# ------------------------------------------------------------------------ # Modified from OFA (https://github.com/OFA-Sys/OFA) # Copyright 2022 The OFA-Sys Team. # All rights reserved. # This source code is licensed under the Apache 2.0 license # found in the LICENSE file in the root directory. # ------------------------------------------------------------------------ # Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 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 @torch.no_grad() 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] @torch.no_grad() 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]) @torch.jit.export 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] @torch.jit.export 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 @torch.jit.export 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 @torch.jit.export 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 @torch.no_grad() 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