tf.compat.v1.distribute.Strategy

A list of devices with a state & compute distribution policy.

tf.compat.v1.distribute.Strategy(
    extended
)

See the guide for overview and examples.

Note: Not all tf.distribute.Strategy implementations currently support TensorFlow's partitioned variables (where a single variable is split across multiple devices) at this time.

Attributes
cluster_resolver	Returns the cluster resolver associated with this strategy. In general, when using a multi-worker tf.distribute strategy such as tf.distribute.experimental.MultiWorkerMirroredStrategy or tf.distribute.experimental.TPUStrategy(), there is a tf.distribute.cluster_resolver.ClusterResolver associated with the strategy used, and such an instance is returned by this property. Strategies that intend to have an associated tf.distribute.cluster_resolver.ClusterResolver must set the relevant attribute, or override this property; otherwise, None is returned by default. Those strategies should also provide information regarding what is returned by this property. Single-worker strategies usually do not have a tf.distribute.cluster_resolver.ClusterResolver, and in those cases this property will return None. The tf.distribute.cluster_resolver.ClusterResolver may be useful when the user needs to access information such as the cluster spec, task type or task id. For example, os.environ['TF_CONFIG'] = json.dumps({ 'cluster': { 'worker': ["localhost:12345", "localhost:23456"], 'ps': ["localhost:34567"] }, 'task': {'type': 'worker', 'index': 0} }) # This implicitly uses TF_CONFIG for the cluster and current task info. strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy() ... if strategy.cluster_resolver.task_type == 'worker': # Perform something that's only applicable on workers. Since we set this # as a worker above, this block will run on this particular instance. elif strategy.cluster_resolver.task_type == 'ps': # Perform something that's only applicable on parameter servers. Since we # set this as a worker above, this block will not run on this particular # instance. For more information, please see tf.distribute.cluster_resolver.ClusterResolver's API docstring.
extended	tf.distribute.StrategyExtended with additional methods.
num_replicas_in_sync	Returns number of replicas over which gradients are aggregated.

Methods

experimental_distribute_dataset

View source

experimental_distribute_dataset(
    dataset, options=None
)

Creates tf.distribute.DistributedDataset from tf.data.Dataset.

The returned tf.distribute.DistributedDataset can be iterated over similar to how regular datasets can. NOTE: The user cannot add any more transformations to a tf.distribute.DistributedDataset.

The following is an example:

strategy = tf.distribute.MirroredStrategy()

# Create a dataset
dataset = dataset_ops.Dataset.TFRecordDataset([
  "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"])

# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)

# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
  # process dataset elements
  strategy.run(replica_fn, args=(x,))

In the code snippet above, the tf.distribute.DistributedDataset dist_dataset is batched by GLOBAL_BATCH_SIZE, and we iterate through it using for x in dist_dataset. x a tf.distribute.DistributedValues containing data for all replicas, which aggregates to a batch of GLOBAL_BATCH_SIZE. tf.distribute.Strategy.run will take care of feeding the right per-replica data in x to the right replica_fn executed on each replica.

What's under the hood of this method, when we say the tf.data.Dataset instance - dataset - gets distributed? It depends on how you set the tf.data.experimental.AutoShardPolicy through tf.data.experimental.DistributeOptions. By default, it is set to tf.data.experimental.AutoShardPolicy.AUTO. In a multi-worker setting, we will first attempt to distribute dataset by detecting whether dataset is being created out of reader datasets (e.g. tf.data.TFRecordDataset, tf.data.TextLineDataset, etc.) and if so, try to shard the input files. Note that there has to be at least one input file per worker. If you have less than one input file per worker, we suggest that you disable dataset sharding across workers, by setting the tf.data.experimental.DistributeOptions.auto_shard_policy to be tf.data.experimental.AutoShardPolicy.OFF.

If the attempt to shard by file is unsuccessful (i.e. the dataset is not read from files), we will shard the dataset evenly at the end by appending a .shard operation to the end of the processing pipeline. This will cause the entire preprocessing pipeline for all the data to be run on every worker, and each worker will do redundant work. We will print a warning if this route is selected.

As mentioned before, within each worker, we will also split the data among all the worker devices (if more than one a present). This will happen even if multi-worker sharding is disabled.

If the above batch splitting and dataset sharding logic is undesirable, please use tf.distribute.Strategy.experimental_distribute_datasets_from_function instead, which does not do any automatic splitting or sharding.

You can also use the element_spec property of the tf.distribute.DistributedDataset instance returned by this API to query the tf.TypeSpec of the elements returned by the iterator. This can be used to set the input_signature property of a tf.function.

strategy = tf.distribute.MirroredStrategy()

# Create a dataset
dataset = dataset_ops.Dataset.TFRecordDataset([
  "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"])

# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)

@tf.function(input_signature=[dist_dataset.element_spec])
def train_step(inputs):
  # train model with inputs
  return

# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
  # process dataset elements
  strategy.run(train_step, args=(x,))

Note: The order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.experimental_distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.

Args
dataset	tf.data.Dataset that will be sharded across all replicas using the rules stated above.
options	tf.distribute.InputOptions used to control options on how this dataset is distributed.

Returns
A tf.distribute.DistributedDataset.

experimental_distribute_datasets_from_function

View source

experimental_distribute_datasets_from_function(
    dataset_fn, options=None
)

Distributes tf.data.Dataset instances created by calls to dataset_fn.

dataset_fn will be called once for each worker in the strategy. Each replica on that worker will dequeue one batch of inputs from the local Dataset (i.e. if a worker has two replicas, two batches will be dequeued from the Dataset every step).

This method can be used for several purposes. For example, where experimental_distribute_dataset is unable to shard the input files, this method might be used to manually shard the dataset (avoiding the slow fallback behavior in experimental_distribute_dataset). In cases where the dataset is infinite, this sharding can be done by creating dataset replicas that differ only in their random seed. experimental_distribute_dataset may also sometimes fail to split the batch across replicas on a worker. In that case, this method can be used where that limitation does not exist.

The dataset_fn should take an tf.distribute.InputContext instance where information about batching and input replication can be accessed.

You can also use the element_spec property of the tf.distribute.DistributedDataset returned by this API to query the tf.TypeSpec of the elements returned by the iterator. This can be used to set the input_signature property of a tf.function.

global_batch_size = 8
def dataset_fn(input_context):
  batch_size = input_context.get_per_replica_batch_size(
                   global_batch_size)
  d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size)
  return d.shard(
      input_context.num_input_pipelines,
      input_context.input_pipeline_id)

strategy = tf.distribute.MirroredStrategy()
ds = strategy.experimental_distribute_datasets_from_function(dataset_fn)

def train(ds):
  @tf.function(input_signature=[ds.element_spec])
  def step_fn(inputs):
    # train the model with inputs
    return inputs

... for batch in ds: ... replica_results = strategy.run(replica_fn, args=(batch,))

train(ds)

Key Point: The tf.data.Dataset returned by dataset_fn should have a per-replica batch size, unlike experimental_distribute_dataset, which uses the global batch size. This may be computed using input_context.get_per_replica_batch_size.

Note: The order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.experimental_distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.

Args
dataset_fn	A function taking a tf.distribute.InputContext instance and returning a tf.data.Dataset.
options	tf.distribute.InputOptions used to control options on how this dataset is distributed.

Returns
A tf.distribute.DistributedDataset.

experimental_local_results

View source

experimental_local_results(
    value
)

Returns the list of all local per-replica values contained in value.

Note: This only returns values on the worker initiated by this client. When using a tf.distribute.Strategy like tf.distribute.experimental.MultiWorkerMirroredStrategy, each worker will be its own client, and this function will only return values computed on that worker.

Args
value	A value returned by experimental_run(), run(), extended.call_for_each_replica(), or a variable created in scope.

Returns
A tuple of values contained in value. If value represents a single value, this returns (value,).

experimental_make_numpy_dataset

View source

experimental_make_numpy_dataset(
    numpy_input, session=None
)

Makes a tf.data.Dataset for input provided via a numpy array.

This avoids adding numpy_input as a large constant in the graph, and copies the data to the machine or machines that will be processing the input.

Note that you will likely need to use tf.distribute.Strategy.experimental_distribute_dataset with the returned dataset to further distribute it with the strategy.

Example:

numpy_input = np.ones([10], dtype=np.float32)
dataset = strategy.experimental_make_numpy_dataset(numpy_input)
dist_dataset = strategy.experimental_distribute_dataset(dataset)

Args
numpy_input	A nest of NumPy input arrays that will be converted into a dataset. Note that lists of Numpy arrays are stacked, as that is normal tf.data.Dataset behavior.
session	(TensorFlow v1.x graph execution only) A session used for initialization.

Returns
A tf.data.Dataset representing numpy_input.

experimental_run

View source

experimental_run(
    fn, input_iterator=None
)

Runs ops in fn on each replica, with inputs from input_iterator.

DEPRECATED: This method is not available in TF 2.x. Please switch to using run instead.

When eager execution is enabled, executes ops specified by fn on each replica. Otherwise, builds a graph to execute the ops on each replica.

Each replica will take a single, different input from the inputs provided by one get_next call on the input iterator.

fn may call tf.distribute.get_replica_context() to access members such as replica_id_in_sync_group.

Key Point: Depending on the tf.distribute.Strategy implementation being used, and whether eager execution is enabled, fn may be called one or more times (once for each replica).

Args
fn	The function to run. The inputs to the function must match the outputs of input_iterator.get_next(). The output must be a tf.nest of Tensors.
input_iterator	(Optional) input iterator from which the inputs are taken.

Returns
Merged return value of fn across replicas. The structure of the return value is the same as the return value from fn. Each element in the structure can either be PerReplica (if the values are unsynchronized), Mirrored (if the values are kept in sync), or Tensor (if running on a single replica).

make_dataset_iterator

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make_dataset_iterator(
    dataset
)

Makes an iterator for input provided via dataset.

DEPRECATED: This method is not available in TF 2.x.

Data from the given dataset will be distributed evenly across all the compute replicas. We will assume that the input dataset is batched by the global batch size. With this assumption, we will make a best effort to divide each batch across all the replicas (one or more workers). If this effort fails, an error will be thrown, and the user should instead use make_input_fn_iterator which provides more control to the user, and does not try to divide a batch across replicas.

The user could also use make_input_fn_iterator if they want to customize which input is fed to which replica/worker etc.

Args
dataset	tf.data.Dataset that will be distributed evenly across all replicas.

Returns
An tf.distribute.InputIterator which returns inputs for each step of the computation. User should call initialize on the returned iterator.

make_input_fn_iterator

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make_input_fn_iterator(
    input_fn, replication_mode=tf.distribute.InputReplicationMode.PER_WORKER
)

Returns an iterator split across replicas created from an input function.

DEPRECATED: This method is not available in TF 2.x.

The input_fn should take an tf.distribute.InputContext object where information about batching and input sharding can be accessed:

def input_fn(input_context):
  batch_size = input_context.get_per_replica_batch_size(global_batch_size)
  d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size)
  return d.shard(input_context.num_input_pipelines,
                 input_context.input_pipeline_id)
with strategy.scope():
  iterator = strategy.make_input_fn_iterator(input_fn)
  replica_results = strategy.experimental_run(replica_fn, iterator)

The tf.data.Dataset returned by input_fn should have a per-replica batch size, which may be computed using input_context.get_per_replica_batch_size.

Args
input_fn	A function taking a tf.distribute.InputContext object and returning a tf.data.Dataset.
replication_mode	an enum value of tf.distribute.InputReplicationMode. Only PER_WORKER is supported currently, which means there will be a single call to input_fn per worker. Replicas will dequeue from the local tf.data.Dataset on their worker.

Returns
An iterator object that should first be .initialize()-ed. It may then either be passed to strategy.experimental_run() or you can iterator.get_next() to get the next value to pass to strategy.extended.call_for_each_replica().

reduce

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reduce(
    reduce_op, value, axis=None
)

Reduce value across replicas.

Given a per-replica value returned by run, say a per-example loss, the batch will be divided across all the replicas. This function allows you to aggregate across replicas and optionally also across batch elements. For example, if you have a global batch size of 8 and 2 replicas, values for examples [0, 1, 2, 3] will be on replica 0 and [4, 5, 6, 7] will be on replica 1. By default, reduce will just aggregate across replicas, returning [0+4, 1+5, 2+6, 3+7]. This is useful when each replica is computing a scalar or some other value that doesn't have a "batch" dimension (like a gradient). More often you will want to aggregate across the global batch, which you can get by specifying the batch dimension as the axis, typically axis=0. In this case it would return a scalar 0+1+2+3+4+5+6+7.

If there is a last partial batch, you will need to specify an axis so that the resulting shape is consistent across replicas. So if the last batch has size 6 and it is divided into [0, 1, 2, 3] and [4, 5], you would get a shape mismatch unless you specify axis=0. If you specify tf.distribute.ReduceOp.MEAN, using axis=0 will use the correct denominator of 6. Contrast this with computing reduce_mean to get a scalar value on each replica and this function to average those means, which will weigh some values 1/8 and others 1/4.

Args
reduce_op	A tf.distribute.ReduceOp value specifying how values should be combined.
value	A "per replica" value, e.g. returned by run to be combined into a single tensor.
axis	Specifies the dimension to reduce along within each replica's tensor. Should typically be set to the batch dimension, or None to only reduce across replicas (e.g. if the tensor has no batch dimension).

Returns
A Tensor.

run

View source

run(
    fn, args=(), kwargs=None, options=None
)

Run fn on each replica, with the given arguments.

Executes ops specified by fn on each replica. If args or kwargs have tf.distribute.DistributedValues, such as those produced by a tf.distribute.DistributedDataset from tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.experimental_distribute_datasets_from_function, when fn is executed on a particular replica, it will be executed with the component of tf.distribute.DistributedValues that correspond to that replica.

fn may call tf.distribute.get_replica_context() to access members such as all_reduce.

All arguments in args or kwargs should either be nest of tensors or tf.distribute.DistributedValues containing tensors or composite tensors.

Key Point: Depending on the implementation of tf.distribute.Strategy and whether eager execution is enabled, fn may be called one or more times. If fn is annotated with tf.function or tf.distribute.Strategy.run is called inside a tf.function, eager execution is disabled and fn is called once (or once per replica, if you are using MirroredStrategy) to generate a Tensorflow graph, which will then be reused for execution with new inputs. Otherwise, if eager execution is enabled, fn will be called every step just like regular python code.

Example usage:

1. Constant tensor input.

strategy = tf.distribute.MirroredStrategy()
tensor_input = tf.constant(3.0)
@tf.function
def replica_fn(input):
  return input*2.0
result = strategy.run(replica_fn, args=(tensor_input,))
result
<tf.Tensor: shape=(), dtype=float32, numpy=6.0>

1. DistributedValues input.

strategy = tf.distribute.MirroredStrategy()
@tf.function
def run():
  def value_fn(value_context):
    return value_context.num_replicas_in_sync
  distributed_values = (
    strategy.experimental_distribute_values_from_function(
      value_fn))
  def replica_fn2(input):
    return input*2
  return strategy.run(replica_fn2, args=(distributed_values,))
result = run()
result
<tf.Tensor: shape=(), dtype=int32, numpy=2>

Args
fn	The function to run. The output must be a tf.nest of Tensors.
args	(Optional) Positional arguments to fn.
kwargs	(Optional) Keyword arguments to fn.
options	(Optional) An instance of tf.distribute.RunOptions specifying the options to run fn.

Returns
Merged return value of fn across replicas. The structure of the return value is the same as the return value from fn. Each element in the structure can either be tf.distribute.DistributedValues, Tensor objects, or Tensors (for example, if running on a single replica).

scope

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scope()

Context manager to make the strategy current and distribute variables.

This method returns a context manager, and is used as follows:

strategy = tf.distribute.MirroredStrategy()
# Variable created inside scope:
with strategy.scope():
  mirrored_variable = tf.Variable(1.)
mirrored_variable
MirroredVariable:{
  0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
}
# Variable created outside scope:
regular_variable = tf.Variable(1.)
regular_variable
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>

What happens when Strategy.scope is entered?

• strategy is installed in the global context as the "current" strategy. Inside this scope, tf.distribute.get_strategy() will now return this strategy. Outside this scope, it returns the default no-op strategy.
• Entering the scope also enters the "cross-replica context". See tf.distribute.StrategyExtended for an explanation on cross-replica and replica contexts.
• Variable creation inside scope is intercepted by the strategy. Each strategy defines how it wants to affect the variable creation. Sync strategies like MirroredStrategy, TPUStrategy and MultiWorkerMiroredStrategy create variables replicated on each replica, whereas ParameterServerStrategy creates variables on the parameter servers. This is done using a custom tf.variable_creator_scope.
• In some strategies, a default device scope may also be entered: in MultiWorkerMiroredStrategy, a default device scope of "/CPU:0" is entered on each worker.

Note: Entering a scope does not automatically distribute a computation, except in the case of high level training framework like keras model.fit. If you're not using model.fit, you need to use strategy.run API to explicitly distribute that computation. See an example in the custom training loop tutorial.

What should be in scope and what should be outside?

There are a number of requirements on what needs to happen inside the scope. However, in places where we have information about which strategy is in use, we often enter the scope for the user, so they don't have to do it explicitly (i.e. calling those either inside or outside the scope is OK).

• Anything that creates variables that should be distributed variables must be in strategy.scope. This can be either by directly putting it in scope, or relying on another API like strategy.run or model.fit to enter it for you. Any variable that is created outside scope will not be distributed and may have performance implications. Common things that create variables in TF: models, optimizers, metrics. These should always be created inside the scope. Another source of variable creation can be a checkpoint restore - when variables are created lazily. Note that any variable created inside a strategy captures the strategy information. So reading and writing to these variables outside the strategy.scope can also work seamlessly, without the user having to enter the scope.
• Some strategy APIs (such as strategy.run and strategy.reduce) which require to be in a strategy's scope, enter the scope for you automatically, which means when using those APIs you don't need to enter the scope yourself.
• When a tf.keras.Model is created inside a strategy.scope, we capture this information. When high level training frameworks methods such as model.compile, model.fit etc are then called on this model, we automatically enter the scope, as well as use this strategy to distribute the training etc. See detailed example in distributed keras tutorial. Note that simply calling the model(..) is not impacted - only high level training framework APIs are. model.compile, model.fit, model.evaluate, model.predict and model.save can all be called inside or outside the scope.
• The following can be either inside or outside the scope: ** Creating the input datasets ** Defining tf.functions that represent your training step ** Saving APIs such as tf.saved_model.save. Loading creates variables, so that should go inside the scope if you want to train the model in a distributed way. ** Checkpoint saving. As mentioned above - checkpoint.restore may sometimes need to be inside scope if it creates variables.

Returns
A context manager.

update_config_proto

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update_config_proto(
    config_proto
)

Returns a copy of config_proto modified for use with this strategy.

DEPRECATED: This method is not available in TF 2.x.

The updated config has something needed to run a strategy, e.g. configuration to run collective ops, or device filters to improve distributed training performance.

Args
config_proto	a tf.ConfigProto object.

Returns
The updated copy of the config_proto.