File size: 31,331 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
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
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
# Copyright 2022 The OFA-Sys Team. 
# All rights reserved.
# This source code is licensed under the Apache 2.0 license 
# found in the LICENSE file in the root directory.

import math
from typing import List, Optional

import torch
import torch.nn as nn
from fairseq.token_generation_constraints import (
    ConstraintState,
    OrderedConstraintState,
    UnorderedConstraintState,
)
from torch import Tensor


class Search(nn.Module):
    def __init__(self, tgt_dict):
        super().__init__()
        self.pad = tgt_dict.pad()
        self.unk = tgt_dict.unk()
        self.eos = tgt_dict.eos()
        self.vocab_size = len(tgt_dict)
        self.src_lengths = torch.tensor(-1)
        self.supports_constraints = False
        self.stop_on_max_len = False

    def step(
        self, step, lprobs, scores, prev_output_tokens=None, original_batch_idxs=None
    ):
        """Take a single search step.

        Args:
            step: the current search step, starting at 0
            lprobs: (bsz x input_beam_size x vocab_size)
                the model's log-probabilities over the vocabulary at the current step
            scores: (bsz x input_beam_size x step)
                the historical model scores of each hypothesis up to this point
            prev_output_tokens: (bsz x step)
                the previously generated oputput tokens
            original_batch_idxs: (bsz)
                the tensor with the batch indices, in the range [0, bsz)
                this is useful in case there has been applied a re-ordering
                and we need to know the orignal indices

        Return: A tuple of (scores, indices, beams) where:
            scores: (bsz x output_beam_size)
                the scores of the chosen elements; output_beam_size can be
                larger than input_beam_size, e.g., we may return
                2*input_beam_size to account for EOS
            indices: (bsz x output_beam_size)
                the indices of the chosen elements
            beams: (bsz x output_beam_size)
                the hypothesis ids of the chosen elements, in the range [0, input_beam_size)
        """
        raise NotImplementedError

    @torch.jit.export
    def set_src_lengths(self, src_lengths):
        self.src_lengths = src_lengths

    @torch.jit.export
    def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int):
        """Initialize constraint states for constrained decoding (if supported).

        Args:
            batch_constraints: (torch.Tensor, optional)
                the list of constraints, in packed form
            beam_size: (int)
                the beam size
        Returns:
            *encoder_out* rearranged according to *new_order*
        """
        pass

    def prune_sentences(self, batch_idxs: Tensor):
        """
        Removes constraint states for completed sentences (if supported).
        This is called from sequence_generator._generate() when sentences are
        deleted from the batch.

        Args:
            batch_idxs: Indices of *sentences* whose constraint state should be *kept*.
        """
        pass

    def update_constraints(self, active_hypos: Tensor):
        """
        Updates the constraint states by selecting the beam items that are retained.
        This is called at each time step of sequence_generator._generate() when
        the set of 2 * {beam_size} candidate hypotheses are reduced to the beam size.

        Args:
            active_hypos: (batch size, beam size)
              list of integers denoting, for each sentence, which beam candidate items
              should be kept.
        """
        pass


class BeamSearch(Search):
    def __init__(self, tgt_dict):
        super().__init__(tgt_dict)
        self.constraint_states = None

    @torch.jit.export
    def step(
        self,
        step: int,
        lprobs,
        scores: Optional[Tensor],
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        bsz, beam_size, vocab_size = lprobs.size()

        if step == 0:
            # at the first step all hypotheses are equally likely, so use
            # only the first beam
            lprobs = lprobs[:, ::beam_size, :].contiguous()
        else:
            # make probs contain cumulative scores for each hypothesis
            assert scores is not None
            lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)

        top_prediction = torch.topk(
            lprobs.view(bsz, -1),
            k=min(
                # Take the best 2 x beam_size predictions. We'll choose the first
                # beam_size of these which don't predict eos to continue with.
                beam_size * 2,
                lprobs.view(bsz, -1).size(1) - 1,  # -1 so we never select pad
            ),
        )
        scores_buf = top_prediction[0]
        indices_buf = top_prediction[1]
        # Project back into relative indices and beams
        beams_buf = indices_buf // vocab_size
        indices_buf = indices_buf.fmod(vocab_size)

        # At this point, beams_buf and indices_buf are single-dim and contain relative indices
        return scores_buf, indices_buf, beams_buf


class PrefixConstrainedBeamSearch(Search):
    def __init__(self, tgt_dict, prefix_allowed_tokens_fn):
        super().__init__(tgt_dict)
        self.prefix_allowed_tokens_fn = prefix_allowed_tokens_fn
        self.stop_on_max_len = True

    @torch.jit.export
    def apply_mask(self, x, prev_output_tokens, original_batch_idxs):
        beam_size = x.shape[0] // original_batch_idxs.shape[0]
        original_batch_idxs = (
            original_batch_idxs.unsqueeze(-1).repeat((1, beam_size)).flatten().tolist()
        )

        mask = torch.full_like(x, -math.inf)
        for sent_i, (sent, batch_i) in enumerate(
            zip(prev_output_tokens, original_batch_idxs)
        ):
            mask[sent_i, :, self.prefix_allowed_tokens_fn(batch_i, sent)] = 0

        return mask

    @torch.jit.export
    def step(
        self,
        step: int,
        lprobs: Tensor,
        scores: Tensor,
        prev_output_tokens: Tensor,
        original_batch_idxs: Tensor,
    ):
        bsz, beam_size, vocab_size = lprobs.size()

        lprobs += self.apply_mask(
            lprobs.view(bsz * beam_size, 1, vocab_size),
            prev_output_tokens,
            original_batch_idxs,
        ).view(bsz, beam_size, vocab_size)

        if step == 0:
            # at the first step all hypotheses are equally likely, so use
            # only the first beam
            lprobs = lprobs[:, ::beam_size, :].contiguous()
        else:
            # make probs contain cumulative scores for each hypothesis
            assert scores is not None
            lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)

        top_prediction = torch.topk(
            lprobs.view(bsz, -1),
            k=min(
                # Take the best beam_size predictions. We'll choose the first
                # beam_size of these which don't predict eos to continue with.
                beam_size,
                lprobs.view(bsz, -1).size(1) - 1,  # -1 so we never select pad
            ),
        )
        scores_buf = top_prediction[0]
        indices_buf = top_prediction[1]
        beams_buf = indices_buf // vocab_size
        indices_buf = indices_buf.fmod(vocab_size)
        return scores_buf, indices_buf, beams_buf


class LexicallyConstrainedBeamSearch(Search):
    """Implements lexically constrained beam search as described in

        Fast Lexically Constrained Decoding with Dynamic Beam
        Allocation for Neural Machine Translation.  Post & Vilar,
        NAACL 2018.  https://www.aclweb.org/anthology/N18-1119/

    and

        Improved Lexically Constrained Decoding for Translation and
        Monolingual Rewriting. Hu et al, NAACL
        2019. https://www.aclweb.org/anthology/N19-1090/

    This is accomplished by maintaining, for each beam hypothesis, a
    ConstraintState object (see constraints.py) that tracks which
    constraints have been generated and using this information to
    shape the beam for each input sentence.
    """

    def __init__(self, tgt_dict, representation):
        super().__init__(tgt_dict)
        self.representation = representation
        self.vocab_size = len(tgt_dict)
        self.num_cands = 0
        self.supports_constraints = True

    @torch.jit.export
    def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int):
        self.constraint_states = []
        for constraint_tensor in batch_constraints:
            if self.representation == "ordered":
                constraint_state = OrderedConstraintState.create(constraint_tensor)
            elif self.representation == "unordered":
                constraint_state = UnorderedConstraintState.create(constraint_tensor)

            self.constraint_states.append([constraint_state for i in range(beam_size)])

    @torch.jit.export
    def prune_sentences(self, batch_idxs: Tensor):
        self.constraint_states = [
            self.constraint_states[i] for i in batch_idxs.tolist()
        ]

    @torch.jit.export
    def update_constraints(self, active_hypos: Tensor):
        if self.constraint_states:
            batch_size = active_hypos.size(0)
            for sentid in range(batch_size):
                self.constraint_states[sentid] = [
                    self.constraint_states[sentid][i] for i in active_hypos[sentid]
                ]

    @torch.jit.export
    def step(
        self,
        step: int,
        lprobs: Tensor,
        scores: Optional[Tensor],
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        """
        A constrained step builds a large candidates list from the following:
        - the top 2 * {beam_size} items over the whole beam
        - for each item in the beam
          - the top {each_k} (default 1)
          - all next constraints
        We then compute the constrained state of each beam item, and assign
        stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so
        on. We then sort by (stripe, score), and truncate the list at
        2 * beam size.

        Args:
            step: the decoder step
            lprobs: (batch size, beam size, target vocab)
                the target-vocab distributions for each item in the beam.
        Retrun: A tuple of (scores, indices, beams, constraints) where:
            scores: (batch, output beam size)
                the scores of the chosen elements
            indices: (batch, output beam size)
                the target vocab indices of the chosen elements
            beams: (batch, output beam size)
                the 0-indexed hypothesis ids of the chosen elements
            constraints: (batch, output beam size)
                the new constraint states
        """
        each_k = 1
        device = lprobs.device

        batch_size, beam_size, vocab_size = lprobs.size()

        self.num_cands = min(
            # Just take the k-best. We'll get another k from the 1-best from each
            # row, plus more from the constraints
            beam_size * 2,
            lprobs.view(batch_size, -1).size(1) - 1,  # -1 so we never select pad
        )

        # STEP 0: Preliminary. Prevent EOS for unfinished hyps across all batch items
        constraint_states = self.constraint_states
        if constraint_states and step > 0:
            not_finished_indices = []
            for sentno, sent_constraints in enumerate(constraint_states):
                for beamno, state in enumerate(sent_constraints):
                    index = sentno * beam_size + beamno
                    if not state.finished:
                        not_finished_indices.append(index)
            not_finished_indices = torch.tensor(not_finished_indices)
            if not_finished_indices.numel() > 0:
                lprobs.view(batch_size * beam_size, -1)[
                    not_finished_indices, self.eos
                ] = -math.inf

        if step == 0:
            # at the first step all hypotheses are equally likely, so use
            # only the first beam entry for each batch item
            lprobs = lprobs[:, ::beam_size, :].contiguous()
        else:
            # make probs contain cumulative scores for each hypothesis
            assert scores is not None
            lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)

        top_prediction = torch.topk(
            lprobs.view(batch_size, -1),
            self.num_cands,
        )
        scores_buf, indices_buf = top_prediction
        # Project back into relative indices and beams
        beams_buf = indices_buf // vocab_size
        indices_buf = indices_buf.fmod(vocab_size)

        # Short circuit if there are no constraints in this batch
        if not constraint_states:
            return scores_buf, indices_buf, beams_buf

        # STEP 1: get top-1 from each hypothesis across all sentences in the batch
        if step > 0:
            top_scores, top_indices = torch.topk(
                lprobs.view(batch_size * beam_size, -1),
                k=each_k,
                dim=1,
            )
            top_scores = top_scores.view(batch_size, -1)
            top_indices = top_indices.view(batch_size, -1)
            scores_buf = torch.cat((scores_buf, top_scores), dim=1)
            indices_buf = torch.cat((indices_buf, top_indices), dim=1)
            new_beams = torch.arange(0, beam_size, device=device).repeat(batch_size, 1)
            beams_buf = torch.cat((beams_buf, new_beams), dim=1)

        # Now, process sentences in the batch one by one.
        new_scores_buf = torch.zeros((batch_size, 2 * beam_size), device=device)
        new_indices_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long()
        new_beams_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long()
        for sentno, states in enumerate(constraint_states):
            scores, indices, beams, new_states = self.step_sentence(
                step,
                sentno,
                lprobs[sentno],
                constraint_states[sentno],
                beams_buf[sentno].clone(),
                indices_buf[sentno].clone(),
                scores_buf[sentno].clone(),
            )
            new_scores_buf[sentno] = scores
            new_indices_buf[sentno] = indices
            new_beams_buf[sentno] = beams
            self.constraint_states[sentno] = new_states

        return new_scores_buf, new_indices_buf, new_beams_buf

    @torch.jit.export
    def step_sentence(
        self,
        step: int,
        sentno: int,
        lprobs: Tensor,
        constraint_states: List[List[ConstraintState]],
        beams_buf: Tensor,
        indices_buf: Tensor,
        scores_buf: Tensor,
    ):
        """Does per-sentence processing. Adds all constraints for each
        hypothesis to the list of candidates; then removes duplicates,
        sorts, and dynamically stripes across the banks. All tensor inputs
        are collapsed to those pertaining to a single input sentence.
        """
        device = lprobs.device

        # STEP 2: Add all constraints for each beam item
        for beamno, state in enumerate(constraint_states):
            next_tokens = torch.tensor(list(state.next_tokens()), device=device).long()
            if next_tokens.numel() != 0:
                indices_buf = torch.cat((indices_buf, next_tokens))
                next_beams = (
                    torch.tensor(beamno, device=device)
                    .repeat(next_tokens.size(0))
                    .long()
                )
                beams_buf = torch.cat((beams_buf, next_beams))
                next_values = lprobs[beamno].take(next_tokens.view(-1))
                scores_buf = torch.cat((scores_buf, next_values))

            # At the 0th time step, there is just one beam item
            if step == 0:
                break

        # STEP 3: Compute the "bank" for each candidate. This is the
        # number of constraints it's generated. We need this so that
        # we can do round-robin allocation of the beam across these
        # banks. If C is the number of constraints, we select the best
        # item in bank C, then the best in bank C-1, etc, followed by
        # the 2nd-best in bank C, the 2nd-best in bank C-1, etc, and so
        # on, until the maximum beam size. We accomplish this by
        # creating a sort key and striping across the banks.

        # Compute the new states for all candidates
        cands_size = indices_buf.size(0)
        constraint_states = [
            constraint_states[beams_buf[i]].advance(indices_buf[i])
            for i in range(cands_size)
        ]

        banks = torch.tensor([state.bank for state in constraint_states], device=device)

        # STEP 4: Sort
        num_constraint_tokens = len(state.tokens)

        # Sort by keys (bank, score) (i.e., sort banks together, and scores
        # within banks). AFAIK pytorch doesn't support either stable sort or
        # multi-key sorting, so we have to hack this.
        MAX_SCORE = -100
        sort_key = (num_constraint_tokens - banks) * MAX_SCORE + scores_buf
        sort_values, sort_indices = sort_key.sort(dim=0, descending=True)
        scores_buf = scores_buf[sort_indices]
        indices_buf = indices_buf[sort_indices]
        beams_buf = beams_buf[sort_indices]
        banks = banks[sort_indices]

        # Sort the constraints to follow suit
        constraint_states = [constraint_states[i] for i in sort_indices]

        # STEP 5: Remove duplicates. The topk calls (overall and
        # per-row) plus the per-row generation of constraints will
        # produce duplicates. Here we remove them.

        def roll(t):
            """Rolls a 1d tensor left by 1.

            [0, 1, 2, 3, 4] becomes [4, 0, 1, 2, 3]
            """
            return torch.cat((t[-1].unsqueeze(0), t[0:-1]), dim=0)

        # We map candidates (beam, token_id) to a single dimension.
        # This is then shifted by 1. We can then easily identify
        # duplicates and create a mask that identifies unique
        # extensions.
        uniques_mask = beams_buf * (self.vocab_size + 1) + indices_buf
        uniques_mask = roll(uniques_mask) != uniques_mask

        # Use the mask to pare down the data structures
        scores_buf = torch.masked_select(scores_buf, uniques_mask)
        indices_buf = torch.masked_select(indices_buf, uniques_mask)
        beams_buf = torch.masked_select(beams_buf, uniques_mask)
        banks = torch.masked_select(banks, uniques_mask)
        i = 1
        for mask in uniques_mask[1:]:
            if not mask:
                constraint_states.pop(i)
            i += mask

        # STEP 6: Assign IDs round-robin across banks, sort, and
        # truncate. Now that the candidates are sorted by (bank,
        # score) and uniqed, we dynamically allocate the {beam_size}
        # beam by striping across the candidates. These stripes will
        # be used as sort keys to do round-robin selection. This is
        # accomplished in a single pass with offsets. Sorting by
        # highest-banks (furthest-along hypotheses) first ensures
        # progress through the constraints.
        #
        # e.g., BANKS: 3 3 3 2 2 2 2 1 1 1 0 0
        # OLD STRIPES: 0 1 2 0 1 2 3 0 1 2 0 1
        # NEW STRIPES: 0 1+4 2+8 0+1 1+5 2+9 3+11 0+2 1+6 2+10 0+3 1+7
        #            = 0 5 10 1 6 11 13 2 7 12 3 8
        #
        # Sorting by this then gives the following banks:
        #
        #             3 2 1 0 3 2 1 0 3 2 1 2
        #
        # We'll take the top {beam_size} of these.
        stripe_offsets = [offset * (len(banks) + 1) for offset in range(len(banks) + 1)]
        stripes = torch.zeros_like(banks)
        cur_bank_count = -1
        cur_bank = banks[0]
        for i, bank in enumerate(banks):
            if bank != cur_bank:
                cur_bank_count = 0
                cur_bank = bank
            else:
                cur_bank_count += 1
            stripes[i] = num_constraint_tokens - bank + stripe_offsets[cur_bank_count]

        # STEP 7: Sort by the stripes values
        sort_values, sort_indices = stripes.sort(dim=0)
        scores_buf = scores_buf[sort_indices]
        indices_buf = indices_buf[sort_indices]
        beams_buf = beams_buf[sort_indices]
        constraint_states = [constraint_states[i] for i in sort_indices]

        # STEP 8: Truncate to the candidates size!
        scores_buf = scores_buf[: self.num_cands]
        indices_buf = indices_buf[: self.num_cands]
        beams_buf = beams_buf[: self.num_cands]

        return scores_buf, indices_buf, beams_buf, constraint_states


class LengthConstrainedBeamSearch(Search):
    def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b):
        super().__init__(tgt_dict)
        self.min_len_a = min_len_a
        self.min_len_b = min_len_b
        self.max_len_a = max_len_a
        self.max_len_b = max_len_b
        self.beam = BeamSearch(tgt_dict)
        self.needs_src_lengths = True

    def step(
        self,
        step: int,
        lprobs,
        scores,
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        min_lens = self.min_len_a * self.src_lengths + self.min_len_b
        max_lens = self.max_len_a * self.src_lengths + self.max_len_b
        lprobs[step < min_lens, :, self.eos] = -math.inf
        lprobs[step >= max_lens, :, self.eos] = 0
        return self.beam.step(step, lprobs, scores)


class DiverseBeamSearch(Search):
    """Diverse Beam Search.

    See "Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
    Models" for details.

    We only implement the Hamming Diversity penalty here, which performed best
    in the original paper.
    """

    def __init__(self, tgt_dict, num_groups, diversity_strength):
        super().__init__(tgt_dict)
        self.num_groups = num_groups
        self.diversity_strength = -diversity_strength
        self.beam = BeamSearch(tgt_dict)

    @torch.jit.export
    def step(
        self,
        step: int,
        lprobs,
        scores,
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        bsz, beam_size, vocab_size = lprobs.size()
        if beam_size % self.num_groups != 0:
            raise ValueError(
                "DiverseBeamSearch requires --beam to be divisible by the number of groups"
            )

        # initialize diversity penalty
        diversity_buf = torch.zeros(lprobs[:, 0, :].size()).to(lprobs)

        scores_G, indices_G, beams_G = [], [], []
        for g in range(self.num_groups):
            lprobs_g = lprobs[:, g :: self.num_groups, :]
            scores_g = scores[:, g :: self.num_groups, :] if step > 0 else None

            # apply diversity penalty
            if g > 0:
                lprobs_g = torch.add(
                    lprobs_g,
                    other=diversity_buf.unsqueeze(1),
                    alpha=self.diversity_strength,
                )
            else:
                lprobs_g = lprobs_g.contiguous()

            scores_buf, indices_buf, beams_buf = self.beam.step(
                step, lprobs_g, scores_g
            )
            beams_buf.mul_(self.num_groups).add_(g)

            scores_G.append(scores_buf.clone())
            indices_G.append(indices_buf.clone())
            beams_G.append(beams_buf.clone())

            # update diversity penalty
            diversity_buf.scatter_add_(
                1, indices_buf, torch.ones(indices_buf.size()).to(diversity_buf)
            )

        # interleave results from different groups
        scores_buf = torch.stack(scores_G, dim=2).view(bsz, -1)
        indices_buf = torch.stack(indices_G, dim=2).view(bsz, -1)
        beams_buf = torch.stack(beams_G, dim=2).view(bsz, -1)
        return scores_buf, indices_buf, beams_buf


class Sampling(Search):
    sampling_topk: int
    sampling_topp: float

    def __init__(self, tgt_dict, sampling_topk=-1, sampling_topp=-1.0):
        super().__init__(tgt_dict)
        self.sampling_topk = sampling_topk
        self.sampling_topp = sampling_topp

    def _sample_topp(self, lprobs):
        """Sample among the smallest set of elements whose cumulative probability mass exceeds p.

        See `"The Curious Case of Neural Text Degeneration"
        (Holtzman et al., 2019) <https://arxiv.org/abs/1904.09751>`_.

        Args:
            lprobs: (bsz x input_beam_size x vocab_size)
                the model's log-probabilities over the vocabulary at the current step

        Return: A tuple of (trimed_probs, truncated_indices) where:
            trimed_probs: (bsz x input_beam_size x ?)
                the model's probabilities over the elements selected to sample from. The
                width of the third dimension is determined by top-P.
            truncated_indices: (bsz x input_beam_size x ?)
                the indices of the chosen elements.
        """
        probs = lprobs.exp_()

        # sort the last dimension (vocab dimension) in descending order
        sorted_probs, sorted_indices = probs.sort(descending=True)

        # compute a mask to indicate the words to be included in the top-P set.
        cumsum_probs = sorted_probs.cumsum(dim=2)
        mask = cumsum_probs.lt(self.sampling_topp)

        # note that mask was computed by 'lt'. One more word needs to be included
        # so that the cumulative probability mass can exceed p.
        cumsum_mask = mask.cumsum(dim=2)
        last_included = cumsum_mask[:, :, -1:]
        last_included.clamp_(0, mask.size()[2] - 1)
        mask = mask.scatter_(2, last_included, 1)

        # truncate unnecessary dims.
        max_dim = last_included.max()
        truncated_mask = mask[:, :, : max_dim + 1]
        truncated_probs = sorted_probs[:, :, : max_dim + 1]
        truncated_indices = sorted_indices[:, :, : max_dim + 1]

        # trim the words that are not in top-P by setting their probabilities
        # to 0, so that they would not be sampled later.
        trim_mask = ~truncated_mask
        trimed_probs = truncated_probs.masked_fill_(trim_mask, 0)
        return trimed_probs, truncated_indices

    @torch.jit.export
    def step(
        self,
        step: int,
        lprobs,
        scores,
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        bsz, beam_size, vocab_size = lprobs.size()

        if step == 0:
            # at the first step all hypotheses are equally likely, so use
            # only the first beam
            lprobs = lprobs[:, ::beam_size, :].contiguous()

        if self.sampling_topp > 0:
            # only sample from the smallest set of words whose cumulative probability mass exceeds p
            probs, top_indices = self._sample_topp(lprobs)
        elif self.sampling_topk > 0:
            # only sample from top-k candidates
            lprobs, top_indices = lprobs.topk(self.sampling_topk)
            probs = lprobs.exp_()
        else:
            probs = lprobs.exp_()

            # dummy data to be consistent with true branch for type check
            top_indices = torch.empty(0).to(probs)
        # sample
        if step == 0:
            indices_buf = torch.multinomial(
                probs.view(bsz, -1),
                beam_size,
                replacement=True,
            ).view(bsz, beam_size)
        else:
            indices_buf = torch.multinomial(
                probs.view(bsz * beam_size, -1),
                1,
                replacement=True,
            ).view(bsz, beam_size)

        if step == 0:
            # expand to beam size
            probs = probs.expand(bsz, beam_size, -1)

        # gather scores
        scores_buf = torch.gather(probs, dim=2, index=indices_buf.unsqueeze(-1))
        scores_buf = scores_buf.log_().view(bsz, -1)

        # remap indices if using top-k or top-P sampling
        if self.sampling_topk > 0 or self.sampling_topp > 0:
            indices_buf = torch.gather(
                top_indices.expand(bsz, beam_size, -1),
                dim=2,
                index=indices_buf.unsqueeze(-1),
            ).squeeze(2)

        if step == 0:
            beams_buf = indices_buf.new_zeros(bsz, beam_size)
        else:
            beams_buf = torch.arange(0, beam_size).to(indices_buf).repeat(bsz, 1)
            # make scores cumulative
            scores_buf.add_(
                torch.gather(scores[:, :, step - 1], dim=1, index=beams_buf)
            )

        return scores_buf, indices_buf, beams_buf


class DiverseSiblingsSearch(Search):
    """
    Beam search with diverse siblings.

    See "A Simple, Fast Diverse Decoding Algorithm for Neural Generation" for details.
    https://arxiv.org/abs/1611.08562

    1/ Calculate hypotheses for each beam
    2/ Intra-sibling ordering
    3/ Rewrite scores
    4/ Choose top K hypotheses

    if diversity_rate == 0 is equivalent to BeamSearch
    """

    def __init__(self, tgt_dict, diversity_rate):
        super().__init__(tgt_dict)
        self.diversity_rate = diversity_rate
        self.beam = BeamSearch(tgt_dict)

    def step(
        self,
        step: int,
        lprobs,
        scores,
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        bsz, beam_size, vocab_size = lprobs.size()
        k = min(
            # Take the best 2 x beam_size predictions. We'll choose the first
            # beam_size of these which don't predict eos to continue with.
            beam_size * 2,
            lprobs.view(bsz, -1).size(1) - 1,  # -1 so we never select pad
        )
        s_list: List[Tensor]
        i_list: List[Tensor]
        s_list = [torch.empty(0).to(lprobs) for i in range(beam_size)]
        i_list = [torch.LongTensor().to(device=lprobs.device) for i in range(beam_size)]
        sibling_score = torch.arange(1, k + 1).to(lprobs) * self.diversity_rate

        if step == 0:
            return self.beam.step(step, lprobs, scores)
        lprobs.add_(scores[:, :, step - 1].unsqueeze(-1))

        # 1/ Calculate hypotheses for each beam
        for i in range(beam_size):
            torch.topk(lprobs[:, i, :].view(bsz, -1), k, out=(s_list[i], i_list[i]))
            i_list[i].fmod_(vocab_size)

            # 2/ Intra-sibling ordering by default from topk + 3/ Rewrite scores
            s_list[i].sub_(sibling_score)

        # 4/ Choose top K hypotheses
        indices = torch.stack(i_list, dim=1).view(bsz, -1)

        final_scores = torch.empty(0).to(lprobs)
        final_indices = torch.LongTensor().to(device=lprobs.device)
        final_beams = torch.LongTensor().to(device=lprobs.device)
        (final_scores, final_indices) = torch.topk(
            torch.stack(s_list, dim=1).view(bsz, -1),
            k,
        )

        final_beams = final_indices // k

        for i in range(bsz):
            final_indices[i] = indices[i][final_indices[i]]

        return final_scores, final_indices, final_beams