tensorflow/tensorflow
### [`v2.5.0`](https://togithub.com/tensorflow/tensorflow/releases/v2.5.0)
### 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](https://togithub.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md).
and [PluggableDevice](https://togithub.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md) interface.
- Add custom ops and kernels through [kernel and op registration C API](https://togithub.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md).
- Register custom graph optimization passes with [graph optimization C API](https://togithub.com/tensorflow/community/blob/master/rfcs/20201027-modular-tensorflow-graph-c-api.md).
- [oneAPI Deep Neural Network Library (oneDNN)](https://togithub.com/oneapi-src/oneDNN) CPU performance optimizations from [Intel-optimized TensorFlow](https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html) 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.
- Keras inputs can now be created directly from arbitrary `tf.TypeSpecs`.
- Two new learning rate schedules added: `tf.keras.optimizers.schedules.CosineDecay` and`tf.keras.optimizers.schedules.CosineDecayRestarts`.
- `tf.data`:
- Exposing `tf.data.experimental.ExternalStatePolicy`, which can be used to control how external state should be handled during dataset serialization or iterator checkpointing.
- Changing `tf.data.experimental.save` to store the type specification of the dataset elements. This avoids the need for explicitly specifying the `element_spec` argument of `tf.data.experimental.load` when loading the previously saved dataset.
- Add `.element_spec` property to `tf.data.DatasetSpec` to access the inner spec. This can be used to extract the structure of nested
datasets.
- Add `tf.data.experimental.AutoShardingPolicy.HINT` which can be used to provide hints to tf.distribute-based auto-sharding as to where in the input pipeline to insert sharding transformations.
- Make tf.data.Options persistent across `tf.function` and `GraphDef` boundaries.
- XLA compilation:
- `tf.function(experimental_compile=True)` has become a stable API, renamed `tf.function(jit_compile=True)`.
- XLA can now compile MirroredStrategy: the step function passed to`strategy.run` can now be annoted with `jit_compile=True`.
- `tf.distribute`:
- Rename `experimental_prefetch_to_device` in `tf.distribute.InputOptions` to `experimental_fetch_to_device` to better reflect the purpose.
- `tf.lite`:
- class `tflite::Subgraph`:
- Removed the `tensors()` method and the non-const overload of the `nodes_and_registration()` method, both of which were previously documented as temporary and to be removed.
- Uses of `tensors()` can be replaced by calling the existing methods `tensors_size()` and `tensor(int)`.
- Uses of the non-const overload of `nodes_and_registration` can be replaced by calling the existing methods `nodes_size()` and `context()`, and then calling the `GetNodeAndRegistration` method in the `TfLiteContext` returned by `context()`.
- NNAPI
- Removed deprecated `Interpreter::UseNNAPI(bool)` C++ API.
- Use `NnApiDelegate()` and related delegate configuration methods directly.
- Replaced the model cache key for models computation algorithm with one guaranteed to be stable across runs.
- 16 bits quantization
- Added int16x8 support for ABS, REDUCE_MAX and REDUCE_MIN operators.
- Additional tests and fixes for ADD and SUB operators.
- Added support for saved model's session initializer through `TFLiteConverter.from_saved_model`.
- Added DEPTH_TO_SPACE support in Post training quantization.
- Added dynamic range quantization support for the BatchMatMul op.
- Both symmetric and asymmetric quantized input tensor are supported.
- Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently only supports float32 input.
- Add 5D support to `SLICE` op.
- TFLite Supports SingatureDef:
- TFLiteConverter exports models with SignatureDef
- Interpreter supports getting a list of signatures and getting callable function for a given signaturedef.
- Add int8 support for `ReshapeV2`.
- Add experimental support for optimization with sparsity.
- Add nominal support for unsigned 32-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.
- Add support for static hash tables through `TFLiteConverter.from_saved_model`.
- The Python TF Lite Interpreter bindings now has an option `experimental_preserve_all_tensors` to aid in debugging conversion.
- Quantized x86 execution defaults to Ruy GEMM library for platforms with AVX support.
- Deprecate `tf.compat.v1.lite.experimental.get_potentially_supported_ops`. Use `tf.lite.TFLiteConverter` directly to check whether a model is convertible.
- Add support to select one of three different built-in op resolvers
- Enabled post training with calibrations for models that require user provided TensorFlow Lite custom op libraries via `converter.target_spec._experimental_custom_op_registerers`. used in Python Interpreter API.
- TF Core:
- Corrected higher-order gradients of control flow constructs (`tf.cond`, `tf.while_loop`, and compositions like `tf.foldl`) computed with `tf.GradientTape` inside a `tf.function`.
- Changed the default step size in `gradient_checker_v2.compute_gradients` to be exactly representable as a binary floating point numbers. This avoids poluting gradient approximations needlessly, which is some cases leads to false negatives in op gradient tests.
- Added `tf.config.experimental.get_memory_info`, returning a dict with the current and peak memory usage. Deprecated `tf.config.experimental.get_memory_usage` in favor of this new function.
- Extended `tf.config.experimental.enable_tensor_float_32_execution` to control Tensor-Float-32 evaluation in RNNs.
- Added a 'experimental_payloads' field to tf.errors.OpError and its subclasses to support more detailed error reporting. This is inspired from Abseil Status payloads: https://github.com/abseil/abseil-cpp/blob/master/absl/status/status.h
- `tf.summary`:
- New `tf.summary.graph` allows manual write of TensorFlow graph (`tf.Graph` or `tf.compat.v1.GraphDef`) as a summary. This is not a replacement for the trace-based API.
- Set `/d2ReducedOptimizeHugeFunctions` by default for Windows builds. This provides a big compile-time speedup, and effectively raises the minimum supported MSVC version to 16.4 (current: 16.8).
- See: https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion
- TensorRT
- Removed the deprecated `session_config` parameter for the TF1-TRT converter `TrtGraphConverter`. Previously, we issued a warning when the value of the parameter is not None.
- The TF2-TRT converter `TrtGraphConverterV2` takes an object of class TrtConversionParams as a parameter. Removed three deprecated fields from this class: `rewriter_config_template`, `is_dynamic_op`, and `max_batch_size`. Previously, we issued a warning when the value of `rewriter_config_template` is not None. We issued an error when the value of `is_dynamic_op` is not True. We didn't use the value for `max_batch_size` for building TensorRT engines. Add parameters `use_dynamic_shape` to enable dynamic shape support. The default is to disable dynamic shape support. Add `dynamic_shape_profile_strategy` for selecting a dynamic shape profile strategy. The default is profile strategy is `Range`.
- Issue a warning when function get_tensorrt_rewriter_config is used.
- TF XLA
- Add new enum value `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED` to `tf.config.experimental.mlir_bridge_rollout` to enable a "safe" mode. This runs the MLIR bridge only when an analysis of the graph only when an analysis of the graph determines that it is safe to run.
- Add new enum value ` MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED' to `tf.config.experimental.mlir_bridge_rollout\` to enable a fallback for the MLIR bridge in a "safe" mode. This runs the MLIR bridge in a FallbackEnabled mode when an analysis of the graph determines that the graph does not have unsupported features.
- Deterministic Op Functionality:
- Add determinism-unimplemented exception-throwing to the segment-sum ops. When the environment variable `TF_DETERMINISTIC_OPS` is set to `"true"` or `"1"` (when op-determinism is expected), an attempt to run the following ops on a GPU will throw `tf.errors.UnimplementedError` (with an understandable message) when `data` is a floating-point type, including complex types (if supported): `tf.math.segment_prod`, `tf.math.segment_sum`, `tf.math.unsorted_segment_mean`, `tf.math.unsorted_segment_sqrt_n`, `tf.math.unsorted_segment_prod`, `tf.math.unsorted_segment_sum`, and therefore also `tf.convert_to_tensor` when `value` is of type `tf.IndexedSlices` (such as in the back prop though `tf.gather` into a dense embedding). See issue [39751](https://togithub.com/tensorflow/tensorflow/issues/39751) which this change addresses, but does not solve. This exception-throwing behavior can be disabled by setting the environment variable `TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS` to `"true"` or
`"1"`. For more information about these changes, see the description in pull request [47772](https://togithub.com/tensorflow/tensorflow/pull/47772).
- In previous versions of TensorFlow, when a GPU was available, `tf.sparse.sparse_dense_matmul` introduced truly random noise in the forward path for data of type `tf.float32` but not for data of type `tf.float64` (for which there was no GPU implementation). In this current release, GPU support for other floating-point types (`tf.float16`, `tf.float64`, `tf.complex64`, and `tf.complex128`) has been added for this op. If you were relying on the determinism of the `tf.float64` CPU implementation being automatically selected because of the absence of the `tf.float64` GPU implementation, you with either need to force the op to run on the CPU or use a different data type.
- Security
- Fixes a heap buffer overflow in `RaggedBinCount` ([CVE-2021-29512](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29512))
- Fixes a heap out of bounds write in `RaggedBinCount` ([CVE-2021-29514](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29514))
- Fixes a type confusion during tensor casts which leads to dereferencing null pointers ([CVE-2021-29513](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29513))
- Fixes a reference binding to null pointer in `MatrixDiag*` ops ([CVE-2021-29515](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29515))
- Fixes a null pointer dereference via invalid Ragged Tensors ([CVE-2021-29516](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29516))
- Fixes a division by zero in `Conv3D` ([CVE-2021-29517](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29517))
- Fixes vulnerabilities where session operations in eager mode lead to null pointer dereferences ([CVE-2021-29518](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29518))
- Fixes a `CHECK`-fail in `SparseCross` caused by type confusion ([CVE-2021-29519](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29519))
- Fixes a segfault in `SparseCountSparseOutput` ([CVE-2021-29521](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29521))
- Fixes a heap buffer overflow in `Conv3DBackprop*` ([CVE-2021-29520](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29520))
- Fixes a division by 0 in `Conv3DBackprop*` ([CVE-2021-29522](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29522))
- Fixes a `CHECK`-fail in `AddManySparseToTensorsMap` ([CVE-2021-29523](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29523))
- Fixes a division by 0 in `Conv2DBackpropFilter` ([CVE-2021-29524](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29524))
- Fixes a division by 0 in `Conv2DBackpropInput` ([CVE-2021-29525](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29525))
- Fixes a division by 0 in `Conv2D` ([CVE-2021-29526](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29526))
- Fixes a division by 0 in `QuantizedConv2D` ([CVE-2021-29527](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29527))
- Fixes a division by 0 in `QuantizedMul` ([CVE-2021-29528](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29528))
- Fixes vulnerabilities caused by invalid validation in `SparseMatrixSparseCholesky` ([CVE-2021-29530](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29530))
- Fixes a heap buffer overflow caused by rounding ([CVE-2021-29529](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29529))
- Fixes a `CHECK`-fail in `tf.raw_ops.EncodePng` ([CVE-2021-29531](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29531))
- Fixes a heap out of bounds read in `RaggedCross` ([CVE-2021-29532](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29532))
- Fixes a `CHECK`-fail in `DrawBoundingBoxes` ([CVE-2021-29533](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29533))
- Fixes a heap buffer overflow in `QuantizedMul` ([CVE-2021-29535](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29535))
- Fixes a `CHECK`-fail in `SparseConcat` ([CVE-2021-29534](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29534))
- Fixes a heap buffer overflow in `QuantizedResizeBilinear` ([CVE-2021-29537](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29537))
- Fixes a heap buffer overflow in `QuantizedReshape` ([CVE-2021-29536](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29536))
- Fixes a division by zero in `Conv2DBackpropFilter` ([CVE-2021-29538](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29538))
- Fixes a heap buffer overflow in `Conv2DBackpropFilter` ([CVE-2021-29540](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29540))
- Fixes a heap buffer overflow in `StringNGrams` ([CVE-2021-29542](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29542))
- Fixes a null pointer dereference in `StringNGrams` ([CVE-2021-29541](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29541))
- Fixes a `CHECK`-fail in `QuantizeAndDequantizeV4Grad` ([CVE-2021-29544](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29544))
- Fixes a `CHECK`-fail in `CTCGreedyDecoder` ([CVE-2021-29543](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29543))
- Fixes a heap buffer overflow in `SparseTensorToCSRSparseMatrix` ([CVE-2021-29545](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29545))
- Fixes a division by 0 in `QuantizedBiasAdd` ([CVE-2021-29546](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29546))
- Fixes a heap out of bounds in `QuantizedBatchNormWithGlobalNormalization` ([CVE-2021-29547](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29547))
- Fixes a division by 0 in `QuantizedBatchNormWithGlobalNormalization` ([CVE-2021-29548](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29548))
- Fixes a division by 0 in `QuantizedAdd` ([CVE-2021-29549](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29549))
- Fixes a division by 0 in `FractionalAvgPool` ([CVE-2021-29550](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29550))
- Fixes an OOB read in `MatrixTriangularSolve` ([CVE-2021-29551](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29551))
- Fixes a heap OOB in `QuantizeAndDequantizeV3` ([CVE-2021-29553](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29553))
- Fixes a `CHECK`-failure in `UnsortedSegmentJoin` ([CVE-2021-29552](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29552))
- Fixes a division by 0 in `DenseCountSparseOutput` ([CVE-2021-29554](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29554))
- Fixes a division by 0 in `FusedBatchNorm` ([CVE-2021-29555](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29555))
- Fixes a division by 0 in `SparseMatMul` ([CVE-2021-29557](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29557))
- Fixes a division by 0 in `Reverse` ([CVE-2021-29556](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29556))
- Fixes a heap buffer overflow in `SparseSplit` ([CVE-2021-29558](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29558))
- Fixes a heap OOB access in unicode ops ([CVE-2021-29559](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29559))
- Fixes a heap buffer overflow in `RaggedTensorToTensor` ([CVE-2021-29560](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29560))
- Fixes a `CHECK`-fail in `LoadAndRemapMatrix` ([CVE-2021-29561](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29561))
- Fixes a `CHECK`-fail in `tf.raw_ops.IRFFT` ([CVE-2021-29562](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29562))
- Fixes a `CHECK`-fail in `tf.raw_ops.RFFT` ([CVE-2021-29563](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29563))
- Fixes a null pointer dereference in `EditDistance` ([CVE-2021-29564](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29564))
- Fixes a null pointer dereference in `SparseFillEmptyRows` ([CVE-2021-29565](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29565))
- Fixes a heap OOB access in `Dilation2DBackpropInput` ([CVE-2021-29566](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29566))
- Fixes a reference binding to null in `ParameterizedTruncatedNormal` ([CVE-2021-29568](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29568))
- Fixes a set of vulnerabilities caused by lack of validation in `SparseDenseCwiseMul` ([CVE-2021-29567](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29567))
- Fixes a heap out of bounds read in `MaxPoolGradWithArgmax` ([CVE-2021-29570](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29570))
- Fixes a heap out of bounds read in `RequantizationRange` ([CVE-2021-29569](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29569))
- Fixes a memory corruption in `DrawBoundingBoxesV2` ([CVE-2021-29571](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29571))
- Fixes a reference binding to nullptr in `SdcaOptimizer` ([CVE-2021-29572](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29572))
- Fixes an overflow and a denial of service in `tf.raw_ops.ReverseSequence` ([CVE-2021-29575](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29575))
- Fixes a division by 0 in `MaxPoolGradWithArgmax` ([CVE-2021-29573](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29573))
- Fixes an undefined behavior in `MaxPool3DGradGrad` ([CVE-2021-29574](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29574))
- Fixes a heap buffer overflow in `MaxPool3DGradGrad` ([CVE-2021-29576](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29576))
- Fixes a heap buffer overflow in `AvgPool3DGrad` ([CVE-2021-29577](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29577))
- Fixes an undefined behavior and a `CHECK`-fail in `FractionalMaxPoolGrad` ([CVE-2021-29580](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29580))
- Fixes a heap buffer overflow in `FractionalAvgPoolGrad` ([CVE-2021-29578](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29578))
- Fixes a heap buffer overflow in `MaxPoolGrad` ([CVE-2021-29579](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29579))
- Fixes a segfault in `CTCBeamSearchDecoder` ([CVE-2021-29581](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29581))
- Fixes a heap OOB read in `tf.raw_ops.Dequantize` ([CVE-2021-29582](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29582))
- Fixes a `CHECK`-fail due to integer overflow ([CVE-2021-29584](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29584))
- Fixes a heap buffer overflow and undefined behavior in `FusedBatchNorm` ([CVE-2021-29583](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29583))
- Fixes a division by zero in padding computation in TFLite ([CVE-2021-29585](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29585))
- Fixes a division by zero in optimized pooling implementations in TFLite ([CVE-2021-29586](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29586))
- Fixes a division by zero in TFLite's implementation of `SpaceToDepth` ([CVE-2021-29587](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29587))
- Fixes a division by zero in TFLite's implementation of `GatherNd` ([CVE-2021-29589](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29589))
- Fixes a division by zero in TFLite's implementation of `TransposeConv` ([CVE-2021-29588](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29588))
- Fixes a heap OOB read in TFLite's implementation of `Minimum` or `Maximum` ([CVE-2021-29590](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29590))
- Fixes a null pointer dereference in TFLite's `Reshape` operator ([CVE-2021-29592](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29592))
- Fixes a stack overflow due to looping TFLite subgraph ([CVE-2021-29591](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29591))
- Fixes a division by zero in TFLite's implementation of `DepthToSpace` ([CVE-2021-29595](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29595))
- Fixes a division by zero in TFLite's convolution code ([CVE-2021-29594](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29594))
- Fixes a division by zero in TFLite's implementation of `EmbeddingLookup` ([CVE-2021-29596](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29596))
- Fixes a division by zero in TFLite's implementation of `BatchToSpaceNd` ([CVE-2021-29593](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29593))
- Fixes a division by zero in TFLite's implementation of `SpaceToBatchNd` ([CVE-2021-29597](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29597))
- Fixes a division by zero in TFLite's implementation of `SVDF` ([CVE-2021-29598](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29598))
- Fixes a division by zero in TFLite's implementation of `Split` ([CVE-2021-29599](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29599))
- Fixes a division by zero in TFLite's implementation of `OneHot` ([CVE-2021-29600](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29600))
- Fixes a division by zero in TFLite's implementation of `DepthwiseConv` ([CVE-2021-29602](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29602))
- Fixes a division by zero in TFLite's implementation of hashtable lookup ([CVE-2021-29604](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29604))
- Fixes a integer overflow in TFLite concatentation ([CVE-2021-29601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29601))
- Fixes a integer overflow in TFLite memory allocation ([CVE-2021-29605](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29605))
- Fixes a heap OOB write in TFLite ([CVE-2021-29603](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29603))
- Fixes a heap OOB read in TFLite ([CVE-2021-29606](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29606))
- Fixes a heap OOB and null pointer dereference in `RaggedTensorToTensor` ([CVE-2021-29608](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29608))
- Fixes vulnerabilities caused by incomplete validation in `SparseAdd` ([CVE-2021-29609](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29609))
- Fixes vulnerabilities caused by incomplete validation in `SparseSparseMinimum` ([CVE-2021-29607](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29607))
- Fixes vulnerabilities caused by incomplete validation in `SparseReshape` ([CVE-2021-29611](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29611))
- Fixes vulnerabilities caused by invalid validation in `QuantizeAndDequantizeV2` ([CVE-2021-29610](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29610))
- Fixes a heap buffer overflow in `BandedTriangularSolve` ([CVE-2021-29612](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29612))
- Fixes vulnerabilities caused by incomplete validation in `tf.raw_ops.CTCLoss` ([CVE-2021-29613](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29613))
- Fixes an interpreter crash from vulnerabilities in `tf.io.decode_raw` ([CVE-2021-29614](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29614))
- Fixes a stack overflow in `ParseAttrValue` with nested tensors ([CVE-2021-29615](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29615))
- Fixes a null dereference in Grappler's `TrySimplify` ([CVE-2021-29616](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29616))
- Fixes a crash in `tf.transpose` with complex inputs ([CVE-2021-29618](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29618))
- Fixes a crash in `tf.strings.substr` due to `CHECK`-fail ([CVE-2021-29617](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29617))
- Fixes a segfault in `tf.raw_ops.SparseCountSparseOutput` ([CVE-2021-29619](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29619))
- Fixes a segfault in `tf.raw_ops.ImmutableConst` ([CVE-2021-29539](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29539))
- Updates `curl` to `7.76.0` to handle [CVE-2020-8169](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8169), [CVE-2020-8177](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8177), [CVE-2020-8231](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8231), [CVE-2020-8284](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8284), [CVE-2020-8285](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8285) and [CVE-2020-8286](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8286).
- Other
- Added `show_debug_info` to `mlir.convert_graph_def` and `mlir.convert_function`.
- Added [Arm Compute Library (ACL)](https://togithub.com/ARM-software/ComputeLibrary) support to `--config=mkl_aarch64` build.
#### Thanks to our Contributors
This release contains contributions from many people at Google, as well as:
8bitmp3, Aaron S. Mondal, Abhilash Mahendrakar, Abhinav Upadhyay, Abhishek Kulkarni, Abolfazl Shahbazi, Adam Hillier, Aditya Kane, Ag Ramesh, ahmedsabie, Albert Villanova Del Moral, Aleksey Vitebskiy, Alex Hoffman, Alexander Bayandin, Alfie Edwards, Aman Kishore, Amogh Joshi, andreABbauer, Andrew Goodbody, Andrzej Pomirski, Artemiy Ryabinkov, Ashish Jha, ather, Ayan Moitra, Bairen Yi, Bart Ribbers, Bas Aarts, Behzad Abghari, Ben Arnao, Ben Barsdell, Benjamin Klimczak, bhack, Brendan Collins, Can Wang, Cheng Ren, Chris Leary, Chris Olivier, Clemens Giuliani, Cloud Han, Corey Cole, Cui, Yifeng, Cuong V. Nguyen, Daniel Moore, Dawid Wojciechowski, Ddavis-2015, Dean Wyatte, Denisa Roberts, dependabot\[bot], Dmitry Volodin, Dominic Jack, Duncan Riach, dushuai, Elena Zhelezina, Eli Osherovich, Erik Smistad, ewsn1593, Felix Fent, fo40225, François Chollet, Frederic Bastien, Freedom" Koan-Sin Tan, fsx950223, ganand1, gbaned, Georgiy Manuilov, gerbauz, Guillaume Klein, Guozhong Zhuang, Harry Slatyer, Harsh188, henri, Henri Woodcock, Hiran Sarkar, Hollow Man, Håkon Sandsmark, I Wayan Dharmana, icysapphire, Ikko Ashimine, Jab Hofmeier, Jack Hessel, Jacob Valdez, Jakub Jatczak, James Bernardi, Jared Smolens, Jason Zaman, jedlimlx, Jenny Plunkett, Jens Elofsson, Jerry Shih, jgehw, Jia Fu Low, Jim Fisher, jpodivin, Julien Stephan, Jungsub Lim, Junha Park, Junhyuk So, justkw, Kaixi Hou, kashyapraval, Kasra Bigdeli, Kazuaki Ishizaki, Keith Mok, Kevin Cheng, kopytjuk, Kristian Hartikainen, ksood12345, Kulin Seth, kushanam, latyas, Lequn Chen, Leslie-Fang, Long M. Lưu, Lukas Geiger, machineko, Mahmoud Abuzaina, Manish, Mao Yunfei, Maozhou, Ge, Marcin Juszkiewicz, Marcin Owsiany, Marconi Jiang, Marcos Pereira, Maria Romanenko Vexlard, Maria Vexlard, Marius Brehler, marload, Martin Kubovčík, Matej, Mateusz Holenko, Maxiwell S. Garcia, Mazhar, mazharul, mbhuiyan, mdfaijul, Michael Gielda, Michael Kuchnik, Michal Szutenberg, Mikhail Stepanov, Milan Straka, Mitchel Humpherys, Mohamed Moselhy, Mohamed Nour Abouelseoud, Måns Bermell, Måns Nilsson, Nathan Luehr, Nico Jahn, Niroop Ammbashankar, Oceania2018, Omri Steiner, Orivej Desh, Oskar Flordal, oujiafan, Patrik Laurell, Paul B. Isaac'S, Paul Klinger, Pawel Piskorski, Pedro Marques, Phat Tran, Piotr Zierhoffer, piyushdatta, Pnikam-Cad, Prashant Kumar, Prateek Gupta, PratsBhatt, Pravin Karandikar, qqq.jq, QQ喵, Quintin, Rama Ketineni, ravikyram, Rehan Guha, rhdong, rmothukuru, Roger Cheng, Rohit Santhanam, rposts, Rsanthanam-Amd, rsun, Rsun-Bdti, Ryan Kuester, ryanking13, Saduf2019, Sami Kama, Samuel Marks, Scott Tseng, Sean Moriarity, Sergey Popov, Sergii Khomenko, Sheng, Yang, shwetaoj, Sidong-Wei, Simon Maurer, Simrit Kaur, Srini511, Srinivasan Narayanamoorthy, Stephan, Stephen Matthews, Sungmann Cho, Sunoru, Suraj Sudhir, Suraj Upadhyay, Taebum Kim, Takayoshi Koizumi, Tamas Bela Feher, Teng Lu, Thibaut Goetghebuer-Planchon, Tomwildenhain-Microsoft, Tony, Traun Leyden, Trent Lo, TVLIgnacy, Tzu-Wei Sung, vaibhav, Vignesh Kothapalli, Vikram Dattu, viktprog, Vinayaka Bandishti, Vincent Abriou, Vishakha Agrawal, Vivek Panyam, Vladimir Silyaev, Võ Văn Nghĩa, wamuir, Wang, Yanzhang, wangsiyu, Waqar Hameed, wxinix, Xiao Yang, xiaohong1031, Xiaoming (Jason) Cui, Xinan Jiang, Yair Ehrenwald, Yajush Vyas, Yasir Modak, Yimei Sun, Yong Tang, Yosshi999, youshenmebutuo, yqtianust, Yuan Tang, yuanbopeng, Yuriy Chernyshov, Yuta Fukasawa, Zachary Deane-Mayer, Zeno Gantner, Zhoulong Jiang, zhuyie, zilinzhu, 彭震东
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This PR contains the following updates:
2.4.0->2.5.0By merging this PR, the below vulnerabilities will be automatically resolved:
Release Notes
tensorflow/tensorflow
### [`v2.5.0`](https://togithub.com/tensorflow/tensorflow/releases/v2.5.0) ### 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](https://togithub.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md). and [PluggableDevice](https://togithub.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md) interface. - Add custom ops and kernels through [kernel and op registration C API](https://togithub.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). - Register custom graph optimization passes with [graph optimization C API](https://togithub.com/tensorflow/community/blob/master/rfcs/20201027-modular-tensorflow-graph-c-api.md). - [oneAPI Deep Neural Network Library (oneDNN)](https://togithub.com/oneapi-src/oneDNN) CPU performance optimizations from [Intel-optimized TensorFlow](https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html) 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. - Keras inputs can now be created directly from arbitrary `tf.TypeSpecs`. - Two new learning rate schedules added: `tf.keras.optimizers.schedules.CosineDecay` and`tf.keras.optimizers.schedules.CosineDecayRestarts`. - `tf.data`: - Exposing `tf.data.experimental.ExternalStatePolicy`, which can be used to control how external state should be handled during dataset serialization or iterator checkpointing. - Changing `tf.data.experimental.save` to store the type specification of the dataset elements. This avoids the need for explicitly specifying the `element_spec` argument of `tf.data.experimental.load` when loading the previously saved dataset. - Add `.element_spec` property to `tf.data.DatasetSpec` to access the inner spec. This can be used to extract the structure of nested datasets. - Add `tf.data.experimental.AutoShardingPolicy.HINT` which can be used to provide hints to tf.distribute-based auto-sharding as to where in the input pipeline to insert sharding transformations. - Make tf.data.Options persistent across `tf.function` and `GraphDef` boundaries. - XLA compilation: - `tf.function(experimental_compile=True)` has become a stable API, renamed `tf.function(jit_compile=True)`. - XLA can now compile MirroredStrategy: the step function passed to`strategy.run` can now be annoted with `jit_compile=True`. - `tf.distribute`: - Rename `experimental_prefetch_to_device` in `tf.distribute.InputOptions` to `experimental_fetch_to_device` to better reflect the purpose. - `tf.lite`: - class `tflite::Subgraph`: - Removed the `tensors()` method and the non-const overload of the `nodes_and_registration()` method, both of which were previously documented as temporary and to be removed. - Uses of `tensors()` can be replaced by calling the existing methods `tensors_size()` and `tensor(int)`. - Uses of the non-const overload of `nodes_and_registration` can be replaced by calling the existing methods `nodes_size()` and `context()`, and then calling the `GetNodeAndRegistration` method in the `TfLiteContext` returned by `context()`. - NNAPI - Removed deprecated `Interpreter::UseNNAPI(bool)` C++ API. - Use `NnApiDelegate()` and related delegate configuration methods directly. - Replaced the model cache key for models computation algorithm with one guaranteed to be stable across runs. - 16 bits quantization - Added int16x8 support for ABS, REDUCE_MAX and REDUCE_MIN operators. - Additional tests and fixes for ADD and SUB operators. - Added support for saved model's session initializer through `TFLiteConverter.from_saved_model`. - Added DEPTH_TO_SPACE support in Post training quantization. - Added dynamic range quantization support for the BatchMatMul op. - Both symmetric and asymmetric quantized input tensor are supported. - Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently only supports float32 input. - Add 5D support to `SLICE` op. - TFLite Supports SingatureDef: - TFLiteConverter exports models with SignatureDef - Interpreter supports getting a list of signatures and getting callable function for a given signaturedef. - Add int8 support for `ReshapeV2`. - Add experimental support for optimization with sparsity. - Add nominal support for unsigned 32-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged. - Add support for static hash tables through `TFLiteConverter.from_saved_model`. - The Python TF Lite Interpreter bindings now has an option `experimental_preserve_all_tensors` to aid in debugging conversion. - Quantized x86 execution defaults to Ruy GEMM library for platforms with AVX support. - Deprecate `tf.compat.v1.lite.experimental.get_potentially_supported_ops`. Use `tf.lite.TFLiteConverter` directly to check whether a model is convertible. - Add support to select one of three different built-in op resolvers - Enabled post training with calibrations for models that require user provided TensorFlow Lite custom op libraries via `converter.target_spec._experimental_custom_op_registerers`. used in Python Interpreter API. - TF Core: - Corrected higher-order gradients of control flow constructs (`tf.cond`, `tf.while_loop`, and compositions like `tf.foldl`) computed with `tf.GradientTape` inside a `tf.function`. - Changed the default step size in `gradient_checker_v2.compute_gradients` to be exactly representable as a binary floating point numbers. This avoids poluting gradient approximations needlessly, which is some cases leads to false negatives in op gradient tests. - Added `tf.config.experimental.get_memory_info`, returning a dict with the current and peak memory usage. Deprecated `tf.config.experimental.get_memory_usage` in favor of this new function. - Extended `tf.config.experimental.enable_tensor_float_32_execution` to control Tensor-Float-32 evaluation in RNNs. - Added a 'experimental_payloads' field to tf.errors.OpError and its subclasses to support more detailed error reporting. This is inspired from Abseil Status payloads: https://github.com/abseil/abseil-cpp/blob/master/absl/status/status.h - `tf.summary`: - New `tf.summary.graph` allows manual write of TensorFlow graph (`tf.Graph` or `tf.compat.v1.GraphDef`) as a summary. This is not a replacement for the trace-based API. - Set `/d2ReducedOptimizeHugeFunctions` by default for Windows builds. This provides a big compile-time speedup, and effectively raises the minimum supported MSVC version to 16.4 (current: 16.8). - See: https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion - TensorRT - Removed the deprecated `session_config` parameter for the TF1-TRT converter `TrtGraphConverter`. Previously, we issued a warning when the value of the parameter is not None. - The TF2-TRT converter `TrtGraphConverterV2` takes an object of class TrtConversionParams as a parameter. Removed three deprecated fields from this class: `rewriter_config_template`, `is_dynamic_op`, and `max_batch_size`. Previously, we issued a warning when the value of `rewriter_config_template` is not None. We issued an error when the value of `is_dynamic_op` is not True. We didn't use the value for `max_batch_size` for building TensorRT engines. Add parameters `use_dynamic_shape` to enable dynamic shape support. The default is to disable dynamic shape support. Add `dynamic_shape_profile_strategy` for selecting a dynamic shape profile strategy. The default is profile strategy is `Range`. - Issue a warning when function get_tensorrt_rewriter_config is used. - TF XLA - Add new enum value `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED` to `tf.config.experimental.mlir_bridge_rollout` to enable a "safe" mode. This runs the MLIR bridge only when an analysis of the graph only when an analysis of the graph determines that it is safe to run. - Add new enum value ` MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED' to `tf.config.experimental.mlir_bridge_rollout\` to enable a fallback for the MLIR bridge in a "safe" mode. This runs the MLIR bridge in a FallbackEnabled mode when an analysis of the graph determines that the graph does not have unsupported features. - Deterministic Op Functionality: - Add determinism-unimplemented exception-throwing to the segment-sum ops. When the environment variable `TF_DETERMINISTIC_OPS` is set to `"true"` or `"1"` (when op-determinism is expected), an attempt to run the following ops on a GPU will throw `tf.errors.UnimplementedError` (with an understandable message) when `data` is a floating-point type, including complex types (if supported): `tf.math.segment_prod`, `tf.math.segment_sum`, `tf.math.unsorted_segment_mean`, `tf.math.unsorted_segment_sqrt_n`, `tf.math.unsorted_segment_prod`, `tf.math.unsorted_segment_sum`, and therefore also `tf.convert_to_tensor` when `value` is of type `tf.IndexedSlices` (such as in the back prop though `tf.gather` into a dense embedding). See issue [39751](https://togithub.com/tensorflow/tensorflow/issues/39751) which this change addresses, but does not solve. This exception-throwing behavior can be disabled by setting the environment variable `TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS` to `"true"` or `"1"`. For more information about these changes, see the description in pull request [47772](https://togithub.com/tensorflow/tensorflow/pull/47772). - In previous versions of TensorFlow, when a GPU was available, `tf.sparse.sparse_dense_matmul` introduced truly random noise in the forward path for data of type `tf.float32` but not for data of type `tf.float64` (for which there was no GPU implementation). In this current release, GPU support for other floating-point types (`tf.float16`, `tf.float64`, `tf.complex64`, and `tf.complex128`) has been added for this op. If you were relying on the determinism of the `tf.float64` CPU implementation being automatically selected because of the absence of the `tf.float64` GPU implementation, you with either need to force the op to run on the CPU or use a different data type. - Security - Fixes a heap buffer overflow in `RaggedBinCount` ([CVE-2021-29512](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29512)) - Fixes a heap out of bounds write in `RaggedBinCount` ([CVE-2021-29514](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29514)) - Fixes a type confusion during tensor casts which leads to dereferencing null pointers ([CVE-2021-29513](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29513)) - Fixes a reference binding to null pointer in `MatrixDiag*` ops ([CVE-2021-29515](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29515)) - Fixes a null pointer dereference via invalid Ragged Tensors ([CVE-2021-29516](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29516)) - Fixes a division by zero in `Conv3D` ([CVE-2021-29517](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29517)) - Fixes vulnerabilities where session operations in eager mode lead to null pointer dereferences ([CVE-2021-29518](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29518)) - Fixes a `CHECK`-fail in `SparseCross` caused by type confusion ([CVE-2021-29519](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29519)) - Fixes a segfault in `SparseCountSparseOutput` ([CVE-2021-29521](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29521)) - Fixes a heap buffer overflow in `Conv3DBackprop*` ([CVE-2021-29520](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29520)) - Fixes a division by 0 in `Conv3DBackprop*` ([CVE-2021-29522](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29522)) - Fixes a `CHECK`-fail in `AddManySparseToTensorsMap` ([CVE-2021-29523](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29523)) - Fixes a division by 0 in `Conv2DBackpropFilter` ([CVE-2021-29524](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29524)) - Fixes a division by 0 in `Conv2DBackpropInput` ([CVE-2021-29525](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29525)) - Fixes a division by 0 in `Conv2D` ([CVE-2021-29526](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29526)) - Fixes a division by 0 in `QuantizedConv2D` ([CVE-2021-29527](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29527)) - Fixes a division by 0 in `QuantizedMul` ([CVE-2021-29528](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29528)) - Fixes vulnerabilities caused by invalid validation in `SparseMatrixSparseCholesky` ([CVE-2021-29530](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29530)) - Fixes a heap buffer overflow caused by rounding ([CVE-2021-29529](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29529)) - Fixes a `CHECK`-fail in `tf.raw_ops.EncodePng` ([CVE-2021-29531](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29531)) - Fixes a heap out of bounds read in `RaggedCross` ([CVE-2021-29532](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29532)) - Fixes a `CHECK`-fail in `DrawBoundingBoxes` ([CVE-2021-29533](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29533)) - Fixes a heap buffer overflow in `QuantizedMul` ([CVE-2021-29535](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29535)) - Fixes a `CHECK`-fail in `SparseConcat` ([CVE-2021-29534](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29534)) - Fixes a heap buffer overflow in `QuantizedResizeBilinear` ([CVE-2021-29537](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29537)) - Fixes a heap buffer overflow in `QuantizedReshape` ([CVE-2021-29536](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29536)) - Fixes a division by zero in `Conv2DBackpropFilter` ([CVE-2021-29538](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29538)) - Fixes a heap buffer overflow in `Conv2DBackpropFilter` ([CVE-2021-29540](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29540)) - Fixes a heap buffer overflow in `StringNGrams` ([CVE-2021-29542](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29542)) - Fixes a null pointer dereference in `StringNGrams` ([CVE-2021-29541](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29541)) - Fixes a `CHECK`-fail in `QuantizeAndDequantizeV4Grad` ([CVE-2021-29544](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29544)) - Fixes a `CHECK`-fail in `CTCGreedyDecoder` ([CVE-2021-29543](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29543)) - Fixes a heap buffer overflow in `SparseTensorToCSRSparseMatrix` ([CVE-2021-29545](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29545)) - Fixes a division by 0 in `QuantizedBiasAdd` ([CVE-2021-29546](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29546)) - Fixes a heap out of bounds in `QuantizedBatchNormWithGlobalNormalization` ([CVE-2021-29547](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29547)) - Fixes a division by 0 in `QuantizedBatchNormWithGlobalNormalization` ([CVE-2021-29548](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29548)) - Fixes a division by 0 in `QuantizedAdd` ([CVE-2021-29549](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29549)) - Fixes a division by 0 in `FractionalAvgPool` ([CVE-2021-29550](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29550)) - Fixes an OOB read in `MatrixTriangularSolve` ([CVE-2021-29551](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29551)) - Fixes a heap OOB in `QuantizeAndDequantizeV3` ([CVE-2021-29553](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29553)) - Fixes a `CHECK`-failure in `UnsortedSegmentJoin` ([CVE-2021-29552](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29552)) - Fixes a division by 0 in `DenseCountSparseOutput` ([CVE-2021-29554](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29554)) - Fixes a division by 0 in `FusedBatchNorm` ([CVE-2021-29555](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29555)) - Fixes a division by 0 in `SparseMatMul` ([CVE-2021-29557](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29557)) - Fixes a division by 0 in `Reverse` ([CVE-2021-29556](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29556)) - Fixes a heap buffer overflow in `SparseSplit` ([CVE-2021-29558](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29558)) - Fixes a heap OOB access in unicode ops ([CVE-2021-29559](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29559)) - Fixes a heap buffer overflow in `RaggedTensorToTensor` ([CVE-2021-29560](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29560)) - Fixes a `CHECK`-fail in `LoadAndRemapMatrix` ([CVE-2021-29561](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29561)) - Fixes a `CHECK`-fail in `tf.raw_ops.IRFFT` ([CVE-2021-29562](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29562)) - Fixes a `CHECK`-fail in `tf.raw_ops.RFFT` ([CVE-2021-29563](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29563)) - Fixes a null pointer dereference in `EditDistance` ([CVE-2021-29564](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29564)) - Fixes a null pointer dereference in `SparseFillEmptyRows` ([CVE-2021-29565](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29565)) - Fixes a heap OOB access in `Dilation2DBackpropInput` ([CVE-2021-29566](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29566)) - Fixes a reference binding to null in `ParameterizedTruncatedNormal` ([CVE-2021-29568](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29568)) - Fixes a set of vulnerabilities caused by lack of validation in `SparseDenseCwiseMul` ([CVE-2021-29567](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29567)) - Fixes a heap out of bounds read in `MaxPoolGradWithArgmax` ([CVE-2021-29570](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29570)) - Fixes a heap out of bounds read in `RequantizationRange` ([CVE-2021-29569](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29569)) - Fixes a memory corruption in `DrawBoundingBoxesV2` ([CVE-2021-29571](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29571)) - Fixes a reference binding to nullptr in `SdcaOptimizer` ([CVE-2021-29572](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29572)) - Fixes an overflow and a denial of service in `tf.raw_ops.ReverseSequence` ([CVE-2021-29575](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29575)) - Fixes a division by 0 in `MaxPoolGradWithArgmax` ([CVE-2021-29573](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29573)) - Fixes an undefined behavior in `MaxPool3DGradGrad` ([CVE-2021-29574](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29574)) - Fixes a heap buffer overflow in `MaxPool3DGradGrad` ([CVE-2021-29576](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29576)) - Fixes a heap buffer overflow in `AvgPool3DGrad` ([CVE-2021-29577](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29577)) - Fixes an undefined behavior and a `CHECK`-fail in `FractionalMaxPoolGrad` ([CVE-2021-29580](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29580)) - Fixes a heap buffer overflow in `FractionalAvgPoolGrad` ([CVE-2021-29578](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29578)) - Fixes a heap buffer overflow in `MaxPoolGrad` ([CVE-2021-29579](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29579)) - Fixes a segfault in `CTCBeamSearchDecoder` ([CVE-2021-29581](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29581)) - Fixes a heap OOB read in `tf.raw_ops.Dequantize` ([CVE-2021-29582](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29582)) - Fixes a `CHECK`-fail due to integer overflow ([CVE-2021-29584](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29584)) - Fixes a heap buffer overflow and undefined behavior in `FusedBatchNorm` ([CVE-2021-29583](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29583)) - Fixes a division by zero in padding computation in TFLite ([CVE-2021-29585](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29585)) - Fixes a division by zero in optimized pooling implementations in TFLite ([CVE-2021-29586](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29586)) - Fixes a division by zero in TFLite's implementation of `SpaceToDepth` ([CVE-2021-29587](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29587)) - Fixes a division by zero in TFLite's implementation of `GatherNd` ([CVE-2021-29589](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29589)) - Fixes a division by zero in TFLite's implementation of `TransposeConv` ([CVE-2021-29588](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29588)) - Fixes a heap OOB read in TFLite's implementation of `Minimum` or `Maximum` ([CVE-2021-29590](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29590)) - Fixes a null pointer dereference in TFLite's `Reshape` operator ([CVE-2021-29592](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29592)) - Fixes a stack overflow due to looping TFLite subgraph ([CVE-2021-29591](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29591)) - Fixes a division by zero in TFLite's implementation of `DepthToSpace` ([CVE-2021-29595](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29595)) - Fixes a division by zero in TFLite's convolution code ([CVE-2021-29594](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29594)) - Fixes a division by zero in TFLite's implementation of `EmbeddingLookup` ([CVE-2021-29596](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29596)) - Fixes a division by zero in TFLite's implementation of `BatchToSpaceNd` ([CVE-2021-29593](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29593)) - Fixes a division by zero in TFLite's implementation of `SpaceToBatchNd` ([CVE-2021-29597](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29597)) - Fixes a division by zero in TFLite's implementation of `SVDF` ([CVE-2021-29598](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29598)) - Fixes a division by zero in TFLite's implementation of `Split` ([CVE-2021-29599](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29599)) - Fixes a division by zero in TFLite's implementation of `OneHot` ([CVE-2021-29600](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29600)) - Fixes a division by zero in TFLite's implementation of `DepthwiseConv` ([CVE-2021-29602](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29602)) - Fixes a division by zero in TFLite's implementation of hashtable lookup ([CVE-2021-29604](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29604)) - Fixes a integer overflow in TFLite concatentation ([CVE-2021-29601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29601)) - Fixes a integer overflow in TFLite memory allocation ([CVE-2021-29605](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29605)) - Fixes a heap OOB write in TFLite ([CVE-2021-29603](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29603)) - Fixes a heap OOB read in TFLite ([CVE-2021-29606](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29606)) - Fixes a heap OOB and null pointer dereference in `RaggedTensorToTensor` ([CVE-2021-29608](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29608)) - Fixes vulnerabilities caused by incomplete validation in `SparseAdd` ([CVE-2021-29609](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29609)) - Fixes vulnerabilities caused by incomplete validation in `SparseSparseMinimum` ([CVE-2021-29607](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29607)) - Fixes vulnerabilities caused by incomplete validation in `SparseReshape` ([CVE-2021-29611](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29611)) - Fixes vulnerabilities caused by invalid validation in `QuantizeAndDequantizeV2` ([CVE-2021-29610](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29610)) - Fixes a heap buffer overflow in `BandedTriangularSolve` ([CVE-2021-29612](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29612)) - Fixes vulnerabilities caused by incomplete validation in `tf.raw_ops.CTCLoss` ([CVE-2021-29613](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29613)) - Fixes an interpreter crash from vulnerabilities in `tf.io.decode_raw` ([CVE-2021-29614](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29614)) - Fixes a stack overflow in `ParseAttrValue` with nested tensors ([CVE-2021-29615](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29615)) - Fixes a null dereference in Grappler's `TrySimplify` ([CVE-2021-29616](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29616)) - Fixes a crash in `tf.transpose` with complex inputs ([CVE-2021-29618](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29618)) - Fixes a crash in `tf.strings.substr` due to `CHECK`-fail ([CVE-2021-29617](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29617)) - Fixes a segfault in `tf.raw_ops.SparseCountSparseOutput` ([CVE-2021-29619](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29619)) - Fixes a segfault in `tf.raw_ops.ImmutableConst` ([CVE-2021-29539](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29539)) - Updates `curl` to `7.76.0` to handle [CVE-2020-8169](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8169), [CVE-2020-8177](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8177), [CVE-2020-8231](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8231), [CVE-2020-8284](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8284), [CVE-2020-8285](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8285) and [CVE-2020-8286](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-8286). - Other - Added `show_debug_info` to `mlir.convert_graph_def` and `mlir.convert_function`. - Added [Arm Compute Library (ACL)](https://togithub.com/ARM-software/ComputeLibrary) support to `--config=mkl_aarch64` build. #### Thanks to our Contributors This release contains contributions from many people at Google, as well as: 8bitmp3, Aaron S. Mondal, Abhilash Mahendrakar, Abhinav Upadhyay, Abhishek Kulkarni, Abolfazl Shahbazi, Adam Hillier, Aditya Kane, Ag Ramesh, ahmedsabie, Albert Villanova Del Moral, Aleksey Vitebskiy, Alex Hoffman, Alexander Bayandin, Alfie Edwards, Aman Kishore, Amogh Joshi, andreABbauer, Andrew Goodbody, Andrzej Pomirski, Artemiy Ryabinkov, Ashish Jha, ather, Ayan Moitra, Bairen Yi, Bart Ribbers, Bas Aarts, Behzad Abghari, Ben Arnao, Ben Barsdell, Benjamin Klimczak, bhack, Brendan Collins, Can Wang, Cheng Ren, Chris Leary, Chris Olivier, Clemens Giuliani, Cloud Han, Corey Cole, Cui, Yifeng, Cuong V. Nguyen, Daniel Moore, Dawid Wojciechowski, Ddavis-2015, Dean Wyatte, Denisa Roberts, dependabot\[bot], Dmitry Volodin, Dominic Jack, Duncan Riach, dushuai, Elena Zhelezina, Eli Osherovich, Erik Smistad, ewsn1593, Felix Fent, fo40225, François Chollet, Frederic Bastien, Freedom" Koan-Sin Tan, fsx950223, ganand1, gbaned, Georgiy Manuilov, gerbauz, Guillaume Klein, Guozhong Zhuang, Harry Slatyer, Harsh188, henri, Henri Woodcock, Hiran Sarkar, Hollow Man, Håkon Sandsmark, I Wayan Dharmana, icysapphire, Ikko Ashimine, Jab Hofmeier, Jack Hessel, Jacob Valdez, Jakub Jatczak, James Bernardi, Jared Smolens, Jason Zaman, jedlimlx, Jenny Plunkett, Jens Elofsson, Jerry Shih, jgehw, Jia Fu Low, Jim Fisher, jpodivin, Julien Stephan, Jungsub Lim, Junha Park, Junhyuk So, justkw, Kaixi Hou, kashyapraval, Kasra Bigdeli, Kazuaki Ishizaki, Keith Mok, Kevin Cheng, kopytjuk, Kristian Hartikainen, ksood12345, Kulin Seth, kushanam, latyas, Lequn Chen, Leslie-Fang, Long M. 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