tf.distribute.ReplicaContext

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Class ReplicaContext

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

Aliases:

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.experimental_run_v2 call.

__init__

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__init__(
    strategy,
    replica_id_in_sync_group
)

Initialize self. See help(type(self)) for accurate signature.

Properties

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.

strategy

The current tf.distribute.Strategy object.

Methods

__enter__

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__enter__()

__exit__

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__exit__(
    exception_type,
    exception_value,
    traceback
)

all_reduce

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all_reduce(
    reduce_op,
    value
)

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 Tensors to all-reduce. The structure must be compatible with tf.nest.

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.experimental_run_v2(step_fn, ...).

See tf.distribute.Strategy.experimental_run_v2 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.