tf.distribute.StrategyExtended

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

tf.distribute.StrategyExtended(
    container_strategy
)

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 tf.distribute.Strategy.run, 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.

Methods

batch_reduce_to

View source

batch_reduce_to(
    reduce_op, value_destination_pairs, options=None
)

Combine multiple reduce_to calls into one for faster execution.

Similar to reduce_to, but accepts a list of (value, destinations) pairs. It's more efficient than reduce each value separately.

This API currently can only be called in cross-replica context. Other variants to reduce values across replicas are:

• tf.distribute.StrategyExtended.reduce_to: the non-batch version of this API.
• tf.distribute.ReplicaContext.all_reduce: the counterpart of this API in replica context. It supports both batched and non-batched all-reduce.
• tf.distribute.Strategy.reduce: a more convenient method to reduce to the host in cross-replica context.

See reduce_to for more information.

@tf.function
def step_fn(var):

  def merge_fn(strategy, value, var):
    # All-reduce the value. Note that `value` here is a
    # `tf.distribute.DistributedValues`.
    reduced = strategy.extended.batch_reduce_to(
        tf.distribute.ReduceOp.SUM, [(value, var)])[0]
    strategy.extended.update(var, lambda var, value: var.assign(value),
        args=(reduced,))

  value = tf.identity(1.)
  tf.distribute.get_replica_context().merge_call(merge_fn,
    args=(value, var))

def run(strategy):
  with strategy.scope():
    v = tf.Variable(0.)
    strategy.run(step_fn, args=(v,))
    return v

run(tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]))
MirroredVariable:{
  0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>,
  1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=2.0>
}
run(tf.distribute.experimental.CentralStorageStrategy(
    compute_devices=["GPU:0", "GPU:1"], parameter_device="CPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>
run(tf.distribute.OneDeviceStrategy("GPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>

colocate_vars_with

View source

colocate_vars_with(
    colocate_with_variable
)

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))

reduce_to

View source

reduce_to(
    reduce_op, value, destinations, options=None
)

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

reduce_to aggregates tf.distribute.DistributedValues and distributed variables. It supports both dense values and tf.IndexedSlices.

This API currently can only be called in cross-replica context. Other variants to reduce values across replicas are:

• tf.distribute.StrategyExtended.batch_reduce_to: the batch version of this API.
• tf.distribute.ReplicaContext.all_reduce: the counterpart of this API in replica context. It supports both batched and non-batched all-reduce.
• tf.distribute.Strategy.reduce: a more convenient method to reduce to the host in cross-replica context.

destinations specifies where to reduce the value to, e.g. "GPU:0". You can also pass in a Tensor, and the destinations will be the device of that tensor. For all-reduce, pass the same to value and destinations.

It can be used in tf.distribute.ReplicaContext.merge_call to write code that works for all tf.distribute.Strategy.

@tf.function
def step_fn(var):

  def merge_fn(strategy, value, var):
    # All-reduce the value. Note that `value` here is a
    # `tf.distribute.DistributedValues`.
    reduced = strategy.extended.reduce_to(tf.distribute.ReduceOp.SUM,
        value, destinations=var)
    strategy.extended.update(var, lambda var, value: var.assign(value),
        args=(reduced,))

  value = tf.identity(1.)
  tf.distribute.get_replica_context().merge_call(merge_fn,
    args=(value, var))

def run(strategy):
  with strategy.scope():
    v = tf.Variable(0.)
    strategy.run(step_fn, args=(v,))
    return v

run(tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]))
MirroredVariable:{
  0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>,
  1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=2.0>
}
run(tf.distribute.experimental.CentralStorageStrategy(
    compute_devices=["GPU:0", "GPU:1"], parameter_device="CPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>
run(tf.distribute.OneDeviceStrategy("GPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>

update

View source

update(
    var, fn, args=(), kwargs=None, group=True
)

Run fn to update var using inputs mirrored to the same devices.

tf.distribute.StrategyExtended.update takes a distributed variable var to be updated, an update function fn, and args and kwargs for fn. It applies fn to each component variable of var and passes corresponding values from args and kwargs. Neither args nor kwargs may contain per-replica values. If they contain mirrored values, they will be unwrapped before calling fn. For example, fn can be assign_add and args can be a mirrored DistributedValues where each component contains the value to be added to this mirrored variable var. Calling update will call assign_add on each component variable of var with the corresponding tensor value on that device.

Example usage:

strategy = tf.distribute.MirroredStrategy(['GPU:0', 'GPU:1']) # With 2
devices
with strategy.scope():
  v = tf.Variable(5.0, aggregation=tf.VariableAggregation.SUM)
def update_fn(v):
  return v.assign(1.0)
result = strategy.extended.update(v, update_fn)
# result is
# Mirrored:{
#  0: tf.Tensor(1.0, shape=(), dtype=float32),
#  1: tf.Tensor(1.0, shape=(), dtype=float32)
# }

If var is mirrored across multiple devices, then this method implements logic as following:

results = {}
for device, v in var:
  with tf.device(device):
    # args and kwargs will be unwrapped if they are mirrored.
    results[device] = fn(v, *args, **kwargs)
return merged(results)

Otherwise, this method returns fn(var, *args, **kwargs) colocated with var.

value_container

View source

value_container(
    value
)

Returns the container that this per-replica value belongs to.

variable_created_in_scope

View source

variable_created_in_scope(
    v
)

Tests whether v was created while this strategy scope was active.

Variables created inside the strategy scope are "owned" by it:

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
  v = tf.Variable(1.)
strategy.extended.variable_created_in_scope(v)
True

Variables created outside the strategy are not owned by it:

strategy = tf.distribute.MirroredStrategy()
v = tf.Variable(1.)
strategy.extended.variable_created_in_scope(v)
False