File size: 21,449 Bytes
9b43833
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
# 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.

try:
    from collections.abc import Iterable
except ImportError:
    from collections import Iterable
import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple

import numpy as np
import torch

from fairseq.file_io import PathManager
from fairseq import utils
import os

logger = logging.getLogger(__name__)


def infer_language_pair(path):
    """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
    src, dst = None, None
    for filename in PathManager.ls(path):
        parts = filename.split(".")
        if len(parts) >= 3 and len(parts[1].split("-")) == 2:
            return parts[1].split("-")
    return src, dst


def collate_tokens(
    values,
    pad_idx,
    eos_idx=None,
    left_pad=False,
    move_eos_to_beginning=False,
    pad_to_length=None,
    pad_to_multiple=1,
    pad_to_bsz=None,
):
    """Convert a list of 1d tensors into a padded 2d tensor."""
    size = max(v.size(0) for v in values)
    size = size if pad_to_length is None else max(size, pad_to_length)
    if pad_to_multiple != 1 and size % pad_to_multiple != 0:
        size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)

    def copy_tensor(src, dst):
        assert dst.numel() == src.numel()
        if move_eos_to_beginning:
            if eos_idx is None:
                # if no eos_idx is specified, then use the last token in src
                dst[0] = src[-1]
            else:
                dst[0] = eos_idx
            dst[1:] = src[:-1]
        else:
            dst.copy_(src)

    if values[0].dim() == 1:
        res = values[0].new(len(values), size).fill_(pad_idx)
    elif values[0].dim() == 2:
        assert move_eos_to_beginning is False
        res = values[0].new(len(values), size, values[0].size(1)).fill_(pad_idx)
    else:
        raise NotImplementedError

    for i, v in enumerate(values):
        copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
    return res


def load_indexed_dataset(
    path, dictionary=None, dataset_impl=None, combine=False, default="cached"
):
    """A helper function for loading indexed datasets.

    Args:
        path (str): path to indexed dataset (e.g., 'data-bin/train')
        dictionary (~fairseq.data.Dictionary): data dictionary
        dataset_impl (str, optional): which dataset implementation to use. If
            not provided, it will be inferred automatically. For legacy indexed
            data we use the 'cached' implementation by default.
        combine (bool, optional): automatically load and combine multiple
            datasets. For example, if *path* is 'data-bin/train', then we will
            combine 'data-bin/train', 'data-bin/train1', ... and return a
            single ConcatDataset instance.
    """
    import fairseq.data.indexed_dataset as indexed_dataset
    from fairseq.data.concat_dataset import ConcatDataset

    datasets = []
    for k in itertools.count():
        path_k = path + (str(k) if k > 0 else "")
        try:
            path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
        except Exception as e:
            if "StorageException: [404] Path not found" in str(e):
                logger.warning(f"path_k: {e} not found")
            else:
                raise e

        dataset_impl_k = dataset_impl
        if dataset_impl_k is None:
            dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
        dataset = indexed_dataset.make_dataset(
            path_k,
            impl=dataset_impl_k or default,
            fix_lua_indexing=True,
            dictionary=dictionary,
        )
        if dataset is None:
            break
        logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
        datasets.append(dataset)
        if not combine:
            break
    if len(datasets) == 0:
        return None
    elif len(datasets) == 1:
        return datasets[0]
    else:
        return ConcatDataset(datasets)


@contextlib.contextmanager
def numpy_seed(seed, *addl_seeds):
    """Context manager which seeds the NumPy PRNG with the specified seed and
    restores the state afterward"""
    if seed is None:
        yield
        return
    if len(addl_seeds) > 0:
        seed = int(hash((seed, *addl_seeds)) % 1e6)
    state = np.random.get_state()
    np.random.seed(seed)
    try:
        yield
    finally:
        np.random.set_state(state)


def collect_filtered(function, iterable, filtered):
    """
    Similar to :func:`filter` but collects filtered elements in ``filtered``.

    Args:
        function (callable): function that returns ``False`` for elements that
            should be filtered
        iterable (iterable): iterable to filter
        filtered (list): list to store filtered elements
    """
    for el in iterable:
        if function(el):
            yield el
        else:
            filtered.append(el)


def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
    def compare_leq(a, b):
        return a <= b if not isinstance(a, tuple) else max(a) <= b

    def check_size(idx):
        if isinstance(max_positions, float) or isinstance(max_positions, int):
            return size_fn(idx) <= max_positions
        elif isinstance(max_positions, dict):
            idx_size = size_fn(idx)
            assert isinstance(idx_size, dict)
            intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
            return all(
                all(
                    a is None or b is None or a <= b
                    for a, b in zip(idx_size[key], max_positions[key])
                )
                for key in intersect_keys
            )
        else:
            # For MultiCorpusSampledDataset, will generalize it later
            if not isinstance(size_fn(idx), Iterable):
                return all(size_fn(idx) <= b for b in max_positions)
            return all(
                a is None or b is None or a <= b
                for a, b in zip(size_fn(idx), max_positions)
            )

    ignored = []
    itr = collect_filtered(check_size, indices, ignored)
    indices = np.fromiter(itr, dtype=np.int64, count=-1)
    return indices, ignored


def filter_by_size(indices, dataset, max_positions, raise_exception=False):
    """
    [deprecated] Filter indices based on their size.
    Use `FairseqDataset::filter_indices_by_size` instead.

    Args:
        indices (List[int]): ordered list of dataset indices
        dataset (FairseqDataset): fairseq dataset instance
        max_positions (tuple): filter elements larger than this size.
            Comparisons are done component-wise.
        raise_exception (bool, optional): if ``True``, raise an exception if
            any elements are filtered (default: False).
    """
    warnings.warn(
        "data_utils.filter_by_size is deprecated. "
        "Use `FairseqDataset::filter_indices_by_size` instead.",
        stacklevel=2,
    )
    if isinstance(max_positions, float) or isinstance(max_positions, int):
        if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
            ignored = indices[dataset.sizes[indices] > max_positions].tolist()
            indices = indices[dataset.sizes[indices] <= max_positions]
        elif (
            hasattr(dataset, "sizes")
            and isinstance(dataset.sizes, list)
            and len(dataset.sizes) == 1
        ):
            ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
            indices = indices[dataset.sizes[0][indices] <= max_positions]
        else:
            indices, ignored = _filter_by_size_dynamic(
                indices, dataset.size, max_positions
            )
    else:
        indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)

    if len(ignored) > 0 and raise_exception:
        raise Exception(
            (
                "Size of sample #{} is invalid (={}) since max_positions={}, "
                "skip this example with --skip-invalid-size-inputs-valid-test"
            ).format(ignored[0], dataset.size(ignored[0]), max_positions)
        )
    if len(ignored) > 0:
        logger.warning(
            (
                "{} samples have invalid sizes and will be skipped, "
                "max_positions={}, first few sample ids={}"
            ).format(len(ignored), max_positions, ignored[:10])
        )
    return indices


def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
    """Filter a list of sample indices. Remove those that are longer
        than specified in max_sizes.

    Args:
        indices (np.array): original array of sample indices
        max_sizes (int or list[int] or tuple[int]): max sample size,
            can be defined separately for src and tgt (then list or tuple)

    Returns:
        np.array: filtered sample array
        list: list of removed indices
    """
    if max_sizes is None:
        return indices, []
    if type(max_sizes) in (int, float):
        max_src_size, max_tgt_size = max_sizes, max_sizes
    else:
        max_src_size, max_tgt_size = max_sizes
    if tgt_sizes is None:
        ignored = indices[src_sizes[indices] > max_src_size]
    else:
        ignored = indices[
            (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
        ]
    if len(ignored) > 0:
        if tgt_sizes is None:
            indices = indices[src_sizes[indices] <= max_src_size]
        else:
            indices = indices[
                (src_sizes[indices] <= max_src_size)
                & (tgt_sizes[indices] <= max_tgt_size)
            ]
    return indices, ignored.tolist()


def batch_by_size(
    indices,
    num_tokens_fn,
    num_tokens_vec=None,
    max_tokens=None,
    max_sentences=None,
    required_batch_size_multiple=1,
    fixed_shapes=None,
):
    """
    Yield mini-batches of indices bucketed by size. Batches may contain
    sequences of different lengths.

    Args:
        indices (List[int]): ordered list of dataset indices
        num_tokens_fn (callable): function that returns the number of tokens at
            a given index
        num_tokens_vec (List[int], optional): precomputed vector of the number
            of tokens for each index in indices (to enable faster batch generation)
        max_tokens (int, optional): max number of tokens in each batch
            (default: None).
        max_sentences (int, optional): max number of sentences in each
            batch (default: None).
        required_batch_size_multiple (int, optional): require batch size to
            be less than N or a multiple of N (default: 1).
        fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
            only be created with the given shapes. *max_sentences* and
            *required_batch_size_multiple* will be ignored (default: None).
    """
    try:
        from fairseq.data.data_utils_fast import (
            batch_by_size_fn,
            batch_by_size_vec,
            batch_fixed_shapes_fast,
        )
    except ImportError:
        raise ImportError(
            "Please build Cython components with: "
            "`python setup.py build_ext --inplace`"
        )
    except ValueError:
        raise ValueError(
            "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
        )

    # added int() to avoid TypeError: an integer is required
    max_tokens = (
        int(max_tokens) if max_tokens is not None else -1
    )
    max_sentences = max_sentences if max_sentences is not None else -1
    bsz_mult = required_batch_size_multiple

    if not isinstance(indices, np.ndarray):
        indices = np.fromiter(indices, dtype=np.int64, count=-1)

    if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
        num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)

    if fixed_shapes is None:
        if num_tokens_vec is None:
            return batch_by_size_fn(
                indices,
                num_tokens_fn,
                max_tokens,
                max_sentences,
                bsz_mult,
            )
        else:
            return batch_by_size_vec(
                indices,
                num_tokens_vec,
                max_tokens,
                max_sentences,
                bsz_mult,
            )

    else:
        fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
        sort_order = np.lexsort(
            [
                fixed_shapes[:, 1].argsort(),  # length
                fixed_shapes[:, 0].argsort(),  # bsz
            ]
        )
        fixed_shapes_sorted = fixed_shapes[sort_order]
        return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)


def post_process(sentence: str, symbol: str):
    if symbol == "sentencepiece":
        sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
    elif symbol == "wordpiece":
        sentence = sentence.replace(" ", "").replace("_", " ").strip()
    elif symbol == "letter":
        sentence = sentence.replace(" ", "").replace("|", " ").strip()
    elif symbol == "silence":
        import re
        sentence = sentence.replace("<SIL>", "")
        sentence = re.sub(' +', ' ', sentence).strip()
    elif symbol == "_EOW":
        sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
    elif symbol in {"subword_nmt", "@@ ", "@@"}:
        if symbol == "subword_nmt":
            symbol = "@@ "
        sentence = (sentence + " ").replace(symbol, "").rstrip()
    elif symbol == "none":
        pass
    elif symbol is not None:
        raise NotImplementedError(f"Unknown post_process option: {symbol}")
    return sentence


def compute_mask_indices(
    shape: Tuple[int, int],
    padding_mask: Optional[torch.Tensor],
    mask_prob: float,
    mask_length: int,
    mask_type: str = "static",
    mask_other: float = 0.0,
    min_masks: int = 0,
    no_overlap: bool = False,
    min_space: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape

    Args:
        shape: the the shape for which to compute masks.
            should be of size 2 where first element is batch size and 2nd is timesteps
        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
        mask_type: how to compute mask lengths
            static = fixed size
            uniform = sample from uniform distribution [mask_other, mask_length*2]
            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
            poisson = sample from possion distribution with lambda = mask length
        min_masks: minimum number of masked spans
        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
    """

    bsz, all_sz = shape
    mask = np.full((bsz, all_sz), False)

    all_num_mask = int(
        # add a random number for probabilistic rounding
        mask_prob * all_sz / float(mask_length)
        + np.random.rand()
    )

    all_num_mask = max(min_masks, all_num_mask)

    mask_idcs = []
    for i in range(bsz):
        if padding_mask is not None:
            sz = all_sz - padding_mask[i].long().sum().item()
            num_mask = int(
                # add a random number for probabilistic rounding
                mask_prob * sz / float(mask_length)
                + np.random.rand()
            )
            num_mask = max(min_masks, num_mask)
        else:
            sz = all_sz
            num_mask = all_num_mask

        if mask_type == "static":
            lengths = np.full(num_mask, mask_length)
        elif mask_type == "uniform":
            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
        elif mask_type == "normal":
            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
            lengths = [max(1, int(round(x))) for x in lengths]
        elif mask_type == "poisson":
            lengths = np.random.poisson(mask_length, size=num_mask)
            lengths = [int(round(x)) for x in lengths]
        else:
            raise Exception("unknown mask selection " + mask_type)

        if sum(lengths) == 0:
            lengths[0] = min(mask_length, sz - 1)

        if no_overlap:
            mask_idc = []

            def arrange(s, e, length, keep_length):
                span_start = np.random.randint(s, e - length)
                mask_idc.extend(span_start + i for i in range(length))

                new_parts = []
                if span_start - s - min_space >= keep_length:
                    new_parts.append((s, span_start - min_space + 1))
                if e - span_start - keep_length - min_space > keep_length:
                    new_parts.append((span_start + length + min_space, e))
                return new_parts

            parts = [(0, sz)]
            min_length = min(lengths)
            for length in sorted(lengths, reverse=True):
                lens = np.fromiter(
                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
                    np.int,
                )
                l_sum = np.sum(lens)
                if l_sum == 0:
                    break
                probs = lens / np.sum(lens)
                c = np.random.choice(len(parts), p=probs)
                s, e = parts.pop(c)
                parts.extend(arrange(s, e, length, min_length))
            mask_idc = np.asarray(mask_idc)
        else:
            min_len = min(lengths)
            if sz - min_len <= num_mask:
                min_len = sz - num_mask - 1

            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)

            mask_idc = np.asarray(
                [
                    mask_idc[j] + offset
                    for j in range(len(mask_idc))
                    for offset in range(lengths[j])
                ]
            )

        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))

    min_len = min([len(m) for m in mask_idcs])
    for i, mask_idc in enumerate(mask_idcs):
        if len(mask_idc) > min_len:
            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
        mask[i, mask_idc] = True

    return mask


def get_mem_usage():
    try:
        import psutil

        mb = 1024 * 1024
        return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
    except ImportError:
        return "N/A"


# lens: torch.LongTensor
# returns: torch.BoolTensor
def lengths_to_padding_mask(lens):
    bsz, max_lens = lens.size(0), torch.max(lens).item()
    mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
    mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
    return mask


# lens: torch.LongTensor
# returns: torch.BoolTensor
def lengths_to_mask(lens):
    return ~lengths_to_padding_mask(lens)


def get_buckets(sizes, num_buckets):
    buckets = np.unique(
        np.percentile(
            sizes,
            np.linspace(0, 100, num_buckets + 1),
            interpolation='lower',
        )[1:]
    )
    return buckets


def get_bucketed_sizes(orig_sizes, buckets):
    sizes = np.copy(orig_sizes)
    assert np.min(sizes) >= 0
    start_val = -1
    for end_val in buckets:
        mask = (sizes > start_val) & (sizes <= end_val)
        sizes[mask] = end_val
        start_val = end_val
    return sizes



def _find_extra_valid_paths(dataset_path: str) -> set:
    paths = utils.split_paths(dataset_path)
    all_valid_paths = set()
    for sub_dir in paths:
        contents = PathManager.ls(sub_dir)
        valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
        all_valid_paths |= {os.path.basename(p) for p in valid_paths}
    # Remove .bin, .idx etc
    roots = {os.path.splitext(p)[0] for p in all_valid_paths}
    return roots


def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
    """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
    if (
        train_cfg.dataset.ignore_unused_valid_subsets
        or train_cfg.dataset.combine_valid_subsets
        or train_cfg.dataset.disable_validation
        or not hasattr(train_cfg.task, "data")
    ):
        return
    other_paths = _find_extra_valid_paths(train_cfg.task.data)
    specified_subsets = train_cfg.dataset.valid_subset.split(",")
    ignored_paths = [p for p in other_paths if p not in specified_subsets]
    if ignored_paths:
        advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
        msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
        raise ValueError(msg)