spec_decode_worker.py 49.3 KB
Newer Older
1
import copy
2
from collections import defaultdict
3
from functools import cached_property
4
from typing import Any, Dict, List, Optional, Set, Tuple, Type
5
6
7

import torch

8
from vllm.config import ParallelConfig, SpeculativeConfig, VllmConfig
9
from vllm.distributed.communication_op import broadcast_tensor_dict
10
from vllm.logger import init_logger
11
from vllm.model_executor.layers.rejection_sampler import RejectionSampler
12
from vllm.model_executor.layers.sampler import SamplerOutput
13
from vllm.model_executor.layers.spec_decode_base_sampler import (
14
    SpecDecodeBaseSampler, SpecDecodeStochasticBaseSampler)
15
16
from vllm.model_executor.layers.typical_acceptance_sampler import (
    TypicalAcceptanceSampler)
17
18
from vllm.sequence import (VLLM_INVALID_TOKEN_ID,
                           CompletionSequenceGroupOutput, ExecuteModelRequest,
19
                           HiddenStates, SequenceGroupMetadata,
20
                           get_all_seq_ids_and_request_ids)
21
from vllm.spec_decode.batch_expansion import BatchExpansionTop1Scorer
22
from vllm.spec_decode.draft_model_runner import TP1DraftModelRunner
23
24
from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                         SpeculativeScorer, SpeculativeScores)
25
from vllm.spec_decode.medusa_worker import MedusaWorker
26
from vllm.spec_decode.metrics import AsyncMetricsCollector
27
from vllm.spec_decode.mlp_speculator_worker import MLPSpeculatorWorker
28
from vllm.spec_decode.mqa_scorer import MQAScorer
29
from vllm.spec_decode.multi_step_worker import MultiStepWorker
30
from vllm.spec_decode.ngram_worker import NGramWorker
31
from vllm.spec_decode.proposer_worker_base import ProposerWorkerBase
32
from vllm.spec_decode.smaller_tp_proposer_worker import SmallerTpProposerWorker
33
from vllm.spec_decode.target_model_runner import TargetModelRunner
34
35
from vllm.spec_decode.util import (Timer, create_logprobs_output,
                                   create_sequence_group_output,
36
                                   get_all_num_logprobs,
37
                                   get_sampled_token_logprobs, nvtx_range,
38
                                   split_batch_by_proposal_len)
39
from vllm.worker.worker import Worker
40
41
42
from vllm.worker.worker_base import LoraNotSupportedWorkerBase, WorkerBase

logger = init_logger(__name__)
43
44


45
46
47
48
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.
    """
49
50
    vllm_config: VllmConfig = kwargs.get("vllm_config")
    speculative_config: SpeculativeConfig = vllm_config.speculative_config
51
52
    assert speculative_config is not None

53
54
55
    draft_worker_kwargs = kwargs.copy()

    kwargs["model_runner_cls"] = TargetModelRunner
56
    target_worker = Worker(*args, **kwargs)
57
58
59
60
    # 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
61

62
63
    draft_worker_config = copy.deepcopy(vllm_config)
    draft_worker_config.model_config = speculative_config.draft_model_config
64
65
66
67
    draft_worker_config.quant_config = VllmConfig._get_quantization_config(
        draft_worker_config.model_config,
        vllm_config.load_config,
    )
68
69
70
    draft_worker_config.parallel_config = speculative_config.draft_parallel_config  # noqa
    # TODO allow draft-model specific load config.

71
72
    # Override draft-model specific worker args.
    draft_worker_kwargs.update(
73
        vllm_config=draft_worker_config,
74
75
76
77
78
79
80
        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,
81
        disable_mqa_scorer=speculative_config.speculative_disable_mqa_scorer,
82
83
        disable_by_batch_size=speculative_config.
        speculative_disable_by_batch_size,
84
85
86
87
88
        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.
89
        typical_acceptance_sampler_posterior_alpha,
90
91
92
        disable_logprobs=speculative_config.disable_logprobs,
        disable_log_stats=speculative_config.disable_log_stats,
    )
93
94
95
96

    return spec_decode_worker


97
98
# Reminder: Please update docs/source/serving/compatibility_matrix.rst
# If the feature combo become valid
99
class SpecDecodeWorker(LoraNotSupportedWorkerBase):
100
101
102
103
104
105
106
107
    """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.

108
    See https://github.com/vllm-project/vllm/pull/2188 and
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
    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.
    """

125
    @classmethod
126
127
    def create_worker(
        cls,
128
        scorer_worker: Worker,
129
        draft_worker_kwargs: Dict[str, Any],
130
        disable_mqa_scorer: bool,
131
        disable_by_batch_size: Optional[int],
132
133
134
        draft_token_acceptance_method: str,
        typical_acceptance_sampler_posterior_threshold: float,
        typical_acceptance_sampler_posterior_alpha: float,
135
        disable_logprobs: bool,
136
        disable_log_stats: bool,
137
138
    ) -> "SpecDecodeWorker":

139
        allow_zero_draft_token_step = True
140
141
142
143
        ngram_prompt_lookup_max = (
            draft_worker_kwargs.pop("ngram_prompt_lookup_max"))
        ngram_prompt_lookup_min = (
            draft_worker_kwargs.pop("ngram_prompt_lookup_min"))
144
145
146
        draft_model_config = draft_worker_kwargs["vllm_config"].model_config
        draft_parallel_config: ParallelConfig = draft_worker_kwargs[
            'vllm_config'].parallel_config
147
148
149
150
151
        if ngram_prompt_lookup_max > 0:
            proposer_worker = NGramWorker(**draft_worker_kwargs)
            proposer_worker.set_ngram_window_size(ngram_prompt_lookup_min,
                                                  ngram_prompt_lookup_max)
        else:
152
153
154
            draft_tp = draft_parallel_config.tensor_parallel_size
            target_tp = scorer_worker.parallel_config.tensor_parallel_size

155
            if draft_model_config.hf_config.model_type == "mlp_speculator":
156
                proposer_worker = MLPSpeculatorWorker(**draft_worker_kwargs)
157
            elif draft_model_config.hf_config.model_type == "medusa":
158
                proposer_worker = MedusaWorker(**draft_worker_kwargs)
159
160
161
162
            else:
                if draft_tp == 1:
                    draft_worker_kwargs[
                        "model_runner_cls"] = TP1DraftModelRunner
163
                else:
164
                    if draft_model_config.hf_config.model_type == "eagle":
165
166
167
                        raise NotImplementedError(
                            "EAGLE does not support TP > 1 yet")

168
                    allow_zero_draft_token_step = False
169
170
                proposer_worker = MultiStepWorker(**draft_worker_kwargs)

171
172
            proposer_worker = SmallerTpProposerWorker.maybe_wrap_worker(
                proposer_worker, draft_tp, target_tp)
173

174
175
176
        logger.info("Configuring SpecDecodeWorker with proposer=%s",
                    type(proposer_worker))

177
178
        spec_decode_sampler: SpecDecodeBaseSampler = None
        if draft_token_acceptance_method == "rejection_sampler":
179
            spec_decode_sampler = RejectionSampler()
180
181
182
183
184
185
        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,
            )
186
187
188
189
190
191
        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(
192
            ) != "FLASH_ATTN":
193
194
195
196
197
                disable_mqa_scorer = True
                logger.info(
                    "[Speculative Decoding] Disabling MQA scorer as the "
                    "MQA is only available with flash attn backend.")

198
199
            if draft_model_config and \
                draft_model_config.max_model_len < \
200
201
202
203
204
205
206
207
208
209
210
211
                    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.")
212

213
214
215
        return SpecDecodeWorker(
            proposer_worker,
            scorer_worker,
216
            disable_mqa_scorer=disable_mqa_scorer,
217
            disable_logprobs=disable_logprobs,
218
            disable_log_stats=disable_log_stats,
219
220
221
            disable_by_batch_size=disable_by_batch_size,
            spec_decode_sampler=spec_decode_sampler,
            allow_zero_draft_token_step=allow_zero_draft_token_step)
222

223
224
    def __init__(
        self,
225
        proposer_worker: ProposerWorkerBase,
226
        scorer_worker: WorkerBase,
227
        spec_decode_sampler: SpecDecodeBaseSampler,
228
        disable_mqa_scorer: bool = False,
229
230
        disable_logprobs: bool = False,
        disable_log_stats: bool = False,
231
        metrics_collector: Optional[AsyncMetricsCollector] = None,
232
        disable_by_batch_size: Optional[int] = None,
233
        allow_zero_draft_token_step: Optional[bool] = True,
234
235
236
237
238
239
240
241
242
243
    ):
        """
        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.
244
245
246
247
248
249
            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.
250
251
            disable_mqa_scorer: If set to True, disable the MQA scorer and use
                the BatchExpansionTop1Scorer instead.
252
253
254
            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.
255
256
            disable_log_stats: If set to True, disable periodic printing of
                speculative stage times.
257
258
            disable_by_batch_size: If the batch size is larger than this,
                disable speculative decoding for new incoming requests.
259
260
            metrics_collector: Helper class for collecting metrics; can be set
                for testing purposes.
261
262
263
            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)
264
265
266
        """
        self.proposer_worker = proposer_worker
        self.scorer_worker = scorer_worker
267
268
269
        scorer_runner = getattr(self.scorer_worker, "model_runner", None)
        self.generators = scorer_runner.get_generators(
        ) if scorer_runner else None
270
        self.disable_by_batch_size = disable_by_batch_size or float("inf")
271
        self.spec_decode_sampler = spec_decode_sampler
272
        self._allow_zero_draft_token_step = allow_zero_draft_token_step
273
        self._metrics = AsyncMetricsCollector(
274
            self.spec_decode_sampler
275
        ) if metrics_collector is None else metrics_collector
276
277
278
279
280
281
282
283
284
        # 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.

285
286
        self.probs_dtype = self.spec_decode_sampler.probs_dtype
        self.token_id_dtype = self.spec_decode_sampler.token_id_dtype
287
        # Lazy initialization.
288
        self.scorer: SpeculativeScorer
289
        self.disable_mqa_scorer = disable_mqa_scorer
290

291
292
293
        # Hidden states from target model to pass to proposer
        # in the subsequent step.
        self.previous_hidden_states: Optional[HiddenStates] = None
294
        self._disable_logprobs = disable_logprobs
295
        self._disable_log_stats = disable_log_stats
296

297
    def init_device(self) -> None:
298
299
300
301
        """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.
302
303
        self.scorer_worker.init_device()
        self.proposer_worker.init_device()
304

305
306
307
308
        # NOTE(cade): load_model is not part of the WorkerBase interface.
        self.scorer_worker.load_model()
        self.proposer_worker.load_model()

309
        self._metrics.init_gpu_tensors(self.rank)
310
311
        self.spec_decode_sampler.init_gpu_tensors(self.rank)

312
313
314
315
316
317
318
319
320
321
322
323
324
        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)
325

326
327
        self._configure_model_sampler_for_spec_decode()

328
329
330
    def load_model(self, *args, **kwargs):
        pass

331
332
333
    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,
334
        which significantly reduces overhead of sampling during verification.
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351

        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
352
353
        (self.scorer_worker.model_runner.model.sampler.
         should_modify_greedy_probs_inplace) = True
354
        self.proposer_worker.set_include_gpu_probs_tensor()
355
        self.proposer_worker.set_should_modify_greedy_probs_inplace()
356

357
    def determine_num_available_blocks(self) -> Tuple[int, int]:
358
359
360
361
362
363
364
365
        """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 = (
366
            self.scorer_worker.determine_num_available_blocks())
367

368
        scorer_cache_block_size_bytes = (
369
            self.scorer_worker.get_cache_block_size_bytes())
370
        proposer_cache_block_size_bytes = (
371
            self.proposer_worker.get_cache_block_size_bytes())
372
373
374
375
376
377

        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

378
379
    def initialize_cache(self, num_gpu_blocks: int,
                         num_cpu_blocks: int) -> None:
380
381
        """Initialize the cache engine of the scorer and proposer workers.
        """
382
383
384
385
        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)
386
387
388

    @torch.inference_mode()
    def execute_model(
389
390
391
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> List[SamplerOutput]:
392
393
        """Perform speculative decoding on the input batch.
        """
394
395
396
        if self.rank != self._driver_rank:
            self._run_non_driver_rank()
            return []
397

398
399
400
401
402
403
404
        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)
405
            return []
406

407
        self._track_finished_requests(execute_model_req)
408
409
410
411
        disable_all_speculation = self._should_disable_all_speculation(
            execute_model_req)
        num_lookahead_slots = execute_model_req.num_lookahead_slots

412
413
414
415
416
        # 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.
417
418
        # 3. No request: There are no requests in the batch, or
        #    none of the requests in the batch have spec decoding enabled.
419
420
        # In any of these cases, the proposer and scorer workers
        # are called normally.
421
422
423
        no_spec = num_lookahead_slots == 0 or disable_all_speculation or all(
            sgm.num_speculative_tokens == 0
            for sgm in execute_model_req.seq_group_metadata_list)
424

425
426
427
428
429
        # 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
430
        # communication to inform them.
431
432
433
434
435
436
437

        # 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.
438
439
        broadcast_dict = dict(
            num_lookahead_slots=num_lookahead_slots,
440
            no_spec=no_spec,
441
442
443
444
445
446
447
448
449
450
            disable_all_speculation=disable_all_speculation,
        )
        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)

451
        if no_spec:
452
453
454
455
456
457
458
459
460
461
462
463
            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

464
465
    def _should_disable_all_speculation(
            self, execute_model_req: ExecuteModelRequest) -> bool:
466
467
        # When the batch size is too large, disable speculative decoding
        # to stop trading off throughput for latency.
468
469
        return (execute_model_req.running_queue_size >=
                self.disable_by_batch_size)
470
471
472
473
474
475
476
477
478
479
480
481
482
483

    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
484

485
486
487
488
    def _serialize_sampler_output_no_logprobs(
            self, execute_model_req: ExecuteModelRequest,
            sampler_output: SamplerOutput) -> SamplerOutput:
        """
489
490
        Creates and returns a `SamplerOutput` with only the token IDs being
        serialized to CPU and populated in `CompletionSequenceGroupOutput`.
491
492
493
494
495
496
497
498
499
500
501
        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 
502
503
            `CompletionSequenceGroupOutput` objects with only token IDs
            populated.
504
        """
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
        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()

        seq_data_entries = (
            (seq_id, seq_data) for sg in \
            execute_model_req.seq_group_metadata_list \
            for seq_id, seq_data in sg.seq_data.items()
        )
522
523
        completion_seq_group_output_list: List[
            CompletionSequenceGroupOutput] = []
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
        for index, ((seq_id, seq_data), needs_prompt_logprobs) in \
            enumerate(zip(seq_data_entries, seq_output_prompt_logprobs)):
            if needs_prompt_logprobs:
                prompt_token_ids = seq_data.get_prompt_token_ids()
                prompt_logprobs = [
                    create_logprobs_output(
                        token_id=p_token_id,
                        token_id_logprob_rank=-1,
                        token_id_logprob=0.0,
                        topk_token_ids=[],
                        topk_logprobs=[],
                    )
                    # no prompt logprobs for the first token
                    for p_token_id in prompt_token_ids[1:]
                ]
            else:
                prompt_logprobs = None

542
543
544
545
546
547
548
549
            completion_seq_group_output_list.append(
                create_sequence_group_output(
                    token_id=sampled_token_ids_list[index][0],
                    token_id_logprob_rank=-1,
                    token_id_logprob=0.0,
                    seq_id=seq_id,
                    topk_token_ids=[],
                    topk_logprobs=[],
550
                    prompt_logprobs=prompt_logprobs))
551
552
        return SamplerOutput(outputs=completion_seq_group_output_list)

553
    @nvtx_range("spec_decode_worker._run_no_spec")
554
555
    def _run_no_spec(self, execute_model_req: ExecuteModelRequest,
                     skip_proposer: bool) -> List[SamplerOutput]:
556
557
        """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
558
559
560
        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.
561
562
        """

563
        sampler_output = self.scorer_worker.execute_model(execute_model_req)
564
565
        assert len(sampler_output) == 1
        sampler_output = sampler_output[0]
566

567
568
569
        # Store hidden states from target model execution.
        hidden_states = sampler_output.hidden_states
        if hidden_states is not None:
570
571
572
573
574
575
            # remove hidden_states for prompt tokens
            if any(seq.is_prompt
                   for seq in execute_model_req.seq_group_metadata_list):
                hidden_states = hidden_states[
                    torch.where(sampler_output.sampled_token_ids -
                                VLLM_INVALID_TOKEN_ID)[0]]
576
577
            if self.previous_hidden_states is None:
                self.previous_hidden_states = HiddenStates(
578
                    hidden_states, execute_model_req.seq_group_metadata_list)
579
580
            else:
                self.previous_hidden_states.update(
581
582
583
584
585
586
587
588
589
590
591
                    hidden_states, execute_model_req.seq_group_metadata_list)

        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)

            self.proposer_worker.execute_model(execute_model_req)
592

593
594
595
596
597
        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
                                    sampler_output)

598
599
        # Clear device tensors from sampler output. This reduces communication
        # overhead when the engine runs in a different process than the workers.
600
601
        sampler_output.sampled_token_probs = None
        sampler_output.sampled_token_ids = None
602
        sampler_output.logprobs = None
603
        return [sampler_output_to_return]
604

605
    def _run_non_driver_rank(self) -> bool:
606
607
608
        """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).
609

610
        Returns True if there are remaining sequences to process.
611
        """
612
613
614
615
616
617
        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"]
618

619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
        # 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()
635

636
        return True
637

638
639
    @nvtx_range("spec_decode_worker._run_speculative_decoding_step")
    def _run_speculative_decoding_step(
640
641
            self, execute_model_req: ExecuteModelRequest,
            num_lookahead_slots: int) -> List[SamplerOutput]:
642
643
644
645
646
647
648
649
        """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.

        Returns a list of SamplerOutput, each containing a single token per
        sequence.
        """
650
        assert num_lookahead_slots == execute_model_req.num_lookahead_slots
651

652
653
654
655
        # Pass last hidden states from target model to proposer
        execute_model_req.previous_hidden_states = self.previous_hidden_states
        self.previous_hidden_states = None

656
657
658
659
        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)
660

661
662
663
664
665
        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")

666
667
        execute_model_req.previous_hidden_states = None

668
669
670
671
672
673
674
675
676
677
678
679
680
681
        with Timer() as scoring_timer:
            proposal_scores = self.scorer.score_proposals(
                execute_model_req,
                proposals,
            )

        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)
682

683
        return self._create_output_sampler_list(
684
            execute_model_req.seq_group_metadata_list,
685
686
            accepted_token_ids,
            target_logprobs=target_logprobs,
687
688
            k=execute_model_req.num_lookahead_slots,
            stage_times=stage_times)
689
690
691
692
693
694
695
696

    @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,
697
    ) -> Tuple[torch.Tensor, torch.Tensor]:
698
699
        """Determine which speculative tokens are accepted using the
        probabilities of each token according to the proposer and scorer models.
700
701
702

        Returns a tuple of Tensors, one for the accepted token ids and one for
        the logprobs according to the scoring model.
703
704
705
706
707
708
709
        """
        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.
710
711
        (_, spec_indices), (_, non_spec_indices) = split_batch_by_proposal_len(
            seq_group_metadata_list, proposal_lens_list)
712
713
        original_indices = spec_indices + non_spec_indices

714
715
        # Get probabilities of target model, including bonus tokens.
        proposal_verifier_probs = proposal_scores.probs[spec_indices]
716
717

        # Get non-speculative sampled tokens from target model.
718
719
        non_spec_token_ids = proposal_scores.token_ids[non_spec_indices]

720
721
722
723
724
725
726
727
728
        # 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]

729
        # Sampler arguments
730
731
732
733
734
735
736
737
        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
            }
738

739
        accepted_token_ids = self.spec_decode_sampler(
740
            target_with_bonus_probs=proposal_verifier_probs,
741
742
743
            bonus_token_ids=bonus_token_ids,
            draft_probs=proposal_probs,
            draft_token_ids=proposal_token_ids,
744
            **sampler_extra_kwargs,
745
746
747
748
749
750
751
752
        )
        # 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])
753
        logprobs = proposal_scores.logprobs
754
755
756
757
        # Rearrange so that results are in the order of the original seq group
        # metadata.
        accepted_token_ids[original_indices] = accepted_token_ids.clone()

758
759
760
        hidden_states = proposal_scores.hidden_states
        if hidden_states is not None:
            # Contract hidden states based on accepted tokens
761
762
            hs_size = hidden_states.shape[-1]

763
764
765
            accepted_index = accepted_token_ids + 1  # Convert -1 to 0
            accepted_index = accepted_index.count_nonzero(dim=1).add_(-1)
            index = accepted_index[:, None, None].expand(-1, 1, hs_size)
766
            second_last_token_hidden_states = hidden_states[:, -2]  # b x d
767
768
            hidden_states = hidden_states.gather(1, index).squeeze(1)  # b x d
            # Store hidden states from target model for subsequent decode step
769
770
771
            self.previous_hidden_states = HiddenStates(
                hidden_states, seq_group_metadata_list,
                second_last_token_hidden_states)
772

773
        return accepted_token_ids, logprobs
774
775
776
777
778

    def _create_output_sampler_list(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        accepted_token_ids: torch.Tensor,  # shape: [batch_size, k+1]
779
        target_logprobs: torch.Tensor,  # shape: [batch_size, k+1, vocab_size]
780
        k: int,
781
        stage_times: Tuple[float, float, float],
782
783
784
785
786
787
    ) -> 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.
        """
788
789
        batch_size, num_steps = accepted_token_ids.shape
        accepted_token_ids_by_step = accepted_token_ids.transpose(0, 1)
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
        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)
810
811
812

        # Get the sequence ids and num_logprobs (sampling parameter) in the
        # batch.
813
814
815
        seq_ids, request_ids_seq_ids_mapping = get_all_seq_ids_and_request_ids(
            seq_group_metadata_list)

816
817
        num_logprobs_per_seq = get_all_num_logprobs(seq_group_metadata_list)

818
        # Serialize tensor to CPU Python list.
819
820
821
        accepted_token_ids_by_step = accepted_token_ids_by_step.tolist()

        # Construct the output on a per-step, per-sequence basis.
822
        sampler_output_list: List[SamplerOutput] = []
823
824
825
        for step_index in range(num_steps):
            if all(token_id == -1
                   for token_id in accepted_token_ids_by_step[step_index]):
826
827
                break

828
            step_output_token_ids: List[CompletionSequenceGroupOutput] = []
829
830
831
            for sequence_index in range(batch_size):
                # Each sequence may have a different num_logprobs; retrieve it.
                num_logprobs = num_logprobs_per_seq[sequence_index]
832
                step_output_token_ids.append(
833
834
835
836
837
838
839
840
841
842
843
844
                    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],
845
846
847
848
                    ))
            sampler_output_list.append(
                SamplerOutput(outputs=step_output_token_ids))

849
850
851
852
853
        # 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)
854
855
        maybe_rejsample_metrics = (
            self._metrics.maybe_collect_rejsample_metrics(k))
856
857
858
        if maybe_rejsample_metrics is not None:
            sampler_output_list[
                0].spec_decode_worker_metrics = maybe_rejsample_metrics
859
860
861
862
863
864

            # Log time spent in each stage periodically.
            # This is periodic because the rejection sampler emits metrics
            # periodically.
            self._maybe_log_stage_times(*stage_times)

865
866
        return sampler_output_list

867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
    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)

883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
    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)

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
    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)

1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
    @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

1032
1033
1034
1035
    @property
    def _driver_rank(self) -> int:
        return 0

1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
    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

1046
1047
1048
1049
1050
1051
1052
1053
    def start_profile(self):
        if isinstance(self.scorer_worker, Worker):
            self.scorer_worker.start_profile()

    def stop_profile(self):
        if isinstance(self.scorer_worker, Worker):
            self.scorer_worker.stop_profile()

1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075

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
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087


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