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- # Copyright (c) 2017-present, Facebook, Inc.
- # All rights reserved.
- #
- # This source code is licensed under the license found in the LICENSE file in
- # the root directory of this source tree. An additional grant of patent rights
- # can be found in the PATENTS file in the same directory.
- import math
- import torch
- class Search(object):
- def __init__(self, tgt_dict):
- self.pad = tgt_dict.pad()
- self.unk = tgt_dict.unk()
- self.eos = tgt_dict.eos()
- self.vocab_size = len(tgt_dict)
- self.scores_buf = None
- self.indices_buf = None
- self.beams_buf = None
- def _init_buffers(self, t):
- if self.scores_buf is None:
- self.scores_buf = t.new()
- self.indices_buf = torch.LongTensor().to(device=t.device)
- self.beams_buf = torch.LongTensor().to(device=t.device)
- def step(self, step, lprobs, scores, beam_size):
- """Take a single search step.
- Args:
- step: the current search step, starting at 0
- lprobs: (bsz x input_beam_size x vocab_size)
- the model's log-probabilities over the vocabulary at the current step
- scores: (bsz x input_beam_size x step)
- the historical model scores of each hypothesis up to this point
- Return: A tuple of (scores, indices, beams) where:
- scores: (bsz x output_beam_size)
- the scores of the chosen elements; output_beam_size can be
- larger than input_beam_size, e.g., we may return
- 2*input_beam_size to account for EOS
- indices: (bsz x output_beam_size)
- the indices of the chosen elements
- beams: (bsz x output_beam_size)
- the hypothesis ids of the chosen elements, in the range [0, input_beam_size)
- """
- raise NotImplementedError
- def set_src_lengths(self, src_lengths):
- self.src_lengths = src_lengths
- class BeamSearch(Search):
- def __init__(self, tgt_dict):
- super().__init__(tgt_dict)
- def step(self, step, lprobs, scores):
- super()._init_buffers(lprobs)
- bsz, beam_size, vocab_size = lprobs.size()
- if step == 0:
- # at the first step all hypotheses are equally likely, so use
- # only the first beam
- lprobs = lprobs[:, ::beam_size, :].contiguous()
- else:
- # make probs contain cumulative scores for each hypothesis
- lprobs.add_(scores[:, :, step - 1].unsqueeze(-1))
- torch.topk(
- lprobs.view(bsz, -1),
- k=min(
- # Take the best 2 x beam_size predictions. We'll choose the first
- # beam_size of these which don't predict eos to continue with.
- beam_size * 2,
- lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad
- ),
- out=(self.scores_buf, self.indices_buf),
- )
- torch.div(self.indices_buf, vocab_size, out=self.beams_buf)
- self.indices_buf.fmod_(vocab_size)
- return self.scores_buf, self.indices_buf, self.beams_buf
- class LengthConstrainedBeamSearch(Search):
- def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b):
- super().__init__(tgt_dict)
- self.min_len_a = min_len_a
- self.min_len_b = min_len_b
- self.max_len_a = max_len_a
- self.max_len_b = max_len_b
- self.beam = BeamSearch(tgt_dict)
- def step(self, step, lprobs, scores):
- min_lens = self.min_len_a * self.src_lengths + self.min_len_b
- max_lens = self.max_len_a * self.src_lengths + self.max_len_b
- lprobs[step < min_lens, :, self.eos] = -math.inf
- lprobs[step == max_lens, :, self.eos] = 0
- lprobs[step > max_lens, :, self.eos] = -math.inf
- return self.beam.step(step, lprobs, scores)
- class DiverseBeamSearch(Search):
- """Diverse Beam Search.
- See "Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
- Models" for details.
- We only implement the Hamming Diversity penalty here, which performed best
- in the original paper.
- """
- def __init__(self, tgt_dict, num_groups, diversity_strength):
- super().__init__(tgt_dict)
- self.num_groups = num_groups
- self.diversity_strength = -diversity_strength
- self.diversity_buf = None
- self.beam = BeamSearch(tgt_dict)
- def step(self, step, lprobs, scores):
- super()._init_buffers(lprobs)
- bsz, beam_size, vocab_size = lprobs.size()
- if beam_size % self.num_groups != 0:
- raise ValueError(
- 'DiverseBeamSearch requires --beam to be divisible by the number of groups'
- )
- # initialize diversity penalty
- if self.diversity_buf is None:
- self.diversity_buf = lprobs.new()
- torch.zeros(lprobs[:, 0, :].size(), out=self.diversity_buf)
- scores_G, indices_G, beams_G = [], [], []
- for g in range(self.num_groups):
- lprobs_g = lprobs[:, g::self.num_groups, :]
- scores_g = scores[:, g::self.num_groups, :] if step > 0 else None
- # apply diversity penalty
- if g > 0:
- lprobs_g = torch.add(lprobs_g, self.diversity_strength, self.diversity_buf.unsqueeze(1))
- else:
- lprobs_g = lprobs_g.contiguous()
- scores_buf, indices_buf, beams_buf = self.beam.step(step, lprobs_g, scores_g)
- beams_buf.mul_(self.num_groups).add_(g)
- scores_G.append(scores_buf.clone())
- indices_G.append(indices_buf.clone())
- beams_G.append(beams_buf.clone())
- # update diversity penalty
- self.diversity_buf.scatter_add_(
- 1,
- indices_buf,
- self.diversity_buf.new_ones(indices_buf.size())
- )
- # interleave results from different groups
- self.scores_buf = torch.stack(scores_G, dim=2, out=self.scores_buf).view(bsz, -1)
- self.indices_buf = torch.stack(indices_G, dim=2, out=self.indices_buf).view(bsz, -1)
- self.beams_buf = torch.stack(beams_G, dim=2, out=self.beams_buf).view(bsz, -1)
- return self.scores_buf, self.indices_buf, self.beams_buf
- class Sampling(Search):
- def __init__(self, tgt_dict, sampling_topk=-1, sampling_temperature=1.):
- super().__init__(tgt_dict)
- self.sampling_topk = sampling_topk
- self.sampling_temperature = sampling_temperature
- def step(self, step, lprobs, scores):
- super()._init_buffers(lprobs)
- bsz, beam_size, vocab_size = lprobs.size()
- if step == 0:
- # at the first step all hypotheses are equally likely, so use
- # only the first beam
- lprobs = lprobs[:, ::beam_size, :].contiguous()
- # we exclude the first two vocab items, one of which is pad
- assert self.pad == 1, 'sampling assumes the first two symbols can be ignored'
- lprobs_nopad = lprobs[:, :, 2:]
- # only sample from top-k candidates
- if self.sampling_topk > 0:
- lprobs_nopad, topk_indices = lprobs_nopad.topk(self.sampling_topk)
- # sampling temperature
- if self.sampling_temperature != 1.:
- lprobs_nopad = lprobs_nopad.div_(self.sampling_temperature)
- # sample
- probs_nopad = lprobs_nopad.exp_()
- if step == 0:
- self.indices_buf = torch.multinomial(
- probs_nopad.view(bsz, -1),
- beam_size,
- replacement=True,
- out=self.indices_buf,
- ).view(bsz, beam_size)
- else:
- self.indices_buf = torch.multinomial(
- probs_nopad.view(bsz * beam_size, -1),
- 1,
- replacement=True,
- out=self.indices_buf,
- ).view(bsz, beam_size)
- if step == 0:
- # expand to beam size
- probs_nopad = probs_nopad.expand(bsz, beam_size, -1)
- # gather scores
- torch.gather(
- probs_nopad,
- dim=2,
- index=self.indices_buf.unsqueeze(-1),
- out=self.scores_buf,
- )
- self.scores_buf = self.scores_buf.log_().view(bsz, -1)
- # remap indices if using top-k sampling
- if self.sampling_topk > 0:
- self.indices_buf = torch.gather(
- topk_indices.expand(bsz, beam_size, -1),
- dim=2,
- index=self.indices_buf.unsqueeze(-1),
- ).squeeze(2)
- # remap indices since we excluded the first two vocab items
- self.indices_buf.add_(2)
- if step == 0:
- self.beams_buf = self.indices_buf.new_zeros(bsz, beam_size)
- else:
- self.beams_buf = torch.arange(0, beam_size, out=self.beams_buf).repeat(bsz, 1)
- # make scores cumulative
- self.scores_buf.add_(
- torch.gather(
- scores[:, :, step - 1],
- dim=1,
- index=self.beams_buf,
- )
- )
- return self.scores_buf, self.indices_buf, self.beams_buf
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