tf.compat.v1.tpu.replicate

Builds a graph operator that runs a replicated TPU computation.

tf.compat.v1.tpu.replicate(
    computation, inputs=None, infeed_queue=None, device_assignment=None, name=None,
    maximum_shapes=None, padding_spec=None, xla_options=None
)

Example for the basic usage that inputs has static shape:

def computation(x):
  x = x + 1
  return tf.math.reduce_mean(x)

x = tf.convert_to_tensor([1., 2., 3.])
y = tf.convert_to_tensor([4., 5., 6.])
tf.compat.v1.tpu.replicate(computation, inputs=[[x], [y]])

If the inputs has dynamic shapes and you would like to automatically bucketize the inputs to avoid XLA recompilation. See the advanced example below:

def computation(x):
  x = x + 1
  return tf.math.reduce_mean(x)

# Assume input tensors in two replicas `x` and `y` both have dynamic shape
# ([None, 2]).
tf.compat.v1.tpu.replicate(
  computation,
  inputs=[x, y],
  maximum_shapes=[tf.TensorShape([None, None])],
  padding_spec=tf.compat.v1.tpu.PaddingSpec.POWER_OF_TWO)

Args
computation	A Python function that builds the computation to replicate.
inputs	A list of lists of input tensors or None (equivalent to [[]]), indexed by [replica_num][input_num]. All replicas must have the same number of inputs. Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with tf.convert_to_tensor.
infeed_queue	If not None, the InfeedQueue from which to append a tuple of arguments as inputs to computation.
device_assignment	If not None, a DeviceAssignment describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if None. The DeviceAssignment may be omitted if each replica of the computation uses only one core, and there is either only one replica, or the number of replicas is equal to the number of cores in the TPU system.
name	(Deprecated) Does nothing.
maximum_shapes	A nested structure of tf.TensorShape representing the shape to which the respective component of each input element in each replica should be padded. Any unknown dimensions (e.g. tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like object) will be padded to the maximum size of that dimension over all replicas. The structure of maximum_shapes needs to be the same as inputs[0].
padding_spec	An enum specified by tpu.PaddingSpec. This describes the padding policy when the inputs to tpu.replicate is dynamic. One usage is to enable automatic bucketizing on the inputs by setting the value to tpu.PaddingSpec.POWER_OF_TWO, which can help to reduce the recompilation in the XLA side.
xla_options	An instance of tpu.XLAOptions which indicates the options passed to XLA compiler. Use None for default options.

Returns

A list of outputs, indexed by [replica_num] each output can be a nested structure same as what computation() returns with a few exceptions. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation.

Raises
ValueError	If all replicas do not have equal numbers of input tensors.
ValueError	If the number of inputs per replica does not match the number of formal parameters to computation.
ValueError	If the static inputs dimensions don't match with the values given in maximum_shapes.
ValueError	If the structure of inputs per replica does not match the structure of maximum_shapes.