3RNN/Lib/site-packages/tensorflow/python/framework/config.py

# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for configuring TensorFlow execution."""

from typing import Union

from tensorflow.python.eager import context
from tensorflow.python.framework import errors
from tensorflow.python.util import _pywrap_determinism
from tensorflow.python.util import _pywrap_tensor_float_32_execution
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export


@tf_export('config.experimental.tensor_float_32_execution_enabled')
def tensor_float_32_execution_enabled():
  """Returns whether TensorFloat-32 is enabled.

  By default, TensorFloat-32 is enabled, but this can be changed with
  `tf.config.experimental.enable_tensor_float_32_execution`.

  Returns:
    True if TensorFloat-32 is enabled (the default) and False otherwise
  """
  return _pywrap_tensor_float_32_execution.is_enabled()


# TODO(b/280688352): Rename or rework this function to make it appear less
# specific to GPUs. TPUs use bfloat16 instead of TensorFloat-32 by default for
# matmuls, yet on TPUs this function also can used to increase the precision of
# matmuls to FP32 by passing enabled=False. It is misleading how the words
# "tensor_float_32" appear in the API name, yet this API affects TPUs which do
# not use TensorFloat-32.
@tf_export('config.experimental.enable_tensor_float_32_execution')
def enable_tensor_float_32_execution(enabled):
  """Enable or disable the use of TensorFloat-32 on supported hardware.

  [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format),
  or TF32 for short, is a math mode for NVIDIA Ampere GPUs and above.
  TensorFloat-32 execution causes certain float32 ops, such as matrix
  multiplications and convolutions, to run much faster on such GPUs but with
  reduced precision. This reduced precision should not impact convergence of
  deep learning models in practice.

  TensorFloat-32 is enabled by default. TensorFloat-32 is only supported on
  NVIDIA GPUs starting with the Ampere generation, so older NVIDIA GPUs will use
  the full float32 precision regardless of whether TensorFloat-32 is enabled or
  not. If you want to use the full float32 precision on all GPUs, you can
  disable TensorFloat-32 execution with this function. For example:

  ```python
  x = tf.fill((1024, 1024), 1.0001)
  y = tf.fill((1024, 1024), 1.)
  # TensorFloat-32 is enabled, so matmul is run with reduced precision
  print(tf.linalg.matmul(x, y)[0, 0])  # 1024.0
  tf.config.experimental.enable_tensor_float_32_execution(False)
  # Matmul is run with full precision
  print(tf.linalg.matmul(x, y)[0, 0])  # ~1024.1
  ```

  To check whether TensorFloat-32 execution is currently enabled, use
  `tf.config.experimental.tensor_float_32_execution_enabled`.

  If TensorFloat-32 is enabled, float32 inputs of supported ops, such as
  `tf.linalg.matmul`, will be rounded from 23 bits of precision to 10 bits of
  precision in most cases. This allows the ops to execute much faster by
  utilizing the GPU's tensor cores. TensorFloat-32 has the same dynamic range as
  float32, meaning it is no more likely to underflow or overflow than float32.
  Ops still use float32 accumulation when TensorFloat-32 is enabled. Enabling or
  disabling TensorFloat-32 only affects Ampere GPUs and above.

  Note TensorFloat-32 is not always used in supported ops, as only inputs of
  certain shapes are supported. Support for more input shapes and more ops may
  be added in the future. As a result, precision of float32 ops may decrease in
  minor versions of TensorFlow.

  TensorFloat-32 is also used for some complex64 ops. Currently, TensorFloat-32
  is used in fewer cases for complex64 as it is for float32.

  Simiarly to GPUs, TPUs also run certain float32 ops, like matrix
  multiplications and convolutions, with lower precision by default. Unlike
  GPUs, TPUs use bfloat16 precision instead of TensorFloat-32 precision for such
  ops. Disabling TensorFloat-32 with this function also causes TPUs to run
  float32 ops with the full float32 precision but with lower performance.

  Args:
    enabled: Bool indicating whether to enable TensorFloat-32 execution.
  """
  _pywrap_tensor_float_32_execution.enable(enabled)


@tf_export('config.threading.get_intra_op_parallelism_threads')
def get_intra_op_parallelism_threads():
  """Get number of threads used within an individual op for parallelism.

  Certain operations like matrix multiplication and reductions can utilize
  parallel threads for speed ups. A value of 0 means the system picks an
  appropriate number.

  Returns:
    Number of parallel threads
  """
  return context.context().intra_op_parallelism_threads


@tf_export('config.threading.set_intra_op_parallelism_threads')
def set_intra_op_parallelism_threads(num_threads):
  """Set number of threads used within an individual op for parallelism.

  Certain operations like matrix multiplication and reductions can utilize
  parallel threads for speed ups. A value of 0 means the system picks an
  appropriate number.

  Args:
    num_threads: Number of parallel threads
  """
  context.context().intra_op_parallelism_threads = num_threads


@tf_export('config.threading.get_inter_op_parallelism_threads')
def get_inter_op_parallelism_threads():
  """Get number of threads used for parallelism between independent operations.

  Determines the number of threads used by independent non-blocking operations.
  0 means the system picks an appropriate number.

  Returns:
    Number of parallel threads
  """
  return context.context().inter_op_parallelism_threads


@tf_export('config.threading.set_inter_op_parallelism_threads')
def set_inter_op_parallelism_threads(num_threads):
  """Set number of threads used for parallelism between independent operations.

  Determines the number of threads used by independent non-blocking operations.
  0 means the system picks an appropriate number.

  Args:
    num_threads: Number of parallel threads
  """
  context.context().inter_op_parallelism_threads = num_threads


@tf_export('config.optimizer.get_jit')
def get_optimizer_jit() -> str:
  """Returns JIT compilation configuration for code inside `tf.function`.

  Possible return values:
     -`"autoclustering"` if
     [autoclustering](https://www.tensorflow.org/xla#auto-clustering) is enabled
     - `""` when no default compilation is applied.
  """
  if context.context().optimizer_jit:
    return 'autoclustering'
  return ''


@tf_export('config.optimizer.set_jit')
@deprecation.deprecated_arg_values(
    None,
    '`True` setting is deprecated, use `autoclustering` instead.',
    warn_once=True,
    jit_config=True)
def set_optimizer_jit(enabled: Union[bool, str]):
  """Configure JIT compilation.

  Note: compilation is only applied to code that is compiled into a
  graph (in TF2 that's only a code inside `tf.function`).

  Args:
    enabled: JIT compilation configuration.
    Possible values:
     - `"autoclustering"` (`True` is a deprecated alias): perform
     [autoclustering](https://www.tensorflow.org/xla#auto-clustering)
       (automatically identify and compile clusters of nodes) on all graphs
       using
     [XLA](https://www.tensorflow.org/xla).
     - `False`: do not automatically compile any graphs.
  """
  autoclustering_enabled = enabled in (True, 'autoclustering')
  context.context().optimizer_jit = autoclustering_enabled


@tf_export('config.optimizer.get_experimental_options')
def get_optimizer_experimental_options():
  """Get experimental optimizer options.

  Refer to tf.config.optimizer.set_experimental_options for a list of current
  options.

  Note that optimizations are only applied in graph mode, (within tf.function).
  In addition, as these are experimental options, the list is subject to change.

  Returns:
    Dictionary of configured experimental optimizer options
  """
  return context.context().get_optimizer_experimental_options()


@tf_export('config.optimizer.set_experimental_options')
def set_optimizer_experimental_options(options):
  """Set experimental optimizer options.

  Note that optimizations are only applied in graph mode, (within tf.function).
  In addition, as these are experimental options, the list is subject to change.

  Args:
    options: Dictionary of experimental optimizer options to configure.
      Valid keys:
      - layout_optimizer: Optimize tensor layouts e.g. This will try to use NCHW
        layout on GPU which is faster.
      - constant_folding: Fold constants Statically infer the value of tensors
        when possible, and materialize the result using constants.
      - shape_optimization: Simplify computations made on shapes.
      - remapping: Remap subgraphs onto more efficient implementations.
      - arithmetic_optimization: Simplify arithmetic ops with common
        sub-expression elimination and arithmetic simplification.
      - dependency_optimization: Control dependency optimizations. Remove
        redundant control dependencies, which may enable other optimization.
        This optimizer is also essential for pruning Identity and NoOp nodes.
      - loop_optimization: Loop optimizations.
      - function_optimization: Function optimizations and inlining.
      - debug_stripper: Strips debug-related nodes from the graph.
      - disable_model_pruning: Disable removal of unnecessary ops from the graph
      - scoped_allocator_optimization: Try to allocate some independent Op
        outputs contiguously in order to merge or eliminate downstream Ops.
      - pin_to_host_optimization: Force small ops onto the CPU.
      - implementation_selector: Enable the swap of kernel implementations based
        on the device placement.
      - auto_mixed_precision: Change certain float32 ops to float16 on Volta
        GPUs and above. Without the use of loss scaling, this can cause
        numerical underflow (see
        `keras.mixed_precision.experimental.LossScaleOptimizer`).
      - disable_meta_optimizer: Disable the entire meta optimizer.
      - min_graph_nodes: The minimum number of nodes in a graph to optimizer.
        For smaller graphs, optimization is skipped.
      - auto_parallel: Automatically parallelizes graphs by splitting along
        the batch dimension
  """
  context.context().set_optimizer_experimental_options(options)


@tf_export('config.get_soft_device_placement')
def get_soft_device_placement():
  """Return status of soft device placement flag.

  If enabled, ops can be placed on different devices than the device explicitly
  assigned by the user. This potentially has a large performance cost due to an
  increase in data communication between devices.

  Some cases where soft_device_placement would modify device assignment are:
    1. no GPU/TPU implementation for the OP
    2. no GPU devices are known or registered
    3. need to co-locate with reftype input(s) which are from CPU
    4. an OP can not be compiled by XLA.  Common for TPU which always requires
         the XLA compiler.

  For TPUs, if this option is true, a feature called automatic outside
  compilation is enabled. Automatic outside compilation will move uncompilable
  ops within a TPU program to instead run on the host. This can be used when
  encountering compilation failures due to unsupported ops.

  Returns:
   A boolean indicating if soft placement is enabled.
  """
  return context.context().soft_device_placement


@tf_export('config.set_soft_device_placement')
def set_soft_device_placement(enabled):
  """Enable or disable soft device placement.

  If enabled, ops can be placed on different devices than the device explicitly
  assigned by the user. This potentially has a large performance cost due to an
  increase in data communication between devices.

  Some cases where soft_device_placement would modify device assignment are:
    1. no GPU/TPU implementation for the OP
    2. no GPU devices are known or registered
    3. need to co-locate with reftype input(s) which are from CPU
    4. an OP can not be compiled by XLA.  Common for TPU which always requires
         the XLA compiler.

  For TPUs, if this option is true, a feature called automatic outside
  compilation is enabled. Automatic outside compilation will move uncompilable
  ops within a TPU program to instead run on the host. This can be used when
  encountering compilation failures due to unsupported ops.

  Note: by default soft device placement is enabled when running in eager mode
  (for convenience) and disabled in graph mode (for performance).

  Args:
    enabled: A boolean indicating whether to enable soft placement.
  """
  context.context().soft_device_placement = enabled


@tf_export('config.experimental.get_device_policy')
def get_device_policy():
  """Gets the current device policy.

  The device policy controls how operations requiring inputs on a specific
  device (e.g., on GPU:0) handle inputs on a different device (e.g. GPU:1).

  This function only gets the device policy for the current thread. Any
  subsequently started thread will again use the default policy.

  Returns:
    Current thread device policy
  """
  device_policy = context.context().device_policy
  if device_policy == context.DEVICE_PLACEMENT_SILENT:
    return 'silent'
  elif device_policy == context.DEVICE_PLACEMENT_SILENT_FOR_INT32:
    return 'silent_for_int32'
  elif device_policy == context.DEVICE_PLACEMENT_WARN:
    return 'warn'
  elif device_policy == context.DEVICE_PLACEMENT_EXPLICIT:
    return 'explicit'
  else:
    # pylint: disable-next=no-value-for-parameter
    raise errors.InternalError(
        f'Got an invalid device policy: {device_policy!r}.')


@tf_export('config.experimental.set_device_policy')
def set_device_policy(device_policy):
  """Sets the current thread device policy.

  The device policy controls how operations requiring inputs on a specific
  device (e.g., on GPU:0) handle inputs on a different device (e.g. GPU:1).

  When using the default, an appropriate policy will be picked automatically.
  The default policy may change over time.

  This function only sets the device policy for the current thread. Any
  subsequently started thread will again use the default policy.

  Args:
    device_policy: A device policy.
      Valid values:
      - None: Switch to a system default.
      - 'warn': Copies the tensors which are not on the right device and logs a
        warning.
      - 'explicit': Raises an error if the placement is not as required.
      - 'silent': Silently copies the tensors. Note that this may hide
        performance problems as there is no notification provided when
        operations are blocked on the tensor being copied between devices.
      - 'silent_for_int32': silently copies `int32` tensors, raising errors on
        the other ones.

  Raises:
      ValueError: If an invalid `device_policy` is passed.
  """
  if device_policy == 'silent':
    context.context().device_policy = context.DEVICE_PLACEMENT_SILENT
  elif device_policy == 'silent_for_int32':
    context.context().device_policy = context.DEVICE_PLACEMENT_SILENT_FOR_INT32
  elif device_policy == 'warn':
    context.context().device_policy = context.DEVICE_PLACEMENT_WARN
  elif device_policy == 'explicit':
    context.context().device_policy = context.DEVICE_PLACEMENT_EXPLICIT
  elif device_policy is None:
    context.context().device_policy = None
  else:
    raise ValueError(
        f'Invalid argument `device_policy`: {device_policy!r}. Please refer to '
        'https://www.tensorflow.org/api_docs/python/tf/config/experimental/set_device_policy '
        'for valid `device_policy` arguments.')


@tf_export('config.experimental.get_synchronous_execution')
def get_synchronous_execution():
  """Gets whether operations are executed synchronously or asynchronously.

  TensorFlow can execute operations synchronously or asynchronously. If
  asynchronous execution is enabled, operations may return "non-ready" handles.

  Returns:
    Current thread execution mode
  """
  return context.context().execution_mode == context.SYNC


@tf_export('config.experimental.set_synchronous_execution')
def set_synchronous_execution(enable):
  """Specifies whether operations are executed synchronously or asynchronously.

  TensorFlow can execute operations synchronously or asynchronously. If
  asynchronous execution is enabled, operations may return "non-ready" handles.

  When `enable` is set to None, an appropriate value will be picked
  automatically. The value picked may change between TensorFlow releases.

  Args:
    enable: Whether operations should be dispatched synchronously.
      Valid values:
      - None: sets the system default.
      - True: executes each operation synchronously.
      - False: executes each operation asynchronously.
  """
  if enable is None:
    context.context().execution_mode = None
  elif enable:
    context.context().execution_mode = context.SYNC
  else:
    context.context().execution_mode = context.ASYNC


@tf_export('config.list_physical_devices',
           'config.experimental.list_physical_devices')
@deprecation.deprecated_endpoints('config.experimental.list_physical_devices')
def list_physical_devices(device_type=None):
  """Return a list of physical devices visible to the host runtime.

  Physical devices are hardware devices present on the host machine. By default
  all discovered CPU and GPU devices are considered visible.

  This API allows querying the physical hardware resources prior to runtime
  initialization. Thus, giving an opportunity to call any additional
  configuration APIs. This is in contrast to `tf.config.list_logical_devices`,
  which triggers runtime initialization in order to list the configured devices.

  The following example lists the number of visible GPUs on the host.

  >>> physical_devices = tf.config.list_physical_devices('GPU')
  >>> print("Num GPUs:", len(physical_devices))
  Num GPUs: ...

  However, the number of GPUs available to the runtime may change during runtime
  initialization due to marking certain devices as not visible or configuring
  multiple logical devices.

  Args:
    device_type: (optional string) Only include devices matching this device
      type. For example "CPU" or "GPU".
    Notes: 1. If provided with any numerical values or any string other than
      supported device type such as 'CPU' it returns an empty list instead of
      raising error. 2. For default value it returns all physical devices

  Returns:
    List of discovered `tf.config.PhysicalDevice` objects
  """
  return context.context().list_physical_devices(device_type)


@tf_export('config.list_logical_devices',
           'config.experimental.list_logical_devices')
@deprecation.deprecated_endpoints('config.experimental.list_logical_devices')
def list_logical_devices(device_type=None):
  """Return a list of logical devices created by runtime.

  Logical devices may correspond to physical devices or remote devices in the
  cluster. Operations and tensors may be placed on these devices by using the
  `name` of the `tf.config.LogicalDevice`.

  Calling `tf.config.list_logical_devices` triggers the runtime to configure any
  `tf.config.PhysicalDevice` visible to the runtime, thereby preventing
  further configuration. To avoid runtime initialization, call
  `tf.config.list_physical_devices` instead.

  For example:

  >>> logical_devices = tf.config.list_logical_devices('GPU')
  >>> if len(logical_devices) > 0:
  ...   # Allocate on GPU:0
  ...   with tf.device(logical_devices[0].name):
  ...     one = tf.constant(1)
  ...   # Allocate on GPU:1
  ...   with tf.device(logical_devices[1].name):
  ...     two = tf.constant(2)

  Args:
    device_type: (optional string) Only include devices matching this device
      type. For example "CPU" or "GPU".
    Notes: 1. If provided with any numerical values or any string other than
      supported device type such as 'CPU' it returns an empty list instead of
      raising error. 2. For default value it returns all logical devices

  Returns:
    List of initialized `LogicalDevice`s
  """
  return context.context().list_logical_devices(device_type=device_type)


@tf_export('config.get_visible_devices',
           'config.experimental.get_visible_devices')
@deprecation.deprecated_endpoints('config.experimental.get_visible_devices')
def get_visible_devices(device_type=None):
  """Get the list of visible physical devices.

  Returns the list of `PhysicalDevice`s currently marked as visible to the
  runtime. A visible device will have at least one `LogicalDevice` associated
  with it once the runtime is initialized.

  The following example verifies all visible GPUs have been disabled:

  >>> physical_devices = tf.config.list_physical_devices('GPU')
  >>> try:
  ...   # Disable all GPUS
  ...   tf.config.set_visible_devices([], 'GPU')
  ...   visible_devices = tf.config.get_visible_devices()
  ...   for device in visible_devices:
  ...     assert device.device_type != 'GPU'
  ... except:
  ...   # Invalid device or cannot modify virtual devices once initialized.
  ...   pass

  Args:
    device_type: (optional string) Only include devices matching this device
      type. For example "CPU" or "GPU".

  Returns:
    List of visible `PhysicalDevice`s
  """
  return context.context().get_visible_devices(device_type)


@tf_export('config.set_visible_devices',
           'config.experimental.set_visible_devices')
@deprecation.deprecated_endpoints('config.experimental.set_visible_devices')
def set_visible_devices(devices, device_type=None):
  """Set the list of visible devices.

  Specifies which `PhysicalDevice` objects are visible to the runtime.
  TensorFlow will only allocate memory and place operations on visible
  physical devices, as otherwise no `LogicalDevice` will be created on them.
  By default all discovered devices are marked as visible.

  The following example demonstrates disabling the first GPU on the machine.

  >>> physical_devices = tf.config.list_physical_devices('GPU')
  >>> try:
  ...   # Disable first GPU
  ...   tf.config.set_visible_devices(physical_devices[1:], 'GPU')
  ...   logical_devices = tf.config.list_logical_devices('GPU')
  ...   # Logical device was not created for first GPU
  ...   assert len(logical_devices) == len(physical_devices) - 1
  ... except:
  ...   # Invalid device or cannot modify virtual devices once initialized.
  ...   pass

  Args:
    devices: List of `PhysicalDevice`s to make visible
    device_type: (optional) Only configure devices matching this device type.
      For example "CPU" or "GPU". Other devices will be left unaltered.

  Raises:
    ValueError: If argument validation fails.
    RuntimeError: Runtime is already initialized.
  """
  context.context().set_visible_devices(devices, device_type)


# TODO(b/188089869): Redesign memory stats related APIs before move them out of
# experimental.
@tf_export('config.experimental.get_memory_info')
def get_memory_info(device):
  """Get memory info for the chosen device, as a dict.

  This function returns a dict containing information about the device's memory
  usage. For example:

  >>> if tf.config.list_physical_devices('GPU'):
  ...   # Returns a dict in the form {'current': <current mem usage>,
  ...   #                             'peak': <peak mem usage>}
  ...   tf.config.experimental.get_memory_info('GPU:0')

  Currently returns the following keys:
    - `'current'`: The current memory used by the device, in bytes.
    - `'peak'`: The peak memory used by the device across the run of the
        program, in bytes. Can be reset with
        `tf.config.experimental.reset_memory_stats`.

  More keys may be added in the future, including device-specific keys.

  Currently only supports GPU and TPU. If called on a CPU device, an exception
  will be raised.

  For GPUs, TensorFlow will allocate all the memory by default, unless changed
  with `tf.config.experimental.set_memory_growth`. The dict specifies only the
  current and peak memory that TensorFlow is actually using, not the memory that
  TensorFlow has allocated on the GPU.

  Args:
    device: Device string to get the memory information for, e.g. `"GPU:0"`,
    `"TPU:0"`. See https://www.tensorflow.org/api_docs/python/tf/device for
      specifying device strings.

  Returns:
    A dict with keys `'current'` and `'peak'`, specifying the current and peak
    memory usage respectively.

  Raises:
    ValueError: No device found with the device name, like '"nonexistent"'.
    ValueError: Invalid device name, like '"GPU"', '"CPU:GPU"', '"CPU:"'.
    ValueError: Multiple devices matched with the device name.
    ValueError: Memory statistics not tracked, like '"CPU:0"'.
  """
  return context.context().get_memory_info(device)


# TODO(b/188089869): Redesign memory stats related APIs before move them out of
# experimental.
# TODO(b/189498350): Unify the behavior on CPU, GPU and TPU.
@tf_export('config.experimental.reset_memory_stats')
def reset_memory_stats(device):
  """Resets the tracked memory stats for the chosen device.

  This function sets the tracked peak memory for a device to the device's
  current memory usage. This allows you to measure the peak memory usage for a
  specific part of your program. For example:

  >>> if tf.config.list_physical_devices('GPU'):
  ...   # Sets the peak memory to the current memory.
  ...   tf.config.experimental.reset_memory_stats('GPU:0')
  ...   # Creates the first peak memory usage.
  ...   x1 = tf.ones(1000 * 1000, dtype=tf.float64)
  ...   del x1 # Frees the memory referenced by `x1`.
  ...   peak1 = tf.config.experimental.get_memory_info('GPU:0')['peak']
  ...   # Sets the peak memory to the current memory again.
  ...   tf.config.experimental.reset_memory_stats('GPU:0')
  ...   # Creates the second peak memory usage.
  ...   x2 = tf.ones(1000 * 1000, dtype=tf.float32)
  ...   del x2
  ...   peak2 = tf.config.experimental.get_memory_info('GPU:0')['peak']
  ...   assert peak2 < peak1  # tf.float32 consumes less memory than tf.float64.

  Currently only supports GPU and TPU. If called on a CPU device, an exception
  will be raised.

  Args:
    device: Device string to reset the memory stats, e.g. `"GPU:0"`, `"TPU:0"`.
      See https://www.tensorflow.org/api_docs/python/tf/device for specifying
      device strings.

  Raises:
    ValueError: No device found with the device name, like '"nonexistent"'.
    ValueError: Invalid device name, like '"GPU"', '"CPU:GPU"', '"CPU:"'.
    ValueError: Multiple devices matched with the device name.
    ValueError: Memory statistics not tracked or clearing memory statistics not
      supported, like '"CPU:0"'.
  """
  context.context().reset_memory_stats(device)


@deprecation.deprecated(
    None,
    "Use tf.config.experimental.get_memory_info(device)['current'] instead.")
@tf_export('config.experimental.get_memory_usage')
def get_memory_usage(device):
  """Get the current memory usage, in bytes, for the chosen device.

  This function is deprecated in favor of
  `tf.config.experimental.get_memory_info`. Calling this function is equivalent
  to calling `tf.config.experimental.get_memory_info()['current']`.

  See https://www.tensorflow.org/api_docs/python/tf/device for specifying device
  strings.

  For example:

  >>> gpu_devices = tf.config.list_physical_devices('GPU')
  >>> if gpu_devices:
  ...   tf.config.experimental.get_memory_usage('GPU:0')

  Does not work for CPU.

  For GPUs, TensorFlow will allocate all the memory by default, unless changed
  with `tf.config.experimental.set_memory_growth`. This function only returns
  the memory that TensorFlow is actually using, not the memory that TensorFlow
  has allocated on the GPU.

  Args:
    device: Device string to get the bytes in use for, e.g. `"GPU:0"`

  Returns:
    Total memory usage in bytes.

  Raises:
    ValueError: Non-existent or CPU device specified.
  """
  return get_memory_info(device)['current']


@tf_export('config.experimental.get_memory_growth')
def get_memory_growth(device):
  """Get if memory growth is enabled for a `PhysicalDevice`.

  If memory growth is enabled for a `PhysicalDevice`, the runtime initialization
  will not allocate all memory on the device.

  For example:

  >>> physical_devices = tf.config.list_physical_devices('GPU')
  >>> try:
  ...   tf.config.experimental.set_memory_growth(physical_devices[0], True)
  ...   assert tf.config.experimental.get_memory_growth(physical_devices[0])
  ... except:
  ...   # Invalid device or cannot modify virtual devices once initialized.
  ...   pass

  Args:
    device: `PhysicalDevice` to query

  Returns:
    A boolean indicating the memory growth setting for the `PhysicalDevice`.

  Raises:
    ValueError: Invalid `PhysicalDevice` specified.
  """
  return context.context().get_memory_growth(device)


@tf_export('config.experimental.set_memory_growth')
def set_memory_growth(device, enable):
  """Set if memory growth should be enabled for a `PhysicalDevice`.

  If memory growth is enabled for a `PhysicalDevice`, the runtime initialization
  will not allocate all memory on the device. Memory growth cannot be configured
  on a `PhysicalDevice` with virtual devices configured.

  For example:

  >>> physical_devices = tf.config.list_physical_devices('GPU')
  >>> try:
  ...   tf.config.experimental.set_memory_growth(physical_devices[0], True)
  ... except:
  ...   # Invalid device or cannot modify virtual devices once initialized.
  ...   pass

  Args:
    device: `PhysicalDevice` to configure
    enable: (Boolean) Whether to enable or disable memory growth

  Raises:
    ValueError: Invalid `PhysicalDevice` specified.
    RuntimeError: Runtime is already initialized.
  """
  context.context().set_memory_growth(device, enable)


@tf_export('config.experimental.get_device_details')
def get_device_details(device):
  """Returns details about a physical devices.

  This API takes in a `tf.config.PhysicalDevice` returned by
  `tf.config.list_physical_devices`. It returns a dict with string keys
  containing various details about the device. Each key is only supported by a
  subset of devices, so you should not assume the returned dict will have any
  particular key.

  >>> gpu_devices = tf.config.list_physical_devices('GPU')
  >>> if gpu_devices:
  ...   details = tf.config.experimental.get_device_details(gpu_devices[0])
  ...   details.get('device_name', 'Unknown GPU')

  Currently, details are only returned for GPUs. This function returns an
  empty dict if passed a non-GPU device.

  The returned dict may have the following keys:
  * `'device_name'`: A human-readable name of the device as a string, e.g.
    "Titan V". Unlike `tf.config.PhysicalDevice.name`, this will be the same for
    multiple devices if each device is the same model. Currently only available
    for GPUs.
  * `'compute_capability'`: The
    [compute capability](https://developer.nvidia.com/cuda-gpus) of the device
    as a tuple of two ints, in the form `(major_version, minor_version)`. Only
    available for NVIDIA GPUs

  Note: This is similar to `tf.sysconfig.get_build_info` in that both functions
  can return information relating to GPUs. However, this function returns
  run-time information about a specific device (such as a GPU's compute
  capability), while `tf.sysconfig.get_build_info` returns compile-time
  information about how TensorFlow was built (such as what version of CUDA
  TensorFlow was built for).

  Args:
    device: A `tf.config.PhysicalDevice` returned by
      `tf.config.list_physical_devices` or `tf.config.get_visible_devices`.

  Returns:
    A dict with string keys.
  """
  return context.context().get_device_details(device)


@tf_export('config.get_logical_device_configuration',
           'config.experimental.get_virtual_device_configuration')
@deprecation.deprecated_endpoints(
    'config.experimental.get_virtual_device_configuration')
def get_logical_device_configuration(device):
  """Get the virtual device configuration for a `tf.config.PhysicalDevice`.

  Returns the list of `tf.config.LogicalDeviceConfiguration`
  objects previously configured by a call to
  `tf.config.set_logical_device_configuration`.

  For example:

  >>> physical_devices = tf.config.list_physical_devices('CPU')
  >>> assert len(physical_devices) == 1, "No CPUs found"
  >>> configs = tf.config.get_logical_device_configuration(
  ...   physical_devices[0])
  >>> try:
  ...   assert configs is None
  ...   tf.config.set_logical_device_configuration(
  ...     physical_devices[0],
  ...     [tf.config.LogicalDeviceConfiguration(),
  ...      tf.config.LogicalDeviceConfiguration()])
  ...   configs = tf.config.get_logical_device_configuration(
  ...     physical_devices[0])
  ...   assert len(configs) == 2
  ... except:
  ...   # Cannot modify virtual devices once initialized.
  ...   pass

  Args:
    device: `PhysicalDevice` to query

  Returns:
    List of `tf.config.LogicalDeviceConfiguration` objects or
    `None` if no virtual device configuration has been set for this physical
    device.
  """
  return context.context().get_logical_device_configuration(device)


@tf_export('config.set_logical_device_configuration',
           'config.experimental.set_virtual_device_configuration')
@deprecation.deprecated_endpoints(
    'config.experimental.set_virtual_device_configuration')
def set_logical_device_configuration(device, logical_devices):
  """Set the logical device configuration for a `tf.config.PhysicalDevice`.

  A visible `tf.config.PhysicalDevice` will by default have a single
  `tf.config.LogicalDevice` associated with it once the runtime is initialized.
  Specifying a list of `tf.config.LogicalDeviceConfiguration` objects allows
  multiple devices to be created on the same `tf.config.PhysicalDevice`.

  Logical device configurations can be modified by calling this function as
  long as the runtime is uninitialized. After the runtime is initialized
  calling this function raises a RuntimeError.

  The following example splits the CPU into 2 logical devices:

  >>> physical_devices = tf.config.list_physical_devices('CPU')
  >>> assert len(physical_devices) == 1, "No CPUs found"
  >>> # Specify 2 virtual CPUs. Note currently memory limit is not supported.
  >>> try:
  ...   tf.config.set_logical_device_configuration(
  ...     physical_devices[0],
  ...     [tf.config.LogicalDeviceConfiguration(),
  ...      tf.config.LogicalDeviceConfiguration()])
  ...   logical_devices = tf.config.list_logical_devices('CPU')
  ...   assert len(logical_devices) == 2
  ...
  ...   tf.config.set_logical_device_configuration(
  ...     physical_devices[0],
  ...     [tf.config.LogicalDeviceConfiguration(),
  ...      tf.config.LogicalDeviceConfiguration(),
  ...      tf.config.LogicalDeviceConfiguration(),
  ...      tf.config.LogicalDeviceConfiguration()])
  ... except:
  ...   # Cannot modify logical devices once initialized.
  ...   pass

  The following example splits the GPU into 2 logical devices with 100 MB each:

  >>> physical_devices = tf.config.list_physical_devices('GPU')
  >>> try:
  ...   tf.config.set_logical_device_configuration(
  ...     physical_devices[0],
  ...     [tf.config.LogicalDeviceConfiguration(memory_limit=100),
  ...      tf.config.LogicalDeviceConfiguration(memory_limit=100)])
  ...
  ...   logical_devices = tf.config.list_logical_devices('GPU')
  ...   assert len(logical_devices) == len(physical_devices) + 1
  ...
  ...   tf.config.set_logical_device_configuration(
  ...     physical_devices[0],
  ...     [tf.config.LogicalDeviceConfiguration(memory_limit=10),
  ...      tf.config.LogicalDeviceConfiguration(memory_limit=10)])
  ... except:
  ...   # Invalid device or cannot modify logical devices once initialized.
  ...   pass

  Args:
    device: The `PhysicalDevice` to configure.
    logical_devices: (optional) List of `tf.config.LogicalDeviceConfiguration`
      objects to allocate for the specified `PhysicalDevice`. If None, the
      default configuration will be used.

  Raises:
    ValueError: If argument validation fails.
    RuntimeError: Runtime is already initialized.
  """
  context.context().set_logical_device_configuration(device, logical_devices)


@tf_export('config.experimental.enable_mlir_bridge')
def enable_mlir_bridge():
  """Enables experimental MLIR-Based TensorFlow Compiler Bridge.

  TensorFlow Compiler Bridge (TF Bridge) is responsible for translating parts
  of TensorFlow graph into a form that can be accepted as an input by a backend
  compiler such as XLA.
  """
  context.context().enable_mlir_bridge = True


@tf_export('config.experimental.disable_mlir_bridge')
def disable_mlir_bridge():
  """Disables experimental MLIR-Based TensorFlow Compiler Bridge."""
  context.context().enable_mlir_bridge = False


@tf_export('config.experimental.enable_op_determinism', v1=[])
def enable_op_determinism():
  """Configures TensorFlow ops to run deterministically.

  When op determinism is enabled, TensorFlow ops will be deterministic. This
  means that if an op is run multiple times with the same inputs on the same
  hardware, it will have the exact same outputs each time. This is useful for
  debugging models. Note that determinism in general comes at the expense of
  lower performance and so your model may run slower when op determinism is
  enabled.

  If you want your TensorFlow program to run deterministically, put the
  following code near the start of your program.

  ```python
  tf.keras.utils.set_random_seed(1)
  tf.config.experimental.enable_op_determinism()
  ```

  Calling `tf.keras.utils.set_random_seed` sets the Python seed, the NumPy seed,
  and the TensorFlow seed. Setting these seeds is necessary to ensure any random
  numbers your program generates are also deterministic.

  By default, op determinism is not enabled, so ops might return different
  results when run with the same inputs. These differences are often caused by
  the use of asynchronous threads within the op nondeterministically changing
  the order in which floating-point numbers are added. Most of these cases of
  nondeterminism occur on GPUs, which have thousands of hardware threads that
  are used to run ops. Enabling determinism directs such ops to use a different
  algorithm, one that does not use threads in a nondeterministic way.

  Another potential source of nondeterminism is `tf.data` based data processing.
  Typically, this can introduce nondeterminsm due to the use of parallelism in
  methods such as `Dataset.map` producing inputs or running stateful ops in a
  nondeterministic order. Enabling determinism will remove such sources of
  nondeterminism.

  Enabling determinism will likely make your model or your `tf.data` data
  processing slower. For example, `Dataset.map` can become several orders of
  magnitude slower when the map function has random ops or other stateful ops.
  See the “Determinism and tf.data” section below for more details. In future
  TensorFlow releases, we plan on improving the performance of determinism,
  especially for common scenarios such as `Dataset.map`.

  Certain ops will raise an `UnimplementedError` because they do not yet have a
  deterministic implementation. Additionally, due to bugs, some ops might be
  nondeterministic and not raise an `UnimplementedError`. If you encounter such
  ops, please [file an issue](https://github.com/tensorflow/tensorflow/issues).

  An example of enabling determinism follows. The
  `tf.nn.softmax_cross_entropy_with_logits` op is run multiple times and the
  output is shown to be the same each time. This example would likely fail when
  run on a GPU if determinism were not enabled, because
  `tf.nn.softmax_cross_entropy_with_logits` uses a nondeterministic algorithm on
  GPUs by default.

  ```python
  labels = tf.random.normal((1, 10000))
  logits = tf.random.normal((1, 10000))
  output = tf.nn.softmax_cross_entropy_with_logits(labels=labels,
                                                   logits=logits)
  for _ in range(5):
    output2 = tf.nn.softmax_cross_entropy_with_logits(labels=labels,
                                                      logits=logits)
    tf.debugging.assert_equal(output, output2)
  ```

  ## Writing deterministic models

  You can make your models deterministic by enabling op determinism. This
  means that you can train a model and finish each run with exactly the same
  trainable variables. This also means that the inferences of your
  previously-trained model will be exactly the same on each run. Typically,
  models can be made deterministic by simply setting the seeds and enabling
  op determinism, as in the example above. However, to guarantee that your
  model operates deterministically, you must meet all the following
  requirements:

  * Call `tf.config.experimental.enable_op_determinism()`, as mentioned above.
  * Reproducibly reset any pseudorandom number generators (PRNGs) you’re using,
    such as by setting the seeds for the default PRNGs in TensorFlow, Python,
    and NumPy, as mentioned above. Note that certain newer NumPy classes like
   ` numpy.random.default_rng` ignore the global NumPy seed, so a seed must be
    explicitly passed to such classes, if used.
  * Use the same hardware configuration in every run.
  * Use the same software environment in every run (OS, checkpoints, version of
    CUDA and TensorFlow, environmental variables, etc). Note that determinism is
    not guaranteed across different versions of TensorFlow.
  * Do not use constructs outside TensorFlow that are nondeterministic, such as
    reading from `/dev/random` or using multiple threads/processes in ways that
    influence TensorFlow’s behavior.
  * Ensure your input pipeline is deterministic. If you use `tf.data`, this is
    done automatically (at the expense of performance). See "Determinism and
    tf.data" below for more information.
  * Do not use `tf.compat.v1.Session` and
    `tf.distribute.experimental.ParameterServerStrategy`, which can introduce
    nondeterminism. Besides ops (including `tf.data` ops), these are the only
    known potential sources of nondeterminism within TensorFlow, (if you
    find more, please file an issue). Note that `tf.compat.v1.Session` is
    required to use the TF1 API, so determinism cannot be guaranteed when using
    the TF1 API.
  * Do not use nondeterministic custom ops.

  ## Additional details on determinism

  For stateful ops to be deterministic, the state of the system must be the same
  every time the op is run. For example the output of `tf.Variable.sparse_read`
  (obviously) depends on both the variable value and the `indices` function
  parameter.  When determinism is enabled, the side effects of stateful ops are
  deterministic.

  TensorFlow’s random ops, such as `tf.random.normal`, will raise a
  `RuntimeError` if determinism is enabled and a seed has not been set. However,
  attempting to generate nondeterministic random numbers using Python or NumPy
  will not raise such errors. Make sure you remember to set the Python and NumPy
  seeds. Calling `tf.keras.utils.set_random_seed` is an easy way to set all
  three seeds.

  Note that latency, memory consumption, throughput, and other performance
  characteristics are *not* made deterministic by enabling op determinism.
  Only op outputs and side effects are made deterministic. Additionally, a model
  may nondeterministically raise a `tf.errors.ResourceExhaustedError` from a
  lack of memory due to the fact that memory consumption is nondeterministic.

  ## Determinism and tf.data

  Enabling deterministic ops makes `tf.data` deterministic in several ways:

  1. For dataset methods with a `deterministic` argument, such as `Dataset.map`
     and `Dataset.batch`, the `deterministic` argument is overridden to be
     `True` irrespective of its setting.
  2. The `tf.data.Option.experimental_deterministic` option is overridden to be
     `True` irrespective of its setting..
  3. In `Dataset.map` and `Dataset.interleave`, if the map or interleave
     function has stateful random ops or other stateful ops, the function will
     run serially instead of in parallel. This means the `num_parallel_calls`
     argument to `map` and `interleave` is effectively ignored.
  4. Prefetching with `Dataset.prefetch` will be disabled if any function run
     as part of the input pipeline has certain stateful ops. Similarly, any
     dataset method with a `num_parallel_calls` argument will be made to run
     serially if any function in the input pipeline has such stateful ops.
     Legacy random ops such as `tf.random.normal` will *not* cause such datasets
     to be changed, but most other stateful ops will.

  Unfortunately, due to (3), performance can be greatly reduced when stateful
  ops are used in `Dataset.map` due to no longer running the map function in
  parallel. A common example of stateful ops used in `Dataset.map` are random
  ops, such as `tf.random.normal`, which are typically used for distortions. One
  way to work around this is to use stateless random ops instead. Alternatively
  you can hoist all random ops into its own separate `Dataset.map` call, making
  the original `Dataset.map` call stateless and thus avoid the need to serialize
  its execution.

  (4) can also cause performance to be reduced, but occurs less frequently than
  (3) because legacy random ops do not cause (4) to take effect. However, unlike
  (3), when there are non-random stateful ops in a user-defined function, every
  `map` and `interleave` dataset is affected, instead of just the `map` or
  `interleave` dataset with the function that has stateful ops. Additionally,
  `prefetch` datasets and any dataset with the `num_parallel_calls` argument are
  also affected.
  """
  _pywrap_determinism.enable(True)


def disable_op_determinism():
  """Disables op determinism."""
  _pywrap_determinism.enable(False)


def is_op_determinism_enabled():
  """Returns True if op determinism is enabled."""
  return _pywrap_determinism.is_enabled()