tf.distribute.HierarchicalCopyAllReduce

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Reduction using hierarchical copy all-reduce.

tf.distribute.HierarchicalCopyAllReduce(
    num_packs=1
)

It reduces to one GPU along edges in some hierarchy and broadcasts back to each GPU along the same path. Before performing all-reduce, tensors will be repacked or aggregated for more efficient cross-device transportation.

This is a reduction created for Nvidia DGX-1 which assumes GPUs connects like that on DGX-1 machine. If you have different GPU inter-connections, it is likely that it would be slower than tf.distribute.ReductionToOneDevice.

Args:

• num_packs: values will be packed in this many splits. num_packs should be greater than or equals 0. When it is zero, no packing will be done.

Raises:

ValueError if num_packs is negative.

Methods

batch_reduce

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batch_reduce(
    reduce_op, value_destination_pairs
)

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

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broadcast(
    tensor, destinations
)

Broadcast the tensor to destinations.

Args:

• tensor: the tensor to broadcast.
• destinations: the broadcast destinations.

Returns:

a Mirrored object.

reduce

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reduce(
    reduce_op, per_replica_value, destinations
)

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