tf.distribute.MirroredStrategy  |  TensorFlow v2.16.1 (original) (raw)

Synchronous training across multiple replicas on one machine.

Inherits From: Strategy

tf.distribute.MirroredStrategy(
    devices=None, cross_device_ops=None
)

Used in the notebooks

This strategy is typically used for training on one machine with multiple GPUs. For TPUs, usetf.distribute.TPUStrategy. To use MirroredStrategy with multiple workers, please refer to tf.distribute.experimental.MultiWorkerMirroredStrategy.

For example, a variable created under a MirroredStrategy is aMirroredVariable. If no devices are specified in the constructor argument of the strategy then it will use all the available GPUs. If no GPUs are found, it will use the available CPUs. Note that TensorFlow treats all CPUs on a machine as a single device, and uses threads internally for parallelism.

strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) with strategy.scope(): x = tf.Variable(1.) x MirroredVariable:{ 0: <tf.Variable ... shape=() dtype=float32, numpy=1.0>, 1: <tf.Variable ... shape=() dtype=float32, numpy=1.0> }

While using distribution strategies, all the variable creation should be done within the strategy's scope. This will replicate the variables across all the replicas and keep them in sync using an all-reduce algorithm.

Variables created inside a MirroredStrategy which is wrapped with atf.function are still MirroredVariables.

x = [] @tf.function # Wrap the function with tf.function. def create_variable(): if not x: x.append(tf.Variable(1.)) return x[0] strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) with strategy.scope(): _ = create_variable() print(x[0]) MirroredVariable:{ 0: <tf.Variable ... shape=() dtype=float32, numpy=1.0>, 1: <tf.Variable ... shape=() dtype=float32, numpy=1.0> }

experimental_distribute_dataset can be used to distribute the dataset across the replicas when writing your own training loop. If you are using .fit and.compile methods available in tf.keras, then tf.keras will handle the distribution for you.

For example:

my_strategy = tf.distribute.MirroredStrategy()
with my_strategy.scope():
  @tf.function
  def distribute_train_epoch(dataset):
    def replica_fn(input):
      # process input and return result
      return result

    total_result = 0
    for x in dataset:
      per_replica_result = my_strategy.run(replica_fn, args=(x,))
      total_result += my_strategy.reduce(tf.distribute.ReduceOp.SUM,
                                         per_replica_result, axis=None)
    return total_result

  dist_dataset = my_strategy.experimental_distribute_dataset(dataset)
  for _ in range(EPOCHS):
    train_result = distribute_train_epoch(dist_dataset)

Methods

distribute_datasets_from_function

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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 atf.distribute.InputContext argument and returns a tf.data.Datasetinstance. 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, whereexperimental_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 thetf.distribute.DistributedDataset returned by this API to query thetf.TypeSpec of the elements returned by the iterator. This can be used to set the input_signature property of a tf.function. Followtf.distribute.DistributedDataset.element_spec to see an example.

For a tutorial on more usage and properties of this method, refer to thetutorial on distributed input). If you are interested in last partial batch handling, read this section.

experimental_distribute_dataset

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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 atf.distribute.DistributedDataset. You can only create an iterator or examine the tf.TypeSpec of the data generated by it. See API docs oftf.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 usetf.distribute.experimental.MultiWorkerMirroredStrategyor 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 usingtf.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 usetf.distribute.Strategy.distribute_datasets_from_functioninstead, which does not do any automatic batching or sharding for you.

For a tutorial on more usage and properties of this method, refer to thetutorial on distributed input. If you are interested in last partial batch handling, read this section.

experimental_distribute_values_from_function

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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.

Example usage:

1. 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>)
2. 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)
3. 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)
4. 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

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experimental_local_results(
    value
)

Returns the list of all local per-replica values contained in value.

gather

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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)>

reduce

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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 of Strategy.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 specifytf.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.

run

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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 kwargshave tf.distribute.DistributedValues, such as those produced by atf.distribute.DistributedDataset fromtf.distribute.Strategy.experimental_distribute_dataset ortf.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 calltf.distribute.get_replica_context() to access members such asall_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 betf.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:

1. 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>
}
2. 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>
3. 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>
}

scope

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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". Seetf.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 like MirroredStrategy, TPUStrategy andMultiWorkerMiroredStrategy create variables replicated on each replica, whereas ParameterServerStrategy creates variables on the parameter servers. This is done using a custom tf.variable_creator_scope.
• In some strategies, a default device scope may also be entered: inMultiWorkerMiroredStrategy, 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 like strategy.run or keras.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 the strategy.scope can also work seamlessly, without the user having to enter the scope.
• Some strategy APIs (such as strategy.run and strategy.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 a strategy.scope, the Model object captures the scope information. When high level training framework methods such as model.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 indistributed keras tutorial. WARNING: Simply calling model(..) does not automatically enter the captured scope -- only high level training framework APIs support this behavior: model.compile, model.fit, model.evaluate, model.predictand model.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.functions 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.