"vscode:/vscode.git/clone" did not exist on "bf0d97d78619b290ed273199ad3800b57b638603"
gpu_model_runner.py 304 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 Callable, 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
    NULL_BLOCK_ID,
126
    create_fast_prefill_custom_backend,
127
    get_dcp_local_seq_lens,
128
129
    reorder_batch_to_split_decodes_and_prefills,
)
130
from vllm.v1.core.sched.output import NewRequestData
131
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
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,
149
    ECConnectorOutput,
150
    KVConnectorOutput,
151
152
153
154
155
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
156
    make_empty_encoder_model_runner_output,
157
)
158
from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
159
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
160
from vllm.v1.sample.logits_processor.interface import LogitsProcessor
161
from vllm.v1.sample.metadata import SamplingMetadata
162
from vllm.v1.sample.rejection_sampler import RejectionSampler
163
from vllm.v1.sample.sampler import Sampler
164
from vllm.v1.spec_decode.dflash import DFlashProposer
165
from vllm.v1.spec_decode.draft_model import DraftModelProposer
166
from vllm.v1.spec_decode.eagle import EagleProposer
167
from vllm.v1.spec_decode.extract_hidden_states import ExtractHiddenStatesProposer
168
from vllm.v1.spec_decode.medusa import MedusaProposer
169
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
170
171
172
173
174
175
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,
)
176
from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
177
from vllm.v1.spec_decode.utils import update_num_computed_tokens_for_batch_change
178
from vllm.v1.structured_output.utils import apply_grammar_bitmask
179
from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
180
181
182
183
184
from vllm.v1.worker import mamba_utils
from vllm.v1.worker.cp_utils import (
    check_attention_cp_compatibility,
    get_total_cp_world_size,
)
185
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
186
from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
187
from vllm.v1.worker.gpu.pool.late_interaction_runner import LateInteractionRunner
188
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
189
from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
190
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
191
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
192
193
194
from vllm.v1.worker.ubatch_utils import (
    UBatchSlices,
    check_ubatch_thresholds,
195
    maybe_create_ubatch_slices,
196
    split_attn_metadata,
197
)
198
from vllm.v1.worker.utils import is_residual_scattered_for_sp
199
from vllm.v1.worker.workspace import lock_workspace
200

201
202
from .utils import (
    AttentionGroup,
203
    KVBlockZeroer,
204
205
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
206
    prepare_kernel_block_sizes,
207
208
    sanity_check_mm_encoder_outputs,
)
209

210
if TYPE_CHECKING:
211
    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
212
    from vllm.v1.spec_decode.ngram_proposer import NgramProposer
213
    from vllm.v1.worker.gpu.mm.encoder_cudagraph import EncoderCudaGraphManager
214
215
216

logger = init_logger(__name__)

217
218
AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
219
PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
220

221

222
223
224
225
226
227
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
228
        logprobs_tensors: LogprobsTensors | None,
229
230
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
231
        vocab_size: int,
232
233
234
235
236
    ):
        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.
237
        self.async_copy_ready_event = torch.Event()
238
239
240
241

        # 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
242
        self.vocab_size = vocab_size
243
        self._logprobs_tensors = logprobs_tensors
244
245
246
247
248

        # 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)
249
            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
250
251
                "cpu", non_blocking=True
            )
252
253
254
255
256
            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
257
            self.async_copy_ready_event.record()
258
259
260

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
261

262
263
        This function blocks until the copy is finished.
        """
264
        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
265
        self.async_copy_ready_event.synchronize()
266

267
268
        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
269
        del self._sampled_token_ids
270
        if max_gen_len == 1:
271
            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
272
273
            for i in self._invalid_req_indices:
                valid_sampled_token_ids[i].clear()
274
275
276
            logprobs_lists = None
            if self._logprobs_tensors_cpu is not None:
                logprobs_lists = self._logprobs_tensors_cpu.tolists()
277
        else:
278
            valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
279
280
                self.sampled_token_ids_cpu,
                self.vocab_size,
281
                self._invalid_req_indices,
282
                logprobs_tensors=self._logprobs_tensors_cpu,
283
            )
284
285
286

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
287
        output.logprobs = logprobs_lists
288
289
290
        return output


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
332
333
334
335
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


336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
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)
357
358
359
            self._model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
                raw_pooler_output=self._raw_pooler_output,
                finished_mask=finished_mask,
360
361
362
363
364
365
366
367
368
369
370
371
372
373
            )
            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


374
375
376
377
378
379
380
381
382
383
384
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
385
    ec_connector_output: ECConnectorOutput | None
386
    cudagraph_stats: CUDAGraphStat | None
387
    slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None
388
389


390
391
392
class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
393
394
    def __init__(
        self,
395
        vllm_config: VllmConfig,
396
        device: torch.device,
397
    ):
398
399
400
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
401
        self.offload_config = vllm_config.offload_config
402
        self.compilation_config = vllm_config.compilation_config
403
404
405
406
407
408
        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
409

410
411
412
413
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
414
        self.device = device
415
416
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
417

418
419
420
        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
421

422
        self.is_pooling_model = model_config.runner_type == "pooling"
423
        self.enable_prompt_embeds = model_config.enable_prompt_embeds
424
        self.is_multimodal_raw_input_only_model = (
425
426
            model_config.is_multimodal_raw_input_only_model
        )
427
        # These will be overridden in load_model()
428
        self.is_multimodal_pruning_enabled = False
429
        self.requires_sequential_video_encoding = False
430
431
432
        # Set to True after init_routed_experts_capturer() completes.
        # Prevents routed experts code from running during profiling/dummy run.
        self.routed_experts_initialized = False
433
        self.max_model_len = model_config.max_model_len
434
435
436

        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
437
        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
438
        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
439
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
440
        self.max_num_reqs = scheduler_config.max_num_seqs
441

442
443
        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
444
        # TODO: Support overlapping micro-batches
445
446
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
447
            self.parallel_config.distributed_executor_backend == "external_launcher"
448
            and len(get_pp_group().ranks) > 1
449
        )
450

451
        # Model-related.
452
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
453
        self.inputs_embeds_size = model_config.get_inputs_embeds_size()
454
        self.attention_chunk_size = model_config.attention_chunk_size
455
        # Only relevant for models using ALiBi (e.g, MPT)
456
        self.use_alibi = model_config.uses_alibi
457

458
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
459
        self.is_mm_prefix_lm = self.model_config.is_mm_prefix_lm
460

461
        # Multi-modal data support
462
        self.mm_registry = MULTIMODAL_REGISTRY
463
        self.uses_mrope = model_config.uses_mrope
464
        self.uses_xdrope_dim = model_config.uses_xdrope_dim
465
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
466
            model_config
467
        )
468

469
470
471
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
472
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
473
474
475
        else:
            self.max_encoder_len = 0

476
477
478
        # Async scheduling
        self.use_async_scheduling = self.scheduler_config.async_scheduling

479
        # Sampler
480
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
481

482
        self.eplb_state: EplbState | None = None
483
484
        # NOTE(yongji): flag to temporarily disable EPLB during scaling up/down
        self.eep_eplb_suppressed = False
485
486
487
488
489
490
        """
        State of the expert parallelism load balancer.

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

491
        # Lazy initializations
492
        # self.model: nn.Module  # Set after load_model
493
        # Initialize in initialize_kv_cache
494
        self.kv_caches: list[torch.Tensor] = []
495
496
497
        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
498
499
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
500
501
        # self.kv_cache_config: KVCacheConfig

502
503
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
504
        self.late_interaction_runner = LateInteractionRunner()
505

506
507
508
        # Encoder CUDA graph manager (initialized after model load if enabled)
        self.encoder_cudagraph_manager: EncoderCudaGraphManager | None = None

509
        self.use_aux_hidden_state_outputs = False
510
511
512
513
514
        # 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:
515
            self.drafter: (
516
                NgramProposer  # noqa: F823
517
                | NgramProposerGPU
518
519
                | SuffixDecodingProposer
                | EagleProposer
520
                | DFlashProposer
521
522
                | DraftModelProposer
                | MedusaProposer
523
                | ExtractHiddenStatesProposer
524
            )
525
            if self.speculative_config.method == "ngram":
526
527
                from vllm.v1.spec_decode.ngram_proposer import NgramProposer

528
                self.drafter = NgramProposer(self.vllm_config)
529
530
531
532
533
534
            elif self.speculative_config.uses_draft_model():
                self.drafter = DraftModelProposer(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    runner=self,
                )
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
            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
                )
552
553
554
            elif self.speculative_config.use_dflash():
                self.drafter = DFlashProposer(self.vllm_config, self.device, self)
                self.use_aux_hidden_state_outputs = True
555
556
            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
557
            elif self.speculative_config.use_eagle():
558
                self.drafter = EagleProposer(self.vllm_config, self.device, self)
559
                if self.speculative_config.method == "eagle3":
560
561
562
                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
563
564
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
565
                    vllm_config=self.vllm_config, device=self.device
566
                )
567
568
569
570
571
            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
572
            else:
573
574
575
576
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
577
            self.rejection_sampler = RejectionSampler(self.sampler)
578

579
        self.num_spec_tokens = 0
580
        self.valid_sampled_token_count_gpu: torch.Tensor | None = None
581
582
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens
583
584
585
586
587
            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
588
589
590
        self.use_async_spec_decode = (
            self.use_async_scheduling and self.num_spec_tokens > 0
        )
591

592
        # Request states.
593
        self.requests: dict[str, CachedRequestState] = {}
594
595
596
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
597
        self.comm_stream = torch.cuda.Stream()
598

599
600
601
602
603
604
605
606
607
        # 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.
608
609
610
611
        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
612
613
614
615
616
        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]
617
618
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
619
            # We need to use the encoder length for encoder-decoder
620
621
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
622
623
624
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
625
            vocab_size=self.model_config.get_vocab_size(),
626
627
            block_sizes=[placeholder_block_size],
            kernel_block_sizes=[placeholder_block_size],
628
            is_spec_decode=bool(self.vllm_config.speculative_config),
629
            logitsprocs=build_logitsprocs(
630
631
632
                self.vllm_config,
                self.device,
                self.pin_memory,
633
                self.is_pooling_model,
634
                custom_logitsprocs,
635
            ),
636
637
            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
638
639
640
641
            # ThinkingTokenBudgetLogitsProcessor also needs output token ids to
            # correctly track think start/end token sequences in async scheduling.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs)
            or self.vllm_config.reasoning_config is not None,
642
            is_pooling_model=self.is_pooling_model,
643
            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
644
        )
645

646
647
648
649
650
        # 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.
651
        self.prepare_inputs_event: torch.Event | None = None
652
653
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
654
            self.prepare_inputs_event = torch.Event()
655

656
657
658
659
660
661
662
663
664
665
666
        # 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 = []

667
        # Cache the device properties.
668
        self._init_device_properties()
669

670
671
672
673
        # Encoder timing registry for observability
        self.encoder_timing_registry: dict[str, EncoderTimingStats] = {}
        self._encoder_timing_lock = threading.Lock()

674
        # Persistent buffers for CUDA graphs.
675
        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
676
677
678
        self.positions = torch.zeros(
            self.max_num_tokens, dtype=torch.int64, device=self.device
        )
679
680
681
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
        self.seq_lens = torch.zeros(
            self.max_num_reqs, dtype=torch.int32, device=self.device
        )
        self.optimistic_seq_lens_cpu = torch.zeros(
            self.max_num_reqs, dtype=torch.int32, pin_memory=self.pin_memory
        )
        self.num_computed_tokens = torch.zeros(
            self.max_num_reqs, dtype=torch.int32, device=self.device
        )
        self.prev_num_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.req_indices = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        # Maps current batch position -> previous batch position (-1 for new reqs)
        self.prev_positions = self._make_buffer(self.max_num_reqs, dtype=torch.int64)
        self.num_scheduled_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )

701
        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
702
703
704
705
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
706
707
708
        # 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.
709
        self.inputs_embeds = self._make_buffer(
710
            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
711
712
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
713
714
        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
715
716
717
718
719
        )
        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
720
            self.max_num_reqs, dtype=torch.int32
721
        )
722

723
724
        # Only relevant for multimodal models
        if self.supports_mm_inputs:
725
726
727
728
729
730
731
            # 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
732

733
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
734
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
735
736
737
738
            # 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
739
740
741
742
743
744

            # 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
745
            self.mrope_positions = self._make_buffer(
746
747
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
748

749
750
751
752
753
754
755
        # 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
            )

756
        # None in the first PP rank. The rest are set after load_model.
757
        self.intermediate_tensors: IntermediateTensors | None = None
758

759
760
761
762
763
764
765
766
        # OPTIMIZATION: Cache the arange tensors rather than creating them
        # every step. Keep in int64 to avoid overflow with long context.
        # - arange_np: immutable [0, 1, 2, ...] used as source for batched computation
        # - query_pos: CpuGpuBuffer for the computed batched arange result
        arange_size = max(self.max_num_reqs + 1, self.max_num_tokens)
        self.arange_np = np.arange(arange_size, dtype=np.int64)
        self.query_pos = self._make_buffer(arange_size, dtype=torch.int64)
        self._arange_scratch = np.empty(arange_size, dtype=np.int64)
767

768
769
770
771
772
        # 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] = {}
773
774
775
776
777
        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(
778
779
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
780

781
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
782
783
784
785

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

786
        self.mm_budget = (
787
            MultiModalBudget(self.vllm_config, self.mm_registry)
788
789
790
            if self.supports_mm_inputs
            else None
        )
791

792
        self.reorder_batch_threshold: int | None = None
793

794
795
796
797
798
        # 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()

799
        # Cached outputs.
800
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
        # 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()

816
        self._draft_token_req_ids: list[str] | None = None
817
        self.transfer_event = torch.Event()
818
        self.sampled_token_ids_pinned_cpu = torch.empty(
819
            (self.max_num_reqs, 1),
820
821
            dtype=torch.int64,
            device="cpu",
822
823
            pin_memory=self.pin_memory,
        )
824

825
826
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
827
        self.valid_sampled_token_count_event: torch.Event | None = None
828
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
829
830
831
832
833
834
        # 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
835
        self.num_accepted_tokens_event: torch.Event | None = None
836
837
        if self.num_spec_tokens:
            self.draft_token_ids_event = torch.Event()
838
            self.num_accepted_tokens_event = torch.Event()
839
840
841
842
843
844
845
846
847
848
849
850
            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,
851
                    dtype=torch.int32,
852
853
854
                    device="cpu",
                    pin_memory=self.pin_memory,
                )
855

856
857
858
859
        # Model weight offloader
        # Make sure this is called before any get_offloader call
        set_offloader(create_offloader(self.offload_config))

860
861
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
862
        self.kv_connector_output: KVConnectorOutput | None = None
863
        self.mamba_state_idx: dict[str, int] = {}
864
        self._mamba_copy_bufs: mamba_utils.MambaCopyBuffers | None = None
865
        self.layerwise_nvtx_hooks_registered = False
866

867
868
869
870
871
872
873
    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

874
    def reset_mm_cache(self) -> None:
875
876
877
878
        """
        Clear the multi-modal cache that was used during profiling,
        but no longer needed during inference.
        """
879
880
        if self.mm_budget:
            self.mm_budget.reset_cache()
881
        self.late_interaction_runner.clear()
882

883
884
885
886
887
888
889
    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()
890
        self.late_interaction_runner.clear()
891

892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
    @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)

936
937
938
939
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
940
941
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
942
            return self.positions[:num_tokens]
943
944
945
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
946
947
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
948
            return self.positions[num_tokens]
949

950
    def _make_buffer(
951
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
952
953
954
955
956
957
958
959
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
960

961
962
963
964
965
966
967
968
969
970
    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

971
    def _init_model_kwargs(self):
972
973
        model_kwargs = dict[str, Any]()

974
        if not self.is_pooling_model:
975
976
            return model_kwargs

977
978
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
979
980
981

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
982
983
984
985
986
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
987
988
989
990
991
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

992
        seq_lens = self.seq_lens[:num_reqs]
993
994
995
996
997
998
999
1000
        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(
1001
1002
            device=self.device
        )
1003
1004
        return model_kwargs

1005
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
1006
1007
        """
        Update the order of requests in the batch based on the attention
1008
        backend's needs. For example, some attention backends (namely MLA) may
1009
1010
1011
1012
1013
1014
        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
1015
        # Attention free models have zero kv_cache_groups, however models
1016
1017
1018
1019
1020
1021
1022
        # 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

1023
1024
1025
1026
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
1027
1028
                decode_threshold=self.reorder_batch_threshold,
            )
1029

1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
    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)

1050
1051
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
1052
        """Initialize attributes from torch.cuda.get_device_properties"""
1053
1054

        self.num_sms = num_compute_units(self.device.index)
1055
1056
1057

    # Note: used for model runner override.
    def _sync_device(self) -> None:
1058
        torch.accelerator.synchronize()
1059

1060
    def _update_states(self, scheduler_output: "SchedulerOutput") -> Callable | None:
1061
1062
1063
1064
1065
1066
        """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.

1067
1068
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
1069
1070
        """
        # Remove finished requests from the cached states.
1071
1072
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
1073
            self.num_prompt_logprobs.pop(req_id, None)
1074
1075
1076
        self.late_interaction_runner.on_requests_finished(
            scheduler_output.finished_req_ids
        )
1077
1078
1079
1080
1081
1082
1083
        # 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:
1084
            self.input_batch.remove_request(req_id)
1085

1086
1087
1088
1089
1090
        # 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)

1091
        # Free the cached encoder outputs.
1092
1093
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
1094

1095
1096
1097
1098
1099
1100
1101
        # 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()
1102
1103
1104
1105
1106
1107
1108
1109
        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)
1110
1111
1112
1113
1114
        # 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:
1115
            self.input_batch.remove_request(req_id)
1116

1117
1118
1119
1120
1121
1122
1123
        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] = []

1124
        reqs_to_add: list[CachedRequestState] = []
1125
1126
        deferred_spec_decode_corrections = []

1127
        # Add new requests to the cached states.
1128
1129
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
1130
1131
1132
1133
1134
1135
            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

1136
            sampling_params = new_req_data.sampling_params
1137
            pooling_params = new_req_data.pooling_params
1138

1139
1140
1141
1142
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
1143
1144
1145
1146
1147
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

1148
1149
            if self.is_pooling_model:
                assert pooling_params is not None
1150
1151
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
1152

1153
                model = cast(VllmModelForPooling, self.get_model())
1154
                to_update = model.pooler.get_pooling_updates(task)
1155
1156
                to_update.apply(pooling_params)

1157
            req_state = CachedRequestState(
1158
                req_id=req_id,
1159
                prompt_token_ids=new_req_data.prompt_token_ids,
1160
                prompt_embeds=new_req_data.prompt_embeds,
1161
                mm_features=new_req_data.mm_features,
1162
                sampling_params=sampling_params,
1163
                pooling_params=pooling_params,
1164
                generator=generator,
1165
1166
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
1167
                output_token_ids=[],
1168
                lora_request=new_req_data.lora_request,
1169
            )
1170
            self.requests[req_id] = req_state
1171
            self.late_interaction_runner.register_request(req_id, pooling_params)
1172

1173
1174
1175
1176
1177
1178
1179
            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
                )

1180
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1181
            if self.uses_mrope:
1182
                self._init_mrope_positions(req_state)
1183

1184
1185
1186
1187
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

1188
            reqs_to_add.append(req_state)
1189
1190
1191
            # Track new requests for ngram_gpu full tensor copy
            if is_ngram_gpu:
                ngram_gpu_new_reqs.append(req_state)
1192

1193
        # Update the states of the running/resumed requests.
1194
        is_last_rank = get_pp_group().is_last_rank
1195
        req_data = scheduler_output.scheduled_cached_reqs
1196
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1197

1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
        # 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,
            )
1213
1214
        if self.use_async_spec_decode:
            self.prev_num_draft_tokens.np.fill(0)
1215

1216
        for i, req_id in enumerate(req_data.req_ids):
1217
            req_state = self.requests[req_id]
1218
1219
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
1220
            resumed_from_preemption = req_id in req_data.resumed_req_ids
1221
            num_output_tokens = req_data.num_output_tokens[i]
1222
            req_index = self.input_batch.req_id_to_index.get(req_id)
1223

1224
1225
1226
1227
            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:
1228
                # first step: num_computed_tokens = 0, spec_tokens = [],
1229
                # prev_num_draft_len = 0.
Jiayi Yan's avatar
Jiayi Yan committed
1230
                # second step: num_computed_tokens = 100(prompt length),
1231
1232
1233
                # 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.
1234
                # num_computed_tokens in first step and second step doesn't contain
1235
1236
1237
                # 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.
1238
1239
1240
                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
                    # Optimistically assume all accepted; queue up a correction
                    # to be called after the model forward to preserve async
                    # scheduling. Corrected on GPU in _prepare_inputs.
                    optimistic_num_accepted = req_state.prev_num_draft_len
                    req_state.output_token_ids.extend([-1] * optimistic_num_accepted)

                    deferred_spec_decode_corrections.append(
                        (req_id, optimistic_num_accepted, req_state)
                    )

                    prev_req_index = (
                        self.input_batch.prev_req_id_to_index.get(req_id)
                        if self.input_batch.prev_req_id_to_index
                        else None
                    )
                    if prev_req_index is not None:
                        self.prev_num_draft_tokens.np[prev_req_index] = (
                            optimistic_num_accepted
                        )
1260

1261
1262
1263
1264
                    if is_ngram_gpu and optimistic_num_accepted > 0:
                        self.input_batch.num_tokens_no_spec[req_index] += (
                            optimistic_num_accepted
                        )
1265

1266
            # Update the cached states.
1267
            req_state.num_computed_tokens = num_computed_tokens
1268
1269

            if not is_last_rank:
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
                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:]
                        )
1290
1291
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
1292
1293
                # failure, or output_token_ids was inflated by the optimistic
                # extend above (async spec decode). Align the cached state.
1294
1295
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
1296
1297
1298
1299
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
1300
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
1301

1302
            # Update the block IDs.
1303
            if not resumed_from_preemption:
1304
1305
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
1306
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
1307
                        block_ids.extend(new_ids)
1308
            else:
1309
                assert req_index is None
1310
                assert new_block_ids is not None
1311
1312
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
1313
                req_state.block_ids = new_block_ids
1314
1315
1316
1317
1318

            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.
1319
1320
1321
1322
1323
1324
1325

                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:]

1326
                reqs_to_add.append(req_state)
1327
1328
1329
                # Track resumed requests for ngram_gpu full tensor copy
                if is_ngram_gpu:
                    ngram_gpu_new_reqs.append(req_state)
1330
1331
1332
                continue

            # Update the persistent batch.
1333
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
1334
            if new_block_ids is not None:
1335
                self.input_batch.block_table.append_row(new_block_ids, req_index)
1336
1337
1338
1339
1340
1341
1342

            # 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)
1343
                self.input_batch.token_ids_cpu[
1344
1345
1346
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
1347

1348
            # Add spec_token_ids to token_ids_cpu.
1349
            self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
1350
1351
1352
1353
1354
            # 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
1355

1356
1357
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
1358
1359
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1360
            self.input_batch.update_req_spec_token_ids(request, scheduled_spec_tokens)
1361

1362
1363
1364
1365
1366
1367
        # 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()
1368

1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
        # 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,
            )

1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
        if deferred_spec_decode_corrections:

            def correct_spec_decode_token_counts():
                valid_sampled_token_count = self._get_valid_sampled_token_count()
                if not valid_sampled_token_count:
                    return
                prev_req_id_to_index = self.input_batch.prev_req_id_to_index
                if not prev_req_id_to_index:
                    return
                for (
                    req_id,
                    optimistic_num_accepted,
                    req_state,
                ) in deferred_spec_decode_corrections:
                    prev_req_index = prev_req_id_to_index.get(req_id)
                    if prev_req_index is None:
                        continue
                    num_accepted = valid_sampled_token_count[prev_req_index] - 1
                    correction = optimistic_num_accepted - num_accepted
                    req_state.num_computed_tokens -= correction
                    cur_req_index = self.input_batch.req_id_to_index.get(req_id)
                    if cur_req_index is None:
                        continue
                    self.input_batch.num_computed_tokens_cpu[cur_req_index] -= (
                        correction
                    )
                    if is_ngram_gpu and correction > 0:
                        self.input_batch.num_tokens_no_spec[cur_req_index] -= correction
                        self.num_tokens_no_spec_gpu[cur_req_index] -= correction

            return correct_spec_decode_token_counts
        else:
            return None

1415
    def _update_states_after_model_execute(
1416
        self, output_token_ids: torch.Tensor, scheduler_output: "SchedulerOutput"
1417
    ) -> None:
1418
1419
1420
1421
1422
1423
1424
1425
        """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.
        """
1426
        if not self.speculative_config or not self.model_config.is_hybrid:
1427
1428
            return

1429
1430
1431
        # TODO: Remove .cpu() sync to enable fully async for hybrid model;
        # Use num_computed_tokens.gpu instead of req.num_computed_tokens to
        # support aligned mamba cache mode.
1432
        # Find the number of accepted tokens for each sequence.
1433
1434
        num_reqs = output_token_ids.size(0)
        self.num_accepted_tokens.gpu[:num_reqs] = (
1435
1436
1437
1438
1439
            (
                torch.cat(
                    [
                        output_token_ids,
                        torch.full(
1440
                            (num_reqs, 1),
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
                            -1,
                            device=output_token_ids.device,
                        ),
                    ],
                    dim=1,
                )
                == -1
            )
            .int()
            .argmax(-1)
        )
1452

1453
        if self.cache_config.mamba_cache_mode == "align":
1454
1455
1456
1457
            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
1458
1459
1460
1461
1462
1463
1464
1465
            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(),
1466
                self._get_mamba_copy_bufs(),
1467
            )
1468
1469
1470
1471
        else:
            self.input_batch.num_accepted_tokens_cpu_tensor[:num_reqs].copy_(
                self.num_accepted_tokens.gpu[:num_reqs], non_blocking=True
            )
1472
1473
            assert self.num_accepted_tokens_event is not None
            self.num_accepted_tokens_event.record()
1474

1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
    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
1494
        self.late_interaction_runner.register_request(req_id, req_state.pooling_params)
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
        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

1510
    def _init_mrope_positions(self, req_state: CachedRequestState):
1511
1512
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1513
1514
1515
1516
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1517
1518

        req_state.mrope_positions, req_state.mrope_position_delta = (
1519
            mrope_model.get_mrope_input_positions(
1520
                req_state.prompt_token_ids,
1521
                req_state.mm_features,
1522
            )
1523
        )
1524

1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
    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,
        )

1538
    def _extract_mm_kwargs(
1539
        self,
1540
1541
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1542
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1543
            return {}
1544

1545
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
1546
        for req in scheduler_output.scheduled_new_reqs:
1547
1548
            for feature in req.mm_features:
                if feature.data is not None:
1549
                    mm_kwargs.append((feature.modality, feature.data))
1550

1551
1552
        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
1553
        for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
1554
1555
1556
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
1557
        ):
1558
            mm_kwargs_combined.update(mm_kwargs_batch)
1559

1560
        return mm_kwargs_combined
1561

1562
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1563
        if not self.is_multimodal_raw_input_only_model:
1564
            return {}
1565

1566
1567
1568
        mm_budget = self.mm_budget
        assert mm_budget is not None

1569
1570
1571
        if not mm_budget.mm_max_toks_per_item:
            return {}  # No tower modalities (embed-only mode)

1572
1573
        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
1574

1575
1576
1577
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1578
        arange_out: np.ndarray,
1579
        cumsum_dtype: np.dtype | None = None,
1580
    ) -> np.ndarray:
1581
        """Get the cumulative sum and batched arange of the given array.
1582
1583
1584
1585
        E.g., [2, 5, 3] -> [2, 7, 10], arange written to
        arange_out[:10] as [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])
1586
1587
1588
1589
1590
1591
1592
        """
        # 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]
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
        np.subtract(
            self.arange_np[:total_num_tokens],
            cumsums_offsets,
            out=arange_out[:total_num_tokens],
        )

        return cu_num_tokens

    def _compute_prev_positions(self, num_reqs: int) -> None:
        """Build prev_positions mapping: current pos -> previous pos (-1 if new).

        Populates self.prev_positions.np[:num_reqs] with the mapping.
        """
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        prev_positions = self.prev_positions.np[:num_reqs]

        if not prev_req_id_to_index:
            prev_positions.fill(-1)
            return
1612

1613
1614
        for i, req_id in enumerate(self.input_batch.req_ids[:num_reqs]):
            prev_positions[i] = prev_req_id_to_index.get(req_id, -1)
1615

1616
    def _prepare_input_ids(
1617
1618
        self,
        scheduler_output: "SchedulerOutput",
1619
        num_reqs: int,
1620
1621
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1622
    ) -> None:
1623
        """Prepare the input IDs for the current batch.
1624

1625
1626
        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
1627
1628
1629
1630
1631
        GPU need to be copied into the corresponding slots into input_ids.

        Uses self.prev_positions[:num_reqs] which maps current pos -> prev pos
        (-1 for new requests).
        """
1632
1633
1634
1635

        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1636
1637
1638
            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)
1639
1640
1641
1642
1643
            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.
1644
1645
        prev_positions = self.prev_positions.np[:num_reqs]
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1646
1647
1648
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1649
1650
        prev_indices: list[int] = []
        common_indices_match = True
1651
        max_flattened_index = -1
1652
1653
        total_num_spec_tokens = 0

1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
        for cur_index in range(num_reqs):
            prev_index = prev_positions[cur_index]
            if prev_index < 0:
                continue
            prev_indices.append(prev_index)
            req_id = self.input_batch.req_ids[cur_index]
            # We need to compute the flattened input_ids index of the
            # last token in each common request.
            draft_len = len(scheduled_spec_tokens.get(req_id, ()))
            total_num_spec_tokens += draft_len
            flattened_index = cu_num_tokens[cur_index].item() - 1
            # 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))
            common_indices_match &= prev_index == flattened_index
            max_flattened_index = max(max_flattened_index, flattened_index)

Jiayi Yan's avatar
Jiayi Yan committed
1683
        num_common_tokens = len(sample_flattened_indices)
1684
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
Jiayi Yan's avatar
Jiayi Yan committed
1685
        if num_common_tokens < total_without_spec:
1686
            # If not all requests are decodes from the last iteration,
1687
            # we need to copy the input_ids_cpu to the GPU first.
1688
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1689
1690
1691
            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
1692
        if num_common_tokens == 0:
1693
            # No requests in common with the previous iteration
1694
            # So input_ids.cpu will have all the input ids.
1695
            return
1696
        if common_indices_match and max_flattened_index == (num_common_tokens - 1):
1697
1698
1699
1700
            # 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
1701
1702
            self.input_ids.gpu[:num_common_tokens].copy_(
                self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0],
1703
1704
                non_blocking=True,
            )
1705
            if self.enable_prompt_embeds:
Jiayi Yan's avatar
Jiayi Yan committed
1706
                self.is_token_ids.gpu[:num_common_tokens] = True
1707
            return
1708
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1709
1710
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1711
        ).to(self.device, non_blocking=True)
1712
        prev_common_req_indices_tensor = torch.tensor(
1713
            prev_indices, dtype=torch.int64, pin_memory=self.pin_memory
1714
        ).to(self.device, non_blocking=True)
1715
1716
        self.input_ids.gpu.scatter_(
            dim=0,
1717
            index=sampled_tokens_index_tensor,
1718
            src=self.input_batch.prev_sampled_token_ids[
1719
1720
1721
                prev_common_req_indices_tensor, 0
            ],
        )
1722

1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
        # 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],
        )

1745
1746
    def _get_encoder_seq_lens(
        self,
1747
        num_scheduled_tokens: dict[str, int],
1748
1749
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1750
        for_cudagraph_capture: bool = False,
1751
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1752
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1753
            return None, None
1754

1755
1756
        # Zero out buffer for padding requests that are not actually scheduled (CGs)
        self.encoder_seq_lens.np[:num_reqs] = 0
1757

1758
1759
        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1760
        for req_id in num_scheduled_tokens:
1761
            req_index = self.input_batch.req_id_to_index[req_id]
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
            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
1774
1775
1776
1777
1778
1779
1780
1781
1782
        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
1783
1784
1785
1786

        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]
1787

1788
        return encoder_seq_lens, encoder_seq_lens_cpu
1789

1790
    def _prepare_inputs(
1791
1792
1793
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1794
1795
    ) -> tuple[
        torch.Tensor,
1796
        SpecDecodeMetadata | None,
1797
    ]:
1798
1799
        """
        :return: tuple[
1800
            logits_indices, spec_decode_metadata,
1801
1802
        ]
        """
1803
1804
1805
1806
1807
1808
1809
        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.
1810
        self.input_batch.block_table.commit_block_table(num_reqs)
1811
1812
1813

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1814
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1815

1816
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
1817
1818
1819
1820
        # self.query_pos.np[:10]: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens = self._get_cumsum_and_arange(
            num_scheduled_tokens, self.query_pos.np
        )
1821
1822

        # Get positions.
1823
1824
1825
        positions_np = (
            self.input_batch.num_computed_tokens_cpu[req_indices]
            + self.query_pos.np[: cu_num_tokens[-1]]
1826
        )
1827

1828
1829
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1830
        if self.uses_mrope:
1831
1832
            self._calc_mrope_positions(scheduler_output)

1833
1834
1835
1836
1837
        # 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)

1838
1839
1840
1841
        # 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.
1842
1843
1844
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1845
        token_indices_tensor = torch.from_numpy(token_indices)
1846

1847
1848
1849
        # 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.
1850
1851
1852
1853
1854
1855
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1856
        if self.enable_prompt_embeds:
1857
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1858
1859
1860
1861
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1862
1863
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896

        # 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:
1897
1898
1899
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1900
1901

                output_idx += num_sched
1902
1903

        # Prepare the attention metadata.
1904
        self.query_start_loc.np[0] = 0
1905
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1906
1907
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1908
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1909
        self.query_start_loc.copy_to_gpu()
1910
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1911

1912
1913
1914
1915
1916
1917
1918
1919
        # Compute optimistic seq_lens (assumes all draft tokens from previous
        # iteration accepted). Store in optimistic_seq_lens_cpu for use by
        # _build_attention_metadata (max_seq_len) and discard_request_mask.
        # seq_lens (GPU) will be computed later using the same optimistic values.
        torch.add(
            self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],
            torch.from_numpy(num_scheduled_tokens),
            out=self.optimistic_seq_lens_cpu[:num_reqs],
1920
        )
1921
1922
1923
1924
1925
1926
        self.optimistic_seq_lens_cpu[num_reqs:].fill_(0)

        # Build prev_positions mapping: current pos -> prev pos (-1 if new).
        # Used for gathering from previous iteration's GPU tensors.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        self._compute_prev_positions(num_reqs)
1927

1928
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1929
1930
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1931
        # Record which requests should not be sampled,
1932
        # so that we could clear the sampled tokens before returning
1933
        self.discard_request_mask.np[:num_reqs] = (
1934
            self.optimistic_seq_lens_cpu[:num_reqs].numpy() < num_tokens_np
1935
        )
1936
        self.discard_request_mask.copy_to_gpu(num_reqs)
1937

1938
1939
1940
1941
        # Sync num_accepted_tokens from CPU (set by
        # _update_states_after_model_execute for hybrid models).
        if self.num_accepted_tokens_event is not None:
            self.num_accepted_tokens_event.synchronize()
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
            # Async mode: condense() reordered indices, use prev_positions mapping
            if self.use_async_scheduling and prev_req_id_to_index:
                prev_idx = self.prev_positions.np[:num_reqs]
                new_mask = prev_idx < 0
                self.num_accepted_tokens.np[:num_reqs] = (
                    self.input_batch.num_accepted_tokens_cpu[
                        np.where(new_mask, 0, prev_idx)
                    ]
                )
                self.num_accepted_tokens.np[:num_reqs][new_mask] = 1
                self.input_batch.num_accepted_tokens_cpu[:num_reqs] = (
                    self.num_accepted_tokens.np[:num_reqs]
                )
            else:
                # Non-async mode: use values directly
                self.num_accepted_tokens.np[:num_reqs] = (
                    self.input_batch.num_accepted_tokens_cpu[:num_reqs]
                )
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
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
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
        else:
            self.num_accepted_tokens.np.fill(1)
            self.num_accepted_tokens.gpu.fill_(1)

        # Update num_computed_tokens on GPU. In async spec decode,
        # CPU values are optimistic (all drafts accepted). The kernel
        # corrects on GPU using the previous step's
        # valid_sampled_token_count_gpu. Otherwise, just copy from CPU.
        if (
            self.use_async_spec_decode
            and self.valid_sampled_token_count_gpu is not None
            and prev_req_id_to_index
        ):
            self.prev_positions.copy_to_gpu(num_reqs)
            self.prev_num_draft_tokens.copy_to_gpu()
            cpu_values = self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs].to(
                device=self.device, non_blocking=True
            )
            update_num_computed_tokens_for_batch_change(
                self.num_computed_tokens,
                self.num_accepted_tokens.gpu[:num_reqs],
                self.prev_positions.gpu[:num_reqs],
                self.valid_sampled_token_count_gpu,
                self.prev_num_draft_tokens.gpu,
                cpu_values,
            )
        else:
            self.num_computed_tokens[:num_reqs].copy_(
                self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],
                non_blocking=True,
            )

        self.req_indices.np[:total_num_scheduled_tokens] = req_indices
        self.req_indices.copy_to_gpu(total_num_scheduled_tokens)
        req_indices_gpu = self.req_indices.gpu[:total_num_scheduled_tokens]

        self.query_pos.copy_to_gpu(total_num_scheduled_tokens)
        self.num_scheduled_tokens.np[:num_reqs] = num_scheduled_tokens
        self.num_scheduled_tokens.copy_to_gpu(num_reqs)
        num_scheduled_tokens_gpu = self.num_scheduled_tokens.gpu[:num_reqs]
        self.positions[:total_num_scheduled_tokens] = (
            self.num_computed_tokens[req_indices_gpu].to(torch.int64)
            + self.query_pos.gpu[:total_num_scheduled_tokens]
        )
        self.seq_lens[:num_reqs] = (
            self.num_computed_tokens[:num_reqs] + num_scheduled_tokens_gpu
        )
        self.seq_lens[num_reqs:].fill_(0)

        self.input_batch.block_table.compute_slot_mapping(
            num_reqs,
            self.query_start_loc.gpu[: num_reqs + 1],
            self.positions[:total_num_scheduled_tokens],
        )

2017
        # Copy the tensors to the GPU.
2018
2019
        self._prepare_input_ids(
            scheduler_output,
2020
            num_reqs,
2021
2022
2023
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
2024

2025
        if self.uses_mrope:
2026
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
2027
2028
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
2029
2030
                non_blocking=True,
            )
2031
2032
2033
2034
2035
2036
        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,
            )
2037
2038
2039
2040
2041
2042
2043
2044
        if self.use_async_spec_decode and (self.uses_mrope or self.uses_xdrope_dim > 0):
            drift = self.num_computed_tokens[req_indices_gpu].to(
                torch.int64
            ) - self.input_batch.num_computed_tokens_cpu_tensor[req_indices].to(
                device=self.device, dtype=torch.int64, non_blocking=True
            )
            target = self.mrope_positions if self.uses_mrope else self.xdrope_positions
            target.gpu[:, :total_num_scheduled_tokens] += drift
2045

2046
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
2047
2048
2049
2050
2051
2052
2053
2054
        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
2055
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
2056
2057
2058
2059
2060
        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)
2061
2062
2063
            # 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)
2064
2065
2066
2067
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
2068
                req_idx = self.input_batch.req_id_to_index[req_id]
2069
2070
                draft_len = len(draft_token_ids)
                num_draft_tokens[req_idx] = draft_len
2071
2072
2073
2074
                if (
                    self.input_batch.num_computed_tokens_cpu[req_idx]
                    >= self.input_batch.num_prompt_tokens[req_idx]
                ):
2075
                    num_decode_draft_tokens[req_idx] = draft_len
2076
            spec_decode_metadata = self._calc_spec_decode_metadata(
2077
2078
                num_draft_tokens, cu_num_tokens
            )
2079
            logits_indices = spec_decode_metadata.logits_indices
2080
            num_sampled_tokens = num_draft_tokens + 1
2081
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
2082
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
2083
2084
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
2085

2086
2087
2088
2089
2090
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
2091
            )
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
2103
        num_tokens: int,
2104
        num_reqs: int,
2105
2106
2107
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
2108
2109
2110
2111
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
2112
        num_scheduled_tokens: dict[str, int] | None = None,
2113
        cascade_attn_prefix_lens: list[list[int]] | None = None,
2114
        slot_mappings: dict[int, torch.Tensor] | None = None,
2115
2116
2117
2118
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
2119
2120
2121
2122
        # Attention metadata is not needed for attention free models
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return {}, None

2123
2124
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs
2125
        assert num_reqs_padded is not None and num_tokens_padded is not None
2126

2127
2128
2129
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
2130

2131
2132
2133
2134
2135
2136
        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:
2137
            max_seq_len = self.optimistic_seq_lens_cpu.numpy()[:num_reqs].max().item()
2138

2139
        kv_cache_groups = self.kv_cache_config.kv_cache_groups
2140

2141
        def _get_block_table(kv_cache_gid: int):
2142
2143
2144
            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):
2145
                blk_table_tensor = torch.zeros(
2146
                    (num_reqs_padded, 1),
2147
                    dtype=torch.int32,
2148
2149
                    device=self.device,
                )
2150
            else:
2151
                blk_table = self.input_batch.block_table[kv_cache_gid]
2152
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
2153

2154
2155
2156
            # Fill unused block table entries with NULL_BLOCK_ID (null block)
            # for CUDAGraph padding. Block 0 is reserved for padding.
            blk_table_tensor[num_reqs:num_reqs_padded].fill_(NULL_BLOCK_ID)
2157
            return blk_table_tensor
2158

2159
2160
2161
        assert slot_mappings is not None
        block_table_gid_0 = _get_block_table(0)
        slot_mapping_gid_0 = slot_mappings[0]
2162

2163
2164
2165
2166
        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()
2167
2168
2169
2170
2171
2172
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs_padded
        ]
        num_prompt_tokens_cpu = self.input_batch.num_prompt_tokens_cpu_tensor[
            :num_reqs_padded
        ]
2173
2174
2175
2176
2177
        seq_lens_cpu = self.optimistic_seq_lens_cpu[:num_reqs_padded]

        # is_prefilling: True if request is still in prefill phase.
        # Used by mamba backends to distinguish actual decodes from
        # short extends.
2178
2179
        is_prefilling = num_computed_tokens_cpu < num_prompt_tokens_cpu

2180
2181
2182
2183
2184
        if self.use_async_spec_decode:
            # GPU tensors are authoritative in async mode.
            seq_lens_cpu = None
            num_computed_tokens_cpu = None

2185
2186
2187
        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],
2188
2189
            seq_lens=self.seq_lens[:num_reqs_padded],
            _seq_lens_cpu=seq_lens_cpu,
2190
            _num_computed_tokens_cpu=num_computed_tokens_cpu,
2191
2192
2193
2194
2195
2196
2197
            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,
2198
            is_prefilling=is_prefilling,
2199
2200
2201
2202
        )

        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
2203
                self.optimistic_seq_lens_cpu[:num_reqs],
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
                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
            )

2222
2223
2224
2225
2226
2227
2228
2229
2230
        # 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
        ] = {}

2231
2232
2233
2234
2235
2236
2237
        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]
2238
            builder = attn_group.get_metadata_builder(ubid or 0)
2239
2240
2241
2242
            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))
2243

2244
2245
2246
2247
2248
2249
2250
            cascade_attn_prefix_len = (
                cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                if cascade_attn_prefix_lens
                else 0
            )

            extra_attn_metadata_args = {}
2251
2252
2253
            if use_spec_decode and isinstance(
                builder, (Mamba2AttentionMetadataBuilder, GDNAttentionMetadataBuilder)
            ):
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
                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
                )
2266
2267
2268
2269
2270
2271
2272
2273
2274
            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,
                )
2275
2276
2277
2278
2279
2280
            else:
                attn_metadata_i = builder.build(
                    common_prefix_len=cascade_attn_prefix_len,
                    common_attn_metadata=common_attn_metadata,
                    **extra_attn_metadata_args,
                )
2281
2282
                if builder.supports_update_block_table:
                    cached_attn_metadata[cache_key] = attn_metadata_i
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305

            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,
2306
                for_cudagraph_capture=for_cudagraph_capture,
2307
            )
2308
            if kv_cache_gid > 0:
2309
2310
                cm.block_table_tensor = _get_block_table(kv_cache_gid)
                cm.slot_mapping = slot_mappings[kv_cache_gid]
2311

2312
            if self.speculative_config and spec_decode_common_attn_metadata is None:
2313
                if isinstance(self.drafter, (EagleProposer, DFlashProposer)):
2314
                    if self.drafter.kv_cache_gid == kv_cache_gid:
2315
                        spec_decode_common_attn_metadata = cm
2316
                else:
2317
                    spec_decode_common_attn_metadata = cm
2318

2319
            for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
2320
                if ubatch_slices is not None:
2321
2322
2323
                    for ubid, _cm in enumerate(split_attn_metadata(ubatch_slices, cm)):
                        _build_attn_group_metadata(kv_cache_gid, attn_gid, _cm, ubid)

2324
                else:
2325
                    _build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
2326

2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
        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]

2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
        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)
            )

2357
        return attn_metadata, spec_decode_common_attn_metadata
2358

2359
2360
2361
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
2362
        num_computed_tokens: np.ndarray,
2363
2364
2365
2366
2367
2368
2369
        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
        """
2370

2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
        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,
2385
                        num_computed_tokens,
2386
2387
2388
2389
2390
2391
2392
2393
                        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
2394

2395
2396
2397
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
2398
        num_computed_tokens: np.ndarray,
2399
        num_common_prefix_blocks: int,
2400
2401
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
    ) -> 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.
        """
2420

2421
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
        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]
2459
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
2460
2461
2462
2463
2464
        # 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.
2465
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
2466
        # common_prefix_len should be a multiple of the block size.
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
        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
        )
2478
2479
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
2480
2481
2482
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
2483
            num_kv_heads=kv_cache_spec.num_kv_heads,
2484
            use_alibi=self.use_alibi,
2485
            use_sliding_window=use_sliding_window,
2486
            use_local_attention=use_local_attention,
2487
            num_sms=self.num_sms,
2488
            dcp_world_size=self.dcp_world_size,
2489
2490
2491
        )
        return common_prefix_len if use_cascade else 0

2492
2493
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
2494
        for index, req_id in enumerate(self.input_batch.req_ids):
2495
2496
2497
            req = self.requests[req_id]
            assert req.mrope_positions is not None

2498
2499
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2500
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
2501
2502
                req.prompt_token_ids, req.prompt_embeds
            )
2503
2504

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
2505
2506
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
            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

2520
2521
2522
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
2523
2524
2525
2526
2527
2528
2529
                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

2530
                assert req.mrope_position_delta is not None
2531
                MRotaryEmbedding.get_next_input_positions_tensor(
2532
                    out=self.mrope_positions.np,
2533
2534
2535
2536
2537
                    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,
                )
2538
2539
2540

                mrope_pos_ptr += completion_part_len

2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
    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

2588
2589
    def _calc_spec_decode_metadata(
        self,
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
        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
2606

2607
2608
2609
2610
2611
        # Step 1.
        # cu_num_sampled_tokens: [4, 5, 8, 9, 11]
        # _arange_scratch[:11]: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
        cu_num_sampled_tokens = self._get_cumsum_and_arange(
            num_sampled_tokens, self._arange_scratch, cumsum_dtype=np.int32
2612
        )
2613
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
2614
        logits_indices = np.repeat(
2615
2616
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
2617
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
2618
        logits_indices += self._arange_scratch[: cu_num_sampled_tokens[-1]]
2619
2620
2621
2622
2623

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

        # Compute the draft logits indices.
2624
        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
2625
2626
2627
        # _arange_scratch[:6]: [0, 1, 2, 0, 1, 0]
        cu_num_draft_tokens = self._get_cumsum_and_arange(
            num_draft_tokens, self._arange_scratch, cumsum_dtype=np.int32
2628
        )
2629
2630
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
2631
2632
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
2633
        # [0, 1, 2, 5, 6, 9]
2634
        target_logits_indices += self._arange_scratch[: cu_num_draft_tokens[-1]]
2635
2636
2637

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
2638
2639
            self.device, non_blocking=True
        )
2640
2641
2642
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
2643
2644
2645
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
2646
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
2647
2648
            self.device, non_blocking=True
        )
2649
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
2650
2651
            self.device, non_blocking=True
        )
2652

2653
2654
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2655
        draft_token_ids = self.input_ids.gpu[logits_indices]
2656
2657
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2658
        return SpecDecodeMetadata(
2659
2660
2661
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2662
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2663
2664
2665
2666
2667
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2668
2669
2670
2671
2672
2673
2674
    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
2675
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2676
2677
2678
2679
2680
        # 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_(
2681
2682
            logits_indices[-1].item()
        )
2683
2684
        # Dispatch for the decoder portion of the model.
        _, batch_desc = self.cudagraph_dispatcher.dispatch(
2685
            num_logits, invalid_modes={CUDAGraphMode.FULL}
2686
2687
        )
        num_logits_padded = batch_desc.num_tokens
2688
2689
2690
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2691
2692
        return logits_indices_padded

2693
    def _batch_mm_inputs_from_scheduler(
2694
2695
        self,
        scheduler_output: "SchedulerOutput",
2696
2697
    ) -> tuple[
        list[str],
2698
        list[tuple[str, MultiModalKwargsItem]],
2699
2700
        list[tuple[str, PlaceholderRange]],
    ]:
2701
        """Batch multimodal inputs from scheduled encoder inputs.
2702
2703
2704

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2705
                inputs.
2706
2707

        Returns:
2708
            A tuple of (mm_hashes, mm_kwargs, mm_lora_refs) where:
2709
2710
            - mm_hashes: List of multimodal hashes for each item
            - mm_kwargs: List of multimodal kwargs for each item
2711
            - mm_lora_refs: List of (req_id, placeholder_range) for each item
2712
        """
2713
2714
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2715
            return [], [], []
2716
2717

        mm_hashes = list[str]()
2718
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
2719
2720
2721
        # Multimodal LoRA reference info to map each multimodal item
        # back to its request & position
        mm_lora_refs = list[tuple[str, PlaceholderRange]]()
2722
2723
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2724
2725

            for mm_input_id in encoder_input_ids:
2726
                mm_feature = req_state.mm_features[mm_input_id]
2727
2728
                if mm_feature.data is None:
                    continue
2729
2730

                mm_hashes.append(mm_feature.identifier)
2731
                mm_kwargs.append((mm_feature.modality, mm_feature.data))
2732
                mm_lora_refs.append((req_id, mm_feature.mm_position))
2733

2734
        return mm_hashes, mm_kwargs, mm_lora_refs
2735

2736
2737
2738
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2739
2740
2741
        mm_hashes, mm_kwargs, mm_lora_refs = self._batch_mm_inputs_from_scheduler(
            scheduler_output
        )
2742
2743

        if not mm_kwargs:
2744
            return []
2745

2746
2747
2748
2749
2750
2751
        should_time = bool(
            self.observability_config
            and self.observability_config.enable_mm_processor_stats
            and scheduler_output.scheduled_encoder_inputs
        )

2752
2753
2754
2755
2756
2757
2758
        # 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.
2759
        model = cast(SupportsMultiModal, self.model)
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774

        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]
2775
                    pos_info.get_num_embeds()
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
                )
                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,
                )

2817
        encoder_outputs: list[torch.Tensor] = []
2818
2819
        # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
        current_item_idx = 0
2820
        for modality, num_items, mm_kwargs_batch in group_and_batch_mm_kwargs(
2821
2822
2823
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
2824
        ):
2825
            batch_outputs: MultiModalEmbeddings
2826

2827
            # EVS and dynamic res video related change.
2828
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2829
            # processing multimodal data. This solves the issue with scheduler
2830
2831
2832
2833
            # 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)
2834
2835
2836
            # dynamic res video for nemotron temporarily uses this hack via
            # requires_sequential_video_encoding
            # because it doesn't yet support video batching.
2837
2838
2839
            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
2840
2841
2842
2843
                (
                    self.is_multimodal_pruning_enabled
                    or self.requires_sequential_video_encoding
                )
2844
2845
2846
                and modality == "video"
                and num_items > 1
            ):
2847
                batch_outputs_lst = list[torch.Tensor]()
2848
2849
2850
2851
2852
2853
                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(
2854
                            group_and_batch_mm_kwargs(
2855
2856
2857
2858
                                [video_mm_kwargs_item],
                                device=self.device,
                                pin_memory=self.pin_memory,
                            )
2859
                        )
2860

2861
2862
2863
                        micro_batch_outputs = model.embed_multimodal(
                            **micro_batch_mm_inputs
                        )
2864

2865
                        batch_outputs_lst.extend(micro_batch_outputs)
2866

2867
                batch_outputs = batch_outputs_lst
2868
2869
            else:
                # Run the encoder.
2870
                # `batch_outputs` is either of the following:
2871
2872
2873
2874
2875
                # 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.
2876
2877
2878
2879

                with self.timed_encoder_operation(
                    should_time, mm_lora_refs, current_item_idx, num_items
                ):
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
                    cudagraph_output = None
                    if (
                        self.encoder_cudagraph_manager is not None
                        and self.encoder_cudagraph_manager.supports_modality(modality)
                    ):
                        cudagraph_output = self.encoder_cudagraph_manager.execute(
                            mm_kwargs_batch,
                        )

                    if cudagraph_output is not None:
                        batch_outputs = cudagraph_output
                    else:
                        batch_outputs = model.embed_multimodal(**mm_kwargs_batch)
2893

2894
2895
            sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=num_items)
            encoder_outputs.extend(batch_outputs)
2896

2897
2898
            current_item_idx += num_items

2899
        # Cache the encoder outputs by mm_hash
2900
        for mm_hash, output in zip(mm_hashes, encoder_outputs):
2901
            self.encoder_cache[mm_hash] = output
2902
2903
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2904

2905
2906
        return encoder_outputs

2907
    def _gather_mm_embeddings(
2908
2909
        self,
        scheduler_output: "SchedulerOutput",
2910
        shift_computed_tokens: int = 0,
2911
2912
2913
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

2914
2915
2916
2917
2918
        # 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]

2919
        mm_embeds = list[torch.Tensor]()
2920
        is_mm_embed = is_mm_embed_buf.cpu
2921
2922
2923
        is_mm_embed[:total_num_scheduled_tokens] = False

        req_start_idx = 0
2924
        should_sync_mrope_positions = False
2925
        should_sync_xdrope_positions = False
2926

2927
        for req_id in self.input_batch.req_ids:
2928
2929
            mm_embeds_req: list[torch.Tensor] = []

2930
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2931
            req_state = self.requests[req_id]
2932
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2933

2934
2935
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2936
2937
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953

                # 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,
2954
2955
                    num_encoder_tokens,
                )
2956
                assert start_idx < end_idx
2957
2958
2959
2960
2961
2962
2963
                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
2964

2965
                mm_hash = mm_feature.identifier
2966
                encoder_output = self.encoder_cache.get(mm_hash, None)
2967
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2968
2969
2970

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]
2971
2972
2973
                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
                else:
                    mm_embeds_item = encoder_output[start_idx:end_idx]
2974

2975
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2976
2977
2978
2979
2980
2981
2982
2983
2984
                # 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
2985
2986
2987
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2988
                assert req_state.mrope_positions is not None
2989
2990
2991
2992
2993
2994
2995
                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,
2996
2997
                    )
                )
2998
2999
3000
3001
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
3002
3003
            req_start_idx += num_scheduled_tokens

3004
        is_mm_embed = is_mm_embed_buf.copy_to_gpu(total_num_scheduled_tokens)
3005
3006
3007

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
3008
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
3009

3010
3011
3012
3013
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

3014
        return mm_embeds, is_mm_embed
3015

3016
    def get_model(self) -> nn.Module:
3017
3018
        if not hasattr(self, "model"):
            raise ValueError("Cannot get model before model has been initialized")
3019
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
3020
            # get raw model out of the cudagraph wrapper.
3021
            return self.model.unwrap()
3022
3023
        return self.model

3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
    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")

3037
3038
3039
        if supports_realtime(model):
            supported_tasks.append("realtime")

3040
3041
        return supported_tasks

3042
3043
3044
3045
3046
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

3047
        return list(model.pooler.get_supported_tasks())
3048

3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
    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)

3059
    def sync_and_slice_intermediate_tensors(
3060
3061
        self,
        num_tokens: int,
3062
        intermediate_tensors: IntermediateTensors | None,
3063
3064
        sync_self: bool,
    ) -> IntermediateTensors:
3065
3066
3067
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
3068
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
3069
3070
3071
3072
3073
3074

        # 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():
3075
                is_scattered = k == "residual" and is_rs
3076
                copy_len = num_tokens // tp if is_scattered else num_tokens
3077
                self.intermediate_tensors[k][:copy_len].copy_(
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
                    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:
3091
3092
3093
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
3094
        if not self.parallel_config.enable_eplb or self.eep_eplb_suppressed:
3095
3096
3097
            return

        assert self.eplb_state is not None
3098
3099
        model = self.get_model()
        assert is_mixture_of_experts(model)
3100
3101
3102
        self.eplb_state.step(
            is_dummy,
            is_profile,
3103
            log_stats=self.parallel_config.eplb_config.log_balancedness,
3104
3105
        )

3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
    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,
        )

3123
3124
3125
3126
3127
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
3128
3129
3130
3131
        kv_connector_output: KVConnectorOutput | None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        num_reqs = self.input_batch.num_reqs
        assert num_reqs == len(self.input_batch.pooling_params), (
3132
3133
            "Either all or none of the requests in a batch must be pooling request"
        )
3134

3135
        hidden_states = hidden_states[:num_scheduled_tokens]
3136
        seq_lens_cpu = self.optimistic_seq_lens_cpu[:num_reqs]
3137

3138
        pooling_metadata = self.input_batch.get_pooling_metadata()
3139
        pooling_metadata.build_pooling_cursor(
3140
3141
3142
3143
            num_scheduled_tokens_np,
            seq_lens_cpu,
            device=hidden_states.device,
            query_start_loc_gpu=self.query_start_loc.gpu[: num_reqs + 1],
3144
        )
3145

3146
3147
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
3148
            hidden_states=hidden_states, pooling_metadata=pooling_metadata
3149
        )
3150
3151
3152
3153
3154

        finished_mask = [
            seq_len == prompt_len
            for seq_len, prompt_len in zip(seq_lens_cpu, pooling_metadata.prompt_lens)
        ]
3155
3156
3157
3158
3159
3160
        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,
        )
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179

        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,
            )

3180
3181
3182
        model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
            raw_pooler_output=raw_pooler_output,
            finished_mask=finished_mask,
3183
        )
3184
3185
        self._sync_device()

3186
        return model_runner_output
3187

3188
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
3189
3190
3191
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
3192
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
3193
3194
3195
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

Patrick von Platen's avatar
Patrick von Platen committed
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
    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

3207
    def _preprocess(
3208
3209
        self,
        scheduler_output: "SchedulerOutput",
3210
        num_input_tokens: int,  # Padded
3211
        intermediate_tensors: IntermediateTensors | None = None,
3212
    ) -> tuple[
3213
3214
        torch.Tensor | None,
        torch.Tensor | None,
3215
        torch.Tensor,
3216
        IntermediateTensors | None,
3217
        dict[str, Any],
3218
        ECConnectorOutput | None,
3219
    ]:
3220
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3221
        is_first_rank = get_pp_group().is_first_rank
3222
        is_encoder_decoder = self.model_config.is_encoder_decoder
3223

3224
3225
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
3226
3227
        ec_connector_output = None

3228
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
3229
            # Run the multimodal encoder if any.
3230
3231
3232
3233
3234
3235
            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)
3236

3237
3238
3239
            # 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.
3240
            inputs_embeds_scheduled = self.model.embed_input_ids(
3241
3242
3243
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
3244
            )
3245

3246
            # TODO(woosuk): Avoid the copy. Optimize.
3247
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
3248

Patrick von Platen's avatar
Patrick von Platen committed
3249
            input_ids, inputs_embeds = self._prepare_mm_inputs(num_input_tokens)
3250
            model_kwargs = {
3251
                **self._init_model_kwargs(),
3252
3253
                **self._extract_mm_kwargs(scheduler_output),
            }
3254
        elif self.enable_prompt_embeds and is_first_rank:
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
            # 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).
3267
3268
3269
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
3270
                .squeeze(1)
3271
            )
3272
3273
3274
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
3275
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
3276
3277
3278
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
3279
            model_kwargs = self._init_model_kwargs()
3280
            input_ids = None
3281
        else:
3282
3283
3284
3285
            # 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.
3286
            input_ids = self.input_ids.gpu[:num_input_tokens]
3287
            inputs_embeds = None
3288
            model_kwargs = self._init_model_kwargs()
3289

3290
        if self.uses_mrope:
3291
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
3292
3293
        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
3294
        else:
3295
            positions = self.positions[:num_input_tokens]
3296
            if num_input_tokens > num_scheduled_tokens:
3297
                self.positions[num_scheduled_tokens:num_input_tokens].zero_()
3298

3299
        if is_first_rank:
3300
3301
            intermediate_tensors = None
        else:
3302
            assert intermediate_tensors is not None
3303
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3304
3305
                num_input_tokens, intermediate_tensors, True
            )
3306

3307
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
3308
3309
3310
3311
3312
3313
3314
            # 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})
3315

3316
3317
3318
3319
3320
3321
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
3322
            ec_connector_output,
3323
        )
3324

3325
    def _sample(
3326
        self,
3327
3328
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3329
    ) -> SamplerOutput:
3330
        # Sample the next token and get logprobs if needed.
3331
        sampling_metadata = self.input_batch.sampling_metadata
3332
3333
3334
        # 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()
3335
        if spec_decode_metadata is None:
3336
            return self.sampler(
3337
3338
3339
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
3340

3341
3342
3343
3344
3345
3346
        # 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)

3347
        sampler_output = self.rejection_sampler(
3348
3349
            spec_decode_metadata,
            None,  # draft_probs
3350
            logits,
3351
3352
            sampling_metadata,
        )
3353
3354
3355
        return sampler_output

    def _bookkeeping_sync(
3356
3357
3358
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
3359
        logits: torch.Tensor | None,
3360
3361
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
3362
        spec_decode_metadata: SpecDecodeMetadata | None,
3363
    ) -> tuple[
3364
        dict[str, int],
3365
        LogprobsLists | None,
3366
        list[list[int]],
3367
        dict[str, LogprobsTensors | None],
3368
3369
3370
        list[str],
        dict[str, int],
        list[int],
3371
    ]:
3372
3373
3374
3375
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

3376
3377
3378
3379
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
3380
3381
3382
3383
        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)
3384

3385
3386
3387
        # 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()
3388
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
3389
3390

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
3391
        sampled_token_ids = sampler_output.sampled_token_ids
3392
        logprobs_tensors = sampler_output.logprobs_tensors
3393
        invalid_req_indices = []
3394
        logprobs_lists = None
3395
3396
3397
3398
3399
3400
        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)
3401
3402
3403
                # 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()
3404
3405
3406

                if logprobs_tensors is not None:
                    logprobs_lists = logprobs_tensors.tolists()
3407
3408
            else:
                # Includes spec decode tokens.
3409
                valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
3410
3411
                    sampled_token_ids,
                    self.input_batch.vocab_size,
3412
                    discard_sampled_tokens_req_indices,
3413
                    logprobs_tensors=logprobs_tensors,
3414
                )
3415
        else:
3416
            valid_sampled_token_ids = []
3417
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
3418
3419
3420
3421
3422
            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.
3423
3424
3425
3426
            # 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
3427
3428
3429
3430
3431
            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
            }
3432

3433
3434
3435
3436
3437
        # 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.
3438
        req_ids = self.input_batch.req_ids
3439
3440
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
3441
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
3442
3443
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
3444

3445
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
3446

3447
            if not sampled_ids:
3448
3449
3450
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
3451
            end_idx = start_idx + num_sampled_ids
3452
3453
3454
3455
            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}"
3456
            )
3457

3458
3459
            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
3460
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
3461

3462
            req_id = req_ids[req_idx]
3463
3464
3465
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

3466
3467
3468
3469
3470
3471
        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
        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,
        )

3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
    @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()

3497
3498
    def _model_forward(
        self,
3499
3500
3501
3502
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
3503
3504
3505
3506
3507
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
3508
        Motivation: We can inspect only this method versus
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
        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,
        )

3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
    @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
        )

3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
    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,
3563
        force_num_active_loras: int | None = None,
3564
        num_encoder_reqs: int = 0,
3565
    ) -> tuple[
3566
3567
        CUDAGraphMode,
        BatchDescriptor,
3568
        bool,
3569
3570
        torch.Tensor | None,
        CUDAGraphStat | None,
3571
    ]:
3572
3573
3574
3575
3576
3577
        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,
3578
        )
3579
3580
3581
3582
3583
        # 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
        )
3584

3585
3586
3587
3588
3589
        # 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)
3590
        )
3591
        has_lora = num_active_loras > 0 if force_has_lora is None else force_has_lora
3592

3593
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
3594
3595
3596

        def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
            return self.cudagraph_dispatcher.dispatch(
3597
3598
3599
                num_tokens=num_tokens,
                has_lora=has_lora,
                uniform_decode=uniform_decode,
3600
                num_active_loras=num_active_loras,
3601
3602
                valid_modes={CUDAGraphMode.NONE} if force_eager else valid_modes,
                invalid_modes={CUDAGraphMode.FULL} if disable_full else None,
3603
3604
            )

3605
        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
3606
            num_tokens_padded, disable_full=use_cascade_attn or has_encoder_output
3607
        )
3608
        num_tokens_padded = batch_descriptor.num_tokens
3609
3610
3611
3612
3613
3614
3615
3616
3617
        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"
            )
3618
3619
3620

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
3621
        should_ubatch, num_tokens_across_dp = False, None
3622
        if self.vllm_config.parallel_config.data_parallel_size > 1:
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
            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,
                )
3633
3634
            )

3635
            # Extract DP-synced values
3636
3637
3638
            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())
3639
3640
3641
                # Re-dispatch with DP padding so we have the correct batch_descriptor
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(
                    num_tokens_padded,
3642
                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
3643
                )
3644
3645
3646
3647
                # 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

3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
        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,
3660
            should_ubatch,
3661
3662
3663
            num_tokens_across_dp,
            cudagraph_stats,
        )
3664

3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
    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

3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
    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

3775
3776
3777
3778
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
3779
        intermediate_tensors: IntermediateTensors | None = None,
3780
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
3781
3782
3783
3784
3785
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
3786

3787
        if self.routed_experts_initialized:
3788
3789
3790
3791
3792
3793
            capturer = RoutedExpertsCapturer.get_instance()
            if capturer is not None:
                capturer.clear_buffer()  # noqa
            else:
                logger.error("RoutedExpertsCapturer not initialized.")

3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
        # 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,
            )

3811
3812
3813
3814
        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)
3815

3816
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3817
3818
3819
3820
3821
        with (
            record_function_or_nullcontext("gpu_model_runner: preprocess"),
            self.synchronize_input_prep(),
        ):
            # Update persistent batch states.
3822
            deferred_state_corrections_fn = self._update_states(scheduler_output)
3823

3824
            if has_ec_transfer() and not get_ec_transfer().is_consumer:
3825
                with self.maybe_get_ec_connector_output(
3826
                    scheduler_output,
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
                    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"
3855
3856
                )

3857
3858
3859
3860
3861
3862
            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
3863

3864
3865
3866
3867
            logits_indices, spec_decode_metadata = self._prepare_inputs(
                scheduler_output,
                num_scheduled_tokens_np,
            )
3868

3869
3870
3871
3872
3873
            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(
3874
                    num_scheduled_tokens_np,
3875
3876
                    self.input_batch.num_computed_tokens_cpu[:num_reqs],
                    scheduler_output.num_common_prefix_blocks,
3877
3878
                )

3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
            (
                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),
            )
3893

3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
            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,
            )

3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
            # 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)
            )
3932
3933
            pad_attn = cudagraph_mode == CUDAGraphMode.FULL

3934
            if self.cache_config.mamba_cache_mode == "align":
3935
3936
3937
3938
3939
3940
                # preprocess_mamba reads req_state.num_computed_tokens (CPU)
                # to decide copy operations, so we must apply deferred
                # corrections before it runs.
                if deferred_state_corrections_fn:
                    deferred_state_corrections_fn()
                    deferred_state_corrections_fn = None
3941
3942
3943
3944
3945
3946
3947
3948
3949
                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(),
3950
                    self._get_mamba_copy_bufs(),
3951
                )
3952
3953
3954
3955
3956
3957
3958
3959
                # preprocess_mamba resets num_accepted_tokens_cpu to 1
                # for requests whose state was copied to a new block.
                # Re-sync to GPU so the mamba kernel reads from the
                # correct initial state slot (init_token_idx = 0).
                self.num_accepted_tokens.np[:num_reqs] = (
                    self.input_batch.num_accepted_tokens_cpu[:num_reqs]
                )
                self.num_accepted_tokens.copy_to_gpu(num_reqs)
3960

3961
3962
3963
            use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
            ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices

3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
            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,
            )

3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
            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,
3987
                    slot_mappings=slot_mappings_by_group,
3988
                )
3989
            )
3990

3991
3992
3993
3994
3995
3996
3997
3998
3999
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
4000
            )
4001

4002
        # Set cudagraph mode to none if calc_kv_scales is true.
4003
4004
4005
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
4006
            cudagraph_mode = CUDAGraphMode.NONE
4007
4008
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
4009

4010
4011
4012
4013
4014
4015
4016
        # 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
        )

4017
4018
        # Run the model.
        # Use persistent buffers for CUDA graphs.
4019
4020
4021
        # 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
4022
4023
        with (
            set_forward_context(
4024
4025
                attn_metadata,
                self.vllm_config,
4026
                num_tokens=num_tokens_padded,
4027
                num_tokens_across_dp=num_tokens_across_dp,
4028
4029
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
4030
                ubatch_slices=ubatch_slices_padded,
4031
                slot_mapping=slot_mappings,
4032
                skip_compiled=has_encoder_input,
4033
            ),
4034
            record_function_or_nullcontext("gpu_model_runner: forward"),
4035
            self.maybe_get_kv_connector_output(
4036
4037
                scheduler_output,
                defer_finalize=defer_kv_connector_finalize,
4038
            ) as kv_connector_output,
4039
        ):
4040
            model_output = self._model_forward(
4041
4042
4043
4044
4045
4046
4047
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

4048
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
4049
            if self.use_aux_hidden_state_outputs:
4050
                # True when EAGLE 3 is used.
4051
4052
                hidden_states, aux_hidden_states = model_output
            else:
4053
                # Common case.
4054
4055
4056
                hidden_states = model_output
                aux_hidden_states = None

4057
4058
4059
4060
4061
            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)
4062
                    hidden_states.kv_connector_output = kv_connector_output
4063
                    self.kv_connector_output = kv_connector_output
4064
                    return hidden_states
4065

4066
                if self.is_pooling_model:
4067
                    # Return the pooling output.
4068
4069
4070
4071
4072
                    return self._pool(
                        hidden_states,
                        num_scheduled_tokens,
                        num_scheduled_tokens_np,
                        kv_connector_output,
4073
                    )
4074
4075

                sample_hidden_states = hidden_states[logits_indices]
4076
                logits = self.model.compute_logits(sample_hidden_states)
4077
4078
4079
4080
            else:
                # Rare case.
                assert not self.is_pooling_model

4081
                sample_hidden_states = hidden_states[logits_indices]
4082
                if not get_pp_group().is_last_rank:
4083
                    all_gather_tensors = {
4084
                        "residual": not is_residual_scattered_for_sp(
4085
                            self.vllm_config, num_tokens_padded
4086
                        )
4087
                    }
4088
                    get_pp_group().send_tensor_dict(
4089
4090
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
4091
4092
                        all_gather_tensors=all_gather_tensors,
                    )
4093
4094
                    logits = None
                else:
4095
                    logits = self.model.compute_logits(sample_hidden_states)
4096

4097
                model_output_broadcast_data: dict[str, Any] = {}
4098
4099
4100
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

4101
                broadcasted = get_pp_group().broadcast_tensor_dict(
4102
4103
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
4104
4105
                assert broadcasted is not None
                logits = broadcasted["logits"]
4106

4107
4108
4109
4110
4111
4112
4113
4114
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
4115
            ec_connector_output,
4116
            cudagraph_stats,
4117
            slot_mappings,
4118
        )
4119
        self.kv_connector_output = kv_connector_output
4120
4121
4122
4123
4124
4125

        # Now the batch has been launched we can wait for corrections from the
        # previous model forward without breaking async scheduling.
        if deferred_state_corrections_fn:
            deferred_state_corrections_fn()

4126
4127
4128
4129
4130
4131
4132
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
        if self.execute_model_state is None:
4133
4134
            kv_connector_output = self.kv_connector_output
            self.kv_connector_output = None
4135
4136
4137
            # 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()
4138
            if not kv_connector_output:
4139
                return None  # type: ignore[return-value]
4140
4141
4142
4143
4144
4145
4146
4147
4148

            # 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
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
4159
            ec_connector_output,
4160
            cudagraph_stats,
4161
            slot_mappings,
4162
4163
4164
4165
4166
4167
4168
4169
4170
        ) = 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
            )
4171

4172
        with record_function_or_nullcontext("gpu_model_runner: sample"):
4173
4174
            sampler_output = self._sample(logits, spec_decode_metadata)

4175
4176
4177
        self._update_states_after_model_execute(
            sampler_output.sampled_token_ids, scheduler_output
        )
4178
4179
        if self.use_async_scheduling:
            pp = get_pp_group()
4180
4181
4182
4183
            # 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:
4184
4185
4186
                self._pp_broadcast_prev_sampled_token_ids(
                    sampler_output.sampled_token_ids
                )
4187

4188
4189
        self._draft_token_ids = None
        self._draft_token_req_ids = None
4190
        self.valid_sampled_token_count_gpu = None
4191
4192
        self.input_batch.prev_sampled_token_ids = None

4193
        def propose_draft_token_ids(sampled_token_ids):
4194
            assert spec_decode_common_attn_metadata is not None
4195
            with record_function_or_nullcontext("gpu_model_runner: draft"):
4196
4197
4198
4199
4200
4201
4202
4203
4204
                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,
4205
                    slot_mappings,
4206
                )
4207
                self._copy_draft_token_ids_to_cpu(scheduler_output)
4208

4209
        spec_config = self.speculative_config
4210
4211
4212
4213
4214
        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
4215
            )
4216
            use_gpu_toks = (
4217
4218
4219
                spec_config.use_eagle()
                or spec_config.uses_draft_model()
                or spec_config.uses_extract_hidden_states()
4220
4221
4222
            ) and not spec_config.disable_padded_drafter_batch
            if use_gpu_toks:
                # EAGLE/DraftModel speculative decoding can use the GPU sampled tokens
4223
                # as inputs, and does not need to wait for bookkeeping to finish.
4224
4225
                assert isinstance(
                    self.drafter,
4226
4227
4228
4229
                    EagleProposer
                    | DFlashProposer
                    | DraftModelProposer
                    | ExtractHiddenStatesProposer,
4230
                )
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
                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(
                            sampled_token_ids,
                            self.requests,
                            self.input_batch,
                            self.discard_request_mask.gpu,
                        )
4243
                    )
4244
4245
4246
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
4247
4248
4249
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
                    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
                    )
4273
4274
4275
4276
4277
4278
4279
4280
                    # 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
4281

4282
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
4283
4284
4285
4286
4287
4288
4289
4290
            (
                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,
4291
4292
4293
4294
4295
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
4296
                scheduler_output.total_num_scheduled_tokens,
4297
                spec_decode_metadata,
4298
            )
4299

4300
        if propose_drafts_after_bookkeeping:
4301
4302
4303
            # 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)
4304

4305
4306
4307
4308
4309
        # 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()
4310

4311
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
4312
            self.eplb_step()
4313

4314
4315
4316
4317
        # self.kv_connector_output may be modified during drafting
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

4318
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
4319
            if self.routed_experts_initialized:
4320
4321
4322
4323
4324
4325
                capturer = RoutedExpertsCapturer.get_instance()
                if capturer is not None:
                    capturer.save_captured_experts(indices=self.slot_mapping)  # noqa
                else:
                    logger.error("RoutedExpertsCapturer not initialized.")

4326
4327
4328
4329
4330
4331
4332
            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,
4333
4334
4335
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
4336
                num_nans_in_logits=num_nans_in_logits,
4337
                cudagraph_stats=cudagraph_stats,
4338
            )
4339

4340
4341
        if not self.use_async_scheduling:
            return output
4342

4343
4344
4345
4346
4347
4348
4349
4350
4351
        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,
4352
                vocab_size=self.input_batch.vocab_size,
4353
4354
4355
4356
4357
            )
        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
4358
            # any requests with sampling params that require output ids.
4359
4360
4361
4362
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
4363
4364
4365

        return async_output

4366
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
4385
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
4403
4404
    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

4405
    def take_draft_token_ids(self) -> DraftTokenIds | None:
4406
        if not self.num_spec_tokens or not self._draft_token_req_ids:
4407
            return None
4408
        draft_token_ids, req_ids = self._get_draft_token_ids_cpu()
4409
        return DraftTokenIds(req_ids, draft_token_ids)
4410

4411
4412
4413
    def _copy_draft_token_ids_to_cpu(
        self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
    ) -> None:
4414
4415
4416
4417
4418
4419
        # 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
        ):
4420
4421
4422
            return
        # We must also set the corresponding request ids.
        self._draft_token_req_ids = self.input_batch.req_ids.copy()
4423

4424
4425
4426
4427
4428
4429
4430
4431
4432
4433
4434
4435
4436
4437
4438
4439
4440
4441
4442
4443
        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()

4444
    def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
4445
        if isinstance(self._draft_token_ids, list):
4446
4447
4448
4449
            return self._draft_token_ids, self.input_batch.req_ids
        req_ids = self._draft_token_req_ids
        if req_ids is None:
            return [], []
4450
4451
4452
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_cpu is not None
        self.draft_token_ids_event.synchronize()
4453
        return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
4454

4455
4456
4457
4458
4459
4460
4461
4462
4463
4464
4465
4466
4467
    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
4468
            assert counts_cpu is not None
4469
4470
4471
            counts_cpu[: counts.shape[0]].copy_(counts, non_blocking=True)
            self.valid_sampled_token_count_event.record()

4472
4473
4474
        if self.use_async_spec_decode:
            # Stash for GPU-side correction in _prepare_inputs.
            self.valid_sampled_token_count_gpu = valid_sampled_tokens_count
4475
4476
4477
4478
4479
        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
4480
4481
        sampled_count_event = self.valid_sampled_token_count_event
        if sampled_count_event is None or prev_sampled_token_ids is None:
4482
4483
4484
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
4485
4486
        assert counts_cpu is not None
        sampled_count_event.synchronize()
4487
4488
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

4489
4490
4491
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
4492
        sampled_token_ids: torch.Tensor | list[list[int]],
4493
4494
4495
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
4496
4497
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
4498
        common_attn_metadata: CommonAttentionMetadata,
4499
        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
4500
    ) -> list[list[int]] | torch.Tensor:
4501
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
4502
4503
4504
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
4505
4506
            from vllm.v1.spec_decode.ngram_proposer import NgramProposer

4507
            assert isinstance(sampled_token_ids, list)
4508
            assert isinstance(self.drafter, NgramProposer)
4509
            draft_token_ids = self.drafter.propose(
4510
                sampled_token_ids,
4511
4512
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
4513
                slot_mappings=slot_mappings,
4514
            )
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
        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,
            )
4552
        elif spec_config.method == "suffix":
4553
4554
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
4555
4556
4557
            draft_token_ids = self.drafter.propose(
                self.input_batch, sampled_token_ids, slot_mappings=slot_mappings
            )
4558
        elif spec_config.method == "medusa":
4559
            assert isinstance(sampled_token_ids, list)
4560
            assert isinstance(self.drafter, MedusaProposer)
4561

4562
4563
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
4564
4565
4566
4567
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
4568
4569
4570
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
4571
                for num_draft, tokens in zip(
4572
4573
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
4574
                    indices.append(offset + len(tokens) - 1)
4575
                    offset += num_draft + 1
4576
                indices = torch.tensor(indices, device=self.device)
4577
4578
                hidden_states = sample_hidden_states[indices]

4579
            draft_token_ids = self.drafter.propose(
4580
4581
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
4582
                slot_mappings=slot_mappings,
4583
            )
4584
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
        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]

4596
            draft_token_ids = self.drafter.propose(
4597
4598
4599
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
4612
4613
                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(
                    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
            )

4614
4615
4616
4617
4618
4619
4620
4621
        elif (
            spec_config.use_eagle()
            or spec_config.use_dflash()
            or spec_config.uses_draft_model()
        ):
            assert isinstance(
                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
            )
4622

4623
            if spec_config.disable_padded_drafter_batch:
4624
4625
4626
                # 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.
4627
4628
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
4629
                    "padded-batch is disabled."
4630
                )
4631
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
4632
4633
4634
4635
4636
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
4637
4638
4639
4640
4641
            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.
4642
4643
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
4644
                    "padded-batch is enabled."
4645
4646
                )
                next_token_ids, valid_sampled_tokens_count = (
4647
4648
4649
4650
                    self.drafter.prepare_next_token_ids_padded(
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
4651
                        self.discard_request_mask.gpu,
4652
                    )
4653
                )
4654
4655
4656
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
4657

4658
            num_rejected_tokens_gpu = None
4659
            if spec_decode_metadata is None:
4660
                token_indices_to_sample = None
4661
                # input_ids can be None for multimodal models.
4662
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
4663
                target_positions = self._get_positions(num_scheduled_tokens)
4664
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
4665
                    assert aux_hidden_states is not None
4666
                    target_hidden_states = torch.cat(
4667
4668
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
4669
4670
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
4671
            else:
4672
                if spec_config.disable_padded_drafter_batch:
4673
                    token_indices_to_sample = None
4674
4675
4676
4677
4678
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
4679
4680
4681
4682
4683
4684
4685
4686
4687
                    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]
4688
                else:
4689
4690
4691
4692
4693
4694
4695
4696
                    (
                        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,
4697
                    )
4698
4699
4700
4701
4702
4703
4704
4705
4706
4707
4708
                    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]
4709

4710
            if self.supports_mm_inputs and self.drafter.supports_mm_inputs:
4711
4712
4713
4714
4715
4716
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
4717

4718
            draft_token_ids = self.drafter.propose(
4719
4720
4721
4722
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
4723
                token_indices_to_sample=token_indices_to_sample,
4724
                sampling_metadata=sampling_metadata,
4725
                common_attn_metadata=common_attn_metadata,
4726
                mm_embed_inputs=mm_embed_inputs,
4727
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
4728
                slot_mappings=slot_mappings,
4729
            )
4730

4731
        return draft_token_ids
4732

4733
4734
4735
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
4736
4737
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
4738
                f"Allowed configs: {allowed_config_names}"
4739
            )
4740
4741
4742
4743
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

4744
    @instrument(span_name="Loading (GPU)")
4745
    def load_model(self, load_dummy_weights: bool = False) -> None:
4746
4747
        """
        Args:
4748
            load_dummy_weights: load dummy weights instead of real weights.
4749
        """
4750
4751
4752
4753
4754
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
4755

4756
4757
4758
4759
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0

4760
4761
4762
        try:
            with DeviceMemoryProfiler() as m:
                time_before_load = time.perf_counter()
4763
4764
                if load_dummy_weights:
                    self.load_config.load_format = "dummy"
4765
4766
4767
4768
4769
4770
4771
                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
4772
                    )
4773
4774
4775
4776
4777
4778
4779
4780
                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
                    ):
4781
4782
4783
                        assert not self.parallel_config.enable_elastic_ep, (
                            "Elastic EP is not supported with drafter model."
                        )
4784
4785
4786
4787
4788
4789
4790
4791
4792
4793
4794
4795
4796
4797
4798
4799
                        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
4800

4801
4802
4803
4804
4805
4806
                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"
                        )
4807

4808
4809
4810
4811
4812
4813
4814
4815
4816
                    # 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:
4817
4818
4819
                        aux_layers = (
                            self.model.get_eagle3_default_aux_hidden_state_layers()
                        )
4820
4821
4822
4823
4824
4825
4826
4827
4828
4829
4830
4831
4832
4833
4834

                    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
4835
        logger.info_once(
4836
4837
            "Model loading took %s GiB memory and %.6f seconds",
            format_gib(self.model_memory_usage),
4838
            time_after_load - time_before_load,
4839
            scope="local",
4840
        )
4841
4842
4843
4844
4845
4846
        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)
4847
        mm_config = self.model_config.multimodal_config
4848
        self.is_multimodal_pruning_enabled = (
4849
            supports_multimodal_pruning(self.get_model())
4850
4851
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
4852
        )
4853
4854
4855
        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
4856

4857
4858
4859
4860
4861
        if (
            is_mixture_of_experts(self.model)
            and self.parallel_config.enable_eplb
            and not load_dummy_weights
        ):
4862
4863
4864
            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(
4865
                self.model,
4866
                self.model_config,
4867
            )
4868
            if self.eplb_state.is_async:
4869
                self.eplb_state.start_async_loop()
4870

4871
        if (
4872
4873
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
4874
        ):
4875
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
4876
            compilation_counter.stock_torch_compile_count += 1
4877
            self.model.compile(fullgraph=True, backend=backend)
4878
            return
4879
        # for other compilation modes, cudagraph behavior is controlled by
Jiayi Yan's avatar
Jiayi Yan committed
4880
        # CudagraphWrapper and CudagraphDispatcher of vllm.
4881
4882

        # wrap the model with full cudagraph wrapper if needed.
4883
4884
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
4885
4886
4887
4888
        if (
            cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.use_ubatching
        ):
4889
4890
4891
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
4892
        elif self.parallel_config.use_ubatching:
4893
            if cudagraph_mode.has_full_cudagraphs():
4894
4895
4896
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
4897
            else:
4898
4899
4900
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
4901

4902
4903
        get_offloader().post_init()

4904
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
4905
4906
4907
4908
4909
4910
4911
4912
4913
4914
4915
4916
4917
4918
4919
        """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

4920
4921
4922
4923
4924
4925
        layer_ids = getattr(hf_config, "eagle_aux_hidden_state_layer_ids", None)
        if not layer_ids:
            dflash_config = getattr(hf_config, "dflash_config", None)
            if dflash_config and isinstance(dflash_config, dict):
                layer_ids = dflash_config.get("target_layer_ids")

4926
4927
4928
4929
4930
        if layer_ids and isinstance(layer_ids, (list, tuple)):
            return tuple(layer_ids)

        return None

4931
4932
4933
4934
4935
4936
4937
4938
4939
4940
4941
4942
4943
    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
4944
            into kernel format (repacking, renaming, etc.)
4945
4946
4947
4948
4949
4950
4951
4952
4953
4954
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
4975
        """
        # 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")
4976
4977
4978
4979
4980
        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)
4981

4982
4983
4984
4985
4986
4987
4988
4989
4990
4991
4992
4993
        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)
4994
4995
4996
4997
4998
4999
5000
5001

        # 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",
5002
        )
5003
5004
5005
5006
5007
5008
5009
        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,
                )
5010

5011
5012
5013
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
5014
        num_scheduled_tokens: dict[str, int],
5015
    ) -> dict[str, LogprobsTensors | None]:
5016
        num_prompt_logprobs_dict = self.num_prompt_logprobs
5017
5018
5019
        if not num_prompt_logprobs_dict:
            return {}

5020
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
5021
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
5022
5023
5024
5025
5026

        # 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():
5027
5028
5029
5030
            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
5031
5032
5033

            # Get metadata for this request.
            request = self.requests[req_id]
5034
5035
5036
5037
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

5038
5039
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
5040
5041
                self.device, non_blocking=True
            )
5042

5043
5044
5045
5046
5047
5048
            # 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(
5049
5050
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
5051
5052
                in_progress_dict[req_id] = logprobs_tensors

5053
            # Determine number of logits to retrieve.
5054
5055
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
5056
            num_remaining_tokens = num_prompt_tokens - start_tok
5057
            if num_tokens <= num_remaining_tokens:
5058
                # This is a chunk, more tokens remain.
5059
5060
5061
                # 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.
5062
5063
5064
5065
5066
                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)
5067
5068
5069
5070
5071
5072
5073
                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
5074
5075
5076
5077
5078

            # 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]
5079
            offset = self.query_start_loc.np[req_idx].item()
5080
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
5081
            logits = self.model.compute_logits(prompt_hidden_states)
5082
5083
5084
5085

            # 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.
5086
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
5087
5088

            # Compute prompt logprobs.
5089
            logprobs = self.sampler.compute_logprobs(logits)
5090
            token_ids, logprobs, ranks, _ = self.sampler.gather_logprobs(
5091
5092
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
5093
5094

            # Transfer GPU->CPU async.
5095
5096
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
5097
5098
5099
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
5100
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
5101
5102
                ranks, non_blocking=True
            )
5103
5104
5105
5106
5107

        # 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]
5108
            del in_progress_dict[req_id]
5109
5110

        # Must synchronize the non-blocking GPU->CPU transfers.
5111
        if prompt_logprobs_dict:
5112
            self._sync_device()
5113
5114
5115

        return prompt_logprobs_dict

5116
5117
    def _get_nans_in_logits(
        self,
5118
        logits: torch.Tensor | None,
5119
5120
5121
5122
5123
5124
5125
5126
5127
5128
5129
    ) -> 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])
5130
5131
5132
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
5133
5134
5135
5136
            return num_nans_in_logits
        except IndexError:
            return {}

5137
    @contextmanager
5138
5139
5140
    def maybe_randomize_inputs(
        self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None
    ):
5141
5142
5143
5144
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
5145
         - during DP rank dummy run
5146
        """
5147

5148
5149
5150
5151
        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
5152
        elif input_ids is not None:
5153
5154
5155
5156

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
5157
                    self.input_ids.gpu,
5158
5159
                    low=0,
                    high=self.model_config.get_vocab_size(),
5160
                )
5161

5162
            logger.debug_once("Randomizing dummy input_ids for DP Rank")
5163
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
5164
5165
            yield
            input_ids.fill_(0)
5166
5167
5168
5169
5170
5171
5172
5173
5174
5175
5176
5177
5178
5179
5180
        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)
5181

5182
5183
5184
5185
5186
5187
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
5188
5189
        assert self.mm_budget is not None

5190
5191
5192
        # 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,
5193
            mm_counts={modality: 1},
5194
            cache=self.mm_budget.cache,
5195
        )
5196
5197
5198
5199
5200
        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"
5201

5202
        return next(
5203
5204
            mm_kwargs_batch
            for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
5205
                [(modality, dummy_mm_item)] * max_items_per_batch,
5206
5207
5208
5209
                device=self.device,
                pin_memory=self.pin_memory,
            )
        )
5210

5211
5212
5213
5214
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
5215
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
5216
5217
        force_attention: bool = False,
        uniform_decode: bool = False,
5218
        allow_microbatching: bool = True,
5219
5220
        skip_eplb: bool = False,
        is_profile: bool = False,
5221
        create_mixed_batch: bool = False,
5222
        remove_lora: bool = True,
Rémi Delacourt's avatar
Rémi Delacourt committed
5223
        is_graph_capturing: bool = False,
5224
        num_active_loras: int = 0,
5225
        profile_seq_lens: int | None = None,
5226
    ) -> tuple[torch.Tensor, torch.Tensor]:
5227
5228
5229
5230
5231
5232
5233
        """
        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.
5234
                - if not set will determine the cudagraph mode based on using
5235
                    the self.cudagraph_dispatcher.
5236
5237
5238
5239
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
5240
            force_attention: If True, always create attention metadata. Used to
5241
5242
5243
5244
                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.
5245
5246
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
5247
            remove_lora: If False, dummy LoRAs are not destroyed after the run
5248
5249
            num_active_loras: Number of distinct active LoRAs to capture for.
                LoRA is activated when num_active_loras > 0.
5250
5251
5252
            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.
5253
        """
5254
5255
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5256
5257
5258
5259
            # 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([])

5260
5261
        assert (
            cudagraph_runtime_mode is None
5262
            or cudagraph_runtime_mode.is_valid_runtime_mode()
5263
        )
5264

5265
        # If cudagraph_mode.decode_mode() == FULL and
5266
        # cudagraph_mode.separate_routine(). This means that we are using
5267
5268
5269
5270
5271
5272
5273
5274
5275
5276
5277
        # 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.
5278
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
5279

5280
5281
5282
        # 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.
5283
        assert num_tokens <= self.max_num_tokens
5284
        max_num_reqs = self.scheduler_config.max_num_seqs
5285
5286
5287
5288
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
5289
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
5290
5291
5292
5293
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
5294
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
5295
5296
5297
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
5298
            assert not create_mixed_batch
5299
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
5300
5301
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
5302
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
5303
5304
5305
5306
5307
5308
        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

5309
5310
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
5311
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
5312
5313
        num_tokens_unpadded = int(num_scheduled_tokens.sum())

5314
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
5315

5316
        _cudagraph_mode, batch_desc, should_ubatch, num_tokens_across_dp, _ = (
5317
5318
5319
5320
5321
5322
5323
5324
5325
5326
5327
5328
5329
5330
5331
5332
5333
            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
5334
5335
5336
5337
                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,
5338
5339
            )
        )
5340
5341
5342

        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
5343
        else:
5344
5345
5346
5347
5348
5349
5350
5351
5352
            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
        )
5353
        ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363
            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,
5364
        )
5365

5366
        attn_metadata: PerLayerAttnMetadata | None = None
5367

5368
5369
5370
5371
5372
5373
5374
        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,
        )

5375
5376
5377
5378
5379
5380
5381
5382
        # _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:
5383
5384
5385
                if profile_seq_lens is not None:
                    seq_lens = profile_seq_lens  # type: ignore[assignment]
                elif create_mixed_batch:
5386
5387
5388
                    # 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
5389
5390
5391
5392
                    seq_lens = torch.tensor(  # type: ignore[assignment]
                        [1] * num_decode_tokens + [num_prefill_tokens + 1],
                        dtype=torch.int,
                    )
5393
5394
                else:
                    seq_lens = max_query_len  # type: ignore[assignment]
5395
5396
5397
                self.optimistic_seq_lens_cpu[:num_reqs] = seq_lens
                self.optimistic_seq_lens_cpu[num_reqs:].fill_(0)
                self.seq_lens.copy_(self.optimistic_seq_lens_cpu, non_blocking=True)
5398

5399
5400
5401
                cum_num_tokens = self._get_cumsum_and_arange(
                    num_scheduled_tokens, self.query_pos.np
                )
5402
5403
                self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
                self.query_start_loc.copy_to_gpu()
5404

5405
5406
5407
5408
5409
5410
                # Sync block table CPU->GPU so cleared rows from
                # remove_request() are visible to the attention metadata
                # builder. Without this, stale block IDs from finished
                # requests can corrupt Mamba state.
                self.input_batch.block_table.commit_block_table(num_reqs_padded)

5411
5412
5413
5414
5415
5416
5417
5418
5419
                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,
5420
                    use_spec_decode=self.speculative_config is not None,
5421
                )
5422

5423
        with self.maybe_dummy_run_with_lora(
5424
5425
5426
5427
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            remove_lora,
5428
            num_active_loras,
5429
        ):
5430
            # Make sure padding doesn't exceed max_num_tokens
5431
            assert num_tokens_padded <= self.max_num_tokens
5432
            model_kwargs = self._init_model_kwargs()
5433
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
Patrick von Platen's avatar
Patrick von Platen committed
5434
5435
                input_ids, inputs_embeds = self._prepare_mm_inputs(num_tokens_padded)

5436
                model_kwargs = {
5437
                    **model_kwargs,
5438
5439
                    **self._dummy_mm_kwargs(num_reqs),
                }
5440
5441
            elif self.enable_prompt_embeds:
                input_ids = None
5442
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
5443
                model_kwargs = self._init_model_kwargs()
5444
            else:
5445
                input_ids = self.input_ids.gpu[:num_tokens_padded]
5446
                inputs_embeds = None
5447

5448
            if self.uses_mrope:
5449
                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
5450
            elif self.uses_xdrope_dim > 0:
5451
                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
5452
            else:
5453
                positions = self.positions[:num_tokens_padded]
5454
5455
5456
5457
5458
5459
5460
5461
5462

            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,
5463
5464
5465
                            device=self.device,
                        )
                    )
5466
5467

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
5468
                    num_tokens_padded, None, False
5469
                )
5470

5471
            if ubatch_slices_padded is not None:
5472
5473
5474
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
5475
                num_tokens_padded = ubatch_slices_padded[0].num_tokens
5476
                if num_tokens_across_dp is not None:
5477
                    num_tokens_across_dp[:] = num_tokens_padded
5478

5479
            with (
5480
                self.maybe_randomize_inputs(input_ids, inputs_embeds),
5481
                set_forward_context(
5482
5483
                    attn_metadata,
                    self.vllm_config,
5484
                    num_tokens=num_tokens_padded,
5485
5486
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
5487
                    batch_descriptor=batch_desc,
5488
                    ubatch_slices=ubatch_slices_padded,
5489
                    slot_mapping=slot_mappings,
5490
5491
                ),
            ):
5492
                outputs = self.model(
5493
5494
5495
5496
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
5497
                    **model_kwargs,
5498
                )
5499

5500
5501
5502
5503
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
5504

5505
5506
5507
            if self.speculative_config and (
                self.speculative_config.use_eagle()
                or self.speculative_config.uses_draft_model()
5508
                or self.speculative_config.uses_extract_hidden_states()
5509
            ):
5510
5511
                assert isinstance(
                    self.drafter,
5512
5513
5514
5515
                    EagleProposer
                    | DFlashProposer
                    | DraftModelProposer
                    | ExtractHiddenStatesProposer,
5516
                )
5517
                assert self.speculative_config is not None
5518
5519
5520
                # 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.
5521
                use_cudagraphs = (
5522
5523
5524
5525
5526
5527
5528
5529
5530
                    (
                        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
5531
5532
5533
5534
5535

                # 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
5536
5537
5538
5539
                if (
                    self.compilation_config.cudagraph_specialize_lora
                    and num_active_loras > 0
                ):
5540
5541
5542
5543
5544
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
Rémi Delacourt's avatar
Rémi Delacourt committed
5545
                    is_graph_capturing=is_graph_capturing,
5546
                    slot_mappings=slot_mappings,
5547
                )
5548

5549
5550
5551
5552
5553
5554
5555
5556
5557
5558
5559
        # 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()

5560
5561
5562
5563
5564
5565
5566
5567
5568
5569
        # 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)

5570
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
5571
5572
5573
5574
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
5575
5576
5577
5578
5579
5580

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
5581
5582
5583
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
5584

5585
5586
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5587
5588
5589
            # MM Encoder only model no need to run sampler.
            return torch.tensor([])

5590
        hidden_states = torch.rand_like(hidden_states)
5591

5592
        logits = self.model.compute_logits(hidden_states)
5593
5594
        num_reqs = logits.size(0)

5595
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
5596
5597
5598
5599
5600
5601
5602
5603
5604
5605
5606
5607
5608
5609
5610

        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)],
5611
            spec_token_ids=[[] for _ in range(num_reqs)],
5612
5613
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
5614
            logitsprocs=LogitsProcessors(),
5615
        )
5616
        try:
5617
5618
5619
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
5620
        except RuntimeError as e:
5621
            if "out of memory" in str(e):
5622
5623
5624
5625
                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 "
5626
5627
                    "initializing the engine."
                ) from e
5628
5629
            else:
                raise e
5630
        if self.speculative_config:
5631
5632
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
5633
5634
                draft_token_ids, self.device
            )
5635
5636
5637
5638
5639
5640

            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
5641
5642
5643
5644
5645
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
5646
            )
5647
5648
5649
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
5650
                logits,
5651
5652
                dummy_metadata,
            )
5653
        return sampler_output
5654

5655
    def _dummy_pooler_run_task(
5656
5657
        self,
        hidden_states: torch.Tensor,
5658
5659
        task: PoolingTask,
    ) -> PoolerOutput:
5660
5661
5662
5663
        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
5664
5665
5666
5667
        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
5668
5669
5670

        req_num_tokens = num_tokens // num_reqs

5671
        dummy_prompt_lens = torch.from_numpy(num_scheduled_tokens_np)
5672
5673
5674
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
5675

5676
        model = cast(VllmModelForPooling, self.get_model())
5677
        dummy_pooling_params = PoolingParams(task=task)
5678
        dummy_pooling_params.verify(self.model_config)
5679
        to_update = model.pooler.get_pooling_updates(task)
5680
5681
        to_update.apply(dummy_pooling_params)

5682
        dummy_metadata = PoolingMetadata(
5683
5684
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
5685
            prompt_token_ids_cpu=dummy_token_ids.cpu(),
5686
            pooling_params=[dummy_pooling_params] * num_reqs,
5687
            pooling_states=[PoolingStates() for i in range(num_reqs)],
5688
        )
5689

5690
        dummy_metadata.build_pooling_cursor(
5691
            num_scheduled_tokens_np,
5692
5693
            seq_lens_cpu=dummy_prompt_lens,
            device=hidden_states.device,
5694
        )
5695

5696
        try:
5697
5698
5699
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
5700
        except RuntimeError as e:
5701
            if "out of memory" in str(e):
5702
                raise RuntimeError(
5703
5704
5705
                    "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 "
5706
5707
                    "initializing the engine."
                ) from e
5708
5709
            else:
                raise e
5710
5711
5712
5713
5714
5715

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
5716
5717
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5718
5719
5720
            # MM Encoder only model not need to run pooler.
            return torch.tensor([])

5721
        # Find the task that has the largest output for subsequent steps
5722
5723
5724
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
5725
5726
5727
5728
5729
5730
            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."
            )
5731

5732
        output_size = dict[PoolingTask, float]()
5733
        for task in supported_pooling_tasks:
5734
5735
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
5736
            output_size[task] = sum(o.nbytes for o in output if o is not None)
5737
5738
5739
5740
            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)
5741

5742
    def profile_run(self) -> None:
5743
        # Profile with multimodal encoder & encoder cache.
5744
        if self.supports_mm_inputs:
5745
5746
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
5747
                logger.info(
5748
                    "Skipping memory profiling for multimodal encoder and "
5749
5750
                    "encoder cache."
                )
5751
5752
5753
5754
5755
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
5769
5770
5771
5772
                    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
                        ]
5773

5774
                        logger.info_once(
5775
5776
5777
5778
5779
5780
                            "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,
5781
                            scope="local",
5782
                        )
5783

5784
5785
5786
5787
5788
                        # Create dummy batch of multimodal inputs.
                        batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                            dummy_modality,
                            max_mm_items_per_batch,
                        )
5789

5790
5791
5792
5793
5794
5795
5796
5797
5798
5799
5800
                        # 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
5801

5802
        # Add `is_profile` here to pre-allocate communication buffers
5803
5804
5805
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
5806
        if get_pp_group().is_last_rank:
5807
5808
5809
5810
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
5811
        else:
5812
            output = None
5813
        self._sync_device()
5814
        del hidden_states, output
5815
        self.encoder_cache.clear()
5816
        gc.collect()
5817

5818
5819
5820
5821
5822
5823
5824
5825
5826
5827
    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

5828
5829
5830
        # 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
5831
        minimal_config = get_kv_cache_config_from_groups(
5832
            self.vllm_config, kv_cache_groups, available_memory=0
5833
        )
5834
        self.cache_config.num_gpu_blocks_override = saved_override
5835

5836
        self.initialize_kv_cache(minimal_config, is_profiling=True)
5837
5838
5839
5840
5841
5842
5843
5844
5845
5846
5847
5848
5849
5850
5851
5852
5853
5854
5855
5856
5857
5858
5859
5860
5861
5862
5863
5864
5865
5866
5867
5868
5869
5870
5871
        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"):
5872
5873
5874
5875
                kv_cache = layer.kv_cache
                layer.kv_cache = (
                    torch.tensor([]) if isinstance(kv_cache, torch.Tensor) else []
                )
5876
5877
5878
5879
5880
5881
5882
5883
5884
5885
5886
5887
5888
5889
5890
5891
5892
5893
5894
5895
5896
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
5920
5921
5922
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
5946
5947
5948
5949
5950
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
5962

        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)]
5963
5964
5965
        for key_set in self.cudagraph_dispatcher.cudagraph_keys.values():
            key_set.clear()
        self.cudagraph_dispatcher.keys_initialized = False
5966
5967
5968
5969
5970
5971
5972
5973
5974
5975
5976
5977
5978
5979
5980
5981
5982
        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)

5983
    @instrument(span_name="Capture model")
5984
    def capture_model(self) -> int:
5985
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
5986
            logger.warning(
5987
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
5988
5989
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
5990
            return 0
5991

5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
        # Initialize encoder CUDA graph manager if enabled.
        # Use get_model() to unwrap CUDAGraphWrapper/UBatchWrapper,
        # because @runtime_checkable Protocol isinstance() checks do not
        # work through __getattr__ forwarding.
        if (
            self.compilation_config.cudagraph_mm_encoder
            and self.supports_mm_inputs
            and self.encoder_cudagraph_manager is None
        ):
            from vllm.model_executor.models.interfaces import (
                SupportsEncoderCudaGraph,
                supports_encoder_cudagraph,
            )
6005
6006
6007
            from vllm.v1.worker.gpu.mm.encoder_cudagraph import (
                EncoderCudaGraphManager,
            )
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018

            raw_model = self.get_model()
            if supports_encoder_cudagraph(raw_model):
                self.encoder_cudagraph_manager = EncoderCudaGraphManager(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    dtype=self.dtype,
                    model=cast(SupportsEncoderCudaGraph, raw_model),
                )
                logger.info("Initialized EncoderCudaGraphManager for vision encoder")

6019
6020
        compilation_counter.num_gpu_runner_capture_triggers += 1

6021
6022
        start_time = time.perf_counter()

6023
6024
6025
        # 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.
6026
        set_cudagraph_capturing_enabled(True)
6027
6028
6029
        with self._freeze_gc(), graph_capture(device=self.device):
            torch.accelerator.synchronize()
            torch.accelerator.empty_cache()
6030
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
6031

6032
6033
6034
6035
            for (
                runtime_mode,
                batch_descs,
            ) in self.cudagraph_dispatcher.get_capture_descs():
6036
                self._capture_cudagraphs(
6037
6038
                    batch_descriptors=batch_descs,
                    cudagraph_runtime_mode=runtime_mode,
6039
                )
6040
                torch.accelerator.synchronize()
6041

6042
6043
6044
6045
            # Capture encoder CUDA graphs if enabled
            if self.encoder_cudagraph_manager is not None:
                self.encoder_cudagraph_manager.capture()

6046
            torch.accelerator.synchronize()
6047
6048
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

6049
6050
6051
        # 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
6052
        # we may do lazy capturing in future that still allows capturing
6053
6054
        # after here.
        set_cudagraph_capturing_enabled(False)
6055

6056
6057
6058
        torch.accelerator.synchronize()
        torch.accelerator.empty_cache()

6059
6060
6061
6062
        # Lock workspace to prevent resizing during execution.
        # Max workspace sizes should have been captured during warmup/profiling.
        lock_workspace()

6063
6064
6065
6066
        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.
6067
        logger.info_once(
6068
6069
6070
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
6071
            scope="local",
6072
        )
6073
        return cuda_graph_size
6074

6075
6076
6077
6078
6079
6080
6081
6082
6083
6084
6085
6086
6087
6088
6089
6090
6091
6092
6093
6094
6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
6105
6106
6107
6108
    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,
        )

6109
6110
    def _capture_cudagraphs(
        self,
6111
        batch_descriptors: list[BatchDescriptor],
6112
6113
6114
6115
        cudagraph_runtime_mode: CUDAGraphMode,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
6116
            and cudagraph_runtime_mode.is_valid_runtime_mode()
6117
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
6118

6119
6120
6121
6122
6123
        if not batch_descriptors:
            return

        uniform_decode = batch_descriptors[0].uniform

6124
6125
        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
6126
6127
            batch_descriptors = tqdm(
                batch_descriptors,
6128
6129
6130
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
6131
6132
6133
                    cudagraph_runtime_mode.name,
                ),
            )
6134

6135
        # We skip EPLB here since we don't want to record dummy metrics
6136
        for batch_desc in batch_descriptors:
6137
            # We currently only capture ubatched graphs when its a FULL
6138
6139
6140
            # 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
6141
            allow_microbatching = (
6142
                self.parallel_config.use_ubatching
6143
6144
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
6145
6146
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
6147
                    num_tokens=batch_desc.num_tokens,
6148
6149
                    uniform_decode=uniform_decode,
                )
6150
            )
6151
6152
            self._warmup_and_capture(
                batch_desc,
6153
6154
6155
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                allow_microbatching=allow_microbatching,
            )
6156
            torch.accelerator.synchronize()
6157
        self.maybe_remove_all_loras(self.lora_config)
6158

6159
6160
6161
6162
6163
    def initialize_attn_backend(
        self,
        kv_cache_config: KVCacheConfig,
        is_profiling: bool = False,
    ) -> None:
6164
6165
6166
        """
        Initialize the attention backends and attention metadata builders.
        """
6167
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
6168

6169
6170
6171
6172
6173
6174
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
6175
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
6176
            layer_type = cast(type[Any], AttentionLayerBase)
6177
            layers = get_layers_from_vllm_config(
6178
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
6179
            )
6180
6181
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
6182
            # Dedupe based on full class name; this is a bit safer than
6183
6184
6185
6186
            # 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.
6187
            for layer_name in kv_cache_group_spec.layer_names:
6188
                attn_backend = layers[layer_name].get_attn_backend()
6189
6190
6191
6192

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
6193
                        attn_backend,  # type: ignore[arg-type]
6194
6195
                    )

6196
6197
6198
                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):
6199
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
6200
                key = (full_cls_name, layer_kv_cache_spec)
6201
6202
6203
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
6204
                attn_backend_layers[key].append(layer_name)
6205
6206
6207
6208
            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()),
            )
6209
6210

        def create_attn_groups(
6211
            attn_backends_map: dict[AttentionGroupKey, list[str]],
6212
            kv_cache_group_id: int,
6213
6214
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
6215
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
6216
                attn_group = AttentionGroup(
6217
                    attn_backend,
6218
                    layer_names,
6219
                    kv_cache_spec,
6220
                    kv_cache_group_id,
6221
6222
                )

6223
6224
6225
                attn_groups.append(attn_group)
            return attn_groups

6226
        attention_backend_maps = []
6227
        attention_backend_list = []
6228
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
6229
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
6230
            attention_backend_maps.append(attn_backends[0])
6231
            attention_backend_list.append(attn_backends[1])
6232
6233

        # Resolve cudagraph_mode before actually initialize metadata_builders
6234
        self._check_and_update_cudagraph_mode(
6235
6236
6237
            attention_backend_list,
            kv_cache_config.kv_cache_groups,
            is_profiling=is_profiling,
6238
        )
6239

6240
6241
6242
        # Check if attention backend supports PCP&DCP and related features.
        check_attention_cp_compatibility(self.vllm_config)

6243
6244
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
6245

6246
6247
6248
6249
6250
6251
6252
6253
6254
6255
6256
6257
6258
6259
6260
    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
6261
6262
                    if not self.parallel_config.use_ubatching
                    else self.parallel_config.num_ubatches,
6263
                )
co63oc's avatar
co63oc committed
6264
        # Calculate reorder batch threshold (if needed)
6265
6266
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
6267
6268
        self.calculate_reorder_batch_threshold()

6269
6270
6271
6272
6273
        # Initialize drafter attention backend
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_draft_model()
        ):
6274
6275
6276
            assert isinstance(
                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
            )
6277
6278
            self.drafter.initialize_attn_backend(kv_cache_config, kernel_block_sizes)

6279
    def _check_and_update_cudagraph_mode(
6280
6281
6282
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
6283
        is_profiling: bool = False,
6284
    ) -> None:
6285
        """
6286
        Resolve the cudagraph_mode when there are multiple attention
6287
        groups with potential conflicting CUDA graph support.
6288
6289
6290
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
6291
        min_cg_support = AttentionCGSupport.ALWAYS
6292
        min_cg_backend_name = None
6293

6294
6295
6296
6297
6298
6299
6300
6301
6302
6303
6304
6305
        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__
6306
6307
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
6308
        assert cudagraph_mode is not None
6309
        # check cudagraph for mixed batch is supported
6310
6311
6312
6313
6314
6315
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
6316
                f"with {min_cg_backend_name} backend (support: "
6317
6318
                f"{min_cg_support})"
            )
6319
6320
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
6321
6322
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
6323
                    "make sure compilation mode is VLLM_COMPILE"
6324
                )
6325
6326
6327
6328
6329
                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"
6330
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6331
                    CUDAGraphMode.FULL_AND_PIECEWISE
6332
                )
6333
6334
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
6335
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6336
                    CUDAGraphMode.FULL_DECODE_ONLY
6337
                )
6338
6339
            logger.warning(msg)

6340
        # check that if we are doing decode full-cudagraphs it is supported
6341
6342
6343
6344
6345
6346
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
6347
                f"with {min_cg_backend_name} backend (support: "
6348
6349
                f"{min_cg_support})"
            )
6350
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
6351
6352
6353
6354
6355
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
6356
                    "attention is compiled piecewise"
6357
6358
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6359
                    CUDAGraphMode.PIECEWISE
6360
                )
6361
            else:
6362
6363
                msg += (
                    "; setting cudagraph_mode=NONE because "
6364
                    "attention is not compiled piecewise"
6365
6366
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6367
                    CUDAGraphMode.NONE
6368
                )
6369
6370
            logger.warning(msg)

6371
6372
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
6373
6374
6375
6376
6377
6378
6379
6380
        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 "
6381
                f"{min_cg_backend_name} (support: {min_cg_support})"
6382
            )
6383
6384
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
6385
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6386
                    CUDAGraphMode.PIECEWISE
6387
                )
6388
6389
            else:
                msg += "; setting cudagraph_mode=NONE"
6390
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
6391
                    CUDAGraphMode.NONE
6392
                )
6393
6394
6395
6396
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
6397
6398
6399
6400
6401
6402
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
6403
                f"supported with {min_cg_backend_name} backend ("
6404
6405
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
6406
                "and make sure compilation mode is VLLM_COMPILE"
6407
            )
6408

6409
6410
6411
6412
        # 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
6413
        # Will be removed in the near future when we have separate cudagraph capture
6414
6415
6416
6417
6418
6419
6420
6421
6422
6423
        # 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
            )

6424
6425
6426
6427
6428
6429
        # For Mamba models with FULL decode cudagraphs, each decode
        # sequence needs one Mamba cache block. The decode cudagraph
        # dispatcher already caps batch sizes at max_num_seqs, so we just
        # need to verify that enough blocks exist. Raising here instead
        # of silently capping cudagraph_capture_sizes avoids unintended
        # restrictions on PIECEWISE (prefill) cudagraphs.
6430
        # See: https://github.com/vllm-project/vllm/issues/34094
6431
        if cudagraph_mode.has_full_cudagraphs() and not is_profiling:
6432
6433
6434
6435
            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:
6436
6437
6438
6439
6440
6441
6442
6443
6444
6445
6446
                num_blocks = self.kv_cache_config.num_blocks
                if self.max_num_reqs > num_blocks:
                    raise ValueError(
                        f"max_num_seqs ({self.max_num_reqs}) exceeds "
                        f"available Mamba cache blocks ({num_blocks}). "
                        f"Each decode sequence requires one Mamba cache "
                        f"block, so CUDA graph capture cannot proceed. "
                        f"Please lower max_num_seqs to at most "
                        f"{num_blocks} or increase "
                        f"gpu_memory_utilization."
                    )
6447

6448
6449
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
6450
        self.compilation_config.cudagraph_mode = cudagraph_mode
6451
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
6452
            cudagraph_mode, self.uniform_decode_query_len
6453
        )
6454

6455
6456
6457
6458
6459
        # 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()
        ):
6460
6461
6462
6463
            assert isinstance(
                self.drafter,
                EagleProposer | DFlashProposer | ExtractHiddenStatesProposer,
            )
6464
6465
            self.drafter.initialize_cudagraph_keys(cudagraph_mode)

6466
6467
    def calculate_reorder_batch_threshold(self) -> None:
        """
6468
6469
6470
6471
        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.
6472
        """
6473
6474
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

6475
        reorder_batch_thresholds: list[int | None] = [
6476
6477
6478
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
6479
6480
6481
6482
6483
        # 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
6484
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
6485

6486
6487
6488
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
6489
6490
        """
        Re-initialize the input batch if the block sizes are different from
6491
6492
6493
6494
        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.
6495
6496
6497

        Args:
            kv_cache_config: The KV cache configuration.
6498
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6499
        """
6500
        block_sizes = []
6501
6502
        max_num_blocks = []
        max_model_len = max(self.max_model_len, self.max_encoder_len)
6503
        for kv_cache_group in kv_cache_config.kv_cache_groups:
6504
6505
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                continue
6506
6507
            block_size = kv_cache_group.kv_cache_spec.block_size
            block_sizes.append(block_size)
6508
            max_num_blocks_per_req = cdiv(
6509
                max_model_len, block_size * get_total_cp_world_size()
6510
6511
            )
            if isinstance(kv_cache_group.kv_cache_spec, MambaSpec):
6512
                max_num_blocks_per_req = (
6513
6514
6515
6516
6517
                    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)
6518

6519
6520
6521
6522
        if (
            block_sizes != self._init_block_sizes
            or kernel_block_sizes != self._init_kernel_block_sizes
        ):
6523
            assert self.offload_config.uva.cpu_offload_gb == 0, (
6524
6525
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
6526
6527
                "for more details."
            )
6528
6529
            self._init_block_sizes = block_sizes
            self._init_kernel_block_sizes = kernel_block_sizes
6530
6531
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
6532
                max_model_len=max_model_len,
6533
6534
6535
6536
6537
                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,
6538
                kernel_block_sizes=kernel_block_sizes,
6539
                max_num_blocks_per_req=max_num_blocks,
6540
                is_spec_decode=bool(self.vllm_config.speculative_config),
6541
                logitsprocs=self.input_batch.logitsprocs,
6542
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
6543
                is_pooling_model=self.is_pooling_model,
6544
6545
            )

6546
6547
6548
6549
6550
6551
6552
6553
6554
        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}"
        )

6555
    def _allocate_kv_cache_tensors(
6556
6557
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
6558
        """
6559
6560
6561
        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.

6562
        Args:
6563
            kv_cache_config: The KV cache config
6564
        Returns:
6565
            dict[str, torch.Tensor]: A map between layer names to their
6566
            corresponding memory buffer for KV cache.
6567
        """
6568
6569
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
6570
6571
6572
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
6573
6574
6575
6576
6577
            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:
6578
6579
6580
6581
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
6582
6583
6584
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
6585
6586
        return kv_cache_raw_tensors

6587
6588
6589
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

6590
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
6591
6592
        if not self.kv_cache_config.kv_cache_groups:
            return
6593
6594
        for attn_groups in self.attn_groups:
            yield from attn_groups
6595

6596
6597
6598
6599
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
6600
        kernel_block_sizes: list[int],
6601
    ) -> dict[str, torch.Tensor]:
6602
        """
6603
        Reshape the KV cache tensors to the desired shape and dtype.
6604

6605
        Args:
6606
6607
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
6608
                correct size but uninitialized shape.
6609
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6610
        Returns:
6611
            Dict[str, torch.Tensor]: A map between layer names to their
6612
6613
            corresponding memory buffer for KV cache.
        """
6614
        kv_caches: dict[str, torch.Tensor] = {}
6615
        has_attn, has_mamba = False, False
6616
6617
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
6618
            attn_backend = group.backend
6619
6620
6621
6622
            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]
6623
            for layer_name in group.layer_names:
6624
6625
                if layer_name in self.runner_only_attn_layers:
                    continue
6626
6627
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
6628
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
6629
                if isinstance(kv_cache_spec, AttentionSpec):
6630
                    has_attn = True
6631
6632
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
6633
6634
6635
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

6636
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
6637
                        kernel_num_blocks,
6638
                        kernel_block_size,
6639
6640
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
6641
6642
                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
6643
                    dtype = kv_cache_spec.dtype
6644
                    try:
6645
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
6646
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
6647
                    except (AttributeError, NotImplementedError):
6648
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
6649
6650
6651
6652
6653
                    # 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.
6654
6655
6656
                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
6657
6658
6659
6660
6661
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
6662
6663
6664
6665
6666
6667
                    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
6668
                elif isinstance(kv_cache_spec, MambaSpec):
6669
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
6670
6671
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
6672
                    storage_offset_bytes = 0
6673
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
6674
6675
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
6676
6677
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
6678
                        target_shape = (num_blocks, *shape)
6679
6680
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
6681
                        assert storage_offset_bytes % dtype_size == 0
6682
6683
6684
6685
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
6686
                            storage_offset=storage_offset_bytes // dtype_size,
6687
                        )
Chen Zhang's avatar
Chen Zhang committed
6688
                        state_tensors.append(tensor)
6689
                        storage_offset_bytes += stride[0] * dtype_size
6690
6691

                    kv_caches[layer_name] = state_tensors
6692
                else:
6693
                    raise NotImplementedError
6694
6695

        if has_attn and has_mamba:
6696
            self._update_hybrid_attention_mamba_layout(kv_caches, kernel_block_sizes)
6697

6698
6699
        return kv_caches

6700
    def _update_hybrid_attention_mamba_layout(
6701
        self, kv_caches: dict[str, torch.Tensor], kernel_block_sizes: list[int]
6702
    ) -> None:
6703
        """
6704
6705
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
6706
6707

        Args:
6708
            kv_caches: The KV cache buffer of each layer.
6709
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6710
6711
        """

6712
6713
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
6714
6715
6716
6717
6718
6719
6720
6721
6722
6723
6724
6725
            if not isinstance(kv_cache_spec, AttentionSpec):
                continue
            block_dim = group.backend.get_kv_cache_block_dim(
                kernel_block_sizes[group.kv_cache_group_id],
                kv_cache_spec.num_kv_heads,
                kv_cache_spec.head_size,
                cache_dtype_str=self.cache_config.cache_dtype,
            )
            # block_dim: 0 means (num_blocks, 2, ...); 1 means (2, num_blocks, ...).
            if block_dim == 0:
                continue
            assert block_dim == 1
6726
            for layer_name in group.layer_names:
6727
                kv_cache = kv_caches[layer_name]
6728
6729
6730
6731
6732
                hidden_size = kv_cache.shape[2:].numel()
                kv_cache.as_strided_(
                    size=kv_cache.shape,
                    stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                )
6733

6734
    def initialize_kv_cache_tensors(
6735
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
6736
    ) -> dict[str, torch.Tensor]:
6737
6738
6739
6740
6741
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
6742
6743
            kernel_block_sizes: The kernel block sizes for each KV cache group.

6744
        Returns:
6745
            Dict[str, torch.Tensor]: A map between layer names to their
6746
6747
            corresponding memory buffer for KV cache.
        """
6748
6749
6750
6751
6752
6753
6754
6755
6756
6757
6758
6759
6760
6761
6762
6763
6764
6765
6766
6767
6768
6769
6770
6771

        # 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
            )
6772

6773
        # Set up cross-layer KV cache sharing
6774
6775
        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)
6776
6777
            kv_caches[layer_name] = kv_caches[target_layer_name]

6778
6779
6780
6781
6782
6783
6784
6785
6786
        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,
        )
6787
6788
6789
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
6790
6791
        self, kv_cache_config: KVCacheConfig
    ) -> None:
6792
6793
6794
6795
6796
6797
6798
6799
6800
6801
6802
6803
6804
6805
6806
6807
6808
6809
        """
        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.
6810
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
6811
6812
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
6813
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
6814
6815
                else:
                    break
6816

6817
6818
6819
6820
6821
    def initialize_kv_cache(
        self,
        kv_cache_config: KVCacheConfig,
        is_profiling: bool = False,
    ) -> None:
6822
6823
6824
6825
6826
6827
        """
        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
        """
6828
        kv_cache_config = deepcopy(kv_cache_config)
6829
        self.kv_cache_config = kv_cache_config
6830
        self._mamba_copy_bufs = None
6831
        self.may_add_encoder_only_layers_to_kv_cache_config()
6832
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
6833
        self.initialize_attn_backend(kv_cache_config, is_profiling=is_profiling)
6834
6835
6836
6837
6838
        # 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.
6839
6840
6841
        kernel_block_sizes = prepare_kernel_block_sizes(
            kv_cache_config, self.attn_groups
        )
6842
        self._kernel_block_sizes = kernel_block_sizes
6843
6844
6845
6846

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

6847
        # Reinitialize need to after initialize_attn_backend
6848
6849
6850
6851
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
6852

6853
6854
6855
        if (
            self.speculative_config
            and self.speculative_config.uses_extract_hidden_states()
6856
        ):
6857
            assert isinstance(self.drafter, ExtractHiddenStatesProposer)
6858
6859
6860
6861
            # 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
6862
        if has_kv_transfer_group():
6863
            kv_transfer_group = get_kv_transfer_group()
6864
6865
6866
6867
6868
6869
6870
            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)
6871
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
6872

6873
6874
6875
6876
6877
6878
    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
6879
6880
6881
6882
6883
6884
6885

    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()
6886
6887
6888
6889
6890
6891
6892
6893
        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)
6894
        self.max_num_kv_tokens = (
6895
6896
6897
6898
6899
6900
6901
            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

6902
6903
6904
        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,
6905
            vllm_config=self.vllm_config,
6906
        )
6907
        self._bind_routed_experts_capturer(routed_experts_capturer)
6908
        self.routed_experts_initialized = True
6909
6910
6911
6912
6913
6914
6915
6916
6917
6918
6919
6920
6921
6922
6923

    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)
6924

6925
6926
6927
6928
6929
    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
6930
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
6931
6932
6933
        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:
6934
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
6935
6936
6937
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
6938
6939
                    dtype=self.kv_cache_dtype,
                )
6940
6941
6942
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
6943
6944
6945
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
6946
6947
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
6948
6949
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
6950

6951
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
6952
        """
6953
        Generates the KVCacheSpec by parsing the kv cache format from each
6954
6955
        Attention module in the static forward context.
        Returns:
6956
            KVCacheSpec: A dictionary mapping layer names to their KV cache
6957
6958
            format. Layers that do not need KV cache are not included.
        """
6959
        if has_ec_transfer() and not get_ec_transfer().is_consumer:
6960
            return {}
6961
        kv_cache_spec: dict[str, KVCacheSpec] = {}
6962
6963
        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
6964
        for layer_name, attn_module in attn_layers.items():
6965
6966
6967
6968
6969
6970
6971
6972
6973
6974
6975
6976
6977
6978
6979
            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
6980

6981
        return kv_cache_spec
6982

6983
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
6984
6985
6986
6987
6988
6989
6990
6991
        # 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.
6992
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
6993
6994
6995
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
6996
        return pinned.tolist()
6997
6998
6999
7000
7001
7002
7003
7004
7005
7006
7007
7008
7009
7010
7011
7012
7013
7014
7015
7016
7017
7018
7019
7020
7021
7022
7023
7024
7025
7026
7027
7028
7029
7030
7031
7032
7033
7034
7035
7036

    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}

7037
        torch.accelerator.synchronize()
7038
7039
7040
7041
7042
        start_time = time.perf_counter()

        try:
            yield
        finally:
7043
            torch.accelerator.synchronize()
7044
7045
7046
7047
7048
7049
7050
7051
7052
7053
            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]
7054
                    stats.encoder_forward_secs += per_request_time
7055
7056
7057
7058
7059
7060
7061
                    stats.num_encoder_calls += 1


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

7062
    encoder_forward_secs: float = 0.0
7063
7064
7065
7066
7067
7068
7069
    """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 {
7070
            "encoder_forward_secs": self.encoder_forward_secs,
7071
7072
            "num_encoder_calls": self.num_encoder_calls,
        }