spec_decode_worker.py 61.2 KB
Newer Older
1
2
# SPDX-License-Identifier: Apache-2.0

3
import copy
4
from collections import defaultdict
5
from functools import cached_property
6
from typing import Any, Dict, List, Optional, Set, Tuple, Type
7
8

import torch
9
import torch.nn as nn
10

11
from vllm.config import ParallelConfig, SpeculativeConfig, VllmConfig
12
13
from vllm.distributed.communication_op import (broadcast_tensor_dict,
                                               tensor_model_parallel_gather)
14
from vllm.logger import init_logger
15
from vllm.model_executor.layers.rejection_sampler import RejectionSampler
16
from vllm.model_executor.layers.sampler import SamplerOutput
17
from vllm.model_executor.layers.spec_decode_base_sampler import (
18
    SpecDecodeBaseSampler, SpecDecodeStochasticBaseSampler)
19
20
from vllm.model_executor.layers.typical_acceptance_sampler import (
    TypicalAcceptanceSampler)
21
from vllm.platforms import current_platform
22
23
from vllm.sequence import (VLLM_INVALID_TOKEN_ID,
                           CompletionSequenceGroupOutput, ExecuteModelRequest,
24
                           HiddenStates, SequenceGroupMetadata,
25
                           get_all_seq_ids_and_request_ids)
26
from vllm.spec_decode.batch_expansion import BatchExpansionTop1Scorer
27
28
29
30

if current_platform.is_cuda_alike():
    from vllm.spec_decode.draft_model_runner import TP1DraftModelRunner

31
32
from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                         SpeculativeScorer, SpeculativeScores)
33
from vllm.spec_decode.medusa_worker import MedusaWorker
34
from vllm.spec_decode.metrics import AsyncMetricsCollector
35
from vllm.spec_decode.mlp_speculator_worker import MLPSpeculatorWorker
36
from vllm.spec_decode.mqa_scorer import MQAScorer
37
from vllm.spec_decode.multi_step_worker import MultiStepWorker
38
from vllm.spec_decode.ngram_worker import NGramWorker
39
from vllm.spec_decode.proposer_worker_base import ProposerWorkerBase
40
from vllm.spec_decode.smaller_tp_proposer_worker import SmallerTpProposerWorker
41
from vllm.spec_decode.target_model_runner import TargetModelRunner
42
43
from vllm.spec_decode.util import (Timer, create_logprobs_output,
                                   create_sequence_group_output,
44
                                   get_all_num_logprobs,
45
                                   get_sampled_token_logprobs, nvtx_range,
46
                                   split_batch_by_proposal_len)
47
48
from vllm.utils import resolve_obj_by_qualname
from vllm.worker.worker_base import LoraNotSupportedWorkerBase, WorkerBase
49
50

logger = init_logger(__name__)
51
52


53
54
55
56
def create_spec_worker(*args, **kwargs) -> "SpecDecodeWorker":
    """Helper method that is the entrypoint for Executors which use
    WorkerWrapper. It constructs a SpecDecodeWorker from the speculative config.
    """
57
58
    vllm_config: VllmConfig = kwargs.get("vllm_config")
    speculative_config: SpeculativeConfig = vllm_config.speculative_config
59
60
    assert speculative_config is not None

61
62
63
64
    if vllm_config.parallel_config.pipeline_parallel_size > 1:
        raise NotImplementedError("Speculative decoding is currently "
                                  "incompatible with pipeline parallelism")

65
66
67
    draft_worker_kwargs = kwargs.copy()

    kwargs["model_runner_cls"] = TargetModelRunner
68
69
70
    target_worker_config = copy.deepcopy(vllm_config)
    target_worker_config.parallel_config.worker_cls =\
        target_worker_config.parallel_config.sd_worker_cls
71
72
73
    cls = resolve_obj_by_qualname(
        target_worker_config.parallel_config.worker_cls)
    target_worker = cls(*args, **kwargs)
74
75
76
77
    # Set the disable_logprobs variable in the TargetModelRunner instance
    # as per its value specified in the SpeculativeConfig.
    target_worker.model_runner.disable_logprobs =\
         speculative_config.disable_logprobs
78

79
80
    draft_worker_config = copy.deepcopy(vllm_config)
    draft_worker_config.model_config = speculative_config.draft_model_config
81
82
83
84
    draft_worker_config.quant_config = VllmConfig._get_quantization_config(
        draft_worker_config.model_config,
        vllm_config.load_config,
    )
85
86
    speculative_config.draft_parallel_config.worker_cls =\
        draft_worker_config.parallel_config.sd_worker_cls
87
88
89
    draft_worker_config.parallel_config = speculative_config.draft_parallel_config  # noqa
    # TODO allow draft-model specific load config.

90
91
    # Override draft-model specific worker args.
    draft_worker_kwargs.update(
92
        vllm_config=draft_worker_config,
93
94
95
96
97
98
99
        ngram_prompt_lookup_max=speculative_config.ngram_prompt_lookup_max,
        ngram_prompt_lookup_min=speculative_config.ngram_prompt_lookup_min,
    )

    spec_decode_worker = SpecDecodeWorker.create_worker(
        scorer_worker=target_worker,
        draft_worker_kwargs=draft_worker_kwargs,
100
        disable_mqa_scorer=speculative_config.speculative_disable_mqa_scorer,
101
102
        disable_by_batch_size=speculative_config.
        speculative_disable_by_batch_size,
103
104
105
106
107
        draft_token_acceptance_method=speculative_config.
        draft_token_acceptance_method,
        typical_acceptance_sampler_posterior_threshold=speculative_config.
        typical_acceptance_sampler_posterior_threshold,
        typical_acceptance_sampler_posterior_alpha=speculative_config.
108
        typical_acceptance_sampler_posterior_alpha,
109
110
        disable_logprobs=speculative_config.disable_logprobs,
        disable_log_stats=speculative_config.disable_log_stats,
111
        num_speculative_tokens=speculative_config.num_speculative_tokens,
112
    )
113
114
115
116

    return spec_decode_worker


117
# Reminder: Please update docs/source/features/compatibility_matrix.md
118
# If the feature combo become valid
119
class SpecDecodeWorker(LoraNotSupportedWorkerBase):
120
121
122
123
124
125
126
127
    """Worker which implements speculative decoding.

    Speculative decoding reduces decoding per-token latency by using a proposal
    method, such as a small draft model, to speculate ahead of a larger LLM. The
    probabilities of the speculative tokens are then determined by the larger
    LLM, after which some verification routine determines which (if any) of the
    speculative tokens are accepted by the larger LLM.

128
    See https://github.com/vllm-project/vllm/pull/2188 and
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
    https://github.com/vllm-project/vllm/pull/3103 for more info.

    The current implementation has the following limitations:
    * Only draft-model proposal is implemented (contributions for more forms are
        welcome!).
    * Only top-1 proposal and scoring are implemented. Tree-attention is left as
        future work.
    * All sequences in a batch must have the same proposal length, or zero. This
        can be improved by having per-sequence speculation in the future.
    * The scoring forward pass is done without an MQA kernel, which is
        suboptimal especially as the batch size, proposal length, and sequence
        lengths grow. Contributions to add a MQA scoring are welcome once
        correctness tests pass.
        More info here https://docs.google.com/document/d/1T-JaS2T1NRfdP51qzqpyakoCXxSXTtORppiwaj5asxA/edit.
    """

145
    @classmethod
146
147
    def create_worker(
        cls,
148
        scorer_worker: WorkerBase,
149
        draft_worker_kwargs: Dict[str, Any],
150
        disable_mqa_scorer: bool,
151
        disable_by_batch_size: Optional[int],
152
153
154
        draft_token_acceptance_method: str,
        typical_acceptance_sampler_posterior_threshold: float,
        typical_acceptance_sampler_posterior_alpha: float,
155
        disable_logprobs: bool,
156
        disable_log_stats: bool,
157
        num_speculative_tokens: int,
158
159
    ) -> "SpecDecodeWorker":

160
        allow_zero_draft_token_step = True
161
        enable_lm_head_weight_load = False
162
        num_spec_prefill_steps = 1
163
164
165
166
        ngram_prompt_lookup_max = (
            draft_worker_kwargs.pop("ngram_prompt_lookup_max"))
        ngram_prompt_lookup_min = (
            draft_worker_kwargs.pop("ngram_prompt_lookup_min"))
167
168
169
        draft_model_config = draft_worker_kwargs["vllm_config"].model_config
        draft_parallel_config: ParallelConfig = draft_worker_kwargs[
            'vllm_config'].parallel_config
170
        if ngram_prompt_lookup_max > 0:
171
172
            draft_worker_kwargs[
                "device_type"] = scorer_worker.device_config.device.type
173
174
175
176
            proposer_worker = NGramWorker(**draft_worker_kwargs)
            proposer_worker.set_ngram_window_size(ngram_prompt_lookup_min,
                                                  ngram_prompt_lookup_max)
        else:
177
178
179
            draft_tp = draft_parallel_config.tensor_parallel_size
            target_tp = scorer_worker.parallel_config.tensor_parallel_size

180
            if draft_model_config.hf_config.model_type == "mlp_speculator":
181
                proposer_worker = MLPSpeculatorWorker(**draft_worker_kwargs)
182
            elif draft_model_config.hf_config.model_type == "medusa":
183
                proposer_worker = MedusaWorker(**draft_worker_kwargs)
184
            else:
185
186
                if draft_tp == 1 or draft_model_config.hf_config.model_type ==\
                        "deepseek_mtp":
187
188
189
                    if current_platform.is_cuda_alike():
                        draft_worker_kwargs[
                            "model_runner_cls"] = TP1DraftModelRunner
190
                else:
191
                    if draft_model_config.hf_config.model_type == "eagle":
192
                        raise NotImplementedError(
193
194
                            f"{draft_model_config.hf_config.model_type} "
                            "does not support TP > 1 yet")
195

196
                    allow_zero_draft_token_step = False
197
198
199
200
201

                # Load lm_head weight for eagle in init_device
                if draft_model_config.hf_config.model_type == "eagle":
                    enable_lm_head_weight_load = True

202
                proposer_worker = MultiStepWorker(**draft_worker_kwargs)
203
204
                if draft_model_config.hf_config.model_type == "deepseek_mtp":
                    num_spec_prefill_steps = num_speculative_tokens
205

206
207
            proposer_worker = SmallerTpProposerWorker.maybe_wrap_worker(
                proposer_worker, draft_tp, target_tp)
208

209
210
211
        logger.info("Configuring SpecDecodeWorker with proposer=%s",
                    type(proposer_worker))

212
213
        spec_decode_sampler: SpecDecodeBaseSampler = None
        if draft_token_acceptance_method == "rejection_sampler":
214
            spec_decode_sampler = RejectionSampler()
215
216
217
218
219
220
        elif draft_token_acceptance_method == "typical_acceptance_sampler":
            spec_decode_sampler = TypicalAcceptanceSampler(
                posterior_threshold=\
                    typical_acceptance_sampler_posterior_threshold,
                posterior_alpha=typical_acceptance_sampler_posterior_alpha,
            )
221
222
223
224
225
226
        logger.info(
            "[Speculative Decoding] Configuring"
            " SpecDecodeWorker with sampler=%s", type(spec_decode_sampler))

        if not disable_mqa_scorer:
            if scorer_worker.model_runner.attn_backend.get_name(
227
            ) != "FLASH_ATTN":
228
229
230
231
232
                disable_mqa_scorer = True
                logger.info(
                    "[Speculative Decoding] Disabling MQA scorer as the "
                    "MQA is only available with flash attn backend.")

233
234
            if draft_model_config and \
                draft_model_config.max_model_len < \
235
236
237
238
239
240
241
242
243
244
245
246
                    scorer_worker.model_config.max_model_len:
                disable_mqa_scorer = True
                logger.info(
                    "[Speculative Decoding] Disabling MQA scorer as the "
                    "draft model max_model_len is smaller than the target "
                    "model max_model_len.")

            if not scorer_worker.model_runner.model_config.enforce_eager:
                disable_mqa_scorer = True
                logger.info(
                    "[Speculative Decoding] Disabling MQA scorer as the "
                    "target model is not running in eager mode.")
247

248
249
250
        return SpecDecodeWorker(
            proposer_worker,
            scorer_worker,
251
            disable_mqa_scorer=disable_mqa_scorer,
252
            disable_logprobs=disable_logprobs,
253
            disable_log_stats=disable_log_stats,
254
255
            disable_by_batch_size=disable_by_batch_size,
            spec_decode_sampler=spec_decode_sampler,
256
            allow_zero_draft_token_step=allow_zero_draft_token_step,
257
258
            enable_lm_head_weight_load=enable_lm_head_weight_load,
            num_spec_prefill_steps=num_spec_prefill_steps)
259

260
261
    def __init__(
        self,
262
        proposer_worker: ProposerWorkerBase,
263
        scorer_worker: WorkerBase,
264
        spec_decode_sampler: SpecDecodeBaseSampler,
265
        disable_mqa_scorer: bool = False,
266
267
        disable_logprobs: bool = False,
        disable_log_stats: bool = False,
268
        metrics_collector: Optional[AsyncMetricsCollector] = None,
269
        disable_by_batch_size: Optional[int] = None,
270
        allow_zero_draft_token_step: Optional[bool] = True,
271
        enable_lm_head_weight_load: Optional[bool] = False,
272
        num_spec_prefill_steps: int = 1,
273
274
275
276
277
278
279
280
281
282
    ):
        """
        Create a SpecDecodeWorker.

        Args:
            proposer_worker: A worker that can produce speculative tokens for
                sequences.
            scorer_worker: A worker that produces probabilities of speculative
                tokens according to some base model. Typically a vanilla vLLM
                Worker.
283
284
285
286
287
288
            spec_decode_sampler: A Torch module used to perform acceptance
                sampling of the draft tokens in the verification step of
                speculative decoding. Currently we support two different 
                types of sampler namely RejectionSampler and
                TypicalAcceptanceSampler. 'spec_decode_sampler' is either an
                instance of RejectionSampler or TypicalAcceptanceSampler.
289
290
            disable_mqa_scorer: If set to True, disable the MQA scorer and use
                the BatchExpansionTop1Scorer instead.
291
292
293
            disable_logprobs: If set to True, token log probabilities will
                not be output in both the draft worker and the target worker.
                If set to False, log probabilities will be output by both.
294
295
            disable_log_stats: If set to True, disable periodic printing of
                speculative stage times.
296
297
            disable_by_batch_size: If the batch size is larger than this,
                disable speculative decoding for new incoming requests.
298
299
            metrics_collector: Helper class for collecting metrics; can be set
                for testing purposes.
300
301
302
            allow_zero_draft_token_step: whether to allow a step where the draft
                model generates no draft token; should disallow when the tp of
                draft model is larger than 1 (TODO: #5814)
303
304
            enable_lm_head_weight_load: whether to load lm_head weight for
                draft models like eagle.
305
306
307
308
            num_spec_prefill_steps: number of speculative prefill steps to run
                before the speculative decoding starts. This is only used when
                the draft model is a deepseek_mtp model that requires prefill
                kv cache separately for each MTP layer.
309
310
311
        """
        self.proposer_worker = proposer_worker
        self.scorer_worker = scorer_worker
312
313
314
        scorer_runner = getattr(self.scorer_worker, "model_runner", None)
        self.generators = scorer_runner.get_generators(
        ) if scorer_runner else None
315
        self.disable_by_batch_size = disable_by_batch_size or float("inf")
316
        self.spec_decode_sampler = spec_decode_sampler
317
        self._allow_zero_draft_token_step = allow_zero_draft_token_step
318
        self._enable_lm_head_weight_load = enable_lm_head_weight_load
319
        self._metrics = AsyncMetricsCollector(
320
            self.spec_decode_sampler
321
        ) if metrics_collector is None else metrics_collector
322
323
324
325
326
327
328
329
330
        # Tracks the sequence IDs that received a bonus token ID in
        # their last forward pass. Needed only if KV cache is being
        # used for token generation such as in the case of MultiStepWorker.
        self._seq_with_bonus_token_in_last_step: Set[int] = set()
        # Tracks the currently active request ids and the sequence IDs
        # corresponding to them
        self._request_id_seq_id_mapping: Dict[str, Set[int]] = defaultdict(set)
        # Tracks if the proposer worker uses the KV cache or not.

331
332
        self.probs_dtype = self.spec_decode_sampler.probs_dtype
        self.token_id_dtype = self.spec_decode_sampler.token_id_dtype
333
        # Lazy initialization.
334
        self.scorer: SpeculativeScorer
335
        self.disable_mqa_scorer = disable_mqa_scorer
336

337
338
339
        # Hidden states from target model to pass to proposer
        # in the subsequent step.
        self.previous_hidden_states: Optional[HiddenStates] = None
340
        self._disable_logprobs = disable_logprobs
341
        self._disable_log_stats = disable_log_stats
342
        self._num_spec_prefill_steps = num_spec_prefill_steps
343

344
    def init_device(self) -> None:
345
346
347
348
        """Initialize both scorer and proposer models.
        """
        # The scorer worker model is initialized first in case the proposer
        # model has a smaller TP degree than the target worker.
349
350
        self.scorer_worker.init_device()
        self.proposer_worker.init_device()
351

352
353
354
355
        # NOTE(cade): load_model is not part of the WorkerBase interface.
        self.scorer_worker.load_model()
        self.proposer_worker.load_model()

356
357
358
359
360
361
362
363
364
365
366
        if self._enable_lm_head_weight_load:
            # NOTE(Shangming): gather lm_head weight when tp enabled
            target_lm_head_weight: torch.Tensor = tensor_model_parallel_gather(
                self.scorer_worker.model_runner.model_runner.model.lm_head.\
                    weight.data,
                    dim=0,
            )

            self.proposer_worker.maybe_load_lm_head_weight(
                target_lm_head_weight)

367
368
369
        self._metrics.init_tensors(self.rank, device_type=self.device)
        self.spec_decode_sampler.init_tensors(self.rank,
                                              device_type=self.device)
370

371
372
373
374
375
376
377
378
379
380
381
382
383
        scorer_cls: Type[SpeculativeScorer]
        if self.disable_mqa_scorer:
            scorer_cls = BatchExpansionTop1Scorer
            logger.info("[Speculative Decoding] Use batch "
                        "expansion for scoring proposals.")
        else:
            scorer_cls = MQAScorer
            logger.info(
                "[Speculative Decoding] Use MQA scorer for scoring proposals.")

        self.scorer = scorer_cls(scorer_worker=self.scorer_worker,
                                 device=self.device,
                                 vocab_size=self._vocab_size)
384

385
386
        self._configure_model_sampler_for_spec_decode()

387
388
389
    def load_model(self, *args, **kwargs):
        pass

390
391
392
    def _configure_model_sampler_for_spec_decode(self):
        """Configure model sampler to emit GPU tensors. This allows spec decode
        to keep data on device without transferring to CPU and serializing,
393
        which significantly reduces overhead of sampling during verification.
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410

        NOTE(cade): This breaks abstraction boundaries pretty badly. The better
        design is to have the "move to CPU and serialize" sampling decision be
        done outside of the model/sampler; this way the "last-mile" worker
        object which interfaces with the scheduler can serialize and incur the
        performance hit as necessary. This allows us to run the worker several
        iterations in a row without incurring the "move to CPU and serialize"
        performance penalty.

        Since this requires a large change to vLLM, we defer it to later and
        temporarily accept this broken abstraction boundary.

        NOTE(cade): This will require a special check if the proposer worker
        does not have a sampler (e.g. ngram speculation).
        """
        (self.scorer_worker.model_runner.model.sampler.include_gpu_probs_tensor
         ) = True
411
412
        (self.scorer_worker.model_runner.model.sampler.
         should_modify_greedy_probs_inplace) = True
413
        self.proposer_worker.set_include_gpu_probs_tensor()
414
        self.proposer_worker.set_should_modify_greedy_probs_inplace()
415

416
    def determine_num_available_blocks(self) -> Tuple[int, int]:
417
418
419
420
421
422
423
424
        """Determine the number of cache blocks to use.

        This is done by profiling the scorer model (which is typically the
        larger of the two). Then the total memory which would be used by the
        scorer cache is divided evenly between the proposer and scorer model KV,
        such that the number of blocks is equal in both KV caches.
        """
        num_gpu_blocks, num_cpu_blocks = (
425
            self.scorer_worker.determine_num_available_blocks())
426

427
        scorer_cache_block_size_bytes = (
428
            self.scorer_worker.get_cache_block_size_bytes())
429
        proposer_cache_block_size_bytes = (
430
            self.proposer_worker.get_cache_block_size_bytes())
431
432
433
434
435
436

        new_num_gpu_blocks = split_num_cache_blocks_evenly(
            scorer_cache_block_size_bytes, proposer_cache_block_size_bytes,
            num_gpu_blocks)
        return new_num_gpu_blocks, num_cpu_blocks

437
438
    def initialize_cache(self, num_gpu_blocks: int,
                         num_cpu_blocks: int) -> None:
439
440
        """Initialize the cache engine of the scorer and proposer workers.
        """
441
442
443
444
        self.scorer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks,
                                            num_cpu_blocks=num_cpu_blocks)
        self.proposer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks,
                                              num_cpu_blocks=num_cpu_blocks)
445

446
447
448
    def get_model(self) -> nn.Module:
        return self.scorer_worker.get_model()

449
450
    @torch.inference_mode()
    def execute_model(
451
452
453
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> List[SamplerOutput]:
454
455
        """Perform speculative decoding on the input batch.
        """
456
457
458
        if self.rank != self._driver_rank:
            self._run_non_driver_rank()
            return []
459

460
461
462
463
464
465
466
        if execute_model_req is None:
            # This signals that there's no more requests to process for now.
            # All workers are running infinite loop with broadcast_tensor_dict,
            # and it stops the loop when the driver broadcasts an empty input.
            # Send an empty input to notify all other workers to stop their
            # execution loop.
            broadcast_tensor_dict({}, src=0)
467
            return []
468

469
        self._track_finished_requests(execute_model_req)
470
471
472
        disable_all_speculation = self._should_disable_all_speculation(
            execute_model_req)
        num_lookahead_slots = execute_model_req.num_lookahead_slots
473
474
475
476
477
478
479
480
481
482
483
484
485
486
        all_prompt = True
        atleast_one_prompt = False
        all_zero_spec_tokens = True
        for sgm in execute_model_req.seq_group_metadata_list:
            all_prompt = all_prompt and sgm.is_prompt
            atleast_one_prompt = atleast_one_prompt or sgm.is_prompt
            all_zero_spec_tokens = all_zero_spec_tokens and (
                sgm.num_speculative_tokens == 0)

        if all_prompt and execute_model_req.seq_group_metadata_list:
            assert num_lookahead_slots == 0, (
                "Prompt only runs should have num_lookahead_slots equal to 0. "
                "This should never happen, please file a bug at "
                "https://github.com/vllm-project/vllm/issues")
487
488
489
490
491
        # Speculative decoding is disabled in the following cases:
        # 1. Prefill phase: Speculative decoding is not
        #    used during the prefill phase.
        # 2. Auto-disable enabled: The running queue size exceeds
        #    the specified threshold.
492
493
        # 3. No request: There are no requests in the batch, or
        #    none of the requests in the batch have spec decoding enabled.
494
495
        # In any of these cases, the proposer and scorer workers
        # are called normally.
496
        # We expect `num_speculative_tokens` to be None for prefills.
497
498
        no_spec = (num_lookahead_slots == 0 or disable_all_speculation
                   or all_zero_spec_tokens)
499

500
501
502
503
504
        # Broadcast how many lookahead slots are scheduled for this step, and
        # whether all speculation is disabled, to all non-driver workers.

        # This is required as if the number of draft model runs changes
        # dynamically, the non-driver workers won't know unless we perform a
505
        # communication to inform them.
506
507
508
509
510
511
512

        # no_spec is used to signal non-driver worker about prefill vs decode
        # stage. This is needed to ensure that order of execution of proposer
        # and scorer is same in both driver and non-driver workers (i.e.,
        # scorer -> proposer for prefill and proposer -> scorer in decode). This
        # order is needed to support models like EAGLE that take scorer states
        # as inputs.
513
514
        broadcast_dict = dict(
            num_lookahead_slots=num_lookahead_slots,
515
            no_spec=no_spec,
516
            disable_all_speculation=disable_all_speculation,
517
518
519
520
521
522
523
524
525
            # When both chunked prefill and speculative decoding are enabled
            # it is possible that the same batch contains both prefill
            # and decodes. If that happens in the scorer we run the batch
            # as one single forward pass. However, in the proposer we
            # run them as 2 different batches - one for prefill and
            # the other for decodes. The variable indicates to the non-driver
            # worker that there are prefills as part of the speculative batch
            # and hence it needs to run an extra prefill forward pass.
            run_spec_proposer_for_prefill=atleast_one_prompt,
526
527
528
529
530
531
532
533
534
        )
        broadcast_tensor_dict(broadcast_dict, src=self._driver_rank)

        assert execute_model_req.seq_group_metadata_list is not None, (
            "speculative decoding requires non-None seq_group_metadata_list")

        self._maybe_disable_speculative_tokens(
            disable_all_speculation, execute_model_req.seq_group_metadata_list)

535
        if no_spec:
536
537
538
539
540
541
542
543
544
545
546
547
            return self._run_no_spec(execute_model_req,
                                     skip_proposer=disable_all_speculation)
        return self._run_speculative_decoding_step(execute_model_req,
                                                   num_lookahead_slots)

    @torch.inference_mode()
    def start_worker_execution_loop(self) -> None:
        """Execute model loop to perform speculative decoding
        in parallel worker."""
        while self._run_non_driver_rank():
            pass

548
549
    def _should_disable_all_speculation(
            self, execute_model_req: ExecuteModelRequest) -> bool:
550
551
        # When the batch size is too large, disable speculative decoding
        # to stop trading off throughput for latency.
552
553
        return (execute_model_req.running_queue_size
                >= self.disable_by_batch_size)
554
555
556
557
558
559
560
561
562
563
564
565
566
567

    def _maybe_disable_speculative_tokens(
            self, disable_all_speculation: bool,
            seq_group_metadata_list: List[SequenceGroupMetadata]) -> None:
        if not disable_all_speculation:
            return

        for seq_group_metadata in seq_group_metadata_list:
            # Once num_speculative_tokens is set to 0, the spec decode
            # of this request will be disabled forever.
            # TODO(comaniac): We currently store spec decoding specific
            # state in the global data structure, but we should maintain
            # this state within spec decode worker.
            seq_group_metadata.num_speculative_tokens = 0
568

569
570
    def _serialize_sampler_output_no_logprobs(
            self, execute_model_req: ExecuteModelRequest,
571
            sampler_output: SamplerOutput) -> List[SamplerOutput]:
572
        """
573
574
        Creates and returns a `SamplerOutput` with only the token IDs being
        serialized to CPU and populated in `CompletionSequenceGroupOutput`.
575
576
577
578
579
580
581
582
583
584
585
        All other parameters in `CompletionSequenceGroupOutput` related to log 
        probabilities are skipped.

        Args:
            execute_model_req (ExecuteModelRequest): The model request that
            was executed.
            sampler_output (SamplerOutput): The output from the sampler with
            only GPU tensors populated.

        Returns:
            SamplerOutput: A new `SamplerOutput` instance containing a list of 
586
587
            `CompletionSequenceGroupOutput` objects with only token IDs
            populated.
588
        """
589
590
591
592
593
594
595
596
597
598
599
600
        seq_output_prompt_logprobs = [
            seq.is_prompt and seq.sampling_params.prompt_logprobs is not None
            and seq.sampling_params.prompt_logprobs > 0
            for seq in execute_model_req.seq_group_metadata_list
        ]
        # ignore slots for prompt tokens that are filled with INVALID_TOKEN_ID
        sampled_token_ids_list = (sampler_output.sampled_token_ids[torch.where(
            # subtracting is faster than testing for equality
            sampler_output.sampled_token_ids - VLLM_INVALID_TOKEN_ID)[0]] \
            if any(seq_output_prompt_logprobs) else \
                sampler_output.sampled_token_ids).tolist()

601
        seq_data_entries = [
602
603
604
            (seq_id, seq_data) for sg in \
            execute_model_req.seq_group_metadata_list \
            for seq_id, seq_data in sg.seq_data.items()
605
        ]
606
607
        completion_seq_group_output_list: List[
            CompletionSequenceGroupOutput] = []
608
609
610
        output_index = 0
        # Make sure the non-terminal prefill chunks are still aligned with
        # their own empty output.
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
        for idx, seq_group_meta in enumerate(
                execute_model_req.seq_group_metadata_list):
            needs_prompt_logprobs = seq_output_prompt_logprobs[idx]
            seq_id, seq_data = seq_data_entries[idx]
            if needs_prompt_logprobs:
                prompt_token_ids = seq_data.get_prompt_token_ids()

                # Some of these sequences may belong to non-terminal chunks,
                # which may still have to report logprobs for prompts.
                start = 1 if seq_data._num_computed_tokens == 0 \
                    else seq_data._num_computed_tokens
                end = (seq_data._num_computed_tokens + \
                       seq_group_meta.token_chunk_size)
                prompt_token_ids = prompt_token_ids[start:end]
                prompt_logprobs = [
                    create_logprobs_output(
                        token_id=p_token_id,
628
629
630
631
                        token_id_logprob_rank=-1,
                        token_id_logprob=0.0,
                        topk_token_ids=[],
                        topk_logprobs=[],
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
                    ) for p_token_id in prompt_token_ids
                ]
            else:
                prompt_logprobs = None

            # Since we can get chunks here, we dont always have a sampled token
            # (only on last chunk) but we still have to provide an output.
            if not seq_group_meta.do_sample:
                completion_seq_group_output_list.append(
                    CompletionSequenceGroupOutput(
                        samples=[], prompt_logprobs=prompt_logprobs))
                continue

            # Sequence with output.
            completion_seq_group_output_list.append(
                create_sequence_group_output(
                    token_id=sampled_token_ids_list[output_index][0],
                    token_id_logprob_rank=-1,
                    token_id_logprob=0.0,
                    seq_id=seq_id,
                    topk_token_ids=[],
                    topk_logprobs=[],
                    prompt_logprobs=prompt_logprobs))
            output_index += 1
656
657

        return [SamplerOutput(outputs=completion_seq_group_output_list)]
658

659
    @nvtx_range("spec_decode_worker._run_no_spec")
660
661
    def _run_no_spec(self, execute_model_req: ExecuteModelRequest,
                     skip_proposer: bool) -> List[SamplerOutput]:
662
663
        """Run a single generation step without any speculation. The input is
        sent to the proposer and scorer model so that the KV cache is consistent
664
665
666
        between the two. When skip_proposer is True, the proposer model is
        not called, meaning that the kv-cache in proposer for requests is not
        updated, so they cannot enable spec decode in the rest decoding.
667
668
        """

669
        sampler_output = self.scorer_worker.execute_model(execute_model_req)
670
671
        assert len(sampler_output) == 1
        sampler_output = sampler_output[0]
672

673
        # Store hidden states from target model execution, BxD.
674
675
        hidden_states = sampler_output.hidden_states
        if hidden_states is not None:
676
677
678
679
680
681
682
            # Only decodes and prefill terminal chunks need a hidden state.
            seq_group_meta_with_hidden = [
                sg for sg in execute_model_req.seq_group_metadata_list
                if sg.do_sample
            ]
            if any(seq.is_prompt for seq in seq_group_meta_with_hidden):
                # Drop hidden_states with no prediction (eg non-terminal chunks)
683
684
685
                hidden_states = hidden_states[
                    torch.where(sampler_output.sampled_token_ids -
                                VLLM_INVALID_TOKEN_ID)[0]]
686
687
            if self.previous_hidden_states is None and len(
                    seq_group_meta_with_hidden):
688
                self.previous_hidden_states = HiddenStates(
689
690
691
692
693
                    hidden_states, seq_group_meta_with_hidden)
            elif self.previous_hidden_states and len(
                    seq_group_meta_with_hidden):
                self.previous_hidden_states.update(hidden_states,
                                                   seq_group_meta_with_hidden)
694
695
696
697
698
699
700
701

        if not skip_proposer:
            # We prepare the prefill hidden states here so that there no
            # additional complexity in worker for spec_decode vs non_spec_decode
            # flow and execute_model doesn't need additional modifications.
            execute_model_req.previous_hidden_states = \
                prepare_prefill_hidden_states(
                    sampler_output.prefill_hidden_states)
702
703
704
            for i in range(self._num_spec_prefill_steps):
                execute_model_req.spec_step_idx = i
                self.proposer_worker.execute_model(execute_model_req)
705

706
707
708
        sampler_output_to_return = (self._serialize_sampler_output_no_logprobs(
            execute_model_req=execute_model_req, sampler_output=sampler_output)
                                    if self._disable_logprobs else
709
                                    [sampler_output])
710

711
712
        # Clear device tensors from sampler output. This reduces communication
        # overhead when the engine runs in a different process than the workers.
713
714
        sampler_output.sampled_token_probs = None
        sampler_output.sampled_token_ids = None
715
        sampler_output.logprobs = None
716
        return sampler_output_to_return
717

718
    def _run_non_driver_rank(self) -> bool:
719
720
721
        """Run proposer and verifier model in non-driver workers. This is used
        for both speculation cases (num_lookahead_slots>0) and non-speculation
        cases (e.g. prefill).
722

723
        Returns True if there are remaining sequences to process.
724
        """
725
726
727
728
729
730
        assert self.rank != self._driver_rank

        data = broadcast_tensor_dict(src=self._driver_rank)
        if not data:
            return False
        num_lookahead_slots = data["num_lookahead_slots"]
731

732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
        # In case of prefill, scorer_worker has to be run before proposer so
        # that the hidden states can be propagated to proposer when needed.
        if data["no_spec"]:
            self.scorer_worker.execute_model()

        if not data["disable_all_speculation"]:
            # Even if num_lookahead_slots is zero, we want to run the
            # proposer model as it may have KV.
            #
            # We run the proposer once per lookahead slot. In the future we
            # should delegate how many times it runs to the proposer.
            for _ in range(max(num_lookahead_slots, 1)):
                self.proposer_worker.execute_model()

        if not data["no_spec"]:
            self.scorer_worker.execute_model()
748
749
            if data["run_spec_proposer_for_prefill"]:
                self.proposer_worker.execute_model()
750

751
        return True
752

753
754
    @nvtx_range("spec_decode_worker._run_speculative_decoding_step")
    def _run_speculative_decoding_step(
755
756
            self, execute_model_req: ExecuteModelRequest,
            num_lookahead_slots: int) -> List[SamplerOutput]:
757
758
759
760
761
        """Execute a single step of speculative decoding.

        This invokes the proposer worker to get k speculative tokens for each
        sequence, then scores each speculative token using the scoring worker.

762
763
764
765
        When `enable_chunked_prefill` is set, scorer will batch decodes and 
        prefills, while proposer will sync its KV-cache by running an extra
        forward on prefills.

766
767
768
        Returns a list of SamplerOutput, each containing a single token per
        sequence.
        """
769
770
        # With prefill chunking, expect requests to have prompts first
        # so that backend gets prefill|decode.
771
        assert num_lookahead_slots == execute_model_req.num_lookahead_slots
772

773
774
775
776
        # Pass last hidden states from target model to proposer
        execute_model_req.previous_hidden_states = self.previous_hidden_states
        self.previous_hidden_states = None

777
778
779
780
        with Timer() as proposal_timer:
            # Generate proposals using draft worker.
            proposals = self.proposer_worker.get_spec_proposals(
                execute_model_req, self._seq_with_bonus_token_in_last_step)
781

782
783
784
785
786
        if not self._allow_zero_draft_token_step and proposals.no_proposals:
            #TODO: Fix it #5814
            raise RuntimeError("Cannot handle cases where distributed draft "
                               "workers generate no tokens")

787
788
        execute_model_req.previous_hidden_states = None

789
790
791
792
793
794
        with Timer() as scoring_timer:
            proposal_scores = self.scorer.score_proposals(
                execute_model_req,
                proposals,
            )

795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
        _, (non_spec_seqs, non_spec_indices) = split_batch_by_proposal_len(
            execute_model_req.seq_group_metadata_list, proposals.proposal_lens)
        # With prefill chunking enabled, `non_spec_seqs` contains prefills too:
        # discard decodes that have already been processed by proposer.
        non_spec_indices = [
            idx for idx in non_spec_indices
            if execute_model_req.seq_group_metadata_list[idx].is_prompt
        ]
        if len(non_spec_indices):
            all_hidden_states = proposal_scores.hidden_states
            if all_hidden_states is not None:
                prefill_hidden_states = all_hidden_states[non_spec_indices]
                execute_model_req.previous_hidden_states = \
                    prepare_prefill_hidden_states(prefill_hidden_states)
            # Sync proposer KV cache for prefills.
            prefill_req = execute_model_req.clone(non_spec_seqs)
811
            # TODO avoid sampling here?
812
813
            self.proposer_worker.execute_model(prefill_req)

814
815
816
817
818
819
820
821
        with Timer() as verification_timer:
            accepted_token_ids, target_logprobs = self._verify_tokens(
                execute_model_req.seq_group_metadata_list, proposal_scores,
                proposals, execute_model_req.num_lookahead_slots)

        stage_times = (proposal_timer.elapsed_time_ms / num_lookahead_slots,
                       scoring_timer.elapsed_time_ms,
                       verification_timer.elapsed_time_ms)
822

823
        return self._create_output_sampler_list(
824
            execute_model_req.seq_group_metadata_list,
825
826
            accepted_token_ids,
            target_logprobs=target_logprobs,
827
828
            prompt_logprobs=proposal_scores.prompt_logprobs
            if not self._disable_logprobs else None,
829
830
            k=execute_model_req.num_lookahead_slots,
            stage_times=stage_times)
831
832
833
834
835
836
837
838

    @nvtx_range("spec_decode_worker._verify_tokens")
    def _verify_tokens(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        proposal_scores: SpeculativeScores,
        proposals: SpeculativeProposals,
        max_proposal_len: int,
839
    ) -> Tuple[torch.Tensor, torch.Tensor]:
840
841
        """Determine which speculative tokens are accepted using the
        probabilities of each token according to the proposer and scorer models.
842
843
844

        Returns a tuple of Tensors, one for the accepted token ids and one for
        the logprobs according to the scoring model.
845
846
847
848
849
850
851
        """
        proposal_lens_list = proposals.proposal_lens.tolist()

        # vLLM currently only supports proposal lens equal to zero or the batch
        # proposal len. This adds some complexity (splitting the batch into spec
        # and non spec sequences) and should be removed in the future. It can be
        # done by supporting per-sequence proposal lens.
852
853
        (_, spec_indices), (_, non_spec_indices) = split_batch_by_proposal_len(
            seq_group_metadata_list, proposal_lens_list)
854
855
        original_indices = spec_indices + non_spec_indices

856
857
        # Get probabilities of target model, including bonus tokens.
        proposal_verifier_probs = proposal_scores.probs[spec_indices]
858
859

        # Get non-speculative sampled tokens from target model.
860
861
        non_spec_token_ids = proposal_scores.token_ids[non_spec_indices]

862
863
864
865
866
867
868
869
870
        # Get bonus tokens from target model.
        bonus_token_ids = proposal_scores.token_ids[spec_indices, -1:]

        # Get probabilities according to proposal method.
        proposal_probs = proposals.proposal_probs[spec_indices]

        # Get proposed tokens.
        proposal_token_ids = proposals.proposal_token_ids[spec_indices]

871
        # Sampler arguments
872
873
874
875
876
877
878
879
        sampler_extra_kwargs: Dict[str, Any] = {}
        if self.generators and isinstance(self.spec_decode_sampler,
                                          SpecDecodeStochasticBaseSampler):
            sampler_extra_kwargs["seeded_seqs"] = {
                idx: self.generators[sgm.request_id]
                for idx, sgm in enumerate(seq_group_metadata_list)
                if sgm.sampling_params.seed is not None
            }
880

881
        accepted_token_ids = self.spec_decode_sampler(
882
            target_with_bonus_probs=proposal_verifier_probs,
883
884
885
            bonus_token_ids=bonus_token_ids,
            draft_probs=proposal_probs,
            draft_token_ids=proposal_token_ids,
886
            **sampler_extra_kwargs,
887
888
889
890
891
892
893
894
        )
        # Append output tokens from non-speculative sequences to
        # the accepted token ids tensor.
        non_spec_token_ids = non_spec_token_ids.expand(-1, max_proposal_len +
                                                       1).clone()
        non_spec_token_ids[:, 1:] = -1
        accepted_token_ids = torch.cat(
            [accepted_token_ids, non_spec_token_ids])
895
        logprobs = proposal_scores.logprobs
896
897
898
899
        # Rearrange so that results are in the order of the original seq group
        # metadata.
        accepted_token_ids[original_indices] = accepted_token_ids.clone()

900
        # B x K+1 x D
901
902
        hidden_states = proposal_scores.hidden_states
        if hidden_states is not None:
903
904
905
906
907
            # Only get terminal hidden states for next step
            terminal_metadata = [
                sg for sg in seq_group_metadata_list if sg.do_sample
            ]

908
            # Contract hidden states based on accepted tokens
909
            hs_size = hidden_states.shape[-1]
910
            accepted_index = accepted_token_ids + 1  # Convert -1 to 0
911
912
            accepted_index = accepted_index.count_nonzero(dim=1).add_(-1)  # b
            # Drop non-terminal prefill chunks hidden states.
913
914
915
916
            hidden_states = hidden_states[accepted_index !=
                                          VLLM_INVALID_TOKEN_ID]
            accepted_index = accepted_index[accepted_index !=
                                            VLLM_INVALID_TOKEN_ID]
917
918
919
920
            assert len(accepted_index) == hidden_states.shape[0] == len(
                terminal_metadata)
            index = accepted_index[:, None, None].expand(-1, 1,
                                                         hs_size)  # b x 1 x d
921
            second_last_token_hidden_states = hidden_states[:, -2]  # b x d
922
923
            hidden_states = hidden_states.gather(1, index).squeeze(1)  # b x d
            # Store hidden states from target model for subsequent decode step
924
            self.previous_hidden_states = HiddenStates(
925
                hidden_states, terminal_metadata,
926
                second_last_token_hidden_states)
927
        return accepted_token_ids, logprobs
928
929
930
931
932

    def _create_output_sampler_list(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        accepted_token_ids: torch.Tensor,  # shape: [batch_size, k+1]
933
        target_logprobs: torch.Tensor,  # shape: [batch_size, k+1, vocab_size]
934
935
        prompt_logprobs: Optional[
            torch.Tensor],  # shape: [nprompt_tokens, vocab_size]
936
        k: int,
937
        stage_times: Tuple[float, float, float],
938
939
940
941
942
943
    ) -> List[SamplerOutput]:
        """Given the accepted token ids, create a list of SamplerOutput.

        The output is padded with -1 tokens such that each sequence has
        the same number of outputs.
        """
944
945
        batch_size, num_steps = accepted_token_ids.shape
        accepted_token_ids_by_step = accepted_token_ids.transpose(0, 1)
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
        if self._disable_logprobs:
            # We are skipping the logprobs. Hence don't serialize the
            # logprobs related tensors from the GPU. Instead create
            # empty/dummy lists.
            (accepted_token_id_ranks_by_step,
            accepted_token_id_logprobs_by_step,
            topk_logprobs_by_step, topk_indices_by_step) =\
            self._create_dummy_logprob_lists(
                batch_size, num_steps,
                self.scorer_worker.model_config.max_logprobs)
        else:
            # Organize input tensors by step instead of by sequence.
            target_logprobs_by_step = target_logprobs.transpose(0, 1)
            # Serialize all tensors into Python lists.
            (accepted_token_id_ranks_by_step,
            accepted_token_id_logprobs_by_step,
            topk_logprobs_by_step, topk_indices_by_step) =\
                self._create_logprob_lists_from_tensors(
                    target_logprobs_by_step, accepted_token_ids_by_step,
                    self.scorer_worker.model_config.max_logprobs)
966
967
968

        # Get the sequence ids and num_logprobs (sampling parameter) in the
        # batch.
969
970
971
        seq_ids, request_ids_seq_ids_mapping = get_all_seq_ids_and_request_ids(
            seq_group_metadata_list)

972
973
        num_logprobs_per_seq = get_all_num_logprobs(seq_group_metadata_list)

974
        # Serialize tensor to CPU Python list.
975
976
977
        accepted_token_ids_by_step = accepted_token_ids_by_step.tolist()

        # Construct the output on a per-step, per-sequence basis.
978
        # Non-terminal prefill chunks will end up here as rows with just -1s
979
980
        # i.e mixed-batch [[-1, 1576], [-1, 29884], [-1, -1], [-1, -1]] while
        # terminal chunks will only have one generated token at time 0.
981
        sampler_output_list: List[SamplerOutput] = []
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047

        # Prefills are not multi-step (return at most 1 token), in order to
        # avoid padding or repetition to fit decodes, we separate them.
        for i, sg in enumerate(seq_group_metadata_list):
            if not sg.is_prompt:
                # Requests are ordered as prefills|decodes=>no more prefills.
                break
            num_logprobs = num_logprobs_per_seq[i]
            seq_kwargs = dict(token_id=-1,
                              token_id_logprob_rank=0,
                              token_id_logprob=-float('inf'),
                              topk_token_ids=[-1] * num_logprobs,
                              topk_logprobs=[-float('inf')] * num_logprobs,
                              seq_id=seq_ids[i])
            # Terminal chunk, has token.
            if sg.do_sample:
                seq_kwargs.update(
                    dict(
                        token_id=accepted_token_ids[i][0].item(),
                        token_id_logprob_rank=accepted_token_id_ranks_by_step[
                            0][i],
                        token_id_logprob=accepted_token_id_logprobs_by_step[0]
                        [i],
                        topk_token_ids=topk_indices_by_step[0][i]
                        [:num_logprobs],
                        # output only so step is 0
                        topk_logprobs=topk_logprobs_by_step[0][i]
                        [:num_logprobs],
                    ))
            needs_plogs = (sg.sampling_params.prompt_logprobs
                           and sg.sampling_params.prompt_logprobs > 0)
            plogs = None
            if prompt_logprobs is not None:
                # Even non-terminal prompt chunks can have logprobs here.
                plogs = prompt_logprobs[i]
            elif needs_plogs:
                # Prompt logprobs are requested but `_disable_logprobs` is set.
                seq_data = next(iter(sg.seq_data.values()))
                # Get only the tokens in this chunk!
                prompt_token_ids = seq_data.get_prompt_token_ids()
                prompt_token_ids = prompt_token_ids[
                    seq_data.
                    _num_computed_tokens:seq_data._num_computed_tokens +
                    sg.token_chunk_size]

                is_first_chunk = seq_data._num_computed_tokens == 0
                # There's no prob generated for the first token in a sequence.
                if is_first_chunk:
                    prompt_token_ids = prompt_token_ids[1:]
                plogs = [
                    create_logprobs_output(
                        token_id=p_token_id,
                        token_id_logprob_rank=-1,
                        token_id_logprob=0.0,
                        topk_token_ids=[],
                        topk_logprobs=[],
                    ) for p_token_id in prompt_token_ids
                ]
            seq_kwargs.update(dict(prompt_logprobs=plogs))

            sampler_output_list.append(
                SamplerOutput(
                    outputs=[create_sequence_group_output(
                        **seq_kwargs)]))  # type: ignore

        # Decodes, create one SamplerOutput per-step (at most K+1).
1048
        for step_index in range(num_steps):
1049
1050
1051
1052
            if all(token_id == -1 for sg, token_id in zip(
                    seq_group_metadata_list,
                    accepted_token_ids_by_step[step_index])
                   if not sg.is_prompt):
1053
1054
                break

1055
            step_output_token_ids: List[CompletionSequenceGroupOutput] = []
1056
            for sequence_index in range(batch_size):
1057
1058
1059
1060
1061
                seq_meta = seq_group_metadata_list[sequence_index]
                # Prompts already processed above.
                if seq_meta.is_prompt:
                    continue

1062
1063
                # Each sequence may have a different num_logprobs; retrieve it.
                num_logprobs = num_logprobs_per_seq[sequence_index]
1064
                step_output_token_ids.append(
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
                    create_sequence_group_output(
                        token_id=accepted_token_ids_by_step[step_index]
                        [sequence_index],
                        token_id_logprob_rank=accepted_token_id_ranks_by_step[
                            step_index][sequence_index],
                        token_id_logprob=accepted_token_id_logprobs_by_step[
                            step_index][sequence_index],
                        seq_id=seq_ids[sequence_index],
                        topk_token_ids=topk_indices_by_step[step_index]
                        [sequence_index][:num_logprobs],
                        topk_logprobs=topk_logprobs_by_step[step_index]
                        [sequence_index][:num_logprobs],
1077
1078
1079
1080
                    ))
            sampler_output_list.append(
                SamplerOutput(outputs=step_output_token_ids))

1081
1082
1083
1084
1085
        # Populate the data structures needed to keep track of sequences with
        # bonus tokens.
        self._track_sequences_with_bonus_tokens(seq_ids,
                                                request_ids_seq_ids_mapping,
                                                accepted_token_ids_by_step)
1086
1087
        maybe_rejsample_metrics = (
            self._metrics.maybe_collect_rejsample_metrics(k))
1088
1089
1090
        if maybe_rejsample_metrics is not None:
            sampler_output_list[
                0].spec_decode_worker_metrics = maybe_rejsample_metrics
1091
1092
1093
1094
1095

            # Log time spent in each stage periodically.
            # This is periodic because the rejection sampler emits metrics
            # periodically.
            self._maybe_log_stage_times(*stage_times)
1096
1097
        # First `n_prefills` entries will contain prefills SamplerOutput when
        # chunked prefill is enabled, the rest is decodes in multi-step format.
1098
1099
        return sampler_output_list

1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
    def _maybe_log_stage_times(self, average_time_per_proposal_tok_ms: float,
                               scoring_time_ms: float,
                               verification_time_ms: float) -> None:
        """Log the speculative stage times. If stat logging is disabled, do
        nothing.
        """
        if self._disable_log_stats:
            return

        logger.info(
            "SpecDecodeWorker stage times: "
            "average_time_per_proposal_tok_ms=%.02f "
            "scoring_time_ms=%.02f verification_time_ms=%.02f",
            average_time_per_proposal_tok_ms, scoring_time_ms,
            verification_time_ms)

1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
    def _create_dummy_logprob_lists(
        self,
        batch_size: int,
        num_steps: int,
        num_top_k: int,
    ) -> Tuple[List[List[int]], List[List[float]],
               List[List[List[Optional[float]]]],
               List[List[List[Optional[int]]]]]:
        """
        Creates and returns four dummy lists representing token probabilities 
        and their ranks.

        This method initializes and returns:
            - The ranks of the accepted tokens, shaped (num_steps, batch_size)
            - The log probabilities of the accepted tokens,
              shaped (num_steps, batch_size)
            - The log probabilities of the top k tokens,
              shaped (num_steps, batch_size, num_top_k)
            - The token IDs of the top k tokens,
              shaped (num_steps, batch_size, num_top_k)

        Args:
            batch_size (int): The size of the batch.
            num_steps (int): The number of steps in the sequence.
            num_top_k (int): The number of top-k token log probabilities to
            return.
        
        Returns:
            A tuple containing four dummy lists as described above.
        """
        accepted_token_id_ranks_by_step = [[-1] * batch_size
                                           for _ in range(num_steps)]
        accepted_token_id_logprobs_by_step = [[0.0] * batch_size
                                              for _ in range(num_steps)]
        topk_logprobs_by_step: List[List[List[Optional[float]]]] = [[
            [None] * num_top_k for _ in range(batch_size)
        ] for _ in range(num_steps)]
        topk_indices_by_step: List[List[List[Optional[int]]]] = [[
            [None] * num_top_k for _ in range(batch_size)
        ] for _ in range(num_steps)]
        return (accepted_token_id_ranks_by_step,
                accepted_token_id_logprobs_by_step, topk_logprobs_by_step,
                topk_indices_by_step)

    def _create_logprob_lists_from_tensors(
        self,
        target_logprobs_by_step: torch.Tensor,
        accepted_token_ids_by_step: torch.Tensor,
        num_top_k: int,
    ) -> Tuple[List[List[int]], List[List[float]],
               List[List[List[Optional[float]]]],
               List[List[List[Optional[int]]]]]:
        """
        Creates and returns four lists representing token probabilities and
        their ranks.

        This method initializes and returns four lists containing:
            - The ranks of the accepted tokens, shaped (num_steps, batch_size)
            - The log probabilities of the accepted tokens,
              shaped (num_steps, batch_size)
            - The log probabilities of the top k tokens,
              shaped (num_steps, batch_size, num_top_k)
            - The token IDs of the top k tokens,
              shaped (num_steps, batch_size, num_top_k)

        Args:
            target_logprobs_by_step (torch.Tensor): Tensor representing the
            log probabilities of the target model,
            shaped (num_steps, batch_size, vocab_size)
            accepted_token_ids_by_step (torch.Tensor): Tensor representing
            the accepted  token_ids, shaped (num_steps, batch_size) 
            num_top_k (int): The number of top-k token log probabilities to
            return.
        
        Returns:
            A tuple containing the lists as described above.
        """
        # Serialize all tensors to CPU Python lists.
        # Get the logprobs/rank of the accepted tokens.
        (accepted_token_id_ranks_by_step_tensor,
         accepted_token_id_logprobs_by_step_tensor
         ) = get_sampled_token_logprobs(
             logprob_tensor=target_logprobs_by_step,
             sampled_token_ids=accepted_token_ids_by_step,
         )
        # Get the top-k logprobs (which may or may not include the
        # logprob of the accepted token).
        (topk_logprobs_by_step_tensor,
         topk_indices_by_step_tensor) = target_logprobs_by_step.topk(
             k=num_top_k,
             dim=-1,
         )
        accepted_token_id_ranks_by_step = (
            accepted_token_id_ranks_by_step_tensor.tolist())
        accepted_token_id_logprobs_by_step = (
            accepted_token_id_logprobs_by_step_tensor.tolist())
        topk_logprobs_by_step = topk_logprobs_by_step_tensor.tolist()
        topk_indices_by_step = topk_indices_by_step_tensor.tolist()
        return (accepted_token_id_ranks_by_step,
                accepted_token_id_logprobs_by_step, topk_logprobs_by_step,
                topk_indices_by_step)

1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
    def _track_finished_requests(self, execute_model_req: ExecuteModelRequest):
        """
        Removes the finished requests and their associated sequence ids from
        internal book keeping data structures.
        """
        for finished_request in execute_model_req.finished_requests_ids:
            for seq_id in self._request_id_seq_id_mapping[finished_request]:
                self._seq_with_bonus_token_in_last_step.discard(seq_id)
            del self._request_id_seq_id_mapping[finished_request]

    def _track_sequences_with_bonus_tokens(
            self, seq_ids: List[int],
            request_ids_seq_ids_mapping: Dict[str, Set[int]],
            accepted_token_ids_by_step: List[List[int]]):
        """
        Updates the internal data structures which keep track of sequences
        which have been assigned bonus tokens in their last forward pass.
        """
        for seq_index, seq_id in enumerate(seq_ids):
            last_token_id = accepted_token_ids_by_step[-1][seq_index]
            if last_token_id == -1:
                self._seq_with_bonus_token_in_last_step.discard(seq_id)
            else:
                self._seq_with_bonus_token_in_last_step.add(seq_id)
        for request_id, sequences in request_ids_seq_ids_mapping.items():
            self._request_id_seq_id_mapping[request_id].update(sequences)

1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
    @cached_property
    def _vocab_size(self) -> int:
        """Get the vocab size of the model and make sure it's consistent between
        draft and target workers.
        """
        vocab_sizes = [
            worker.vocab_size
            for worker in [self.proposer_worker, self.scorer_worker]
        ]
        assert all(vocab_sizes[0] == vocab_size for vocab_size in vocab_sizes)
        return vocab_sizes[0]

    @property
    def rank(self):
        return self.scorer_worker.rank

    @property
    def device(self):
        return self.scorer_worker.device

1265
1266
1267
1268
    @property
    def _driver_rank(self) -> int:
        return 0

1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
    def get_cache_block_size_bytes(self):
        """Return the size of a cache block in bytes.
        
        This function is only used to compose workers within a SpecDecodeWorker.
        We leave composing a SpecDecodeWorker within a SpecDecodeWorker
        undefined for now, although it could be implemented in the future.
        See https://arxiv.org/abs/2308.04623.
        """
        raise NotImplementedError

1279
    def start_profile(self):
1280
        if isinstance(self.scorer_worker, WorkerBase):
1281
1282
1283
            self.scorer_worker.start_profile()

    def stop_profile(self):
1284
        if isinstance(self.scorer_worker, WorkerBase):
1285
1286
            self.scorer_worker.stop_profile()

1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308

def split_num_cache_blocks_evenly(scorer_cache_block_size_bytes: int,
                                  proposer_cache_block_size_bytes: int,
                                  total_num_gpu_blocks: int) -> int:
    """Given total_num_gpu_blocks, the number of GPU blocks that could be
    allocate to the target model, this function calculates how many blocks
    should be given to the draft and target model.

    Note that usually the block size, in bytes, of each model is different,
    as it's a function of number of KV/layer, number of heads, and hidden
    dimension size.

    Since the target and draft models allocate the same number of blocks, we
    simply calculate the number of blocks where if allocated by both models,
    the total memory usage from KV cache is no larger than the number of
    blocks allocatable by the target model alone.
    """
    new_num_gpu_blocks = int(
        total_num_gpu_blocks * scorer_cache_block_size_bytes /
        (proposer_cache_block_size_bytes + scorer_cache_block_size_bytes))

    return new_num_gpu_blocks
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320


def prepare_prefill_hidden_states(
        prefill_hidden_states: torch.Tensor) -> HiddenStates:
    # For prefill step in proposer, we run the model for N-1 tokens
    # because Nth token will be processed in the first decode step. For
    # N-1 tokens, the input should be 0:N-1 hidden states which should
    # be concatanated with 1:N token (since output of scorer has to be
    # the input for proposer). Therefore, we shift the hidden states to
    # align n-1th hidden state with nth token.
    return HiddenStates(prefill_hidden_states.roll(
        shifts=1, dims=0)) if prefill_hidden_states is not None else None