distribution_strategy_context.py

# 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.
# ==============================================================================
"""Utility to get tf.distribute.Strategy related contexts."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import contextlib
import threading

from tensorflow.python import tf2
from tensorflow.python.framework import ops
from tensorflow.python.util.lazy_loader import LazyLoader
from tensorflow.python.util.tf_export import tf_export


# There is a circular dependency between this and the `distribute_lib` module.
# So we load it lazily to work around this.
distribute_lib = LazyLoader(
    "distribute_lib", globals(),
    "tensorflow.python.distribute.distribute_lib")

# ------------------------------------------------------------------------------
# Internal API for setting the current thread mode as being either in a
# replica or cross-replica context for a particular tf.distribute.Strategy.


class _ThreadMode(object):

  def __init__(self, dist, cross, replica):
    self.strategy = dist
    self.cross_replica_context = cross
    self.replica_context = replica


class _CrossReplicaThreadMode(_ThreadMode):

  def __init__(self, strategy):
    _ThreadMode.__init__(self, strategy, strategy, None)


class _InReplicaThreadMode(_ThreadMode):

  def __init__(self, replica_ctx):
    _ThreadMode.__init__(self, replica_ctx.strategy, None, replica_ctx)


def _push_per_thread_mode(context):
  ops.get_default_graph()._distribution_strategy_stack.append(context)  # pylint: disable=protected-access


def _pop_per_thread_mode():
  ops.get_default_graph()._distribution_strategy_stack.pop(-1)  # pylint: disable=protected-access


class _DefaultReplicaThreadMode(_ThreadMode):
  """Type of default value returned by `_get_per_thread_mode()`.

  Used when the thread-local stack is empty.
  """

  def __init__(self):
    _ThreadMode.__init__(self, _get_default_strategy(), None,
                         _get_default_replica_context())


def _get_per_thread_mode():
  try:
    return ops.get_default_graph()._distribution_strategy_stack[-1]  # pylint: disable=protected-access
  except (AttributeError, IndexError):
    return _get_default_replica_mode()


_variable_sync_on_read_context = threading.local()


@tf_export("__internal__.distribute.variable_sync_on_read_context", v1=[])
@contextlib.contextmanager
def variable_sync_on_read_context():
  """A context that forces SyncOnReadVariable to aggregate upon reading.

  This context is useful if one wants to read the aggregated value out of a
  SyncOnReadVariable in replica context. By default the aggregation is turned
  off per the definition of SyncOnReadVariable.

  When reading a SyncOnReadVariable in cross-replica context, aggregation is
  always turned on so there is no need for such context.

  By reading a SyncOnReadVariable, we mean:
    1. Convert the variable to a tensor using `convert_to_tensor`.
    2. Calling `variable.value()` or `variable.read_value()`.

  Example usage:

  ```
  strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
  with strategy.scope():
    v = tf.Variable(1.0, synchronization=tf.VariableSynchronization.ON_READ,
      aggregation=tf.VariableAggregation.SUM)

  def replica_fn():
    return v + 10.0

  non_aggregated = strategy.run(replica_fn)
  print(non_aggregated) # PerReplica: {0: 11.0, 1: 11.0}

  def replica_fn():
    with variable_sync_on_read_context():
      return v + 10.0

  aggregated = strategy.run(replica_fn)
  print(aggregated) # PerReplica: {0: 12.0, 1: 12.0}
  ```

  Yields:
    Context manager for aggregating SyncOnReadVariable upon reading.
  """
  try:
    _variable_sync_on_read_context.entered = True
    yield
  finally:
    _variable_sync_on_read_context.entered = False


def in_variable_sync_on_read_context():
  try:
    return _variable_sync_on_read_context.entered
  except AttributeError:
    return False

# ------------------------------------------------------------------------------
# Public API for accessing the current thread mode


@tf_export("distribute.get_replica_context")
def get_replica_context():
  """Returns the current `tf.distribute.ReplicaContext` or `None`.

  Returns `None` if in a cross-replica context.

  Note that execution:

  1. starts in the default (single-replica) replica context (this function
     will return the default `ReplicaContext` object);
  2. switches to cross-replica context (in which case this will return
     `None`) when entering a `with tf.distribute.Strategy.scope():` block;
  3. switches to a (non-default) replica context inside `strategy.run(fn, ...)`;
  4. if `fn` calls `get_replica_context().merge_call(merge_fn, ...)`, then
     inside `merge_fn` you are back in the cross-replica context (and again
     this function will return `None`).

  Most `tf.distribute.Strategy` methods may only be executed in
  a cross-replica context, in a replica context you should use the
  API of the `tf.distribute.ReplicaContext` object returned by this
  method instead.

  ```
  assert tf.distribute.get_replica_context() is not None  # default
  with strategy.scope():
    assert tf.distribute.get_replica_context() is None

    def f():
      replica_context = tf.distribute.get_replica_context()  # for strategy
      assert replica_context is not None
      tf.print("Replica id: ", replica_context.replica_id_in_sync_group,
               " of ", replica_context.num_replicas_in_sync)

    strategy.run(f)
  ```

  Returns:
    The current `tf.distribute.ReplicaContext` object when in a replica context
    scope, else `None`.

    Within a particular block, exactly one of these two things will be true:

    * `get_replica_context()` returns non-`None`, or
    * `tf.distribute.is_cross_replica_context()` returns True.
  """
  return _get_per_thread_mode().replica_context


def get_cross_replica_context():
  """Returns the current tf.distribute.Strategy if in a cross-replica context.

  DEPRECATED: Please use `in_cross_replica_context()` and
  `get_strategy()` instead.

  Returns:
    Returns the current `tf.distribute.Strategy` object in a cross-replica
    context, or `None`.

    Exactly one of `get_replica_context()` and `get_cross_replica_context()`
    will return `None` in a particular block.
  """
  return _get_per_thread_mode().cross_replica_context


@tf_export("distribute.in_cross_replica_context")
def in_cross_replica_context():
  """Returns `True` if in a cross-replica context.

  See `tf.distribute.get_replica_context` for details.

  ```
  assert not tf.distribute.in_cross_replica_context()
  with strategy.scope():
    assert tf.distribute.in_cross_replica_context()

    def f():
      assert not tf.distribute.in_cross_replica_context()

    strategy.run(f)
  ```

  Returns:
    `True` if in a cross-replica context (`get_replica_context()` returns
    `None`), or `False` if in a replica context (`get_replica_context()` returns
    non-`None`).
  """
  return _get_per_thread_mode().cross_replica_context is not None


@tf_export("distribute.get_strategy")
def get_strategy():
  """Returns the current `tf.distribute.Strategy` object.

  Typically only used in a cross-replica context:

  ```
  if tf.distribute.in_cross_replica_context():
    strategy = tf.distribute.get_strategy()
    ...
  ```

  Returns:
    A `tf.distribute.Strategy` object. Inside a `with strategy.scope()` block,
    it returns `strategy`, otherwise it returns the default (single-replica)
    `tf.distribute.Strategy` object.
  """
  return _get_per_thread_mode().strategy


@tf_export("distribute.has_strategy")
def has_strategy():
  """Return if there is a current non-default `tf.distribute.Strategy`.

  ```
  assert not tf.distribute.has_strategy()
  with strategy.scope():
    assert tf.distribute.has_strategy()
  ```

  Returns:
    True if inside a `with strategy.scope():`.
  """
  return get_strategy() is not _get_default_strategy()


def get_strategy_and_replica_context():
  per_thread_mode = _get_per_thread_mode()
  return (per_thread_mode.strategy, per_thread_mode.replica_context)


@tf_export("distribute.experimental_set_strategy")
def experimental_set_strategy(strategy):
  """Set a `tf.distribute.Strategy` as current without `with strategy.scope()`.

  ```
  tf.distribute.experimental_set_strategy(strategy1)
  f()
  tf.distribute.experimental_set_strategy(strategy2)
  g()
  tf.distribute.experimental_set_strategy(None)
  h()
  ```

  is equivalent to:

  ```
  with strategy1.scope():
    f()
  with strategy2.scope():
    g()
  h()
  ```

  In general, you should use the `with strategy.scope():` API, but this
  alternative may be convenient in notebooks where you would have to put
  each cell in a `with strategy.scope():` block.

  Note: This should only be called outside of any TensorFlow scope to
  avoid improper nesting.

  Args:
    strategy: A `tf.distribute.Strategy` object or None.

  Raises:
    RuntimeError: If called inside a `with strategy.scope():`.
  """
  old_scope = ops.get_default_graph()._global_distribute_strategy_scope  # pylint: disable=protected-access
  if old_scope is not None:
    old_scope.__exit__(None, None, None)
    ops.get_default_graph()._global_distribute_strategy_scope = None  # pylint: disable=protected-access
  if has_strategy():
    raise RuntimeError(
        "Must not be called inside a `tf.distribute.Strategy` scope.")
  if strategy is not None:
    new_scope = strategy.scope()
    new_scope.__enter__()
    ops.get_default_graph()._global_distribute_strategy_scope = new_scope  # pylint: disable=protected-access


# ------------------------------------------------------------------------------
# Internal helpers.


@contextlib.contextmanager
def enter_or_assert_strategy(strategy):
  if has_strategy():
    _assert_strategy(strategy)
    yield
  else:
    with strategy.scope():
      yield


# ------------------------------------------------------------------------------
# Defaults that are used when no tf.distribute.Strategy is explicitly created.
# We create them lazily in a function so that we can workaround the circular
# dependency on distribute_lib. See lazy loader at the top of this file.

_defaults = {
    "strategy": None,
    "replica_context": None,
    "replica_mode": None
}
# Note: These need to be different locks since _get_default_replica_context
# calls _get_default_strategy inside its lock, and them using the same lock
# can lead to deadlock.
_default_strategy_lock = threading.Lock()
_default_replica_context_lock = threading.Lock()
_default_replica_mode_lock = threading.Lock()


def _assert_strategy(strategy):
  if not has_strategy():
    raise RuntimeError('Need to be inside "with strategy.scope()" for %s' %
                       (strategy,))
  current_strategy = get_strategy()
  if current_strategy is not strategy:
    raise RuntimeError(
        "Mixing different tf.distribute.Strategy objects: %s is not %s" %
        (current_strategy, strategy))


def _get_default_strategy():
  if _defaults["strategy"] is None:
    # Avoid race condition causing two defaults to be created
    with _default_strategy_lock:
      if _defaults["strategy"] is None:
        # pylint: disable=protected-access
        # Make sure distribute_lib module is loaded by accessing some member.
        _ = distribute_lib._creating_default_strategy_singleton
        distribute_lib._creating_default_strategy_singleton = True
        if tf2.enabled():
          _defaults["strategy"] = distribute_lib._DefaultDistributionStrategy()
        else:
          _defaults["strategy"] = (
              distribute_lib._DefaultDistributionStrategyV1())
        distribute_lib._creating_default_strategy_singleton = False
        # pylint: enable=protected-access
  return _defaults["strategy"]


def _get_default_replica_context():
  if _defaults["replica_context"] is None:
    # Avoid race condition causing two defaults to be created
    with _default_replica_context_lock:
      if _defaults["replica_context"] is None:
        # pylint: disable=protected-access
        _defaults["replica_context"] = distribute_lib._DefaultReplicaContext(
            _get_default_strategy(), replica_id_in_sync_group=0)
        # pylint: enable=protected-access
  return _defaults["replica_context"]


def _get_default_replica_mode():
  if _defaults["replica_mode"] is None:
    # Avoid race condition causing two defaults to be created
    with _default_replica_mode_lock:
      if _defaults["replica_mode"] is None:
        _defaults["replica_mode"] = _DefaultReplicaThreadMode()
  return _defaults["replica_mode"]


# Aliases for compatibility with old names.
get_distribution_strategy = get_strategy
has_distribution_strategy = has_strategy