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# 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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()
@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. TensorFloat-32
execution causes certain float32 ops, such as matrix multiplications and
convolutions, to run much faster on Ampere 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
Ampere GPUs, so all other hardware 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 Ampere, you can disable TensorFloat-32 execution
with this function. For example:
```python
x = tf.fill((2, 2), 1.0001)
y = tf.fill((2, 2), 1.)
# TensorFloat-32 is enabled, so matmul is run with reduced precision
print(tf.linalg.matmul(x, y)) # [[2., 2.], [2., 2.]]
tf.config.experimental.enable_tensor_float_32_execution(False)
# Matmul is run with full precision
print(tf.linalg.matmul(x, y)) # [[2.0002, 2.0002], [2.0002, 2.0002]]
```
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 subsequent GPUs that
support TensorFloat-32.
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.
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.
"""
context.context().set_optimizer_experimental_options(options)
@tf_export('config.get_soft_device_placement')
def get_soft_device_placement():
"""Get if soft device placement is enabled.
If enabled, an op will be placed on CPU if any of the following are true
1. there's no GPU 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
If disabled, the placement is strict and CPU fallback is not allowed.
An error is raised when an Op cannot be placed onto its intended device.
Returns:
If soft placement is enabled.
"""
return context.context().soft_device_placement
@tf_export('config.set_soft_device_placement')
def set_soft_device_placement(enabled):
"""Set if soft device placement is enabled.
If enabled, an op will be placed on CPU if any of the following are true
1. there's no GPU 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
Args:
enabled: 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:
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".
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".
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.
DO NOT USE, DEV AND TESTING ONLY AT THE MOMENT.
NOTE: MLIR-Based TensorFlow Compiler is under active development and has
missing features, please refrain from using. This API exists for development
and testing only.
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.enable_mlir_graph_optimization')
def enable_mlir_graph_optimization():
"""Enables experimental MLIR-Based TensorFlow Compiler Optimizations.
DO NOT USE, DEV AND TESTING ONLY AT THE MOMENT.
NOTE: MLIR-Based TensorFlow Compiler is under active development and has
missing features, please refrain from using. This API exists for development
and testing only.
TensorFlow Compiler Optimizations are responsible general graph level
optimizations that in the current stack mostly done by Grappler graph
optimizers.
"""
context.context().enable_mlir_graph_optimization = 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.disable_mlir_graph_optimization')
def disable_mlir_graph_optimization():
"""Disables experimental MLIR-Based TensorFlow Compiler Optimizations."""
context.context().enable_mlir_graph_optimization = False
def enable_op_determinism():
"""Enable op determinism.
When enabled, many ops will be made deterministic. This means that if you run
the same op multiple times, it will have the same outputs (and stateful ops
will have the same side effects). This function is described in [the
determinism
RFC](https://github.com/tensorflow/community/blob/master/rfcs/20210119-determinism.md).
The determinism functionality is not yet complete. Certain ops will raise a
NotImplemented error when run after determinism is enabled, because they do
not yet have a deterministic implementation. Certain other ops will instead
silently run nondeterministically, either because the NotImplemented error has
not been added yet or that the TensorFlow developers do not yet know the op is
nondeterministic. This function will not be exported as part of the TensorFlow
API until all known nondeterministic ops raise a NotImplemented error.
Currently, enabling determinism after certain ops have already been run may
cause future runs of such ops to be run nondeterministically. This is because
Autotune for ops like Conv2D may select and cache a nondeterministic
algorithm, which will still be used once determinism is enabled. It is
therefore recommended to enable determinism only before running any ops.
"""
_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()