model_runner.py 50.9 KB
Newer Older
Woosuk Kwon's avatar
Woosuk Kwon committed
1
2
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
"""
NOTE: Coding style guide for this file:
This model runner is shared by all models: text and multimodal, generative
and embedding, public and private. As a result, this file must only contain
code that is common to every model. Model-specific behavior belongs in the
appropriate model-specific files.

In other words:
* Be paranoid about changing this file. It should remain stable.
* Be even more paranoid about adding new lines. It should remain minimal.

Even for shared features (for example, different parallelism modes), keep the
complexity out of this path. The less common the feature, the more it should be
hidden. Prefer utility functions defined elsewhere and call them from here,
instead of embedding feature-specific logic directly.
"""

20
import functools
Woosuk Kwon's avatar
Woosuk Kwon committed
21
22
23
import gc
import time
from copy import deepcopy
24
from typing import Any, NamedTuple
Woosuk Kwon's avatar
Woosuk Kwon committed
25
26
27
28
29
30
31

import numpy as np
import torch
import torch.nn as nn

from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
32
from vllm.distributed.parallel_state import (
33
    get_dcp_group,
34
35
36
    get_pp_group,
    prepare_communication_buffer_for_model,
)
37
from vllm.forward_context import BatchDescriptor, set_forward_context
Woosuk Kwon's avatar
Woosuk Kwon committed
38
39
from vllm.logger import init_logger
from vllm.model_executor.model_loader import get_model_loader
40
from vllm.multimodal import MULTIMODAL_REGISTRY
41
from vllm.sequence import IntermediateTensors
42
from vllm.tasks import SupportedTask
43
from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
Woosuk Kwon's avatar
Woosuk Kwon committed
44
45
46
from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
from vllm.v1.kv_cache_interface import KVCacheConfig
47
from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
48
from vllm.v1.worker.cp_utils import check_attention_cp_compatibility
49
from vllm.v1.worker.gpu.async_utils import AsyncOutput, AsyncPoolingOutput
Woosuk Kwon's avatar
Woosuk Kwon committed
50
from vllm.v1.worker.gpu.attn_utils import (
51
    build_slot_mappings_by_layer,
Woosuk Kwon's avatar
Woosuk Kwon committed
52
53
54
55
56
    get_kv_cache_spec,
    init_attn_backend,
    init_kv_cache,
)
from vllm.v1.worker.gpu.block_table import BlockTables
57
from vllm.v1.worker.gpu.buffer_utils import async_copy_to_gpu
58
from vllm.v1.worker.gpu.cp_utils import prepare_dcp_local_seq_lens
59
60
61
62
63
64
from vllm.v1.worker.gpu.cudagraph_utils import (
    BatchExecutionDescriptor,
    ModelCudaGraphManager,
    get_uniform_token_count,
)
from vllm.v1.worker.gpu.dp_utils import sync_cudagraph_and_dp_padding
Woosuk Kwon's avatar
Woosuk Kwon committed
65
66
67
from vllm.v1.worker.gpu.input_batch import (
    InputBatch,
    InputBuffers,
68
    combine_sampled_and_draft_tokens,
69
    expand_idx_mapping,
70
    get_num_sampled_and_rejected,
71
    post_update,
72
    post_update_pool,
73
74
    prepare_pos_seq_lens,
    prepare_prefill_inputs,
Woosuk Kwon's avatar
Woosuk Kwon committed
75
)
76
77
78
79
80
from vllm.v1.worker.gpu.kv_connector import (
    NO_OP_KV_CONNECTOR,
    KVConnector,
    get_kv_connector,
)
81
from vllm.v1.worker.gpu.lora_utils import LoraState
82
from vllm.v1.worker.gpu.mm.encoder_cache import EncoderCache
83
from vllm.v1.worker.gpu.model_states import init_model_state
84
from vllm.v1.worker.gpu.pool.pooling_runner import PoolingRunner
85
from vllm.v1.worker.gpu.pp_utils import pp_broadcast, pp_receive
86
from vllm.v1.worker.gpu.sample.output import SamplerOutput
87
from vllm.v1.worker.gpu.sample.prompt_logprob import PromptLogprobsWorker
88
from vllm.v1.worker.gpu.sample.sampler import Sampler
89
from vllm.v1.worker.gpu.spec_decode import init_speculator
90
91
92
from vllm.v1.worker.gpu.spec_decode.eagle.eagle3_utils import (
    set_eagle3_aux_hidden_state_layers,
)
93
from vllm.v1.worker.gpu.spec_decode.rejection_sampler import RejectionSampler
94
from vllm.v1.worker.gpu.spec_decode.utils import DraftTokensHandler
95
from vllm.v1.worker.gpu.states import RequestState
96
from vllm.v1.worker.gpu.structured_outputs import StructuredOutputsWorker
Woosuk Kwon's avatar
Woosuk Kwon committed
97
98
99
100
101
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin

logger = init_logger(__name__)


102
class GPUModelRunner(LoRAModelRunnerMixin):
103
    def __init__(self, vllm_config: VllmConfig, device: torch.device):
Woosuk Kwon's avatar
Woosuk Kwon committed
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.compilation_config = vllm_config.compilation_config
        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config

        self.device = device
        self.dtype = self.model_config.dtype
        self.kv_cache_dtype = self.dtype
        if self.cache_config.cache_dtype != "auto":
            # Quantized KV cache.
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                self.cache_config.cache_dtype
            ]

        self.vocab_size = self.model_config.get_vocab_size()
        self.max_model_len = self.model_config.max_model_len
        self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
        self.max_num_reqs = self.scheduler_config.max_num_seqs
128
        self.is_encoder_decoder = self.model_config.is_encoder_decoder
129

Woosuk Kwon's avatar
Woosuk Kwon committed
130
131
132
133
        self.use_async_scheduling = self.scheduler_config.async_scheduling
        self.output_copy_stream = torch.cuda.Stream(self.device)
        self.output_copy_event = torch.cuda.Event()

134
135
136
137
138
139
140
141
142
143
        # Pipeline parallelism.
        self.pp_size = self.parallel_config.pipeline_parallel_size
        self.use_pp = self.pp_size > 1
        if self.use_pp:
            self.is_first_pp_rank = get_pp_group().is_first_rank
            self.is_last_pp_rank = get_pp_group().is_last_rank
        else:
            self.is_first_pp_rank = True
            self.is_last_pp_rank = True

144
145
146
147
        # Data parallelism.
        self.dp_size = self.parallel_config.data_parallel_size
        self.dp_rank = self.parallel_config.data_parallel_rank

148
149
150
151
152
153
        # Decode context parallelism.
        self.dcp_size = self.parallel_config.decode_context_parallel_size
        self.use_dcp = self.dcp_size > 1
        self.dcp_rank = get_dcp_group().rank_in_group if self.use_dcp else 0
        self.cp_interleave = self.parallel_config.cp_kv_cache_interleave_size

154
155
156
157
158
159
160
161
162
        # Multimodal
        self.mm_registry = MULTIMODAL_REGISTRY
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
            self.model_config
        )
        self.encoder_cache = None
        if self.supports_mm_inputs and self.is_first_pp_rank:
            self.encoder_cache = EncoderCache()

163
        # Speculative decoding.
164
        self.speculator = None
165
        self.num_speculative_steps = 0
166
        self.use_aux_hidden_state_outputs = False
167
        use_strict_rejection_sampling = False
168
169
        if self.speculative_config is not None:
            self.num_speculative_steps = self.speculative_config.num_speculative_tokens
170
171
172
173
            use_strict_rejection_sampling = (
                self.speculative_config.rejection_sample_method == "strict"
            )

174
175
176
177
178
179
180
181
182
183
184
185
            if self.is_last_pp_rank:
                self.speculator = init_speculator(self.vllm_config, self.device)

            if self.speculative_config.method == "eagle3":
                # EAGLE3 may require auxiliary hidden states from target model outputs.
                self.use_aux_hidden_state_outputs = True
                if self.pp_size > 1:
                    raise ValueError("EAGLE3 with pipeline parallel is not supported.")

        # Draft tokens propagation - for spec-dec + struct outputs.
        self.draft_tokens_handler = DraftTokensHandler(self.device)

186
187
188
189
        # Pooling models.
        self.is_pooling_model = self.model_config.runner_type == "pooling"
        self.pooling_runner: PoolingRunner | None = None

190
        # General request states.
Woosuk Kwon's avatar
Woosuk Kwon committed
191
192
193
194
        self.req_states = RequestState(
            max_num_reqs=self.max_num_reqs,
            max_model_len=self.max_model_len,
            max_num_batched_tokens=self.max_num_tokens,
195
            num_speculative_steps=self.num_speculative_steps,
Woosuk Kwon's avatar
Woosuk Kwon committed
196
197
            vocab_size=self.vocab_size,
            device=self.device,
198
            cache_draft_logits=not use_strict_rejection_sampling,
Woosuk Kwon's avatar
Woosuk Kwon committed
199
200
201
202
203
204
        )
        self.input_buffers = InputBuffers(
            max_num_reqs=self.max_num_reqs,
            max_num_tokens=self.max_num_tokens,
            device=self.device,
        )
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232

        self.sampler: Sampler | None = None
        self.rejection_sampler: RejectionSampler | None = None
        self.prompt_logprobs_worker: PromptLogprobsWorker | None = None
        self.structured_outputs_worker: StructuredOutputsWorker | None = None
        if self.is_last_pp_rank and not self.is_pooling_model:
            # Initialize sampling-related workers.
            # These components are only set up on the last PP rank and
            # for generative (non-pooling) models.
            self.sampler = Sampler(
                max_num_reqs=self.max_num_reqs,
                vocab_size=self.vocab_size,
                device=self.device,
                req_states=self.req_states,
                logprobs_mode=self.model_config.logprobs_mode,
                num_speculative_tokens=self.num_speculative_steps + 1,
            )
            self.rejection_sampler = RejectionSampler(
                self.sampler,
                num_speculative_steps=self.num_speculative_steps,
                use_strict_rejection_sampling=use_strict_rejection_sampling,
            )
            self.prompt_logprobs_worker = PromptLogprobsWorker(self.max_num_reqs)
            self.structured_outputs_worker = StructuredOutputsWorker(
                max_num_logits=self.max_num_reqs * (self.num_speculative_steps + 1),
                vocab_size=self.vocab_size,
                device=self.device,
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
233
234

        # CUDA graphs.
235
236
        self.decode_query_len = self.num_speculative_steps + 1
        self.cudagraph_manager = ModelCudaGraphManager(
237
238
            self.vllm_config,
            self.device,
239
240
            self.compilation_config.cudagraph_mode,
            decode_query_len=self.decode_query_len,
241
        )
242
243
        # LoRA-related workers.
        self.lora_state = LoraState(max_num_reqs=self.max_num_reqs)
244
        # KV Connector if configured.
245
246
        self.kv_connector: KVConnector = NO_OP_KV_CONNECTOR

247
        # For transferring state from execute_model to subsequent sample_tokens call.
248
        self.execute_model_state: ExecuteModelState | None = None
249

250
251
252
253
    def update_max_model_len(self, max_model_len: int) -> None:
        self.max_model_len = max_model_len
        self.req_states.max_model_len = max_model_len

254
255
256
    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        tasks: list[SupportedTask] = []
        if self.model_config.runner_type == "generate":
257
            tasks.extend(self.model_state.get_supported_generation_tasks())
258
259
260
261
262
        if self.is_pooling_model:
            # Do not rely on pooling_runner here, since this information is needed
            # on the first PP rank, while pooling_runner is only initialized
            # on the last PP rank.
            tasks.extend(PoolingRunner.get_supported_tasks(self.model))
263
        return tuple(tasks)
Woosuk Kwon's avatar
Woosuk Kwon committed
264
265
266
267
268
269
270
271
272
273
274
275
276

    def load_model(self, *args, **kwargs) -> None:
        time_before_load = time.perf_counter()
        with DeviceMemoryProfiler() as m:
            model_loader = get_model_loader(self.vllm_config.load_config)
            logger.info("Loading model from scratch...")

            self.model = model_loader.load_model(
                vllm_config=self.vllm_config,
                model_config=self.vllm_config.model_config,
            )
            if self.lora_config:
                self.model = self.load_lora_model(
277
                    self.model, self.vllm_config, self.device
Woosuk Kwon's avatar
Woosuk Kwon committed
278
                )
279
280
281
282
283

            if self.use_aux_hidden_state_outputs:
                assert self.speculative_config is not None
                set_eagle3_aux_hidden_state_layers(self.model, self.speculative_config)
            if self.speculator is not None:
284
                self.speculator.load_model(self.model)
Woosuk Kwon's avatar
Woosuk Kwon committed
285
286
287
288
        time_after_load = time.perf_counter()

        self.model_memory_usage = m.consumed_memory
        logger.info(
289
290
            "Model loading took %s GiB and %.6f seconds",
            format_gib(m.consumed_memory),
Woosuk Kwon's avatar
Woosuk Kwon committed
291
292
293
            time_after_load - time_before_load,
        )

294
        prepare_communication_buffer_for_model(self.model)
295
        if self.speculator is not None:
296
            prepare_communication_buffer_for_model(self.speculator.model)
297

298
        # Initialize the components that require the model.
299
        self.model_state = init_model_state(
300
301
            self.vllm_config, self.model, self.encoder_cache, self.device
        )
302
        if self.is_pooling_model and self.is_last_pp_rank:
303
            self.pooling_runner = PoolingRunner(self.model)
304

Woosuk Kwon's avatar
Woosuk Kwon committed
305
306
307
    def get_model(self) -> nn.Module:
        return self.model

308
309
310
311
312
    @functools.cached_property
    def main_stream(self) -> torch.cuda.Stream:
        # Cache the default CUDA stream to avoid lookup overhead.
        return torch.cuda.current_stream(self.device)

Woosuk Kwon's avatar
Woosuk Kwon committed
313
314
315
316
317
318
319
320
321
322
323
    def get_kv_cache_spec(self):
        return get_kv_cache_spec(self.vllm_config)

    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        kv_cache_config = deepcopy(kv_cache_config)
        self.kv_cache_config = kv_cache_config
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
        ]

324
325
326
327
328
329
330
331
332
        block_table_max_model_len = self.max_model_len
        if self.is_encoder_decoder:
            # Cross-attention block tables need to index encoder tokens
            # (e.g., Whisper ~1500), which can exceed decoder max_model_len.
            block_table_max_model_len = max(
                block_table_max_model_len,
                getattr(self.model_config.hf_config, "max_source_positions", 0),
            )

Woosuk Kwon's avatar
Woosuk Kwon committed
333
334
335
336
        self.block_tables = BlockTables(
            block_sizes=block_sizes,
            max_num_reqs=self.max_num_reqs,
            max_num_batched_tokens=self.max_num_tokens,
337
            max_model_len=block_table_max_model_len,
Woosuk Kwon's avatar
Woosuk Kwon committed
338
            device=self.device,
339
340
341
            cp_size=self.dcp_size,
            cp_rank=self.dcp_rank,
            cp_interleave=self.cp_interleave,
Woosuk Kwon's avatar
Woosuk Kwon committed
342
343
        )

344
        self.attn_backends, self.attn_groups = init_attn_backend(
345
            self.kv_cache_config, self.vllm_config, self.device
Woosuk Kwon's avatar
Woosuk Kwon committed
346
        )
347
        check_attention_cp_compatibility(self.vllm_config)
348
        if self.speculator is not None:
349
350
            # HACK(woosuk)
            self.speculator.set_attn(
351
                self.model_state,
352
353
354
                self.kv_cache_config,
                self.block_tables,
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
355
356

        self.kv_caches: list[torch.Tensor] = []
357
        kv_caches_dict = init_kv_cache(
Woosuk Kwon's avatar
Woosuk Kwon committed
358
359
360
361
362
363
            self.kv_caches,
            self.compilation_config.static_forward_context,
            self.kv_cache_config,
            self.attn_backends,
            self.device,
        )
364
365
        self.kv_connector = get_kv_connector(self.vllm_config, kv_caches_dict)

Woosuk Kwon's avatar
Woosuk Kwon committed
366
367
    @torch.inference_mode()
    def _dummy_run(
368
369
370
371
372
373
        self,
        num_tokens: int,
        *args,
        skip_attn: bool = True,
        uniform_decode: bool = False,
        **kwargs,
374
    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
375
        # Create a dummy scheduler output.
376
        num_reqs = min(num_tokens, self.max_num_reqs)
377
        if uniform_decode:
378
379
380
381
382
383
384
385
386
387
            # HACK(lucas): for now since the worker is shared between MRV1 and MRV2,
            # and for spec-decode with MTP we want to make sure the dummy runs use
            # 1+num_speculative_tokens we use max here, this will likely be eventually
            # changed in the worker: https://github.com/vllm-project/vllm/pull/35243
            num_tokens = max(num_tokens, self.decode_query_len)
            num_reqs = num_tokens // self.decode_query_len
            assert num_tokens % self.decode_query_len == 0
        num_tokens_per_request = [num_tokens // num_reqs] * num_reqs
        num_tokens_per_request[-1] += num_tokens % num_reqs

388
389
        assert sum(num_tokens_per_request) == num_tokens
        num_scheduled_tokens = {
390
            f"_dummy_req_{i}": n for i, n in enumerate(num_tokens_per_request)
391
392
393
394
395
        }
        dummy_scheduler_output = SchedulerOutput.make_empty()
        dummy_scheduler_output.total_num_scheduled_tokens = num_tokens
        dummy_scheduler_output.num_scheduled_tokens = num_scheduled_tokens

396
397
398
        # Disable any use of KVConnector for dummy runs.
        self.kv_connector.set_disabled(True)

399
400
        # For non-first PP ranks, create dummy intermediate_tensors.
        intermediate_tensors = None
401
        if not self.is_first_pp_rank:
402
403
404
405
406
407
            intermediate_tensors = self.model.make_empty_intermediate_tensors(
                batch_size=num_tokens,
                dtype=self.model_config.dtype,
                device=self.device,
            )

408
409
        # Execute the model.
        self.execute_model(
410
411
412
413
            dummy_scheduler_output,
            intermediate_tensors=intermediate_tensors,
            dummy_run=True,
            skip_attn_for_dummy_run=skip_attn,
414
        )
415
        self.kv_connector.set_disabled(False)
416
417

        # Non-last PP ranks don't produce output for sampling.
418
        if not self.is_last_pp_rank:
419
420
            return None, None

421
        assert self.execute_model_state is not None
422
423
424
425
426
427
        input_batch = self.execute_model_state.input_batch
        attn_metadata = self.execute_model_state.attn_metadata
        slot_mappings_by_layer = self.execute_model_state.slot_mappings_by_layer
        hidden_states = self.execute_model_state.hidden_states
        aux_hidden_states = self.execute_model_state.aux_hidden_states
        num_tokens_across_dp = self.execute_model_state.num_tokens_across_dp
428
        self.execute_model_state = None
429
430
431

        # dummy run the eagle speculator's propose to ensure DP/EP sync.
        if self.speculator is not None:
432
            assert self.sampler is not None
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
            self.speculator.propose(
                input_batch=input_batch,
                attn_metadata=attn_metadata,
                slot_mappings=slot_mappings_by_layer,
                last_hidden_states=hidden_states,
                aux_hidden_states=aux_hidden_states,
                num_sampled=torch.ones(
                    input_batch.num_reqs, dtype=torch.int32, device=self.device
                ),
                num_rejected=torch.zeros(
                    input_batch.num_reqs, dtype=torch.int32, device=self.device
                ),
                last_sampled=self.req_states.last_sampled_tokens,
                next_prefill_tokens=self.req_states.next_prefill_tokens,
                temperature=self.sampler.sampling_states.temperature.gpu,
                seeds=self.sampler.sampling_states.seeds.gpu,
449
                draft_logits_out=self.req_states.draft_logits,
450
451
452
453
454
                num_tokens_across_dp=num_tokens_across_dp,
                dummy_run=True,
                skip_attn_for_dummy_run=skip_attn,
            )

455
        assert hidden_states is not None  # Last PP rank always has hidden_states
456
        sample_hidden_states = hidden_states[input_batch.logits_indices]
Woosuk Kwon's avatar
Woosuk Kwon committed
457
458
459
        return hidden_states, sample_hidden_states

    @torch.inference_mode()
460
    def _dummy_sampler_run(self, hidden_states: torch.Tensor) -> None:
Woosuk Kwon's avatar
Woosuk Kwon committed
461
462
        num_reqs = hidden_states.shape[0]
        logits = self.model.compute_logits(hidden_states)
463
464
        dummy_input_batch = InputBatch.make_dummy(
            num_reqs, num_reqs, self.input_buffers
465
        )
466

467
468
469
        # NOTE(woosuk): During the initial memory profiling, the sampler may skip
        # top_k, top_p, and logprobs, using less GPU memory than what is possible
        # during actual execution.
470
471
        assert self.sampler is not None
        self.sampler(logits, dummy_input_batch)
Woosuk Kwon's avatar
Woosuk Kwon committed
472

473
474
475
476
477
    @torch.inference_mode()
    def _dummy_pooler_run(self, hidden_states: torch.Tensor) -> None:
        assert self.pooling_runner is not None
        self.pooling_runner.dummy_pooler_run(hidden_states)

Woosuk Kwon's avatar
Woosuk Kwon committed
478
479
480
    @torch.inference_mode()
    def profile_run(self) -> None:
        hidden_states, sample_hidden_states = self._dummy_run(
481
            self.max_num_tokens, skip_attn=True
Woosuk Kwon's avatar
Woosuk Kwon committed
482
        )
483

484
        # Only run sampler/pooler on last PP rank (non-last ranks return None).
485
        if self.is_last_pp_rank:
486
            assert sample_hidden_states is not None
487
488
489
490
            if self.pooling_runner is None:
                self._dummy_sampler_run(sample_hidden_states)
            else:
                self._dummy_pooler_run(hidden_states)
491

492
        torch.accelerator.synchronize()
Woosuk Kwon's avatar
Woosuk Kwon committed
493
494
495
496
        del hidden_states, sample_hidden_states
        gc.collect()

    def reset_mm_cache(self) -> None:
497
498
        if self.encoder_cache is not None:
            self.encoder_cache.reset_mm_cache()
499
500

    def reset_encoder_cache(self) -> None:
501
502
        if self.encoder_cache is not None:
            self.encoder_cache.reset_encoder_cache()
Woosuk Kwon's avatar
Woosuk Kwon committed
503
504
505
506
507

    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
        # SP is not supported yet.
        return num_scheduled_tokens

508
509
510
511
    def profile_cudagraph_memory(self) -> int:
        # NOTE(woosuk): It is TBD whether we keep this API or not.
        return 0

Woosuk Kwon's avatar
Woosuk Kwon committed
512
513
514
515
516
517
518
519
520
    @torch.inference_mode()
    def capture_model(self) -> int:
        if not self.cudagraph_manager.needs_capture():
            logger.warning(
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
            return 0

521
522
523
524
525
526
527
528
        # TODO (zhanqiu): support CUDA graph for PP.
        if self.use_pp:
            logger.warning_once(
                "Skipping CUDA graph capture because pipeline parallel is "
                "enabled. Pipeline parallel is currently eager-only.",
            )
            return 0

Woosuk Kwon's avatar
Woosuk Kwon committed
529
        start_time = time.perf_counter()
530
        gc.collect()
531
        torch.accelerator.empty_cache()
Woosuk Kwon's avatar
Woosuk Kwon committed
532
533
534
535
        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

        with self.maybe_setup_dummy_loras(self.lora_config):
            self.cudagraph_manager.capture(
536
537
538
539
540
541
                self.model,
                self.model_state,
                self.input_buffers,
                self.block_tables,
                self.attn_groups,
                self.kv_cache_config,
542
                has_lora=self.lora_config is not None,
543
                use_aux_hidden_state_outputs=self.use_aux_hidden_state_outputs,
Woosuk Kwon's avatar
Woosuk Kwon committed
544
            )
545
            if self.speculator is not None:
546
                self.speculator.capture_model()
Woosuk Kwon's avatar
Woosuk Kwon committed
547
548
549
550
551
552
553
554
555
556
557
558
559

        end_time = time.perf_counter()
        end_free_gpu_memory = torch.cuda.mem_get_info()[0]
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
        return cuda_graph_size

560
    def finish_requests(self, scheduler_output: SchedulerOutput) -> None:
561
        finished_req_ids = scheduler_output.finished_req_ids
562
563
564
        preempted_req_ids = scheduler_output.preempted_req_ids
        if preempted_req_ids:
            finished_req_ids = finished_req_ids.union(preempted_req_ids)
565
        for req_id in finished_req_ids:
Woosuk Kwon's avatar
Woosuk Kwon committed
566
            self.req_states.remove_request(req_id)
567
568
            if self.encoder_cache is not None:
                self.encoder_cache.remove_request(req_id)
569
570
            if self.prompt_logprobs_worker is not None:
                self.prompt_logprobs_worker.remove_request(req_id)
571
            self.lora_state.remove_request(req_id)
572

573
    def free_states(self, scheduler_output: SchedulerOutput) -> None:
574
        if self.encoder_cache is not None:
575
            for mm_hash in scheduler_output.free_encoder_mm_hashes:
576
                self.encoder_cache.free_encoder_cache(mm_hash)
Woosuk Kwon's avatar
Woosuk Kwon committed
577

578
    def add_requests(self, scheduler_output: SchedulerOutput) -> None:
Woosuk Kwon's avatar
Woosuk Kwon committed
579
        for new_req_data in scheduler_output.scheduled_new_reqs:
580
581
            assert new_req_data.prompt_token_ids is not None
            assert new_req_data.prefill_token_ids is not None
Woosuk Kwon's avatar
Woosuk Kwon committed
582
            req_id = new_req_data.req_id
583
            prompt_len = len(new_req_data.prompt_token_ids)
Woosuk Kwon's avatar
Woosuk Kwon committed
584
585
            self.req_states.add_request(
                req_id=req_id,
586
                prompt_len=prompt_len,
587
                all_token_ids=new_req_data.prefill_token_ids,
Woosuk Kwon's avatar
Woosuk Kwon committed
588
589
590
                num_computed_tokens=new_req_data.num_computed_tokens,
            )
            req_index = self.req_states.req_id_to_index[req_id]
591

592
593
            if self.encoder_cache is not None:
                self.encoder_cache.add_request(req_id, new_req_data.mm_features)
594

595
            self.model_state.add_request(req_index, new_req_data)
596
597
598
            self.block_tables.append_block_ids(
                req_index, new_req_data.block_ids, overwrite=True
            )
599
            self.lora_state.add_request(req_id, req_index, new_req_data.lora_request)
Woosuk Kwon's avatar
Woosuk Kwon committed
600

601
602
            if self.is_last_pp_rank and new_req_data.sampling_params is not None:
                assert self.sampler is not None
603
604
605
                self.sampler.add_request(
                    req_index, prompt_len, new_req_data.sampling_params
                )
606
                assert self.prompt_logprobs_worker is not None
607
608
609
610
                self.prompt_logprobs_worker.add_request(
                    req_id, req_index, new_req_data.sampling_params
                )

611
612
        if scheduler_output.scheduled_new_reqs:
            self.req_states.apply_staged_writes()
613
            self.model_state.apply_staged_writes()
614
615
        if self.sampler is not None:
            self.sampler.apply_staged_writes()
616
617

    def update_requests(self, scheduler_output: SchedulerOutput) -> None:
Woosuk Kwon's avatar
Woosuk Kwon committed
618
        # Add new blocks for the existing requests.
619
620
        reqs = scheduler_output.scheduled_cached_reqs
        for req_new_block_ids, req_id in zip(reqs.new_block_ids, reqs.req_ids):
Woosuk Kwon's avatar
Woosuk Kwon committed
621
            if req_new_block_ids is not None:
622
                req_index = self.req_states.req_id_to_index[req_id]
623
624
625
                self.block_tables.append_block_ids(
                    req_index, req_new_block_ids, overwrite=False
                )
Woosuk Kwon's avatar
Woosuk Kwon committed
626
627

    def prepare_inputs(
628
        self, scheduler_output: SchedulerOutput, batch_desc: BatchExecutionDescriptor
Woosuk Kwon's avatar
Woosuk Kwon committed
629
630
    ) -> InputBatch:
        num_tokens = scheduler_output.total_num_scheduled_tokens
631
        num_tokens_after_padding = batch_desc.num_tokens
Woosuk Kwon's avatar
Woosuk Kwon committed
632
        assert num_tokens > 0
633
634
        num_tokens_per_req = scheduler_output.num_scheduled_tokens
        num_reqs = len(num_tokens_per_req)
Woosuk Kwon's avatar
Woosuk Kwon committed
635
636
637

        # Decode first, then prefill.
        # batch_idx -> req_id
638
639
640
        req_ids = sorted(num_tokens_per_req, key=num_tokens_per_req.get)  # type: ignore[arg-type]
        numtoks_iter = map(num_tokens_per_req.get, req_ids)
        num_scheduled_tokens = np.fromiter(numtoks_iter, dtype=np.int32, count=num_reqs)
Woosuk Kwon's avatar
Woosuk Kwon committed
641

642
643
        idx_mapping_iter = map(self.req_states.req_id_to_index.get, req_ids)
        idx_mapping_np = np.fromiter(idx_mapping_iter, dtype=np.int32, count=num_reqs)
644
        idx_mapping = async_copy_to_gpu(idx_mapping_np, device=self.device)
Woosuk Kwon's avatar
Woosuk Kwon committed
645

646
        # Get the number of draft tokens for each request.
647
648
        draft_tokens = scheduler_output.scheduled_spec_decode_tokens
        if not draft_tokens:
649
650
651
            # No draft token scheduled (common case).
            total_num_draft_tokens = 0
            total_num_logits = num_reqs
652
            cu_num_logits_np = np.arange(num_reqs + 1, dtype=np.int32)
653
654
655
            cu_num_logits = torch.arange(
                num_reqs + 1, device=self.device, dtype=torch.int32
            )
656
            expanded_idx_mapping = idx_mapping
657
658
659
            expanded_local_pos = torch.zeros(
                num_reqs, dtype=torch.int32, device=self.device
            )
660
        else:
661
662
            num_draft_tokens = np.fromiter(
                (len(draft_tokens.get(req_id, ())) for req_id in req_ids),
663
                dtype=np.int32,
664
                count=num_reqs,
665
666
667
668
            )
            total_num_draft_tokens = int(num_draft_tokens.sum())
            total_num_logits = num_reqs + total_num_draft_tokens

669
670
671
672
            num_logits = num_draft_tokens + 1
            cu_num_logits_np = np.empty(num_reqs + 1, dtype=np.int32)
            cu_num_logits_np[0] = 0
            np.cumsum(num_logits, out=cu_num_logits_np[1:])
673
            cu_num_logits = async_copy_to_gpu(cu_num_logits_np, device=self.device)
674

675
            max_expand_len = self.num_speculative_steps + 1
676
            expanded_idx_mapping, expanded_local_pos = expand_idx_mapping(
677
                idx_mapping, total_num_logits, cu_num_logits, max_expand_len
678
679
            )

680
        # Get query_start_loc.
681
682
        # num_reqs_padded is None for PIECEWISE graphs (no request padding needed)
        num_reqs_padded = batch_desc.num_reqs or num_reqs
683
684
685
        query_start_loc_np = np.empty(self.max_num_reqs + 1, dtype=np.int32)
        query_start_loc_np[0] = 0
        np.cumsum(num_scheduled_tokens, out=query_start_loc_np[1 : num_reqs + 1])
686
687
        # Pad for full CUDA graph mode.
        # Some attention backends like FA3 require query_start_loc to be non-decreasing.
688
        query_start_loc_np[num_reqs + 1 :] = num_tokens
689
        async_copy_to_gpu(query_start_loc_np, out=self.input_buffers.query_start_loc)
690
691
        query_start_loc_np = query_start_loc_np[: num_reqs_padded + 1]
        query_start_loc = self.input_buffers.query_start_loc[: num_reqs_padded + 1]
692

693
694
695
696
697
698
699
700
701
702
703
        # Get prefill tokens if any.
        if self.req_states.any_prefills(idx_mapping_np):
            prepare_prefill_inputs(
                self.input_buffers.input_ids,
                self.req_states.next_prefill_tokens,
                idx_mapping,
                query_start_loc,
                self.req_states.all_token_ids.gpu,
                self.req_states.prefill_len.gpu,
                self.req_states.num_computed_tokens.gpu,
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
704

705
706
707
        # Prepare positions and seq_lens.
        prepare_pos_seq_lens(
            idx_mapping,
708
709
            query_start_loc,
            self.req_states.num_computed_tokens.gpu,
710
711
712
            self.input_buffers.positions,
            self.input_buffers.seq_lens,
        )
713
        seq_lens = self.input_buffers.seq_lens[:num_reqs_padded]
714

715
        dcp_local_seq_lens = None
716
717
        if self.use_dcp:
            # Prepare dcp local seq_lens.
718
719
            prepare_dcp_local_seq_lens(
                self.input_buffers.dcp_local_seq_lens,
720
                self.input_buffers.seq_lens,
721
                num_reqs,
722
723
724
                self.dcp_size,
                self.dcp_rank,
                self.cp_interleave,
725
            )
726
            dcp_local_seq_lens = self.input_buffers.dcp_local_seq_lens[:num_reqs_padded]
727

728
        # Some input token ids are directly read from the last sampled tokens
729
730
        # and draft tokens. Also, get the logits indices to sample tokens from.
        logits_indices = combine_sampled_and_draft_tokens(
731
            self.input_buffers.input_ids,
Woosuk Kwon's avatar
Woosuk Kwon committed
732
733
            idx_mapping,
            self.req_states.last_sampled_tokens,
734
            query_start_loc,
735
736
            seq_lens,
            self.req_states.prefill_len.gpu,
737
738
739
            self.req_states.draft_tokens,
            cu_num_logits,
            total_num_logits,
Woosuk Kwon's avatar
Woosuk Kwon committed
740
741
742
743
744
        )

        return InputBatch(
            req_ids=req_ids,
            num_reqs=num_reqs,
745
            num_reqs_after_padding=num_reqs_padded,
Woosuk Kwon's avatar
Woosuk Kwon committed
746
747
            idx_mapping=idx_mapping,
            idx_mapping_np=idx_mapping_np,
748
            expanded_idx_mapping=expanded_idx_mapping,
749
            expanded_local_pos=expanded_local_pos,
Woosuk Kwon's avatar
Woosuk Kwon committed
750
751
752
            num_scheduled_tokens=num_scheduled_tokens,
            num_tokens=num_tokens,
            num_tokens_after_padding=num_tokens_after_padding,
753
            num_draft_tokens=total_num_draft_tokens,
754
            query_start_loc=query_start_loc,
Woosuk Kwon's avatar
Woosuk Kwon committed
755
            query_start_loc_np=query_start_loc_np,
756
            seq_lens=seq_lens,
757
758
759
            dcp_local_seq_lens=dcp_local_seq_lens,
            input_ids=self.input_buffers.input_ids[:num_tokens_after_padding],
            positions=self.input_buffers.positions[:num_tokens_after_padding],
Woosuk Kwon's avatar
Woosuk Kwon committed
760
            logits_indices=logits_indices,
761
            cu_num_logits=cu_num_logits,
762
            cu_num_logits_np=cu_num_logits_np,
763
            has_structured_output_reqs=scheduler_output.has_structured_output_requests,
Woosuk Kwon's avatar
Woosuk Kwon committed
764
765
        )

766
767
768
    def prepare_attn(
        self, input_batch: InputBatch
    ) -> tuple[tuple[torch.Tensor, ...], torch.Tensor]:
769
770
771
772
773
774
775
        # Block tables: num_kv_cache_groups x [num_reqs_padded, max_num_blocks].
        block_tables = self.block_tables.gather_block_tables(
            input_batch.idx_mapping,
            num_reqs_padded=input_batch.num_reqs_after_padding,
        )
        # Slot mappings: [num_kv_cache_groups, num_tokens_padded].
        # Kernel pads beyond num_tokens with PAD_SLOT_ID.
776
777
778
779
        slot_mappings = self.block_tables.compute_slot_mappings(
            input_batch.idx_mapping,
            input_batch.query_start_loc,
            input_batch.positions,
780
            num_tokens_padded=input_batch.num_tokens_after_padding,
781
782
783
784
785
786
787
788
789
790
791
792
        )
        return block_tables, slot_mappings

    def prepare_dummy_attn(
        self, input_batch: InputBatch
    ) -> tuple[tuple[torch.Tensor, ...], torch.Tensor]:
        block_tables = self.block_tables.get_dummy_block_tables(input_batch.num_reqs)
        slot_mappings = self.block_tables.get_dummy_slot_mappings(
            input_batch.num_tokens
        )
        return block_tables, slot_mappings

Woosuk Kwon's avatar
Woosuk Kwon committed
793
794
795
796
797
    def sample(
        self,
        hidden_states: torch.Tensor,
        input_batch: InputBatch,
        grammar_output: GrammarOutput | None,
798
    ) -> tuple[SamplerOutput, torch.Tensor, torch.Tensor]:
Woosuk Kwon's avatar
Woosuk Kwon committed
799
800
801
802
        sample_hidden_states = hidden_states[input_batch.logits_indices]
        logits = self.model.compute_logits(sample_hidden_states)
        if grammar_output is not None:
            # Apply grammar bitmask to the logits in-place.
803
            assert self.structured_outputs_worker is not None
804
805
806
807
808
809
            self.structured_outputs_worker.apply_grammar_bitmask(
                logits,
                input_batch,
                grammar_output.structured_output_request_ids,
                grammar_output.grammar_bitmask,
            )
810

811
812
        if input_batch.num_draft_tokens == 0:
            # No draft tokens (common case).
813
814
            assert self.sampler is not None
            sampler_output = self.sampler(logits, input_batch)
815
        else:
816
            # Rejection sampling for spec decoding.
817
            assert self.rejection_sampler is not None
818
819
820
821
            sampler_output = self.rejection_sampler(
                logits,
                input_batch,
                # Draft logits are needed for probabilistic rejection sampling.
822
                self.req_states.draft_logits,
823
            )
824
825
826
827

        # Get the number of sampled and rejected tokens.
        # For chunked prefills, num_sampled and num_rejected are both 0.
        num_sampled, num_rejected = get_num_sampled_and_rejected(
828
            sampler_output.num_sampled,
829
830
831
832
833
            input_batch.seq_lens,
            input_batch.cu_num_logits,
            input_batch.idx_mapping,
            self.req_states.prefill_len.gpu,
        )
834
        return sampler_output, num_sampled, num_rejected
Woosuk Kwon's avatar
Woosuk Kwon committed
835
836
837
838

    def postprocess(
        self,
        input_batch: InputBatch,
839
840
        sampled_tokens: torch.Tensor,
        num_sampled: torch.Tensor,
841
        num_rejected: torch.Tensor,
842
843
    ) -> None:
        # Update the number of computed tokens.
844
845
846
847
848
        if self.is_last_pp_rank:
            assert self.sampler is not None
            output_bin_counts = self.sampler.penalties_state.output_bin_counts
        else:
            output_bin_counts = None
849
        post_update(
850
            input_batch.idx_mapping,
851
            self.req_states.num_computed_tokens.gpu,
852
            self.req_states.last_sampled_tokens,
853
            output_bin_counts,
854
855
            sampled_tokens,
            num_sampled,
856
            num_rejected,
857
            input_batch.query_start_loc,
858
859
            self.req_states.all_token_ids.gpu,
            self.req_states.total_len.gpu,
Woosuk Kwon's avatar
Woosuk Kwon committed
860
        )
861
862

        # Update the number of computed prefill tokens.
Woosuk Kwon's avatar
Woosuk Kwon committed
863
        idx_mapping_np = input_batch.idx_mapping_np
864
        computed_prefill = self.req_states.num_computed_prefill_tokens
865
866
867
        computed_prefill[idx_mapping_np] += input_batch.num_scheduled_tokens
        np.minimum(
            computed_prefill, self.req_states.prefill_len.np, out=computed_prefill
Woosuk Kwon's avatar
Woosuk Kwon committed
868
869
870
871
872
873
        )

    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: SchedulerOutput,
874
        intermediate_tensors: IntermediateTensors | None = None,
Woosuk Kwon's avatar
Woosuk Kwon committed
875
        dummy_run: bool = False,
876
        skip_attn_for_dummy_run: bool = False,
877
    ) -> ModelRunnerOutput | IntermediateTensors | None:
878
879
880
881
882
883
884
885
886
        if not dummy_run:
            # Update the request states.
            self.finish_requests(scheduler_output)
            self.free_states(scheduler_output)
            self.add_requests(scheduler_output)
            self.update_requests(scheduler_output)
            self.block_tables.apply_staged_writes()
            if scheduler_output.total_num_scheduled_tokens == 0:
                # No need to run the model.
887
888
                empty_output = self.kv_connector.no_forward(scheduler_output)
                return empty_output
Woosuk Kwon's avatar
Woosuk Kwon committed
889

890
891
892
893
894
895
896
897
        # Get batch descriptor and sync across DP ranks.
        num_reqs = len(scheduler_output.num_scheduled_tokens)
        num_toks = scheduler_output.total_num_scheduled_tokens
        max_query_len = max(scheduler_output.num_scheduled_tokens.values())
        uniform_tok_count = get_uniform_token_count(num_reqs, num_toks, max_query_len)

        batch_desc = self.cudagraph_manager.dispatch(
            num_reqs, num_toks, uniform_tok_count
898
        )
899
        num_tokens_across_dp = None
900

901
902
903
904
905
906
907
908
909
910
911
912
913
        skip_compiled = False
        if self.is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
            # Encoder-decoder models such as Whisper should run eager/non-compiled
            # when encoder inputs are scheduled, because this step updates
            # cross-attention cache with dynamic encoder outputs.
            # Override batch_desc to NONE.
            skip_compiled = True
            batch_desc = BatchExecutionDescriptor(
                cg_mode=CUDAGraphMode.NONE,
                num_tokens=num_toks,
                num_reqs=num_reqs,
            )

914
915
916
917
918
919
920
921
922
        if self.dp_size > 1:
            batch_desc, num_tokens_across_dp = sync_cudagraph_and_dp_padding(
                self.cudagraph_manager,
                batch_desc,
                num_toks,
                num_reqs,
                uniform_tok_count,
                self.dp_size,
                self.dp_rank,
923
            )
924
925

        if batch_desc.num_tokens == 0:
926
            # All DP ranks have zero tokens to run.
927
928
            empty_output = self.kv_connector.no_forward(scheduler_output)
            return empty_output
929
930
931
932

        if not dummy_run:
            # Common case.
            # Prepare all the inputs and copy to the input buffers.
933
            input_batch = self.prepare_inputs(scheduler_output, batch_desc)
934
935
            block_tables, slot_mappings = self.prepare_attn(input_batch)

936
937
            if self.lora_config:
                # Activate LoRA adapters.
938
                lora_inputs = self.lora_state.make_lora_inputs(
939
940
941
                    input_batch.req_ids,
                    input_batch.idx_mapping_np,
                    input_batch.num_scheduled_tokens,
Woosuk Kwon's avatar
Woosuk Kwon committed
942
                )
943
944
                self._set_active_loras(*lora_inputs)
        else:
945
            # No actual tokens to run. A dummy run for DP or memory profiling.
946
            input_batch = InputBatch.make_dummy(
947
948
949
                batch_desc.num_reqs or num_reqs,
                batch_desc.num_tokens,
                self.input_buffers,
950
            )
951
            if not skip_attn_for_dummy_run:
952
953
954
955
                block_tables, slot_mappings = self.prepare_dummy_attn(input_batch)
            else:
                block_tables = None
                slot_mappings = None
956
            # FIXME(woosuk): Fix warmup for LoRA.
Woosuk Kwon's avatar
Woosuk Kwon committed
957

958
959
960
961
962
963
964
965
966
967
        attn_metadata = None
        slot_mappings_by_layer = None
        if not (dummy_run and skip_attn_for_dummy_run):
            assert slot_mappings is not None
            slot_mappings_by_layer = build_slot_mappings_by_layer(
                slot_mappings, self.kv_cache_config
            )
            assert block_tables is not None
            attn_metadata = self.model_state.prepare_attn(
                input_batch,
968
                batch_desc.cg_mode,
969
970
971
972
973
974
                block_tables,
                slot_mappings,
                self.attn_groups,
                self.kv_cache_config,
            )

975
        inputs_embeds = None
976
        if self.supports_mm_inputs and self.is_first_pp_rank:
977
978
            # Run MM encoder (if needed) and get multimodal embeddings.
            # Only first PP rank prepares multimodal embeddings.
979
980
            # NOTE(woosuk): We must call get_mm_embeddings even during dummy runs
            # to obtain inputs_embeds, because the compiled model expects this input.
981
982
983
984
985
986
            inputs_embeds = self.model_state.get_mm_embeddings(
                scheduler_output.scheduled_encoder_inputs,
                input_batch,
                self.req_states,
            )

987
988
989
        model_inputs = {
            "input_ids": input_batch.input_ids,
            "positions": input_batch.positions,
990
            "inputs_embeds": inputs_embeds,
991
            "intermediate_tensors": intermediate_tensors,
992
993
994
995
996
997
998
999
            # NOTE: Values returned by `prepare_inputs` will override the default
            # values above.
            **self.model_state.prepare_inputs(input_batch, self.req_states),
        }
        if not self.is_first_pp_rank:
            # Update for non-first PP ranks.
            model_inputs["input_ids"] = None
            model_inputs["inputs_embeds"] = None
1000
            assert intermediate_tensors is not None
1001

Woosuk Kwon's avatar
Woosuk Kwon committed
1002
        # Run model.
1003
        if batch_desc.cg_mode == CUDAGraphMode.FULL:
1004
            # Use explicit cudagraph replay for FULL mode.
Woosuk Kwon's avatar
Woosuk Kwon committed
1005
1006
            # NOTE(woosuk): Here, we don't need to pass the input tensors,
            # because they are already copied to the CUDA graph input buffers.
1007
            self.kv_connector.pre_forward(scheduler_output)
1008
            model_output = self.cudagraph_manager.run_fullgraph(batch_desc)
1009
1010
1011
1012
1013
            if self.use_aux_hidden_state_outputs:
                hidden_states, aux_hidden_states = model_output
            else:
                hidden_states = model_output
                aux_hidden_states = None
Woosuk Kwon's avatar
Woosuk Kwon committed
1014
        else:
1015
1016
1017
1018
1019
1020
            # For piecewise and eager mode, just call model().
            batch_descriptor = BatchDescriptor(
                num_tokens=input_batch.num_tokens_after_padding,
                has_lora=self.lora_config is not None,
            )

Woosuk Kwon's avatar
Woosuk Kwon committed
1021
            with set_forward_context(
1022
                attn_metadata,
Woosuk Kwon's avatar
Woosuk Kwon committed
1023
1024
                self.vllm_config,
                num_tokens=input_batch.num_tokens_after_padding,
1025
                cudagraph_runtime_mode=batch_desc.cg_mode,
Woosuk Kwon's avatar
Woosuk Kwon committed
1026
                num_tokens_across_dp=num_tokens_across_dp,
1027
                batch_descriptor=batch_descriptor,
1028
                slot_mapping=slot_mappings_by_layer,
1029
                skip_compiled=skip_compiled,
Woosuk Kwon's avatar
Woosuk Kwon committed
1030
            ):
1031
                self.kv_connector.pre_forward(scheduler_output)
1032
                model_output = self.model(**model_inputs)
1033
1034
1035
1036
1037
                if self.use_aux_hidden_state_outputs:
                    hidden_states, aux_hidden_states = model_output
                else:
                    hidden_states = model_output
                    aux_hidden_states = None
Woosuk Kwon's avatar
Woosuk Kwon committed
1038

1039
        kv_connector_output = self.kv_connector.post_forward(scheduler_output)
1040
1041
1042
1043
1044
1045
1046
1047
        self.execute_model_state = ExecuteModelState(
            input_batch=input_batch,
            attn_metadata=attn_metadata,
            slot_mappings_by_layer=slot_mappings_by_layer,
            hidden_states=hidden_states,
            aux_hidden_states=aux_hidden_states,
            kv_connector_output=kv_connector_output,
            num_tokens_across_dp=num_tokens_across_dp,
1048
        )
1049

1050
        if not self.is_last_pp_rank:
1051
1052
1053
1054
1055
            # Non-last PP rank: return IntermediateTensors for sending.
            assert isinstance(hidden_states, IntermediateTensors)
            hidden_states.kv_connector_output = kv_connector_output
            return hidden_states
        # Last rank (or no PP): hidden_states is a tensor for sampling.
1056
        assert isinstance(hidden_states, torch.Tensor)
Woosuk Kwon's avatar
Woosuk Kwon committed
1057
1058
1059
1060
        return None

    @torch.inference_mode()
    def sample_tokens(
1061
        self, grammar_output: GrammarOutput | None
1062
    ) -> AsyncOutput | ModelRunnerOutput | None:
1063
1064
1065
        if self.execute_model_state is None:
            # The prior execute_model call must have failed.
            return None
1066
1067
1068
1069
1070
1071
1072
1073

        input_batch = self.execute_model_state.input_batch
        attn_metadata = self.execute_model_state.attn_metadata
        slot_mappings_by_layer = self.execute_model_state.slot_mappings_by_layer
        hidden_states = self.execute_model_state.hidden_states
        aux_hidden_states = self.execute_model_state.aux_hidden_states
        kv_connector_output = self.execute_model_state.kv_connector_output
        num_tokens_across_dp = self.execute_model_state.num_tokens_across_dp
1074
        self.execute_model_state = None
Woosuk Kwon's avatar
Woosuk Kwon committed
1075

1076
        if not self.is_last_pp_rank:
1077
1078
1079
1080
            # Non-last PP rank: hidden_states is None because this rank produced
            # IntermediateTensors instead of final hidden states. Receive the
            # sampled tokens broadcast from the last rank and update local state.
            sampled, num_sampled, num_rejected = pp_receive(
1081
                input_batch.num_reqs, max_sample_len=self.num_speculative_steps + 1
1082
            )
1083
            self.postprocess(input_batch, sampled, num_sampled, num_rejected)
1084
1085
1086
            return None

        # Last rank: sample tokens
1087
        sampler_output, num_sampled, num_rejected = self.sample(
1088
            hidden_states, input_batch, grammar_output
Woosuk Kwon's avatar
Woosuk Kwon committed
1089
        )
1090
1091

        if self.use_pp:
1092
            # Broadcast to non-last PP ranks (handles spec decode multi-token).
1093
            pp_broadcast(sampler_output.sampled_token_ids, num_sampled, num_rejected)
1094

1095
        assert self.prompt_logprobs_worker is not None
1096
1097
1098
1099
        prompt_logprobs_dict = self.prompt_logprobs_worker.compute_prompt_logprobs(
            self.model.compute_logits,
            hidden_states,
            input_batch,
1100
            self.req_states.all_token_ids.gpu,
1101
            self.req_states.num_computed_tokens.gpu,
1102
            self.req_states.prompt_len.np,
1103
1104
1105
            self.req_states.prefill_len.np,
            self.req_states.num_computed_prefill_tokens,
        )
1106
1107
1108
1109
1110
1111
1112
1113

        # Prepare the model runner output.
        model_runner_output = ModelRunnerOutput(
            req_ids=input_batch.req_ids,
            # NOTE(woosuk): req_id_to_index is unused in this model runner.
            # Only for compatibility with the existing model runner and scheduler.
            req_id_to_index={req_id: i for i, req_id in enumerate(input_batch.req_ids)},
            sampled_token_ids=None,  # type: ignore
1114
            prompt_logprobs_dict=prompt_logprobs_dict,  # type: ignore[arg-type]
1115
            kv_connector_output=kv_connector_output,
1116
1117
1118
1119
        )
        async_output = AsyncOutput(
            model_runner_output=model_runner_output,
            sampler_output=sampler_output,
1120
            num_sampled_tokens=num_sampled,
1121
            main_stream=self.main_stream,
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
            copy_stream=self.output_copy_stream,
            copy_event=self.output_copy_event,
        )

        # Postprocess results and update request states.
        # NOTE: This is intentionally done after creating the AsyncOutput,
        # ensuring that `copy_event` is recorded before calling postprocess.
        # This sequencing may slightly reduce latency as async D2H copy does not
        # need to wait for the postprocess to finish.
        self.postprocess(
1132
            input_batch, sampler_output.sampled_token_ids, num_sampled, num_rejected
Woosuk Kwon's avatar
Woosuk Kwon committed
1133
        )
1134
        if self.speculator is not None:
1135
            assert self.sampler is not None
1136
            draft_tokens = self.speculator.propose(
1137
                input_batch,
1138
1139
                attn_metadata,
                slot_mappings_by_layer,
1140
                hidden_states,
1141
                aux_hidden_states,
1142
1143
                num_sampled,
                num_rejected,
1144
1145
1146
1147
                self.req_states.last_sampled_tokens,
                self.req_states.next_prefill_tokens,
                self.sampler.sampling_states.temperature.gpu,
                self.sampler.sampling_states.seeds.gpu,
1148
                self.req_states.draft_logits,
1149
                num_tokens_across_dp=num_tokens_across_dp,
1150
            )
1151
            self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens
1152
            self.draft_tokens_handler.set_draft_tokens(input_batch, draft_tokens)
1153
1154
1155
1156

        if self.use_async_scheduling:
            return async_output
        return async_output.get_output()
1157
1158
1159

    def take_draft_token_ids(self) -> DraftTokenIds | None:
        return self.draft_tokens_handler.get_draft_tokens()
1160
1161
1162
1163
1164
1165
1166

    @torch.inference_mode()
    def pool(self) -> AsyncPoolingOutput | ModelRunnerOutput | None:
        if self.execute_model_state is None:
            # The prior execute_model call must have failed.
            return None

1167
1168
1169
        input_batch = self.execute_model_state.input_batch
        hidden_states = self.execute_model_state.hidden_states
        kv_connector_output = self.execute_model_state.kv_connector_output
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
        self.execute_model_state = None

        if not self.is_last_pp_rank:
            self.postprocess_pool(input_batch)
            return None

        assert self.pooling_runner is not None
        pooler_output, is_valid = self.pooling_runner.pool(
            hidden_states, input_batch, self.req_states
        )
        self.postprocess_pool(input_batch)

        # Build the model runner output.
        model_runner_output = ModelRunnerOutput(
            req_ids=input_batch.req_ids,
            req_id_to_index={req_id: i for i, req_id in enumerate(input_batch.req_ids)},
            kv_connector_output=kv_connector_output,
        )
        async_output = AsyncPoolingOutput(
            model_runner_output=model_runner_output,
            pooler_output=pooler_output,
            is_valid=is_valid,
            main_stream=self.main_stream,
            copy_stream=self.output_copy_stream,
            copy_event=self.output_copy_event,
        )
        if self.use_async_scheduling:
            return async_output
        return async_output.get_output()

    def postprocess_pool(self, input_batch: InputBatch) -> None:
        # Update the number of computed tokens.
        post_update_pool(
            input_batch.idx_mapping,
            self.req_states.num_computed_tokens.gpu,
            input_batch.query_start_loc,
        )

        # Update the number of computed prefill tokens.
        idx_mapping_np = input_batch.idx_mapping_np
        computed_prefill = self.req_states.num_computed_prefill_tokens
        computed_prefill[idx_mapping_np] += input_batch.num_scheduled_tokens
        np.minimum(
            computed_prefill, self.req_states.prefill_len.np, out=computed_prefill
        )
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224


class ExecuteModelState(NamedTuple):
    input_batch: InputBatch
    attn_metadata: dict[str, Any] | None
    slot_mappings_by_layer: dict[str, torch.Tensor] | None
    hidden_states: torch.Tensor | IntermediateTensors
    aux_hidden_states: list[torch.Tensor] | None
    kv_connector_output: KVConnectorOutput | None
    num_tokens_across_dp: torch.Tensor | None