tf.distribute.experimental.TPUStrategy

Synchronous training on TPUs and TPU Pods.

Inherits From: Strategy

tf.distribute.experimental.TPUStrategy(
    tpu_cluster_resolver=None, device_assignment=None
)

To construct a TPUStrategy object, you need to run the initialization code as below:

resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)

While using distribution strategies, the variables created within the strategy's scope will be replicated across all the replicas and can be kept in sync using all-reduce algorithms.

To run TF2 programs on TPUs, you can either use .compile and .fit APIs in tf.keras with TPUStrategy, or write your own customized training loop by calling strategy.run directly. Note that TPUStrategy doesn't support pure eager execution, so please make sure the function passed into strategy.run is a tf.function or strategy.run is called inside a tf.function if eager behavior is enabled.

Args
tpu_cluster_resolver	A tf.distribute.cluster_resolver.TPUClusterResolver, which provides information about the TPU cluster.
device_assignment	Optional tf.tpu.experimental.DeviceAssignment to specify the placement of replicas on the TPU cluster.

Attributes
cluster_resolver	Returns the cluster resolver associated with this strategy. tf.distribute.experimental.TPUStrategy provides the associated tf.distribute.cluster_resolver.ClusterResolver. If the user provides one in __init__, that instance is returned; if the user does not, a default tf.distribute.cluster_resolver.TPUClusterResolver is provided.
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, 1, 2],
    num_replicas=4)
strategy = tf.distribute.TPUStrategy(
    resolver, experimental_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, options=None
)

Creates tf.distribute.DistributedDataset from tf.data.Dataset.

The returned tf.distribute.DistributedDataset can be iterated over similar to how regular datasets can. NOTE: The user cannot add any more transformations to a tf.distribute.DistributedDataset.

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 `tf.distribute.DistributedDataset`
for x in dist_dataset:
  # process dataset elements
  strategy.run(replica_fn, args=(x,))

In the code snippet above, the tf.distribute.DistributedDataset dist_dataset is batched by GLOBAL_BATCH_SIZE, and we iterate through it using for x in dist_dataset. x a tf.distribute.DistributedValues containing data for all replicas, which aggregates to a batch of GLOBAL_BATCH_SIZE. tf.distribute.Strategy.run will take care of feeding the right per-replica data in x to the right replica_fn executed on each replica.

What's under the hood of this method, when we say the tf.data.Dataset instance - dataset - gets distributed? It depends on how you set the tf.data.experimental.AutoShardPolicy through tf.data.experimental.DistributeOptions. By default, it is set to tf.data.experimental.AutoShardPolicy.AUTO. In a multi-worker setting, we will first attempt to distribute dataset by detecting whether dataset is being created out of reader datasets (e.g. tf.data.TFRecordDataset, tf.data.TextLineDataset, etc.) and if so, try 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 disable dataset sharding across workers, by setting the tf.data.experimental.DistributeOptions.auto_shard_policy to be tf.data.experimental.AutoShardPolicy.OFF.

If the attempt to shard by file is unsuccessful (i.e. the dataset is not read from files), 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 route is selected.

As mentioned before, within each worker, we will also split the data among all the worker devices (if more than one a present). This will happen even if multi-worker sharding is disabled.

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

Note: The order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.experimental_distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.

Args
dataset	tf.data.Dataset that will be sharded across all replicas using the rules stated above.
options	tf.distribute.InputOptions used to control options on how this dataset is distributed.

Returns
A tf.distribute.DistributedDataset.

experimental_distribute_datasets_from_function

View source

experimental_distribute_datasets_from_function(
    dataset_fn, options=None
)

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.

You can also use the element_spec property of the tf.distribute.DistributedDataset 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.

global_batch_size = 8
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)

strategy = tf.distribute.MirroredStrategy()
ds = strategy.experimental_distribute_datasets_from_function(dataset_fn)

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

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

train(ds)

Key Point: The tf.data.Dataset retur