File size: 9,866 Bytes
6705032 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
import torch.nn as nn
import torch
from torch.autograd import Variable
import copy
import numpy as np
class Seq2Seq(nn.Module):
"""
Build Seqence-to-Sequence.
Parameters:
* `encoder`- encoder of seq2seq model. e.g. roberta
* `decoder`- decoder of seq2seq model. e.g. transformer
* `config`- configuration of encoder model.
* `beam_size`- beam size for beam search.
* `max_length`- max length of target for beam search.
* `sos_id`- start of symbol ids in target for beam search.
* `eos_id`- end of symbol ids in target for beam search.
"""
def __init__(self, encoder, decoder, config, mse_loss_weight=0.95, ce_loss_weight=0.05, beam_size=None, max_length=None, sos_id=None, eos_id=None, ):
super(Seq2Seq, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.config = config
self.register_buffer(
"bias", torch.tril(torch.ones(
(1024, 1024), dtype=torch.uint8)).view(1, 1024, 1024)
)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.lm_head = nn.Linear(
config.hidden_size, config.vocab_size, bias=False)
self.lm_head.weight = self.encoder.embeddings.word_embeddings.weight
self.lsm = nn.LogSoftmax(dim=-1)
self.pred_dense = nn.Linear(config.hidden_size, 1, bias=True)
self.sigmoid = nn.Sigmoid()
self.mse_loss_weight = mse_loss_weight
self.ce_loss_weight = ce_loss_weight
self.beam_size = beam_size
self.max_length = max_length
self.sos_id = sos_id
self.eos_id = eos_id
def forward(self, source_ids, exist=None, target_ids=None):
if target_ids is None or exist is None:
return self.generate(source_ids)
mask = source_ids.ne(1)[:, None, :]*source_ids.ne(1)[:, :, None]
encoder_output = self.encoder(
source_ids, attention_mask=mask, use_cache=True)
ids = torch.cat((source_ids, target_ids), -1)
mask = self.bias[:,
source_ids.size(-1):ids.size(-1), :ids.size(-1)].bool()
mask = mask & ids[:, None, :].ne(1)
out = self.decoder(target_ids, attention_mask=mask,
past_key_values=encoder_output.past_key_values).last_hidden_state
lm_logits = self.lm_head(out[..., 1:, :])
# Shift so that tokens < n predict n
active_loss = target_ids[..., 2:].ne(1).view(-1)
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = target_ids[..., 2:].contiguous()
exist_labels = exist.contiguous()
pred_out = out[..., 0, :]
pred_sigmoid = self.sigmoid(self.pred_dense(pred_out))
# Flatten the tokens
loss_fct_code = nn.CrossEntropyLoss(ignore_index=-1)
loss_fct_pred = nn.MSELoss(reduction="mean")
loss_code = loss_fct_code(shift_logits.view(-1, shift_logits.size(-1))[active_loss],
shift_labels.view(-1)[active_loss])
loss_pred = loss_fct_pred(pred_sigmoid, exist_labels)
loss = loss_pred * self.mse_loss_weight + loss_code * self.ce_loss_weight
outputs = loss, loss*active_loss.sum(), active_loss.sum(), loss_pred, loss_code
return outputs
def generate(self, source_ids):
mask = source_ids.ne(1)[:, None, :] * source_ids.ne(1)[:, :, None]
encoder_output = self.encoder(
source_ids, attention_mask=mask, use_cache=True)
preds = []
predicates = []
zero = torch.cuda.LongTensor(1).fill_(0)
source_len = list(source_ids.ne(1).sum(-1).cpu().numpy())
for i in range(source_ids.shape[0]):
context = [[x[i:i+1, :, :source_len[i]].repeat(self.beam_size, 1, 1, 1) for x in y]
for y in encoder_output.past_key_values]
beam = Beam(self.beam_size, self.sos_id, self.eos_id)
input_ids = beam.getCurrentState()
context_ids = source_ids[i:i+1,
:source_len[i]].repeat(self.beam_size, 1)
predicate = []
for _ in range(self.max_length):
if beam.done():
break
ids = torch.cat((context_ids, input_ids), -1)
mask = self.bias[:,
context_ids.size(-1):ids.size(-1), :ids.size(-1)].bool()
mask = mask & ids[:, None, :].ne(1)
out = self.decoder(input_ids, attention_mask=mask,
past_key_values=context).last_hidden_state
hidden_states = out[:, -1, :]
if out.size(1) == 1:
pred_sigmoid = self.sigmoid(self.pred_dense(
hidden_states.view(-1, 1, hidden_states.size(-1))))
predicate.append(
pred_sigmoid.view(-1, pred_sigmoid.size(-1)))
#predicate.append(pred_sigmoid.view(-1, pred_sigmoid.size(-1)).cpu().numpy())# ZM modified
out = self.lsm(self.lm_head(hidden_states)).data
beam.advance(out)
input_ids.data.copy_(input_ids.data.index_select(
0, beam.getCurrentOrigin()))
input_ids = torch.cat((input_ids, beam.getCurrentState()), -1)
hyp = beam.getHyp(beam.getFinal())
pred = beam.buildTargetTokens(hyp)[:self.beam_size]
pred = [torch.cat([x.view(-1) for x in p] + [zero] *
(self.max_length-len(p))).view(1, -1) for p in pred]
predicates.append(predicate[0][0])# ZM modified
preds.append(torch.cat(pred, 0).unsqueeze(0))
preds = torch.cat(preds, 0)
predicates = torch.tensor(predicates, device="cuda")# ZM modified
return preds, predicates
class Beam(object):
def __init__(self, size, sos, eos):
self.size = size
self.tt = torch.cuda
# The score for each translation on the beam.
self.scores = self.tt.FloatTensor(size).zero_()
# The backpointers at each time-step.
self.prevKs = []
# The outputs at each time-step.
self.nextYs = [self.tt.LongTensor(size)
.fill_(0)]
self.nextYs[0][0] = sos
# Has EOS topped the beam yet.
self._eos = eos
self.eosTop = False
# Time and k pair for finished.
self.finished = []
def getCurrentState(self):
"Get the outputs for the current timestep."
batch = self.tt.LongTensor(self.nextYs[-1]).view(-1, 1)
return batch
def getCurrentOrigin(self):
"Get the backpointers for the current timestep."
return self.prevKs[-1]
def advance(self, wordLk):
"""
Given prob over words for every last beam `wordLk` and attention
`attnOut`: Compute and update the beam search.
Parameters:
* `wordLk`- probs of advancing from the last step (K x words)
* `attnOut`- attention at the last step
Returns: True if beam search is complete.
"""
numWords = wordLk.size(1)
# Sum the previous scores.
if len(self.prevKs) > 0:
beamLk = wordLk + self.scores.unsqueeze(1).expand_as(wordLk)
# Don't let EOS have children.
for i in range(self.nextYs[-1].size(0)):
if self.nextYs[-1][i] == self._eos:
beamLk[i] = -1e20
else:
beamLk = wordLk[0]
flatBeamLk = beamLk.view(-1)
bestScores, bestScoresId = flatBeamLk.topk(self.size, 0, True, True)
self.scores = bestScores
# bestScoresId is flattened beam x word array, so calculate which
# word and beam each score came from
prevK = bestScoresId // numWords
self.prevKs.append(prevK)
self.nextYs.append((bestScoresId - prevK * numWords))
for i in range(self.nextYs[-1].size(0)):
if self.nextYs[-1][i] == self._eos:
s = self.scores[i]
self.finished.append((s, len(self.nextYs) - 1, i))
# End condition is when top-of-beam is EOS and no global score.
if self.nextYs[-1][0] == self._eos:
self.eosTop = True
def done(self):
return self.eosTop and len(self.finished) >= self.size
def getFinal(self):
if len(self.finished) == 0:
self.finished.append((self.scores[0], len(self.nextYs) - 1, 0))
self.finished.sort(key=lambda a: -a[0])
if len(self.finished) != self.size:
unfinished = []
for i in range(self.nextYs[-1].size(0)):
if self.nextYs[-1][i] != self._eos:
s = self.scores[i]
unfinished.append((s, len(self.nextYs) - 1, i))
unfinished.sort(key=lambda a: -a[0])
self.finished += unfinished[:self.size-len(self.finished)]
return self.finished[:self.size]
def getHyp(self, beam_res):
"""
Walk back to construct the full hypothesis.
"""
hyps = []
for _, timestep, k in beam_res:
hyp = []
for j in range(len(self.prevKs[:timestep]) - 1, -1, -1):
hyp.append(self.nextYs[j+1][k])
k = self.prevKs[j][k]
hyps.append(hyp[::-1])
return hyps
def buildTargetTokens(self, preds):
sentence = []
for pred in preds:
tokens = []
for tok in pred:
if tok == self._eos:
break
tokens.append(tok)
sentence.append(tokens)
return sentence
|