gpu_input_batch.py 34.3 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
# Datastructures defining a GPU input batch
4
5

from dataclasses import dataclass
6
from typing import Optional, cast
7
8
9

import numpy as np
import torch
10
from typing_extensions import deprecated
11

12
from vllm.lora.request import LoRARequest
13
14
from vllm.multimodal.inputs import (MultiModalKwargs, MultiModalKwargsItem,
                                    PlaceholderRange)
15
from vllm.pooling_params import PoolingParams
16
from vllm.sampling_params import SamplingParams, SamplingType
17
from vllm.utils import swap_dict_values
18
from vllm.v1.outputs import LogprobsTensors
19
from vllm.v1.pool.metadata import PoolingMetadata
20
from vllm.v1.sample.logits_processor import (BatchUpdateBuilder,
21
22
                                             LogitsProcessors,
                                             MoveDirectionality)
23
from vllm.v1.sample.metadata import SamplingMetadata
24
from vllm.v1.spec_decode.utils import is_spec_decode_unsupported
25
from vllm.v1.utils import copy_slice
26
from vllm.v1.worker.block_table import MultiGroupBlockTable
27
28
29
30
31
32


@dataclass
class CachedRequestState:

    req_id: str
33
    prompt_token_ids: list[int]
34
    mm_kwargs: list[MultiModalKwargsItem]
35
    mm_positions: list[PlaceholderRange]
36
37
    sampling_params: Optional[SamplingParams]
    pooling_params: Optional[PoolingParams]
38
39
    generator: Optional[torch.Generator]

40
    block_ids: tuple[list[int], ...]
41
    num_computed_tokens: int
42
    output_token_ids: list[int]
zhuwenwen's avatar
zhuwenwen committed
43
    spec_token_ids: list[int] = None
44

45
46
47
    mrope_positions: Optional[torch.Tensor] = None
    mrope_position_delta: Optional[int] = None

48
49
    lora_request: Optional[LoRARequest] = None

50
51
52
    def __post_init__(self):
        self.num_prompt_tokens = len(self.prompt_token_ids)

53
54
    @property
    def num_tokens(self) -> int:
55
56
        return self.num_prompt_tokens + len(self.output_token_ids)

57
58
59
60
61
    # Temporary back-compatibility for plugins that define model runner
    @property
    @deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
                "removed in v0.13. Please use `mm_kwargs` instead.")
    def mm_inputs(self) -> list[MultiModalKwargs]:
62
        return [MultiModalKwargs([item]) for item in self.mm_kwargs]
63

64
65
66
67
68
    def get_token_id(self, idx: int) -> int:
        if idx < self.num_prompt_tokens:
            return self.prompt_token_ids[idx]
        else:
            return self.output_token_ids[idx - self.num_prompt_tokens]
69
70
71
72
73


class InputBatch:

    def __init__(
74
75
76
77
78
79
80
81
        self,
        max_num_reqs: int,
        max_model_len: int,
        max_num_batched_tokens: int,
        device: torch.device,
        pin_memory: bool,
        vocab_size: int,
        block_sizes: list[int],  # The block_size of each kv cache group
82
        logitsprocs: Optional[LogitsProcessors] = None,
83
        is_spec_decode: bool = False,
84
        is_pooling_model: bool = False,
85
    ):
86
        self.is_pooling_model = is_pooling_model
87
        self.is_spec_decode = is_spec_decode
88
89
        self.max_num_reqs = max_num_reqs
        self.max_model_len = max_model_len
90
        self.max_num_batched_tokens = max_num_batched_tokens
91
92
        self.device = device
        self.pin_memory = pin_memory
93
        self.vocab_size = vocab_size
94

95
96
        self._req_ids: list[Optional[str]] = []
        self.req_id_to_index: dict[str, int] = {}
97

98
99
        # TODO(woosuk): This buffer could be too large if max_model_len is big.
        # Find a way to reduce the CPU memory usage.
100
101
        # This buffer is not directly transferred to the GPU, so it does not
        # need to be pinned.
102
103
104
105
        self.token_ids_cpu_tensor = torch.zeros(
            (max_num_reqs, max_model_len),
            device="cpu",
            dtype=torch.int32,
106
            pin_memory=False,
107
108
        )
        self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
109
        self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32)
110
        self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
111
        self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
112
113
114
115
116
117
118
119
        self.num_computed_tokens_cpu_tensor = torch.zeros(
            (max_num_reqs, ),
            device="cpu",
            dtype=torch.int32,
            pin_memory=pin_memory,
        )
        self.num_computed_tokens_cpu = \
            self.num_computed_tokens_cpu_tensor.numpy()
120

121
        # Block table.
122
        self.block_table = MultiGroupBlockTable(
123
            max_num_reqs=max_num_reqs,
124
            max_model_len=max_model_len,
125
            max_num_batched_tokens=max_num_batched_tokens,
126
            pin_memory=pin_memory,
127
            device=device,
128
            block_sizes=block_sizes,
129
130
131
132
133
134
135
136
137
138
139
        )

        # Sampling-related.
        self.temperature = torch.empty((max_num_reqs, ),
                                       dtype=torch.float32,
                                       device=device)
        self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
                                                  dtype=torch.float32,
                                                  device="cpu",
                                                  pin_memory=pin_memory)
        self.temperature_cpu = self.temperature_cpu_tensor.numpy()
140
141
        self.greedy_reqs: set[str] = set()
        self.random_reqs: set[str] = set()
142
143
144
145
146
147
148
149
150

        self.top_p = torch.empty((max_num_reqs, ),
                                 dtype=torch.float32,
                                 device=device)
        self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
                                            dtype=torch.float32,
                                            device="cpu",
                                            pin_memory=pin_memory)
        self.top_p_cpu = self.top_p_cpu_tensor.numpy()
151
        self.top_p_reqs: set[str] = set()
152
153
154
155
156
157
158
159
160

        self.top_k = torch.empty((max_num_reqs, ),
                                 dtype=torch.int32,
                                 device=device)
        self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
                                            dtype=torch.int32,
                                            device="cpu",
                                            pin_memory=pin_memory)
        self.top_k_cpu = self.top_k_cpu_tensor.numpy()
161
        self.top_k_reqs: set[str] = set()
162

163
164
        # IDs of requests which do not support spec decoding
        self.spec_decode_unsupported_reqs: set[str] = set()
165

166
167
168
169
170
171
172
173
174
175
        # Frequency penalty related data structures
        self.frequency_penalties = torch.empty((max_num_reqs, ),
                                               dtype=torch.float,
                                               device=device)
        self.frequency_penalties_cpu_tensor = torch.empty(
            (max_num_reqs, ),
            dtype=torch.float,
            device="cpu",
            pin_memory=pin_memory)
        self.frequency_penalties_cpu = \
176
            self.frequency_penalties_cpu_tensor.numpy()
177
        self.frequency_penalties_reqs: set[str] = set()
178
179
180
181
182
183
184
185
186

        # Presence penalty related data structures
        self.presence_penalties = torch.empty((max_num_reqs, ),
                                              dtype=torch.float,
                                              device=device)
        self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
                                                         dtype=torch.float,
                                                         device="cpu",
                                                         pin_memory=pin_memory)
187
188
        self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
        )
189
        self.presence_penalties_reqs: set[str] = set()
190
191
192
193
194
195
196
197
198
199
200

        # Repetition penalty related data structures
        self.repetition_penalties = torch.empty((max_num_reqs, ),
                                                dtype=torch.float,
                                                device=device)
        self.repetition_penalties_cpu_tensor = torch.empty(
            (max_num_reqs, ),
            dtype=torch.float,
            device="cpu",
            pin_memory=pin_memory)
        self.repetition_penalties_cpu = \
201
            self.repetition_penalties_cpu_tensor.numpy()
202
        self.repetition_penalties_reqs: set[str] = set()
203

204
205
206
        # lora related
        self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
                                             dtype=np.int32)
207
208
        self.lora_id_to_request_ids: dict[int, set[str]] = {}
        self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
209

210
        # req_index -> generator
211
212
        # NOTE(woosuk): The indices of the requests that do not have their own
        # generator should not be included in the dictionary.
213
        self.generators: dict[int, torch.Generator] = {}
214

215
        self.num_logprobs: dict[str, int] = {}
216
217
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
218
        self.num_prompt_logprobs: dict[str, int] = {}
219

220
221
222
        # To accumulate prompt logprobs tensor chunks across prefill steps.
        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}

223
224
225
226
227
228
        # Internal representation of per-step batch state changes, used for
        # reordering persistent batch and generating logitsprocs batch state
        # updates. Should reset each step.
        self.batch_update_builder = BatchUpdateBuilder()

        # TODO convert this to LogitsProcessor
229
        self.has_allowed_token_ids: set[str] = set()
230
231
        # NOTE(lufang): In the mask tensor, if the corresponding token allowed,
        # the value is False. Since we use masked_fill_ to set -inf.
232
233
        self.allowed_token_ids_mask: Optional[torch.Tensor] = None
        self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
234

235
236
237
        # req_index -> bad_words_token_ids
        self.bad_words_token_ids: dict[int, list[list[int]]] = {}

238
239
240
        self.logits_processing_needs_token_ids = np.zeros(max_num_reqs,
                                                          dtype=bool)

241
        self.req_output_token_ids: list[Optional[list[int]]] = []
242

243
244
245
246
        # Store provided logitsprocs. If none are provided, initialize empty
        # data structure
        self.logitsprocs = logitsprocs or LogitsProcessors()

247
248
249
        # This is updated each time the batch constituents change.
        self.sampling_metadata = self._make_sampling_metadata()

250
251
        self.pooling_params: dict[str, PoolingParams] = {}

252
    @property
253
    def req_ids(self) -> list[str]:
254
255
        # None elements should only be present transiently
        # while performing state updates to the batch.
256
        return cast(list[str], self._req_ids)
257

258
    def _register_add_request(self, request: "CachedRequestState") -> int:
259
260
261
262
263
264
265
266
267
268
269
270
271
272
        """Track add-request operations for logits processors.
        Not applicable to pooling models.
        """

        # Detailed added request metadata is only required for non-pooling
        # models, to support logitsprocs
        assert request.sampling_params

        # Fill the next empty index if there is one.
        if (new_req_index := self.batch_update_builder.pop_removed()) is None:
            # Append to end otherwise.
            new_req_index = self.num_reqs

        assert new_req_index < self.max_num_reqs
273
        self.batch_update_builder.added.append(
274
275
276
            (new_req_index, request.sampling_params, request.prompt_token_ids,
             request.output_token_ids))
        return new_req_index
277

278
279
280
    def add_request(
        self,
        request: "CachedRequestState",
281
    ) -> int:
282
283
284
285
286
        if not self.is_pooling_model:
            # New request index bookkeeping for autoregressive models.
            req_index = self._register_add_request(request)
        else:
            req_index = self.num_reqs
287
288

        req_id = request.req_id
289
290
291
292
293
294
295
        if req_index == len(self._req_ids):
            self._req_ids.append(req_id)
            self.req_output_token_ids.append(request.output_token_ids)
        else:
            self._req_ids[req_index] = req_id
            self.req_output_token_ids[req_index] = request.output_token_ids

296
297
298
299
        self.req_id_to_index[req_id] = req_index

        # Copy the prompt token ids and output token ids.
        num_prompt_tokens = len(request.prompt_token_ids)
300
        self.num_prompt_tokens[req_index] = num_prompt_tokens
301
302
303
304
305
306
        self.token_ids_cpu[
            req_index, :num_prompt_tokens] = request.prompt_token_ids
        start_idx = num_prompt_tokens
        end_idx = start_idx + len(request.output_token_ids)
        self.token_ids_cpu[req_index,
                           start_idx:end_idx] = request.output_token_ids
zhuwenwen's avatar
zhuwenwen committed
307
308
309
310
311
312
313
        
        num_spec_tokens = 0
        if request.spec_token_ids != None:
            num_spec_tokens = len(request.spec_token_ids)
            self.token_ids_cpu[req_index,
                            end_idx:end_idx + num_spec_tokens] = request.spec_token_ids

314
315
        # Number of token ids in token_ids_cpu.
        # NOTE(woosuk): This may include spec decode tokens.
zhuwenwen's avatar
zhuwenwen committed
316
        self.num_tokens[req_index] = request.num_tokens + num_spec_tokens
317
318
        # Number of tokens without spec decode tokens.
        self.num_tokens_no_spec[req_index] = request.num_tokens
319
320

        self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
321
        self.block_table.add_row(request.block_ids, req_index)
322

323
        if sampling_params := request.sampling_params:
324
325
326
            if (self.is_spec_decode
                    and is_spec_decode_unsupported(sampling_params)):
                self.spec_decode_unsupported_reqs.add(req_id)
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
            if sampling_params.sampling_type == SamplingType.GREEDY:
                # Avoid later division by zero.
                self.temperature_cpu[req_index] = -1.0
                self.greedy_reqs.add(req_id)
            else:
                self.temperature_cpu[req_index] = sampling_params.temperature
                self.random_reqs.add(req_id)

            self.top_p_cpu[req_index] = sampling_params.top_p
            if sampling_params.top_p < 1:
                self.top_p_reqs.add(req_id)
            top_k = sampling_params.top_k
            if 0 < top_k < self.vocab_size:
                self.top_k_reqs.add(req_id)
            else:
                top_k = self.vocab_size
            self.top_k_cpu[req_index] = top_k
            self.frequency_penalties_cpu[
                req_index] = sampling_params.frequency_penalty
            if sampling_params.frequency_penalty != 0.0:
                self.frequency_penalties_reqs.add(req_id)
            self.presence_penalties_cpu[
                req_index] = sampling_params.presence_penalty
            if sampling_params.presence_penalty != 0.0:
                self.presence_penalties_reqs.add(req_id)
            self.repetition_penalties_cpu[
                req_index] = sampling_params.repetition_penalty
            if sampling_params.repetition_penalty != 1.0:
                self.repetition_penalties_reqs.add(req_id)

            # NOTE(woosuk): self.generators should not include the requests that
            # do not have their own generator.
            if request.generator is not None:
                self.generators[req_index] = request.generator

            if sampling_params.logprobs is not None:
363
364
365
                self.num_logprobs[req_id] = (self.vocab_size
                                             if sampling_params.logprobs == -1
                                             else sampling_params.logprobs)
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
            if sampling_params.prompt_logprobs is not None:
                self.num_prompt_logprobs[
                    req_id] = sampling_params.prompt_logprobs

            if sampling_params.allowed_token_ids:
                self.has_allowed_token_ids.add(req_id)
                if self.allowed_token_ids_mask_cpu_tensor is None:
                    # Lazy allocation for this tensor, which can be large.
                    # False means we don't fill with -inf.
                    self.allowed_token_ids_mask = torch.zeros(
                        self.max_num_reqs,
                        self.vocab_size,
                        dtype=torch.bool,
                        device=self.device)
                    self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
                        self.max_num_reqs,
                        self.vocab_size,
                        dtype=torch.bool,
                        device="cpu")
                self.allowed_token_ids_mask_cpu_tensor[req_index] = True
386
                # False means we don't fill with -inf.
387
388
                self.allowed_token_ids_mask_cpu_tensor[req_index][
                    sampling_params.allowed_token_ids] = False
389

390
391
392
            if sampling_params.bad_words_token_ids:
                self.bad_words_token_ids[
                    req_index] = sampling_params.bad_words_token_ids
393
394
395
396
        elif pooling_params := request.pooling_params:
            self.pooling_params[req_id] = pooling_params
            self.logits_processing_needs_token_ids[req_index] = (
                pooling_params.requires_token_ids)
397
        else:
398
            raise NotImplementedError(request)
399

400
401
402
403
404
405
406
407
408
409
410
411
412
        # Add request lora ID
        if request.lora_request:
            lora_id = request.lora_request.lora_int_id
            if lora_id not in self.lora_id_to_request_ids:
                self.lora_id_to_request_ids[lora_id] = set()

            self.request_lora_mapping[req_index] = lora_id
            self.lora_id_to_request_ids[lora_id].add(request.req_id)
            self.lora_id_to_lora_request[lora_id] = request.lora_request
        else:
            # No LoRA
            self.request_lora_mapping[req_index] = 0

413
414
        return req_index

415
    def remove_request(self, req_id: str) -> Optional[int]:
416
        """This method must always be followed by a call to condense().
417

418
419
420
421
422
423
        Args:
          req_id: request to remove

        Returns:
          Removed request index, or `None` if `req_id` not recognized
        """
424

425
426
427
        req_index = self.req_id_to_index.pop(req_id, None)
        if req_index is None:
            return None
428
429
430
431
        if not self.is_pooling_model:
            # Autoregressive models require bookkeeping of removed requests to
            # support logitsprocs.
            self.batch_update_builder.removed_append(req_index)
432
433
        self._req_ids[req_index] = None
        self.req_output_token_ids[req_index] = None
434
435
436
437
438

        self.greedy_reqs.discard(req_id)
        self.random_reqs.discard(req_id)
        self.top_p_reqs.discard(req_id)
        self.top_k_reqs.discard(req_id)
439
        self.spec_decode_unsupported_reqs.discard(req_id)
440
441
442
        self.frequency_penalties_reqs.discard(req_id)
        self.presence_penalties_reqs.discard(req_id)
        self.repetition_penalties_reqs.discard(req_id)
443
444
        self.generators.pop(req_index, None)
        self.num_logprobs.pop(req_id, None)
445
        self.num_prompt_logprobs.pop(req_id, None)
446
        self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
447
448
449
450
451
452
453
454
455
456

        # LoRA
        lora_id = self.request_lora_mapping[req_index]
        if lora_id != 0:
            self.lora_id_to_request_ids[lora_id].discard(req_id)
            if len(self.lora_id_to_request_ids[lora_id]) == 0:
                self.lora_id_to_request_ids.pop(lora_id)
                self.lora_id_to_lora_request.pop(lora_id)
            self.request_lora_mapping[req_index] = 0

457
458
        self.has_allowed_token_ids.discard(req_id)
        if self.allowed_token_ids_mask_cpu_tensor is not None:
459
            # False means we don't fill with -inf.
460
            self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
461
        self.bad_words_token_ids.pop(req_index, None)
462
        self.pooling_params.pop(req_id, None)
463
464
        return req_index

465
    def swap_states(self, i1: int, i2: int) -> None:
466
467
        # For autoregressive models, track detailed request reordering info
        # to support logitsprocs
468
469
        self.batch_update_builder.moved.append(
            (i1, i2, MoveDirectionality.SWAP))
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
        old_id_i1 = self._req_ids[i1]
        old_id_i2 = self._req_ids[i2]
        self._req_ids[i1], self._req_ids[i2] =\
            self._req_ids[i2], self._req_ids[i1] # noqa
        self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\
            self.req_output_token_ids[i2], self.req_output_token_ids[i1]
        assert old_id_i1 is not None and old_id_i2 is not None
        self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\
            self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1]
        self.num_tokens[i1], self.num_tokens[i2] =\
            self.num_tokens[i2], self.num_tokens[i1]
        self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\
            self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1]
        self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\
            self.num_prompt_tokens[i2], self.num_prompt_tokens[i1]
        self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\
            self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1]
        self.temperature_cpu[i1], self.temperature_cpu[i2] =\
            self.temperature_cpu[i2], self.temperature_cpu[i1]
        self.top_p_cpu[i1], self.top_p_cpu[i2] =\
            self.top_p_cpu[i2], self.top_p_cpu[i1]
        self.top_k_cpu[i1], self.top_k_cpu[i2] =\
            self.top_k_cpu[i2], self.top_k_cpu[i1]
        self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\
            self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1]
        self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\
            self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1]
        self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\
            self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1]

500
501
502
503
504
505
506
507
508
        # NOTE: the following is unsafe
        # self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
        #     self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
        # instead, we need to temporiarily copy the data for one of the indices
        # TODO(lucas): optimize this by only copying valid indices
        tmp = self.token_ids_cpu[i1, ...].copy()
        self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...]
        self.token_ids_cpu[i2, ...] = tmp

509
510
        swap_dict_values(self.generators, i1, i2)
        swap_dict_values(self.bad_words_token_ids, i1, i2)
511
512
513

        self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\
            self.request_lora_mapping[i2], self.request_lora_mapping[i1]
514
515
516
517
518
519

        if self.allowed_token_ids_mask_cpu_tensor is not None:
            self.allowed_token_ids_mask_cpu_tensor[i1], \
                self.allowed_token_ids_mask_cpu_tensor[i2] =\
                self.allowed_token_ids_mask_cpu_tensor[i2], \
                    self.allowed_token_ids_mask_cpu_tensor[i1]
520
521
        self.block_table.swap_row(i1, i2)

522
523
524
525
526
527
    def condense(self) -> None:
        """Slide non-empty requests down into lower, empty indices.

        Any consecutive empty indices at the very end of the list are not
        filled.

528
        Args:
529
530
531
532
533
          empty_req_indices: empty indices which may be filled.

        Returns:
          swaps: list of (from,to) swap tuples for moved requests
          empty_req_indices: indices not filled by condensation
534
        """
535
536
537
538
539
540
541
542
        num_reqs = self.num_reqs

        if self.is_pooling_model:
            # Will be contiguous in pooling case, just trim the lists.
            del self._req_ids[num_reqs:]
            del self.req_output_token_ids[num_reqs:]
            return

543
544
545
546
        if not (empty_req_indices := self.batch_update_builder.removed):
            # All removed requests were replaced by added requests, or else no
            # requests were removed at all. No condense() needed
            return
547
        if num_reqs == 0:
548
            # The batched states are empty.
549
550
            self._req_ids.clear()
            self.req_output_token_ids.clear()
551
552
553
554
            return

        # NOTE(woosuk): This function assumes that the empty_req_indices
        # is sorted in descending order.
555
        last_req_index = num_reqs + len(empty_req_indices) - 1
556
557
558
559
560
561
        while empty_req_indices:
            # Find the largest non-empty index.
            while last_req_index in empty_req_indices:
                last_req_index -= 1

            # Find the smallest empty index.
562
563
            empty_index = self.batch_update_builder.peek_removed()
            assert empty_index is not None
564
565
566
            if empty_index >= last_req_index:
                break

567
568
569
            # Move active request down into empty request
            # index.
            self.batch_update_builder.pop_removed()
570
571
            # Autoregressive models require detailed tracking of condense
            # operations to support logitsprocs
572
573
574
            self.batch_update_builder.moved.append(
                (last_req_index, empty_index,
                 MoveDirectionality.UNIDIRECTIONAL))
575
576
            req_id = self._req_ids[last_req_index]
            output_token_ids = self.req_output_token_ids[last_req_index]
577
            assert req_id is not None
578
579
580
581
            self._req_ids[empty_index] = req_id
            self._req_ids[last_req_index] = None
            self.req_output_token_ids[empty_index] = output_token_ids
            self.req_output_token_ids[last_req_index] = None
582
583
            self.req_id_to_index[req_id] = empty_index

584
585
586
587
            num_tokens = self.num_tokens[last_req_index]
            self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[
                last_req_index, :num_tokens]
            self.num_tokens[empty_index] = num_tokens
588
589
            self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[
                last_req_index]
590
591
            self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[
                last_req_index]
592
593
            self.num_computed_tokens_cpu[
                empty_index] = self.num_computed_tokens_cpu[last_req_index]
594
            self.block_table.move_row(last_req_index, empty_index)
595
596
597
598
            self.temperature_cpu[empty_index] = self.temperature_cpu[
                last_req_index]
            self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]
            self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index]
599
600
601
602
603
604
            self.frequency_penalties_cpu[
                empty_index] = self.frequency_penalties_cpu[last_req_index]
            self.presence_penalties_cpu[
                empty_index] = self.presence_penalties_cpu[last_req_index]
            self.repetition_penalties_cpu[
                empty_index] = self.repetition_penalties_cpu[last_req_index]
605
606
607
608
            generator = self.generators.pop(last_req_index, None)
            if generator is not None:
                self.generators[empty_index] = generator

609
610
611
            self.request_lora_mapping[empty_index] = self.request_lora_mapping[
                last_req_index]

612
            # TODO convert these to LogitsProcessors
613
614
615
616
617
            if self.allowed_token_ids_mask_cpu_tensor is not None:
                self.allowed_token_ids_mask_cpu_tensor[
                    empty_index] = self.allowed_token_ids_mask_cpu_tensor[
                        last_req_index]

618
619
620
621
            bad_words_token_ids = self.bad_words_token_ids.pop(
                last_req_index, None)
            if bad_words_token_ids is not None:
                self.bad_words_token_ids[empty_index] = bad_words_token_ids
622

623
624
625
            # Decrement last_req_index since it is now empty.
            last_req_index -= 1

626
        # Trim lists to the batch size.
627
628
        del self._req_ids[num_reqs:]
        del self.req_output_token_ids[num_reqs:]
629

630
    def refresh_metadata(self):
631
        """Apply any batch updates to sampling metadata."""
632

633
634
635
636
637
638
639
640
        if self.is_pooling_model:
            # Batch changes every step for pooling models.
            self.sampling_metadata = self._make_sampling_metadata()
            return

        # For non-pooling models - generate and apply logitsprocs update;
        # reset batch update tracking.
        # Update sampling metadata if batch state is changed.
641
642
643
644
645
        batch_update = self.batch_update_builder.get_and_reset(self.num_reqs)
        for logit_proc in self.logitsprocs.all:
            logit_proc.update_state(batch_update)
        if batch_update:
            self.sampling_metadata = self._make_sampling_metadata()
646
647
648

    def _make_sampling_metadata(self) -> SamplingMetadata:
        num_reqs = self.num_reqs
649
650
651
652
653
        if not self.all_greedy:
            temperature = copy_slice(self.temperature_cpu_tensor,
                                     self.temperature, num_reqs)
        else:
            temperature = None
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
        if not self.no_top_p:
            copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs)
        if not self.no_top_k:
            copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs)

        if not self.no_penalties:
            # Since syncing these tensors is expensive only copy them
            # if necessary i.e. if there are requests which require
            # penalties to be applied during sampling.
            copy_slice(self.frequency_penalties_cpu_tensor,
                       self.frequency_penalties, num_reqs)
            copy_slice(self.presence_penalties_cpu_tensor,
                       self.presence_penalties, num_reqs)
            copy_slice(self.repetition_penalties_cpu_tensor,
                       self.repetition_penalties, num_reqs)

670
671
672
        needs_prompt_token_ids = (
            not self.no_penalties
            or self.logits_processing_needs_token_ids[:num_reqs].any())
673
674
675
676
677
        if needs_prompt_token_ids:
            # The prompt tokens are used only for applying penalties or
            # step pooling during the sampling/pooling process.
            # Hence copy these tensors only when there are requests which
            # need penalties/step_pooler to be applied.
678
679
680
            prompt_token_ids = self._make_prompt_token_ids_tensor()
        else:
            prompt_token_ids = None
681

682
683
684
685
686
687
688
        allowed_token_ids_mask: Optional[torch.Tensor] = None
        if not self.no_allowed_token_ids:
            assert self.allowed_token_ids_mask is not None
            copy_slice(self.allowed_token_ids_mask_cpu_tensor,
                       self.allowed_token_ids_mask, num_reqs)
            allowed_token_ids_mask = self.allowed_token_ids_mask[:num_reqs]

689
        return SamplingMetadata(
690
            temperature=temperature,
691
692
            all_greedy=self.all_greedy,
            all_random=self.all_random,
693
694
            top_p=None if self.no_top_p else self.top_p[:num_reqs],
            top_k=None if self.no_top_k else self.top_k[:num_reqs],
695
696
            generators=self.generators,
            max_num_logprobs=self.max_num_logprobs,
697
698
699
700
            prompt_token_ids=prompt_token_ids,
            frequency_penalties=self.frequency_penalties[:num_reqs],
            presence_penalties=self.presence_penalties[:num_reqs],
            repetition_penalties=self.repetition_penalties[:num_reqs],
701
            output_token_ids=cast(list[list[int]], self.req_output_token_ids),
702
            no_penalties=self.no_penalties,
703
            allowed_token_ids_mask=allowed_token_ids_mask,
704
            bad_words_token_ids=self.bad_words_token_ids,
705
            logitsprocs=self.logitsprocs,
706
707
        )

708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
    @property
    def pooling_metadata(self) -> PoolingMetadata:
        if len(self.pooling_params) == 0:
            pooling_params = []
        else:
            # Note, for now this assumes that all request in the batch
            # are either sampling or pooling requests
            assert len(self.req_ids) == len(self.pooling_params)
            pooling_params = [
                self.pooling_params[req_id] for req_id in self.req_ids
            ]

        return PoolingMetadata(
            prompt_lens=torch.from_numpy(
                self.num_prompt_tokens[:self.num_reqs]).to(self.device),
            prompt_token_ids=self.sampling_metadata.prompt_token_ids,
            pooling_params=pooling_params,
        )

727
728
729
730
731
732
    def _make_prompt_token_ids_tensor(self) -> torch.Tensor:
        max_prompt_len = self.num_prompt_tokens[:self.num_reqs].max()
        prompt_token_ids_cpu_tensor = torch.empty(
            (self.num_reqs, max_prompt_len),
            device="cpu",
            dtype=torch.int64,
733
734
            pin_memory=self.pin_memory,
        )
735
        prompt_token_ids = prompt_token_ids_cpu_tensor.numpy()
736
737
        prompt_token_ids[:] = self.token_ids_cpu[:self.
                                                 num_reqs, :max_prompt_len]
738
739
740
741
742
743
744
        # Use the value of vocab_size as a pad since we don't have a
        # token_id of this value.
        for i in range(self.num_reqs):
            prompt_token_ids[i, self.num_prompt_tokens[i]:] = self.vocab_size
        return prompt_token_ids_cpu_tensor.to(device=self.device,
                                              non_blocking=True)

745
746
    def make_lora_inputs(
        self, num_scheduled_tokens: np.ndarray
747
    ) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
        """
        Given the num_scheduled_tokens for each request in the batch, return
        datastructures used to activate the current LoRAs.
        Returns:
            1. prompt_lora_mapping: A tuple of size self.num_reqs where,
               prompt_lora_mapping[i] is the LoRA id to use for the ith prompt.
            2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens)
               where, token_lora_mapping[i] is the LoRA id to use for ith token.
            3. lora_requests: Set of relevant LoRA requests.
        """

        req_lora_mapping = self.request_lora_mapping[:self.num_reqs]
        prompt_lora_mapping = tuple(req_lora_mapping)
        token_lora_mapping = tuple(
            req_lora_mapping.repeat(num_scheduled_tokens))
763
        active_lora_requests: set[LoRARequest] = set(
764
765
766
767
            self.lora_id_to_lora_request.values())

        return prompt_lora_mapping, token_lora_mapping, active_lora_requests

768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
    @property
    def num_reqs(self) -> int:
        return len(self.req_id_to_index)

    @property
    def all_greedy(self) -> bool:
        return len(self.random_reqs) == 0

    @property
    def all_random(self) -> bool:
        return len(self.greedy_reqs) == 0

    @property
    def no_top_p(self) -> bool:
        return len(self.top_p_reqs) == 0

    @property
    def no_top_k(self) -> bool:
        return len(self.top_k_reqs) == 0

788
789
790
791
792
793
    @property
    def no_penalties(self) -> bool:
        return (len(self.presence_penalties_reqs) == 0
                and len(self.frequency_penalties_reqs) == 0
                and len(self.repetition_penalties_reqs) == 0)

794
    @property
795
796
    def max_num_logprobs(self) -> Optional[int]:
        return max(self.num_logprobs.values()) if self.num_logprobs else None
797
798
799

    @property
    def no_prompt_logprob(self) -> bool:
800
        return not self.num_prompt_logprobs
801
802
803
804

    @property
    def no_allowed_token_ids(self) -> bool:
        return len(self.has_allowed_token_ids) == 0