model_runner.py 37.8 KB
Newer Older
Woosuk Kwon's avatar
Woosuk Kwon committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import gc
import time
from copy import deepcopy
from typing import Any

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

from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
from vllm.forward_context import set_forward_context
from vllm.logger import init_logger
from vllm.model_executor.model_loader import get_model_loader
17
from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
Woosuk Kwon's avatar
Woosuk Kwon committed
18
19
20
21
22
23
24
25
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
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    LogprobsTensors,
    ModelRunnerOutput,
)
26
from vllm.v1.worker.gpu.async_utils import AsyncOutput
Woosuk Kwon's avatar
Woosuk Kwon committed
27
28
29
30
31
32
33
from vllm.v1.worker.gpu.attn_utils import (
    build_attn_metadata,
    get_kv_cache_spec,
    init_attn_backend,
    init_kv_cache,
)
from vllm.v1.worker.gpu.block_table import BlockTables
34
from vllm.v1.worker.gpu.buffer_utils import UvaBufferPool
Woosuk Kwon's avatar
Woosuk Kwon committed
35
from vllm.v1.worker.gpu.cudagraph_utils import CudaGraphManager
36
37
38
39
from vllm.v1.worker.gpu.dp_utils import (
    get_batch_metadata_across_dp,
    make_num_tokens_across_dp,
)
Woosuk Kwon's avatar
Woosuk Kwon committed
40
41
42
from vllm.v1.worker.gpu.input_batch import (
    InputBatch,
    InputBuffers,
43
    combine_sampled_and_draft_tokens,
44
    expand_idx_mapping,
45
    get_num_sampled_and_rejected,
46
    post_update,
47
48
    prepare_pos_seq_lens,
    prepare_prefill_inputs,
Woosuk Kwon's avatar
Woosuk Kwon committed
49
)
50
from vllm.v1.worker.gpu.sample.logprob import compute_prompt_logprobs
51
from vllm.v1.worker.gpu.sample.metadata import SamplingMetadata
52
from vllm.v1.worker.gpu.sample.output import SamplerOutput
53
from vllm.v1.worker.gpu.sample.sampler import Sampler
54
from vllm.v1.worker.gpu.spec_decode import init_speculator
55
from vllm.v1.worker.gpu.spec_decode.rejection_sample import rejection_sample
56
from vllm.v1.worker.gpu.states import RequestState
57
from vllm.v1.worker.gpu.structured_outputs import StructuredOutputsWorker
Woosuk Kwon's avatar
Woosuk Kwon committed
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin

logger = init_logger(__name__)


class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
    ):
        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.is_pooling_model = False

        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
95
        self.inputs_embeds_size = self.model_config.get_inputs_embeds_size()
Woosuk Kwon's avatar
Woosuk Kwon committed
96
97
98
99
100
101
102
103
104
105
106

        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()
        if self.use_async_scheduling:
            self.input_prep_event = torch.cuda.Event()
            self.structured_outputs_event = torch.cuda.Event()
        else:
            self.input_prep_event = None
            self.structured_outputs_event = None

107
108
109
        if self.speculative_config is not None:
            self.do_spec_decode = True
            self.num_speculative_steps = self.speculative_config.num_speculative_tokens
110
            self.speculator = init_speculator(self.vllm_config, self.device)
111
112
113
        else:
            self.do_spec_decode = False
            self.num_speculative_steps = 0
114
            self.speculator = None
115

Woosuk Kwon's avatar
Woosuk Kwon committed
116
117
118
119
        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,
120
            num_speculative_steps=self.num_speculative_steps,
Woosuk Kwon's avatar
Woosuk Kwon committed
121
122
123
124
125
126
            vocab_size=self.vocab_size,
            device=self.device,
        )
        self.input_buffers = InputBuffers(
            max_num_reqs=self.max_num_reqs,
            max_num_tokens=self.max_num_tokens,
127
            inputs_embeds_size=self.inputs_embeds_size,
Woosuk Kwon's avatar
Woosuk Kwon committed
128
129
130
131
132
133
134
            vocab_size=self.vocab_size,
            dtype=self.dtype,
            device=self.device,
        )
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)

        # CUDA graphs.
135
        self.cudagraph_manager = CudaGraphManager(self.vllm_config, self.device)
136
137
138
139
140
141
142
143
144
145
        # Structured outputs worker.
        self.structured_outputs_worker = StructuredOutputsWorker(
            max_num_logits=self.max_num_reqs * (self.num_speculative_steps + 1),
            vocab_size=self.vocab_size,
        )

        # Buffers for CPU-to-GPU copies.
        self.tmp_idx_mapping = UvaBufferPool(self.max_num_reqs, torch.int32)
        self.tmp_cu_num_logits = UvaBufferPool(self.max_num_reqs + 1, torch.int32)
        self.tmp_query_start_loc = UvaBufferPool(self.max_num_reqs + 1, torch.int32)
Woosuk Kwon's avatar
Woosuk Kwon committed
146

147
148
149
150
    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

Woosuk Kwon's avatar
Woosuk Kwon committed
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
    def get_supported_tasks(self) -> tuple[str]:
        return ("generate",)

    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(
                    self.model,
                    self.vllm_config,
                    self.device,
                )
170
171
            if self.do_spec_decode:
                self.speculator.load_model(self.model)
Woosuk Kwon's avatar
Woosuk Kwon committed
172
173
174
175
        time_after_load = time.perf_counter()

        self.model_memory_usage = m.consumed_memory
        logger.info(
176
177
            "Model loading took %s GiB and %.6f seconds",
            format_gib(m.consumed_memory),
Woosuk Kwon's avatar
Woosuk Kwon committed
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
            time_after_load - time_before_load,
        )

    def get_model(self) -> nn.Module:
        return self.model

    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
        ]

        self.block_tables = BlockTables(
            block_sizes=block_sizes,
            max_num_reqs=self.max_num_reqs,
            max_num_batched_tokens=self.max_num_tokens,
            max_model_len=self.max_model_len,
            device=self.device,
        )

        self.attn_backends, self.attn_metadata_builders = init_attn_backend(
            self.kv_cache_config,
            self.vllm_config,
            self.device,
        )
208
209
210
211
212
213
214
215
        if self.do_spec_decode:
            # HACK(woosuk)
            self.speculator.set_attn(
                self.kv_cache_config,
                self.attn_metadata_builders,
                self.block_tables,
            )

216
217
218
        # TODO(woosuk): Support other backends.
        if not all(b.get_name() == "FLASH_ATTN" for b in self.attn_backends.values()):
            raise NotImplementedError("Only FLASH_ATTN backend is supported currently.")
Woosuk Kwon's avatar
Woosuk Kwon committed
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239

        self.kv_caches: list[torch.Tensor] = []
        init_kv_cache(
            self.kv_caches,
            self.compilation_config.static_forward_context,
            self.kv_cache_config,
            self.attn_backends,
            self.device,
        )
        # Attention groups are not supported.
        self.attn_groups = []  # type: ignore

    def prepare_dummy_attn_metadata(self, input_batch: InputBatch) -> None:
        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
        )
        attn_metadata = build_attn_metadata(
            attn_metadata_builders=self.attn_metadata_builders,
            num_reqs=input_batch.num_reqs,
            num_tokens=input_batch.num_tokens,
240
241
242
            query_start_loc_gpu=input_batch.query_start_loc,
            query_start_loc_cpu=torch.from_numpy(input_batch.query_start_loc_np),
            seq_lens=input_batch.seq_lens,
243
            max_seq_len=self.max_model_len,
Woosuk Kwon's avatar
Woosuk Kwon committed
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
            block_tables=block_tables,
            slot_mappings=slot_mappings,
            kv_cache_config=self.kv_cache_config,
        )
        input_batch.attn_metadata = attn_metadata

    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
        *args,
        skip_attn: bool = True,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        num_reqs = min(num_tokens, self.max_num_reqs)
        input_batch = InputBatch.make_dummy(
            num_reqs=num_reqs,
            num_tokens=num_tokens,
            input_buffers=self.input_buffers,
            device=self.device,
        )
        if not skip_attn:
            self.prepare_dummy_attn_metadata(input_batch)

268
269
        dp_size = self.parallel_config.data_parallel_size
        num_tokens_across_dp = make_num_tokens_across_dp(dp_size, num_tokens)
Woosuk Kwon's avatar
Woosuk Kwon committed
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
        num_sampled_tokens = np.ones(input_batch.num_reqs, dtype=np.int32)
        with (
            self.maybe_dummy_run_with_lora(
                self.lora_config,
                input_batch.num_scheduled_tokens,
                num_sampled_tokens,
            ),
            set_forward_context(
                input_batch.attn_metadata,
                self.vllm_config,
                num_tokens=num_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
            ),
        ):
            hidden_states = self.model(
                input_ids=input_batch.input_ids,
                positions=input_batch.positions,
            )
            sample_hidden_states = hidden_states[input_batch.logits_indices]
        return hidden_states, sample_hidden_states

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> None:
        num_reqs = hidden_states.shape[0]
        sampling_metadata = SamplingMetadata.make_dummy(
            num_reqs=num_reqs,
            device=self.device,
        )
        logits = self.model.compute_logits(hidden_states)
        self.sampler(logits, sampling_metadata)

    @torch.inference_mode()
    def profile_run(self) -> None:
        hidden_states, sample_hidden_states = self._dummy_run(
            self.max_num_tokens,
            skip_attn=True,
        )
        self._dummy_sampler_run(sample_hidden_states)
311
        if self.do_spec_decode:
312
            num_tokens_across_dp = make_num_tokens_across_dp(
313
                self.parallel_config.data_parallel_size, self.max_num_tokens
314
315
316
317
318
319
            )
            self.speculator.run_model(
                self.max_num_tokens,
                attn_metadata=None,
                num_tokens_across_dp=num_tokens_across_dp,
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
        torch.cuda.synchronize()
        del hidden_states, sample_hidden_states
        gc.collect()

    def reset_mm_cache(self) -> None:
        pass

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

    @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

        start_time = time.perf_counter()
341
        gc.collect()
342
        torch.cuda.empty_cache()
Woosuk Kwon's avatar
Woosuk Kwon committed
343
344
345
346
347
348
349
350
351
352
        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

        with self.maybe_setup_dummy_loras(self.lora_config):
            self.cudagraph_manager.capture(
                model=self.model,
                input_buffers=self.input_buffers,
                block_tables=self.block_tables,
                attn_metadata_builders=self.attn_metadata_builders,
                kv_cache_config=self.kv_cache_config,
            )
353
354
            if self.do_spec_decode:
                self.speculator.capture_model()
Woosuk Kwon's avatar
Woosuk Kwon committed
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375

        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

    def warmup_for_prefill(self) -> None:
        # For FlashInfer, we would like to execute a dummy prefill run
        # to trigger JIT compilation.
        if all("FLASHINFER" in b.get_name() for b in self.attn_backends.values()):
            self._dummy_run(self.max_num_tokens, skip_attn=False)
            torch.cuda.synchronize()

    def update_states(self, scheduler_output: SchedulerOutput) -> None:
376
377
378
        if scheduler_output.preempted_req_ids is not None:
            for req_id in scheduler_output.preempted_req_ids:
                self.req_states.remove_request(req_id)
Woosuk Kwon's avatar
Woosuk Kwon committed
379
380
381
382
383
        for req_id in scheduler_output.finished_req_ids:
            self.req_states.remove_request(req_id)

        # Add new requests.
        for new_req_data in scheduler_output.scheduled_new_reqs:
384
385
386
            assert new_req_data.prompt_token_ids is not None
            assert new_req_data.prefill_token_ids is not None
            assert new_req_data.sampling_params is not None
Woosuk Kwon's avatar
Woosuk Kwon committed
387
388
389
390
391
392
393
394
395
396
397
            req_id = new_req_data.req_id
            self.req_states.add_request(
                req_id=req_id,
                prompt_len=len(new_req_data.prompt_token_ids),
                prefill_token_ids=new_req_data.prefill_token_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
                sampling_params=new_req_data.sampling_params,
                lora_request=new_req_data.lora_request,
            )

            req_index = self.req_states.req_id_to_index[req_id]
398
399
400
            self.block_tables.append_block_ids(
                req_index, new_req_data.block_ids, overwrite=True
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
401
402
403
404
405
406
407

        # Add new blocks for the existing requests.
        cached_reqs = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(cached_reqs.req_ids):
            req_index = self.req_states.req_id_to_index[req_id]
            req_new_block_ids = cached_reqs.new_block_ids[i]
            if req_new_block_ids is not None:
408
409
410
                self.block_tables.append_block_ids(
                    req_index, req_new_block_ids, overwrite=False
                )
Woosuk Kwon's avatar
Woosuk Kwon committed
411

412
413
414
        self.req_states.apply_staged_writes()
        self.block_tables.apply_staged_writes()

Woosuk Kwon's avatar
Woosuk Kwon committed
415
416
417
418
419
420
421
422
423
424
425
426
    def prepare_inputs(
        self,
        scheduler_output: SchedulerOutput,
        num_tokens_after_padding: int,
    ) -> InputBatch:
        num_tokens = scheduler_output.total_num_scheduled_tokens
        assert num_tokens > 0
        num_reqs = len(scheduler_output.num_scheduled_tokens)

        # Decode first, then prefill.
        # batch_idx -> req_id
        req_ids = sorted(
427
428
            scheduler_output.num_scheduled_tokens.keys(),
            key=lambda k: scheduler_output.num_scheduled_tokens[k],
Woosuk Kwon's avatar
Woosuk Kwon committed
429
430
431
432
433
434
435
436
        )
        num_scheduled_tokens = np.array(
            [scheduler_output.num_scheduled_tokens[i] for i in req_ids], dtype=np.int32
        )

        idx_mapping_list = [
            self.req_states.req_id_to_index[req_id] for req_id in req_ids
        ]
437
438
        idx_mapping_np = np.array(idx_mapping_list, dtype=np.int32)
        idx_mapping = self.tmp_idx_mapping.copy_to_gpu(idx_mapping_np)
Woosuk Kwon's avatar
Woosuk Kwon committed
439

440
441
442
443
444
        # Get the number of draft tokens for each request.
        if not scheduler_output.scheduled_spec_decode_tokens:
            # No draft token scheduled (common case).
            total_num_draft_tokens = 0
            total_num_logits = num_reqs
445
            cu_num_logits_np = np.arange(num_reqs + 1, dtype=np.int32)
446
447
448
            cu_num_logits = torch.arange(
                num_reqs + 1, device=self.device, dtype=torch.int32
            )
449
            expanded_idx_mapping = idx_mapping
450
451
452
453
454
455
456
457
458
459
460
461
        else:
            draft_tokens = scheduler_output.scheduled_spec_decode_tokens
            num_draft_tokens = np.array(
                [
                    len(draft_tokens[req_id]) if req_id in draft_tokens else 0
                    for req_id in req_ids
                ],
                dtype=np.int32,
            )
            total_num_draft_tokens = int(num_draft_tokens.sum())
            total_num_logits = num_reqs + total_num_draft_tokens

462
463
464
465
466
467
468
469
470
471
472
            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:])
            cu_num_logits = self.tmp_cu_num_logits.copy_to_gpu(cu_num_logits_np)

            expanded_idx_mapping = expand_idx_mapping(
                idx_mapping,
                total_num_logits,
                cu_num_logits,
                max_expand_len=self.num_speculative_steps + 1,
473
474
            )

Woosuk Kwon's avatar
Woosuk Kwon committed
475
476
477
        # Block tables: num_kv_cache_groups x [num_reqs, max_num_blocks]
        block_tables = self.block_tables.gather_block_tables(idx_mapping)

478
        # Get query_start_loc.
479
480
481
        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])
482
483
        # Pad for full CUDA graph mode.
        # Some attention backends like FA3 require query_start_loc to be non-decreasing.
484
485
486
487
488
489
490
491
        query_start_loc_np[num_reqs + 1 :] = num_tokens
        self.tmp_query_start_loc.copy_to_gpu(
            query_start_loc_np,
            out=self.input_buffers.query_start_loc,
        )
        query_start_loc_np = query_start_loc_np[: num_reqs + 1]
        query_start_loc_cpu = torch.from_numpy(query_start_loc_np)
        query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
492

493
        # Get prefill tokens.
494
        prepare_prefill_inputs(
495
496
497
            self.input_buffers.input_ids,
            self.req_states.next_prefill_tokens,
            idx_mapping,
498
            query_start_loc,
499
500
            self.req_states.prefill_token_ids.gpu,
            self.req_states.prefill_len.gpu,
501
            self.req_states.num_computed_tokens.gpu,
Woosuk Kwon's avatar
Woosuk Kwon committed
502
503
        )

504
505
506
        # Prepare positions and seq_lens.
        prepare_pos_seq_lens(
            idx_mapping,
507
508
            query_start_loc,
            self.req_states.num_computed_tokens.gpu,
509
510
511
512
513
514
            self.input_buffers.positions,
            self.input_buffers.seq_lens,
        )
        seq_lens = self.input_buffers.seq_lens[:num_reqs]

        # Some input token ids are directly read from the last sampled tokens
515
516
        # and draft tokens. Also, get the logits indices to sample tokens from.
        logits_indices = combine_sampled_and_draft_tokens(
517
            self.input_buffers.input_ids,
Woosuk Kwon's avatar
Woosuk Kwon committed
518
519
            idx_mapping,
            self.req_states.last_sampled_tokens,
520
            query_start_loc,
521
522
            seq_lens,
            self.req_states.prefill_len.gpu,
523
524
525
            self.req_states.draft_tokens,
            cu_num_logits,
            total_num_logits,
Woosuk Kwon's avatar
Woosuk Kwon committed
526
527
528
529
        )

        # Compute slot mappings: [num_kv_cache_groups, num_tokens]
        slot_mappings = self.block_tables.compute_slot_mappings(
530
            query_start_loc, self.input_buffers.positions[:num_tokens]
Woosuk Kwon's avatar
Woosuk Kwon committed
531
532
533
534
535
536
537
        )

        # Layer name -> attention metadata.
        attn_metadata = build_attn_metadata(
            attn_metadata_builders=self.attn_metadata_builders,
            num_reqs=num_reqs,
            num_tokens=num_tokens,
538
            query_start_loc_gpu=query_start_loc,
539
            query_start_loc_cpu=query_start_loc_cpu,
Woosuk Kwon's avatar
Woosuk Kwon committed
540
            seq_lens=self.input_buffers.seq_lens,
541
            max_seq_len=self.max_model_len,
Woosuk Kwon's avatar
Woosuk Kwon committed
542
543
544
545
546
            block_tables=block_tables,
            slot_mappings=slot_mappings,
            kv_cache_config=self.kv_cache_config,
        )

547
        input_ids = self.input_buffers.input_ids[:num_tokens_after_padding]
548
        positions = self.input_buffers.positions[:num_tokens_after_padding]
Woosuk Kwon's avatar
Woosuk Kwon committed
549
550
551
552
553
        return InputBatch(
            req_ids=req_ids,
            num_reqs=num_reqs,
            idx_mapping=idx_mapping,
            idx_mapping_np=idx_mapping_np,
554
            expanded_idx_mapping=expanded_idx_mapping,
Woosuk Kwon's avatar
Woosuk Kwon committed
555
556
557
            num_scheduled_tokens=num_scheduled_tokens,
            num_tokens=num_tokens,
            num_tokens_after_padding=num_tokens_after_padding,
558
            num_draft_tokens=total_num_draft_tokens,
559
            query_start_loc=query_start_loc,
Woosuk Kwon's avatar
Woosuk Kwon committed
560
            query_start_loc_np=query_start_loc_np,
561
            seq_lens=seq_lens,
Woosuk Kwon's avatar
Woosuk Kwon committed
562
563
564
565
            input_ids=input_ids,
            positions=positions,
            attn_metadata=attn_metadata,
            logits_indices=logits_indices,
566
            cu_num_logits=cu_num_logits,
567
            cu_num_logits_np=cu_num_logits_np,
Woosuk Kwon's avatar
Woosuk Kwon committed
568
569
570
571
572
573
574
575
        )

    def sample(
        self,
        hidden_states: torch.Tensor,
        input_batch: InputBatch,
        sampling_metadata: SamplingMetadata,
        grammar_output: GrammarOutput | None,
576
    ) -> tuple[SamplerOutput, torch.Tensor, torch.Tensor]:
Woosuk Kwon's avatar
Woosuk Kwon committed
577
578
579
580
        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.
581
582
583
584
585
586
            self.structured_outputs_worker.apply_grammar_bitmask(
                logits,
                input_batch,
                grammar_output.structured_output_request_ids,
                grammar_output.grammar_bitmask,
            )
587

588
        # Sample tokens and compute logprobs (if needed).
Woosuk Kwon's avatar
Woosuk Kwon committed
589
        sampler_output = self.sampler(logits, sampling_metadata)
590
591
592

        if input_batch.num_draft_tokens == 0:
            # No draft tokens (common case).
593
594
595
            num_sampled = torch.ones(
                input_batch.num_reqs, dtype=torch.int32, device=self.device
            )
596
        else:
597
            # Rejection sampling for spec decoding.
598
599
600
601
602
603
604
605
            input_ids = input_batch.input_ids[input_batch.logits_indices]
            sampled_tokens, num_sampled = rejection_sample(
                sampler_output.sampled_token_ids,
                input_ids,
                input_batch.cu_num_logits,
                self.num_speculative_steps,
            )
            sampler_output.sampled_token_ids = sampled_tokens
606
607
608
609
610
611
612
613
614
615

        # 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(
            num_sampled,
            input_batch.seq_lens,
            input_batch.cu_num_logits,
            input_batch.idx_mapping,
            self.req_states.prefill_len.gpu,
        )
616
        return sampler_output, num_sampled, num_rejected
Woosuk Kwon's avatar
Woosuk Kwon committed
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631

    def compute_prompt_logprobs(
        self,
        hidden_states: torch.Tensor,
        input_batch: InputBatch,
    ) -> dict[str, LogprobsTensors]:
        idx_mapping_np = input_batch.idx_mapping_np
        needs_prompt_logprobs = self.req_states.needs_prompt_logprobs[idx_mapping_np]
        if not np.any(needs_prompt_logprobs):
            # No request asks for prompt logprobs.
            return {}

        prompt_lens = self.req_states.prompt_len[idx_mapping_np]
        # NOTE(woosuk): -1 because the last prompt token's hidden state is not
        # needed for prompt logprobs.
632
633
        computed_prefill = self.req_states.num_computed_prefill_tokens[idx_mapping_np]
        includes_prompt = computed_prefill < prompt_lens - 1
Woosuk Kwon's avatar
Woosuk Kwon committed
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
        # NOTE(woosuk): If the request was resumed after preemption, its prompt
        # logprobs must have been computed before preemption. Skip.
        resumed_after_prompt = (
            prompt_lens < self.req_states.prefill_len.np[idx_mapping_np]
        )
        needs_prompt_logprobs &= includes_prompt & ~resumed_after_prompt
        if not np.any(needs_prompt_logprobs):
            return {}

        # Just to be safe, clone the input ids.
        n = input_batch.num_tokens
        # Shift the input ids by one.
        token_ids = torch.empty_like(input_batch.input_ids[:n])
        token_ids[: n - 1] = input_batch.input_ids[1:n]
        # To avoid out-of-bound access, set the last token id to 0.
        token_ids[n - 1] = 0

        # Handle chunked prompts.
652
653
        pos_after_step = computed_prefill + input_batch.num_scheduled_tokens
        is_prompt_chunked = pos_after_step < prompt_lens
654
655
        prefill_token_ids = self.req_states.prefill_token_ids.gpu
        query_start_loc_np = input_batch.query_start_loc_np
Woosuk Kwon's avatar
Woosuk Kwon committed
656
657
658
659
660
661
662
        for i, req_id in enumerate(input_batch.req_ids):
            if not needs_prompt_logprobs[i]:
                continue
            if not is_prompt_chunked[i]:
                continue
            # The prompt is chunked. Get the next prompt token.
            req_idx = input_batch.idx_mapping_np[i]
663
664
665
            idx = int(query_start_loc_np[i + 1] - 1)
            # NOTE(woosuk): This triggers two GPU operations.
            next_prompt_token = prefill_token_ids[req_idx, pos_after_step[i]]
Woosuk Kwon's avatar
Woosuk Kwon committed
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
            token_ids[idx] = next_prompt_token

        # NOTE(woosuk): We mask out logprobs for negative tokens.
        prompt_logprobs, prompt_ranks = compute_prompt_logprobs(
            token_ids,
            hidden_states[:n],
            self.model.compute_logits,
        )

        prompt_token_ids = token_ids.unsqueeze(-1)
        prompt_logprobs_dict: dict[str, LogprobsTensors] = {}
        for i, req_id in enumerate(input_batch.req_ids):
            if not needs_prompt_logprobs[i]:
                continue

681
682
            start_idx = query_start_loc_np[i]
            end_idx = query_start_loc_np[i + 1]
Woosuk Kwon's avatar
Woosuk Kwon committed
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
            assert start_idx < end_idx, (
                f"start_idx ({start_idx}) >= end_idx ({end_idx})"
            )
            logprobs = LogprobsTensors(
                logprob_token_ids=prompt_token_ids[start_idx:end_idx],
                logprobs=prompt_logprobs[start_idx:end_idx],
                selected_token_ranks=prompt_ranks[start_idx:end_idx],
            )

            req_extra_data = self.req_states.extra_data[req_id]
            prompt_logprobs_list = req_extra_data.in_progress_prompt_logprobs
            if is_prompt_chunked[i]:
                # Prompt is chunked. Do not return the logprobs yet.
                prompt_logprobs_list.append(logprobs)
                continue

            if prompt_logprobs_list:
                # Merge the in-progress logprobs.
                prompt_logprobs_list.append(logprobs)
                logprobs = LogprobsTensors(
                    logprob_token_ids=torch.cat(
                        [x.logprob_token_ids for x in prompt_logprobs_list]
                    ),
                    logprobs=torch.cat([x.logprobs for x in prompt_logprobs_list]),
                    selected_token_ranks=torch.cat(
                        [x.selected_token_ranks for x in prompt_logprobs_list]
                    ),
                )
                prompt_logprobs_list.clear()

            prompt_logprobs_dict[req_id] = logprobs
        return prompt_logprobs_dict

    def postprocess(
        self,
        input_batch: InputBatch,
719
720
        sampled_tokens: torch.Tensor,
        num_sampled: torch.Tensor,
721
        num_rejected: torch.Tensor,
722
723
    ) -> None:
        # Update the number of computed tokens.
724
        post_update(
725
            input_batch.idx_mapping,
726
            self.req_states.num_computed_tokens.gpu,
727
            self.req_states.last_sampled_tokens,
728
            self.req_states.output_bin_counts,
729
730
            sampled_tokens,
            num_sampled,
731
            num_rejected,
732
            input_batch.query_start_loc,
Woosuk Kwon's avatar
Woosuk Kwon committed
733
        )
734
735

        # Update the number of computed prefill tokens.
Woosuk Kwon's avatar
Woosuk Kwon committed
736
        idx_mapping_np = input_batch.idx_mapping_np
737
738
739
740
741
        computed_prefill = self.req_states.num_computed_prefill_tokens
        # TODO(woosuk): Simplify this.
        computed_prefill[idx_mapping_np] = np.minimum(
            computed_prefill[idx_mapping_np] + input_batch.num_scheduled_tokens,
            self.req_states.prefill_len.np[idx_mapping_np],
Woosuk Kwon's avatar
Woosuk Kwon committed
742
743
        )

744
745
746
747
748
749
750
751
    @torch.inference_mode()
    def propose_draft(
        self,
        input_batch: InputBatch,
        sampling_metadata: SamplingMetadata,
        last_hidden_states: torch.Tensor,
        aux_hidden_states: list[torch.Tensor] | None,
        num_sampled: torch.Tensor,
752
        num_rejected: torch.Tensor,
753
754
    ) -> torch.Tensor:
        assert self.speculator is not None
755
756
757
758
759
760
        last_sampled_tokens = self.req_states.last_sampled_tokens[
            input_batch.idx_mapping
        ]
        next_prefill_tokens = self.req_states.next_prefill_tokens[
            input_batch.idx_mapping
        ]
761
762
763
764
765
766
        draft_tokens = self.speculator.propose(
            input_batch,
            sampling_metadata,
            last_hidden_states,
            aux_hidden_states,
            num_sampled,
767
            num_rejected,
768
            last_sampled_tokens,
769
770
771
772
            next_prefill_tokens,
        )
        return draft_tokens

Woosuk Kwon's avatar
Woosuk Kwon committed
773
774
775
776
777
    def get_cudagraph_and_dp_padding(
        self,
        scheduler_output: SchedulerOutput,
    ) -> tuple[CUDAGraphMode, int, torch.Tensor | None]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
778
779
        dp_size = self.parallel_config.data_parallel_size
        if dp_size == 1:
Woosuk Kwon's avatar
Woosuk Kwon committed
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
            # No DP. Only consider CUDA graphs.
            if total_num_scheduled_tokens == 0:
                # Special case: no tokens to run.
                return CUDAGraphMode.NONE, 0, None

            cudagraph_size = self.cudagraph_manager.get_cudagraph_size(
                scheduler_output, total_num_scheduled_tokens
            )
            if cudagraph_size is not None:
                # Use full CUDA graph.
                return CUDAGraphMode.FULL, cudagraph_size, None
            # Fall back to eager mode.
            # TODO(woosuk): Support piecewise CUDA graphs.
            return CUDAGraphMode.NONE, total_num_scheduled_tokens, None

        # Consider DP padding and CUDA graph.
        if total_num_scheduled_tokens == 0:
            # Special handling is needed for 0.
            cudagraph_size_before_dp: int | None = 0
        else:
            cudagraph_size_before_dp = self.cudagraph_manager.get_cudagraph_size(
                scheduler_output, total_num_scheduled_tokens
            )
            if cudagraph_size_before_dp is None:
                cudagraph_size_before_dp = -1

        assert cudagraph_size_before_dp is not None
807
        dp_rank = self.parallel_config.data_parallel_rank
Woosuk Kwon's avatar
Woosuk Kwon committed
808
809
810
        num_tokens_across_dp, cudagraph_size_across_dp = get_batch_metadata_across_dp(
            total_num_scheduled_tokens,
            cudagraph_size_before_dp,
811
812
            dp_size,
            dp_rank,
Woosuk Kwon's avatar
Woosuk Kwon committed
813
814
815
816
817
818
819
820
821
822
        )
        if all(cudagraph_size_across_dp >= 0):
            # If all ranks can use CUDA graph, pad to the maximum number of tokens
            # across DP and use CUDA graph.
            num_tokens_after_padding = int(cudagraph_size_across_dp.max().item())
            cudagraph_mode = CUDAGraphMode.FULL
        else:
            # If any of the ranks cannot use CUDA graph, use eager mode for all ranks.
            # No padding is needed except for ranks that have no tokens to run.
            num_tokens_across_dp = torch.clamp(num_tokens_across_dp, min=1)
823
            num_tokens_after_padding = num_tokens_across_dp[dp_rank]
Woosuk Kwon's avatar
Woosuk Kwon committed
824
825
826
827
828
829
830
831
832
833
834
835
836
            cudagraph_mode = CUDAGraphMode.NONE
        return cudagraph_mode, num_tokens_after_padding, num_tokens_across_dp

    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: SchedulerOutput,
        intermediate_tensors: Any | None = None,
        dummy_run: bool = False,
    ) -> ModelRunnerOutput | None:
        assert intermediate_tensors is None
        if scheduler_output.total_num_scheduled_tokens == 0 and not dummy_run:
            # No need to run the model.
837
838
            self.update_states(scheduler_output)
            return EMPTY_MODEL_RUNNER_OUTPUT
Woosuk Kwon's avatar
Woosuk Kwon committed
839
840
841
842

        cudagraph_mode, num_tokens_after_padding, num_tokens_across_dp = (
            self.get_cudagraph_and_dp_padding(scheduler_output)
        )
843
844
845
846
847
848
849
850
851
852
853
854
        self.update_states(scheduler_output)
        if num_tokens_after_padding == 0:
            # All DP ranks have zero tokens to run.
            return EMPTY_MODEL_RUNNER_OUTPUT

        if not dummy_run:
            # Common case.
            # Prepare all the inputs and copy to the input buffers.
            input_batch = self.prepare_inputs(
                scheduler_output,
                num_tokens_after_padding,
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
855

856
857
858
859
860
861
862
863
864
865
866
            pos = input_batch.positions[input_batch.logits_indices]
            sampling_metadata = self.req_states.make_sampling_metadata(
                input_batch.expanded_idx_mapping, input_batch.idx_mapping_np, pos
            )

            if self.lora_config:
                # Activate LoRA adapters.
                lora_inputs = self.req_states.make_lora_inputs(
                    input_batch.req_ids,
                    input_batch.idx_mapping_np,
                    input_batch.num_scheduled_tokens,
Woosuk Kwon's avatar
Woosuk Kwon committed
867
                )
868
869
870
871
872
873
874
875
876
877
878
879
                self._set_active_loras(*lora_inputs)
        else:
            # No actual tokens to run. A dummy run for DP.
            num_reqs = min(num_tokens_after_padding, self.max_num_reqs)
            input_batch = InputBatch.make_dummy(
                num_reqs=num_reqs,
                num_tokens=num_tokens_after_padding,
                input_buffers=self.input_buffers,
                device=self.device,
            )
            self.prepare_dummy_attn_metadata(input_batch)
            sampling_metadata = None
Woosuk Kwon's avatar
Woosuk Kwon committed
880
881
882
883
884
885
886
887
888
889
890

        # Run model.
        if cudagraph_mode == CUDAGraphMode.FULL:
            # Run CUDA graph.
            # NOTE(woosuk): Here, we don't need to pass the input tensors,
            # because they are already copied to the CUDA graph input buffers.
            hidden_states = self.cudagraph_manager.run(
                input_batch.num_tokens_after_padding
            )
        else:
            # Run PyTorch model in eager mode.
891
            # TODO(woosuk): Support piecewise CUDA graph.
Woosuk Kwon's avatar
Woosuk Kwon committed
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
            with set_forward_context(
                input_batch.attn_metadata,
                self.vllm_config,
                num_tokens=input_batch.num_tokens_after_padding,
                cudagraph_runtime_mode=cudagraph_mode,
                num_tokens_across_dp=num_tokens_across_dp,
            ):
                hidden_states = self.model(
                    input_ids=input_batch.input_ids,
                    positions=input_batch.positions,
                )

        self.execute_model_state = hidden_states, input_batch, sampling_metadata
        return None

    @torch.inference_mode()
    def sample_tokens(
        self,
        grammar_output: GrammarOutput | None,
    ) -> AsyncOutput | ModelRunnerOutput:
        assert self.execute_model_state is not None
        hidden_states, input_batch, sampling_metadata = self.execute_model_state
        self.execute_model_state = None  # type: ignore
        assert sampling_metadata is not None

917
        sampler_output, num_sampled, num_rejected = self.sample(
Woosuk Kwon's avatar
Woosuk Kwon committed
918
919
920
            hidden_states, input_batch, sampling_metadata, grammar_output
        )
        prompt_logprobs_dict = self.compute_prompt_logprobs(hidden_states, input_batch)
921
922
923
924
925
926
927
928

        # 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
929
            prompt_logprobs_dict=prompt_logprobs_dict,  # type: ignore[arg-type]
930
931
932
933
        )
        async_output = AsyncOutput(
            model_runner_output=model_runner_output,
            sampler_output=sampler_output,
934
            num_sampled_tokens=num_sampled,
935
936
937
938
939
940
941
942
943
944
            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(
945
            input_batch, sampler_output.sampled_token_ids, num_sampled, num_rejected
Woosuk Kwon's avatar
Woosuk Kwon committed
946
        )
947
        if self.do_spec_decode:
948
            draft_tokens = self.propose_draft(
949
950
951
952
                input_batch,
                sampling_metadata,
                hidden_states,
                None,  # aux_hidden_states
953
954
                num_sampled,
                num_rejected,
955
            )
956
            self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens
957
958
959
960

        if self.use_async_scheduling:
            return async_output
        return async_output.get_output()