Source code for fairseq.modules.beamable_mm

# 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

[docs]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
[docs] def forward(self, input1, input2): if ( not 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 =[0, :, :], input2[0, :, :]) else: output = input1.bmm(input2) return output.view(bsz, 1, -1) else: return input1.bmm(input2)
[docs] def set_beam_size(self, beam_size): self.beam_size = beam_size