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tf.distribute.MirroredStrategy

TensorFlow 1 version View source on GitHub

Synchronous training across multiple replicas on one machine.

Inherits From: Strategy

tf.distribute.MirroredStrategy(
    devices=None, cross_device_ops=None
)

Used in the notebooks

Used in the guide Used in the tutorials

This strategy is typically used for training on one machine with multiple GPUs. For TPUs, use tf.distribute.experimental.TPUStrategy. To use MirroredStrategy with multiple workers, please refer to tf.distribute.experimental.MultiWorkerMirroredStrategy.

For example, a variable created under a MirroredStrategy is a MirroredVariable. If no devices are specified in the constructor argument of the strategy then it will use all the available GPUs. If no GPUs are found, it will use the available CPUs. Note that TensorFlow treats all CPUs on a machine as a single device, and uses threads internally for parallelism.

strategy = tf.distribute.MirroredStrategy() 
with strategy.scope(): 
  x = tf.Variable(1.) 
x 
MirroredVariable:{ 
    0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0> 
  } 

While using distribution strategies, all the variable creation should be done within the strategy's scope. This will replicate the variables across all the replicas and keep them in sync using an all-reduce algorithm.

Variables created inside a MirroredStrategy which is wrapped with a tf.function are still MirroredVariables.

x = [] 
@tf.function  # Wrap the function with tf.function. 
def create_variable(): 
  if not x: 
    x.append(tf.Variable(1.)) 
strategy = tf.distribute.MirroredStrategy() 
with strategy.scope(): 
  create_variable() 
  print (x[0]) 
MirroredVariable:{ 
    0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0> 
  } 

experimental_distribute_dataset can be used to distribute the dataset across the replicas when writing your own training loop. If you are using .fit and .compile methods available in tf.keras, then tf.keras will handle the distribution for you.

For example:

my_strategy = tf.distribute.MirroredStrategy()
with my_strategy.scope():
  @tf.function
  def distribute_train_epoch(dataset):
    def replica_fn(input):
      # process input and return result
      return result

    total_result = 0
    for x in dataset:
      per_replica_result = my_strategy.run(replica_fn, args=(x,))
      total_result += my_strategy.reduce(tf.distribute.ReduceOp.SUM,
                                         per_replica_result, axis=None)
    return total_result

  dist_dataset = my_strategy.experimental_distribute_dataset(dataset)
  for _ in range(EPOCHS):
    train_result = distribute_train_epoch(dist_dataset)

Args:

  • devices: a list of device strings such as ['/gpu:0', '/gpu:1']. If None, all available GPUs are used. If no GPUs are found, CPU is used.
  • cross_device_ops: optional, a descedant of CrossDeviceOps. If this is not set, NcclAllReduce() will be used by default. One would customize this if NCCL isn't available or if a special implementation that exploits the particular hardware is available.

Attributes:

  • extended: tf.distribute.StrategyExtended with additional methods.
  • num_replicas_in_sync: Returns number of replicas over which gradients are aggregated.

Methods

experimental_assign_to_logical_device

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experimental_assign_to_logical_device(
    tensor, logical_device_id
)

Adds annotation that tensor will be assigned to a logical device.

NOTE: This API is only supported in TPUStrategy for now. This adds an annotation to tensor specifying that operations on tensor will be invoked on logical core device id logical_device_id. When model parallelism is used, the default behavior is that all ops are placed on zero-th logical device.


# Initializing TPU system with 2 logical devices and 4 replicas.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment = tf.tpu.experimental.DeviceAssignment.build(
    topology,
    computation_shape=[1, 1, 2],
    num_replicas=4)
strategy = tf.distribute.experimental.TPUStrategy(
    resolver, device_assignment=device_assignment)
iterator = iter(inputs)

@tf.function()
def step_fn(inputs):
  output = tf.add(inputs, inputs)

  // Add operation will be executed on logical device 0.
  output = strategy.experimental_assign_to_logical_device(output, 0)
  return output

strategy.run(step_fn, args=(next(iterator),))

Args:

  • tensor: Input tensor to annotate.
  • logical_device_id: Id of the logical core to which the tensor will be assigned.

Raises:

  • ValueError: The logical device id presented is not consistent with total number of partitions specified by the device assignment.

Returns:

Annotated tensor with idential value as tensor.

experimental_distribute_dataset

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experimental_distribute_dataset(
    dataset
)

Distributes a tf.data.Dataset instance provided via dataset.

The returned distributed dataset can be iterated over similar to how regular datasets can. NOTE: Currently, the user cannot add any more transformations to a distributed dataset.

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 distributed dataset
for x in dist_dataset:
  # process dataset elements
  strategy.run(train_step, args=(x,))

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).

In a multi-worker setting, we will first attempt to distribute the dataset by attempting to detect whether the dataset is being created out of ReaderDatasets (e.g. TFRecordDataset, TextLineDataset, etc.) and if so, attempting 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 should disable distributing your dataset using the method below.

If that attempt is unsuccessful (e.g. the dataset is created from a Dataset.range), 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 method of sharding is selected.

You can disable dataset sharding across workers using the auto_shard_policy option in tf.data.experimental.DistributeOptions.

Within each worker, we will also split the data among all the worker devices (if more than one a present), and this will happen even if multi-worker sharding is disabled using the method above.

If the above batch splitting and dataset sharding logic is undesirable, please use 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 distributed dataset 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 distributed dataset
for x in dist_dataset:
  # process dataset elements
  strategy.run(train_step, args=(x,))

Args:

  • dataset: tf.data.Dataset that will be sharded across all replicas using the rules stated above.

Returns:

A "distributed Dataset", which acts like a tf.data.Dataset except it produces "per-replica" values.

experimental_distribute_datasets_from_function

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experimental_distribute_datasets_from_function(
    dataset_fn
)

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:

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)

inputs = strategy.experimental_distribute_datasets_from_function(dataset_fn)

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

IMPORTANT: 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.

To query the tf.TypeSpec of the elements in the distributed dataset returned by this API, you need to use the element_spec property of the distributed iterator. This tf.TypeSpec can be used to set the input_signature property of a tf.function.

# If you want to specify `input_signature` for a `tf.function` you must
# first create the iterator.
iterator = iter(inputs)

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

for _ in range(steps):
  strategy.run(replica_fn_with_signature,
      args=(next(iterator),))

Args:

Returns:

A "distributed Dataset", which acts like a tf.data.Dataset except it produces "per-replica" values.

experimental_distribute_values_from_function

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experimental_distribute_values_from_function(
    value_fn
)

Generates tf.distribute.DistributedValues from value_fn.

This function is to generate tf.distribute.DistributedValues to pass into run, reduce, or other methods that take distributed values when not using datasets.

Args:

  • value_fn: The function to run to generate values. It is called for each replica with tf.distribute.ValueContext as the sole argument. It must return a Tensor or a type that can be converted to a Tensor.

Returns:

A tf.distribute.DistributedValues containing a value for each replica.

Example usage:

  1. Return constant value per replica:
strategy = tf.distribute.MirroredStrategy() 
def value_fn(ctx): 
  return tf.constant(1.) 
distributed_values = ( 
     strategy.experimental_distribute_values_from_function( 
       value_fn)) 
local_result = strategy.experimental_local_results(distributed_values) 
local_result 
(<tf.Tensor: shape=(), dtype=float32, numpy=1.0>,) 
  1. Distribute values in array based on replica_id:
strategy = tf.distribute.MirroredStrategy() 
array_value = np.array([3., 2., 1.]) 
def value_fn(ctx): 
  return array_value[ctx.replica_id_in_sync_group] 
distributed_values = ( 
     strategy.experimental_distribute_values_from_function( 
       value_fn)) 
local_result = strategy.experimental_local_results(distributed_values) 
local_result 
(3.0,) 
  1. Specify values using num_replicas_in_sync:
strategy = tf.distribute.MirroredStrategy() 
def value_fn(ctx): 
  return ctx.num_replicas_in_sync 
distributed_values = ( 
     strategy.experimental_distribute_values_from_function( 
       value_fn)) 
local_result = strategy.experimental_local_results(distributed_values) 
local_result 
(1,) 
  1. Place values on devices and distribute:
strategy = tf.distribute.TPUStrategy()
worker_devices = strategy.extended.worker_devices
multiple_values = []
for i in range(strategy.num_replicas_in_sync):
  with tf.device(worker_devices[i]):
    multiple_values.append(tf.constant(1.0))

def value_fn(ctx):
  return multiple_values[ctx.replica_id]

distributed_values = strategy.
  experimental_distribute_values_from_function(
  value_fn)

experimental_local_results

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experimental_local_results(
    value
)

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

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

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experimental_make_numpy_dataset(
    numpy_input
)

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 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.

Returns:

A tf.data.Dataset representing numpy_input.

experimental_replicate_to_logical_devices

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experimental_replicate_to_logical_devices(
    tensor
)

Adds annotation that tensor will be replicated to all logical devices.

NOTE: This API is only supported in TPUStrategy for now. This adds an annotation to tensor tensor specifying that operations on tensor will be invoked on all logical devices.

# Initializing TPU system with 2 logical devices and 4 replicas.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment = tf.tpu.experimental.DeviceAssignment.build(
    topology,
    computation_shape=[1, 1, 2],
    num_replicas=4)
strategy = tf.distribute.experimental.TPUStrategy(
    resolver, device_assignment=device_assignment)

iterator = iter(inputs)

@tf.function()
def step_fn(inputs):
  images, labels = inputs
  images = strategy.experimental_split_to_logical_devices(
    inputs, [1, 2, 4, 1])

  // model() function will be executed on 8 logical devices with `inputs`
  // split 2 * 4  ways.
  output = model(inputs)

  // For loss calculation, all logical devices share the same logits
  // and labels.
  labels = strategy.experimental_replicate_to_logical_devices(labels)
  output = strategy.experimental_replicate_to_logical_devices(output)
  loss = loss_fn(labels, output)

  return loss

strategy.run(step_fn, args=(next(iterator),))

Args: tensor: Input tensor to annotate.

Returns:

Annotated tensor with idential value as tensor.

experimental_split_to_logical_devices

View source

experimental_split_to_logical_devices(
    tensor, partition_dimensions
)

Adds annotation that tensor will be split across logical devices.

NOTE: This API is only supported in TPUStrategy for now. This adds an annotation to tensor tensor specifying that operations on tensor will be be split among multiple logical devices. Tensor tensor will be split across dimensions specified by partition_dimensions. The dimensions of tensor must be divisible by corresponding value in partition_dimensions.

For example, for system with 8 logical devices, if tensor is an image tensor with shape (batch_size, width, height, channel) and partition_dimensions is [1, 2, 4, 1], then tensor will be split 2 in width dimension and 4 way in height dimension and the split tensor values will be fed into 8 logical devices.

# Initializing TPU system with 8 logical devices and 1 replica.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
device_assignment = tf.tpu.experimental.DeviceAssignment.build(
    topology,
    computation_shape=[2, 2, 2],
    num_replicas=1)
strategy = tf.distribute.experimental.TPUStrategy(
    resolver, device_assignment=device_assignment)

iterator = iter(inputs)

@tf.function()
def step_fn(inputs):
  inputs = strategy.experimental_split_to_logical_devices(
    inputs, [1, 2, 4, 1])

  // model() function will be executed on 8 logical devices with `inputs`
  // split 2 * 4  ways.
  output = model(inputs)
  return output

strategy.run(step_fn, args=(next(iterator),))

Args: tensor: Input tensor to annotate. partition_dimensions: An unnested list of integers with the size equal to rank of tensor specifying how tensor will be partitioned. The product of all elements in partition_dimensions must be equal to the total number of logical devices per replica.

Raises:

  • ValueError: 1) If the size of partition_dimensions does not equal to rank of tensor or 2) if product of elements of partition_dimensions does not match the number of logical devices per replica defined by the implementing DistributionStrategy's device specification or 3) if a known size of tensor is not divisible by corresponding value in partition_dimensions.

Returns:

Annotated tensor with idential value as tensor.

reduce

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reduce(
    reduce_op, value, axis
)

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

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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 "distributed Dataset" or experimental_distribute_values_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.

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

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()

Returns a context manager selecting this Strategy as current.

Inside a with strategy.scope(): code block, this thread will use a variable creator set by strategy, and will enter its "cross-replica context".

Returns:

A context manager.