tf.distribute.experimental.ParameterServerStrategy

TensorFlow 1 version View source on GitHub

An asynchronous multi-worker parameter server tf.distribute strategy.

Inherits From: Strategy

tf.distribute.experimental.ParameterServerStrategy(
    cluster_resolver=None
)

This strategy requires two roles: workers and parameter servers. Variables and updates to those variables will be assigned to parameter servers and other operations are assigned to workers.

When each worker has more than one GPU, operations will be replicated on all GPUs. Even though operations may be replicated, variables are not and each worker shares a common view for which parameter server a variable is assigned to.

By default it uses TFConfigClusterResolver to detect configurations for multi-worker training. This requires a 'TF_CONFIG' environment variable and the 'TF_CONFIG' must have a cluster spec.

This class assumes each worker is running the same code independently, but parameter servers are running a standard server. This means that while each worker will synchronously compute a single gradient update across all GPUs, updates between workers proceed asynchronously. Operations that occur only on the first replica (such as incrementing the global step), will occur on the first replica of every worker.

It is expected to call call_for_each_replica(fn, ...) for any operations which potentially can be replicated across replicas (i.e. multiple GPUs) even if there is only CPU or one GPU. When defining the fn, extra caution needs to be taken:

1) It is generally not recommended to open a device scope under the strategy's scope. A device scope (i.e. calling tf.device) will be merged with or override the device for operations but will not change the device for variables.

2) It is also not recommended to open a colocation scope (i.e. calling tf.compat.v1.colocate_with) under the strategy's scope. For colocating variables, use strategy.extended.colocate_vars_with instead. Colocation of ops will possibly create device assignment conflicts.

For Example:

strategy = tf.distribute.experimental.ParameterServerStrategy()
run_config = tf.estimator.RunConfig(
    experimental_distribute.train_distribute=strategy)
estimator = tf.estimator.Estimator(config=run_config)
tf.estimator.train_and_evaluate(estimator,...)

Args:

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.

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

View source

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

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. 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

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:

  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

View source

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

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.

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

View source

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

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 "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

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.