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A one-machine strategy that puts all variables on a single device.
Inherits From: Strategy
tf.distribute.experimental.CentralStorageStrategy(
compute_devices=None, parameter_device=None
)
Variables are assigned to local CPU or the only GPU. If there is more than one GPU, compute operations (other than variable update operations) will be replicated across all GPUs.
For Example:
strategy = tf.distribute.experimental.CentralStorageStrategy()
# Create a dataset
ds = tf.data.Dataset.range(5).batch(2)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(ds)
with strategy.scope():
@tf.function
def train_step(val):
return val + 1
# Iterate over the distributed dataset
for x in dist_dataset:
# process dataset elements
strategy.run(train_step, args=(x,))
Attributes | |
---|---|
cluster_resolver
|
Returns the cluster resolver associated with this strategy.
In general, when using a multi-worker Strategies that intend to have an associated
Single-worker strategies usually do not have a
The
For more information, please see
|
extended
|
tf.distribute.StrategyExtended with additional methods.
|
num_replicas_in_sync
|
Returns number of replicas over which gradients are aggregated. |
Methods
distribute_datasets_from_function
distribute_datasets_from_function(
dataset_fn, options=None
)
Distributes tf.data.Dataset
instances created by calls to dataset_fn
.
The argument dataset_fn
that users pass in is an input function that has a
tf.distribute.InputContext
argument and returns a tf.data.Dataset
instance. It is expected that the returned dataset from dataset_fn
is
already batched by per-replica batch size (i.e. global batch size divided by
the number of replicas in sync) and sharded.
tf.distribute.Strategy.distribute_datasets_from_function
does
not batch or shard the tf.data.Dataset
instance
returned from the input function. dataset_fn
will be called on the CPU
device of each of the workers and each generates a dataset where every
replica on that worker will dequeue one batch of inputs (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. First, it allows you to
specify your own batching and sharding logic. (In contrast,
tf.distribute.experimental_distribute_dataset
does batching and sharding
for you.) 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.
The dataset_fn
should take an tf.distribute.InputContext
instance where
information about batching and input replication can be accessed.
You can use 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
. Follow
tf.distribute.DistributedDataset.element_spec
to see an example.
For a tutorial on more usage and properties of this method, refer to the tutorial on distributed input). If you are interested in last partial batch handling, read this section.
Args | |
---|---|
dataset_fn
|
A function taking a tf.distribute.InputContext instance and
returning a tf.data.Dataset .
|
options
|
tf.distribute.InputOptions used to control options on how this
dataset is distributed.
|
Returns | |
---|---|
A tf.distribute.DistributedDataset .
|
experimental_distribute_dataset
experimental_distribute_dataset(
dataset, options=None
)
Distributes a tf.data.Dataset instance provided via dataset.
The returned dataset is a wrapped strategy dataset which creates a multidevice iterator under the hood. It prefetches the input data to the specified devices on the worker. The returned distributed dataset can be iterated over similar to how regular datasets can.
For Example:
strategy = tf.distribute.CentralStorageStrategy() # with 1 CPU and 1 GPU
dataset = tf.data.Dataset.range(10).batch(2)
dist_dataset = strategy.experimental_distribute_dataset(dataset)
for x in dist_dataset:
print(x) # Prints PerReplica values [0, 1], [2, 3],...
Args:
dataset: tf.data.Dataset
to be prefetched to device.
options: tf.distribute.InputOptions
used to control options on how this
dataset is distributed.
Returns | |
---|---|
A "distributed Dataset " that the caller can iterate over.
|
experimental_distribute_values_from_function
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:
Return constant value per replica:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
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>,
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>)
Distribute values in array based on replica_id:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
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, 2.0)
Specify values using num_replicas_in_sync:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
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
(2, 2)
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_in_sync_group] distributed_values = strategy. experimental_distribute_values_from_function( value_fn)
experimental_local_results
experimental_local_results(
value
)
Returns the list of all local per-replica values contained in value
.
In CentralStorageStrategy
there is a single worker so the value returned
will be all the values on that worker.
Args | |
---|---|
value
|
A value returned by 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,).
|
gather
gather(
value, axis
)
Gather value
across replicas along axis
to the current device.
Given a tf.distribute.DistributedValues
or tf.Tensor
-like
object value
, this API gathers and concatenates value
across replicas
along the axis
-th dimension. The result is copied to the "current" device,
which would typically be the CPU of the worker on which the program is
running. For tf.distribute.TPUStrategy
, it is the first TPU host. For
multi-client tf.distribute.MultiWorkerMirroredStrategy
, this is the CPU of
each worker.
This API can only be called in the cross-replica context. For a counterpart
in the replica context, see tf.distribute.ReplicaContext.all_gather
.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
# A DistributedValues with component tensor of shape (2, 1) on each replica
distributed_values = strategy.experimental_distribute_values_from_function(lambda _: tf.identity(tf.constant([[1], [2]])))
@tf.function
def run():
return strategy.gather(distributed_values, axis=0)
run()
<tf.Tensor: shape=(4, 1), dtype=int32, numpy=
array([[1],
[2],
[1],
[2]], dtype=int32)>
Consider the following example for more combinations:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1", "GPU:2", "GPU:3"])
single_tensor = tf.reshape(tf.range(6), shape=(1,2,3))
distributed_values = strategy.experimental_distribute_values_from_function(lambda _: tf.identity(single_tensor))
@tf.function
def run(axis):
return strategy.gather(distributed_values, axis=axis)
axis=0
run(axis)
<tf.Tensor: shape=(4, 2, 3), dtype=int32, numpy=
array([[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]]], dtype=int32)>
axis=1
run(axis)
<tf.Tensor: shape=(1, 8, 3), dtype=int32, numpy=
array([[[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5]]], dtype=int32)>
axis=2
run(axis)
<tf.Tensor: shape=(1, 2, 12), dtype=int32, numpy=
array([[[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5, 3, 4, 5]]], dtype=int32)>
Args | |
---|---|
value
|
a tf.distribute.DistributedValues instance, e.g. returned by
Strategy.run , to be combined into a single tensor. It can also be a
regular tensor when used with tf.distribute.OneDeviceStrategy or the
default strategy. The tensors that constitute the DistributedValues
can only be dense tensors with non-zero rank, NOT a tf.IndexedSlices .
|
axis
|
0-D int32 Tensor. Dimension along which to gather. Must be in the range [0, rank(value)). |
Returns | |
---|---|
A Tensor that's the concatenation of value across replicas along
axis dimension.
|
reduce
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
.
For Example:
strategy = tf.distribute.experimental.CentralStorageStrategy(
compute_devices=['CPU:0', 'GPU:0'], parameter_device='CPU:0')
ds = tf.data.Dataset.range(10)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(ds)
with strategy.scope():
@tf.function
def train_step(val):
# pass through
return val
# Iterate over the distributed dataset
for x in dist_dataset:
result = strategy.run(train_step, args=(x,))
result = strategy.reduce(tf.distribute.ReduceOp.SUM, result,
axis=None).numpy()
# result: array([ 4, 6, 8, 10])
result = strategy.reduce(tf.distribute.ReduceOp.SUM, result, axis=0).numpy()
# result: 28
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
run(
fn, args=(), kwargs=None, options=None
)
Run fn
on each replica, with the given arguments.
In CentralStorageStrategy
, fn
is called on each of the compute
replicas, with the provided "per replica" arguments specific to that device.
Args | |
---|---|
fn
|
The function to run. The output must be a tf.nest of Tensor s.
|
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 | |
---|---|
Return value from running fn .
|
scope
scope()
Context manager to make the strategy current and distribute variables.
This method returns a context manager, and is used as follows:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
# Variable created inside scope:
with strategy.scope():
mirrored_variable = tf.Variable(1.)
mirrored_variable
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>,
1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=1.0>
}
# Variable created outside scope:
regular_variable = tf.Variable(1.)
regular_variable
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
What happens when Strategy.scope is entered?
strategy
is installed in the global context as the "current" strategy. Inside this scope,tf.distribute.get_strategy()
will now return this strategy. Outside this scope, it returns the default no-op strategy.- Entering the scope also enters the "cross-replica context". See
tf.distribute.StrategyExtended
for an explanation on cross-replica and replica contexts. - Variable creation inside
scope
is intercepted by the strategy. Each strategy defines how it wants to affect the variable creation. Sync strategies likeMirroredStrategy
,TPUStrategy
andMultiWorkerMiroredStrategy
create variables replicated on each replica, whereasParameterServerStrategy
creates variables on the parameter servers. This is done using a customtf.variable_creator_scope
. - In some strategies, a default device scope may also be entered: in
MultiWorkerMiroredStrategy
, a default device scope of "/CPU:0" is entered on each worker.
What should be in scope and what should be outside?
There are a number of requirements on what needs to happen inside the scope. However, in places where we have information about which strategy is in use, we often enter the scope for the user, so they don't have to do it explicitly (i.e. calling those either inside or outside the scope is OK).
- Anything that creates variables that should be distributed variables
must be called in a
strategy.scope
. This can be accomplished either by directly calling the variable creating function within the scope context, or by relying on another API likestrategy.run
orkeras.Model.fit
to automatically enter it for you. Any variable that is created outside scope will not be distributed and may have performance implications. Some common objects that create variables in TF are Models, Optimizers, Metrics. Such objects should always be initialized in the scope, and any functions that may lazily create variables (e.g.,Model.call()
, tracing atf.function
, etc.) should similarly be called within scope. Another source of variable creation can be a checkpoint restore - when variables are created lazily. Note that any variable created inside a strategy captures the strategy information. So reading and writing to these variables outside thestrategy.scope
can also work seamlessly, without the user having to enter the scope. - Some strategy APIs (such as
strategy.run
andstrategy.reduce
) which require to be in a strategy's scope, enter the scope automatically, which means when using those APIs you don't need to explicitly enter the scope yourself. - When a
tf.keras.Model
is created inside astrategy.scope
, the Model object captures the scope information. When high level training framework methods such asmodel.compile
,model.fit
, etc. are then called, the captured scope will be automatically entered, and the associated strategy will be used to distribute the training etc. See a detailed example in distributed keras tutorial. WARNING: Simply callingmodel(..)
does not automatically enter the captured scope -- only high level training framework APIs support this behavior:model.compile
,model.fit
,model.evaluate
,model.predict
andmodel.save
can all be called inside or outside the scope. - The following can be either inside or outside the scope:
- Creating the input datasets
- Defining
tf.function
s that represent your training step - Saving APIs such as
tf.saved_model.save
. Loading creates variables, so that should go inside the scope if you want to train the model in a distributed way. - Checkpoint saving. As mentioned above -
checkpoint.restore
may sometimes need to be inside scope if it creates variables.
Returns | |
---|---|
A context manager. |