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tf.distribute.ReplicaContext

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tf.distribute.Strategy API when in a replica context.

tf.distribute.ReplicaContext(
    strategy, replica_id_in_sync_group
)

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.

Attributes
devices The devices this replica is to be executed on, as a tuple of strings.
num_replicas_in_sync Returns number of replicas over which gradients are aggregated.
replica_id_in_sync_group 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.
strategy The current tf.distribute.Strategy object.

Methods

all_reduce

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all_reduce(
    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.

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 Tensors 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 values from each replica.

merge_call

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merge_call(
    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.

__enter__

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__enter__()

__exit__

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__exit__(
    exception_type, exception_value, traceback
)

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/distribute/ReplicaContext