods, e.g. if x.shape.rank == 1: x = tf.expand_dims(x, axis=-1). Functional models as well as Sequential models built with an explicit input shape are not affected.
  • The methods Model.to_yaml() and keras.models.model_from_yaml have been replaced to raise a RuntimeError as they can be abused to cause arbitrary code execution. It is recommended to use JSON serialization instead of YAML, or, a better alternative, serialize to H5.
  • LinearModel and WideDeepModel are moved to the tf.compat.v1.keras.models. namespace (tf.compat.v1.keras.models.LinearModel and tf.compat.v1.keras.models.WideDeepModel), and their experimental endpoints (tf.keras.experimental.models.LinearModel and tf.keras.experimental.models.WideDeepModel) are being deprecated.
  • RNG behavior change for all tf.keras.initializers classes. For any class constructed with a fixed seed, it will no longer generate same value when invoked multiple times. Instead, it will return different value, but a determinisitic sequence. This change will make the initialize behavior align between v1 and v2.
• tf.lite:
  • Rename fields SignatureDef table in schema to maximize the parity with TF SavedModel’s Signature concept.
  • Deprecate Makefile builds. Makefile users need to migrate their builds to CMake or Bazel. Please refer to the Build TensorFlow Lite with CMake and Build TensorFlow Lite for ARM boards for the migration.
  • Deprecate tflite::OpResolver::GetDelegates. The list returned by TfLite’s BuiltinOpResolver::GetDelegates is now always empty. Instead, recommend using new method tflite::OpResolver::GetDelegateCreators in order to achieve lazy initialization on TfLite delegate instances.
• TF Core:
  • tf.Graph.get_name_scope() now always returns a string, as documented. Previously, when called within name_scope("") or name_scope(None) contexts, it returned None; now it returns the empty string.
  • tensorflow/core/ir/ contains a new MLIR-based Graph dialect that is isomorphic to GraphDef and will be used to replace GraphDef-based (e.g., Grappler) optimizations.
  • Deprecated and removed attrs() function in shape inference. All attributes should be queried by name now (rather than range returned) to enable changing the underlying storage there.
  • The following Python symbols were accidentally added in earlier versions of TensorFlow and now are removed. Each symbol has a replacement that should be used instead, but note the replacement’s argument names are different.
    • tf.quantize_and_dequantize_v4 (accidentally introduced in TensorFlow 2.4): Use tf.quantization.quantize_and_dequantize_v2 instead.
    • tf.batch_mat_mul_v3 (accidentally introduced in TensorFlow 2.6): Use tf.linalg.matmul instead.
    • tf.sparse_segment_sum_grad (accidentally introduced in TensorFlow 2.6): Use tf.raw_ops.SparseSegmentSumGrad instead. Directly calling this op is typically not necessary, as it is automatically used when computing the gradient of tf.sparse.segment_sum.
  • Renaming of tensorflow::int64 to int_64_t in numerous places (the former is an alias for the latter) which could result in needing to regenerate selective op registration headers else execution would fail with unregistered kernels error.

Major Features and Improvements

• Improvements to the TensorFlow debugging experience:
  
  
  

  • Previously, TensorFlow error stack traces involved many internal frames, which could be challenging to read through, while not being actionable for end users. As of TF 2.7, TensorFlow filters internal frames in most errors that it raises, to keep stack traces short, readable, and focused on what’s actionable for end users (their own code).

    
    

    This behavior can be disabled by calling tf.debugging.disable_traceback_filtering(), and can be re-enabled via tf.debugging.enable_traceback_filtering(). If you are debugging a TensorFlow-internal issue (e.g. to prepare a TensorFlow PR), make sure to disable traceback filtering. You can check whether this feature is currently enabled by calling tf.debugging.is_traceback_filtering_enabled().

    
    

    Note that this feature is only available with Python 3.7 or higher.

  
  

  • Improve the informativeness of error messages raised by Keras Layer.__call__(), by adding the full list of argument values passed to the layer in every exception.

  
  
  
  



• Introduce the tf.compat.v1.keras.utils.track_tf1_style_variables decorator, which enables using large classes of tf1-style variable_scope, get_variable, and compat.v1.layer-based components from within TF2 models running with TF2 behavior enabled.



• tf.data:
  
  
  

  • tf.data service now supports auto-sharding. Users specify the sharding policy with tf.data.experimental.service.ShardingPolicy enum. It can be one of OFF (equivalent to today’s "parallel_epochs" mode), DYNAMIC (equivalent to today’s "distributed_epoch" mode), or one of the static sharding policies: FILE, DATA, FILE_OR_DATA, or HINT (corresponding to values of tf.data.experimental.AutoShardPolicy).

    
    Static sharding (auto-sharding) requires the number of tf.data service workers be fixed. Users need to specify the worker addresses in tensorflow.data.experimental.DispatcherConfig.
  
  

  • tf.data.experimental.service.register_dataset now accepts optional compression argument.

  
  
  
  


• Keras:


• tf.keras.layers.Conv now includes a public convolution_op method. This method can be used to simplify the implementation of Conv subclasses. There are two primary ways to use this new method. The first is to use the method directly in your own call method: python class StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs): mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True) return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var + 1e-10)) Alternatively, you can override convolution_op: python class StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs, kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2], keepdims=True) # Author code uses std + 1e-5 return super().convolution_op(inputs, (kernel - mean) / tf.sqrt(var + 1e-10))


• Added merge_state() method to tf.keras.metrics.Metric for use in distributed computations.


• Added sparse and ragged options to tf.keras.layers.TextVectorization to allow for SparseTensor and RaggedTensor outputs from the layer.


• distribute.experimental.rpc package:
  
  
  

  • distribute.experimental.rpc package introduces APIs to create a GRPC based server to register tf.function methods and a GRPC client to invoke remote registered methods. RPC APIs are intended for multi-client setups i.e. server and clients are started in separate binaries independently.

  
  

  • Example usage to create server:

    
    

       server = tf.distribute.experimental.rpc.Server.create("grpc", 
               "127.0.0.1:1234")
       @tf.function(input_signature=[
         tf.TensorSpec([], tf.int32),
         tf.TensorSpec([], dtypes.int32)
       ])
       def _remote_multiply(a, b):
         return tf.math.multiply(a, b)
    
    
       server.register("multiply", _remote_multiply)
    

  • Example usage to create client:

    bc(python). client = tf.distribute.experimental.rpc.Client.create(“grpc”, address) a = tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32) result = client.multiply(a, b)

  
  
  
  


• tf.lite:
  
  
  
  • Add experimental API experimental_from_jax to support conversion from Jax models to TensorFlow Lite.
  
  
  • Support uint32 data type for cast op.
  
  
  • Add experimental quantization debugger tf.lite.QuantizationDebugger
  
  
  
  


• Extension Types
  
  
  

  • Add experimental API to define new Python classes that can be handled by TensorFlow APIs. To create an extension type, simply define a Python class with tf.experimental.ExtensionType as its base, and use type annotations to specify the type for each field. E.g.:

    
    bc(python). class MaskedTensor(tf.experimental.ExtensionType): values: tf.Tensor mask: tf.Tensor

    The tf.ExtensionType base class works similarly to @typing.NamedTuple@ and “dataclasses.dataclass</tt>":https://docs.python.org/3/library/dataclasses.html#dataclasses.dataclass from the standard Python library.</li> <li>Extension types are supported by Keras, tf.data, TF&#45;hub, SavedModel, tf.function, control flow ops, py&#95;function, and distribution strategy.</li> <li>Add "dispatch decorators" that can be used to override the default behavior of TensorFlow ops (such as tf.add@ or tf.concat) when they are applied to ExtensionType values.

  
  

  • The BatchableExtensionType API can be used to define extension types that support APIs that make use of batching, such as tf.data.Dataset and tf.map_fn.

  
  
  
  

Bug Fixes and Other Changes

• TF Core:
  • Random number generation (RNG) system
    • Add argument alg to tf.random.stateless_* functions to explicitly select the RNG algorithm.
    • Add tf.nn.experimental.stateless_dropout, a stateless version of tf.nn.dropout.
    • tf.random.Generator now can be created inside the scope of tf.distribute.experimental.ParameterServerStrategy and tf.distribute.experimental.CentralStorageStrategy.
  • Add an experimental session config tf.experimental.disable_functional_ops_lowering which disables functional control flow op lowering optimization. This is useful when executing within a portable runtime where control flow op kernels may not be loaded due to selective registration.
  • Add a new experimental argument experimental_is_anonymous to tf.lookup.StaticHashTable.__init__ to create the table in anonymous mode. In this mode, the table resource can only be accessed via resource handles (not resource names) and will be deleted automatically when all resource handles pointing to it are gone.
• tf.data:
  • Introduce the tf.data.experimental.at API which provides random access for input pipelines that consist of transformations that support random access. The initial set of transformations that support random access includes: tf.data.Dataset.from_tensor_slices,@tf.data.Dataset.shuffle@, tf.data.Dataset.batch, tf.data.Dataset.shard, tf.data.Dataset.map, and tf.data.Dataset.range.
  • Promote tf.data.Options.experimental_deterministic API to tf.data.Options.deterministic and deprecate the experimental endpoint.
  • Move autotuning options from@tf.data.Options.experimental_optimization.autotune*@ to a newly created tf.data.Options.autotune.* and remove support for tf.data.Options.experimental_optimization.autotune_buffers.
  • Add support for user-defined names of tf.data core Python API, which can be used to disambiguate tf.data events in TF Profiler Trace Viewer.
  • Promote tf.data.experimental.sample_from_datasets API to tf.data.Dataset.sample_from_datasets and deprecate the experimental endpoint.
• TF SavedModel:
  • Custom gradients are now saved by default. See tf.saved_model.SaveOptions to disable this.
• XLA:
  • Add a new API that allows custom call functions to signal errors. The old API will be deprecated in a future release. See https://www.tensorflow.org/xla/custom_call for details.
  • XLA:GPU reductions are deterministic by default (reductions within jit_compile=True are now deterministic).
  • XLA:GPU works with Horovod (OSS contribution by Trent Lo from NVidia)
• tf.saved_model.save:
  • When saving a model, not specifying a namespace whitelist for custom ops with a namespace will now default to allowing rather than rejecting them all.

Thanks to our Contributors

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

8bitmp3, Abhilash Majumder, abhilash1910, AdeshChoudhar, Adrian Garcia Badaracco, Adrian Ratiu, ag.ramesh, Aleksandr Nikolaev, Alexander Bosch, Alexander Grund, Annie Tallund, Anush Elangovan, Artem Sokolovskii, azazhu, Balint Cristian, Bas Aarts, Ben Barsdell, bhack, cfRod, Cheney-Wang, Cheng Ren, Christopher Bate, collin, Danila Bespalov, David Datascientist, Deven Desai, Ehsan Kia, Ellie, Fan Du, fo40225, Frederic Bastien, fsx950223, Gauri1 Deshpande, geetachavan1, Guillaume Klein, guozhong.zhuang, helen, Håkon Sandsmark, japm48, jgehw, Jinzhe Zeng, Jonathan Dekhtiar, Kai Zhu, Kaixi Hou, Kanvi Khanna, Koan-Sin Tan, Koki Ibukuro, Kulin Seth, KumaTea, Kun-Lu, Lemo, lipracer, liuyuanqiang, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, metarutaiga, Michal Szutenberg, nammbash, Neil Girdhar, Nishidha Panpaliya, Nyadla-Sys, Patrice Vignola, Peter Kasting, Philipp Hack, PINTO0309, Prateek Gupta, puneeshkhanna, Rahul Butani, Rajeshwar Reddy T, Reza Rahimi, RinozaJiffry, rmothukuru, Rohit Santhanam, Saduf2019, Samuel Marks, sclarkson, Sergii Khomenko, Sheng, Yang, Sidong-Wei, slowy07, Srinivasan Narayanamoorthy, Srishti Srivastava, stanley, Stella Alice Schlotter, Steven I Reeves, stevenireeves, svobora, Takayoshi Koizumi, Tamas Bela Feher, Thibaut Goetghebuer-Planchon, Trent Lo, Twice, Varghese, Jojimon, Vishnuvardhan Janapati, Wang Yanzhang, Wang,Quintin, William Muir, William Raveane, Yasuhiro Matsumoto, Yi Li, Yong Tang, zhaozheng09, Zhoulong Jiang, zzpmiracle