TensorFlow 1 version
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View source on GitHub
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A distribution strategy for synchronous training on multiple workers.
Inherits From: Strategy
tf.distribute.experimental.MultiWorkerMirroredStrategy(
communication=tf.distribute.experimental.CollectiveCommunication.AUTO,
cluster_resolver=None
)
This strategy implements synchronous distributed training across multiple
workers, each with potentially multiple GPUs. Similar to
tf.distribute.MirroredStrategy, it creates copies of all variables in the
model on each device across all workers.
It uses CollectiveOps's implementation of multi-worker all-reduce to to keep variables in sync. A collective op is a single op in the TensorFlow graph which can automatically choose an all-reduce algorithm in the TensorFlow runtime according to hardware, network topology and tensor sizes.
By default it uses all local GPUs or CPU for single-worker training.
When 'TF_CONFIG' environment variable is set, it parses cluster_spec, task_type and task_id from 'TF_CONFIG' and turns into a multi-worker strategy which mirrores models on GPUs of all machines in a cluster. In the current implementation, it uses all GPUs in a cluster and it assumes all workers have the same number of GPUs.
You can also pass a distribute.cluster_resolver.ClusterResolver instance
when instantiating the strategy. The task_type, task_id etc. will be parsed
from the resolver instance instead of from the TF_CONFIG env var.
It supports both eager mode and graph mode. However, for eager mode, it has to set up the eager context in its constructor and therefore all ops in eager mode have to run after the strategy object is created.
Args | |
|---|---|
communication
|
optional Enum of type
distribute.experimental.CollectiveCommunication. This provides a way
for the user to override the choice of collective op communication.
Possible values include AUTO, RING, and NCCL.
|
cluster_resolver
|
optional distribute.cluster_resolver.ClusterResolver
object. The default ClusterResolver that is used is the
TFConfigClusterResolver which is instantiated from the TF_CONFIG env
var.
|
Attributes | |
|---|---|
extended
|
tf.distribute.StrategyExtended with additional methods.
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num_replicas_in_sync
|
Returns number of replicas over which gradients are aggregated. |
Methods
experimental_distribute_dataset
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.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
experimental_distribute_datasets_from_function instead.
You can disable dataset sharding across workers using the auto_shard
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.
| 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
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.experimental_run_v2(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 acts like a tf.data.Dataset except
it produces "per-replica" values.
|
experimental_local_results
experimental_local_results(
value
)
Returns the list of all local per-replica values contained in value.
| Args | |
|---|---|
value
|
A value returned by experimental_run(), experimental_run_v2(),
extended.call_for_each_replica(), or a variable created in scope.
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| Returns | |
|---|---|
A tuple of values contained in value. If value represents a single
value, this returns (value,).
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experimental_make_numpy_dataset
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.
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experimental_run_v2
experimental_run_v2(
fn, args=(), kwargs=None
)
Run fn on each replica, with the given arguments.
Executes ops specified by fn on each replica. If args or kwargs have
"per-replica" values, such as those produced by a "distributed Dataset",
when fn is executed on a particular replica, it will be executed with the
component of those "per-replica" values 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
per-replica objects containing tensors or composite tensors.
| Args | |
|---|---|
fn
|
The function to run. The output must be a tf.nest of Tensors.
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args
|
(Optional) Positional arguments to fn.
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kwargs
|
(Optional) Keyword arguments to fn.
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| 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 "per-replica" Tensor objects or Tensors
(for example, if running on a single replica).
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reduce
reduce(
reduce_op, value, axis
)
Reduce 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, 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.
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value
|
A "per replica" value, e.g. returned by experimental_run_v2 to
be combined into a single tensor.
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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.
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scope
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 MultiWorkerMirroredStrategy, all variables created inside
`strategy.scope() will be mirrored on all replicas of each worker.
Moreover, it also sets a default device scope so that ops without
specified devices will end up on the correct worker.
| Returns | |
|---|---|
| A context manager to use for creating variables with this strategy. |