Spaces:
Sleeping
Sleeping
File size: 8,150 Bytes
6fc683c |
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 |
# 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
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 FairseqNATModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
import torch
from fairseq.models.nat.nonautoregressive_transformer import NATransformerEncoder, NATransformerDecoder, NATransformerModel
import logging
import random
from contextlib import contextmanager
logger = logging.getLogger(__name__)
@contextmanager
def torch_seed(seed):
state = torch.random.get_rng_state()
state_cuda = torch.cuda.random.get_rng_state()
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
try:
yield
finally:
torch.random.set_rng_state(state)
torch.cuda.random.set_rng_state(state_cuda)
@register_model("block")
class BlockNAT(FairseqNATModel):
forward_decoder = NATransformerModel.forward_decoder
initialize_output_tokens = NATransformerModel.initialize_output_tokens
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
@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, glat=None, **kwargs
):
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
nonpad_positions = tgt_tokens.ne(self.pad)
mask_positions = prev_output_tokens.eq(self.unk) & nonpad_positions
mask_lens = (mask_positions).sum(1)
l2r_positions = prev_output_tokens.ne(self.unk) & prev_output_tokens.ne(self.pad)
l2r_lens = (l2r_positions).sum(1)
rand_seed = random.randint(0, 19260817)
glat_info = None
if glat and tgt_tokens is not None:
with torch.no_grad():
with torch_seed(rand_seed):
word_ins_out = self.decoder(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
pred_tokens = word_ins_out.argmax(-1)
same_num = ((pred_tokens == tgt_tokens) & mask_positions).sum(1)
input_mask = torch.ones_like(nonpad_positions)
bsz, seq_len = tgt_tokens.size()
for li in range(bsz):
target_num = (((mask_lens[li] - same_num[li].sum()).float()) * glat['context_p']).long()
if target_num > 0:
input_mask[li].scatter_(dim=0, index=(torch.randperm(mask_lens[li])[:target_num].cuda() +
l2r_lens[li]).cuda(), value=0)
input_mask = input_mask.eq(1)
tgt_mask = input_mask.masked_fill(~mask_positions, False)
glat_prev_output_tokens = prev_output_tokens.masked_fill(~input_mask, 0) + tgt_tokens.masked_fill(
input_mask, 0)
glat_tgt_tokens = tgt_tokens.masked_fill(~tgt_mask, self.pad)
prev_output_tokens, tgt_tokens = glat_prev_output_tokens, glat_tgt_tokens
glat_info = {
"glat_accu": (same_num.sum() / mask_lens.sum()).item(),
"glat_context_p": glat['context_p'],
}
with torch_seed(rand_seed):
word_ins_out = self.decoder(
normalize=False,
prev_output_tokens=prev_output_tokens,
encoder_out=encoder_out,
)
ret = {
"word_ins": {
"out": word_ins_out,
"tgt": tgt_tokens,
"mask": tgt_tokens.ne(self.pad),
"ls": self.args.label_smoothing,
"nll_loss": True,
}
}
if glat_info is not None:
ret.update(glat_info)
return ret
@register_model_architecture(
"block", "block_6e6d512"
)
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(
"block", "block"
)
def block_architecture(args):
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", args.encoder_embed_dim*4)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", args.encoder_embed_dim//64)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", args.decoder_embed_dim*4)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", args.decoder_embed_dim//64)
base_architecture(args)
@register_model_architecture(
"block", "block_base"
)
def base_architecture2(args):
base_architecture(args)
|