tf.distribute.OneDeviceStrategy

A distribution strategy for running on a single device.

Inherits From: Strategy

tf.distribute.OneDeviceStrategy(
    device
)

Using this strategy will place any variables created in its scope on the specified device. Input distributed through this strategy will be prefetched to the specified device. Moreover, any functions called via strategy.run will also be placed on the specified device as well.

Typical usage of this strategy could be testing your code with the tf.distribute.Strategy API before switching to other strategies which actually distribute to multiple devices/machines.

For example:

strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")

with strategy.scope():
  v = tf.Variable(1.0)
  print(v.device)  # /job:localhost/replica:0/task:0/device:GPU:0

def step_fn(x):
  return x * 2

result = 0
for i in range(10):
  result += strategy.run(step_fn, args=(i,))
print(result)  # 90

Args
`device`	Device string identifier for the device on which the variables should be placed. See class docs for more details on how the device is used. Examples: "/cpu:0", "/gpu:0", "/device:CPU:0", "/device:GPU:0"

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`

View source

experimental_assign_to_logical_device(
    tensor, logical_device_id
)

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`

View source

experimental_distribute_dataset(
    dataset
)

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

In this case, there is only one device, so this is only a thin wrapper around the input dataset. It will, however, prefetch the input data to the specified device. The returned distributed dataset can be iterated over similar to how regular datasets can.

Example:

strategy = tf.distribute.OneDeviceStrategy()
dataset = tf.data.Dataset.range(10).batch(2)
dist_dataset = strategy.experimental_distribute_dataset(dataset)
for x in dist_dataset:
  print(x)  # [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`

View source

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 and one device so dataset_fn is called once.

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,))

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`

View source

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:

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>,)

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,)

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,)

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`

View source

experimental_local_results(
    value
)

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

In OneDeviceStrategy, the value is always expected to be a single value, so the result is just the value in a tuple.

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
)

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

experimental_replicate_to_logical_devices(
    tensor
)

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

experimental_split_to_logical_devices(
    tensor, partition_dimensions
)

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

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`.

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(
    reduce_op, value, axis
)

Reduce value across replicas.

In OneDeviceStrategy, there is only one replica, so if axis=None, value is simply returned. If axis is specified as something other than None, such as axis=0, value is reduced along that axis and returned.

Example:

t = tf.range(10)

result = strategy.reduce(tf.distribute.ReduceOp.SUM, t, axis=None).numpy()
# result: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

result = strategy.reduce(tf.distribute.ReduceOp.SUM, t, axis=0).numpy()
# result: 45

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.

In OneDeviceStrategy, fn is simply called within a device scope for the given device, with the provided arguments.

Args
`fn`	The function to run. The output must be a `tf.nest` of `Tensor`s.
`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

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".

In OneDeviceStrategy, all variables created inside strategy.scope() will be on device specified at strategy construction time. See example in the docs for this class.

Returns
A context manager to use for creating variables with this strategy.

tf.distribute.OneDeviceStrategy

For example:

Args

Attributes

Methods

experimental_assign_to_logical_device

experimental_distribute_dataset

Example:

experimental_distribute_datasets_from_function

experimental_distribute_values_from_function

Example usage:

experimental_local_results

experimental_make_numpy_dataset

Example:

experimental_replicate_to_logical_devices

experimental_split_to_logical_devices

reduce

Example:

run

scope

`experimental_assign_to_logical_device`

`experimental_distribute_dataset`

`experimental_distribute_datasets_from_function`

`experimental_distribute_values_from_function`

`experimental_local_results`

`experimental_make_numpy_dataset`

`experimental_replicate_to_logical_devices`

`experimental_split_to_logical_devices`

`reduce`

`run`

`scope`