File size: 11,868 Bytes
f9d7028 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 |
# python wrapper for fairseq-interactive command line tool
#!/usr/bin/env python3 -u
# 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.
"""
Translate raw text with a trained model. Batches data on-the-fly.
"""
import os
import ast
from collections import namedtuple
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.token_generation_constraints import pack_constraints, unpack_constraints
from fairseq_cli.generate import get_symbols_to_strip_from_output
import codecs
PWD = os.path.dirname(__file__)
Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints")
Translation = namedtuple("Translation", "src_str hypos pos_scores alignments")
def make_batches(
lines, cfg, task, max_positions, encode_fn, constrainted_decoding=False
):
def encode_fn_target(x):
return encode_fn(x)
if constrainted_decoding:
# Strip (tab-delimited) contraints, if present, from input lines,
# store them in batch_constraints
batch_constraints = [list() for _ in lines]
for i, line in enumerate(lines):
if "\t" in line:
lines[i], *batch_constraints[i] = line.split("\t")
# Convert each List[str] to List[Tensor]
for i, constraint_list in enumerate(batch_constraints):
batch_constraints[i] = [
task.target_dictionary.encode_line(
encode_fn_target(constraint),
append_eos=False,
add_if_not_exist=False,
)
for constraint in constraint_list
]
if constrainted_decoding:
constraints_tensor = pack_constraints(batch_constraints)
else:
constraints_tensor = None
tokens, lengths = task.get_interactive_tokens_and_lengths(lines, encode_fn)
itr = task.get_batch_iterator(
dataset=task.build_dataset_for_inference(
tokens, lengths, constraints=constraints_tensor
),
max_tokens=cfg.dataset.max_tokens,
max_sentences=cfg.dataset.batch_size,
max_positions=max_positions,
ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
).next_epoch_itr(shuffle=False)
for batch in itr:
ids = batch["id"]
src_tokens = batch["net_input"]["src_tokens"]
src_lengths = batch["net_input"]["src_lengths"]
constraints = batch.get("constraints", None)
yield Batch(
ids=ids,
src_tokens=src_tokens,
src_lengths=src_lengths,
constraints=constraints,
)
class Translator:
"""
Wrapper class to handle the interaction with fairseq model class for translation
"""
def __init__(
self, data_dir, checkpoint_path, batch_size=25, constrained_decoding=False
):
self.constrained_decoding = constrained_decoding
self.parser = options.get_generation_parser(interactive=True)
# buffer_size is currently not used but we just initialize it to batch
# size + 1 to avoid any assertion errors.
if self.constrained_decoding:
self.parser.set_defaults(
path=checkpoint_path,
num_workers=-1,
constraints="ordered",
batch_size=batch_size,
buffer_size=batch_size + 1,
)
else:
self.parser.set_defaults(
path=checkpoint_path,
remove_bpe="subword_nmt",
num_workers=-1,
batch_size=batch_size,
buffer_size=batch_size + 1,
)
args = options.parse_args_and_arch(self.parser, input_args=[data_dir])
# we are explictly setting src_lang and tgt_lang here
# generally the data_dir we pass contains {split}-{src_lang}-{tgt_lang}.*.idx files from
# which fairseq infers the src and tgt langs(if these are not passed). In deployment we dont
# use any idx files and only store the SRC and TGT dictionaries.
args.source_lang = "SRC"
args.target_lang = "TGT"
# since we are truncating sentences to max_seq_len in engine, we can set it to False here
args.skip_invalid_size_inputs_valid_test = False
# we have custom architechtures in this folder and we will let fairseq
# import this
args.user_dir = os.path.join(PWD, "model_configs")
self.cfg = convert_namespace_to_omegaconf(args)
utils.import_user_module(self.cfg.common)
if self.cfg.interactive.buffer_size < 1:
self.cfg.interactive.buffer_size = 1
if self.cfg.dataset.max_tokens is None and self.cfg.dataset.batch_size is None:
self.cfg.dataset.batch_size = 1
assert (
not self.cfg.generation.sampling
or self.cfg.generation.nbest == self.cfg.generation.beam
), "--sampling requires --nbest to be equal to --beam"
assert (
not self.cfg.dataset.batch_size
or self.cfg.dataset.batch_size <= self.cfg.interactive.buffer_size
), "--batch-size cannot be larger than --buffer-size"
# Fix seed for stochastic decoding
# if self.cfg.common.seed is not None and not self.cfg.generation.no_seed_provided:
# np.random.seed(self.cfg.common.seed)
# utils.set_torch_seed(self.cfg.common.seed)
# if not self.constrained_decoding:
# self.use_cuda = torch.cuda.is_available() and not self.cfg.common.cpu
# else:
# self.use_cuda = False
self.use_cuda = torch.cuda.is_available() and not self.cfg.common.cpu
# Setup task, e.g., translation
self.task = tasks.setup_task(self.cfg.task)
# Load ensemble
overrides = ast.literal_eval(self.cfg.common_eval.model_overrides)
self.models, self._model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(self.cfg.common_eval.path),
arg_overrides=overrides,
task=self.task,
suffix=self.cfg.checkpoint.checkpoint_suffix,
strict=(self.cfg.checkpoint.checkpoint_shard_count == 1),
num_shards=self.cfg.checkpoint.checkpoint_shard_count,
)
# Set dictionaries
self.src_dict = self.task.source_dictionary
self.tgt_dict = self.task.target_dictionary
# Optimize ensemble for generation
for model in self.models:
if model is None:
continue
if self.cfg.common.fp16:
model.half()
if (
self.use_cuda
and not self.cfg.distributed_training.pipeline_model_parallel
):
model.cuda()
model.prepare_for_inference_(self.cfg)
# Initialize generator
self.generator = self.task.build_generator(self.models, self.cfg.generation)
self.tokenizer = None
self.bpe = None
# # Handle tokenization and BPE
# self.tokenizer = self.task.build_tokenizer(self.cfg.tokenizer)
# self.bpe = self.task.build_bpe(self.cfg.bpe)
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
self.align_dict = utils.load_align_dict(self.cfg.generation.replace_unk)
self.max_positions = utils.resolve_max_positions(
self.task.max_positions(), *[model.max_positions() for model in self.models]
)
def encode_fn(self, x):
if self.tokenizer is not None:
x = self.tokenizer.encode(x)
if self.bpe is not None:
x = self.bpe.encode(x)
return x
def decode_fn(self, x):
if self.bpe is not None:
x = self.bpe.decode(x)
if self.tokenizer is not None:
x = self.tokenizer.decode(x)
return x
def translate(self, inputs, constraints=None):
if self.constrained_decoding and constraints is None:
raise ValueError("Constraints cant be None in constrained decoding mode")
if not self.constrained_decoding and constraints is not None:
raise ValueError("Cannot pass constraints during normal translation")
if constraints:
constrained_decoding = True
modified_inputs = []
for _input, constraint in zip(inputs, constraints):
modified_inputs.append(_input + f"\t{constraint}")
inputs = modified_inputs
else:
constrained_decoding = False
start_id = 0
results = []
final_translations = []
for batch in make_batches(
inputs,
self.cfg,
self.task,
self.max_positions,
self.encode_fn,
constrained_decoding,
):
bsz = batch.src_tokens.size(0)
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
constraints = batch.constraints
if self.use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
if constraints is not None:
constraints = constraints.cuda()
sample = {
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
},
}
translations = self.task.inference_step(
self.generator, self.models, sample, constraints=constraints
)
list_constraints = [[] for _ in range(bsz)]
if constrained_decoding:
list_constraints = [unpack_constraints(c) for c in constraints]
for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], self.tgt_dict.pad())
constraints = list_constraints[i]
results.append(
(
start_id + id,
src_tokens_i,
hypos,
{
"constraints": constraints,
},
)
)
# sort output to match input order
for id_, src_tokens, hypos, _ in sorted(results, key=lambda x: x[0]):
src_str = ""
if self.src_dict is not None:
src_str = self.src_dict.string(
src_tokens, self.cfg.common_eval.post_process
)
# Process top predictions
for hypo in hypos[: min(len(hypos), self.cfg.generation.nbest)]:
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo["tokens"].int().cpu(),
src_str=src_str,
alignment=hypo["alignment"],
align_dict=self.align_dict,
tgt_dict=self.tgt_dict,
extra_symbols_to_ignore=get_symbols_to_strip_from_output(
self.generator
),
)
detok_hypo_str = self.decode_fn(hypo_str)
final_translations.append(detok_hypo_str)
return final_translations
|