tf.nondifferentiable_batch_function

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,
    enable_large_batch_splitting=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.
enable_large_batch_splitting The value of this option doesn't affect processing output given the same input; it affects implementation details as stated below: 1. Improve batching efficiency by eliminating unnecessary adding. 2.max_batch_size specifies the limit of input and allowed_batch_sizes specifies the limit of a task to be processed. API user can give an input of size 128 when 'max_execution_batch_size' is 32 -> implementation can split input of 128 into 4 x 32, schedule concurrent processing, and then return concatenated results corresponding to 128.

The decorated function will return the unbatched computation output Tensors.