File size: 11,436 Bytes
ee21b96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
306
307
308
309
310
311
312
313
314
315
316
317
#!/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 ast
import fileinput
import logging
import math
import os
import sys
import time
from argparse import Namespace
from collections import namedtuple

import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils
from fairseq.dataclass.configs import FairseqConfig
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


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)
logger = logging.getLogger("fairseq_cli.interactive")


Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints")
Translation = namedtuple("Translation", "src_str hypos pos_scores alignments")


def buffered_read(input, buffer_size):
    buffer = []
    with fileinput.input(files=[input], openhook=fileinput.hook_encoded("utf-8")) as h:
        for src_str in h:
            buffer.append(src_str.strip())
            if len(buffer) >= buffer_size:
                yield buffer
                buffer = []

    if len(buffer) > 0:
        yield buffer


def make_batches(lines, cfg, task, max_positions, encode_fn):
    def encode_fn_target(x):
        return encode_fn(x)

    if cfg.generation.constraints:
        # 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 cfg.generation.constraints:
        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,
        )


def main(cfg: FairseqConfig):
    if isinstance(cfg, Namespace):
        cfg = convert_namespace_to_omegaconf(cfg)

    start_time = time.time()
    total_translate_time = 0

    utils.import_user_module(cfg.common)

    if cfg.interactive.buffer_size < 1:
        cfg.interactive.buffer_size = 1
    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
        cfg.dataset.batch_size = 1

    assert (
        not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
    ), "--sampling requires --nbest to be equal to --beam"
    assert (
        not cfg.dataset.batch_size
        or cfg.dataset.batch_size <= cfg.interactive.buffer_size
    ), "--batch-size cannot be larger than --buffer-size"

    logger.info(cfg)

    # Fix seed for stochastic decoding
    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
        np.random.seed(cfg.common.seed)
        utils.set_torch_seed(cfg.common.seed)

    use_cuda = torch.cuda.is_available() and not cfg.common.cpu

    # Setup task, e.g., translation
    task = tasks.setup_task(cfg.task)

    # Load ensemble
    overrides = ast.literal_eval(cfg.common_eval.model_overrides)
    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
    models, _model_args = checkpoint_utils.load_model_ensemble(
        utils.split_paths(cfg.common_eval.path),
        arg_overrides=overrides,
        task=task,
        suffix=cfg.checkpoint.checkpoint_suffix,
        strict=(cfg.checkpoint.checkpoint_shard_count == 1),
        num_shards=cfg.checkpoint.checkpoint_shard_count,
    )

    # Set dictionaries
    src_dict = task.source_dictionary
    tgt_dict = task.target_dictionary

    # Optimize ensemble for generation
    for model in models:
        if model is None:
            continue
        if cfg.common.fp16:
            model.half()
        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
            model.cuda()
        model.prepare_for_inference_(cfg)

    # Initialize generator
    generator = task.build_generator(models, cfg.generation)

    # Handle tokenization and BPE
    tokenizer = task.build_tokenizer(cfg.tokenizer)
    bpe = task.build_bpe(cfg.bpe)

    def encode_fn(x):
        if tokenizer is not None:
            x = tokenizer.encode(x)
        if bpe is not None:
            x = bpe.encode(x)
        return x

    def decode_fn(x):
        if bpe is not None:
            x = bpe.decode(x)
        if tokenizer is not None:
            x = tokenizer.decode(x)
        return x

    # Load alignment dictionary for unknown word replacement
    # (None if no unknown word replacement, empty if no path to align dictionary)
    align_dict = utils.load_align_dict(cfg.generation.replace_unk)

    max_positions = utils.resolve_max_positions(
        task.max_positions(), *[model.max_positions() for model in models]
    )

    if cfg.generation.constraints:
        logger.warning(
            "NOTE: Constrained decoding currently assumes a shared subword vocabulary."
        )

    if cfg.interactive.buffer_size > 1:
        logger.info("Sentence buffer size: %s", cfg.interactive.buffer_size)
    logger.info("NOTE: hypothesis and token scores are output in base 2")
    logger.info("Type the input sentence and press return:")
    start_id = 0
    for inputs in buffered_read(cfg.interactive.input, cfg.interactive.buffer_size):
        results = []
        for batch in make_batches(inputs, cfg, task, max_positions, encode_fn):
            bsz = batch.src_tokens.size(0)
            src_tokens = batch.src_tokens
            src_lengths = batch.src_lengths
            constraints = batch.constraints
            if 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,
                },
            }
            translate_start_time = time.time()
            translations = task.inference_step(
                generator, models, sample, constraints=constraints
            )
            translate_time = time.time() - translate_start_time
            total_translate_time += translate_time
            list_constraints = [[] for _ in range(bsz)]
            if cfg.generation.constraints:
                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], tgt_dict.pad())
                constraints = list_constraints[i]
                results.append(
                    (
                        start_id + id,
                        src_tokens_i,
                        hypos,
                        {
                            "constraints": constraints,
                            "time": translate_time / len(translations),
                        },
                    )
                )

        # sort output to match input order
        for id_, src_tokens, hypos, info in sorted(results, key=lambda x: x[0]):
            src_str = ''
            if src_dict is not None:
                src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
                print("S-{}\t{}".format(id_, src_str))
                print("W-{}\t{:.3f}\tseconds".format(id_, info["time"]))
                for constraint in info["constraints"]:
                    print(
                        "C-{}\t{}".format(
                            id_, tgt_dict.string(constraint, cfg.common_eval.post_process)
                        )
                    )

            # Process top predictions
            for hypo in hypos[: min(len(hypos), 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=align_dict,
                    tgt_dict=tgt_dict,
                    remove_bpe=cfg.common_eval.post_process,
                    extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator),
                )
                detok_hypo_str = decode_fn(hypo_str)
                score = hypo["score"] / math.log(2)  # convert to base 2
                # original hypothesis (after tokenization and BPE)
                print("H-{}\t{}\t{}".format(id_, score, hypo_str))
                # detokenized hypothesis
                print("D-{}\t{}\t{}".format(id_, score, detok_hypo_str))
                print(
                    "P-{}\t{}".format(
                        id_,
                        " ".join(
                            map(
                                lambda x: "{:.4f}".format(x),
                                # convert from base e to base 2
                                hypo["positional_scores"].div_(math.log(2)).tolist(),
                            )
                        ),
                    )
                )
                if cfg.generation.print_alignment:
                    alignment_str = " ".join(
                        ["{}-{}".format(src, tgt) for src, tgt in alignment]
                    )
                    print("A-{}\t{}".format(id_, alignment_str))

        # update running id_ counter
        start_id += len(inputs)

    logger.info(
        "Total time: {:.3f} seconds; translation time: {:.3f}".format(
            time.time() - start_time, total_translate_time
        )
    )


def cli_main():
    parser = options.get_interactive_generation_parser()
    args = options.parse_args_and_arch(parser)
    distributed_utils.call_main(convert_namespace_to_omegaconf(args), main)


if __name__ == "__main__":
    cli_main()