|TensorFlow 1 version||View source on GitHub|
TPU distribution strategy implementation.
Compat aliases for migration
See Migration guide for more details.
Used in the notebooks
|Used in the guide|
__init__( tpu_cluster_resolver=None, device_assignment=None )
Synchronous training in TPU donuts or Pods.
To construct a TPUStrategy object, you need to run the initialization code as below:
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=FLAGS.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 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
.fit APIs in
tf.keras with TPUStrategy, or write your own customized
training loop by calling
strategy.experimental_run_v2 directly. Note that
TPUStrategy doesn't support pure eager execution, so please make sure the
function passed into
strategy.experimental_run_v2 is a
strategy.experimental_run_v2 us called inside a
tf.function if running
in eager mode.
tpu_cluster_resolver: A tf.distribute.cluster_resolver.TPUClusterResolver, which provides information about the TPU cluster.
tf.tpu.experimental.DeviceAssignmentto specify the placement of replicas on the TPU cluster. Currently only supports the usecase of using a single core within a TPU cluster.
tf.distribute.StrategyExtended with additional methods.
Returns number of replicas over which gradients are aggregated.
Distributes a tf.data.Dataset instance provided via
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.experimental_run_v2(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. In this case, consider using
You can disable dataset sharding across workers using the
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,
experimental_distribute_datasets_from_function instead, which
does not do any automatic splitting or sharding.
tf.data.Datasetthat will be sharded across all replicas using the rules stated above.
Dataset", which acts like a
it produces "per-replica" values.
tf.data.Dataset instances created by calls to
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
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.
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.experimental_run_v2(replica_fn, args=(batch,))
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
dataset_fn: A function taking a
tf.distribute.InputContextinstance and returning a
Dataset", which acts like a
it produces "per-replica" values.
Returns the list of all local per-replica values contained in
value: A value returned by
extended.call_for_each_replica(), or a variable created in
A tuple of values contained in
value represents a single
value, this returns
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
Note that you will likely need to use
with the returned dataset to further distribute it with the strategy.
numpy_input = np.ones(, dtype=np.float32) dataset = strategy.experimental_make_numpy_dataset(numpy_input) dist_dataset = strategy.experimental_distribute_dataset(dataset)
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
experimental_run_v2( fn, args=(), kwargs=None )
See base class.
reduce( reduce_op, value, axis )
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=0. In this case it would return a
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
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
tf.distribute.ReduceOpvalue specifying how values should be combined.
value: A "per replica" value, e.g. returned by
experimental_run_v2to 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
Noneto only reduce across replicas (e.g. if the tensor has no batch dimension).
Returns a context manager selecting this Strategy as current.
with strategy.scope(): code block, this thread
will use a variable creator set by
strategy, and will
enter its "cross-replica context".
A context manager.