TensorFlow 1 version | View source on GitHub |
An multi-worker tf.distribute strategy with parameter servers.
Inherits From: Strategy
tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver, variable_partitioner=None
)
Parameter server training is a common data-parallel method to scale up a machine learning model on multiple machines. A parameter server training cluster consists of workers and parameter servers. Variables are created on parameter servers and they are read and updated by workers in each step. By default, workers read and update these variables independently without synchronizing with each other. Under this configuration, it is known as asynchronous training.
In TensorFlow 2, we recommend an architecture based on central coordination
for parameter server training. Each worker and parameter server runs a
tf.distribute.Server
, and on top of that, a coordinator task is responsible
for creating resources on workers and parameter servers, dispatching
functions, and coordinating the training. The coordinator uses a
tf.distribute.experimental.coordinator.ClusterCoordinator
to coordinate the
cluster, and a tf.distribute.experimental.ParameterServerStrategy
to define
variables on parameter servers and computation on workers.
For the training to work, the coordinator dispatches tf.function
s to be
executed on remote workers. Upon receiving requests from the coordinator, a
worker executes the tf.function
by reading the variables from parameter
servers, executing the ops, and updating the variables on the parameter
servers. Each of the worker only processes the requests from the coordinator,
and communicates with parameter servers, without direct interactions with
other workers in the cluster.
As a result, failures of some workers do not prevent the cluster from continuing the work, and this allows the cluster to train with instances that can be occasionally unavailable (e.g. preemptible or spot instances). The coordinator and parameter servers though, must be available at all times for the cluster to make progress.
Note that the coordinator is not one of the training workers. Instead, it
creates resources such as variables and datasets, dispatchs tf.function
s,
saves checkpoints and so on. In addition to workers, parameter servers and
the coordinator, an optional evaluator can be run on the side that
periodically reads the checkpoints saved by the coordinator and runs
evaluations against each checkpoint.
ParameterServerStrategy
is supported with two training APIs: Custom
Training Loop (CTL)
and Keras Training API, also known as Model.fit
. CTL is recommended
when users prefer to define the details of their training loop, and
Model.fit
is recommended when users prefer a high-level abstraction and
handling of training.
When using a CTL, ParameterServerStrategy
has to work in conjunction with a
tf.distribute.experimental.coordinator.ClusterCoordinator
object.
When using Model.fit
, currently only the
tf.keras.utils.experimental.DatasetCreator
input type is supported.
Example code for coordinator
This section provides code snippets that are intended to be run on (the only)
one task that is designated as the coordinator. Note that cluster_resolver
,
variable_partitioner
, and dataset_fn
arguments are explained in the
following "Cluster setup", "Variable partitioning", and "Dataset preparation"
sections.
With a CTL,
# Prepare a strategy to use with the cluster and variable partitioning info.
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver=...,
variable_partitioner=...)
coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
strategy=strategy)
# Prepare a distribute dataset that will place datasets on the workers.
distributed_dataset = coordinator.create_per_worker_dataset(dataset_fn=...)
with strategy.scope():
model = ...
optimizer, metrics = ... # Keras optimizer/metrics are great choices
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
checkpoint_manager = tf.train.CheckpointManager(
checkpoint, checkpoint_dir, max_to_keep=2)
# `load_checkpoint` infers initial epoch from `optimizer.iterations`.
initial_epoch = load_checkpoint(checkpoint_manager) or 0
@tf.function
def worker_fn(iterator):
def replica_fn(inputs):
batch_data, labels = inputs
# calculate gradient, applying gradient, metrics update etc.
strategy.run(replica_fn, args=(next(iterator),))
for epoch in range(initial_epoch, num_epoch):
distributed_iterator = iter(distributed_dataset) # Reset iterator state.
for step in range(steps_per_epoch):
# Asynchronously schedule the `worker_fn` to be executed on an arbitrary
# worker. This call returns immediately.
coordinator.schedule(worker_fn, args=(distributed_iterator,))
# `join` blocks until all scheduled `worker_fn`s finish execution. Once it
# returns, we can read the metrics and save checkpoints as needed.
coordinator.join()
logging.info('Metric result: %r', metrics.result())
train_accuracy.reset_states()
checkpoint_manager.save()
With Model.fit
,
# Prepare a strategy to use with the cluster and variable partitioning info.
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver=...,
variable_partitioner=...)
# A dataset function takes a `input_context` and returns a `Dataset`
def dataset_fn(input_context):
dataset = tf.data.Dataset.from_tensors(...)
return dataset.repeat().shard(...).batch(...).prefetch(...)
# With `Model.fit`, a `DatasetCreator` needs to be used.
input = tf.keras.utils.experimental.DatasetCreator(dataset_fn=...)
with strategy.scope():
model = ... # Make sure the `Model` is created within scope.
model.compile(optimizer="rmsprop", loss="mse", steps_per_execution=..., ...)
# Optional callbacks to checkpoint the model, back up the progress, etc.
callbacks = [tf.keras.callbacks.ModelCheckpoint(...), ...]
# `steps_per_epoch` is required with `ParameterServerStrategy`.
model.fit(input, epochs=..., steps_per_epoch=..., callbacks=callbacks)
Example code for worker and parameter servers
In addition to the coordinator, there should be tasks designated as "worker" or "ps". They should run the following code to start a TensorFlow server, waiting for coordinator's requests:
# Provide a `tf.distribute.cluster_resolver.ClusterResolver` that serves
# the cluster information. See below "Cluster setup" section.
cluster_resolver = ...
server = tf.distribute.Server(
cluster_resolver.cluster_spec(),
job_name=cluster_resolver.task_type,
task_index=cluster_resolver.task_id,
protocol="grpc")
# Blocking the process that starts a server from exiting.
server.join()
Cluster setup
In order for the tasks in the cluster to know other tasks' addresses,
a tf.distribute.cluster_resolver.ClusterResolver
is required to be used
in coordinator, worker, and ps. The
tf.distribute.cluster_resolver.ClusterResolver
is responsible for providing
the cluster information, as well as the task type and id of the current task.
See tf.distribute.cluster_resolver.ClusterResolver
for more information.
If TF_CONFIG
environment variable is set, a
tf.distribute.cluster_resolver.TFConfigClusterResolver
should be used as
well.
Since there are assumptions in
tf.distribute.experimental.ParameterServerStrategy
around the naming of the
task types, "chief", "ps", and "worker" should be used in the
tf.distribute.cluster_resolver.ClusterResolver
to refer to the coordinator,
parameter servers, and workers, respectively.
The following example demonstrates setting TF_CONFIG
for the task designated
as a parameter server (task type "ps") and index 1 (the second task), in a
cluster with 1 chief, 2 parameter servers, and 3 workers. Note that it needs
to be set before the use of
tf.distribute.cluster_resolver.TFConfigClusterResolver
.
Example code for cluster setup:
os.environ['TF_CONFIG'] = '''
{
"cluster": {
"chief": ["chief.example.com:2222"],
"ps": ["ps0.example.com:2222", "ps1.example.com:2222"],
"worker": ["worker0.example.com:2222", "worker1.example.com:2222",
"worker2.example.com:2222"]
},
"task": {
"type": "ps",
"index": 1
}
}
'''
If you prefer to run the same binary for all tasks, you will need to let the binary branch into different roles at the beginning of the program:
# If coordinator, create a strategy and start the training program.
if cluster_resolver.task_type == 'chief':
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver)
...
# If worker/ps, create a server
elif cluster_resolver.task_type in ("worker", "ps"):
server = tf.distribute.Server(...)
...
Alternatively, you can also start a bunch of TensorFlow servers in advance and connect to them later. The coordinator can be in the same cluster or on any machine that has connectivity to workers and parameter servers. This is covered in our guide and tutorial.
Variable creation with strategy.scope()
tf.distribute.experimental.ParameterServerStrategy
follows the
tf.distribute
API contract where variable creation is expected to be inside
the context manager returned by strategy.scope()
, in order to be correctly
placed on parameter servers in a round-robin manner:
# In this example, we're assuming having 3 ps.
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver=...)
coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
strategy=strategy)
# Variables should be created inside scope to be placed on parameter servers.
# If created outside scope such as `v1` here, it would be placed on the
# coordinator.
v1 = tf.Variable(initial_value=0.0)
with strategy.scope():
v2 = tf.Variable(initial_value=1.0)
v3 = tf.Variable(initial_value=2.0)
v4 = tf.Variable(initial_value=3.0)
v5 = tf.Variable(initial_value=4.0)
# v2 through v5 are created in scope and are distributed on parameter servers.
# Default placement is round-robin but the order should not be relied on.
assert v2.device == "/job:ps/replica:0/task:0/device:CPU:0"
assert v3.device == "/job:ps/replica:0/task:1/device:CPU:0"
assert v4.device == "/job:ps/replica:0/task:2/device:CPU:0"
assert v5.device == "/job:ps/replica:0/task:0/device:CPU:0"
See distribute.Strategy.scope
for more information.
Variable partitioning
Having dedicated servers to store variables means being able to divide up, or "shard" the variables across the ps. Partitioning large variable among ps is a commonly used technique to boost training throughput and mitigate memory constraints. It enables parallel computations and updates on different shards of a variable, and often yields better load balancing across parameter servers. Without sharding, models with large variables (e.g, embeddings) that can't fit into one machine's memory would otherwise be unable to train.
With tf.distribute.experimental.ParameterServerStrategy
, if a
variable_partitioner
is provided to __init__
and certain conditions are
satisfied, the resulting variables created in scope are sharded across the
parameter servers, in a round-robin fashion. The variable reference returned
from tf.Variable
becomes a type that serves as the container of the sharded
variables. One can access variables
attribute of this container for the
actual variable components. If building model with tf.Module
or Keras,
the variable components are collected in the variables
alike attributes.
class Dense(tf.Module):
def __init__(self, name=None):
super().__init__(name=name)
self.w = tf.Variable(tf.random.normal([100, 10]), name='w')
def __call__(self, x):
return x * self.w
# Partition the dense layer into 2 shards.
variable_partitioner = (
tf.distribute.experimental.partitioners.FixedShardsPartitioner(
num_shards = 2))
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver=...,
variable_partitioner = variable_partitioner)
with strategy.scope():
dense = Dense()
assert len(dense.variables) == 2
assert isinstance(dense.variables[0], tf.Variable)
assert isinstance(dense.variables[1], tf.Variable)
assert dense.variables[0].shape == (50, 10)
assert dense.variables[1].shape == (50, 10)
The sharded variable container can be converted to a Tensor
via
tf.convert_to_tensor
. This means the container can be directly used in most
Python Ops where such Tensor
conversion automatically happens. For example,
in the above code snippet, x * self.w
would implicitly apply the said tensor
conversion. Note that such conversion can be expensive, as the variable
components need to be transferred from multiple parameter servers to where
the value is used.
tf.nn.embedding_lookup
on the other hand doesn't apply the tensor
conversion, and performs parallel lookups on the variable components instead.
This is crucial to scale up embedding lookups when the embedding table
variable is large.
When a partitioned variable is saved to a SavedModel
, it will be saved as if
it is one single variable. This improves serving efficiency by eliminating
a number of Ops that handle the partiton aspects.
Known limitations of variable partitioning:
Number of partitions must not change across Checkpoint saving/loading.
After saving partitioned variables to a SavedModel, the SavedModel can't be loaded via
tf.saved_model.load
.Partition variable doesn't directly work with
tf.GradientTape
, please use thevariables
attributes to get the actual variable components and use them in gradient APIs instead.
Dataset preparation
With tf.distribute.experimental.ParameterServerStrategy
, a dataset is
created in each of the workers to be used for training. This is done by
creating a dataset_fn
that takes no argument and returns a
tf.data.Dataset
, and passing the dataset_fn
into
tf.distribute.experimental.coordinator.
ClusterCoordinator.create_per_worker_dataset
. We recommend the dataset to be
shuffled and repeated to have the examples run through the training as evenly
as possible.
def dataset_fn():
filenames = ...
dataset = tf.data.Dataset.from_tensor_slices(filenames)
# Dataset is recommended to be shuffled, and repeated.
return dataset.shuffle(buffer_size=...).repeat().batch(batch_size=...)
coordinator =
tf.distribute.experimental.coordinator.ClusterCoordinator(strategy=...)
distributed_dataset = coordinator.create_per_worker_dataset(dataset_fn)
Limitations
tf.distribute.experimental.ParameterServerStrategy
in TF2 is experimental, and the API is subject to further changes.When using
Model.fit
,tf.distribute.experimental.ParameterServerStrategy
must be used with atf.keras.utils.experimental.DatasetCreator
, andsteps_per_epoch
must be specified.
Args | |
---|---|
cluster_resolver
|
a tf.distribute.cluster_resolver.ClusterResolver
object.
|
variable_partitioner
|
a distribute.experimental.partitioners.Partitioner that specifies
how to partition variables. If None , variables will not be
partitioned.
|
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
)
Creates tf.distribute.DistributedDataset
from tf.data.Dataset
.
The returned tf.distribute.DistributedDataset
can be iterated over
similar to regular datasets.
NOTE: The user cannot add any more transformations to a
tf.distribute.DistributedDataset
. You can only create an iterator or
examine the tf.TypeSpec
of the data generated by it. See API docs of
tf.distribute.DistributedDataset
to learn more.
The following is an example:
global_batch_size = 2
# Passing the devices is optional.
strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
# Create a dataset
dataset = tf.data.Dataset.range(4).batch(global_batch_size)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)
@tf.function
def replica_fn(input):
return input*2
result = []
# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
# process dataset elements
result.append(strategy.run(replica_fn, args=(x,)))
print(result)
[PerReplica:{
0: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([0])>,
1: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([2])>
}, PerReplica:{
0: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([4])>,
1: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([6])>
}]
Three key actions happening under the hood of this method are batching, sharding, and prefetching.
In the code snippet above, dataset
is batched by global_batch_size
, and
calling experimental_distribute_dataset
on it rebatches dataset
to a
new batch size that is equal to the global batch size divided by the number
of replicas in sync. We iterate through it using a Pythonic for loop.
x
is a tf.distribute.DistributedValues
containing data for all replicas,
and each replica gets data of the new 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.
Sharding contains autosharding across multiple workers and within every
worker. First, in multi-worker distributed training (i.e. when you use
tf.distribute.experimental.MultiWorkerMirroredStrategy
or tf.distribute.TPUStrategy
), autosharding a dataset over a set of
workers means that each worker is assigned a subset of the entire dataset
(if the right tf.data.experimental.AutoShardPolicy
is set). This is to
ensure that at each step, a global batch size of non-overlapping dataset
elements will be processed by each worker. Autosharding has a couple of
different options that can be specified using
tf.data.experimental.DistributeOptions
. Then, sharding within each worker
means the method will split the data among all the worker devices (if more
than one a present). This will happen regardless of multi-worker
autosharding.
By default, this method adds a prefetch transformation at the end of the
user provided tf.data.Dataset
instance. The argument to the prefetch
transformation which is buffer_size
is equal to the number of replicas in
sync.
If the above batch splitting and dataset sharding logic is undesirable,
please use
tf.distribute.Strategy.distribute_datasets_from_function
instead, which does not do any automatic batching or sharding for you.
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
|
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_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
.
Args | |
---|---|
value
|
A value returned by experimental_run() , run(), or a variable
created in scope`.
|
Returns | |
---|---|
A tuple of values contained in value where ith element corresponds to
ith replica. 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 and return result on current device.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def step_fn():
i = tf.distribute.get_replica_context().replica_id_in_sync_group
return tf.identity(i)
per_replica_result = strategy.run(step_fn)
total = strategy.reduce("SUM", per_replica_result, axis=None)
total
<tf.Tensor: shape=(), dtype=int32, numpy=1>
To see how this would look with multiple replicas, consider the same example with MirroredStrategy with 2 GPUs:
strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
def step_fn():
i = tf.distribute.get_replica_context().replica_id_in_sync_group
return tf.identity(i)
per_replica_result = strategy.run(step_fn)
# Check devices on which per replica result is:
strategy.experimental_local_results(per_replica_result)[0].device
# /job:localhost/replica:0/task:0/device:GPU:0
strategy.experimental_local_results(per_replica_result)[1].device
# /job:localhost/replica:0/task:0/device:GPU:1
total = strategy.reduce("SUM", per_replica_result, axis=None)
# Check device on which reduced result is:
total.device
# /job:localhost/replica:0/task:0/device:CPU:0
This API is typically used for aggregating the results returned from different replicas, for reporting etc. For example, loss computed from different replicas can be averaged using this API before printing.
There are a number of different tf.distribute APIs for reducing values across replicas:
tf.distribute.ReplicaContext.all_reduce
: This differs fromStrategy.reduce
in that it is for replica context and does not copy the results to the host device.all_reduce
should be typically used for reductions inside the training step such as gradients.tf.distribute.StrategyExtended.reduce_to
andtf.distribute.StrategyExtended.batch_reduce_to
: These APIs are more advanced versions ofStrategy.reduce
as they allow customizing the destination of the result. They are also called in cross replica context.
What should axis be?
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 by specifying the axis parameter accordingly.
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. With axis=None
, reduce
will
aggregate only 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 or loss).
strategy.reduce("sum", per_replica_result, axis=None)
Sometimes, you will want to aggregate across both the global batch and
all replicas. You can get this behavior 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
.
strategy.reduce("sum", per_replica_result, axis=0)
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. Allows using string representation of the enum such as
"SUM", "MEAN".
|
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 OneDeviceStrategy or default strategy.
|
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
)
Invokes fn
on each replica, with the given arguments.
This method is the primary way to distribute your computation with a
tf.distribute object. It invokes fn
on each replica. If args
or kwargs
have tf.distribute.DistributedValues
, such as those produced by a
tf.distribute.DistributedDataset
from
tf.distribute.Strategy.experimental_distribute_dataset
or
tf.distribute.Strategy.distribute_datasets_from_function
,
when fn
is executed on a particular replica, it will be executed with the
component of tf.distribute.DistributedValues
that correspond to that
replica.
fn
is invoked under a replica context. fn
may call
tf.distribute.get_replica_context()
to access members such as
all_reduce
. Please see the module-level docstring of tf.distribute for the
concept of replica context.
All arguments in args
or kwargs
can be a nested structure of tensors,
e.g. a list of tensors, in which case args
and kwargs
will be passed to
the fn
invoked on each replica. Or args
or kwargs
can be
tf.distribute.DistributedValues
containing tensors or composite tensors,
i.e. tf.compat.v1.TensorInfo.CompositeTensor
, in which case each fn
call
will get the component of a tf.distribute.DistributedValues
corresponding
to its replica. Note that arbitrary Python values that are not of the types
above are not supported.
Example usage:
- Constant tensor input.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
tensor_input = tf.constant(3.0)
@tf.function
def replica_fn(input):
return input*2.0
result = strategy.run(replica_fn, args=(tensor_input,))
result
PerReplica:{
0: <tf.Tensor: shape=(), dtype=float32, numpy=6.0>,
1: <tf.Tensor: shape=(), dtype=float32, numpy=6.0>
}
- DistributedValues input.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
@tf.function
def run():
def value_fn(value_context):
return value_context.num_replicas_in_sync
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
def replica_fn2(input):
return input*2
return strategy.run(replica_fn2, args=(distributed_values,))
result = run()
result
<tf.Tensor: shape=(), dtype=int32, numpy=4>
- Use
tf.distribute.ReplicaContext
to allreduce values.
strategy = tf.distribute.MirroredStrategy(["gpu:0", "gpu:1"])
@tf.function
def run():
def value_fn(value_context):
return tf.constant(value_context.replica_id_in_sync_group)
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
def replica_fn(input):
return tf.distribute.get_replica_context().all_reduce("sum", input)
return strategy.run(replica_fn, args=(distributed_values,))
result = run()
result
PerReplica:{
0: <tf.Tensor: shape=(), dtype=int32, numpy=1>,
1: <tf.Tensor: shape=(), dtype=int32, numpy=1>
}
Args | |
---|---|
fn
|
The function to run on each replica. |
args
|
Optional positional arguments to fn . Its element can be a tensor,
a nested structure of tensors or a tf.distribute.DistributedValues .
|
kwargs
|
Optional keyword arguments to fn . Its element can be a tensor,
a nested structure of tensors or a tf.distribute.DistributedValues .
|
options
|
An optional 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 tf.distribute.DistributedValues , Tensor
objects, or Tensor s (for example, if running on a single replica).
|
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. |