tf.distribute.experimental.CentralStorageStrategy

TensorFlow 1 version View source on GitHub

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

Inherits From: Strategy

Used in the notebooks

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.run(train_step, args=(x,))

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

View source

Adds annotation that tensor will be assigned to a 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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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.

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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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.run(replica_fn, args=(batch,))

Args
dataset_fn A function taking a tf.distribute.InputContext instance and returning a tf.data.Dataset.

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

experimental_distribute_values_from_function

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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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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 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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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

View source

Adds annotation that tensor will be replicated to 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

Adds annotation that tensor will be split across logical devices.

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

View source

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.

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.run(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 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 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.
options (Optional) An instance of tf.distribute.RunOptions specifying the options to run fn.

Returns
Return value from running fn.

scope

View source

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.