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

tf.distribute.DistributedDataset

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Represents a dataset distributed among devices and machines.

A tf.distribute.DistributedDataset could be thought of as a "distributed" dataset. When you use tf.distribute API to scale training to multiple devices or machines, you also need to distribute the input data, which leads to a tf.distribute.DistributedDataset instance, instead of atf.data.Dataset instance in the non-distributed case. In TF 2.x,tf.distribute.DistributedDataset objects are Python iterables.

There are two APIs to create a tf.distribute.DistributedDataset object:tf.distribute.Strategy.experimental_distribute_dataset(dataset)andtf.distribute.Strategy.distribute_datasets_from_function(dataset_fn).When to use which? When you have a tf.data.Dataset instance, and the regular batch splitting (i.e. re-batch the input tf.data.Dataset instance with a new batch size that is equal to the global batch size divided by the number of replicas in sync) and autosharding (i.e. thetf.data.experimental.AutoShardPolicy options) work for you, use the former API. Otherwise, if you are not using a canonical tf.data.Dataset instance, or you would like to customize the batch splitting or sharding, you can wrap these logic in a dataset_fn and use the latter API. Both API handles prefetch to device for the user. For more details and examples, follow the links to the APIs.

There are two main usages of a DistributedDataset object:

1. Iterate over it to generate the input for a single device or multiple devices, which is a tf.distribute.DistributedValues instance. To do this, you can:
   • use a pythonic for-loop construct:
     global_batch_size = 4
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
dataset = tf.data.Dataset.from_tensors(([1.],[1.])).repeat(4).batch(global_batch_size)
dist_dataset = strategy.experimental_distribute_dataset(dataset)
@tf.function
def train_step(input):
features, labels = input
return labels - 0.3 * features
for x in dist_dataset:
# train_step trains the model using the dataset elements

loss = strategy.run(train_step, args=(x,))
print("Loss is", loss)
Loss is PerReplica:{
0: tf.Tensor(
[[0.7]
[0.7]], shape=(2, 1), dtype=float32),
1: tf.Tensor(
[[0.7]
[0.7]], shape=(2, 1), dtype=float32)
}

Placing the loop inside a tf.function will give a performance boost. However break and return are currently not supported if the loop is placed inside a tf.function. We also don't support placing the loop inside a tf.function when usingtf.distribute.experimental.MultiWorkerMirroredStrategy ortf.distribute.experimental.TPUStrategy with multiple workers.

• use __iter__ to create an explicit iterator, which is of typetf.distribute.DistributedIterator
  global_batch_size = 4
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
train_dataset = tf.data.Dataset.from_tensors(([1.],[1.])).repeat(50).batch(global_batch_size)
train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)
@tf.function
def distributed_train_step(dataset_inputs):
def train_step(input):
loss = tf.constant(0.1)
return loss
per_replica_losses = strategy.run(train_step, args=(dataset_inputs,))
return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,axis=None)
EPOCHS = 2
STEPS = 3
for epoch in range(EPOCHS):
total_loss = 0.0
num_batches = 0
dist_dataset_iterator = iter(train_dist_dataset)
for _ in range(STEPS):
total_loss += distributed_train_step(next(dist_dataset_iterator))
num_batches += 1
average_train_loss = total_loss / num_batches
template = ("Epoch {}, Loss: {:.4f}")
print (template.format(epoch+1, average_train_loss))
Epoch 1, Loss: 0.2000
Epoch 2, Loss: 0.2000
  
  To achieve a performance improvement, you can also wrap the strategy.runcall with a tf.range inside a tf.function. This runs multiple steps in atf.function. Autograph will convert it to a tf.while_loop on the worker. However, it is less flexible comparing with running a single step insidetf.function. For example, you cannot run things eagerly or arbitrary python code within the steps.

1. Inspect the tf.TypeSpec of the data generated by DistributedDataset.
   tf.distribute.DistributedDataset generatestf.distribute.DistributedValues as input to the devices. If you pass the input to a tf.function and would like to specify the shape and type of each Tensor argument to the function, you can pass a tf.TypeSpec object to the input_signature argument of the tf.function. To get thetf.TypeSpec of the input, you can use the element_spec property of thetf.distribute.DistributedDataset or tf.distribute.DistributedIteratorobject.
   For example:
   global_batch_size = 4
epochs = 1
steps_per_epoch = 1
mirrored_strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
dataset = tf.data.Dataset.from_tensors(([2.])).repeat(100).batch(global_batch_size)
dist_dataset = mirrored_strategy.experimental_distribute_dataset(dataset)
@tf.function(input_signature=[dist_dataset.element_spec])
def train_step(per_replica_inputs):
def step_fn(inputs):
return tf.square(inputs)
return mirrored_strategy.run(step_fn, args=(per_replica_inputs,))
for _ in range(epochs):
iterator = iter(dist_dataset)
for _ in range(steps_per_epoch):
output = train_step(next(iterator))
print(output)
PerReplica:{
0: tf.Tensor(
[[4.]
[4.]], shape=(2, 1), dtype=float32),
1: tf.Tensor(
[[4.]
[4.]], shape=(2, 1), dtype=float32)
}
   

Visit the tutorialon distributed input for more examples and caveats.

Methods

__iter__

View source

__iter__()

Creates an iterator for the tf.distribute.DistributedDataset.

The returned iterator implements the Python Iterator protocol.

Example usage:

global_batch_size = 4 strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4]).repeat().batch(global_batch_size) distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset)) print(next(distributed_iterator)) PerReplica:{ 0: tf.Tensor([1 2], shape=(2,), dtype=int32), 1: tf.Tensor([3 4], shape=(2,), dtype=int32) }