Tensorflow

Latest version: v2.16.1

Safety actively analyzes 640296 Python packages for vulnerabilities to keep your Python projects secure.

Scan your dependencies

Page 8 of 17

2.4.0

Not secure
\ Major Features and Improvements

* `tf.distribute` introduces experimental support for asynchronous training of
models via the
[`tf.distribute.experimental.ParameterServerStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/ParameterServerStrategy)
API. Please see the
[tutorial](https://www.tensorflow.org/tutorials/distribute/parameter_server_training)
to learn more.

* [`MultiWorkerMirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy)
is now a stable API and is no longer considered experimental. Some of the
major improvements involve handling peer failure and many bug fixes. Please
check out the detailed tutorial on
[Multi-worker training with Keras](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras).

* Introduces experimental support for a new module named
[`tf.experimental.numpy`](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy)
which is a NumPy-compatible API for writing TF programs. See the
[detailed guide](https://www.tensorflow.org/guide/tf_numpy) to learn more.
Additional details below.

* Adds Support for
[TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)
on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for
NVIDIA Ampere based GPUs and is enabled by default.

* A major refactoring of the internals of the Keras Functional API has been
completed, that should improve the reliability, stability, and performance
of constructing Functional models.

* Keras mixed precision API
[`tf.keras.mixed_precision`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision?version=nightly)
is no longer experimental and allows the use of 16-bit floating point
formats during training, improving performance by up to 3x on GPUs and 60%
on TPUs. Please see below for additional details.

* TensorFlow Profiler now supports profiling `MultiWorkerMirroredStrategy` and
tracing multiple workers using the
[sampling mode API](https://www.tensorflow.org/guide/profiler#profiling_apis).

* TFLite Profiler for Android is available. See the detailed
[guide](https://www.tensorflow.org/lite/performance/measurement#trace_tensorflow_lite_internals_in_android)
to learn more.

* TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

Breaking Changes

* TF Core:

* Certain float32 ops run in lower precision on Ampere based GPUs,
including matmuls and convolutions, due to the use of
[TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/).
Specifically, inputs to such ops are rounded from 23 bits of precision
to 10 bits of precision. This is unlikely to cause issues in practice
for deep learning models. In some cases, TensorFloat-32 is also used for
complex64 ops. TensorFloat-32 can be disabled by running
`tf.config.experimental.enable_tensor_float_32_execution(False)`.
* The byte layout for string tensors across the C-API has been updated to
match TF Core/C++; i.e., a contiguous array of
`tensorflow::tstring`/`TF_TString`s.
* C-API functions `TF_StringDecode`, `TF_StringEncode`, and
`TF_StringEncodedSize` are no longer relevant and have been removed; see
`core/platform/ctstring.h` for string access/modification in C.
* `tensorflow.python`, `tensorflow.core` and `tensorflow.compiler` modules
are now hidden. These modules are not part of TensorFlow public API.
* `tf.raw_ops.Max` and `tf.raw_ops.Min` no longer accept inputs of type
`tf.complex64` or `tf.complex128`, because the behavior of these ops is
not well defined for complex types.
* XLA:CPU and XLA:GPU devices are no longer registered by default. Use
`TF_XLA_FLAGS=--tf_xla_enable_xla_devices` if you really need them, but
this flag will eventually be removed in subsequent releases.

* `tf.keras`:

* The `steps_per_execution` argument in `model.compile()` is no longer
experimental; if you were passing `experimental_steps_per_execution`,
rename it to `steps_per_execution` in your code. This argument controls
the number of batches to run during each `tf.function` call when calling
`model.fit()`. Running multiple batches inside a single `tf.function`
call can greatly improve performance on TPUs or small models with a
large Python overhead.
* A **major refactoring** of the internals of the Keras Functional API may
affect code that is relying on certain internal details:
* Code that uses `isinstance(x, tf.Tensor)` instead of `tf.is_tensor` when
checking Keras symbolic inputs/outputs should switch to using
`tf.is_tensor`.
* Code that is overly dependent on the exact names attached to symbolic
tensors (e.g. assumes there will be ":0" at the end of the inputs,
treats names as unique identifiers instead of using `tensor.ref()`,
etc.) may break.
* Code that uses full path for `get_concrete_function` to trace Keras
symbolic inputs directly should switch to building matching
`tf.TensorSpec`s directly and tracing the `TensorSpec` objects.
* Code that relies on the exact number and names of the op layers that
TensorFlow operations were converted into may have changed.
* Code that uses `tf.map_fn`/`tf.cond`/`tf.while_loop`/control flow as op
layers and happens to work before TF 2.4. These will explicitly be
unsupported now. Converting these ops to Functional API op layers was
unreliable before TF 2.4, and prone to erroring incomprehensibly or
being silently buggy.
* Code that directly asserts on a Keras symbolic value in cases where ops
like `tf.rank` used to return a static or symbolic value depending on if
the input had a fully static shape or not. Now these ops always return
symbolic values.
* Code already susceptible to leaking tensors outside of graphs becomes
slightly more likely to do so now.
* Code that tries directly getting gradients with respect to symbolic
Keras inputs/outputs. Use `GradientTape` on the actual Tensors passed to
the already-constructed model instead.
* Code that requires very tricky shape manipulation via converted op
layers in order to work, where the Keras symbolic shape inference proves
insufficient.
* Code that tries manually walking a `tf.keras.Model` layer by layer and
assumes layers only ever have one positional argument. This assumption
doesn't hold true before TF 2.4 either, but is more likely to cause
issues now.
* Code that manually enters `keras.backend.get_graph()` before building a
functional model is no longer needed.
* Start enforcing input shape assumptions when calling Functional API
Keras models. This may potentially break some users, in case there is a
mismatch between the shape used when creating `Input` objects in a
Functional model, and the shape of the data passed to that model. You
can fix this mismatch by either calling the model with correctly-shaped
data, or by relaxing `Input` shape assumptions (note that you can pass
shapes with `None` entries for axes that are meant to be dynamic). You
can also disable the input checking entirely by setting
`model.input_spec = None`.
* Several changes have been made to
`tf.keras.mixed_precision.experimental`. Note that it is now recommended
to use the non-experimental `tf.keras.mixed_precision` API.
* `AutoCastVariable.dtype` now refers to the actual variable dtype, not
the dtype it will be casted to.
* When mixed precision is enabled, `tf.keras.layers.Embedding` now outputs
a float16 or bfloat16 tensor instead of a float32 tensor.
* The property
`tf.keras.mixed_precision.experimental.LossScaleOptimizer.loss_scale` is
now a tensor, not a `LossScale` object. This means to get a loss scale
of a `LossScaleOptimizer` as a tensor, you must now call
`opt.loss_scale`instead of `opt.loss_scale()`.
* The property `should_cast_variables` has been removed from
`tf.keras.mixed_precision.experimental.Policy`
* When passing a `tf.mixed_precision.experimental.DynamicLossScale` to
`tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the
`DynamicLossScale`'s multiplier must be 2.
* When passing a `tf.mixed_precision.experimental.DynamicLossScale` to
`tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the weights
of the `DynanmicLossScale` are copied into the `LossScaleOptimizer`
instead of being reused. This means modifying the weights of the
`DynamicLossScale` will no longer affect the weights of the
LossScaleOptimizer, and vice versa.
* The global policy can no longer be set to a non-floating point policy in
`tf.keras.mixed_precision.experimental.set_policy`
* In `Layer.call`, `AutoCastVariable`s will no longer be casted within
`MirroredStrategy.run` or `ReplicaContext.merge_call`. This is because a
thread local variable is used to determine whether `AutoCastVariable`s
are casted, and those two functions run with a different thread. Note
this only applies if one of these two functions is called within
`Layer.call`; if one of those two functions calls `Layer.call`,
`AutoCastVariable`s will still be casted.

* `tf.data`:

* `tf.data.experimental.service.DispatchServer` now takes a config tuple
instead of individual arguments. Usages should be updated to
`tf.data.experimental.service.DispatchServer(dispatcher_config)`.
* `tf.data.experimental.service.WorkerServer` now takes a config tuple
instead of individual arguments. Usages should be updated to
`tf.data.experimental.service.WorkerServer(worker_config)`.

* `tf.distribute`:

* Removes `tf.distribute.Strategy.experimental_make_numpy_dataset`. Please
use `tf.data.Dataset.from_tensor_slices` instead.
* Renames `experimental_hints` in
`tf.distribute.StrategyExtended.reduce_to`,
`tf.distribute.StrategyExtended.batch_reduce_to`,
`tf.distribute.ReplicaContext.all_reduce` to `options`.
* Renames `tf.distribute.experimental.CollectiveHints` to
`tf.distribute.experimental.CommunicationOptions`.
* Renames `tf.distribute.experimental.CollectiveCommunication` to
`tf.distribute.experimental.CommunicationImplementation`.
* Renames
`tf.distribute.Strategy.experimental_distribute_datasets_from_function`
to `distribute_datasets_from_function` as it is no longer experimental.
* Removes `tf.distribute.Strategy.experimental_run_v2` method, which was
deprecated in TF 2.2.

* `tf.lite`:

* `tf.quantization.quantize_and_dequantize_v2` has been introduced, which
updates the gradient definition for quantization which is outside the
range to be 0. To simulate the V1 the behavior of
`tf.quantization.quantize_and_dequantize(...)` use
`tf.grad_pass_through(tf.quantization.quantize_and_dequantize_v2)(...)`.

* Building TensorFlow:

* Windows platform builds: TensorFlow on Windows under MSVC is now built
with `--copt=/experimental:preprocessor
--host_copt=/experimental:preprocessor` (see `.bazelrc` for more
details). Builds including TensorFlow may fail with unexpected syntax
errors if these flags are absent. See also
[this thread on SIG Build](https://groups.google.com/a/tensorflow.org/g/build/c/LbAw8RILvTg/m/ttnuhYU2BgAJ).

Known Caveats

* `tf.keras.mixed_precision`
* When using mixed precision, calling `RMSprop.apply_gradients` or
`Nadam.apply_gradients` outside a `tf.function` does not work and will
raise the AttributeError "Tensor.op is meaningless when eager execution
is enabled". See this
[issue](https://github.com/tensorflow/tensorflow/issues/45536) for
details and a workaround.

Bug Fixes and Other Changes

TF Core:

* Introduces experimental support for a new module named
[`tf.experimental.numpy`](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy),
which is a NumPy-compatible API for writing TF programs. This module
provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
(e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
seamless in most cases. See
[tensorflow/python/ops/numpy_ops/README.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/numpy_ops/README.md)
for details of what operations are supported and what are the differences
from NumPy.
* `tf.types.experimental.TensorLike` is a new `Union` type that can be used as
type annotation for variables representing a Tensor or a value that can be
converted to Tensor by `tf.convert_to_tensor`.
* Calling ops with a python constants or numpy values is now consistent with
tf.convert_to_tensor behavior. This avoids operations like tf.reshape
truncating inputs such as from int64 to int32.
* Adds `tf.sparse.map_values` to apply a function to the `.value`s of
`SparseTensor` arguments.
* The Python bitwise operators for `Tensor` (`__and__`, `__or__`, `__xor__`
and `__invert__` now support non-`bool` arguments and apply the
corresponding bitwise ops. `bool` arguments continue to be supported and
dispatch to logical ops. This brings them more in line with Python and NumPy
behavior.
* Adds `tf.SparseTensor.with_values`. This returns a new SparseTensor with the
same sparsity pattern, but with new provided values. It is similar to the
`with_values` function of `RaggedTensor`.
* Adds `StatelessCase` op, and uses it if none of case branches has stateful
ops.
* Adds `tf.config.experimental.get_memory_usage` to return total memory usage
of the device.
* Adds gradients for `RaggedTensorToVariant` and `RaggedTensorFromVariant`.
* Improve shape inference of nested function calls by supporting constant
folding across Arg nodes which makes more static values available to shape
inference functions.
* `tf.debugging`:
* `tf.debugging.assert_shapes()` now works on `SparseTensor`s (Fixes
[36268](https://github.com/tensorflow/tensorflow/issues/36268)).
* GPU
* Adds Support for
[TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)
on Ampere based GPUs.TensorFloat-32, or TF32 for short, is a math mode
for NVIDIA Ampere based GPUs which causes certain float32 ops, such as
matrix multiplications and convolutions, to run much faster on Ampere
GPUs but with reduced precision. This reduced precision has not been
found to effect convergence quality of deep learning models in practice.
TensorFloat-32 is enabled by default, but can be disabled with
`tf.config.experimental.enable_tensor_float_32_execution`.
* `tf.math`:
* Adds `tf.math.erfcinv`, the inverse to `tf.math.erfc`.
* `tf.nn`:
* `tf.nn.max_pool2d` now supports explicit padding.
* `tf.image`:
* Adds deterministic `tf.image.stateless_random_*` functions for each
`tf.image.random_*` function. Added a new op
`stateless_sample_distorted_bounding_box` which is a deterministic
version of `sample_distorted_bounding_box` op. Given the same seed,
these stateless functions/ops produce the same results independent of
how many times the function is called, and independent of global seed
settings.
* Adds deterministic `tf.image.resize` backprop CUDA kernels for
`method=ResizeMethod.BILINEAR` (the default method). Enable by setting
the environment variable `TF_DETERMINISTIC_OPS` to `"true"` or `"1"`.
* `tf.print`:
* Bug fix in `tf.print()` with `OrderedDict` where if an `OrderedDict`
didn't have the keys sorted, the keys and values were not being printed
in accordance with their correct mapping.
* `tf.train.Checkpoint`:
* Now accepts a `root` argument in the initialization, which generates a
checkpoint with a root object. This allows users to create a
`Checkpoint` object that is compatible with Keras `model.save_weights()`
and `model.load_weights`. The checkpoint is also compatible with the
checkpoint saved in the `variables/` folder in the SavedModel.
* When restoring, `save_path` can be a path to a SavedModel. The function
will automatically find the checkpoint in the SavedModel.

`tf.data`:

* Adds new `tf.data.experimental.service.register_dataset` and
`tf.data.experimental.service.from_dataset_id` APIs to enable one process to
register a dataset with the tf.data service, and another process to consume
data from the dataset.
* Adds support for dispatcher fault tolerance. To enable fault tolerance,
configure a `work_dir` when running your dispatcher server and set
`dispatcher_fault_tolerance=True`. The dispatcher will store its state to
`work_dir`, so that on restart it can continue from its previous state after
restart.
* Adds support for sharing dataset graphs via shared filesystem instead of
over RPC. This reduces load on the dispatcher, improving performance of
distributing datasets. For this to work, the dispatcher's `work_dir` must be
accessible from workers. If the worker fails to read from the `work_dir`, it
falls back to using RPC for dataset graph transfer.
* Adds support for a new "distributed_epoch" processing mode. This processing
mode distributes a dataset across all tf.data workers, instead of having
each worker process the full dataset. See
[the tf.data service docs](https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#understand_processing_mode)
to learn more.
* Adds optional `exclude_cols` parameter to CsvDataset. This parameter is the
complement of `select_cols`; at most one of these should be specified.
* We have implemented an optimization which reorders data-discarding
transformations such as `take` and `shard` to happen earlier in the dataset
when it is safe to do so. The optimization can be disabled via the
`experimental_optimization.reorder_data_discarding_ops` dataset option.
* `tf.data.Options` were previously immutable and can now be overridden.
* `tf.data.Dataset.from_generator` now supports Ragged and Sparse tensors with
a new `output_signature` argument, which allows `from_generator` to produce
any type describable by a `tf.TypeSpec`.
* `tf.data.experimental.AUTOTUNE` is now available in the core API as
`tf.data.AUTOTUNE`.

`tf.distribute`:

* Introduces experimental support for asynchronous training of models via
`tf.distribute.experimental.ParameterServerStrategy`:
* Replaces the existing
`tf.distribute.experimental.ParameterServerStrategy` symbol with a new
class that is for parameter server training in TF2. Usage of the old
symbol, usually with Estimator API, should be **replaced** with
[`tf.compat.v1.distribute.experimental.ParameterServerStrategy`].
* Added `tf.distribute.experimental.coordinator.*` namespace, including
the main API `ClusterCoordinator` for coordinating the training cluster,
the related data structure `RemoteValue` and `PerWorkerValue`.
* `MultiWorkerMirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy)
is now a stable API and is no longer considered experimental. Some of the
major improvements involve handling peer failure and many bug fixes. Please
check out the detailed tutorial on
[Multi-worer training with Keras](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras).
* Adds `tf.distribute.Strategy.gather` and
`tf.distribute.ReplicaContext.all_gather` APIs to support gathering dense
distributed values.
* Fixes various issues with saving a distributed model.

`tf.keras`:

* Improvements from the Functional API refactoring:
* Functional model construction does not need to maintain a global
workspace graph, removing memory leaks especially when building many
models or very large models.
* Functional model construction should be ~8-10% faster on average.
* Functional models can now contain non-symbolic values in their call
inputs inside of the first positional argument.
* Several classes of TF ops that were not reliably converted to Keras
layers during functional API construction should now work,
e.g.`tf.image.ssim_multiscale`
* Error messages when Functional API construction goes wrong (and when ops
cannot be converted to Keras layers automatically) should be clearer and
easier to understand.
* `Optimizer.minimize` can now accept a loss `Tensor` and a `GradientTape` as
an alternative to accepting a `callable` loss.
* Adds `beta` hyperparameter to
[FTRL](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Ftrl)
optimizer classes (Keras and others) to match
[FTRL paper](https://research.google.com/pubs/archive/41159.pdf).
* `Optimizer.__init__` now accepts a `gradient_aggregator` to allow for
customization of how gradients are aggregated across devices, as well as
`gradients_transformers` to allow for custom gradient transformations (such
as gradient clipping).
* Improvements to Keras preprocessing layers:
* TextVectorization can now accept a vocabulary list or file as an init
arg.
* Normalization can now accept mean and variance values as init args.
* In `Attention` and `AdditiveAttention` layers, the `call()` method now
accepts a `return_attention_scores` argument. When set to True, the layer
returns the attention scores as an additional output argument.
* Adds `tf.metrics.log_cosh` and `tf.metrics.logcosh` API entrypoints with the
same implementation as their `tf.losses` equivalent.
* For Keras model, the individual call of `Model.evaluate` uses no cached data
for evaluation, while `Model.fit` uses cached data when `validation_data`
arg is provided for better performance.
* Adds a `save_traces` argument to `model.save`/ `tf.keras.models.save_model`
which determines whether the SavedModel format stores the Keras model/layer
call functions. The traced functions allow Keras to revive custom models and
layers without the original class definition, but if this isn't required the
tracing can be disabled with the added option.
* The `tf.keras.mixed_precision` API is now non-experimental. The
non-experimental API differs from the experimental API in several ways.
* `tf.keras.mixed_precision.Policy` no longer takes in a
`tf.mixed_precision. experimental.LossScale` in the constructor, and no
longer has a `LossScale` associated with it. Instead, `Model.compile`
will automatically wrap the optimizer with a `LossScaleOptimizer` using
dynamic loss scaling if `Policy.name` is "mixed_float16".
* `tf.keras.mixed_precision.LossScaleOptimizer`'s constructor takes in
different arguments. In particular, it no longer takes in a `LossScale`,
and there is no longer a `LossScale` associated with the
`LossScaleOptimizer`. Instead, `LossScaleOptimizer` directly implements
fixed or dynamic loss scaling. See the documentation of
[`tf.keras.mixed_precision.experimental.LossScaleOptimizer`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/experimental/LossScaleOptimizer?version=nightly)
for details on the differences between the experimental
`LossScaleOptimizer` and the new non-experimental `LossScaleOptimizer`.
* `tf.mixed_precision.experimental.LossScale` and its subclasses are
deprecated, as all of its functionality now exists within
`tf.keras.mixed_precision.LossScaleOptimizer`

`tf.lite`:

* `TFLiteConverter`:
* Support optional flags `inference_input_type` and
`inference_output_type` for full integer quantized models. This allows
users to modify the model input and output type to integer types
(`tf.int8`, `tf.uint8`) instead of defaulting to float type
(`tf.float32`).
* NNAPI
* Adds NNAPI Delegation support for requantization use cases by converting
the operation into a dequantize-quantize pair.
* Removes deprecated `Interpreter.setUseNNAPI(boolean)` Java API. Use
`Interpreter.Options.setUseNNAPI` instead.
* Deprecates `Interpreter::UseNNAPI(bool)` C++ API. Use `NnApiDelegate()`
and related delegate configuration methods directly.
* Deprecates `Interpreter::SetAllowFp16PrecisionForFp32(bool)` C++ API.
Prefer controlling this via delegate options, e.g.
`tflite::StatefulNnApiDelegate::Options::allow_fp16'
or`TfLiteGpuDelegateOptionsV2::is_precision_loss_allowed`.
* GPU
* GPU acceleration now supports quantized models by default
* `DynamicBuffer::AddJoinedString()` will now add a separator if the first
string to be joined is empty.
* Adds support for cumulative sum (cumsum), both as builtin op and MLIR
conversion.

`TensorRT`

* Issues a warning when the `session_config` parameter for the TF1 converter
is used or the `rewrite_config_template` field in the TF2 converter
parameter object is used.

TPU Enhancements:

* Adds support for the `beta` parameter of the FTRL optimizer for TPU
embeddings. Users of other TensorFlow platforms can implement equivalent
behavior by adjusting the `l2` parameter.

XLA Support:

* xla.experimental.compile is deprecated, use
`tf.function(experimental_compile=True)` instead.
* Adds `tf.function.experimental_get_compiler_ir` which returns compiler IR
(currently 'hlo' and 'optimized_hlo') for given input for given function.

Security:

* Fixes an undefined behavior causing a segfault in `tf.raw_ops.Switch`,
([CVE-2020-15190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15190))
* Fixes three vulnerabilities in conversion to DLPack format
* [CVE-2020-15191](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15191),
* [CVE-2020-15192](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15192),
* [CVE-2020-15193](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15193)
* Fixes two vulnerabilities in `SparseFillEmptyRowsGrad`
* [CVE-2020-15194](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15194),
* [CVE-2020-15195](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15195)
* Fixes several vulnerabilities in `RaggedCountSparseOutput` and
`SparseCountSparseOutput` operations
* [CVE-2020-15196](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15196),
* [CVE-2020-15197](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15197),
* [CVE-2020-15198](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15198),
* [CVE-2020-15199](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15199),
* [CVE-2020-15200](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15200),
* [CVE-2020-15201](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15201)
* Fixes an integer truncation vulnerability in code using the work sharder
API,
([CVE-2020-15202](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15202))
* Fixes a format string vulnerability in `tf.strings.as_string`,
([CVE-2020-15203](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15203))
* Fixes segfault raised by calling session-only ops in eager mode,
([CVE-2020-15204](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15204))
* Fixes data leak and potential ASLR violation from `tf.raw_ops.StringNGrams`,
([CVE-2020-15205](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15205))
* Fixes segfaults caused by incomplete `SavedModel` validation,
([CVE-2020-15206](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15206))
* Fixes a data corruption due to a bug in negative indexing support in TFLite,
([CVE-2020-15207](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15207))
* Fixes a data corruption due to dimension mismatch in TFLite,
([CVE-2020-15208](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15208))
* Fixes several vulnerabilities in TFLite saved model format
* [CVE-2020-15209](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209),
* [CVE-2020-15210](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15210),
* [CVE-2020-15211](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15211)
* Fixes several vulnerabilities in TFLite implementation of segment sum
* [CVE-2020-15212](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15212),
* [CVE-2020-15213](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15213),
* [CVE-2020-15214](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15214)
* Fixes a segfault in `tf.quantization.quantize_and_dequantize`,
([CVE-2020-15265](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15265))
* Fixes an undefined behavior float cast causing a crash,
([CVE-2020-15266](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15266))
* Fixes a lack of validation in `tf.raw_ops.DataFormatVecPermute` and
`tf.raw_ops.DataFormatDimMap` which can cause uninitialized memory access,
read outside bounds of arrays, data corruption and segmentation faults
([CVE-2020-26267](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26267))
* Fixes a crash caused by writing to read only memory region
([CVE-2020-26268](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26268))
* Fixes a heap out of bounds access in filesystem globbing implementation
([CVE-2020-26269](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26269))

Other:

* We have replaced uses of "whitelist" and "blacklist" with "allowlist" and
"denylist" where possible. Please see
[this list](https://developers.google.com/style/word-list#blacklist) for
more context.
* Adds `tf.config.experimental.mlir_bridge_rollout` which will help us rollout
the new MLIR TPU bridge.
* Adds `tf.experimental.register_filesystem_plugin` to load modular filesystem
plugins from Python

Thanks to our Contributors

This release contains contributions from many people at Google as well as the
following external contributors:

8bitmp3, aaa.jq, Abhineet Choudhary, Abolfazl Shahbazi, acxz, Adam Hillier,
Adrian Garcia Badaracco, Ag Ramesh, ahmedsabie, Alan Anderson, Alexander Grund,
Alexandre Lissy, Alexey Ivanov, Amedeo Cavallo, anencore94, Aniket Kumar Singh,
Anthony Platanios, Ashwin Phadke, Balint Cristian, Basit Ayantunde, bbbboom, Ben
Barsdell, Benjamin Chetioui, Benjamin Peterson, bhack, Bhanu Prakash Bandaru
Venkata, Biagio Montaruli, Brent M. Spell, bubblebooy, bzhao, cfRod, Cheng Chen,
Cheng(Kit) Chen, Chris Tessum, Christian, chuanqiw, codeadmin_peritiae,
COTASPAR, CuiYifeng, danielknobe, danielyou0230, dannyfriar, daria,
DarrenZhang01, Denisa Roberts, dependabot[bot], Deven Desai, Dmitry Volodin,
Dmitry Zakharov, drebain, Duncan Riach, Eduard Feicho, Ehsan Toosi, Elena
Zhelezina, emlaprise2358, Eugene Kuznetsov, Evaderan-Lab, Evgeniy Polyakov,
Fausto Morales, Felix Johnny, fo40225, Frederic Bastien, Fredrik Knutsson,
fsx950223, Gaurav Singh, Gauri1 Deshpande, George Grzegorz Pawelczak, gerbauz,
Gianluca Baratti, Giorgio Arena, Gmc2, Guozhong Zhuang, Hannes Achleitner,
Harirai, HarisWang, Harsh188, hedgehog91, Hemal Mamtora, Hideto Ueno, Hugh Ku,
Ian Beauregard, Ilya Persky, jacco, Jakub Beránek, Jan Jongboom, Javier Montalt
Tordera, Jens Elofsson, Jerry Shih, jerryyin, jgehw, Jinjing Zhou, jma, jmsmdy,
Johan Nordström, John Poole, Jonah Kohn, Jonathan Dekhtiar, jpodivin, Jung Daun,
Kai Katsumata, Kaixi Hou, Kamil Rakoczy, Kaustubh Maske Patil, Kazuaki Ishizaki,
Kedar Sovani, Koan-Sin Tan, Koki Ibukuro, Krzysztof Laskowski, Kushagra Sharma,
Kushan Ahmadian, Lakshay Tokas, Leicong Li, levinxo, Lukas Geiger, Maderator,
Mahmoud Abuzaina, Mao Yunfei, Marius Brehler, markf, Martin Hwasser, Martin
Kubovčík, Matt Conley, Matthias, mazharul, mdfaijul, Michael137, MichelBr,
Mikhail Startsev, Milan Straka, Ml-0, Myung-Hyun Kim, Måns Nilsson, Nathan
Luehr, ngc92, nikochiko, Niranjan Hasabnis, nyagato_00, Oceania2018, Oleg Guba,
Ongun Kanat, OscarVanL, Patrik Laurell, Paul Tanger, Peter Sobot, Phil Pearl,
PlusPlusUltra, Poedator, Prasad Nikam, Rahul-Kamat, Rajeshwar Reddy T,
redwrasse, Rickard, Robert Szczepanski, Rohan Lekhwani, Sam Holt, Sami Kama,
Samuel Holt, Sandeep Giri, sboshin, Sean Settle, settle, Sharada Shiddibhavi,
Shawn Presser, ShengYang1, Shi,Guangyong, Shuxiang Gao, Sicong Li, Sidong-Wei,
Srihari Humbarwadi, Srinivasan Narayanamoorthy, Steenu Johnson, Steven Clarkson,
stjohnso98, Tamas Bela Feher, Tamas Nyiri, Tarandeep Singh, Teng Lu, Thibaut
Goetghebuer-Planchon, Tim Bradley, Tomasz Strejczek, Tongzhou Wang, Torsten
Rudolf, Trent Lo, Ty Mick, Tzu-Wei Sung, Varghese, Jojimon, Vignesh Kothapalli,
Vishakha Agrawal, Vividha, Vladimir Menshakov, Vladimir Silyaev, VoVAllen, Võ
Văn Nghĩa, wondertx, xiaohong1031, Xiaoming (Jason) Cui, Xinan Jiang, Yair
Ehrenwald, Yasir Modak, Yasuhiro Matsumoto, Yimei Sun, Yiwen Li, Yixing, Yoav
Ramon, Yong Tang, Yong Wu, yuanbopeng, Yunmo Koo, Zhangqiang, Zhou Peng,
ZhuBaohe, zilinzhu, zmx

2.3.4

Not secure
This release introduces several vulnerability fixes:

* Fixes a heap out of bounds access in sparse reduction operations
([CVE-2021-37635](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37635))
* Fixes a floating point exception in `SparseDenseCwiseDiv`
([CVE-2021-37636](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37636))
* Fixes a null pointer dereference in `CompressElement`
([CVE-2021-37637](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37637))
* Fixes a null pointer dereference in `RaggedTensorToTensor`
([CVE-2021-37638](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37638))
* Fixes a null pointer dereference and a heap OOB read arising from operations
restoring tensors
([CVE-2021-37639](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37639))
* Fixes an integer division by 0 in sparse reshaping
([CVE-2021-37640](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37640))
* Fixes a division by 0 in `ResourceScatterDiv`
([CVE-2021-37642](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37642))
* Fixes a heap OOB in `RaggedGather`
([CVE-2021-37641](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37641))
* Fixes a `std::abort` raised from `TensorListReserve`
([CVE-2021-37644](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37644))
* Fixes a null pointer dereference in `MatrixDiagPartOp`
([CVE-2021-37643](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37643))
* Fixes an integer overflow due to conversion to unsigned
([CVE-2021-37645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37645))
* Fixes a bad allocation error in `StringNGrams` caused by integer conversion
([CVE-2021-37646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37646))
* Fixes a null pointer dereference in `SparseTensorSliceDataset`
([CVE-2021-37647](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37647))
* Fixes an incorrect validation of `SaveV2` inputs
([CVE-2021-37648](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37648))
* Fixes a null pointer dereference in `UncompressElement`
([CVE-2021-37649](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37649))
* Fixes a segfault and a heap buffer overflow in
`{Experimental,}DatasetToTFRecord`
([CVE-2021-37650](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37650))
* Fixes a heap buffer overflow in `FractionalAvgPoolGrad`
([CVE-2021-37651](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37651))
* Fixes a use after free in boosted trees creation
([CVE-2021-37652](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37652))
* Fixes a division by 0 in `ResourceGather`
([CVE-2021-37653](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37653))
* Fixes a heap OOB and a `CHECK` fail in `ResourceGather`
([CVE-2021-37654](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37654))
* Fixes a heap OOB in `ResourceScatterUpdate`
([CVE-2021-37655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37655))
* Fixes an undefined behavior arising from reference binding to nullptr in
`RaggedTensorToSparse`
([CVE-2021-37656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37656))
* Fixes an undefined behavior arising from reference binding to nullptr in
`MatrixDiagV*` ops
([CVE-2021-37657](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37657))
* Fixes an undefined behavior arising from reference binding to nullptr in
`MatrixSetDiagV*` ops
([CVE-2021-37658](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37658))
* Fixes an undefined behavior arising from reference binding to nullptr and
heap OOB in binary cwise ops
([CVE-2021-37659](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37659))
* Fixes a division by 0 in inplace operations
([CVE-2021-37660](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37660))
* Fixes a crash caused by integer conversion to unsigned
([CVE-2021-37661](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37661))
* Fixes an undefined behavior arising from reference binding to nullptr in
boosted trees
([CVE-2021-37662](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37662))
* Fixes a heap OOB in boosted trees
([CVE-2021-37664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37664))
* Fixes vulnerabilities arising from incomplete validation in `QuantizeV2`
([CVE-2021-37663](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37663))
* Fixes vulnerabilities arising from incomplete validation in MKL
requantization
([CVE-2021-37665](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37665))
* Fixes an undefined behavior arising from reference binding to nullptr in
`RaggedTensorToVariant`
([CVE-2021-37666](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37666))
* Fixes an undefined behavior arising from reference binding to nullptr in
unicode encoding
([CVE-2021-37667](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37667))
* Fixes an FPE in `tf.raw_ops.UnravelIndex`
([CVE-2021-37668](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37668))
* Fixes a crash in NMS ops caused by integer conversion to unsigned
([CVE-2021-37669](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37669))
* Fixes a heap OOB in `UpperBound` and `LowerBound`
([CVE-2021-37670](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37670))
* Fixes an undefined behavior arising from reference binding to nullptr in map
operations
([CVE-2021-37671](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37671))
* Fixes a heap OOB in `SdcaOptimizerV2`
([CVE-2021-37672](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37672))
* Fixes a `CHECK`-fail in `MapStage`
([CVE-2021-37673](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37673))
* Fixes a vulnerability arising from incomplete validation in `MaxPoolGrad`
([CVE-2021-37674](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37674))
* Fixes an undefined behavior arising from reference binding to nullptr in
shape inference
([CVE-2021-37676](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37676))
* Fixes a division by 0 in most convolution operators
([CVE-2021-37675](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37675))
* Fixes vulnerabilities arising from missing validation in shape inference for
`Dequantize`
([CVE-2021-37677](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37677))
* Fixes an arbitrary code execution due to YAML deserialization
([CVE-2021-37678](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37678))
* Fixes a heap OOB in nested `tf.map_fn` with `RaggedTensor`s
([CVE-2021-37679](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37679))
* Fixes a division by zero in TFLite
([CVE-2021-37680](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37680))
* Fixes an NPE in TFLite
([CVE-2021-37681](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37681))
* Fixes a vulnerability arising from use of unitialized value in TFLite
([CVE-2021-37682](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37682))
* Fixes an FPE in TFLite division operations
([CVE-2021-37683](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37683))
* Fixes an FPE in TFLite pooling operations
([CVE-2021-37684](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37684))
* Fixes an infinite loop in TFLite
([CVE-2021-37686](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37686))
* Fixes a heap OOB in TFLite
([CVE-2021-37685](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37685))
* Fixes a heap OOB in TFLite's `Gather*` implementations
([CVE-2021-37687](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37687))
* Fixes an undefined behavior arising from null pointer dereference in TFLite
([CVE-2021-37688](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37688))
* Fixes an undefined behavior arising from null pointer dereference in TFLite
MLIR optimizations
([CVE-2021-37689](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37689))
* Fixes a FPE in LSH in TFLite
([CVE-2021-37691](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37691))
* Fixes a segfault on strings tensors with mismatched dimensions, arising in
Go code
([CVE-2021-37692](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37692))
* Fixes a use after free and a potential segfault in shape inference functions
([CVE-2021-37690](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37690))
* Updates `curl` to `7.77.0` to handle
[CVE-2021-22876](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-22876),
[CVE-2021-22897](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-22897),
[CVE-2021-22898](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-22898),
and
[CVE-2021-22901](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-22901).

2.3.3

Not secure
This release introduces several vulnerability fixes:

* 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).

2.3.2

Not secure
Bug Fixes and Other Changes

* Fixes an access to unitialized memory in Eigen code
([CVE-2020-26266](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26266))
* Fixes a security vulnerability caused by lack of validation in
`tf.raw_ops.DataFormatVecPermute` and `tf.raw_ops.DataFormatDimMap`
([CVE-2020-26267](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26267))
* Fixes a vulnerability caused by attempting to write to immutable memory
region in `tf.raw_ops.ImmutableConst`
([CVE-2020-26268](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26268)
* Fixes a `CHECK`-fail in LSTM with zero-length input
([CVE-2020-26270](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26270))
* Fixes a security vulnerability caused by accessing heap data outside of
bounds when loading a specially crafted `SavedModel`
([CVE-2020-26271](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26271))
* Solves an OOM issue on TPUs when XLA contexts use fused average updates
* Updates `libjpeg-turbo` to `2.0.5` to handle
[CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).
* Updates `junit` to `4.13.1` to handle
[CVE-2020-15250](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15250).
* Updates `PCRE` to `8.44` to handle
[CVE-2019-20838](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-20838)
and
[CVE-2020-14155](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-14155).
* Updates `sqlite3` to `3.44.0` to keep in sync with master branch.

2.3.1

Not secure
Bug Fixes and Other Changes

* Fixes an undefined behavior causing a segfault in `tf.raw_ops.Switch`
([CVE-2020-15190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15190))
* Fixes three vulnerabilities in conversion to DLPack format
([CVE-2020-15191](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15191),
[CVE-2020-15192](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15192),
[CVE-2020-15193](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15193))
* Fixes two vulnerabilities in `SparseFillEmptyRowsGrad`
([CVE-2020-15194](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15194),
[CVE-2020-15195](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15195))
* Fixes several vulnerabilities in `RaggedCountSparseOutput` and
`SparseCountSparseOutput` operations
([CVE-2020-15196](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15196),
[CVE-2020-15197](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15197),
[CVE-2020-15198](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15198),
[CVE-2020-15199](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15199),
[CVE-2020-15200](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15200),
[CVE-2020-15201](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15201))
* Fixes an integer truncation vulnerability in code using the work sharder API
([CVE-2020-15202](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15202))
* Fixes a format string vulnerability in `tf.strings.as_string`
([CVE-2020-15203](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15203))
* Fixes segfault raised by calling session-only ops in eager mode
([CVE-2020-15204](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15204))
* Fixes data leak and potential ASLR violation from `tf.raw_ops.StringNGrams`
([CVE-2020-15205](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15205))
* Fixes segfaults caused by incomplete `SavedModel` validation
([CVE-2020-15206](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15206))
* Fixes a data corruption due to a bug in negative indexing support in TFLite
([CVE-2020-15207](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15207))
* Fixes a data corruption due to dimension mismatch in TFLite
([CVE-2020-15208](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15208))
* Fixes several vulnerabilities in TFLite saved model format
([CVE-2020-15209](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209),
[CVE-2020-15210](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15210),
[CVE-2020-15211](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15211))
* Fixes several vulnerabilities in TFLite implementation of segment sum
([CVE-2020-15212](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15212),
[CVE-2020-15213](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15213),
[CVE-2020-15214](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15214))
* Updates `sqlite3` to `3.33.00` to handle
[CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358).
* Fixes deprecated usage of `collections` API
* Removes `scipy` dependency from `setup.py` since TensorFlow does not need it
to install the pip package

2.3.0

Not secure
Major Features and Improvements

* `tf.data` adds two new mechanisms to solve input pipeline bottlenecks and
save resources:

* [snapshot](https://www.tensorflow.org/api_docs/python/tf/data/experimental/snapshot)
* [tf.data service](https://www.tensorflow.org/api_docs/python/tf/data/experimental/service).

In addition checkout the detailed
[guide](https://www.tensorflow.org/guide/data_performance_analysis) for
analyzing input pipeline performance with TF Profiler.

* [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy)
is now a stable API and no longer considered experimental for TensorFlow.
(earlier `tf.distribute.experimental.TPUStrategy`).

* [TF Profiler](https://www.tensorflow.org/guide/profiler) introduces two new
tools: a memory profiler to visualize your model’s memory usage over time
and a [python tracer](https://www.tensorflow.org/guide/profiler#events)
which allows you to trace python function calls in your model. Usability
improvements include better diagnostic messages and
[profile options](https://tensorflow.org/guide/profiler#collect_performance_data)
to customize the host and device trace verbosity level.

* Introduces experimental support for Keras Preprocessing Layers API
([`tf.keras.layers.experimental.preprocessing.*`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing?version=nightly))
to handle data preprocessing operations, with support for composite tensor
inputs. Please see below for additional details on these layers.

* TFLite now properly supports dynamic shapes during conversion and inference.
We’ve also added opt-in support on Android and iOS for
[XNNPACK](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/delegates/xnnpack),
a highly optimized set of CPU kernels, as well as opt-in support for
[executing quantized models on the GPU](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/gpu_advanced.md#running-quantized-models-experimental).

* Libtensorflow packages are available in GCS starting this release. We have
also started to
[release a nightly version of these packages](https://github.com/tensorflow/tensorflow#official-builds).

* The experimental Python API
[`tf.debugging.experimental.enable_dump_debug_info()`](https://www.tensorflow.org/api_docs/python/tf/debugging/experimental/enable_dump_debug_info)
now allows you to instrument a TensorFlow program and dump debugging
information to a directory on the file system. The directory can be read and
visualized by a new interactive dashboard in TensorBoard 2.3 called
[Debugger V2](https://www.tensorflow.org/tensorboard/debugger_v2), which
reveals the details of the TensorFlow program including graph structures,
history of op executions at the Python (eager) and intra-graph levels, the
runtime dtype, shape, and numerical composition of tensors, as well as their
code locations.

Breaking Changes

* Increases the **minimum bazel version** required to build TF to **3.1.0**.
* `tf.data`
* Makes the following (breaking) changes to the `tf.data`.
* C++ API: - `IteratorBase::RestoreInternal`,
`IteratorBase::SaveInternal`, and `DatasetBase::CheckExternalState`
become pure-virtual and subclasses are now expected to provide an
implementation.
* The deprecated `DatasetBase::IsStateful` method is removed in favor of
`DatasetBase::CheckExternalState`.
* Deprecated overrides of `DatasetBase::MakeIterator` and
`MakeIteratorFromInputElement` are removed.
* The signature of `tensorflow::data::IteratorBase::SaveInternal` and
`tensorflow::data::IteratorBase::SaveInput` has been extended with
`SerializationContext` argument to enable overriding the default policy
for the handling external state during iterator checkpointing. This is
not a backwards compatible change and all subclasses of `IteratorBase`
*need to be updated* accordingly.
* `tf.keras`
* Add a new `BackupAndRestore` callback for handling distributed training
failures & restarts. Please take a look at this
[tutorial](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras)
for details on how to use the callback.
* `tf.image.extract_glimpse` has been updated to correctly process the case
where `centered=False` and `normalized=False`. This is a breaking change as
the output is different from (incorrect) previous versions. Note this
breaking change only impacts `tf.image.extract_glimpse` and
`tf.compat.v2.image.extract_glimpse` API endpoints. The behavior of
`tf.compat.v1.image.extract_glimpse` does not change. The behavior of
existing C++ kernel `ExtractGlimpse` does not change either, so saved models
using `tf.raw_ops.ExtractGlimpse` will not be impacted.

Known Caveats

* `tf.lite`
* Keras-based LSTM models must be converted with an explicit batch size in
the input layer.

Bug Fixes and Other Changes

TF Core:

* Set `tf2_behavior` to 1 to enable V2 for early loading cases.
* Add `execute_fn_for_device function` to dynamically choose the
implementation based on underlying device placement.
* Eager:
* Add `reduce_logsumexp` benchmark with experiment compile.
* Give `EagerTensor`s a meaningful `__array__` implementation.
* Add another version of defun matmul for performance analysis.
* `tf.function`/AutoGraph:
* `AutoGraph` now includes into TensorFlow loops any variables that are
closed over by local functions. Previously, such variables were
sometimes incorrectly ignored.
* functions returned by the `get_concrete_function` method of
`tf.function` objects can now be called with arguments consistent with
the original arguments or type specs passed to `get_concrete_function`.
This calling convention is now the preferred way to use concrete
functions with nested values and composite tensors. Please check the
[guide](https://www.tensorflow.org/guide/concrete_function) for more
details on `concrete_ function`.
* Update `tf.function`'s `experimental_relax_shapes` to handle composite
tensors appropriately.
* Optimize `tf.function` invocation, by removing redundant list converter.
* `tf.function` will retrace when called with a different variable instead
of simply using the `dtype` & `shape`.
* [Improve support](https://github.com/tensorflow/tensorflow/issues/33862)
for dynamically-sized TensorArray inside `tf.function`.
* `tf.math`:
* Narrow down `argmin`/`argmax` contract to always return the smallest
index for ties.
* `tf.math.reduce_variance` and `tf.math.reduce_std` return correct
computation for complex types and no longer support integer types.
* Add Bessel functions of order 0,1 to `tf.math.special`.
* `tf.divide` now always returns a tensor to be consistent with
documentation and other APIs.
* `tf.image`:
* Replaced
[`tf.image.non_max_suppression_padded`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/image/non_max_suppression_padded?hl=en)
with a new implementation that supports batched inputs, which is
considerably faster on TPUs and GPUs. Boxes with area=0 will be ignored.
Existing usage with single inputs should still work as before.
* `tf.linalg`
* Add `tf.linalg.banded_triangular_solve`.
* `tf.random`:
* Add `tf.random.stateless_parameterized_truncated_normal`.
* `tf.ragged`:
* Add `tf.ragged.cross` and `tf.ragged.cross_hashed` operations.
* `tf.RaggedTensor`:
* `RaggedTensor.to_tensor()` now preserves static shape.
* Add `tf.strings.format()` and `tf.print()` to support RaggedTensors.
* `tf.saved_model`:
* `tf.function` from SavedModel no longer ignores args after a
`RaggedTensor` when selecting the concrete function to run.
* Fix save model issue for ops with a list of functions.
* Add `tf.saved_model.LoadOptions` with
[`experimental_io_device`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/saved_model/LoadOptions?hl=en)
as arg with default value `None` to choose the I/O device for loading
models and weights.
* Update `tf.saved_model.SaveOptions` with
[`experimental_io_device`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/saved_model/SaveOptions?hl=en)
as arg with default value `None` to choose the I/O device for saving
models and weights.
* Mutable tables now restore checkpointed values when loaded from
SavedModel.
* The user object metadata field in the SavedModel proto has been
deprecated as part of the updates to Keras SavedModel. Keras was the
only consumer of this field prior to the update.
* GPU
* TF 2.3 includes PTX kernels only for
[compute capability](https://developer.nvidia.com/cuda-gpus) 7.0 to
reduce the TF pip binary size. Earlier releases included PTX for a
variety of older compute capabilities.
* Remove environmental variable `TF_USE_CUDNN`.
* Others
* Retain parent namescope for ops added inside
`tf.while_loop`/`tf.cond`/`tf.switch_case`.
* Update `tf.vectorized_map` to support vectorizing `tf.while_loop` and
TensorList operations.
* `tf.custom_gradient` can now be applied to functions that accept nested
structures of `tensors` as inputs (instead of just a list of tensors).
Note that Python structures such as tuples and lists now won't be
treated as tensors, so if you still want them to be treated that way,
you need to wrap them with `tf.convert_to_tensor`.
* No lowering on gradient case op when input is `DeviceIndex` op.
* Extend the ragged version of `tf.gather` to support `batch_dims` and
`axis` args.
* Update `tf.map_fn` to support RaggedTensors and SparseTensors.
* Deprecate `tf.group`. It is not useful in eager mode.
* Add CPU and GPU implementation of modified variation of
[`FTRL`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ApplyFtrl)/[`FTRLV2`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ApplyFtrlV2)
that can triggerred by `multiply_linear_by_lr` allowing a learning rate
of zero.

`tf.data`:

* `tf.data.experimental.dense_to_ragged_batch` works correctly with tuples.
* `tf.data.experimental.dense_to_ragged_batch` to output variable ragged rank.
* `tf.data.experimental.cardinality` is now a method on `tf.data.Dataset`.
* `tf.data.Dataset` now supports `len(Dataset)` when the cardinality is
finite.

`tf.distribute`:

* Expose experimental
[`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/distribute/DistributedDataset?hl=en)
and
[`tf.distribute.DistributedIterator`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/distribute/DistributedIterator)
to distribute input data when using `tf.distribute` to scale training on
multiple devices.
* Added a
[`get_next_as_optional`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/distribute/DistributedIterator?hl=en#get_next_as_optional)
method for
[`tf.distribute.DistributedIterator`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/distribute/DistributedIterator?hl=en)
class to return a `tf.experimental.Optional` instance that contains the
next value for all replicas or none instead of raising an out of range
error. Also see *new*
[guide on input distribution](https://www.tensorflow.org/tutorials/distribute/input).
* Allow var.assign on MirroredVariables with aggregation=NONE in replica
context. Previously this would raise an error. We now allow this because
many users and library writers find using `.assign` in replica context to be
more convenient, instead of having to use `Strategy.extended.update` which
was the previous way of updating variables in this situation.
* `tf.distribute.experimental.MultiWorkerMirroredStrategy` adds support for
partial batches. Workers running out of data now continue to participate in
the training with empty inputs, instead of raising an error. Learn more
about
[partial batches here](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).
* Improve the performance of reading metrics eagerly under
`tf.distribute.experimental.MultiWorkerMirroredStrategy`.
* Fix the issue that `strategy.reduce()` inside `tf.function` may raise
exceptions when the values to reduce are from loops or if-clauses.
* Fix the issue that `tf.distribute.MirroredStrategy` cannot be used together
with `tf.distribute.experimental.MultiWorkerMirroredStrategy`.
* Add a `tf.distribute.cluster_resolver.TPUClusterResolver.connect` API to
simplify TPU initialization.
* Add `tf.distribute.Strategy.gather` and
`tf.distribute.ReplicaContext.all_gather` methods to gather and concatenate
`tf.distribute.DistributedValues` across workers and devices.

`tf.keras`:

* Introduces experimental preprocessing layers API
(`tf.keras.layers.experimental.preprocessing`) to handle data preprocessing
operations such as categorical feature encoding, text vectorization, data
normalization, and data discretization (binning). The newly added layers
provide a replacement for the legacy feature column API, and support
composite tensor inputs.
* Added **categorical data** processing layers:
* `IntegerLookup` & `StringLookup`: build an index of categorical feature
values
* `CategoryEncoding`: turn integer-encoded categories into one-hot,
multi-hot, or tf-idf encoded representations
* `CategoryCrossing`: create new categorical features representing
co-occurrences of previous categorical feature values
* `Hashing`: the hashing trick, for large-vocabulary categorical features
* `Discretization`: turn continuous numerical features into categorical
features by binning their values
* Improved **image preprocessing** layers: `CenterCrop`, `Rescaling`
* Improved **image augmentation** layers: `RandomCrop`, `RandomFlip`,
`RandomTranslation`, `RandomRotation`, `RandomHeight`, `RandomWidth`,
`RandomZoom`, `RandomContrast`
* Improved **`TextVectorization`** layer, which handles string tokenization,
n-gram generation, and token encoding
* The `TextVectorization` layer now accounts for the mask_token as part of
the vocabulary size when output_mode='int'. This means that, if you have
a max_tokens value of 5000, your output will have 5000 unique values
(not 5001 as before).
* Change the return value of `TextVectorization.get_vocabulary()` from
`byte` to `string`. Users who previously were calling 'decode' on the
output of this method should no longer need to do so.
* Introduce new Keras dataset generation utilities :
* **[`image_dataset_from_directory`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image_dataset_from_directory)**
is a utility based on `tf.data.Dataset`, meant to replace the legacy
`ImageDataGenerator`. It takes you from a structured directory of images
to a labeled dataset, in one function call. Note that it doesn't perform
image data augmentation (which is meant to be done using preprocessing
layers).
* **[`text_dataset_from_directory`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory)**
takes you from a structured directory of text files to a labeled
dataset, in one function call.
* **[`timeseries_dataset_from_array`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/timeseries_dataset_from_array)**
is a `tf.data.Dataset`-based replacement of the legacy
`TimeseriesGenerator`. It takes you from an array of timeseries data to
a dataset of shifting windows with their targets.
* Added
[`experimental_steps_per_execution`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/Model?hl=en#compile)
arg to `model.compile` to indicate the number of batches to run per
`tf.function` call. This can speed up Keras Models on TPUs up to 3x.
* Extends `tf.keras.layers.Lambda` layers to support multi-argument lambdas,
and keyword arguments when calling the layer.
* Functional models now get constructed if *any* tensor in a layer call's
arguments/keyword arguments comes from a keras input. Previously the
functional api would only work if all of the elements in the first argument
to the layer came from a keras input.
* Clean up `BatchNormalization` layer's `trainable` property to act like
standard python state when it's used inside `tf.functions` (frozen at
tracing time), instead of acting like a pseudo-variable whose updates *kind
of sometimes* get reflected in already-traced `tf.function` traces.
* Add the `Conv1DTranspose` layer.
* Refine the semantics of `SensitivitySpecificityBase` derived metrics. See
the updated API docstrings for
[`tf.keras.metrics.SensitivityAtSpecificity`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/metrics/SensitivityAtSpecificity)
and
[`tf.keras.metrics.SpecificityAtSensitivty`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/metrics/SpecificityAtSensitivity).

`tf.lite`:

* Converter
* Restored `inference_input_type` and `inference_output_type` flags in TF
2.x TFLiteConverter (backward compatible with TF 1.x) to support integer
(tf.int8, tf.uint8) input and output types in post training full integer
quantized models.
* Added support for converting and resizing models with dynamic
(placeholder) dimensions. Previously, there was only limited support for
dynamic batch size, and even that did not guarantee that the model could
be properly resized at runtime.
* Enabled experimental support for a new quantization mode with 16-bit
activations and 8-bit weights. See
`lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8`.
* CPU
* Fix an issue w/ dynamic weights and `Conv2D` on x86.
* Add a runtime Android flag for enabling `XNNPACK` for optimized CPU
performance.
* Add a runtime iOS flag for enabling `XNNPACK` for optimized CPU
performance.
* Add a compiler flag to enable building a TFLite library that applies
`XNNPACK` delegate automatically when the model has a `fp32` operation.
* GPU
* Allow GPU acceleration starting with internal graph nodes
* Experimental support for quantized models with the Android GPU delegate
* Add GPU delegate whitelist.
* Rename GPU whitelist -> compatibility (list).
* Improve GPU compatibility list entries from crash reports.
* NNAPI
* Set default value for
`StatefulNnApiDelegate::Options::max_number_delegated_partitions` to 3.
* Add capability to disable `NNAPI` CPU and check `NNAPI` Errno.
* Fix crashes when using `NNAPI` with target accelerator specified with
model containing Conv2d or FullyConnected or LSTM nodes with quantized
weights.
* Fix `ANEURALNETWORKS_BAD_DATA` execution failures with
`sum`/`max`/`min`/`reduce` operations with `scalar` inputs.
* Hexagon
* TFLite Hexagon Delegate out of experimental.
* Experimental `int8` support for most hexagon ops.
* Experimental per-channel quant support for `conv` in Hexagon delegate.
* Support dynamic batch size in C++ API.
* CoreML
* Opensource CoreML delegate
* Misc
* Enable building Android TFLite targets on Windows
* Add support for `BatchMatMul`.
* Add support for `half_pixel_centers` with `ResizeNearestNeighbor`.
* Add 3D support for `BatchToSpaceND`.
* Add 5D support for `BroadcastSub`, `Maximum`, `Minimum`, `Transpose` and
`BroadcastDiv`.
* Rename `kTfLiteActRelu1` to `kTfLiteActReluN1To1`.
* Enable flex delegate on tensorflow.lite.Interpreter Python package.
* Add `Buckettize`, `SparseCross` and `BoostedTreesBucketize` to the flex
whitelist.
* Add support for selective registration of flex ops.
* Add missing kernels for flex delegate whitelisted ops.
* Fix issue when using direct `ByteBuffer` inputs with graphs that have
dynamic shapes.
* Fix error checking supported operations in a model containing
`HardSwish`.

Packaging Support

* Added `tf.sysconfig.get_build_info()`. Returns a dict that describes the
build environment of the currently installed TensorFlow package, e.g. the
NVIDIA CUDA and NVIDIA CuDNN versions used when TensorFlow was built.

Profiler

* Fix a subtle use-after-free issue in `XStatVisitor::RefValue()`.

TPU Enhancements

* Adds 3D mesh support in TPU configurations ops.
* Added TPU code for `FTRL` with `multiply_linear_by_lr`.
* Silently adds a new file system registry at `gstpu`.
* Support `restartType` in cloud tpu client.
* Depend on a specific version of google-api-python-client.
* Fixes apiclient import.

Tracing and Debugging

* Add a `TFE_Py_Execute` traceme.

XLA Support

* Implement stable `argmin` and `argmax`

Thanks to our Contributors

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

90244958880bigcat_chenASIC, Abdul Baseer Khan, Abhineet Choudhary, Abolfazl
Shahbazi, Adam Hillier, ag.ramesh, Agoniii, Ajay P, Alex Hoffman, Alexander
Bayandin, Alexander Grund, Alexandre Abadie, Alexey Rogachevskiy, amoitra,
Andrew Stevens, Angus-Luo, Anshuman Tripathy, Anush Elangovan, Artem Mavrin,
Ashutosh Hathidara, autoih, Ayushman Kumar, ayushmankumar7, Bairen Yi, Bas
Aarts, Bastian Eichenberger, Ben Barsdell, bhack, Bharat Raghunathan, Biagio
Montaruli, Bigcat-Himax, blueyi, Bryan Cutler, Byambaa, Carlos
Hernandez-Vaquero, Chen Lei, Chris Knorowski, Christian Clauss, chuanqiw,
CuiYifeng, Daniel Situnayake, Daria Zhuravleva, Dayananda-V, Deven Desai, Devi
Sandeep Endluri, Dmitry Zakharov, Dominic Jack, Duncan Riach, Edgar Liberis,
Ehsan Toosi, ekuznetsov139, Elena Zhelezina, Eugene Kuznetsov, Eugene
Mikhantiev, Evgenii Zheltonozhskii, Fabio Di Domenico, Fausto Morales, Fei Sun,
feihugis, Felix E. Klee, flyingcat, Frederic Bastien, Fredrik Knutsson, frreiss,
fsx950223, ganler, Gaurav Singh, Georgios Pinitas, Gian Marco Iodice, Giorgio
Arena, Giuseppe Rossini, Gregory Keith, Guozhong Zhuang, gurushantj, Hahn
Anselm, Harald Husum, Harjyot Bagga, Hristo Vrigazov, Ilya Persky, Ir1d, Itamar
Turner-Trauring, jacco, Jake Tae, Janosh Riebesell, Jason Zaman, jayanth, Jeff
Daily, Jens Elofsson, Jinzhe Zeng, JLZ, Jonas Skog, Jonathan Dekhtiar, Josh
Meyer, Joshua Chia, Judd, justkw, Kaixi Hou, Kam D Kasravi, Kamil Rakoczy, Karol
Gugala, Kayou, Kazuaki Ishizaki, Keith Smiley, Khaled Besrour, Kilaru Yasaswi
Sri Chandra Gandhi, Kim, Young Soo, Kristian Hartikainen, Kwabena W. Agyeman,
Leslie-Fang, Leslie-Fang-Intel, Li, Guizi, Lukas Geiger, Lutz Roeder, M\U00E5Ns
Nilsson, Mahmoud Abuzaina, Manish, Marcel Koester, Marcin Sielski, marload,
Martin Jul, Matt Conley, mdfaijul, Meng, Peng, Meteorix, Michael Käufl,
Michael137, Milan Straka, Mitchell Vitez, Ml-0, Mokke Meguru, Mshr-H, nammbash,
Nathan Luehr, naumkin, Neeraj Bhadani, ngc92, Nick Morgan, nihui, Niranjan
Hasabnis, Niranjan Yadla, Nishidha Panpaliya, Oceania2018, oclyke, Ouyang Jin,
OverLordGoldDragon, Owen Lyke, Patrick Hemmer, Paul Andrey, Peng Sun,
periannath, Phil Pearl, Prashant Dandriyal, Prashant Kumar, Rahul Huilgol, Rajan
Singh, Rajeshwar Reddy T, rangjiaheng, Rishit Dagli, Rohan Reddy, rpalakkal,
rposts, Ruan Kunliang, Rushabh Vasani, Ryohei Ikegami, Semun Lee, Seo-Inyoung,
Sergey Mironov, Sharada Shiddibhavi, ShengYang1, Shraiysh Vaishay, Shunya Ueta,
shwetaoj, Siyavash Najafzade, Srinivasan Narayanamoorthy, Stephan Uphoff,
storypku, sunchenggen, sunway513, Sven-Hendrik Haase, Swapnil Parekh, Tamas Bela
Feher, Teng Lu, tigertang, tomas, Tomohiro Ubukata, tongxuan.ltx, Tony Tonev,
Tzu-Wei Huang, Téo Bouvard, Uday Bondhugula, Vaibhav Jade, Vijay Tadikamalla,
Vikram Dattu, Vincent Abriou, Vishnuvardhan Janapati, Vo Van Nghia, VoVAllen,
Will Battel, William D. Irons, wyzhao, Xiaoming (Jason) Cui, Xiaoquan Kong,
Xinan Jiang, xutianming, Yair Ehrenwald, Yasir Modak, Yasuhiro Matsumoto, Yixing
Fu, Yong Tang, Yuan Tang, zhaozheng09, Zilin Zhu, zilinzhu, 张志豪

Page 8 of 17

© 2024 Safety CLI Cybersecurity Inc. All Rights Reserved.