wehub-resource-sync
190 lines
7.5 KiB
Python
190 lines
7.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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This file is to re-implemented the low-rank and beam approximation of CRF layer
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Proposed by:
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Sun, Zhiqing, et al.
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Fast Structured Decoding for Sequence Models
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https://arxiv.org/abs/1910.11555
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The CRF implementation is mainly borrowed from
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https://github.com/kmkurn/pytorch-crf/blob/master/torchcrf/__init__.py
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"""
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import numpy as np
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import torch
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import torch.nn as nn
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def logsumexp(x, dim=1):
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return torch.logsumexp(x.float(), dim=dim).type_as(x)
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class DynamicCRF(nn.Module):
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"""Dynamic CRF layer is used to approximate the traditional
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Conditional Random Fields (CRF)
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$P(y | x) = 1/Z(x) exp(sum_i s(y_i, x) + sum_i t(y_{i-1}, y_i, x))$
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where in this function, we assume the emition scores (s) are given,
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and the transition score is a |V| x |V| matrix $M$
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in the following two aspects:
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(1) it used a low-rank approximation for the transition matrix:
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$M = E_1 E_2^T$
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(2) it used a beam to estimate the normalizing factor Z(x)
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"""
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def __init__(self, num_embedding, low_rank=32, beam_size=64):
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super().__init__()
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self.E1 = nn.Embedding(num_embedding, low_rank)
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self.E2 = nn.Embedding(num_embedding, low_rank)
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self.vocb = num_embedding
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self.rank = low_rank
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self.beam = beam_size
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def extra_repr(self):
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return "vocab_size={}, low_rank={}, beam_size={}".format(
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self.vocb, self.rank, self.beam
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)
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def forward(self, emissions, targets, masks, beam=None):
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"""
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Compute the conditional log-likelihood of a sequence of target tokens given emission scores
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Args:
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emissions (`~torch.Tensor`): Emission score are usually the unnormalized decoder output
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``(batch_size, seq_len, vocab_size)``. We assume batch-first
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targets (`~torch.LongTensor`): Sequence of target token indices
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``(batch_size, seq_len)
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masks (`~torch.ByteTensor`): Mask tensor with the same size as targets
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Returns:
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`~torch.Tensor`: approximated log-likelihood
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"""
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numerator = self._compute_score(emissions, targets, masks)
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denominator = self._compute_normalizer(emissions, targets, masks, beam)
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return numerator - denominator
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def forward_decoder(self, emissions, masks=None, beam=None):
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"""
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Find the most likely output sequence using Viterbi algorithm.
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Args:
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emissions (`~torch.Tensor`): Emission score are usually the unnormalized decoder output
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``(batch_size, seq_len, vocab_size)``. We assume batch-first
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masks (`~torch.ByteTensor`): Mask tensor with the same size as targets
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Returns:
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`~torch.LongTensor`: decoded sequence from the CRF model
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"""
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return self._viterbi_decode(emissions, masks, beam)
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def _compute_score(self, emissions, targets, masks=None):
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batch_size, seq_len = targets.size()
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emission_scores = emissions.gather(2, targets[:, :, None])[:, :, 0] # B x T
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transition_scores = (self.E1(targets[:, :-1]) * self.E2(targets[:, 1:])).sum(2)
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scores = emission_scores
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scores[:, 1:] += transition_scores
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if masks is not None:
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scores = scores * masks.type_as(scores)
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return scores.sum(-1)
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def _compute_normalizer(self, emissions, targets=None, masks=None, beam=None):
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# HACK: we include "target" which is a hueristic for training
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# HACK: we use a beam of tokens to approximate the normalizing factor (which is bad?)
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beam = beam if beam is not None else self.beam
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batch_size, seq_len = emissions.size()[:2]
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if targets is not None:
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_emissions = emissions.scatter(2, targets[:, :, None], np.float("inf"))
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beam_targets = _emissions.topk(beam, 2)[1]
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beam_emission_scores = emissions.gather(2, beam_targets)
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else:
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beam_emission_scores, beam_targets = emissions.topk(beam, 2)
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beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D
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beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D
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beam_transition_matrix = torch.bmm(
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beam_transition_score1.view(-1, beam, self.rank),
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beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2),
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)
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beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam)
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# compute the normalizer in the log-space
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score = beam_emission_scores[:, 0] # B x K
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for i in range(1, seq_len):
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next_score = score[:, :, None] + beam_transition_matrix[:, i - 1]
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next_score = logsumexp(next_score, dim=1) + beam_emission_scores[:, i]
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if masks is not None:
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score = torch.where(masks[:, i : i + 1], next_score, score)
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else:
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score = next_score
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# Sum (log-sum-exp) over all possible tags
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return logsumexp(score, dim=1)
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def _viterbi_decode(self, emissions, masks=None, beam=None):
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# HACK: we use a beam of tokens to approximate the normalizing factor (which is bad?)
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beam = beam if beam is not None else self.beam
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batch_size, seq_len = emissions.size()[:2]
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beam_emission_scores, beam_targets = emissions.topk(beam, 2)
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beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D
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beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D
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beam_transition_matrix = torch.bmm(
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beam_transition_score1.view(-1, beam, self.rank),
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beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2),
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)
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beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam)
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traj_tokens, traj_scores = [], []
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finalized_tokens, finalized_scores = [], []
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# compute the normalizer in the log-space
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score = beam_emission_scores[:, 0] # B x K
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dummy = (
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torch.arange(beam, device=score.device).expand(*score.size()).contiguous()
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)
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for i in range(1, seq_len):
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traj_scores.append(score)
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_score = score[:, :, None] + beam_transition_matrix[:, i - 1]
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_score, _index = _score.max(dim=1)
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_score = _score + beam_emission_scores[:, i]
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if masks is not None:
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score = torch.where(masks[:, i : i + 1], _score, score)
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index = torch.where(masks[:, i : i + 1], _index, dummy)
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else:
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score, index = _score, _index
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traj_tokens.append(index)
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# now running the back-tracing and find the best
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best_score, best_index = score.max(dim=1)
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finalized_tokens.append(best_index[:, None])
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finalized_scores.append(best_score[:, None])
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for idx, scs in zip(reversed(traj_tokens), reversed(traj_scores)):
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previous_index = finalized_tokens[-1]
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finalized_tokens.append(idx.gather(1, previous_index))
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finalized_scores.append(scs.gather(1, previous_index))
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finalized_tokens.reverse()
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finalized_tokens = torch.cat(finalized_tokens, 1)
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finalized_tokens = beam_targets.gather(2, finalized_tokens[:, :, None])[:, :, 0]
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finalized_scores.reverse()
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finalized_scores = torch.cat(finalized_scores, 1)
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finalized_scores[:, 1:] = finalized_scores[:, 1:] - finalized_scores[:, :-1]
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return finalized_scores, finalized_tokens
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