gpu_model_runner.py 155 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import gc
5
import itertools
6
import time
7
8
from collections import defaultdict
from collections.abc import Iterator
9
from contextlib import contextmanager
10
from copy import deepcopy
11
from typing import TYPE_CHECKING, Any, Optional, Union, cast
12
13
14
15
16

import numpy as np
import torch
import torch.distributed
import torch.nn as nn
17
from tqdm import tqdm
18

19
import vllm.envs as envs
20
from vllm.attention import Attention, AttentionType
21
from vllm.attention.backends.abstract import AttentionBackend
22
from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
23
from vllm.compilation.counter import compilation_counter
24
25
26
from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig,
27
                         get_layers_from_vllm_config, update_config)
28
from vllm.distributed.eplb.eplb_state import EplbState
29
30
from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group)
31
from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
32
from vllm.distributed.parallel_state import (
33
    get_pp_group, get_tp_group, graph_capture, is_global_first_rank,
34
    prepare_communication_buffer_for_model)
35
36
from vllm.forward_context import (BatchDescriptor, DPMetadata,
                                  set_forward_context)
37
from vllm.logger import init_logger
38
39
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.mamba.abstract import MambaBase
40
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
41
from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
42
from vllm.model_executor.models.interfaces import (is_mixture_of_experts,
43
                                                   supports_eagle3,
44
45
46
                                                   supports_transcription)
from vllm.model_executor.models.interfaces_base import (
    VllmModelForPooling, is_pooling_model, is_text_generation_model)
47
from vllm.multimodal import MULTIMODAL_REGISTRY
48
from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargsItem,
49
                                    PlaceholderRange)
50
from vllm.multimodal.utils import group_mm_kwargs_by_modality
51
from vllm.pooling_params import PoolingParams
52
from vllm.sampling_params import SamplingType
53
from vllm.sequence import IntermediateTensors, PoolerOutput
54
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
55
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
56
57
58
                        GiB_bytes, LazyLoader, cdiv, check_use_alibi,
                        get_dtype_size, is_pin_memory_available, round_up,
                        supports_dynamo)
59
from vllm.v1.attention.backends.utils import (
60
    AttentionCGSupport, AttentionMetadataBuilder, CommonAttentionMetadata,
61
    create_fast_prefill_custom_backend,
62
    reorder_batch_to_split_decodes_and_prefills)
63
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
64
65
from vllm.v1.kv_cache_interface import (AttentionSpec,
                                        ChunkedLocalAttentionSpec,
66
                                        EncoderOnlyAttentionSpec,
67
                                        FullAttentionSpec, KVCacheConfig,
68
69
                                        KVCacheGroupSpec, KVCacheSpec,
                                        MambaSpec, SlidingWindowSpec)
70
71
from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput,
                             DraftTokenIds, LogprobsTensors, ModelRunnerOutput)
72
from vllm.v1.pool.metadata import PoolingMetadata
73
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
74
from vllm.v1.sample.metadata import SamplingMetadata
75
from vllm.v1.sample.rejection_sampler import RejectionSampler
76
from vllm.v1.sample.sampler import Sampler
77
from vllm.v1.spec_decode.eagle import EagleProposer
78
from vllm.v1.spec_decode.medusa import MedusaProposer
79
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
80
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
81
from vllm.v1.utils import CpuGpuBuffer
82
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
83
from vllm.v1.worker.kv_connector_model_runner_mixin import (
84
    KVConnectorModelRunnerMixin, KVConnectorOutput)
85
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
86

87
88
89
90
from .utils import (AttentionGroup, MultiModalBudget,
                    add_kv_sharing_layers_to_kv_cache_groups, bind_kv_cache,
                    gather_mm_placeholders, sanity_check_mm_encoder_outputs,
                    scatter_mm_placeholders)
91

92
if TYPE_CHECKING:
93
94
    import xgrammar as xgr

95
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
96
    from vllm.v1.core.sched.output import SchedulerOutput
97
98
else:
    xgr = LazyLoader("xgr", globals(), "xgrammar")
99
100
101
102

logger = init_logger(__name__)


103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):

    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        self._model_runner_output = model_runner_output
        self._invalid_req_indices = invalid_req_indices

        # Event on the copy stream so we can synchronize the non-blocking copy.
        self._async_copy_ready_event = torch.cuda.Event()

        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids

        # Initiate the copy on a separate stream, but do not synchronize it.
        default_stream = torch.cuda.current_stream()
        with torch.cuda.stream(async_output_copy_stream):
            async_output_copy_stream.wait_stream(default_stream)
            self._sampled_token_ids_cpu = self._sampled_token_ids.to(
                'cpu', non_blocking=True)
            self._async_copy_ready_event.record()

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
        
        This function blocks until the copy is finished.
        """
        self._async_copy_ready_event.synchronize()

        # Release the device tensor once the copy has completed
        del self._sampled_token_ids

        valid_sampled_token_ids = self._sampled_token_ids_cpu.tolist()
        for i in self._invalid_req_indices:
            valid_sampled_token_ids[i].clear()

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
        return output


150
class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
151
152
153

    def __init__(
        self,
154
        vllm_config: VllmConfig,
155
        device: torch.device,
156
    ):
157
158
159
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
160
        self.compilation_config = vllm_config.compilation_config
161
162
163
164
165
166
        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
167

168
169
170
171
        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
        set_cpu_offload_max_bytes(
            int(self.cache_config.cpu_offload_gb * 1024**3))

172
173
174
175
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
176
        self.device = device
177
178
179
180
181
182
183
184
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
        if cache_config.cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        else:
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                cache_config.cache_dtype]

185
        self.is_pooling_model = (model_config.runner_type == 'pooling')
186
187
188
        self.is_multimodal_raw_input_only_model = (
            model_config.is_multimodal_raw_input_only_model)

189
190
        self.max_model_len = model_config.max_model_len
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
191
        self.max_num_reqs = scheduler_config.max_num_seqs
192
193

        # Model-related.
194
195
        self.num_query_heads = model_config.get_num_attention_heads(
            parallel_config)
196
        self.hidden_size = model_config.get_hidden_size()
197
        self.attention_chunk_size = model_config.attention_chunk_size
198
199
        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)
200

201
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
202

203
        # Multi-modal data support
204
        self.mm_registry = MULTIMODAL_REGISTRY
205
        self.uses_mrope = model_config.uses_mrope
206
207
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
            model_config)
208

209
        # Sampler
210
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
211

212
213
214
215
216
217
218
        self.eplb_state: Optional[EplbState] = None
        """
        State of the expert parallelism load balancer.

        Will be lazily initialized when the model is loaded.
        """

219
        # Lazy initializations
220
        # self.model: nn.Module  # Set after load_model
221
        # Initialize in initialize_kv_cache
222
        self.kv_caches: list[torch.Tensor] = []
223
224
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
225
226
        # self.kv_cache_config: KVCacheConfig

227
228
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
229

230
        self.use_aux_hidden_state_outputs = False
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
                self.drafter = EagleProposer(self.vllm_config, self.device,
                                             self)  # type: ignore
                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
                    vllm_config=self.vllm_config,
                    device=self.device)  # type: ignore
            else:
                raise ValueError("Unknown speculative decoding method: "
                                 f"{self.speculative_config.method}")
            self.rejection_sampler = RejectionSampler()
251

252
        # Request states.
253
        self.requests: dict[str, CachedRequestState] = {}
254

255
256
257
258
259
260
261
262
263
        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
264
265
266
267
268
269
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
            max_model_len=self.max_model_len,
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
270
            vocab_size=self.model_config.get_vocab_size(),
271
            block_sizes=[self.cache_config.block_size],
272
            is_spec_decode=bool(self.vllm_config.speculative_config),
273
274
275
276
277
            logitsprocs=build_logitsprocs(
                self.vllm_config, self.device, self.pin_memory,
                self.is_pooling_model,
                self.vllm_config.model_config.logits_processors),
            is_pooling_model=self.is_pooling_model,
278
        )
279

280
281
282
283
        self.use_async_scheduling = self.scheduler_config.async_scheduling
        self.async_output_copy_stream = torch.cuda.Stream() if \
            self.use_async_scheduling else None

284
        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
285
286
287
        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
288
289
290
291
        if self.compilation_config.cudagraph_capture_sizes and \
                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE:
            self.cudagraph_batch_sizes = list(
                reversed(self.compilation_config.cudagraph_capture_sizes))
292

293
        # Cache the device properties.
294
        self._init_device_properties()
295

296
        # Persistent buffers for CUDA graphs.
297
298
299
300
301
302
303
304
305
306
307
        self.input_ids = self._make_buffer(self.max_num_tokens,
                                           dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens,
                                           dtype=torch.int64)
        self.query_start_loc = self._make_buffer(self.max_num_reqs + 1,
                                                 dtype=torch.int32)
        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
        self.inputs_embeds = torch.zeros(
            (self.max_num_tokens, self.hidden_size),
            dtype=self.dtype,
            device=self.device)
308
309

        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
310
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
311
312
313
314
            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
315
316
317
318
319
320

            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
321
322
            self.mrope_positions = self._make_buffer(
                (3, self.max_num_tokens + 1), dtype=torch.int64)
323

324
325
        # None in the first PP rank. The rest are set after load_model.
        self.intermediate_tensors: Optional[IntermediateTensors] = None
326

327
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
328
        # Keep in int64 to avoid overflow with long context
329
        self.arange_np = np.arange(max(self.max_num_reqs + 1,
330
331
                                       self.max_model_len,
                                       self.max_num_tokens),
332
                                   dtype=np.int64)
333

334
335
336
337
338
        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}
339
340
341
342
343
344
        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

        self.kv_sharing_fast_prefill_logits_indices = None
        if self.cache_config.kv_sharing_fast_prefill:
            self.kv_sharing_fast_prefill_logits_indices = torch.zeros(
                self.max_num_tokens, dtype=torch.int32, device=self.device)
345

346
347
348
349
350
351
        self.uniform_decode_query_len = 1 if not self.speculative_config else \
            1 + self.speculative_config.num_speculative_tokens

        # Cudagraph dispatcher for runtime cudagraph dispatching.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)

352
353
354
355
        self.mm_budget = (MultiModalBudget(
            self.model_config,
            self.scheduler_config,
            self.mm_registry,
356
        ) if self.supports_mm_inputs else None)
357

358
359
        self.reorder_batch_threshold: Optional[int] = None

360
361
362
363
364
        # Attention layers that are only in the KVCacheConfig of the runner
        # (e.g., KV sharing, encoder-only attention), but not in the
        # KVCacheConfig of the scheduler.
        self.runner_only_attn_layers: set[str] = set()

365
366
367
        # Cached outputs.
        self._draft_token_ids: Optional[Union[list[list[int]],
                                              torch.Tensor]] = None
368
369
370
371
372
        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
373
            pin_memory=self.pin_memory)
374

375
376
377
378
379
380
    def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
        return CpuGpuBuffer(*args,
                            dtype=dtype,
                            device=self.device,
                            pin_memory=self.pin_memory)

381
382
383
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

384
        if not self.is_pooling_model:
385
386
            return model_kwargs

387
388
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
389
390
391
392
393
394
395
396
397
398
399

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
            if param.extra_kwargs is not None and \
            (token_types := param.extra_kwargs.get(
                "compressed_token_type_ids")) is not None:
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

400
        seq_lens = self.seq_lens.gpu[:num_reqs]
401
402
403
404
405
406
407
408
409
410
411
        token_type_ids = []

        for i in range(num_reqs):
            pos = token_type_id_requests.get(i, seq_lens[i])
            ids = (torch.arange(seq_lens[i]) >= pos).int()
            token_type_ids.append(ids)

        model_kwargs["token_type_ids"] = torch.concat(token_type_ids).to(
            device=self.device)
        return model_kwargs

412
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
413
414
        """
        Update the order of requests in the batch based on the attention
415
        backend's needs. For example, some attention backends (namely MLA) may
416
417
418
419
420
421
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
422
423
424
425
426
427
428
429
        # Attention free models have zero kv_cache_goups, however models
        # like Mamba are also attention free but use the kv_cache for
        # keeping its internal state. This is why we check the number
        # of kv_cache groups instead of solely checking
        # for self.model_config.is_attention_free.
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return

430
431
432
433
434
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
                decode_threshold=self.reorder_batch_threshold)
435

436
437
438
439
440
441
442
443
444
445
446
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
        """Initialize attributes from torch.cuda.get_device_properties
        """
        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

    # Note: used for model runner override.
    def _sync_device(self) -> None:
        torch.cuda.synchronize()

447
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
448
449
450
451
452
453
        """Update the cached states and the persistent batch with the scheduler
        output.

        The updated states are used by the `_prepare_inputs` function to create
        the input GPU tensors for the model.

454
455
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
456
457
        """
        # Remove finished requests from the cached states.
458
459
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
460
461
462
463
464
465
466
        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
467
            self.input_batch.remove_request(req_id)
468
469

        # Free the cached encoder outputs.
470
471
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
472

473
474
475
476
477
478
479
480
481
482
483
484
485
        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
486
            self.input_batch.remove_request(req_id)
487

488
        reqs_to_add: list[CachedRequestState] = []
489
        # Add new requests to the cached states.
490
491
492
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
493
            pooling_params = new_req_data.pooling_params
494

495
496
            if sampling_params and \
                sampling_params.sampling_type == SamplingType.RANDOM_SEED:
497
498
499
500
501
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

502
503
            if self.is_pooling_model:
                assert pooling_params is not None
504
505
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
506

507
                model = cast(VllmModelForPooling, self.get_model())
508
                to_update = model.pooler.get_pooling_updates(task)
509
510
                to_update.apply(pooling_params)

511
            req_state = CachedRequestState(
512
                req_id=req_id,
513
                prompt_token_ids=new_req_data.prompt_token_ids,
514
                mm_kwargs=new_req_data.mm_kwargs,
515
                mm_positions=new_req_data.mm_positions,
516
                mm_hashes=new_req_data.mm_hashes,
517
                sampling_params=sampling_params,
518
                pooling_params=pooling_params,
519
                generator=generator,
520
521
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
522
                output_token_ids=[],
523
                lora_request=new_req_data.lora_request,
524
            )
525
526
            self.requests[req_id] = req_state

527
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
528
            if self.uses_mrope:
529
                self._init_mrope_positions(req_state)
530

531
            reqs_to_add.append(req_state)
532

533
        # Update the states of the running/resumed requests.
534
        is_last_rank = get_pp_group().is_last_rank
535
536
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
537
            req_state = self.requests[req_id]
538
539
540
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
            resumed_from_preemption = req_data.resumed_from_preemption[i]
541

542
            # Update the cached states.
543
            req_state.num_computed_tokens = num_computed_tokens
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560

            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
                num_new_tokens = (num_computed_tokens + len(new_token_ids) -
                                  req_state.num_tokens)
                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
                    req_state.output_token_ids.extend(
                        new_token_ids[-num_new_tokens:])

561
            # Update the block IDs.
562
            if not resumed_from_preemption:
563
564
565
566
567
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
                    for block_ids, new_ids in zip(req_state.block_ids,
                                                  new_block_ids):
                        block_ids.extend(new_ids)
568
            else:
569
                assert new_block_ids is not None
570
571
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
572
                req_state.block_ids = new_block_ids
573
574
575
576
577
578

            req_index = self.input_batch.req_id_to_index.get(req_id)
            if req_index is None:
                # The request is not in the persistent batch.
                # The request was either preempted and resumed later, or was not
                # scheduled in the previous step and needs to be added again.
579
                reqs_to_add.append(req_state)
580
581
582
583
                continue

            # Update the persistent batch.
            self.input_batch.num_computed_tokens_cpu[req_index] = (
584
                num_computed_tokens)
585
586
587
            if new_block_ids is not None:
                self.input_batch.block_table.append_row(
                    new_block_ids, req_index)
588
589
590
591
592
593
594

            # For the last rank, we don't need to update the token_ids_cpu
            # because the sampled tokens are already cached.
            if not is_last_rank:
                # Add new_token_ids to token_ids_cpu.
                start_token_index = num_computed_tokens
                end_token_index = num_computed_tokens + len(new_token_ids)
595
                self.input_batch.token_ids_cpu[
596
597
598
599
600
                    req_index,
                    start_token_index:end_token_index] = new_token_ids
                self.input_batch.num_tokens_no_spec[
                    req_index] = end_token_index
                self.input_batch.num_tokens[req_index] = end_token_index
601

602
603
604
605
606
607
608
609
610
611
612
613
            # Add spec_token_ids to token_ids_cpu.
            spec_token_ids = (
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, ()))
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
                    req_index, start_index:end_token_index] = spec_token_ids
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens

614
615
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
616
617
        for request in reqs_to_add:
            self.input_batch.add_request(request)
618

619
620
621
622
623
624
        # Condense the batched states if there are gaps left by removed requests
        self.input_batch.condense()
        # Allow attention backend to reorder the batch, potentially
        self._may_reorder_batch(scheduler_output)
        # Refresh batch metadata with any pending updates.
        self.input_batch.refresh_metadata()
625

626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
    def _init_mrope_positions(self, req_state: CachedRequestState):
        image_grid_thw = []
        video_grid_thw = []
        second_per_grid_ts = []
        audio_feature_lengths = []
        use_audio_in_video = False
        for mm_item in req_state.mm_kwargs:
            mm_input = mm_item.get_data()
            if (t := mm_input.get("image_grid_thw")) is not None:
                image_grid_thw.append(t.tolist())
            if (t := mm_input.get("video_grid_thw")) is not None:
                video_grid_thw.append(t.tolist())
            if (t := mm_input.get("second_per_grid_ts")) is not None:
                second_per_grid_ts.append(t)
            if (t := mm_input.get("audio_feature_lengths")) is not None:
                audio_feature_lengths.append(t)
            if mm_input.get("use_audio_in_video") is True:
                use_audio_in_video = True

        req_state.mrope_positions, req_state.mrope_position_delta = \
            MRotaryEmbedding.get_input_positions_tensor(
                req_state.prompt_token_ids,
                hf_config=self.model_config.hf_config,
                image_grid_thw=image_grid_thw,
                video_grid_thw=video_grid_thw,
                second_per_grid_ts=second_per_grid_ts,
                audio_feature_lengths=audio_feature_lengths,
                use_audio_in_video=use_audio_in_video,
            )

656
    def _extract_mm_kwargs(
657
        self,
658
659
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
660
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
661
            return {}
662

663
664
665
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
            mm_kwargs.extend(req.mm_kwargs)
666

667
668
669
670
671
672
673
674
        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
                device=self.device,
                pin_memory=self.pin_memory,
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
675

676
        return mm_kwargs_combined
677

678
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
679
        if not self.is_multimodal_raw_input_only_model:
680
            return {}
681

682
683
684
685
686
        mm_budget = self.mm_budget
        assert mm_budget is not None

        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
687

688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
        cumsum_dtype: Optional[np.dtype] = None,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Get the cumulative sum and batched arange of the given array.
        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
        # Equivalent to but faster than:
        # np.concatenate([np.arange(n) for n in num_tokens])
        """
        # Step 1. [2, 5, 3] -> [2, 7, 10]
        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
        total_num_tokens = cu_num_tokens[-1]
        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        arange = self.arange_np[:total_num_tokens] - cumsums_offsets

        return cu_num_tokens, arange

708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
    def _prepare_input_ids(self, total_num_scheduled_tokens: int,
                           cu_num_tokens: np.ndarray) -> None:
        """Prepare the input IDs for the current batch.
        
        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
        GPU need to be copied into the corresponding slots into input_ids."""

        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        assert prev_req_id_to_index is not None
        flattened_indices = []
        prev_common_req_indices = []
        indices_match = True
        max_flattened_index = -1
        for req_id, cur_index in self.input_batch.req_id_to_index.items():
            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
                prev_common_req_indices.append(prev_index)
                # We need to compute the flattened input_ids index of the
                # last token in each common request.
                flattened_index = cu_num_tokens[cur_index].item() - 1
                flattened_indices.append(flattened_index)
                indices_match &= (prev_index == flattened_index)
                max_flattened_index = max(max_flattened_index, flattened_index)
        num_commmon_tokens = len(flattened_indices)
        if num_commmon_tokens < total_num_scheduled_tokens:
            # If not all requests are decodes from the last iteration,
            # We need to copy the input_ids_cpu to the GPU first.
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
            # So input_ids_cpu will have all the input ids.
            return
        if indices_match and max_flattened_index == (num_commmon_tokens - 1):
            # Common-case optimization: the batch is unchanged
            # and no reordering happened.
            # The indices are both the same permutation of 0..N-1 so
            # we can copy directly using a single slice.
            self.input_ids.gpu[:num_commmon_tokens].copy_(
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens,
                                                        0],
                non_blocking=True)
            return
        # Upload the index tensors asynchronously
        # so the scatter can be non-blocking.
        input_ids_index_tensor = torch.tensor(flattened_indices,
                                              dtype=torch.int64,
                                              pin_memory=self.pin_memory).to(
                                                  self.device,
                                                  non_blocking=True)
        prev_common_req_indices_tensor = torch.tensor(
            prev_common_req_indices,
            dtype=torch.int64,
            pin_memory=self.pin_memory).to(self.device, non_blocking=True)
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
                prev_common_req_indices_tensor, 0])

775
    def _prepare_inputs(
776
777
        self,
        scheduler_output: "SchedulerOutput",
778
779
    ) -> tuple[dict[str, Any], torch.Tensor, Optional[SpecDecodeMetadata],
               np.ndarray, Optional[CommonAttentionMetadata], int]:
780
781
782
783
784
785
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
            logits_indices, spec_decode_metadata
        ]
        """
786
787
788
789
790
791
792
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0

        # OPTIMIZATION: Start copying the block table first.
        # This way, we can overlap the copy with the following CPU operations.
793
        self.input_batch.block_table.commit_block_table(num_reqs)
794
795

        # Get the number of scheduled tokens for each request.
796
797
798
799
        req_ids = self.input_batch.req_ids
        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
        max_num_scheduled_tokens = max(tokens)
800
801
802

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
803
804
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
805

806
807
808
809
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens, arange = self._get_cumsum_and_arange(
            num_scheduled_tokens)
810
811

        # Get positions.
812
        positions_np = self.positions.np[:total_num_scheduled_tokens]
813
814
815
816
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

817
818
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
819
        if self.uses_mrope:
820
821
            self._calc_mrope_positions(scheduler_output)

822
823
824
825
        # Get token indices.
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
        # where M is the max_model_len.
826
827
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
828

829
830
831
832
        # NOTE(woosuk): We use torch.index_select instead of np.take here
        # because torch.index_select is much faster than np.take for large
        # tensors.
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
833
                           0,
834
                           torch.from_numpy(token_indices),
835
                           out=self.input_ids.cpu[:total_num_scheduled_tokens])
836

837
838
839
840
        self.input_batch.block_table.compute_slot_mapping(
            req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(
            total_num_scheduled_tokens)
841
842

        # Prepare the attention metadata.
843
844
        self.query_start_loc.np[0] = 0
        self.query_start_loc.np[1:num_reqs + 1] = cu_num_tokens
845
846
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
847
848
849
        self.query_start_loc.np[num_reqs + 1:].fill(cu_num_tokens[-1])
        self.query_start_loc.copy_to_gpu()
        query_start_loc = self.query_start_loc.gpu[:num_reqs + 1]
850

851
        self.seq_lens.np[:num_reqs] = (
852
853
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)
854
        # Fill unused with 0 for full cuda graph mode.
855
856
857
858
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
        seq_lens = self.seq_lens.gpu[:num_reqs]
        max_seq_len = self.seq_lens.np[:num_reqs].max().item()
859
860

        # Copy the tensors to the GPU.
861
862
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

863
        if self.uses_mrope:
864
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
865
866
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
867
868
869
                non_blocking=True)
        else:
            # Common case (1D positions)
870
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
871

872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
        use_spec_decode = len(
            scheduler_output.scheduled_spec_decode_tokens) > 0
        if not use_spec_decode:
            # NOTE(woosuk): Due to chunked prefills, the batch may contain
            # partial requests. While we should not sample any token
            # from these partial requests, we do so for simplicity.
            # We will ignore the sampled tokens from the partial requests.
            # TODO: Support prompt logprobs.
            logits_indices = query_start_loc[1:] - 1
            spec_decode_metadata = None
        else:
            # Get the number of draft tokens for each request.
            # Iterate over the dictionary rather than all requests since not all
            # requests have draft tokens.
            num_draft_tokens = np.zeros(num_reqs, dtype=np.int32)
            for req_id, draft_token_ids in (
                    scheduler_output.scheduled_spec_decode_tokens.items()):
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)

            spec_decode_metadata = self._calc_spec_decode_metadata(
                num_draft_tokens, cu_num_tokens)
            logits_indices = spec_decode_metadata.logits_indices

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
898
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
899
900
                logits_indices)

901
        attn_metadata: dict[str, Any] = {}
902

903
        # Used in the below loop.
904
905
        query_start_loc_cpu = self.query_start_loc.cpu[:num_reqs + 1]
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
906
907
908
909
        num_computed_tokens_cpu = (
            self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs])
        spec_decode_common_attn_metadata = None

910
911
912
913
914
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
                self.kv_cache_config.kv_cache_groups):

915
916
917
918
919
920
921
            if isinstance(kv_cache_group_spec.kv_cache_spec,
                          EncoderOnlyAttentionSpec):
                # Encoder-only layers do not have KV cache, so we need to
                # create a dummy block table and slot mapping for them.
                blk_table_tensor = torch.zeros(
                    (num_reqs, 1),
                    dtype=torch.int32,
922
923
924
925
926
927
928
                    device=self.device,
                )
                slot_mapping = torch.zeros(
                    (total_num_scheduled_tokens, ),
                    dtype=torch.int64,
                    device=self.device,
                )
929
930
931
932
933
934
935
936
937
938
939
940
941
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
                blk_table_tensor = blk_table.get_device_tensor()[:num_reqs]
                slot_mapping = blk_table.slot_mapping[:
                                                      total_num_scheduled_tokens]

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
                blk_table.slot_mapping[total_num_scheduled_tokens:].fill_(-1)
                num_common_prefix_blocks = (
                    scheduler_output.
                    num_common_prefix_blocks[kv_cache_group_id])
942

943
            common_attn_metadata = CommonAttentionMetadata(
944
945
946
947
948
                query_start_loc=query_start_loc,
                query_start_loc_cpu=query_start_loc_cpu,
                seq_lens=seq_lens,
                seq_lens_cpu=seq_lens_cpu,
                num_computed_tokens_cpu=num_computed_tokens_cpu,
949
950
951
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
952
                max_seq_len=max_seq_len,
953
954
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
955
956
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
957
                causal=True,
958
959
960
961
962
963
            )

            if self.speculative_config and \
                spec_decode_common_attn_metadata is None:
                spec_decode_common_attn_metadata = common_attn_metadata

964
965
966
967
968
969
970
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
                builder = attn_group.metadata_builder
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
971
                        num_common_prefix_blocks,
972
973
974
                        kv_cache_group_spec.kv_cache_spec,
                        builder,
                    )
975

976
977
978
                attn_metadata_i = (builder.build(
                    common_prefix_len=common_prefix_len,
                    common_attn_metadata=common_attn_metadata,
979
980
                ))

981
982
                for layer_name in attn_group.layer_names:
                    attn_metadata[layer_name] = attn_metadata_i
983

984
985
986
987
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

988
989
990
        return (attn_metadata, logits_indices, spec_decode_metadata,
                num_scheduled_tokens, spec_decode_common_attn_metadata,
                max_num_scheduled_tokens)
991

992
993
994
995
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
996
997
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
    ) -> int:
        """Compute the length of the common prefix for cascade attention.

        NOTE(woosuk): The common prefix length returned by this function
        represents the length used specifically for cascade attention, not the
        actual number of tokens shared between requests. When cascade attention
        is disabled (use_cascade=False), this function returns 0 even if
        requests share common tokens. Additionally, the common prefix length is
        truncated to a multiple of the block size and may be further truncated
        due to implementation details explained below.

        Args:
            num_scheduled_tokens: Number of tokens scheduled per request.
            num_common_prefix_blocks: Number of shared KV cache blocks.

        Returns:
            int: Length of common prefix in tokens.
        """
1016
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
        if common_prefix_len == 0:
            # Common case.
            return 0

        # NOTE(woosuk): Cascade attention uses two attention kernels: one
        # for the common prefix and the other for the rest. For the first
        # kernel, we concatenate all the query tokens (possibly from
        # different requests) and treat them as if they are from the same
        # request. Then, we use bi-directional attention to process the
        # common prefix in the KV cache. Importantly, this means that the
        # first kernel does not do any masking.

        # Consider the following example:
        # Request 1's input query: [D, E, X]
        # Request 1's kv cache: [A, B, C, D, E, X]
        # Request 1's num_computed_tokens: 3 (i.e., [A, B, C])
        # Request 2's input query: [E, Y]
        # Request 2's kv cache: [A, B, C, D, E, Y]
        # Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D])

        # If we use [A, B, C, D, E] as the common prefix, then the
        # first kernel will compute the bi-directional attention between
        # input query [D, E, X, E, Y] and common prefix [A, B, C, D, E].
        # However, this is wrong because D in Request 1 should not attend to
        # E in the common prefix (i.e., we need masking).
        # To avoid this, [A, B, C, D] should be the common prefix.
        # That is, the common prefix should be capped by the minimum
        # num_computed_tokens among the requests, and plus one to include
        # the first token of the query.

        # In practice, we use [A, B, C] as the common prefix, instead of
        # [A, B, C, D] (i.e., the common prefix is capped by the minimum
        # num_computed_tokens, without plus one).
        # This is because of an implementation detail: We want to always
        # use two kernels for cascade attention. Let's imagine:
        # Request 3's input query: [D]
        # Request 3's kv cache: [A, B, C, D]
1054
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
        # If we use [A, B, C, D] as the common prefix for Request 1-3,
        # then Request 3 will be processed only by the first kernel,
        # and the second kernel will get an empty input. While this is not
        # a fundamental problem, our current implementation does not support
        # this case.
        num_reqs = len(num_scheduled_tokens)
        common_prefix_len = min(
            common_prefix_len,
            self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
        # common_prefix_len should be a multiple of the block size.
1065
1066
1067
1068
1069
        common_prefix_len = (common_prefix_len // kv_cache_spec.block_size *
                             kv_cache_spec.block_size)
        use_sliding_window = (isinstance(kv_cache_spec, SlidingWindowSpec) or
                              (isinstance(kv_cache_spec, FullAttentionSpec)
                               and kv_cache_spec.sliding_window is not None))
1070
1071
1072
1073
        use_local_attention = (
            isinstance(kv_cache_spec, ChunkedLocalAttentionSpec)
            or (isinstance(kv_cache_spec, FullAttentionSpec)
                and kv_cache_spec.attention_chunk_size is not None))
1074
1075
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1076
1077
1078
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1079
            num_kv_heads=kv_cache_spec.num_kv_heads,
1080
            use_alibi=self.use_alibi,
1081
            use_sliding_window=use_sliding_window,
1082
            use_local_attention=use_local_attention,
1083
1084
1085
1086
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

1087
1088
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1089
        for index, req_id in enumerate(self.input_batch.req_ids):
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
            req = self.requests[req_id]
            assert req.mrope_positions is not None

            num_computed_tokens = \
                self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = \
                scheduler_output.num_scheduled_tokens[req_id]
            num_prompt_tokens = len(req.prompt_token_ids)

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
                prompt_part_len = max(0,
                                      num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(
                    0, num_scheduled_tokens - prompt_part_len)
            else:
                prompt_part_len = num_scheduled_tokens
                completion_part_len = 0

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

            if prompt_part_len > 0:
                # prompt's mrope_positions are pre-computed
                dst_start = mrope_pos_ptr
                dst_end = mrope_pos_ptr + prompt_part_len
                src_start = num_computed_tokens
                src_end = num_computed_tokens + prompt_part_len

1117
1118
                self.mrope_positions.cpu[:, dst_start:dst_end] = (
                    req.mrope_positions[:, src_start:src_end])
1119
1120
1121
1122
1123
1124
1125
                mrope_pos_ptr += prompt_part_len

            if completion_part_len > 0:
                # compute completion's mrope_positions on-the-fly
                dst_start = mrope_pos_ptr
                dst_end = mrope_pos_ptr + completion_part_len

1126
                MRotaryEmbedding.get_next_input_positions_tensor(
1127
                    out=self.mrope_positions.np,
1128
1129
1130
1131
1132
                    out_offset=dst_start,
                    mrope_position_delta=req.mrope_position_delta,
                    context_len=num_computed_tokens + prompt_part_len,
                    num_new_tokens=completion_part_len,
                )
1133
1134
1135

                mrope_pos_ptr += completion_part_len

1136
1137
    def _calc_spec_decode_metadata(
        self,
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
        num_draft_tokens: np.ndarray,
        cu_num_scheduled_tokens: np.ndarray,
    ) -> SpecDecodeMetadata:
        # Inputs:
        # cu_num_scheduled_tokens:  [  4, 104, 107, 207, 209]
        # num_draft_tokens:         [  3,   0,   2,   0,   1]
        # Outputs:
        # cu_num_draft_tokens:      [  3,   3,   5,   5,   6]
        # logits_indices:           [  0,   1,   2,   3, 103, 104, 105, 106,
        #                            206, 207, 208]
        # target_logits_indices:    [  0,   1,   2,   5,   6,   9]
        # bonus_logits_indices:     [  3,   4,   7,   8,  10]

        # Compute the logits indices.
        # [4, 1, 3, 1, 2]
        num_sampled_tokens = num_draft_tokens + 1
1154
1155
1156
1157
1158
1159

        # Step 1. cu_num_sampled_tokens: [4, 5, 8, 9, 11]
        # arange: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
        cu_num_sampled_tokens, arange = self._get_cumsum_and_arange(
            num_sampled_tokens, cumsum_dtype=np.int32)
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1160
1161
        logits_indices = np.repeat(
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
1162
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1163
1164
1165
1166
1167
1168
        logits_indices += arange

        # Compute the bonus logits indices.
        bonus_logits_indices = cu_num_sampled_tokens - 1

        # Compute the draft logits indices.
1169
1170
1171
1172
        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
        # arange: [0, 1, 2, 0, 1, 0]
        cu_num_draft_tokens, arange = self._get_cumsum_and_arange(
            num_draft_tokens, cumsum_dtype=np.int32)
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens)
        # [0, 1, 2, 5, 6, 9]
        target_logits_indices += arange

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
            self.device, non_blocking=True)
        logits_indices = torch.from_numpy(logits_indices).to(self.device,
                                                             non_blocking=True)
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
            self.device, non_blocking=True)
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1187
1188
            self.device, non_blocking=True)

1189
1190
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1191
        draft_token_ids = self.input_ids.gpu[logits_indices]
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

        metadata = SpecDecodeMetadata(
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )
        return metadata

1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
    def _prepare_kv_sharing_fast_prefill(
        self,
        logits_indices: torch.Tensor,
    ) -> torch.Tensor:
        assert self.kv_sharing_fast_prefill_logits_indices is not None
        num_logits = logits_indices.shape[0]
        assert num_logits > 0
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(
            logits_indices)
        # There might have leftover indices in logits_indices[num_logits:]
        # from previous iterations, whose values may be greater than the
        # batch size in the current iteration. To ensure indices are always
        # valid, we fill the padded indices with the last index.
        self.kv_sharing_fast_prefill_logits_indices[num_logits:].fill_(
            logits_indices[-1].item())
        if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
                and num_logits <= self.cudagraph_batch_sizes[-1]):
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
            num_logits_padded = self.vllm_config.pad_for_cudagraph(num_logits)
        else:
            num_logits_padded = num_logits
        logits_indices_padded = (
            self.kv_sharing_fast_prefill_logits_indices[:num_logits_padded])
        return logits_indices_padded

1230
    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
1231
1232
1233
1234
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return
        # Batch the multi-modal inputs.
1235
        mm_kwargs = list[MultiModalKwargsItem]()
1236
1237
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1238
1239
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1240
1241

            for mm_input_id in encoder_input_ids:
1242
                mm_hash = req_state.mm_hashes[mm_input_id]
1243
                mm_kwargs.append(req_state.mm_kwargs[mm_input_id])
1244
1245
                mm_hashes_pos.append(
                    (mm_hash, req_state.mm_positions[mm_input_id]))
1246
1247
1248
1249
1250
1251
1252
1253
1254

        # Batch mm inputs as much as we can: if a request in the batch has
        # multiple modalities or a different modality than the previous one,
        # we process it separately to preserve item order.
        # FIXME(ywang96): This is a hacky way to deal with multiple modalities
        # in the same batch while still being able to benefit from batching
        # multimodal inputs. The proper solution should be reordering the
        # encoder outputs.
        encoder_outputs = []
1255
1256
        for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
1257
                device=self.device,
1258
1259
                pin_memory=self.pin_memory,
        ):
1260
1261
1262
1263
1264
1265
1266
1267
            # Run the encoder.
            # `curr_group_outputs` is either of the following:
            # 1. A tensor of shape (num_items, feature_size, hidden_size)
            # in case feature_size is fixed across all multimodal items.
            # 2. A list or tuple (length: num_items) of tensors, each of shape
            # (feature_size, hidden_size) in case the feature size is dynamic
            # depending on the input multimodal items.
            curr_group_outputs = self.model.get_multimodal_embeddings(
1268
                **mm_kwargs_group)
1269

1270
1271
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1272
                expected_num_items=num_items,
1273
1274
            )

1275
1276
            for output in curr_group_outputs:
                encoder_outputs.append(output)
1277

1278
1279
1280
        # Cache the encoder outputs by mm_hash
        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
            self.encoder_cache[mm_hash] = scatter_mm_placeholders(
1281
1282
1283
1284
1285
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1286
1287
        self,
        scheduler_output: "SchedulerOutput",
1288
        shift_computed_tokens: int = 0,
1289
    ) -> list[torch.Tensor]:
1290
        mm_embeds: list[torch.Tensor] = []
1291
        for req_id in self.input_batch.req_ids:
1292
1293
1294
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
1295
1296
            num_computed_tokens = \
                req_state.num_computed_tokens + shift_computed_tokens
1297
            mm_positions = req_state.mm_positions
1298
            mm_hashes = req_state.mm_hashes
1299
            for i, pos_info in enumerate(mm_positions):
1300
1301
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317

                # The encoder output is needed if the two ranges overlap:
                # [num_computed_tokens,
                #  num_computed_tokens + num_scheduled_tokens) and
                # [start_pos, start_pos + num_encoder_tokens)
                if start_pos >= num_computed_tokens + num_scheduled_tokens:
                    # The encoder output is not needed in this step.
                    break
                if start_pos + num_encoder_tokens <= num_computed_tokens:
                    # The encoder output is already processed and stored
                    # in the decoder's KV cache.
                    continue

                start_idx = max(num_computed_tokens - start_pos, 0)
                end_idx = min(
                    num_computed_tokens - start_pos + num_scheduled_tokens,
1318
1319
                    num_encoder_tokens,
                )
1320
                assert start_idx < end_idx
1321
1322
1323
1324
1325

                mm_hash = mm_hashes[i]
                encoder_output = self.encoder_cache.get(mm_hash, None)
                assert encoder_output is not None,\
                    f"Encoder cache miss for {mm_hash}."
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]

                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
                mm_embeds.append(mm_embeds_item)
        return mm_embeds
1336

1337
    def get_model(self) -> nn.Module:
1338
1339
1340
        # get raw model out of the cudagraph wrapper.
        if isinstance(self.model, CUDAGraphWrapper):
            return self.model.unwrap()
1341
1342
        return self.model

1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
    def get_supported_generation_tasks(self) -> list[GenerationTask]:
        model = self.get_model()
        supported_tasks = list[GenerationTask]()

        if is_text_generation_model(model):
            supported_tasks.append("generate")

        if supports_transcription(model):
            if model.supports_transcription_only:
                return ["transcription"]

            supported_tasks.append("transcription")

        return supported_tasks

1358
1359
1360
1361
1362
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1363
1364
1365
1366
1367
1368
        supported_tasks = list(model.pooler.get_supported_tasks())

        if (self.scheduler_config.chunked_prefill_enabled
                and "encode" in supported_tasks):
            supported_tasks.remove("encode")

1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
            logger.debug_once("Chunked prefill is not supported with "
                              "encode task which using ALL pooling. "
                              "Please turn off chunked prefill by "
                              "`--no-enable-chunked-prefill` before using it.")

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
                logger.debug_once(
                    "Score API is only enabled for num_labels == 1.")
1380
1381

        return supported_tasks
1382

1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        tasks = list[SupportedTask]()

        if self.model_config.runner_type == "generate":
            tasks.extend(self.get_supported_generation_tasks())
        if self.model_config.runner_type == "pooling":
            tasks.extend(self.get_supported_pooling_tasks())

        return tuple(tasks)

1393
1394
1395
1396
1397
1398
1399
1400
1401
    def apply_grammar_bitmask(
        self,
        scheduler_output: "SchedulerOutput",
        logits: torch.Tensor,
    ):
        grammar_bitmask = scheduler_output.grammar_bitmask
        if grammar_bitmask is None:
            return

1402
1403
1404
1405
1406
1407
1408
1409
1410
        # We receive the structured output bitmask from the scheduler,
        # compacted to contain bitmasks only for structured output requests.
        # The order of the requests in the bitmask is not guaranteed to be the
        # same as the order of the requests in the gpu runner's batch. We need
        # to sort the bitmask to match the order of the requests used here.

        # Get the batch indices of the structured output requests.
        # Keep track of the number of speculative tokens scheduled for every
        # request in the batch, as the logit indices are offset by this amount.
1411
        struct_out_req_batch_indices: dict[str, int] = {}
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
        cumulative_offset = 0
        seq = sorted(self.input_batch.req_id_to_index.items(),
                     key=lambda x: x[1])
        for req_id, batch_index in seq:
            logit_index = batch_index + cumulative_offset
            cumulative_offset += len(
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
            if req_id in scheduler_output.structured_output_request_ids:
                struct_out_req_batch_indices[req_id] = logit_index

        out_indices = []

        # Reorder the bitmask to match the order of the requests in the batch.
1425
1426
1427
1428
        sorted_bitmask = np.full(shape=(logits.shape[0],
                                        grammar_bitmask.shape[1]),
                                 fill_value=-1,
                                 dtype=grammar_bitmask.dtype)
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
        cumulative_index = 0
        seq = sorted(scheduler_output.structured_output_request_ids.items(),
                     key=lambda x: x[1])
        for req_id, _ in seq:
            logit_index = struct_out_req_batch_indices[req_id]
            num_spec_tokens = len(
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
            for i in range(1 + num_spec_tokens):
                sorted_bitmask[logit_index + i] = \
                    grammar_bitmask[cumulative_index + i]
                out_indices.append(logit_index + i)
            cumulative_index += 1 + num_spec_tokens
        grammar_bitmask = sorted_bitmask
1442

1443
        # If the length of out indices and the logits have the same shape
1444
1445
        # we don't need to pass indices to the kernel,
        # since the bitmask is already aligned with the logits.
1446
        skip_out_indices = len(out_indices) == logits.shape[0]
1447

1448
1449
        # Serialization of np.ndarray is much more efficient than a tensor,
        # so we receive it in that format.
1450
        grammar_bitmask = torch.from_numpy(grammar_bitmask).contiguous()
1451

1452
        xgr.apply_token_bitmask_inplace(
1453
1454
            logits,
            grammar_bitmask.to(self.device, non_blocking=True),
1455
            indices=out_indices if not skip_out_indices else None,
1456
1457
        )

1458
1459
1460
1461
1462
1463
1464
    def sync_and_slice_intermediate_tensors(
            self, num_tokens: int, intermediate_tensors: IntermediateTensors,
            sync_self: bool) -> IntermediateTensors:

        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1465
        enabled_sp = self.compilation_config.pass_config. \
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
            enable_sequence_parallelism
        if enabled_sp:
            # When sequence parallelism is enabled, we always pad num_tokens
            # to be a multiple of tensor_parallel_size (tp) earlier
            assert num_tokens % tp == 0
        is_residual_scattered = tp > 1 and enabled_sp \
            and num_tokens % tp == 0

        # When sequence parallelism is enabled, the "residual" tensor is sharded
        # across tensor parallel ranks, so each rank only needs its own slice.
        if sync_self:
            assert intermediate_tensors is not None
            for k, v in intermediate_tensors.items():
1479
                is_scattered = k == "residual" and is_residual_scattered
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
                copy_len = num_tokens // tp if is_scattered else \
                    num_tokens
                self.intermediate_tensors[k][:copy_len].copy_(
                    v[:copy_len], non_blocking=True)

        return IntermediateTensors({
            k:
            v[:num_tokens // tp]
            if k == "residual" and is_residual_scattered else v[:num_tokens]
            for k, v in self.intermediate_tensors.items()
        })

1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
    def eplb_step(self,
                  is_dummy: bool = False,
                  is_profile: bool = False) -> None:
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
1502
1503
        model = self.get_model()
        assert is_mixture_of_experts(model)
1504
        self.eplb_state.step(
1505
            model,
1506
1507
            is_dummy,
            is_profile,
1508
            log_stats=self.parallel_config.eplb_config.log_balancedness,
1509
1510
        )

1511
1512
    def get_dp_padding(self,
                       num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
1513
1514
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
1515
1516
1517
1518
1519
1520
1521
1522
1523

        # For DP: Don't pad when setting enforce_eager.
        # This lets us set enforce_eager on the prefiller in a P/D setup and
        # still use CUDA graphs (enabled by this padding) on the decoder.
        #
        # TODO(tms) : There are many cases where padding is enabled for
        # prefills, causing unnecessary and excessive padding of activations.

        if dp_size == 1 or self.vllm_config.model_config.enforce_eager:
1524
            # Early exit.
1525
            return 0, None
1526
1527
1528
1529

        num_tokens_across_dp = DPMetadata.num_tokens_across_dp(
            num_tokens, dp_size, dp_rank)
        max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp).item()
1530
1531
1532
1533
1534
        num_tokens_after_padding = torch.tensor([max_tokens_across_dp_cpu] *
                                                dp_size,
                                                device="cpu",
                                                dtype=torch.int32)
        return max_tokens_across_dp_cpu - num_tokens, num_tokens_after_padding
1535

1536
1537
1538
1539
1540
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
1541
        kv_connector_output: Optional[KVConnectorOutput],
1542
1543
1544
1545
1546
1547
    ) -> ModelRunnerOutput:
        assert self.input_batch.num_reqs ==\
            len(self.input_batch.pooling_params), \
        "Either all or none of the requests in" \
        " a batch must be pooling request"

1548
        hidden_states = hidden_states[:num_scheduled_tokens]
1549
        pooling_metadata = self.input_batch.get_pooling_metadata()
1550
1551
        pooling_metadata.build_pooling_cursor(num_scheduled_tokens_np.tolist(),
                                              device=hidden_states.device)
1552
        seq_lens_cpu = self.seq_lens.cpu[:self.input_batch.num_reqs]
1553

1554
        # Pooling models D2H & synchronize occurs in pooler.py:build_output
1555
        raw_pooler_output = self.model.pooler(
1556
            hidden_states=hidden_states, pooling_metadata=pooling_metadata)
1557
1558
1559

        pooler_output: list[Optional[torch.Tensor]] = []
        for raw_output, seq_len, prompt_len in zip(
1560
                raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
1561

1562
1563
            output = raw_output.data if seq_len == prompt_len else None
            pooler_output.append(output)
1564
1565
1566
1567
1568
1569
1570
1571

        return ModelRunnerOutput(
            req_ids=self.input_batch.req_ids,
            req_id_to_index=self.input_batch.req_id_to_index,
            sampled_token_ids=[],
            logprobs=None,
            prompt_logprobs_dict={},
            pooler_output=pooler_output,
1572
            kv_connector_output=kv_connector_output,
1573
1574
        )

1575
1576
1577
1578
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
1579
        intermediate_tensors: Optional[IntermediateTensors] = None,
1580
    ) -> Union[ModelRunnerOutput, AsyncModelRunnerOutput, IntermediateTensors]:
1581
        self._update_states(scheduler_output)
1582
        if not scheduler_output.total_num_scheduled_tokens:
1583
1584
1585
            if not has_kv_transfer_group():
                # Return empty ModelRunnerOutput if there's no work to do.
                return EMPTY_MODEL_RUNNER_OUTPUT
Robert Shaw's avatar
Robert Shaw committed
1586

1587
1588
            return self.kv_connector_no_forward(scheduler_output,
                                                self.vllm_config)
1589

1590
1591
1592
1593
1594
1595
        if self.cache_config.kv_sharing_fast_prefill:
            assert not self.input_batch.num_prompt_logprobs, (
                "--kv-sharing-fast-prefill produces incorrect logprobs for "
                "prompt tokens, tokens, please disable it when the requests "
                "need prompt logprobs")

1596
        # Prepare the decoder inputs.
1597
1598
        (attn_metadata, logits_indices, spec_decode_metadata,
         num_scheduled_tokens_np, spec_decode_common_attn_metadata,
1599
         max_query_len) = self._prepare_inputs(scheduler_output)
1600

1601
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
1602
        if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
1603
                and not envs.VLLM_DISABLE_PAD_FOR_CUDAGRAPH
1604
                and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
1605
            # Use CUDA graphs.
1606
            # Add padding to the batch size.
1607
            num_input_tokens = self.vllm_config.pad_for_cudagraph(
1608
1609
1610
                num_scheduled_tokens)
        else:
            # Eager mode.
1611
1612
1613
            # Pad tokens to multiple of tensor_parallel_size when
            # enabled collective fusion for SP
            tp_size = self.vllm_config.parallel_config.tensor_parallel_size
1614
            if self.compilation_config.pass_config. \
1615
1616
1617
1618
                enable_sequence_parallelism and tp_size > 1:
                num_input_tokens = round_up(num_scheduled_tokens, tp_size)
            else:
                num_input_tokens = num_scheduled_tokens
1619

1620
        # Padding for DP
1621
1622
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_input_tokens)
        num_input_tokens += num_pad
1623

1624
1625
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
1626
        if self.supports_mm_inputs:
1627
1628
1629
1630
1631
1632
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)
        else:
            mm_embeds = []

1633
        if self.supports_mm_inputs and get_pp_group().is_first_rank:
1634
1635
1636
            # NOTE(woosuk): To unify token ids and soft tokens (vision
            # embeddings), we always use embeddings (rather than token ids)
            # as input to the multimodal model, even when the input is text.
1637
            inputs_embeds_scheduled = self.model.get_input_embeddings(
1638
                input_ids=self.input_ids.gpu[:num_scheduled_tokens],
1639
1640
                multimodal_embeddings=mm_embeds or None,
            )
1641

1642
            # TODO(woosuk): Avoid the copy. Optimize.
1643
1644
1645
            self.inputs_embeds[:num_scheduled_tokens].copy_(
                inputs_embeds_scheduled)

1646
            input_ids = None
1647
            inputs_embeds = self.inputs_embeds[:num_input_tokens]
1648
1649
1650
1651
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
1652
        else:
1653
1654
1655
1656
            # For text-only models, we use token ids as input.
            # While it is possible to use embeddings as input just like the
            # multimodal models, it is not desirable for performance since
            # then the embedding layer is not included in the CUDA graph.
1657
            input_ids = self.input_ids.gpu[:num_input_tokens]
1658
            inputs_embeds = None
1659
            model_kwargs = self._init_model_kwargs(num_input_tokens)
1660
        if self.uses_mrope:
1661
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
1662
        else:
1663
            positions = self.positions.gpu[:num_input_tokens]
1664

1665
1666
1667
        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
1668
1669
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                num_input_tokens, intermediate_tensors, True)
1670

1671
1672
1673
1674
1675
1676
        uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
            num_scheduled_tokens == self.input_batch.num_reqs * max_query_len)
        batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens,
                                           uniform_decode=uniform_decode)
        cudagraph_runtime_mode, batch_descriptor = \
            self.cudagraph_dispatcher.dispatch(batch_descriptor)
1677

1678
        # Run the model.
1679
        # Use persistent buffers for CUDA graphs.
1680
1681
1682
1683
1684
        with set_forward_context(
                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
1685
1686
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                batch_descriptor=batch_descriptor,
1687
1688
        ), self.maybe_get_kv_connector_output(
                scheduler_output) as kv_connector_output:
1689

Robert Shaw's avatar
Robert Shaw committed
1690
            model_output = self.model(
1691
                input_ids=input_ids,
1692
                positions=positions,
1693
                intermediate_tensors=intermediate_tensors,
1694
                inputs_embeds=inputs_embeds,
1695
                **model_kwargs,
1696
            )
1697
1698

        if self.use_aux_hidden_state_outputs:
Robert Shaw's avatar
Robert Shaw committed
1699
            hidden_states, aux_hidden_states = model_output
1700
        else:
Robert Shaw's avatar
Robert Shaw committed
1701
            hidden_states = model_output
1702
1703
            aux_hidden_states = None

1704
1705
1706
1707
1708
1709
1710
        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        broadcast_pp_output = \
            self.parallel_config.distributed_executor_backend \
            == "external_launcher" and len(get_pp_group().ranks) > 0
1711
        if not get_pp_group().is_last_rank:
1712
            # For mid-pipeline stages, return the hidden states.
1713
            assert isinstance(hidden_states, IntermediateTensors)
1714
            if not broadcast_pp_output:
1715
                hidden_states.kv_connector_output = kv_connector_output
1716
1717
1718
1719
1720
                return hidden_states
            get_pp_group().send_tensor_dict(hidden_states.tensors,
                                            all_gather_group=get_tp_group())
            logits = None
        else:
1721
            if self.is_pooling_model:
1722
                return self._pool(hidden_states, num_scheduled_tokens,
1723
                                  num_scheduled_tokens_np, kv_connector_output)
1724

1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
            sample_hidden_states = hidden_states[logits_indices]
            logits = self.model.compute_logits(sample_hidden_states, None)
        if broadcast_pp_output:
            model_output_broadcast_data = {
                "logits": logits.contiguous(),
            } if logits is not None else {}
            model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
                model_output_broadcast_data, src=len(get_pp_group().ranks) - 1)
            assert model_output_broadcast_data is not None
            logits = model_output_broadcast_data["logits"]
1735

1736
1737
1738
1739
        # Apply structured output bitmasks if present
        if scheduler_output.grammar_bitmask is not None:
            self.apply_grammar_bitmask(scheduler_output, logits)

1740
        # Sample the next token and get logprobs if needed.
1741
        sampling_metadata = self.input_batch.sampling_metadata
1742
        if spec_decode_metadata is None:
1743
            sampler_output = self.sampler(
1744
1745
1746
1747
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
1748
1749
1750
1751
            # When indexing with a tensor (bonus_logits_indices), PyTorch
            # creates a new tensor with separate storage from the original
            # logits tensor. This means any in-place operations on bonus_logits
            # won't affect the original logits tensor.
1752
            assert logits is not None
1753
            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
1754
            sampler_output = self.sampler(
1755
                logits=bonus_logits,
1756
1757
1758
                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
1759

1760
1761
1762
            # Just like `bonus_logits`, `target_logits` is a new tensor with
            # separate storage from the original `logits` tensor. Therefore,
            # it is safe to update `target_logits` in place.
1763
            target_logits = logits[spec_decode_metadata.target_logits_indices]
1764
            output_token_ids = self.rejection_sampler(
1765
                spec_decode_metadata,
1766
                None,  # draft_probs
1767
                target_logits,
1768
                bonus_token_ids,
1769
1770
                sampling_metadata,
            )
1771
            sampler_output.sampled_token_ids = output_token_ids
1772

1773
1774
1775
1776
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

1777
1778
        # TODO(woosuk): The following loop can be slow since it iterates over
        # the requests one by one. Optimize.
1779
1780
        discard_sampled_tokens_req_indices = []
        for i, req_id in enumerate(self.input_batch.req_ids):
1781
1782
1783
            req_state = self.requests[req_id]
            seq_len = (req_state.num_computed_tokens +
                       scheduler_output.num_scheduled_tokens[req_id])
1784
            if seq_len < req_state.num_tokens:
1785
                # Ignore the sampled token for partial prefills.
1786
                # Rewind the generator state as if the token was not sampled.
1787
                # This relies on cuda-specific torch-internal impl details
1788
1789
1790
1791
1792
1793
                generator = self.input_batch.generators.get(i)
                if generator is not None:
                    generator.set_offset(generator.get_offset() - 4)
                # Record the index of the request that should not be sampled,
                # so that we could clear the sampled tokens before returning.
                discard_sampled_tokens_req_indices.append(i)
1794

1795
1796
1797
1798
1799
1800
        # Copy some objects so they don't get modified after returning.
        # This is important when using async scheduling.
        req_ids_output_copy = self.input_batch.req_ids.copy()
        req_id_to_index_output_copy = \
            self.input_batch.req_id_to_index.copy()

1801
1802
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
1803
1804
1805
1806
1807
1808
        logprobs_tensors = sampler_output.logprobs_tensors
        logprobs_lists = logprobs_tensors.tolists() \
            if logprobs_tensors is not None else None

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
1809
            hidden_states[:num_scheduled_tokens],
1810
            scheduler_output.num_scheduled_tokens,
1811
1812
        )

1813
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
1814
        sampled_token_ids = sampler_output.sampled_token_ids
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
        if not self.use_async_scheduling:
            # Get the valid generated tokens.
            max_gen_len = sampled_token_ids.shape[-1]
            if max_gen_len == 1:
                # No spec decode tokens.
                valid_sampled_token_ids = self._to_list(sampled_token_ids)
            else:
                # Includes spec decode tokens.
                valid_sampled_token_ids = self.rejection_sampler.parse_output(
                    sampled_token_ids,
                    self.input_batch.vocab_size,
                )
            # Mask out the sampled tokens that should not be sampled.
            for i in discard_sampled_tokens_req_indices:
                valid_sampled_token_ids[i].clear()
1830
        else:
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
            valid_sampled_token_ids = []
            invalid_req_indices = list(discard_sampled_tokens_req_indices)
            invalid_req_indices_set = set(invalid_req_indices)
            assert sampled_token_ids.shape[-1] == 1

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
            self.input_batch.prev_sampled_token_ids = \
                sampled_token_ids
            self.input_batch.prev_sampled_token_ids_invalid_indices = \
                invalid_req_indices_set
            self.input_batch.prev_req_id_to_index = {
                req_id: i
                for i, req_id in enumerate(self.input_batch.req_ids)
                if i not in invalid_req_indices_set
            }
1848

1849
1850
1851
1852
1853
        # Cache the sampled tokens in the model runner, so that the scheduler
        # doesn't need to send them back.
        # NOTE(woosuk): As an exception, when using PP, the scheduler sends
        # the sampled tokens back, because there's no direct communication
        # between the first-stage worker and the last-stage worker.
1854
        req_ids = self.input_batch.req_ids
1855
1856
1857
1858
1859
1860
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
                sampled_ids = [-1] if \
                    req_idx not in invalid_req_indices_set else None
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
            assert end_idx <= self.max_model_len, (
                "Sampled token IDs exceed the max model length. "
                f"Total number of tokens: {end_idx} > max_model_len: "
                f"{self.max_model_len}")

            self.input_batch.token_ids_cpu[req_idx,
                                           start_idx:end_idx] = sampled_ids
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
1875

1876
            req_id = req_ids[req_idx]
1877
1878
1879
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

1880
        if self.speculative_config:
1881
            assert spec_decode_common_attn_metadata is not None
1882
            self._draft_token_ids = self.propose_draft_token_ids(
1883
1884
1885
1886
1887
1888
1889
                scheduler_output,
                valid_sampled_token_ids,
                sampling_metadata,
                hidden_states,
                sample_hidden_states,
                aux_hidden_states,
                spec_decode_metadata,
1890
                spec_decode_common_attn_metadata,
1891
1892
1893
1894
            )

        self.eplb_step()

1895
1896
1897
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
1898
1899
1900
1901
            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
1902
            kv_connector_output=kv_connector_output,
1903
1904
1905
            num_nans_in_logits=num_nans_in_logits,
        )

1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
        if not self.use_async_scheduling:
            return output

        return AsyncGPUModelRunnerOutput(
            model_runner_output=output,
            sampled_token_ids=sampled_token_ids,
            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
    def take_draft_token_ids(self) -> Optional[DraftTokenIds]:
        if self._draft_token_ids is None:
            return None
        req_ids = self.input_batch.req_ids
        if isinstance(self._draft_token_ids, torch.Tensor):
            draft_token_ids = self._draft_token_ids.tolist()
        else:
            draft_token_ids = self._draft_token_ids
        self._draft_token_ids = None
        return DraftTokenIds(req_ids, draft_token_ids)

1927
1928
1929
1930
1931
1932
1933
1934
1935
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
        sampled_token_ids: list[list[int]],
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
        aux_hidden_states: Optional[torch.Tensor],
        spec_decode_metadata: Optional[SpecDecodeMetadata],
1936
        common_attn_metadata: CommonAttentionMetadata,
1937
    ) -> Union[list[list[int]], torch.Tensor]:
1938
1939
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
1940
            assert isinstance(self.drafter, NgramProposer)
1941
            draft_token_ids = self.propose_ngram_draft_token_ids(
1942
                sampled_token_ids)
1943
1944
        elif self.speculative_config.method == "medusa":
            assert isinstance(self.drafter, MedusaProposer)
1945
1946
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
1947
1948
1949
1950
1951
1952
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
                for num_draft, tokens in zip(
                        spec_decode_metadata.num_draft_tokens,
1953
                        sampled_token_ids):
1954
1955
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
1956
                indices = torch.tensor(indices, device=self.device)
1957
1958
                hidden_states = sample_hidden_states[indices]

1959
            draft_token_ids = self.drafter.propose(
1960
1961
1962
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
1963
        elif self.speculative_config.use_eagle():
1964
1965
            assert isinstance(self.drafter, EagleProposer)
            # TODO(woosuk): Refactor the loop.
1966
            req_ids = self.input_batch.req_ids
1967
            next_token_ids: list[int] = []
1968
            for i, token_ids in enumerate(sampled_token_ids):
1969
1970
1971
1972
1973
1974
                if token_ids:
                    # Common case.
                    next_token_id = token_ids[-1]
                else:
                    # Partial prefill (rare case).
                    # Get the next token id from the request state.
1975
                    req_id = req_ids[i]
1976
1977
1978
1979
1980
                    req_state = self.requests[req_id]
                    seq_len = (req_state.num_computed_tokens +
                               scheduler_output.num_scheduled_tokens[req_id])
                    next_token_id = req_state.get_token_id(seq_len)
                next_token_ids.append(next_token_id)
1981
1982
1983
            next_token_ids = torch.tensor(next_token_ids,
                                          dtype=torch.int32,
                                          device=self.device)
Jiayi Yao's avatar
Jiayi Yao committed
1984

1985
1986
            if spec_decode_metadata is None:
                # input_ids can be None for multimodal models.
1987
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
1988
                # TODO(woosuk): Support M-RoPE.
1989
                target_positions = self.positions.gpu[:num_scheduled_tokens]
1990
                if self.use_aux_hidden_state_outputs:
1991
1992
1993
                    target_hidden_states = torch.cat(
                        [h[:num_scheduled_tokens] for h in aux_hidden_states],
                        dim=-1)
1994
1995
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
1996
1997
1998
1999
            else:
                # TODO(woosuk): Refactor this.
                num_draft_tokens = spec_decode_metadata.num_draft_tokens
                num_rejected_tokens = [
2000
                    n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
2001
2002
                    for i, n in enumerate(num_draft_tokens)
                ]
2003
2004
2005
2006
2007
2008
                num_rejected_tokens_cpu = torch.tensor(num_rejected_tokens,
                                                       dtype=torch.int32)
                common_attn_metadata, token_indices =\
                    self.drafter.prepare_inputs(
                    common_attn_metadata, num_rejected_tokens_cpu)

2009
                target_token_ids = self.input_ids.gpu[token_indices]
2010
                # TODO(woosuk): Support M-RoPE.
2011
                target_positions = self.positions.gpu[token_indices]
2012
                if self.use_aux_hidden_state_outputs:
2013
2014
                    target_hidden_states = torch.cat(
                        [h[token_indices] for h in aux_hidden_states], dim=-1)
2015
2016
                else:
                    target_hidden_states = hidden_states[token_indices]
2017
            mm_embeds = None
2018
            if self.supports_mm_inputs:
2019
2020
2021
                mm_embeds = self._gather_mm_embeddings(scheduler_output,
                                                       shift_computed_tokens=1)

2022
            draft_token_ids = self.drafter.propose(
2023
2024
2025
2026
2027
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
                sampling_metadata=sampling_metadata,
2028
                common_attn_metadata=common_attn_metadata,
2029
                mm_embeds=mm_embeds,
2030
            )
2031
        return draft_token_ids
2032

2033
    def propose_ngram_draft_token_ids(
2034
        self,
2035
2036
        sampled_token_ids: list[list[int]],
    ) -> list[list[int]]:
2037
        # TODO(woosuk): Optimize.
2038
        req_ids = self.input_batch.req_ids
2039
        draft_token_ids: list[list[int]] = []
2040
2041
2042
        for i, sampled_ids in enumerate(sampled_token_ids):
            num_sampled_ids = len(sampled_ids)
            if not num_sampled_ids:
2043
2044
2045
2046
                # Skip speculative decoding.
                draft_token_ids.append([])
                continue

2047
2048
            # Skip requests that require sampling parameters that are not
            # supported with speculative decoding.
2049
            req_id = req_ids[i]
2050
            if req_id in self.input_batch.spec_decode_unsupported_reqs:
2051
2052
2053
                draft_token_ids.append([])
                continue

2054
2055
            num_tokens = self.input_batch.num_tokens_no_spec[i]
            if num_tokens >= self.max_model_len:
2056
2057
2058
2059
                # Skip requests that have already reached the max model length.
                draft_token_ids.append([])
                continue

2060
            drafter_output = self.drafter.propose(
2061
                self.input_batch.token_ids_cpu[i, :num_tokens])
2062
2063
2064
2065
2066
2067
            if drafter_output is None or len(drafter_output) == 0:
                draft_token_ids.append([])
            else:
                draft_token_ids.append(drafter_output.tolist())
        return draft_token_ids

2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
            assert config_name in allowed_config_names, \
                f"Config `{config_name}` not supported. " \
                f"Allowed configs: {allowed_config_names}"
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2078
2079
2080
2081
2082
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2083
        logger.info("Starting to load model %s...", self.model_config.model)
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
            num_local_physical_experts = torch.empty(1,
                                                     dtype=torch.int32,
                                                     device="cpu")
            torch.distributed.broadcast(num_local_physical_experts,
                                        group=get_ep_group().cpu_group,
                                        group_src=0)
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
            global_expert_load, old_global_expert_indices = (
                EplbState.recv_state())
            num_logical_experts = global_expert_load.shape[1]
2097
            self.parallel_config.eplb_config.num_redundant_experts = (
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
                num_local_physical_experts * new_ep_size - num_logical_experts)
            assert old_global_expert_indices.shape[
                1] % num_local_physical_experts == 0
            old_ep_size = old_global_expert_indices.shape[
                1] // num_local_physical_experts
            rank_mapping = {
                old_ep_rank: old_ep_rank
                for old_ep_rank in range(old_ep_size)
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2112
        with DeviceMemoryProfiler() as m:
2113
            time_before_load = time.perf_counter()
2114
            model_loader = get_model_loader(self.load_config)
2115
2116
2117
            logger.info("Loading model from scratch...")
            self.model = model_loader.load_model(
                vllm_config=self.vllm_config, model_config=self.model_config)
2118
2119
2120
2121
2122
2123
            if self.lora_config:
                self.model = self.load_lora_model(self.model,
                                                  self.model_config,
                                                  self.scheduler_config,
                                                  self.lora_config,
                                                  self.device)
2124
2125
2126
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2127
            if self.use_aux_hidden_state_outputs:
2128
2129
2130
2131
2132
2133
2134
                if supports_eagle3(self.model):
                    self.model.set_aux_hidden_state_layers(
                        self.model.get_eagle3_aux_hidden_state_layers())
                else:
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
                        "aux_hidden_state_outputs was requested")
2135
            time_after_load = time.perf_counter()
2136
        self.model_memory_usage = m.consumed_memory
2137
2138
        logger.info("Model loading took %.4f GiB and %.6f seconds",
                    self.model_memory_usage / GiB_bytes,
2139
                    time_after_load - time_before_load)
2140
        prepare_communication_buffer_for_model(self.model)
2141

2142
2143
2144
2145
2146
2147
2148
2149
        if is_mixture_of_experts(
                self.model) and self.parallel_config.enable_eplb:
            logger.info("EPLB is enabled for model %s.",
                        self.model_config.model)
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2150
2151
2152
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2153
2154
            )

2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
        if (
            self.vllm_config.compilation_config.level == \
                CompilationLevel.DYNAMO_AS_IS and supports_dynamo()
        ):
            backend = self.vllm_config.compilation_config.init_backend(
                self.vllm_config)
            compilation_counter.dynamo_as_is_count += 1
            self.model.compile(
                fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
                backend=backend)
2165
2166
2167
2168
2169
2170
2171
2172
2173
            return
        # for other compilation levels, cudagraph behavior is controlled by
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
            self.model = CUDAGraphWrapper(self.model,
                                          self.vllm_config,
                                          runtime_mode=CUDAGraphMode.FULL)
2174

2175
2176
2177
2178
2179
    def reload_weights(self) -> None:
        assert getattr(self, "model", None) is not None, \
            "Cannot reload weights before model is loaded."
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
2180
2181
        model = self.get_model()
        model_loader.load_weights(model, model_config=self.model_config)
2182

2183
2184
2185
2186
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
2187
        model = self.get_model()
2188
        TensorizerLoader.save_model(
2189
            model,
2190
            tensorizer_config=tensorizer_config,
2191
            model_config=self.model_config,
2192
2193
        )

2194
2195
2196
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
2197
        num_scheduled_tokens: dict[str, int],
2198
    ) -> dict[str, Optional[LogprobsTensors]]:
2199
2200
2201
2202
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

2203
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
2204
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
2205
2206
2207
2208
2209

        # Since prompt logprobs are a rare feature, prioritize simple,
        # maintainable loop over optimal performance.
        completed_prefill_reqs = []
        for req_id, num_prompt_logprobs in num_prompt_logprobs_dict.items():
2210
            num_tokens = num_scheduled_tokens[req_id]
2211
2212
2213
2214
2215
2216
2217

            # Get metadata for this request.
            request = self.requests[req_id]
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
                self.device, non_blocking=True)

2218
2219
2220
2221
2222
2223
2224
2225
2226
            # Set up target LogprobsTensors object.
            logprobs_tensors = in_progress_dict.get(req_id)
            if not logprobs_tensors:
                # Create empty logprobs CPU tensors for the entire prompt.
                # If chunked, we'll copy in slice by slice.
                logprobs_tensors = LogprobsTensors.empty_cpu(
                    num_prompt_tokens - 1, num_prompt_logprobs + 1)
                in_progress_dict[req_id] = logprobs_tensors

2227
            # Determine number of logits to retrieve.
2228
2229
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
2230
            num_remaining_tokens = num_prompt_tokens - start_tok
2231
            if num_tokens <= num_remaining_tokens:
2232
                # This is a chunk, more tokens remain.
2233
2234
2235
                # In the == case, there are no more prompt logprobs to produce
                # but we want to defer returning them to the next step where we
                # have new generated tokens to return.
2236
2237
2238
2239
2240
                num_logits = num_tokens
            else:
                # This is the last chunk of prompt tokens to return.
                num_logits = num_remaining_tokens
                completed_prefill_reqs.append(req_id)
2241
2242
2243
2244
2245
2246
2247
                prompt_logprobs_dict[req_id] = logprobs_tensors

            if num_logits <= 0:
                # This can happen for the final chunk if we prefilled exactly
                # (num_prompt_tokens - 1) tokens for this request in the prior
                # step. There are no more prompt logprobs to produce.
                continue
2248
2249
2250
2251
2252

            # Get the logits corresponding to this req's prompt tokens.
            # If this is a partial request (i.e. chunked prefill),
            # then there is prompt logprob generated for each index.
            req_idx = self.input_batch.req_id_to_index[req_id]
2253
            offset = self.query_start_loc.np[req_idx].item()
2254
2255
2256
2257
2258
2259
2260
2261
2262
            prompt_hidden_states = hidden_states[offset:offset + num_logits]
            logits = self.model.compute_logits(prompt_hidden_states, None)

            # Get the "target" tokens for each index. For prompt at index i,
            # the token at prompt index i+1 is the "sampled" token we want
            # to gather the logprob for.
            tgt_token_ids = prompt_token_ids[start_tok:start_tok + num_logits]

            # Compute prompt logprobs.
2263
2264
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
2265
2266
2267
                logprobs, num_prompt_logprobs, tgt_token_ids)

            # Transfer GPU->CPU async.
2268
2269
2270
2271
2272
2273
2274
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
                token_ids, non_blocking=True)
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs,
                                                         non_blocking=True)
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
                ranks, non_blocking=True)
2275
2276
2277
2278
2279

        # Remove requests that have completed prefill from the batch
        # num_prompt_logprobs_dict.
        for req_id in completed_prefill_reqs:
            del num_prompt_logprobs_dict[req_id]
2280
            del in_progress_dict[req_id]
2281
2282

        # Must synchronize the non-blocking GPU->CPU transfers.
2283
        if prompt_logprobs_dict:
2284
            self._sync_device()
2285
2286
2287

        return prompt_logprobs_dict

2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
    def _get_nans_in_logits(
        self,
        logits: Optional[torch.Tensor],
    ) -> dict[str, int]:
        try:
            if logits is None:
                return {req_id: 0 for req_id in self.input_batch.req_ids}

            num_nans_in_logits = {}
            num_nans_for_index = logits.isnan().sum(dim=-1).cpu().numpy()
            for req_id in self.input_batch.req_ids:
                req_index = self.input_batch.req_id_to_index[req_id]
                num_nans_in_logits[req_id] = (
                    int(num_nans_for_index[req_index])
                    if num_nans_for_index is not None
                    and req_index < logits.shape[0] else 0)
            return num_nans_in_logits
        except IndexError:
            return {}

2308
2309
2310
2311
2312
2313
    @contextmanager
    def maybe_randomize_inputs(self, input_ids: torch.Tensor):
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
2314
         - during DP rank dummy run
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
        """
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        randomize_inputs = envs.VLLM_RANDOMIZE_DP_DUMMY_INPUTS and dp_size > 1
        if not randomize_inputs:
            yield
        else:
            import functools

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
2326
                    self.input_ids.gpu,
2327
2328
2329
2330
                    low=0,
                    high=self.model_config.get_vocab_size(),
                    dtype=input_ids.dtype)

2331
            logger.debug_once("Randomizing dummy data for DP Rank")
2332
2333
2334
2335
2336
            input_ids.copy_(rand_input_ids()[:input_ids.size(0)],
                            non_blocking=True)
            yield
            input_ids.fill_(0)

2337
2338
2339
2340
2341
2342
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
2343
2344
        assert self.mm_budget is not None

2345
2346
2347
2348
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
            seq_len=self.max_num_tokens,
            mm_counts={modality: 1},
2349
            cache=self.mm_budget.cache,
2350
2351
2352
2353
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
2354
2355
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
2356

2357
2358
        return next(mm_kwargs_group
                    for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
2359
                        dummy_mm_items,
2360
2361
2362
                        device=self.device,
                        pin_memory=self.pin_memory,
                    ))
2363

2364
2365
2366
2367
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
2368
2369
2370
        cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
        force_attention: bool = False,
        uniform_decode: bool = False,
2371
2372
        skip_eplb: bool = False,
        is_profile: bool = False,
2373
        remove_lora: bool = True,
2374
    ) -> tuple[torch.Tensor, torch.Tensor]:
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
        """
        Run a dummy forward pass to warm up/profile run or capture the
        CUDA graph for the model.

        Args:
            num_tokens: Number of tokens to run the dummy forward pass.
            cudagraph_runtime_mode: used to control the behavior.
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
2386
            force_attention: If True, always create attention metadata. Used to
2387
2388
2389
2390
                warm up attention backend when mode is NONE.
            uniform_decode: If True, the batch is a uniform decode batch.
            skip_eplb: If True, skip EPLB state update.
            is_profile: If True, this is a profile run.
2391
            remove_lora: If False, dummy LoRAs are not destroyed after the run
2392
2393
2394
2395
        """
        assert cudagraph_runtime_mode in {
            CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL
        }
2396

2397
        # Padding for DP
2398
2399
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
        num_tokens += num_pad
2400

2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
        # If cudagraph_mode.decode_mode() == FULL and
        # cudagraph_mode.seperate_routine(). This means that we are using
        # different graphs and/or modes for mixed prefill-decode batches vs.
        # uniform decode batches. A uniform decode batch means that all
        # requests have identical query length, except a potential virtual
        # request (shorter) in the batch account for padding.
        # Uniform decode batch could either be common pure decode, where
        # max_query_len == 1, or speculative decode, where
        # max_query_len == 1 + num_spec_decode_tokens.

        # When setting max_query_len = 1, we switch to and capture the optimized
        # routine of FA2 for pure decode, i.e., Flashdecode + an optimization
        # for GQA/MQA.
        max_query_len = self.uniform_decode_query_len if uniform_decode else \
                                                                num_tokens

2417
2418
2419
2420
2421
        # Set num_scheduled_tokens based on num_tokens and max_num_seqs
        # for dummy run with LoRA so that the num_reqs collectively
        # has num_tokens in total.
        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
        max_num_reqs = self.scheduler_config.max_num_seqs
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
        if uniform_decode:
            num_reqs = cdiv(num_tokens, max_query_len)
            assert num_reqs <= max_num_reqs, \
                "Do not capture num_reqs > max_num_reqs for uniform batch"
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
        else:
            num_reqs = min(num_tokens, max_num_reqs)
            min_tokens_per_req = num_tokens // num_reqs
            num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
            num_scheduled_tokens_list[-1] += num_tokens % num_reqs

2435
2436
2437
2438
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
        num_scheduled_tokens = np.array(num_scheduled_tokens_list,
                                        dtype=np.int32)
2439

2440
        attn_metadata: Optional[dict[str, Any]] = None
2441
2442
2443

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
2444
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
2445
2446
            attn_metadata = {}

2447
            # Make sure max_model_len is used at the graph capture time.
2448
2449
2450
            self.seq_lens.np[:num_reqs] = self.max_model_len
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
2451

2452
2453
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
                    self.kv_cache_config.kv_cache_groups):
2454
                common_attn_metadata = CommonAttentionMetadata(
2455
2456
                    query_start_loc=self.query_start_loc.gpu[:num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs +
2457
                                                                 1],
2458
2459
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
2460
2461
2462
2463
                    num_computed_tokens_cpu=self.input_batch.
                    num_computed_tokens_cpu_tensor[:num_reqs],
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
2464
                    max_query_len=max_query_len,
2465
                    max_seq_len=self.max_model_len,
2466
2467
2468
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id].get_device_tensor()[:num_reqs],
                    slot_mapping=self.input_batch.
2469
2470
                    block_table[kv_cache_group_id].slot_mapping[:num_tokens],
                    causal=True)
2471

2472
2473
2474
2475
2476
                for attn_group in self.attn_groups[kv_cache_group_id]:
                    attn_metadata_i = attn_group.metadata_builder\
                        .build_for_cudagraph_capture(common_attn_metadata)
                    for layer_name in kv_cache_group_spec.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
2477

2478
        with self.maybe_dummy_run_with_lora(self.lora_config,
2479
                                            num_scheduled_tokens, remove_lora):
2480
            if self.supports_mm_inputs:
2481
2482
                input_ids = None
                inputs_embeds = self.inputs_embeds[:num_tokens]
2483
2484
2485
2486
                model_kwargs = {
                    **self._init_model_kwargs(num_tokens),
                    **self._dummy_mm_kwargs(num_reqs),
                }
2487
            else:
2488
                input_ids = self.input_ids.gpu[:num_tokens]
2489
                inputs_embeds = None
2490
                model_kwargs = self._init_model_kwargs(num_tokens)
2491

2492
            if self.uses_mrope:
2493
                positions = self.mrope_positions.gpu[:, :num_tokens]
2494
            else:
2495
                positions = self.positions.gpu[:num_tokens]
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505

            if get_pp_group().is_first_rank:
                intermediate_tensors = None
            else:
                if self.intermediate_tensors is None:
                    self.intermediate_tensors = (
                        self.model.make_empty_intermediate_tensors(
                            batch_size=self.max_num_tokens,
                            dtype=self.model_config.dtype,
                            device=self.device))
2506
2507
2508

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                    num_tokens, None, False)
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
            if cudagraph_runtime_mode == CUDAGraphMode.NONE:
                batch_descriptor = None
            else:
                # filter out the valid batch descriptor
                _cg_mode, batch_descriptor = \
                    self.cudagraph_dispatcher.dispatch(
                        BatchDescriptor(num_tokens=num_tokens,
                                        uniform_decode=uniform_decode))
                # sanity check
                assert cudagraph_runtime_mode == _cg_mode, (
                    f"Cudagraph runtime mode mismatch at dummy_run. "
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}.")
2521

2522
            with self.maybe_randomize_inputs(input_ids), set_forward_context(
2523
2524
2525
                    attn_metadata,
                    self.vllm_config,
                    num_tokens=num_tokens,
2526
2527
2528
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
                    batch_descriptor=batch_descriptor):
2529
                outputs = self.model(
2530
2531
2532
2533
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
2534
                    **model_kwargs,
2535
                )
2536

2537
2538
2539
2540
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
2541

2542
            if self.speculative_config and self.speculative_config.use_eagle():
2543
2544
2545
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
        # This is necessary to avoid blocking DP.
        # For dummy runs, we typically skip EPLB since we don't have any real
        # requests to process.
        # However, in DP settings, there may be cases when some DP ranks do
        # not have any requests to process, so they're executing dummy batches.
        # In such cases, we still have to trigger EPLB to make sure
        # ranks execute the rearrangement in synchronization.
        if not skip_eplb:
            self.eplb_step(is_dummy=True, is_profile=is_profile)

2556
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
2557
        return hidden_states, hidden_states[logit_indices]
2558
2559
2560
2561
2562
2563

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
2564
2565
2566
2567
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
        hidden_states = torch.rand_like(hidden_states)
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590

        logits = self.model.compute_logits(hidden_states, None)
        num_reqs = logits.size(0)

        dummy_tensors = lambda v: torch.full(
            (num_reqs, ), v, device=self.device)

        dummy_metadata = SamplingMetadata(
            temperature=dummy_tensors(0.5),
            all_greedy=False,
            all_random=False,
            top_p=dummy_tensors(0.9),
            top_k=dummy_tensors(logits.size(1) - 1),
            generators={},
            max_num_logprobs=None,
            no_penalties=True,
            prompt_token_ids=None,
            frequency_penalties=dummy_tensors(0.1),
            presence_penalties=dummy_tensors(0.1),
            repetition_penalties=dummy_tensors(0.1),
            output_token_ids=[[] for _ in range(num_reqs)],
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
2591
            logitsprocs=LogitsProcessors(),
2592
        )
2593
        try:
2594
2595
            sampler_output = self.sampler(logits=logits,
                                          sampling_metadata=dummy_metadata)
2596
2597
2598
2599
2600
2601
2602
2603
2604
        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
                    "CUDA out of memory occurred when warming up sampler with "
                    f"{num_reqs} dummy requests. Please try lowering "
                    "`max_num_seqs` or `gpu_memory_utilization` when "
                    "initializing the engine.") from e
            else:
                raise e
2605
        if self.speculative_config:
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
                draft_token_ids, self.device)

            num_tokens = sum(len(ids) for ids in draft_token_ids)
            # draft_probs = torch.randn(
            #     num_tokens, logits.shape[-1], device=self.device,
            #     dtype=logits.dtype)
            draft_probs = None
            target_logits = torch.randn(num_tokens,
                                        logits.shape[-1],
                                        device=self.device,
                                        dtype=logits.dtype)
            # NOTE(woosuk): Here, we should use int32 because the sampler uses
            # int32 for bonus_token_ids. If the dtype mismatches, re-compilation
            # will occur at runtime.
            bonus_token_ids = torch.zeros(num_reqs,
                                          device=self.device,
                                          dtype=torch.int32)
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
2632
        return sampler_output
2633

2634
    def _dummy_pooler_run_task(
2635
2636
        self,
        hidden_states: torch.Tensor,
2637
2638
        task: PoolingTask,
    ) -> PoolerOutput:
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
        num_tokens = hidden_states.shape[0]
        max_num_reqs = self.scheduler_config.max_num_seqs
        num_reqs = min(num_tokens, max_num_reqs)
        min_tokens_per_req = num_tokens // num_reqs
        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs

        req_num_tokens = num_tokens // num_reqs

2650
        dummy_prompt_lens = torch.tensor(
2651
2652
            num_scheduled_tokens_list,
            device="cpu",
2653
2654
2655
2656
        )
        dummy_token_ids = torch.zeros((num_reqs, req_num_tokens),
                                      dtype=torch.int32,
                                      device=self.device)
2657

2658
        model = cast(VllmModelForPooling, self.get_model())
2659
2660
        dummy_pooling_params = PoolingParams(task=task)
        to_update = model.pooler.get_pooling_updates(task)
2661
2662
        to_update.apply(dummy_pooling_params)

2663
        dummy_metadata = PoolingMetadata(
2664
2665
2666
2667
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
2668

2669
2670
2671
        dummy_metadata.build_pooling_cursor(num_scheduled_tokens_list,
                                            device=hidden_states.device)

2672
        try:
2673
            return model.pooler(hidden_states=hidden_states,
2674
                                pooling_metadata=dummy_metadata)
2675
2676
2677
        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
2678
2679
2680
                    "CUDA out of memory occurred when warming up pooler "
                    f"({task=}) with {num_reqs} dummy requests. Please try "
                    "lowering `max_num_seqs` or `gpu_memory_utilization` when "
2681
2682
2683
                    "initializing the engine.") from e
            else:
                raise e
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
        output_size = dict[PoolingTask, float]()
        for task in self.get_supported_pooling_tasks():
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
            output_size[task] = output.get_data_nbytes()
            del output  # Allow GC

        max_task = max(output_size.items(), key=lambda x: x[1])[0]
        return self._dummy_pooler_run_task(hidden_states, max_task)
2700

2701
    def profile_run(self) -> None:
2702
        # Profile with multimodal encoder & encoder cache.
2703
        if self.supports_mm_inputs:
2704
            if self.model_config.multimodal_config.skip_mm_profiling:
2705
                logger.info(
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
                    "Skipping memory profiling for multimodal encoder and "
                    "encoder cache.")
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                # TODO: handle encoder-decoder models once we support them.
                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
                    # NOTE: Currently model is profiled with a single non-text
                    # modality with the max possible input tokens even when
                    # it supports multiple.
2717
2718
2719
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
                    max_mm_items_per_batch = mm_budget \
                        .max_items_per_batch_by_modality[dummy_modality]
2720
2721
2722
2723
2724
2725
2726
2727
2728

                    logger.info(
                        "Encoder cache will be initialized with a budget of "
                        "%s tokens, and profiled with %s %s items of the "
                        "maximum feature size.",
                        encoder_budget,
                        max_mm_items_per_batch,
                        dummy_modality,
                    )
2729

2730
2731
2732
2733
2734
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
2735

2736
2737
2738
2739
                    # Run multimodal encoder.
                    dummy_encoder_outputs = \
                        self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs)
2740

2741
2742
2743
2744
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
2745

2746
2747
2748
                    # Cache the dummy encoder outputs.
                    self.encoder_cache["tmp"] = dict(
                        enumerate(dummy_encoder_outputs))
2749

2750
        # Add `is_profile` here to pre-allocate communication buffers
2751
        hidden_states, last_hidden_states \
2752
            = self._dummy_run(self.max_num_tokens, is_profile=True)
2753
        if get_pp_group().is_last_rank:
2754
2755
2756
2757
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
2758
        else:
2759
            output = None
2760
        self._sync_device()
2761
        del hidden_states, output
2762
        self.encoder_cache.clear()
2763
        gc.collect()
2764
2765

    def capture_model(self) -> None:
2766
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
2767
            logger.warning(
2768
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
2769
                "ensure `cudagraph_mode` was not manually set to `NONE`")
2770
            return
2771
2772
        else:
            self.initialize_cudagraph_capture()
2773

2774
2775
        compilation_counter.num_gpu_runner_capture_triggers += 1

2776
2777
2778
        start_time = time.perf_counter()
        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()

2794
2795
2796
        # Trigger CUDA graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
2797
        set_cudagraph_capturing_enabled(True)
2798
        with freeze_gc(), graph_capture(device=self.device):
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
            cudagraph_mode = self.compilation_config.cudagraph_mode
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()

                compilation_cases = list(reversed(self.cudagraph_batch_sizes))
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
                    uniform_decode=False)

            # Capture full cudagraph for uniform decode batches if we have
            # dont already have full mixed prefill-decode cudagraphs
            if cudagraph_mode.decode_mode() == CUDAGraphMode.FULL and \
                cudagraph_mode.separate_routine():
                max_num_tokens = self.scheduler_config.max_num_seqs * \
                        self.uniform_decode_query_len
                decode_cudagraph_batch_sizes = [
                    x for x in self.cudagraph_batch_sizes if
                    x <= max_num_tokens and x >= self.uniform_decode_query_len
                ]
                compilation_cases_decode = list(
                    reversed(decode_cudagraph_batch_sizes))
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
                    uniform_decode=True)

        # Disable cudagraph capturing globally, so any unexpected cudagraph
        # capturing will be detected and raise an error after here.
        # Note: We don't put it into graph_capture context manager because
2829
        # we may do lazy capturing in future that still allows capturing
2830
2831
        # after here.
        set_cudagraph_capturing_enabled(False)
2832
2833
2834
2835
2836
2837
2838
2839

        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))
2840

2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
    def _capture_cudagraphs(self, compilation_cases: list[int],
                            cudagraph_runtime_mode: CUDAGraphMode,
                            uniform_decode: bool):
        assert cudagraph_runtime_mode != CUDAGraphMode.NONE and \
            cudagraph_runtime_mode in [CUDAGraphMode.FULL,
                                        CUDAGraphMode.PIECEWISE]

        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
                    cudagraph_runtime_mode.name))
        # We skip EPLB here since we don't want to record dummy metrics
        for num_tokens in compilation_cases:
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
                force_attention = (
                    cudagraph_runtime_mode == CUDAGraphMode.FULL)
                self._dummy_run(num_tokens,
                                cudagraph_runtime_mode=CUDAGraphMode.NONE,
                                force_attention=force_attention,
                                uniform_decode=uniform_decode,
2870
2871
                                skip_eplb=True,
                                remove_lora=False)
2872
2873
2874
            self._dummy_run(num_tokens,
                            cudagraph_runtime_mode=cudagraph_runtime_mode,
                            uniform_decode=uniform_decode,
2875
2876
2877
                            skip_eplb=True,
                            remove_lora=False)
        self.maybe_remove_all_loras(self.lora_config)
2878

2879
2880
2881
2882
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
2883
2884
2885
2886
2887
2888
        assert len(self.attn_groups) == 0, \
            "Attention backends are already initialized"

        def get_attn_backends_for_layers(
                layer_names: list[str]
        ) -> dict[type[AttentionBackend], list[str]]:
2889
2890
2891
            layers = get_layers_from_vllm_config(self.vllm_config,
                                                 AttentionLayerBase,
                                                 layer_names)
2892
2893
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
2894
            # Dedupe based on full class name; this is a bit safer than
2895
2896
2897
2898
2899
            # using the class itself as the key because when we create dynamic
            # attention backend subclasses (e.g. ChunkedLocalAttention) unless
            # they are cached correctly, there will be different objects per
            # layer.
            for layer_name in layer_names:
2900
                attn_backend = layers[layer_name].get_attn_backend()
2901
2902
2903
2904
2905
2906
2907

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
                key = attn_backend.full_cls_name()
                attn_backends[key] = attn_backend
                attn_backend_layers[key].append(layer_name)
            return {
                attn_backends[k]: v
                for k, v in attn_backend_layers.items()
            }

        def create_attn_groups(
            attn_backends_map: dict[AttentionBackend, list[str]],
            kv_cache_spec: KVCacheSpec,
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
            for attn_backend, layer_names in attn_backends_map.items():
                attn_metadata_builder_i = attn_backend.get_builder_cls()(
                    kv_cache_spec,
                    layer_names,
                    self.vllm_config,
                    self.device,
                )
                attn_group = AttentionGroup(attn_backend,
                                            attn_metadata_builder_i,
                                            layer_names)
                attn_groups.append(attn_group)
            return attn_groups

        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
2935
            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
2936
2937
            attn_backends = get_attn_backends_for_layers(
                kv_cache_group_spec.layer_names)
2938
2939
            self.attn_groups.append(
                create_attn_groups(attn_backends, kv_cache_spec))
2940

co63oc's avatar
co63oc committed
2941
        # Calculate reorder batch threshold (if needed)
2942
2943
        self.calculate_reorder_batch_threshold()

2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
    def initialize_cudagraph_capture(self) -> None:
        min_cg_support = AttentionCGSupport.ALWAYS
        min_cg_builder_name = None

        for attn_group in self._attn_group_iterator():
            builder = attn_group.metadata_builder
            if builder.cudagraph_support.value < min_cg_support.value:
                min_cg_support = builder.cudagraph_support
                min_cg_builder_name = builder.__class__.__name__

        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
        if cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL \
            and min_cg_support != AttentionCGSupport.ALWAYS:
            msg = (f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                   f"with {min_cg_builder_name} backend (support: "
                   f"{min_cg_support})")
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
                msg += "; please try cudagraph_mode=PIECEWISE, and "\
                    "make sure compilation level is piecewise"
                raise ValueError(msg)

            # attempt to resolve the full cudagraph related mode
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=FULL_AND_PIECEWISE"
                cudagraph_mode = self.compilation_config.cudagraph_mode = \
                    CUDAGraphMode.FULL_AND_PIECEWISE
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
                cudagraph_mode = self.compilation_config.cudagraph_mode = \
                    CUDAGraphMode.FULL_DECODE_ONLY
            logger.warning(msg)

        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
        if (cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and self.uniform_decode_query_len > 1 and min_cg_support.value
                < AttentionCGSupport.UNIFORM_BATCH.value):
            msg = (f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
                   f" with spec-decode for attention backend "
                   f"{min_cg_builder_name} (support: {min_cg_support})")
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
                cudagraph_mode = self.compilation_config.cudagraph_mode = \
                    CUDAGraphMode.PIECEWISE
            else:
                msg += "; setting cudagraph_mode=NONE"
                cudagraph_mode = self.compilation_config.cudagraph_mode = \
                    CUDAGraphMode.NONE
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
        if cudagraph_mode.has_full_cudagraphs() \
            and min_cg_support == AttentionCGSupport.NEVER:
            raise ValueError(f"CUDAGraphMode.{cudagraph_mode.name} is not "
                             f"supported with {min_cg_builder_name} backend ("
                             f"support:{min_cg_support}) "
                             "; please try cudagraph_mode=PIECEWISE, "
                             "and make sure compilation level is piecewise")

        # Trigger cudagraph dispatching keys initialization here (after
        # initializing attn backends).
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
            self.compilation_config.cudagraph_mode,
            self.uniform_decode_query_len)

3013
3014
3015
3016
3017
    def calculate_reorder_batch_threshold(self) -> None:
        """
        Check that if any backends reorder batches; that the reordering
        is compatible (e.g., decode threshold is the same)
        """
3018
3019
3020
        for group in self._attn_group_iterator():
            attn_metadata_builder_i = group.metadata_builder

3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
            # check that if any backends reorder batches; that the reordering
            # is compatible (e.g., decode threshold is the same)
            reorder_batch_threshold_i = (
                attn_metadata_builder_i.reorder_batch_threshold)
            if reorder_batch_threshold_i is not None:
                if self.reorder_batch_threshold is not None:
                    if reorder_batch_threshold_i != \
                        self.reorder_batch_threshold:
                        raise ValueError(
                            f"Attention backend reorders decodes with "
                            f"threshold {reorder_batch_threshold_i} but other "
                            f"backend uses threshold "
                            f"{self.reorder_batch_threshold}")
                else:
                    self.reorder_batch_threshold = reorder_batch_threshold_i

3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
    def may_reinitialize_input_batch(self,
                                     kv_cache_config: KVCacheConfig) -> None:
        """
        Re-initialize the input batch if the block sizes are different from
        `[self.cache_config.block_size]`. This usually happens when there
        are multiple KV cache groups.

        Args:
            kv_cache_config: The KV cache configuration.
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
        ]
        if block_sizes != [self.cache_config.block_size]:
            assert self.cache_config.cpu_offload_gb == 0, (
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
                "for more details.")
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
                max_model_len=self.max_model_len,
                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
                block_sizes=block_sizes,
3064
                is_spec_decode=bool(self.vllm_config.speculative_config),
3065
3066
                logitsprocs=self.input_batch.logitsprocs,
                is_pooling_model=self.is_pooling_model,
3067
3068
            )

3069
3070
    def _allocate_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
3071
        """
3072
3073
3074
        Initializes the KV cache buffer with the correct size. The buffer needs
        to be reshaped to the desired shape before being used by the models.

3075
        Args:
3076
            kv_cache_config: The KV cache config
3077
        Returns:
3078
            dict[str, torch.Tensor]: A map between layer names to their
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
            corresponding memory buffer for KV cache.
         """
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
            tensor = torch.zeros(kv_cache_tensor.size,
                                 dtype=torch.int8,
                                 device=self.device)
            for layer_name in kv_cache_tensor.shared_by:
                kv_cache_raw_tensors[layer_name] = tensor

        layer_names = set()
        for group in kv_cache_config.kv_cache_groups:
3091
3092
3093
3094
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
3095
3096
3097
3098
        assert layer_names == set(kv_cache_raw_tensors.keys(
        )), "Some layers are not correctly initialized"
        return kv_cache_raw_tensors

3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

    def _kv_cache_spec_attn_group_iterator(
            self) -> Iterator[tuple[KVCacheSpec, AttentionGroup]]:
        if not self.kv_cache_config.kv_cache_groups:
            return
        for kv_cache_spec_id, attn_groups in enumerate(self.attn_groups):
            for attn_group in attn_groups:
                yield self.kv_cache_config.kv_cache_groups[
                    kv_cache_spec_id].kv_cache_spec, attn_group

3111
3112
3113
3114
3115
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
3116
        """
3117
        Reshape the KV cache tensors to the desired shape and dtype.
3118

3119
        Args:
3120
3121
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
3122
                correct size but uninitialized shape.
3123
        Returns:
3124
            Dict[str, torch.Tensor]: A map between layer names to their
3125
3126
            corresponding memory buffer for KV cache.
        """
3127
        kv_caches: dict[str, torch.Tensor] = {}
3128
        has_attn, has_mamba = False, False
3129
3130
3131
        for kv_cache_spec, group in self._kv_cache_spec_attn_group_iterator():
            attn_backend = group.backend
            for layer_name in group.layer_names:
3132
3133
                if layer_name in self.runner_only_attn_layers:
                    continue
3134
3135
3136
3137
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
                num_blocks = (raw_tensor.numel() //
                              kv_cache_spec.page_size_bytes)
3138
                if isinstance(kv_cache_spec, AttentionSpec):
3139
                    has_attn = True
3140
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
3141
3142
3143
                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    dtype = kv_cache_spec.dtype
3144
                    try:
3145
3146
                        kv_cache_stride_order = \
                            attn_backend.get_kv_cache_stride_order()
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
                        assert len(kv_cache_stride_order) == len(
                            kv_cache_shape)
                    except (AttributeError, NotImplementedError):
                        kv_cache_stride_order = tuple(
                            range(len(kv_cache_shape)))
                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
                    kv_cache_shape = tuple(kv_cache_shape[i]
                                           for i in kv_cache_stride_order)
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
3164
3165
3166
                    kv_caches[layer_name] = kv_cache_raw_tensors[
                        layer_name].view(dtype).view(kv_cache_shape).permute(
                            *inv_order)
Chen Zhang's avatar
Chen Zhang committed
3167
                elif isinstance(kv_cache_spec, MambaSpec):
3168
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
3169
3170
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
3171
3172
3173
3174
3175
3176
                    storage_offset_bytes = 0
                    for (shape, dtype) in zip(kv_cache_spec.shapes,
                                              kv_cache_spec.dtypes):
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
                            kv_cache_spec.page_size_bytes // dtype_size)
Chen Zhang's avatar
Chen Zhang committed
3177
                        target_shape = (num_blocks, *shape)
3178
3179
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
3180
                        assert storage_offset_bytes % dtype_size == 0
3181
3182
3183
3184
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
3185
                            storage_offset=storage_offset_bytes // dtype_size,
3186
                        )
Chen Zhang's avatar
Chen Zhang committed
3187
                        state_tensors.append(tensor)
3188
                        storage_offset_bytes += stride[0] * dtype_size
3189
3190

                    kv_caches[layer_name] = state_tensors
3191
                else:
3192
                    raise NotImplementedError
3193
3194

        if has_attn and has_mamba:
3195
            self._update_hybrid_attention_mamba_layout(kv_caches)
3196

3197
3198
        return kv_caches

3199
3200
    def _update_hybrid_attention_mamba_layout(
            self, kv_caches: dict[str, torch.Tensor]) -> None:
3201
        """
3202
3203
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
3204
3205

        Args:
3206
            kv_caches: The KV cache buffer of each layer.
3207
3208
        """

3209
3210
        for kv_cache_spec, group in self._kv_cache_spec_attn_group_iterator():
            for layer_name in group.layer_names:
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
                kv_cache = kv_caches[layer_name]
                if (isinstance(kv_cache_spec, AttentionSpec)
                        and kv_cache.shape[0] == 2):
                    assert kv_cache.shape[1] != 2, \
                        "Fail to determine whether the layout is " \
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for " \
                        f"a tensor of shape {kv_cache.shape}"
                    hidden_size = kv_cache.shape[2:].numel()
                    kv_cache.as_strided_(size=kv_cache.shape,
                                         stride=(hidden_size, 2 * hidden_size,
                                                 *kv_cache.stride()[2:]))
3222

3223
3224
3225
3226
3227
3228
3229
3230
    def initialize_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        Returns:
3231
            Dict[str, torch.Tensor]: A map between layer names to their
3232
3233
3234
3235
3236
3237
3238
            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
        kv_caches = self._reshape_kv_cache_tensors(kv_cache_config,
                                                   kv_cache_raw_tensors)
3239

3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
        # Set up cross-layer KV cache sharing
        for layer_name, target_layer_name in self.shared_kv_cache_layers.items(
        ):
            logger.debug("%s reuses KV cache of %s", layer_name,
                         target_layer_name)
            kv_caches[layer_name] = kv_caches[target_layer_name]

        bind_kv_cache(kv_caches,
                      self.compilation_config.static_forward_context,
                      self.kv_caches)
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
            self, kv_cache_config: KVCacheConfig) -> None:
        """
        Add layers that re-use KV cache to KV cache group of its target layer.
        Mapping of KV cache tensors happens in `initialize_kv_cache_tensors()`
        """
        if not self.shared_kv_cache_layers:
            # No cross-layer KV sharing, return
            return

        add_kv_sharing_layers_to_kv_cache_groups(
            self.shared_kv_cache_layers,
            kv_cache_config.kv_cache_groups,
            self.runner_only_attn_layers,
        )

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
3272
3273
3274
3275
3276
3277
3278
3279
            attn_layers = get_layers_from_vllm_config(self.vllm_config,
                                                      Attention)
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
                    self.kv_sharing_fast_prefill_eligible_layers.add(
                        layer_name)
                else:
                    break
3280

3281
3282
3283
3284
3285
3286
3287
    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
3288
        kv_cache_config = deepcopy(kv_cache_config)
3289
3290
        self.kv_cache_config = kv_cache_config
        self.may_reinitialize_input_batch(kv_cache_config)
3291
        self.may_add_encoder_only_layers_to_kv_cache_config()
3292
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
3293
3294
3295
        self.initialize_attn_backend(kv_cache_config)
        kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)

3296
3297
3298
3299
3300
3301
        if self.speculative_config and self.speculative_config.use_eagle():
            assert isinstance(self.drafter, EagleProposer)
            # validate all draft model layers belong to the same kv cache
            # group
            self.drafter.validate_same_kv_cache_group(kv_cache_config)

Robert Shaw's avatar
Robert Shaw committed
3302
3303
        if has_kv_transfer_group():
            get_kv_transfer_group().register_kv_caches(kv_caches)
3304
3305
3306
            if self.device.type == 'xpu':
                get_kv_transfer_group().set_host_xfer_buffer_ops(
                    copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
3307

3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
        """
        Add encoder-only layers to the KV cache config.
        """
        block_size = self.vllm_config.cache_config.block_size
        use_mla = self.vllm_config.model_config.use_mla
        encoder_only_attn_specs: dict[AttentionSpec,
                                      list[str]] = defaultdict(list)
        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
                attn_spec = EncoderOnlyAttentionSpec(
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
                    dtype=self.kv_cache_dtype,
                    use_mla=use_mla)
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
            assert len(
                encoder_only_attn_specs
            ) == 1, "Only support one encoder-only attention spec now"
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec))

3335
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
3336
        """
3337
        Generates the KVCacheSpec by parsing the kv cache format from each
3338
3339
        Attention module in the static forward context.
        Returns:
3340
            KVCacheSpec: A dictionary mapping layer names to their KV cache
3341
3342
3343
3344
            format. Layers that do not need KV cache are not included.
        """

        block_size = self.vllm_config.cache_config.block_size
3345
        use_mla = self.vllm_config.model_config.use_mla
3346
        kv_cache_spec: dict[str, KVCacheSpec] = {}
Chen Zhang's avatar
Chen Zhang committed
3347
3348
        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
            if (kv_tgt_layer :=
                    attn_module.kv_sharing_target_layer_name) is not None:
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue

3361
            # TODO: Support other attention modules, e.g., cross-attention
3362
3363
            # TODO(lucas): move the attention specs into the model layers like
            # the attention backends
3364
            if attn_module.attn_type == AttentionType.DECODER:
3365
3366
3367
3368
3369
3370
3371
3372
                if attn_module.sliding_window is not None:
                    kv_cache_spec[layer_name] = SlidingWindowSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        sliding_window=attn_module.sliding_window,
                        use_mla=use_mla)
3373
3374
                elif self.attention_chunk_size is not None \
                        and isinstance(attn_module, ChunkedLocalAttention):
3375
                    kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec(
3376
3377
3378
3379
3380
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        attention_chunk_size=self.attention_chunk_size,
3381
                        use_mla=use_mla)
3382
3383
3384
3385
3386
3387
3388
                else:
                    kv_cache_spec[layer_name] = FullAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        use_mla=use_mla)
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
            elif attn_module.attn_type in (AttentionType.ENCODER,
                                           AttentionType.ENCODER_ONLY):
                # encoder-only attention does not need KV cache.
                continue
            elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                raise NotImplementedError
            else:
                raise ValueError(
                    f"Unknown attention type: {attn_module.attn_type}")

3399
        mamba_layers = get_layers_from_vllm_config(self.vllm_config, MambaBase)
Chen Zhang's avatar
Chen Zhang committed
3400
3401
3402
3403
3404
3405
3406
3407
        if len(mamba_layers) > 0:
            if self.vllm_config.speculative_config is not None:
                raise NotImplementedError(
                    "Mamba with speculative decoding is not supported yet.")
            if self.vllm_config.cache_config.enable_prefix_caching:
                raise NotImplementedError(
                    "Prefix caching is not supported for Mamba yet.")
            max_model_len = self.vllm_config.model_config.max_model_len
3408

3409
3410
            page_size_padded = (
                self.vllm_config.cache_config.mamba_page_size_padded)
3411

Chen Zhang's avatar
Chen Zhang committed
3412
3413
3414
3415
3416
            # Set block_size to max_model_len, so that mamba model will always
            # have only one block in the KV cache.
            for layer_name, mamba_module in mamba_layers.items():
                kv_cache_spec[layer_name] = MambaSpec(
                    shapes=mamba_module.get_state_shape(),
3417
                    dtypes=mamba_module.get_state_dtype(),
3418
                    block_size=max_model_len,
3419
3420
                    page_size_padded=page_size_padded,
                    mamba_type=mamba_module.mamba_type)
3421

3422
        return kv_cache_spec
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437

    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
        pinned = self.sampled_token_ids_pinned_cpu[:sampled_token_ids.shape[0]]
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()