tf.distribute.ReductionToOneDevice

TensorFlow 1 version View source on GitHub

Always do reduction to one device first and then do broadcasting.

Inherits From: CrossDeviceOps

Batch reduction is done by reduction on each element one by one.

  mirrored_strategy = tf.distribute.MirroredStrategy(
    cross_device_ops=tf.distribute.ReductionToOneDevice())

reduce_to_device the intermediate device to reduce to. If None, reduce to the first device in destinations of the reduce() method.
accumulation_fn a function that does accumulation. If None, then tf.math.add_n is used.

Methods

batch_reduce

View source

Reduce PerReplica objects in a batch.

Reduce each first element in value_destination_pairs to each second element which indicates the destinations.

This can be faster than multiple individual reduces because we can fuse several tensors into one or multiple packs before reduction.

Args
reduce_op An instance of tf.distribute.ReduceOp that indicates how the per_replica_value will be reduced.
value_destination_pairs a list or a tuple of PerReplica objects (or tensors with device set if there is one device) and destinations.

Returns
a list of Mirrored objects.

Raises
ValueError if value_destination_pairs is not an iterable of tuples of PerReplica objects and destinations.

broadcast

View source

Broadcast the tensor to destinations.

Args
tensor the tensor to broadcast.
destinations the broadcast destinations.

Returns
a Mirrored object.

reduce

View source

Reduce per_replica_value to destinations.

It runs the reduction operation defined by reduce_op and put the result on destinations.

Args
reduce_op An instance of tf.distribute.ReduceOp that indicates how per_replica_value will be reduced.
per_replica_value a PerReplica object or a tensor with device set.
destinations the reduction destinations.

Returns
a Mirrored object.

Raises
ValueError if per_replica_value can't be converted to a PerReplica object or if destinations aren't strings, Variables or DistributedValues