gpu_model_runner.py 300 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
from vllm.model_executor.layers.mamba.ops.ssu_dispatch import (
    initialize_mamba_ssu_backend,
)
62
63
64
65
from vllm.model_executor.layers.rotary_embedding import (
    MRotaryEmbedding,
    XDRotaryEmbedding,
)
66
from vllm.model_executor.model_loader import get_model_loader
67
68
69
70
from vllm.model_executor.model_loader.reload import (
    finalize_layerwise_reload,
    initialize_layerwise_reload,
)
71
from vllm.model_executor.models.interfaces import (
72
    MultiModalEmbeddings,
73
    SupportsMRoPE,
74
    SupportsMultiModal,
75
    SupportsXDRoPE,
76
77
78
79
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
80
    supports_realtime,
81
    supports_transcription,
82
    supports_xdrope,
83
)
84
from vllm.model_executor.models.interfaces_base import (
85
86
87
88
    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
89
90
91
92
93
from vllm.model_executor.offloader import (
    create_offloader,
    get_offloader,
    set_offloader,
)
94
from vllm.multimodal import MULTIMODAL_REGISTRY
95
from vllm.multimodal.encoder_budget import MultiModalBudget
96
97
98
99
100
from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
101
from vllm.multimodal.utils import group_and_batch_mm_kwargs
102
from vllm.platforms import current_platform
103
from vllm.pooling_params import PoolingParams
104
from vllm.sampling_params import SamplingType
105
from vllm.sequence import IntermediateTensors
106
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
107
from vllm.tracing import instrument
108
from vllm.utils import length_from_prompt_token_ids_or_embeds
109
from vllm.utils.math_utils import cdiv, round_up
110
from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
111
from vllm.utils.nvtx_pytorch_hooks import PytHooks
112
from vllm.utils.platform_utils import is_pin_memory_available, num_compute_units
113
114
from vllm.utils.torch_utils import (
    get_dtype_size,
115
    is_quantized_kv_cache,
116
117
    kv_cache_dtype_str_to_dtype,
)
118
119
from vllm.v1.attention.backend import (
    AttentionBackend,
120
    AttentionCGSupport,
121
    AttentionMetadata,
122
    AttentionMetadataBuilder,
123
    AttentionType,
124
    CommonAttentionMetadata,
125
)
126
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
127
from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadataBuilder
128
from vllm.v1.attention.backends.utils import (
129
    NULL_BLOCK_ID,
130
    create_fast_prefill_custom_backend,
131
    get_dcp_local_seq_lens,
132
133
    reorder_batch_to_split_decodes_and_prefills,
)
134
from vllm.v1.core.sched.output import NewRequestData
135
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
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,
153
    ECConnectorOutput,
154
    KVConnectorOutput,
155
156
157
158
159
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
160
    make_empty_encoder_model_runner_output,
161
)
162
from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
163
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
164
from vllm.v1.sample.logits_processor.interface import LogitsProcessor
165
from vllm.v1.sample.metadata import SamplingMetadata
166
from vllm.v1.sample.rejection_sampler import RejectionSampler
167
from vllm.v1.sample.sampler import Sampler
168
from vllm.v1.spec_decode.dflash import DFlashProposer
169
from vllm.v1.spec_decode.draft_model import DraftModelProposer
170
from vllm.v1.spec_decode.eagle import EagleProposer
171
from vllm.v1.spec_decode.extract_hidden_states import ExtractHiddenStatesProposer
172
from vllm.v1.spec_decode.medusa import MedusaProposer
173
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
174
175
176
177
178
179
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,
)
180
from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
181
from vllm.v1.spec_decode.utils import update_num_computed_tokens_for_batch_change
182
from vllm.v1.structured_output.utils import apply_grammar_bitmask
183
from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
184
185
186
187
188
from vllm.v1.worker import mamba_utils
from vllm.v1.worker.cp_utils import (
    check_attention_cp_compatibility,
    get_total_cp_world_size,
)
189
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
190
from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
191
from vllm.v1.worker.gpu.pool.late_interaction_runner import LateInteractionRunner
192
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
193
from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
194
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
195
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
196
197
198
from vllm.v1.worker.ubatch_utils import (
    UBatchSlices,
    check_ubatch_thresholds,
199
    maybe_create_ubatch_slices,
200
    split_attn_metadata,
201
)
202
from vllm.v1.worker.utils import is_residual_scattered_for_sp
203
from vllm.v1.worker.workspace import lock_workspace
204

205
206
from .utils import (
    AttentionGroup,
207
    KVBlockZeroer,
208
209
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
210
    prepare_kernel_block_sizes,
211
212
    sanity_check_mm_encoder_outputs,
)
213

214
if TYPE_CHECKING:
215
    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
216
    from vllm.v1.spec_decode.ngram_proposer import NgramProposer
217
    from vllm.v1.worker.encoder_cudagraph import EncoderCudaGraphManager
218
219
220

logger = init_logger(__name__)

221
222
AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
223
PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
224

225

226
227
228
229
230
231
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
232
        logprobs_tensors: LogprobsTensors | None,
233
234
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
235
        vocab_size: int,
236
237
238
239
240
    ):
        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.
241
        self.async_copy_ready_event = torch.Event()
242
243
244
245

        # 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
246
        self.vocab_size = vocab_size
247
        self._logprobs_tensors = logprobs_tensors
248
249
250
251
252

        # 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)
253
            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
254
255
                "cpu", non_blocking=True
            )
256
257
258
259
260
            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
261
            self.async_copy_ready_event.record()
262
263
264

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
265

266
267
        This function blocks until the copy is finished.
        """
268
        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
269
        self.async_copy_ready_event.synchronize()
270

271
272
        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
273
        del self._sampled_token_ids
274
        if max_gen_len == 1:
275
            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
276
277
            for i in self._invalid_req_indices:
                valid_sampled_token_ids[i].clear()
278
279
280
            logprobs_lists = None
            if self._logprobs_tensors_cpu is not None:
                logprobs_lists = self._logprobs_tensors_cpu.tolists()
281
        else:
282
            valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
283
284
                self.sampled_token_ids_cpu,
                self.vocab_size,
285
                self._invalid_req_indices,
286
                logprobs_tensors=self._logprobs_tensors_cpu,
287
            )
288
289
290

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
291
        output.logprobs = logprobs_lists
292
293
294
        return output


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
336
337
338
339
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


340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
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)
361
362
363
            self._model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
                raw_pooler_output=self._raw_pooler_output,
                finished_mask=finished_mask,
364
365
366
367
368
369
370
371
372
373
374
375
376
377
            )
            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


378
379
380
381
382
383
384
385
386
387
388
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
389
    ec_connector_output: ECConnectorOutput | None
390
    cudagraph_stats: CUDAGraphStat | None
391
    slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None
392
393


394
395
396
class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
397
398
    def __init__(
        self,
399
        vllm_config: VllmConfig,
400
        device: torch.device,
401
    ):
402
403
404
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
405
        self.offload_config = vllm_config.offload_config
406
        self.compilation_config = vllm_config.compilation_config
407
408
409
410
411
412
        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
413

414
415
416
417
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
418
        self.device = device
419
420
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
421

422
423
424
        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
425

426
        self.is_pooling_model = model_config.runner_type == "pooling"
427
        self.enable_prompt_embeds = model_config.enable_prompt_embeds
428
        self.is_multimodal_raw_input_only_model = (
429
430
            model_config.is_multimodal_raw_input_only_model
        )
431
        # These will be overridden in load_model()
432
        self.is_multimodal_pruning_enabled = False
433
        self.requires_sequential_video_encoding = False
434
435
436
        # Set to True after init_routed_experts_capturer() completes.
        # Prevents routed experts code from running during profiling/dummy run.
        self.routed_experts_initialized = False
437
        self.max_model_len = model_config.max_model_len
438
439
440

        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
441
        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
442
        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
443
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
444
        self.max_num_reqs = scheduler_config.max_num_seqs
445

446
447
        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
448
        # TODO: Support overlapping micro-batches
449
450
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
451
            self.parallel_config.distributed_executor_backend == "external_launcher"
452
            and len(get_pp_group().ranks) > 1
453
        )
454

455
        # Model-related.
456
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
457
        self.inputs_embeds_size = model_config.get_inputs_embeds_size()
458
        # Only relevant for models using ALiBi (e.g, MPT)
459
        self.use_alibi = model_config.uses_alibi
460

461
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
462
        self.is_mm_prefix_lm = self.model_config.is_mm_prefix_lm
463

464
        # Multi-modal data support
465
        self.mm_registry = MULTIMODAL_REGISTRY
466
        self.uses_mrope = model_config.uses_mrope
467
        self.uses_xdrope_dim = model_config.uses_xdrope_dim
468
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
469
            model_config
470
        )
471

472
473
474
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
475
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
476
477
478
        else:
            self.max_encoder_len = 0

479
480
481
        # Async scheduling
        self.use_async_scheduling = self.scheduler_config.async_scheduling

482
        # Sampler
483
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
484

485
        self.eplb_state: EplbState | None = None
486
487
        # NOTE(yongji): flag to temporarily disable EPLB during scaling up/down
        self.eep_eplb_suppressed = False
488
489
490
491
492
493
        """
        State of the expert parallelism load balancer.

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

494
        # Lazy initializations
495
        # self.model: nn.Module  # Set after load_model
496
        # Initialize in initialize_kv_cache
497
        self.kv_caches: list[torch.Tensor] = []
498
499
500
        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
501
502
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
503
504
        # self.kv_cache_config: KVCacheConfig

505
506
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
507
        self.late_interaction_runner = LateInteractionRunner()
508

509
510
511
        # Encoder CUDA graph manager (initialized after model load if enabled)
        self.encoder_cudagraph_manager: EncoderCudaGraphManager | None = None

512
        self.use_aux_hidden_state_outputs = False
513
514
515
516
517
        # 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:
518
            self.drafter: (
519
                NgramProposer  # noqa: F823
520
                | NgramProposerGPU
521
522
                | SuffixDecodingProposer
                | EagleProposer
523
                | DFlashProposer
524
525
                | DraftModelProposer
                | MedusaProposer
526
                | ExtractHiddenStatesProposer
527
            )
528
            if self.speculative_config.method == "ngram":
529
530
                from vllm.v1.spec_decode.ngram_proposer import NgramProposer

531
                self.drafter = NgramProposer(self.vllm_config)
532
533
534
535
536
537
            elif self.speculative_config.uses_draft_model():
                self.drafter = DraftModelProposer(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    runner=self,
                )
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
            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
                )
555
556
557
            elif self.speculative_config.use_dflash():
                self.drafter = DFlashProposer(self.vllm_config, self.device, self)
                self.use_aux_hidden_state_outputs = True
558
559
            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
560
            elif self.speculative_config.use_eagle():
561
                self.drafter = EagleProposer(self.vllm_config, self.device, self)
562
                if self.speculative_config.method == "eagle3":
563
564
565
                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
566
567
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
568
                    vllm_config=self.vllm_config, device=self.device
569
                )
570
571
572
573
574
            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
575
            else:
576
577
578
579
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
580
            self.rejection_sampler = RejectionSampler(self.sampler)
581

582
        self.num_spec_tokens = 0
583
        self.valid_sampled_token_count_gpu: torch.Tensor | None = None
584
585
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens
586
587
588
589
590
            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
591
592
593
        self.use_async_spec_decode = (
            self.use_async_scheduling and self.num_spec_tokens > 0
        )
594

595
        # Request states.
596
        self.requests: dict[str, CachedRequestState] = {}
597
598
599
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
600

601
602
603
604
605
606
607
608
609
        # 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.
610
611
612
613
        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
614
615
616
617
618
        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]
619
620
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
621
            # We need to use the encoder length for encoder-decoder
622
623
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
624
625
626
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
627
            vocab_size=self.model_config.get_vocab_size(),
628
629
            block_sizes=[placeholder_block_size],
            kernel_block_sizes=[placeholder_block_size],
630
            is_spec_decode=bool(self.vllm_config.speculative_config),
631
            logitsprocs=build_logitsprocs(
632
633
634
                self.vllm_config,
                self.device,
                self.pin_memory,
635
                self.is_pooling_model,
636
                custom_logitsprocs,
637
            ),
638
639
            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
640
641
642
643
            # 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,
644
            is_pooling_model=self.is_pooling_model,
645
            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
646
        )
647

648
649
650
651
652
        # 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.
653
        self.prepare_inputs_event: torch.Event | None = None
654
655
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
656
            self.prepare_inputs_event = torch.Event()
657

658
659
660
661
662
663
664
665
666
667
668
        # 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 = []

669
        # Cache the device properties.
670
        self._init_device_properties()
671

672
673
674
675
        # Encoder timing registry for observability
        self.encoder_timing_registry: dict[str, EncoderTimingStats] = {}
        self._encoder_timing_lock = threading.Lock()

676
        # Persistent buffers for CUDA graphs.
677
        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
678
679
680
        self.positions = torch.zeros(
            self.max_num_tokens, dtype=torch.int64, device=self.device
        )
681
682
683
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
        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
        )

703
        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
704
705
706
707
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
708
709
710
        # 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.
711
        self.inputs_embeds = self._make_buffer(
712
            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
713
714
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
715
716
        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
717
718
719
720
721
        )
        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
722
            self.max_num_reqs, dtype=torch.int32
723
        )
724
725

        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
726
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
727
728
729
730
            # 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
731
732
733
734
735
736

            # 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
737
            self.mrope_positions = self._make_buffer(
738
739
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
740

741
742
743
744
745
746
747
        # 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
            )

748
        # None in the first PP rank. The rest are set after load_model.
749
        self.intermediate_tensors: IntermediateTensors | None = None
750

751
752
753
754
755
756
757
758
        # 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)
759

760
761
762
763
764
        # 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] = {}
765
766
767
768
769
        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(
770
771
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
772

773
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
774
775
776
777

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

778
        self.mm_budget = (
779
            MultiModalBudget(self.vllm_config, self.mm_registry)
780
781
782
            if self.supports_mm_inputs
            else None
        )
783

784
        self.reorder_batch_threshold: int | None = None
785

786
787
788
789
790
        # 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()

791
        # Cached outputs.
792
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
        # 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()

808
        self._draft_token_req_ids: list[str] | None = None
809
        self.transfer_event = torch.Event()
810
        self.sampled_token_ids_pinned_cpu = torch.empty(
811
            (self.max_num_reqs, 1),
812
813
            dtype=torch.int64,
            device="cpu",
814
815
            pin_memory=self.pin_memory,
        )
816

817
818
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
819
        self.valid_sampled_token_count_event: torch.Event | None = None
820
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
821
822
823
824
825
826
        # 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
827
        self.num_accepted_tokens_event: torch.Event | None = None
828
829
        if self.num_spec_tokens:
            self.draft_token_ids_event = torch.Event()
830
            self.num_accepted_tokens_event = torch.Event()
831
832
833
834
835
836
837
838
839
840
841
842
            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,
843
                    dtype=torch.int32,
844
845
846
                    device="cpu",
                    pin_memory=self.pin_memory,
                )
847

848
849
850
851
        # Model weight offloader
        # Make sure this is called before any get_offloader call
        set_offloader(create_offloader(self.offload_config))

852
853
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
854
        self.kv_connector_output: KVConnectorOutput | None = None
855
        self.mamba_state_idx: dict[str, int] = {}
856
        self._mamba_copy_bufs: mamba_utils.MambaCopyBuffers | None = None
857
        self.layerwise_nvtx_hooks_registered = False
858

859
860
861
862
863
864
865
    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

866
    def reset_mm_cache(self) -> None:
867
868
869
870
        """
        Clear the multi-modal cache that was used during profiling,
        but no longer needed during inference.
        """
871
872
        if self.mm_budget:
            self.mm_budget.reset_cache()
873
        self.late_interaction_runner.clear()
874

875
876
877
878
879
880
881
    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()
882
        self.late_interaction_runner.clear()
883

884
885
886
887
888
889
890
891
892
    @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.
        """
893
        if not is_quantized_kv_cache(self.cache_config.cache_dtype):
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
            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)

928
929
930
931
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
932
933
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
934
            return self.positions[:num_tokens]
935
936
937
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
938
939
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
940
            return self.positions[num_tokens]
941

942
    def _make_buffer(
943
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
944
945
946
947
948
949
950
951
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
952

953
954
955
956
957
958
959
960
961
962
    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

963
    def _init_model_kwargs(self):
964
965
        model_kwargs = dict[str, Any]()

966
        if not self.is_pooling_model:
967
968
            return model_kwargs

969
970
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
971
972
973

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
974
975
976
977
978
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
979
980
981
982
983
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

984
        seq_lens = self.seq_lens[:num_reqs]
985
986
987
988
989
990
991
992
        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(
993
994
            device=self.device
        )
995
996
        return model_kwargs

997
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
998
999
        """
        Update the order of requests in the batch based on the attention
1000
        backend's needs. For example, some attention backends (namely MLA) may
1001
1002
1003
1004
1005
1006
        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
1007
        # Attention free models have zero kv_cache_groups, however models
1008
1009
1010
1011
1012
1013
1014
        # 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

1015
1016
1017
1018
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
1019
1020
                decode_threshold=self.reorder_batch_threshold,
            )
1021

1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
    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)

1042
1043
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
1044
        """Initialize attributes from torch.cuda.get_device_properties"""
1045
1046

        self.num_sms = num_compute_units(self.device.index)
1047
1048
1049

    # Note: used for model runner override.
    def _sync_device(self) -> None:
1050
        torch.accelerator.synchronize()
1051

1052
1053
1054
1055
1056
1057
1058
    def _get_or_create_async_output_copy_stream(self) -> torch.cuda.Stream:
        stream = self.async_output_copy_stream
        if stream is None:
            stream = torch.cuda.Stream()
            self.async_output_copy_stream = stream
        return stream

1059
    def _update_states(self, scheduler_output: "SchedulerOutput") -> Callable | None:
1060
1061
1062
1063
1064
1065
        """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.

1066
1067
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
1068
1069
        """
        # Remove finished requests from the cached states.
1070
1071
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
1072
            self.num_prompt_logprobs.pop(req_id, None)
1073
1074
1075
        self.late_interaction_runner.on_requests_finished(
            scheduler_output.finished_req_ids
        )
1076
1077
1078
1079
1080
1081
1082
        # 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:
1083
            self.input_batch.remove_request(req_id)
1084

1085
1086
1087
1088
1089
        # 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)

1090
        # Free the cached encoder outputs.
1091
1092
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
1093

1094
1095
1096
1097
1098
1099
1100
        # 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()
1101
1102
1103
1104
1105
1106
1107
1108
        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)
1109
1110
1111
1112
1113
        # 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:
1114
            self.input_batch.remove_request(req_id)
1115

1116
1117
1118
1119
1120
1121
1122
        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] = []

1123
        reqs_to_add: list[CachedRequestState] = []
1124
1125
        deferred_spec_decode_corrections = []

1126
        # Add new requests to the cached states.
1127
1128
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
1129
1130
1131
1132
1133
1134
            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

1135
            sampling_params = new_req_data.sampling_params
1136
            pooling_params = new_req_data.pooling_params
1137

1138
1139
1140
1141
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
1142
1143
1144
1145
1146
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

1147
1148
            if self.is_pooling_model:
                assert pooling_params is not None
1149
1150
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
1151

1152
                model = cast(VllmModelForPooling, self.get_model())
1153
                to_update = model.pooler.get_pooling_updates(task)
1154
1155
                to_update.apply(pooling_params)

1156
            req_state = CachedRequestState(
1157
                req_id=req_id,
1158
                prompt_token_ids=new_req_data.prompt_token_ids,
1159
                prompt_embeds=new_req_data.prompt_embeds,
1160
                mm_features=new_req_data.mm_features,
1161
                sampling_params=sampling_params,
1162
                pooling_params=pooling_params,
1163
                generator=generator,
1164
1165
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
1166
                output_token_ids=[],
1167
                lora_request=new_req_data.lora_request,
1168
            )
1169
            self.requests[req_id] = req_state
1170
            self.late_interaction_runner.register_request(req_id, pooling_params)
1171

1172
1173
1174
1175
1176
1177
1178
            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
                )

1179
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1180
            if self.uses_mrope:
1181
                self._init_mrope_positions(req_state)
1182

1183
1184
1185
1186
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

1187
            reqs_to_add.append(req_state)
1188
1189
1190
            # Track new requests for ngram_gpu full tensor copy
            if is_ngram_gpu:
                ngram_gpu_new_reqs.append(req_state)
1191

1192
        # Update the states of the running/resumed requests.
1193
        is_last_rank = get_pp_group().is_last_rank
1194
        req_data = scheduler_output.scheduled_cached_reqs
1195
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1196

1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
        # 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,
            )
1212
1213
        if self.use_async_spec_decode:
            self.prev_num_draft_tokens.np.fill(0)
1214

1215
        for i, req_id in enumerate(req_data.req_ids):
1216
            req_state = self.requests[req_id]
1217
1218
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
1219
            resumed_from_preemption = req_id in req_data.resumed_req_ids
1220
            num_output_tokens = req_data.num_output_tokens[i]
1221
            req_index = self.input_batch.req_id_to_index.get(req_id)
1222

1223
1224
1225
1226
            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:
1227
                # first step: num_computed_tokens = 0, spec_tokens = [],
1228
                # prev_num_draft_len = 0.
Jiayi Yan's avatar
Jiayi Yan committed
1229
                # second step: num_computed_tokens = 100(prompt length),
1230
1231
1232
                # 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.
1233
                # num_computed_tokens in first step and second step doesn't contain
1234
1235
1236
                # 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.
1237
1238
1239
                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
                    # 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
                        )
1259

1260
1261
1262
1263
                    if is_ngram_gpu and optimistic_num_accepted > 0:
                        self.input_batch.num_tokens_no_spec[req_index] += (
                            optimistic_num_accepted
                        )
1264

1265
            # Update the cached states.
1266
            req_state.num_computed_tokens = num_computed_tokens
1267
1268

            if not is_last_rank:
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
                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:]
                        )
1289
1290
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
1291
1292
                # failure, or output_token_ids was inflated by the optimistic
                # extend above (async spec decode). Align the cached state.
1293
1294
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
1295
1296
1297
1298
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
1299
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
1300

1301
            # Update the block IDs.
1302
            if not resumed_from_preemption:
1303
1304
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
1305
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
1306
                        block_ids.extend(new_ids)
1307
            else:
1308
                assert req_index is None
1309
                assert new_block_ids is not None
1310
1311
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
1312
                req_state.block_ids = new_block_ids
1313
1314
1315
1316
1317

            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.
1318
1319
1320
1321
1322
1323
1324

                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:]

1325
                reqs_to_add.append(req_state)
1326
1327
1328
                # Track resumed requests for ngram_gpu full tensor copy
                if is_ngram_gpu:
                    ngram_gpu_new_reqs.append(req_state)
1329
1330
1331
                continue

            # Update the persistent batch.
1332
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
1333
            if new_block_ids is not None:
1334
                self.input_batch.block_table.append_row(new_block_ids, req_index)
1335
1336
1337
1338
1339
1340
1341

            # 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)
1342
                self.input_batch.token_ids_cpu[
1343
1344
1345
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
1346

1347
            # Add spec_token_ids to token_ids_cpu.
1348
            self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
1349
1350
1351
1352
1353
            # 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
1354

1355
1356
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
1357
1358
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1359
            self.input_batch.update_req_spec_token_ids(request, scheduled_spec_tokens)
1360

1361
1362
1363
1364
1365
1366
        # 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()
1367

1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
        # 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,
            )

1380
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
        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

1414
    def _update_states_after_model_execute(
1415
        self, output_token_ids: torch.Tensor, scheduler_output: "SchedulerOutput"
1416
    ) -> None:
1417
1418
1419
1420
1421
1422
1423
1424
        """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.
        """
1425
        if not self.speculative_config or not self.model_config.is_hybrid:
1426
1427
            return

1428
1429
1430
        # 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.
1431
1432
1433
        # Count the number of accepted tokens for each sequence.
        # Valid tokens are contiguous from position 0, so counting non-(-1)
        # tokens gives us the first -1 position (i.e., number of accepted).
1434
        num_reqs = output_token_ids.size(0)
1435
        self.num_accepted_tokens.gpu[:num_reqs] = (output_token_ids != -1).sum(dim=1)
1436

1437
        if self.cache_config.mamba_cache_mode == "align":
1438
1439
1440
1441
            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
1442
1443
1444
1445
1446
1447
1448
1449
            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(),
1450
                self._get_mamba_copy_bufs(),
1451
            )
1452
1453
1454
1455
        else:
            self.input_batch.num_accepted_tokens_cpu_tensor[:num_reqs].copy_(
                self.num_accepted_tokens.gpu[:num_reqs], non_blocking=True
            )
1456
1457
            assert self.num_accepted_tokens_event is not None
            self.num_accepted_tokens_event.record()
1458

1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
    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
1478
        self.late_interaction_runner.register_request(req_id, req_state.pooling_params)
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
        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

1494
    def _init_mrope_positions(self, req_state: CachedRequestState):
1495
1496
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1497
1498
1499
1500
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1501
1502

        req_state.mrope_positions, req_state.mrope_position_delta = (
1503
            mrope_model.get_mrope_input_positions(
1504
                req_state.prompt_token_ids,
1505
                req_state.mm_features,
1506
            )
1507
        )
1508

1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
    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,
        )

1522
    def _extract_mm_kwargs(
1523
        self,
1524
1525
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1526
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1527
            return {}
1528

1529
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
1530
        for req in scheduler_output.scheduled_new_reqs:
1531
1532
            for feature in req.mm_features:
                if feature.data is not None:
1533
                    mm_kwargs.append((feature.modality, feature.data))
1534

1535
1536
        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
1537
        for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
1538
1539
1540
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
1541
        ):
1542
            mm_kwargs_combined.update(mm_kwargs_batch)
1543

1544
        return mm_kwargs_combined
1545

1546
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1547
        if not self.is_multimodal_raw_input_only_model:
1548
            return {}
1549

1550
1551
1552
        mm_budget = self.mm_budget
        assert mm_budget is not None

1553
1554
1555
        if not mm_budget.mm_max_toks_per_item:
            return {}  # No tower modalities (embed-only mode)

1556
1557
        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
1558

1559
1560
1561
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1562
        arange_out: np.ndarray,
1563
        cumsum_dtype: np.dtype | None = None,
1564
    ) -> np.ndarray:
1565
        """Get the cumulative sum and batched arange of the given array.
1566
1567
1568
1569
        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])
1570
1571
1572
1573
1574
1575
1576
        """
        # 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]
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
        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
1596

1597
1598
        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)
1599

1600
    def _prepare_input_ids(
1601
1602
        self,
        scheduler_output: "SchedulerOutput",
1603
        num_reqs: int,
1604
1605
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1606
    ) -> None:
1607
        """Prepare the input IDs for the current batch.
1608

1609
1610
        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
1611
1612
1613
1614
1615
        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).
        """
1616
1617
1618
1619

        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1620
1621
1622
            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)
1623
1624
1625
1626
1627
            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.
1628
1629
        prev_positions = self.prev_positions.np[:num_reqs]
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1630
1631
1632
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1633
1634
        prev_indices: list[int] = []
        common_indices_match = True
1635
        max_flattened_index = -1
1636
1637
        total_num_spec_tokens = 0

1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
        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
1667
        num_common_tokens = len(sample_flattened_indices)
1668
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
Jiayi Yan's avatar
Jiayi Yan committed
1669
        if num_common_tokens < total_without_spec:
1670
            # If not all requests are decodes from the last iteration,
1671
            # we need to copy the input_ids_cpu to the GPU first.
1672
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1673
1674
1675
            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
1676
        if num_common_tokens == 0:
1677
            # No requests in common with the previous iteration
1678
            # So input_ids.cpu will have all the input ids.
1679
            return
1680
        if common_indices_match and max_flattened_index == (num_common_tokens - 1):
1681
1682
1683
1684
            # 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
1685
1686
            self.input_ids.gpu[:num_common_tokens].copy_(
                self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0],
1687
1688
                non_blocking=True,
            )
1689
            if self.enable_prompt_embeds:
Jiayi Yan's avatar
Jiayi Yan committed
1690
                self.is_token_ids.gpu[:num_common_tokens] = True
1691
            return
1692
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1693
1694
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1695
        ).to(self.device, non_blocking=True)
1696
        prev_common_req_indices_tensor = torch.tensor(
1697
            prev_indices, dtype=torch.int64, pin_memory=self.pin_memory
1698
        ).to(self.device, non_blocking=True)
1699
1700
        self.input_ids.gpu.scatter_(
            dim=0,
1701
            index=sampled_tokens_index_tensor,
1702
            src=self.input_batch.prev_sampled_token_ids[
1703
1704
1705
                prev_common_req_indices_tensor, 0
            ],
        )
1706

1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
        # 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],
        )

1729
1730
    def _get_encoder_seq_lens(
        self,
1731
        num_scheduled_tokens: dict[str, int],
1732
1733
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1734
        for_cudagraph_capture: bool = False,
1735
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1736
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1737
            return None, None
1738

1739
1740
        # Zero out buffer for padding requests that are not actually scheduled (CGs)
        self.encoder_seq_lens.np[:num_reqs] = 0
1741

1742
1743
        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1744
        for req_id in num_scheduled_tokens:
1745
            req_index = self.input_batch.req_id_to_index[req_id]
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
            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
1758
1759
1760
1761
1762
1763
1764
1765
1766
        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
1767
1768
1769
1770

        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]
1771

1772
        return encoder_seq_lens, encoder_seq_lens_cpu
1773

1774
    def _prepare_inputs(
1775
1776
1777
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1778
1779
    ) -> tuple[
        torch.Tensor,
1780
        SpecDecodeMetadata | None,
1781
    ]:
1782
1783
        """
        :return: tuple[
1784
            logits_indices, spec_decode_metadata,
1785
1786
        ]
        """
1787
1788
1789
1790
1791
1792
1793
        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.
1794
        self.input_batch.block_table.commit_block_table(num_reqs)
1795
1796
1797

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1798
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1799

1800
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
1801
1802
1803
1804
        # 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
        )
1805
1806

        # Get positions.
1807
1808
1809
        positions_np = (
            self.input_batch.num_computed_tokens_cpu[req_indices]
            + self.query_pos.np[: cu_num_tokens[-1]]
1810
        )
1811

1812
1813
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1814
        if self.uses_mrope:
1815
1816
            self._calc_mrope_positions(scheduler_output)

1817
1818
1819
1820
1821
        # 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)

1822
1823
1824
1825
        # 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.
1826
1827
1828
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1829
        token_indices_tensor = torch.from_numpy(token_indices)
1830

1831
1832
1833
        # 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.
1834
1835
1836
1837
1838
1839
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1840
        if self.enable_prompt_embeds:
1841
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1842
1843
1844
1845
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1846
1847
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880

        # 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:
1881
1882
1883
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1884
1885

                output_idx += num_sched
1886
1887

        # Prepare the attention metadata.
1888
        self.query_start_loc.np[0] = 0
1889
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1890
1891
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1892
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1893
        self.query_start_loc.copy_to_gpu()
1894
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1895

1896
1897
1898
1899
1900
1901
1902
1903
        # 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],
1904
        )
1905
1906
1907
1908
1909
1910
        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)
1911

1912
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1913
1914
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1915
        # Record which requests should not be sampled,
1916
        # so that we could clear the sampled tokens before returning
1917
        self.discard_request_mask.np[:num_reqs] = (
1918
            self.optimistic_seq_lens_cpu[:num_reqs].numpy() < num_tokens_np
1919
        )
1920
        self.discard_request_mask.copy_to_gpu(num_reqs)
1921

1922
1923
1924
1925
        # 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()
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
            # 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]
                )
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
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
            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],
        )

2001
        # Copy the tensors to the GPU.
2002
2003
        self._prepare_input_ids(
            scheduler_output,
2004
            num_reqs,
2005
2006
2007
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
2008

2009
        if self.uses_mrope:
2010
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
2011
2012
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
2013
2014
                non_blocking=True,
            )
2015
2016
2017
2018
2019
2020
        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,
            )
2021
2022
2023
2024
2025
2026
2027
2028
        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
2029

2030
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
2031
2032
2033
2034
2035
2036
2037
2038
        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
2039
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
2040
2041
2042
2043
2044
        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)
2045
2046
2047
            # 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)
2048
2049
2050
2051
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
2052
                req_idx = self.input_batch.req_id_to_index[req_id]
2053
2054
                draft_len = len(draft_token_ids)
                num_draft_tokens[req_idx] = draft_len
2055
2056
2057
2058
                if (
                    self.input_batch.num_computed_tokens_cpu[req_idx]
                    >= self.input_batch.num_prompt_tokens[req_idx]
                ):
2059
                    num_decode_draft_tokens[req_idx] = draft_len
2060
            spec_decode_metadata = self._calc_spec_decode_metadata(
2061
2062
                num_draft_tokens, cu_num_tokens
            )
2063
            logits_indices = spec_decode_metadata.logits_indices
2064
            num_sampled_tokens = num_draft_tokens + 1
2065
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
2066
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
2067
2068
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
2069

2070
2071
2072
2073
2074
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
2075
            )
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
2087
        num_tokens: int,
2088
        num_reqs: int,
2089
2090
2091
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
2092
2093
2094
2095
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
2096
        num_scheduled_tokens: dict[str, int] | None = None,
2097
        cascade_attn_prefix_lens: list[list[int]] | None = None,
2098
        slot_mappings: dict[int, torch.Tensor] | None = None,
2099
2100
2101
2102
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
2103
2104
2105
2106
        # Attention metadata is not needed for attention free models
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return {}, None

2107
2108
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs
2109
        assert num_reqs_padded is not None and num_tokens_padded is not None
2110

2111
2112
2113
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
2114

2115
2116
2117
2118
2119
2120
        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:
2121
            max_seq_len = self.optimistic_seq_lens_cpu.numpy()[:num_reqs].max().item()
2122

2123
        kv_cache_groups = self.kv_cache_config.kv_cache_groups
2124

2125
        def _get_block_table(kv_cache_gid: int):
2126
2127
2128
            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):
2129
                blk_table_tensor = torch.zeros(
2130
                    (num_reqs_padded, 1),
2131
                    dtype=torch.int32,
2132
2133
                    device=self.device,
                )
2134
            else:
2135
                blk_table = self.input_batch.block_table[kv_cache_gid]
2136
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
2137

2138
2139
2140
            # 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)
2141
            return blk_table_tensor
2142

2143
2144
2145
        assert slot_mappings is not None
        block_table_gid_0 = _get_block_table(0)
        slot_mapping_gid_0 = slot_mappings[0]
2146

2147
2148
2149
2150
        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()
2151
2152
2153
2154
2155
2156
        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
        ]
2157
2158
2159
2160
2161
        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.
2162
2163
        is_prefilling = num_computed_tokens_cpu < num_prompt_tokens_cpu

2164
2165
2166
2167
2168
        if self.use_async_spec_decode:
            # GPU tensors are authoritative in async mode.
            seq_lens_cpu = None
            num_computed_tokens_cpu = None

2169
2170
2171
        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],
2172
2173
            seq_lens=self.seq_lens[:num_reqs_padded],
            _seq_lens_cpu=seq_lens_cpu,
2174
            _num_computed_tokens_cpu=num_computed_tokens_cpu,
2175
2176
2177
2178
2179
2180
2181
            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,
2182
            is_prefilling=is_prefilling,
2183
2184
2185
2186
        )

        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
2187
                self.optimistic_seq_lens_cpu[:num_reqs],
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
                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
            )

2206
2207
2208
2209
2210
2211
2212
2213
2214
        # 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
        ] = {}

2215
2216
2217
2218
2219
2220
2221
        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]
2222
            builder = attn_group.get_metadata_builder(ubid or 0)
2223
2224
2225
2226
            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))
2227

2228
2229
2230
2231
2232
2233
2234
            cascade_attn_prefix_len = (
                cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                if cascade_attn_prefix_lens
                else 0
            )

            extra_attn_metadata_args = {}
2235
2236
2237
            if use_spec_decode and isinstance(
                builder, (Mamba2AttentionMetadataBuilder, GDNAttentionMetadataBuilder)
            ):
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
                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
                )
2250
2251
2252
2253
2254
2255
2256
2257
2258
            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,
                )
2259
2260
2261
2262
2263
2264
            else:
                attn_metadata_i = builder.build(
                    common_prefix_len=cascade_attn_prefix_len,
                    common_attn_metadata=common_attn_metadata,
                    **extra_attn_metadata_args,
                )
2265
2266
                if builder.supports_update_block_table:
                    cached_attn_metadata[cache_key] = attn_metadata_i
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289

            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,
2290
                for_cudagraph_capture=for_cudagraph_capture,
2291
            )
2292
            if kv_cache_gid > 0:
2293
2294
                cm.block_table_tensor = _get_block_table(kv_cache_gid)
                cm.slot_mapping = slot_mappings[kv_cache_gid]
2295

2296
            if self.speculative_config and spec_decode_common_attn_metadata is None:
2297
                if isinstance(self.drafter, (EagleProposer, DFlashProposer)):
2298
                    if self.drafter.kv_cache_gid == kv_cache_gid:
2299
                        spec_decode_common_attn_metadata = cm
2300
                else:
2301
                    spec_decode_common_attn_metadata = cm
2302

2303
            for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
2304
                if ubatch_slices is not None:
2305
2306
2307
                    for ubid, _cm in enumerate(split_attn_metadata(ubatch_slices, cm)):
                        _build_attn_group_metadata(kv_cache_gid, attn_gid, _cm, ubid)

2308
                else:
2309
                    _build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
2310

2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
        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

2323
2324
            # Set mm_prefix_range for all attention metadata
            self._set_mm_prefix_range_for_metadata(attn_metadata, req_doc_ranges)
2325

2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
        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)
            )

2336
        return attn_metadata, spec_decode_common_attn_metadata
2337

2338
2339
2340
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
2341
        num_computed_tokens: np.ndarray,
2342
2343
2344
2345
2346
2347
2348
        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
        """
2349

2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
        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,
2364
                        num_computed_tokens,
2365
2366
2367
2368
2369
2370
2371
2372
                        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
2373

2374
2375
2376
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
2377
        num_computed_tokens: np.ndarray,
2378
        num_common_prefix_blocks: int,
2379
2380
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
    ) -> 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.
        """
2399

2400
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
        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]
2438
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
2439
2440
2441
2442
2443
        # 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.
2444
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
2445
        # common_prefix_len should be a multiple of the block size.
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
        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
        )
2457
2458
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
2459
2460
2461
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
2462
            num_kv_heads=kv_cache_spec.num_kv_heads,
2463
            use_alibi=self.use_alibi,
2464
            use_sliding_window=use_sliding_window,
2465
            use_local_attention=use_local_attention,
2466
            num_sms=self.num_sms,
2467
            dcp_world_size=self.dcp_world_size,
2468
2469
2470
        )
        return common_prefix_len if use_cascade else 0

2471
2472
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
2473
        for index, req_id in enumerate(self.input_batch.req_ids):
2474
2475
2476
            req = self.requests[req_id]
            assert req.mrope_positions is not None

2477
2478
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2479
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
2480
2481
                req.prompt_token_ids, req.prompt_embeds
            )
2482
2483

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
2484
2485
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
            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

2499
2500
2501
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
2502
2503
2504
2505
2506
2507
2508
                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

2509
                assert req.mrope_position_delta is not None
2510
                MRotaryEmbedding.get_next_input_positions_tensor(
2511
                    out=self.mrope_positions.np,
2512
2513
2514
2515
2516
                    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,
                )
2517
2518
2519

                mrope_pos_ptr += completion_part_len

2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
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
    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

2567
2568
    def _calc_spec_decode_metadata(
        self,
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
        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
2585

2586
2587
2588
2589
2590
        # 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
2591
        )
2592
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
2593
        logits_indices = np.repeat(
2594
2595
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
2596
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
2597
        logits_indices += self._arange_scratch[: cu_num_sampled_tokens[-1]]
2598
2599
2600
2601
2602

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

        # Compute the draft logits indices.
2603
        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
2604
2605
2606
        # _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
2607
        )
2608
2609
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
2610
2611
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
2612
        # [0, 1, 2, 5, 6, 9]
2613
        target_logits_indices += self._arange_scratch[: cu_num_draft_tokens[-1]]
2614
2615
2616

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
2617
2618
            self.device, non_blocking=True
        )
2619
2620
2621
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
2622
2623
2624
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
2625
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
2626
2627
            self.device, non_blocking=True
        )
2628
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
2629
2630
            self.device, non_blocking=True
        )
2631

2632
2633
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2634
        draft_token_ids = self.input_ids.gpu[logits_indices]
2635
2636
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2637
        return SpecDecodeMetadata(
2638
2639
2640
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2641
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2642
2643
2644
2645
2646
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2647
2648
2649
2650
2651
2652
2653
    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
2654
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2655
2656
2657
2658
2659
        # 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_(
2660
2661
            logits_indices[-1].item()
        )
2662
2663
        # Dispatch for the decoder portion of the model.
        _, batch_desc = self.cudagraph_dispatcher.dispatch(
2664
            num_logits, invalid_modes={CUDAGraphMode.FULL}
2665
2666
        )
        num_logits_padded = batch_desc.num_tokens
2667
2668
2669
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2670
2671
        return logits_indices_padded

2672
    def _batch_mm_inputs_from_scheduler(
2673
2674
        self,
        scheduler_output: "SchedulerOutput",
2675
2676
    ) -> tuple[
        list[str],
2677
        list[tuple[str, MultiModalKwargsItem]],
2678
2679
        list[tuple[str, PlaceholderRange]],
    ]:
2680
        """Batch multimodal inputs from scheduled encoder inputs.
2681
2682
2683

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2684
                inputs.
2685
2686

        Returns:
2687
            A tuple of (mm_hashes, mm_kwargs, mm_lora_refs) where:
2688
2689
            - mm_hashes: List of multimodal hashes for each item
            - mm_kwargs: List of multimodal kwargs for each item
2690
            - mm_lora_refs: List of (req_id, placeholder_range) for each item
2691
        """
2692
2693
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2694
            return [], [], []
2695
2696

        mm_hashes = list[str]()
2697
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
2698
2699
2700
        # Multimodal LoRA reference info to map each multimodal item
        # back to its request & position
        mm_lora_refs = list[tuple[str, PlaceholderRange]]()
2701
2702
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2703
2704

            for mm_input_id in encoder_input_ids:
2705
                mm_feature = req_state.mm_features[mm_input_id]
2706
2707
                if mm_feature.data is None:
                    continue
2708
2709

                mm_hashes.append(mm_feature.identifier)
2710
                mm_kwargs.append((mm_feature.modality, mm_feature.data))
2711
                mm_lora_refs.append((req_id, mm_feature.mm_position))
2712

2713
        return mm_hashes, mm_kwargs, mm_lora_refs
2714

2715
2716
2717
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2718
2719
2720
        mm_hashes, mm_kwargs, mm_lora_refs = self._batch_mm_inputs_from_scheduler(
            scheduler_output
        )
2721
2722

        if not mm_kwargs:
2723
            return []
2724

2725
2726
2727
2728
2729
2730
        should_time = bool(
            self.observability_config
            and self.observability_config.enable_mm_processor_stats
            and scheduler_output.scheduled_encoder_inputs
        )

2731
2732
2733
2734
2735
2736
2737
        # 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.
2738
        model = cast(SupportsMultiModal, self.model)
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753

        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]
2754
                    pos_info.get_num_embeds()
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
                )
                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)

2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
            # Only set connector mapping if the model actually has a connector.
            # Some multimodal models inherit a stub `get_num_mm_connector_tokens`
            # from `SupportsMultiModal`, which returns None and should not be
            # treated as a signal that connector LoRA is supported.
            mm_mapping = (
                self.model.get_mm_mapping()  # type: ignore[attr-defined]
                if hasattr(self.model, "get_mm_mapping")
                else None
            )
            if (
                mm_mapping is not None
                and mm_mapping.connector
                and hasattr(self.model, "get_num_mm_connector_tokens")
            ):
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
                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,
                )

2809
        encoder_outputs: list[torch.Tensor] = []
2810
2811
        # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
        current_item_idx = 0
2812
        for modality, num_items, mm_kwargs_batch in group_and_batch_mm_kwargs(
2813
2814
2815
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
2816
        ):
2817
            batch_outputs: MultiModalEmbeddings
2818

2819
            # EVS and dynamic res video related change.
2820
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2821
            # processing multimodal data. This solves the issue with scheduler
2822
2823
2824
2825
            # 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)
2826
2827
2828
            # dynamic res video for nemotron temporarily uses this hack via
            # requires_sequential_video_encoding
            # because it doesn't yet support video batching.
2829
2830
2831
            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
2832
2833
2834
2835
                (
                    self.is_multimodal_pruning_enabled
                    or self.requires_sequential_video_encoding
                )
2836
2837
2838
                and modality == "video"
                and num_items > 1
            ):
2839
                batch_outputs_lst = list[torch.Tensor]()
2840
2841
2842
2843
2844
2845
                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(
2846
                            group_and_batch_mm_kwargs(
2847
2848
2849
2850
                                [video_mm_kwargs_item],
                                device=self.device,
                                pin_memory=self.pin_memory,
                            )
2851
                        )
2852

2853
2854
2855
                        micro_batch_outputs = model.embed_multimodal(
                            **micro_batch_mm_inputs
                        )
2856

2857
                        batch_outputs_lst.extend(micro_batch_outputs)
2858

2859
                batch_outputs = batch_outputs_lst
2860
2861
            else:
                # Run the encoder.
2862
                # `batch_outputs` is either of the following:
2863
2864
2865
2866
2867
                # 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.
2868
2869
2870
2871

                with self.timed_encoder_operation(
                    should_time, mm_lora_refs, current_item_idx, num_items
                ):
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
                    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)
2885

2886
2887
            sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=num_items)
            encoder_outputs.extend(batch_outputs)
2888

2889
2890
            current_item_idx += num_items

2891
        # Cache the encoder outputs by mm_hash
2892
        for mm_hash, output in zip(mm_hashes, encoder_outputs):
2893
            self.encoder_cache[mm_hash] = output
2894
2895
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2896

2897
2898
        return encoder_outputs

2899
    def _gather_mm_embeddings(
2900
2901
        self,
        scheduler_output: "SchedulerOutput",
2902
        shift_computed_tokens: int = 0,
2903
2904
2905
2906
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

        mm_embeds = list[torch.Tensor]()
2907
2908
2909
        is_mm_embed = torch.zeros(
            total_num_scheduled_tokens, dtype=torch.bool, device="cpu"
        )
2910
2911

        req_start_idx = 0
2912
        should_sync_mrope_positions = False
2913
        should_sync_xdrope_positions = False
2914

2915
        for req_id in self.input_batch.req_ids:
2916
2917
            mm_embeds_req: list[torch.Tensor] = []

2918
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2919
            req_state = self.requests[req_id]
2920
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2921

2922
2923
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2924
2925
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941

                # 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,
2942
2943
                    num_encoder_tokens,
                )
2944
                assert start_idx < end_idx
2945
2946
2947
2948
2949
2950
2951
                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
2952

2953
                mm_hash = mm_feature.identifier
2954
                encoder_output = self.encoder_cache.get(mm_hash, None)
2955
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2956
2957
2958

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]
2959
2960
2961
                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
                else:
                    mm_embeds_item = encoder_output[start_idx:end_idx]
2962

2963
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2964
2965
2966
2967
2968
2969
2970
2971
2972
                # 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
2973
2974
2975
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2976
                assert req_state.mrope_positions is not None
2977
2978
2979
2980
2981
2982
2983
                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,
2984
2985
                    )
                )
2986
2987
2988
2989
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2990
2991
            req_start_idx += num_scheduled_tokens

2992
2993
        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2994
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2995

2996
2997
2998
2999
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

3000
        return mm_embeds, is_mm_embed
3001

3002
    def get_model(self) -> nn.Module:
3003
3004
        if not hasattr(self, "model"):
            raise ValueError("Cannot get model before model has been initialized")
3005
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
3006
            # get raw model out of the cudagraph wrapper.
3007
            return self.model.unwrap()
3008
3009
        return self.model

3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
    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")

3023
3024
3025
        if supports_realtime(model):
            supported_tasks.append("realtime")

3026
3027
        return supported_tasks

3028
3029
3030
3031
3032
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

3033
        return list(model.pooler.get_supported_tasks())
3034

3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
    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)

3045
    def sync_and_slice_intermediate_tensors(
3046
3047
        self,
        num_tokens: int,
3048
        intermediate_tensors: IntermediateTensors | None,
3049
3050
        sync_self: bool,
    ) -> IntermediateTensors:
3051
3052
3053
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
3054
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
3055
3056
3057
3058
3059
3060

        # 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():
3061
                is_scattered = k == "residual" and is_rs
3062
                copy_len = num_tokens // tp if is_scattered else num_tokens
3063
                self.intermediate_tensors[k][:copy_len].copy_(
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
                    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:
3077
3078
3079
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
3080
        if not self.parallel_config.enable_eplb or self.eep_eplb_suppressed:
3081
3082
3083
            return

        assert self.eplb_state is not None
3084
3085
        model = self.get_model()
        assert is_mixture_of_experts(model)
3086
3087
3088
        self.eplb_state.step(
            is_dummy,
            is_profile,
3089
            log_stats=self.parallel_config.eplb_config.log_balancedness,
3090
3091
        )

3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
    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,
        )

3109
3110
3111
3112
3113
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
3114
3115
3116
3117
        kv_connector_output: KVConnectorOutput | None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        num_reqs = self.input_batch.num_reqs
        assert num_reqs == len(self.input_batch.pooling_params), (
3118
3119
            "Either all or none of the requests in a batch must be pooling request"
        )
3120

3121
        hidden_states = hidden_states[:num_scheduled_tokens]
3122
        seq_lens_cpu = self.optimistic_seq_lens_cpu[:num_reqs]
3123

3124
        pooling_metadata = self.input_batch.get_pooling_metadata()
3125
        pooling_metadata.build_pooling_cursor(
3126
3127
3128
3129
            num_scheduled_tokens_np,
            seq_lens_cpu,
            device=hidden_states.device,
            query_start_loc_gpu=self.query_start_loc.gpu[: num_reqs + 1],
3130
        )
3131

3132
3133
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
3134
            hidden_states=hidden_states, pooling_metadata=pooling_metadata
3135
        )
3136
3137
3138
3139
3140

        finished_mask = [
            seq_len == prompt_len
            for seq_len, prompt_len in zip(seq_lens_cpu, pooling_metadata.prompt_lens)
        ]
3141
3142
3143
3144
3145
3146
        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,
        )
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157

        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

3158
3159
3160
        if not current_platform.is_cuda_alike():
            # cpu/xpu runners cannot use the CUDA stream/event-based wrapper.
            model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
3161
3162
3163
                raw_pooler_output=raw_pooler_output,
                finished_mask=finished_mask,
            )
3164
3165
            self._sync_device()
            return model_runner_output
3166

3167
3168
        return AsyncGPUPoolingModelRunnerOutput(
            model_runner_output=model_runner_output,
3169
3170
            raw_pooler_output=raw_pooler_output,
            finished_mask=finished_mask,
3171
            async_output_copy_stream=self._get_or_create_async_output_copy_stream(),
3172
        )
3173

3174
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
3175
3176
3177
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
3178
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
3179
3180
3181
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

Patrick von Platen's avatar
Patrick von Platen committed
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
    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

3193
    def _preprocess(
3194
3195
        self,
        scheduler_output: "SchedulerOutput",
3196
        num_input_tokens: int,  # Padded
3197
        intermediate_tensors: IntermediateTensors | None = None,
3198
    ) -> tuple[
3199
3200
        torch.Tensor | None,
        torch.Tensor | None,
3201
        torch.Tensor,
3202
        IntermediateTensors | None,
3203
        dict[str, Any],
3204
        ECConnectorOutput | None,
3205
    ]:
3206
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3207
        is_first_rank = get_pp_group().is_first_rank
3208
        is_encoder_decoder = self.model_config.is_encoder_decoder
3209

3210
3211
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
3212
3213
        ec_connector_output = None

3214
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
3215
            # Run the multimodal encoder if any.
3216
3217
3218
3219
3220
3221
            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)
3222

3223
3224
3225
            # 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.
3226
            inputs_embeds_scheduled = self.model.embed_input_ids(
3227
3228
3229
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
3230
            )
3231

3232
            # TODO(woosuk): Avoid the copy. Optimize.
3233
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
3234

Patrick von Platen's avatar
Patrick von Platen committed
3235
            input_ids, inputs_embeds = self._prepare_mm_inputs(num_input_tokens)
3236
            model_kwargs = {
3237
                **self._init_model_kwargs(),
3238
3239
                **self._extract_mm_kwargs(scheduler_output),
            }
3240
        elif self.enable_prompt_embeds and is_first_rank:
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
            # 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).
3253
3254
3255
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
3256
                .squeeze(1)
3257
            )
3258
3259
3260
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
3261
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
3262
3263
3264
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
3265
            model_kwargs = self._init_model_kwargs()
3266
            input_ids = None
3267
        else:
3268
3269
3270
3271
            # 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.
3272
            input_ids = self.input_ids.gpu[:num_input_tokens]
3273
            inputs_embeds = None
3274
            model_kwargs = self._init_model_kwargs()
3275

3276
        if self.uses_mrope:
3277
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
3278
3279
        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
3280
        else:
3281
            positions = self.positions[:num_input_tokens]
3282
            if num_input_tokens > num_scheduled_tokens:
3283
                self.positions[num_scheduled_tokens:num_input_tokens].zero_()
3284

3285
        if is_first_rank:
3286
3287
            intermediate_tensors = None
        else:
3288
            assert intermediate_tensors is not None
3289
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3290
3291
                num_input_tokens, intermediate_tensors, True
            )
3292

3293
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
3294
3295
3296
3297
3298
3299
3300
            # 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})
3301

3302
3303
3304
3305
3306
3307
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
3308
            ec_connector_output,
3309
        )
3310

3311
    def _sample(
3312
        self,
3313
3314
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3315
    ) -> SamplerOutput:
3316
        # Sample the next token and get logprobs if needed.
3317
        sampling_metadata = self.input_batch.sampling_metadata
3318
3319
3320
        # 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()
3321
        if spec_decode_metadata is None:
3322
            return self.sampler(
3323
3324
3325
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
3326

3327
3328
3329
3330
3331
3332
        # 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)

3333
        sampler_output = self.rejection_sampler(
3334
3335
            spec_decode_metadata,
            None,  # draft_probs
3336
            logits,
3337
3338
            sampling_metadata,
        )
3339
3340
3341
        return sampler_output

    def _bookkeeping_sync(
3342
3343
3344
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
3345
        logits: torch.Tensor | None,
3346
3347
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
3348
        spec_decode_metadata: SpecDecodeMetadata | None,
3349
    ) -> tuple[
3350
        dict[str, int],
3351
        LogprobsLists | None,
3352
        list[list[int]],
3353
        dict[str, LogprobsTensors | None],
3354
3355
3356
        list[str],
        dict[str, int],
        list[int],
3357
    ]:
3358
3359
3360
3361
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

3362
3363
3364
3365
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
3366
3367
3368
3369
        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)
3370

3371
3372
3373
        # 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()
3374
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
3375
3376

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
3377
        sampled_token_ids = sampler_output.sampled_token_ids
3378
        logprobs_tensors = sampler_output.logprobs_tensors
3379
        invalid_req_indices = []
3380
        logprobs_lists = None
3381
3382
3383
3384
3385
3386
        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)
3387
3388
3389
                # 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()
3390
3391
3392

                if logprobs_tensors is not None:
                    logprobs_lists = logprobs_tensors.tolists()
3393
3394
            else:
                # Includes spec decode tokens.
3395
                valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
3396
3397
                    sampled_token_ids,
                    self.input_batch.vocab_size,
3398
                    discard_sampled_tokens_req_indices,
3399
                    logprobs_tensors=logprobs_tensors,
3400
                )
3401
        else:
3402
            valid_sampled_token_ids = []
3403
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
3404
3405
3406
3407
3408
            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.
3409
3410
3411
3412
            # 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
3413
3414
3415
3416
3417
            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
            }
3418

3419
3420
3421
3422
3423
        # 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.
3424
        req_ids = self.input_batch.req_ids
3425
3426
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
3427
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
3428
3429
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
3430

3431
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
3432

3433
            if not sampled_ids:
3434
3435
3436
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
3437
            end_idx = start_idx + num_sampled_ids
3438
3439
3440
3441
            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}"
3442
            )
3443

3444
3445
            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
3446
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
3447

3448
            req_id = req_ids[req_idx]
3449
3450
3451
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

3452
3453
3454
3455
3456
3457
        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
        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,
        )

3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
    @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()

3483
3484
    def _model_forward(
        self,
3485
3486
3487
3488
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
3489
3490
3491
3492
3493
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
3494
        Motivation: We can inspect only this method versus
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
        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,
        )

3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
    @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
        )

3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
    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,
3549
        force_num_active_loras: int | None = None,
3550
        num_encoder_reqs: int = 0,
3551
    ) -> tuple[
3552
3553
        CUDAGraphMode,
        BatchDescriptor,
3554
        bool,
3555
3556
        torch.Tensor | None,
        CUDAGraphStat | None,
3557
    ]:
3558
3559
3560
3561
3562
3563
        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,
3564
        )
3565
3566
3567
3568
3569
        # 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
        )
3570

3571
3572
3573
3574
3575
        # 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)
3576
        )
3577
        has_lora = num_active_loras > 0 if force_has_lora is None else force_has_lora
3578

3579
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
3580
3581
3582

        def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
            return self.cudagraph_dispatcher.dispatch(
3583
3584
3585
                num_tokens=num_tokens,
                has_lora=has_lora,
                uniform_decode=uniform_decode,
3586
                num_active_loras=num_active_loras,
3587
3588
                valid_modes={CUDAGraphMode.NONE} if force_eager else valid_modes,
                invalid_modes={CUDAGraphMode.FULL} if disable_full else None,
3589
3590
            )

3591
        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
3592
            num_tokens_padded, disable_full=use_cascade_attn or has_encoder_output
3593
        )
3594
        num_tokens_padded = batch_descriptor.num_tokens
3595
3596
3597
3598
3599
3600
3601
3602
3603
        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"
            )
3604
3605
3606

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
3607
        should_ubatch, num_tokens_across_dp = False, None
3608
        if self.vllm_config.parallel_config.data_parallel_size > 1:
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
            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,
                )
3619
3620
            )

3621
            # Extract DP-synced values
3622
3623
3624
            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())
3625
3626
3627
                # Re-dispatch with DP padding so we have the correct batch_descriptor
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(
                    num_tokens_padded,
3628
                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
3629
                )
3630
3631
3632
3633
                # 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

3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
        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,
3646
            should_ubatch,
3647
3648
3649
            num_tokens_across_dp,
            cudagraph_stats,
        )
3650

3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
    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

3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
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
    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

3761
3762
3763
3764
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
3765
        intermediate_tensors: IntermediateTensors | None = None,
3766
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
3767
3768
3769
3770
3771
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
3772

3773
        if self.routed_experts_initialized:
3774
3775
3776
3777
3778
3779
            capturer = RoutedExpertsCapturer.get_instance()
            if capturer is not None:
                capturer.clear_buffer()  # noqa
            else:
                logger.error("RoutedExpertsCapturer not initialized.")

3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
        # 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,
            )

3797
3798
3799
3800
        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)
3801

3802
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3803
3804
3805
3806
3807
        with (
            record_function_or_nullcontext("gpu_model_runner: preprocess"),
            self.synchronize_input_prep(),
        ):
            # Update persistent batch states.
3808
            deferred_state_corrections_fn = self._update_states(scheduler_output)
3809

3810
            if has_ec_transfer() and not get_ec_transfer().is_consumer:
3811
                with self.maybe_get_ec_connector_output(
3812
                    scheduler_output,
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
                    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"
3841
3842
                )

3843
3844
3845
3846
3847
3848
            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
3849

3850
3851
3852
3853
            logits_indices, spec_decode_metadata = self._prepare_inputs(
                scheduler_output,
                num_scheduled_tokens_np,
            )
3854

3855
3856
3857
3858
3859
            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(
3860
                    num_scheduled_tokens_np,
3861
3862
                    self.input_batch.num_computed_tokens_cpu[:num_reqs],
                    scheduler_output.num_common_prefix_blocks,
3863
3864
                )

3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
            (
                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),
            )
3879

3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
            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,
            )

3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
            # 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)
            )
3918
3919
            pad_attn = cudagraph_mode == CUDAGraphMode.FULL

3920
            if self.cache_config.mamba_cache_mode == "align":
3921
3922
3923
3924
3925
3926
                # 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
3927
3928
3929
3930
3931
3932
3933
3934
3935
                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(),
3936
                    self._get_mamba_copy_bufs(),
3937
                )
3938
3939
3940
3941
3942
3943
3944
3945
                # 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)
3946

3947
3948
3949
            use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
            ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices

3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
            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,
            )

3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
            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,
3973
                    slot_mappings=slot_mappings_by_group,
3974
                )
3975
            )
3976

3977
3978
3979
3980
3981
3982
3983
3984
3985
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
3986
            )
3987

3988
        # Set cudagraph mode to none if calc_kv_scales is true.
3989
3990
3991
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
3992
            cudagraph_mode = CUDAGraphMode.NONE
3993
3994
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
3995

3996
3997
3998
3999
4000
4001
4002
        # 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
        )

4003
4004
        # Run the model.
        # Use persistent buffers for CUDA graphs.
4005
4006
4007
        # 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
4008
4009
        with (
            set_forward_context(
4010
4011
                attn_metadata,
                self.vllm_config,
4012
                num_tokens=num_tokens_padded,
4013
                num_tokens_across_dp=num_tokens_across_dp,
4014
4015
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
4016
                ubatch_slices=ubatch_slices_padded,
4017
                slot_mapping=slot_mappings,
4018
                skip_compiled=has_encoder_input,
4019
            ),
4020
            record_function_or_nullcontext("gpu_model_runner: forward"),
4021
            self.maybe_get_kv_connector_output(
4022
4023
                scheduler_output,
                defer_finalize=defer_kv_connector_finalize,
4024
            ) as kv_connector_output,
4025
        ):
4026
            model_output = self._model_forward(
4027
4028
4029
4030
4031
4032
4033
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

4034
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
4035
            if self.use_aux_hidden_state_outputs:
4036
                # True when EAGLE 3 is used.
4037
4038
                hidden_states, aux_hidden_states = model_output
            else:
4039
                # Common case.
4040
4041
4042
                hidden_states = model_output
                aux_hidden_states = None

4043
4044
4045
4046
4047
            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)
4048
                    hidden_states.kv_connector_output = kv_connector_output
4049
                    self.kv_connector_output = kv_connector_output
4050
                    return hidden_states
4051

4052
                if self.is_pooling_model:
4053
                    # Return the pooling output.
4054
4055
4056
4057
4058
                    return self._pool(
                        hidden_states,
                        num_scheduled_tokens,
                        num_scheduled_tokens_np,
                        kv_connector_output,
4059
                    )
4060
4061

                sample_hidden_states = hidden_states[logits_indices]
4062
                logits = self.model.compute_logits(sample_hidden_states)
4063
4064
4065
4066
            else:
                # Rare case.
                assert not self.is_pooling_model

4067
                sample_hidden_states = hidden_states[logits_indices]
4068
                if not get_pp_group().is_last_rank:
4069
                    all_gather_tensors = {
4070
                        "residual": not is_residual_scattered_for_sp(
4071
                            self.vllm_config, num_tokens_padded
4072
                        )
4073
                    }
4074
                    get_pp_group().send_tensor_dict(
4075
4076
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
4077
4078
                        all_gather_tensors=all_gather_tensors,
                    )
4079
4080
                    logits = None
                else:
4081
                    logits = self.model.compute_logits(sample_hidden_states)
4082

4083
                model_output_broadcast_data: dict[str, Any] = {}
4084
4085
4086
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

4087
                broadcasted = get_pp_group().broadcast_tensor_dict(
4088
4089
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
4090
4091
                assert broadcasted is not None
                logits = broadcasted["logits"]
4092

4093
4094
4095
4096
4097
4098
4099
4100
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
4101
            ec_connector_output,
4102
            cudagraph_stats,
4103
            slot_mappings,
4104
        )
4105
        self.kv_connector_output = kv_connector_output
4106
4107
4108
4109
4110
4111

        # 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()

4112
4113
4114
4115
4116
4117
4118
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
        if self.execute_model_state is None:
4119
4120
            kv_connector_output = self.kv_connector_output
            self.kv_connector_output = None
4121
4122
4123
            # 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()
4124
            if not kv_connector_output:
4125
                return None  # type: ignore[return-value]
4126
4127
4128
4129
4130
4131
4132
4133
4134

            # 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
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
4145
            ec_connector_output,
4146
            cudagraph_stats,
4147
            slot_mappings,
4148
4149
4150
4151
4152
4153
4154
4155
4156
        ) = 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
            )
4157

4158
        with record_function_or_nullcontext("gpu_model_runner: sample"):
4159
4160
            sampler_output = self._sample(logits, spec_decode_metadata)

4161
4162
4163
        self._update_states_after_model_execute(
            sampler_output.sampled_token_ids, scheduler_output
        )
4164
4165
        if self.use_async_scheduling:
            pp = get_pp_group()
4166
4167
4168
4169
            # 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:
4170
4171
4172
                self._pp_broadcast_prev_sampled_token_ids(
                    sampler_output.sampled_token_ids
                )
4173

4174
4175
        self._draft_token_ids = None
        self._draft_token_req_ids = None
4176
        self.valid_sampled_token_count_gpu = None
4177
4178
        self.input_batch.prev_sampled_token_ids = None

4179
        def propose_draft_token_ids(sampled_token_ids):
4180
            assert spec_decode_common_attn_metadata is not None
4181
            with record_function_or_nullcontext("gpu_model_runner: draft"):
4182
4183
4184
4185
4186
4187
4188
4189
4190
                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,
4191
                    slot_mappings,
4192
                )
4193
                self._copy_draft_token_ids_to_cpu(scheduler_output)
4194

4195
        spec_config = self.speculative_config
4196
4197
        propose_drafts_after_bookkeeping = False
        if spec_config is not None:
4198
            # Decide whether to run the drafter or zero out draft tokens.
4199
4200
4201
            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
4202
            )
4203
            use_gpu_toks = (
4204
4205
4206
                spec_config.use_eagle()
                or spec_config.uses_draft_model()
                or spec_config.uses_extract_hidden_states()
4207
4208
4209
            ) and not spec_config.disable_padded_drafter_batch
            if use_gpu_toks:
                # EAGLE/DraftModel speculative decoding can use the GPU sampled tokens
4210
                # as inputs, and does not need to wait for bookkeeping to finish.
4211
4212
                assert isinstance(
                    self.drafter,
4213
4214
4215
4216
                    EagleProposer
                    | DFlashProposer
                    | DraftModelProposer
                    | ExtractHiddenStatesProposer,
4217
                )
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
                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,
                        )
4230
                    )
4231
4232
4233
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
4254
4255
            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
                    )
4256
4257
            else:
                propose_drafts_after_bookkeeping = input_fits_in_drafter
4258

4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
            if not input_fits_in_drafter:
                # Zero out draft tokens so the scheduler doesn't schedule
                # stale drafts from the previous step.
                # For Nemotron-H: it is necessary to zero out the draft tokens,
                # otherwise the stale tokens will corrupt Mamba recurrent
                # state and logprobs for sequences near max_model_len.
                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)

4270
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
4271
4272
4273
4274
4275
4276
4277
4278
            (
                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,
4279
4280
4281
4282
4283
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
4284
                scheduler_output.total_num_scheduled_tokens,
4285
                spec_decode_metadata,
4286
            )
4287

4288
        if propose_drafts_after_bookkeeping:
4289
4290
4291
            # 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)
4292

4293
4294
4295
4296
4297
        # 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()
4298

4299
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
4300
            self.eplb_step()
4301

4302
4303
4304
4305
        # self.kv_connector_output may be modified during drafting
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

4306
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
4307
            if self.routed_experts_initialized:
4308
4309
4310
4311
4312
4313
                capturer = RoutedExpertsCapturer.get_instance()
                if capturer is not None:
                    capturer.save_captured_experts(indices=self.slot_mapping)  # noqa
                else:
                    logger.error("RoutedExpertsCapturer not initialized.")

4314
4315
4316
4317
4318
4319
4320
            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,
4321
4322
4323
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
4324
                num_nans_in_logits=num_nans_in_logits,
4325
                cudagraph_stats=cudagraph_stats,
4326
            )
4327

4328
4329
        if not self.use_async_scheduling:
            return output
4330

4331
4332
4333
4334
4335
4336
4337
4338
        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,
4339
                async_output_copy_stream=self._get_or_create_async_output_copy_stream(),
4340
                vocab_size=self.input_batch.vocab_size,
4341
4342
4343
4344
4345
            )
        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
4346
            # any requests with sampling params that require output ids.
4347
4348
4349
4350
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
4351
4352
4353

        return async_output

4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
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
    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

4393
    def take_draft_token_ids(self) -> DraftTokenIds | None:
4394
        if not self.num_spec_tokens or not self._draft_token_req_ids:
4395
            return None
4396
        draft_token_ids, req_ids = self._get_draft_token_ids_cpu()
4397
        return DraftTokenIds(req_ids, draft_token_ids)
4398

4399
4400
4401
    def _copy_draft_token_ids_to_cpu(
        self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
    ) -> None:
4402
4403
4404
4405
4406
4407
        # 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
        ):
4408
4409
4410
            return
        # We must also set the corresponding request ids.
        self._draft_token_req_ids = self.input_batch.req_ids.copy()
4411

4412
4413
4414
4415
4416
4417
4418
4419
4420
4421
4422
4423
4424
4425
4426
4427
4428
4429
4430
4431
        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()

4432
    def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
4433
        if isinstance(self._draft_token_ids, list):
4434
4435
4436
4437
            return self._draft_token_ids, self.input_batch.req_ids
        req_ids = self._draft_token_req_ids
        if req_ids is None:
            return [], []
4438
4439
4440
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_cpu is not None
        self.draft_token_ids_event.synchronize()
4441
        return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
4442

4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
    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
4456
            assert counts_cpu is not None
4457
4458
4459
            counts_cpu[: counts.shape[0]].copy_(counts, non_blocking=True)
            self.valid_sampled_token_count_event.record()

4460
4461
4462
        if self.use_async_spec_decode:
            # Stash for GPU-side correction in _prepare_inputs.
            self.valid_sampled_token_count_gpu = valid_sampled_tokens_count
4463
4464
4465
4466
4467
        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
4468
4469
        sampled_count_event = self.valid_sampled_token_count_event
        if sampled_count_event is None or prev_sampled_token_ids is None:
4470
4471
4472
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
4473
4474
        assert counts_cpu is not None
        sampled_count_event.synchronize()
4475
4476
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

4477
4478
4479
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
4480
        sampled_token_ids: torch.Tensor | list[list[int]],
4481
4482
4483
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
4484
4485
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
4486
        common_attn_metadata: CommonAttentionMetadata,
4487
        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
4488
    ) -> list[list[int]] | torch.Tensor:
4489
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
4490
4491
4492
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
4493
4494
            from vllm.v1.spec_decode.ngram_proposer import NgramProposer

4495
            assert isinstance(sampled_token_ids, list)
4496
            assert isinstance(self.drafter, NgramProposer)
4497
            draft_token_ids = self.drafter.propose(
4498
                sampled_token_ids,
4499
4500
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
4501
                slot_mappings=slot_mappings,
4502
            )
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
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
        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,
            )
4540
        elif spec_config.method == "suffix":
4541
4542
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
4543
4544
4545
            draft_token_ids = self.drafter.propose(
                self.input_batch, sampled_token_ids, slot_mappings=slot_mappings
            )
4546
        elif spec_config.method == "medusa":
4547
            assert isinstance(sampled_token_ids, list)
4548
            assert isinstance(self.drafter, MedusaProposer)
4549

4550
4551
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
4552
4553
4554
4555
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
4556
4557
4558
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
4559
                for num_draft, tokens in zip(
4560
4561
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
4562
                    indices.append(offset + len(tokens) - 1)
4563
                    offset += num_draft + 1
4564
                indices = torch.tensor(indices, device=self.device)
4565
4566
                hidden_states = sample_hidden_states[indices]

4567
            draft_token_ids = self.drafter.propose(
4568
4569
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
4570
                slot_mappings=slot_mappings,
4571
            )
4572
4573
4574
4575
4576
4577
4578
4579
4580
4581
4582
4583
        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]

4584
            draft_token_ids = self.drafter.propose(
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
4598
4599
4600
4601
                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
            )

4602
4603
4604
4605
4606
4607
4608
4609
        elif (
            spec_config.use_eagle()
            or spec_config.use_dflash()
            or spec_config.uses_draft_model()
        ):
            assert isinstance(
                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
            )
4610

4611
            if spec_config.disable_padded_drafter_batch:
4612
4613
4614
                # 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.
4615
4616
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
4617
                    "padded-batch is disabled."
4618
                )
4619
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
4620
4621
4622
4623
4624
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
4625
4626
4627
4628
4629
            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.
4630
4631
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
4632
                    "padded-batch is enabled."
4633
4634
                )
                next_token_ids, valid_sampled_tokens_count = (
4635
4636
4637
4638
                    self.drafter.prepare_next_token_ids_padded(
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
4639
                        self.discard_request_mask.gpu,
4640
                    )
4641
                )
4642
4643
4644
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
4645

4646
            num_rejected_tokens_gpu = None
4647
            if spec_decode_metadata is None:
4648
                token_indices_to_sample = None
4649
                # input_ids can be None for multimodal models.
4650
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
4651
                target_positions = self._get_positions(num_scheduled_tokens)
4652
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
4653
                    assert aux_hidden_states is not None
4654
                    target_hidden_states = torch.cat(
4655
4656
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
4657
4658
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
4659
            else:
4660
                if spec_config.disable_padded_drafter_batch:
4661
                    token_indices_to_sample = None
4662
4663
4664
4665
4666
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
4667
4668
4669
4670
4671
4672
4673
4674
4675
                    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]
4676
                else:
4677
4678
4679
4680
4681
4682
4683
4684
                    (
                        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,
4685
                    )
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
4696
                    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]
4697

4698
            if self.supports_mm_inputs and self.drafter.supports_mm_inputs:
4699
4700
4701
4702
4703
4704
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
4705

4706
            draft_token_ids = self.drafter.propose(
4707
4708
4709
4710
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
4711
                token_indices_to_sample=token_indices_to_sample,
4712
                sampling_metadata=sampling_metadata,
4713
                common_attn_metadata=common_attn_metadata,
4714
                mm_embed_inputs=mm_embed_inputs,
4715
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
4716
                slot_mappings=slot_mappings,
4717
            )
4718

4719
        return draft_token_ids
4720

4721
4722
4723
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
4724
4725
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
4726
                f"Allowed configs: {allowed_config_names}"
4727
            )
4728
4729
4730
4731
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

4732
    @instrument(span_name="Loading (GPU)")
4733
    def load_model(self, load_dummy_weights: bool = False) -> None:
4734
4735
        """
        Args:
4736
            load_dummy_weights: load dummy weights instead of real weights.
4737
        """
4738
4739
4740
4741
4742
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
4743

4744
4745
4746
4747
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0

4748
4749
4750
        try:
            with DeviceMemoryProfiler() as m:
                time_before_load = time.perf_counter()
4751
4752
                if load_dummy_weights:
                    self.load_config.load_format = "dummy"
4753
4754
4755
4756
4757
4758
4759
                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
4760
                    )
4761
4762
4763
4764
4765
4766
4767
4768
                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
                    ):
4769
4770
4771
                        assert not self.parallel_config.enable_elastic_ep, (
                            "Elastic EP is not supported with drafter model."
                        )
4772
4773
4774
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
                        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
4788

4789
4790
4791
4792
4793
4794
                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"
                        )
4795

4796
4797
4798
4799
4800
4801
4802
4803
4804
                    # 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:
4805
4806
4807
                        aux_layers = (
                            self.model.get_eagle3_default_aux_hidden_state_layers()
                        )
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
4818
4819
4820
4821
4822

                    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
4823
        logger.info_once(
4824
4825
            "Model loading took %s GiB memory and %.6f seconds",
            format_gib(self.model_memory_usage),
4826
            time_after_load - time_before_load,
4827
            scope="local",
4828
        )
4829
4830
4831
4832
4833
4834
        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)
4835
        mm_config = self.model_config.multimodal_config
4836
        self.is_multimodal_pruning_enabled = (
4837
            supports_multimodal_pruning(self.get_model())
4838
4839
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
4840
        )
4841
4842
4843
        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
4844

4845
4846
4847
4848
4849
        if (
            is_mixture_of_experts(self.model)
            and self.parallel_config.enable_eplb
            and not load_dummy_weights
        ):
4850
4851
4852
            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(
4853
                self.model,
4854
                self.model_config,
4855
            )
4856
            if self.eplb_state.is_async:
4857
                self.eplb_state.start_async_loop()
4858

4859
        if (
4860
4861
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
4862
        ):
4863
4864
4865
            from vllm.env_override import _apply_constrain_to_fx_strides_patch

            _apply_constrain_to_fx_strides_patch()
4866
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
4867
            compilation_counter.stock_torch_compile_count += 1
4868
            self.model.compile(fullgraph=True, backend=backend)
4869
            return
4870
        # for other compilation modes, cudagraph behavior is controlled by
Jiayi Yan's avatar
Jiayi Yan committed
4871
        # CudagraphWrapper and CudagraphDispatcher of vllm.
4872
4873

        # wrap the model with full cudagraph wrapper if needed.
4874
4875
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
4876
4877
4878
4879
        if (
            cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.use_ubatching
        ):
4880
4881
4882
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
4883
        elif self.parallel_config.use_ubatching:
4884
            if cudagraph_mode.has_full_cudagraphs():
4885
4886
4887
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
4888
            else:
4889
4890
4891
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
4892

4893
4894
        get_offloader().post_init()

4895
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
4896
4897
4898
4899
4900
4901
4902
4903
4904
4905
4906
4907
4908
4909
4910
        """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

4911
4912
4913
4914
4915
4916
        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")

4917
4918
4919
4920
4921
        if layer_ids and isinstance(layer_ids, (list, tuple)):
            return tuple(layer_ids)

        return None

4922
4923
4924
4925
4926
4927
4928
4929
4930
4931
4932
4933
4934
    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
4935
            into kernel format (repacking, renaming, etc.)
4936
4937
4938
4939
4940
4941
4942
4943
4944
4945
4946
4947
4948
4949
4950
4951
4952
4953
4954
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
        """
        # 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")
4967
4968
4969
4970
4971
        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)
4972

4973
4974
4975
4976
4977
4978
4979
4980
4981
4982
4983
4984
        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)
4985
4986
4987
4988
4989
4990
4991
4992

        # 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",
4993
        )
4994
4995
4996
4997
4998
4999
5000
        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,
                )
5001

5002
5003
5004
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
5005
        num_scheduled_tokens: dict[str, int],
5006
    ) -> dict[str, LogprobsTensors | None]:
5007
        num_prompt_logprobs_dict = self.num_prompt_logprobs
5008
5009
5010
        if not num_prompt_logprobs_dict:
            return {}

5011
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
5012
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
5013
5014
5015
5016
5017

        # 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():
5018
5019
5020
5021
            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
5022
5023
5024

            # Get metadata for this request.
            request = self.requests[req_id]
5025
5026
5027
5028
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

5029
5030
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
5031
5032
                self.device, non_blocking=True
            )
5033

5034
5035
5036
5037
5038
5039
            # 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(
5040
5041
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
5042
5043
                in_progress_dict[req_id] = logprobs_tensors

5044
            # Determine number of logits to retrieve.
5045
5046
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
5047
            num_remaining_tokens = num_prompt_tokens - start_tok
5048
            if num_tokens <= num_remaining_tokens:
5049
                # This is a chunk, more tokens remain.
5050
5051
5052
                # 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.
5053
5054
5055
5056
5057
                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)
5058
5059
5060
5061
5062
5063
5064
                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
5065
5066
5067
5068
5069

            # 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]
5070
            offset = self.query_start_loc.np[req_idx].item()
5071
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
5072
            logits = self.model.compute_logits(prompt_hidden_states)
5073
5074
5075
5076

            # 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.
5077
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
5078
5079

            # Compute prompt logprobs.
5080
            logprobs = self.sampler.compute_logprobs(logits)
5081
            token_ids, logprobs, ranks, _ = self.sampler.gather_logprobs(
5082
5083
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
5084
5085

            # Transfer GPU->CPU async.
5086
5087
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
5088
5089
5090
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
5091
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
5092
5093
                ranks, non_blocking=True
            )
5094
5095
5096
5097
5098

        # 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]
5099
            del in_progress_dict[req_id]
5100
5101

        # Must synchronize the non-blocking GPU->CPU transfers.
5102
        if prompt_logprobs_dict:
5103
            self._sync_device()
5104
5105
5106

        return prompt_logprobs_dict

5107
5108
    def _get_nans_in_logits(
        self,
5109
        logits: torch.Tensor | None,
5110
5111
5112
5113
5114
5115
5116
5117
5118
5119
5120
    ) -> 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])
5121
5122
5123
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
5124
5125
5126
5127
            return num_nans_in_logits
        except IndexError:
            return {}

5128
    @contextmanager
5129
5130
5131
    def maybe_randomize_inputs(
        self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None
    ):
5132
5133
5134
5135
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
5136
         - during DP rank dummy run
5137
        """
5138

5139
5140
5141
5142
        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
5143
        elif input_ids is not None:
5144
5145
5146
5147

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
5148
                    self.input_ids.gpu,
5149
5150
                    low=0,
                    high=self.model_config.get_vocab_size(),
5151
                )
5152

5153
            logger.debug_once("Randomizing dummy input_ids for DP Rank")
5154
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
5155
5156
            yield
            input_ids.fill_(0)
5157
5158
5159
5160
5161
5162
5163
5164
5165
5166
5167
5168
5169
5170
5171
        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)
5172

5173
5174
5175
5176
5177
5178
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
5179
5180
        assert self.mm_budget is not None

5181
5182
5183
        # 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,
5184
            mm_counts={modality: 1},
5185
            cache=self.mm_budget.cache,
5186
        )
5187
5188
5189
5190
5191
        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"
5192

5193
        return next(
5194
5195
            mm_kwargs_batch
            for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
5196
                [(modality, dummy_mm_item)] * max_items_per_batch,
5197
5198
5199
5200
                device=self.device,
                pin_memory=self.pin_memory,
            )
        )
5201

5202
5203
5204
5205
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
5206
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
5207
5208
        force_attention: bool = False,
        uniform_decode: bool = False,
5209
        allow_microbatching: bool = True,
5210
5211
        skip_eplb: bool = False,
        is_profile: bool = False,
5212
        create_mixed_batch: bool = False,
5213
        remove_lora: bool = True,
Rémi Delacourt's avatar
Rémi Delacourt committed
5214
        is_graph_capturing: bool = False,
5215
        num_active_loras: int = 0,
5216
        profile_seq_lens: int | None = None,
5217
    ) -> tuple[torch.Tensor, torch.Tensor]:
5218
5219
5220
5221
5222
5223
5224
        """
        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.
5225
                - if not set will determine the cudagraph mode based on using
5226
                    the self.cudagraph_dispatcher.
5227
5228
5229
5230
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
5231
            force_attention: If True, always create attention metadata. Used to
5232
5233
5234
5235
                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.
5236
5237
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
5238
            remove_lora: If False, dummy LoRAs are not destroyed after the run
5239
5240
            num_active_loras: Number of distinct active LoRAs to capture for.
                LoRA is activated when num_active_loras > 0.
5241
5242
5243
            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.
5244
        """
5245
5246
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5247
5248
5249
5250
            # 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([])

5251
5252
        assert (
            cudagraph_runtime_mode is None
5253
            or cudagraph_runtime_mode.is_valid_runtime_mode()
5254
        )
5255

5256
        # If cudagraph_mode.decode_mode() == FULL and
5257
        # cudagraph_mode.separate_routine(). This means that we are using
5258
5259
5260
5261
5262
5263
5264
5265
5266
5267
5268
        # 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.
5269
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
5270

5271
5272
5273
        # 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.
5274
        assert num_tokens <= self.max_num_tokens
5275
        max_num_reqs = self.scheduler_config.max_num_seqs
5276
5277
5278
5279
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
5280
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
5281
5282
5283
5284
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
5285
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
5286
5287
5288
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
5289
            assert not create_mixed_batch
5290
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
5291
5292
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
5293
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
5294
5295
5296
5297
5298
5299
        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

5300
5301
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
5302
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
5303
5304
        num_tokens_unpadded = int(num_scheduled_tokens.sum())

5305
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
5306

5307
        _cudagraph_mode, batch_desc, should_ubatch, num_tokens_across_dp, _ = (
5308
5309
5310
5311
5312
5313
5314
5315
5316
5317
5318
5319
5320
5321
5322
5323
5324
            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
5325
5326
5327
5328
                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,
5329
5330
            )
        )
5331
5332
5333

        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
5334
        else:
5335
5336
5337
5338
5339
5340
5341
5342
5343
            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
        )
5344
        ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
5345
5346
5347
5348
5349
5350
5351
5352
5353
5354
            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,
5355
        )
5356

5357
        attn_metadata: PerLayerAttnMetadata | None = None
5358

5359
        slot_mappings_by_group, slot_mappings = self._get_slot_mappings(
5360
            num_tokens_padded=num_tokens_padded,
5361
5362
5363
5364
5365
            num_reqs_padded=num_reqs_padded,
            num_tokens_unpadded=num_tokens_unpadded,
            ubatch_slices=ubatch_slices_padded,
        )

5366
5367
5368
5369
5370
5371
        # Dummy runs have no real slot assignments — fill with -1 so
        # concat_and_cache kernels skip the KV write.
        if slot_mappings_by_group is not None:
            for sm in slot_mappings_by_group.values():
                sm.fill_(-1)

5372
5373
5374
5375
5376
5377
5378
5379
        # _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:
5380
5381
5382
                if profile_seq_lens is not None:
                    seq_lens = profile_seq_lens  # type: ignore[assignment]
                elif create_mixed_batch:
5383
5384
5385
                    # 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
5386
5387
5388
5389
                    seq_lens = torch.tensor(  # type: ignore[assignment]
                        [1] * num_decode_tokens + [num_prefill_tokens + 1],
                        dtype=torch.int,
                    )
5390
5391
                else:
                    seq_lens = max_query_len  # type: ignore[assignment]
5392
5393
5394
                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)
5395

5396
5397
5398
                cum_num_tokens = self._get_cumsum_and_arange(
                    num_scheduled_tokens, self.query_pos.np
                )
5399
5400
                self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
                self.query_start_loc.copy_to_gpu()
5401

5402
5403
5404
5405
5406
5407
                # 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)

5408
5409
5410
5411
5412
5413
5414
5415
5416
                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,
5417
                    use_spec_decode=self.speculative_config is not None,
5418
                )
5419

5420
        with self.maybe_dummy_run_with_lora(
5421
5422
5423
5424
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            remove_lora,
5425
            num_active_loras,
5426
        ):
5427
            # Make sure padding doesn't exceed max_num_tokens
5428
            assert num_tokens_padded <= self.max_num_tokens
5429
            model_kwargs = self._init_model_kwargs()
5430
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
Patrick von Platen's avatar
Patrick von Platen committed
5431
5432
                input_ids, inputs_embeds = self._prepare_mm_inputs(num_tokens_padded)

5433
                model_kwargs = {
5434
                    **model_kwargs,
5435
5436
                    **self._dummy_mm_kwargs(num_reqs),
                }
5437
5438
            elif self.enable_prompt_embeds:
                input_ids = None
5439
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
5440
                model_kwargs = self._init_model_kwargs()
5441
            else:
5442
                input_ids = self.input_ids.gpu[:num_tokens_padded]
5443
                inputs_embeds = None
5444

5445
            if self.uses_mrope:
5446
                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
5447
            elif self.uses_xdrope_dim > 0:
5448
                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
5449
            else:
5450
                positions = self.positions[:num_tokens_padded]
5451
5452
5453
5454
5455
5456
5457
5458
5459

            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,
5460
5461
5462
                            device=self.device,
                        )
                    )
5463
5464

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
5465
                    num_tokens_padded, None, False
5466
                )
5467

5468
            if ubatch_slices_padded is not None:
5469
5470
5471
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
5472
                num_tokens_padded = ubatch_slices_padded[0].num_tokens
5473
                if num_tokens_across_dp is not None:
5474
                    num_tokens_across_dp[:] = num_tokens_padded
5475

5476
            with (
5477
                self.maybe_randomize_inputs(input_ids, inputs_embeds),
5478
                set_forward_context(
5479
5480
                    attn_metadata,
                    self.vllm_config,
5481
                    num_tokens=num_tokens_padded,
5482
5483
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
5484
                    batch_descriptor=batch_desc,
5485
                    ubatch_slices=ubatch_slices_padded,
5486
                    slot_mapping=slot_mappings,
5487
5488
                ),
            ):
5489
                outputs = self.model(
5490
5491
5492
5493
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
5494
                    **model_kwargs,
5495
                )
5496

5497
5498
5499
5500
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
5501

5502
5503
5504
            if self.speculative_config and (
                self.speculative_config.use_eagle()
                or self.speculative_config.uses_draft_model()
5505
                or self.speculative_config.uses_extract_hidden_states()
5506
            ):
5507
5508
                assert isinstance(
                    self.drafter,
5509
5510
5511
5512
                    EagleProposer
                    | DFlashProposer
                    | DraftModelProposer
                    | ExtractHiddenStatesProposer,
5513
                )
5514
                assert self.speculative_config is not None
5515
5516
5517
                # 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.
5518
                use_cudagraphs = (
5519
5520
5521
5522
5523
5524
5525
5526
5527
                    (
                        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
5528
5529
5530
5531
5532

                # 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
5533
5534
5535
5536
                if (
                    self.compilation_config.cudagraph_specialize_lora
                    and num_active_loras > 0
                ):
5537
5538
5539
5540
5541
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
Rémi Delacourt's avatar
Rémi Delacourt committed
5542
                    is_graph_capturing=is_graph_capturing,
5543
                    slot_mappings=slot_mappings,
5544
                )
5545

5546
5547
5548
5549
5550
5551
5552
5553
5554
5555
5556
        # 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()

5557
5558
5559
5560
5561
5562
5563
5564
5565
5566
        # 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)

5567
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
5568
5569
5570
5571
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
5572
5573
5574
5575
5576
5577

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
5578
5579
5580
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
5581

5582
5583
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5584
5585
5586
            # MM Encoder only model no need to run sampler.
            return torch.tensor([])

5587
        hidden_states = torch.rand_like(hidden_states)
5588

5589
        logits = self.model.compute_logits(hidden_states)
5590
5591
        num_reqs = logits.size(0)

5592
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
5593
5594
5595
5596
5597
5598
5599
5600
5601

        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,
Vedant V Jhaveri's avatar
Vedant V Jhaveri committed
5602
            logprob_token_ids=None,
5603
5604
5605
5606
5607
5608
            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)],
5609
            spec_token_ids=[[] for _ in range(num_reqs)],
5610
5611
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
5612
            logitsprocs=LogitsProcessors(),
5613
        )
5614
        try:
5615
5616
5617
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
5618
        except RuntimeError as e:
5619
            if "out of memory" in str(e):
5620
5621
5622
5623
                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 "
5624
5625
                    "initializing the engine."
                ) from e
5626
5627
            else:
                raise e
5628
        if self.speculative_config:
5629
5630
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
5631
5632
                draft_token_ids, self.device
            )
5633
5634
5635
5636
5637
5638

            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
5639
5640
5641
5642
5643
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
5644
            )
5645
5646
5647
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
5648
                logits,
5649
5650
                dummy_metadata,
            )
5651
        return sampler_output
5652

5653
    def _dummy_pooler_run_task(
5654
5655
        self,
        hidden_states: torch.Tensor,
5656
5657
        task: PoolingTask,
    ) -> PoolerOutput:
5658
5659
5660
5661
        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
5662
5663
5664
5665
        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
5666
5667
5668

        req_num_tokens = num_tokens // num_reqs

5669
        dummy_prompt_lens = torch.from_numpy(num_scheduled_tokens_np)
5670
5671
5672
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
5673

5674
        model = cast(VllmModelForPooling, self.get_model())
5675
        dummy_pooling_params = PoolingParams(task=task)
5676
        dummy_pooling_params.verify(self.model_config)
5677
        to_update = model.pooler.get_pooling_updates(task)
5678
5679
        to_update.apply(dummy_pooling_params)

5680
        dummy_metadata = PoolingMetadata(
5681
5682
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
5683
            prompt_token_ids_cpu=dummy_token_ids.cpu(),
5684
            pooling_params=[dummy_pooling_params] * num_reqs,
5685
            pooling_states=[PoolingStates() for i in range(num_reqs)],
5686
        )
5687

5688
        dummy_metadata.build_pooling_cursor(
5689
            num_scheduled_tokens_np,
5690
5691
            seq_lens_cpu=dummy_prompt_lens,
            device=hidden_states.device,
5692
        )
5693

5694
        try:
5695
5696
5697
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
5698
        except RuntimeError as e:
5699
            if "out of memory" in str(e):
5700
                raise RuntimeError(
5701
5702
5703
                    "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 "
5704
5705
                    "initializing the engine."
                ) from e
5706
5707
            else:
                raise e
5708
5709
5710
5711
5712
5713

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
5714
5715
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5716
5717
5718
            # MM Encoder only model not need to run pooler.
            return torch.tensor([])

5719
        # Find the task that has the largest output for subsequent steps
5720
5721
5722
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
5723
5724
5725
5726
5727
5728
            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."
            )
5729

5730
        output_size = dict[PoolingTask, float]()
5731
        for task in supported_pooling_tasks:
5732
5733
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
5734
            output_size[task] = sum(o.nbytes for o in output if o is not None)
5735
5736
5737
5738
            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)
5739

5740
    def profile_run(self) -> None:
5741
        # Profile with multimodal encoder & encoder cache.
5742
        if self.supports_mm_inputs:
5743
5744
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
5745
                logger.info(
5746
                    "Skipping memory profiling for multimodal encoder and "
5747
5748
                    "encoder cache."
                )
5749
5750
5751
5752
5753
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
5754
5755
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
5769
5770
                    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
                        ]
5771

5772
                        logger.info_once(
5773
5774
5775
5776
5777
5778
                            "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,
5779
                            scope="local",
5780
                        )
5781

5782
5783
5784
5785
5786
                        # Create dummy batch of multimodal inputs.
                        batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                            dummy_modality,
                            max_mm_items_per_batch,
                        )
5787

5788
5789
5790
5791
5792
5793
5794
5795
5796
5797
5798
                        # 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
5799

5800
        # Add `is_profile` here to pre-allocate communication buffers
5801
5802
5803
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
5804
        if get_pp_group().is_last_rank:
5805
5806
5807
5808
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
5809
        else:
5810
            output = None
5811
        self._sync_device()
5812
        del hidden_states, output
5813
        self.encoder_cache.clear()
5814
        gc.collect()
5815

5816
5817
5818
5819
5820
5821
5822
5823
5824
5825
    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

5826
5827
5828
        # 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
5829
        minimal_config = get_kv_cache_config_from_groups(
5830
            self.vllm_config, kv_cache_groups, available_memory=0
5831
        )
5832
        self.cache_config.num_gpu_blocks_override = saved_override
5833

5834
        self.initialize_kv_cache(minimal_config, is_profiling=True)
5835
5836
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
        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"):
5870
5871
5872
5873
                kv_cache = layer.kv_cache
                layer.kv_cache = (
                    torch.tensor([]) if isinstance(kv_cache, torch.Tensor) else []
                )
5874
5875
5876
5877
5878
5879
5880
            # Clean up quantized KV cache scale views
            # (int8_per_token_head, fp8_per_token_head)
            if hasattr(layer, "impl"):
                if hasattr(layer.impl, "_k_scale_cache"):
                    layer.impl._k_scale_cache = None
                if hasattr(layer.impl, "_v_scale_cache"):
                    layer.impl._v_scale_cache = None
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
5963
5964
5965
5966
5967

        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)]
5968
5969
5970
        for key_set in self.cudagraph_dispatcher.cudagraph_keys.values():
            key_set.clear()
        self.cudagraph_dispatcher.keys_initialized = False
5971
5972
5973
5974
5975
5976
5977
5978
5979
5980
5981
5982
5983
5984
5985
5986
5987
        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)

5988
    @instrument(span_name="Capture model")
5989
    def capture_model(self) -> int:
5990
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
5991
            logger.warning(
5992
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
5993
5994
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
5995
            return 0
5996

5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
        # 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,
            )
6010
            from vllm.v1.worker.encoder_cudagraph import (
6011
6012
                EncoderCudaGraphManager,
            )
6013
6014
6015
6016
6017
6018
6019
6020
6021
6022
6023

            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")

6024
6025
        compilation_counter.num_gpu_runner_capture_triggers += 1

6026
6027
        start_time = time.perf_counter()

6028
6029
6030
        # 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.
6031
        set_cudagraph_capturing_enabled(True)
6032
6033
6034
        with self._freeze_gc(), graph_capture(device=self.device):
            torch.accelerator.synchronize()
            torch.accelerator.empty_cache()
6035
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
6036

6037
6038
6039
6040
            for (
                runtime_mode,
                batch_descs,
            ) in self.cudagraph_dispatcher.get_capture_descs():
6041
                self._capture_cudagraphs(
6042
6043
                    batch_descriptors=batch_descs,
                    cudagraph_runtime_mode=runtime_mode,
6044
                )
6045
                torch.accelerator.synchronize()
6046

6047
6048
6049
6050
            # Capture encoder CUDA graphs if enabled
            if self.encoder_cudagraph_manager is not None:
                self.encoder_cudagraph_manager.capture()

6051
            torch.accelerator.synchronize()
6052
6053
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

6054
6055
6056
        # 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
6057
        # we may do lazy capturing in future that still allows capturing
6058
6059
        # after here.
        set_cudagraph_capturing_enabled(False)
6060

6061
6062
6063
        torch.accelerator.synchronize()
        torch.accelerator.empty_cache()

6064
6065
6066
6067
        # Lock workspace to prevent resizing during execution.
        # Max workspace sizes should have been captured during warmup/profiling.
        lock_workspace()

6068
6069
6070
6071
        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.
6072
        logger.info_once(
6073
6074
6075
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
6076
            scope="local",
6077
        )
6078
        return cuda_graph_size
6079

6080
6081
6082
6083
6084
6085
6086
6087
6088
6089
6090
6091
6092
6093
6094
6095
6096
6097
6098
6099
6100
    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,
6101
                profile_seq_lens=profile_seq_lens,
6102
6103
6104
6105
6106
6107
6108
6109
6110
6111
6112
6113
6114
            )
        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,
        )

6115
6116
    def _capture_cudagraphs(
        self,
6117
        batch_descriptors: list[BatchDescriptor],
6118
6119
6120
6121
        cudagraph_runtime_mode: CUDAGraphMode,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
6122
            and cudagraph_runtime_mode.is_valid_runtime_mode()
6123
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
6124

6125
6126
6127
6128
6129
        if not batch_descriptors:
            return

        uniform_decode = batch_descriptors[0].uniform

6130
6131
        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
6132
6133
            batch_descriptors = tqdm(
                batch_descriptors,
6134
6135
6136
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
6137
6138
6139
                    cudagraph_runtime_mode.name,
                ),
            )
6140

6141
        # We skip EPLB here since we don't want to record dummy metrics
6142
        for batch_desc in batch_descriptors:
6143
            # We currently only capture ubatched graphs when its a FULL
6144
6145
6146
            # 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
6147
            allow_microbatching = (
6148
                self.parallel_config.use_ubatching
6149
6150
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
6151
6152
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
6153
                    num_tokens=batch_desc.num_tokens,
6154
6155
                    uniform_decode=uniform_decode,
                )
6156
            )
6157
6158
            self._warmup_and_capture(
                batch_desc,
6159
6160
6161
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                allow_microbatching=allow_microbatching,
            )
6162
            torch.accelerator.synchronize()
6163
        self.maybe_remove_all_loras(self.lora_config)
6164

6165
6166
6167
6168
6169
    def initialize_attn_backend(
        self,
        kv_cache_config: KVCacheConfig,
        is_profiling: bool = False,
    ) -> None:
6170
6171
6172
        """
        Initialize the attention backends and attention metadata builders.
        """
6173
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
6174

6175
6176
6177
6178
6179
6180
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
6181
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
6182
            layer_type = cast(type[Any], AttentionLayerBase)
6183
            layers = get_layers_from_vllm_config(
6184
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
6185
            )
6186
6187
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
6188
            # Dedupe based on full class name; this is a bit safer than
6189
6190
6191
6192
            # 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.
6193
            for layer_name in kv_cache_group_spec.layer_names:
6194
                attn_backend = layers[layer_name].get_attn_backend()
6195
6196
6197
6198

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
6199
                        attn_backend,  # type: ignore[arg-type]
6200
6201
                    )

6202
6203
6204
                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):
6205
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
6206
                key = (full_cls_name, layer_kv_cache_spec)
6207
6208
6209
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
6210
                attn_backend_layers[key].append(layer_name)
6211
6212
6213
6214
            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()),
            )
6215
6216

        def create_attn_groups(
6217
            attn_backends_map: dict[AttentionGroupKey, list[str]],
6218
            kv_cache_group_id: int,
6219
6220
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
6221
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
6222
                attn_group = AttentionGroup(
6223
                    attn_backend,
6224
                    layer_names,
6225
                    kv_cache_spec,
6226
                    kv_cache_group_id,
6227
6228
                )

6229
6230
6231
                attn_groups.append(attn_group)
            return attn_groups

6232
        attention_backend_maps = []
6233
        attention_backend_list = []
6234
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
6235
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
6236
            attention_backend_maps.append(attn_backends[0])
6237
            attention_backend_list.append(attn_backends[1])
6238
6239

        # Resolve cudagraph_mode before actually initialize metadata_builders
6240
        self._check_and_update_cudagraph_mode(
6241
6242
6243
            attention_backend_list,
            kv_cache_config.kv_cache_groups,
            is_profiling=is_profiling,
6244
        )
6245

6246
6247
6248
        # Check if attention backend supports PCP&DCP and related features.
        check_attention_cp_compatibility(self.vllm_config)

6249
6250
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
6251

6252
6253
6254
6255
6256
6257
6258
6259
6260
6261
6262
6263
6264
6265
6266
    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
6267
6268
                    if not self.parallel_config.use_ubatching
                    else self.parallel_config.num_ubatches,
6269
                )
co63oc's avatar
co63oc committed
6270
        # Calculate reorder batch threshold (if needed)
6271
6272
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
6273
6274
        self.calculate_reorder_batch_threshold()

6275
6276
6277
6278
6279
        # Initialize drafter attention backend
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_draft_model()
        ):
6280
6281
6282
            assert isinstance(
                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
            )
6283
6284
            self.drafter.initialize_attn_backend(kv_cache_config, kernel_block_sizes)

6285
    def _check_and_update_cudagraph_mode(
6286
6287
6288
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
6289
        is_profiling: bool = False,
6290
    ) -> None:
6291
        """
6292
        Resolve the cudagraph_mode when there are multiple attention
6293
        groups with potential conflicting CUDA graph support.
6294
6295
6296
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
6297
        min_cg_support = AttentionCGSupport.ALWAYS
6298
        min_cg_attn_backend = None
6299

6300
6301
6302
6303
6304
6305
6306
6307
6308
6309
6310
        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
6311
6312
6313
6314
6315
6316
6317
6318
6319
6320
                    min_cg_attn_backend = attn_backend.__name__
        cudagraph_mode = self.compilation_config.resolve_cudagraph_mode_and_sizes(
            min_cg_support,
            min_cg_attn_backend,
            self.uniform_decode_query_len,
            self.parallel_config.tensor_parallel_size,
            self.kv_cache_config,
            self.max_num_reqs,
            is_profiling=is_profiling,
        )
6321
6322
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
6323
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
6324
            cudagraph_mode, self.uniform_decode_query_len
6325
        )
6326

6327
6328
6329
6330
6331
        # 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()
        ):
6332
6333
6334
6335
            assert isinstance(
                self.drafter,
                EagleProposer | DFlashProposer | ExtractHiddenStatesProposer,
            )
6336
6337
            self.drafter.initialize_cudagraph_keys(cudagraph_mode)

6338
6339
    def calculate_reorder_batch_threshold(self) -> None:
        """
6340
6341
6342
6343
        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.
6344
        """
6345
6346
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

6347
        reorder_batch_thresholds: list[int | None] = [
6348
6349
6350
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
6351
6352
6353
6354
6355
        # 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
6356
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
6357

6358
6359
6360
6361
6362
6363
6364
6365
6366
6367
6368
6369
6370
6371
6372
6373
6374
6375
6376
6377
6378
6379
6380
6381
6382
6383
6384
6385
6386
6387
6388
6389
6390
6391
6392
6393
6394
6395
6396
6397
    def _set_mm_prefix_range_for_metadata(
        self,
        attn_metadata: Any,
        req_doc_ranges: dict[int, list[tuple[int, int]]],
    ) -> None:
        """Set mm_prefix_range for all attention metadata objects.

        This method handles both list and non-list attention metadata,
        computing mm_prefix_range_tensor once and sharing it across all
        metadata objects to avoid redundant host-to-device transfers.
        """
        from vllm.v1.attention.backends.triton_attn import (
            TritonAttentionMetadata,
        )

        # Get all metadata objects from either list or dict structure
        metadata_list = []
        if isinstance(attn_metadata, list):
            for ub_metadata in attn_metadata:
                metadata_list.extend(ub_metadata.values())
        else:
            metadata_list.extend(attn_metadata.values())

        # Set mm_prefix_range for all metadata and compute tensor once
        shared_tensor = None
        for metadata in metadata_list:
            metadata.mm_prefix_range = req_doc_ranges  # type: ignore[attr-defined]

            # Only compute tensor for TritonAttentionMetadata
            if isinstance(metadata, TritonAttentionMetadata):
                if shared_tensor is None:
                    shared_tensor = (
                        TritonAttentionMetadata.compute_mm_prefix_range_tensor(
                            req_doc_ranges,
                            metadata.seq_lens.shape[0],  # type: ignore[attr-defined]
                            metadata.seq_lens.device,  # type: ignore[attr-defined]
                        )
                    )
                metadata.mm_prefix_range_tensor = shared_tensor

6398
6399
6400
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
6401
6402
        """
        Re-initialize the input batch if the block sizes are different from
6403
6404
6405
6406
        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.
6407
6408
6409

        Args:
            kv_cache_config: The KV cache configuration.
6410
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6411
        """
6412
        block_sizes = []
6413
6414
        max_num_blocks = []
        max_model_len = max(self.max_model_len, self.max_encoder_len)
6415
        for kv_cache_group in kv_cache_config.kv_cache_groups:
6416
6417
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                continue
6418
6419
            block_size = kv_cache_group.kv_cache_spec.block_size
            block_sizes.append(block_size)
6420
            max_num_blocks_per_req = cdiv(
6421
                max_model_len, block_size * get_total_cp_world_size()
6422
6423
            )
            if isinstance(kv_cache_group.kv_cache_spec, MambaSpec):
6424
                max_num_blocks_per_req = (
6425
6426
6427
6428
6429
                    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)
6430

6431
6432
6433
6434
6435
6436
        if (
            block_sizes != self._init_block_sizes
            or kernel_block_sizes != self._init_kernel_block_sizes
        ):
            self._init_block_sizes = block_sizes
            self._init_kernel_block_sizes = kernel_block_sizes
6437
6438
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
6439
                max_model_len=max_model_len,
6440
6441
6442
6443
6444
                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,
6445
                kernel_block_sizes=kernel_block_sizes,
6446
                max_num_blocks_per_req=max_num_blocks,
6447
                is_spec_decode=bool(self.vllm_config.speculative_config),
6448
                logitsprocs=self.input_batch.logitsprocs,
6449
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
6450
                is_pooling_model=self.is_pooling_model,
6451
6452
            )

6453
6454
6455
6456
6457
6458
6459
6460
6461
        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}"
        )

6462
    def _allocate_kv_cache_tensors(
6463
6464
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
6465
        """
6466
6467
6468
        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.

6469
        Args:
6470
            kv_cache_config: The KV cache config
6471
        Returns:
6472
            dict[str, torch.Tensor]: A map between layer names to their
6473
            corresponding memory buffer for KV cache.
6474
        """
6475
6476
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
6477
6478
6479
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
6480
6481
6482
6483
6484
            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:
6485
6486
6487
6488
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
6489
6490
6491
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
6492
6493
        return kv_cache_raw_tensors

6494
6495
6496
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

6497
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
6498
6499
        if not self.kv_cache_config.kv_cache_groups:
            return
6500
6501
        for attn_groups in self.attn_groups:
            yield from attn_groups
6502

6503
6504
6505
6506
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
6507
        kernel_block_sizes: list[int],
6508
    ) -> dict[str, torch.Tensor]:
6509
        """
6510
        Reshape the KV cache tensors to the desired shape and dtype.
6511

6512
        Args:
6513
6514
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
6515
                correct size but uninitialized shape.
6516
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6517
        Returns:
6518
            Dict[str, torch.Tensor]: A map between layer names to their
6519
6520
            corresponding memory buffer for KV cache.
        """
6521
        kv_caches: dict[str, torch.Tensor] = {}
6522
        has_attn, has_mamba = False, False
6523
6524
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
6525
            attn_backend = group.backend
6526
6527
6528
6529
            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]
6530
            for layer_name in group.layer_names:
6531
6532
                if layer_name in self.runner_only_attn_layers:
                    continue
6533
6534
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
6535
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
6536
                if isinstance(kv_cache_spec, AttentionSpec):
6537
                    has_attn = True
6538
6539
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
6540
6541
6542
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

6543
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
6544
                        kernel_num_blocks,
6545
                        kernel_block_size,
6546
6547
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
6548
6549
                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
6550
                    dtype = kv_cache_spec.dtype
6551
                    try:
6552
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
6553
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
6554
                    except (AttributeError, NotImplementedError):
6555
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
6556
6557
6558
6559
6560
                    # 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.
6561
6562
6563
                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
6564
6565
6566
6567
6568
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
6569
6570
6571
6572
6573
6574
                    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
6575
                elif isinstance(kv_cache_spec, MambaSpec):
6576
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
6577
6578
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
6579
                    storage_offset_bytes = 0
6580
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
6581
6582
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
6583
6584
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
6585
                        target_shape = (num_blocks, *shape)
6586
6587
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
6588
                        assert storage_offset_bytes % dtype_size == 0
6589
6590
6591
6592
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
6593
                            storage_offset=storage_offset_bytes // dtype_size,
6594
                        )
Chen Zhang's avatar
Chen Zhang committed
6595
                        state_tensors.append(tensor)
6596
                        storage_offset_bytes += stride[0] * dtype_size
6597
6598

                    kv_caches[layer_name] = state_tensors
6599
                else:
6600
                    raise NotImplementedError
6601
6602

        if has_attn and has_mamba:
6603
            self._update_hybrid_attention_mamba_layout(kv_caches, kernel_block_sizes)
6604

6605
6606
        return kv_caches

6607
    def _update_hybrid_attention_mamba_layout(
6608
        self, kv_caches: dict[str, torch.Tensor], kernel_block_sizes: list[int]
6609
    ) -> None:
6610
        """
6611
6612
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
6613
6614

        Args:
6615
            kv_caches: The KV cache buffer of each layer.
6616
            kernel_block_sizes: The kernel block sizes for each KV cache group.
6617
6618
        """

6619
6620
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
6621
6622
6623
6624
6625
6626
6627
6628
6629
6630
6631
6632
            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
6633
            for layer_name in group.layer_names:
6634
                kv_cache = kv_caches[layer_name]
6635
6636
6637
6638
6639
                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:]),
                )
6640

6641
    def initialize_kv_cache_tensors(
6642
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
6643
    ) -> dict[str, torch.Tensor]:
6644
6645
6646
6647
6648
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
6649
6650
            kernel_block_sizes: The kernel block sizes for each KV cache group.

6651
        Returns:
6652
            Dict[str, torch.Tensor]: A map between layer names to their
6653
6654
            corresponding memory buffer for KV cache.
        """
6655
6656
6657
6658
6659
6660
6661
6662
6663
6664
6665
6666
6667
6668
6669
6670
6671
6672
6673
6674
6675
6676
6677
6678

        # 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
            )
6679

6680
        # Set up cross-layer KV cache sharing
6681
6682
        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)
6683
6684
            kv_caches[layer_name] = kv_caches[target_layer_name]

6685
6686
6687
6688
6689
6690
6691
6692
6693
        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,
        )
6694
6695
6696
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
6697
6698
        self, kv_cache_config: KVCacheConfig
    ) -> None:
6699
6700
6701
6702
6703
6704
6705
6706
6707
6708
6709
6710
6711
6712
6713
6714
6715
6716
        """
        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.
6717
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
6718
6719
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
6720
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
6721
6722
                else:
                    break
6723

6724
6725
6726
6727
6728
    def initialize_kv_cache(
        self,
        kv_cache_config: KVCacheConfig,
        is_profiling: bool = False,
    ) -> None:
6729
6730
6731
6732
6733
6734
        """
        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
        """
6735
        kv_cache_config = deepcopy(kv_cache_config)
6736
        self.kv_cache_config = kv_cache_config
6737
        self._mamba_copy_bufs = None
6738
        self.may_add_encoder_only_layers_to_kv_cache_config()
6739
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
6740
        self.initialize_attn_backend(kv_cache_config, is_profiling=is_profiling)
6741
        initialize_mamba_ssu_backend(self.vllm_config.mamba_config)
6742
6743
6744
6745
6746
        # 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.
6747
6748
6749
        kernel_block_sizes = prepare_kernel_block_sizes(
            kv_cache_config, self.attn_groups
        )
6750
        self._kernel_block_sizes = kernel_block_sizes
6751
6752
6753
6754

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

6755
        # Reinitialize need to after initialize_attn_backend
6756
6757
6758
6759
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
6760

6761
6762
6763
        if (
            self.speculative_config
            and self.speculative_config.uses_extract_hidden_states()
6764
        ):
6765
            assert isinstance(self.drafter, ExtractHiddenStatesProposer)
6766
6767
6768
6769
            # validate all draft model layers belong to the same kv cache
            # group
            self.drafter.validate_same_kv_cache_group(kv_cache_config)

6770
        if has_kv_transfer_group() and not is_profiling:
6771
            kv_transfer_group = get_kv_transfer_group()
6772
6773
6774
6775
6776
6777
6778
            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)
6779
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
6780

6781
6782
6783
6784
6785
6786
    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
6787
6788
6789
6790
6791
6792
6793

    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()
6794
6795
6796
6797
6798
6799
6800
6801
        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)
6802
        self.max_num_kv_tokens = (
6803
6804
6805
6806
6807
6808
6809
            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

6810
6811
6812
        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,
6813
            vllm_config=self.vllm_config,
6814
        )
6815
        self._bind_routed_experts_capturer(routed_experts_capturer)
6816
        self.routed_experts_initialized = True
6817
6818
6819
6820
6821
6822
6823
6824
6825
6826
6827
6828
6829
6830
6831

    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)
6832

6833
6834
6835
6836
6837
    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
6838
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
6839
6840
6841
        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:
6842
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
6843
6844
6845
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
6846
6847
                    dtype=self.kv_cache_dtype,
                )
6848
6849
6850
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
6851
6852
6853
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
6854
6855
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
6856
6857
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
6858

6859
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
6860
        """
6861
        Generates the KVCacheSpec by parsing the kv cache format from each
6862
6863
        Attention module in the static forward context.
        Returns:
6864
            KVCacheSpec: A dictionary mapping layer names to their KV cache
6865
6866
            format. Layers that do not need KV cache are not included.
        """
6867
        if has_ec_transfer() and not get_ec_transfer().is_consumer:
6868
            return {}
6869
        kv_cache_spec: dict[str, KVCacheSpec] = {}
6870
6871
        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
6872
        for layer_name, attn_module in attn_layers.items():
6873
6874
6875
6876
6877
6878
6879
6880
6881
6882
6883
6884
6885
6886
6887
            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
6888

6889
        return kv_cache_spec
6890

6891
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
6892
6893
6894
6895
6896
6897
6898
6899
        # 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.
6900
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
6901
6902
6903
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
6904
        return pinned.tolist()
6905
6906
6907
6908
6909
6910
6911
6912
6913
6914
6915
6916
6917
6918
6919
6920
6921
6922
6923
6924
6925
6926
6927
6928
6929
6930
6931
6932
6933
6934
6935
6936
6937
6938
6939
6940
6941
6942
6943
6944

    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}

6945
        torch.accelerator.synchronize()
6946
6947
6948
6949
6950
        start_time = time.perf_counter()

        try:
            yield
        finally:
6951
            torch.accelerator.synchronize()
6952
6953
6954
6955
6956
6957
6958
6959
6960
6961
            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]
6962
                    stats.encoder_forward_secs += per_request_time
6963
6964
6965
6966
6967
6968
6969
                    stats.num_encoder_calls += 1


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

6970
    encoder_forward_secs: float = 0.0
6971
6972
6973
6974
6975
6976
6977
    """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 {
6978
            "encoder_forward_secs": self.encoder_forward_secs,
6979
6980
            "num_encoder_calls": self.num_encoder_calls,
        }