sampler.py 24.7 KB
Newer Older
1
"""A layer that samples the next tokens from the model's outputs."""
2
from typing import Dict, List, Optional, Tuple
Woosuk Kwon's avatar
Woosuk Kwon committed
3
4
5
6

import torch
import torch.nn as nn

Woosuk Kwon's avatar
Woosuk Kwon committed
7
from vllm.model_executor.input_metadata import InputMetadata
8
9
from vllm.model_executor.parallel_utils.communication_op import (
    tensor_model_parallel_all_gather)
10
from vllm.sampling_params import SamplingParams, SamplingType
11
12
from vllm.sequence import (PromptLogprobs, SampleLogprobs, SamplerOutput,
                           SequenceData, SequenceGroupOutputs, SequenceOutputs)
Woosuk Kwon's avatar
Woosuk Kwon committed
13

14
_SAMPLING_EPS = 1e-5
Woosuk Kwon's avatar
Minor  
Woosuk Kwon committed
15

16

Woosuk Kwon's avatar
Woosuk Kwon committed
17
class Sampler(nn.Module):
18
19
20
21
22
23
24
25
26
27
28
29
30
    """Samples the next tokens from the model's outputs.

    This layer does the following:
    1. Discard the hidden states that are not used for sampling (i.e., all
        tokens except the final one in each prompt).
    2. Compute the logits for the next tokens.
    3. Apply presence and frequency penalties.
    4. Apply temperature scaling.
    5. Apply top-p and top-k truncation.
    6. Sample the next tokens.
    Here, each sequence group within the batch can have different sampling
    parameters (e.g., sampling method, temperature, top-p, top-k, etc.).
    """
Woosuk Kwon's avatar
Woosuk Kwon committed
31

Woosuk Kwon's avatar
Woosuk Kwon committed
32
    def __init__(self, vocab_size: int) -> None:
33
        super().__init__()
Woosuk Kwon's avatar
Woosuk Kwon committed
34
        self.vocab_size = vocab_size
Woosuk Kwon's avatar
Woosuk Kwon committed
35
36
37

    def forward(
        self,
Woosuk Kwon's avatar
Woosuk Kwon committed
38
        embedding: torch.Tensor,
Woosuk Kwon's avatar
Woosuk Kwon committed
39
40
        hidden_states: torch.Tensor,
        input_metadata: InputMetadata,
41
        embedding_bias: Optional[torch.Tensor] = None,
42
    ) -> SamplerOutput:
43
44
        # Get the hidden states that we use for sampling.
        hidden_states = _prune_hidden_states(hidden_states, input_metadata)
Woosuk Kwon's avatar
Woosuk Kwon committed
45
46

        # Get the logits for the next tokens.
47
48
        logits = _get_logits(hidden_states, embedding, embedding_bias,
                             self.vocab_size)
Woosuk Kwon's avatar
Woosuk Kwon committed
49

50
51
52
        # Apply presence and frequency penalties.
        output_tokens = _get_output_tokens(input_metadata)
        assert len(output_tokens) == logits.shape[0]
53
54
        presence_penalties, frequency_penalties = _get_penalties(
            input_metadata)
55
56
        assert len(presence_penalties) == logits.shape[0]
        assert len(frequency_penalties) == logits.shape[0]
57
        logits = _apply_penalties(logits, output_tokens, presence_penalties,
58
                                  frequency_penalties)
59

60
61
62
63
        # Apply temperature scaling.
        temperatures = _get_temperatures(input_metadata)
        assert len(temperatures) == logits.shape[0]
        if any(t != 1.0 for t in temperatures):
64
65
66
            t = torch.tensor(temperatures,
                             dtype=logits.dtype,
                             device=logits.device)
67
68
69
            # Use in-place division to avoid creating a new tensor.
            logits.div_(t.unsqueeze(dim=1))

Woosuk Kwon's avatar
Woosuk Kwon committed
70
71
        # Apply top-p and top-k truncation.
        top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
72
        assert len(top_ps) == len(top_ks) == logits.shape[0]
73
74
75
        do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
        do_top_k = any(k != self.vocab_size for k in top_ks)
        if do_top_p or do_top_k:
76
77
78
79
80
            logits = _apply_top_p_top_k(logits, top_ps, top_ks)

        # We use float32 for probabilities and log probabilities.
        # Compute the probabilities.
        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
Zhuohan Li's avatar
Zhuohan Li committed
81
82
83
        # Compute the log probabilities.
        # Use log_softmax to ensure numerical stability.
        logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
84

Woosuk Kwon's avatar
Woosuk Kwon committed
85
        # Sample the next tokens.
86
87
88
89
90
91
        sample_results = _sample(probs, logprobs, input_metadata)
        # Get the logprobs query results.
        prompt_logprobs, sample_logprobs = _get_logprobs(
            logprobs, input_metadata, sample_results)
        return _build_sampler_output(sample_results, input_metadata,
                                     prompt_logprobs, sample_logprobs)
92
93


94
95
96
97
98
99
100
def _get_logits(hidden_states: torch.Tensor, embedding: torch.Tensor,
                embedding_bias: Optional[torch.Tensor],
                vocab_size: int) -> torch.Tensor:
    # Get the logits for the next tokens.
    logits = torch.matmul(hidden_states, embedding.t())
    if embedding_bias is not None:
        logits += embedding_bias
101
    logits = tensor_model_parallel_all_gather(logits)
102
103
104
105
106
    # Remove paddings in vocab (if any).
    logits = logits[:, :vocab_size]
    return logits


107
108
109
110
def _prune_hidden_states(
    hidden_states: torch.Tensor,
    input_metadata: InputMetadata,
) -> torch.Tensor:
111
    selected_token_indices: List[int] = []
112
    start_idx = 0
113
    for i, seq_group in enumerate(input_metadata.seq_groups):
114
        seq_ids, sampling_params = seq_group
115
116
117
        if i < input_metadata.num_prompts:
            assert len(seq_ids) == 1, "Prompt input should have only one seq."
            prompt_len = input_metadata.prompt_lens[i]
118
119
120
121
            if sampling_params.prompt_logprobs is not None:
                selected_token_indices.extend(
                    range(start_idx, start_idx + prompt_len - 1))
            selected_token_indices.append(start_idx + prompt_len - 1)
122
123
124
            start_idx += prompt_len
        else:
            num_seqs = len(seq_ids)
125
126
            selected_token_indices.extend(
                range(start_idx, start_idx + num_seqs))
127
128
            start_idx += num_seqs

129
130
131
132
    selected_token_indices = torch.tensor(selected_token_indices,
                                          dtype=torch.long,
                                          device=hidden_states.device)
    return hidden_states.index_select(0, selected_token_indices)
133
134


135
def _get_penalties(
136
        input_metadata: InputMetadata) -> Tuple[List[float], List[float]]:
137
138
139
    # Collect the presence and frequency penalties.
    presence_penalties: List[float] = []
    frequency_penalties: List[float] = []
140
    for i, seq_group in enumerate(input_metadata.seq_groups):
141
142
143
        seq_ids, sampling_params = seq_group
        p = sampling_params.presence_penalty
        f = sampling_params.frequency_penalty
144
145
146
147
148
149
150
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            # NOTE: We do not apply presence and frequency penalties for the
            # prompt token positions where we don't sample new tokens.
            prompt_len = input_metadata.prompt_lens[i]
            presence_penalties += [0] * (prompt_len - 1)
            frequency_penalties += [0] * (prompt_len - 1)
151
152
        presence_penalties += [p] * len(seq_ids)
        frequency_penalties += [f] * len(seq_ids)
153
154
155
    return presence_penalties, frequency_penalties


156
def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
157
    output_tokens: List[List[int]] = []
158
159
160
161
162
163
164
165
    for i, seq_group in enumerate(input_metadata.seq_groups):
        seq_ids, sampling_params = seq_group
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            # NOTE: prompt token positions do not need output tokens to
            # compute penalties.
            prompt_len = input_metadata.prompt_lens[i]
            output_tokens.extend([] for _ in range(prompt_len - 1))
166
        for seq_id in seq_ids:
167
168
169
170
171
172
173
174
175
176
177
            seq_data = input_metadata.seq_data[seq_id]
            output_tokens.append(seq_data.output_token_ids)
    return output_tokens


def _apply_penalties(
    logits: torch.Tensor,
    output_tokens: List[List[int]],
    presence_penalties: List[float],
    frequency_penalties: List[float],
) -> torch.Tensor:
178
    num_seqs, vocab_size = logits.shape
179
180
181
182
183
    for i in range(num_seqs):
        if not output_tokens[i]:
            continue
        p = presence_penalties[i]
        f = frequency_penalties[i]
184
        if abs(p) < _SAMPLING_EPS and abs(f) < _SAMPLING_EPS:
185
            continue
186
187
188
        break
    else:
        # Return early if all sequences have zero penalties.
189
190
        return logits

191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
    max_output_len = max(len(tokens) for tokens in output_tokens)
    padded_output_tokens = [
        tokens + [vocab_size] * (max_output_len - len(tokens))
        for tokens in output_tokens
    ]
    output_tokens_tensor = torch.tensor(padded_output_tokens,
                                        dtype=torch.long,
                                        device=logits.device)

    # Compute the bin counts for the output tokens.
    # vocab_size + 1 for padding.
    bin_counts = torch.zeros((num_seqs, vocab_size + 1),
                             dtype=torch.long,
                             device=logits.device)
    bin_counts.scatter_add_(1, output_tokens_tensor,
                            torch.ones_like(output_tokens_tensor))
    bin_counts = bin_counts[:, :vocab_size]  # Remove the padding bin.
208

209
210
211
212
213
214
    frequency_penalties = torch.tensor(frequency_penalties,
                                       dtype=logits.dtype,
                                       device=logits.device)
    presence_penalties = torch.tensor(presence_penalties,
                                      dtype=logits.dtype,
                                      device=logits.device)
215
216
217

    # We follow the definition in OpenAI API.
    # Refer to https://platform.openai.com/docs/api-reference/parameter-details
218
219
    logits -= frequency_penalties.unsqueeze(dim=1) * bin_counts
    logits -= presence_penalties.unsqueeze(dim=1) * (bin_counts > 0)
220
221
222
    return logits


223
def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
224
225
    # Collect the temperatures for the logits.
    temperatures: List[float] = []
226
    for i, seq_group in enumerate(input_metadata.seq_groups):
227
228
        seq_ids, sampling_params = seq_group
        temperature = sampling_params.temperature
229
        if temperature < _SAMPLING_EPS:
230
231
232
233
            # NOTE: Zero temperature means deterministic sampling
            # (i.e., greedy sampling or beam search).
            # Set the temperature to 1 to avoid division by zero.
            temperature = 1.0
234
235
236
237
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            prompt_len = input_metadata.prompt_lens[i]
            temperatures += [temperature] * (prompt_len - 1)
238
        temperatures += [temperature] * len(seq_ids)
239
240
241
    return temperatures


Woosuk Kwon's avatar
Woosuk Kwon committed
242
def _get_top_p_top_k(
243
    input_metadata: InputMetadata,
Woosuk Kwon's avatar
Woosuk Kwon committed
244
245
    vocab_size: int,
) -> Tuple[List[float], List[int]]:
246
    top_ps: List[float] = []
Woosuk Kwon's avatar
Woosuk Kwon committed
247
    top_ks: List[int] = []
248
    for i, seq_group in enumerate(input_metadata.seq_groups):
249
        seq_ids, sampling_params = seq_group
Woosuk Kwon's avatar
Woosuk Kwon committed
250
251
252
253
254
        top_p = sampling_params.top_p
        # k should not be greater than the vocab size.
        top_k = min(sampling_params.top_k, vocab_size)
        # k=-1 means no truncation.
        top_k = vocab_size if top_k == -1 else top_k
255
256
257
258
259
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            prompt_len = input_metadata.prompt_lens[i]
            top_ps += [top_p] * (prompt_len - 1)
            top_ks += [top_k] * (prompt_len - 1)
260
261
        top_ps += [top_p] * len(seq_ids)
        top_ks += [top_k] * len(seq_ids)
Woosuk Kwon's avatar
Woosuk Kwon committed
262
    return top_ps, top_ks
263
264


Woosuk Kwon's avatar
Woosuk Kwon committed
265
def _apply_top_p_top_k(
266
    logits: torch.Tensor,
267
268
    top_ps: List[float],
    top_ks: List[int],
269
) -> torch.Tensor:
270
271
272
    p = torch.tensor(top_ps, dtype=logits.dtype, device=logits.device)
    k = torch.tensor(top_ks, dtype=torch.int, device=logits.device)
    logits_sort, logits_idx = logits.sort(dim=-1, descending=True)
Woosuk Kwon's avatar
Woosuk Kwon committed
273
274

    # Apply top-p.
275
276
    probs_sort = logits_sort.softmax(dim=-1)
    probs_sum = probs_sort.cumsum(dim=-1)
Woosuk Kwon's avatar
Woosuk Kwon committed
277
    top_p_mask = (probs_sum - probs_sort) > p.unsqueeze(dim=1)
278
    logits_sort[top_p_mask] = -float("inf")
Woosuk Kwon's avatar
Woosuk Kwon committed
279
280
281

    # Apply top-k.
    # Create a mask for the top-k elements.
282
283
    top_k_mask = torch.arange(logits_idx.shape[-1], device=logits_idx.device)
    top_k_mask = top_k_mask.expand(logits_idx.shape[0], -1)
Woosuk Kwon's avatar
Woosuk Kwon committed
284
    top_k_mask = top_k_mask >= k.unsqueeze(dim=1)
285
    logits_sort[top_k_mask] = -float("inf")
Woosuk Kwon's avatar
Woosuk Kwon committed
286
287

    # Re-sort the probabilities.
288
289
290
291
    logits = torch.gather(logits_sort,
                          dim=-1,
                          index=torch.argsort(logits_idx, dim=-1))
    return logits
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
def _greedy_sample(
    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
    logprobs: torch.Tensor,
) -> List[Tuple[List[int], List[int]]]:
    samples = torch.argmax(logprobs, dim=-1).cpu()
    sample_idx = 0
    results = []
    for seq_group in selected_seq_groups:
        seq_ids, _ = seq_group
        num_parent_seqs = len(seq_ids)
        assert num_parent_seqs == 1, (
            "Greedy sampling should have only one seq.")
        parent_ids = list(range(num_parent_seqs))
        next_token_ids = [samples[sample_idx].item()]
        results.append((next_token_ids, parent_ids))
        sample_idx += num_parent_seqs
    assert sample_idx == logprobs.size(0)
    return results


def _random_sample(
    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
    is_prompts: List[bool],
317
    probs: torch.Tensor,
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
) -> List[Tuple[List[int], List[int]]]:
    # Find the maximum best_of value of the prompt phase requests.
    max_best_of = 1
    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
        if is_prompt:
            seq_ids, sampling_params = seq_group
            max_best_of = max(max_best_of, sampling_params.best_of)
    random_samples = torch.multinomial(probs,
                                       num_samples=max_best_of,
                                       replacement=True).cpu()
    sample_idx = 0
    results = []
    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
        seq_ids, sampling_params = seq_group
        num_parent_seqs = len(seq_ids)
        if is_prompt:
            # Prompt phase.
            assert num_parent_seqs == 1, (
                "Prompt input should have only one seq.")
            parent_ids = [0] * sampling_params.best_of
            next_token_ids = random_samples[
                sample_idx, :sampling_params.best_of].tolist()
        else:
            # Generation phase.
            parent_ids = list(range(num_parent_seqs))
            next_token_ids = random_samples[sample_idx:sample_idx +
                                            num_parent_seqs, 0].tolist()
        results.append((next_token_ids, parent_ids))
        sample_idx += num_parent_seqs
    assert sample_idx == probs.size(0)
    return results


def _beam_search_sample(
    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
    is_prompts: List[bool],
    seq_data: Dict[int, SequenceData],
355
    logprobs: torch.Tensor,
356
357
358
359
360
361
362
363
) -> List[Tuple[List[int], List[int]]]:
    # We sample 2 * beam_width candidates to make sure that with high
    # probability we can get `beam_width` candidates in addition to
    # the finished sequences for the next iteration. See
    # https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
    # for details. See also HF reference:
    # https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
    #
364
    # NOTE: Beam search is not vectorized, so its speed can be slower than
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
    # other sampling methods.
    sample_idx = 0
    results = []
    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
        seq_ids, sampling_params = seq_group
        num_parent_seqs = len(seq_ids)
        beam_width = sampling_params.best_of
        seq_group_logprobs = logprobs[sample_idx:sample_idx + num_parent_seqs]
        if is_prompt:
            # Prompt phase.
            assert num_parent_seqs == 1, (
                "Prompt input should have only one seq.")
            parent_ids = [0] * (2 * beam_width)
            _, next_token_ids = torch.topk(seq_group_logprobs[0],
                                           2 * beam_width)
            next_token_ids = next_token_ids.tolist()
        else:
            # Generation phase.
            cumulative_logprobs = [
                seq_data[seq_id].cumulative_logprob for seq_id in seq_ids
            ]
            cumulative_logprobs = torch.tensor(
                cumulative_logprobs,
                dtype=torch.float,
                device=seq_group_logprobs.device)
            seq_group_logprobs = (seq_group_logprobs +
                                  cumulative_logprobs.unsqueeze(dim=1))
            _, topk_ids = torch.topk(seq_group_logprobs.flatten(),
                                     2 * beam_width)
            topk_ids = topk_ids.tolist()
            vocab_size = seq_group_logprobs.size(-1)
            parent_ids = [i // vocab_size for i in topk_ids]
            next_token_ids = [i % vocab_size for i in topk_ids]
        results.append((next_token_ids, parent_ids))
        sample_idx += num_parent_seqs
    assert sample_idx == logprobs.size(0)
    return results
402
403
404
405
406
407


def _sample(
    probs: torch.Tensor,
    logprobs: torch.Tensor,
    input_metadata: InputMetadata,
408
) -> List[Tuple[List[int], List[int]]]:
409
    categorized_seq_group_ids = {t: [] for t in SamplingType}
410
    categorized_sample_indices = {t: [] for t in SamplingType}
411
    start_idx = 0
412
413
    for i, seq_group in enumerate(input_metadata.seq_groups):
        seq_ids, sampling_params = seq_group
414
        sampling_type = sampling_params.sampling_type
415
416
417
418
419
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            # NOTE: prompt token positions do not need sample, skip
            prompt_len = input_metadata.prompt_lens[i]
            start_idx += prompt_len - 1
420
421
        categorized_seq_group_ids[sampling_type].append(i)
        num_seqs = len(seq_ids)
422
        categorized_sample_indices[sampling_type].extend(
423
424
            range(start_idx, start_idx + num_seqs))
        start_idx += num_seqs
425
426

    sample_results_dict: Dict[int, Tuple[List[int], List[int]]] = {}
427
428
429
430
    for sampling_type in SamplingType:
        seq_group_ids = categorized_seq_group_ids[sampling_type]
        seq_groups = [input_metadata.seq_groups[i] for i in seq_group_ids]
        is_prompts = [i < input_metadata.num_prompts for i in seq_group_ids]
431
432
        sample_indices = categorized_sample_indices[sampling_type]
        num_tokens = len(sample_indices)
433
434
435
        if num_tokens == 0:
            continue
        if sampling_type == SamplingType.GREEDY:
436
            category_logprobs = logprobs[sample_indices]
437
438
            sample_results = _greedy_sample(seq_groups, category_logprobs)
        elif sampling_type == SamplingType.RANDOM:
439
            category_probs = probs[sample_indices]
440
441
442
            sample_results = _random_sample(seq_groups, is_prompts,
                                            category_probs)
        elif sampling_type == SamplingType.BEAM:
443
            category_logprobs = logprobs[sample_indices]
444
445
446
            sample_results = _beam_search_sample(seq_groups, is_prompts,
                                                 input_metadata.seq_data,
                                                 category_logprobs)
447
        else:
448
            raise ValueError(f"Unsupported sampling type: {sampling_type}")
449
        sample_results_dict.update(zip(seq_group_ids, sample_results))
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
    sample_results = [
        sample_results_dict[i] for i in range(len(input_metadata.seq_groups))
    ]
    return sample_results


def _get_logprobs(
    logprobs: torch.Tensor,
    input_metadata: InputMetadata,
    sample_results: List[Tuple[List[int], List[int]]],
) -> Tuple[List[Optional[List[Optional[Dict[int, float]]]]], List[List[Dict[
        int, float]]]]:
    # Prepare query indices
    batched_logprobs_query_seq_indices: List[int] = []
    batched_logprobs_query_token_indices: List[int] = []
    largest_num_logprobs = 0
    sample_idx = 0
    for i, (seq_group, sample_result) in enumerate(
            zip(input_metadata.seq_groups, sample_results)):
        seq_ids, sampling_params = seq_group
        next_token_ids, parent_ids = sample_result
        num_parent_seqs = len(seq_ids)
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            largest_num_logprobs = max(largest_num_logprobs,
                                       sampling_params.prompt_logprobs)
            prompt_len = input_metadata.prompt_lens[i]
            prompt_tokens = input_metadata.seq_data[
                seq_ids[0]].prompt_token_ids
480
            batched_logprobs_query_seq_indices.extend(
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
                sample_idx + j for j in range(prompt_len - 1))
            batched_logprobs_query_token_indices.extend(
                token_id for token_id in prompt_tokens[1:])
            sample_idx += prompt_len - 1
        batched_logprobs_query_seq_indices.extend(
            [sample_idx + parent_id for parent_id in parent_ids])
        batched_logprobs_query_token_indices.extend(next_token_ids)
        if sampling_params.logprobs is not None:
            largest_num_logprobs = max(largest_num_logprobs,
                                       sampling_params.logprobs)
        sample_idx += num_parent_seqs
    assert sample_idx == logprobs.size(0)

    # Batched query for logprobs of selected token
    batched_logprobs_query_result = logprobs[[
        batched_logprobs_query_seq_indices,
        batched_logprobs_query_token_indices
    ]].cpu()

    # Batched query for logprobs of topk tokens
    if largest_num_logprobs > 0:
        top_logprobs, top_token_ids = torch.topk(logprobs,
                                                 largest_num_logprobs,
                                                 dim=-1)
        top_logprobs = top_logprobs.cpu()
        top_token_ids = top_token_ids.cpu()
    else:
        top_logprobs, top_token_ids = None, None

    # Gather results
    result_prompt_logprobs: List[Optional[PromptLogprobs]] = []
    result_sample_logprobs: List[SampleLogprobs] = []
    sample_idx = 0
    query_result_idx = 0
    for i, (seq_group, sample_result) in enumerate(
            zip(input_metadata.seq_groups, sample_results)):
        seq_ids, sampling_params = seq_group
        next_token_ids, parent_ids = sample_result

        # Prompt logprobs
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            num_logprobs = sampling_params.prompt_logprobs
            prompt_len = input_metadata.prompt_lens[i]
            prompt_tokens = input_metadata.seq_data[
                seq_ids[0]].prompt_token_ids
            group_prompt_logprobs: PromptLogprobs = [None]
            for token_id in prompt_tokens[1:]:
                prompt_logprobs_dict = {
                    token_id:
                    batched_logprobs_query_result[query_result_idx].item()
                }
                if num_logprobs > 0:
                    prompt_logprobs_dict.update(
                        zip(top_token_ids[sample_idx, :num_logprobs].tolist(),
                            top_logprobs[sample_idx, :num_logprobs].tolist()))
                group_prompt_logprobs.append(prompt_logprobs_dict)
                sample_idx += 1
                query_result_idx += 1
            result_prompt_logprobs.append(group_prompt_logprobs)
        else:
            result_prompt_logprobs.append(None)

        # Sample logprobs
        num_logprobs = sampling_params.logprobs
        if num_logprobs is None:
            num_logprobs = 0
        group_sample_logprobs: SampleLogprobs = []
        for next_token_id, parent_id in zip(next_token_ids, parent_ids):
            sample_logprobs_dict = {
                next_token_id:
                batched_logprobs_query_result[query_result_idx].item()
            }
            query_result_idx += 1
            if num_logprobs > 0:
                sample_logprobs_dict.update(
                    zip(
                        top_token_ids[sample_idx +
                                      parent_id, :num_logprobs].tolist(),
                        top_logprobs[sample_idx +
                                     parent_id, :num_logprobs].tolist()))
            group_sample_logprobs.append(sample_logprobs_dict)
        result_sample_logprobs.append(group_sample_logprobs)
        sample_idx += len(seq_ids)

    return result_prompt_logprobs, result_sample_logprobs


def _build_sampler_output(
    sample_results: List[Tuple[List[int], List[int]]],
    input_metadata: InputMetadata,
    prompt_logprobs: List[Optional[PromptLogprobs]],
    sample_logprobs: List[SampleLogprobs],
) -> SamplerOutput:
    sampler_output = []
    for (seq_group, sample_result, group_prompt_logprobs,
         group_sample_logprobs) in zip(input_metadata.seq_groups,
                                       sample_results, prompt_logprobs,
                                       sample_logprobs):
        seq_ids, _ = seq_group
        next_token_ids, parent_ids = sample_result
        seq_outputs = []
        for parent_id, next_token_id, logprobs in zip(parent_ids,
                                                      next_token_ids,
                                                      group_sample_logprobs):
            seq_outputs.append(
                SequenceOutputs(seq_ids[parent_id], next_token_id, logprobs))
        sampler_output.append(
            SequenceGroupOutputs(seq_outputs, group_prompt_logprobs))
    return sampler_output