wehub-resource-sync
451 lines
17 KiB
Python
451 lines
17 KiB
Python
# 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 torch
|
|
import torch.nn.functional as F
|
|
from fairseq import utils
|
|
from fairseq.iterative_refinement_generator import DecoderOut
|
|
from fairseq.models import register_model, register_model_architecture
|
|
from fairseq.models.nat import FairseqNATDecoder, FairseqNATEncoder, FairseqNATModel, ensemble_decoder, ensemble_encoder
|
|
from fairseq.models.transformer import Embedding
|
|
from fairseq.modules.transformer_sentence_encoder import init_bert_params
|
|
from typing import Any, Dict, List, Optional, Tuple
|
|
from torch import Tensor
|
|
|
|
def _mean_pooling(enc_feats, src_masks):
|
|
# enc_feats: T x B x C
|
|
# src_masks: B x T or None
|
|
if src_masks is None:
|
|
enc_feats = enc_feats.mean(0)
|
|
else:
|
|
src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats)
|
|
enc_feats = (
|
|
(enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None]
|
|
).sum(0)
|
|
return enc_feats
|
|
|
|
|
|
def _argmax(x, dim):
|
|
return (x == x.max(dim, keepdim=True)[0]).type_as(x)
|
|
|
|
|
|
def _uniform_assignment(src_lens, trg_lens):
|
|
max_trg_len = trg_lens.max()
|
|
steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size
|
|
# max_trg_len
|
|
index_t = utils.new_arange(trg_lens, max_trg_len).float()
|
|
index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len
|
|
index_t = torch.round(index_t).long().detach()
|
|
return index_t
|
|
|
|
|
|
@register_model("nonautoregressive_transformer")
|
|
class NATransformerModel(FairseqNATModel):
|
|
|
|
@staticmethod
|
|
def add_args(parser):
|
|
FairseqNATModel.add_args(parser)
|
|
|
|
parser.add_argument(
|
|
"--src-embedding-copy",
|
|
action="store_true",
|
|
help="copy encoder word embeddings as the initial input of the decoder",
|
|
)
|
|
|
|
@classmethod
|
|
def build_encoder(cls, args, tgt_dict, embed_tokens):
|
|
encoder = NATransformerEncoder(args, tgt_dict, embed_tokens)
|
|
if getattr(args, "apply_bert_init", False):
|
|
encoder.apply(init_bert_params)
|
|
return encoder
|
|
|
|
@classmethod
|
|
def build_decoder(cls, args, tgt_dict, embed_tokens):
|
|
decoder = NATransformerDecoder(args, tgt_dict, embed_tokens)
|
|
if getattr(args, "apply_bert_init", False):
|
|
decoder.apply(init_bert_params)
|
|
return decoder
|
|
|
|
def forward(
|
|
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
|
|
):
|
|
# encoding
|
|
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
|
|
|
|
# decoding
|
|
word_ins_out = self.decoder(
|
|
normalize=False,
|
|
prev_output_tokens=prev_output_tokens,
|
|
encoder_out=encoder_out,
|
|
)
|
|
|
|
return {
|
|
"word_ins": {
|
|
"out": word_ins_out,
|
|
"tgt": tgt_tokens,
|
|
"mask": tgt_tokens.ne(self.pad),
|
|
"ls": self.args.label_smoothing,
|
|
"nll_loss": True,
|
|
}
|
|
}
|
|
|
|
def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
|
|
step = decoder_out.step
|
|
output_tokens = decoder_out.output_tokens
|
|
output_scores = decoder_out.output_scores
|
|
history = decoder_out.history
|
|
|
|
# execute the decoder
|
|
output_masks = output_tokens.ne(self.pad)
|
|
_scores, _tokens = self.decoder(
|
|
normalize=True,
|
|
prev_output_tokens=output_tokens,
|
|
encoder_out=encoder_out,
|
|
step=step,
|
|
).max(-1)
|
|
|
|
output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
|
|
output_scores.masked_scatter_(output_masks, _scores[output_masks])
|
|
if history is not None:
|
|
history.append(output_tokens.clone())
|
|
|
|
return decoder_out._replace(
|
|
output_tokens=output_tokens,
|
|
output_scores=output_scores,
|
|
attn=None,
|
|
history=history,
|
|
)
|
|
|
|
|
|
class NATransformerDecoder(FairseqNATDecoder):
|
|
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
|
|
super().__init__(
|
|
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
|
|
)
|
|
self.dictionary = dictionary
|
|
self.bos = dictionary.bos()
|
|
self.unk = dictionary.unk()
|
|
self.eos = dictionary.eos()
|
|
|
|
self.encoder_embed_dim = args.encoder_embed_dim
|
|
self.src_embedding_copy = getattr(args, "src_embedding_copy", False)
|
|
if self.src_embedding_copy:
|
|
self.copy_attn = torch.nn.Linear(self.embed_dim, self.embed_dim, bias=False)
|
|
|
|
@ensemble_decoder
|
|
def forward(self, normalize, encoder_out, prev_output_tokens, step=0, **unused):
|
|
features, _ = self.extract_features(
|
|
prev_output_tokens,
|
|
encoder_out=encoder_out,
|
|
embedding_copy=(step == 0) & self.src_embedding_copy,
|
|
)
|
|
decoder_out = self.output_layer(features)
|
|
return F.log_softmax(decoder_out, -1) if normalize else decoder_out
|
|
|
|
def extract_features(
|
|
self,
|
|
prev_output_tokens,
|
|
encoder_out=None,
|
|
early_exit=None,
|
|
embedding_copy=False,
|
|
**unused
|
|
):
|
|
"""
|
|
Similar to *forward* but only return features.
|
|
|
|
Inputs:
|
|
prev_output_tokens: Tensor(B, T)
|
|
encoder_out: a dictionary of hidden states and masks
|
|
|
|
Returns:
|
|
tuple:
|
|
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
|
|
- a dictionary with any model-specific outputs
|
|
the LevenshteinTransformer decoder has full-attention to all generated tokens
|
|
"""
|
|
positions = (
|
|
self.embed_positions(prev_output_tokens)
|
|
if self.embed_positions is not None
|
|
else None
|
|
)
|
|
# embedding
|
|
if embedding_copy:
|
|
src_embd = encoder_out["encoder_embedding"][0]
|
|
if len(encoder_out["encoder_padding_mask"]) > 0:
|
|
src_mask = encoder_out["encoder_padding_mask"][0]
|
|
else:
|
|
src_mask = None
|
|
|
|
bsz, seq_len = prev_output_tokens.size()
|
|
attn_score = torch.bmm(self.copy_attn(positions),
|
|
(src_embd + encoder_out['encoder_pos'][0]).transpose(1, 2))
|
|
if src_mask is not None:
|
|
attn_score = attn_score.masked_fill(src_mask.unsqueeze(1).expand(-1, seq_len, -1), float('-inf'))
|
|
attn_weight = F.softmax(attn_score, dim=-1)
|
|
x = torch.bmm(attn_weight, src_embd)
|
|
mask_target_x, decoder_padding_mask = self.forward_embedding(prev_output_tokens)
|
|
output_mask = prev_output_tokens.eq(self.unk)
|
|
cat_x = torch.cat([mask_target_x.unsqueeze(2), x.unsqueeze(2)], dim=2).view(-1, x.size(2))
|
|
# torch.arange(bsz * seq_len).cuda()
|
|
x = cat_x.index_select(dim=0, index=torch.arange(bsz * seq_len).cuda() * 2 +
|
|
output_mask.view(-1).long()).reshape(bsz, seq_len, x.size(2))
|
|
else:
|
|
|
|
x, decoder_padding_mask = self.forward_embedding(prev_output_tokens)
|
|
|
|
# B x T x C -> T x B x C
|
|
x = x.transpose(0, 1)
|
|
positions = positions.transpose(0, 1)
|
|
attn = None
|
|
inner_states = [x]
|
|
|
|
# decoder layers
|
|
for i, layer in enumerate(self.layers):
|
|
|
|
# early exit from the decoder.
|
|
if (early_exit is not None) and (i >= early_exit):
|
|
break
|
|
|
|
if positions is not None:
|
|
x += positions
|
|
x = self.dropout_module(x)
|
|
|
|
x, attn, _ = layer(
|
|
x,
|
|
encoder_out["encoder_out"][0]
|
|
if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0)
|
|
else None,
|
|
encoder_out["encoder_padding_mask"][0]
|
|
if (
|
|
encoder_out is not None
|
|
and len(encoder_out["encoder_padding_mask"]) > 0
|
|
)
|
|
else None,
|
|
self_attn_mask=None,
|
|
self_attn_padding_mask=decoder_padding_mask,
|
|
)
|
|
inner_states.append(x)
|
|
|
|
if self.layer_norm:
|
|
x = self.layer_norm(x)
|
|
|
|
# T x B x C -> B x T x C
|
|
x = x.transpose(0, 1)
|
|
|
|
if self.project_out_dim is not None:
|
|
x = self.project_out_dim(x)
|
|
|
|
return x, {"attn": attn, "inner_states": inner_states}
|
|
|
|
def forward_embedding(self, prev_output_tokens, states=None):
|
|
# embed tokens
|
|
if states is None:
|
|
x = self.embed_tokens(prev_output_tokens)
|
|
if self.project_in_dim is not None:
|
|
x = self.project_in_dim(x)
|
|
else:
|
|
x = states
|
|
|
|
decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
|
|
return x, decoder_padding_mask
|
|
|
|
def forward_copying_source(self, src_embeds, src_masks, tgt_masks):
|
|
length_sources = src_masks.sum(1)
|
|
length_targets = tgt_masks.sum(1)
|
|
mapped_inputs = _uniform_assignment(length_sources, length_targets).masked_fill(
|
|
~tgt_masks, 0
|
|
)
|
|
copied_embedding = torch.gather(
|
|
src_embeds,
|
|
1,
|
|
mapped_inputs.unsqueeze(-1).expand(
|
|
*mapped_inputs.size(), src_embeds.size(-1)
|
|
),
|
|
)
|
|
return copied_embedding
|
|
|
|
|
|
class NATransformerEncoder(FairseqNATEncoder):
|
|
def __init__(self, args, dictionary, embed_tokens):
|
|
super().__init__(args, dictionary, embed_tokens)
|
|
|
|
@ensemble_encoder
|
|
def forward(
|
|
self,
|
|
src_tokens,
|
|
src_lengths: Optional[torch.Tensor] = None,
|
|
return_all_hiddens: bool = False,
|
|
token_embeddings: Optional[torch.Tensor] = None,
|
|
):
|
|
encoder_padding_mask = src_tokens.eq(self.padding_idx)
|
|
has_pads = (src_tokens.device.type == "xla" or encoder_padding_mask.any())
|
|
|
|
x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings)
|
|
|
|
encoder_pos = self.embed_positions(src_tokens)
|
|
# account for padding while computing the representation
|
|
if encoder_padding_mask is not None:
|
|
x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
|
|
|
|
# B x T x C -> T x B x C
|
|
x = x.transpose(0, 1)
|
|
|
|
encoder_states = []
|
|
|
|
if return_all_hiddens:
|
|
encoder_states.append(x)
|
|
# encoder layers
|
|
for layer in self.layers:
|
|
x = layer(
|
|
x, encoder_padding_mask=encoder_padding_mask if has_pads else None
|
|
)
|
|
if return_all_hiddens:
|
|
assert encoder_states is not None
|
|
encoder_states.append(x)
|
|
|
|
if self.layer_norm is not None:
|
|
x = self.layer_norm(x)
|
|
|
|
# The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
|
|
# `forward` so we use a dictionary instead.
|
|
# TorchScript does not support mixed values so the values are all lists.
|
|
# The empty list is equivalent to None.
|
|
return {
|
|
"encoder_out": [x], # T x B x C
|
|
"encoder_padding_mask": [encoder_padding_mask], # B x T
|
|
"encoder_embedding": [encoder_embedding], # B x T x C
|
|
"encoder_pos": [encoder_pos],
|
|
"encoder_states": encoder_states, # List[T x B x C]
|
|
"src_tokens": [],
|
|
"src_lengths": [],
|
|
}
|
|
|
|
def forward_embedding(
|
|
self, src_tokens, token_embedding: Optional[torch.Tensor] = None
|
|
):
|
|
# embed tokens and positions
|
|
if token_embedding is None:
|
|
token_embedding = self.embed_tokens(src_tokens)
|
|
x = embed = token_embedding
|
|
if self.embed_positions is not None:
|
|
x = embed + self.embed_positions(src_tokens)
|
|
if self.layernorm_embedding is not None:
|
|
x = self.layernorm_embedding(x)
|
|
x = self.dropout_module(x)
|
|
if self.quant_noise is not None:
|
|
x = self.quant_noise(x)
|
|
return x, embed
|
|
|
|
@torch.jit.export
|
|
def reorder_encoder_out(self, encoder_out: 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*
|
|
"""
|
|
if len(encoder_out["encoder_out"]) == 0:
|
|
new_encoder_out = []
|
|
else:
|
|
new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
|
|
if len(encoder_out["encoder_padding_mask"]) == 0:
|
|
new_encoder_padding_mask = []
|
|
else:
|
|
new_encoder_padding_mask = [
|
|
encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
|
|
]
|
|
if len(encoder_out["encoder_embedding"]) == 0:
|
|
new_encoder_embedding = []
|
|
else:
|
|
new_encoder_embedding = [
|
|
encoder_out["encoder_embedding"][0].index_select(0, new_order)
|
|
]
|
|
if len(encoder_out["encoder_pos"]) == 0:
|
|
new_encoder_pos = []
|
|
else:
|
|
new_encoder_pos = [
|
|
encoder_out["encoder_pos"][0].index_select(0, new_order)
|
|
]
|
|
|
|
if len(encoder_out["src_tokens"]) == 0:
|
|
src_tokens = []
|
|
else:
|
|
src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]
|
|
|
|
if len(encoder_out["src_lengths"]) == 0:
|
|
src_lengths = []
|
|
else:
|
|
src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]
|
|
|
|
encoder_states = encoder_out["encoder_states"]
|
|
if len(encoder_states) > 0:
|
|
for idx, state in enumerate(encoder_states):
|
|
encoder_states[idx] = state.index_select(1, new_order)
|
|
|
|
return {
|
|
"encoder_out": new_encoder_out, # T x B x C
|
|
"encoder_padding_mask": new_encoder_padding_mask, # B x T
|
|
"encoder_embedding": new_encoder_embedding, # B x T x C
|
|
"encoder_pos": new_encoder_pos,
|
|
"encoder_states": encoder_states, # List[T x B x C]
|
|
"src_tokens": src_tokens, # B x T
|
|
"src_lengths": src_lengths, # B x 1
|
|
}
|
|
|
|
@register_model_architecture(
|
|
"nonautoregressive_transformer", "nonautoregressive_transformer"
|
|
)
|
|
def base_architecture(args):
|
|
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
|
|
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
|
|
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
|
|
args.encoder_layers = getattr(args, "encoder_layers", 6)
|
|
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
|
|
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
|
|
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
|
|
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
|
|
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
|
|
args.decoder_ffn_embed_dim = getattr(
|
|
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
|
|
)
|
|
args.decoder_layers = getattr(args, "decoder_layers", 6)
|
|
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
|
|
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
|
|
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
|
|
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
|
|
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
|
|
args.activation_fn = getattr(args, "activation_fn", "relu")
|
|
args.dropout = getattr(args, "dropout", 0.1)
|
|
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
|
|
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
|
|
args.share_decoder_input_output_embed = getattr(
|
|
args, "share_decoder_input_output_embed", False
|
|
)
|
|
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
|
|
args.no_token_positional_embeddings = getattr(
|
|
args, "no_token_positional_embeddings", False
|
|
)
|
|
args.adaptive_input = getattr(args, "adaptive_input", False)
|
|
args.apply_bert_init = getattr(args, "apply_bert_init", False)
|
|
|
|
args.decoder_output_dim = getattr(
|
|
args, "decoder_output_dim", args.decoder_embed_dim
|
|
)
|
|
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
|
|
|
|
# --- special arguments ---
|
|
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
|
|
|
|
|
|
@register_model_architecture(
|
|
"nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de"
|
|
)
|
|
def nonautoregressive_transformer_wmt_en_de(args):
|
|
base_architecture(args)
|