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Additional APIs for algorithms that need to be distribution-aware.

Some common use cases of functions on this page:

  • Locality

tf.distribute.DistributedValues can have the same locality as a distributed variable, which leads to a mirrored value residing on the same devices as the variable (as opposed to the compute devices). Such values may be passed to a call to tf.distribute.StrategyExtended.update to update the value of a variable. You may use tf.distribute.StrategyExtended.colocate_vars_with to give a variable the same locality as another variable. You may convert a "PerReplica" value to a variable's locality by using tf.distribute.StrategyExtended.reduce_to or tf.distribute.StrategyExtended.batch_reduce_to.

  • How to update a distributed variable

A distributed variable is variables created on multiple devices. As discussed in the glossary, mirrored variable and SyncOnRead variable are two examples. The standard pattern for updating distributed variables is to:

  1. In your function passed to, compute a list of (update, variable) pairs. For example, the update might be a gradient of the loss with respect to the variable.
  2. Switch to cross-replica mode by calling tf.distribute.get_replica_context().merge_call() with the updates and variables as arguments.
  3. Call tf.distribute.StrategyExtended.reduce_to(VariableAggregation.SUM, t, v) (for one variable) or tf.distribute.StrategyExtended.batch_reduce_to (for a list of variables) to sum the updates.
  4. Call tf.distribute.StrategyExtended.update(v) for each variable to update its value.

Steps 2 through 4 are done automatically by class tf.keras.optimizers.Optimizer if you call its tf.keras.optimizers.Optimizer.apply_gradients method in a replica context.

In fact, a higher-level solution to update a distributed variable is by calling assign on the variable as you would do to a regular tf.Variable. You can call the method in both replica context and cross-replica context. For a mirrored variable, calling assign in replica context requires you to specify the aggregation type in the variable constructor. In that case, the context switching and sync described in steps 2 through 4 are handled for you. If you call assign on mirrored variable in cross-replica context, you can only assign a single value or assign values from another mirrored variable or a mirrored tf.distribute.DistributedValues. For a SyncOnRead variable, in replica context, you can simply call assign on it and no aggregation happens under the hood. In cross-replica context, you can only assign a single value to a SyncOnRead variable. One example case is restoring from a checkpoint: if the aggregation type of the variable is tf.VariableAggregation.SUM, it is assumed that replica values were added before checkpointing, so at the time of restoring, the value is divided by the number of replicas and then assigned to each replica; if the aggregation type is tf.VariableAggregation.MEAN, the value is assigned to each replica directly.

experimental_require_static_shapes Returns True if static shape is required; False otherwise.
parameter_devices Returns the tuple of all devices used to place variables.
worker_devices Returns the tuple of all devices used to for compute replica execution.



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Combine multiple reduce_to calls into one for faster execution.

reduce_op Reduction type, an instance of tf.distribute.ReduceOp enum.
value_destination_pairs A sequence of (value, destinations) pairs. See reduce_to() for a description.
experimental_hints A tf.distrbute.experimental.CollectiveHints. Hints to perform collective operations.

A list of mirrored values, one per pair in value_destination_pairs.


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Scope that controls which devices variables will be created on.

No operations should be added to the graph inside this scope, it should only be used when creating variables (some implementations work by changing variable creation, others work by using a tf.compat.v1.colocate_with() scope).

This may only be used inside self.scope().

Example usage:

with strategy.scope():
  var1 = tf.Variable(...)
  with strategy.extended.colocate_vars_with(var1):
    # var2 and var3 will be created on the same device(s) as var1
    var2 = tf.Variable(...)
    var3 = tf.Variable(...)

  def fn(v1, v2, v3):
    # operates on v1 from var1, v2 from var2, and v3 from var3

  # `fn` runs on every device `var1` is on, `var2` and `var3` will be there
  # too.
  strategy.extended.update(var1, fn, args=(var2, var3))

colocate_with_variable A variable created in this strategy's scope(). Variables created while in the returned context manager will be on the same set of devices as colocate_with_variable.

A context manager.


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Combine (via e.g. sum or mean) values across replicas.

reduce_op Reduction type, an instance