gpu_model_runner.py 270 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 Iterator, Sequence
11
from contextlib import contextmanager
12
from copy import copy, deepcopy
13
from dataclasses import dataclass
14
from functools import reduce
王敏's avatar
王敏 committed
15
from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast, Optional
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.attention.layer import Attention, MLAAttention
25
from vllm.compilation.counter import compilation_counter
26
from vllm.compilation.cuda_graph import CUDAGraphStat, CUDAGraphWrapper
27
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
28
from vllm.config import (
29
    CompilationMode,
30
31
32
33
34
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
35
from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
36
from vllm.distributed.eplb.eplb_state import EplbState
37
from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
38
from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
39
from vllm.distributed.parallel_state import (
40
    get_dcp_group,
41
42
43
44
45
46
    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
47
48
49
50
from vllm.forward_context import (
    BatchDescriptor,
    set_forward_context,
)
51
from vllm.logger import init_logger
52
from vllm.lora.layers import LoRAMapping, LoRAMappingType
53
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
54
55
56
from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
    RoutedExpertsCapturer,
)
57
58
59
60
from vllm.model_executor.layers.rotary_embedding import (
    MRotaryEmbedding,
    XDRotaryEmbedding,
)
61
from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
62
from vllm.model_executor.models.interfaces import (
63
    MultiModalEmbeddings,
64
    SupportsMRoPE,
65
    SupportsMultiModal,
66
    SupportsXDRoPE,
67
68
69
70
71
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
72
    supports_xdrope,
73
)
74
from vllm.model_executor.models.interfaces_base import (
75
76
77
78
    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
79
from vllm.multimodal import MULTIMODAL_REGISTRY
80
81
82
83
84
from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
85
from vllm.multimodal.utils import group_mm_kwargs_by_modality
86
from vllm.pooling_params import PoolingParams
87
from vllm.sampling_params import SamplingType
88
from vllm.sequence import IntermediateTensors
89
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
90
from vllm.utils import length_from_prompt_token_ids_or_embeds
91
from vllm.utils.jsontree import json_map_leaves
92
from vllm.utils.math_utils import cdiv, round_up
93
from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
94
from vllm.utils.nvtx_pytorch_hooks import PytHooks
95
from vllm.utils.platform_utils import is_pin_memory_available
96
97
98
99
from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
)
100
101
from vllm.v1.attention.backend import (
    AttentionBackend,
102
    AttentionCGSupport,
103
    AttentionMetadata,
104
    AttentionMetadataBuilder,
105
    AttentionType,
106
    CommonAttentionMetadata,
107
108
    MultipleOf,
)
109
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
110
from vllm.v1.attention.backends.utils import (
111
    create_fast_prefill_custom_backend,
112
    get_dcp_local_seq_lens,
113
114
    reorder_batch_to_split_decodes_and_prefills,
)
115
from vllm.v1.core.sched.output import NewRequestData
116
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
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,
134
    ECConnectorOutput,
135
    KVConnectorOutput,
136
137
138
139
140
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
141
    make_empty_encoder_model_runner_output,
142
)
143
from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
144
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
145
from vllm.v1.sample.logits_processor.interface import LogitsProcessor
146
from vllm.v1.sample.metadata import SamplingMetadata
147
from vllm.v1.sample.rejection_sampler import RejectionSampler
王敏's avatar
王敏 committed
148
from vllm.v1.sample.rejection_sampler_opt import OptRejectionSampler
149
from vllm.v1.sample.sampler import Sampler
150
from vllm.v1.spec_decode.draft_model import DraftModelProposer
151
from vllm.v1.spec_decode.eagle import EagleProposer
152
from vllm.v1.spec_decode.medusa import MedusaProposer
153
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
154
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
155
from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
156
from vllm.v1.structured_output.utils import apply_grammar_bitmask
157
from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
158
159
160
161
162
from vllm.v1.worker import mamba_utils
from vllm.v1.worker.cp_utils import (
    check_attention_cp_compatibility,
    get_total_cp_world_size,
)
163
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
164
from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
165
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
166
from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
167
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
168
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
169
170
171
from vllm.v1.worker.ubatch_utils import (
    UBatchSlices,
    check_ubatch_thresholds,
172
    maybe_create_ubatch_slices,
173
    split_attn_metadata,
174
)
175
from vllm.v1.worker.utils import is_residual_scattered_for_sp
176
from vllm.v1.worker.workspace import lock_workspace
177

178
179
180
181
182
183
184
from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    sanity_check_mm_encoder_outputs,
)
王敏's avatar
王敏 committed
185
from vllm.v1.spec_decode.utils import DraftProbs
186

187
if TYPE_CHECKING:
188
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
189
    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
190
191
192

logger = init_logger(__name__)

193
194
AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
195
PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
196

197

198
199
200
201
202
203
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
204
        logprobs_tensors: LogprobsTensors | None,
205
206
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
207
        vocab_size: int,
208
209
210
211
212
    ):
        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.
213
        self.async_copy_ready_event = torch.Event()
214
215
216
217

        # 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
218
        self.vocab_size = vocab_size
219
        self._logprobs_tensors = logprobs_tensors
220
221
222
223
224

        # 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)
225
            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
226
227
                "cpu", non_blocking=True
            )
228
229
230
231
232
            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
233
            self.async_copy_ready_event.record()
234
235
236

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
237

238
239
        This function blocks until the copy is finished.
        """
240
        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
241
        self.async_copy_ready_event.synchronize()
242

243
244
        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
245
        del self._sampled_token_ids
246
        if max_gen_len == 1:
247
            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
248
249
            for i in self._invalid_req_indices:
                valid_sampled_token_ids[i].clear()
250
251
252
            logprobs_lists = None
            if self._logprobs_tensors_cpu is not None:
                logprobs_lists = self._logprobs_tensors_cpu.tolists()
253
        else:
254
            valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
255
256
                self.sampled_token_ids_cpu,
                self.vocab_size,
257
                self._invalid_req_indices,
258
                logprobs_tensors=self._logprobs_tensors_cpu,
259
            )
260
261
262

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
263
        output.logprobs = logprobs_lists
264
265
266
        return output


267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
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)
288
            raw_pooler_output_cpu = json_map_leaves(
289
290
291
292
                lambda x: None if x is None else x.to("cpu", non_blocking=True),
                self._raw_pooler_output,
            )
            self.async_copy_ready_event.record()
293
294
295
296
            self._model_runner_output.pooler_output = [
                out if include else None
                for out, include in zip(raw_pooler_output_cpu, finished_mask)
            ]
297
298
299
300
301
302
303
304
305
306
307
308

    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


309
310
311
class ExecuteModelState(NamedTuple):
    """Ephemeral cached state transferred between execute_model() and
    sample_tokens(), after execute_model() returns None."""
312

313
314
315
316
317
318
319
    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
320
    ec_connector_output: ECConnectorOutput | None
321
    cudagraph_stats: CUDAGraphStat | None
322
    slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None
323
324


325
326
327
class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
328
329
    def __init__(
        self,
330
        vllm_config: VllmConfig,
331
        device: torch.device,
332
    ):
333
334
335
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
336
        self.compilation_config = vllm_config.compilation_config
337
338
339
340
341
342
        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
343

344
        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
345
346

        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
347

348
349
350
351
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
352
        self.device = device
353
354
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
355

356
357
358
        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
359

360
        self.is_pooling_model = model_config.runner_type == "pooling"
361
        self.enable_prompt_embeds = model_config.enable_prompt_embeds
362
        self.is_multimodal_raw_input_only_model = (
363
364
            model_config.is_multimodal_raw_input_only_model
        )
365
366
        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
367
        self.max_model_len = model_config.max_model_len
368
369
370

        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
371
        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
372
        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
373
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
374
        self.max_num_reqs = scheduler_config.max_num_seqs
375

376
377
378
379
380
        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
381
            self.parallel_config.distributed_executor_backend == "external_launcher"
382
            and len(get_pp_group().ranks) > 1
383
        )
384

385
        # Model-related.
386
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
387
        self.inputs_embeds_size = model_config.get_inputs_embeds_size()
388
        self.attention_chunk_size = model_config.attention_chunk_size
389
        # Only relevant for models using ALiBi (e.g, MPT)
390
        self.use_alibi = model_config.uses_alibi
391

392
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
393
        self.is_mm_prefix_lm = self.model_config.is_mm_prefix_lm
394

395
        # Multi-modal data support
396
        self.mm_registry = MULTIMODAL_REGISTRY
397
        self.uses_mrope = model_config.uses_mrope
398
        self.uses_xdrope_dim = model_config.uses_xdrope_dim
guanyu1's avatar
guanyu1 committed
399
        self.use_1d_mrope = self.uses_mrope and envs.VLLM_1D_MROPE
400
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
401
402
            model_config
        )
403

404
405
406
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
407
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
408
409
410
        else:
            self.max_encoder_len = 0

411
412
413
        # Async scheduling
        self.use_async_scheduling = self.scheduler_config.async_scheduling

414
        # Sampler
415
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
416

417
        self.eplb_state: EplbState | None = None
418
419
420
421
422
423
        """
        State of the expert parallelism load balancer.

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

424
        # Lazy initializations
425
        # self.model: nn.Module  # Set after load_model
426
        # Initialize in initialize_kv_cache
427
        self.kv_caches: list[torch.Tensor] = []
428
429
430
        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
431
432
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
433
434
        # self.kv_cache_config: KVCacheConfig

435
436
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
437

438
        self.use_aux_hidden_state_outputs = False
439
440
441
442
443
        # 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:
444
            self.drafter: (
445
446
447
448
449
                NgramProposer
                | SuffixDecodingProposer
                | EagleProposer
                | DraftModelProposer
                | MedusaProposer
450
            )
451
452
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
453
454
455
456
457
458
            elif self.speculative_config.uses_draft_model():
                self.drafter = DraftModelProposer(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    runner=self,
                )
459
460
            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
461
            elif self.speculative_config.use_eagle():
462
                self.drafter = EagleProposer(self.vllm_config, self.device, self)
463
                if self.speculative_config.method == "eagle3":
464
465
466
                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
467
468
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
469
                    vllm_config=self.vllm_config, device=self.device
470
                )
471
            else:
472
473
474
475
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
王敏's avatar
王敏 committed
476
477
478
479
480
            
            if not envs.VLLM_REJECT_SAMPLE_OPT:
                self.rejection_sampler = RejectionSampler(self.sampler)
            else:
                self.rejection_sampler = OptRejectionSampler(self.sampler)
481

482
483
484
        self.num_spec_tokens = 0
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens
485
486
487
488
489
            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
490

491
        # Request states.
492
        self.requests: dict[str, CachedRequestState] = {}
493
494
495
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
496
        self.comm_stream = torch.cuda.Stream()
497

498
499
500
501
502
503
504
505
506
        # 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.
507
508
509
510
        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
511
512
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
513
514
515
            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
516
517
518
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
519
            vocab_size=self.model_config.get_vocab_size(),
520
            block_sizes=[self.cache_config.block_size],
521
            kernel_block_sizes=[self.cache_config.block_size],
522
            is_spec_decode=bool(self.vllm_config.speculative_config),
523
            logitsprocs=build_logitsprocs(
524
525
526
                self.vllm_config,
                self.device,
                self.pin_memory,
527
                self.is_pooling_model,
528
                custom_logitsprocs,
529
            ),
530
531
532
            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
533
            is_pooling_model=self.is_pooling_model,
534
            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
535
        )
536

537
538
539
540
541
        # 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.
542
        self.prepare_inputs_event: torch.Event | None = None
543
544
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
545
            self.prepare_inputs_event = torch.Event()
546

547
        # self.cudagraph_batch_sizes sorts in ascending order.
548
549
550
551
        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
552
553
            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
554
            )
555

556
        # Cache the device properties.
557
        self._init_device_properties()
558

559
560
561
562
        # Encoder timing registry for observability
        self.encoder_timing_registry: dict[str, EncoderTimingStats] = {}
        self._encoder_timing_lock = threading.Lock()

563
        # Persistent buffers for CUDA graphs.
564
565
566
567
568
        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
569
        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
570
        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
571
572
573
574
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
575
576
577
        # 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.
578
        self.inputs_embeds = self._make_buffer(
579
            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
580
581
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
582
583
        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
584
585
586
587
588
589
590
        )
        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
591

592
593
        # Only relevant for multimodal models
        if self.supports_mm_inputs:
594
595
596
597
598
599
600
            # Double buffer to avoid race condition: previous iteration's async
            # copy may still be reading from CPU while current iteration writes.
            self.is_mm_embed_buffers = [
                self._make_buffer(self.max_num_tokens, dtype=torch.bool),
                self._make_buffer(self.max_num_tokens, dtype=torch.bool),
            ]
            self.is_mm_embed_idx = 0
601
602

        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
603
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
604
605
606
607
            # 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
608
609
610
611
612
613

            # 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
guanyu1's avatar
guanyu1 committed
614
615
616
617
618
619
620
621
            if self.use_1d_mrope:
                self.mrope_positions = self._make_buffer(
                    3 * (self.max_num_tokens + 1), dtype=torch.int64
                )
            else:
                self.mrope_positions = self._make_buffer(
                    (3, self.max_num_tokens + 1), dtype=torch.int64
                )
622
623
624
625
626
627
        # 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
            )
628

629
        # None in the first PP rank. The rest are set after load_model.
630
        self.intermediate_tensors: IntermediateTensors | None = None
631

632
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
633
        # Keep in int64 to avoid overflow with long context
634
635
636
637
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
638

639
640
641
642
643
        # 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] = {}
644
645
646
647
648
        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(
649
650
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
651

652
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
653
654
655
656

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

657
        self.mm_budget = (
658
            MultiModalBudget(self.vllm_config, self.mm_registry)
659
660
661
            if self.supports_mm_inputs
            else None
        )
662

663
        self.reorder_batch_threshold: int | None = None
664

665
666
667
668
669
        # 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()

670
        # Cached outputs.
671
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
672
        self._draft_token_req_ids: list[str] | None = None
673
        self.transfer_event = torch.Event()
674
        self.sampled_token_ids_pinned_cpu = torch.empty(
675
            (self.max_num_reqs, 1),
676
677
            dtype=torch.int64,
            device="cpu",
678
679
            pin_memory=self.pin_memory,
        )
680

681
682
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
683
        self.valid_sampled_token_count_event: torch.Event | None = None
684
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
        # 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
        if self.num_spec_tokens:
            self.draft_token_ids_event = torch.Event()
            self.draft_token_ids_copy_stream = torch.cuda.Stream()
            self.draft_token_ids_cpu = torch.empty(
                (self.max_num_reqs, self.num_spec_tokens),
                dtype=torch.int64,
                device="cpu",
                pin_memory=self.pin_memory,
            )
            if self.use_async_scheduling:
                self.valid_sampled_token_count_event = torch.Event()
                self.valid_sampled_token_count_copy_stream = torch.cuda.Stream()
                self.valid_sampled_token_count_cpu = torch.empty(
                    self.max_num_reqs,
                    dtype=torch.int64,
                    device="cpu",
                    pin_memory=self.pin_memory,
                )
709

710
711
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
712
        self.kv_connector_output: KVConnectorOutput | None = None
713
        self.mamba_state_idx: dict[str, int] = {}
714
        self.layerwise_nvtx_hooks_registered = False
715

王敏's avatar
王敏 committed
716
717
        self.draft_probs : Optional[DraftProbs] = None

718
719
720
721
722
723
724
    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

725
726
727
728
    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
    @torch.inference_mode()
    def init_fp8_kv_scales(self) -> None:
        """
        Re-initialize the KV cache and FP8 scales after waking from sleep.
        1. Zero out the KV cache tensors to remove garbage data from re-allocation.
        2. Reset Attention layer scaling factors (_k_scale, _v_scale) to 1.0.
          If these are left at 0.0 (default after wake_up), all KV cache values
          become effectively zero, causing gibberish output.
        """
        if not self.cache_config.cache_dtype.startswith("fp8"):
            return

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

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

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

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

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

773
774
775
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
guanyu1's avatar
guanyu1 committed
776
777
778
779
                if self.use_1d_mrope:
                    return self.mrope_positions.gpu[: 3 * num_tokens].view(
                        num_tokens, 3
                    ).T
780
                return self.mrope_positions.gpu[:, :num_tokens]
781
782
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
783
784
785
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
guanyu1's avatar
guanyu1 committed
786
787
                if self.use_1d_mrope:
                    return self.mrope_positions.gpu.view(-1, 3)[num_tokens].T
788
                return self.mrope_positions.gpu[:, num_tokens]
789
790
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
791
            return self.positions.gpu[num_tokens]
guanyu1's avatar
guanyu1 committed
792
        
793
    def _make_buffer(
794
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
795
796
797
798
799
800
801
802
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
guanyu1's avatar
guanyu1 committed
803
    
guanyu1's avatar
guanyu1 committed
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
    def _copy_mrope_positions_to_gpu(self, num_tokens: int) -> None:
        if not self.uses_mrope:
            return
        if self.use_1d_mrope:
            num_values = 3 * num_tokens
            self.mrope_positions.gpu[:num_values].copy_(
                self.mrope_positions.cpu[:num_values],
                non_blocking=True,
            )
            return
        
        self.mrope_positions.gpu[:, :num_tokens].copy_(
            self.mrope_positions.cpu[:, :num_tokens],
            non_blocking=True,
        )

    def _copy_xdrope_positions_to_gpu(self, num_tokens: int) -> None:
        if self.uses_xdrope_dim <= 0:
            return
        
        self.xdrope_positions.gpu[:, :num_tokens].copy_(
            self.xdrope_positions.cpu[:, :num_tokens],
            non_blocking=True,
        )
828

829
    def _init_model_kwargs(self):
830
831
        model_kwargs = dict[str, Any]()

832
        if not self.is_pooling_model:
833
834
            return model_kwargs

835
836
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
837
838
839

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
840
841
842
843
844
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
845
846
847
848
849
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

850
        seq_lens = self.seq_lens.gpu[:num_reqs]
851
852
853
854
855
856
857
858
        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(
859
860
            device=self.device
        )
861
        return model_kwargs
862

863
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
864
865
        """
        Update the order of requests in the batch based on the attention
866
        backend's needs. For example, some attention backends (namely MLA) may
867
868
869
870
871
872
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
873
874
875
876
877
878
879
880
        # Attention free models have zero kv_cache_goups, however models
        # like Mamba are also attention free but use the kv_cache for
        # keeping its internal state. This is why we check the number
        # of kv_cache groups instead of solely checking
        # for self.model_config.is_attention_free.
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return

881
882
883
884
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
885
886
                decode_threshold=self.reorder_batch_threshold,
            )
887

888
889
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
890
        """Initialize attributes from torch.cuda.get_device_properties"""
891
892
893
894
895
896
897
        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

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

898
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
899
900
901
902
903
904
        """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.

905
906
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
907
908
        """
        # Remove finished requests from the cached states.
909
910
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
911
            self.num_prompt_logprobs.pop(req_id, None)
912
913
914
915
916
917
918
        # 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:
919
            self.input_batch.remove_request(req_id)
920

王敏's avatar
王敏 committed
921
922
923
924
        # prune draft probs of finished requests
        if envs.VLLM_REJECT_SAMPLE_OPT and self.draft_probs is not None and len(scheduler_output.finished_req_ids) > 0:
            self.draft_probs.prune(list(scheduler_output.finished_req_ids))

925
        # Free the cached encoder outputs.
926
927
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
928

929
930
931
932
933
934
935
        # 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()
936
937
938
939
940
941
942
943
        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)
944
945
946
947
948
        # 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:
949
            self.input_batch.remove_request(req_id)
950

951
        reqs_to_add: list[CachedRequestState] = []
952
        # Add new requests to the cached states.
953
954
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
955
956
957
958
959
960
            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

961
            sampling_params = new_req_data.sampling_params
962
            pooling_params = new_req_data.pooling_params
963

964
965
966
967
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
968
969
970
971
972
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

973
974
            if self.is_pooling_model:
                assert pooling_params is not None
975
976
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
977

978
                model = cast(VllmModelForPooling, self.get_model())
979
                to_update = model.pooler.get_pooling_updates(task)
980
981
                to_update.apply(pooling_params)

982
            req_state = CachedRequestState(
983
                req_id=req_id,
984
                prompt_token_ids=new_req_data.prompt_token_ids,
985
                prompt_embeds=new_req_data.prompt_embeds,
986
                mm_features=new_req_data.mm_features,
987
                sampling_params=sampling_params,
988
                pooling_params=pooling_params,
989
                generator=generator,
990
991
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
992
                output_token_ids=[],
993
                lora_request=new_req_data.lora_request,
994
            )
995
            self.requests[req_id] = req_state
996

997
998
999
1000
1001
1002
1003
            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
                )

1004
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1005
            if self.uses_mrope:
1006
                self._init_mrope_positions(req_state)
1007

1008
1009
1010
1011
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

1012
            reqs_to_add.append(req_state)
1013

1014
        # Update the states of the running/resumed requests.
1015
        is_last_rank = get_pp_group().is_last_rank
1016
        req_data = scheduler_output.scheduled_cached_reqs
1017
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1018
1019
1020
1021
1022

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

1023
        for i, req_id in enumerate(req_data.req_ids):
1024
            req_state = self.requests[req_id]
1025
1026
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
1027
            resumed_from_preemption = req_id in req_data.resumed_req_ids
1028
            num_output_tokens = req_data.num_output_tokens[i]
1029
            req_index = self.input_batch.req_id_to_index.get(req_id)
1030

1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
            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:
                # fist step: num_computed_tokens = 0, spec_tokens = [],
                # prev_num_draft_len = 0.
                # second step: num_computed_tokens = 100(prompt lenth),
                # 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.
                # num_computed_tokens in first step and second step does't contain
                # 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.
1045
1046
1047
1048
1049
1050
1051
1052
1053
                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
                    assert self.input_batch.prev_req_id_to_index is not None
                    prev_req_index = self.input_batch.prev_req_id_to_index[req_id]
                    num_accepted = valid_sampled_token_count[prev_req_index] - 1
                    num_rejected = req_state.prev_num_draft_len - num_accepted
                    num_computed_tokens -= num_rejected
                    req_state.output_token_ids.extend([-1] * num_accepted)
1054

1055
            # Update the cached states.
1056
            req_state.num_computed_tokens = num_computed_tokens
1057
1058
1059
1060
1061
1062
1063
1064

            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
1065
1066
1067
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
1068
1069
1070
1071
                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:
1072
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
1073
1074
1075
1076
1077
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
1078
1079
1080
1081
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
1082
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
1083

1084
            # Update the block IDs.
1085
            if not resumed_from_preemption:
1086
1087
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
1088
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
1089
                        block_ids.extend(new_ids)
1090
            else:
1091
                assert req_index is None
1092
                assert new_block_ids is not None
1093
1094
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
1095
                req_state.block_ids = new_block_ids
1096
1097
1098
1099
1100

            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.
1101
1102
1103
1104
1105
1106
1107

                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:]

1108
                reqs_to_add.append(req_state)
1109
1110
1111
                continue

            # Update the persistent batch.
1112
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
1113
            if new_block_ids is not None:
1114
                self.input_batch.block_table.append_row(new_block_ids, req_index)
1115
1116
1117
1118
1119
1120

            # 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
zhuwenwen's avatar
zhuwenwen committed
1121
                end_token_index = num_computed_tokens + len(new_token_ids)
1122
                self.input_batch.token_ids_cpu[
1123
1124
1125
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
1126

1127
            # Add spec_token_ids to token_ids_cpu.
1128
            self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
1129

1130
1131
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
1132
1133
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1134
            self.input_batch.update_req_spec_token_ids(request, scheduled_spec_tokens)
1135

1136
1137
1138
1139
1140
        # 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.
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
        repeat_counts = None
        if envs.VLLM_REJECT_SAMPLE_OPT and \
                scheduler_output.scheduled_spec_decode_tokens:
            repeat_counts = [1] * self.input_batch.num_reqs
            for req_id, draft_token_ids in (
                    scheduler_output.scheduled_spec_decode_tokens.items()):
                req_idx = self.input_batch.req_id_to_index.get(req_id)
                if req_idx is not None:
                    repeat_counts[req_idx] += len(draft_token_ids)
            repeat_counts = torch.tensor(repeat_counts, dtype=torch.int32, device="cpu")
        self.input_batch.refresh_metadata(repeat_counts)
1152

1153
    def _update_states_after_model_execute(
1154
        self, output_token_ids: torch.Tensor, scheduler_output: "SchedulerOutput"
1155
    ) -> None:
1156
1157
1158
1159
1160
1161
1162
1163
        """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.
        """
1164
        if not self.speculative_config or not self.model_config.is_hybrid:
1165
1166
1167
            return

        # Find the number of accepted tokens for each sequence.
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
        num_accepted_tokens = (
            (
                torch.cat(
                    [
                        output_token_ids,
                        torch.full(
                            (output_token_ids.size(0), 1),
                            -1,
                            device=output_token_ids.device,
                        ),
                    ],
                    dim=1,
                )
                == -1
            )
            .int()
            .argmax(-1)
            .cpu()
            .numpy()
        )
1188
1189
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
        if self.cache_config.mamba_cache_mode == "align":
            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(),
            )
1200

1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
    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
        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
1234

1235
    def _init_mrope_positions(self, req_state: CachedRequestState):
1236
1237
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1238
1239
1240
1241
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1242
1243

        req_state.mrope_positions, req_state.mrope_position_delta = (
1244
            mrope_model.get_mrope_input_positions(
1245
                req_state.prompt_token_ids,
1246
                req_state.mm_features,
1247
            )
1248
        )
1249

1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
    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,
        )
1262

1263
    def _extract_mm_kwargs(
1264
        self,
1265
1266
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1267
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1268
            return {}
1269

1270
1271
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
1272
1273
1274
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
1275

1276
1277
1278
        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1279
1280
1281
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
1282
1283
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
1284

1285
        return mm_kwargs_combined
1286
1287

    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1288
        if not self.is_multimodal_raw_input_only_model:
1289
            return {}
1290

1291
1292
        mm_budget = self.mm_budget
        assert mm_budget is not None
1293

1294
1295
        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
1296

1297
1298
1299
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1300
        cumsum_dtype: np.dtype | None = None,
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
    ) -> tuple[np.ndarray, np.ndarray]:
        """Get the cumulative sum and batched arange of the given array.
        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
        # Equivalent to but faster than:
        # np.concatenate([np.arange(n) for n in num_tokens])
        """
        # Step 1. [2, 5, 3] -> [2, 7, 10]
        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
        total_num_tokens = cu_num_tokens[-1]
        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        arange = self.arange_np[:total_num_tokens] - cumsums_offsets

        return cu_num_tokens, arange

1317
    def _prepare_input_ids(
1318
1319
1320
1321
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1322
    ) -> None:
1323
        """Prepare the input IDs for the current batch.
1324

1325
1326
1327
1328
1329
1330
1331
        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
        GPU need to be copied into the corresponding slots into input_ids."""

        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1332
1333
1334
            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)
1335
1336
1337
1338
1339
1340
1341
            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        assert prev_req_id_to_index is not None
1342
1343
1344
1345
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1346
1347
        indices_match = True
        max_flattened_index = -1
1348
1349
1350
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1351
1352
1353
1354
1355
        for req_id, cur_index in self.input_batch.req_id_to_index.items():
            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
                prev_common_req_indices.append(prev_index)
                # We need to compute the flattened input_ids index of the
                # last token in each common request.
1356
1357
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1358
                flattened_index = cu_num_tokens[cur_index].item() - 1
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
                # 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))
1374
                indices_match &= prev_index == flattened_index
1375
                max_flattened_index = max(max_flattened_index, flattened_index)
1376
1377
1378
        num_commmon_tokens = len(sample_flattened_indices)
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
        if num_commmon_tokens < total_without_spec:
1379
1380
1381
            # If not all requests are decodes from the last iteration,
            # We need to copy the input_ids_cpu to the GPU first.
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1382
1383
1384
            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)
1385
1386
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1387
            # So input_ids.cpu will have all the input ids.
1388
1389
1390
1391
1392
1393
1394
            return
        if indices_match and max_flattened_index == (num_commmon_tokens - 1):
            # Common-case optimization: the batch is unchanged
            # and no reordering happened.
            # The indices are both the same permutation of 0..N-1 so
            # we can copy directly using a single slice.
            self.input_ids.gpu[:num_commmon_tokens].copy_(
1395
1396
1397
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1398
1399
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1400
            return
1401
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1402
1403
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1404
        ).to(self.device, non_blocking=True)
1405
        prev_common_req_indices_tensor = torch.tensor(
1406
1407
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1408
1409
        self.input_ids.gpu.scatter_(
            dim=0,
1410
            index=sampled_tokens_index_tensor,
1411
            src=self.input_batch.prev_sampled_token_ids[
1412
1413
1414
                prev_common_req_indices_tensor, 0
            ],
        )
1415

1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
        # 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],
        )
1437

1438
1439
    def _get_encoder_seq_lens(
        self,
1440
        num_scheduled_tokens: dict[str, int],
1441
1442
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1443
        for_cudagraph_capture: bool = False,
1444
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1445
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1446
            return None, None
1447

1448
1449
        # Zero out buffer for padding requests that are not actually scheduled (CGs)
        self.encoder_seq_lens.np[:num_reqs] = 0
1450

1451
1452
        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1453
        for req_id in num_scheduled_tokens:
1454
            req_index = self.input_batch.req_id_to_index[req_id]
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
            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
1467
1468
1469
1470
1471
1472
1473
1474
1475
        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
1476

1477
1478
1479
        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]
1480

1481
        return encoder_seq_lens, encoder_seq_lens_cpu
1482

1483
    def _prepare_inputs(
1484
1485
1486
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1487
1488
    ) -> tuple[
        torch.Tensor,
1489
        SpecDecodeMetadata | None,
1490
    ]:
1491
1492
        """
        :return: tuple[
1493
            logits_indices, spec_decode_metadata,
1494
1495
        ]
        """
1496
1497
1498
1499
1500
1501
1502
        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.
1503
        self.input_batch.block_table.commit_block_table(num_reqs)
1504
1505
1506

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1507
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1508

1509
1510
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1511
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1512
1513

        # Get positions.
1514
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1515
1516
1517
1518
1519
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1520

1521
1522
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1523
        if self.uses_mrope:
1524
1525
            self._calc_mrope_positions(scheduler_output)

1526
1527
1528
1529
1530
        # 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)

1531
1532
1533
1534
        # 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.
1535
1536
1537
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1538
        token_indices_tensor = torch.from_numpy(token_indices)
1539

1540
1541
1542
        # 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.
1543
1544
1545
1546
1547
1548
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1549
        if self.enable_prompt_embeds:
1550
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1551
1552
1553
1554
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1555
1556
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589

        # 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:
1590
1591
1592
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1593
1594

                output_idx += num_sched
1595

1596
1597
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1598
1599

        # Prepare the attention metadata.
1600
        self.query_start_loc.np[0] = 0
1601
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1602
1603
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1604
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1605
        self.query_start_loc.copy_to_gpu()
1606
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1607

1608
        self.seq_lens.np[:num_reqs] = (
1609
1610
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1611
        # Fill unused with 0 for full cuda graph mode.
1612
1613
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1614

1615
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1616
1617
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1618
        # Record which requests should not be sampled,
1619
        # so that we could clear the sampled tokens before returning
1620
1621
        self.discard_request_mask.np[:num_reqs] = (
            self.seq_lens.np[:num_reqs] < num_tokens_np
1622
        )
1623
        self.discard_request_mask.copy_to_gpu(num_reqs)
1624

1625
        # Copy the tensors to the GPU.
1626
1627
1628
1629
1630
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
guanyu1's avatar
guanyu1 committed
1631
        print(f'======================={total_num_scheduled_tokens=}')
1632
        if self.uses_mrope:
1633
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
guanyu1's avatar
guanyu1 committed
1634
            self._copy_mrope_positions_to_gpu(total_num_scheduled_tokens)
1635
1636
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
guanyu1's avatar
guanyu1 committed
1637
            self._copy_xdrope_positions_to_gpu(total_num_scheduled_tokens)
1638
1639
        else:
            # Common case (1D positions)
1640
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1641

1642
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1643
1644
1645
1646
1647
1648
1649
1650
        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
1651
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1652
1653
1654
1655
1656
        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)
1657
1658
1659
            # 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)
1660
1661
1662
1663
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1664
1665
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1666
1667
1668
1669
1670
                if (
                    self.input_batch.num_computed_tokens_cpu[req_idx]
                    >= self.input_batch.num_prompt_tokens[req_idx]
                ):
                    num_decode_draft_tokens[req_idx] = len(draft_token_ids)
王敏's avatar
王敏 committed
1671
1672
1673
1674
1675

            spec_decode_ids = None
            if envs.VLLM_REJECT_SAMPLE_OPT:
                spec_decode_ids = scheduler_output.scheduled_spec_decode_tokens.keys()

1676
            spec_decode_metadata = self._calc_spec_decode_metadata(
王敏's avatar
王敏 committed
1677
                num_draft_tokens, cu_num_tokens, spec_decode_ids
1678
            )
1679
            logits_indices = spec_decode_metadata.logits_indices
1680
            num_sampled_tokens = num_draft_tokens + 1
1681
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1682
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1683
1684
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1685

1686
1687
1688
1689
1690
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1691
            )
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
1703
        num_tokens: int,
1704
        num_reqs: int,
1705
1706
1707
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
1708
1709
1710
1711
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
1712
        num_scheduled_tokens: dict[str, int] | None = None,
1713
        cascade_attn_prefix_lens: list[list[int]] | None = None,
1714
        slot_mappings: dict[int, torch.Tensor] | None = None,
1715
1716
1717
1718
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
1719
1720
1721
1722
        # Attention metadata is not needed for attention free models
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return {}, None

1723
1724
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs
1725
        assert num_reqs_padded is not None and num_tokens_padded is not None
1726

1727
1728
1729
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1730

1731
1732
1733
1734
1735
1736
1737
1738
        if for_cudagraph_capture:
            # For some attention backends (e.g. FA) with sliding window models we need
            # to make sure the backend see a max_seq_len that is larger to the sliding
            # window size when capturing to make sure the correct kernel is selected.
            max_seq_len = self.max_model_len
        else:
            max_seq_len = self.seq_lens.np[:num_reqs].max().item()

1739
1740
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1741
1742
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1743
1744
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1745

1746
        kv_cache_groups = self.kv_cache_config.kv_cache_groups
1747

1748
        def _get_block_table(kv_cache_gid: int):
1749
1750
1751
            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):
1752
                blk_table_tensor = torch.zeros(
1753
                    (num_reqs_padded, 1),
1754
                    dtype=torch.int32,
1755
1756
                    device=self.device,
                )
1757
            else:
1758
                blk_table = self.input_batch.block_table[kv_cache_gid]
1759
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
1760

1761
1762
1763
            # Fill unused with -1. Needed for reshape_and_cache in full cuda
            # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
            blk_table_tensor[num_reqs:num_reqs_padded].fill_(-1)
1764
            return blk_table_tensor
1765

1766
1767
1768
        assert slot_mappings is not None
        block_table_gid_0 = _get_block_table(0)
        slot_mapping_gid_0 = slot_mappings[0]
1769

1770
1771
        if self.model_config.enable_return_routed_experts:
            self.slot_mapping = slot_mapping_gid_0[:num_tokens].cpu().numpy()
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
        cm_base = CommonAttentionMetadata(
            query_start_loc=self.query_start_loc.gpu[: num_reqs_padded + 1],
            query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs_padded + 1],
            seq_lens=self.seq_lens.gpu[:num_reqs_padded],
            _seq_lens_cpu=self.seq_lens.cpu[:num_reqs_padded],
            _num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                :num_reqs_padded
            ],
            num_reqs=num_reqs_padded,
            num_actual_tokens=num_tokens_padded,
            max_query_len=max_query_len,
            max_seq_len=max_seq_len,
            block_table_tensor=block_table_gid_0,
            slot_mapping=slot_mapping_gid_0,
            causal=True,
        )

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

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

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

1810
1811
1812
1813
1814
1815
1816
1817
1818
        # 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
        ] = {}

1819
1820
1821
1822
1823
1824
1825
        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]
1826
            builder = attn_group.get_metadata_builder(ubid or 0)
1827
1828
1829
1830
            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))
1831

1832
1833
1834
1835
            cascade_attn_prefix_len = (
                cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                if cascade_attn_prefix_lens
                else 0
1836
1837
            )

1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
            extra_attn_metadata_args = {}
            if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
                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
                )
1852
1853
1854
1855
1856
1857
1858
1859
1860
            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,
                )
1861
1862
1863
1864
1865
1866
            else:
                attn_metadata_i = builder.build(
                    common_prefix_len=cascade_attn_prefix_len,
                    common_attn_metadata=common_attn_metadata,
                    **extra_attn_metadata_args,
                )
1867
1868
                if builder.supports_update_block_table:
                    cached_attn_metadata[cache_key] = attn_metadata_i
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891

            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,
1892
                for_cudagraph_capture=for_cudagraph_capture,
1893
            )
1894
            if kv_cache_gid > 0:
1895
1896
                cm.block_table_tensor = _get_block_table(kv_cache_gid)
                cm.slot_mapping = slot_mappings[kv_cache_gid]
1897

1898
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1899
                if isinstance(self.drafter, EagleProposer):
1900
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1901
                        spec_decode_common_attn_metadata = cm
1902
                else:
1903
                    spec_decode_common_attn_metadata = cm
1904

1905
            for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
1906
                if ubatch_slices is not None:
1907
1908
1909
                    for ubid, _cm in enumerate(split_attn_metadata(ubatch_slices, cm)):
                        _build_attn_group_metadata(kv_cache_gid, attn_gid, _cm, ubid)

1910
                else:
1911
                    _build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
1912

1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
        if self.is_mm_prefix_lm:
            req_doc_ranges = {}
            for req_id in self.input_batch.req_ids:
                image_doc_ranges = []
                req_state = self.requests[req_id]
                for mm_feature in req_state.mm_features:
                    pos_info = mm_feature.mm_position
                    img_doc_range = pos_info.extract_embeds_range()
                    image_doc_ranges.extend(img_doc_range)
                req_idx = self.input_batch.req_id_to_index[req_id]
                req_doc_ranges[req_idx] = image_doc_ranges

            if isinstance(attn_metadata, list):
                for ub_metadata in attn_metadata:
                    for _metadata in ub_metadata.values():
                        _metadata.mm_prefix_range = req_doc_ranges  # type: ignore[attr-defined]
            else:
                for _metadata in attn_metadata.values():
                    _metadata.mm_prefix_range = req_doc_ranges  # type: ignore[attr-defined]
1932

1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
        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)
            )

1943
        return attn_metadata, spec_decode_common_attn_metadata
1944

1945
1946
1947
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
1948
        num_computed_tokens: np.ndarray,
1949
1950
1951
1952
1953
1954
1955
        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
        """
1956

1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
        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,
1971
                        num_computed_tokens,
1972
1973
1974
1975
1976
1977
                        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
1978

1979
        return cascade_attn_prefix_lens if use_cascade_attn else None
1980

1981
1982
1983
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
1984
        num_computed_tokens: np.ndarray,
1985
        num_common_prefix_blocks: int,
1986
1987
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
    ) -> 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.
        """
2006

2007
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
        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]
2045
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
2046
2047
2048
2049
2050
        # 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.
2051
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
2052
        # common_prefix_len should be a multiple of the block size.
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
        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
        )
2064
2065
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
2066
2067
2068
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
2069
            num_kv_heads=kv_cache_spec.num_kv_heads,
2070
            use_alibi=self.use_alibi,
2071
            use_sliding_window=use_sliding_window,
2072
            use_local_attention=use_local_attention,
2073
            num_sms=self.num_sms,
2074
            dcp_world_size=self.dcp_world_size,
2075
2076
2077
        )
        return common_prefix_len if use_cascade else 0

2078
2079
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
guanyu1's avatar
guanyu1 committed
2080
2081
2082
2083
2084
2085
2086
        if self.use_1d_mrope:
            mrope_positions_token_major = self.mrope_positions.cpu.view(
                self.max_num_tokens + 1, 3
            )
            mrope_positions_token_major_np = self.mrope_positions.np.reshape(
                self.max_num_tokens + 1, 3
            )
2087
        for index, req_id in enumerate(self.input_batch.req_ids):
2088
2089
2090
            req = self.requests[req_id]
            assert req.mrope_positions is not None

2091
2092
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2093
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
2094
2095
                req.prompt_token_ids, req.prompt_embeds
            )
2096
2097

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
2098
2099
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
            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

guanyu1's avatar
guanyu1 committed
2113
2114
2115
2116
2117
2118
2119
2120
                if self.use_1d_mrope:
                    mrope_positions_token_major[dst_start:dst_end, :].copy_(
                        req.mrope_positions[:, src_start:src_end].transpose(0, 1)
                    )
                else:
                    self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                        :, src_start:src_end
                    ]
2121
2122
2123
2124
2125
2126
2127
                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

2128
                assert req.mrope_position_delta is not None
guanyu1's avatar
guanyu1 committed
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
                if self.use_1d_mrope:
                    values = np.arange(
                        req.mrope_position_delta + num_computed_tokens + prompt_part_len,
                        req.mrope_position_delta
                        + num_computed_tokens
                        + prompt_part_len
                        + completion_part_len,
                        dtype=mrope_positions_token_major_np.dtype,
                    )
                    mrope_positions_token_major_np[dst_start:dst_end, :] = values[
                        :, None
                    ]
                else:
                    MRotaryEmbedding.get_next_input_positions_tensor(
                        out=self.mrope_positions.np,
                        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,
                    )
2149
2150
2151

                mrope_pos_ptr += completion_part_len

2152
2153
2154
2155
2156
    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
2157

2158
2159
2160
2161
2162
            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
            )
2163

2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
            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

2199
2200
    def _calc_spec_decode_metadata(
        self,
2201
2202
        num_draft_tokens: np.ndarray,
        cu_num_scheduled_tokens: np.ndarray,
王敏's avatar
王敏 committed
2203
        spec_decode_ids: Optional[list[str]] = None
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
    ) -> 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
2218
2219
2220
2221

        # Step 1. cu_num_sampled_tokens: [4, 5, 8, 9, 11]
        # arange: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
        cu_num_sampled_tokens, arange = self._get_cumsum_and_arange(
2222
2223
            num_sampled_tokens, cumsum_dtype=np.int32
        )
2224
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
2225
        logits_indices = np.repeat(
2226
2227
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
2228
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
2229
2230
2231
2232
2233
2234
        logits_indices += arange

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

        # Compute the draft logits indices.
2235
2236
2237
        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
        # arange: [0, 1, 2, 0, 1, 0]
        cu_num_draft_tokens, arange = self._get_cumsum_and_arange(
2238
2239
            num_draft_tokens, cumsum_dtype=np.int32
        )
2240
2241
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
2242
2243
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
2244
2245
        # [0, 1, 2, 5, 6, 9]
        target_logits_indices += arange
2246
        draft_token_indices = target_logits_indices + 1
2247

2248
        # TODO: Optimize the CPU -> GPU copy.
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
        # cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
        #     self.device, non_blocking=True
        # )
        # cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
        #     self.device, non_blocking=True
        # )
        # logits_indices = torch.from_numpy(logits_indices).to(
        #     self.device, non_blocking=True
        # )
        # target_logits_indices = torch.from_numpy(target_logits_indices).to(
        #     self.device, non_blocking=True
        # )
        # bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
        #     self.device, non_blocking=True
        # )

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

        # Optimize the H2D in the process of creating spec decode metadata
        fused_meta_data = cu_num_draft_tokens.tolist() + cu_num_sampled_tokens.tolist()\
              + logits_indices.tolist() + target_logits_indices.tolist() + bonus_logits_indices.tolist()\
              + draft_token_indices.tolist()
        
        fused_meta_data_len = np.array([len(cu_num_draft_tokens), len(cu_num_sampled_tokens),\
                                        len(logits_indices), len(target_logits_indices),\
                                            len(bonus_logits_indices), len(draft_token_indices)], dtype=np.int32)
        cu_fused_meta_data_len = np.cumsum(fused_meta_data_len, dtype=np.int32)
        fused_meta_data = torch.tensor(
            fused_meta_data, dtype=torch.int32, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
        
        cu_num_draft_tokens = fused_meta_data[:cu_fused_meta_data_len[0]]
        cu_num_sampled_tokens = fused_meta_data[cu_fused_meta_data_len[0]:cu_fused_meta_data_len[1]]
        logits_indices = fused_meta_data[cu_fused_meta_data_len[1]:cu_fused_meta_data_len[2]]
        target_logits_indices = fused_meta_data[cu_fused_meta_data_len[2]:cu_fused_meta_data_len[3]]
        bonus_logits_indices = fused_meta_data[cu_fused_meta_data_len[3]:cu_fused_meta_data_len[4]]
        draft_token_indices = fused_meta_data[cu_fused_meta_data_len[4]:cu_fused_meta_data_len[5]]

2290

2291
2292
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2293
        draft_token_ids = self.input_ids.gpu[logits_indices]
2294
        draft_token_ids = draft_token_ids[draft_token_indices]
2295

2296
        return SpecDecodeMetadata(
2297
2298
2299
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2300
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2301
2302
2303
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
王敏's avatar
王敏 committed
2304
            spec_decode_ids=spec_decode_ids,
2305
2306
        )

2307
2308
2309
2310
2311
2312
2313
    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
2314
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2315
2316
2317
2318
2319
        # 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_(
2320
2321
            logits_indices[-1].item()
        )
2322
2323
2324
2325
2326
        # Dispatch for the decoder portion of the model.
        _, batch_desc = self.cudagraph_dispatcher.dispatch(
            num_logits, disable_full=True
        )
        num_logits_padded = batch_desc.num_tokens
2327
2328
2329
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2330
2331
        return logits_indices_padded

2332
    def _batch_mm_inputs_from_scheduler(
2333
2334
        self,
        scheduler_output: "SchedulerOutput",
2335
2336
2337
2338
2339
    ) -> tuple[
        list[str],
        list[MultiModalKwargsItem],
        list[tuple[str, PlaceholderRange]],
    ]:
2340
        """Batch multimodal inputs from scheduled encoder inputs.
2341
2342
2343

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2344
                inputs.
2345
2346

        Returns:
2347
            A tuple of (mm_hashes, mm_kwargs, mm_lora_refs) where:
2348
2349
            - mm_hashes: List of multimodal hashes for each item
            - mm_kwargs: List of multimodal kwargs for each item
2350
            - mm_lora_refs: List of (req_id, placeholder_range) for each item
2351
        """
2352
2353
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2354
            return [], [], []
2355
2356

        mm_hashes = list[str]()
2357
        mm_kwargs = list[MultiModalKwargsItem]()
2358
2359
2360
        # Multimodal LoRA reference info to map each multimodal item
        # back to its request & position
        mm_lora_refs = list[tuple[str, PlaceholderRange]]()
2361
2362
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2363
2364

            for mm_input_id in encoder_input_ids:
2365
                mm_feature = req_state.mm_features[mm_input_id]
2366
2367
                if mm_feature.data is None:
                    continue
2368
2369

                mm_hashes.append(mm_feature.identifier)
2370
                mm_kwargs.append(mm_feature.data)
2371
                mm_lora_refs.append((req_id, mm_feature.mm_position))
2372

2373
        return mm_hashes, mm_kwargs, mm_lora_refs
2374

2375
2376
2377
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2378
        mm_hashes, mm_kwargs, mm_lora_refs = self._batch_mm_inputs_from_scheduler(
2379
2380
            scheduler_output
        )
2381
2382

        if not mm_kwargs:
2383
            return []
2384

2385
2386
2387
2388
2389
2390
        should_time = bool(
            self.observability_config
            and self.observability_config.enable_mm_processor_stats
            and scheduler_output.scheduled_encoder_inputs
        )

2391
2392
2393
2394
2395
2396
2397
        # 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.
2398
        model = cast(SupportsMultiModal, self.model)
2399
2400
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
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455

        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]
                    pos_info.get_num_embeds
                )
                prompt_lora_mapping.append(lora_id)
                token_lora_mapping.extend([lora_id] * num_tokens)
                encoder_token_counts.append(num_tokens)

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

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

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

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

                self.lora_manager.set_active_adapters(
                    lora_requests,
                    connector_mapping,
                )

2456
        encoder_outputs: list[torch.Tensor] = []
2457
2458
        # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
        current_item_idx = 0
2459
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
2460
2461
2462
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
2463
        ):
2464
            curr_group_outputs: MultiModalEmbeddings
2465
2466

            # EVS-related change.
2467
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2468
            # processing multimodal data. This solves the issue with scheduler
2469
2470
2471
2472
            # 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)
2473
2474
2475
2476
2477
2478
2479
            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
                self.is_multimodal_pruning_enabled
                and modality == "video"
                and num_items > 1
            ):
2480
                curr_group_outputs_lst = list[torch.Tensor]()
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
                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(
                            group_mm_kwargs_by_modality(
                                [video_mm_kwargs_item],
                                device=self.device,
                                pin_memory=self.pin_memory,
                            )
2492
                        )
2493

2494
2495
2496
                        micro_batch_outputs = model.embed_multimodal(
                            **micro_batch_mm_inputs
                        )
2497

2498
                        curr_group_outputs_lst.extend(micro_batch_outputs)
2499
2500

                curr_group_outputs = curr_group_outputs_lst
2501
2502
2503
2504
2505
2506
2507
2508
            else:
                # Run the encoder.
                # `curr_group_outputs` is either of the following:
                # 1. A tensor of shape (num_items, feature_size, hidden_size)
                # in case feature_size is fixed across all multimodal items.
                # 2. A list or tuple (length: num_items) of tensors,
                # each of shape (feature_size, hidden_size) in case the feature
                # size is dynamic depending on the input multimodal items.
2509
2510
2511
2512
2513

                with self.timed_encoder_operation(
                    should_time, mm_lora_refs, current_item_idx, num_items
                ):
                    curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
2514

2515
2516
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
2517
                expected_num_items=num_items,
2518
            )
2519
            encoder_outputs.extend(curr_group_outputs)
2520

2521
2522
            current_item_idx += num_items

2523
        # Cache the encoder outputs by mm_hash
2524
        for mm_hash, output in zip(mm_hashes, encoder_outputs):
2525
            self.encoder_cache[mm_hash] = output
2526
2527
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2528

2529
        return encoder_outputs
2530
2531

    def _gather_mm_embeddings(
2532
2533
        self,
        scheduler_output: "SchedulerOutput",
2534
        shift_computed_tokens: int = 0,
2535
2536
2537
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

2538
2539
2540
2541
2542
        # Swap to the other buffer to avoid race condition with previous
        # iteration's async copy that may still be reading from CPU.
        self.is_mm_embed_idx = 1 - self.is_mm_embed_idx
        is_mm_embed_buf = self.is_mm_embed_buffers[self.is_mm_embed_idx]

2543
        mm_embeds = list[torch.Tensor]()
2544
        is_mm_embed = is_mm_embed_buf.cpu
2545
2546
2547
        is_mm_embed[:total_num_scheduled_tokens] = False

        req_start_idx = 0
2548
        should_sync_mrope_positions = False
2549
        should_sync_xdrope_positions = False
2550

2551
        for req_id in self.input_batch.req_ids:
2552
2553
            mm_embeds_req: list[torch.Tensor] = []

2554
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2555
            req_state = self.requests[req_id]
2556
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2557

2558
2559
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2560
2561
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577

                # 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,
2578
2579
                    num_encoder_tokens,
                )
2580
                assert start_idx < end_idx
2581
2582
2583
2584
2585
2586
2587
                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
2588

2589
                mm_hash = mm_feature.identifier
2590
                encoder_output = self.encoder_cache.get(mm_hash, None)
2591
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2592
2593
2594

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]
2595
2596
2597
                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
                else:
                    mm_embeds_item = encoder_output[start_idx:end_idx]
2598

2599
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2600
2601
2602
2603
2604
2605
2606
2607
2608
                # 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
2609
2610
2611
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2612
                assert req_state.mrope_positions is not None
2613
2614
2615
2616
2617
2618
2619
                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,
2620
2621
                    )
                )
2622
2623
2624
2625
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2626
2627
            req_start_idx += num_scheduled_tokens

2628
        is_mm_embed = is_mm_embed_buf.copy_to_gpu(total_num_scheduled_tokens)
2629
2630
2631

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
guanyu1's avatar
guanyu1 committed
2632
            self._copy_mrope_positions_to_gpu(total_num_scheduled_tokens)
2633

2634
2635
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
guanyu1's avatar
guanyu1 committed
2636
            self._copy_xdrope_positions_to_gpu(total_num_scheduled_tokens)
2637

2638
        return mm_embeds, is_mm_embed
2639

2640
    def get_model(self) -> nn.Module:
2641
        # get raw model out of the cudagraph wrapper.
2642
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2643
            return self.model.unwrap()
2644
2645
        return self.model

2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
    def get_supported_generation_tasks(self) -> list[GenerationTask]:
        model = self.get_model()
        supported_tasks = list[GenerationTask]()

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

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

            supported_tasks.append("transcription")

        return supported_tasks

2661
2662
2663
2664
2665
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2666
2667
        supported_tasks = list(model.pooler.get_supported_tasks())

2668
2669
2670
2671
        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2672
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2673
2674

        return supported_tasks
2675

2676
2677
2678
2679
2680
2681
2682
2683
2684
    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)
2685

2686
    def sync_and_slice_intermediate_tensors(
2687
2688
        self,
        num_tokens: int,
2689
        intermediate_tensors: IntermediateTensors | None,
2690
2691
        sync_self: bool,
    ) -> IntermediateTensors:
2692
2693
2694
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2695
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2696
2697
2698
2699
2700
2701

        # 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():
2702
                is_scattered = k == "residual" and is_rs
2703
                copy_len = num_tokens // tp if is_scattered else num_tokens
2704
                self.intermediate_tensors[k][:copy_len].copy_(
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
                    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:
2718
2719
2720
2721
2722
2723
2724
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2725
2726
        model = self.get_model()
        assert is_mixture_of_experts(model)
2727
2728
2729
        self.eplb_state.step(
            is_dummy,
            is_profile,
2730
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2731
2732
        )

2733
2734
2735
2736
2737
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
2738
2739
2740
2741
        kv_connector_output: KVConnectorOutput | None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        num_reqs = self.input_batch.num_reqs
        assert num_reqs == len(self.input_batch.pooling_params), (
2742
2743
            "Either all or none of the requests in a batch must be pooling request"
        )
2744

2745
        hidden_states = hidden_states[:num_scheduled_tokens]
2746
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
2747

2748
        pooling_metadata = self.input_batch.get_pooling_metadata()
2749
        pooling_metadata.build_pooling_cursor(
2750
            num_scheduled_tokens_np, seq_lens_cpu, device=hidden_states.device
2751
        )
2752

2753
2754
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
2755
            hidden_states=hidden_states, pooling_metadata=pooling_metadata
2756
        )
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780

        finished_mask = [
            seq_len == prompt_len
            for seq_len, prompt_len in zip(seq_lens_cpu, pooling_metadata.prompt_lens)
        ]

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

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

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

2781
        raw_pooler_output = json_map_leaves(
2782
            lambda x: None if x is None else x.to("cpu", non_blocking=True),
2783
2784
            raw_pooler_output,
        )
2785
2786
2787
2788
        model_runner_output.pooler_output = [
            out if include else None
            for out, include in zip(raw_pooler_output, finished_mask)
        ]
2789
        self._sync_device()
2790

2791
        return model_runner_output
2792

2793
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
2794
2795
2796
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2797
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
2798
2799
2800
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

Patrick von Platen's avatar
Patrick von Platen committed
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
    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

2812
    def _preprocess(
2813
2814
        self,
        scheduler_output: "SchedulerOutput",
2815
        num_input_tokens: int,  # Padded
2816
        intermediate_tensors: IntermediateTensors | None = None,
2817
    ) -> tuple[
2818
2819
        torch.Tensor | None,
        torch.Tensor | None,
2820
        torch.Tensor,
2821
        IntermediateTensors | None,
2822
        dict[str, Any],
2823
        ECConnectorOutput | None,
2824
    ]:
2825
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2826
        is_first_rank = get_pp_group().is_first_rank
2827
        is_encoder_decoder = self.model_config.is_encoder_decoder
2828

2829
2830
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2831
2832
        ec_connector_output = None

2833
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
2834
            # Run the multimodal encoder if any.
2835
2836
2837
2838
2839
2840
            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)
2841

2842
2843
2844
            # 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.
2845
            inputs_embeds_scheduled = self.model.embed_input_ids(
2846
2847
2848
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2849
            )
2850

2851
            # TODO(woosuk): Avoid the copy. Optimize.
2852
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2853

Patrick von Platen's avatar
Patrick von Platen committed
2854
            input_ids, inputs_embeds = self._prepare_mm_inputs(num_input_tokens)
2855
            model_kwargs = {
2856
                **self._init_model_kwargs(),
2857
2858
                **self._extract_mm_kwargs(scheduler_output),
            }
2859
        elif self.enable_prompt_embeds and is_first_rank:
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
            # 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).
2872
2873
2874
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2875
                .squeeze(1)
2876
            )
2877
2878
2879
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2880
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2881
2882
2883
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2884
            model_kwargs = self._init_model_kwargs()
2885
            input_ids = None
2886
        else:
2887
2888
2889
2890
            # 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.
2891
            input_ids = self.input_ids.gpu[:num_input_tokens]
2892
            inputs_embeds = None
2893
            model_kwargs = self._init_model_kwargs()
2894

guanyu1's avatar
guanyu1 committed
2895
        positions = self._get_positions(num_input_tokens)
2896

2897
        if is_first_rank:
2898
2899
            intermediate_tensors = None
        else:
2900
            assert intermediate_tensors is not None
2901
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2902
2903
                num_input_tokens, intermediate_tensors, True
            )
2904

2905
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
2906
2907
2908
2909
2910
2911
2912
            # 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})
2913

2914
2915
2916
2917
2918
2919
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
2920
            ec_connector_output,
2921
        )
2922

2923
    def _sample(
2924
        self,
2925
2926
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2927
    ) -> SamplerOutput:
2928
        # Sample the next token and get logprobs if needed.
2929
        sampling_metadata = self.input_batch.sampling_metadata
2930
2931
2932
        # 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()
2933
        if spec_decode_metadata is None:
2934
            return self.sampler(
2935
2936
2937
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2938

2939
2940
2941
2942
2943
2944
        # 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)

2945
        sampler_output = self.rejection_sampler(
2946
            spec_decode_metadata,
王敏's avatar
王敏 committed
2947
2948
            None if self.draft_probs is None else \
                self.draft_probs.get_probs(spec_decode_metadata.spec_decode_ids),  # draft_probs
2949
            logits,
2950
2951
            sampling_metadata,
        )
2952
2953
2954
        return sampler_output

    def _bookkeeping_sync(
2955
2956
2957
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2958
        logits: torch.Tensor | None,
2959
2960
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2961
        spec_decode_metadata: SpecDecodeMetadata | None,
2962
    ) -> tuple[
2963
        dict[str, int],
2964
        LogprobsLists | None,
2965
        list[list[int]],
2966
        dict[str, LogprobsTensors | None],
2967
2968
2969
        list[str],
        dict[str, int],
        list[int],
2970
    ]:
2971
2972
2973
2974
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2975
2976
2977
2978
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
2979
2980
2981
2982
        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)
2983

2984
2985
2986
        # 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()
2987
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2988

2989
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2990
        sampled_token_ids = sampler_output.sampled_token_ids
2991
        logprobs_tensors = sampler_output.logprobs_tensors
2992
        invalid_req_indices = []
2993
        logprobs_lists = None
2994
2995
2996
2997
2998
2999
        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)
3000
3001
3002
                # 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()
3003
3004
3005

                if logprobs_tensors is not None:
                    logprobs_lists = logprobs_tensors.tolists()
3006
3007
            else:
                # Includes spec decode tokens.
3008
                valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
3009
3010
                    sampled_token_ids,
                    self.input_batch.vocab_size,
3011
                    discard_sampled_tokens_req_indices,
3012
                    logprobs_tensors=logprobs_tensors,
3013
                )
3014
        else:
3015
            valid_sampled_token_ids = []
3016
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
3017
3018
3019
3020
3021
            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.
3022
3023
3024
3025
            # 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
3026
3027
3028
3029
3030
            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
            }
3031

3032
3033
3034
3035
3036
        # 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.
3037
        req_ids = self.input_batch.req_ids
3038
3039
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
3040
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
3041
3042
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
3043

3044
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
3045

3046
3047
3048
3049
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
3050
            end_idx = start_idx + num_sampled_ids
3051
3052
3053
            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: "
3054
                f"{self.max_model_len}"
3055
            )
3056

3057
            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
3058
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
3059
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
3060

3061
            req_id = req_ids[req_idx]
3062
3063
3064
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

3065
3066
3067
3068
3069
3070
        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
        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,
        )

3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
    @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()

3096
3097
    def _model_forward(
        self,
3098
3099
3100
3101
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
3102
3103
3104
3105
3106
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
3107
        Motivation: We can inspect only this method versus
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
        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,
        )

3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
    @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
        )

3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
    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,
3162
        num_encoder_reqs: int = 0,
3163
    ) -> tuple[
3164
3165
        CUDAGraphMode,
        BatchDescriptor,
3166
        bool,
3167
3168
        torch.Tensor | None,
        CUDAGraphStat | None,
3169
    ]:
3170
3171
3172
3173
3174
3175
        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,
3176
        )
3177
3178
3179
3180
3181
        # 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
        )
3182
3183
3184
3185
3186
3187
3188

        has_lora = (
            len(self.input_batch.lora_id_to_lora_request) > 0
            if force_has_lora is None
            else force_has_lora
        )

3189
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
3190
        dispatch_cudagraph = (
3191
            lambda num_tokens, disable_full: self.cudagraph_dispatcher.dispatch(
3192
3193
3194
                num_tokens=num_tokens,
                has_lora=has_lora,
                uniform_decode=uniform_decode,
3195
                disable_full=disable_full,
3196
3197
3198
3199
3200
            )
            if not force_eager
            else (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
        )

3201
        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
3202
            num_tokens_padded, use_cascade_attn or has_encoder_output
3203
        )
3204
        num_tokens_padded = batch_descriptor.num_tokens
3205
3206
3207
3208
3209
3210
3211
3212
3213
        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"
            )
3214
3215
3216

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
3217
        should_ubatch, num_tokens_across_dp = False, None
3218
3219
3220
3221
3222
3223
3224
3225
3226
        if self.vllm_config.parallel_config.data_parallel_size > 1:
            # Disable DP padding when running eager to avoid excessive padding when
            # running prefills. This lets us set cudagraph_mode="NONE" on the prefiller
            # in a P/D setup and still use CUDA graphs (enabled by this padding) on the
            # decoder.
            allow_dp_padding = (
                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            )

3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
            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,
                    allow_dp_padding=allow_dp_padding,
                    num_tokens_padded=num_tokens_padded,
                    uniform_decode=uniform_decode,
                    num_scheduled_tokens_per_request=num_scheduled_tokens_np,
                    cudagraph_mode=cudagraph_mode.value,
                )
3238
3239
            )

3240
            # Extract DP-synced values
3241
3242
3243
            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())
3244
3245
3246
3247
3248
                # Re-dispatch with DP padding so we have the correct batch_descriptor
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(
                    num_tokens_padded,
                    disable_full=synced_cudagraph_mode <= CUDAGraphMode.PIECEWISE.value,
                )
3249
3250
3251
3252
                # 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

3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
        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,
3265
            should_ubatch,
3266
3267
3268
            num_tokens_across_dp,
            cudagraph_stats,
        )
3269

3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
    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
3305

3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
    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

3380
3381
3382
3383
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
3384
        intermediate_tensors: IntermediateTensors | None = None,
3385
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
3386
3387
3388
3389
3390
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
3391

3392
3393
3394
3395
3396
3397
        if self.vllm_config.model_config.enable_return_routed_experts:
            capturer = RoutedExpertsCapturer.get_instance()
            if capturer is not None:
                capturer.clear_buffer()  # noqa
            else:
                logger.error("RoutedExpertsCapturer not initialized.")
3398

3399
3400
3401
3402
        if scheduler_output.preempted_req_ids and has_kv_transfer_group():
            get_kv_transfer_group().handle_preemptions(
                scheduler_output.preempted_req_ids
            )
3403

3404
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3405
3406
3407
3408
3409
3410
        with (
            record_function_or_nullcontext("gpu_model_runner: preprocess"),
            self.synchronize_input_prep(),
        ):
            # Update persistent batch states.
            self._update_states(scheduler_output)
3411

3412
3413
            if has_ec_transfer() and get_ec_transfer().is_producer:
                with self.maybe_get_ec_connector_output(
3414
                    scheduler_output,
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
                    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"
3443
                )
3444

3445
3446
3447
3448
3449
3450
            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
3451

3452
3453
3454
3455
            logits_indices, spec_decode_metadata = self._prepare_inputs(
                scheduler_output,
                num_scheduled_tokens_np,
            )
3456

3457
3458
3459
3460
3461
            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(
3462
                    num_scheduled_tokens_np,
3463
3464
                    self.input_batch.num_computed_tokens_cpu[:num_reqs],
                    scheduler_output.num_common_prefix_blocks,
3465
                )
3466

3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
            (
                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),
            )
3481

3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
            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,
            )

3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
            # 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)
            )
3520
3521
            pad_attn = cudagraph_mode == CUDAGraphMode.FULL

3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
            if self.cache_config.mamba_cache_mode == "align":
                mamba_utils.preprocess_mamba(
                    scheduler_output,
                    self.kv_cache_config,
                    self.cache_config,
                    self.mamba_state_idx,
                    self.input_batch,
                    self.requests,
                    self.compilation_config.static_forward_context,
                    self.model.get_mamba_state_copy_func(),
                )

3534
3535
3536
            use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
            ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices

3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
            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,
            )

3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
            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,
3560
                    slot_mappings=slot_mappings_by_group,
3561
                )
3562
            )
3563
3564
3565
3566
3567
3568
3569

            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
3570
3571
3572
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
3573
            )
3574

3575
        # Set cudagraph mode to none if calc_kv_scales is true.
3576
3577
3578
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
3579
            cudagraph_mode = CUDAGraphMode.NONE
3580
3581
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
3582

3583
3584
3585
3586
3587
3588
3589
        # 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
        )

3590
3591
        # Run the model.
        # Use persistent buffers for CUDA graphs.
3592
3593
        with (
            set_forward_context(
3594
3595
                attn_metadata,
                self.vllm_config,
3596
                num_tokens=num_tokens_padded,
3597
                num_tokens_across_dp=num_tokens_across_dp,
3598
3599
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
3600
                ubatch_slices=ubatch_slices_padded,
3601
                slot_mapping=slot_mappings,
3602
                skip_compiled=has_encoder_input,
3603
            ),
3604
            record_function_or_nullcontext("gpu_model_runner: forward"),
3605
3606
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
3607
            model_output = self._model_forward(
3608
3609
3610
3611
3612
3613
3614
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

3615
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
3616
            if self.use_aux_hidden_state_outputs:
3617
                # True when EAGLE 3 is used.
3618
3619
                hidden_states, aux_hidden_states = model_output
            else:
3620
                # Common case.
3621
3622
3623
                hidden_states = model_output
                aux_hidden_states = None

3624
3625
3626
3627
3628
            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)
3629
                    hidden_states.kv_connector_output = kv_connector_output
3630
                    self.kv_connector_output = kv_connector_output
3631
                    return hidden_states
3632

3633
                if self.is_pooling_model:
3634
                    # Return the pooling output.
3635
3636
3637
3638
3639
                    return self._pool(
                        hidden_states,
                        num_scheduled_tokens,
                        num_scheduled_tokens_np,
                        kv_connector_output,
3640
                    )
3641
3642

                sample_hidden_states = hidden_states[logits_indices]
3643
                logits = self.model.compute_logits(sample_hidden_states)
3644
3645
3646
3647
            else:
                # Rare case.
                assert not self.is_pooling_model

3648
                sample_hidden_states = hidden_states[logits_indices]
3649
                if not get_pp_group().is_last_rank:
3650
                    all_gather_tensors = {
3651
                        "residual": not is_residual_scattered_for_sp(
3652
                            self.vllm_config, num_tokens_padded
3653
                        )
3654
                    }
3655
                    get_pp_group().send_tensor_dict(
3656
3657
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
3658
3659
                        all_gather_tensors=all_gather_tensors,
                    )
3660
3661
                    logits = None
                else:
3662
                    logits = self.model.compute_logits(sample_hidden_states)
3663

3664
                model_output_broadcast_data: dict[str, Any] = {}
3665
3666
3667
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

3668
                broadcasted = get_pp_group().broadcast_tensor_dict(
3669
3670
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
3671
3672
                assert broadcasted is not None
                logits = broadcasted["logits"]
3673

3674
3675
3676
3677
3678
3679
3680
3681
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3682
            ec_connector_output,
3683
            cudagraph_stats,
3684
            slot_mappings,
3685
        )
3686
        self.kv_connector_output = kv_connector_output
3687
3688
3689
3690
3691
3692
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
3693
3694
3695
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

3696
3697
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
3698
            if not kv_connector_output:
3699
                return None  # type: ignore[return-value]
3700
3701
3702
3703
3704
3705
3706
3707
3708

            # 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
3709

3710
3711
3712
3713
3714
3715
3716
3717
3718
        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3719
            ec_connector_output,
3720
            cudagraph_stats,
3721
            slot_mappings,
3722
3723
3724
3725
3726
3727
3728
3729
3730
        ) = 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
            )
3731

3732
        with record_function_or_nullcontext("gpu_model_runner: sample"):
3733
3734
            sampler_output = self._sample(logits, spec_decode_metadata)

3735
3736
3737
3738
        self._update_states_after_model_execute(
            sampler_output.sampled_token_ids, scheduler_output
        )

3739
3740
        self._draft_token_ids = None
        self._draft_token_req_ids = None
3741
3742
        self.input_batch.prev_sampled_token_ids = None

3743
3744
        def propose_draft_token_ids(sampled_token_ids):
            assert spec_decode_common_attn_metadata is not None
3745
            with record_function_or_nullcontext("gpu_model_runner: draft"):
3746
3747
3748
3749
3750
3751
3752
3753
3754
                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,
3755
                    slot_mappings,
3756
                )
3757
                self._copy_draft_token_ids_to_cpu(scheduler_output)
3758

3759
        spec_config = self.speculative_config
3760
3761
3762
3763
3764
        propose_drafts_after_bookkeeping = False
        if spec_config is not None:
            input_fits_in_drafter = spec_decode_common_attn_metadata is not None and (
                spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
                <= self.effective_drafter_max_model_len
3765
            )
3766
3767
3768
3769
3770
            use_gpu_toks = (
                spec_config.use_eagle() or spec_config.uses_draft_model()
            ) and not spec_config.disable_padded_drafter_batch
            if use_gpu_toks:
                # EAGLE/DraftModel speculative decoding can use the GPU sampled tokens
3771
                # as inputs, and does not need to wait for bookkeeping to finish.
3772
                assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
                sampled_token_ids = sampler_output.sampled_token_ids
                if input_fits_in_drafter:
                    propose_draft_token_ids(sampled_token_ids)
                elif self.valid_sampled_token_count_event is not None:
                    assert spec_decode_common_attn_metadata is not None
                    next_token_ids, valid_sampled_tokens_count = (
                        self.drafter.prepare_next_token_ids_padded(
                            spec_decode_common_attn_metadata,
                            sampled_token_ids,
                            self.requests,
                            self.input_batch,
                            self.discard_request_mask.gpu,
                        )
3786
                    )
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
                    # Since we couldn't run the drafter,
                    # just use zeros for the draft tokens.
                    self._draft_token_ids = torch.zeros(
                        1, device=self.device, dtype=torch.int32
                    ).expand(len(self.input_batch.req_ids), self.num_spec_tokens)
                    self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)
            else:
                propose_drafts_after_bookkeeping = input_fits_in_drafter
3798

3799
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
3800
3801
3802
3803
3804
3805
3806
3807
            (
                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,
3808
3809
3810
3811
3812
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
3813
                scheduler_output.total_num_scheduled_tokens,
3814
                spec_decode_metadata,
3815
            )
3816

3817
        if propose_drafts_after_bookkeeping:
3818
3819
3820
            # 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)
3821

3822
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
3823
            self.eplb_step()
3824

3825
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
3826
3827
3828
3829
3830
3831
3832
            if self.model_config.enable_return_routed_experts:
                capturer = RoutedExpertsCapturer.get_instance()
                if capturer is not None:
                    capturer.save_captured_experts(indices=self.slot_mapping)  # noqa
                else:
                    logger.error("RoutedExpertsCapturer not initialized.")

3833
3834
3835
3836
3837
3838
3839
            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,
3840
3841
3842
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
3843
                num_nans_in_logits=num_nans_in_logits,
3844
                cudagraph_stats=cudagraph_stats,
3845
            )
3846

3847
3848
        if not self.use_async_scheduling:
            return output
3849

3850
3851
3852
3853
3854
3855
3856
3857
3858
        with record_function_or_nullcontext(
            "gpu_model_runner: AsyncGPUModelRunnerOutput"
        ):
            async_output = AsyncGPUModelRunnerOutput(
                model_runner_output=output,
                sampled_token_ids=sampler_output.sampled_token_ids,
                logprobs_tensors=sampler_output.logprobs_tensors,
                invalid_req_indices=invalid_req_indices,
                async_output_copy_stream=self.async_output_copy_stream,
3859
                vocab_size=self.input_batch.vocab_size,
3860
3861
3862
3863
3864
            )
        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
3865
            # any requests with sampling params that require output ids.
3866
3867
3868
3869
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
3870

3871
        return async_output
3872

3873
    def take_draft_token_ids(self) -> DraftTokenIds | None:
3874
        if not self.num_spec_tokens or not self._draft_token_req_ids:
3875
            return None
3876
        draft_token_ids, req_ids = self._get_draft_token_ids_cpu()
3877
3878
        return DraftTokenIds(req_ids, draft_token_ids)

3879
3880
3881
    def _copy_draft_token_ids_to_cpu(
        self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
    ) -> None:
3882
3883
3884
3885
3886
3887
        # 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
        ):
3888
3889
3890
            return
        # We must also set the corresponding request ids.
        self._draft_token_req_ids = self.input_batch.req_ids.copy()
3891

3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
        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()

3912
    def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
3913
        if isinstance(self._draft_token_ids, list):
3914
3915
3916
3917
            return self._draft_token_ids, self.input_batch.req_ids
        req_ids = self._draft_token_req_ids
        if req_ids is None:
            return [], []
3918
3919
3920
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_cpu is not None
        self.draft_token_ids_event.synchronize()
3921
        return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
3922

3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
3935
    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
3936
            assert counts_cpu is not None
3937
3938
3939
3940
3941
3942
3943
3944
            counts_cpu[: counts.shape[0]].copy_(counts, non_blocking=True)
            self.valid_sampled_token_count_event.record()

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

    def _get_valid_sampled_token_count(self) -> list[int]:
        # Wait until valid_sampled_tokens_count is copied to cpu,
        prev_sampled_token_ids = self.input_batch.prev_sampled_token_ids
3945
3946
        sampled_count_event = self.valid_sampled_token_count_event
        if sampled_count_event is None or prev_sampled_token_ids is None:
3947
3948
3949
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
3950
3951
        assert counts_cpu is not None
        sampled_count_event.synchronize()
3952
3953
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

3954
3955
3956
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
3957
        sampled_token_ids: torch.Tensor | list[list[int]],
3958
3959
3960
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
3961
3962
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3963
        common_attn_metadata: CommonAttentionMetadata,
3964
        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
3965
    ) -> list[list[int]] | torch.Tensor:
3966
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3967
3968
3969
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
3970
            assert isinstance(sampled_token_ids, list)
3971
            assert isinstance(self.drafter, NgramProposer)
3972
            draft_token_ids = self.drafter.propose(
3973
                sampled_token_ids,
3974
3975
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
3976
                slot_mappings=slot_mappings,
3977
            )
3978
        elif spec_config.method == "suffix":
3979
3980
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
3981
3982
3983
            draft_token_ids = self.drafter.propose(
                self.input_batch, sampled_token_ids, slot_mappings=slot_mappings
            )
3984
        elif spec_config.method == "medusa":
3985
            assert isinstance(sampled_token_ids, list)
3986
            assert isinstance(self.drafter, MedusaProposer)
3987

3988
3989
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
3990
3991
3992
3993
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
3994
3995
3996
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
3997
                for num_draft, tokens in zip(
3998
3999
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
4000
4001
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
4002
                indices = torch.tensor(indices, device=self.device)
4003
4004
                hidden_states = sample_hidden_states[indices]

4005
            draft_token_ids = self.drafter.propose(
4006
4007
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
4008
                slot_mappings=slot_mappings,
4009
            )
4010
4011
        elif spec_config.use_eagle() or spec_config.uses_draft_model():
            assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
4012

4013
            if spec_config.disable_padded_drafter_batch:
4014
4015
4016
                # 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.
4017
4018
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
4019
                    "padded-batch is disabled."
4020
                )
4021
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
4022
4023
4024
4025
4026
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
4027
4028
4029
4030
4031
            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.
4032
4033
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
4034
                    "padded-batch is enabled."
4035
4036
                )
                next_token_ids, valid_sampled_tokens_count = (
4037
4038
4039
4040
4041
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
4042
                        self.discard_request_mask.gpu,
4043
                    )
4044
                )
4045
4046
4047
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
4048

4049
            num_rejected_tokens_gpu = None
4050
            if spec_decode_metadata is None:
4051
                token_indices_to_sample = None
4052
                # input_ids can be None for multimodal models.
4053
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
4054
                target_positions = self._get_positions(num_scheduled_tokens)
4055
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
4056
                    assert aux_hidden_states is not None
4057
                    target_hidden_states = torch.cat(
4058
4059
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
4060
4061
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
4062
            else:
4063
                if spec_config.disable_padded_drafter_batch:
4064
                    token_indices_to_sample = None
4065
4066
4067
4068
4069
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
4070
4071
4072
4073
4074
4075
4076
4077
4078
                    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]
4079
                else:
4080
4081
4082
4083
4084
4085
4086
4087
                    (
                        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,
4088
                    )
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
                    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]
4100

4101
            if self.supports_mm_inputs:
4102
4103
4104
4105
4106
4107
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
4108

王敏's avatar
王敏 committed
4109
            draft_result = self.drafter.propose(
4110
4111
4112
4113
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
4114
                last_token_indices=token_indices_to_sample,
4115
                sampling_metadata=sampling_metadata,
4116
                common_attn_metadata=common_attn_metadata,
4117
                mm_embed_inputs=mm_embed_inputs,
4118
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
4119
                slot_mappings=slot_mappings,
4120
            )
4121

王敏's avatar
王敏 committed
4122
4123
4124
4125
4126
4127
4128
4129
4130
4131
4132
4133
4134
            if not envs.VLLM_REJECT_SAMPLE_OPT:
                draft_token_ids = draft_result
            else:
                draft_token_ids, draft_probs = draft_result

            if envs.VLLM_REJECT_SAMPLE_OPT:
                draft_req_ids = list(scheduler_output.num_scheduled_tokens.keys())
                if self.draft_probs is None:
                    self.draft_probs = DraftProbs(
                        draft_probs, draft_req_ids)
                else:
                    self.draft_probs.update(draft_probs, draft_req_ids)

4135
        return draft_token_ids
4136

4137
4138
4139
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
4140
4141
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
4142
                f"Allowed configs: {allowed_config_names}"
4143
            )
4144
4145
4146
4147
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

4148
4149
4150
4151
4152
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
4153
4154
4155
4156
4157
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
4158
4159
4160
4161
4162
        global_expert_loads, old_global_expert_indices_per_model, rank_mapping = (
            EplbState.get_eep_state(self.parallel_config)
            if eep_scale_up
            else (None, None, None)
        )
4163

4164
4165
4166
4167
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0

4168
4169
4170
4171
4172
4173
        try:
            with DeviceMemoryProfiler() as m:
                time_before_load = time.perf_counter()
                model_loader = get_model_loader(self.load_config)
                self.model = model_loader.load_model(
                    vllm_config=self.vllm_config, model_config=self.model_config
4174
                )
4175
4176
4177
                if self.lora_config:
                    self.model = self.load_lora_model(
                        self.model, self.vllm_config, self.device
4178
                    )
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
                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
                    ):
                        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,
                        )
4194

4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
                        global_expert_load = (
                            global_expert_loads[eplb_models]
                            if global_expert_loads
                            else None
                        )
                        old_global_expert_indices = (
                            old_global_expert_indices_per_model[eplb_models]
                            if old_global_expert_indices_per_model
                            else None
                        )
                        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,
                            global_expert_load,
                            old_global_expert_indices,
                            rank_mapping,
                        )
                        eplb_models += 1
4217

4218
4219
4220
4221
4222
4223
                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"
                        )
4224

4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
                    # 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:
                        aux_layers = self.model.get_eagle3_aux_hidden_state_layers()
4235

4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
                    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
4250
        logger.info_once(
4251
4252
            "Model loading took %s GiB memory and %.6f seconds",
            format_gib(self.model_memory_usage),
4253
            time_after_load - time_before_load,
4254
            scope="local",
4255
        )
4256
        prepare_communication_buffer_for_model(self.model)
4257
4258
4259
4260
        if (drafter := getattr(self, "drafter", None)) and (
            drafter_model := getattr(drafter, "model", None)
        ):
            prepare_communication_buffer_for_model(drafter_model)
4261
        mm_config = self.model_config.multimodal_config
4262
        self.is_multimodal_pruning_enabled = (
4263
            supports_multimodal_pruning(self.get_model())
4264
4265
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
4266
        )
4267

4268
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
            global_expert_load = (
                global_expert_loads[eplb_models] if global_expert_loads else None
            )
            old_global_expert_indices = (
                old_global_expert_indices_per_model[eplb_models]
                if old_global_expert_indices_per_model
                else None
            )
            assert self.eplb_state is not None
            self.eplb_state.add_model(
4280
                self.model,
4281
                self.model_config,
4282
4283
4284
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
4285
            )
4286
4287
            if self.eplb_state.is_async:
                self.eplb_state.start_async_loop(rank_mapping=rank_mapping)
4288

4289
        if (
4290
4291
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
4292
        ):
4293
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
4294
            compilation_counter.stock_torch_compile_count += 1
4295
            self.model.compile(fullgraph=True, backend=backend)
4296
            return
4297
        # for other compilation modes, cudagraph behavior is controlled by
4298
4299
4300
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
4301
4302
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
4303
4304
4305
4306
        if (
            cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.use_ubatching
        ):
4307
4308
4309
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
4310
        elif self.parallel_config.use_ubatching:
4311
            if cudagraph_mode.has_full_cudagraphs():
4312
4313
4314
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
4315
            else:
4316
4317
4318
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
4319

4320
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
4321
4322
4323
4324
4325
4326
4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
        """Extract Eagle3 auxiliary layer indices from speculative config.

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

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

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

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

        return None
4343

4344
    def reload_weights(self) -> None:
4345
        assert getattr(self, "model", None) is not None, (
4346
            "Cannot reload weights before model is loaded."
4347
        )
4348
4349
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
4350
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
4351

4352
4353
4354
4355
4356
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
4357
            self.get_model(),
4358
            tensorizer_config=tensorizer_config,
4359
            model_config=self.model_config,
4360
4361
        )

4362
4363
4364
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
4365
        num_scheduled_tokens: dict[str, int],
4366
    ) -> dict[str, LogprobsTensors | None]:
4367
        num_prompt_logprobs_dict = self.num_prompt_logprobs
4368
4369
4370
        if not num_prompt_logprobs_dict:
            return {}

4371
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
4372
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
4373
4374
4375
4376
4377

        # 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():
4378
4379
4380
4381
            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
4382
4383
4384

            # Get metadata for this request.
            request = self.requests[req_id]
4385
4386
4387
4388
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

4389
4390
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
4391
4392
                self.device, non_blocking=True
            )
4393

4394
4395
4396
4397
4398
4399
            # 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(
4400
4401
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
4402
4403
                in_progress_dict[req_id] = logprobs_tensors

4404
            # Determine number of logits to retrieve.
4405
4406
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
4407
            num_remaining_tokens = num_prompt_tokens - start_tok
4408
            if num_tokens <= num_remaining_tokens:
4409
                # This is a chunk, more tokens remain.
4410
4411
4412
                # 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.
4413
4414
4415
4416
4417
                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)
4418
4419
4420
4421
4422
4423
4424
                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
4425
4426
4427
4428
4429

            # 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]
4430
            offset = self.query_start_loc.np[req_idx].item()
4431
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
4432
            logits = self.model.compute_logits(prompt_hidden_states)
4433
4434
4435
4436

            # 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.
4437
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
4438
4439

            # Compute prompt logprobs.
4440
4441
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
4442
4443
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
4444
4445

            # Transfer GPU->CPU async.
4446
4447
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
4448
4449
4450
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
4451
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
4452
4453
                ranks, non_blocking=True
            )
4454
4455
4456
4457
4458

        # 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]
4459
            del in_progress_dict[req_id]
4460
4461

        # Must synchronize the non-blocking GPU->CPU transfers.
4462
        if prompt_logprobs_dict:
4463
            self._sync_device()
4464
4465
4466

        return prompt_logprobs_dict

4467
4468
    def _get_nans_in_logits(
        self,
4469
        logits: torch.Tensor | None,
4470
4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
    ) -> 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])
4481
4482
4483
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
4484
4485
4486
4487
            return num_nans_in_logits
        except IndexError:
            return {}

4488
    @contextmanager
4489
4490
4491
    def maybe_randomize_inputs(
        self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None
    ):
4492
4493
4494
4495
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
4496
         - during DP rank dummy run
4497
        """
4498

4499
4500
4501
4502
        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
4503
        elif input_ids is not None:
4504
4505
4506
4507

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
4508
                    self.input_ids.gpu,
4509
4510
                    low=0,
                    high=self.model_config.get_vocab_size(),
4511
                )
4512

4513
            logger.debug_once("Randomizing dummy input_ids for DP Rank")
4514
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
4515
4516
            yield
            input_ids.fill_(0)
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
        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)
4532

4533
4534
4535
4536
4537
4538
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
4539
4540
        assert self.mm_budget is not None

4541
4542
4543
        # 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,
4544
            mm_counts={modality: 1},
4545
            cache=self.mm_budget.cache,
4546
        )
4547
4548
4549
4550
4551
        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"
4552

4553
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
4554

4555
4556
4557
4558
4559
4560
4561
4562
        return next(
            mm_kwargs_group
            for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                dummy_mm_items,
                device=self.device,
                pin_memory=self.pin_memory,
            )
        )
4563

4564
4565
4566
4567
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
4568
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
4569
4570
        force_attention: bool = False,
        uniform_decode: bool = False,
4571
        allow_microbatching: bool = True,
4572
4573
        skip_eplb: bool = False,
        is_profile: bool = False,
4574
        create_mixed_batch: bool = False,
4575
        remove_lora: bool = True,
4576
        activate_lora: bool = False,
Rémi Delacourt's avatar
Rémi Delacourt committed
4577
        is_graph_capturing: bool = False,
4578
    ) -> tuple[torch.Tensor, torch.Tensor]:
4579
4580
4581
4582
4583
4584
4585
        """
        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.
4586
                - if not set will determine the cudagraph mode based on using
4587
                    the self.cudagraph_dispatcher.
4588
4589
4590
4591
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
4592
            force_attention: If True, always create attention metadata. Used to
4593
4594
4595
4596
                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.
4597
4598
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
4599
            remove_lora: If False, dummy LoRAs are not destroyed after the run
4600
            activate_lora: If False, dummy_run is performed without LoRAs.
4601
        """
4602
4603
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
4604
4605
4606
4607
            # 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([])

4608
4609
4610
4611
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
4612

4613
        # If cudagraph_mode.decode_mode() == FULL and
4614
        # cudagraph_mode.separate_routine(). This means that we are using
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
        # 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.
4626
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
4627

4628
4629
4630
4631
4632
        # Set num_scheduled_tokens based on num_tokens and max_num_seqs
        # for dummy run with LoRA so that the num_reqs collectively
        # has num_tokens in total.
        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
        max_num_reqs = self.scheduler_config.max_num_seqs
4633
4634
4635
4636
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
4637
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
4638
4639
4640
4641
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
4642
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
4643
4644
4645
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
4646
            assert not create_mixed_batch
4647
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
4648
4649
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
4650
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
4651
4652
4653
4654
4655
4656
        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

4657
4658
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
4659
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
4660
4661
        num_tokens_unpadded = int(num_scheduled_tokens.sum())

4662
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
4663

4664
        _cudagraph_mode, batch_desc, should_ubatch, num_tokens_across_dp, _ = (
4665
4666
4667
4668
4669
4670
4671
4672
4673
4674
4675
4676
4677
4678
4679
4680
4681
4682
            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
                force_has_lora=activate_lora,
4683
            )
4684
        )
4685
4686
4687

        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
4688
        else:
4689
4690
4691
4692
            assert cudagraph_runtime_mode == _cudagraph_mode, (
                f"Cudagraph runtime mode mismatch in dummy_run. "
                f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
            )
4693

4694
4695
4696
4697
        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
        )
4698
        ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
4699
4700
4701
4702
4703
4704
4705
4706
4707
4708
            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,
4709
        )
4710

4711
        attn_metadata: PerLayerAttnMetadata | None = None
4712

4713
4714
4715
4716
4717
4718
4719
        slot_mappings_by_group, slot_mappings = self._get_slot_mappings(
            num_tokens_padded=num_tokens,
            num_reqs_padded=num_reqs_padded,
            num_tokens_unpadded=num_tokens_unpadded,
            ubatch_slices=ubatch_slices_padded,
        )

4720
4721
        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
4722
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
4723
4724
4725
4726
4727
4728
            if create_mixed_batch:
                # 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
                seq_lens = [1] * num_decode_tokens + [num_prefill_tokens + 1]
            else:
4729
4730
4731
4732
4733
                if not envs.VLLM_USE_PIECEWISE:
                    seq_lens = max_query_len
                else:
                    # Make sure max_model_len is used at the graph capture time.
                    seq_lens = self.max_model_len
4734
            self.seq_lens.np[:num_reqs] = seq_lens
4735
4736
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
4737

4738
4739
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
4740
4741
            self.query_start_loc.copy_to_gpu()

4742
            pad_attn = cudagraph_runtime_mode == CUDAGraphMode.FULL
4743
            attn_metadata, _ = self._build_attention_metadata(
4744
4745
4746
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
4747
                ubatch_slices=ubatch_slices_padded if pad_attn else ubatch_slices,
4748
                for_cudagraph_capture=is_graph_capturing,
4749
                slot_mappings=slot_mappings_by_group,
4750
            )
4751

4752
        with self.maybe_dummy_run_with_lora(
4753
4754
4755
4756
4757
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
4758
        ):
4759
            # Make sure padding doesn't exceed max_num_tokens
4760
            assert num_tokens_padded <= self.max_num_tokens
4761
            model_kwargs = self._init_model_kwargs()
4762
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
Patrick von Platen's avatar
Patrick von Platen committed
4763
4764
                input_ids, inputs_embeds = self._prepare_mm_inputs(num_tokens_padded)

4765
                model_kwargs = {
4766
                    **model_kwargs,
4767
4768
                    **self._dummy_mm_kwargs(num_reqs),
                }
4769
4770
            elif self.enable_prompt_embeds:
                input_ids = None
4771
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
4772
                model_kwargs = self._init_model_kwargs()
4773
            else:
王敏's avatar
王敏 committed
4774
4775
4776
                self.input_ids.gpu[:num_tokens_padded] = torch.randint(0, self.model_config.get_vocab_size(),
                                                                        (num_tokens_padded,),
                                                                        dtype=torch.int32)
4777
                input_ids = self.input_ids.gpu[:num_tokens_padded]
4778
                inputs_embeds = None
4779

guanyu1's avatar
guanyu1 committed
4780
4781
4782
4783
4784
4785
4786
            # if self.uses_mrope:
            #     positions = self.mrope_positions.gpu[:, :num_tokens_padded]
            # elif self.uses_xdrope_dim > 0:
            #     positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
            # else:
            #     positions = self.positions.gpu[:num_tokens_padded]
            positions = self._get_positions(num_tokens_padded)
4787
4788
4789
4790
4791
4792
4793
4794
            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,
4795
4796
4797
                            device=self.device,
                        )
                    )
4798
4799

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
4800
                    num_tokens_padded, None, False
4801
                )
4802

4803
            if ubatch_slices_padded is not None:
4804
4805
4806
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
4807
                num_tokens_padded = ubatch_slices_padded[0].num_tokens
4808
                if num_tokens_across_dp is not None:
4809
                    num_tokens_across_dp[:] = num_tokens_padded
4810

4811
            with (
4812
                self.maybe_randomize_inputs(input_ids, inputs_embeds),
4813
                set_forward_context(
4814
4815
                    attn_metadata,
                    self.vllm_config,
4816
                    num_tokens=num_tokens_padded,
4817
4818
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
4819
                    batch_descriptor=batch_desc,
4820
                    ubatch_slices=ubatch_slices_padded,
4821
                    slot_mapping=slot_mappings,
4822
4823
                ),
            ):
4824
                outputs = self.model(
4825
4826
4827
4828
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
4829
                    **model_kwargs,
4830
                )
4831

4832
4833
4834
4835
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
4836

4837
4838
4839
4840
4841
4842
            if self.speculative_config and (
                self.speculative_config.use_eagle()
                or self.speculative_config.uses_draft_model()
            ):
                assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
                assert self.speculative_config is not None
4843
4844
4845
                # 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.
4846
                use_cudagraphs = (
4847
4848
4849
4850
4851
4852
4853
4854
4855
                    (
                        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
4856
4857
4858
4859
4860
4861
4862
4863
4864
4865
4866

                # 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
                if self.compilation_config.cudagraph_specialize_lora and activate_lora:
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
Rémi Delacourt's avatar
Rémi Delacourt committed
4867
                    is_graph_capturing=is_graph_capturing,
4868
                    slot_mappings=slot_mappings,
4869
                )
4870

4871
4872
4873
4874
4875
4876
4877
4878
4879
4880
4881
        # 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()

4882
4883
4884
4885
4886
4887
4888
4889
4890
4891
        # 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)

4892
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
4893
4894
4895
4896
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
4897
4898
4899
4900
4901
4902

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
4903
4904
4905
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
4906

4907
4908
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
4909
4910
4911
            # MM Encoder only model no need to run sampler.
            return torch.tensor([])

4912
        hidden_states = torch.rand_like(hidden_states)
4913

4914
        logits = self.model.compute_logits(hidden_states)
4915
4916
        num_reqs = logits.size(0)

4917
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
4918
4919
4920
4921
4922
4923
4924
4925
4926
4927
4928
4929
4930
4931
4932

        dummy_metadata = SamplingMetadata(
            temperature=dummy_tensors(0.5),
            all_greedy=False,
            all_random=False,
            top_p=dummy_tensors(0.9),
            top_k=dummy_tensors(logits.size(1) - 1),
            generators={},
            max_num_logprobs=None,
            no_penalties=True,
            prompt_token_ids=None,
            frequency_penalties=dummy_tensors(0.1),
            presence_penalties=dummy_tensors(0.1),
            repetition_penalties=dummy_tensors(0.1),
            output_token_ids=[[] for _ in range(num_reqs)],
4933
            spec_token_ids=[[] for _ in range(num_reqs)],
4934
4935
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
4936
            logitsprocs=LogitsProcessors(),
4937
        )
4938
        try:
4939
4940
4941
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
4942
        except RuntimeError as e:
4943
            if "out of memory" in str(e):
4944
4945
4946
4947
                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 "
4948
4949
                    "initializing the engine."
                ) from e
4950
4951
            else:
                raise e
4952
        if self.speculative_config:
4953
4954
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
4955
4956
                draft_token_ids, self.device
            )
4957
4958

            num_tokens = sum(len(ids) for ids in draft_token_ids)
4959
4960
4961
            # draft_probs = torch.randn(
            #     num_tokens, logits.shape[-1], device=self.device,
            #     dtype=logits.dtype)
王敏's avatar
王敏 committed
4962
4963
4964
4965
4966
4967
4968
            
            if not envs.VLLM_REJECT_SAMPLE_OPT:
                draft_probs = None
            else:
                draft_probs = torch.randn(
                    num_reqs, self.speculative_config.num_speculative_tokens, logits.shape[-1], device=self.device,
                    dtype=logits.dtype)
4969
                dummy_metadata.all_greedy = True
王敏's avatar
王敏 committed
4970

4971
4972
4973
4974
4975
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
4976
            )
4977
4978
4979
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
4980
                logits,
4981
4982
                dummy_metadata,
            )
4983
        return sampler_output
4984

4985
    def _dummy_pooler_run_task(
4986
4987
        self,
        hidden_states: torch.Tensor,
4988
4989
        task: PoolingTask,
    ) -> PoolerOutput:
4990
4991
4992
4993
        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
4994
4995
4996
4997
        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
4998
4999
5000

        req_num_tokens = num_tokens // num_reqs

5001
        dummy_prompt_lens = torch.from_numpy(num_scheduled_tokens_np)
5002
5003
5004
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
5005

5006
        model = cast(VllmModelForPooling, self.get_model())
5007
        dummy_pooling_params = PoolingParams(task=task)
5008
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
5009
        to_update = model.pooler.get_pooling_updates(task)
5010
5011
        to_update.apply(dummy_pooling_params)

5012
        dummy_metadata = PoolingMetadata(
5013
5014
5015
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
5016
            pooling_states=[PoolingStates() for i in range(num_reqs)],
5017
        )
5018

5019
        dummy_metadata.build_pooling_cursor(
5020
            num_scheduled_tokens_np,
5021
5022
            seq_lens_cpu=dummy_prompt_lens,
            device=hidden_states.device,
5023
        )
5024

5025
        try:
5026
5027
5028
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
5029
        except RuntimeError as e:
5030
            if "out of memory" in str(e):
5031
                raise RuntimeError(
5032
5033
5034
                    "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 "
5035
5036
                    "initializing the engine."
                ) from e
5037
5038
            else:
                raise e
5039
5040
5041
5042
5043
5044

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
5045
5046
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5047
5048
5049
            # MM Encoder only model not need to run pooler.
            return torch.tensor([])

5050
        # Find the task that has the largest output for subsequent steps
5051
5052
5053
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
5054
5055
5056
5057
5058
5059
            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."
            )
5060

5061
        output_size = dict[PoolingTask, float]()
5062
        for task in supported_pooling_tasks:
5063
5064
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
5065
            output_size[task] = sum(o.nbytes for o in output if o is not None)
5066
5067
5068
5069
            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)
5070

5071
    def profile_run(self) -> None:
5072
        # Profile with multimodal encoder & encoder cache.
5073
        if self.supports_mm_inputs:
5074
5075
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
5076
                logger.info(
5077
                    "Skipping memory profiling for multimodal encoder and "
5078
5079
                    "encoder cache."
                )
5080
5081
5082
5083
5084
5085
5086
5087
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
                    # NOTE: Currently model is profiled with a single non-text
                    # modality with the max possible input tokens even when
                    # it supports multiple.
5088
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
5089
5090
5091
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
5092
5093
5094
5095
5096
5097
5098
5099
5100

                    logger.info(
                        "Encoder cache will be initialized with a budget of "
                        "%s tokens, and profiled with %s %s items of the "
                        "maximum feature size.",
                        encoder_budget,
                        max_mm_items_per_batch,
                        dummy_modality,
                    )
5101

5102
5103
5104
5105
5106
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
5107

5108
                    # Run multimodal encoder.
5109
                    dummy_encoder_outputs = self.model.embed_multimodal(
5110
5111
                        **batched_dummy_mm_inputs
                    )
5112

5113
5114
5115
5116
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
5117
5118
                    for i, output in enumerate(dummy_encoder_outputs):
                        self.encoder_cache[f"tmp_{i}"] = output
5119

5120
        # Add `is_profile` here to pre-allocate communication buffers
5121
5122
5123
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
5124
        if get_pp_group().is_last_rank:
5125
5126
5127
5128
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
5129
        else:
5130
            output = None
5131
        self._sync_device()
5132
        del hidden_states, output
5133
        self.encoder_cache.clear()
5134
        gc.collect()
5135

5136
    def capture_model(self) -> int:
5137
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
5138
            logger.warning(
5139
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
5140
5141
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
5142
            return 0
5143

5144
5145
        compilation_counter.num_gpu_runner_capture_triggers += 1

5146
5147
        start_time = time.perf_counter()

5148
5149
5150
5151
5152
5153
5154
5155
5156
5157
5158
5159
5160
5161
        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()
5162
                    gc.collect()
5163

5164
5165
5166
        # 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.
5167
        set_cudagraph_capturing_enabled(True)
5168
        with freeze_gc(), graph_capture(device=self.device):
5169
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
5170

5171
5172
5173
5174
            for (
                runtime_mode,
                batch_descs,
            ) in self.cudagraph_dispatcher.get_capture_descs():
5175
                self._capture_cudagraphs(
5176
5177
                    batch_descriptors=batch_descs,
                    cudagraph_runtime_mode=runtime_mode,
5178
                )
5179

5180
5181
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]
5182
5183
5184
5185

        # 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
5186
        # we may do lazy capturing in future that still allows capturing
5187
5188
        # after here.
        set_cudagraph_capturing_enabled(False)
5189

5190
5191
5192
5193
        # Lock workspace to prevent resizing during execution.
        # Max workspace sizes should have been captured during warmup/profiling.
        lock_workspace()

5194
5195
5196
5197
        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.
5198
        logger.info_once(
5199
5200
5201
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
5202
            scope="local",
5203
        )
5204
        return cuda_graph_size
5205

5206
5207
    def _capture_cudagraphs(
        self,
5208
        batch_descriptors: list[BatchDescriptor],
5209
5210
5211
5212
5213
5214
        cudagraph_runtime_mode: CUDAGraphMode,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
5215

5216
5217
5218
5219
5220
5221
5222
5223
5224
5225
5226
5227
5228
5229
        if not batch_descriptors:
            return

        uniform_decode = batch_descriptors[0].uniform
        force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL

        dummy_run = functools.partial(
            self._dummy_run,
            uniform_decode=uniform_decode,
            skip_eplb=True,
            remove_lora=False,
            force_attention=force_attention,
        )

5230
5231
        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
5232
5233
            batch_descriptors = tqdm(
                batch_descriptors,
5234
5235
5236
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
5237
5238
5239
                    cudagraph_runtime_mode.name,
                ),
            )
5240

5241
        # We skip EPLB here since we don't want to record dummy metrics
5242
5243
5244
5245
        for batch_desc in batch_descriptors:
            num_tokens = batch_desc.num_tokens
            activate_lora = batch_desc.has_lora

5246
            # We currently only capture ubatched graphs when its a FULL
5247
5248
5249
            # 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
5250
            allow_microbatching = (
5251
                self.parallel_config.use_ubatching
5252
5253
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
5254
5255
5256
5257
5258
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
5259
            )
5260

5261
5262
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
5263
                # But be careful, warm up with `NONE` is orthogonal to
5264
5265
5266
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
5267
                dummy_run(
5268
5269
5270
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    allow_microbatching=allow_microbatching,
5271
                    activate_lora=activate_lora,
5272
                )
5273
5274
5275

            # Capture run
            dummy_run(
5276
5277
5278
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                allow_microbatching=allow_microbatching,
5279
                activate_lora=activate_lora,
Rémi Delacourt's avatar
Rémi Delacourt committed
5280
                is_graph_capturing=True,
5281
            )
5282
        self.maybe_remove_all_loras(self.lora_config)
5283

5284
5285
5286
5287
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
5288
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
5289

5290
5291
5292
5293
5294
5295
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
5296
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
5297
            layer_type = cast(type[Any], AttentionLayerBase)
5298
            layers = get_layers_from_vllm_config(
5299
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
5300
            )
5301
5302
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
5303
            # Dedupe based on full class name; this is a bit safer than
5304
5305
5306
5307
            # 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.
5308
            for layer_name in kv_cache_group_spec.layer_names:
5309
                attn_backend = layers[layer_name].get_attn_backend()
5310
5311
5312
5313

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
5314
                        attn_backend,  # type: ignore[arg-type]
5315
5316
                    )

5317
5318
5319
                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):
5320
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
5321
                key = (full_cls_name, layer_kv_cache_spec)
5322
5323
5324
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
5325
                attn_backend_layers[key].append(layer_name)
5326
5327
5328
5329
            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()),
            )
5330
5331

        def create_attn_groups(
5332
            attn_backends_map: dict[AttentionGroupKey, list[str]],
5333
            kv_cache_group_id: int,
5334
5335
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
5336
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
5337
                attn_group = AttentionGroup(
5338
                    attn_backend,
5339
                    layer_names,
5340
                    kv_cache_spec,
5341
                    kv_cache_group_id,
5342
                )
5343

5344
5345
5346
                attn_groups.append(attn_group)
            return attn_groups

5347
        attention_backend_maps = []
5348
        attention_backend_list = []
5349
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
5350
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
5351
            attention_backend_maps.append(attn_backends[0])
5352
            attention_backend_list.append(attn_backends[1])
5353
5354

        # Resolve cudagraph_mode before actually initialize metadata_builders
5355
5356
5357
        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
5358

5359
5360
5361
        # Check if attention backend supports PCP&DCP and related features.
        check_attention_cp_compatibility(self.vllm_config)

5362
5363
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
5364

5365
5366
5367
5368
5369
5370
5371
5372
5373
5374
5375
5376
5377
5378
5379
    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
5380
5381
                    if not self.parallel_config.use_ubatching
                    else self.parallel_config.num_ubatches,
5382
                )
co63oc's avatar
co63oc committed
5383
        # Calculate reorder batch threshold (if needed)
5384
5385
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
5386
5387
        self.calculate_reorder_batch_threshold()

5388
    def _check_and_update_cudagraph_mode(
5389
5390
5391
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
5392
    ) -> None:
5393
        """
5394
        Resolve the cudagraph_mode when there are multiple attention
5395
        groups with potential conflicting CUDA graph support.
5396
5397
5398
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
5399
        min_cg_support = AttentionCGSupport.ALWAYS
5400
        min_cg_backend_name = None
5401

5402
5403
5404
5405
5406
        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()
5407

5408
5409
5410
5411
5412
5413
                cg_support = builder_cls.get_cudagraph_support(
                    self.vllm_config, kv_cache_group.kv_cache_spec
                )
                if cg_support.value < min_cg_support.value:
                    min_cg_support = cg_support
                    min_cg_backend_name = attn_backend.__name__
5414
5415
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
5416
        assert cudagraph_mode is not None
5417
        # check cudagraph for mixed batch is supported
5418
5419
5420
5421
5422
5423
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
5424
                f"with {min_cg_backend_name} backend (support: "
5425
5426
                f"{min_cg_support})"
            )
5427
5428
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
5429
5430
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
5431
                    "make sure compilation mode is VLLM_COMPILE"
5432
                )
5433
5434
5435
5436
5437
                raise ValueError(msg)

            # attempt to resolve the full cudagraph related mode
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=FULL_AND_PIECEWISE"
5438
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5439
                    CUDAGraphMode.FULL_AND_PIECEWISE
5440
                )
5441
5442
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
5443
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5444
                    CUDAGraphMode.FULL_DECODE_ONLY
5445
                )
5446
5447
            logger.warning(msg)

5448
        # check that if we are doing decode full-cudagraphs it is supported
5449
5450
5451
5452
        if not envs.VLLM_USE_PIECEWISE:
            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and min_cg_support == AttentionCGSupport.NEVER
5453
            ):
5454
5455
5456
5457
                msg = (
                    f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                    f"with {min_cg_backend_name} backend (support: "
                    f"{min_cg_support})"
5458
                )
5459
5460
5461
5462
5463
5464
5465
5466
5467
5468
5469
5470
5471
5472
5473
5474
5475
5476
5477
5478
                if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
                    self.compilation_config.splitting_ops_contain_attention()
                    or self.compilation_config.use_inductor_graph_partition
                ):
                    msg += (
                        "; setting cudagraph_mode=PIECEWISE because "
                        "attention is compiled piecewise"
                    )
                    cudagraph_mode = self.compilation_config.cudagraph_mode = (
                        CUDAGraphMode.PIECEWISE
                    )
                else:
                    msg += (
                        "; setting cudagraph_mode=NONE because "
                        "attention is not compiled piecewise"
                    )
                    cudagraph_mode = self.compilation_config.cudagraph_mode = (
                        CUDAGraphMode.NONE
                    )
                logger.warning(msg)
5479

5480
5481
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
5482
5483
5484
5485
5486
5487
5488
5489
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and self.uniform_decode_query_len > 1
            and min_cg_support.value < AttentionCGSupport.UNIFORM_BATCH.value
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
                f" with spec-decode for attention backend "
5490
                f"{min_cg_backend_name} (support: {min_cg_support})"
5491
            )
5492
5493
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
5494
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5495
                    CUDAGraphMode.PIECEWISE
5496
                )
5497
5498
            else:
                msg += "; setting cudagraph_mode=NONE"
5499
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5500
                    CUDAGraphMode.NONE
5501
                )
5502
5503
5504
5505
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
5506
5507
5508
5509
5510
5511
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
5512
                f"supported with {min_cg_backend_name} backend ("
5513
5514
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
5515
                "and make sure compilation mode is VLLM_COMPILE"
5516
            )
5517

5518
5519
5520
5521
        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
        # we need to adjust the cudagraph sizes to be a multiple of the uniform
        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
5522
        # Will be removed in the near future when we have separate cudagraph capture
5523
5524
5525
5526
5527
5528
5529
5530
5531
        # sizes for decode and mixed prefill-decode.
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and cudagraph_mode.separate_routine()
            and self.uniform_decode_query_len > 1
        ):
            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
            )
5532
5533
5534
5535
            capture_sizes = self.compilation_config.cudagraph_capture_sizes
            self.cudagraph_batch_sizes = (
                capture_sizes if capture_sizes is not None else []
            )
5536

5537
5538
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
5539
        self.compilation_config.cudagraph_mode = cudagraph_mode
5540
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
5541
            cudagraph_mode, self.uniform_decode_query_len
5542
        )
5543

5544
5545
5546
5547
5548
        # Initialize eagle's cudagraph dispatcher if using eagle spec decode.
        if self.speculative_config and self.speculative_config.use_eagle():
            assert isinstance(self.drafter, EagleProposer)
            self.drafter.initialize_cudagraph_keys(cudagraph_mode)

5549
5550
    def calculate_reorder_batch_threshold(self) -> None:
        """
5551
5552
5553
5554
        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.
5555
        """
5556
5557
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

5558
        reorder_batch_thresholds: list[int | None] = [
5559
5560
5561
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
5562
5563
5564
5565
5566
        # 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
5567
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
5568

5569
5570
5571
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
5572
5573
    ) -> int:
        """
5574
5575
5576
5577
5578
        Select a block size that is supported by all backends and is a factor of
        kv_manager_block_size.

        If kv_manager_block_size is supported by all backends, return it directly.
        Otherwise, return the max supported size.
5579

5580
5581
5582
5583
5584
        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

        Returns:
5585
            The selected block size
5586
5587

        Raises:
5588
            ValueError: If no valid block size found
5589
5590
        """

王敏's avatar
王敏 committed
5591
5592
5593
5594
        #exclude indexer backend
        def _participates_in_block_size_selection(backend: type[AttentionBackend]) -> bool:
            return not getattr(backend, "exclude_from_block_size_selection", False)

5595
5596
5597
5598
5599
5600
5601
5602
        def block_size_is_supported(
            backends: list[type[AttentionBackend]], block_size: int
        ) -> bool:
            """
            Check if the block size is supported by all backends.
            """
            for backend in backends:
                is_supported = False
5603
                for supported_size in backend.get_supported_kernel_block_sizes():
5604
5605
5606
5607
5608
5609
5610
5611
5612
5613
5614
5615
                    if isinstance(supported_size, int):
                        if block_size == supported_size:
                            is_supported = True
                    elif isinstance(supported_size, MultipleOf):
                        if block_size % supported_size.base == 0:
                            is_supported = True
                    else:
                        raise ValueError(f"Unknown supported size: {supported_size}")
                if not is_supported:
                    return False
            return True

zhuwenwen's avatar
zhuwenwen committed
5616
5617
5618
5619
        all_backends = [group.backend for group in attn_groups]
        backends = [
            b for b in all_backends
            if _participates_in_block_size_selection(b)
5620
            ]
zhuwenwen's avatar
zhuwenwen committed
5621

5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
5634
5635
5636
5637
5638

        # Case 1: if the block_size of kv cache manager is supported by all backends,
        # return it directly
        if block_size_is_supported(backends, kv_manager_block_size):
            return kv_manager_block_size

        # Case 2: otherwise, the block_size must be an `int`-format supported size of
        # at least one backend. Iterate over all `int`-format supported sizes in
        # descending order and return the first one that is supported by all backends.
        # Simple proof:
        # If the supported size b is in MultipleOf(x_i) format for all attention
        # backends i, and b a factor of kv_manager_block_size, then
        # kv_manager_block_size also satisfies MultipleOf(x_i) for all i. We will
        # return kv_manager_block_size in case 1.
        all_int_supported_sizes = set(
            supported_size
            for backend in backends
5639
            for supported_size in backend.get_supported_kernel_block_sizes()
5640
5641
            if isinstance(supported_size, int)
        )
5642

5643
5644
5645
5646
5647
5648
        for supported_size in sorted(all_int_supported_sizes, reverse=True):
            if kv_manager_block_size % supported_size != 0:
                continue
            if block_size_is_supported(backends, supported_size):
                return supported_size
        raise ValueError(f"No common block size for {kv_manager_block_size}. ")
5649

5650
5651
5652
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
5653
5654
5655
5656
5657
5658
5659
        """
        Re-initialize the input batch if the block sizes are different from
        `[self.cache_config.block_size]`. This usually happens when there
        are multiple KV cache groups.

        Args:
            kv_cache_config: The KV cache configuration.
5660
            kernel_block_sizes: The kernel block sizes for each KV cache group.
5661
5662
5663
5664
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
5665
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
5666
        ]
5667
5668
5669
5670
5671
5672
5673
5674
5675
5676
5677
5678
5679
5680
5681
5682
5683
5684
        max_num_blocks = []
        max_model_len = max(self.max_model_len, self.max_encoder_len)
        for i, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                continue
            max_num_blocks_per_req = cdiv(
                max_model_len, block_sizes[i] * get_total_cp_world_size()
            )
            if isinstance(kv_cache_group.kv_cache_spec, MambaSpec):
                mamba_blocks_per_req = (
                    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_per_req = max(
                    max_num_blocks_per_req, mamba_blocks_per_req
                )
            max_num_blocks.append(max_num_blocks_per_req)
5685
5686
5687
5688

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
5689
5690
5691
            assert self.cache_config.cpu_offload_gb == 0, (
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
5692
5693
                "for more details."
            )
5694
5695
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
5696
                max_model_len=max_model_len,
5697
5698
5699
5700
5701
                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,
5702
                kernel_block_sizes=kernel_block_sizes,
5703
                max_num_blocks_per_req=max_num_blocks,
5704
                is_spec_decode=bool(self.vllm_config.speculative_config),
5705
                logitsprocs=self.input_batch.logitsprocs,
5706
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
5707
                is_pooling_model=self.is_pooling_model,
5708
5709
            )

5710
    def _allocate_kv_cache_tensors(
5711
5712
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
5713
        """
5714
5715
5716
        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.

5717
        Args:
5718
            kv_cache_config: The KV cache config
5719
        Returns:
5720
            dict[str, torch.Tensor]: A map between layer names to their
5721
            corresponding memory buffer for KV cache.
5722
        """
5723
5724
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
5725
5726
5727
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
5728
5729
5730
5731
5732
            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:
5733
5734
5735
5736
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
5737
5738
5739
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
5740
5741
        return kv_cache_raw_tensors

5742
5743
5744
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

5745
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
5746
5747
        if not self.kv_cache_config.kv_cache_groups:
            return
5748
5749
        for attn_groups in self.attn_groups:
            yield from attn_groups
5750

5751
5752
5753
5754
5755
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
    def _prepare_kernel_block_sizes(self, kv_cache_config: KVCacheConfig) -> list[int]:
        """
        Generate kernel_block_sizes that matches each block_size.

        For attention backends that support virtual block splitting,
        use the supported block sizes from the backend.
        For other backends (like Mamba), use the same block size (no splitting).

        Args:
            kv_cache_config: The KV cache configuration.

        Returns:
            list[int]: List of kernel block sizes for each cache group.
        """
        kernel_block_sizes = []
5766
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
5767
5768
5769
5770
5771
5772
            kv_cache_spec = kv_cache_group.kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                # All layers in the UniformTypeKVCacheSpecs have the same type,
                # Pick an arbitrary one to dispatch.
                kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
5773
                continue
5774
            elif isinstance(kv_cache_spec, AttentionSpec):
5775
5776
5777
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
5778
                attn_groups = self.attn_groups[kv_cache_gid]
5779
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
5780
                selected_kernel_size = self.select_common_block_size(
5781
5782
5783
                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
5784
            elif isinstance(kv_cache_spec, MambaSpec):
5785
5786
                # This is likely Mamba or other non-attention cache,
                # no splitting.
5787
                kernel_block_sizes.append(kv_cache_spec.block_size)
5788
5789
5790
5791
5792
5793
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

5794
5795
5796
5797
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
5798
        kernel_block_sizes: list[int],
5799
    ) -> dict[str, torch.Tensor]:
5800
        """
5801
        Reshape the KV cache tensors to the desired shape and dtype.
5802

5803
        Args:
5804
5805
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
5806
                correct size but uninitialized shape.
5807
            kernel_block_sizes: The kernel block sizes for each KV cache group.
5808
        Returns:
5809
            Dict[str, torch.Tensor]: A map between layer names to their
5810
5811
            corresponding memory buffer for KV cache.
        """
5812
        kv_caches: dict[str, torch.Tensor] = {}
5813
        has_attn, has_mamba = False, False
5814
5815
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
5816
            attn_backend = group.backend
5817
5818
5819
5820
            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]
5821
            for layer_name in group.layer_names:
5822
5823
                if layer_name in self.runner_only_attn_layers:
                    continue
5824
5825
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
5826
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
5827
                if isinstance(kv_cache_spec, AttentionSpec):
5828
                    has_attn = True
5829
5830
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
5831
5832
5833
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

5834
                    if envs.VLLM_USE_FLASH_ATTN_PA and not self.vllm_config.model_config.use_mla:
5835
                        key_cache_shape, value_cache_shape = attn_backend.get_kv_cache_shape(
5836
5837
                            kernel_num_blocks,
                            kernel_block_size,
5838
5839
                            kv_cache_spec.num_kv_heads,
                            kv_cache_spec.head_size,
5840
5841
                            cache_dtype_str=self.cache_config.cache_dtype,
                        )
5842
5843
5844
                        dtype = kv_cache_spec.dtype
                        try:
                            key_stride_order, value_stride_order = attn_backend.get_kv_cache_stride_order()
5845
5846
                            assert len(key_stride_order) == len(key_stride_order)
                            assert len(value_stride_order) == len(value_cache_shape)
5847
                        except (AttributeError, NotImplementedError):
5848
5849
                            key_stride_order = tuple(range(len(key_cache_shape)))
                            value_stride_order = tuple(range(len(value_cache_shape)))
5850
5851
5852
5853
5854
                        # 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.
5855
5856
5857
5858
                        key_cache_shape = tuple(
                            key_cache_shape[i] for i in key_stride_order)
                        value_cache_shape = tuple(
                            value_cache_shape[i] for i in value_stride_order)
5859
5860
5861
5862
5863
5864
5865
5866
5867
5868
5869
5870
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
5881
5882
5883
5884
5885
                        # Maintain original KV shape view.
                        inv_key_order = [
                            key_stride_order.index(i)
                            for i in range(len(key_stride_order))
                        ]
                        inv_value_order = [
                            value_stride_order.index(i)
                            for i in range(len(value_stride_order))
                        ]
                        
                        raw_tensor = kv_cache_raw_tensors[layer_name].view(dtype)
                        total_elements = raw_tensor.numel()
                        key_elements = (key_cache_shape[0] * key_cache_shape[1] * 
                                        key_cache_shape[2] * key_cache_shape[3])
                        value_elements = (value_cache_shape[0] * value_cache_shape[1] *
                                        value_cache_shape[2] * value_cache_shape[3])

                        assert total_elements == key_elements + value_elements

                        key_cache = raw_tensor[:key_elements].view(key_cache_shape).permute(
                            *inv_key_order)
                        value_cache = raw_tensor[key_elements:].view(value_cache_shape).permute(
                            *inv_value_order)
                        kv_caches[layer_name] = (key_cache, value_cache)

                    else:
                        kv_cache_shape = attn_backend.get_kv_cache_shape(
5886
5887
                            kernel_num_blocks,
                            kernel_block_size,
5888
5889
                            kv_cache_spec.num_kv_heads,
                            kv_cache_spec.head_size,
5890
5891
                            cache_dtype_str=self.cache_config.cache_dtype,
                        )
5892
5893
                        dtype = kv_cache_spec.dtype
                        try:
5894
5895
                            kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
                            assert len(kv_cache_stride_order) == len(kv_cache_shape)
5896
                        except (AttributeError, NotImplementedError):
5897
                            kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
5898
5899
5900
5901
5902
                        # 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.
5903
5904
5905
                        kv_cache_shape = tuple(
                            kv_cache_shape[i] for i in kv_cache_stride_order
                        )
5906
5907
5908
5909
5910
                        # Maintain original KV shape view.
                        inv_order = [
                            kv_cache_stride_order.index(i)
                            for i in range(len(kv_cache_stride_order))
                        ]
5911
5912
5913
5914
5915
5916
                        kv_caches[layer_name] = (
                            kv_cache_raw_tensors[layer_name]
                            .view(dtype)
                            .view(kv_cache_shape)
                            .permute(*inv_order)
                        )
5917

Chen Zhang's avatar
Chen Zhang committed
5918
                elif isinstance(kv_cache_spec, MambaSpec):
5919
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
5920
5921
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
5922
                    storage_offset_bytes = 0
5923
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
5924
5925
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
5926
5927
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
5928
                        target_shape = (num_blocks, *shape)
5929
5930
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
5931
                        assert storage_offset_bytes % dtype_size == 0
5932
5933
5934
5935
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
5936
                            storage_offset=storage_offset_bytes // dtype_size,
5937
                        )
Chen Zhang's avatar
Chen Zhang committed
5938
                        state_tensors.append(tensor)
5939
                        storage_offset_bytes += stride[0] * dtype_size
5940
5941

                    kv_caches[layer_name] = state_tensors
5942
                else:
5943
                    raise NotImplementedError
5944
5945

        if has_attn and has_mamba:
5946
            self._update_hybrid_attention_mamba_layout(kv_caches)
5947

5948
5949
        return kv_caches

5950
    def _update_hybrid_attention_mamba_layout(
5951
5952
        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
5953
        """
5954
5955
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
5956
5957

        Args:
5958
            kv_caches: The KV cache buffer of each layer.
5959
5960
        """

5961
5962
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
5963
            for layer_name in group.layer_names:
5964
                kv_cache = kv_caches[layer_name]
5965
5966
5967
5968
                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
5969
                        f"a tensor of shape {kv_cache.shape}"
5970
                    )
5971
                    hidden_size = kv_cache.shape[2:].numel()
5972
5973
5974
5975
                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
5976

5977
    def initialize_kv_cache_tensors(
5978
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
5979
    ) -> dict[str, torch.Tensor]:
5980
5981
5982
5983
5984
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
5985
5986
            kernel_block_sizes: The kernel block sizes for each KV cache group.

5987
        Returns:
5988
            Dict[str, torch.Tensor]: A map between layer names to their
5989
5990
            corresponding memory buffer for KV cache.
        """
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014

        # 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
            )
6015

6016
        # Set up cross-layer KV cache sharing
6017
6018
        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)
6019
6020
            kv_caches[layer_name] = kv_caches[target_layer_name]

6021
6022
6023
6024
6025
6026
6027
6028
6029
        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,
        )
6030
6031
6032
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
6033
6034
        self, kv_cache_config: KVCacheConfig
    ) -> None:
6035
6036
6037
6038
6039
6040
6041
6042
6043
6044
6045
6046
6047
6048
6049
6050
6051
6052
        """
        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.
6053
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
6054
6055
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
6056
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
6057
6058
                else:
                    break
6059

6060
6061
6062
6063
6064
6065
6066
    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
6067
        kv_cache_config = deepcopy(kv_cache_config)
6068
        self.kv_cache_config = kv_cache_config
6069
        self.may_add_encoder_only_layers_to_kv_cache_config()
6070
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
6071
        self.initialize_attn_backend(kv_cache_config)
6072
6073
6074
6075
6076
6077
        # 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.
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)
6078
6079
6080
6081

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

6082
        # Reinitialize need to after initialize_attn_backend
6083
6084
6085
6086
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
6087

6088
6089
6090
6091
6092
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_draft_model()
        ):
            assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
6093
6094
            # validate all draft model layers belong to the same kv cache
            # group
zhuwenwen's avatar
zhuwenwen committed
6095
            self.drafter.validate_same_kv_cache_group(kv_cache_config)
6096

Robert Shaw's avatar
Robert Shaw committed
6097
        if has_kv_transfer_group():
6098
            kv_transfer_group = get_kv_transfer_group()
6099
6100
6101
6102
6103
6104
6105
            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)
6106
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
6107

6108
6109
6110
6111
6112
6113
6114
6115
6116
6117
6118
6119
6120
6121
6122
6123
6124
        if self.model_config.enable_return_routed_experts:
            self.init_routed_experts_capturer()

    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()
        block_size = self.cache_config.block_size
        self.max_num_kv_tokens = (
            self.kv_cache_config.num_blocks // len(self.kv_cache_config.kv_cache_groups)
            + 1
        ) * block_size
        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,
6125
            vllm_config=self.vllm_config,
6126
6127
        )

6128
6129
6130
6131
6132
    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
6133
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
6134
6135
6136
        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:
6137
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
6138
6139
6140
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
6141
6142
                    dtype=self.kv_cache_dtype,
                )
6143
6144
6145
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
6146
6147
6148
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
6149
6150
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
6151
6152
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
6153

6154
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
6155
        """
6156
        Generates the KVCacheSpec by parsing the kv cache format from each
6157
6158
        Attention module in the static forward context.
        Returns:
6159
            KVCacheSpec: A dictionary mapping layer names to their KV cache
6160
6161
            format. Layers that do not need KV cache are not included.
        """
6162
6163
        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
6164
        kv_cache_spec: dict[str, KVCacheSpec] = {}
6165
6166
        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
6167
        for layer_name, attn_module in attn_layers.items():
6168
6169
6170
            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
6171
6172
6173
6174
6175
6176
6177
6178
6179
                # 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
6180
6181
6182
            # 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
6183

6184
        return kv_cache_spec
6185

6186
6187
6188
6189
6190
6191
6192
6193
6194
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
6195
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
6196
6197
6198
6199
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()
6200
6201
6202
6203
6204
6205
6206
6207
6208
6209
6210
6211
6212
6213
6214
6215
6216
6217
6218
6219
6220
6221
6222
6223
6224
6225
6226
6227
6228
6229
6230
6231
6232
6233
6234
6235
6236
6237
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
6254
6255
6256
6257
6258
6259
6260
6261
6262
6263
6264
6265
6266
6267
6268
6269
6270
6271
6272
6273
6274
6275

    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}

        torch.cuda.synchronize()
        start_time = time.perf_counter()

        try:
            yield
        finally:
            torch.cuda.synchronize()
            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]
                    stats.encoder_forward_time += per_request_time
                    stats.num_encoder_calls += 1


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

    encoder_forward_time: float = 0.0
    """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 {
            "encoder_forward_time": self.encoder_forward_time,
            "num_encoder_calls": self.num_encoder_calls,
        }