tf.nondifferentiable_batch_function

TensorFlow 1 version View source on GitHub

Batches the computation done by the decorated function.

tf.nondifferentiable_batch_function(
    num_batch_threads, max_batch_size, batch_timeout_micros,
    allowed_batch_sizes=None, max_enqueued_batches=10, autograph=True
)

So, for example, in the following code

@batch_function(1, 2, 3)
def layer(a):
  return tf.matmul(a, a)

b = layer(w)

if more than one session.run call is simultaneously trying to compute b the values of w will be gathered, non-deterministically concatenated along the first axis, and only one thread will run the computation. See the documentation of the Batch op for more details.

Assumes that all arguments of the decorated function are Tensors which will be batched along their first dimension.

SparseTensor is not supported. The return value of the decorated function must be a Tensor or a list/tuple of Tensors.

num_batch_threads Number of scheduling threads for processing batches of work. Determines the number of batches processed in parallel.
max_batch_size Batch sizes will never be bigger than this.
batch_timeout_micros Maximum number of microseconds to wait before outputting an incomplete batch.
allowed_batch_sizes Optional list of allowed batch sizes. If left empty, does nothing. Otherwise, supplies a list of batch sizes, causing the op to pad batches up to one of those sizes. The entries must increase monotonically, and the final entry must equal max_batch_size.
max_enqueued_batches The maximum depth of the batch queue. Defaults to 10.
autograph Whether to use autograph to compile python and eager style code for efficient graph-mode execution.

The decorated function will return the unbatched computation output Tensors.