tf.distribute.DistributedValues

Base class for representing distributed values.

A subclass instance of tf.distribute.DistributedValues is created when creating variables within a distribution strategy, iterating a tf.distribute.DistributedDataset or through tf.distribute.Strategy.run. This base class should never be instantiated directly. tf.distribute.DistributedValues contains a value per replica. Depending on the subclass, the values could either be synced on update, synced on demand, or never synced.

Two representative types of tf.distribute.DistributedValues are tf.types.experimental.PerReplica and tf.types.experimental.Mirrored values.

PerReplica values exist on the worker devices, with a different value for each replica. They are produced by iterating through a distributed dataset returned by tf.distribute.Strategy.experimental_distribute_dataset (Example 1, below) and tf.distribute.Strategy.distribute_datasets_from_function. They are also the typical result returned by tf.distribute.Strategy.run (Example 2).

Mirrored values are like PerReplica values, except we know that the value on all replicas are the same. Mirrored values are kept synchronized by the distribution strategy in use, while PerReplica values are left unsynchronized. Mirrored values typically represent model weights. We can safely read a Mirrored value in a cross-replica context by using the value on any replica, while PerReplica values should not be read or manipulated in a cross-replica context."

tf.distribute.DistributedValues can be reduced via strategy.reduce to obtain a single value across replicas (Example 4), used as input into tf.distribute.Strategy.run (Example 3), or collected to inspect the per-replica values using tf.distribute.Strategy.experimental_local_results (Example 5).

Example usages:

Created from a tf.distribute.DistributedDataset:

strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
dataset = tf.data.Dataset.from_tensor_slices([5., 6., 7., 8.]).batch(2)
dataset_iterator = iter(strategy.experimental_distribute_dataset(dataset))
distributed_values = next(dataset_iterator)
distributed_values
PerReplica:{
  0: <tf.Tensor: shape=(1,), dtype=float32, numpy=array([5.], dtype=float32)>,
  1: <tf.Tensor: shape=(1,), dtype=float32, numpy=array([6.], dtype=float32)>
}

Returned by run:

strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
@tf.function
def run():
  ctx = tf.distribute.get_replica_context()
  return ctx.replica_id_in_sync_group
distributed_values = strategy.run(run)
distributed_values
PerReplica:{
  0: <tf.Tensor: shape=(), dtype=int32, numpy=0>,
  1: <tf.Tensor: shape=(), dtype=int32, numpy=1>
}

As input into run:

strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
dataset = tf.data.Dataset.from_tensor_slices([5., 6., 7., 8.]).batch(2)
dataset_iterator = iter(strategy.experimental_distribute_dataset(dataset))
distributed_values = next(dataset_iterator)
@tf.function
def run(input):
  return input + 1.0
updated_value = strategy.run(run, args=(distributed_values,))
updated_value
PerReplica:{
  0: <tf.Tensor: shape=(1,), dtype=float32, numpy=array([6.], dtype=float32)>,
  1: <tf.Tensor: shape=(1,), dtype=float32, numpy=array([7.], dtype=float32)>
}

As input into reduce:

strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
dataset = tf.data.Dataset.from_tensor_slices([5., 6., 7., 8.]).batch(2)
dataset_iterator = iter(strategy.experimental_distribute_dataset(dataset))
distributed_values = next(dataset_iterator)
reduced_value = strategy.reduce(tf.distribute.ReduceOp.SUM,
                                distributed_values,
                                axis = 0)
reduced_value
<tf.Tensor: shape=(), dtype=float32, numpy=11.0>

How to inspect per-replica values locally:

strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
dataset = tf.data.Dataset.from_tensor_slices([5., 6., 7., 8.]).batch(2)
dataset_iterator = iter(strategy.experimental_distribute_dataset(dataset))
per_replica_values = strategy.experimental_local_results(
   distributed_values)
per_replica_values
(<tf.Tensor: shape=(1,), dtype=float32, numpy=array([5.], dtype=float32)>,
 <tf.Tensor: shape=(1,), dtype=float32, numpy=array([6.], dtype=float32)>)

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Example usages:

tf.distribute.DistributedValues