Release 2.8.0

Major Features and Improvements

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.raw_ops.Bucketize op on CPU.
    • tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.
    • tf.random.normal op for output data type tf.float32 on CPU.
    • tf.random.uniform op for output data type tf.float32 on CPU.
    • tf.random.categorical op for output data type tf.int64 on CPU.

• tensorflow.experimental.tensorrt:

  • conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and allow_build_at_runtime.
  • Added a new parameter called save_gpu_specific_engines to the .save() function inside TrtGraphConverterV2. When False, the .save() function won't save any TRT engines that have been built. When True (default), the original behavior is preserved.
  • TrtGraphConverterV2 provides a new API called .summary() which outputs a summary of the inference converted by TF-TRT. It namely shows each TRTEngineOp with their input(s)' and output(s)' shape and dtype. A detailed version of the summary is available which prints additionally all the TensorFlow OPs included in each of the TRTEngineOps.

• tf.tpu.experimental.embedding:

  • tf.tpu.experimental.embedding.FeatureConfig now takes an additional argument output_shape which can specify the shape of the output activation for the feature.
  • tf.tpu.experimental.embedding.TPUEmbedding now has the same behavior as tf.tpu.experimental.embedding.serving_embedding_lookup which can take arbitrary rank of dense and sparse tensor. For ragged tensor, though the input tensor remains to be rank 2, the activations now can be rank 2 or above by specifying the output shape in the feature config or via the build method.

• Add tf.config.experimental.enable_op_determinism, which makes TensorFlow ops run deterministically at the cost of performance. Replaces the TF_DETERMINISTIC_OPS environmental variable, which is now deprecated. The "Bug Fixes and Other Changes" section lists more determinism-related changes.

• (Since TF 2.7) Add PluggableDevice support to TensorFlow Profiler.

Bug Fixes and Other Changes

• tf.data:

  • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
  • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.
    • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
    • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.

• tf.lite:

  • Adds GPU Delegation support for serialization to Java API. This boosts initialization time up to 90% when OpenCL is available.
  • Deprecated Interpreter::SetNumThreads, in favor of InterpreterBuilder::SetNumThreads.

• tf.keras:

  • Adds tf.compat.v1.keras.utils.get_or_create_layer to aid migration to TF2 by enabling tracking of nested keras models created in TF1-style, when used with the tf.compat.v1.keras.utils.track_tf1_style_variables decorator.
  • Added a tf.keras.layers.experimental.preprocessing.HashedCrossing layer which applies the hashing trick to the concatenation of crossed scalar inputs. This provides a stateless way to try adding feature crosses of integer or string data to a model.
  • Removed keras.layers.experimental.preprocessing.CategoryCrossing. Users should migrate to the HashedCrossing layer or use tf.sparse.cross/tf.ragged.cross directly.
  • Added additional standardize and split modes to TextVectorization:
    • standardize="lower" will lowercase inputs.
    • standardize="string_punctuation" will remove all puncuation.
    • split="character" will split on every unicode character.
  • Added an output_mode argument to the Discretization and Hashing layers with the same semantics as other preprocessing layers. All categorical preprocessing layers now support output_mode.
  • All preprocessing layer output will follow the compute dtype of a tf.keras.mixed_precision.Policy, unless constructed with output_mode="int" in which case output will be tf.int64. The output type of any preprocessing layer can be controlled individually by passing a dtype argument to the layer.
  • tf.random.Generator for keras initializers and all RNG code.
  • Added 3 new APIs for enable/disable/check the usage of tf.random.Generator in keras backend, which will be the new backend for all the RNG in Keras. We plan to switch on the new code path by default in tf 2.8, and the behavior change will likely to cause some breakage on user side (eg if the test is checking against some golden nubmer). These 3 APIs will allow user to disable and switch back to legacy behavior if they prefer. In future (eg TF 2.10), we expect to totally remove the legacy code path (stateful random Ops), and these 3 APIs will be removed as well.
  • tf.keras.callbacks.experimental.BackupAndRestore is now available as tf.keras.callbacks.BackupAndRestore. The experimental endpoint is deprecated and will be removed in a future release.
  • tf.keras.experimental.SidecarEvaluator is now available as tf.keras.utils.SidecarEvaluator. The experimental endpoint is deprecated and will be removed in a future release.
  • Metrics update and collection logic in default Model.train_step() is now customizable via overriding Model.compute_metrics().
  • Losses computation logic in default Model.train_step() is now customizable via overriding Model.compute_loss().
  • jit_compile added to Model.compile() on an opt-in basis to compile the model's training step with XLA. Note that jit_compile=True may not necessarily work for all models.

• Deterministic Op Functionality:

  • Fix regression in deterministic selection of deterministic cuDNN convolution algorithms, a regression that was introduced in v2.5. Note that nondeterministic out-of-memory events while selecting algorithms could still lead to nondeterminism, although this is very unlikely. This additional, unlikely source will be eliminated in a later version.
  • Add determinsitic GPU implementations of:
    • tf.function(jit_compile=True)'s that use Scatter.
    • (since v2.7) Stateful ops used in tf.data.Dataset
    • (since v2.7) tf.convert_to_tensor when fed with (sparse) tf.IndexedSlices (because it uses tf.math.unsorted_segment_sum)
    • (since v2.7) tf.gather backprop (because tf.convert_to_tensor reduces tf.gather's (sparse) tf.IndexedSlices gradients into its dense params input)
    • (since v2.7) tf.math.segment_mean
    • (since v2.7) tf.math.segment_prod
    • (since v2.7) tf.math.segment_sum
    • (since v2.7) tf.math.unsorted_segment_mean
    • (since v2.7) tf.math.unsorted_segment_prod
    • (since v2.7) tf.math.unsorted_segment_sum
    • (since v2.7) tf.math.unsorted_segment_sqrt
    • (since v2.7) tf.nn.ctc_loss (resolved, possibly in prior release, and confirmed with tests)
    • (since v2.7)tf.nn.sparse_softmax_crossentropy_with_logits
  • (since v2.7) Run tf.scatter_nd and other related scatter functions, such as tf.tensor_scatter_nd_update, on CPU (with significant performance penalty).
  • Add determinism-unimplemented exception-throwing to the following ops. When op-determinism is expected (i.e. after tf.config.experimental.enable_op_determinism has been called), an attempt to use the specified paths through the following ops on a GPU will cause tf.errors.UnimplementedError (with an understandable message), unless otherwise specified, to be thrown.
    • FakeQuantWithMinMaxVarsGradient and FakeQuantWithMinMaxVarsPerChannelGradient
    • (since v2.7) tf.compat.v1.get_seed if the global random seed has not yet been set (via tf.random.set_seed). Throws RuntimeError from Python or InvalidArgument from C++
    • (since v2.7) tf.compat.v1.nn.fused_batch_norm backprop to offset when is_training=False
    • (since v2.7) tf.image.adjust_contrast forward
    • (since v2.7) tf.image.resize with method=ResizeMethod.NEAREST backprop
    • (since v2.7) tf.linalg.svd
    • (since v2.7) tf.math.bincount
    • (since v2.7) tf.nn.depthwise_conv2d backprop to filter when not using cuDNN convolution
    • (since v2.7) tf.nn.dilation2d gradient
    • (since v2.7) tf.nn.max_pool_with_argmax gradient
    • (since v2.7) tf.raw_ops.DebugNumericSummary and tf.raw_ops.DebugNumericSummaryV2
    • (since v2.7) tf.timestamp. Throws FailedPrecondition
    • (since v2.7) tf.Variable.scatter_add (and other scatter methods, both on ref and resource variables)
    • (since v2.7) The random-number-generating ops in the tf.random module when the global random seed has not yet been set (via tf.random.set_seed). Throws RuntimeError from Python or InvalidArgument from C++

• TensorFlow-oneDNN no longer supports explicit use of oneDNN blocked tensor format, e.g., setting the environment variable TF_ENABLE_MKL_NATIVE_FORMAT will not have any effect.

• TensorFlow has been validated on Windows Subsystem for Linux 2 (aka WSL 2) for both GPUs and CPUs.

• Due to security issues (see section below), all boosted trees code has been deprecated. Users should switch to TensorFlow Decision Forests. TF's boosted trees code will be eliminated before the branch cut for TF 2.9 and will no longer be present since that release.

Security

• Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
• Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
• Fixes a heap OOB access in Dequantize (CVE-2022-21726)
• Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
• Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
• Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
• Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
• Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
• Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
• Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
• Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
• Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
• Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
• Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
• Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
• Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
• Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
• Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
• Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
• Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
• Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
• Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
• Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
• Fixes an integer overflow in TFLite (CVE-2022-23559)
• Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
• Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
• Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
• Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
• Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
• Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
• Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
• Fixes a heap OOB write in Grappler (CVE-2022-23566)
• Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
• Fixes a null-dereference when specializing tensor type (CVE-2022-23570)
• Fixes a crash when type cannot be specialized (CVE-2022-23572)
• Fixes a heap OOB read/write in SpecializeType (CVE-2022-23574)
• Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
• Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
• Fixes a null dereference in GetInitOp (CVE-2022-23577)
• Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
• Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
• Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
• Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
• Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
• Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)
• Fixes a use after free in DecodePng kernel (CVE-2022-23584)
• Fixes a memory leak in decoding PNG images (CVE-2022-23585)
• Fixes multiple CHECK-fails in function.cc (CVE-2022-23586)
• Fixes multiple CHECK-fails due to attempting to build a reference tensor (CVE-2022-23588)
• Fixes an integer overflow in Grappler cost estimation of crop and resize operation (CVE-2022-23587)
• Fixes a null pointer dereference in Grappler's IsConstant (CVE-2022-23589)
• Fixes a CHECK failure in constant folding (CVE-2021-41197)
• Fixes a stack overflow due to self-recursive function in GraphDef (CVE-2022-23591)
• Fixes a heap OOB access in RunForwardTypeInference (CVE-2022-23592)
• Fixes a crash due to erroneous StatusOr (CVE-2022-23590)
• Fixes multiple crashes and heap OOB accesses in TFG dialect (MLIR) (CVE-2022-23594)
• Fixes a segfault in simplifyBroadcast (MLIR) (CVE-2022-23593)
• Fixes a null pointer dereference in BuildXlaCompilationCache (XLA) (CVE-2022-23595)
• Updates icu to 69.1 to handle CVE-2020-10531

Thanks to our Contributors

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

8bitmp3, Adam Lanicek, ag.ramesh, alesapin, Andrew Goodbody, annasuheyla, Ariel Elkin, Arnab Dutta, Ben Barsdell, bhack, cfRod, Chengji Yao, Christopher Bate, dan, Dan F-M, David Korczynski, DEKHTIARJonathan, dengzhiyuan, Deven Desai, Duncan Riach, Eli Osherovich, Ewout Ter Hoeven, ez2take, Faijul Amin, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, Georgiy Manuilov, Guilherme De Lázari, Guozhong Zhuang, H1Gdev, homuler, Hongxu Jia, Jacky_Yin, jayfurmanek, jgehw, Jhalak Patel, Jinzhe Zeng, Johan Gunnarsson, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, Kevin Cheng, Koan-Sin Tan, Kruglov-Dmitry, Kun Lu, Lemo, Lequn Chen, long.chen, Louis Sugy, Mahmoud Abuzaina, Mao, Marius Brehler, Mark Harfouche, Martin Patz, Maxiwell S. Garcia, Meenakshi Venkataraman, Michael Melesse, Mrinal Tyagi, Måns Nilsson, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Oktay Ozturk, Patrice Vignola, Pawel-Polyai, Rama Ketineni, Ramesh Sampath, Reza Rahimi, Rob Suderman, Robert Kalmar, Rohit Santhanam, Sachin Muradi, Saduf2019, Samuel Marks, Shi,Guangyong, Sidong-Wei, Srinivasan Narayanamoorthy, Srishti Srivastava, Steven I Reeves, stevenireeves, Supernovae, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Thomas Schmeyer, tilakrayal, Valery Mironov, Victor Guo, Vignesh Kothapalli, Vishnuvardhan Janapati, wamuir, Wang,Quintin, William Muir, William Raveane, Yash Goel, Yimei Sun, Yong Tang, Yuduo Wu