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STT-tensorflow/tensorflow/python/distribute/distribute_lib.py
Rick Chao 4d13d6416d Make cluster_resolver standard property in tf.distribute strategies. 
PiperOrigin-RevId: 317771299
Change-Id: I71b5c585cef7bd7ef80e66b75e30287fddcf89e2
2020-06-22 18:19:14 -07:00

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# Copyright 2018 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.
# ==============================================================================
# pylint: disable=line-too-long
"""Library for running a computation across multiple devices.
The intent of this library is that you can write an algorithm in a stylized way
and it will be usable with a variety of different `tf.distribute.Strategy`
implementations. Each descendant will implement a different strategy for
distributing the algorithm across multiple devices/machines. Furthermore, these
changes can be hidden inside the specific layers and other library classes that
need special treatment to run in a distributed setting, so that most users'
model definition code can run unchanged. The `tf.distribute.Strategy` API works
the same way with eager and graph execution.
*Guides*
* [TensorFlow v2.x](https://www.tensorflow.org/guide/distributed_training)
* [TensorFlow v1.x](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/distribute_strategy.ipynb)
*Tutorials*
* [Distributed Training Tutorials](https://www.tensorflow.org/tutorials/distribute/)
The tutorials cover how to use `tf.distribute.Strategy` to do distributed
training with native Keras APIs, custom training loops,
and Esitmator APIs. They also cover how to save/load model when using
`tf.distribute.Strategy`.
*Glossary*
* _Data parallelism_ is where we run multiple copies of the model
on different slices of the input data. This is in contrast to
_model parallelism_ where we divide up a single copy of a model
across multiple devices.
Note: we only support data parallelism for now, but
hope to add support for model parallelism in the future.
* A _device_ is a CPU or accelerator (e.g. GPUs, TPUs) on some machine that
TensorFlow can run operations on (see e.g. `tf.device`). You may have multiple
devices on a single machine, or be connected to devices on multiple
machines. Devices used to run computations are called _worker devices_.
Devices used to store variables are _parameter devices_. For some strategies,
such as `tf.distribute.MirroredStrategy`, the worker and parameter devices
will be the same (see mirrored variables below). For others they will be
different. For example, `tf.distribute.experimental.CentralStorageStrategy`
puts the variables on a single device (which may be a worker device or may be
the CPU), and `tf.distribute.experimental.ParameterServerStrategy` puts the
variables on separate machines called _parameter servers_ (see below).
* A _replica_ is one copy of the model, running on one slice of the
input data. Right now each replica is executed on its own
worker device, but once we add support for model parallelism
a replica may span multiple worker devices.
* A _host_ is the CPU device on a machine with worker devices, typically
used for running input pipelines.
* A _worker_ is defined to be the physical machine(s) containing the physical
devices (e.g. GPUs, TPUs) on which the replicated computation is executed. A
worker may contain one or more replicas, but contains at least one
replica. Typically one worker will correspond to one machine, but in the case
of very large models with model parallelism, one worker may span multiple
machines. We typically run one input pipeline per worker, feeding all the
replicas on that worker.
* _Synchronous_, or more commonly _sync_, training is where the updates from
each replica are aggregated together before updating the model variables. This
is in contrast to _asynchronous_, or _async_ training, where each replica
updates the model variables independently. You may also have replicas
partitioned into groups which are in sync within each group but async between
groups.
* _Parameter servers_: These are machines that hold a single copy of
parameters/variables, used by some strategies (right now just
`tf.distribute.experimental.ParameterServerStrategy`). All replicas that want
to operate on a variable retrieve it at the beginning of a step and send an
update to be applied at the end of the step. These can in priniciple support
either sync or async training, but right now we only have support for async
training with parameter servers. Compare to
`tf.distribute.experimental.CentralStorageStrategy`, which puts all variables
on a single device on the same machine (and does sync training), and
`tf.distribute.MirroredStrategy`, which mirrors variables to multiple devices
(see below).
* _Replica context_ vs. _Cross-replica context_ vs _Update context_
A _replica context_ applies
when you execute the computation function that was called with `strategy.run`.
Conceptually, you're in replica context when executing the computation
function that is being replicated.
An _update context_ is entered in a `tf.distribute.StrategyExtended.update`
call.
An _cross-replica context_ is entered when you enter a `strategy.scope`. This
is useful for calling `tf.distribute.Strategy` methods which operate across
the replicas (like `reduce_to()`). By default you start in a _replica context_
(the "default single _replica context_") and then some methods can switch you
back and forth.
* _Distributed value_: Distributed value is represented by the base class
`tf.distribute.DistributedValues`. `tf.distribute.DistributedValues` is useful
to represent values on multiple devices, and it contains a map from replica id
to values. Two representative kinds of `tf.distribute.DistributedValues` are
"PerReplica" and "Mirrored" values.
"PerReplica" values exist on the worker
devices, with a different value for each replica. They are produced by
iterating through a distributed dataset returned by
`tf.distribute.Strategy.experimental_distribute_dataset` and
`tf.distribute.Strategy.experimental_distribute_datasets_from_function`. They
are also the typical result returned by
`tf.distribute.Strategy.run`.
"Mirrored" values are like "PerReplica" values, except we know that the value
on all replicas are the same. We can safely read a "Mirrored" value in a
cross-replica context by using the value on any replica.
* _Unwrapping_ and _merging_: Consider calling a function `fn` on multiple
replicas, like `strategy.run(fn, args=[w])` with an
argument `w` that is a `tf.distribute.DistributedValues`. This means `w` will
have a map taking replica id `0` to `w0`, replica id `1` to `w1`, etc.
`strategy.run()` unwraps `w` before calling `fn`, so it calls `fn(w0)` on
device `d0`, `fn(w1)` on device `d1`, etc. It then merges the return
values from `fn()`, which leads to one common object if the returned values
are the same object from every replica, or a `DistributedValues` object
otherwise.
* _Reductions_ and _all-reduce_: A _reduction_ is a method of aggregating
multiple values into one value, like "sum" or "mean". If a strategy is doing
sync training, we will perform a reduction on the gradients to a parameter
from all replicas before applying the update. _All-reduce_ is an algorithm for
performing a reduction on values from multiple devices and making the result
available on all of those devices.
* _Mirrored variables_: These are variables that are created on multiple
devices, where we keep the variables in sync by applying the same
updates to every copy. Mirrored variables are created with
`tf.Variable(...synchronization=tf.VariableSynchronization.ON_WRITE...)`.
Normally they are only used in synchronous training.
* _SyncOnRead variables_
_SyncOnRead variables_ are created by
`tf.Variable(...synchronization=tf.VariableSynchronization.ON_READ...)`, and
they are created on multiple devices. In replica context, each
component variable on the local replica can perform reads and writes without
synchronization with each other. When the
_SyncOnRead variable_ is read in cross-replica context, the values from
component variables are aggregated and returned.
_SyncOnRead variables_ bring a lot of custom configuration difficulty to the
underlying logic, so we do not encourage users to instantiate and use
_SyncOnRead variable_ on their own. We have mainly used _SyncOnRead
variables_ for use cases such as batch norm and metrics. For performance
reasons, we often don't need to keep these statistics in sync every step and
they can be accumulated on each replica independently. The only time we want
to sync them is reporting or checkpointing, which typically happens in
cross-replica context. _SyncOnRead variables_ are also often used by advanced
users who want to control when variable values are aggregated. For example,
users sometimes want to maintain gradients independently on each replica for a
couple of steps without aggregation.
* _Distribute-aware layers_
Layers are generally called in a replica context, except when defining a
Keras functional model. `tf.distribute.in_cross_replica_context` will let you
determine which case you are in. If in a replica context,
the `tf.distribute.get_replica_context` function will return the default
replica context outside a strategy scope, `None` within a strategy scope, and
a `tf.distribute.ReplicaContext` object inside a strategy scope and within a
`tf.distribute.Strategy.run` function. The `ReplicaContext` object has an
`all_reduce` method for aggregating across all replicas.
Note that we provide a default version of `tf.distribute.Strategy` that is
used when no other strategy is in scope, that provides the same API with
reasonable default behavior.
"""
# pylint: enable=line-too-long
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import enum # pylint: disable=g-bad-import-order
import threading
import weakref
import six
from tensorflow.python.autograph.core import ag_ctx as autograph_ctx
from tensorflow.python.autograph.impl import api as autograph
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import iterator_ops
from tensorflow.python.distribute import collective_util
from tensorflow.python.distribute import device_util
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.distribute import numpy_dataset
from tensorflow.python.distribute import reduce_util
from tensorflow.python.eager import context as eager_context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import monitoring
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import custom_gradient
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import summary_ops_v2
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops.losses import loss_reduction
from tensorflow.python.ops.losses import losses_impl
from tensorflow.python.platform import tf_logging
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.tf_export import tf_export
from tensorflow.tools.docs import doc_controls
# ------------------------------------------------------------------------------
# Context tracking whether in a strategy.update() or .update_non_slot() call.
_update_replica_id = threading.local()
def get_update_replica_id():
"""Get the current device if in a `tf.distribute.Strategy.update()` call."""
try:
return _update_replica_id.current
except AttributeError:
return None
class UpdateContext(object):
"""Context manager when you are in `update()` or `update_non_slot()`."""
def __init__(self, replica_id):
self._replica_id = replica_id
self._old_replica_id = None
def __enter__(self):
self._old_replica_id = get_update_replica_id()
_update_replica_id.current = self._replica_id
def __exit__(self, exception_type, exception_value, traceback):
del exception_type, exception_value, traceback
_update_replica_id.current = self._old_replica_id
# ------------------------------------------------------------------------------
# Public utility functions.
@tf_export(v1=["distribute.get_loss_reduction"])
def get_loss_reduction():
"""`tf.distribute.ReduceOp` corresponding to the last loss reduction.
This is used to decide whether loss should be scaled in optimizer (used only
for estimator + v1 optimizer use case).
Returns:
`tf.distribute.ReduceOp` corresponding to the last loss reduction for
estimator and v1 optimizer use case. `tf.distribute.ReduceOp.SUM` otherwise.
"""
if not distribution_strategy_context.get_strategy()._scale_loss_for_estimator: # pylint: disable=protected-access
# If we are not in Estimator context then return 'SUM'. We do not need to
# scale loss in the optimizer.
return reduce_util.ReduceOp.SUM
last_reduction = ops.get_default_graph()._last_loss_reduction # pylint: disable=protected-access
if (last_reduction == losses_impl.Reduction.SUM or
last_reduction == loss_reduction.ReductionV2.SUM):
return reduce_util.ReduceOp.SUM
return reduce_util.ReduceOp.MEAN
# ------------------------------------------------------------------------------
# Internal API for validating the current thread mode
def _require_cross_replica_or_default_context_extended(extended):
"""Verify in cross-replica context."""
context = _get_per_thread_mode()
cross_replica = context.cross_replica_context
if cross_replica is not None and cross_replica.extended is extended:
return
if context is _get_default_replica_mode():
return
strategy = extended._container_strategy() # pylint: disable=protected-access
# We have an error to report, figure out the right message.
if context.strategy is not strategy:
_wrong_strategy_scope(strategy, context)
assert cross_replica is None
raise RuntimeError("Method requires being in cross-replica context, use "
"get_replica_context().merge_call()")
def _wrong_strategy_scope(strategy, context):
# Figure out the right error message.
if not distribution_strategy_context.has_strategy():
raise RuntimeError(
'Need to be inside "with strategy.scope()" for %s' %
(strategy,))
else:
raise RuntimeError(
"Mixing different tf.distribute.Strategy objects: %s is not %s" %
(context.strategy, strategy))
def require_replica_context(replica_ctx):
"""Verify in `replica_ctx` replica context."""
context = _get_per_thread_mode()
if context.replica_context is replica_ctx: return
# We have an error to report, figure out the right message.
if context.replica_context is None:
raise RuntimeError("Need to be inside `call_for_each_replica()`")
if context.strategy is replica_ctx.strategy:
# Two different ReplicaContexts with the same tf.distribute.Strategy.
raise RuntimeError("Mismatching ReplicaContext.")
raise RuntimeError(
"Mismatching tf.distribute.Strategy objects: %s is not %s." %
(context.strategy, replica_ctx.strategy))
def _require_strategy_scope_strategy(strategy):
"""Verify in a `strategy.scope()` in this thread."""
context = _get_per_thread_mode()
if context.strategy is strategy: return
_wrong_strategy_scope(strategy, context)
def _require_strategy_scope_extended(extended):
"""Verify in a `distribution_strategy.scope()` in this thread."""
context = _get_per_thread_mode()
if context.strategy.extended is extended: return
# Report error.
strategy = extended._container_strategy() # pylint: disable=protected-access
_wrong_strategy_scope(strategy, context)
# ------------------------------------------------------------------------------
# Internal context managers used to implement the DistributionStrategy
# base class
class _CurrentDistributionContext(object):
"""Context manager setting the current `tf.distribute.Strategy`.
Also: overrides the variable creator and optionally the current device.
"""
def __init__(self,
strategy,
var_creator_scope,
var_scope=None,
default_device=None):
self._context = distribution_strategy_context._CrossReplicaThreadMode( # pylint: disable=protected-access
strategy)
self._var_creator_scope = var_creator_scope
self._var_scope = var_scope
if default_device:
self._device_scope = ops.device(default_device)
else:
self._device_scope = None
self._same_scope_again_count = 0
def __enter__(self):
# Allow this scope to be entered if this strategy is already in scope.
if distribution_strategy_context.has_strategy():
_require_cross_replica_or_default_context_extended(
self._context.strategy.extended)
self._same_scope_again_count += 1
else:
_push_per_thread_mode(self._context)
if self._var_scope:
self._var_scope.__enter__()
self._var_creator_scope.__enter__()
if self._device_scope:
self._device_scope.__enter__()
return self._context.strategy
def __exit__(self, exception_type, exception_value, traceback):
if self._same_scope_again_count > 0:
self._same_scope_again_count -= 1
return
if self._device_scope:
try:
self._device_scope.__exit__(exception_type, exception_value, traceback)
except RuntimeError as e:
six.raise_from(
RuntimeError("Device scope nesting error: move call to "
"tf.distribute.set_strategy() out of `with` scope."),
e)
try:
self._var_creator_scope.__exit__(
exception_type, exception_value, traceback)
except RuntimeError as e:
six.raise_from(
RuntimeError("Variable creator scope nesting error: move call to "
"tf.distribute.set_strategy() out of `with` scope."),
e)
if self._var_scope:
try:
self._var_scope.__exit__(exception_type, exception_value, traceback)
except RuntimeError as e:
six.raise_from(
RuntimeError("Variable scope nesting error: move call to "
"tf.distribute.set_strategy() out of `with` scope."),
e)
_pop_per_thread_mode()
# TODO(yuefengz): add more replication modes.
@tf_export("distribute.InputReplicationMode")
class InputReplicationMode(enum.Enum):
"""Replication mode for input function.
* `PER_WORKER`: The input function will be called on each worker
independently, creating as many input pipelines as number of workers.
Replicas will dequeue from the local Dataset on their worker.
`tf.distribute.Strategy` doesn't manage any state sharing between such
separate input pipelines.
"""
PER_WORKER = "PER_WORKER"
@tf_export("distribute.InputContext")
class InputContext(object):
"""A class wrapping information needed by an input function.
This is a context class that is passed to the user's input function and
contains information about the compute replicas and input pipelines. The
number of compute replicas (in sync training) helps compute the local batch
size from the desired global batch size for each replica. The input pipeline
information can be used to return a different subset of the input in each
replica (for e.g. shard the input pipeline, use a different input
source etc).
"""
def __init__(self,
num_input_pipelines=1,
input_pipeline_id=0,
num_replicas_in_sync=1):
"""Initializes an InputContext object.
Args:
num_input_pipelines: the number of input pipelines in a cluster.
input_pipeline_id: the current input pipeline id, should be an int in
[0,`num_input_pipelines`).
num_replicas_in_sync: the number of replicas that are in sync.
"""
self._num_input_pipelines = num_input_pipelines
self._input_pipeline_id = input_pipeline_id
self._num_replicas_in_sync = num_replicas_in_sync
@property
def num_replicas_in_sync(self):
"""Returns the number of compute replicas in sync."""
return self._num_replicas_in_sync
@property
def input_pipeline_id(self):
"""Returns the input pipeline ID."""
return self._input_pipeline_id
@property
def num_input_pipelines(self):
"""Returns the number of input pipelines."""
return self._num_input_pipelines
def get_per_replica_batch_size(self, global_batch_size):
"""Returns the per-replica batch size.
Args:
global_batch_size: the global batch size which should be divisible by
`num_replicas_in_sync`.
Returns:
the per-replica batch size.
Raises:
ValueError: if `global_batch_size` not divisible by
`num_replicas_in_sync`.
"""
if global_batch_size % self._num_replicas_in_sync != 0:
raise ValueError("The `global_batch_size` %r is not divisible by "
"`num_replicas_in_sync` %r " %
(global_batch_size, self._num_replicas_in_sync))
return global_batch_size // self._num_replicas_in_sync
def __str__(self):
return "tf.distribute.InputContext(input pipeline id {}, total: {})".format(
self.input_pipeline_id, self.num_input_pipelines)
@tf_export("distribute.experimental.ValueContext", v1=[])
class ValueContext(object):
"""A class wrapping information needed by a distribute function.
This is a context class that is passed to the `value_fn` in
`strategy.experimental_distribute_values_from_function` and contains
information about the compute replicas. The `num_replicas_in_sync` and
`replica_id` can be used to customize the value on each replica.
Example usage:
1. Directly constructed.
>>> def value_fn(context):
... return context.replica_id_in_sync_group/context.num_replicas_in_sync
>>> context = tf.distribute.experimental.ValueContext(
... replica_id_in_sync_group=2, num_replicas_in_sync=4)
>>> per_replica_value = value_fn(context)
>>> per_replica_value
0.5
2. Passed in by `experimental_distribute_values_from_function`.
>>> strategy = tf.distribute.MirroredStrategy()
>>> def value_fn(value_context):
... return value_context.num_replicas_in_sync
>>> distributed_values = (
... strategy.experimental_distribute_values_from_function(
... value_fn))
>>> local_result = strategy.experimental_local_results(distributed_values)
>>> local_result
(1,)
"""
def __init__(self,
replica_id_in_sync_group=0,
num_replicas_in_sync=1):
"""Initializes an ValueContext object.
Args:
replica_id_in_sync_group: the current replica_id, should be an int in
[0,`num_replicas_in_sync`).
num_replicas_in_sync: the number of replicas that are in sync.
"""
self._replica_id_in_sync_group = replica_id_in_sync_group
self._num_replicas_in_sync = num_replicas_in_sync
@property
def num_replicas_in_sync(self):
"""Returns the number of compute replicas in sync."""
return self._num_replicas_in_sync
@property
def replica_id_in_sync_group(self):
"""Returns the replica ID."""
return self._replica_id_in_sync_group
def __str__(self):
return (("tf.distribute.ValueContext(replica id {}, "
" total replicas in sync: ""{})")
.format(self.replica_id_in_sync_group, self.num_replicas_in_sync))
@tf_export("distribute.RunOptions")
class RunOptions(
collections.namedtuple("RunOptions", [
"experimental_enable_dynamic_batch_size",
"experimental_bucketizing_dynamic_shape",
])):
"""Run options for `strategy.run`.
This can be used to hold some strategy specific configs.
Attributes:
experimental_enable_dynamic_batch_size: Boolean. Only applies to
TPUStrategy. Default to True. If True, TPUStrategy will enable dynamic
padder to support dynamic batch size for the inputs. Otherwise only static
shape inputs are allowed.
experimental_bucketizing_dynamic_shape: Boolean. Only applies to
TPUStrategy. Default to False. If True, TPUStrategy will automatic
bucketize inputs passed into `run` if the input shape is
dynamic. This is a performance optimization to reduce XLA recompilation,
which should not have impact on correctness.
"""
def __new__(cls,
experimental_enable_dynamic_batch_size=True,
experimental_bucketizing_dynamic_shape=False):
return super(RunOptions,
cls).__new__(cls, experimental_enable_dynamic_batch_size,
experimental_bucketizing_dynamic_shape)
@tf_export("distribute.InputOptions", v1=[])
class InputOptions(
collections.namedtuple("InputOptions", [
"experimental_prefetch_to_device",
])):
"""Run options for `experimental_distribute_dataset(s_from_function)`.
This can be used to hold some strategy specific configs.
```python
# Setup TPUStrategy
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
dataset = tf.data.Dataset.range(16)
distributed_dataset_on_host = (
strategy.experimental_distribute_dataset(
dataset,
tf.distribute.InputOptions(
experimental_prefetch_to_device=False)))
```
Attributes:
experimental_prefetch_to_device: Boolean. Currently only applies to
TPUStrategy. Defaults to True. If True, dataset elements will be
prefetched to accelerator device memory. When False, dataset elements are
prefetched to host device memory. Must be False when using TPUEmbedding
API.
"""
def __new__(cls, experimental_prefetch_to_device=True):
return super(InputOptions, cls).__new__(cls,
experimental_prefetch_to_device)
# ------------------------------------------------------------------------------
# Base classes for all distribution strategies.
# Base class for v1 Strategy and v2 Strategy classes. For API's specific to
# v1/v2 Strategy, add to implementing classes of StrategyBase.
# pylint: disable=line-too-long
class StrategyBase(object):
"""A state & compute distribution policy on a list of devices.
See [the guide](https://www.tensorflow.org/guide/distributed_training)
for overview and examples. See `tf.distribute.StrategyExtended` and
[`tf.distribute`](https://www.tensorflow.org/api_docs/python/tf/distribute)
for a glossory of concepts mentioned on this page such as "per-replica",
_replica_, and _reduce_.
In short:
* To use it with Keras `compile`/`fit`,
[please
read](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_keras).
* You may pass descendant of `tf.distribute.Strategy` to
`tf.estimator.RunConfig` to specify how a `tf.estimator.Estimator`
should distribute its computation. See
[guide](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_estimator_limited_support).
* Otherwise, use `tf.distribute.Strategy.scope` to specify that a
strategy should be used when building an executing your model.
(This puts you in the "cross-replica context" for this strategy, which
means the strategy is put in control of things like variable placement.)
* If you are writing a custom training loop, you will need to call a few more
methods,
[see the
guide](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_custom_training_loops):
* Start by either creating a `tf.data.Dataset` normally or using
`tf.distribute.experimental_make_numpy_dataset` to make a dataset out of
a `numpy` array.
* Use `tf.distribute.Strategy.experimental_distribute_dataset` to convert
a `tf.data.Dataset` to something that produces "per-replica" values.
If you want to manually specify how the dataset should be partitioned
across replicas, use
`tf.distribute.Strategy.experimental_distribute_datasets_from_function`
instead.
* Use `tf.distribute.Strategy.run` to run a function
once per replica, taking values that may be "per-replica" (e.g.
from a `tf.distribute.DistributedDataset` object) and returning
"per-replica" values.
This function is executed in "replica context", which means each
operation is performed separately on each replica.
* Finally use a method (such as `tf.distribute.Strategy.reduce`) to
convert the resulting "per-replica" values into ordinary `Tensor`s.
A custom training loop can be as simple as:
```
with my_strategy.scope():
@tf.function
def distribute_train_epoch(dataset):
def replica_fn(input):
# process input and return result
return result
total_result = 0
for x in dataset:
per_replica_result = my_strategy.run(replica_fn, args=(x,))
total_result += my_strategy.reduce(tf.distribute.ReduceOp.SUM,
per_replica_result, axis=None)
return total_result
dist_dataset = my_strategy.experimental_distribute_dataset(dataset)
for _ in range(EPOCHS):
train_result = distribute_train_epoch(dist_dataset)
```
This takes an ordinary `dataset` and `replica_fn` and runs it
distributed using a particular `tf.distribute.Strategy` named
`my_strategy` above. Any variables created in `replica_fn` are created
using `my_strategy`'s policy, and library functions called by
`replica_fn` can use the `get_replica_context()` API to implement
distributed-specific behavior.
You can use the `reduce` API to aggregate results across replicas and use
this as a return value from one iteration over a
`tf.distribute.DistributedDataset`. Or
you can use `tf.keras.metrics` (such as loss, accuracy, etc.) to
accumulate metrics across steps in a given epoch.
See the
[custom training loop
tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training)
for a more detailed example.
Note: `tf.distribute.Strategy` currently does not support TensorFlow's
partitioned variables (where a single variable is split across multiple
devices) at this time.
"""
# pylint: enable=line-too-long
# TODO(josh11b): Partitioned computations, state; sharding
# TODO(josh11b): Model parallelism: "replicas" with multiple devices; shuffling
def __init__(self, extended):
self._extended = extended
# Flag that is used to indicate whether distribution strategy is used with
# Estimator. This is required for backward compatibility of loss scaling
# when using v1 optimizer with estimator.
self._scale_loss_for_estimator = False
if not hasattr(extended, "_retrace_functions_for_each_device"):
# pylint: disable=protected-access
# `extended._retrace_functions_for_each_device` dictates
# whether the same function will be retraced when it is called on
# different devices.
try:
extended._retrace_functions_for_each_device = (
len(extended.worker_devices) > 1)
distribution_strategy_replica_gauge.get_cell("num_replicas").set(
self.num_replicas_in_sync)
except: # pylint: disable=bare-except
# Default for the case where extended.worker_devices can't return
# a sensible value.
extended._retrace_functions_for_each_device = True
# Below are the dicts of axis(int) -> `tf.function`.
self._mean_reduce_helper_fns = {}
self._reduce_sum_fns = {}
@property
def extended(self):
"""`tf.distribute.StrategyExtended` with additional methods."""
return self._extended
@tf_contextlib.contextmanager
def _scale_loss_for_estimator_enabled(self):
"""Scope which sets a flag used for scaling losses in optimizer.
Yields:
`_scale_loss_for_estimator_enabled` is a context manager with a
side effect, but doesn't return a value.
"""
self._scale_loss_for_estimator = True
try:
yield
finally:
self._scale_loss_for_estimator = False
# pylint: disable=line-too-long
def scope(self):
"""Context manager to make the strategy current and distribute variables.
This method returns a context manager, and is used as follows:
>>> strategy = tf.distribute.MirroredStrategy()
>>> # Variable created inside scope:
>>> with strategy.scope():
... mirrored_variable = tf.Variable(1.)
>>> mirrored_variable
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
}
>>> # Variable created outside scope:
>>> regular_variable = tf.Variable(1.)
>>> regular_variable
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
_What happens when Strategy.scope is entered?_
* `strategy` is installed in the global context as the "current" strategy.
Inside this scope, `tf.distribute.get_strategy()` will now return this
strategy. Outside this scope, it returns the default no-op strategy.
* Entering the scope also enters the "cross-replica context". See
`tf.distribute.StrategyExtended` for an explanation on cross-replica and
replica contexts.
* Variable creation inside `scope` is intercepted by the strategy. Each
strategy defines how it wants to affect the variable creation. Sync
strategies like `MirroredStrategy`, `TPUStrategy` and
`MultiWorkerMiroredStrategy` create variables replicated on each replica,
whereas `ParameterServerStrategy` creates variables on the parameter
servers. This is done using a custom `tf.variable_creator_scope`.
* In some strategies, a default device scope may also be entered: in
`MultiWorkerMiroredStrategy`, a default device scope of "/CPU:0" is
entered on each worker.
Note: Entering a scope does not automatically distribute a computation, except
in the case of high level training framework like keras `model.fit`. If
you're not using `model.fit`, you
need to use `strategy.run` API to explicitly distribute that computation.
See an example in the [custom training loop tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training).
_What should be in scope and what should be outside?_
There are a number of requirements on what needs to happen inside the scope.
However, in places where we have information about which strategy is in use,
we often enter the scope for the user, so they don't have to do it
explicitly (i.e. calling those either inside or outside the scope is OK).
* Anything that creates variables that should be distributed variables
must be in `strategy.scope`. This can be either by directly putting it in
scope, or relying on another API like `strategy.run` or `model.fit` to
enter it for you. Any variable that is created outside scope will not be
distributed and may have performance implications. Common things that
create variables in TF: models, optimizers, metrics. These should always
be created inside the scope. Another source of variable creation can be
a checkpoint restore - when variables are created lazily. Note that any
variable created inside a strategy captures the strategy information. So
reading and writing to these variables outside the `strategy.scope` can
also work seamlessly, without the user having to enter the scope.
* Some strategy APIs (such as `strategy.run` and `strategy.reduce`) which
require to be in a strategy's scope, enter the scope for you
automatically, which means when using those APIs you don't need to
enter the scope yourself.
* When a `tf.keras.Model` is created inside a `strategy.scope`, we capture
this information. When high level training frameworks methods such as
`model.compile`, `model.fit` etc are then called
on this model, we automatically enter the scope, as well as use this
strategy to distribute the training etc. See
detailed example in [distributed keras tutorial](https://www.tensorflow.org/tutorials/distribute/keras).
Note that simply calling the `model(..)` is not impacted - only high
level training framework APIs are. `model.compile`, `model.fit`,
`model.evaluate`, `model.predict` and `model.save` can all be called
inside or outside the scope.
* The following can be either inside or outside the scope:
** Creating the input datasets
** Defining `tf.function`s that represent your training step
** Saving APIs such as `tf.saved_model.save`. Loading creates variables,
so that should go inside the scope if you want to train the model in a
distributed way.
** Checkpoint saving. As mentioned above - `checkpoint.restore` may
sometimes need to be inside scope if it creates variables.
Returns:
A context manager.
"""
return self._extended._scope(self) # pylint: disable=protected-access
# pylint: enable=line-too-long
@doc_controls.do_not_doc_inheritable # DEPRECATED, moving to `extended`
def colocate_vars_with(self, colocate_with_variable):
"""DEPRECATED: use extended.colocate_vars_with() instead."""
return self._extended.colocate_vars_with(colocate_with_variable)
@doc_controls.do_not_generate_docs # DEPRECATED: TF 1.x only
def make_dataset_iterator(self, dataset):
"""DEPRECATED TF 1.x ONLY."""
return self._extended._make_dataset_iterator(dataset) # pylint: disable=protected-access
@doc_controls.do_not_generate_docs # DEPRECATED: TF 1.x only
def make_input_fn_iterator(self,
input_fn,
replication_mode=InputReplicationMode.PER_WORKER):
"""DEPRECATED TF 1.x ONLY."""
if replication_mode != InputReplicationMode.PER_WORKER:
raise ValueError(
"Input replication mode not supported: %r" % replication_mode)
with self.scope():
return self.extended._make_input_fn_iterator( # pylint: disable=protected-access
input_fn, replication_mode=replication_mode)
@deprecation.deprecated(
"2020-09-30", "Please use tf.data.Dataset.from_tensor_slices instead")
def experimental_make_numpy_dataset(self, numpy_input):
"""Makes a `tf.data.Dataset` from a numpy array.
This avoids adding `numpy_input` as a large constant in the graph,
and copies the data to the machine or machines that will be processing
the input.
Note that you will likely need to use `experimental_distribute_dataset`
with the returned dataset to further distribute it with the strategy.
Example:
>>> strategy = tf.distribute.MirroredStrategy()
>>> numpy_input = np.ones([10], dtype=np.float32)
>>> dataset = strategy.experimental_make_numpy_dataset(numpy_input)
>>> dataset
<TensorSliceDataset shapes: (), types: tf.float32>
>>> dataset = dataset.batch(2)
>>> dist_dataset = strategy.experimental_distribute_dataset(dataset)
Args:
numpy_input: a nest of NumPy input arrays that will be converted into a
dataset. Note that the NumPy arrays are stacked, as that is normal
`tf.data.Dataset` behavior.
Returns:
A `tf.data.Dataset` representing `numpy_input`.
"""
return self.extended.experimental_make_numpy_dataset(
numpy_input, session=None)
@doc_controls.do_not_generate_docs # DEPRECATED: TF 1.x only
def experimental_run(self, fn, input_iterator=None):
"""DEPRECATED TF 1.x ONLY."""
with self.scope():
args = (input_iterator.get_next(),) if input_iterator is not None else ()
return self.run(fn, args=args)
def experimental_distribute_dataset(self, dataset, options=None):
"""Creates `tf.distribute.DistributedDataset` from `tf.data.Dataset`.
The returned `tf.distribute.DistributedDataset` can be iterated over
similar to how regular datasets can.
NOTE: The user cannot add any more transformations to a
`tf.distribute.DistributedDataset`.
The following is an example:
```python
strategy = tf.distribute.MirroredStrategy()
# Create a dataset
dataset = dataset_ops.Dataset.TFRecordDataset([
"/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"])
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)
# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
# process dataset elements
strategy.run(replica_fn, args=(x,))
```
In the code snippet above, the `tf.distribute.DistributedDataset`
`dist_dataset` is batched by `GLOBAL_BATCH_SIZE`, and we iterate through it
using `for x in dist_dataset`. `x` a `tf.distribute.DistributedValues`
containing data for all replicas, which aggregates to a batch of
`GLOBAL_BATCH_SIZE`. `tf.distribute.Strategy.run` will take care of feeding
the right per-replica data in `x` to the right `replica_fn` executed on each
replica.
What's under the hood of this method, when we say the `tf.data.Dataset`
instance - `dataset` - gets distributed? It depends on how you set the
`tf.data.experimental.AutoShardPolicy` through
`tf.data.experimental.DistributeOptions`. By default, it is set to
`tf.data.experimental.AutoShardPolicy.AUTO`. In a multi-worker setting, we
will first attempt to distribute `dataset` by detecting whether `dataset` is
being created out of reader datasets (e.g. `tf.data.TFRecordDataset`,
`tf.data.TextLineDataset`, etc.) and if so, try to shard the input files.
Note that there has to be at least one input file per worker. If you have
less than one input file per worker, we suggest that you disable dataset
sharding across workers, by setting the
`tf.data.experimental.DistributeOptions.auto_shard_policy` to be
`tf.data.experimental.AutoShardPolicy.OFF`.
If the attempt to shard by file is unsuccessful (i.e. the dataset is not
read from files), we will shard the dataset evenly at the end by
appending a `.shard` operation to the end of the processing pipeline. This
will cause the entire preprocessing pipeline for all the data to be run on
every worker, and each worker will do redundant work. We will print a
warning if this route is selected.
As mentioned before, within each worker, we will also split the data among
all the worker devices (if more than one a present). This will happen
even if multi-worker sharding is disabled.
If the above batch splitting and dataset sharding logic is undesirable,
please use
`tf.distribute.Strategy.experimental_distribute_datasets_from_function`
instead, which does not do any automatic splitting or sharding.
You can also use the `element_spec` property of the
`tf.distribute.DistributedDataset` instance returned by this API to query
the `tf.TypeSpec` of the elements returned
by the iterator. This can be used to set the `input_signature` property
of a `tf.function`.
```python
strategy = tf.distribute.MirroredStrategy()
# Create a dataset
dataset = dataset_ops.Dataset.TFRecordDataset([
"/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"])
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)
@tf.function(input_signature=[dist_dataset.element_spec])
def train_step(inputs):
# train model with inputs
return
# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
# process dataset elements
strategy.run(train_step, args=(x,))
```
Note: The order in which the data is processed by the workers when using
`tf.distribute.Strategy.experimental_distribute_dataset` or
`tf.distribute.Strategy.experimental_distribute_datasets_from_function` is
not guaranteed. This is typically required if you are using
`tf.distribute` to scale prediction. You can however insert an index for
each element in the batch and order outputs accordingly. Refer to [this
snippet](https://www.tensorflow.org/tutorials/distribute/input#caveats)
for an example of how to order outputs.
Args:
dataset: `tf.data.Dataset` that will be sharded across all replicas using
the rules stated above.
options: `tf.distribute.InputOptions` used to control options on how this
dataset is distributed.
Returns:
A `tf.distribute.DistributedDataset`.
"""
return self._extended._experimental_distribute_dataset(dataset, options) # pylint: disable=protected-access
def experimental_distribute_datasets_from_function(self, dataset_fn,
options=None):
"""Distributes `tf.data.Dataset` instances created by calls to `dataset_fn`.
`dataset_fn` will be called once for each worker in the strategy. Each
replica on that worker will dequeue one batch of inputs from the local
`Dataset` (i.e. if a worker has two replicas, two batches will be dequeued
from the `Dataset` every step).
This method can be used for several purposes. For example, where
`experimental_distribute_dataset` is unable to shard the input files, this
method might be used to manually shard the dataset (avoiding the slow
fallback behavior in `experimental_distribute_dataset`). In cases where the
dataset is infinite, this sharding can be done by creating dataset replicas
that differ only in their random seed.
`experimental_distribute_dataset` may also sometimes fail to split the
batch across replicas on a worker. In that case, this method can be used
where that limitation does not exist.
The `dataset_fn` should take an `tf.distribute.InputContext` instance where
information about batching and input replication can be accessed.
You can also use the `element_spec` property of the
`tf.distribute.DistributedDataset` returned by this API to query the
`tf.TypeSpec` of the elements returned by the iterator. This can be used to
set the `input_signature` property of a `tf.function`.
>>> global_batch_size = 8
>>> def dataset_fn(input_context):
... batch_size = input_context.get_per_replica_batch_size(
... global_batch_size)
... d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size)
... return d.shard(
... input_context.num_input_pipelines,
... input_context.input_pipeline_id)
>>> strategy = tf.distribute.MirroredStrategy()
>>> ds = strategy.experimental_distribute_datasets_from_function(dataset_fn)
>>> def train(ds):
... @tf.function(input_signature=[ds.element_spec])
... def step_fn(inputs):
... # train the model with inputs
... return inputs
... for batch in ds:
... replica_results = strategy.run(replica_fn, args=(batch,))
>>> train(ds)
IMPORTANT: The `tf.data.Dataset` returned by `dataset_fn` should have a
per-replica batch size, unlike `experimental_distribute_dataset`, which uses
the global batch size. This may be computed using
`input_context.get_per_replica_batch_size`.
Note: The order in which the data is processed by the workers when using
`tf.distribute.Strategy.experimental_distribute_dataset` or
`tf.distribute.Strategy.experimental_distribute_datasets_from_function` is
not guaranteed. This is typically required if you are using
`tf.distribute` to scale prediction. You can however insert an index for
each element in the batch and order outputs accordingly. Refer to [this
snippet](https://www.tensorflow.org/tutorials/distribute/input#caveats)
for an example of how to order outputs.
Args:
dataset_fn: A function taking a `tf.distribute.InputContext` instance and
returning a `tf.data.Dataset`.
options: `tf.distribute.InputOptions` used to control options on how this
dataset is distributed.
Returns:
A `tf.distribute.DistributedDataset`.
"""
return self._extended._experimental_distribute_datasets_from_function( # pylint: disable=protected-access
dataset_fn, options)
def run(self, fn, args=(), kwargs=None, options=None):
"""Run `fn` on each replica, with the given arguments.
Executes ops specified by `fn` on each replica. If `args` or `kwargs` have
`tf.distribute.DistributedValues`, such as those produced by a
`tf.distribute.DistributedDataset` from
`tf.distribute.Strategy.experimental_distribute_dataset` or
`tf.distribute.Strategy.experimental_distribute_datasets_from_function`,
when `fn` is executed on a particular replica, it will be executed with the
component of `tf.distribute.DistributedValues` that correspond to that
replica.
`fn` may call `tf.distribute.get_replica_context()` to access members such
as `all_reduce`.
All arguments in `args` or `kwargs` should either be nest of tensors or
`tf.distribute.DistributedValues` containing tensors or composite tensors.
IMPORTANT: Depending on the implementation of `tf.distribute.Strategy` and
whether eager execution is enabled, `fn` may be called one or more times. If
`fn` is annotated with `tf.function` or `tf.distribute.Strategy.run` is
called inside a `tf.function`, eager execution is disabled and `fn` is
called once (or once per replica, if you are using MirroredStrategy) to
generate a Tensorflow graph, which will then be reused for execution with
new inputs. Otherwise, if eager execution is enabled, `fn` will be called
every step just like regular python code.
Example usage:
1. Constant tensor input.
>>> strategy = tf.distribute.MirroredStrategy()
>>> tensor_input = tf.constant(3.0)
>>> @tf.function
... def replica_fn(input):
... return input*2.0
>>> result = strategy.run(replica_fn, args=(tensor_input,))
>>> result
<tf.Tensor: shape=(), dtype=float32, numpy=6.0>
2. DistributedValues input.
>>> strategy = tf.distribute.MirroredStrategy()
>>> @tf.function
... def run():
... def value_fn(value_context):
... return value_context.num_replicas_in_sync
... distributed_values = (
... strategy.experimental_distribute_values_from_function(
... value_fn))
... def replica_fn2(input):
... return input*2
... return strategy.run(replica_fn2, args=(distributed_values,))
>>> result = run()
>>> result
<tf.Tensor: shape=(), dtype=int32, numpy=2>
Args:
fn: The function to run. The output must be a `tf.nest` of `Tensor`s.
args: (Optional) Positional arguments to `fn`.
kwargs: (Optional) Keyword arguments to `fn`.
options: (Optional) An instance of `tf.distribute.RunOptions` specifying
the options to run `fn`.
Returns:
Merged return value of `fn` across replicas. The structure of the return
value is the same as the return value from `fn`. Each element in the
structure can either be `tf.distribute.DistributedValues`, `Tensor`
objects, or `Tensor`s (for example, if running on a single replica).
"""
del options
if not isinstance(args, (list, tuple)):
raise ValueError(
"positional args must be a list or tuple, got {}".format(type(args)))
with self.scope():
# tf.distribute supports Eager functions, so AutoGraph should not be
# applied when when the caller is also in Eager mode.
fn = autograph.tf_convert(
fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
# TODO(b/151224785): Remove deprecated alias.
@doc_controls.do_not_doc_inheritable # DEPRECATED
@deprecation.deprecated(None, "renamed to `run`")
def experimental_run_v2(self, fn, args=(), kwargs=None, options=None):
return self.run(fn, args=args, kwargs=kwargs, options=options)
def reduce(self, reduce_op, value, axis):
"""Reduce `value` across replicas.
Given a per-replica value returned by `run`, say a
per-example loss, the batch will be divided across all the replicas. This
function allows you to aggregate across replicas and optionally also across
batch elements. For example, if you have a global batch size of 8 and 2
replicas, values for examples `[0, 1, 2, 3]` will be on replica 0 and
`[4, 5, 6, 7]` will be on replica 1. By default, `reduce` will just
aggregate across replicas, returning `[0+4, 1+5, 2+6, 3+7]`. This is useful
when each replica is computing a scalar or some other value that doesn't
have a "batch" dimension (like a gradient). More often you will want to
aggregate across the global batch, which you can get by specifying the batch
dimension as the `axis`, typically `axis=0`. In this case it would return a
scalar `0+1+2+3+4+5+6+7`.
If there is a last partial batch, you will need to specify an axis so
that the resulting shape is consistent across replicas. So if the last
batch has size 6 and it is divided into [0, 1, 2, 3] and [4, 5], you
would get a shape mismatch unless you specify `axis=0`. If you specify
`tf.distribute.ReduceOp.MEAN`, using `axis=0` will use the correct
denominator of 6. Contrast this with computing `reduce_mean` to get a
scalar value on each replica and this function to average those means,
which will weigh some values `1/8` and others `1/4`.
Args:
reduce_op: A `tf.distribute.ReduceOp` value specifying how values should
be combined.
value: A "per replica" value, e.g. returned by `run` to
be combined into a single tensor.
axis: Specifies the dimension to reduce along within each
replica's tensor. Should typically be set to the batch dimension, or
`None` to only reduce across replicas (e.g. if the tensor has no batch
dimension).
Returns:
A `Tensor`.
"""
# TODO(josh11b): support `value` being a nest.
_require_cross_replica_or_default_context_extended(self._extended)
if isinstance(reduce_op, six.string_types):
reduce_op = reduce_util.ReduceOp(reduce_op.upper())
if axis is None:
return self._extended._reduce(reduce_op, value) # pylint: disable=protected-access
if reduce_op == reduce_util.ReduceOp.SUM:
def reduce_sum(v):
return math_ops.reduce_sum(v, axis=axis)
if eager_context.executing_eagerly():
# As some strategies (e.g. TPUStrategy) doesn't support pure eager
# execution, wrap the `reduce_sum_fn` with a `tf.function` so it can be
# run from eager mode. Cache the tf.function by `axis` to avoid the
# same function to be traced again.
if axis not in self._reduce_sum_fns:
def reduce_sum_fn(v):
return self.run(reduce_sum, args=(v,))
self._reduce_sum_fns[axis] = def_function.function(reduce_sum_fn)
value = self._reduce_sum_fns[axis](value)
else:
value = self.run(reduce_sum, args=(value,))
return self._extended._reduce(reduce_op, value) # pylint: disable=protected-access
if reduce_op != reduce_util.ReduceOp.MEAN:
raise TypeError("Expected `reduce_op` to be a `tf.distribute.ReduceOp`, "
"not: %r" % reduce_op)
# TODO(josh11b): Support list/tuple and tensor axis values.
if not isinstance(axis, six.integer_types):
raise TypeError("Expected `axis` to be an integer not: %r" % axis)
def mean_reduce_helper(v, axis=axis):
"""Computes the numerator and denominator on each replica."""
numer = math_ops.reduce_sum(v, axis=axis)
if v.shape.rank is not None:
# Note(joshl): We support axis < 0 to be consistent with the
# tf.math.reduce_* operations.
if axis < 0:
if axis + v.shape.rank < 0:
raise ValueError(
"`axis` = %r out of range for `value` with rank %d" %
(axis, v.shape.rank))
axis += v.shape.rank
elif axis >= v.shape.rank:
raise ValueError(
"`axis` = %r out of range for `value` with rank %d" %
(axis, v.shape.rank))
# TF v2 returns `None` for unknown dimensions and an integer for
# known dimension, whereas TF v1 returns tensor_shape.Dimension(None)
# or tensor_shape.Dimension(integer). `dimension_value` hides this
# difference, always returning `None` or an integer.
dim = tensor_shape.dimension_value(v.shape[axis])
if dim is not None:
# By returning a python value in the static shape case, we can
# maybe get a fast path for reducing the denominator.
# TODO(b/151871486): Remove array_ops.identity after we fallback to
# simple reduction if inputs are all on CPU.
return numer, array_ops.identity(
constant_op.constant(dim, dtype=dtypes.int64))
elif axis < 0:
axis = axis + array_ops.rank(v)
# TODO(b/151871486): Remove array_ops.identity after we fallback to simple
# reduction if inputs are all on CPU.
denom = array_ops.identity(
array_ops.shape_v2(v, out_type=dtypes.int64)[axis])
# TODO(josh11b): Should we cast denom to v.dtype here instead of after the
# reduce is complete?
return numer, denom
if eager_context.executing_eagerly():
# As some strategies (e.g. TPUStrategy) doesn't support pure eager
# execution, wrap the `mean_reduce_helper` with a `tf.function` so it can
# be run from eager mode. Cache the tf.function by `axis` to avoid the
# same function to be traced again.
if axis not in self._mean_reduce_helper_fns:
def mean_reduce_fn(v):
return self.run(mean_reduce_helper, args=(v,))
self._mean_reduce_helper_fns[axis] = def_function.function(
mean_reduce_fn)
numer, denom = self._mean_reduce_helper_fns[axis](value)
else:
numer, denom = self.run(mean_reduce_helper, args=(value,))
# TODO(josh11b): Should batch reduce here instead of doing two.
numer = self._extended._reduce(reduce_util.ReduceOp.SUM, numer) # pylint: disable=protected-access
denom = self._extended._reduce(reduce_util.ReduceOp.SUM, denom) # pylint: disable=protected-access
denom = math_ops.cast(denom, numer.dtype)
return math_ops.truediv(numer, denom)
@doc_controls.do_not_doc_inheritable # DEPRECATED
def unwrap(self, value):
"""Returns the list of all local per-replica values contained in `value`.
DEPRECATED: Please use `experimental_local_results` instead.
Note: This only returns values on the workers initiated by this client.
When using a `tf.distribute.Strategy` like
`tf.distribute.experimental.MultiWorkerMirroredStrategy`, each worker
will be its own client, and this function will only return values
computed on that worker.
Args:
value: A value returned by `experimental_run()`,
`extended.call_for_each_replica()`, or a variable created in `scope`.
Returns:
A tuple of values contained in `value`. If `value` represents a single
value, this returns `(value,).`
"""
return self._extended._local_results(value) # pylint: disable=protected-access
def experimental_local_results(self, value):
"""Returns the list of all local per-replica values contained in `value`.
Note: This only returns values on the worker initiated by this client.
When using a `tf.distribute.Strategy` like
`tf.distribute.experimental.MultiWorkerMirroredStrategy`, each worker
will be its own client, and this function will only return values
computed on that worker.
Args:
value: A value returned by `experimental_run()`, `run()`,
`extended.call_for_each_replica()`, or a variable created in `scope`.
Returns:
A tuple of values contained in `value`. If `value` represents a single
value, this returns `(value,).`
"""
return self._extended._local_results(value) # pylint: disable=protected-access
@doc_controls.do_not_doc_inheritable # DEPRECATED: TF v1.x only
def group(self, value, name=None):
"""Shortcut for `tf.group(self.experimental_local_results(value))`."""
return self._extended._group(value, name) # pylint: disable=protected-access
@property
def num_replicas_in_sync(self):
"""Returns number of replicas over which gradients are aggregated."""
return self._extended._num_replicas_in_sync # pylint: disable=protected-access
@doc_controls.do_not_doc_inheritable # DEPRECATED: see doc string
def configure(self,
session_config=None,
cluster_spec=None,
task_type=None,
task_id=None):
# pylint: disable=g-doc-return-or-yield,g-doc-args
"""DEPRECATED: use `update_config_proto` instead.
Configures the strategy class.
DEPRECATED: This method's functionality has been split into the strategy
constructor and `update_config_proto`. In the future, we will allow passing
cluster and config_proto to the constructor to configure the strategy. And
`update_config_proto` can be used to update the config_proto based on the
specific strategy.
"""
return self._extended._configure( # pylint: disable=protected-access
session_config, cluster_spec, task_type, task_id)
@doc_controls.do_not_generate_docs # DEPRECATED
def update_config_proto(self, config_proto):
"""DEPRECATED TF 1.x ONLY."""
return self._extended._update_config_proto(config_proto) # pylint: disable=protected-access
def __deepcopy__(self, memo):
# First do a regular deepcopy of `self`.
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = result
for k, v in self.__dict__.items():
setattr(result, k, copy.deepcopy(v, memo))
# One little fix-up: we want `result._extended` to reference `result`
# instead of `self`.
result._extended._container_strategy_weakref = weakref.ref(result) # pylint: disable=protected-access
return result
def __copy__(self):
raise RuntimeError("Must only deepcopy DistributionStrategy.")
@property
def cluster_resolver(self):
"""Returns the cluster resolver associated with this strategy.
In general, when using a multi-worker `tf.distribute` strategy such as
`tf.distribute.experimental.MultiWorkerMirroredStrategy` or
`tf.distribute.experimental.TPUStrategy()`, there is a
`tf.distribute.cluster_resolver.ClusterResolver` associated with the
strategy used, and such an instance is returned by this property.
Strategies that intend to have an associated
`tf.distribute.cluster_resolver.ClusterResolver` must set the
relevant attribute, or override this property; otherwise, `None` is returned
by default. Those strategies should also provide information regarding what
is returned by this property.
Single-worker strategies usually do not have a
`tf.distribute.cluster_resolver.ClusterResolver`, and in those cases this
property will return `None`.
The `tf.distribute.cluster_resolver.ClusterResolver` may be useful when the
user needs to access information such as the cluster spec, task type or task
id. For example,
```python
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["localhost:12345", "localhost:23456"],
'ps': ["localhost:34567"]
},
'task': {'type': 'worker', 'index': 0}
})
# This implicitly uses TF_CONFIG for the cluster and current task info.
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
...
if strategy.cluster_resolver.task_type == 'worker':
# Perform something that's only applicable on workers. Since we set this
# as a worker above, this block will run on this particular instance.
elif strategy.cluster_resolver.task_type == 'ps':
# Perform something that's only applicable on parameter servers. Since we
# set this as a worker above, this block will not run on this particular
# instance.
```
For more information, please see
`tf.distribute.cluster_resolver.ClusterResolver`'s API docstring.
Returns:
The cluster resolver associated with this strategy. Returns `None` if a
cluster resolver is not applicable or available in this strategy.
"""
if hasattr(self.extended, "_cluster_resolver"):
return self.extended._cluster_resolver # pylint: disable=protected-access
return None
@tf_export("distribute.Strategy", v1=[]) # pylint: disable=g-missing-docstring
class Strategy(StrategyBase):
__doc__ = StrategyBase.__doc__
def experimental_assign_to_logical_device(self, tensor, logical_device_id):
"""Adds annotation that `tensor` will be assigned to a logical device.
NOTE: This API is only supported in TPUStrategy for now.
This adds an annotation to `tensor` specifying that operations on
`tensor` will be invoked on logical core device id `logical_device_id`.
When model parallelism is used, the default behavior is that all ops
are placed on zero-th logical device.
```python
# Initializing TPU system with 2 logical devices and 4 replicas.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment = tf.tpu.experimental.DeviceAssignment.build(
topology,
computation_shape=[1, 1, 1, 2],
num_replicas=4)
strategy = tf.distribute.TPUStrategy(
resolver, experimental_device_assignment=device_assignment)
iterator = iter(inputs)
@tf.function()
def step_fn(inputs):
output = tf.add(inputs, inputs)
# Add operation will be executed on logical device 0.
output = strategy.experimental_assign_to_logical_device(output, 0)
return output
strategy.run(step_fn, args=(next(iterator),))
```
Args:
tensor: Input tensor to annotate.
logical_device_id: Id of the logical core to which the tensor will be
assigned.
Raises:
ValueError: The logical device id presented is not consistent with total
number of partitions specified by the device assignment.
Returns:
Annotated tensor with idential value as `tensor`.
"""
return self._extended._experimental_assign_to_logical_device( # pylint: disable=protected-access
tensor, logical_device_id)
def experimental_split_to_logical_devices(self, tensor, partition_dimensions):
"""Adds annotation that `tensor` will be split across logical devices.
NOTE: This API is only supported in TPUStrategy for now.
This adds an annotation to tensor `tensor` specifying that operations on
`tensor` will be be split among multiple logical devices. Tensor `tensor`
will be split across dimensions specified by `partition_dimensions`.
The dimensions of `tensor` must be divisible by corresponding value in
`partition_dimensions`.
For example, for system with 8 logical devices, if `tensor` is an image
tensor with shape (batch_size, width, height, channel) and
`partition_dimensions` is [1, 2, 4, 1], then `tensor` will be split
2 in width dimension and 4 way in height dimension and the split
tensor values will be fed into 8 logical devices.
```python
# Initializing TPU system with 8 logical devices and 1 replica.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment = tf.tpu.experimental.DeviceAssignment.build(
topology,
computation_shape=[1, 2, 2, 2],
num_replicas=1)
strategy = tf.distribute.TPUStrategy(
resolver, experimental_device_assignment=device_assignment)
iterator = iter(inputs)
@tf.function()
def step_fn(inputs):
inputs = strategy.experimental_split_to_logical_devices(
inputs, [1, 2, 4, 1])
# model() function will be executed on 8 logical devices with `inputs`
# split 2 * 4 ways.
output = model(inputs)
return output
strategy.run(step_fn, args=(next(iterator),))
```
Args:
tensor: Input tensor to annotate.
partition_dimensions: An unnested list of integers with the size equal to
rank of `tensor` specifying how `tensor` will be partitioned. The
product of all elements in `partition_dimensions` must be equal to the
total number of logical devices per replica.
Raises:
ValueError: 1) If the size of partition_dimensions does not equal to rank
of `tensor` or 2) if product of elements of `partition_dimensions` does
not match the number of logical devices per replica defined by the
implementing DistributionStrategy's device specification or
3) if a known size of `tensor` is not divisible by corresponding
value in `partition_dimensions`.
Returns:
Annotated tensor with idential value as `tensor`.
"""
return self._extended._experimental_split_to_logical_devices( # pylint: disable=protected-access
tensor, partition_dimensions)
def experimental_replicate_to_logical_devices(self, tensor):
"""Adds annotation that `tensor` will be replicated to all logical devices.
NOTE: This API is only supported in TPUStrategy for now.
This adds an annotation to tensor `tensor` specifying that operations on
`tensor` will be invoked on all logical devices.
```python
# Initializing TPU system with 2 logical devices and 4 replicas.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment = tf.tpu.experimental.DeviceAssignment.build(
topology,
computation_shape=[1, 1, 1, 2],
num_replicas=4)
strategy = tf.distribute.TPUStrategy(
resolver, experimental_device_assignment=device_assignment)
iterator = iter(inputs)
@tf.function()
def step_fn(inputs):
images, labels = inputs
images = strategy.experimental_split_to_logical_devices(
inputs, [1, 2, 4, 1])
# model() function will be executed on 8 logical devices with `inputs`
# split 2 * 4 ways.
output = model(inputs)
# For loss calculation, all logical devices share the same logits
# and labels.
labels = strategy.experimental_replicate_to_logical_devices(labels)
output = strategy.experimental_replicate_to_logical_devices(output)
loss = loss_fn(labels, output)
return loss
strategy.run(step_fn, args=(next(iterator),))
```
Args:
tensor: Input tensor to annotate.
Returns:
Annotated tensor with idential value as `tensor`.
"""
return self._extended._experimental_replicate_to_logical_devices(tensor) # pylint: disable=protected-access
def experimental_distribute_values_from_function(self, value_fn):
"""Generates `tf.distribute.DistributedValues` from `value_fn`.
This function is to generate `tf.distribute.DistributedValues` to pass
into `run`, `reduce`, or other methods that take
distributed values when not using datasets.
Args:
value_fn: The function to run to generate values. It is called for
each replica with `tf.distribute.ValueContext` as the sole argument. It
must return a Tensor or a type that can be converted to a Tensor.
Returns:
A `tf.distribute.DistributedValues` containing a value for each replica.
Example usage:
1. Return constant value per replica:
>>> strategy = tf.distribute.MirroredStrategy()
>>> def value_fn(ctx):
... return tf.constant(1.)
>>> distributed_values = (
... strategy.experimental_distribute_values_from_function(
... value_fn))
>>> local_result = strategy.experimental_local_results(distributed_values)
>>> local_result
(<tf.Tensor: shape=(), dtype=float32, numpy=1.0>,)
2. Distribute values in array based on replica_id:
>>> strategy = tf.distribute.MirroredStrategy()
>>> array_value = np.array([3., 2., 1.])
>>> def value_fn(ctx):
... return array_value[ctx.replica_id_in_sync_group]
>>> distributed_values = (
... strategy.experimental_distribute_values_from_function(
... value_fn))
>>> local_result = strategy.experimental_local_results(distributed_values)
>>> local_result
(3.0,)
3. Specify values using num_replicas_in_sync:
>>> strategy = tf.distribute.MirroredStrategy()
>>> def value_fn(ctx):
... return ctx.num_replicas_in_sync
>>> distributed_values = (
... strategy.experimental_distribute_values_from_function(
... value_fn))
>>> local_result = strategy.experimental_local_results(distributed_values)
>>> local_result
(1,)
4. Place values on devices and distribute:
```
strategy = tf.distribute.TPUStrategy()
worker_devices = strategy.extended.worker_devices
multiple_values = []
for i in range(strategy.num_replicas_in_sync):
with tf.device(worker_devices[i]):
multiple_values.append(tf.constant(1.0))
def value_fn(ctx):
return multiple_values[ctx.replica_id_in_sync_group]
distributed_values = strategy.
experimental_distribute_values_from_function(
value_fn)
```
"""
return self._extended._experimental_distribute_values_from_function( # pylint: disable=protected-access
value_fn)
# TF v1.x version has additional deprecated APIs
@tf_export(v1=["distribute.Strategy"])
class StrategyV1(StrategyBase):
"""A list of devices with a state & compute distribution policy.
See [the guide](https://www.tensorflow.org/guide/distribute_strategy)
for overview and examples.
Note: Not all `tf.distribute.Strategy` implementations currently support
TensorFlow's partitioned variables (where a single variable is split across
multiple devices) at this time.
"""
def make_dataset_iterator(self, dataset):
"""Makes an iterator for input provided via `dataset`.
DEPRECATED: This method is not available in TF 2.x.
Data from the given dataset will be distributed evenly across all the
compute replicas. We will assume that the input dataset is batched by the
global batch size. With this assumption, we will make a best effort to
divide each batch across all the replicas (one or more workers).
If this effort fails, an error will be thrown, and the user should instead
use `make_input_fn_iterator` which provides more control to the user, and
does not try to divide a batch across replicas.
The user could also use `make_input_fn_iterator` if they want to
customize which input is fed to which replica/worker etc.
Args:
dataset: `tf.data.Dataset` that will be distributed evenly across all
replicas.
Returns:
An `tf.distribute.InputIterator` which returns inputs for each step of the
computation. User should call `initialize` on the returned iterator.
"""
return self._extended._make_dataset_iterator(dataset) # pylint: disable=protected-access
def make_input_fn_iterator(self, # pylint: disable=useless-super-delegation
input_fn,
replication_mode=InputReplicationMode.PER_WORKER):
"""Returns an iterator split across replicas created from an input function.
DEPRECATED: This method is not available in TF 2.x.
The `input_fn` should take an `tf.distribute.InputContext` object where
information about batching and input sharding can be accessed:
```
def input_fn(input_context):
batch_size = input_context.get_per_replica_batch_size(global_batch_size)
d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size)
return d.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
with strategy.scope():
iterator = strategy.make_input_fn_iterator(input_fn)
replica_results = strategy.experimental_run(replica_fn, iterator)
```
The `tf.data.Dataset` returned by `input_fn` should have a per-replica
batch size, which may be computed using
`input_context.get_per_replica_batch_size`.
Args:
input_fn: A function taking a `tf.distribute.InputContext` object and
returning a `tf.data.Dataset`.
replication_mode: an enum value of `tf.distribute.InputReplicationMode`.
Only `PER_WORKER` is supported currently, which means there will be
a single call to `input_fn` per worker. Replicas will dequeue from the
local `tf.data.Dataset` on their worker.
Returns:
An iterator object that should first be `.initialize()`-ed. It may then
either be passed to `strategy.experimental_run()` or you can
`iterator.get_next()` to get the next value to pass to
`strategy.extended.call_for_each_replica()`.
"""
return super(StrategyV1, self).make_input_fn_iterator(
input_fn, replication_mode)
def experimental_make_numpy_dataset(self, numpy_input, session=None):
"""Makes a tf.data.Dataset for input provided via a numpy array.
This avoids adding `numpy_input` as a large constant in the graph,
and copies the data to the machine or machines that will be processing
the input.
Note that you will likely need to use
tf.distribute.Strategy.experimental_distribute_dataset
with the returned dataset to further distribute it with the strategy.
Example:
```
numpy_input = np.ones([10], dtype=np.float32)
dataset = strategy.experimental_make_numpy_dataset(numpy_input)
dist_dataset = strategy.experimental_distribute_dataset(dataset)
```
Args:
numpy_input: A nest of NumPy input arrays that will be converted into a
dataset. Note that lists of Numpy arrays are stacked, as that is normal
`tf.data.Dataset` behavior.
session: (TensorFlow v1.x graph execution only) A session used for
initialization.
Returns:
A `tf.data.Dataset` representing `numpy_input`.
"""
return self.extended.experimental_make_numpy_dataset(
numpy_input, session=session)
def experimental_run(self, fn, input_iterator=None): # pylint: disable=useless-super-delegation
"""Runs ops in `fn` on each replica, with inputs from `input_iterator`.
DEPRECATED: This method is not available in TF 2.x. Please switch
to using `run` instead.
When eager execution is enabled, executes ops specified by `fn` on each
replica. Otherwise, builds a graph to execute the ops on each replica.
Each replica will take a single, different input from the inputs provided by
one `get_next` call on the input iterator.
`fn` may call `tf.distribute.get_replica_context()` to access members such
as `replica_id_in_sync_group`.
IMPORTANT: Depending on the `tf.distribute.Strategy` implementation being
used, and whether eager execution is enabled, `fn` may be called one or more
times (once for each replica).
Args:
fn: The function to run. The inputs to the function must match the outputs
of `input_iterator.get_next()`. The output must be a `tf.nest` of
`Tensor`s.
input_iterator: (Optional) input iterator from which the inputs are taken.
Returns:
Merged return value of `fn` across replicas. The structure of the return
value is the same as the return value from `fn`. Each element in the
structure can either be `PerReplica` (if the values are unsynchronized),
`Mirrored` (if the values are kept in sync), or `Tensor` (if running on a
single replica).
"""
return super(StrategyV1, self).experimental_run(
fn, input_iterator)
def reduce(self, reduce_op, value, axis=None):
return super(StrategyV1, self).reduce(reduce_op, value, axis)
reduce.__doc__ = StrategyBase.reduce.__doc__
def update_config_proto(self, config_proto):
"""Returns a copy of `config_proto` modified for use with this strategy.
DEPRECATED: This method is not available in TF 2.x.
The updated config has something needed to run a strategy, e.g.
configuration to run collective ops, or device filters to improve
distributed training performance.
Args:
config_proto: a `tf.ConfigProto` object.
Returns:
The updated copy of the `config_proto`.
"""
return self._extended._update_config_proto(config_proto) # pylint: disable=protected-access
# NOTE(josh11b): For any strategy that needs to support tf.compat.v1,
# instead descend from StrategyExtendedV1.
@tf_export("distribute.StrategyExtended", v1=[])
class StrategyExtendedV2(object):
"""Additional APIs for algorithms that need to be distribution-aware.
Note: For most usage of `tf.distribute.Strategy`, there should be no need to
call these methods, since TensorFlow libraries (such as optimizers) already
call these methods when needed on your behalf.
Some common use cases of functions on this page:
* _Locality_
`tf.distribute.DistributedValues` can have the same _locality_ as a
_distributed variable_, which leads to a mirrored value residing on the same
devices as the variable (as opposed to the compute devices). Such values may
be passed to a call to `tf.distribute.StrategyExtended.update` to update the
value of a variable. You may use
`tf.distribute.StrategyExtended.colocate_vars_with` to give a variable the
same locality as another variable. You may convert a "PerReplica" value to a
variable's locality by using `tf.distribute.StrategyExtended.reduce_to` or
`tf.distribute.StrategyExtended.batch_reduce_to`.
* _How to update a distributed variable_
A distributed variable is variables created on multiple devices. As discussed
in the [glossary](https://www.tensorflow.org/api_docs/python/tf/distribute),
mirrored variable and SyncOnRead variable are two examples. The standard
pattern for updating distributed variables is to:
1. In your function passed to `tf.distribute.Strategy.run`,
compute a list of (update, variable) pairs. For example, the update might
be a gradient of the loss with respect to the variable.
2. Switch to cross-replica mode by calling
`tf.distribute.get_replica_context().merge_call()` with the updates and
variables as arguments.
3. Call
`tf.distribute.StrategyExtended.reduce_to(VariableAggregation.SUM, t, v)`
(for one variable) or `tf.distribute.StrategyExtended.batch_reduce_to`
(for a list of variables) to sum the updates.
4. Call `tf.distribute.StrategyExtended.update(v)` for each variable to update
its value.
Steps 2 through 4 are done automatically by class
`tf.keras.optimizers.Optimizer` if you call its
`tf.keras.optimizers.Optimizer.apply_gradients` method in a replica context.
In fact, a higher-level solution to update a distributed variable is by
calling `assign` on the variable as you would do to a regular `tf.Variable`.
You can call the method in both _replica context_ and _cross-replica context_.
For a _mirrored variable_, calling `assign` in _replica context_ requires you
to specify the `aggregation` type in the variable constructor. In that case,
the context switching and sync described in steps 2 through 4 are handled for
you. If you call `assign` on _mirrored variable_ in _cross-replica context_,
you can only assign a single value or assign values from another mirrored
variable or a mirrored `tf.distribute.DistributedValues`. For a _SyncOnRead
variable_, in _replica context_, you can simply call `assign` on it and no
aggregation happens under the hood. In _cross-replica context_, you can only
assign a single value to a SyncOnRead variable. One example case is restoring
from a checkpoint: if the `aggregation` type of the variable is
`tf.VariableAggregation.SUM`, it is assumed that replica values were added
before checkpointing, so at the time of restoring, the value is divided by
the number of replicas and then assigned to each replica; if the `aggregation`
type is `tf.VariableAggregation.MEAN`, the value is assigned to each replica
directly.
"""
def __init__(self, container_strategy):
self._container_strategy_weakref = weakref.ref(container_strategy)
self._default_device = None
# This property is used to determine if we should set drop_remainder=True
# when creating Datasets from numpy array inputs.
self._require_static_shapes = False
def _container_strategy(self):
"""Get the containing `tf.distribute.Strategy`.
This should not generally be needed except when creating a new
`ReplicaContext` and to validate that the caller is in the correct
`scope()`.
Returns:
The `tf.distribute.Strategy` such that `strategy.extended` is `self`.
"""
container_strategy = self._container_strategy_weakref()
assert container_strategy is not None
return container_strategy
def _scope(self, strategy):
"""Implementation of tf.distribute.Strategy.scope()."""
def creator_with_resource_vars(next_creator, **kwargs):
"""Variable creator to use in `_CurrentDistributionContext`."""
_require_strategy_scope_extended(self)
kwargs["use_resource"] = True
kwargs["distribute_strategy"] = strategy
# Unwrap `initial_value` if it is a `CheckpointInitialValue` to avoid
# dereferencing a `Tensor` that is without a `name`. We still need to
# propagate the metadata it's holding.
if isinstance(kwargs["initial_value"], trackable.CheckpointInitialValue):
checkpoint_restore_uid = kwargs[
"initial_value"].checkpoint_position.restore_uid
kwargs["initial_value"] = kwargs["initial_value"].wrapped_value
else:
checkpoint_restore_uid = None
created = self._create_variable(next_creator, **kwargs)
if checkpoint_restore_uid is not None:
# pylint: disable=protected-access
# Let the checkpointing infrastructure know that the variable was
# already restored so it doesn't waste memory loading the value again.
created._maybe_initialize_trackable()
created._update_uid = checkpoint_restore_uid
# pylint: enable=protected-access
return created
def distributed_getter(getter, *args, **kwargs):
if not self._allow_variable_partition():
if kwargs.pop("partitioner", None) is not None:
tf_logging.log_first_n(
tf_logging.WARN, "Partitioned variables are disabled when using "
"current tf.distribute.Strategy.", 1)
return getter(*args, **kwargs)
return _CurrentDistributionContext(
strategy,
variable_scope.variable_creator_scope(creator_with_resource_vars),
variable_scope.variable_scope(
variable_scope.get_variable_scope(),
custom_getter=distributed_getter), self._default_device)
def _allow_variable_partition(self):
return False
def _create_variable(self, next_creator, **kwargs):
# Note: should support "colocate_with" argument.
raise NotImplementedError("must be implemented in descendants")
def variable_created_in_scope(self, v):
"""Tests whether `v` was created while this strategy scope was active.
Variables created inside the strategy scope are "owned" by it:
>>> strategy = tf.distribute.MirroredStrategy()
>>> with strategy.scope():
... v = tf.Variable(1.)
>>> strategy.extended.variable_created_in_scope(v)
True
Variables created outside the strategy are not owned by it:
>>> strategy = tf.distribute.MirroredStrategy()
>>> v = tf.Variable(1.)
>>> strategy.extended.variable_created_in_scope(v)
False
Args:
v: A `tf.Variable` instance.
Returns:
True if `v` was created inside the scope, False if not.
"""
return v._distribute_strategy == self._container_strategy_weakref() # pylint: disable=protected-access
def colocate_vars_with(self, colocate_with_variable):
"""Scope that controls which devices variables will be created on.
No operations should be added to the graph inside this scope, it
should only be used when creating variables (some implementations
work by changing variable creation, others work by using a
tf.compat.v1.colocate_with() scope).
This may only be used inside `self.scope()`.
Example usage:
```
with strategy.scope():
var1 = tf.Variable(...)
with strategy.extended.colocate_vars_with(var1):
# var2 and var3 will be created on the same device(s) as var1
var2 = tf.Variable(...)
var3 = tf.Variable(...)
def fn(v1, v2, v3):
# operates on v1 from var1, v2 from var2, and v3 from var3
# `fn` runs on every device `var1` is on, `var2` and `var3` will be there
# too.
strategy.extended.update(var1, fn, args=(var2, var3))
```
Args:
colocate_with_variable: A variable created in this strategy's `scope()`.
Variables created while in the returned context manager will be on the
same set of devices as `colocate_with_variable`.
Returns:
A context manager.
"""
def create_colocated_variable(next_creator, **kwargs):
_require_strategy_scope_extended(self)
kwargs["use_resource"] = True
kwargs["colocate_with"] = colocate_with_variable
return next_creator(**kwargs)
_require_strategy_scope_extended(self)
self._validate_colocate_with_variable(colocate_with_variable)
return variable_scope.variable_creator_scope(create_colocated_variable)
def _validate_colocate_with_variable(self, colocate_with_variable):
"""Validate `colocate_with_variable` argument to `colocate_vars_with`."""
pass
def _experimental_assign_to_logical_device(self, tensor, logical_device_id):
raise NotImplementedError("This method should be overriden by "
"sub-classes which support model parallelism.")
def _experimental_split_to_logical_devices(self, tensor,
partition_dimensions):
raise NotImplementedError("This method should be overriden by "
"sub-classes which support model parallelism.")
def _experimental_replicate_to_logical_devices(self, tensor):
raise NotImplementedError("This method should be overriden by "
"sub-classes which support model parallelism.")
def _make_dataset_iterator(self, dataset):
raise NotImplementedError("must be implemented in descendants")
def _make_input_fn_iterator(self, input_fn, replication_mode):
raise NotImplementedError("must be implemented in descendants")
def _experimental_distribute_dataset(self, dataset, options):
raise NotImplementedError("must be implemented in descendants")
def _experimental_distribute_datasets_from_function(self, dataset_fn,
options):
raise NotImplementedError("must be implemented in descendants")
def _experimental_distribute_values_from_function(self, value_fn):
raise NotImplementedError("must be implemented in descendants")
def _reduce(self, reduce_op, value):
# Default implementation until we have an implementation for each strategy.
dst = device_util.current() or self._default_device or "/device:CPU:0"
return self._local_results(self.reduce_to(reduce_op, value, dst))[0]
def reduce_to(self, reduce_op, value, destinations, experimental_hints=None):
"""Combine (via e.g. sum or mean) values across replicas.
Args:
reduce_op: Reduction type, an instance of `tf.distribute.ReduceOp` enum.
value: A per-replica value with one value per replica.
destinations: A mirrored variable, a per-replica tensor, or a device
string. The return value will be copied to all destination devices (or
all the devices where the `destinations` value resides). To perform an
all-reduction, pass `value` to `destinations`.
experimental_hints: A `tf.distrbute.experimental.CollectiveHints`. Hints
to perform collective operations.
Returns:
A tensor or value mirrored to `destinations`.
"""
# TODO(josh11b): More docstring
_require_cross_replica_or_default_context_extended(self)
assert not isinstance(destinations, (list, tuple))
assert not isinstance(reduce_op, variable_scope.VariableAggregation)
if isinstance(reduce_op, six.string_types):
reduce_op = reduce_util.ReduceOp(reduce_op.upper())
assert (reduce_op == reduce_util.ReduceOp.SUM or
reduce_op == reduce_util.ReduceOp.MEAN)
if experimental_hints is None:
experimental_hints = collective_util.Hints()
return self._reduce_to(reduce_op, value, destinations, experimental_hints)
def _reduce_to(self, reduce_op, value, destinations, experimental_hints):
raise NotImplementedError("must be implemented in descendants")
def batch_reduce_to(self,
reduce_op,
value_destination_pairs,
experimental_hints=None):
"""Combine multiple `reduce_to` calls into one for faster execution.
Args:
reduce_op: Reduction type, an instance of `tf.distribute.ReduceOp` enum.
value_destination_pairs: A sequence of (value, destinations) pairs. See
`reduce_to()` for a description.
experimental_hints: A `tf.distrbute.experimental.CollectiveHints`. Hints
to perform collective operations.
Returns:
A list of mirrored values, one per pair in `value_destination_pairs`.
"""
# TODO(josh11b): More docstring
_require_cross_replica_or_default_context_extended(self)
assert not isinstance(reduce_op, variable_scope.VariableAggregation)
if isinstance(reduce_op, six.string_types):
reduce_op = reduce_util.ReduceOp(reduce_op.upper())
if experimental_hints is None:
experimental_hints = collective_util.Hints()
return self._batch_reduce_to(reduce_op, value_destination_pairs,
experimental_hints)
def _batch_reduce_to(self, reduce_op, value_destination_pairs,
experimental_hints):
return [
self.reduce_to(
reduce_op, t, destinations=v, experimental_hints=experimental_hints)
for t, v in value_destination_pairs
]
def update(self, var, fn, args=(), kwargs=None, group=True):
"""Run `fn` to update `var` using inputs mirrored to the same devices.
`tf.distribute.StrategyExtended.update` takes a distributed variable `var`
to be updated, an update function `fn`, and `args` and `kwargs` for `fn`. It
applies `fn` to each component variable of `var` and passes corresponding
values from `args` and `kwargs`. Neither `args` nor `kwargs` may contain
per-replica values. If they contain mirrored values, they will be unwrapped
before calling `fn`. For example, `fn` can be `assign_add` and `args` can be
a mirrored DistributedValues where each component contains the value to be
added to this mirrored variable `var`. Calling `update` will call
`assign_add` on each component variable of `var` with the corresponding
tensor value on that device.
Example usage:
```python
strategy = tf.distribute.MirroredStrategy(['/gpu:0', '/gpu:1']) # With 2 devices
with strategy.scope():
v = tf.Variable(5.0, aggregation=tf.VariableAggregation.SUM)
def update_fn(v):
return v.assign(1.0)
result = strategy.extended.update(v, update_fn)
# result is
# Mirrored:{
# 0: tf.Tensor(1.0, shape=(), dtype=float32),
# 1: tf.Tensor(1.0, shape=(), dtype=float32)
# }
```
If `var` is mirrored across multiple devices, then this method implements
logic as following:
```python
results = {}
for device, v in var:
with tf.device(device):
# args and kwargs will be unwrapped if they are mirrored.
results[device] = fn(v, *args, **kwargs)
return merged(results)
```
Otherwise, this method returns `fn(var, *args, **kwargs)` colocated with
`var`.
Args:
var: Variable, possibly mirrored to multiple devices, to operate on.
fn: Function to call. Should take the variable as the first argument.
args: Tuple or list. Additional positional arguments to pass to `fn()`.
kwargs: Dict with keyword arguments to pass to `fn()`.
group: Boolean. Defaults to True. If False, the return value will be
unwrapped.
Returns:
By default, the merged return value of `fn` across all replicas. The
merged result has dependencies to make sure that if it is evaluated at
all, the side effects (updates) will happen on every replica. If instead
"group=False" is specified, this function will return a nest of lists
where each list has an element per replica, and the caller is responsible
for ensuring all elements are executed.
"""
_require_cross_replica_or_default_context_extended(self)
if kwargs is None:
kwargs = {}
fn = autograph.tf_convert(
fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
with self._container_strategy().scope():
return self._update(var, fn, args, kwargs, group)
def _update(self, var, fn, args, kwargs, group):
raise NotImplementedError("must be implemented in descendants")
@doc_controls.do_not_generate_docs
def update_non_slot(
self, colocate_with, fn, args=(), kwargs=None, group=True):
"""Runs `fn(*args, **kwargs)` on `colocate_with` devices.
Used to update non-slot variables.
Args:
colocate_with: Devices returned by `non_slot_devices()`.
fn: Function to execute.
args: Tuple or list. Positional arguments to pass to `fn()`.
kwargs: Dict with keyword arguments to pass to `fn()`.
group: Boolean. Defaults to True. If False, the return value will be
unwrapped.
Returns:
Return value of `fn`, possibly merged across devices.
"""
_require_cross_replica_or_default_context_extended(self)
if kwargs is None:
kwargs = {}
fn = autograph.tf_convert(
fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
with self._container_strategy().scope():
return self._update_non_slot(colocate_with, fn, args, kwargs, group)
def _update_non_slot(self, colocate_with, fn, args, kwargs, group):
raise NotImplementedError("must be implemented in descendants")
def _local_results(self, distributed_value):
raise NotImplementedError("must be implemented in descendants")
def value_container(self, value):
"""Returns the container that this per-replica `value` belongs to.
Args:
value: A value returned by `run()` or a variable created in `scope()`.
Returns:
A container that `value` belongs to.
If value does not belong to any container (including the case of
container having been destroyed), returns the value itself.
`value in experimental_local_results(value_container(value))` will
always be true.
"""
raise NotImplementedError("must be implemented in descendants")
def _group(self, value, name=None):
"""Implementation of `group`."""
value = nest.flatten(self._local_results(value))
if len(value) != 1 or name is not None:
return control_flow_ops.group(value, name=name)
# Special handling for the common case of one op.
v, = value
if hasattr(v, "op"):
v = v.op
return v
@property
def experimental_require_static_shapes(self):
"""Returns `True` if static shape is required; `False` otherwise."""
return self._require_static_shapes
@property
def _num_replicas_in_sync(self):
"""Returns number of replicas over which gradients are aggregated."""
raise NotImplementedError("must be implemented in descendants")
@property
def worker_devices(self):
"""Returns the tuple of all devices used to for compute replica execution.
"""
# TODO(josh11b): More docstring
raise NotImplementedError("must be implemented in descendants")
@property
def parameter_devices(self):
"""Returns the tuple of all devices used to place variables."""
# TODO(josh11b): More docstring
raise NotImplementedError("must be implemented in descendants")
@doc_controls.do_not_generate_docs
def non_slot_devices(self, var_list):
"""Device(s) for non-slot variables.
This method returns non-slot devices where non-slot variables are placed.
Users can create non-slot variables on these devices by using a block:
```python
with tf.distribute.StrategyExtended.colocate_vars_with(tf.distribute.StrategyExtended.non_slot_devices(...)):
...
```
Args:
var_list: The list of variables being optimized, needed with the
default `tf.distribute.Strategy`.
Returns:
A sequence of devices for non-slot variables.
"""
raise NotImplementedError("must be implemented in descendants")
def _configure(self,
session_config=None,
cluster_spec=None,
task_type=None,
task_id=None):
"""Configures the strategy class."""
del session_config, cluster_spec, task_type, task_id
def _update_config_proto(self, config_proto):
return copy.deepcopy(config_proto)
def _in_multi_worker_mode(self):
"""Whether this strategy indicates working in multi-worker settings.
Multi-worker training refers to the setup where the training is
distributed across multiple workers, as opposed to the case where
only a local process performs the training. This function is
used by higher-level apis such as Keras' `model.fit()` to infer
for example whether or not a distribute coordinator should be run,
and thus TensorFlow servers should be started for communication
with other servers in the cluster, or whether or not saving/restoring
checkpoints is relevant for preemption fault tolerance.
Subclasses should override this to provide whether the strategy is
currently in multi-worker setup.
Experimental. Signature and implementation are subject to change.
"""
raise NotImplementedError("must be implemented in descendants")
@tf_export(v1=["distribute.StrategyExtended"]) # pylint: disable=missing-docstring
class StrategyExtendedV1(StrategyExtendedV2):
__doc__ = StrategyExtendedV2.__doc__
def experimental_make_numpy_dataset(self, numpy_input, session=None):
"""Makes a dataset for input provided via a numpy array.
This avoids adding `numpy_input` as a large constant in the graph,
and copies the data to the machine or machines that will be processing
the input.
Args:
numpy_input: A nest of NumPy input arrays that will be distributed evenly
across all replicas. Note that lists of Numpy arrays are stacked, as
that is normal `tf.data.Dataset` behavior.
session: (TensorFlow v1.x graph execution only) A session used for
initialization.
Returns:
A `tf.data.Dataset` representing `numpy_input`.
"""
_require_cross_replica_or_default_context_extended(self)
return self._experimental_make_numpy_dataset(numpy_input, session=session)
def _experimental_make_numpy_dataset(self, numpy_input, session):
raise NotImplementedError("must be implemented in descendants")
def broadcast_to(self, tensor, destinations):
"""Mirror a tensor on one device to all worker devices.
Args:
tensor: A Tensor value to broadcast.
destinations: A mirrored variable or device string specifying the
destination devices to copy `tensor` to.
Returns:
A value mirrored to `destinations` devices.
"""
assert destinations is not None # from old strategy.broadcast()
# TODO(josh11b): More docstring
_require_cross_replica_or_default_context_extended(self)
assert not isinstance(destinations, (list, tuple))
return self._broadcast_to(tensor, destinations)
def _broadcast_to(self, tensor, destinations):
raise NotImplementedError("must be implemented in descendants")
def experimental_run_steps_on_iterator(self,
fn,
iterator,
iterations=1,
initial_loop_values=None):
"""DEPRECATED: please use `run` instead.
Run `fn` with input from `iterator` for `iterations` times.
This method can be used to run a step function for training a number of
times using input from a dataset.
Args:
fn: function to run using this distribution strategy. The function must
have the following signature: `def fn(context, inputs)`. `context` is an
instance of `MultiStepContext` that will be passed when `fn` is run.
`context` can be used to specify the outputs to be returned from `fn`
by calling `context.set_last_step_output`. It can also be used to
capture non tensor outputs by `context.set_non_tensor_output`. See
`MultiStepContext` documentation for more information. `inputs` will
have same type/structure as `iterator.get_next()`. Typically, `fn`
will use `call_for_each_replica` method of the strategy to distribute
the computation over multiple replicas.
iterator: Iterator of a dataset that represents the input for `fn`. The
caller is responsible for initializing the iterator as needed.
iterations: (Optional) Number of iterations that `fn` should be run.
Defaults to 1.
initial_loop_values: (Optional) Initial values to be passed into the
loop that runs `fn`. Defaults to `None`. # TODO(priyag): Remove
initial_loop_values argument when we have a mechanism to infer the
outputs of `fn`.
Returns:
Returns the `MultiStepContext` object which has the following properties,
among other things:
- run_op: An op that runs `fn` `iterations` times.
- last_step_outputs: A dictionary containing tensors set using
`context.set_last_step_output`. Evaluating this returns the value of
the tensors after the last iteration.
- non_tensor_outputs: A dictionary containing anything that was set by
`fn` by calling `context.set_non_tensor_output`.
"""
_require_cross_replica_or_default_context_extended(self)
with self._container_strategy().scope():
return self._experimental_run_steps_on_iterator(fn, iterator, iterations,
initial_loop_values)
def _experimental_run_steps_on_iterator(self, fn, iterator, iterations,
initial_loop_values):
raise NotImplementedError("must be implemented in descendants")
def call_for_each_replica(self, fn, args=(), kwargs=None):
"""Run `fn` once per replica.
`fn` may call `tf.get_replica_context()` to access methods such as
`replica_id_in_sync_group` and `merge_call()`.
`merge_call()` is used to communicate between the replicas and
re-enter the cross-replica context. All replicas pause their execution
having encountered a `merge_call()` call. After that the
`merge_fn`-function is executed. Its results are then unwrapped and
given back to each replica call. After that execution resumes until
`fn` is complete or encounters another `merge_call()`. Example:
```python
# Called once in "cross-replica" context.
def merge_fn(distribution, three_plus_replica_id):
# sum the values across replicas
return sum(distribution.experimental_local_results(three_plus_replica_id))
# Called once per replica in `distribution`, in a "replica" context.
def fn(three):
replica_ctx = tf.get_replica_context()
v = three + replica_ctx.replica_id_in_sync_group
# Computes the sum of the `v` values across all replicas.
s = replica_ctx.merge_call(merge_fn, args=(v,))
return s + v
with distribution.scope():
# in "cross-replica" context
...
merged_results = distribution.run(fn, args=[3])
# merged_results has the values from every replica execution of `fn`.
# This statement prints a list:
print(distribution.experimental_local_results(merged_results))
```
Args:
fn: function to run (will be run once per replica).
args: Tuple or list with positional arguments for `fn`.
kwargs: Dict with keyword arguments for `fn`.
Returns:
Merged return value of `fn` across all replicas.
"""
_require_cross_replica_or_default_context_extended(self)
if kwargs is None:
kwargs = {}
with self._container_strategy().scope():
return self._call_for_each_replica(fn, args, kwargs)
def _call_for_each_replica(self, fn, args, kwargs):
raise NotImplementedError("must be implemented in descendants")
def read_var(self, v):
"""Reads the value of a variable.
Returns the aggregate value of a replica-local variable, or the
(read-only) value of any other variable.
Args:
v: A variable allocated within the scope of this `tf.distribute.Strategy`.
Returns:
A tensor representing the value of `v`, aggregated across replicas if
necessary.
"""
raise NotImplementedError("must be implemented in descendants")
@property
def experimental_between_graph(self):
"""Whether the strategy uses between-graph replication or not.
This is expected to return a constant value that will not be changed
throughout its life cycle.
"""
raise NotImplementedError("must be implemented in descendants")
@property
def experimental_should_init(self):
"""Whether initialization is needed."""
raise NotImplementedError("must be implemented in descendants")
@property
def should_checkpoint(self):
"""Whether checkpointing is needed."""
raise NotImplementedError("must be implemented in descendants")
@property
def should_save_summary(self):
"""Whether saving summaries is needed."""
raise NotImplementedError("must be implemented in descendants")
# A note about the difference between the context managers
# `ReplicaContext` (defined here) and `_CurrentDistributionContext`
# (defined above) used by `tf.distribute.Strategy.scope()`:
#
# * a ReplicaContext is only present during a `run()`
# call (except during a `merge_run` call) and in such a scope it
# will be returned by calls to `get_replica_context()`. Implementers of new
# Strategy descendants will frequently also need to
# define a descendant of ReplicaContext, and are responsible for
# entering and exiting this context.
#
# * Strategy.scope() sets up a variable_creator scope that
# changes variable creation calls (e.g. to make mirrored
# variables). This is intended as an outer scope that users enter once
# around their model creation and graph definition. There is no
# anticipated need to define descendants of _CurrentDistributionContext.
# It sets the current Strategy for purposes of
# `get_strategy()` and `has_strategy()`
# and switches the thread mode to a "cross-replica context".
@tf_export("distribute.ReplicaContext")
class ReplicaContext(object):
"""`tf.distribute.Strategy` API when in a replica context.
You can use `tf.distribute.get_replica_context` to get an instance of
`ReplicaContext`. This should be inside your replicated step function, such
as in a `tf.distribute.Strategy.run` call.
"""
def __init__(self, strategy, replica_id_in_sync_group):
self._strategy = strategy
self._thread_context = distribution_strategy_context._InReplicaThreadMode( # pylint: disable=protected-access
self)
self._replica_id_in_sync_group = replica_id_in_sync_group
self._summary_recording_distribution_strategy = None
def __enter__(self):
_push_per_thread_mode(self._thread_context)
def replica_id_is_zero():
return math_ops.equal(self._replica_id_in_sync_group,
constant_op.constant(0))
summary_state = summary_ops_v2._summary_state # pylint: disable=protected-access
self._summary_recording_distribution_strategy = (
summary_state.is_recording_distribution_strategy)
summary_state.is_recording_distribution_strategy = replica_id_is_zero
def __exit__(self, exception_type, exception_value, traceback):
summary_state = summary_ops_v2._summary_state # pylint: disable=protected-access
summary_state.is_recording_distribution_strategy = (
self._summary_recording_distribution_strategy)
_pop_per_thread_mode()
def merge_call(self, merge_fn, args=(), kwargs=None):
"""Merge args across replicas and run `merge_fn` in a cross-replica context.
This allows communication and coordination when there are multiple calls
to the step_fn triggered by a call to `strategy.run(step_fn, ...)`.
See `tf.distribute.Strategy.run` for an explanation.
If not inside a distributed scope, this is equivalent to:
```
strategy = tf.distribute.get_strategy()
with cross-replica-context(strategy):
return merge_fn(strategy, *args, **kwargs)
```
Args:
merge_fn: Function that joins arguments from threads that are given as
PerReplica. It accepts `tf.distribute.Strategy` object as
the first argument.
args: List or tuple with positional per-thread arguments for `merge_fn`.
kwargs: Dict with keyword per-thread arguments for `merge_fn`.
Returns:
The return value of `merge_fn`, except for `PerReplica` values which are
unpacked.
"""
require_replica_context(self)
if kwargs is None:
kwargs = {}
merge_fn = autograph.tf_convert(
merge_fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
return self._merge_call(merge_fn, args, kwargs)
def _merge_call(self, merge_fn, args, kwargs):
"""Default implementation for single replica."""
_push_per_thread_mode( # thread-local, so not needed with multiple threads
distribution_strategy_context._CrossReplicaThreadMode(self._strategy)) # pylint: disable=protected-access
try:
return merge_fn(self._strategy, *args, **kwargs)
finally:
_pop_per_thread_mode()
@property
def num_replicas_in_sync(self):
"""Returns number of replicas over which gradients are aggregated."""
return self._strategy.num_replicas_in_sync
@property
def replica_id_in_sync_group(self):
"""Returns the id of the replica being defined.
This identifies the replica that is part of a sync group. Currently we
assume that all sync groups contain the same number of replicas. The value
of the replica id can range from 0 to `num_replica_in_sync` - 1.
NOTE: This is not guaranteed to be the same ID as the XLA replica ID use
for low-level operations such as collective_permute.
"""
require_replica_context(self)
return self._replica_id_in_sync_group
@property
def strategy(self):
"""The current `tf.distribute.Strategy` object."""
return self._strategy
@property
def devices(self):
"""The devices this replica is to be executed on, as a tuple of strings."""
require_replica_context(self)
return (device_util.current(),)
def all_reduce(self, reduce_op, value, experimental_hints=None):
"""All-reduces the given `value Tensor` nest across replicas.
If `all_reduce` is called in any replica, it must be called in all replicas.
The nested structure and `Tensor` shapes must be identical in all replicas.
IMPORTANT: The ordering of communications must be identical in all replicas.
Example with two replicas:
Replica 0 `value`: {'a': 1, 'b': [40, 1]}
Replica 1 `value`: {'a': 3, 'b': [ 2, 98]}
If `reduce_op` == `SUM`:
Result (on all replicas): {'a': 4, 'b': [42, 99]}
If `reduce_op` == `MEAN`:
Result (on all replicas): {'a': 2, 'b': [21, 49.5]}
Args:
reduce_op: Reduction type, an instance of `tf.distribute.ReduceOp` enum.
value: The nested structure of `Tensor`s to all-reduce. The structure must
be compatible with `tf.nest`.
experimental_hints: A `tf.distrbute.experimental.CollectiveHints`. Hints
to perform collective operations.
Returns:
A `Tensor` nest with the reduced `value`s from each replica.
"""
if isinstance(reduce_op, six.string_types):
reduce_op = reduce_util.ReduceOp(reduce_op.upper())
if experimental_hints is None:
experimental_hints = collective_util.Hints()
def batch_all_reduce(strategy, *value_flat):
return strategy.extended.batch_reduce_to(
reduce_op, [(v, _batch_reduce_destination(v)) for v in value_flat],
experimental_hints)
if reduce_op in [reduce_util.ReduceOp.SUM, reduce_util.ReduceOp.MEAN]:
# TODO(cjfj): Work out why `batch_reduce` doesn't return the correct grad.
@custom_gradient.custom_gradient
def grad_wrapper(*xs):
ys = self.merge_call(batch_all_reduce, args=xs)
# The gradient of an all-sum is itself an all-sum (all-mean, likewise).
return ys, lambda *dy_s: self.all_reduce(reduce_op, dy_s)
return nest.pack_sequence_as(value, grad_wrapper(*nest.flatten(value)))
else:
# TODO(cjfj): Implement gradients for other reductions.
reduced = nest.pack_sequence_as(
value, self.merge_call(batch_all_reduce, args=nest.flatten(value)))
return nest.map_structure(array_ops.prevent_gradient, reduced)
# TODO(josh11b): Implement `start_all_reduce(method, t)` for efficient
# all-reduce. It would return a function returning the result of reducing `t`
# across all replicas. The caller would wait to call this function until they
# needed the reduce result, allowing an efficient implementation:
# * With eager execution, the reduction could be performed asynchronously
# in the background, not blocking until the result was needed.
# * When constructing a graph, it could batch up all reduction requests up
# to that point that the first result is needed. Most likely this can be
# implemented in terms of `merge_call()` and `batch_reduce_to()`.
def _batch_reduce_destination(x):
"""Returns the destinations for batch all-reduce."""
if isinstance(x, ops.Tensor):
# If this is a one device strategy.
return x.device
else:
return x
# ------------------------------------------------------------------------------
_creating_default_strategy_singleton = False
class _DefaultDistributionStrategy(StrategyV1):
"""Default `tf.distribute.Strategy` if none is explicitly selected."""
def __init__(self):
if not _creating_default_strategy_singleton:
raise RuntimeError("Should only create a single instance of "
"_DefaultDistributionStrategy")
super(_DefaultDistributionStrategy, self).__init__(
_DefaultDistributionExtended(self))
def __deepcopy__(self, memo):
del memo
raise RuntimeError("Should only create a single instance of "
"_DefaultDistributionStrategy")
class _DefaultDistributionContext(object):
"""Context manager setting the default `tf.distribute.Strategy`."""
def __init__(self, strategy):
def creator(next_creator, **kwargs):
_require_strategy_scope_strategy(strategy)
return next_creator(**kwargs)
self._var_creator_scope = variable_scope.variable_creator_scope(creator)
self._strategy = strategy
self._nested_count = 0
def __enter__(self):
# Allow this scope to be entered if this strategy is already in scope.
if distribution_strategy_context.has_strategy():
raise RuntimeError("Must not nest tf.distribute.Strategy scopes.")
if self._nested_count == 0:
self._var_creator_scope.__enter__()
self._nested_count += 1
return self._strategy
def __exit__(self, exception_type, exception_value, traceback):
self._nested_count -= 1
if self._nested_count == 0:
try:
self._var_creator_scope.__exit__(
exception_type, exception_value, traceback)
except RuntimeError as e:
six.raise_from(
RuntimeError("Variable creator scope nesting error: move call to "
"tf.distribute.set_strategy() out of `with` scope."),
e)
class _DefaultDistributionExtended(StrategyExtendedV1):
"""Implementation of _DefaultDistributionStrategy."""
def __init__(self, container_strategy):
super(_DefaultDistributionExtended, self).__init__(container_strategy)
self._retrace_functions_for_each_device = False
def _scope(self, strategy):
"""Context manager setting a variable creator and `self` as current."""
return _DefaultDistributionContext(strategy)
def colocate_vars_with(self, colocate_with_variable):
"""Does not require `self.scope`."""
_require_strategy_scope_extended(self)
return ops.colocate_with(colocate_with_variable)
def variable_created_in_scope(self, v):
return v._distribute_strategy is None # pylint: disable=protected-access
def _experimental_distribute_dataset(self, dataset, options):
return dataset
def _experimental_distribute_datasets_from_function(self, dataset_fn,
options):
return dataset_fn(InputContext())
def _experimental_distribute_values_from_function(self, value_fn):
return value_fn(ValueContext())
def _make_dataset_iterator(self, dataset):
return _DefaultDistributionExtended.DefaultInputIterator(dataset)
def _make_input_fn_iterator(self,
input_fn,
replication_mode=InputReplicationMode.PER_WORKER):
dataset = input_fn(InputContext())
return _DefaultDistributionExtended.DefaultInputIterator(dataset)
def _experimental_make_numpy_dataset(self, numpy_input, session):
numpy_flat = nest.flatten(numpy_input)
vars_flat = tuple(
variable_scope.variable(array_ops.zeros(i.shape, i.dtype),
trainable=False, use_resource=True)
for i in numpy_flat
)
for v, i in zip(vars_flat, numpy_flat):
numpy_dataset.init_var_from_numpy(v, i, session)
vars_nested = nest.pack_sequence_as(numpy_input, vars_flat)
return dataset_ops.Dataset.from_tensor_slices(vars_nested)
def _broadcast_to(self, tensor, destinations):
if destinations is None:
return tensor
else:
raise NotImplementedError("TODO")
def _call_for_each_replica(self, fn, args, kwargs):
with ReplicaContext(
self._container_strategy(),
replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
return fn(*args, **kwargs)
def _reduce_to(self, reduce_op, value, destinations, experimental_hints):
# TODO(josh11b): Use destinations?
del reduce_op, destinations, experimental_hints
return value
def _update(self, var, fn, args, kwargs, group):
# The implementations of _update() and _update_non_slot() are identical
# except _update() passes `var` as the first argument to `fn()`.
return self._update_non_slot(var, fn, (var,) + tuple(args), kwargs, group)
def _update_non_slot(self, colocate_with, fn, args, kwargs, should_group):
# TODO(josh11b): Figure out what we should be passing to UpdateContext()
# once that value is used for something.
with UpdateContext(colocate_with):
result = fn(*args, **kwargs)
if should_group:
return result
else:
return nest.map_structure(self._local_results, result)
def read_var(self, replica_local_var):
return array_ops.identity(replica_local_var)
def _local_results(self, distributed_value):
return (distributed_value,)
def value_container(self, value):
return value
@property
def _num_replicas_in_sync(self):
return 1
@property
def worker_devices(self):
raise RuntimeError("worker_devices() method unsupported by default "
"tf.distribute.Strategy.")
@property
def parameter_devices(self):
raise RuntimeError("parameter_devices() method unsupported by default "
"tf.distribute.Strategy.")
def non_slot_devices(self, var_list):
return min(var_list, key=lambda x: x.name)
def _in_multi_worker_mode(self):
"""Whether this strategy indicates working in multi-worker settings."""
# Default strategy doesn't indicate multi-worker training.
return False
@property
def should_checkpoint(self):
return True
@property
def should_save_summary(self):
return True
# TODO(priyag): This should inherit from `InputIterator`, once dependency
# issues have been resolved.
class DefaultInputIterator(object):
"""Default implementation of `InputIterator` for default strategy."""
def __init__(self, dataset):
self._dataset = dataset
if eager_context.executing_eagerly():
self._iterator = dataset_ops.make_one_shot_iterator(dataset)
else:
self._iterator = dataset_ops.make_initializable_iterator(dataset)
def get_next(self):
return self._iterator.get_next()
def get_next_as_optional(self):
return iterator_ops.get_next_as_optional(self._iterator)
@deprecated(None, "Use the iterator's `initializer` property instead.")
def initialize(self):
"""Initialize underlying iterators.
Returns:
A list of any initializer ops that should be run.
"""
if eager_context.executing_eagerly():
self._iterator = self._dataset.make_one_shot_iterator()
return []
else:
return [self._iterator.initializer]
@property
def initializer(self):
"""Returns a list of ops that initialize the iterator."""
return self.initialize()
# TODO(priyag): Delete this once all strategies use global batch size.
@property
def _global_batch_size(self):
"""Global and per-replica batching are equivalent for this strategy."""
return True
# ------------------------------------------------------------------------------
# We haven't yet implemented deserialization for DistributedVariables.
# So here we catch any attempts to deserialize variables
# when using distribution strategies.
# pylint: disable=protected-access
_original_from_proto = resource_variable_ops._from_proto_fn
def _from_proto_fn(v, import_scope=None):
if distribution_strategy_context.has_strategy():
raise NotImplementedError(
"Deserialization of variables is not yet supported when using a "
"tf.distribute.Strategy.")
else:
return _original_from_proto(v, import_scope=import_scope)
resource_variable_ops._from_proto_fn = _from_proto_fn
# pylint: enable=protected-access
#-------------------------------------------------------------------------------
# Shorthand for some methods from distribution_strategy_context.
_push_per_thread_mode = distribution_strategy_context._push_per_thread_mode # pylint: disable=protected-access
_get_per_thread_mode = distribution_strategy_context._get_per_thread_mode # pylint: disable=protected-access
_pop_per_thread_mode = distribution_strategy_context._pop_per_thread_mode # pylint: disable=protected-access
_get_default_replica_mode = (
distribution_strategy_context._get_default_replica_mode) # pylint: disable=protected-access
# ------------------------------------------------------------------------------
# Metrics to track which distribution strategy is being called
distribution_strategy_gauge = monitoring.StringGauge(
"/tensorflow/api/distribution_strategy",
"Gauge to track the type of distribution strategy used.", "TFVersion")
distribution_strategy_replica_gauge = monitoring.IntGauge(
"/tensorflow/api/distribution_strategy/replica",
"Gauge to track the number of replica each distribution strategy used.",
"CountType")
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