File size: 15,831 Bytes
8437114
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
#!/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 pre-processed data with a trained model.
"""

import ast
import logging
import math
import os
import sys
from argparse import Namespace
from itertools import chain

import numpy as np
import torch
from fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progress_bar
from fairseq.logging.meters import StopwatchMeter, TimeMeter
from omegaconf import DictConfig


def main(cfg: DictConfig):

    if isinstance(cfg, Namespace):
        cfg = convert_namespace_to_omegaconf(cfg)

    assert cfg.common_eval.path is not None, "--path required for generation!"
    assert (
        not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam
    ), "--sampling requires --nbest to be equal to --beam"
    assert (
        cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw"
    ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)"

    if cfg.common_eval.results_path is not None:
        os.makedirs(cfg.common_eval.results_path, exist_ok=True)
        output_path = os.path.join(
            cfg.common_eval.results_path,
            "generate-{}.txt".format(cfg.dataset.gen_subset),
        )
        with open(output_path, "w", buffering=1, encoding="utf-8") as h:
            return _main(cfg, h)
    else:
        return _main(cfg, sys.stdout)


def get_symbols_to_strip_from_output(generator):
    if hasattr(generator, "symbols_to_strip_from_output"):
        return generator.symbols_to_strip_from_output
    else:
        return {generator.eos}


def _main(cfg: DictConfig, output_file):
    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=output_file,
    )
    logger = logging.getLogger("fairseq_cli.generate")

    utils.import_user_module(cfg.common)

    if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
        cfg.dataset.max_tokens = 12000
    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

    # Load dataset splits
    task = tasks.setup_task(cfg.task)


    # Set dictionaries
    try:
        src_dict = getattr(task, "source_dictionary", None)
    except NotImplementedError:
        src_dict = None
    tgt_dict = task.target_dictionary

    overrides = ast.literal_eval(cfg.common_eval.model_overrides)

    # Load ensemble
    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
    models, saved_cfg = 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,
    )

    # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
    task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)

    if cfg.generation.lm_path is not None:
        overrides["data"] = cfg.task.data

        try:
            lms, _ = checkpoint_utils.load_model_ensemble(
                [cfg.generation.lm_path], arg_overrides=overrides, task=None
            )
        except:
            logger.warning(
                f"Failed to load language model! Please make sure that the language model dict is the same "
                f"as target dict and is located in the data dir ({cfg.task.data})"
            )
            raise

        assert len(lms) == 1
    else:
        lms = [None]

    # Optimize ensemble for generation
    for model in chain(models, lms):
        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)

    # 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)

    # Load dataset (possibly sharded)
    itr = task.get_batch_iterator(
        dataset=task.dataset(cfg.dataset.gen_subset),
        max_tokens=cfg.dataset.max_tokens,
        max_sentences=cfg.dataset.batch_size,
        max_positions=utils.resolve_max_positions(
            task.max_positions(), *[m.max_positions() for m in models]
        ),
        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
        required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
        seed=cfg.common.seed,
        num_shards=cfg.distributed_training.distributed_world_size,
        shard_id=cfg.distributed_training.distributed_rank,
        num_workers=cfg.dataset.num_workers,
        data_buffer_size=cfg.dataset.data_buffer_size,
    ).next_epoch_itr(shuffle=False)
    progress = progress_bar.progress_bar(
        itr,
        log_format=cfg.common.log_format,
        log_interval=cfg.common.log_interval,
        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
    )

    # Initialize generator
    gen_timer = StopwatchMeter()

    extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight}
    generator = task.build_generator(
        models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs
    )

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

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

    scorer = scoring.build_scorer(cfg.scoring, tgt_dict)

    num_sentences = 0
    has_target = True
    wps_meter = TimeMeter()
    for sample in progress:
        sample = utils.move_to_cuda(sample) if use_cuda else sample
        if "net_input" not in sample:
            continue

        prefix_tokens = None
        if cfg.generation.prefix_size > 0:
            prefix_tokens = sample["target"][:, : cfg.generation.prefix_size]

        constraints = None
        if "constraints" in sample:
            constraints = sample["constraints"]

        gen_timer.start()
        hypos = task.inference_step(
            generator,
            models,
            sample,
            prefix_tokens=prefix_tokens,
            constraints=constraints,
        )
        num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
        gen_timer.stop(num_generated_tokens)

        for i, sample_id in enumerate(sample["id"].tolist()):
            has_target = sample["target"] is not None

            # Remove padding
            if "src_tokens" in sample["net_input"]:
                src_tokens = utils.strip_pad(
                    sample["net_input"]["src_tokens"][i, :], tgt_dict.pad()
                )
            else:
                src_tokens = None

            target_tokens = None
            if has_target:
                target_tokens = (
                    utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
                )

            # Either retrieve the original sentences or regenerate them from tokens.
            if align_dict is not None:
                src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text(
                    sample_id
                )
                target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text(
                    sample_id
                )
            else:
                if src_dict is not None:
                    src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
                else:
                    src_str = ""
                if has_target:
                    target_str = tgt_dict.string(
                        target_tokens,
                        cfg.common_eval.post_process,
                        escape_unk=True,
                        extra_symbols_to_ignore=get_symbols_to_strip_from_output(
                            generator
                        ),
                    )

            src_str = decode_fn(src_str)
            if has_target:
                target_str = decode_fn(target_str)

            if not cfg.common_eval.quiet:
                if src_dict is not None:
                    print("S-{}\t{}".format(sample_id, src_str), file=output_file)
                if has_target:
                    print("T-{}\t{}".format(sample_id, target_str), file=output_file)

            # Process top predictions
            for j, hypo in enumerate(hypos[i][: 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)
                if not cfg.common_eval.quiet:
                    score = hypo["score"] / math.log(2)  # convert to base 2
                    # original hypothesis (after tokenization and BPE)
                    print(
                        "H-{}\t{}\t{}".format(sample_id, score, hypo_str),
                        file=output_file,
                    )
                    # detokenized hypothesis
                    print(
                        "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str),
                        file=output_file,
                    )
                    print(
                        "P-{}\t{}".format(
                            sample_id,
                            " ".join(
                                map(
                                    lambda x: "{:.4f}".format(x),
                                    # convert from base e to base 2
                                    hypo["positional_scores"]
                                    .div_(math.log(2))
                                    .tolist(),
                                )
                            ),
                        ),
                        file=output_file,
                    )

                    if cfg.generation.print_alignment == "hard":
                        print(
                            "A-{}\t{}".format(
                                sample_id,
                                " ".join(
                                    [
                                        "{}-{}".format(src_idx, tgt_idx)
                                        for src_idx, tgt_idx in alignment
                                    ]
                                ),
                            ),
                            file=output_file,
                        )
                    if cfg.generation.print_alignment == "soft":
                        print(
                            "A-{}\t{}".format(
                                sample_id,
                                " ".join(
                                    [
                                        ",".join(src_probs)
                                        for src_probs in alignment
                                    ]
                                ),
                            ),
                            file=output_file,
                        )

                    if cfg.generation.print_step:
                        print(
                            "I-{}\t{}".format(sample_id, hypo["steps"]),
                            file=output_file,
                        )

                    if cfg.generation.retain_iter_history:
                        for step, h in enumerate(hypo["history"]):
                            _, h_str, _ = utils.post_process_prediction(
                                hypo_tokens=h["tokens"].int().cpu(),
                                src_str=src_str,
                                alignment=None,
                                align_dict=None,
                                tgt_dict=tgt_dict,
                                remove_bpe=None,
                            )
                            print(
                                "E-{}_{}\t{}".format(sample_id, step, h_str),
                                file=output_file,
                            )

                # Score only the top hypothesis
                if has_target and j == 0:
                    if align_dict is not None or cfg.common_eval.post_process is not None:
                        # Convert back to tokens for evaluation with unk replacement and/or without BPE
                        target_tokens = tgt_dict.encode_line(
                            target_str, add_if_not_exist=True
                        )
                        hypo_tokens = tgt_dict.encode_line(
                            detok_hypo_str, add_if_not_exist=True
                        )
                    if hasattr(scorer, "add_string"):
                        scorer.add_string(target_str, detok_hypo_str)
                    else:
                        scorer.add(target_tokens, hypo_tokens)

        wps_meter.update(num_generated_tokens)
        progress.log({"wps": round(wps_meter.avg)})
        num_sentences += (
            sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
        )

    logger.info("NOTE: hypothesis and token scores are output in base 2")
    logger.info(
        "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format(
            num_sentences,
            gen_timer.n,
            gen_timer.sum,
            num_sentences / gen_timer.sum,
            1.0 / gen_timer.avg,
        )
    )
    if has_target:
        if cfg.bpe and not cfg.generation.sacrebleu:
            if cfg.common_eval.post_process:
                logger.warning(
                    "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization"
                )
            else:
                logger.warning(
                    "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words.  Use --sacrebleu for standard 13a BLEU tokenization"
                )
        # use print to be consistent with other main outputs: S-, H-, T-, D- and so on
        print(
            "Generate {} with beam={}: {}".format(
                cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string()
            ),
            file=output_file,
        )

    return scorer


def cli_main():
    parser = options.get_generation_parser()
    # TODO: replace this workaround with refactoring of `AudioPretraining`
    parser.add_argument(
        '--arch', '-a', metavar='ARCH', default="wav2vec2",
        help='Model architecture. For constructing tasks that rely on '
             'model args (e.g. `AudioPretraining`)'
    )
    args = options.parse_args_and_arch(parser)
    main(args)


if __name__ == "__main__":
    cli_main()