tf.distribute.experimental.CentralStorageStrategy

TensorFlow 1 version View source on GitHub

Class CentralStorageStrategy

A one-machine strategy that puts all variables on a single device.

Inherits From: Strategy

Aliases:

Used in the guide:

Variables are assigned to local CPU or the only GPU. If there is more than one GPU, compute operations (other than variable update operations) will be replicated across all GPUs.

For Example:

strategy = tf.distribute.experimental.CentralStorageStrategy()
# Create a dataset
ds = tf.data.Dataset.range(5).batch(2)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(ds)

with strategy.scope():
  @tf.function
  def train_step(val):
    return val + 1

  # Iterate over the distributed dataset
  for x in dist_dataset:
    # process dataset elements
    strategy.experimental_run_v2(train_step, args=(x,))

__init__

View source

__init__(
    compute_devices=None,
    parameter_device=None
)

Initialize self. See help(type(self)) for accurate signature.

Properties

extended

tf.distribute.StrategyExtended with additional methods.

num_replicas_in_sync

Returns number of replicas over which gradients are aggregated.

Methods

experimental_distribute_dataset

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experimental_distribute_dataset(dataset)

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

The returned dataset is a wrapped strategy dataset which creates a multidevice iterator under the hood. It prefetches the input data to the specified devices on the worker. 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.

For Example:

strategy = tf.distribute.CentralStorageStrategy()  # with 1 CPU and 1 GPU
dataset = tf.data.Dataset.range(10).batch(2)
dist_dataset = strategy.experimental_distribute_dataset(dataset)
for x in dist_dataset:
  print(x)  # Prints PerReplica values [0, 1], [2, 3],...

Args: dataset: tf.data.Dataset to be prefetched to device.

Returns:

A "distributed Dataset" that the caller can iterate over.

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. In this case, we only have one worker so dataset_fn is called once. Each replica on this worker will then dequeue a batch of elements from this local dataset.

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

For Example:

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.experimental_run_v2(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.

Args:

Returns:

A "distributed Dataset", which the caller can iterate over like regular datasets.

experimental_local_results

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experimental_local_results(value)

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

In CentralStorageStrategy there is a single worker so the value returned will be all the values on that worker.

Args:

  • value: A value returned by experimental_run(), experimental_run_v2(), 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_run_v2

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experimental_run_v2(
    fn,
    args=(),
    kwargs=None
)

Run fn on each replica, with the given arguments.

In CentralStorageStrategy, fn is called on each of the compute replicas, with the provided "per replica" arguments specific to that device.

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.

Returns:

Return value from running fn.

reduce

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reduce(
    reduce_op,
    value,
    axis
)

Reduce value across replicas.

Given a per-replica value returned by experimental_run_v2, 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.

For Example:

strategy = tf.distribute.experimental.CentralStorageStrategy(
    compute_devices=['CPU:0', 'GPU:0'], parameter_device='CPU:0')
ds = tf.data.Dataset.range(10)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(ds)

with strategy.scope():
  @tf.function
  def train_step(val):
    # pass through
    return val

  # Iterate over the distributed dataset
  for x in dist_dataset:
    result = strategy.experimental_run_v2(train_step, args=(x,))

result = strategy.reduce(tf.distribute.ReduceOp.SUM, result,
                         axis=None).numpy()
# result: array([ 4,  6,  8, 10])

result = strategy.reduce(tf.distribute.ReduceOp.SUM, result, axis=0).numpy()
# result: 28

Args:

  • reduce_op: A tf.distribute.ReduceOp value specifying how values should be combined.
  • value: A "per replica" value, e.g. returned by experimental_run_v2 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.

scope

View source

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.