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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 math | |
from typing import Dict, List, Optional | |
import sys | |
import torch | |
import torch.nn as nn | |
from fairseq import search, utils | |
from fairseq.models import FairseqIncrementalDecoder | |
from torch import Tensor | |
from fairseq.ngram_repeat_block import NGramRepeatBlock | |
from data import data_utils | |
class SequenceGenerator(nn.Module): | |
def __init__( | |
self, | |
models, | |
tgt_dict, | |
beam_size=1, | |
max_len_a=0, | |
max_len_b=200, | |
max_len=0, | |
min_len=1, | |
normalize_scores=True, | |
len_penalty=1.0, | |
unk_penalty=0.0, | |
temperature=1.0, | |
match_source_len=False, | |
no_repeat_ngram_size=0, | |
search_strategy=None, | |
eos=None, | |
symbols_to_strip_from_output=None, | |
lm_model=None, | |
lm_weight=1.0, | |
constraint_trie=None, | |
constraint_range=None, | |
gen_code=False, | |
gen_box=False, | |
ignore_eos=False, | |
zero_shot=False | |
): | |
"""Generates translations of a given source sentence. | |
Args: | |
models (List[~fairseq.models.FairseqModel]): ensemble of models, | |
currently support fairseq.models.TransformerModel for scripting | |
beam_size (int, optional): beam width (default: 1) | |
max_len_a/b (int, optional): generate sequences of maximum length | |
ax + b, where x is the source length | |
max_len (int, optional): the maximum length of the generated output | |
(not including end-of-sentence) | |
min_len (int, optional): the minimum length of the generated output | |
(not including end-of-sentence) | |
normalize_scores (bool, optional): normalize scores by the length | |
of the output (default: True) | |
len_penalty (float, optional): length penalty, where <1.0 favors | |
shorter, >1.0 favors longer sentences (default: 1.0) | |
unk_penalty (float, optional): unknown word penalty, where <0 | |
produces more unks, >0 produces fewer (default: 0.0) | |
temperature (float, optional): temperature, where values | |
>1.0 produce more uniform samples and values <1.0 produce | |
sharper samples (default: 1.0) | |
match_source_len (bool, optional): outputs should match the source | |
length (default: False) | |
""" | |
super().__init__() | |
if isinstance(models, EnsembleModel): | |
self.model = models | |
else: | |
self.model = EnsembleModel(models) | |
self.gen_code = gen_code | |
self.gen_box = gen_box | |
self.ignore_eos = ignore_eos | |
self.tgt_dict = tgt_dict | |
self.pad = tgt_dict.pad() | |
self.unk = tgt_dict.unk() | |
self.bos = tgt_dict.bos() | |
self.eos = tgt_dict.eos() if eos is None else eos | |
self.symbols_to_strip_from_output = ( | |
symbols_to_strip_from_output.union({self.eos}) | |
if symbols_to_strip_from_output is not None | |
else {self.bos, self.eos} | |
) | |
self.vocab_size = len(tgt_dict) | |
self.beam_size = beam_size | |
# the max beam size is the dictionary size - 1, since we never select pad | |
self.beam_size = min(beam_size, self.vocab_size - 1) | |
self.max_len_a = max_len_a | |
self.max_len_b = max_len_b | |
self.min_len = min_len | |
self.max_len = max_len or self.model.max_decoder_positions() | |
self.normalize_scores = normalize_scores | |
self.len_penalty = len_penalty | |
self.unk_penalty = unk_penalty | |
self.temperature = temperature | |
self.match_source_len = match_source_len | |
self.zero_shot = zero_shot | |
if no_repeat_ngram_size > 0: | |
self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) | |
else: | |
self.repeat_ngram_blocker = None | |
assert temperature > 0, "--temperature must be greater than 0" | |
self.search = ( | |
search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy | |
) | |
# We only need to set src_lengths in LengthConstrainedBeamSearch. | |
# As a module attribute, setting it would break in multithread | |
# settings when the model is shared. | |
self.should_set_src_lengths = ( | |
hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths | |
) | |
self.model.eval() | |
self.lm_model = lm_model | |
self.lm_weight = lm_weight | |
if self.lm_model is not None: | |
self.lm_model.eval() | |
self.constraint_trie = constraint_trie | |
self.constraint_start = None | |
self.constraint_end = None | |
if constraint_range is not None: | |
constraint_start, constraint_end = constraint_range.split(',') | |
self.constraint_start = int(constraint_start) | |
self.constraint_end = int(constraint_end) | |
def cuda(self): | |
self.model.cuda() | |
return self | |
def forward( | |
self, | |
sample: Dict[str, Dict[str, Tensor]], | |
prefix_tokens: Optional[Tensor] = None, | |
bos_token: Optional[int] = None, | |
): | |
"""Generate a batch of translations. | |
Args: | |
sample (dict): batch | |
prefix_tokens (torch.LongTensor, optional): force decoder to begin | |
with these tokens | |
bos_token (int, optional): beginning of sentence token | |
(default: self.eos) | |
""" | |
return self._generate(sample, prefix_tokens, bos_token=bos_token) | |
# TODO(myleott): unused, deprecate after pytorch-translate migration | |
def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None): | |
"""Iterate over a batched dataset and yield individual translations. | |
Args: | |
cuda (bool, optional): use GPU for generation | |
timer (StopwatchMeter, optional): time generations | |
""" | |
for sample in data_itr: | |
s = utils.move_to_cuda(sample) if cuda else sample | |
if "net_input" not in s: | |
continue | |
input = s["net_input"] | |
# model.forward normally channels prev_output_tokens into the decoder | |
# separately, but SequenceGenerator directly calls model.encoder | |
encoder_input = { | |
k: v for k, v in input.items() if k != "prev_output_tokens" | |
} | |
if timer is not None: | |
timer.start() | |
with torch.no_grad(): | |
hypos = self.generate(encoder_input) | |
if timer is not None: | |
timer.stop(sum(len(h[0]["tokens"]) for h in hypos)) | |
for i, id in enumerate(s["id"].data): | |
# remove padding | |
src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad) | |
ref = ( | |
utils.strip_pad(s["target"].data[i, :], self.pad) | |
if s["target"] is not None | |
else None | |
) | |
yield id, src, ref, hypos[i] | |
def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs) -> List[List[Dict[str, Tensor]]]: | |
"""Generate translations. Match the api of other fairseq generators. | |
Args: | |
models (List[~fairseq.models.FairseqModel]): ensemble of models | |
sample (dict): batch | |
prefix_tokens (torch.LongTensor, optional): force decoder to begin | |
with these tokens | |
constraints (torch.LongTensor, optional): force decoder to include | |
the list of constraints | |
bos_token (int, optional): beginning of sentence token | |
(default: self.eos) | |
""" | |
return self._generate(models, sample, **kwargs) | |
def _generate( | |
self, | |
models, | |
sample: Dict[str, Dict[str, Tensor]], | |
prefix_tokens: Optional[Tensor] = None, | |
constraints: Optional[Tensor] = None, | |
bos_token: Optional[int] = None, | |
): | |
model = EnsembleModel(models) | |
incremental_states = torch.jit.annotate( | |
List[Dict[str, Dict[str, Optional[Tensor]]]], | |
[ | |
torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) | |
for i in range(model.models_size) | |
], | |
) | |
net_input = sample["net_input"] | |
if "src_tokens" in net_input: | |
src_tokens = net_input["src_tokens"] | |
# length of the source text being the character length except EndOfSentence and pad | |
src_lengths = ( | |
(src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) | |
) | |
elif "source" in net_input: | |
src_tokens = net_input["source"] | |
src_lengths = ( | |
net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) | |
if net_input["padding_mask"] is not None | |
else torch.tensor(src_tokens.size(-1)).to(src_tokens) | |
) | |
elif "features" in net_input: | |
src_tokens = net_input["features"] | |
src_lengths = ( | |
net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) | |
if net_input["padding_mask"] is not None | |
else torch.tensor(src_tokens.size(-1)).to(src_tokens) | |
) | |
else: | |
raise Exception("expected src_tokens or source in net input. input keys: " + str(net_input.keys())) | |
# bsz: total number of sentences in beam | |
# Note that src_tokens may have more than 2 dimensions (i.e. audio features) | |
bsz, src_len = src_tokens.size()[:2] | |
beam_size = self.beam_size | |
if constraints is not None and not self.search.supports_constraints: | |
raise NotImplementedError( | |
"Target-side constraints were provided, but search method doesn't support them" | |
) | |
# Initialize constraints, when active | |
self.search.init_constraints(constraints, beam_size) | |
max_len: int = -1 | |
if self.match_source_len: | |
max_len = src_lengths.max().item() | |
else: | |
max_len = int(self.max_len_a * src_len + self.max_len_b) | |
assert ( | |
self.min_len <= max_len | |
), "min_len cannot be larger than max_len, please adjust these!" | |
# compute the encoder output for each beam | |
with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): | |
encoder_outs = model.forward_encoder(net_input) | |
# placeholder of indices for bsz * beam_size to hold tokens and accumulative scores | |
new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) | |
new_order = new_order.to(src_tokens.device).long() | |
encoder_outs = model.reorder_encoder_out(encoder_outs, new_order) | |
# ensure encoder_outs is a List. | |
assert encoder_outs is not None | |
# initialize buffers | |
scores = ( | |
torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() | |
) # +1 for eos; pad is never chosen for scoring | |
tokens = ( | |
torch.zeros(bsz * beam_size, max_len + 2) | |
.to(src_tokens) | |
.long() | |
.fill_(self.pad) | |
) # +2 for eos and pad | |
# tokens[:, 0] = self.eos if bos_token is None else bos_token | |
tokens[:, 0] = self.bos | |
attn: Optional[Tensor] = None | |
# A list that indicates candidates that should be ignored. | |
# For example, suppose we're sampling and have already finalized 2/5 | |
# samples. Then cands_to_ignore would mark 2 positions as being ignored, | |
# so that we only finalize the remaining 3 samples. | |
cands_to_ignore = ( | |
torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) | |
) # forward and backward-compatible False mask | |
# list of completed sentences | |
finalized = torch.jit.annotate( | |
List[List[Dict[str, Tensor]]], | |
[torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], | |
) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step | |
# a boolean array indicating if the sentence at the index is finished or not | |
finished = [False for i in range(bsz)] | |
num_remaining_sent = bsz # number of sentences remaining | |
# number of candidate hypos per step | |
cand_size = 2 * beam_size # 2 x beam size in case half are EOS | |
# offset arrays for converting between different indexing schemes | |
bbsz_offsets = ( | |
(torch.arange(0, bsz) * beam_size) | |
.unsqueeze(1) | |
.type_as(tokens) | |
.to(src_tokens.device) | |
) | |
cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) | |
reorder_state: Optional[Tensor] = None | |
batch_idxs: Optional[Tensor] = None | |
original_batch_idxs: Optional[Tensor] = None | |
if "id" in sample and isinstance(sample["id"], Tensor): | |
original_batch_idxs = sample["id"] | |
else: | |
original_batch_idxs = torch.arange(0, bsz).type_as(tokens) | |
for step in range(max_len + 1): # one extra step for EOS marker | |
# reorder decoder internal states based on the prev choice of beams | |
if reorder_state is not None: | |
if batch_idxs is not None: | |
# update beam indices to take into account removed sentences | |
corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( | |
batch_idxs | |
) | |
reorder_state.view(-1, beam_size).add_( | |
corr.unsqueeze(-1) * beam_size | |
) | |
original_batch_idxs = original_batch_idxs[batch_idxs] | |
model.reorder_incremental_state(incremental_states, reorder_state) | |
encoder_outs = model.reorder_encoder_out( | |
encoder_outs, reorder_state | |
) | |
with torch.autograd.profiler.record_function("EnsembleModel: forward_decoder"): | |
lprobs, avg_attn_scores = model.forward_decoder( | |
tokens[:, : step + 1], | |
encoder_outs, | |
incremental_states, | |
self.temperature, | |
constraint_trie=self.constraint_trie, | |
constraint_start=self.constraint_start, | |
constraint_end=self.constraint_end, | |
gen_code=self.gen_code, | |
zero_shot=self.zero_shot, | |
prefix_tokens=prefix_tokens | |
) | |
if self.lm_model is not None: | |
lm_out = self.lm_model(tokens[:, : step + 1]) | |
probs = self.lm_model.get_normalized_probs( | |
lm_out, log_probs=True, sample=None | |
) | |
probs = probs[:, -1, :] * self.lm_weight | |
lprobs += probs | |
# handle prefix tokens (possibly with different lengths) | |
if ( | |
prefix_tokens is not None | |
and step < prefix_tokens.size(1) | |
and step < max_len | |
): | |
lprobs, tokens, scores = self._prefix_tokens( | |
step, lprobs, scores, tokens, prefix_tokens, beam_size | |
) | |
elif step < self.min_len: | |
# minimum length constraint (does not apply if using prefix_tokens) | |
lprobs[:, self.eos] = -math.inf | |
lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) | |
lprobs[:, self.pad] = -math.inf # never select pad | |
lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty | |
if (self.gen_code or self.gen_box) and step < max_len: | |
lprobs[:, :4] = -math.inf | |
if self.gen_box: | |
lprobs[:, -1] = -math.inf | |
if (step + 1) % 5 == 0: | |
lprobs[:, self.constraint_start:59457] = -math.inf | |
else: | |
lprobs[:, 59457:] = -math.inf | |
# handle max length constraint | |
if step >= max_len: | |
lprobs[:, : self.eos] = -math.inf | |
lprobs[:, self.eos + 1 :] = -math.inf | |
if self.ignore_eos: | |
lprobs[:, self.eos] = 1 | |
# Record attention scores, only support avg_attn_scores is a Tensor | |
if avg_attn_scores is not None: | |
if attn is None: | |
attn = torch.empty( | |
bsz * beam_size, avg_attn_scores.size(1), max_len + 2 | |
).to(scores) | |
attn[:, :, step + 1].copy_(avg_attn_scores) | |
scores = scores.type_as(lprobs) | |
eos_bbsz_idx = torch.empty(0).to( | |
tokens | |
) # indices of hypothesis ending with eos (finished sentences) | |
eos_scores = torch.empty(0).to( | |
scores | |
) # scores of hypothesis ending with eos (finished sentences) | |
if self.should_set_src_lengths: | |
self.search.set_src_lengths(src_lengths) | |
if self.repeat_ngram_blocker is not None: | |
lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) | |
# Shape: (batch, cand_size) | |
cand_scores, cand_indices, cand_beams = self.search.step( | |
step, | |
lprobs.view(bsz, -1, self.vocab_size), | |
scores.view(bsz, beam_size, -1)[:, :, :step], | |
tokens[:, : step + 1], | |
original_batch_idxs, | |
) | |
# cand_bbsz_idx contains beam indices for the top candidate | |
# hypotheses, with a range of values: [0, bsz*beam_size), | |
# and dimensions: [bsz, cand_size] | |
cand_bbsz_idx = cand_beams.add(bbsz_offsets) | |
# finalize hypotheses that end in eos | |
# Shape of eos_mask: (batch size, beam size) | |
eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) | |
eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) | |
# only consider eos when it's among the top beam_size indices | |
# Now we know what beam item(s) to finish | |
# Shape: 1d list of absolute-numbered | |
eos_bbsz_idx = torch.masked_select( | |
cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] | |
) | |
finalized_sents: List[int] = [] | |
if eos_bbsz_idx.numel() > 0: | |
eos_scores = torch.masked_select( | |
cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] | |
) | |
finalized_sents = self.finalize_hypos( | |
step, | |
eos_bbsz_idx, | |
eos_scores, | |
tokens, | |
scores, | |
finalized, | |
finished, | |
beam_size, | |
attn, | |
src_lengths, | |
max_len, | |
) | |
num_remaining_sent -= len(finalized_sents) | |
assert num_remaining_sent >= 0 | |
if num_remaining_sent == 0: | |
break | |
if self.search.stop_on_max_len and step >= max_len: | |
break | |
assert step < max_len, f"{step} < {max_len}" | |
# Remove finalized sentences (ones for which {beam_size} | |
# finished hypotheses have been generated) from the batch. | |
if len(finalized_sents) > 0: | |
new_bsz = bsz - len(finalized_sents) | |
# construct batch_idxs which holds indices of batches to keep for the next pass | |
batch_mask = torch.ones( | |
bsz, dtype=torch.bool, device=cand_indices.device | |
) | |
batch_mask[finalized_sents] = False | |
# TODO replace `nonzero(as_tuple=False)` after TorchScript supports it | |
batch_idxs = torch.arange( | |
bsz, device=cand_indices.device | |
).masked_select(batch_mask) | |
# Choose the subset of the hypothesized constraints that will continue | |
self.search.prune_sentences(batch_idxs) | |
eos_mask = eos_mask[batch_idxs] | |
cand_beams = cand_beams[batch_idxs] | |
bbsz_offsets.resize_(new_bsz, 1) | |
cand_bbsz_idx = cand_beams.add(bbsz_offsets) | |
cand_scores = cand_scores[batch_idxs] | |
cand_indices = cand_indices[batch_idxs] | |
if prefix_tokens is not None: | |
prefix_tokens = prefix_tokens[batch_idxs] | |
src_lengths = src_lengths[batch_idxs] | |
cands_to_ignore = cands_to_ignore[batch_idxs] | |
scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) | |
tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) | |
if attn is not None: | |
attn = attn.view(bsz, -1)[batch_idxs].view( | |
new_bsz * beam_size, attn.size(1), -1 | |
) | |
bsz = new_bsz | |
else: | |
batch_idxs = None | |
# Set active_mask so that values > cand_size indicate eos hypos | |
# and values < cand_size indicate candidate active hypos. | |
# After, the min values per row are the top candidate active hypos | |
# Rewrite the operator since the element wise or is not supported in torchscript. | |
eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) | |
active_mask = torch.add( | |
eos_mask.type_as(cand_offsets) * cand_size, | |
cand_offsets[: eos_mask.size(1)], | |
) | |
# get the top beam_size active hypotheses, which are just | |
# the hypos with the smallest values in active_mask. | |
# {active_hypos} indicates which {beam_size} hypotheses | |
# from the list of {2 * beam_size} candidates were | |
# selected. Shapes: (batch size, beam size) | |
new_cands_to_ignore, active_hypos = torch.topk( | |
active_mask, k=beam_size, dim=1, largest=False | |
) | |
# update cands_to_ignore to ignore any finalized hypos. | |
cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] | |
# Make sure there is at least one active item for each sentence in the batch. | |
assert (~cands_to_ignore).any(dim=1).all() | |
# update cands_to_ignore to ignore any finalized hypos | |
# {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam | |
# can be selected more than once). | |
active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) | |
active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) | |
active_bbsz_idx = active_bbsz_idx.view(-1) | |
active_scores = active_scores.view(-1) | |
# copy tokens and scores for active hypotheses | |
# Set the tokens for each beam (can select the same row more than once) | |
tokens[:, : step + 1] = torch.index_select( | |
tokens[:, : step + 1], dim=0, index=active_bbsz_idx | |
) | |
# Select the next token for each of them | |
tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( | |
cand_indices, dim=1, index=active_hypos | |
) | |
if step > 0: | |
scores[:, :step] = torch.index_select( | |
scores[:, :step], dim=0, index=active_bbsz_idx | |
) | |
scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( | |
cand_scores, dim=1, index=active_hypos | |
) | |
# Update constraints based on which candidates were selected for the next beam | |
self.search.update_constraints(active_hypos) | |
# copy attention for active hypotheses | |
if attn is not None: | |
attn[:, :, : step + 2] = torch.index_select( | |
attn[:, :, : step + 2], dim=0, index=active_bbsz_idx | |
) | |
# reorder incremental state in decoder | |
reorder_state = active_bbsz_idx | |
# sort by score descending | |
for sent in range(len(finalized)): | |
scores = torch.tensor( | |
[float(elem["score"].item()) for elem in finalized[sent]] | |
) | |
_, sorted_scores_indices = torch.sort(scores, descending=True) | |
finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] | |
finalized[sent] = torch.jit.annotate( | |
List[Dict[str, Tensor]], finalized[sent] | |
) | |
return finalized | |
def _prefix_tokens( | |
self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int | |
): | |
"""Handle prefix tokens""" | |
prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) | |
prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) | |
prefix_mask = prefix_toks.ne(self.pad) | |
if self.constraint_trie is None: | |
lprobs[prefix_mask] = torch.min(prefix_lprobs) - 1 | |
else: | |
lprobs[prefix_mask] = -math.inf | |
lprobs[prefix_mask] = lprobs[prefix_mask].scatter( | |
-1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] | |
) | |
# if prefix includes eos, then we should make sure tokens and | |
# scores are the same across all beams | |
eos_mask = prefix_toks.eq(self.eos) | |
if eos_mask.any(): | |
# validate that the first beam matches the prefix | |
first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ | |
:, 0, 1 : step + 1 | |
] | |
eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] | |
target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] | |
assert (first_beam == target_prefix).all() | |
# copy tokens, scores and lprobs from the first beam to all beams | |
tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) | |
scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) | |
lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) | |
return lprobs, tokens, scores | |
def replicate_first_beam(self, tensor, mask, beam_size: int): | |
tensor = tensor.view(-1, beam_size, tensor.size(-1)) | |
tensor[mask] = tensor[mask][:, :1, :] | |
return tensor.view(-1, tensor.size(-1)) | |
def finalize_hypos( | |
self, | |
step: int, | |
bbsz_idx, | |
eos_scores, | |
tokens, | |
scores, | |
finalized: List[List[Dict[str, Tensor]]], | |
finished: List[bool], | |
beam_size: int, | |
attn: Optional[Tensor], | |
src_lengths, | |
max_len: int, | |
): | |
"""Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. | |
A sentence is finalized when {beam_size} finished items have been collected for it. | |
Returns number of sentences (not beam items) being finalized. | |
These will be removed from the batch and not processed further. | |
Args: | |
bbsz_idx (Tensor): | |
""" | |
assert bbsz_idx.numel() == eos_scores.numel() | |
# clone relevant token and attention tensors. | |
# tokens is (batch * beam, max_len). So the index_select | |
# gets the newly EOS rows, then selects cols 1..{step + 2} | |
tokens_clone = tokens.index_select(0, bbsz_idx)[ | |
:, 1 : step + 2 | |
] # skip the first index, which is EOS | |
tokens_clone[:, step] = self.eos | |
attn_clone = ( | |
attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] | |
if attn is not None | |
else None | |
) | |
# compute scores per token position | |
pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] | |
pos_scores[:, step] = eos_scores | |
# convert from cumulative to per-position scores | |
pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] | |
# normalize sentence-level scores | |
if self.normalize_scores: | |
eos_scores /= (step + 1) ** self.len_penalty | |
# cum_unfin records which sentences in the batch are finished. | |
# It helps match indexing between (a) the original sentences | |
# in the batch and (b) the current, possibly-reduced set of | |
# sentences. | |
cum_unfin: List[int] = [] | |
prev = 0 | |
for f in finished: | |
if f: | |
prev += 1 | |
else: | |
cum_unfin.append(prev) | |
cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx) | |
unfin_idx = bbsz_idx // beam_size | |
sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx) | |
# Create a set of "{sent}{unfin_idx}", where | |
# "unfin_idx" is the index in the current (possibly reduced) | |
# list of sentences, and "sent" is the index in the original, | |
# unreduced batch | |
# For every finished beam item | |
# sentence index in the current (possibly reduced) batch | |
seen = (sent << 32) + unfin_idx | |
unique_seen: List[int] = torch.unique(seen).tolist() | |
if self.match_source_len: | |
condition = step > torch.index_select(src_lengths, 0, unfin_idx) | |
eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores) | |
sent_list: List[int] = sent.tolist() | |
for i in range(bbsz_idx.size()[0]): | |
# An input sentence (among those in a batch) is finished when | |
# beam_size hypotheses have been collected for it | |
if len(finalized[sent_list[i]]) < beam_size: | |
if attn_clone is not None: | |
# remove padding tokens from attn scores | |
hypo_attn = attn_clone[i] | |
else: | |
hypo_attn = torch.empty(0) | |
finalized[sent_list[i]].append( | |
{ | |
"tokens": tokens_clone[i], | |
"score": eos_scores[i], | |
"attention": hypo_attn, # src_len x tgt_len | |
"alignment": torch.empty(0), | |
"positional_scores": pos_scores[i], | |
} | |
) | |
newly_finished: List[int] = [] | |
for unique_s in unique_seen: | |
# check termination conditions for this sentence | |
unique_sent: int = unique_s >> 32 | |
unique_unfin_idx: int = unique_s - (unique_sent << 32) | |
if not finished[unique_sent] and self.is_finished( | |
step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size | |
): | |
finished[unique_sent] = True | |
newly_finished.append(unique_unfin_idx) | |
return newly_finished | |
def is_finished( | |
self, | |
step: int, | |
unfin_idx: int, | |
max_len: int, | |
finalized_sent_len: int, | |
beam_size: int, | |
): | |
""" | |
Check whether decoding for a sentence is finished, which | |
occurs when the list of finalized sentences has reached the | |
beam size, or when we reach the maximum length. | |
""" | |
assert finalized_sent_len <= beam_size | |
if finalized_sent_len == beam_size or step == max_len: | |
return True | |
return False | |
class EnsembleModel(nn.Module): | |
"""A wrapper around an ensemble of models.""" | |
def __init__(self, models): | |
super().__init__() | |
self.models_size = len(models) | |
# method '__len__' is not supported in ModuleList for torch script | |
self.single_model = models[0] | |
self.models = nn.ModuleList(models) | |
self.has_incremental: bool = False | |
if all( | |
hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) | |
for m in models | |
): | |
self.has_incremental = True | |
def forward(self): | |
pass | |
def has_encoder(self): | |
return hasattr(self.single_model, "encoder") | |
def has_incremental_states(self): | |
return self.has_incremental | |
def max_decoder_positions(self): | |
return min([m.max_decoder_positions() for m in self.models if hasattr(m, "max_decoder_positions")] + [sys.maxsize]) | |
def forward_encoder(self, net_input: Dict[str, Tensor]): | |
if not self.has_encoder(): | |
return None | |
return [model.encoder.forward_torchscript(net_input) for model in self.models] | |
def forward_decoder( | |
self, | |
tokens, | |
encoder_outs: List[Dict[str, List[Tensor]]], | |
incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], | |
temperature: float = 1.0, | |
constraint_trie=None, | |
constraint_start=None, | |
constraint_end=None, | |
gen_code=False, | |
zero_shot=False, | |
prefix_tokens=None | |
): | |
log_probs = [] | |
avg_attn: Optional[Tensor] = None | |
encoder_out: Optional[Dict[str, List[Tensor]]] = None | |
code_mask = (tokens.new_ones(tokens.size(0))*gen_code).bool() | |
for i, model in enumerate(self.models): | |
if self.has_encoder(): | |
encoder_out = encoder_outs[i] | |
# decode each model | |
if self.has_incremental_states(): | |
decoder_out = model.decoder.forward( | |
tokens, | |
code_masks=code_mask, | |
encoder_out=encoder_out, | |
incremental_state=incremental_states[i], | |
) | |
else: | |
if hasattr(model, "decoder"): | |
decoder_out = model.decoder.forward(tokens, code_masks=code_mask, encoder_out=encoder_out) | |
else: | |
decoder_out = model.forward(tokens) | |
attn: Optional[Tensor] = None | |
decoder_len = len(decoder_out) | |
if decoder_len > 1 and decoder_out[1] is not None: | |
if isinstance(decoder_out[1], Tensor): | |
attn = decoder_out[1] | |
else: | |
attn_holder = decoder_out[1]["attn"] | |
if isinstance(attn_holder, Tensor): | |
attn = attn_holder | |
elif attn_holder is not None: | |
attn = attn_holder[0] | |
if attn is not None: | |
attn = attn[:, -1, :] | |
decoder_out_tuple = ( | |
decoder_out[0][:, -1:, :].div_(temperature), | |
None if decoder_len <= 1 else decoder_out[1], | |
) | |
beam_size = decoder_out_tuple[0].size(0) // prefix_tokens.size(0) if prefix_tokens is not None else 0 | |
if constraint_trie is not None and not zero_shot: | |
assert constraint_start is None and constraint_end is None | |
constraint_masks = decoder_out_tuple[0].new_zeros(decoder_out_tuple[0].size()).bool() | |
constraint_prefix_tokens = tokens.tolist() | |
for token_index, constraint_prefix_token in enumerate(constraint_prefix_tokens): | |
prefix_len = prefix_tokens[token_index // beam_size].ne(1).sum().item() if prefix_tokens is not None else 0 | |
if len(constraint_prefix_token) > prefix_len: | |
constraint_prefix_token = [0] + constraint_prefix_token[prefix_len+1:] | |
constraint_nodes = constraint_trie.get_next_layer(constraint_prefix_token) | |
constraint_masks[token_index][:, constraint_nodes] = True | |
else: | |
constraint_masks[token_index] = True | |
decoder_out_tuple[0].masked_fill_(~constraint_masks, -math.inf) | |
if constraint_start is not None and constraint_end is not None and not zero_shot: | |
assert constraint_trie is None | |
decoder_out_tuple[0][:, :, 4:constraint_start] = -math.inf | |
decoder_out_tuple[0][:, :, constraint_end:] = -math.inf | |
probs = model.get_normalized_probs( | |
decoder_out_tuple, log_probs=True, sample=None | |
) | |
if constraint_trie is not None and zero_shot: | |
assert constraint_start is None and constraint_end is None | |
constraint_masks = decoder_out_tuple[0].new_zeros(decoder_out_tuple[0].size()).bool() | |
constraint_prefix_tokens = tokens.tolist() | |
for token_index, constraint_prefix_token in enumerate(constraint_prefix_tokens): | |
constraint_nodes = constraint_trie.get_next_layer(constraint_prefix_token) | |
constraint_masks[token_index][:, constraint_nodes] = True | |
probs.masked_fill_(~constraint_masks, -math.inf) | |
if constraint_start is not None and constraint_end is not None and zero_shot: | |
assert constraint_trie is None | |
probs[:, :, 4:constraint_start] = -math.inf | |
probs[:, :, constraint_end:] = -math.inf | |
probs = probs[:, -1, :] | |
if self.models_size == 1: | |
return probs, attn | |
log_probs.append(probs) | |
if attn is not None: | |
if avg_attn is None: | |
avg_attn = attn | |
else: | |
avg_attn.add_(attn) | |
avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( | |
self.models_size | |
) | |
if avg_attn is not None: | |
avg_attn.div_(self.models_size) | |
return avg_probs, avg_attn | |
def reorder_encoder_out( | |
self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order | |
): | |
""" | |
Reorder encoder output according to *new_order*. | |
Args: | |
encoder_out: output from the ``forward()`` method | |
new_order (LongTensor): desired order | |
Returns: | |
*encoder_out* rearranged according to *new_order* | |
""" | |
new_outs: List[Dict[str, List[Tensor]]] = [] | |
if not self.has_encoder(): | |
return new_outs | |
for i, model in enumerate(self.models): | |
assert encoder_outs is not None | |
new_outs.append( | |
model.encoder.reorder_encoder_out(encoder_outs[i], new_order) | |
) | |
return new_outs | |
def reorder_incremental_state( | |
self, | |
incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], | |
new_order, | |
): | |
if not self.has_incremental_states(): | |
return | |
for i, model in enumerate(self.models): | |
model.decoder.reorder_incremental_state_scripting( | |
incremental_states[i], new_order | |
) | |
class SequenceGeneratorWithAlignment(SequenceGenerator): | |
def __init__( | |
self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs | |
): | |
"""Generates translations of a given source sentence. | |
Produces alignments following "Jointly Learning to Align and | |
Translate with Transformer Models" (Garg et al., EMNLP 2019). | |
Args: | |
left_pad_target (bool, optional): Whether or not the | |
hypothesis should be left padded or not when they are | |
teacher forced for generating alignments. | |
""" | |
super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs) | |
self.left_pad_target = left_pad_target | |
if print_alignment == "hard": | |
self.extract_alignment = utils.extract_hard_alignment | |
elif print_alignment == "soft": | |
self.extract_alignment = utils.extract_soft_alignment | |
def generate(self, models, sample, **kwargs): | |
finalized = super()._generate(sample, **kwargs) | |
src_tokens = sample["net_input"]["src_tokens"] | |
bsz = src_tokens.shape[0] | |
beam_size = self.beam_size | |
( | |
src_tokens, | |
src_lengths, | |
prev_output_tokens, | |
tgt_tokens, | |
) = self._prepare_batch_for_alignment(sample, finalized) | |
if any(getattr(m, "full_context_alignment", False) for m in self.model.models): | |
attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens) | |
else: | |
attn = [ | |
finalized[i // beam_size][i % beam_size]["attention"].transpose(1, 0) | |
for i in range(bsz * beam_size) | |
] | |
if src_tokens.device != "cpu": | |
src_tokens = src_tokens.to("cpu") | |
tgt_tokens = tgt_tokens.to("cpu") | |
attn = [i.to("cpu") for i in attn] | |
# Process the attn matrix to extract hard alignments. | |
for i in range(bsz * beam_size): | |
alignment = self.extract_alignment( | |
attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos | |
) | |
finalized[i // beam_size][i % beam_size]["alignment"] = alignment | |
return finalized | |
def _prepare_batch_for_alignment(self, sample, hypothesis): | |
src_tokens = sample["net_input"]["src_tokens"] | |
bsz = src_tokens.shape[0] | |
src_tokens = ( | |
src_tokens[:, None, :] | |
.expand(-1, self.beam_size, -1) | |
.contiguous() | |
.view(bsz * self.beam_size, -1) | |
) | |
src_lengths = sample["net_input"]["src_lengths"] | |
src_lengths = ( | |
src_lengths[:, None] | |
.expand(-1, self.beam_size) | |
.contiguous() | |
.view(bsz * self.beam_size) | |
) | |
prev_output_tokens = data_utils.collate_tokens( | |
[beam["tokens"] for example in hypothesis for beam in example], | |
self.pad, | |
self.eos, | |
self.left_pad_target, | |
move_eos_to_beginning=True, | |
) | |
tgt_tokens = data_utils.collate_tokens( | |
[beam["tokens"] for example in hypothesis for beam in example], | |
self.pad, | |
self.eos, | |
self.left_pad_target, | |
move_eos_to_beginning=False, | |
) | |
return src_tokens, src_lengths, prev_output_tokens, tgt_tokens | |
class EnsembleModelWithAlignment(EnsembleModel): | |
"""A wrapper around an ensemble of models.""" | |
def __init__(self, models): | |
super().__init__(models) | |
def forward_align(self, src_tokens, src_lengths, prev_output_tokens): | |
avg_attn = None | |
for model in self.models: | |
decoder_out = model(src_tokens, src_lengths, prev_output_tokens) | |
attn = decoder_out[1]["attn"][0] | |
if avg_attn is None: | |
avg_attn = attn | |
else: | |
avg_attn.add_(attn) | |
if len(self.models) > 1: | |
avg_attn.div_(len(self.models)) | |
return avg_attn | |