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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
class BeamableMM(nn.Module):
"""This module provides an optimized MM for beam decoding with attention.
It leverage the fact that the source-side of the input is replicated beam
times and the target-side of the input is of width one. This layer speeds up
inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)}
with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}.
"""
def __init__(self, beam_size=None):
super(BeamableMM, self).__init__()
self.beam_size = beam_size
def forward(self, input1, input2):
if (
not self.training
and self.beam_size is not None # test mode
and input1.dim() == 3 # beam size is set
and input1.size(1) # only support batched input
== 1 # single time step update
):
bsz, beam = input1.size(0), self.beam_size
# bsz x 1 x nhu --> bsz/beam x beam x nhu
input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1)
# bsz x sz2 x nhu --> bsz/beam x sz2 x nhu
input2 = input2.unfold(0, beam, beam)[:, :, :, 0]
# use non batched operation if bsz = beam
if input1.size(0) == 1:
output = torch.mm(input1[0, :, :], input2[0, :, :])
else:
output = input1.bmm(input2)
return output.view(bsz, 1, -1)
else:
return input1.bmm(input2)
def set_beam_size(self, beam_size):
self.beam_size = beam_size
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