TensorFlow 1 version | View source on GitHub |
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.
|
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
experimental_assign_to_logical_device(
tensor, logical_device_id
)
Adds annotation that tensor
will be assigned to a 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
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,))
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
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)
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_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()
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>,)
- Distribute values in array based on replica_id:
strategy = tf.distribute.MirroredStrategy()
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,)
- Specify values using num_replicas_in_sync:
strategy = tf.distribute.MirroredStrategy()
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
(1,)
- 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
.
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
)
Makes a tf.data.Dataset
from a numpy array. (deprecated)
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:
strategy = tf.distribute.MirroredStrategy()
numpy_input = np.ones([10], dtype=np.float32)
dataset = strategy.experimental_make_numpy_dataset(numpy_input)
dataset
<TensorSliceDataset shapes: (), types: tf.float32>
dataset = dataset.batch(2)
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 the NumPy arrays are stacked, as that is normal
tf.data.Dataset behavior.
|
Returns | |
---|---|
A tf.data.Dataset representing numpy_input .
|
experimental_replicate_to_logical_devices
experimental_replicate_to_logical_devices(
tensor
)
Adds annotation that tensor
will be replicated to all logical devices.
# 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):
images, labels = inputs
images = strategy.experimental_split_to_logical_devices(
inputs, [1, 2, 4, 1])
# model() function will be executed on 8 logical devices with `inputs`
# split 2 * 4 ways.
output = model(inputs)
# For loss calculation, all logical devices share the same logits
# and labels.
labels = strategy.experimental_replicate_to_logical_devices(labels)
output = strategy.experimental_replicate_to_logical_devices(output)
loss = loss_fn(labels, output)
return loss
strategy.run(step_fn, args=(next(iterator),))
Args: tensor: Input tensor to annotate.
Returns | |
---|---|
Annotated tensor with idential value as tensor .
|
experimental_split_to_logical_devices
experimental_split_to_logical_devices(
tensor, partition_dimensions
)
Adds annotation that tensor
will be split across logical devices.
For example, for system with 8 logical devices, if tensor
is an image
tensor with shape (batch_size, width, height, channel) and
partition_dimensions
is [1, 2, 4, 1], then tensor
will be split
2 in width dimension and 4 way in height dimension and the split
tensor values will be fed into 8 logical devices.
# Initializing TPU system with 8 logical devices and 1 replica.
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, 2, 2, 2],
num_replicas=1)
strategy = tf.distribute.TPUStrategy(
resolver, experimental_device_assignment=device_assignment)
iterator = iter(inputs)
@tf.function()
def step_fn(inputs):
inputs = strategy.experimental_split_to_logical_devices(
inputs, [1, 2, 4, 1])
# model() function will be executed on 8 logical devices with `inputs`
# split 2 * 4 ways.
output = model(inputs)
return output
strategy.run(step_fn, args=(next(iterator),))
Args:
tensor: Input tensor to annotate.
partition_dimensions: An unnested list of integers with the size equal to
rank of tensor
specifying how tensor
will be partitioned. The
product of all elements in partition_dimensions
must be equal to the
total number of logical devices per replica.
Raises | |
---|---|
ValueError
|
1) If the size of partition_dimensions does not equal to rank
of |
Returns | |
---|---|
Annotated tensor with idential value as tensor .
|
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
.
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
)
See base class.
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()
# 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>
}
# 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 in
strategy.scope
. This can be either by directly putting it in scope, or relying on another API likestrategy.run
ormodel.fit
to enter it for you. Any variable that is created outside scope will not be distributed and may have performance implications. Common things that create variables in TF: models, optimizers, metrics. These should always be created inside the 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 for you automatically, which means when using those APIs you don't need to enter the scope yourself. - When a
tf.keras.Model
is created inside astrategy.scope
, we capture this information. When high level training frameworks methods such asmodel.compile
,model.fit
etc are then called on this model, we automatically enter the scope, as well as use this strategy to distribute the training etc. See detailed example in distributed keras tutorial. Note that simply calling themodel(..)
is not impacted - only high level training framework APIs are.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 astf.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. |