description: Synchronous training on TPUs and TPU Pods.

tf.distribute.experimental.TPUStrategy

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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.

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 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.

IMPORTANT: The tf.data.Dataset returned by dataset_fn should have a per-replica batch size, unlike experimental_distribute_dataset, which uses the global batch size. This may be computed using input_context.get_per_replica_batch_size.

Note: If you are using TPUStrategy, the order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.

Note: Stateful dataset transformations are currently not supported with tf.distribute.experimental_distribute_dataset or tf.distribute.distribute_datasets_from_function. Any stateful ops that the dataset may have are currently ignored. For example, if your dataset has a map_fn that uses tf.random.uniform to rotate an image, then you have a dataset graph that depends on state (i.e the random seed) on the local machine where the python process is being executed.

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.

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 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 happending 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.

Note: for autosharding across multiple workers, the default mode is tf.data.experimental.AutoShardPolicy.AUTO. This mode will attempt to shard the input dataset by files if the dataset is being created out of reader datasets (e.g. tf.data.TFRecordDataset, tf.data.TextLineDataset, etc.) or otherwise shard the dataset by data, where each of the workers will read the entire dataset and only process the shard assigned to it. However, if you have less than one input file per worker, we suggest that you disable dataset autosharding across workers by setting the tf.data.experimental.DistributeOptions.auto_shard_policy to be tf.data.experimental.AutoShardPolicy.OFF.

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.

Note: If you are using TPUStrategy, the order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.

Note: Stateful dataset transformations are currently not supported with tf.distribute.experimental_distribute_dataset or tf.distribute.distribute_datasets_from_function. Any stateful ops that the dataset may have are currently ignored. For example, if your dataset has a map_fn that uses tf.random.uniform to rotate an image, then you have a dataset graph that depends on state (i.e the random seed) on the local machine where the python process is being executed.

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.

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>)

1. 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)

1. 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)

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

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experimental_local_results(
    value
)

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

Note: This only returns values on the worker initiated by this client. When using a tf.distribute.Strategy like tf.distribute.experimental.MultiWorkerMirroredStrategy, each worker will be its own client, and this function will only return values computed on that worker.

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 MultiWorkerMirroredStrategy, this is 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.

Note: For all strategies except tf.distribute.TPUStrategy, the input value on different replicas must have the same rank, and their shapes must be the same in all dimensions except the axis-th dimension. In other words, their shapes cannot be different in a dimension d where d does not equal to the axis argument. For example, given a tf.distribute.DistributedValues with component tensors of shape (1, 2, 3) and (1, 3, 3) on two replicas, you can call gather(..., axis=1, ...) on it, but not gather(..., axis=0, ...) or gather(..., axis=2, ...). However, for tf.distribute.TPUStrategy.gather, all tensors must have exactly the same rank and same shape.

Note: Given a tf.distribute.DistributedValues value, its component tensors must have a non-zero rank. Otherwise, consider using tf.expand_dims before gathering them.

>>> 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.

Note: The result is copied to the "current" device - which would typically be the CPU of the worker on which the program is running. For TPUStrategy, it is the first TPU host. For multi client MultiWorkerMirroredStrategy, this is CPU of each worker.

There are a number of different tf.distribute APIs for reducing values across replicas: * tf.distribute.ReplicaContext.all_reduce: This differs from Strategy.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 and tf.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 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.

run

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run(
    fn, args=(), kwargs=None, options=None
)

See base class.

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". 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 like MirroredStrategy, TPUStrategy and MultiWorkerMiroredStrategy 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: in MultiWorkerMiroredStrategy, a default device scope of "/CPU:0" is entered on each worker.

Note: Entering a scope does not automatically distribute a computation, except in the case of high level training framework like keras model.fit. If you're not using model.fit, you need to use strategy.run API to explicitly distribute that computation. See an example in the custom training loop tutorial.

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 like strategy.run or model.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 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 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 a strategy.scope, we capture this information. When high level training frameworks methods such as model.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 the model(..) is not impacted - only high level training framework APIs are. model.compile, model.fit, model.evaluate, model.predict and 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.