gpu_model_runner.py 290 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import functools
5
import gc
6
import itertools
7
import threading
8
import time
9
from collections import defaultdict
10
from collections.abc import Iterable, Iterator, Sequence
11
from contextlib import contextmanager
12
from copy import copy, deepcopy
13
from dataclasses import dataclass, replace
14
from functools import reduce
15
from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
16
17
18
19
20

import numpy as np
import torch
import torch.distributed
import torch.nn as nn
21
from tqdm import tqdm
22

23
import vllm.envs as envs
24
from vllm.compilation.counter import compilation_counter
25
from vllm.compilation.cuda_graph import CUDAGraphStat, CUDAGraphWrapper
26
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
27
from vllm.config import (
28
    CompilationMode,
29
30
31
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
32
    set_current_vllm_config,
33
34
    update_config,
)
35
from vllm.config.cache import CacheConfig
36
from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
37
from vllm.distributed.eplb.eplb_state import EplbState
38
from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
39
from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
40
from vllm.distributed.parallel_state import (
41
    get_dcp_group,
42
43
44
45
46
47
    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
48
49
50
51
from vllm.forward_context import (
    BatchDescriptor,
    set_forward_context,
)
52
from vllm.logger import init_logger
53
from vllm.lora.layers import LoRAMapping, LoRAMappingType
54
from vllm.model_executor.layers.attention import Attention, MLAAttention
55
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
56
57
58
from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
    RoutedExpertsCapturer,
)
59
60
61
62
from vllm.model_executor.layers.rotary_embedding import (
    MRotaryEmbedding,
    XDRotaryEmbedding,
)
63
from vllm.model_executor.model_loader import get_model_loader
64
65
66
67
from vllm.model_executor.model_loader.reload import (
    finalize_layerwise_reload,
    initialize_layerwise_reload,
)
68
from vllm.model_executor.models.interfaces import (
69
    MultiModalEmbeddings,
70
    SupportsMRoPE,
71
    SupportsMultiModal,
72
    SupportsXDRoPE,
73
74
75
76
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
77
    supports_realtime,
78
    supports_transcription,
79
    supports_xdrope,
80
)
81
from vllm.model_executor.models.interfaces_base import (
82
83
84
85
    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
86
87
88
89
90
from vllm.model_executor.offloader import (
    create_offloader,
    get_offloader,
    set_offloader,
)
91
from vllm.multimodal import MULTIMODAL_REGISTRY
92
from vllm.multimodal.encoder_budget import MultiModalBudget
93
94
95
96
97
from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
98
from vllm.multimodal.utils import group_and_batch_mm_kwargs
99
from vllm.platforms import current_platform
100
from vllm.pooling_params import PoolingParams
101
from vllm.sampling_params import SamplingType
102
from vllm.sequence import IntermediateTensors
103
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
104
from vllm.tracing import instrument
105
from vllm.utils import length_from_prompt_token_ids_or_embeds
106
from vllm.utils.math_utils import cdiv, round_up
107
from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
108
from vllm.utils.nvtx_pytorch_hooks import PytHooks
109
from vllm.utils.platform_utils import is_pin_memory_available, num_compute_units
110
111
112
113
from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
)
114
115
from vllm.v1.attention.backend import (
    AttentionBackend,
116
    AttentionCGSupport,
117
    AttentionMetadata,
118
    AttentionMetadataBuilder,
119
    AttentionType,
120
    CommonAttentionMetadata,
121
)
122
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
123
from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadataBuilder
124
from vllm.v1.attention.backends.utils import (
125
    create_fast_prefill_custom_backend,
126
    get_dcp_local_seq_lens,
127
128
    reorder_batch_to_split_decodes_and_prefills,
)
129
from vllm.v1.core.sched.output import NewRequestData
130
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
148
    ECConnectorOutput,
149
    KVConnectorOutput,
150
151
152
153
154
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
155
    make_empty_encoder_model_runner_output,
156
)
157
from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
158
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
159
from vllm.v1.sample.logits_processor.interface import LogitsProcessor
160
from vllm.v1.sample.metadata import SamplingMetadata
161
from vllm.v1.sample.rejection_sampler import RejectionSampler
162
from vllm.v1.sample.sampler import Sampler
163
from vllm.v1.spec_decode.draft_model import DraftModelProposer
164
from vllm.v1.spec_decode.eagle import EagleProposer
165
from vllm.v1.spec_decode.extract_hidden_states import ExtractHiddenStatesProposer
166
from vllm.v1.spec_decode.medusa import MedusaProposer
167
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
168
169
170
171
172
173
from vllm.v1.spec_decode.ngram_proposer_gpu import (
    NgramProposerGPU,
    copy_num_valid_draft_tokens,
    update_ngram_gpu_tensors_incremental,
    update_scheduler_for_invalid_drafts,
)
174
from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
175
from vllm.v1.structured_output.utils import apply_grammar_bitmask
176
from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
177
178
179
180
181
from vllm.v1.worker import mamba_utils
from vllm.v1.worker.cp_utils import (
    check_attention_cp_compatibility,
    get_total_cp_world_size,
)
182
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
183
from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
184
from vllm.v1.worker.gpu.pool.late_interaction_runner import LateInteractionRunner
185
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
186
from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
187
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
188
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
189
190
191
from vllm.v1.worker.ubatch_utils import (
    UBatchSlices,
    check_ubatch_thresholds,
192
    maybe_create_ubatch_slices,
193
    split_attn_metadata,
194
)
195
from vllm.v1.worker.utils import is_residual_scattered_for_sp
196
from vllm.v1.worker.workspace import lock_workspace
197

198
199
from .utils import (
    AttentionGroup,
200
    KVBlockZeroer,
201
202
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
203
    prepare_kernel_block_sizes,
204
205
    sanity_check_mm_encoder_outputs,
)
206

207
if TYPE_CHECKING:
208
    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
209
    from vllm.v1.spec_decode.ngram_proposer import NgramProposer
210
211
212

logger = init_logger(__name__)

213
214
AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
215
PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
216

217

218
219
220
221
222
223
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
224
        logprobs_tensors: LogprobsTensors | None,
225
226
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
227
        vocab_size: int,
228
229
230
231
232
    ):
        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.
233
        self.async_copy_ready_event = torch.Event()
234
235
236
237

        # 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
238
        self.vocab_size = vocab_size
239
        self._logprobs_tensors = logprobs_tensors
240
241
242
243
244

        # 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)
245
            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
246
247
                "cpu", non_blocking=True
            )
248
249
250
251
252
            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
253
            self.async_copy_ready_event.record()
254
255
256

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
257

258
259
        This function blocks until the copy is finished.
        """
260
        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
261
        self.async_copy_ready_event.synchronize()
262

263
264
        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
265
        del self._sampled_token_ids
266
        if max_gen_len == 1:
267
            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
268
269
            for i in self._invalid_req_indices:
                valid_sampled_token_ids[i].clear()
270
271
272
            logprobs_lists = None
            if self._logprobs_tensors_cpu is not None:
                logprobs_lists = self._logprobs_tensors_cpu.tolists()
273
        else:
274
            valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
275
276
                self.sampled_token_ids_cpu,
                self.vocab_size,
277
                self._invalid_req_indices,
278
                logprobs_tensors=self._logprobs_tensors_cpu,
279
            )
280
281
282

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
283
        output.logprobs = logprobs_lists
284
285
286
        return output


287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
def _copy_pooler_output_to_cpu(
    raw_pooler_output: PoolerOutput, finished_mask: list[bool]
) -> list[torch.Tensor | None]:
    num_reqs = len(finished_mask)

    if isinstance(raw_pooler_output, torch.Tensor):
        if raw_pooler_output.shape[0] != num_reqs:
            raise ValueError(
                "Pooler output batch size does not match finished mask size: "
                f"{raw_pooler_output.shape[0]} != {num_reqs}."
            )

        num_finished = sum(finished_mask)
        if num_finished == 0:
            return [None] * num_reqs
        if num_finished == num_reqs:
            return list(raw_pooler_output.to("cpu", non_blocking=True))

        # partial finished
        finished_indices = [i for i, include in enumerate(finished_mask) if include]
        index_tensor = torch.tensor(
            finished_indices, device=raw_pooler_output.device, dtype=torch.long
        )
        finished_outputs = raw_pooler_output.index_select(0, index_tensor).to(
            "cpu", non_blocking=True
        )
        partial_pooler_output: list[torch.Tensor | None] = [None] * num_reqs
        for i, out in zip(finished_indices, finished_outputs):
            partial_pooler_output[i] = out
        return partial_pooler_output

    assert isinstance(raw_pooler_output, list)
    if len(raw_pooler_output) != num_reqs:
        raise ValueError(
            "Pooler output batch size does not match finished mask size: "
            f"{len(raw_pooler_output)} != {num_reqs}."
        )

    pooler_output: list[torch.Tensor | None] = [None] * num_reqs
    for i, (out, include) in enumerate(zip(raw_pooler_output, finished_mask)):
        if include and out is not None:
            pooler_output[i] = out.to("cpu", non_blocking=True)
    return pooler_output


332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
class AsyncGPUPoolingModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        raw_pooler_output: PoolerOutput,
        finished_mask: list[bool],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        self._model_runner_output = model_runner_output

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

        # Keep a reference to the device tensors to avoid them being
        # deallocated until we finish copying it to the host.
        self._raw_pooler_output = raw_pooler_output

        # 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)
353
354
355
            self._model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
                raw_pooler_output=self._raw_pooler_output,
                finished_mask=finished_mask,
356
357
358
359
360
361
362
363
364
365
366
367
368
369
            )
            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 tensors once the copy has completed.
        del self._raw_pooler_output
        return self._model_runner_output


370
371
372
373
374
375
376
377
378
379
380
class ExecuteModelState(NamedTuple):
    """Ephemeral cached state transferred between execute_model() and
    sample_tokens(), after execute_model() returns None."""

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
381
    ec_connector_output: ECConnectorOutput | None
382
    cudagraph_stats: CUDAGraphStat | None
383
    slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None
384
385


386
387
388
class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
389
390
    def __init__(
        self,
391
        vllm_config: VllmConfig,
392
        device: torch.device,
393
    ):
394
395
396
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
397
        self.offload_config = vllm_config.offload_config
398
        self.compilation_config = vllm_config.compilation_config
399
400
401
402
403
404
        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
405

406
407
408
409
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
410
        self.device = device
411
412
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
413

414
415
416
        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
417

418
        self.is_pooling_model = model_config.runner_type == "pooling"
419
        self.enable_prompt_embeds = model_config.enable_prompt_embeds
420
        self.is_multimodal_raw_input_only_model = (
421
422
            model_config.is_multimodal_raw_input_only_model
        )
423
        # These will be overridden in load_model()
424
        self.is_multimodal_pruning_enabled = False
425
        self.requires_sequential_video_encoding = False
426
427
428
        # Set to True after init_routed_experts_capturer() completes.
        # Prevents routed experts code from running during profiling/dummy run.
        self.routed_experts_initialized = False
429
        self.max_model_len = model_config.max_model_len
430
431
432

        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
433
        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
434
        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
435
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
436
        self.max_num_reqs = scheduler_config.max_num_seqs
437

438
439
        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
440
        # TODO: Support overlapping micro-batches
441
442
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
443
            self.parallel_config.distributed_executor_backend == "external_launcher"
444
            and len(get_pp_group().ranks) > 1
445
        )
446

447
        # Model-related.
448
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
449
        self.inputs_embeds_size = model_config.get_inputs_embeds_size()
450
        self.attention_chunk_size = model_config.attention_chunk_size
451
        # Only relevant for models using ALiBi (e.g, MPT)
452
        self.use_alibi = model_config.uses_alibi
453

454
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
455
        self.is_mm_prefix_lm = self.model_config.is_mm_prefix_lm
456

457
        # Multi-modal data support
458
        self.mm_registry = MULTIMODAL_REGISTRY
459
        self.uses_mrope = model_config.uses_mrope
460
        self.uses_xdrope_dim = model_config.uses_xdrope_dim
461
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
462
            model_config
463
        )
464

465
466
467
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
468
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
469
470
471
        else:
            self.max_encoder_len = 0

472
473
474
        # Async scheduling
        self.use_async_scheduling = self.scheduler_config.async_scheduling

475
        # Sampler
476
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
477

478
        self.eplb_state: EplbState | None = None
479
480
        # NOTE(yongji): flag to temporarily disable EPLB during scaling up/down
        self.eep_eplb_suppressed = False
481
482
483
484
485
486
        """
        State of the expert parallelism load balancer.

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

487
        # Lazy initializations
488
        # self.model: nn.Module  # Set after load_model
489
        # Initialize in initialize_kv_cache
490
        self.kv_caches: list[torch.Tensor] = []
491
492
493
        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
494
495
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
496
497
        # self.kv_cache_config: KVCacheConfig

498
499
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
500
        self.late_interaction_runner = LateInteractionRunner()
501

502
        self.use_aux_hidden_state_outputs = False
503
504
505
506
507
        # 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:
508
            self.drafter: (
509
                NgramProposer  # noqa: F823
510
                | NgramProposerGPU
511
512
513
514
                | SuffixDecodingProposer
                | EagleProposer
                | DraftModelProposer
                | MedusaProposer
515
                | ExtractHiddenStatesProposer
516
            )
517
            if self.speculative_config.method == "ngram":
518
519
                from vllm.v1.spec_decode.ngram_proposer import NgramProposer

520
                self.drafter = NgramProposer(self.vllm_config)
521
522
523
524
525
526
            elif self.speculative_config.uses_draft_model():
                self.drafter = DraftModelProposer(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    runner=self,
                )
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
            elif self.speculative_config.use_ngram_gpu():
                self.drafter = NgramProposerGPU(self.vllm_config, self.device, self)
                self.num_tokens_no_spec_gpu = torch.zeros(
                    self.max_num_reqs, dtype=torch.int32, device=device
                )
                self.token_ids_gpu_tensor = torch.zeros(
                    self.max_num_reqs,
                    self.max_model_len,
                    dtype=torch.int32,
                    device=device,
                )
                self._ngram_pinned_idx_buf = torch.zeros(
                    self.max_num_reqs, dtype=torch.long, pin_memory=True
                )
                self._ngram_pinned_val_buf = torch.zeros(
                    self.max_num_reqs, dtype=torch.int32, pin_memory=True
                )
544
545
            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
546
            elif self.speculative_config.use_eagle():
547
                self.drafter = EagleProposer(self.vllm_config, self.device, self)
548
                if self.speculative_config.method == "eagle3":
549
550
551
                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
552
553
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
554
                    vllm_config=self.vllm_config, device=self.device
555
                )
556
557
558
559
560
            elif self.speculative_config.method == "extract_hidden_states":
                self.drafter = ExtractHiddenStatesProposer(
                    vllm_config=self.vllm_config, device=self.device
                )
                self.use_aux_hidden_state_outputs = True
561
            else:
562
563
564
565
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
566
            self.rejection_sampler = RejectionSampler(self.sampler)
567

568
569
570
        self.num_spec_tokens = 0
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens
571
572
573
574
575
            draft_config = self.speculative_config.draft_model_config
            if draft_config is not None and draft_config.max_model_len is not None:
                self.effective_drafter_max_model_len = draft_config.max_model_len
            else:
                self.effective_drafter_max_model_len = self.max_model_len
576

577
        # Request states.
578
        self.requests: dict[str, CachedRequestState] = {}
579
580
581
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
582
        self.comm_stream = torch.cuda.Stream()
583

584
585
586
587
588
589
590
591
592
        # 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.
593
594
595
596
        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
597
598
599
600
601
        placeholder_block_size = (
            self.cache_config.block_size or CacheConfig.DEFAULT_BLOCK_SIZE
        )
        self._init_block_sizes = [placeholder_block_size]
        self._init_kernel_block_sizes = [placeholder_block_size]
602
603
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
604
            # We need to use the encoder length for encoder-decoder
605
606
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
607
608
609
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
610
            vocab_size=self.model_config.get_vocab_size(),
611
612
            block_sizes=[placeholder_block_size],
            kernel_block_sizes=[placeholder_block_size],
613
            is_spec_decode=bool(self.vllm_config.speculative_config),
614
            logitsprocs=build_logitsprocs(
615
616
617
                self.vllm_config,
                self.device,
                self.pin_memory,
618
                self.is_pooling_model,
619
                custom_logitsprocs,
620
            ),
621
622
623
            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
624
            is_pooling_model=self.is_pooling_model,
625
            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
626
        )
627

628
629
630
631
632
        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
633
        self.prepare_inputs_event: torch.Event | None = None
634
635
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
636
            self.prepare_inputs_event = torch.Event()
637

638
639
640
641
642
643
644
645
646
647
648
        # self.cudagraph_batch_sizes sorts in ascending order.
        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
            )
        else:
            self.cudagraph_batch_sizes = []

649
        # Cache the device properties.
650
        self._init_device_properties()
651

652
653
654
655
        # Encoder timing registry for observability
        self.encoder_timing_registry: dict[str, EncoderTimingStats] = {}
        self._encoder_timing_lock = threading.Lock()

656
        # Persistent buffers for CUDA graphs.
657
658
659
660
661
        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
        )
662
        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
663
        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
664
665
666
667
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
668
669
670
        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
671
        self.inputs_embeds = self._make_buffer(
672
            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
673
674
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
675
676
        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
677
678
679
680
681
682
683
        )
        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
684

685
686
        # Only relevant for multimodal models
        if self.supports_mm_inputs:
687
688
689
690
691
692
693
            # Double buffer to avoid race condition: previous iteration's async
            # copy may still be reading from CPU while current iteration writes.
            self.is_mm_embed_buffers = [
                self._make_buffer(self.max_num_tokens, dtype=torch.bool),
                self._make_buffer(self.max_num_tokens, dtype=torch.bool),
            ]
            self.is_mm_embed_idx = 0
694

695
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
696
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
697
698
699
700
            # 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
701
702
703
704
705
706

            # 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
707
            self.mrope_positions = self._make_buffer(
708
709
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
710

711
712
713
714
715
716
717
        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            # Similar to mrope but use assigned dimension number for RoPE, 4 as default.
            self.xdrope_positions = self._make_buffer(
                (self.uses_xdrope_dim, self.max_num_tokens + 1), dtype=torch.int64
            )

718
        # None in the first PP rank. The rest are set after load_model.
719
        self.intermediate_tensors: IntermediateTensors | None = None
720

721
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
722
        # Keep in int64 to avoid overflow with long context
723
724
725
726
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
727

728
729
730
731
732
        # 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] = {}
733
734
735
736
737
        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(
738
739
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
740

741
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
742

743
744
745
746
747
748
749
750
751
752
753
754
755
        # When spec decode is active, the mamba backend classifies requests
        # with query_len <= reorder_batch_threshold as "decodes". Prefill
        # chunks that fall under this threshold get processed via the decode
        # path, which stores intermediate states at sequential slots. We must
        # set num_accepted_tokens to the chunk's query_len for those requests
        # so the next iteration reads from the correct final-state slot.
        # Prefills that went through the actual prefill path should keep the
        # default value of 1 (the prefill path stores state at slot 0 only).
        self.needs_prefill_as_decode_slots: bool = False
        self.prefill_as_decode_num_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )

756
757
758
        # Cudagraph dispatcher for runtime cudagraph dispatching.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)

759
        self.mm_budget = (
760
            MultiModalBudget(self.vllm_config, self.mm_registry)
761
762
763
            if self.supports_mm_inputs
            else None
        )
764

765
        self.reorder_batch_threshold: int | None = None
766

767
768
769
770
771
        # 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()

772
        # Cached outputs.
773
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
        # N-gram GPU path: async D2H buffer/event for per-request valid draft counts.
        self._num_valid_draft_tokens: torch.Tensor | None = None
        self._num_valid_draft_tokens_cpu: torch.Tensor | None = None
        self._num_valid_draft_tokens_event: torch.cuda.Event | None = None
        self._num_valid_draft_tokens_copy_stream: torch.cuda.Stream | None = None
        if (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        ):
            self._num_valid_draft_tokens_cpu = torch.empty(
                self.max_num_reqs, dtype=torch.int32, pin_memory=self.pin_memory
            )
            self._num_valid_draft_tokens_event = torch.cuda.Event()
            self._num_valid_draft_tokens_copy_stream = torch.cuda.Stream()

789
        self._draft_token_req_ids: list[str] | None = None
790
        self.transfer_event = torch.Event()
791
        self.sampled_token_ids_pinned_cpu = torch.empty(
792
            (self.max_num_reqs, 1),
793
794
            dtype=torch.int64,
            device="cpu",
795
796
            pin_memory=self.pin_memory,
        )
797

798
799
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
800
        self.valid_sampled_token_count_event: torch.Event | None = None
801
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
802
803
804
805
806
807
        # We also copy the drafted tokens to the CPU asynchronously,
        # in case we need them for structured outputs.
        self.draft_token_ids_event: torch.Event | None = None
        self.draft_token_ids_copy_stream: torch.cuda.Stream | None = None
        self.valid_sampled_token_count_cpu: torch.Tensor | None = None
        self.draft_token_ids_cpu: torch.Tensor | None = None
808
        self.num_accepted_tokens_event: torch.Event | None = None
809
810
        if self.num_spec_tokens:
            self.draft_token_ids_event = torch.Event()
811
            self.num_accepted_tokens_event = torch.Event()
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
            self.draft_token_ids_copy_stream = torch.cuda.Stream()
            self.draft_token_ids_cpu = torch.empty(
                (self.max_num_reqs, self.num_spec_tokens),
                dtype=torch.int64,
                device="cpu",
                pin_memory=self.pin_memory,
            )
            if self.use_async_scheduling:
                self.valid_sampled_token_count_event = torch.Event()
                self.valid_sampled_token_count_copy_stream = torch.cuda.Stream()
                self.valid_sampled_token_count_cpu = torch.empty(
                    self.max_num_reqs,
                    dtype=torch.int64,
                    device="cpu",
                    pin_memory=self.pin_memory,
                )
828

829
830
831
832
        # Model weight offloader
        # Make sure this is called before any get_offloader call
        set_offloader(create_offloader(self.offload_config))

833
834
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
835
        self.kv_connector_output: KVConnectorOutput | None = None
836
        self.mamba_state_idx: dict[str, int] = {}
837
        self._mamba_copy_bufs: mamba_utils.MambaCopyBuffers | None = None
838
        self.layerwise_nvtx_hooks_registered = False
839

840
841
842
843
844
845
846
    def update_max_model_len(self, max_model_len: int) -> None:
        self.max_model_len = max_model_len
        if self.speculative_config:
            draft_config = self.speculative_config.draft_model_config
            if draft_config is None or draft_config.max_model_len is None:
                self.effective_drafter_max_model_len = self.max_model_len

847
    def reset_mm_cache(self) -> None:
848
849
850
851
        """
        Clear the multi-modal cache that was used during profiling,
        but no longer needed during inference.
        """
852
853
        if self.mm_budget:
            self.mm_budget.reset_cache()
854
        self.late_interaction_runner.clear()
855

856
857
858
859
860
861
862
    def reset_encoder_cache(self) -> None:
        """Clear the GPU-side encoder cache storing vision embeddings.

        This should be called when model weights are updated to ensure
        stale embeddings computed with old weights are not reused.
        """
        self.encoder_cache.clear()
863
        self.late_interaction_runner.clear()
864

865
866
867
868
869
870
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
898
899
900
901
902
903
904
905
906
907
908
    @torch.inference_mode()
    def init_fp8_kv_scales(self) -> None:
        """
        Re-initialize the KV cache and FP8 scales after waking from sleep.
        1. Zero out the KV cache tensors to remove garbage data from re-allocation.
        2. Reset Attention layer scaling factors (_k_scale, _v_scale) to 1.0.
          If these are left at 0.0 (default after wake_up), all KV cache values
          become effectively zero, causing gibberish output.
        """
        if not self.cache_config.cache_dtype.startswith("fp8"):
            return

        kv_caches = getattr(self, "kv_caches", [])
        for cache_tensor in kv_caches:
            if cache_tensor is not None:
                cache_tensor.zero_()

        k_attr_names = ("_k_scale", "k_scale")
        v_attr_names = ("_v_scale", "v_scale")

        attn_layers = self.compilation_config.static_forward_context
        for name, module in attn_layers.items():
            if isinstance(module, (Attention, MLAAttention)):
                # TODO: Generally, scale is 1.0 if user uses on-the-fly fp8
                # kvcache quant. However, to get better accuracy, compression
                # frameworks like llm-compressors allow users to tune the
                # scale. We may need to restore the specific calibrated scales
                # here in the future.
                k_scale_val, v_scale_val = 1.0, 1.0

                # Processing K Scale
                for attr in k_attr_names:
                    if hasattr(module, attr):
                        param = getattr(module, attr)
                        if isinstance(param, torch.Tensor):
                            param.fill_(k_scale_val)

                # Processing V Scale
                for attr in v_attr_names:
                    if hasattr(module, attr):
                        param = getattr(module, attr)
                        if isinstance(param, torch.Tensor):
                            param.fill_(v_scale_val)

909
910
911
912
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
913
914
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
915
916
917
918
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
919
920
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
921
922
            return self.positions.gpu[num_tokens]

923
    def _make_buffer(
924
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
925
926
927
928
929
930
931
932
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
933

934
935
936
937
938
939
940
941
942
943
    def _get_mamba_copy_bufs(self) -> mamba_utils.MambaCopyBuffers:
        if self._mamba_copy_bufs is None:
            self._mamba_copy_bufs = mamba_utils.MambaCopyBuffers.create(
                self.max_num_reqs,
                self.kv_cache_config,
                self.model.get_mamba_state_copy_func(),
                self._make_buffer,
            )
        return self._mamba_copy_bufs

944
    def _init_model_kwargs(self):
945
946
        model_kwargs = dict[str, Any]()

947
        if not self.is_pooling_model:
948
949
            return model_kwargs

950
951
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
952
953
954

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
955
956
957
958
959
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
960
961
962
963
964
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

965
        seq_lens = self.seq_lens.gpu[:num_reqs]
966
967
968
969
970
971
972
973
        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(
974
975
            device=self.device
        )
976
977
        return model_kwargs

978
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
979
980
        """
        Update the order of requests in the batch based on the attention
981
        backend's needs. For example, some attention backends (namely MLA) may
982
983
984
985
986
987
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
Jiayi Yan's avatar
Jiayi Yan committed
988
        # Attention free models have zero kv_cache_groups, however models
989
990
991
992
993
994
995
        # 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

996
997
998
999
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
1000
1001
                decode_threshold=self.reorder_batch_threshold,
            )
1002

1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
    def _init_kv_zero_meta(self) -> None:
        """One-time precomputation for _zero_block_ids.

        Delegates to KVBlockZeroer.init_meta with the runner's state.
        Called from gpu_worker.py outside the CuMem pool context.
        """
        self._kv_block_zeroer = KVBlockZeroer(self.device, self.pin_memory)
        self._kv_block_zeroer.init_meta(
            attn_groups_iter=self._kv_cache_spec_attn_group_iterator(),
            kernel_block_sizes=self._kernel_block_sizes,
            cache_dtype=self.cache_config.cache_dtype,
            runner_only_attn_layers=self.runner_only_attn_layers,
            static_forward_context=(self.compilation_config.static_forward_context),
        )

    def _zero_block_ids(self, block_ids: list[int]) -> None:
        """Zero the KV cache memory for the given block IDs."""
        if hasattr(self, "_kv_block_zeroer"):
            self._kv_block_zeroer.zero_block_ids(block_ids)

1023
1024
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
1025
        """Initialize attributes from torch.cuda.get_device_properties"""
1026
1027

        self.num_sms = num_compute_units(self.device.index)
1028
1029
1030

    # Note: used for model runner override.
    def _sync_device(self) -> None:
1031
        torch.accelerator.synchronize()
1032

1033
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
1034
1035
1036
1037
1038
1039
        """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.

1040
1041
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
1042
1043
        """
        # Remove finished requests from the cached states.
1044
1045
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
1046
            self.num_prompt_logprobs.pop(req_id, None)
1047
1048
1049
        self.late_interaction_runner.on_requests_finished(
            scheduler_output.finished_req_ids
        )
1050
1051
1052
1053
1054
1055
1056
        # 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:
1057
            self.input_batch.remove_request(req_id)
1058

1059
1060
1061
1062
1063
        # Zero GPU memory for freshly allocated cache blocks to prevent
        # stale NaN/data from corrupting attention or SSM computation.
        if scheduler_output.new_block_ids_to_zero:
            self._zero_block_ids(scheduler_output.new_block_ids_to_zero)

1064
        # Free the cached encoder outputs.
1065
1066
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
1067

1068
1069
1070
1071
1072
1073
1074
        # 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()
1075
1076
1077
1078
1079
1080
1081
1082
        resumed_req_ids = scheduler_output.scheduled_cached_reqs.resumed_req_ids
        # NOTE(zhuohan): cached_req_ids and resumed_req_ids are usually disjoint,
        # so `(scheduled_req_ids - resumed_req_ids) == scheduled_req_ids` holds
        # apart from the forced-preemption case in reset_prefix_cache. And in
        # that case we include the resumed_req_ids in the unscheduled set so
        # that they get cleared from the persistent batch before being re-scheduled
        # in the normal resumed request path.
        unscheduled_req_ids = cached_req_ids - (scheduled_req_ids - resumed_req_ids)
1083
1084
1085
1086
1087
        # 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:
1088
            self.input_batch.remove_request(req_id)
1089

1090
1091
1092
1093
1094
1095
1096
        is_ngram_gpu = (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        )
        if is_ngram_gpu:
            ngram_gpu_new_reqs: list[CachedRequestState] = []

1097
        reqs_to_add: list[CachedRequestState] = []
1098
        # Add new requests to the cached states.
1099
1100
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
1101
1102
1103
1104
1105
1106
            if req_id in self.requests:
                # For streaming case only.
                req_state = self._update_streaming_request(req_id, new_req_data)
                reqs_to_add.append(req_state)
                continue

1107
            sampling_params = new_req_data.sampling_params
1108
            pooling_params = new_req_data.pooling_params
1109

1110
1111
1112
1113
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
1114
1115
1116
1117
1118
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

1119
1120
            if self.is_pooling_model:
                assert pooling_params is not None
1121
1122
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
1123

1124
                model = cast(VllmModelForPooling, self.get_model())
1125
                to_update = model.pooler.get_pooling_updates(task)
1126
1127
                to_update.apply(pooling_params)

1128
            req_state = CachedRequestState(
1129
                req_id=req_id,
1130
                prompt_token_ids=new_req_data.prompt_token_ids,
1131
                prompt_embeds=new_req_data.prompt_embeds,
1132
                mm_features=new_req_data.mm_features,
1133
                sampling_params=sampling_params,
1134
                pooling_params=pooling_params,
1135
                generator=generator,
1136
1137
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
1138
                output_token_ids=[],
1139
                lora_request=new_req_data.lora_request,
1140
            )
1141
            self.requests[req_id] = req_state
1142
            self.late_interaction_runner.register_request(req_id, pooling_params)
1143

1144
1145
1146
1147
1148
1149
1150
            if sampling_params and sampling_params.prompt_logprobs is not None:
                self.num_prompt_logprobs[req_id] = (
                    self.input_batch.vocab_size
                    if sampling_params.prompt_logprobs == -1
                    else sampling_params.prompt_logprobs
                )

1151
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1152
            if self.uses_mrope:
1153
                self._init_mrope_positions(req_state)
1154

1155
1156
1157
1158
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

1159
            reqs_to_add.append(req_state)
1160
1161
1162
            # Track new requests for ngram_gpu full tensor copy
            if is_ngram_gpu:
                ngram_gpu_new_reqs.append(req_state)
1163

1164
        # Update the states of the running/resumed requests.
1165
        is_last_rank = get_pp_group().is_last_rank
1166
        req_data = scheduler_output.scheduled_cached_reqs
1167
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1168

1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
        # Save scheduler-allocated spec lengths before trimming so
        # prev_num_draft_len keeps the optimistic count for rejection correction.
        original_num_spec_per_req: dict[str, int] = {}
        if (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        ):
            for req_id, toks in scheduled_spec_tokens.items():
                original_num_spec_per_req[req_id] = len(toks)
            update_scheduler_for_invalid_drafts(
                self._num_valid_draft_tokens_event,
                self._num_valid_draft_tokens_cpu,
                scheduler_output,
                self.input_batch.req_id_to_index,
            )

1185
1186
1187
1188
        # Wait until valid_sampled_tokens_count is copied to cpu,
        # then use it to update actual num_computed_tokens of each request.
        valid_sampled_token_count = self._get_valid_sampled_token_count()

1189
        for i, req_id in enumerate(req_data.req_ids):
1190
            req_state = self.requests[req_id]
1191
1192
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
1193
            resumed_from_preemption = req_id in req_data.resumed_req_ids
1194
            num_output_tokens = req_data.num_output_tokens[i]
1195
            req_index = self.input_batch.req_id_to_index.get(req_id)
1196

1197
1198
1199
1200
            if req_state.prev_num_draft_len and self.use_async_scheduling:
                # prev_num_draft_len is used in async scheduling mode with
                # spec decode. it indicates if need to update num_computed_tokens
                # of the request. for example:
1201
                # first step: num_computed_tokens = 0, spec_tokens = [],
1202
                # prev_num_draft_len = 0.
Jiayi Yan's avatar
Jiayi Yan committed
1203
                # second step: num_computed_tokens = 100(prompt length),
1204
1205
1206
                # spec_tokens = [a,b], prev_num_draft_len = 0.
                # third step: num_computed_tokens = 100 + 2, spec_tokens = [c,d],
                # prev_num_draft_len = 2.
1207
                # num_computed_tokens in first step and second step doesn't contain
1208
1209
1210
                # the spec tokens length, but in third step it contains the
                # spec tokens length. we only need to update num_computed_tokens
                # when prev_num_draft_len > 0.
1211
1212
1213
1214
1215
1216
1217
1218
1219
                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
                    assert self.input_batch.prev_req_id_to_index is not None
                    prev_req_index = self.input_batch.prev_req_id_to_index[req_id]
                    num_accepted = valid_sampled_token_count[prev_req_index] - 1
                    num_rejected = req_state.prev_num_draft_len - num_accepted
                    num_computed_tokens -= num_rejected
                    req_state.output_token_ids.extend([-1] * num_accepted)
1220

1221
1222
1223
                    if is_ngram_gpu and num_accepted > 0 and req_index is not None:
                        self.input_batch.num_tokens_no_spec[req_index] += num_accepted

1224
            # Update the cached states.
1225
            req_state.num_computed_tokens = num_computed_tokens
1226
1227

            if not is_last_rank:
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
                if not req_data.new_token_ids:
                    # Async scheduled PP: Sampled tokens propagated via GPU broadcast.
                    new_token_ids: list[int] = []
                else:
                    # Non-async scheduling with PP: The scheduler sends
                    # sampled token ids 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:]
                        )
1248
1249
1250
1251
1252
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
1253
1254
1255
1256
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
1257
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
1258

1259
            # Update the block IDs.
1260
            if not resumed_from_preemption:
1261
1262
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
1263
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
1264
                        block_ids.extend(new_ids)
1265
            else:
1266
                assert req_index is None
1267
                assert new_block_ids is not None
1268
1269
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
1270
                req_state.block_ids = new_block_ids
1271
1272
1273
1274
1275

            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.
1276
1277
1278
1279
1280
1281
1282

                if self.use_async_scheduling and num_output_tokens > 0:
                    # We must recover the output token ids for resumed requests in the
                    # async scheduling case, so that correct input_ids are obtained.
                    resumed_token_ids = req_data.all_token_ids[req_id]
                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]

1283
                reqs_to_add.append(req_state)
1284
1285
1286
                # Track resumed requests for ngram_gpu full tensor copy
                if is_ngram_gpu:
                    ngram_gpu_new_reqs.append(req_state)
1287
1288
1289
                continue

            # Update the persistent batch.
1290
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
1291
            if new_block_ids is not None:
1292
                self.input_batch.block_table.append_row(new_block_ids, req_index)
1293
1294
1295
1296
1297
1298
1299

            # 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)
1300
                self.input_batch.token_ids_cpu[
1301
1302
1303
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
1304

1305
            # Add spec_token_ids to token_ids_cpu.
1306
            self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
1307
1308
1309
1310
1311
            # Restore scheduler-side draft count after ngram trimming.
            if original_num_spec_per_req:
                orig = original_num_spec_per_req.get(req_id, 0)
                if orig != req_state.prev_num_draft_len:
                    req_state.prev_num_draft_len = orig
1312

1313
1314
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
1315
1316
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1317
            self.input_batch.update_req_spec_token_ids(request, scheduled_spec_tokens)
1318

1319
1320
1321
1322
1323
1324
        # 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()
1325

1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
        # Incrementally update ngram_gpu tensors after batch is stable
        if is_ngram_gpu:
            update_ngram_gpu_tensors_incremental(
                self.input_batch,
                self.token_ids_gpu_tensor,
                self.num_tokens_no_spec_gpu,
                ngram_gpu_new_reqs,
                self.device,
                _pinned_idx_buf=self._ngram_pinned_idx_buf,
                _pinned_val_buf=self._ngram_pinned_val_buf,
            )

1338
    def _update_states_after_model_execute(
1339
        self, output_token_ids: torch.Tensor, scheduler_output: "SchedulerOutput"
1340
    ) -> None:
1341
1342
1343
1344
1345
1346
1347
1348
        """Update the cached states after model execution.

        This is used for MTP/EAGLE for hybrid models, as in linear attention,
        only the last token's state is kept. In MTP/EAGLE, for draft tokens
        the state are kept util we decide how many tokens are accepted for
        each sequence, and a shifting is done during the next iteration
        based on the number of accepted tokens.
        """
1349
        if not self.speculative_config or not self.model_config.is_hybrid:
1350
1351
1352
            return

        # Find the number of accepted tokens for each sequence.
1353
1354
        num_reqs = output_token_ids.size(0)
        self.num_accepted_tokens.gpu[:num_reqs] = (
1355
1356
1357
1358
1359
            (
                torch.cat(
                    [
                        output_token_ids,
                        torch.full(
1360
                            (num_reqs, 1),
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
                            -1,
                            device=output_token_ids.device,
                        ),
                    ],
                    dim=1,
                )
                == -1
            )
            .int()
            .argmax(-1)
        )
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
        spec_decode_active = bool(scheduler_output.scheduled_spec_decode_tokens)
        if self.needs_prefill_as_decode_slots and spec_decode_active:
            mamba_utils.update_accepted_tokens_for_prefill_as_decode(
                self.input_batch,
                self.prefill_as_decode_num_tokens,
                self.num_accepted_tokens.gpu,
                scheduler_output,
                self.reorder_batch_threshold,
                num_reqs,
            )

1383
        if self.cache_config.mamba_cache_mode == "align":
1384
1385
1386
1387
            for i, num_tokens in enumerate(
                self.num_accepted_tokens.gpu[:num_reqs].cpu().numpy()
            ):
                self.input_batch.num_accepted_tokens_cpu[i] = num_tokens
1388
1389
1390
1391
1392
1393
1394
1395
            mamba_utils.postprocess_mamba(
                scheduler_output,
                self.kv_cache_config,
                self.input_batch,
                self.requests,
                self.mamba_state_idx,
                self.compilation_config.static_forward_context,
                self.model.get_mamba_state_copy_func(),
1396
                self._get_mamba_copy_bufs(),
1397
            )
1398
1399
1400
1401
        else:
            self.input_batch.num_accepted_tokens_cpu_tensor[:num_reqs].copy_(
                self.num_accepted_tokens.gpu[:num_reqs], non_blocking=True
            )
1402
1403
            assert self.num_accepted_tokens_event is not None
            self.num_accepted_tokens_event.record()
1404

1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
    def _update_streaming_request(
        self, req_id: str, new_req_data: NewRequestData
    ) -> CachedRequestState:
        """Updates streaming session request from `scheduled_new_reqs`.

        Removes the request from InputBatch (if present), updates the cached
        state, and prepares it for re-addition to the batch.

        NOTE: prompt_token_ids includes intermediate output tokens - tokens
        previously generated but now are input context (part of the prompt).
        """
        self.input_batch.remove_request(req_id)
        req_state = self.requests[req_id]

        req_state.prompt_token_ids = new_req_data.prompt_token_ids
        req_state.mm_features = new_req_data.mm_features
        req_state.prompt_embeds = new_req_data.prompt_embeds
        req_state.sampling_params = new_req_data.sampling_params
        req_state.pooling_params = new_req_data.pooling_params
1424
        self.late_interaction_runner.register_request(req_id, req_state.pooling_params)
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
        req_state.block_ids = new_req_data.block_ids
        req_state.num_computed_tokens = new_req_data.num_computed_tokens
        req_state.num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
            req_state.prompt_token_ids, req_state.prompt_embeds
        )

        # Clear `output_token_ids` as previous output tokens are now part of
        # `prompt_token_ids`.
        req_state.output_token_ids.clear()

        if self.uses_mrope:
            self._init_mrope_positions(req_state)

        return req_state

1440
    def _init_mrope_positions(self, req_state: CachedRequestState):
1441
1442
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1443
1444
1445
1446
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1447
1448

        req_state.mrope_positions, req_state.mrope_position_delta = (
1449
            mrope_model.get_mrope_input_positions(
1450
                req_state.prompt_token_ids,
1451
                req_state.mm_features,
1452
            )
1453
        )
1454

1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
    def _init_xdrope_positions(self, req_state: CachedRequestState):
        model = self.get_model()
        xdrope_model = cast(SupportsXDRoPE, model)
        assert req_state.prompt_token_ids is not None, (
            "XD-RoPE requires prompt_token_ids to be available."
        )
        assert supports_xdrope(model), "XD-RoPE support is not implemented."

        req_state.xdrope_positions = xdrope_model.get_xdrope_input_positions(
            req_state.prompt_token_ids,
            req_state.mm_features,
        )

1468
    def _extract_mm_kwargs(
1469
        self,
1470
1471
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1472
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1473
            return {}
1474

1475
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
1476
        for req in scheduler_output.scheduled_new_reqs:
1477
1478
            for feature in req.mm_features:
                if feature.data is not None:
1479
                    mm_kwargs.append((feature.modality, feature.data))
1480

1481
1482
        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
1483
        for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
1484
1485
1486
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
1487
        ):
1488
            mm_kwargs_combined.update(mm_kwargs_batch)
1489

1490
        return mm_kwargs_combined
1491

1492
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1493
        if not self.is_multimodal_raw_input_only_model:
1494
            return {}
1495

1496
1497
1498
        mm_budget = self.mm_budget
        assert mm_budget is not None

1499
1500
1501
        if not mm_budget.mm_max_toks_per_item:
            return {}  # No tower modalities (embed-only mode)

1502
1503
        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
1504

1505
1506
1507
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1508
        cumsum_dtype: np.dtype | None = None,
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
    ) -> 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

1525
    def _prepare_input_ids(
1526
1527
1528
1529
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1530
    ) -> None:
1531
        """Prepare the input IDs for the current batch.
1532

1533
1534
1535
1536
1537
1538
1539
        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)
1540
1541
1542
            if self.enable_prompt_embeds:
                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
1543
1544
1545
1546
1547
1548
1549
            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
1550
1551
1552
1553
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1554
1555
        indices_match = True
        max_flattened_index = -1
1556
1557
1558
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1559
1560
1561
1562
1563
        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.
1564
1565
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1566
                flattened_index = cu_num_tokens[cur_index].item() - 1
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
                # example: cu_num_tokens = [2, 5, 8], draft_tokens = [1, 2, 2]
                # sample_flattened_indices = [0, 2, 5]
                # spec_flattened_indices = [1,   3, 4,    6, 7]
                sample_flattened_indices.append(flattened_index - draft_len)
                spec_flattened_indices.extend(
                    range(flattened_index - draft_len + 1, flattened_index + 1)
                )
                start = prev_index * self.num_spec_tokens
                # prev_draft_token_indices is used to find which draft_tokens_id
                # should be copied to input_ids
                # example: prev draft_tokens_id [[1,2], [3,4], [5, 6]]
                # flatten draft_tokens_id [1,2,3,4,5,6]
                # draft_len of each request [1, 2, 1]
                # then prev_draft_token_indices is [0,   2, 3,   4]
                prev_draft_token_indices.extend(range(start, start + draft_len))
1582
                indices_match &= prev_index == flattened_index
1583
                max_flattened_index = max(max_flattened_index, flattened_index)
Jiayi Yan's avatar
Jiayi Yan committed
1584
        num_common_tokens = len(sample_flattened_indices)
1585
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
Jiayi Yan's avatar
Jiayi Yan committed
1586
        if num_common_tokens < total_without_spec:
1587
1588
1589
            # 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)
1590
1591
1592
            if self.enable_prompt_embeds:
                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
Jiayi Yan's avatar
Jiayi Yan committed
1593
        if num_common_tokens == 0:
1594
            # No requests in common with the previous iteration
1595
            # So input_ids.cpu will have all the input ids.
1596
            return
Jiayi Yan's avatar
Jiayi Yan committed
1597
        if indices_match and max_flattened_index == (num_common_tokens - 1):
1598
1599
1600
1601
            # 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.
Jiayi Yan's avatar
Jiayi Yan committed
1602
1603
            self.input_ids.gpu[:num_common_tokens].copy_(
                self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0],
1604
1605
                non_blocking=True,
            )
1606
            if self.enable_prompt_embeds:
Jiayi Yan's avatar
Jiayi Yan committed
1607
                self.is_token_ids.gpu[:num_common_tokens] = True
1608
            return
1609
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1610
1611
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1612
        ).to(self.device, non_blocking=True)
1613
        prev_common_req_indices_tensor = torch.tensor(
1614
1615
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1616
1617
        self.input_ids.gpu.scatter_(
            dim=0,
1618
            index=sampled_tokens_index_tensor,
1619
            src=self.input_batch.prev_sampled_token_ids[
1620
1621
1622
                prev_common_req_indices_tensor, 0
            ],
        )
1623

1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
        # Scatter the draft tokens after the sampled tokens are scattered.
        if self._draft_token_ids is None or not spec_flattened_indices:
            return

        assert isinstance(self._draft_token_ids, torch.Tensor)
        draft_tokens_index_tensor = torch.tensor(
            spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
        prev_draft_token_indices_tensor = torch.tensor(
            prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)

        # because input_ids dtype is torch.int32,
        # so convert draft_token_ids to torch.int32 here.
        draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)

        self.input_ids.gpu.scatter_(
            dim=0,
            index=draft_tokens_index_tensor,
            src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
        )

1646
1647
    def _get_encoder_seq_lens(
        self,
1648
        num_scheduled_tokens: dict[str, int],
1649
1650
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1651
        for_cudagraph_capture: bool = False,
1652
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1653
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1654
            return None, None
1655

1656
1657
        # Zero out buffer for padding requests that are not actually scheduled (CGs)
        self.encoder_seq_lens.np[:num_reqs] = 0
1658

1659
1660
        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1661
        for req_id in num_scheduled_tokens:
1662
            req_index = self.input_batch.req_id_to_index[req_id]
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
            req_state = self.requests[req_id]
            if req_state.mm_features is None:
                self.encoder_seq_lens.np[req_index] = 0
                continue

            # Get the total number of encoder input tokens for running encoder requests
            # whether encoding is finished or not so that cross-attention knows how
            # many encoder tokens to attend to.
            encoder_input_tokens = sum(
                feature.mm_position.length for feature in req_state.mm_features
            )
            self.encoder_seq_lens.np[req_index] = encoder_input_tokens
1675
1676
1677
1678
1679
1680
1681
1682
1683
        if for_cudagraph_capture:
            # During CUDA graph capture, we need to use realistic encoder lengths
            # so that max_seqlen_k is captured with the correct value.
            max_encoder_len = getattr(
                self.model_config.hf_config,
                "max_source_positions",
                self.max_encoder_len,
            )
            self.encoder_seq_lens.np[:num_reqs] = max_encoder_len
1684
1685
1686
1687

        self.encoder_seq_lens.copy_to_gpu(num_reqs)
        encoder_seq_lens = self.encoder_seq_lens.gpu[:num_reqs]
        encoder_seq_lens_cpu = self.encoder_seq_lens.np[:num_reqs]
1688

1689
        return encoder_seq_lens, encoder_seq_lens_cpu
1690

1691
    def _prepare_inputs(
1692
1693
1694
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1695
1696
    ) -> tuple[
        torch.Tensor,
1697
        SpecDecodeMetadata | None,
1698
    ]:
1699
1700
        """
        :return: tuple[
1701
            logits_indices, spec_decode_metadata,
1702
1703
        ]
        """
1704
1705
1706
1707
1708
1709
1710
        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.
1711
        self.input_batch.block_table.commit_block_table(num_reqs)
1712
1713
1714

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1715
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1716

1717
1718
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1719
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1720
1721

        # Get positions.
1722
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1723
1724
1725
1726
1727
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1728

1729
1730
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1731
        if self.uses_mrope:
1732
1733
            self._calc_mrope_positions(scheduler_output)

1734
1735
1736
1737
1738
        # Calculate XD-RoPE positions.
        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            self._calc_xdrope_positions(scheduler_output)

1739
1740
1741
1742
        # 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.
1743
1744
1745
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1746
        token_indices_tensor = torch.from_numpy(token_indices)
1747

1748
1749
1750
        # 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.
1751
1752
1753
1754
1755
1756
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1757
        if self.enable_prompt_embeds:
1758
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1759
1760
1761
1762
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1763
1764
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797

        # Because we did not pre-allocate a massive prompt_embeds CPU tensor on
        # the InputBatch, we need to fill in the prompt embeds into the expected
        # spots in the GpuModelRunner's pre-allocated prompt_embeds tensor.
        if self.input_batch.req_prompt_embeds:
            output_idx = 0
            for req_idx in range(num_reqs):
                num_sched = num_scheduled_tokens[req_idx]

                # Skip if this request doesn't have embeddings
                if req_idx not in self.input_batch.req_prompt_embeds:
                    output_idx += num_sched
                    continue

                # Skip if no tokens scheduled
                if num_sched <= 0:
                    output_idx += num_sched
                    continue

                req_embeds = self.input_batch.req_prompt_embeds[req_idx]
                start_pos = self.input_batch.num_computed_tokens_cpu[req_idx]

                # Skip if trying to read beyond available embeddings
                if start_pos >= req_embeds.shape[0]:
                    output_idx += num_sched
                    continue

                # Copy available embeddings
                end_pos = start_pos + num_sched
                actual_end = min(end_pos, req_embeds.shape[0])
                actual_num_sched = actual_end - start_pos

                if actual_num_sched > 0:
1798
1799
1800
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1801
1802

                output_idx += num_sched
1803

1804
1805
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1806
1807

        # Prepare the attention metadata.
1808
        self.query_start_loc.np[0] = 0
1809
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1810
1811
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1812
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1813
        self.query_start_loc.copy_to_gpu()
1814
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1815

1816
        self.seq_lens.np[:num_reqs] = (
1817
1818
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1819
        # Fill unused with 0 for full cuda graph mode.
1820
1821
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1822

1823
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1824
1825
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1826
        # Record which requests should not be sampled,
1827
        # so that we could clear the sampled tokens before returning
1828
1829
        self.discard_request_mask.np[:num_reqs] = (
            self.seq_lens.np[:num_reqs] < num_tokens_np
1830
        )
1831
        self.discard_request_mask.copy_to_gpu(num_reqs)
1832

1833
        # Copy the tensors to the GPU.
1834
1835
1836
1837
1838
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1839

1840
        if self.uses_mrope:
1841
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1842
1843
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1844
1845
                non_blocking=True,
            )
1846
1847
1848
1849
1850
1851
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
                non_blocking=True,
            )
1852
1853
        else:
            # Common case (1D positions)
1854
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1855

1856
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1857
1858
1859
1860
1861
1862
1863
1864
        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
1865
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1866
1867
1868
1869
1870
        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)
1871
1872
1873
            # For chunked prefills, use -1 as mask rather than 0, as guided
            # decoding may rollback speculative tokens.
            num_decode_draft_tokens = np.full(num_reqs, -1, dtype=np.int32)
1874
1875
1876
1877
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1878
1879
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1880
1881
1882
1883
1884
                if (
                    self.input_batch.num_computed_tokens_cpu[req_idx]
                    >= self.input_batch.num_prompt_tokens[req_idx]
                ):
                    num_decode_draft_tokens[req_idx] = len(draft_token_ids)
1885
            spec_decode_metadata = self._calc_spec_decode_metadata(
1886
1887
                num_draft_tokens, cu_num_tokens
            )
1888
            logits_indices = spec_decode_metadata.logits_indices
1889
            num_sampled_tokens = num_draft_tokens + 1
1890
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1891
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1892
1893
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1894

1895
1896
1897
1898
1899
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1900
            )
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
1912
        num_tokens: int,
1913
        num_reqs: int,
1914
1915
1916
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
1917
1918
1919
1920
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
1921
        num_scheduled_tokens: dict[str, int] | None = None,
1922
        cascade_attn_prefix_lens: list[list[int]] | None = None,
1923
        slot_mappings: dict[int, torch.Tensor] | None = None,
1924
1925
1926
1927
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
1928
1929
1930
1931
        # Attention metadata is not needed for attention free models
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return {}, None

1932
1933
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs
1934
        assert num_reqs_padded is not None and num_tokens_padded is not None
1935

1936
1937
1938
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1939

1940
1941
1942
1943
1944
1945
1946
1947
        if for_cudagraph_capture:
            # For some attention backends (e.g. FA) with sliding window models we need
            # to make sure the backend see a max_seq_len that is larger to the sliding
            # window size when capturing to make sure the correct kernel is selected.
            max_seq_len = self.max_model_len
        else:
            max_seq_len = self.seq_lens.np[:num_reqs].max().item()

1948
        if use_spec_decode:
1949
1950
            if self.num_accepted_tokens_event is not None:
                self.num_accepted_tokens_event.synchronize()
1951
            self.num_accepted_tokens.np[:num_reqs] = (
1952
1953
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1954
1955
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1956

1957
        kv_cache_groups = self.kv_cache_config.kv_cache_groups
1958

1959
        def _get_block_table(kv_cache_gid: int):
1960
1961
1962
            assert num_reqs_padded is not None and num_tokens_padded is not None
            kv_cache_spec = kv_cache_groups[kv_cache_gid].kv_cache_spec
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
1963
                blk_table_tensor = torch.zeros(
1964
                    (num_reqs_padded, 1),
1965
                    dtype=torch.int32,
1966
1967
                    device=self.device,
                )
1968
            else:
1969
                blk_table = self.input_batch.block_table[kv_cache_gid]
1970
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
1971

1972
1973
1974
            # Fill unused with -1. Needed for reshape_and_cache in full cuda
            # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
            blk_table_tensor[num_reqs:num_reqs_padded].fill_(-1)
1975
            return blk_table_tensor
1976

1977
1978
1979
        assert slot_mappings is not None
        block_table_gid_0 = _get_block_table(0)
        slot_mapping_gid_0 = slot_mappings[0]
1980

1981
1982
1983
1984
        if self.routed_experts_initialized:
            attn_gid = self.routed_experts_attn_gid
            slot_mapping_attn = slot_mappings[attn_gid]
            self.slot_mapping = slot_mapping_attn[:num_tokens].cpu().numpy()
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
        cm_base = CommonAttentionMetadata(
            query_start_loc=self.query_start_loc.gpu[: num_reqs_padded + 1],
            query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs_padded + 1],
            seq_lens=self.seq_lens.gpu[:num_reqs_padded],
            _seq_lens_cpu=self.seq_lens.cpu[:num_reqs_padded],
            _num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                :num_reqs_padded
            ],
            num_reqs=num_reqs_padded,
            num_actual_tokens=num_tokens_padded,
            max_query_len=max_query_len,
            max_seq_len=max_seq_len,
            block_table_tensor=block_table_gid_0,
            slot_mapping=slot_mapping_gid_0,
            causal=True,
        )

        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
                self.seq_lens.cpu[:num_reqs],
                self.dcp_world_size,
                self.dcp_rank,
                self.parallel_config.cp_kv_cache_interleave_size,
            )
            self.dcp_local_seq_lens.cpu[num_reqs:].fill_(0)
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs_padded)

            cm_base.dcp_local_seq_lens = self.dcp_local_seq_lens.gpu[:num_reqs_padded]
            cm_base.dcp_local_seq_lens_cpu = self.dcp_local_seq_lens.cpu[
                :num_reqs_padded
            ]

        if logits_indices is not None and self.cache_config.kv_sharing_fast_prefill:
            cm_base.num_logits_indices = logits_indices.size(0)
            cm_base.logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
                logits_indices
            )

2023
2024
2025
2026
2027
2028
2029
2030
2031
        # Cache attention metadata builds across hybrid KV-cache groups
        # The only thing that changes between different hybrid KV-cache groups when the
        # same metadata builder and KVCacheSpec is the same is the block table, so we
        # can cache the attention metadata builds and just update the block table using
        # `builder.update_block_table` if the builder supports it.
        cached_attn_metadata: dict[
            tuple[KVCacheSpec, type[AttentionMetadataBuilder]], AttentionMetadata
        ] = {}

2032
2033
2034
2035
2036
2037
2038
        def _build_attn_group_metadata(
            kv_cache_gid: int,
            attn_gid: int,
            common_attn_metadata: CommonAttentionMetadata,
            ubid: int | None = None,
        ) -> None:
            attn_group = self.attn_groups[kv_cache_gid][attn_gid]
2039
            builder = attn_group.get_metadata_builder(ubid or 0)
2040
2041
2042
2043
            kv_cache_spec = kv_cache_groups[kv_cache_gid].kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                kv_cache_spec = kv_cache_spec.kv_cache_specs[attn_group.layer_names[0]]
            cache_key = (kv_cache_spec, type(builder))
2044

2045
2046
2047
2048
2049
2050
            cascade_attn_prefix_len = (
                cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                if cascade_attn_prefix_lens
                else 0
            )

2051
2052
            if isinstance(builder, Mamba2AttentionMetadataBuilder):
                self.needs_prefill_as_decode_slots = True
2053
            extra_attn_metadata_args = {}
2054
2055
2056
            if use_spec_decode and isinstance(
                builder, (Mamba2AttentionMetadataBuilder, GDNAttentionMetadataBuilder)
            ):
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
                assert ubid is None, "UBatching not supported with GDN yet"
                extra_attn_metadata_args = dict(
                    num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs_padded],
                    num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                        :num_reqs_padded
                    ],
                )

            if for_cudagraph_capture:
                attn_metadata_i = builder.build_for_cudagraph_capture(
                    common_attn_metadata
                )
2069
2070
2071
2072
2073
2074
2075
2076
2077
            elif (
                cache_key in cached_attn_metadata
                and builder.supports_update_block_table
            ):
                attn_metadata_i = builder.update_block_table(
                    cached_attn_metadata[cache_key],
                    common_attn_metadata.block_table_tensor,
                    common_attn_metadata.slot_mapping,
                )
2078
2079
2080
2081
2082
2083
            else:
                attn_metadata_i = builder.build(
                    common_prefix_len=cascade_attn_prefix_len,
                    common_attn_metadata=common_attn_metadata,
                    **extra_attn_metadata_args,
                )
2084
2085
                if builder.supports_update_block_table:
                    cached_attn_metadata[cache_key] = attn_metadata_i
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108

            if ubid is None:
                assert isinstance(attn_metadata, dict)
                attn_metadata_dict = attn_metadata
            else:
                assert isinstance(attn_metadata, list)
                attn_metadata_dict = attn_metadata[ubid]

            for layer_name in attn_group.layer_names:
                attn_metadata_dict[layer_name] = attn_metadata_i

        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        spec_decode_common_attn_metadata = None
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_groups):
            cm = copy(cm_base)  # shallow copy

            # Basically only the encoder seq_lens, block_table and slot_mapping change
            # for each kv_cache_group.
            cm.encoder_seq_lens, cm.encoder_seq_lens_cpu = self._get_encoder_seq_lens(
                num_scheduled_tokens or {},
                kv_cache_group.kv_cache_spec,
                num_reqs_padded,
2109
                for_cudagraph_capture=for_cudagraph_capture,
2110
            )
2111
            if kv_cache_gid > 0:
2112
2113
                cm.block_table_tensor = _get_block_table(kv_cache_gid)
                cm.slot_mapping = slot_mappings[kv_cache_gid]
2114

2115
            if self.speculative_config and spec_decode_common_attn_metadata is None:
2116
                if isinstance(self.drafter, EagleProposer):
2117
                    if self.drafter.kv_cache_gid == kv_cache_gid:
2118
                        spec_decode_common_attn_metadata = cm
2119
                else:
2120
                    spec_decode_common_attn_metadata = cm
2121

2122
            for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
2123
                if ubatch_slices is not None:
2124
2125
2126
                    for ubid, _cm in enumerate(split_attn_metadata(ubatch_slices, cm)):
                        _build_attn_group_metadata(kv_cache_gid, attn_gid, _cm, ubid)

2127
                else:
2128
                    _build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
2129

2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
        if self.is_mm_prefix_lm:
            req_doc_ranges = {}
            for req_id in self.input_batch.req_ids:
                image_doc_ranges = []
                req_state = self.requests[req_id]
                for mm_feature in req_state.mm_features:
                    pos_info = mm_feature.mm_position
                    img_doc_range = pos_info.extract_embeds_range()
                    image_doc_ranges.extend(img_doc_range)
                req_idx = self.input_batch.req_id_to_index[req_id]
                req_doc_ranges[req_idx] = image_doc_ranges

            if isinstance(attn_metadata, list):
                for ub_metadata in attn_metadata:
                    for _metadata in ub_metadata.values():
                        _metadata.mm_prefix_range = req_doc_ranges  # type: ignore[attr-defined]
            else:
                for _metadata in attn_metadata.values():
                    _metadata.mm_prefix_range = req_doc_ranges  # type: ignore[attr-defined]

2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
        if spec_decode_common_attn_metadata is not None and (
            num_reqs != num_reqs_padded or num_tokens != num_tokens_padded
        ):
            # Currently the drafter still only uses piecewise cudagraphs (and modifies
            # the attention metadata in directly), and therefore does not want to use
            # padded attention metadata.
            spec_decode_common_attn_metadata = (
                spec_decode_common_attn_metadata.unpadded(num_tokens, num_reqs)
            )

2160
        return attn_metadata, spec_decode_common_attn_metadata
2161

2162
2163
2164
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
2165
        num_computed_tokens: np.ndarray,
2166
2167
2168
2169
2170
2171
2172
        num_common_prefix_blocks: list[int],
    ) -> list[list[int]] | None:
        """
        :return: Optional[cascade_attn_prefix_lens]
            cascade_attn_prefix_lens is 2D: ``[kv_cache_group_id][attn_group_idx]``,
            None if we should not use cascade attention
        """
2173

2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
        use_cascade_attn = False
        num_kv_cache_groups = len(self.kv_cache_config.kv_cache_groups)
        cascade_attn_prefix_lens: list[list[int]] = [
            [] for _ in range(num_kv_cache_groups)
        ]

        for kv_cache_gid in range(num_kv_cache_groups):
            for attn_group in self.attn_groups[kv_cache_gid]:
                if isinstance(attn_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                    cascade_attn_prefix_len = 0
                else:
                    # 0 if cascade attention should not be used
                    cascade_attn_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
2188
                        num_computed_tokens,
2189
2190
2191
2192
2193
2194
2195
2196
                        num_common_prefix_blocks[kv_cache_gid],
                        attn_group.kv_cache_spec,
                        attn_group.get_metadata_builder(),
                    )
                cascade_attn_prefix_lens[kv_cache_gid].append(cascade_attn_prefix_len)
                use_cascade_attn |= cascade_attn_prefix_len > 0

        return cascade_attn_prefix_lens if use_cascade_attn else None
2197

2198
2199
2200
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
2201
        num_computed_tokens: np.ndarray,
2202
        num_common_prefix_blocks: int,
2203
2204
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
    ) -> 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.
        """
2223

2224
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
        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]
2262
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
2263
2264
2265
2266
2267
        # 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.
2268
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
2269
        # common_prefix_len should be a multiple of the block size.
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
        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
        )
        use_local_attention = isinstance(kv_cache_spec, ChunkedLocalAttentionSpec) or (
            isinstance(kv_cache_spec, FullAttentionSpec)
            and kv_cache_spec.attention_chunk_size is not None
        )
2281
2282
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
2283
2284
2285
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
2286
            num_kv_heads=kv_cache_spec.num_kv_heads,
2287
            use_alibi=self.use_alibi,
2288
            use_sliding_window=use_sliding_window,
2289
            use_local_attention=use_local_attention,
2290
            num_sms=self.num_sms,
2291
            dcp_world_size=self.dcp_world_size,
2292
2293
2294
        )
        return common_prefix_len if use_cascade else 0

2295
2296
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
2297
        for index, req_id in enumerate(self.input_batch.req_ids):
2298
2299
2300
            req = self.requests[req_id]
            assert req.mrope_positions is not None

2301
2302
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2303
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
2304
2305
                req.prompt_token_ids, req.prompt_embeds
            )
2306
2307

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
2308
2309
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
            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

2323
2324
2325
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
2326
2327
2328
2329
2330
2331
2332
                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

2333
                assert req.mrope_position_delta is not None
2334
                MRotaryEmbedding.get_next_input_positions_tensor(
2335
                    out=self.mrope_positions.np,
2336
2337
2338
2339
2340
                    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,
                )
2341
2342
2343

                mrope_pos_ptr += completion_part_len

2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
    def _calc_xdrope_positions(self, scheduler_output: "SchedulerOutput"):
        xdrope_pos_ptr = 0
        for index, req_id in enumerate(self.input_batch.req_ids):
            req = self.requests[req_id]
            assert req.xdrope_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 = length_from_prompt_token_ids_or_embeds(
                req.prompt_token_ids, req.prompt_embeds
            )

            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 xdrope_positions are pre-computed
                dst_start = xdrope_pos_ptr
                dst_end = xdrope_pos_ptr + prompt_part_len
                src_start = num_computed_tokens
                src_end = num_computed_tokens + prompt_part_len

                self.xdrope_positions.cpu[:, dst_start:dst_end] = req.xdrope_positions[
                    :, src_start:src_end
                ]
                xdrope_pos_ptr += prompt_part_len

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

                XDRotaryEmbedding.get_next_input_positions_tensor(
                    out=self.xdrope_positions.np,
                    out_offset=dst_start,
                    context_len=num_computed_tokens + prompt_part_len,
                    num_new_tokens=completion_part_len,
                )

                xdrope_pos_ptr += completion_part_len

2391
2392
    def _calc_spec_decode_metadata(
        self,
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
        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
2409
2410
2411
2412

        # 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(
2413
2414
            num_sampled_tokens, cumsum_dtype=np.int32
        )
2415
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
2416
        logits_indices = np.repeat(
2417
2418
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
2419
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
2420
2421
2422
2423
2424
2425
        logits_indices += arange

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

        # Compute the draft logits indices.
2426
2427
2428
        # 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(
2429
2430
            num_draft_tokens, cumsum_dtype=np.int32
        )
2431
2432
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
2433
2434
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
2435
2436
2437
2438
2439
        # [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(
2440
2441
            self.device, non_blocking=True
        )
2442
2443
2444
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
2445
2446
2447
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
2448
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
2449
2450
            self.device, non_blocking=True
        )
2451
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
2452
2453
            self.device, non_blocking=True
        )
2454

2455
2456
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2457
        draft_token_ids = self.input_ids.gpu[logits_indices]
2458
2459
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2460
        return SpecDecodeMetadata(
2461
2462
2463
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2464
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2465
2466
2467
2468
2469
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2470
2471
2472
2473
2474
2475
2476
    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
2477
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2478
2479
2480
2481
2482
        # 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_(
2483
2484
            logits_indices[-1].item()
        )
2485
2486
        # Dispatch for the decoder portion of the model.
        _, batch_desc = self.cudagraph_dispatcher.dispatch(
2487
            num_logits, invalid_modes={CUDAGraphMode.FULL}
2488
2489
        )
        num_logits_padded = batch_desc.num_tokens
2490
2491
2492
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2493
2494
        return logits_indices_padded

2495
    def _batch_mm_inputs_from_scheduler(
2496
2497
        self,
        scheduler_output: "SchedulerOutput",
2498
2499
    ) -> tuple[
        list[str],
2500
        list[tuple[str, MultiModalKwargsItem]],
2501
2502
        list[tuple[str, PlaceholderRange]],
    ]:
2503
        """Batch multimodal inputs from scheduled encoder inputs.
2504
2505
2506

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2507
                inputs.
2508
2509

        Returns:
2510
            A tuple of (mm_hashes, mm_kwargs, mm_lora_refs) where:
2511
2512
            - mm_hashes: List of multimodal hashes for each item
            - mm_kwargs: List of multimodal kwargs for each item
2513
            - mm_lora_refs: List of (req_id, placeholder_range) for each item
2514
        """
2515
2516
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2517
            return [], [], []
2518
2519

        mm_hashes = list[str]()
2520
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
2521
2522
2523
        # Multimodal LoRA reference info to map each multimodal item
        # back to its request & position
        mm_lora_refs = list[tuple[str, PlaceholderRange]]()
2524
2525
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2526
2527

            for mm_input_id in encoder_input_ids:
2528
                mm_feature = req_state.mm_features[mm_input_id]
2529
2530
                if mm_feature.data is None:
                    continue
2531
2532

                mm_hashes.append(mm_feature.identifier)
2533
                mm_kwargs.append((mm_feature.modality, mm_feature.data))
2534
                mm_lora_refs.append((req_id, mm_feature.mm_position))
2535

2536
        return mm_hashes, mm_kwargs, mm_lora_refs
2537

2538
2539
2540
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2541
2542
2543
        mm_hashes, mm_kwargs, mm_lora_refs = self._batch_mm_inputs_from_scheduler(
            scheduler_output
        )
2544
2545

        if not mm_kwargs:
2546
            return []
2547

2548
2549
2550
2551
2552
2553
        should_time = bool(
            self.observability_config
            and self.observability_config.enable_mm_processor_stats
            and scheduler_output.scheduled_encoder_inputs
        )

2554
2555
2556
2557
2558
2559
2560
        # 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.
2561
        model = cast(SupportsMultiModal, self.model)
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576

        if self.lora_config and self.lora_manager.supports_tower_connector_lora():
            # Build LoRA mappings independently for encoder inputs
            # (encoder batch structure is different from main batch)
            prompt_lora_mapping = []
            token_lora_mapping = []
            lora_requests = set()
            encoder_token_counts = []

            for req_id, pos_info in mm_lora_refs:
                req_idx = self.input_batch.req_id_to_index[req_id]
                lora_id = int(self.input_batch.request_lora_mapping[req_idx])

                # Prefer pos_info.get_num_embeds to count precise MM embedding tokens.
                num_tokens = self.model.get_num_mm_encoder_tokens(  # type: ignore[attr-defined]
2577
                    pos_info.get_num_embeds()
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
                )
                prompt_lora_mapping.append(lora_id)
                token_lora_mapping.extend([lora_id] * num_tokens)
                encoder_token_counts.append(num_tokens)

                if lora_id > 0:
                    lora_request = self.input_batch.lora_id_to_lora_request.get(lora_id)
                    if lora_request is not None:
                        lora_requests.add(lora_request)

            # Set tower adapter mapping
            tower_mapping = LoRAMapping(
                tuple(token_lora_mapping),
                tuple(prompt_lora_mapping),
                is_prefill=True,
                type=LoRAMappingType.TOWER,
            )
            self.lora_manager.set_active_adapters(lora_requests, tower_mapping)

            if hasattr(self.model, "get_num_mm_connector_tokens"):
                post_op_counts = [
                    self.model.get_num_mm_connector_tokens(num_tokens)  # type: ignore[attr-defined]
                    for num_tokens in encoder_token_counts
                ]

                connector_token_mapping = np.repeat(
                    np.array(prompt_lora_mapping, dtype=np.int32),
                    np.array(post_op_counts, dtype=np.int32),
                )
                connector_mapping = LoRAMapping(
                    index_mapping=tuple(connector_token_mapping.tolist()),
                    prompt_mapping=tuple(prompt_lora_mapping),
                    is_prefill=True,
                    type=LoRAMappingType.CONNECTOR,
                )

                self.lora_manager.set_active_adapters(
                    lora_requests,
                    connector_mapping,
                )

2619
        encoder_outputs: list[torch.Tensor] = []
2620
2621
        # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
        current_item_idx = 0
2622
        for modality, num_items, mm_kwargs_batch in group_and_batch_mm_kwargs(
2623
2624
2625
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
2626
        ):
2627
            batch_outputs: MultiModalEmbeddings
2628

2629
            # EVS and dynamic res video related change.
2630
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2631
            # processing multimodal data. This solves the issue with scheduler
2632
2633
2634
2635
            # putting too many video samples into a single batch. Scheduler
            # uses pruned vision tokens count to compare it versus compute
            # budget which is incorrect (Either input media size or non-pruned
            # output vision tokens count should be considered)
2636
2637
2638
            # dynamic res video for nemotron temporarily uses this hack via
            # requires_sequential_video_encoding
            # because it doesn't yet support video batching.
2639
2640
2641
            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
2642
2643
2644
2645
                (
                    self.is_multimodal_pruning_enabled
                    or self.requires_sequential_video_encoding
                )
2646
2647
2648
                and modality == "video"
                and num_items > 1
            ):
2649
                batch_outputs_lst = list[torch.Tensor]()
2650
2651
2652
2653
2654
2655
                for video_idx in range(num_items):
                    video_mm_kwargs_item = mm_kwargs[current_item_idx + video_idx]
                    with self.timed_encoder_operation(
                        should_time, mm_lora_refs, current_item_idx + video_idx, 1
                    ):
                        _, _, micro_batch_mm_inputs = next(
2656
                            group_and_batch_mm_kwargs(
2657
2658
2659
2660
                                [video_mm_kwargs_item],
                                device=self.device,
                                pin_memory=self.pin_memory,
                            )
2661
                        )
2662

2663
2664
2665
                        micro_batch_outputs = model.embed_multimodal(
                            **micro_batch_mm_inputs
                        )
2666

2667
                        batch_outputs_lst.extend(micro_batch_outputs)
2668

2669
                batch_outputs = batch_outputs_lst
2670
2671
            else:
                # Run the encoder.
2672
                # `batch_outputs` is either of the following:
2673
2674
2675
2676
2677
                # 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.
2678
2679
2680
2681

                with self.timed_encoder_operation(
                    should_time, mm_lora_refs, current_item_idx, num_items
                ):
2682
                    batch_outputs = model.embed_multimodal(**mm_kwargs_batch)
2683

2684
2685
            sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=num_items)
            encoder_outputs.extend(batch_outputs)
2686

2687
2688
            current_item_idx += num_items

2689
        # Cache the encoder outputs by mm_hash
2690
        for mm_hash, output in zip(mm_hashes, encoder_outputs):
2691
            self.encoder_cache[mm_hash] = output
2692
2693
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2694

2695
2696
        return encoder_outputs

2697
    def _gather_mm_embeddings(
2698
2699
        self,
        scheduler_output: "SchedulerOutput",
2700
        shift_computed_tokens: int = 0,
2701
2702
2703
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

2704
2705
2706
2707
2708
        # Swap to the other buffer to avoid race condition with previous
        # iteration's async copy that may still be reading from CPU.
        self.is_mm_embed_idx = 1 - self.is_mm_embed_idx
        is_mm_embed_buf = self.is_mm_embed_buffers[self.is_mm_embed_idx]

2709
        mm_embeds = list[torch.Tensor]()
2710
        is_mm_embed = is_mm_embed_buf.cpu
2711
2712
2713
        is_mm_embed[:total_num_scheduled_tokens] = False

        req_start_idx = 0
2714
        should_sync_mrope_positions = False
2715
        should_sync_xdrope_positions = False
2716

2717
        for req_id in self.input_batch.req_ids:
2718
2719
            mm_embeds_req: list[torch.Tensor] = []

2720
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2721
            req_state = self.requests[req_id]
2722
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2723

2724
2725
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2726
2727
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743

                # 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,
2744
2745
                    num_encoder_tokens,
                )
2746
                assert start_idx < end_idx
2747
2748
2749
2750
2751
2752
2753
                curr_embeds_start, curr_embeds_end = (
                    pos_info.get_embeds_indices_in_range(start_idx, end_idx)
                )
                # If there are no embeddings in the current range, we skip
                # gathering the embeddings.
                if curr_embeds_start == curr_embeds_end:
                    continue
2754

2755
                mm_hash = mm_feature.identifier
2756
                encoder_output = self.encoder_cache.get(mm_hash, None)
2757
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2758
2759
2760

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]
2761
2762
2763
                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
                else:
                    mm_embeds_item = encoder_output[start_idx:end_idx]
2764

2765
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2766
2767
2768
2769
2770
2771
2772
2773
2774
                # OR mask for overlapping mm_features (use_audio_in_video)
                if is_embed is None:
                    is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                        True
                    )
                else:
                    is_mm_embed[
                        req_start_pos + start_idx : req_start_pos + end_idx
                    ] |= is_embed
2775
2776
2777
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2778
                assert req_state.mrope_positions is not None
2779
2780
2781
2782
2783
2784
2785
                should_sync_mrope_positions = True
                mm_embeds_req, new_mrope_positions, new_delta = (
                    self.model.recompute_mrope_positions(
                        input_ids=req_state.prompt_token_ids,
                        multimodal_embeddings=mm_embeds_req,
                        mrope_positions=req_state.mrope_positions,
                        num_computed_tokens=req_state.num_computed_tokens,
2786
2787
                    )
                )
2788
2789
2790
2791
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2792
2793
            req_start_idx += num_scheduled_tokens

2794
        is_mm_embed = is_mm_embed_buf.copy_to_gpu(total_num_scheduled_tokens)
2795
2796
2797

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2798
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2799

2800
2801
2802
2803
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

2804
        return mm_embeds, is_mm_embed
2805

2806
    def get_model(self) -> nn.Module:
2807
2808
        if not hasattr(self, "model"):
            raise ValueError("Cannot get model before model has been initialized")
2809
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2810
            # get raw model out of the cudagraph wrapper.
2811
            return self.model.unwrap()
2812
2813
        return self.model

2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
    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")

2827
2828
2829
        if supports_realtime(model):
            supported_tasks.append("realtime")

2830
2831
        return supported_tasks

2832
2833
2834
2835
2836
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2837
2838
        supported_tasks = list(model.pooler.get_supported_tasks())

2839
2840
2841
2842
        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2843
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2844
2845

        return supported_tasks
2846

2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
    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)

2857
    def sync_and_slice_intermediate_tensors(
2858
2859
        self,
        num_tokens: int,
2860
        intermediate_tensors: IntermediateTensors | None,
2861
2862
        sync_self: bool,
    ) -> IntermediateTensors:
2863
2864
2865
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2866
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2867
2868
2869
2870
2871
2872

        # 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():
2873
                is_scattered = k == "residual" and is_rs
2874
                copy_len = num_tokens // tp if is_scattered else num_tokens
2875
                self.intermediate_tensors[k][:copy_len].copy_(
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
                    v[:copy_len], non_blocking=True
                )

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

    def eplb_step(self, is_dummy: bool = False, is_profile: bool = False) -> None:
2889
2890
2891
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
2892
        if not self.parallel_config.enable_eplb or self.eep_eplb_suppressed:
2893
2894
2895
            return

        assert self.eplb_state is not None
2896
2897
        model = self.get_model()
        assert is_mixture_of_experts(model)
2898
2899
2900
        self.eplb_state.step(
            is_dummy,
            is_profile,
2901
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2902
2903
        )

2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
    def setup_eplb_from_mapping(
        self,
        expanded_physical_to_logical: torch.Tensor,
        old_num_physical_experts: int,
    ) -> None:
        model = self.get_model()
        assert is_mixture_of_experts(model)

        self.eplb_state = EplbState.from_mapping(
            model=model,
            model_config=self.model_config,
            device=self.device,
            parallel_config=self.parallel_config,
            expanded_physical_to_logical=expanded_physical_to_logical,
            num_valid_physical_experts=old_num_physical_experts,
        )

2921
2922
2923
2924
2925
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
2926
2927
2928
2929
        kv_connector_output: KVConnectorOutput | None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        num_reqs = self.input_batch.num_reqs
        assert num_reqs == len(self.input_batch.pooling_params), (
2930
2931
            "Either all or none of the requests in a batch must be pooling request"
        )
2932

2933
        hidden_states = hidden_states[:num_scheduled_tokens]
2934
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
2935

2936
        pooling_metadata = self.input_batch.get_pooling_metadata()
2937
        pooling_metadata.build_pooling_cursor(
2938
2939
2940
2941
            num_scheduled_tokens_np,
            seq_lens_cpu,
            device=hidden_states.device,
            query_start_loc_gpu=self.query_start_loc.gpu[: num_reqs + 1],
2942
        )
2943

2944
2945
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
2946
            hidden_states=hidden_states, pooling_metadata=pooling_metadata
2947
        )
2948
2949
2950
2951
2952

        finished_mask = [
            seq_len == prompt_len
            for seq_len, prompt_len in zip(seq_lens_cpu, pooling_metadata.prompt_lens)
        ]
2953
2954
2955
2956
2957
2958
        raw_pooler_output = self.late_interaction_runner.postprocess_pooler_output(
            raw_pooler_output=raw_pooler_output,
            pooling_params=pooling_metadata.pooling_params,
            req_ids=self.input_batch.req_ids,
            finished_mask=finished_mask,
        )
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977

        model_runner_output = ModelRunnerOutput(
            req_ids=self.input_batch.req_ids.copy(),
            req_id_to_index=self.input_batch.req_id_to_index.copy(),
            kv_connector_output=kv_connector_output,
        )

        if raw_pooler_output is None or not any(finished_mask):
            model_runner_output.pooler_output = [None] * num_reqs
            return model_runner_output

        if self.use_async_scheduling:
            return AsyncGPUPoolingModelRunnerOutput(
                model_runner_output=model_runner_output,
                raw_pooler_output=raw_pooler_output,
                finished_mask=finished_mask,
                async_output_copy_stream=self.async_output_copy_stream,
            )

2978
2979
2980
        model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
            raw_pooler_output=raw_pooler_output,
            finished_mask=finished_mask,
2981
        )
2982
2983
        self._sync_device()

2984
        return model_runner_output
2985

2986
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
2987
2988
2989
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2990
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
2991
2992
2993
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

Patrick von Platen's avatar
Patrick von Platen committed
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
    def _prepare_mm_inputs(
        self, num_tokens: int
    ) -> tuple[torch.Tensor | None, torch.Tensor]:
        if self.model.requires_raw_input_tokens:
            input_ids = self.input_ids.gpu[:num_tokens]
        else:
            input_ids = None

        inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
        return input_ids, inputs_embeds

3005
    def _preprocess(
3006
3007
        self,
        scheduler_output: "SchedulerOutput",
3008
        num_input_tokens: int,  # Padded
3009
        intermediate_tensors: IntermediateTensors | None = None,
3010
    ) -> tuple[
3011
3012
        torch.Tensor | None,
        torch.Tensor | None,
3013
        torch.Tensor,
3014
        IntermediateTensors | None,
3015
        dict[str, Any],
3016
        ECConnectorOutput | None,
3017
    ]:
3018
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3019
        is_first_rank = get_pp_group().is_first_rank
3020
        is_encoder_decoder = self.model_config.is_encoder_decoder
3021

3022
3023
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
3024
3025
        ec_connector_output = None

3026
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
3027
            # Run the multimodal encoder if any.
3028
3029
3030
3031
3032
3033
            with self.maybe_get_ec_connector_output(
                scheduler_output,
                encoder_cache=self.encoder_cache,
            ) as ec_connector_output:
                self._execute_mm_encoder(scheduler_output)
                mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
3034

3035
3036
3037
            # 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.
3038
            inputs_embeds_scheduled = self.model.embed_input_ids(
3039
3040
3041
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
3042
            )
3043

3044
            # TODO(woosuk): Avoid the copy. Optimize.
3045
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
3046

Patrick von Platen's avatar
Patrick von Platen committed
3047
            input_ids, inputs_embeds = self._prepare_mm_inputs(num_input_tokens)
3048
            model_kwargs = {
3049
                **self._init_model_kwargs(),
3050
3051
                **self._extract_mm_kwargs(scheduler_output),
            }
3052
        elif self.enable_prompt_embeds and is_first_rank:
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
            # Get the input embeddings for the tokens that are not input embeds,
            # then put them into the appropriate positions.
            # TODO(qthequartermasterman): Since even when prompt embeds are
            # enabled, (a) not all requests will use prompt embeds, and (b)
            # after the initial prompt is processed, the rest of the generated
            # tokens will be token ids, it is not desirable to have the
            # embedding layer outside of the CUDA graph all the time. The v0
            # engine avoids this by "double compiling" the CUDA graph, once
            # with input_ids and again with inputs_embeds, for all num_tokens.
            # If a batch only has token ids, then including the embedding layer
            # in the CUDA graph will be more performant (like in the else case
            # below).
3065
3066
3067
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
3068
                .squeeze(1)
3069
            )
3070
3071
3072
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
3073
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
3074
3075
3076
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
3077
            model_kwargs = self._init_model_kwargs()
3078
            input_ids = None
3079
        else:
3080
3081
3082
3083
            # 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.
3084
            input_ids = self.input_ids.gpu[:num_input_tokens]
3085
            inputs_embeds = None
3086
            model_kwargs = self._init_model_kwargs()
3087

3088
        if self.uses_mrope:
3089
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
3090
3091
        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
3092
        else:
3093
            positions = self.positions.gpu[:num_input_tokens]
3094

3095
        if is_first_rank:
3096
3097
            intermediate_tensors = None
        else:
3098
            assert intermediate_tensors is not None
3099
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3100
3101
                num_input_tokens, intermediate_tensors, True
            )
3102

3103
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
3104
3105
3106
3107
3108
3109
3110
            # Run the encoder, just like we do with other multimodal inputs.
            # For an encoder-decoder model, our processing here is a bit
            # simpler, because the outputs are just passed to the decoder.
            # We are not doing any prompt replacement. We also will only
            # ever have a single encoder input.
            encoder_outputs = self._execute_mm_encoder(scheduler_output)
            model_kwargs.update({"encoder_outputs": encoder_outputs})
3111

3112
3113
3114
3115
3116
3117
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
3118
            ec_connector_output,
3119
        )
3120

3121
    def _sample(
3122
        self,
3123
3124
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3125
    ) -> SamplerOutput:
3126
        # Sample the next token and get logprobs if needed.
3127
        sampling_metadata = self.input_batch.sampling_metadata
3128
3129
3130
        # Update output token ids with tokens sampled in last step
        # if async scheduling and required by current sampling params.
        self.input_batch.update_async_output_token_ids()
3131
        if spec_decode_metadata is None:
3132
            return self.sampler(
3133
3134
3135
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
3136

3137
3138
3139
3140
3141
3142
        # Update spec_token_ids with real draft tokens from pre step only when
        # output_token_ids is needed (penalties or bad_words are in use).
        if self.use_async_scheduling and self._draft_token_req_ids is not None:
            draft_token_ids_cpu, _ = self._get_draft_token_ids_cpu()
            self.input_batch.update_async_spec_token_ids(draft_token_ids_cpu)

3143
        sampler_output = self.rejection_sampler(
3144
3145
            spec_decode_metadata,
            None,  # draft_probs
3146
            logits,
3147
3148
            sampling_metadata,
        )
3149
3150
3151
        return sampler_output

    def _bookkeeping_sync(
3152
3153
3154
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
3155
        logits: torch.Tensor | None,
3156
3157
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
3158
        spec_decode_metadata: SpecDecodeMetadata | None,
3159
    ) -> tuple[
3160
        dict[str, int],
3161
        LogprobsLists | None,
3162
        list[list[int]],
3163
        dict[str, LogprobsTensors | None],
3164
3165
3166
        list[str],
        dict[str, int],
        list[int],
3167
    ]:
3168
3169
3170
3171
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

3172
3173
3174
3175
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
3176
3177
3178
3179
        for i in discard_sampled_tokens_req_indices:
            gen = self.input_batch.generators.get(int(i))
            if gen is not None:
                gen.set_offset(gen.get_offset() - 4)
3180

3181
3182
3183
        # 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()
3184
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
3185
3186

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
3187
        sampled_token_ids = sampler_output.sampled_token_ids
3188
        logprobs_tensors = sampler_output.logprobs_tensors
3189
        invalid_req_indices = []
3190
        logprobs_lists = None
3191
3192
3193
3194
3195
3196
        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)
3197
3198
3199
                # Mask out the sampled tokens that should not be sampled.
                for i in discard_sampled_tokens_req_indices:
                    valid_sampled_token_ids[int(i)].clear()
3200
3201
3202

                if logprobs_tensors is not None:
                    logprobs_lists = logprobs_tensors.tolists()
3203
3204
            else:
                # Includes spec decode tokens.
3205
                valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
3206
3207
                    sampled_token_ids,
                    self.input_batch.vocab_size,
3208
                    discard_sampled_tokens_req_indices,
3209
                    logprobs_tensors=logprobs_tensors,
3210
                )
3211
        else:
3212
            valid_sampled_token_ids = []
3213
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
3214
3215
3216
3217
3218
            invalid_req_indices_set = set(invalid_req_indices)

            # 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.
3219
3220
3221
3222
            # With spec decoding, this is done in propose_draft_token_ids().
            if self.input_batch.prev_sampled_token_ids is None:
                assert sampled_token_ids.shape[-1] == 1
                self.input_batch.prev_sampled_token_ids = sampled_token_ids
3223
3224
3225
3226
3227
            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
            }
3228

3229
3230
3231
3232
3233
        # 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.
3234
        req_ids = self.input_batch.req_ids
3235
3236
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
3237
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
3238
3239
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
3240

3241
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
3242

3243
            if not sampled_ids:
3244
3245
3246
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
3247
            end_idx = start_idx + num_sampled_ids
3248
3249
3250
3251
            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}"
3252
            )
3253

3254
3255
            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
3256
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
3257

3258
            req_id = req_ids[req_idx]
3259
3260
3261
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

3262
3263
3264
3265
3266
3267
        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
        return (
            num_nans_in_logits,
            logprobs_lists,
            valid_sampled_token_ids,
            prompt_logprobs_dict,
            req_ids_output_copy,
            req_id_to_index_output_copy,
            invalid_req_indices,
        )

3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
    @contextmanager
    def synchronize_input_prep(self):
        if self.prepare_inputs_event is None:
            yield
            return

        # Ensure prior step has finished with reused CPU tensors.
        # This is required in the async scheduling case because
        # the CPU->GPU transfer happens async.
        self.prepare_inputs_event.synchronize()
        try:
            yield
        finally:
            self.prepare_inputs_event.record()

3293
3294
    def _model_forward(
        self,
3295
3296
3297
3298
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
3299
3300
3301
3302
3303
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
3304
        Motivation: We can inspect only this method versus
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
        the whole execute_model, which has additional logic.

        Args:
            input_ids: Input token IDs
            positions: Token positions
            intermediate_tensors: Tensors from previous pipeline stages
            inputs_embeds: Input embeddings (alternative to input_ids)
            **model_kwargs: Additional model arguments

        Returns:
            Model output tensor
        """
        return self.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
            **model_kwargs,
        )

3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
    @staticmethod
    def _is_uniform_decode(
        max_num_scheduled_tokens: int,
        uniform_decode_query_len: int,
        num_tokens: int,
        num_reqs: int,
        force_uniform_decode: bool | None = None,
    ) -> bool:
        """
        Checks if it's a decode batch with same amount scheduled tokens
        across all requests.
        """
        return (
            (
                (max_num_scheduled_tokens == uniform_decode_query_len)
                and (num_tokens == max_num_scheduled_tokens * num_reqs)
            )
            if force_uniform_decode is None
            else force_uniform_decode
        )

3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
    def _determine_batch_execution_and_padding(
        self,
        num_tokens: int,
        num_reqs: int,
        num_scheduled_tokens_np: np.ndarray,
        max_num_scheduled_tokens: int,
        use_cascade_attn: bool,
        allow_microbatching: bool = True,
        force_eager: bool = False,
        # For cudagraph capture TODO(lucas): Refactor how we capture cudagraphs (will
        # be improved in model runner v2)
        force_uniform_decode: bool | None = None,
        force_has_lora: bool | None = None,
3359
        force_num_active_loras: int | None = None,
3360
        num_encoder_reqs: int = 0,
3361
    ) -> tuple[
3362
3363
        CUDAGraphMode,
        BatchDescriptor,
3364
        bool,
3365
3366
        torch.Tensor | None,
        CUDAGraphStat | None,
3367
    ]:
3368
3369
3370
3371
3372
3373
        uniform_decode = self._is_uniform_decode(
            max_num_scheduled_tokens=max_num_scheduled_tokens,
            uniform_decode_query_len=self.uniform_decode_query_len,
            num_tokens=num_tokens,
            num_reqs=num_reqs,
            force_uniform_decode=force_uniform_decode,
3374
        )
3375
3376
3377
3378
3379
        # Encoder-decoder models only support CG for decoder_step > 0 (no enc_output
        # is present). Also, chunked-prefill is disabled, so batch are uniform.
        has_encoder_output = (
            self.model_config.is_encoder_decoder and num_encoder_reqs > 0
        )
3380

3381
3382
3383
3384
3385
        # Compute LoRA state for cudagraph dispatch
        num_active_loras = (
            force_num_active_loras
            if force_num_active_loras is not None
            else len(self.input_batch.lora_id_to_lora_request)
3386
        )
3387
        has_lora = num_active_loras > 0 if force_has_lora is None else force_has_lora
3388

3389
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
3390
3391
3392

        def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
            return self.cudagraph_dispatcher.dispatch(
3393
3394
3395
                num_tokens=num_tokens,
                has_lora=has_lora,
                uniform_decode=uniform_decode,
3396
                num_active_loras=num_active_loras,
3397
3398
                valid_modes={CUDAGraphMode.NONE} if force_eager else valid_modes,
                invalid_modes={CUDAGraphMode.FULL} if disable_full else None,
3399
3400
            )

3401
        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
3402
            num_tokens_padded, disable_full=use_cascade_attn or has_encoder_output
3403
        )
3404
        num_tokens_padded = batch_descriptor.num_tokens
3405
3406
3407
3408
3409
3410
3411
3412
3413
        if self.compilation_config.pass_config.enable_sp:
            assert (
                batch_descriptor.num_tokens
                % self.vllm_config.parallel_config.tensor_parallel_size
                == 0
            ), (
                "Sequence parallelism requires num_tokens to be "
                "a multiple of tensor parallel size"
            )
3414
3415
3416

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
3417
        should_ubatch, num_tokens_across_dp = False, None
3418
        if self.vllm_config.parallel_config.data_parallel_size > 1:
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
                coordinate_batch_across_dp(
                    num_tokens_unpadded=num_tokens,
                    parallel_config=self.parallel_config,
                    allow_microbatching=allow_microbatching,
                    num_tokens_padded=num_tokens_padded,
                    uniform_decode=uniform_decode,
                    num_scheduled_tokens_per_request=num_scheduled_tokens_np,
                    cudagraph_mode=cudagraph_mode.value,
                )
3429
3430
            )

3431
            # Extract DP-synced values
3432
3433
3434
            if num_tokens_across_dp is not None:
                dp_rank = self.parallel_config.data_parallel_rank
                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
3435
3436
3437
                # Re-dispatch with DP padding so we have the correct batch_descriptor
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(
                    num_tokens_padded,
3438
                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
3439
                )
3440
3441
3442
3443
                # Assert to make sure the agreed upon token count is correct otherwise
                # num_tokens_across_dp will no-longer be valid
                assert batch_descriptor.num_tokens == num_tokens_padded

3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
        cudagraph_stats = None
        if self.vllm_config.observability_config.cudagraph_metrics:
            cudagraph_stats = CUDAGraphStat(
                num_unpadded_tokens=num_tokens,
                num_padded_tokens=batch_descriptor.num_tokens,
                num_paddings=batch_descriptor.num_tokens - num_tokens,
                runtime_mode=str(cudagraph_mode),
            )

        return (
            cudagraph_mode,
            batch_descriptor,
3456
            should_ubatch,
3457
3458
3459
            num_tokens_across_dp,
            cudagraph_stats,
        )
3460

3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
    def _register_layerwise_nvtx_hooks(self) -> None:
        """
        Register layerwise NVTX hooks if --enable-layerwise-nvtx-tracing is enabled
        to trace detailed information of each layer or module in the model.
        """

        if (
            self.vllm_config.observability_config.enable_layerwise_nvtx_tracing
            and not self.layerwise_nvtx_hooks_registered
        ):
            if self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE:
                logger.debug_once(
                    "layerwise NVTX tracing is not supported when CUDA graph is "
                    "turned off; you may observe part or all of the model "
                    "missing NVTX markers"
                )

            # In STOCK_TORCH_COMPILE mode, after registering hooks here,
            # the __call__ function of nn.module will be recompiled with
            # fullgraph=True. Since nvtx.range_push/pop are not traceable
            # by torch dynamo, we can't register hook functions here
            # because hook functions will also be traced by torch dynamo.
            if (
                self.vllm_config.compilation_config.mode
                == CompilationMode.STOCK_TORCH_COMPILE
            ):
                logger.debug_once(
                    "layerwise NVTX tracing is not supported when "
                    "CompilationMode is STOCK_TORCH_COMPILE, skipping "
                    "function hooks registration"
                )
            else:
                pyt_hooks = PytHooks()
                pyt_hooks.register_hooks(self.model, self.model.__class__.__name__)
                self.layerwise_nvtx_hooks_registered = True

3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
    def _get_slot_mappings(
        self,
        num_tokens_padded: int,
        num_reqs_padded: int,
        num_tokens_unpadded: int,
        ubatch_slices: "UBatchSlices | None" = None,
    ) -> tuple[
        dict[int, torch.Tensor] | None,
        dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
    ]:
        """
        Build slot mappings in both formats needed by the system.

        Args:
            num_tokens_padded: Total number of tokens (padded)
            num_reqs_padded: Total number of requests (padded)
            num_tokens_unpadded: Actual number of tokens (unpadded)
            ubatch_slices: Optional ubatch slicing info for DBO

        Returns:
            A tuple of:
            - slot_mappings_by_gid: dict[int, torch.Tensor] for attention metadata
            - slot_mappings_by_layer: dict[str, torch.Tensor] or list for ForwardContext
        """
        if not (
            hasattr(self, "kv_cache_config")
            and self.kv_cache_config is not None
            and len(self.kv_cache_config.kv_cache_groups) > 0
        ):
            return None, None

        def _get_slot_mapping(kv_cache_gid: int):
            assert num_reqs_padded is not None and num_tokens_padded is not None
            kv_cache_spec = self.kv_cache_config.kv_cache_groups[
                kv_cache_gid
            ].kv_cache_spec
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
                slot_mapping = torch.zeros(
                    (num_tokens_padded,),
                    dtype=torch.int64,
                    device=self.device,
                )
            else:
                blk_table = self.input_batch.block_table[kv_cache_gid]
                slot_mapping = blk_table.slot_mapping.gpu[:num_tokens_padded]

            # Fill unused with -1. Needed for reshape_and_cache in full cuda
            # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
            slot_mapping[num_tokens_unpadded:num_tokens_padded].fill_(-1)

            return slot_mapping

        slot_mappings_by_gid = {
            gid: _get_slot_mapping(gid)
            for gid, _ in enumerate(self.kv_cache_config.kv_cache_groups)
        }

        slot_mappings_by_layer: dict[str, torch.Tensor] = {}
        for gid, kv_cache_group in enumerate(self.kv_cache_config.kv_cache_groups):
            slot_mapping = slot_mappings_by_gid[gid]
            for layer_name in kv_cache_group.layer_names:
                slot_mappings_by_layer[layer_name] = slot_mapping

        if ubatch_slices is not None:
            result: list[dict[str, torch.Tensor]] = []
            for ubatch in ubatch_slices:
                sliced_mappings: dict[str, torch.Tensor] = {}
                for layer_name, slot_mapping in slot_mappings_by_layer.items():
                    sliced_mappings[layer_name] = slot_mapping[ubatch.token_slice]
                result.append(sliced_mappings)
            return slot_mappings_by_gid, result

        return slot_mappings_by_gid, slot_mappings_by_layer

3571
3572
3573
3574
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
3575
        intermediate_tensors: IntermediateTensors | None = None,
3576
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
3577
3578
3579
3580
3581
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
3582

3583
        if self.routed_experts_initialized:
3584
3585
3586
3587
3588
3589
            capturer = RoutedExpertsCapturer.get_instance()
            if capturer is not None:
                capturer.clear_buffer()  # noqa
            else:
                logger.error("RoutedExpertsCapturer not initialized.")

3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
        # If ngram_gpu is used, we need to copy the scheduler_output to avoid
        # the modification has influence on the scheduler_output in engine core process.
        # The replace is much faster than deepcopy.
        if (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        ):
            num_scheduled_tokens_copy = scheduler_output.num_scheduled_tokens.copy()
            spec_decode_tokens_copy = (
                scheduler_output.scheduled_spec_decode_tokens.copy()
            )
            scheduler_output = replace(
                scheduler_output,
                num_scheduled_tokens=num_scheduled_tokens_copy,
                scheduled_spec_decode_tokens=spec_decode_tokens_copy,
            )

3607
3608
3609
3610
        if has_kv_transfer_group():
            kv_connector_metadata = scheduler_output.kv_connector_metadata
            assert kv_connector_metadata is not None
            get_kv_transfer_group().handle_preemptions(kv_connector_metadata)
3611

3612
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3613
3614
3615
3616
3617
3618
        with (
            record_function_or_nullcontext("gpu_model_runner: preprocess"),
            self.synchronize_input_prep(),
        ):
            # Update persistent batch states.
            self._update_states(scheduler_output)
3619

3620
            if has_ec_transfer() and not get_ec_transfer().is_consumer:
3621
                with self.maybe_get_ec_connector_output(
3622
                    scheduler_output,
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
                    encoder_cache=self.encoder_cache,
                ) as ec_connector_output:
                    self._execute_mm_encoder(scheduler_output)
                    return make_empty_encoder_model_runner_output(scheduler_output)

            if not num_scheduled_tokens:
                if (
                    self.parallel_config.distributed_executor_backend
                    == "external_launcher"
                    and self.parallel_config.data_parallel_size > 1
                ):
                    # this is a corner case when both external launcher
                    # and DP are enabled, num_scheduled_tokens could be
                    # 0, and has_unfinished_requests in the outer loop
                    # returns True. before returning early here we call
                    # dummy run to ensure coordinate_batch_across_dp
                    # is called into to avoid out of sync issues.
                    self._dummy_run(1)
                if not has_kv_transfer_group():
                    # Return empty ModelRunnerOutput if no work to do.
                    return EMPTY_MODEL_RUNNER_OUTPUT
                return self.kv_connector_no_forward(scheduler_output, self.vllm_config)

            if self.cache_config.kv_sharing_fast_prefill:
                assert not self.num_prompt_logprobs, (
                    "--kv-sharing-fast-prefill produces incorrect "
                    "logprobs for prompt tokens, tokens, please disable "
                    "it when the requests need prompt logprobs"
3651
3652
                )

3653
3654
3655
3656
3657
3658
            num_reqs = self.input_batch.num_reqs
            req_ids = self.input_batch.req_ids
            tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
            num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
            max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())
            num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
3659

3660
3661
3662
3663
            logits_indices, spec_decode_metadata = self._prepare_inputs(
                scheduler_output,
                num_scheduled_tokens_np,
            )
3664

3665
3666
3667
3668
3669
            cascade_attn_prefix_lens = None
            # Disable cascade attention when using microbatching (DBO)
            if self.cascade_attn_enabled and not self.parallel_config.use_ubatching:
                # Pre-compute cascade attention prefix lengths
                cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
3670
                    num_scheduled_tokens_np,
3671
3672
                    self.input_batch.num_computed_tokens_cpu[:num_reqs],
                    scheduler_output.num_common_prefix_blocks,
3673
3674
                )

3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
            (
                cudagraph_mode,
                batch_desc,
                should_ubatch,
                num_tokens_across_dp,
                cudagraph_stats,
            ) = self._determine_batch_execution_and_padding(
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs,
                num_scheduled_tokens_np=num_scheduled_tokens_np,
                max_num_scheduled_tokens=max_num_scheduled_tokens,
                use_cascade_attn=cascade_attn_prefix_lens is not None,
                num_encoder_reqs=len(scheduler_output.scheduled_encoder_inputs),
            )
3689

3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
            logger.debug(
                "Running batch with cudagraph_mode: %s, batch_descriptor: %s, "
                "should_ubatch: %s, num_tokens_across_dp: %s",
                cudagraph_mode,
                batch_desc,
                should_ubatch,
                num_tokens_across_dp,
            )

            num_tokens_padded = batch_desc.num_tokens
            num_reqs_padded = (
                batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
            )
            ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
                should_ubatch,
                num_scheduled_tokens_np,
                num_tokens_padded,
                num_reqs_padded,
                self.parallel_config.num_ubatches,
            )

            logger.debug(
                "ubatch_slices: %s, ubatch_slices_padded: %s",
                ubatch_slices,
                ubatch_slices_padded,
            )

3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
            # True if any attention backend handles KV cache update separately
            # from forward() (i.e., forward_includes_kv_cache_update=False). When true,
            # slot_mappings must use padded dimensions to match the key/value tensors.
            has_separate_kv_update = not all(
                all(
                    g.backend.forward_includes_kv_cache_update
                    for g in self.attn_groups[id]
                )
                for id, spec in enumerate(self.kv_cache_config.kv_cache_groups)
                if not isinstance(spec.kv_cache_spec, EncoderOnlyAttentionSpec)
            )
3728
3729
            pad_attn = cudagraph_mode == CUDAGraphMode.FULL

3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
            if self.cache_config.mamba_cache_mode == "align":
                mamba_utils.preprocess_mamba(
                    scheduler_output,
                    self.kv_cache_config,
                    self.cache_config,
                    self.mamba_state_idx,
                    self.input_batch,
                    self.requests,
                    self.compilation_config.static_forward_context,
                    self.model.get_mamba_state_copy_func(),
3740
                    self._get_mamba_copy_bufs(),
3741
3742
                )

3743
3744
3745
            use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
            ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices

3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
            slot_mappings_by_group, slot_mappings = self._get_slot_mappings(
                num_tokens_padded=num_tokens_padded
                if pad_attn or has_separate_kv_update
                else num_tokens_unpadded,
                num_reqs_padded=(
                    num_reqs_padded if pad_attn or has_separate_kv_update else num_reqs
                ),
                num_tokens_unpadded=num_tokens_unpadded,
                ubatch_slices=ubatch_slices_padded,
            )

3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
            attn_metadata, spec_decode_common_attn_metadata = (
                self._build_attention_metadata(
                    num_tokens=num_tokens_unpadded,
                    num_tokens_padded=num_tokens_padded if pad_attn else None,
                    num_reqs=num_reqs,
                    num_reqs_padded=num_reqs_padded if pad_attn else None,
                    max_query_len=max_num_scheduled_tokens,
                    ubatch_slices=ubatch_slices_attn,
                    logits_indices=logits_indices,
                    use_spec_decode=use_spec_decode,
                    num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
                    cascade_attn_prefix_lens=cascade_attn_prefix_lens,
3769
                    slot_mappings=slot_mappings_by_group,
3770
                )
3771
            )
3772

3773
3774
3775
3776
3777
3778
3779
3780
3781
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
3782
            )
3783

3784
        # Set cudagraph mode to none if calc_kv_scales is true.
3785
3786
3787
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
3788
            cudagraph_mode = CUDAGraphMode.NONE
3789
3790
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
3791

3792
3793
3794
3795
3796
3797
3798
        # Encoder-decoder models can only compile the pure decode steps where no
        # encoder inputs are present. Use eager for the first pass.
        num_encoder_reqs = len(scheduler_output.scheduled_encoder_inputs)
        has_encoder_input = (
            self.model_config.is_encoder_decoder and num_encoder_reqs > 0
        )

3799
3800
        # Run the model.
        # Use persistent buffers for CUDA graphs.
3801
3802
3803
        # When spec decode is enabled, defer connector finalization
        # (wait_for_save + clear metadata) until after draft model runs.
        defer_kv_connector_finalize = self.speculative_config is not None
3804
3805
        with (
            set_forward_context(
3806
3807
                attn_metadata,
                self.vllm_config,
3808
                num_tokens=num_tokens_padded,
3809
                num_tokens_across_dp=num_tokens_across_dp,
3810
3811
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
3812
                ubatch_slices=ubatch_slices_padded,
3813
                slot_mapping=slot_mappings,
3814
                skip_compiled=has_encoder_input,
3815
            ),
3816
            record_function_or_nullcontext("gpu_model_runner: forward"),
3817
            self.maybe_get_kv_connector_output(
3818
3819
                scheduler_output,
                defer_finalize=defer_kv_connector_finalize,
3820
            ) as kv_connector_output,
3821
        ):
3822
            model_output = self._model_forward(
3823
3824
3825
3826
3827
3828
3829
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

3830
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
3831
            if self.use_aux_hidden_state_outputs:
3832
                # True when EAGLE 3 is used.
3833
3834
                hidden_states, aux_hidden_states = model_output
            else:
3835
                # Common case.
3836
3837
3838
                hidden_states = model_output
                aux_hidden_states = None

3839
3840
3841
3842
3843
            if not self.broadcast_pp_output:
                # Common case.
                if not get_pp_group().is_last_rank:
                    # Return the intermediate tensors.
                    assert isinstance(hidden_states, IntermediateTensors)
3844
                    hidden_states.kv_connector_output = kv_connector_output
3845
                    self.kv_connector_output = kv_connector_output
3846
                    return hidden_states
3847

3848
                if self.is_pooling_model:
3849
                    # Return the pooling output.
3850
3851
3852
3853
3854
                    return self._pool(
                        hidden_states,
                        num_scheduled_tokens,
                        num_scheduled_tokens_np,
                        kv_connector_output,
3855
                    )
3856
3857

                sample_hidden_states = hidden_states[logits_indices]
3858
                logits = self.model.compute_logits(sample_hidden_states)
3859
3860
3861
3862
            else:
                # Rare case.
                assert not self.is_pooling_model

3863
                sample_hidden_states = hidden_states[logits_indices]
3864
                if not get_pp_group().is_last_rank:
3865
                    all_gather_tensors = {
3866
                        "residual": not is_residual_scattered_for_sp(
3867
                            self.vllm_config, num_tokens_padded
3868
                        )
3869
                    }
3870
                    get_pp_group().send_tensor_dict(
3871
3872
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
3873
3874
                        all_gather_tensors=all_gather_tensors,
                    )
3875
3876
                    logits = None
                else:
3877
                    logits = self.model.compute_logits(sample_hidden_states)
3878

3879
                model_output_broadcast_data: dict[str, Any] = {}
3880
3881
3882
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

3883
                broadcasted = get_pp_group().broadcast_tensor_dict(
3884
3885
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
3886
3887
                assert broadcasted is not None
                logits = broadcasted["logits"]
3888

3889
3890
3891
3892
3893
3894
3895
3896
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3897
            ec_connector_output,
3898
            cudagraph_stats,
3899
            slot_mappings,
3900
        )
3901
        self.kv_connector_output = kv_connector_output
3902
3903
3904
3905
3906
3907
3908
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
        if self.execute_model_state is None:
3909
3910
            kv_connector_output = self.kv_connector_output
            self.kv_connector_output = None
3911
3912
3913
            # receive sampled token ids from the last PP rank.
            if self.use_async_scheduling and get_pp_group().world_size > 1:
                self._pp_receive_prev_sampled_token_ids_to_input_batch()
3914
            if not kv_connector_output:
3915
                return None  # type: ignore[return-value]
3916
3917
3918
3919
3920
3921
3922
3923
3924

            # In case of PP with kv transfer, we need to pass through the
            # kv_connector_output
            if kv_connector_output.is_empty():
                return EMPTY_MODEL_RUNNER_OUTPUT

            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
            output.kv_connector_output = kv_connector_output
            return output
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3935
            ec_connector_output,
3936
            cudagraph_stats,
3937
            slot_mappings,
3938
3939
3940
3941
3942
3943
3944
3945
3946
        ) = self.execute_model_state
        # Clear ephemeral state.
        self.execute_model_state = None

        # Apply structured output bitmasks if present.
        if grammar_output is not None:
            apply_grammar_bitmask(
                scheduler_output, grammar_output, self.input_batch, logits
            )
3947

3948
        with record_function_or_nullcontext("gpu_model_runner: sample"):
3949
3950
            sampler_output = self._sample(logits, spec_decode_metadata)

3951
3952
3953
        self._update_states_after_model_execute(
            sampler_output.sampled_token_ids, scheduler_output
        )
3954
3955
        if self.use_async_scheduling:
            pp = get_pp_group()
3956
3957
3958
3959
            # For torchrun external_launcher PP mode with broadcast_pp_output=True,
            # PP outputs have been broadcasted to all ranks at logits computation.
            # Therefore, here is no need to send sampled token ids again in this case.
            if not self.broadcast_pp_output and pp.world_size > 1 and pp.is_last_rank:
3960
3961
3962
                self._pp_broadcast_prev_sampled_token_ids(
                    sampler_output.sampled_token_ids
                )
3963

3964
3965
        self._draft_token_ids = None
        self._draft_token_req_ids = None
3966
3967
        self.input_batch.prev_sampled_token_ids = None

3968
        def propose_draft_token_ids(sampled_token_ids):
3969
            assert spec_decode_common_attn_metadata is not None
3970
            with record_function_or_nullcontext("gpu_model_runner: draft"):
3971
3972
3973
3974
3975
3976
3977
3978
3979
                self._draft_token_ids = self.propose_draft_token_ids(
                    scheduler_output,
                    sampled_token_ids,
                    self.input_batch.sampling_metadata,
                    hidden_states,
                    sample_hidden_states,
                    aux_hidden_states,
                    spec_decode_metadata,
                    spec_decode_common_attn_metadata,
3980
                    slot_mappings,
3981
                )
3982
                self._copy_draft_token_ids_to_cpu(scheduler_output)
3983

3984
        spec_config = self.speculative_config
3985
3986
3987
3988
3989
        propose_drafts_after_bookkeeping = False
        if spec_config is not None:
            input_fits_in_drafter = spec_decode_common_attn_metadata is not None and (
                spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
                <= self.effective_drafter_max_model_len
3990
            )
3991
            use_gpu_toks = (
3992
3993
3994
                spec_config.use_eagle()
                or spec_config.uses_draft_model()
                or spec_config.uses_extract_hidden_states()
3995
3996
3997
            ) and not spec_config.disable_padded_drafter_batch
            if use_gpu_toks:
                # EAGLE/DraftModel speculative decoding can use the GPU sampled tokens
3998
                # as inputs, and does not need to wait for bookkeeping to finish.
3999
4000
4001
4002
                assert isinstance(
                    self.drafter,
                    EagleProposer | DraftModelProposer | ExtractHiddenStatesProposer,
                )
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
                sampled_token_ids = sampler_output.sampled_token_ids
                if input_fits_in_drafter:
                    propose_draft_token_ids(sampled_token_ids)
                elif self.valid_sampled_token_count_event is not None:
                    assert spec_decode_common_attn_metadata is not None
                    next_token_ids, valid_sampled_tokens_count = (
                        self.drafter.prepare_next_token_ids_padded(
                            spec_decode_common_attn_metadata,
                            sampled_token_ids,
                            self.requests,
                            self.input_batch,
                            self.discard_request_mask.gpu,
                        )
4016
                    )
4017
4018
4019
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
                    self._draft_token_ids = torch.zeros(
                        1, device=self.device, dtype=torch.int32
                    ).expand(len(self.input_batch.req_ids), self.num_spec_tokens)
                    self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)
            elif (
                spec_config.use_ngram_gpu()
                and not spec_config.disable_padded_drafter_batch
            ):
                assert isinstance(self.drafter, NgramProposerGPU)
                sampled_token_ids = sampler_output.sampled_token_ids
                if input_fits_in_drafter:
                    propose_draft_token_ids(sampled_token_ids)
                elif self.valid_sampled_token_count_event is not None:
                    assert spec_decode_common_attn_metadata is not None
                    next_token_ids, valid_sampled_tokens_count, _ = (
                        self.drafter.update_token_ids_ngram(
                            sampled_token_ids,
                            self.input_batch,
                            self.token_ids_gpu_tensor,
                            self.num_tokens_no_spec_gpu,
                            self.discard_request_mask.gpu,
                        )
                    )
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
4046
4047
4048
4049
4050
4051
4052
4053
                    # Since we couldn't run the drafter,
                    # just use zeros for the draft tokens.
                    self._draft_token_ids = torch.zeros(
                        1, device=self.device, dtype=torch.int32
                    ).expand(len(self.input_batch.req_ids), self.num_spec_tokens)
                    self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)
            else:
                propose_drafts_after_bookkeeping = input_fits_in_drafter
4054

4055
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
4056
4057
4058
4059
4060
4061
4062
4063
            (
                num_nans_in_logits,
                logprobs_lists,
                valid_sampled_token_ids,
                prompt_logprobs_dict,
                req_ids_output_copy,
                req_id_to_index_output_copy,
                invalid_req_indices,
4064
4065
4066
4067
4068
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
4069
                scheduler_output.total_num_scheduled_tokens,
4070
                spec_decode_metadata,
4071
            )
4072

4073
        if propose_drafts_after_bookkeeping:
4074
4075
4076
            # ngram and other speculative decoding methods use the sampled
            # tokens on the CPU, so they are run after bookkeeping.
            propose_draft_token_ids(valid_sampled_token_ids)
4077

4078
4079
4080
4081
4082
        # Finalize KV connector (wait_for_save + clear metadata) after
        # draft model runs. Deferred from target model forward to allow
        # draft model to also save its KV cache.
        if spec_config is not None:
            self.finalize_kv_connector()
4083

4084
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
4085
            self.eplb_step()
4086

4087
4088
4089
4090
        # self.kv_connector_output may be modified during drafting
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

4091
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
4092
            if self.routed_experts_initialized:
4093
4094
4095
4096
4097
4098
                capturer = RoutedExpertsCapturer.get_instance()
                if capturer is not None:
                    capturer.save_captured_experts(indices=self.slot_mapping)  # noqa
                else:
                    logger.error("RoutedExpertsCapturer not initialized.")

4099
4100
4101
4102
4103
4104
4105
            output = ModelRunnerOutput(
                req_ids=req_ids_output_copy,
                req_id_to_index=req_id_to_index_output_copy,
                sampled_token_ids=valid_sampled_token_ids,
                logprobs=logprobs_lists,
                prompt_logprobs_dict=prompt_logprobs_dict,
                kv_connector_output=kv_connector_output,
4106
4107
4108
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
4109
                num_nans_in_logits=num_nans_in_logits,
4110
                cudagraph_stats=cudagraph_stats,
4111
            )
4112

4113
4114
        if not self.use_async_scheduling:
            return output
4115

4116
4117
4118
4119
4120
4121
4122
4123
4124
        with record_function_or_nullcontext(
            "gpu_model_runner: AsyncGPUModelRunnerOutput"
        ):
            async_output = AsyncGPUModelRunnerOutput(
                model_runner_output=output,
                sampled_token_ids=sampler_output.sampled_token_ids,
                logprobs_tensors=sampler_output.logprobs_tensors,
                invalid_req_indices=invalid_req_indices,
                async_output_copy_stream=self.async_output_copy_stream,
4125
                vocab_size=self.input_batch.vocab_size,
4126
4127
4128
4129
4130
            )
        with record_function_or_nullcontext(
            "gpu_model_runner: set_async_sampled_token_ids"
        ):
            # Save ref of sampled_token_ids CPU tensor if the batch contains
4131
            # any requests with sampling params that require output ids.
4132
4133
4134
4135
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
4136
4137
4138

        return async_output

4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
    def _pp_broadcast_prev_sampled_token_ids(
        self, sampled_token_ids: torch.Tensor
    ) -> None:
        """Broadcast sampled token ids (GPU) from last PP stage"""
        pp = get_pp_group()
        assert pp.is_last_rank
        # `prev_sampled_token_ids` is expected to have shape [num_reqs, 1].
        assert sampled_token_ids.dim() == 2 and sampled_token_ids.shape[-1] == 1, (
            "PP+async expects sampled_token_ids to have shape [num_reqs, 1]"
        )
        torch.distributed.broadcast(
            sampled_token_ids, src=pp.rank, group=pp.device_group
        )

    def _pp_receive_prev_sampled_token_ids_to_input_batch(self) -> None:
        """Receive sampled token ids broadcast from last PP stage"""
        pp = get_pp_group()
        assert not pp.is_last_rank
        num_reqs = self.input_batch.num_reqs
        # `prev_sampled_token_ids` is expected to have shape [num_reqs, 1].
        recv = torch.empty((num_reqs, 1), dtype=torch.int32, device=self.device)
        torch.distributed.broadcast(recv, src=pp.last_rank, group=pp.device_group)
        self.input_batch.prev_sampled_token_ids = recv

        # construct `prev_req_id_to_index` here so `_prepare_input_ids`
        # can map req_id -> previous batch row
        discard_req_indices = np.nonzero(self.discard_request_mask.np[:num_reqs])[0]
        discard_req_indices_set = set(discard_req_indices)
        prev_req_id_to_index: dict[str, int] = {}
        for i, req_id in enumerate(self.input_batch.req_ids):
            if i in discard_req_indices_set:
                continue
            prev_req_id_to_index[req_id] = i
            # PP+async scheduling: advance per-request local cached output length by
            # appending a placeholder (-1) token id.
            if (req_state := self.requests.get(req_id)) is not None:
                req_state.output_token_ids.append(-1)
        self.input_batch.prev_req_id_to_index = prev_req_id_to_index

4178
    def take_draft_token_ids(self) -> DraftTokenIds | None:
4179
        if not self.num_spec_tokens or not self._draft_token_req_ids:
4180
            return None
4181
        draft_token_ids, req_ids = self._get_draft_token_ids_cpu()
4182
        return DraftTokenIds(req_ids, draft_token_ids)
4183

4184
4185
4186
    def _copy_draft_token_ids_to_cpu(
        self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
    ) -> None:
4187
4188
4189
4190
4191
4192
        # Check if we need to copy draft tokens to CPU. In async scheduling,
        # we only copy when needed for structured output, penalties or bad_words.
        if self.use_async_scheduling and not (
            scheduler_output.has_structured_output_requests
            or self.input_batch.sampling_metadata.output_token_ids
        ):
4193
4194
4195
            return
        # We must also set the corresponding request ids.
        self._draft_token_req_ids = self.input_batch.req_ids.copy()
4196

4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
        draft_token_ids: torch.Tensor = self._draft_token_ids
        if not torch.is_tensor(draft_token_ids):
            return
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_copy_stream is not None
        assert self.draft_token_ids_cpu is not None
        default_stream = torch.cuda.current_stream()
        num_reqs = draft_token_ids.shape[0]
        with torch.cuda.stream(self.draft_token_ids_copy_stream):
            if not zeros_only:
                # Trigger async copy of draft token ids to cpu.
                self.draft_token_ids_copy_stream.wait_stream(default_stream)
                self.draft_token_ids_cpu[:num_reqs].copy_(
                    draft_token_ids, non_blocking=True
                )
            else:
                # No copy needed, just zero-out cpu tensor.
                self.draft_token_ids_cpu[:num_reqs] = 0
            self.draft_token_ids_event.record()

4217
    def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
4218
        if isinstance(self._draft_token_ids, list):
4219
4220
4221
4222
            return self._draft_token_ids, self.input_batch.req_ids
        req_ids = self._draft_token_req_ids
        if req_ids is None:
            return [], []
4223
4224
4225
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_cpu is not None
        self.draft_token_ids_event.synchronize()
4226
        return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
4227

4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
    def _copy_valid_sampled_token_count(
        self, next_token_ids: torch.Tensor, valid_sampled_tokens_count: torch.Tensor
    ) -> None:
        if self.valid_sampled_token_count_event is None:
            return

        default_stream = torch.cuda.current_stream()
        # Initialize a new stream to overlap the copy operation with
        # prepare_input of draft model.
        with torch.cuda.stream(self.valid_sampled_token_count_copy_stream):
            self.valid_sampled_token_count_copy_stream.wait_stream(default_stream)  # type: ignore
            counts = valid_sampled_tokens_count
            counts_cpu = self.valid_sampled_token_count_cpu
4241
            assert counts_cpu is not None
4242
4243
4244
4245
4246
4247
4248
4249
            counts_cpu[: counts.shape[0]].copy_(counts, non_blocking=True)
            self.valid_sampled_token_count_event.record()

        self.input_batch.prev_sampled_token_ids = next_token_ids.unsqueeze(1)

    def _get_valid_sampled_token_count(self) -> list[int]:
        # Wait until valid_sampled_tokens_count is copied to cpu,
        prev_sampled_token_ids = self.input_batch.prev_sampled_token_ids
4250
4251
        sampled_count_event = self.valid_sampled_token_count_event
        if sampled_count_event is None or prev_sampled_token_ids is None:
4252
4253
4254
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
4255
4256
        assert counts_cpu is not None
        sampled_count_event.synchronize()
4257
4258
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

4259
4260
4261
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
4262
        sampled_token_ids: torch.Tensor | list[list[int]],
4263
4264
4265
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
4266
4267
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
4268
        common_attn_metadata: CommonAttentionMetadata,
4269
        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
4270
    ) -> list[list[int]] | torch.Tensor:
4271
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
4272
4273
4274
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
4275
4276
            from vllm.v1.spec_decode.ngram_proposer import NgramProposer

4277
            assert isinstance(sampled_token_ids, list)
4278
            assert isinstance(self.drafter, NgramProposer)
4279
            draft_token_ids = self.drafter.propose(
4280
                sampled_token_ids,
4281
4282
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
4283
                slot_mappings=slot_mappings,
4284
            )
4285
4286
4287
4288
4289
4290
4291
4292
4293
4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
4311
4312
4313
4314
4315
4316
4317
4318
4319
4320
4321
        elif spec_config.use_ngram_gpu():
            assert isinstance(self.drafter, NgramProposerGPU)
            (
                next_token_ids,
                valid_sampled_tokens_count,
                valid_sampled_token_ids_gpu,
            ) = self.drafter.update_token_ids_ngram(
                sampled_token_ids,
                self.input_batch,
                self.token_ids_gpu_tensor,
                self.num_tokens_no_spec_gpu,
                self.discard_request_mask.gpu,
            )
            self._copy_valid_sampled_token_count(
                next_token_ids, valid_sampled_tokens_count
            )

            batch_size = next_token_ids.shape[0]

            draft_token_ids, num_valid_draft_tokens = self.drafter.propose(
                self.num_tokens_no_spec_gpu[:batch_size],
                self.token_ids_gpu_tensor[:batch_size],
                valid_sampled_token_ids_gpu,
                valid_sampled_tokens_count,
            )

            # Cache valid draft counts for scheduler-side trimming.
            self._num_valid_draft_tokens = num_valid_draft_tokens

            # Async D2H copy on a dedicated stream.
            copy_num_valid_draft_tokens(
                self._num_valid_draft_tokens_cpu,
                self._num_valid_draft_tokens_copy_stream,
                self._num_valid_draft_tokens_event,
                self._num_valid_draft_tokens,
                self.input_batch.num_reqs,
            )
4322
        elif spec_config.method == "suffix":
4323
4324
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
4325
4326
4327
            draft_token_ids = self.drafter.propose(
                self.input_batch, sampled_token_ids, slot_mappings=slot_mappings
            )
4328
        elif spec_config.method == "medusa":
4329
            assert isinstance(sampled_token_ids, list)
4330
            assert isinstance(self.drafter, MedusaProposer)
4331

4332
4333
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
4334
4335
4336
4337
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
4338
4339
4340
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
4341
                for num_draft, tokens in zip(
4342
4343
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
4344
                    indices.append(offset + len(tokens) - 1)
4345
                    offset += num_draft + 1
4346
                indices = torch.tensor(indices, device=self.device)
4347
4348
                hidden_states = sample_hidden_states[indices]

4349
            draft_token_ids = self.drafter.propose(
4350
4351
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
4352
                slot_mappings=slot_mappings,
4353
            )
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
        elif spec_config.uses_extract_hidden_states():
            assert isinstance(self.drafter, ExtractHiddenStatesProposer)
            assert isinstance(sampled_token_ids, torch.Tensor), (
                "sampled_token_ids should be a torch.Tensor for "
                "extract_hidden_states method."
            )
            if not self.use_aux_hidden_state_outputs or aux_hidden_states is None:
                raise ValueError(
                    "aux_hidden_states are required when using `extract_hidden_states`"
                )
            target_hidden_states = [h[:num_scheduled_tokens] for h in aux_hidden_states]

4366
            draft_token_ids = self.drafter.propose(
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
                sampled_token_ids=sampled_token_ids,
                target_hidden_states=target_hidden_states,
                common_attn_metadata=common_attn_metadata,
                slot_mappings=slot_mappings,
            )
            next_token_ids, valid_sampled_tokens_count = (
                self.drafter.prepare_next_token_ids_padded(
                    common_attn_metadata,
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    self.discard_request_mask.gpu,
                )
            )
            self._copy_valid_sampled_token_count(
                next_token_ids, valid_sampled_tokens_count
            )

4385
4386
        elif spec_config.use_eagle() or spec_config.uses_draft_model():
            assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
4387

4388
            if spec_config.disable_padded_drafter_batch:
4389
4390
4391
                # When padded-batch is disabled, the sampled_token_ids should be
                # the cpu-side list[list[int]] of valid sampled tokens for each
                # request, with invalid requests having empty lists.
4392
4393
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
4394
                    "padded-batch is disabled."
4395
                )
4396
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
4397
4398
4399
4400
4401
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
4402
4403
4404
4405
4406
            else:
                # When using padded-batch, the sampled_token_ids should be
                # the gpu tensor of sampled tokens for each request, of shape
                # (num_reqs, num_spec_tokens + 1) with rejected tokens having
                # value -1.
4407
4408
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
4409
                    "padded-batch is enabled."
4410
4411
                )
                next_token_ids, valid_sampled_tokens_count = (
4412
4413
4414
4415
4416
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
4417
                        self.discard_request_mask.gpu,
4418
                    )
4419
                )
4420
4421
4422
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
4423

4424
            num_rejected_tokens_gpu = None
4425
            if spec_decode_metadata is None:
4426
                token_indices_to_sample = None
4427
                # input_ids can be None for multimodal models.
4428
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
4429
                target_positions = self._get_positions(num_scheduled_tokens)
4430
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
4431
                    assert aux_hidden_states is not None
4432
                    target_hidden_states = torch.cat(
4433
4434
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
4435
4436
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
4437
            else:
4438
                if spec_config.disable_padded_drafter_batch:
4439
                    token_indices_to_sample = None
4440
4441
4442
4443
4444
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
4445
4446
4447
4448
4449
4450
4451
4452
4453
                    target_token_ids = self.input_ids.gpu[token_indices]
                    target_positions = self._get_positions(token_indices)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[token_indices] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[token_indices]
4454
                else:
4455
4456
4457
4458
4459
4460
4461
4462
                    (
                        common_attn_metadata,
                        token_indices_to_sample,
                        num_rejected_tokens_gpu,
                    ) = self.drafter.prepare_inputs_padded(
                        common_attn_metadata,
                        spec_decode_metadata,
                        valid_sampled_tokens_count,
4463
                    )
4464
4465
4466
4467
4468
4469
4470
4471
4472
4473
4474
                    total_num_tokens = common_attn_metadata.num_actual_tokens
                    # When padding the batch, token_indices is just a range
                    target_token_ids = self.input_ids.gpu[:total_num_tokens]
                    target_positions = self._get_positions(total_num_tokens)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[:total_num_tokens] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[:total_num_tokens]
4475

4476
            if self.supports_mm_inputs and self.drafter.supports_mm_inputs:
4477
4478
4479
4480
4481
4482
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
4483

4484
            draft_token_ids = self.drafter.propose(
4485
4486
4487
4488
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
4489
                token_indices_to_sample=token_indices_to_sample,
4490
                sampling_metadata=sampling_metadata,
4491
                common_attn_metadata=common_attn_metadata,
4492
                mm_embed_inputs=mm_embed_inputs,
4493
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
4494
                slot_mappings=slot_mappings,
4495
            )
4496

4497
        return draft_token_ids
4498

4499
4500
4501
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
4502
4503
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
4504
                f"Allowed configs: {allowed_config_names}"
4505
            )
4506
4507
4508
4509
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

4510
    @instrument(span_name="Loading (GPU)")
4511
    def load_model(self, load_dummy_weights: bool = False) -> None:
4512
4513
        """
        Args:
4514
            load_dummy_weights: load dummy weights instead of real weights.
4515
        """
4516
4517
4518
4519
4520
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
4521

4522
4523
4524
4525
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0

4526
4527
4528
        try:
            with DeviceMemoryProfiler() as m:
                time_before_load = time.perf_counter()
4529
4530
                if load_dummy_weights:
                    self.load_config.load_format = "dummy"
4531
4532
4533
4534
4535
4536
4537
                model_loader = get_model_loader(self.load_config)
                self.model = model_loader.load_model(
                    vllm_config=self.vllm_config, model_config=self.model_config
                )
                if self.lora_config:
                    self.model = self.load_lora_model(
                        self.model, self.vllm_config, self.device
4538
                    )
4539
4540
4541
4542
4543
4544
4545
4546
                if hasattr(self, "drafter"):
                    logger.info_once("Loading drafter model...")
                    self.drafter.load_model(self.model)
                    if (
                        hasattr(self.drafter, "model")
                        and is_mixture_of_experts(self.drafter.model)
                        and self.parallel_config.enable_eplb
                    ):
4547
4548
4549
                        assert not self.parallel_config.enable_elastic_ep, (
                            "Elastic EP is not supported with drafter model."
                        )
4550
4551
4552
4553
4554
4555
4556
4557
4558
4559
4560
4561
4562
4563
4564
4565
                        spec_config = self.vllm_config.speculative_config
                        assert spec_config is not None
                        assert spec_config.draft_model_config is not None
                        logger.info_once(
                            "EPLB is enabled for drafter model %s.",
                            spec_config.draft_model_config.model,
                        )
                        if self.eplb_state is None:
                            self.eplb_state = EplbState(
                                self.parallel_config, self.device
                            )
                        self.eplb_state.add_model(
                            self.drafter.model,
                            spec_config.draft_model_config,
                        )
                        eplb_models += 1
4566

4567
4568
4569
4570
4571
4572
                if self.use_aux_hidden_state_outputs:
                    if not supports_eagle3(self.get_model()):
                        raise RuntimeError(
                            "Model does not support EAGLE3 interface but "
                            "aux_hidden_state_outputs was requested"
                        )
4573

4574
4575
4576
4577
4578
4579
4580
4581
4582
                    # Try to get auxiliary layers from speculative config,
                    # otherwise use model's default layers
                    aux_layers = self._get_eagle3_aux_layers_from_config()
                    if aux_layers:
                        logger.info(
                            "Using auxiliary layers from speculative config: %s",
                            aux_layers,
                        )
                    else:
4583
4584
4585
                        aux_layers = (
                            self.model.get_eagle3_default_aux_hidden_state_layers()
                        )
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
4598
4599
4600

                    self.model.set_aux_hidden_state_layers(aux_layers)
                time_after_load = time.perf_counter()
            self.model_memory_usage = m.consumed_memory
        except torch.cuda.OutOfMemoryError as e:
            msg = (
                "Failed to load model - not enough GPU memory. "
                "Try lowering --gpu-memory-utilization to free memory for weights, "
                "increasing --tensor-parallel-size, or using --quantization. "
                "See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
                "for more tips."
            )
            combined_msg = f"{msg} (original error: {e})"
            logger.error(combined_msg)
            raise e
4601
        logger.info_once(
4602
4603
            "Model loading took %s GiB memory and %.6f seconds",
            format_gib(self.model_memory_usage),
4604
            time_after_load - time_before_load,
4605
            scope="local",
4606
        )
4607
4608
4609
4610
4611
4612
        if not load_dummy_weights:
            prepare_communication_buffer_for_model(self.model)
            if (drafter := getattr(self, "drafter", None)) and (
                drafter_model := getattr(drafter, "model", None)
            ):
                prepare_communication_buffer_for_model(drafter_model)
4613
        mm_config = self.model_config.multimodal_config
4614
        self.is_multimodal_pruning_enabled = (
4615
            supports_multimodal_pruning(self.get_model())
4616
4617
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
4618
        )
4619
4620
4621
        self.requires_sequential_video_encoding = hasattr(
            self.get_model(), "requires_sequential_video_encoding"
        )  # Temporary hack for dynamic res video w/o support for bs>1 yet
4622

4623
4624
4625
4626
4627
        if (
            is_mixture_of_experts(self.model)
            and self.parallel_config.enable_eplb
            and not load_dummy_weights
        ):
4628
4629
4630
            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
            assert self.eplb_state is not None
            self.eplb_state.add_model(
4631
                self.model,
4632
                self.model_config,
4633
            )
4634
            if self.eplb_state.is_async:
4635
                self.eplb_state.start_async_loop()
4636

4637
        if (
4638
4639
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
4640
        ):
4641
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
4642
            compilation_counter.stock_torch_compile_count += 1
4643
            self.model.compile(fullgraph=True, backend=backend)
4644
            return
4645
        # for other compilation modes, cudagraph behavior is controlled by
Jiayi Yan's avatar
Jiayi Yan committed
4646
        # CudagraphWrapper and CudagraphDispatcher of vllm.
4647
4648

        # wrap the model with full cudagraph wrapper if needed.
4649
4650
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
4651
4652
4653
4654
        if (
            cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.use_ubatching
        ):
4655
4656
4657
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
4658
        elif self.parallel_config.use_ubatching:
4659
            if cudagraph_mode.has_full_cudagraphs():
4660
4661
4662
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
4663
            else:
4664
4665
4666
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
4667

4668
4669
        get_offloader().post_init()

4670
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
4671
4672
4673
4674
4675
4676
4677
4678
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
4693
        """Extract Eagle3 auxiliary layer indices from speculative config.

        These indices specify which hidden states from the base model should
        be used as auxiliary inputs for the Eagle3 drafter model during
        speculative decoding.

        Returns:
            Tuple of layer indices if found in draft model config,
            None otherwise.
        """
        if not (self.speculative_config and self.speculative_config.draft_model_config):
            return None

        hf_config = self.speculative_config.draft_model_config.hf_config
        if not hasattr(hf_config, "eagle_aux_hidden_state_layer_ids"):
            return None

        layer_ids = hf_config.eagle_aux_hidden_state_layer_ids
        if layer_ids and isinstance(layer_ids, (list, tuple)):
            return tuple(layer_ids)

        return None

4694
4695
4696
4697
4698
4699
4700
4701
4702
4703
4704
4705
4706
    def reload_weights(
        self,
        weights_iterator: Iterable[tuple[str, torch.Tensor]] | None = None,
        weights_path: str | None = None,
        is_checkpoint_format: bool = True,
    ) -> None:
        """
        Reload weights from a weights iterator or from disk

        :param weights_iterator: weights to load into model
        :param weights_path: path to load weights from if weights_iterator is not
            provided. Use path of original model if neither is provided.
        :param is_checkpoint_format: set to False if weights have already been processed
Jiayi Yan's avatar
Jiayi Yan committed
4707
            into kernel format (repacking, renaming, etc.)
4708
4709
4710
4711
4712
4713
4714
4715
4716
4717
4718
4719
4720
4721
4722
4723
4724
4725
4726
4727
4728
4729
4730
4731
4732
4733
4734
4735
4736
4737
4738
4739
4740
4741
4742
4743
4744
4745
4746
4747
4748
4749
4750
4751
4752
4753
4754
4755
4756
4757
4758
4759
4760
4761
4762
4763
4764
4765
4766
4767
4768
        """
        # TODO(@kylesayrs): generalize to all runners and loaders
        # argument validation
        if weights_iterator is None and not is_checkpoint_format:
            logger.warning(
                "Reloading from disk means that weights will be in checkpoint format. "
                "Please use `is_checkpoint_format=True` "
                "to avoid weight reloading errors"
            )

        model = self.get_model()
        weights_to_load = {name for name, _ in model.named_parameters()}
        counter_before_reloading = time.perf_counter()

        # load weights from disk if none are provided
        if weights_iterator is None:
            model_loader = get_model_loader(self.load_config)
            if not hasattr(model_loader, "get_all_weights"):
                raise NotImplementedError(
                    f"Model reloading with `{self.load_config.load_format}` format"
                )

            if weights_path is not None:
                self.model_config.model = weights_path
            weights_iterator = model_loader.get_all_weights(self.model_config, model)
            weights_iterator = cast(
                Iterable[tuple[str, torch.Tensor]], weights_iterator
            )

        # begin loading weights
        logger.info_once("Reloading weights inplace...", scope="local")
        load_device = (
            self.vllm_config.load_config.device or self.vllm_config.device_config.device
        )
        with torch.device(load_device):
            if is_checkpoint_format:
                # load weights from checkpoint/ original model format
                initialize_layerwise_reload(model)
                loaded_weights = model.load_weights(weights_iterator)
                finalize_layerwise_reload(model, self.model_config)

            else:
                # load weights from kernel format
                logger.warning_once(
                    "Reloading with `is_checkpoint_format=True` requires that "
                    "weights be in kernel format and already sharded",
                    scope="local",
                )
                loaded_weights = set()
                for name, loaded_weight in weights_iterator:
                    param = model.get_parameter(name)  # TODO: buffers?
                    param.copy_(loaded_weight)
                    loaded_weights.add(name)

        # logging and validation
        counter_after_reloading = time.perf_counter()
        diff_seconds = counter_after_reloading - counter_before_reloading
        logger.info_once(
            "Reloading and processing weights took %.2f seconds",
            diff_seconds,
            scope="local",
4769
        )
4770
4771
4772
4773
4774
4775
4776
        if self.model_config.quantization is None and loaded_weights is not None:
            weights_not_loaded = weights_to_load - loaded_weights
            if weights_not_loaded:
                logger.warning(
                    "Following weights were not loaded from checkpoint: %s",
                    weights_not_loaded,
                )
4777

4778
4779
4780
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
4781
        num_scheduled_tokens: dict[str, int],
4782
    ) -> dict[str, LogprobsTensors | None]:
4783
        num_prompt_logprobs_dict = self.num_prompt_logprobs
4784
4785
4786
        if not num_prompt_logprobs_dict:
            return {}

4787
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
4788
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
4789
4790
4791
4792
4793

        # 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():
4794
4795
4796
4797
            num_tokens = num_scheduled_tokens.get(req_id)
            if num_tokens is None:
                # This can happen if the request was preempted in prefill stage.
                continue
4798
4799
4800

            # Get metadata for this request.
            request = self.requests[req_id]
4801
4802
4803
4804
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

4805
4806
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
4807
4808
                self.device, non_blocking=True
            )
4809

4810
4811
4812
4813
4814
4815
            # 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(
4816
4817
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
4818
4819
                in_progress_dict[req_id] = logprobs_tensors

4820
            # Determine number of logits to retrieve.
4821
4822
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
4823
            num_remaining_tokens = num_prompt_tokens - start_tok
4824
            if num_tokens <= num_remaining_tokens:
4825
                # This is a chunk, more tokens remain.
4826
4827
4828
                # 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.
4829
4830
4831
4832
4833
                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)
4834
4835
4836
4837
4838
4839
4840
                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
4841
4842
4843
4844
4845

            # 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]
4846
            offset = self.query_start_loc.np[req_idx].item()
4847
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
4848
            logits = self.model.compute_logits(prompt_hidden_states)
4849
4850
4851
4852

            # 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.
4853
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
4854
4855

            # Compute prompt logprobs.
4856
            logprobs = self.sampler.compute_logprobs(logits)
4857
            token_ids, logprobs, ranks, _ = self.sampler.gather_logprobs(
4858
4859
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
4860
4861

            # Transfer GPU->CPU async.
4862
4863
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
4864
4865
4866
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
4867
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
4868
4869
                ranks, non_blocking=True
            )
4870
4871
4872
4873
4874

        # 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]
4875
            del in_progress_dict[req_id]
4876
4877

        # Must synchronize the non-blocking GPU->CPU transfers.
4878
        if prompt_logprobs_dict:
4879
            self._sync_device()
4880
4881
4882

        return prompt_logprobs_dict

4883
4884
    def _get_nans_in_logits(
        self,
4885
        logits: torch.Tensor | None,
4886
4887
4888
4889
4890
4891
4892
4893
4894
4895
4896
    ) -> 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])
4897
4898
4899
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
4900
4901
4902
4903
            return num_nans_in_logits
        except IndexError:
            return {}

4904
    @contextmanager
4905
4906
4907
    def maybe_randomize_inputs(
        self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None
    ):
4908
4909
4910
4911
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
4912
         - during DP rank dummy run
4913
        """
4914

4915
4916
4917
4918
        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
4919
        elif input_ids is not None:
4920
4921
4922
4923

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
4924
                    self.input_ids.gpu,
4925
4926
                    low=0,
                    high=self.model_config.get_vocab_size(),
4927
                )
4928

4929
            logger.debug_once("Randomizing dummy input_ids for DP Rank")
4930
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
4931
4932
            yield
            input_ids.fill_(0)
4933
4934
4935
4936
4937
4938
4939
4940
4941
4942
4943
4944
4945
4946
4947
        else:

            @functools.cache
            def rand_inputs_embeds() -> torch.Tensor:
                return torch.randn_like(
                    self.inputs_embeds.gpu,
                )

            assert inputs_embeds is not None
            logger.debug_once("Randomizing dummy inputs_embeds for DP Rank")
            inputs_embeds.copy_(
                rand_inputs_embeds()[: inputs_embeds.size(0)], non_blocking=True
            )
            yield
            inputs_embeds.fill_(0)
4948

4949
4950
4951
4952
4953
4954
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
4955
4956
        assert self.mm_budget is not None

4957
4958
4959
        # Don't use `max_items_per_batch` here to avoid redundant computation
        dummy_mm_inputs = self.mm_registry.get_dummy_mm_inputs(
            self.model_config,
4960
            mm_counts={modality: 1},
4961
            cache=self.mm_budget.cache,
4962
        )
4963
4964
4965
4966
4967
        dummy_mm_item = dummy_mm_inputs["mm_kwargs"][modality][0]

        # We use the cache so that the item is saved to the cache,
        # but not read from the cache
        assert dummy_mm_item is not None, "Item should not already be cached"
4968

4969
        return next(
4970
4971
            mm_kwargs_batch
            for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
4972
                [(modality, dummy_mm_item)] * max_items_per_batch,
4973
4974
4975
4976
                device=self.device,
                pin_memory=self.pin_memory,
            )
        )
4977

4978
4979
4980
4981
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
4982
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
4983
4984
        force_attention: bool = False,
        uniform_decode: bool = False,
4985
        allow_microbatching: bool = True,
4986
4987
        skip_eplb: bool = False,
        is_profile: bool = False,
4988
        create_mixed_batch: bool = False,
4989
        remove_lora: bool = True,
Rémi Delacourt's avatar
Rémi Delacourt committed
4990
        is_graph_capturing: bool = False,
4991
        num_active_loras: int = 0,
4992
        profile_seq_lens: int | None = None,
4993
    ) -> tuple[torch.Tensor, torch.Tensor]:
4994
4995
4996
4997
4998
4999
5000
        """
        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.
5001
                - if not set will determine the cudagraph mode based on using
5002
                    the self.cudagraph_dispatcher.
5003
5004
5005
5006
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
5007
            force_attention: If True, always create attention metadata. Used to
5008
5009
5010
5011
                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.
5012
5013
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
5014
            remove_lora: If False, dummy LoRAs are not destroyed after the run
5015
5016
            num_active_loras: Number of distinct active LoRAs to capture for.
                LoRA is activated when num_active_loras > 0.
5017
5018
5019
            profile_seq_lens: If provided, use this value for seq_lens instead
                of max_query_len. Used to profile attention workspace that
                scales with context length.
5020
        """
5021
5022
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5023
5024
5025
5026
            # The current dummy run only covers LM execution, so we can skip it.
            # mm encoder dummy run may need to add in the future.
            return torch.tensor([]), torch.tensor([])

5027
5028
        assert (
            cudagraph_runtime_mode is None
5029
            or cudagraph_runtime_mode.is_valid_runtime_mode()
5030
        )
5031

5032
        # If cudagraph_mode.decode_mode() == FULL and
5033
        # cudagraph_mode.separate_routine(). This means that we are using
5034
5035
5036
5037
5038
5039
5040
5041
5042
5043
5044
        # 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.
5045
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
5046

5047
5048
5049
        # 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.
5050
        assert num_tokens <= self.max_num_tokens
5051
        max_num_reqs = self.scheduler_config.max_num_seqs
5052
5053
5054
5055
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
5056
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
5057
5058
5059
5060
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
5061
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
5062
5063
5064
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
5065
            assert not create_mixed_batch
5066
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
5067
5068
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
5069
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
5070
5071
5072
5073
5074
5075
        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

5076
5077
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
5078
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
5079
5080
        num_tokens_unpadded = int(num_scheduled_tokens.sum())

5081
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
5082

5083
        _cudagraph_mode, batch_desc, should_ubatch, num_tokens_across_dp, _ = (
5084
5085
5086
5087
5088
5089
5090
5091
5092
5093
5094
5095
5096
5097
5098
5099
5100
            self._determine_batch_execution_and_padding(
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs,
                num_scheduled_tokens_np=num_scheduled_tokens,
                max_num_scheduled_tokens=max_query_len,
                use_cascade_attn=False,
                allow_microbatching=allow_microbatching,
                force_eager=is_profile
                or (cudagraph_runtime_mode == CUDAGraphMode.NONE),
                # `force_uniform_decode` is used for cudagraph capture; because for
                # capturing mixed prefill-decode batches, we sometimes use
                # num_tokens == num_reqs which looks like a uniform decode batch to the
                # dispatcher; but we actually want to capture a piecewise cudagraph
                force_uniform_decode=uniform_decode,
                # `force_has_lora` is used for cudagraph capture; because LoRA is
                # activated later in the context manager, but we need to know the
                # LoRA state when determining the batch descriptor for capture
5101
5102
5103
5104
                force_has_lora=num_active_loras > 0,
                # `force_num_active_loras` is used for cudagraph capture; because we
                # need to capture graphs for specific num_active_loras counts
                force_num_active_loras=num_active_loras,
5105
5106
            )
        )
5107
5108
5109

        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
5110
        else:
5111
5112
5113
5114
5115
5116
5117
5118
5119
            assert cudagraph_runtime_mode == _cudagraph_mode, (
                f"Cudagraph runtime mode mismatch in dummy_run. "
                f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
            )

        num_tokens_padded = batch_desc.num_tokens
        num_reqs_padded = (
            batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
        )
5120
        ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
5121
5122
5123
5124
5125
5126
5127
5128
5129
5130
            should_ubatch,
            num_scheduled_tokens,
            num_tokens_padded,
            num_reqs_padded,
            self.vllm_config.parallel_config.num_ubatches,
        )
        logger.debug(
            "ubatch_slices: %s, ubatch_slices_padded: %s",
            ubatch_slices,
            ubatch_slices_padded,
5131
        )
5132

5133
        attn_metadata: PerLayerAttnMetadata | None = None
5134

5135
5136
5137
5138
5139
5140
5141
        slot_mappings_by_group, slot_mappings = self._get_slot_mappings(
            num_tokens_padded=num_tokens,
            num_reqs_padded=num_reqs_padded,
            num_tokens_unpadded=num_tokens_unpadded,
            ubatch_slices=ubatch_slices_padded,
        )

5142
5143
5144
5145
5146
5147
5148
5149
        # _dummy_run shares pinned CPU buffers (seq_lens, query_start_loc,
        # etc.) with execute_model.  It must participate in the same event
        # protocol so that back-to-back dummy/real steps don't overwrite
        # pinned memory while a prior non_blocking H2D DMA is still reading.
        with self.synchronize_input_prep():
            # If force_attention is True, we always capture attention.
            # Otherwise, it only happens for cudagraph_runtime_mode=FULL.
            if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
5150
5151
5152
                if profile_seq_lens is not None:
                    seq_lens = profile_seq_lens  # type: ignore[assignment]
                elif create_mixed_batch:
5153
5154
5155
                    # In the mixed batch mode (used for FI warmup), we use
                    # shorter sequence lengths to run faster.
                    # TODO(luka) better system for describing dummy batches
5156
                    seq_lens = [1] * num_decode_tokens + [num_prefill_tokens + 1]  # type: ignore[assignment]
5157
5158
5159
5160
5161
                else:
                    seq_lens = max_query_len  # type: ignore[assignment]
                self.seq_lens.np[:num_reqs] = seq_lens
                self.seq_lens.np[num_reqs:] = 0
                self.seq_lens.copy_to_gpu()
5162

5163
5164
5165
                cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
                self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
                self.query_start_loc.copy_to_gpu()
5166

5167
5168
5169
5170
5171
5172
5173
5174
5175
                pad_attn = cudagraph_runtime_mode == CUDAGraphMode.FULL
                attn_metadata, _ = self._build_attention_metadata(
                    num_tokens=num_tokens_unpadded,
                    num_tokens_padded=num_tokens_padded if pad_attn else None,
                    num_reqs=num_reqs_padded,
                    max_query_len=max_query_len,
                    ubatch_slices=(ubatch_slices_padded if pad_attn else ubatch_slices),
                    for_cudagraph_capture=is_graph_capturing,
                    slot_mappings=slot_mappings_by_group,
5176
                    use_spec_decode=self.speculative_config is not None,
5177
                )
5178

5179
        with self.maybe_dummy_run_with_lora(
5180
5181
5182
5183
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            remove_lora,
5184
            num_active_loras,
5185
        ):
5186
            # Make sure padding doesn't exceed max_num_tokens
5187
            assert num_tokens_padded <= self.max_num_tokens
5188
            model_kwargs = self._init_model_kwargs()
5189
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
Patrick von Platen's avatar
Patrick von Platen committed
5190
5191
                input_ids, inputs_embeds = self._prepare_mm_inputs(num_tokens_padded)

5192
                model_kwargs = {
5193
                    **model_kwargs,
5194
5195
                    **self._dummy_mm_kwargs(num_reqs),
                }
5196
5197
            elif self.enable_prompt_embeds:
                input_ids = None
5198
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
5199
                model_kwargs = self._init_model_kwargs()
5200
            else:
5201
                input_ids = self.input_ids.gpu[:num_tokens_padded]
5202
                inputs_embeds = None
5203

5204
            if self.uses_mrope:
5205
                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
5206
            elif self.uses_xdrope_dim > 0:
5207
                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
5208
            else:
5209
                positions = self.positions.gpu[:num_tokens_padded]
5210
5211
5212
5213
5214
5215
5216
5217
5218

            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,
5219
5220
5221
                            device=self.device,
                        )
                    )
5222
5223

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
5224
                    num_tokens_padded, None, False
5225
                )
5226

5227
            if ubatch_slices_padded is not None:
5228
5229
5230
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
5231
                num_tokens_padded = ubatch_slices_padded[0].num_tokens
5232
                if num_tokens_across_dp is not None:
5233
                    num_tokens_across_dp[:] = num_tokens_padded
5234

5235
            with (
5236
                self.maybe_randomize_inputs(input_ids, inputs_embeds),
5237
                set_forward_context(
5238
5239
                    attn_metadata,
                    self.vllm_config,
5240
                    num_tokens=num_tokens_padded,
5241
5242
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
5243
                    batch_descriptor=batch_desc,
5244
                    ubatch_slices=ubatch_slices_padded,
5245
                    slot_mapping=slot_mappings,
5246
5247
                ),
            ):
5248
                outputs = self.model(
5249
5250
5251
5252
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
5253
                    **model_kwargs,
5254
                )
5255

5256
5257
5258
5259
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
5260

5261
5262
5263
            if self.speculative_config and (
                self.speculative_config.use_eagle()
                or self.speculative_config.uses_draft_model()
5264
                or self.speculative_config.uses_extract_hidden_states()
5265
            ):
5266
5267
5268
5269
                assert isinstance(
                    self.drafter,
                    EagleProposer | DraftModelProposer | ExtractHiddenStatesProposer,
                )
5270
                assert self.speculative_config is not None
5271
5272
5273
                # Eagle currently only supports PIECEWISE cudagraphs.
                # Therefore only use cudagraphs if the main model uses PIECEWISE
                # NOTE(lucas): this is a hack, need to clean up.
5274
                use_cudagraphs = (
5275
5276
5277
5278
5279
5280
5281
5282
5283
                    (
                        is_graph_capturing
                        and cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    )
                    or (
                        not is_graph_capturing
                        and cudagraph_runtime_mode != CUDAGraphMode.NONE
                    )
                ) and not self.speculative_config.enforce_eager
5284
5285
5286
5287
5288

                # Note(gnovack) - We need to disable cudagraphs for one of the two
                # lora cases when cudagraph_specialize_lora is enabled. This is a
                # short term mitigation for issue mentioned in
                # https://github.com/vllm-project/vllm/issues/28334
5289
5290
5291
5292
                if (
                    self.compilation_config.cudagraph_specialize_lora
                    and num_active_loras > 0
                ):
5293
5294
5295
5296
5297
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
Rémi Delacourt's avatar
Rémi Delacourt committed
5298
                    is_graph_capturing=is_graph_capturing,
5299
                    slot_mappings=slot_mappings,
5300
                )
5301

5302
5303
5304
5305
5306
5307
5308
5309
5310
5311
5312
        # We register layerwise NVTX hooks here after the first dynamo tracing is
        # done to avoid nvtx operations in hook functions being traced by
        # torch dynamo and causing graph breaks.
        # Note that for DYNAMO_ONCE and VLLM_COMPILE mode,
        # compiled model's dynamo tracing is only done once and the compiled model's
        # __call__ function is replaced by calling the compiled function.
        # So it's safe to register hooks here. Hooks will be registered to
        # both compiled and uncompiled models but they will never
        # be called on the compiled model execution path.
        self._register_layerwise_nvtx_hooks()

5313
5314
5315
5316
5317
5318
5319
5320
5321
5322
        # 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)

5323
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
5324
5325
5326
5327
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
5328
5329
5330
5331
5332
5333

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
5334
5335
5336
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
5337

5338
5339
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5340
5341
5342
            # MM Encoder only model no need to run sampler.
            return torch.tensor([])

5343
        hidden_states = torch.rand_like(hidden_states)
5344

5345
        logits = self.model.compute_logits(hidden_states)
5346
5347
        num_reqs = logits.size(0)

5348
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
5349
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363

        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)],
5364
            spec_token_ids=[[] for _ in range(num_reqs)],
5365
5366
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
5367
            logitsprocs=LogitsProcessors(),
5368
        )
5369
        try:
5370
5371
5372
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
5373
        except RuntimeError as e:
5374
            if "out of memory" in str(e):
5375
5376
5377
5378
                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 "
5379
5380
                    "initializing the engine."
                ) from e
5381
5382
            else:
                raise e
5383
        if self.speculative_config:
5384
5385
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
5386
5387
                draft_token_ids, self.device
            )
5388
5389
5390
5391
5392
5393

            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
5394
5395
5396
5397
5398
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
5399
            )
5400
5401
5402
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
5403
                logits,
5404
5405
                dummy_metadata,
            )
5406
        return sampler_output
5407

5408
    def _dummy_pooler_run_task(
5409
5410
        self,
        hidden_states: torch.Tensor,
5411
5412
        task: PoolingTask,
    ) -> PoolerOutput:
5413
5414
5415
5416
        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
5417
5418
5419
5420
        num_scheduled_tokens_np = np.full(num_reqs, min_tokens_per_req)
        num_scheduled_tokens_np[-1] += num_tokens % num_reqs
        assert np.sum(num_scheduled_tokens_np) == num_tokens
        assert len(num_scheduled_tokens_np) == num_reqs
5421
5422
5423

        req_num_tokens = num_tokens // num_reqs

5424
        dummy_prompt_lens = torch.from_numpy(num_scheduled_tokens_np)
5425
5426
5427
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
5428

5429
        model = cast(VllmModelForPooling, self.get_model())
5430
        dummy_pooling_params = PoolingParams(task=task)
5431
        dummy_pooling_params.verify(self.model_config)
5432
        to_update = model.pooler.get_pooling_updates(task)
5433
5434
        to_update.apply(dummy_pooling_params)

5435
        dummy_metadata = PoolingMetadata(
5436
5437
5438
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
5439
            pooling_states=[PoolingStates() for i in range(num_reqs)],
5440
        )
5441

5442
        dummy_metadata.build_pooling_cursor(
5443
            num_scheduled_tokens_np,
5444
5445
            seq_lens_cpu=dummy_prompt_lens,
            device=hidden_states.device,
5446
        )
5447

5448
        try:
5449
5450
5451
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
5452
        except RuntimeError as e:
5453
            if "out of memory" in str(e):
5454
                raise RuntimeError(
5455
5456
5457
                    "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 "
5458
5459
                    "initializing the engine."
                ) from e
5460
5461
            else:
                raise e
5462
5463
5464
5465
5466
5467

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
5468
5469
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5470
5471
5472
            # MM Encoder only model not need to run pooler.
            return torch.tensor([])

5473
        # Find the task that has the largest output for subsequent steps
5474
5475
5476
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
5477
5478
5479
5480
5481
5482
            raise RuntimeError(
                f"Model {self.model_config.model} does not support "
                "any pooling tasks. See "
                "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                "to learn more."
            )
5483

5484
        output_size = dict[PoolingTask, float]()
5485
        for task in supported_pooling_tasks:
5486
5487
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
5488
            output_size[task] = sum(o.nbytes for o in output if o is not None)
5489
5490
5491
5492
            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)
5493

5494
    def profile_run(self) -> None:
5495
        # Profile with multimodal encoder & encoder cache.
5496
        if self.supports_mm_inputs:
5497
5498
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
5499
                logger.info(
5500
                    "Skipping memory profiling for multimodal encoder and "
5501
5502
                    "encoder cache."
                )
5503
5504
5505
5506
5507
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
5508
5509
5510
5511
5512
5513
5514
5515
5516
5517
5518
5519
5520
5521
5522
5523
5524
                    if not mm_budget.mm_max_toks_per_item:
                        # All modality limits are 0 — embedding-only mode.
                        # Budget is non-zero for embedding storage, but
                        # there's no encoder to profile.
                        logger.info(
                            "Skipping encoder profiling for embedding-only "
                            "mode (all modality limits=0 with "
                            "enable_mm_embeds=True).",
                        )
                    else:
                        # NOTE: Currently model is profiled with a single
                        # non-text modality with the max possible input
                        # tokens even when it supports multiple.
                        dummy_modality = mm_budget.get_modality_with_max_tokens()
                        max_mm_items_per_batch = mm_budget.mm_max_items_per_batch[
                            dummy_modality
                        ]
5525

5526
                        logger.info_once(
5527
5528
5529
5530
5531
5532
                            "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,
5533
                            scope="local",
5534
                        )
5535

5536
5537
5538
5539
5540
                        # Create dummy batch of multimodal inputs.
                        batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                            dummy_modality,
                            max_mm_items_per_batch,
                        )
5541

5542
5543
5544
5545
5546
5547
5548
5549
5550
5551
5552
                        # Run multimodal encoder.
                        dummy_encoder_outputs = self.model.embed_multimodal(
                            **batched_dummy_mm_inputs
                        )

                        sanity_check_mm_encoder_outputs(
                            dummy_encoder_outputs,
                            expected_num_items=max_mm_items_per_batch,
                        )
                        for i, output in enumerate(dummy_encoder_outputs):
                            self.encoder_cache[f"tmp_{i}"] = output
5553

5554
        # Add `is_profile` here to pre-allocate communication buffers
5555
5556
5557
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
5558
        if get_pp_group().is_last_rank:
5559
5560
5561
5562
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
5563
        else:
5564
            output = None
5565
        self._sync_device()
5566
        del hidden_states, output
5567
        self.encoder_cache.clear()
5568
        gc.collect()
5569

5570
5571
5572
5573
5574
5575
5576
5577
5578
5579
    def _init_minimal_kv_cache_for_profiling(self) -> None:
        from vllm.v1.core.kv_cache_utils import (
            get_kv_cache_config_from_groups,
            get_kv_cache_groups,
        )

        kv_cache_spec = self.get_kv_cache_spec()
        kv_cache_groups = get_kv_cache_groups(self.vllm_config, kv_cache_spec)
        min_blocks = self.compilation_config.max_cudagraph_capture_size or 1

5580
5581
5582
        # Temporarily change num_gpu_blocks_override to allocate a minimal KV cache
        saved_override = self.cache_config.num_gpu_blocks_override
        self.cache_config.num_gpu_blocks_override = min_blocks
5583
        minimal_config = get_kv_cache_config_from_groups(
5584
            self.vllm_config, kv_cache_groups, available_memory=0
5585
        )
5586
        self.cache_config.num_gpu_blocks_override = saved_override
5587
5588
5589
5590
5591
5592
5593
5594
5595
5596
5597
5598
5599
5600
5601
5602
5603
5604
5605
5606
5607
5608
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
5634
5635
5636
5637
5638
5639
5640
5641
5642
5643
5644
5645
5646
5647
5648
5649
5650
5651
5652
5653
5654
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
5666
5667
5668
5669
5670
5671
5672
5673
5674
5675
5676
5677
5678
5679
5680
5681
5682
5683
5684
5685
5686
5687
5688
5689
5690
5691
5692
5693
5694
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
5705
5706
5707
5708
5709
5710
5711

        self.initialize_kv_cache(minimal_config)
        self.cache_config.num_gpu_blocks = minimal_config.num_blocks

        logger.debug("Initialized minimal KV cache for CUDA graph profiling")

    @staticmethod
    @contextmanager
    def _freeze_gc():
        gc.collect()
        should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
        if should_freeze:
            gc.freeze()
        try:
            yield
        finally:
            if should_freeze:
                gc.unfreeze()
                gc.collect()

    def _cleanup_profiling_kv_cache(self) -> None:
        torch.accelerator.synchronize()
        if hasattr(self, "kv_caches") and self.kv_caches:
            for i in range(len(self.kv_caches)):
                self.kv_caches[i] = None  # type: ignore
            self.kv_caches.clear()
        if hasattr(self, "cross_layers_kv_cache"):
            self.cross_layers_kv_cache = None
            self.cross_layers_attn_backend = None
        if hasattr(self, "attn_groups"):
            self.attn_groups.clear()
        if hasattr(self, "kv_cache_config"):
            delattr(self, "kv_cache_config")
        self.cache_config.num_gpu_blocks = None

        for layer in self.compilation_config.static_forward_context.values():
            if hasattr(layer, "kv_cache"):
                layer.kv_cache = []

        gc.collect()
        torch.accelerator.empty_cache()

        logger.debug("Cleaned up profiling KV cache and CUDA graphs")

    @torch.inference_mode()
    def profile_cudagraph_memory(self) -> int:
        with set_current_vllm_config(self.vllm_config):
            self._init_minimal_kv_cache_for_profiling()

        saved_num_cudagraph_captured = compilation_counter.num_cudagraph_captured

        capture_descs = self.cudagraph_dispatcher.get_capture_descs()

        total_graphs = sum(len(descs) for _, descs in capture_descs)
        if total_graphs == 0:
            logger.debug("No CUDA graphs will be captured, skipping profiling")
            self._cleanup_profiling_kv_cache()
            return 0

        logger.info(
            "Profiling CUDA graph memory: %s",
            ", ".join(
                f"{mode.name}={len(descs)} (largest={descs[0].num_tokens})"
                for mode, descs in capture_descs
                if descs
            ),
        )

        # Use a temporary pool for profiling to avoid fragmentation in the main pool.
        profiling_pool = current_platform.graph_pool_handle()
        original_pools: dict[int, Any] = {}
        for instance in list(CUDAGraphWrapper._all_instances):
            original_pools[id(instance)] = instance.graph_pool
            instance.graph_pool = profiling_pool

        set_cudagraph_capturing_enabled(True)
        with self._freeze_gc(), graph_capture(device=self.device):
            shared_memory_estimate = {}
            per_graph_estimate = {}
            torch.accelerator.synchronize()
            torch.accelerator.empty_cache()

            for mode, descs in capture_descs:
                profile_descs = descs[:2]
                mem_samples: list[int] = []

                for i, desc in enumerate(profile_descs):
                    mem_before = torch.cuda.mem_get_info()[0]
                    self._warmup_and_capture(
                        desc,
                        cudagraph_runtime_mode=mode,
                        profile_seq_lens=(
                            min(
                                self.max_model_len,
                                self.max_num_tokens // desc.num_tokens,
                            )
                            if mode == CUDAGraphMode.FULL and i == 0
                            else None
                        ),
                    )
                    torch.accelerator.synchronize()
                    free_after = torch.cuda.mem_get_info()[0]
                    mem_samples.append(mem_before - free_after)

                first_capture = mem_samples[0]
                # Use at least 1 MiB per graph for driver overhead
                per_graph = max(mem_samples[1] if len(mem_samples) > 1 else 0, 1 << 20)

                shared_memory_estimate[mode] = first_capture
                per_graph_estimate[mode] = per_graph * (len(descs) - 1)

                logger.debug(
                    "Estimated %s CUDA graph memory: "
                    "%.2f MiB first-capture + (%d-1) × %.2f MiB per-graph",
                    mode.name,
                    first_capture / (1 << 20),
                    len(descs),
                    per_graph / (1 << 20),
                )

        set_cudagraph_capturing_enabled(False)
        CUDAGraphWrapper.clear_all_graphs()
        for instance in list(CUDAGraphWrapper._all_instances):
            if id(instance) in original_pools:
                instance.graph_pool = original_pools[id(instance)]
5712
5713
5714
        for key_set in self.cudagraph_dispatcher.cudagraph_keys.values():
            key_set.clear()
        self.cudagraph_dispatcher.keys_initialized = False
5715
5716
5717
5718
5719
5720
5721
5722
5723
5724
5725
5726
5727
5728
5729
5730
5731
        self.maybe_remove_all_loras(self.lora_config)
        self._cleanup_profiling_kv_cache()
        compilation_counter.num_cudagraph_captured = saved_num_cudagraph_captured

        # FULL and PIECEWISE graphs share the global pool at runtime and are
        # never replayed concurrently, so the pool overlays their memory.
        # Take the max to avoid double-counting the overlap.
        total_estimate = max(shared_memory_estimate.values()) + sum(
            per_graph_estimate.values()
        )
        logger.info(
            "Estimated CUDA graph memory: %.2f GiB total",
            total_estimate / (1 << 30),
        )

        return int(total_estimate)

5732
    @instrument(span_name="Capture model")
5733
    def capture_model(self) -> int:
5734
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
5735
            logger.warning(
5736
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
5737
5738
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
5739
            return 0
5740

5741
5742
        compilation_counter.num_gpu_runner_capture_triggers += 1

5743
5744
        start_time = time.perf_counter()

5745
5746
5747
        # 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.
5748
        set_cudagraph_capturing_enabled(True)
5749
5750
5751
        with self._freeze_gc(), graph_capture(device=self.device):
            torch.accelerator.synchronize()
            torch.accelerator.empty_cache()
5752
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
5753

5754
5755
5756
5757
            for (
                runtime_mode,
                batch_descs,
            ) in self.cudagraph_dispatcher.get_capture_descs():
5758
                self._capture_cudagraphs(
5759
5760
                    batch_descriptors=batch_descs,
                    cudagraph_runtime_mode=runtime_mode,
5761
                )
5762
                torch.accelerator.synchronize()
5763

5764
            torch.accelerator.synchronize()
5765
5766
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

5767
5768
5769
        # 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
5770
        # we may do lazy capturing in future that still allows capturing
5771
5772
        # after here.
        set_cudagraph_capturing_enabled(False)
5773

5774
5775
5776
        torch.accelerator.synchronize()
        torch.accelerator.empty_cache()

5777
5778
5779
5780
        # Lock workspace to prevent resizing during execution.
        # Max workspace sizes should have been captured during warmup/profiling.
        lock_workspace()

5781
5782
5783
5784
        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
5785
        logger.info_once(
5786
5787
5788
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
5789
            scope="local",
5790
        )
5791
        return cuda_graph_size
5792

5793
5794
5795
5796
5797
5798
5799
5800
5801
5802
5803
5804
5805
5806
5807
5808
5809
5810
5811
5812
5813
5814
5815
5816
5817
5818
5819
5820
5821
5822
5823
5824
5825
5826
    def _warmup_and_capture(
        self,
        desc: BatchDescriptor,
        cudagraph_runtime_mode: CUDAGraphMode,
        profile_seq_lens: int | None = None,
        allow_microbatching: bool = False,
        num_warmups: int | None = None,
    ):
        if num_warmups is None:
            num_warmups = self.compilation_config.cudagraph_num_of_warmups
        force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
        for _ in range(num_warmups):
            self._dummy_run(
                desc.num_tokens,
                cudagraph_runtime_mode=CUDAGraphMode.NONE,
                force_attention=force_attention,
                uniform_decode=desc.uniform,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
                num_active_loras=desc.num_active_loras,
            )
        self._dummy_run(
            desc.num_tokens,
            cudagraph_runtime_mode=cudagraph_runtime_mode,
            uniform_decode=desc.uniform,
            allow_microbatching=allow_microbatching,
            skip_eplb=True,
            remove_lora=False,
            num_active_loras=desc.num_active_loras,
            is_graph_capturing=True,
            profile_seq_lens=profile_seq_lens,
        )

5827
5828
    def _capture_cudagraphs(
        self,
5829
        batch_descriptors: list[BatchDescriptor],
5830
5831
5832
5833
        cudagraph_runtime_mode: CUDAGraphMode,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
5834
            and cudagraph_runtime_mode.is_valid_runtime_mode()
5835
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
5836

5837
5838
5839
5840
5841
        if not batch_descriptors:
            return

        uniform_decode = batch_descriptors[0].uniform

5842
5843
        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
5844
5845
            batch_descriptors = tqdm(
                batch_descriptors,
5846
5847
5848
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
5849
5850
5851
                    cudagraph_runtime_mode.name,
                ),
            )
5852

5853
        # We skip EPLB here since we don't want to record dummy metrics
5854
        for batch_desc in batch_descriptors:
5855
            # We currently only capture ubatched graphs when its a FULL
5856
5857
5858
            # cudagraph, a uniform decode batch, and the number of tokens
            # is above the threshold. Otherwise we just capture a non-ubatched
            # version of the graph
5859
            allow_microbatching = (
5860
                self.parallel_config.use_ubatching
5861
5862
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
5863
5864
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
5865
                    num_tokens=batch_desc.num_tokens,
5866
5867
                    uniform_decode=uniform_decode,
                )
5868
            )
5869
5870
            self._warmup_and_capture(
                batch_desc,
5871
5872
5873
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                allow_microbatching=allow_microbatching,
            )
5874
            torch.accelerator.synchronize()
5875
        self.maybe_remove_all_loras(self.lora_config)
5876

5877
5878
5879
5880
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
5881
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
5882

5883
5884
5885
5886
5887
5888
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
5889
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
5890
            layer_type = cast(type[Any], AttentionLayerBase)
5891
            layers = get_layers_from_vllm_config(
5892
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
5893
            )
5894
5895
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
5896
            # Dedupe based on full class name; this is a bit safer than
5897
5898
5899
5900
            # 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.
5901
            for layer_name in kv_cache_group_spec.layer_names:
5902
                attn_backend = layers[layer_name].get_attn_backend()
5903
5904
5905
5906

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
5907
                        attn_backend,  # type: ignore[arg-type]
5908
5909
                    )

5910
5911
5912
                full_cls_name = attn_backend.full_cls_name()
                layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
                if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
5913
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
5914
                key = (full_cls_name, layer_kv_cache_spec)
5915
5916
5917
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
5918
                attn_backend_layers[key].append(layer_name)
5919
5920
5921
5922
            return (
                {attn_backends[k]: v for k, v in attn_backend_layers.items()},
                set(group_key.attn_backend for group_key in attn_backends.values()),
            )
5923
5924

        def create_attn_groups(
5925
            attn_backends_map: dict[AttentionGroupKey, list[str]],
5926
            kv_cache_group_id: int,
5927
5928
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
5929
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
5930
                attn_group = AttentionGroup(
5931
                    attn_backend,
5932
                    layer_names,
5933
                    kv_cache_spec,
5934
                    kv_cache_group_id,
5935
5936
                )

5937
5938
5939
                attn_groups.append(attn_group)
            return attn_groups

5940
        attention_backend_maps = []
5941
        attention_backend_list = []
5942
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
5943
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
5944
            attention_backend_maps.append(attn_backends[0])
5945
            attention_backend_list.append(attn_backends[1])
5946
5947

        # Resolve cudagraph_mode before actually initialize metadata_builders
5948
5949
5950
        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
5951

5952
5953
5954
        # Check if attention backend supports PCP&DCP and related features.
        check_attention_cp_compatibility(self.vllm_config)

5955
5956
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
5957

5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
5970
5971
5972
    def initialize_metadata_builders(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
        """
        Create the metadata builders for all KV cache groups and attn groups.
        """
        for kv_cache_group_id in range(len(kv_cache_config.kv_cache_groups)):
            for attn_group in self.attn_groups[kv_cache_group_id]:
                attn_group.create_metadata_builders(
                    self.vllm_config,
                    self.device,
                    kernel_block_sizes[kv_cache_group_id]
                    if kv_cache_group_id < len(kernel_block_sizes)
                    else None,
                    num_metadata_builders=1
5973
5974
                    if not self.parallel_config.use_ubatching
                    else self.parallel_config.num_ubatches,
5975
                )
co63oc's avatar
co63oc committed
5976
        # Calculate reorder batch threshold (if needed)
5977
5978
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
5979
5980
        self.calculate_reorder_batch_threshold()

5981
5982
5983
5984
5985
5986
5987
5988
        # Initialize drafter attention backend
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_draft_model()
        ):
            assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
            self.drafter.initialize_attn_backend(kv_cache_config, kernel_block_sizes)

5989
    def _check_and_update_cudagraph_mode(
5990
5991
5992
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
5993
    ) -> None:
5994
        """
5995
        Resolve the cudagraph_mode when there are multiple attention
5996
        groups with potential conflicting CUDA graph support.
5997
5998
5999
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
6000
        min_cg_support = AttentionCGSupport.ALWAYS
6001
        min_cg_backend_name = None
6002

6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014
        for attn_backend_set, kv_cache_group in zip(
            attention_backends, kv_cache_groups
        ):
            for attn_backend in attn_backend_set:
                builder_cls = attn_backend.get_builder_cls()

                cg_support = builder_cls.get_cudagraph_support(
                    self.vllm_config, kv_cache_group.kv_cache_spec
                )
                if cg_support.value < min_cg_support.value:
                    min_cg_support = cg_support
                    min_cg_backend_name = attn_backend.__name__
6015
6016
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
6017
        assert cudagraph_mode is not None
6018
        # check cudagraph for mixed batch is supported
6019
6020
6021
6022
6023
6024
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
6025
                f"with {min_cg_backend_name} backend (support: "
6026
6027
                f"{min_cg_support})"
            )
6028
6029
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
6030
6031
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
6032
                    "make sure compilation mode is VLLM_COMPILE"
6033
                )
6034
6035
6036
6037
6038
                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"
6039
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6040
                    CUDAGraphMode.FULL_AND_PIECEWISE
6041
                )
6042
6043
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
6044
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6045
                    CUDAGraphMode.FULL_DECODE_ONLY
6046
                )
6047
6048
            logger.warning(msg)

6049
        # check that if we are doing decode full-cudagraphs it is supported
6050
6051
6052
6053
6054
6055
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
6056
                f"with {min_cg_backend_name} backend (support: "
6057
6058
                f"{min_cg_support})"
            )
6059
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
6060
6061
6062
6063
6064
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
6065
                    "attention is compiled piecewise"
6066
6067
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6068
                    CUDAGraphMode.PIECEWISE
6069
                )
6070
            else:
6071
6072
                msg += (
                    "; setting cudagraph_mode=NONE because "
6073
                    "attention is not compiled piecewise"
6074
6075
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6076
                    CUDAGraphMode.NONE
6077
                )
6078
6079
            logger.warning(msg)

6080
6081
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
6082
6083
6084
6085
6086
6087
6088
6089
        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 "
6090
                f"{min_cg_backend_name} (support: {min_cg_support})"
6091
            )
6092
6093
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
6094
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6095
                    CUDAGraphMode.PIECEWISE
6096
                )
6097
6098
            else:
                msg += "; setting cudagraph_mode=NONE"
6099
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6100
                    CUDAGraphMode.NONE
6101
                )
6102
6103
6104
6105
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
6106
6107
6108
6109
6110
6111
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
6112
                f"supported with {min_cg_backend_name} backend ("
6113
6114
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
6115
                "and make sure compilation mode is VLLM_COMPILE"
6116
            )
6117

6118
6119
6120
6121
        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
        # we need to adjust the cudagraph sizes to be a multiple of the uniform
        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
6122
        # Will be removed in the near future when we have separate cudagraph capture
6123
6124
6125
6126
6127
6128
6129
6130
6131
6132
        # sizes for decode and mixed prefill-decode.
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and cudagraph_mode.separate_routine()
            and self.uniform_decode_query_len > 1
        ):
            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
            )

6133
6134
6135
6136
6137
6138
6139
6140
6141
6142
6143
6144
6145
6146
6147
6148
        # If the model has Mamba layers and cudagraph mode includes FULL
        # decode, cap cudagraph capture sizes to the number of available
        # Mamba cache blocks. Each decode request needs one conv_state
        # cache line, so capture batch sizes cannot exceed num_blocks.
        # Only FULL decode graphs are affected because PIECEWISE captures
        # run GDN/Mamba ops eagerly (prefill path, no causal_conv1d_update).
        # See: https://github.com/vllm-project/vllm/issues/34094
        if cudagraph_mode.has_full_cudagraphs():
            has_mamba = any(
                isinstance(g.kv_cache_spec, MambaSpec) for g in kv_cache_groups
            )
            if has_mamba and self.kv_cache_config is not None:
                self.compilation_config.adjust_cudagraph_sizes_for_mamba_cache(
                    self.kv_cache_config.num_blocks
                )

6149
6150
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
6151
        self.compilation_config.cudagraph_mode = cudagraph_mode
6152
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
6153
            cudagraph_mode, self.uniform_decode_query_len
6154
        )
6155

6156
6157
6158
6159
6160
6161
        # Initialize drafter's cudagraph dispatcher if using spec decode.
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_extract_hidden_states()
        ):
            assert isinstance(self.drafter, EagleProposer | ExtractHiddenStatesProposer)
6162
6163
            self.drafter.initialize_cudagraph_keys(cudagraph_mode)

6164
6165
    def calculate_reorder_batch_threshold(self) -> None:
        """
6166
6167
6168
6169
        Choose the minimum reorder batch threshold from all attention groups.
        Backends should be able to support lower threshold then what they request
        just may have a performance penalty due to that backend treating decodes
        as prefills.
6170
        """
6171
6172
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

6173
        reorder_batch_thresholds: list[int | None] = [
6174
6175
6176
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
6177
6178
6179
6180
6181
        # If there are no attention groups (attention-free model) or no backend
        # reports a threshold, leave reordering disabled.
        if len(reorder_batch_thresholds) == 0:
            self.reorder_batch_threshold = None
            return
6182
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
6183

6184
6185
6186
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
6187
6188
        """
        Re-initialize the input batch if the block sizes are different from
6189
6190
6191
6192
        what it was originally created with. This happens when the final
        block size (determined after model loading) differs from the
        placeholder used during __init__, or when there are multiple
        KV cache groups.
6193
6194
6195

        Args:
            kv_cache_config: The KV cache configuration.
6196
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6197
        """
6198
        block_sizes = []
6199
6200
        max_num_blocks = []
        max_model_len = max(self.max_model_len, self.max_encoder_len)
6201
        for kv_cache_group in kv_cache_config.kv_cache_groups:
6202
6203
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                continue
6204
6205
            block_size = kv_cache_group.kv_cache_spec.block_size
            block_sizes.append(block_size)
6206
            max_num_blocks_per_req = cdiv(
6207
                max_model_len, block_size * get_total_cp_world_size()
6208
6209
            )
            if isinstance(kv_cache_group.kv_cache_spec, MambaSpec):
6210
                max_num_blocks_per_req = (
6211
6212
6213
6214
6215
                    max_num_blocks_per_req
                    if self.cache_config.enable_prefix_caching
                    else 1
                ) + kv_cache_group.kv_cache_spec.num_speculative_blocks
            max_num_blocks.append(max_num_blocks_per_req)
6216

6217
6218
6219
6220
        if (
            block_sizes != self._init_block_sizes
            or kernel_block_sizes != self._init_kernel_block_sizes
        ):
6221
            assert self.offload_config.uva.cpu_offload_gb == 0, (
6222
6223
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
6224
6225
                "for more details."
            )
6226
6227
            self._init_block_sizes = block_sizes
            self._init_kernel_block_sizes = kernel_block_sizes
6228
6229
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
6230
                max_model_len=max_model_len,
6231
6232
6233
6234
6235
                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,
6236
                kernel_block_sizes=kernel_block_sizes,
6237
                max_num_blocks_per_req=max_num_blocks,
6238
                is_spec_decode=bool(self.vllm_config.speculative_config),
6239
                logitsprocs=self.input_batch.logitsprocs,
6240
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
6241
                is_pooling_model=self.is_pooling_model,
6242
6243
            )

6244
6245
6246
6247
6248
6249
6250
6251
6252
        assert self._init_block_sizes == block_sizes, (
            f"InputBatch block_sizes {self._init_block_sizes} != "
            f"kv_cache block_sizes {block_sizes}"
        )
        assert self._init_kernel_block_sizes == kernel_block_sizes, (
            f"InputBatch kernel_block_sizes {self._init_kernel_block_sizes} "
            f"!= kv_cache kernel_block_sizes {kernel_block_sizes}"
        )

6253
    def _allocate_kv_cache_tensors(
6254
6255
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
6256
        """
6257
6258
6259
        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.

6260
        Args:
6261
            kv_cache_config: The KV cache config
6262
        Returns:
6263
            dict[str, torch.Tensor]: A map between layer names to their
6264
            corresponding memory buffer for KV cache.
6265
        """
6266
6267
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
6268
6269
6270
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
6271
6272
6273
6274
6275
            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:
6276
6277
6278
6279
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
6280
6281
6282
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
6283
6284
        return kv_cache_raw_tensors

6285
6286
6287
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

6288
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
6289
6290
        if not self.kv_cache_config.kv_cache_groups:
            return
6291
6292
        for attn_groups in self.attn_groups:
            yield from attn_groups
6293

6294
6295
6296
6297
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
6298
        kernel_block_sizes: list[int],
6299
    ) -> dict[str, torch.Tensor]:
6300
        """
6301
        Reshape the KV cache tensors to the desired shape and dtype.
6302

6303
        Args:
6304
6305
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
6306
                correct size but uninitialized shape.
6307
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6308
        Returns:
6309
            Dict[str, torch.Tensor]: A map between layer names to their
6310
6311
            corresponding memory buffer for KV cache.
        """
6312
        kv_caches: dict[str, torch.Tensor] = {}
6313
        has_attn, has_mamba = False, False
6314
6315
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
6316
            attn_backend = group.backend
6317
6318
6319
6320
            if group.kv_cache_group_id == len(kernel_block_sizes):
                # There may be a last group for layers without kv cache.
                continue
            kernel_block_size = kernel_block_sizes[group.kv_cache_group_id]
6321
            for layer_name in group.layer_names:
6322
6323
                if layer_name in self.runner_only_attn_layers:
                    continue
6324
6325
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
6326
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
6327
                if isinstance(kv_cache_spec, AttentionSpec):
6328
                    has_attn = True
6329
6330
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
6331
6332
6333
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

6334
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
6335
                        kernel_num_blocks,
6336
                        kernel_block_size,
6337
6338
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
6339
6340
                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
6341
                    dtype = kv_cache_spec.dtype
6342
                    try:
6343
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
6344
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
6345
                    except (AttributeError, NotImplementedError):
6346
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
6347
6348
6349
6350
6351
                    # 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.
6352
6353
6354
                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
6355
6356
6357
6358
6359
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
6360
6361
6362
6363
6364
6365
                    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
6366
                elif isinstance(kv_cache_spec, MambaSpec):
6367
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
6368
6369
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
6370
                    storage_offset_bytes = 0
6371
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
6372
6373
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
6374
6375
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
6376
                        target_shape = (num_blocks, *shape)
6377
6378
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
6379
                        assert storage_offset_bytes % dtype_size == 0
6380
6381
6382
6383
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
6384
                            storage_offset=storage_offset_bytes // dtype_size,
6385
                        )
Chen Zhang's avatar
Chen Zhang committed
6386
                        state_tensors.append(tensor)
6387
                        storage_offset_bytes += stride[0] * dtype_size
6388
6389

                    kv_caches[layer_name] = state_tensors
6390
                else:
6391
                    raise NotImplementedError
6392
6393

        if has_attn and has_mamba:
6394
            self._update_hybrid_attention_mamba_layout(kv_caches)
6395

6396
6397
        return kv_caches

6398
    def _update_hybrid_attention_mamba_layout(
6399
6400
        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
6401
        """
6402
6403
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
6404
6405

        Args:
6406
            kv_caches: The KV cache buffer of each layer.
6407
6408
        """

6409
6410
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
6411
            for layer_name in group.layer_names:
6412
                kv_cache = kv_caches[layer_name]
6413
6414
6415
6416
                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 "
6417
                        f"a tensor of shape {kv_cache.shape}"
6418
                    )
6419
                    hidden_size = kv_cache.shape[2:].numel()
6420
6421
6422
6423
                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
6424

6425
    def initialize_kv_cache_tensors(
6426
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
6427
    ) -> dict[str, torch.Tensor]:
6428
6429
6430
6431
6432
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
6433
6434
            kernel_block_sizes: The kernel block sizes for each KV cache group.

6435
        Returns:
6436
            Dict[str, torch.Tensor]: A map between layer names to their
6437
6438
            corresponding memory buffer for KV cache.
        """
6439
6440
6441
6442
6443
6444
6445
6446
6447
6448
6449
6450
6451
6452
6453
6454
6455
6456
6457
6458
6459
6460
6461
6462

        # Try creating KV caches optimized for kv-connector transfers
        cache_dtype = self.cache_config.cache_dtype
        if self.use_uniform_kv_cache(self.attn_groups, cache_dtype):
            kv_caches, cross_layers_kv_cache, attn_backend = (
                self.allocate_uniform_kv_caches(
                    kv_cache_config,
                    self.attn_groups,
                    cache_dtype,
                    self.device,
                    kernel_block_sizes,
                )
            )
            self.cross_layers_kv_cache = cross_layers_kv_cache
            self.cross_layers_attn_backend = attn_backend
        else:
            # Fallback to the general case
            # 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, kernel_block_sizes
            )
6463

6464
        # Set up cross-layer KV cache sharing
6465
6466
        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)
6467
6468
            kv_caches[layer_name] = kv_caches[target_layer_name]

6469
6470
6471
6472
6473
6474
6475
6476
6477
        num_attn_module = (
            2 if self.model_config.hf_config.model_type == "longcat_flash" else 1
        )
        bind_kv_cache(
            kv_caches,
            self.compilation_config.static_forward_context,
            self.kv_caches,
            num_attn_module,
        )
6478
6479
6480
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
6481
6482
        self, kv_cache_config: KVCacheConfig
    ) -> None:
6483
6484
6485
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
6497
6498
6499
6500
        """
        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.
6501
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
6502
6503
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
6504
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
6505
6506
                else:
                    break
6507

6508
6509
6510
6511
6512
6513
6514
    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
        """
6515
        kv_cache_config = deepcopy(kv_cache_config)
6516
        self.kv_cache_config = kv_cache_config
6517
        self._mamba_copy_bufs = None
6518
        self.may_add_encoder_only_layers_to_kv_cache_config()
6519
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
6520
        self.initialize_attn_backend(kv_cache_config)
6521
6522
6523
6524
6525
        # The kernel block size for all KV cache groups. For example, if
        # kv_cache_manager uses block_size 256 for a given group, but the attention
        # backends for that group only supports block_size 64, we will return
        # kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
        # tokens each.
6526
6527
6528
        kernel_block_sizes = prepare_kernel_block_sizes(
            kv_cache_config, self.attn_groups
        )
6529
        self._kernel_block_sizes = kernel_block_sizes
6530
6531
6532
6533

        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

6534
        # Reinitialize need to after initialize_attn_backend
6535
6536
6537
6538
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
6539

6540
6541
6542
        if (
            self.speculative_config
            and self.speculative_config.uses_extract_hidden_states()
6543
        ):
6544
            assert isinstance(self.drafter, ExtractHiddenStatesProposer)
6545
6546
6547
6548
            # 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
6549
        if has_kv_transfer_group():
6550
            kv_transfer_group = get_kv_transfer_group()
6551
6552
6553
6554
6555
6556
6557
            if self.cross_layers_kv_cache is not None:
                assert self.cross_layers_attn_backend is not None
                kv_transfer_group.register_cross_layers_kv_cache(
                    self.cross_layers_kv_cache, self.cross_layers_attn_backend
                )
            else:
                kv_transfer_group.register_kv_caches(kv_caches)
6558
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
6559

6560
6561
6562
6563
6564
6565
    def _get_attention_kv_cache_gid(self) -> int:
        """Find the KV cache group index for attention layers."""
        for gid, group in enumerate(self.kv_cache_config.kv_cache_groups):
            if isinstance(group.kv_cache_spec, AttentionSpec):
                return gid
        return 0
6566
6567
6568
6569
6570
6571
6572

    def init_routed_experts_capturer(self):
        logger.info(
            "Initializing routed experts capturer, enable_return_routed_experts: %s",
            self.model_config.enable_return_routed_experts,
        )
        routed_experts_capturer = RoutedExpertsCapturer.create()
6573
6574
6575
6576
6577
6578
6579
6580
        self.routed_experts_attn_gid = self._get_attention_kv_cache_gid()
        min_block_size = min(
            [
                group.kv_cache_spec.block_size
                for group in self.kv_cache_config.kv_cache_groups
            ]
        )
        num_groups = len(self.kv_cache_config.kv_cache_groups)
6581
        self.max_num_kv_tokens = (
6582
6583
6584
6585
6586
6587
6588
            self.kv_cache_config.num_blocks // num_groups
        ) * min_block_size
        dcp_size = self.vllm_config.parallel_config.decode_context_parallel_size
        pcp_size = self.vllm_config.parallel_config.prefill_context_parallel_size
        if pcp_size * dcp_size > 1:
            self.max_num_kv_tokens *= pcp_size * dcp_size

6589
6590
6591
        routed_experts_capturer.init_buffer(
            max_num_batched_tokens=self.scheduler_config.max_num_batched_tokens,
            max_num_kv_tokens=self.max_num_kv_tokens,
6592
            vllm_config=self.vllm_config,
6593
        )
6594
        self._bind_routed_experts_capturer(routed_experts_capturer)
6595
        self.routed_experts_initialized = True
6596
6597
6598
6599
6600
6601
6602
6603
6604
6605
6606
6607
6608
6609
6610

    def _bind_routed_experts_capturer(self, capturer: RoutedExpertsCapturer) -> None:
        from vllm.model_executor.layers.fused_moe.layer import FusedMoE
        from vllm.model_executor.layers.fused_moe.router.base_router import (
            BaseRouter,
        )

        for module in self.compilation_config.static_forward_context.values():
            if isinstance(module, FusedMoE) and isinstance(module.router, BaseRouter):
                layer_id = module.layer_id

                def _capture_fn(topk_ids, _layer_id=layer_id, _capturer=capturer):
                    _capturer.capture(_layer_id, topk_ids)

                module.router.set_capture_fn(_capture_fn)
6611

6612
6613
6614
6615
6616
    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
6617
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
6618
6619
6620
        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:
6621
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
6622
6623
6624
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
6625
6626
                    dtype=self.kv_cache_dtype,
                )
6627
6628
6629
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
6630
6631
6632
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
6633
6634
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
6635
6636
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
6637

6638
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
6639
        """
6640
        Generates the KVCacheSpec by parsing the kv cache format from each
6641
6642
        Attention module in the static forward context.
        Returns:
6643
            KVCacheSpec: A dictionary mapping layer names to their KV cache
6644
6645
            format. Layers that do not need KV cache are not included.
        """
6646
        if has_ec_transfer() and not get_ec_transfer().is_consumer:
6647
            return {}
6648
        kv_cache_spec: dict[str, KVCacheSpec] = {}
6649
6650
        layer_type = cast(type[Any], AttentionLayerBase)
        attn_layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
Chen Zhang's avatar
Chen Zhang committed
6651
        for layer_name, attn_module in attn_layers.items():
6652
6653
6654
6655
6656
6657
6658
6659
6660
6661
6662
6663
6664
6665
6666
            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # 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
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
6667

6668
        return kv_cache_spec
6669

6670
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
6671
6672
6673
6674
6675
6676
6677
6678
        # 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.
6679
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
6680
6681
6682
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
6683
        return pinned.tolist()
6684
6685
6686
6687
6688
6689
6690
6691
6692
6693
6694
6695
6696
6697
6698
6699
6700
6701
6702
6703
6704
6705
6706
6707
6708
6709
6710
6711
6712
6713
6714
6715
6716
6717
6718
6719
6720
6721
6722
6723

    def get_encoder_timing_stats(self) -> dict[str, dict[str, float | int]]:
        """
        Get encoder timing stats for all requests and clear the registry.

        Returns:
            Dictionary mapping request_id to stats dict.
        """
        with self._encoder_timing_lock:
            stats = {
                req_id: stats_obj.to_dict()
                for req_id, stats_obj in self.encoder_timing_registry.items()
            }
            self.encoder_timing_registry.clear()
            return stats

    @contextmanager
    def timed_encoder_operation(
        self,
        should_time: bool,
        group_lora_refs: list[tuple[str, Any]],
        current_item_idx: int,
        num_items: int,
    ):
        """
        Context manager to time encoder forward operations.

        Args:
            should_time: Whether timing is enabled
            group_lora_refs: Full list of (request_id, pos_info) tuples
            current_item_idx: Starting index for this group
            num_items: Number of items in this group
        """
        if not should_time:
            yield
            return

        group_refs = group_lora_refs[current_item_idx : current_item_idx + num_items]
        group_request_ids = {req_id for req_id, _ in group_refs}

6724
        torch.accelerator.synchronize()
6725
6726
6727
6728
6729
        start_time = time.perf_counter()

        try:
            yield
        finally:
6730
            torch.accelerator.synchronize()
6731
6732
6733
6734
6735
6736
6737
6738
6739
6740
            elapsed = time.perf_counter() - start_time

            per_request_time = elapsed / max(len(group_request_ids), 1)

            with self._encoder_timing_lock:
                for req_id in group_request_ids:
                    if req_id not in self.encoder_timing_registry:
                        self.encoder_timing_registry[req_id] = EncoderTimingStats()

                    stats = self.encoder_timing_registry[req_id]
6741
                    stats.encoder_forward_secs += per_request_time
6742
6743
6744
6745
6746
6747
6748
                    stats.num_encoder_calls += 1


@dataclass
class EncoderTimingStats:
    """Per-request timing statistics for encoder forward pass."""

6749
    encoder_forward_secs: float = 0.0
6750
6751
6752
6753
6754
6755
6756
    """Time spent in vision encoder forward pass (seconds)."""

    num_encoder_calls: int = 0
    """Number of times encoder was called for this request."""

    def to_dict(self) -> dict[str, float | int]:
        return {
6757
            "encoder_forward_secs": self.encoder_forward_secs,
6758
6759
            "num_encoder_calls": self.num_encoder_calls,
        }