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tf.contrib.distribute.MultiWorkerAllReduce

Class MultiWorkerAllReduce

Inherits From: AllReduceCrossDeviceOps

Defined in tensorflow/python/distribute/cross_device_ops.py.

All-reduce algorithms for distributed TensorFlow.

__init__

__init__(
    worker_devices,
    num_gpus_per_worker,
    all_reduce_spec=('pscpu/pscpu', 2, -1),
    num_packs=0,
    agg_small_grads_max_bytes=0,
    agg_small_grads_max_group=10
)

Initialize the all-reduce algorithm.

Args:

  • worker_devices: a list of device strings for workers participating in all-reduce.
  • num_gpus_per_worker: number of GPU devices per worker.
  • all_reduce_spec: a tuple or a named tuple or a list of tuples specifying the all-reduce algorithm.
    1. The first element of a tuple is the name of the all-reduce algorithm. Valid algorithm names are: "nccl", "nccl/xring", "nccl/rechd", "nccl/pscpu", "xring", "pscpu", "psgpu", "pscpu/pscpu". Algorithms with a "/" are hierarchical, so two all-reduces are executed, the first one aggregates tensors within a worker and the second aggregates across workers.
    2. The second element of a tuple is the number of shards when doing all-reduce. Let's say its values is M, each tensor after packing will be split into M shards and then M parallel all-reduces would be performed before finally they are concatenated backed into a complete tensor.
    3. The third element is the maximum size of tensors that will be applicable for the algorithm specified by the first element. For example, if all_reduce_spec=[("nccl", 2, 1024), ("pscpu/pscpu", 2, -1)], tensors with size not larger than 1024 bytes will be applied a 2-shard "nccl" all-reduce and other tensors will be applied a 2-shard "pscpu/pscpu" algorithm. The third elements should be in increasing order across tuples and end with -1 which indicates infinity.
  • num_packs: see AllReduceCrossDeviceOps.
  • agg_small_grads_max_bytes: see AllReduceCrossDeviceOps.
  • agg_small_grads_max_group: see AllReduceCrossDeviceOps.

Methods

batch_reduce

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.

Args:

  • reduce_op: Indicates how per_replica_value will be reduced. Accepted values are tf.distribute.ReduceOp.SUM, tf.distribute.ReduceOp.MEAN.
  • value_destination_pairs: a list or a tuple of tuples 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 a list or a tuple of tuples of PerReplica objects and destinations

broadcast

broadcast(
    tensor,
    destinations
)

Broadcast the tensor to destinations.

Args:

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

Returns:

a Mirrored object.

reduce

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:

Returns:

a Mirrored object.

Raises:

  • ValueError: if per_replica_value is not a PerReplica object.