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|
TPU distribution strategy implementation.
Inherits From: Strategy
tf.compat.v1.distribute.experimental.TPUStrategy(
tpu_cluster_resolver=None, steps_per_run=None, device_assignment=None
)
Args | |
|---|---|
tpu_cluster_resolver
|
A tf.distribute.cluster_resolver.TPUClusterResolver, which provides information about the TPU cluster. |
steps_per_run
|
Number of steps to run on device before returning to the host. Note that this can have side-effects on performance, hooks, metrics, summaries etc. This parameter is only used when Distribution Strategy is used with estimator or keras. |
device_assignment
|
Optional tf.tpu.experimental.DeviceAssignment to
specify the placement of replicas on the TPU cluster. Currently only
supports the usecase of using a single core within a TPU cluster.
|
Attributes | |
|---|---|
extended
|
tf.distribute.StrategyExtended with additional methods.
|
num_replicas_in_sync
|
Returns number of replicas over which gradients are aggregated. |
steps_per_run
|
DEPRECATED: use .extended.steps_per_run instead. |
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.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
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,))
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 | |
|---|---|
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(), 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
experimental_make_numpy_dataset(
numpy_input, session=None
)
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 tf.distribute.Strategy.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.
|
session
|
(TensorFlow v1.x graph execution only) A session used for initialization. |
| Returns | |
|---|---|
A tf.data.Dataset representing numpy_input.
|
experimental_run
experimental_run(
fn, input_iterator=None
)
Runs ops in fn on each replica, with inputs from input_iterator.
DEPRECATED: This method is not available in TF 2.x. Please switch
to using run instead.
When eager execution is enabled, executes ops specified by fn on each
replica. Otherwise, builds a graph to execute the ops on each replica.
Each replica will take a single, different input from the inputs provided by
one get_next call on the input iterator.
fn may call tf.distribute.get_replica_context() to access members such
as replica_id_in_sync_group.
| Args | |
|---|---|
fn
|
The function to run. The inputs to the function must match the outputs
of input_iterator.get_next(). The output must be a tf.nest of
Tensors.
|
input_iterator
|
(Optional) input iterator from which the inputs are taken. |
| 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 PerReplica (if the values are unsynchronized),
Mirrored (if the values are kept in sync), or Tensor (if running on a
single replica).
|
make_dataset_iterator
make_dataset_iterator(
dataset
)
Makes an iterator for input provided via dataset.
DEPRECATED: This method is not available in TF 2.x.
Data from the given dataset will be distributed evenly across all the
compute replicas. 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).
If this effort fails, an error will be thrown, and the user should instead
use make_input_fn_iterator which provides more control to the user, and
does not try to divide a batch across replicas.
The user could also use make_input_fn_iterator if they want to
customize which input is fed to which replica/worker etc.
| Args | |
|---|---|
dataset
|
tf.data.Dataset that will be distributed evenly across all
replicas.
|
| Returns | |
|---|---|
An tf.distribute.InputIterator which returns inputs for each step of the
computation. User should call initialize on the returned iterator.
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make_input_fn_iterator
make_input_fn_iterator(
input_fn, replication_mode=tf.distribute.InputReplicationMode.PER_WORKER
)
Returns an iterator split across replicas created from an input function.
DEPRECATED: This method is not available in TF 2.x.
The input_fn should take an tf.distribute.InputContext object where
information about batching and input sharding can be accessed:
def input_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)
with strategy.scope():
iterator = strategy.make_input_fn_iterator(input_fn)
replica_results = strategy.experimental_run(replica_fn, iterator)
The tf.data.Dataset returned by input_fn should have a per-replica
batch size, which may be computed using
input_context.get_per_replica_batch_size.
| Args | |
|---|---|
input_fn
|
A function taking a tf.distribute.InputContext object and
returning a tf.data.Dataset.
|
replication_mode
|
an enum value of tf.distribute.InputReplicationMode.
Only PER_WORKER is supported currently, which means there will be
a single call to input_fn per worker. Replicas will dequeue from the
local tf.data.Dataset on their worker.
|
| Returns | |
|---|---|
An iterator object that should first be .initialize()-ed. It may then
either be passed to strategy.experimental_run() or you can
iterator.get_next() to get the next value to pass to
strategy.extended.call_for_each_replica().
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reduce
reduce(
reduce_op, value, axis=None
)
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).
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| Returns | |
|---|---|
A Tensor.
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run
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
"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.
Users can pass strategy specific options to options argument. An example
to enable bucketizing dynamic shapes in TPUStrategy.run
is:
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(tpu='')
options = tf.distribute.RunOptions()
options.experimental_bucketizing_dynamic_shape = True
iterator = iter(inputs)
@tf.function()
def step_fn(inputs):
output = tf.reduce_sum(inputs)
return output
strategy.run(step_fn, args=(next(iterator),),
options=options)
| 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 "per-replica" Tensor objects or Tensors
(for example, if running on a single replica).
|
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".
| Returns | |
|---|---|
| A context manager. |
update_config_proto
update_config_proto(
config_proto
)
Returns a copy of config_proto modified for use with this strategy.
DEPRECATED: This method is not available in TF 2.x.
The updated config has something needed to run a strategy, e.g. configuration to run collective ops, or device filters to improve distributed training performance.
| Args | |
|---|---|
config_proto
|
a tf.ConfigProto object.
|
| Returns | |
|---|---|
The updated copy of the config_proto.
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