gpu_model_runner.py 269 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
399
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
400
401
            model_config
        )
402

403
404
405
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
406
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
407
408
409
        else:
            self.max_encoder_len = 0

410
411
412
        # Async scheduling
        self.use_async_scheduling = self.scheduler_config.async_scheduling

413
        # Sampler
414
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
415

416
        self.eplb_state: EplbState | None = None
417
418
419
420
421
422
        """
        State of the expert parallelism load balancer.

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

423
        # Lazy initializations
424
        # self.model: nn.Module  # Set after load_model
425
        # Initialize in initialize_kv_cache
426
        self.kv_caches: list[torch.Tensor] = []
427
428
429
        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
430
431
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
432
433
        # self.kv_cache_config: KVCacheConfig

434
435
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
436

437
        self.use_aux_hidden_state_outputs = False
438
439
440
441
442
        # 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:
443
            self.drafter: (
444
445
446
447
448
                NgramProposer
                | SuffixDecodingProposer
                | EagleProposer
                | DraftModelProposer
                | MedusaProposer
449
            )
450
451
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
452
453
454
455
456
457
            elif self.speculative_config.uses_draft_model():
                self.drafter = DraftModelProposer(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    runner=self,
                )
458
459
            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
460
            elif self.speculative_config.use_eagle():
461
                self.drafter = EagleProposer(self.vllm_config, self.device, self)
462
                if self.speculative_config.method == "eagle3":
463
464
465
                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
466
467
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
468
                    vllm_config=self.vllm_config, device=self.device
469
                )
470
            else:
471
472
473
474
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
王敏's avatar
王敏 committed
475
476
477
478
479
            
            if not envs.VLLM_REJECT_SAMPLE_OPT:
                self.rejection_sampler = RejectionSampler(self.sampler)
            else:
                self.rejection_sampler = OptRejectionSampler(self.sampler)
480

481
482
483
        self.num_spec_tokens = 0
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens
484
485
486
487
488
            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
489

490
        # Request states.
491
        self.requests: dict[str, CachedRequestState] = {}
492
493
494
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
495
        self.comm_stream = torch.cuda.Stream()
496

497
498
499
500
501
502
503
504
505
        # 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.
506
507
508
509
        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
510
511
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
512
513
514
            # 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),
515
516
517
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
518
            vocab_size=self.model_config.get_vocab_size(),
519
            block_sizes=[self.cache_config.block_size],
520
            kernel_block_sizes=[self.cache_config.block_size],
521
            is_spec_decode=bool(self.vllm_config.speculative_config),
522
            logitsprocs=build_logitsprocs(
523
524
525
                self.vllm_config,
                self.device,
                self.pin_memory,
526
                self.is_pooling_model,
527
                custom_logitsprocs,
528
            ),
529
530
531
            # 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),
532
            is_pooling_model=self.is_pooling_model,
533
            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
534
        )
535

536
537
538
539
540
        # 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.
541
        self.prepare_inputs_event: torch.Event | None = None
542
543
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
544
            self.prepare_inputs_event = torch.Event()
545

546
        # self.cudagraph_batch_sizes sorts in ascending order.
547
548
549
550
        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
551
552
            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
553
            )
554

555
        # Cache the device properties.
556
        self._init_device_properties()
557

558
559
560
561
        # Encoder timing registry for observability
        self.encoder_timing_registry: dict[str, EncoderTimingStats] = {}
        self._encoder_timing_lock = threading.Lock()

562
        # Persistent buffers for CUDA graphs.
563
564
565
566
567
        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
        )
568
        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
569
        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
570
571
572
573
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
574
575
576
        # 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.
577
        self.inputs_embeds = self._make_buffer(
578
            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
579
580
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
581
582
        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
583
584
585
586
587
588
589
        )
        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
        )
590

591
592
        # Only relevant for multimodal models
        if self.supports_mm_inputs:
593
594
595
596
597
598
599
            # 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
600
601

        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
602
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
603
604
605
606
            # 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
607
608
609
610
611
612

            # 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
613
            self.mrope_positions = self._make_buffer(
614
615
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
616

617
618
619
620
621
622
        # 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
            )
623

624
        # None in the first PP rank. The rest are set after load_model.
625
        self.intermediate_tensors: IntermediateTensors | None = None
626

627
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
628
        # Keep in int64 to avoid overflow with long context
629
630
631
632
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
633

634
635
636
637
638
        # 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] = {}
639
640
641
642
643
        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(
644
645
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
646

647
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
648
649
650
651

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

652
        self.mm_budget = (
653
            MultiModalBudget(self.vllm_config, self.mm_registry)
654
655
656
            if self.supports_mm_inputs
            else None
        )
657

658
        self.reorder_batch_threshold: int | None = None
659

660
661
662
663
664
        # 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()

665
        # Cached outputs.
666
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
667
        self._draft_token_req_ids: list[str] | None = None
668
        self.transfer_event = torch.Event()
669
        self.sampled_token_ids_pinned_cpu = torch.empty(
670
            (self.max_num_reqs, 1),
671
672
            dtype=torch.int64,
            device="cpu",
673
674
            pin_memory=self.pin_memory,
        )
675

676
677
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
678
        self.valid_sampled_token_count_event: torch.Event | None = None
679
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
        # 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,
                )
704

705
706
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
707
        self.kv_connector_output: KVConnectorOutput | None = None
708
        self.mamba_state_idx: dict[str, int] = {}
709
        self.layerwise_nvtx_hooks_registered = False
710

王敏's avatar
王敏 committed
711
712
        self.draft_probs : Optional[DraftProbs] = None

713
714
715
716
717
718
719
    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

720
721
722
723
    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
    @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)

768
769
770
771
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
772
773
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
774
775
776
777
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
778
779
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
780
781
            return self.positions.gpu[num_tokens]

782
    def _make_buffer(
783
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
784
785
786
787
788
789
790
791
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
guanyu1's avatar
guanyu1 committed
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
    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,
        )
816

817
    def _init_model_kwargs(self):
818
819
        model_kwargs = dict[str, Any]()

820
        if not self.is_pooling_model:
821
822
            return model_kwargs

823
824
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
825
826
827

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
828
829
830
831
832
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
833
834
835
836
837
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

838
        seq_lens = self.seq_lens.gpu[:num_reqs]
839
840
841
842
843
844
845
846
        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(
847
848
            device=self.device
        )
849
        return model_kwargs
850

851
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
852
853
        """
        Update the order of requests in the batch based on the attention
854
        backend's needs. For example, some attention backends (namely MLA) may
855
856
857
858
859
860
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
861
862
863
864
865
866
867
868
        # 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

869
870
871
872
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
873
874
                decode_threshold=self.reorder_batch_threshold,
            )
875

876
877
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
878
        """Initialize attributes from torch.cuda.get_device_properties"""
879
880
881
882
883
884
885
        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()

886
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
887
888
889
890
891
892
        """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.

893
894
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
895
896
        """
        # Remove finished requests from the cached states.
897
898
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
899
            self.num_prompt_logprobs.pop(req_id, None)
900
901
902
903
904
905
906
        # 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:
907
            self.input_batch.remove_request(req_id)
908

王敏's avatar
王敏 committed
909
910
911
912
        # 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))

913
        # Free the cached encoder outputs.
914
915
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
916

917
918
919
920
921
922
923
        # 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()
924
925
926
927
928
929
930
931
        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)
932
933
934
935
936
        # 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:
937
            self.input_batch.remove_request(req_id)
938

939
        reqs_to_add: list[CachedRequestState] = []
940
        # Add new requests to the cached states.
941
942
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
943
944
945
946
947
948
            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

949
            sampling_params = new_req_data.sampling_params
950
            pooling_params = new_req_data.pooling_params
951

952
953
954
955
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
956
957
958
959
960
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

961
962
            if self.is_pooling_model:
                assert pooling_params is not None
963
964
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
965

966
                model = cast(VllmModelForPooling, self.get_model())
967
                to_update = model.pooler.get_pooling_updates(task)
968
969
                to_update.apply(pooling_params)

970
            req_state = CachedRequestState(
971
                req_id=req_id,
972
                prompt_token_ids=new_req_data.prompt_token_ids,
973
                prompt_embeds=new_req_data.prompt_embeds,
974
                mm_features=new_req_data.mm_features,
975
                sampling_params=sampling_params,
976
                pooling_params=pooling_params,
977
                generator=generator,
978
979
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
980
                output_token_ids=[],
981
                lora_request=new_req_data.lora_request,
982
            )
983
            self.requests[req_id] = req_state
984

985
986
987
988
989
990
991
            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
                )

992
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
993
            if self.uses_mrope:
994
                self._init_mrope_positions(req_state)
995

996
997
998
999
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

1000
            reqs_to_add.append(req_state)
1001

1002
        # Update the states of the running/resumed requests.
1003
        is_last_rank = get_pp_group().is_last_rank
1004
        req_data = scheduler_output.scheduled_cached_reqs
1005
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
1006
1007
1008
1009
1010

        # 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()

1011
        for i, req_id in enumerate(req_data.req_ids):
1012
            req_state = self.requests[req_id]
1013
1014
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
1015
            resumed_from_preemption = req_id in req_data.resumed_req_ids
1016
            num_output_tokens = req_data.num_output_tokens[i]
1017
            req_index = self.input_batch.req_id_to_index.get(req_id)
1018

1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
            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.
1033
1034
1035
1036
1037
1038
1039
1040
1041
                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)
1042

1043
            # Update the cached states.
1044
            req_state.num_computed_tokens = num_computed_tokens
1045
1046
1047
1048
1049
1050
1051
1052

            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.
1053
1054
1055
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
1056
1057
1058
1059
                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:
1060
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
1061
1062
1063
1064
1065
            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:
1066
1067
1068
1069
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
1070
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
1071

1072
            # Update the block IDs.
1073
            if not resumed_from_preemption:
1074
1075
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
1076
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
1077
                        block_ids.extend(new_ids)
1078
            else:
1079
                assert req_index is None
1080
                assert new_block_ids is not None
1081
1082
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
1083
                req_state.block_ids = new_block_ids
1084
1085
1086
1087
1088

            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.
1089
1090
1091
1092
1093
1094
1095

                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:]

1096
                reqs_to_add.append(req_state)
1097
1098
1099
                continue

            # Update the persistent batch.
1100
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
1101
            if new_block_ids is not None:
1102
                self.input_batch.block_table.append_row(new_block_ids, req_index)
1103
1104
1105
1106
1107
1108

            # 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
1109
                end_token_index = num_computed_tokens + len(new_token_ids)
1110
                self.input_batch.token_ids_cpu[
1111
1112
1113
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
1114

1115
            # Add spec_token_ids to token_ids_cpu.
1116
            self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
1117

1118
1119
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
1120
1121
        for request in reqs_to_add:
            self.input_batch.add_request(request)
1122
            self.input_batch.update_req_spec_token_ids(request, scheduled_spec_tokens)
1123

1124
1125
1126
1127
1128
        # 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.
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
        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)
1140

1141
    def _update_states_after_model_execute(
1142
        self, output_token_ids: torch.Tensor, scheduler_output: "SchedulerOutput"
1143
    ) -> None:
1144
1145
1146
1147
1148
1149
1150
1151
        """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.
        """
1152
        if not self.speculative_config or not self.model_config.is_hybrid:
1153
1154
1155
            return

        # Find the number of accepted tokens for each sequence.
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
        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()
        )
1176
1177
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
        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(),
            )
1188

1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
    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
1222

1223
    def _init_mrope_positions(self, req_state: CachedRequestState):
1224
1225
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1226
1227
1228
1229
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1230
1231

        req_state.mrope_positions, req_state.mrope_position_delta = (
1232
            mrope_model.get_mrope_input_positions(
1233
                req_state.prompt_token_ids,
1234
                req_state.mm_features,
1235
            )
1236
        )
1237

1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
    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,
        )
1250

1251
    def _extract_mm_kwargs(
1252
        self,
1253
1254
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1255
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1256
            return {}
1257

1258
1259
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
1260
1261
1262
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
1263

1264
1265
1266
        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1267
1268
1269
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
1270
1271
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
1272

1273
        return mm_kwargs_combined
1274
1275

    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1276
        if not self.is_multimodal_raw_input_only_model:
1277
            return {}
1278

1279
1280
        mm_budget = self.mm_budget
        assert mm_budget is not None
1281

1282
1283
        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
1284

1285
1286
1287
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1288
        cumsum_dtype: np.dtype | None = None,
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
    ) -> 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

1305
    def _prepare_input_ids(
1306
1307
1308
1309
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1310
    ) -> None:
1311
        """Prepare the input IDs for the current batch.
1312

1313
1314
1315
1316
1317
1318
1319
        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)
1320
1321
1322
            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)
1323
1324
1325
1326
1327
1328
1329
            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
1330
1331
1332
1333
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1334
1335
        indices_match = True
        max_flattened_index = -1
1336
1337
1338
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1339
1340
1341
1342
1343
        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.
1344
1345
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1346
                flattened_index = cu_num_tokens[cur_index].item() - 1
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
                # 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))
1362
                indices_match &= prev_index == flattened_index
1363
                max_flattened_index = max(max_flattened_index, flattened_index)
1364
1365
1366
        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:
1367
1368
1369
            # 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)
1370
1371
1372
            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)
1373
1374
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1375
            # So input_ids.cpu will have all the input ids.
1376
1377
1378
1379
1380
1381
1382
            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_(
1383
1384
1385
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1386
1387
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1388
            return
1389
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1390
1391
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1392
        ).to(self.device, non_blocking=True)
1393
        prev_common_req_indices_tensor = torch.tensor(
1394
1395
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1396
1397
        self.input_ids.gpu.scatter_(
            dim=0,
1398
            index=sampled_tokens_index_tensor,
1399
            src=self.input_batch.prev_sampled_token_ids[
1400
1401
1402
                prev_common_req_indices_tensor, 0
            ],
        )
1403

1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
        # 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],
        )
1425

1426
1427
    def _get_encoder_seq_lens(
        self,
1428
        num_scheduled_tokens: dict[str, int],
1429
1430
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1431
        for_cudagraph_capture: bool = False,
1432
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1433
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1434
            return None, None
1435

1436
1437
        # Zero out buffer for padding requests that are not actually scheduled (CGs)
        self.encoder_seq_lens.np[:num_reqs] = 0
1438

1439
1440
        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1441
        for req_id in num_scheduled_tokens:
1442
            req_index = self.input_batch.req_id_to_index[req_id]
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
            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
1455
1456
1457
1458
1459
1460
1461
1462
1463
        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
1464

1465
1466
1467
        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]
1468

1469
        return encoder_seq_lens, encoder_seq_lens_cpu
1470

1471
    def _prepare_inputs(
1472
1473
1474
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1475
1476
    ) -> tuple[
        torch.Tensor,
1477
        SpecDecodeMetadata | None,
1478
    ]:
1479
1480
        """
        :return: tuple[
1481
            logits_indices, spec_decode_metadata,
1482
1483
        ]
        """
1484
1485
1486
1487
1488
1489
1490
        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.
1491
        self.input_batch.block_table.commit_block_table(num_reqs)
1492
1493
1494

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1495
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1496

1497
1498
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1499
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1500
1501

        # Get positions.
1502
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1503
1504
1505
1506
1507
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1508

1509
1510
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1511
        if self.uses_mrope:
1512
1513
            self._calc_mrope_positions(scheduler_output)

1514
1515
1516
1517
1518
        # 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)

1519
1520
1521
1522
        # 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.
1523
1524
1525
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1526
        token_indices_tensor = torch.from_numpy(token_indices)
1527

1528
1529
1530
        # 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.
1531
1532
1533
1534
1535
1536
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1537
        if self.enable_prompt_embeds:
1538
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1539
1540
1541
1542
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1543
1544
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577

        # 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:
1578
1579
1580
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1581
1582

                output_idx += num_sched
1583

1584
1585
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1586
1587

        # Prepare the attention metadata.
1588
        self.query_start_loc.np[0] = 0
1589
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1590
1591
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1592
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1593
        self.query_start_loc.copy_to_gpu()
1594
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1595

1596
        self.seq_lens.np[:num_reqs] = (
1597
1598
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1599
        # Fill unused with 0 for full cuda graph mode.
1600
1601
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1602

1603
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1604
1605
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1606
        # Record which requests should not be sampled,
1607
        # so that we could clear the sampled tokens before returning
1608
1609
        self.discard_request_mask.np[:num_reqs] = (
            self.seq_lens.np[:num_reqs] < num_tokens_np
1610
        )
1611
        self.discard_request_mask.copy_to_gpu(num_reqs)
1612

1613
        # Copy the tensors to the GPU.
1614
1615
1616
1617
1618
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1619
        if self.uses_mrope:
1620
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
guanyu1's avatar
guanyu1 committed
1621
            self._copy_mrope_positions_to_gpu(total_num_scheduled_tokens)
1622
1623
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
guanyu1's avatar
guanyu1 committed
1624
            self._copy_xdrope_positions_to_gpu(total_num_scheduled_tokens)
1625
1626
        else:
            # Common case (1D positions)
1627
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1628

1629
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1630
1631
1632
1633
1634
1635
1636
1637
        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
1638
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1639
1640
1641
1642
1643
        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)
1644
1645
1646
            # 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)
1647
1648
1649
1650
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1651
1652
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1653
1654
1655
1656
1657
                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
1658
1659
1660
1661
1662

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

1663
            spec_decode_metadata = self._calc_spec_decode_metadata(
王敏's avatar
王敏 committed
1664
                num_draft_tokens, cu_num_tokens, spec_decode_ids
1665
            )
1666
            logits_indices = spec_decode_metadata.logits_indices
1667
            num_sampled_tokens = num_draft_tokens + 1
1668
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1669
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1670
1671
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1672

1673
1674
1675
1676
1677
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1678
            )
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
1690
        num_tokens: int,
1691
        num_reqs: int,
1692
1693
1694
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
1695
1696
1697
1698
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
1699
        num_scheduled_tokens: dict[str, int] | None = None,
1700
        cascade_attn_prefix_lens: list[list[int]] | None = None,
1701
        slot_mappings: dict[int, torch.Tensor] | None = None,
1702
1703
1704
1705
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
1706
1707
1708
1709
        # Attention metadata is not needed for attention free models
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return {}, None

1710
1711
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs
1712
        assert num_reqs_padded is not None and num_tokens_padded is not None
1713

1714
1715
1716
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1717

1718
1719
1720
1721
1722
1723
1724
1725
        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()

1726
1727
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1728
1729
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1730
1731
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1732

1733
        kv_cache_groups = self.kv_cache_config.kv_cache_groups
1734

1735
        def _get_block_table(kv_cache_gid: int):
1736
1737
1738
            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):
1739
                blk_table_tensor = torch.zeros(
1740
                    (num_reqs_padded, 1),
1741
                    dtype=torch.int32,
1742
1743
                    device=self.device,
                )
1744
            else:
1745
                blk_table = self.input_batch.block_table[kv_cache_gid]
1746
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
1747

1748
1749
1750
            # 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)
1751
            return blk_table_tensor
1752

1753
1754
1755
        assert slot_mappings is not None
        block_table_gid_0 = _get_block_table(0)
        slot_mapping_gid_0 = slot_mappings[0]
1756

1757
1758
        if self.model_config.enable_return_routed_experts:
            self.slot_mapping = slot_mapping_gid_0[:num_tokens].cpu().numpy()
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
        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
            )

1797
1798
1799
1800
1801
1802
1803
1804
1805
        # 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
        ] = {}

1806
1807
1808
1809
1810
1811
1812
        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]
1813
            builder = attn_group.get_metadata_builder(ubid or 0)
1814
1815
1816
1817
            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))
1818

1819
1820
1821
1822
            cascade_attn_prefix_len = (
                cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                if cascade_attn_prefix_lens
                else 0
1823
1824
            )

1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
            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
                )
1839
1840
1841
1842
1843
1844
1845
1846
1847
            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,
                )
1848
1849
1850
1851
1852
1853
            else:
                attn_metadata_i = builder.build(
                    common_prefix_len=cascade_attn_prefix_len,
                    common_attn_metadata=common_attn_metadata,
                    **extra_attn_metadata_args,
                )
1854
1855
                if builder.supports_update_block_table:
                    cached_attn_metadata[cache_key] = attn_metadata_i
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878

            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,
1879
                for_cudagraph_capture=for_cudagraph_capture,
1880
            )
1881
            if kv_cache_gid > 0:
1882
1883
                cm.block_table_tensor = _get_block_table(kv_cache_gid)
                cm.slot_mapping = slot_mappings[kv_cache_gid]
1884

1885
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1886
                if isinstance(self.drafter, EagleProposer):
1887
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1888
                        spec_decode_common_attn_metadata = cm
1889
                else:
1890
                    spec_decode_common_attn_metadata = cm
1891

1892
            for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
1893
                if ubatch_slices is not None:
1894
1895
1896
                    for ubid, _cm in enumerate(split_attn_metadata(ubatch_slices, cm)):
                        _build_attn_group_metadata(kv_cache_gid, attn_gid, _cm, ubid)

1897
                else:
1898
                    _build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
1899

1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
        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]
1919

1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
        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)
            )

1930
        return attn_metadata, spec_decode_common_attn_metadata
1931

1932
1933
1934
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
1935
        num_computed_tokens: np.ndarray,
1936
1937
1938
1939
1940
1941
1942
        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
        """
1943

1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
        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,
1958
                        num_computed_tokens,
1959
1960
1961
1962
1963
1964
                        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
1965

1966
        return cascade_attn_prefix_lens if use_cascade_attn else None
1967

1968
1969
1970
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
1971
        num_computed_tokens: np.ndarray,
1972
        num_common_prefix_blocks: int,
1973
1974
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
    ) -> 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.
        """
1993

1994
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
        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]
2032
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
2033
2034
2035
2036
2037
        # 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.
2038
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
2039
        # common_prefix_len should be a multiple of the block size.
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
        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
        )
2051
2052
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
2053
2054
2055
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
2056
            num_kv_heads=kv_cache_spec.num_kv_heads,
2057
            use_alibi=self.use_alibi,
2058
            use_sliding_window=use_sliding_window,
2059
            use_local_attention=use_local_attention,
2060
            num_sms=self.num_sms,
2061
            dcp_world_size=self.dcp_world_size,
2062
2063
2064
        )
        return common_prefix_len if use_cascade else 0

2065
2066
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
guanyu1's avatar
guanyu1 committed
2067
2068
2069
2070
2071
2072
2073
        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
            )
2074
        for index, req_id in enumerate(self.input_batch.req_ids):
2075
2076
2077
            req = self.requests[req_id]
            assert req.mrope_positions is not None

2078
2079
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2080
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
2081
2082
                req.prompt_token_ids, req.prompt_embeds
            )
2083
2084

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
2085
2086
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
            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
2100
2101
2102
2103
2104
2105
2106
2107
                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
                    ]
2108
2109
2110
2111
2112
2113
2114
                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

2115
                assert req.mrope_position_delta is not None
guanyu1's avatar
guanyu1 committed
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
                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,
                    )
2136
2137
2138

                mrope_pos_ptr += completion_part_len

2139
2140
2141
2142
2143
    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
2144

2145
2146
2147
2148
2149
            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
            )
2150

2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
            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

2186
2187
    def _calc_spec_decode_metadata(
        self,
2188
2189
        num_draft_tokens: np.ndarray,
        cu_num_scheduled_tokens: np.ndarray,
王敏's avatar
王敏 committed
2190
        spec_decode_ids: Optional[list[str]] = None
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
    ) -> 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
2205
2206
2207
2208

        # 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(
2209
2210
            num_sampled_tokens, cumsum_dtype=np.int32
        )
2211
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
2212
        logits_indices = np.repeat(
2213
2214
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
2215
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
2216
2217
2218
2219
2220
2221
        logits_indices += arange

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

        # Compute the draft logits indices.
2222
2223
2224
        # 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(
2225
2226
            num_draft_tokens, cumsum_dtype=np.int32
        )
2227
2228
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
2229
2230
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
2231
2232
        # [0, 1, 2, 5, 6, 9]
        target_logits_indices += arange
2233
        draft_token_indices = target_logits_indices + 1
2234

2235
        # TODO: Optimize the CPU -> GPU copy.
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
        # 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]]

2277

2278
2279
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2280
        draft_token_ids = self.input_ids.gpu[logits_indices]
2281
        draft_token_ids = draft_token_ids[draft_token_indices]
2282

2283
        return SpecDecodeMetadata(
2284
2285
2286
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2287
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2288
2289
2290
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
王敏's avatar
王敏 committed
2291
            spec_decode_ids=spec_decode_ids,
2292
2293
        )

2294
2295
2296
2297
2298
2299
2300
    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
2301
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2302
2303
2304
2305
2306
        # 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_(
2307
2308
            logits_indices[-1].item()
        )
2309
2310
2311
2312
2313
        # 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
2314
2315
2316
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2317
2318
        return logits_indices_padded

2319
    def _batch_mm_inputs_from_scheduler(
2320
2321
        self,
        scheduler_output: "SchedulerOutput",
2322
2323
2324
2325
2326
    ) -> tuple[
        list[str],
        list[MultiModalKwargsItem],
        list[tuple[str, PlaceholderRange]],
    ]:
2327
        """Batch multimodal inputs from scheduled encoder inputs.
2328
2329
2330

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2331
                inputs.
2332
2333

        Returns:
2334
            A tuple of (mm_hashes, mm_kwargs, mm_lora_refs) where:
2335
2336
            - mm_hashes: List of multimodal hashes for each item
            - mm_kwargs: List of multimodal kwargs for each item
2337
            - mm_lora_refs: List of (req_id, placeholder_range) for each item
2338
        """
2339
2340
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2341
            return [], [], []
2342
2343

        mm_hashes = list[str]()
2344
        mm_kwargs = list[MultiModalKwargsItem]()
2345
2346
2347
        # Multimodal LoRA reference info to map each multimodal item
        # back to its request & position
        mm_lora_refs = list[tuple[str, PlaceholderRange]]()
2348
2349
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2350
2351

            for mm_input_id in encoder_input_ids:
2352
                mm_feature = req_state.mm_features[mm_input_id]
2353
2354
                if mm_feature.data is None:
                    continue
2355
2356

                mm_hashes.append(mm_feature.identifier)
2357
                mm_kwargs.append(mm_feature.data)
2358
                mm_lora_refs.append((req_id, mm_feature.mm_position))
2359

2360
        return mm_hashes, mm_kwargs, mm_lora_refs
2361

2362
2363
2364
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2365
        mm_hashes, mm_kwargs, mm_lora_refs = self._batch_mm_inputs_from_scheduler(
2366
2367
            scheduler_output
        )
2368
2369

        if not mm_kwargs:
2370
            return []
2371

2372
2373
2374
2375
2376
2377
        should_time = bool(
            self.observability_config
            and self.observability_config.enable_mm_processor_stats
            and scheduler_output.scheduled_encoder_inputs
        )

2378
2379
2380
2381
2382
2383
2384
        # 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.
2385
        model = cast(SupportsMultiModal, self.model)
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
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

        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,
                )

2443
        encoder_outputs: list[torch.Tensor] = []
2444
2445
        # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
        current_item_idx = 0
2446
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
2447
2448
2449
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
2450
        ):
2451
            curr_group_outputs: MultiModalEmbeddings
2452
2453

            # EVS-related change.
2454
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2455
            # processing multimodal data. This solves the issue with scheduler
2456
2457
2458
2459
            # 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)
2460
2461
2462
2463
2464
2465
2466
            # 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
            ):
2467
                curr_group_outputs_lst = list[torch.Tensor]()
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
                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,
                            )
2479
                        )
2480

2481
2482
2483
                        micro_batch_outputs = model.embed_multimodal(
                            **micro_batch_mm_inputs
                        )
2484

2485
                        curr_group_outputs_lst.extend(micro_batch_outputs)
2486
2487

                curr_group_outputs = curr_group_outputs_lst
2488
2489
2490
2491
2492
2493
2494
2495
            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.
2496
2497
2498
2499
2500

                with self.timed_encoder_operation(
                    should_time, mm_lora_refs, current_item_idx, num_items
                ):
                    curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
2501

2502
2503
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
2504
                expected_num_items=num_items,
2505
            )
2506
            encoder_outputs.extend(curr_group_outputs)
2507

2508
2509
            current_item_idx += num_items

2510
        # Cache the encoder outputs by mm_hash
2511
        for mm_hash, output in zip(mm_hashes, encoder_outputs):
2512
            self.encoder_cache[mm_hash] = output
2513
2514
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2515

2516
        return encoder_outputs
2517
2518

    def _gather_mm_embeddings(
2519
2520
        self,
        scheduler_output: "SchedulerOutput",
2521
        shift_computed_tokens: int = 0,
2522
2523
2524
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

2525
2526
2527
2528
2529
        # 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]

2530
        mm_embeds = list[torch.Tensor]()
2531
        is_mm_embed = is_mm_embed_buf.cpu
2532
2533
2534
        is_mm_embed[:total_num_scheduled_tokens] = False

        req_start_idx = 0
2535
        should_sync_mrope_positions = False
2536
        should_sync_xdrope_positions = False
2537

2538
        for req_id in self.input_batch.req_ids:
2539
2540
            mm_embeds_req: list[torch.Tensor] = []

2541
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2542
            req_state = self.requests[req_id]
2543
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2544

2545
2546
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2547
2548
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564

                # 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,
2565
2566
                    num_encoder_tokens,
                )
2567
                assert start_idx < end_idx
2568
2569
2570
2571
2572
2573
2574
                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
2575

2576
                mm_hash = mm_feature.identifier
2577
                encoder_output = self.encoder_cache.get(mm_hash, None)
2578
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2579
2580
2581

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]
2582
2583
2584
                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
                else:
                    mm_embeds_item = encoder_output[start_idx:end_idx]
2585

2586
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2587
2588
2589
2590
2591
2592
2593
2594
2595
                # 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
2596
2597
2598
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2599
                assert req_state.mrope_positions is not None
2600
2601
2602
2603
2604
2605
2606
                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,
2607
2608
                    )
                )
2609
2610
2611
2612
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2613
2614
            req_start_idx += num_scheduled_tokens

2615
        is_mm_embed = is_mm_embed_buf.copy_to_gpu(total_num_scheduled_tokens)
2616
2617
2618

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
guanyu1's avatar
guanyu1 committed
2619
            self._copy_mrope_positions_to_gpu(total_num_scheduled_tokens)
2620

2621
2622
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
guanyu1's avatar
guanyu1 committed
2623
            self._copy_xdrope_positions_to_gpu(total_num_scheduled_tokens)
2624

2625
        return mm_embeds, is_mm_embed
2626

2627
    def get_model(self) -> nn.Module:
2628
        # get raw model out of the cudagraph wrapper.
2629
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2630
            return self.model.unwrap()
2631
2632
        return self.model

2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
    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

2648
2649
2650
2651
2652
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2653
2654
        supported_tasks = list(model.pooler.get_supported_tasks())

2655
2656
2657
2658
        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2659
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2660
2661

        return supported_tasks
2662

2663
2664
2665
2666
2667
2668
2669
2670
2671
    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)
2672

2673
    def sync_and_slice_intermediate_tensors(
2674
2675
        self,
        num_tokens: int,
2676
        intermediate_tensors: IntermediateTensors | None,
2677
2678
        sync_self: bool,
    ) -> IntermediateTensors:
2679
2680
2681
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2682
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2683
2684
2685
2686
2687
2688

        # 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():
2689
                is_scattered = k == "residual" and is_rs
2690
                copy_len = num_tokens // tp if is_scattered else num_tokens
2691
                self.intermediate_tensors[k][:copy_len].copy_(
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
                    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:
2705
2706
2707
2708
2709
2710
2711
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2712
2713
        model = self.get_model()
        assert is_mixture_of_experts(model)
2714
2715
2716
        self.eplb_state.step(
            is_dummy,
            is_profile,
2717
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2718
2719
        )

2720
2721
2722
2723
2724
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
2725
2726
2727
2728
        kv_connector_output: KVConnectorOutput | None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        num_reqs = self.input_batch.num_reqs
        assert num_reqs == len(self.input_batch.pooling_params), (
2729
2730
            "Either all or none of the requests in a batch must be pooling request"
        )
2731

2732
        hidden_states = hidden_states[:num_scheduled_tokens]
2733
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
2734

2735
        pooling_metadata = self.input_batch.get_pooling_metadata()
2736
        pooling_metadata.build_pooling_cursor(
2737
            num_scheduled_tokens_np, seq_lens_cpu, device=hidden_states.device
2738
        )
2739

2740
2741
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
2742
            hidden_states=hidden_states, pooling_metadata=pooling_metadata
2743
        )
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767

        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,
            )

2768
        raw_pooler_output = json_map_leaves(
2769
            lambda x: None if x is None else x.to("cpu", non_blocking=True),
2770
2771
            raw_pooler_output,
        )
2772
2773
2774
2775
        model_runner_output.pooler_output = [
            out if include else None
            for out, include in zip(raw_pooler_output, finished_mask)
        ]
2776
        self._sync_device()
2777

2778
        return model_runner_output
2779

2780
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
2781
2782
2783
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2784
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
2785
2786
2787
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

Patrick von Platen's avatar
Patrick von Platen committed
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
    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

2799
    def _preprocess(
2800
2801
        self,
        scheduler_output: "SchedulerOutput",
2802
        num_input_tokens: int,  # Padded
2803
        intermediate_tensors: IntermediateTensors | None = None,
2804
    ) -> tuple[
2805
2806
        torch.Tensor | None,
        torch.Tensor | None,
2807
        torch.Tensor,
2808
        IntermediateTensors | None,
2809
        dict[str, Any],
2810
        ECConnectorOutput | None,
2811
    ]:
2812
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2813
        is_first_rank = get_pp_group().is_first_rank
2814
        is_encoder_decoder = self.model_config.is_encoder_decoder
2815

2816
2817
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2818
2819
        ec_connector_output = None

2820
        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
2821
            # Run the multimodal encoder if any.
2822
2823
2824
2825
2826
2827
            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)
2828

2829
2830
2831
            # 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.
2832
            inputs_embeds_scheduled = self.model.embed_input_ids(
2833
2834
2835
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2836
            )
2837

2838
            # TODO(woosuk): Avoid the copy. Optimize.
2839
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2840

Patrick von Platen's avatar
Patrick von Platen committed
2841
            input_ids, inputs_embeds = self._prepare_mm_inputs(num_input_tokens)
2842
            model_kwargs = {
2843
                **self._init_model_kwargs(),
2844
2845
                **self._extract_mm_kwargs(scheduler_output),
            }
2846
        elif self.enable_prompt_embeds and is_first_rank:
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
            # 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).
2859
2860
2861
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2862
                .squeeze(1)
2863
            )
2864
2865
2866
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2867
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2868
2869
2870
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2871
            model_kwargs = self._init_model_kwargs()
2872
            input_ids = None
2873
        else:
2874
2875
2876
2877
            # 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.
2878
            input_ids = self.input_ids.gpu[:num_input_tokens]
2879
            inputs_embeds = None
2880
            model_kwargs = self._init_model_kwargs()
2881

guanyu1's avatar
guanyu1 committed
2882
        positions = self._get_positions(num_input_tokens)
2883

2884
        if is_first_rank:
2885
2886
            intermediate_tensors = None
        else:
2887
            assert intermediate_tensors is not None
2888
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2889
2890
                num_input_tokens, intermediate_tensors, True
            )
2891

2892
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
2893
2894
2895
2896
2897
2898
2899
            # 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})
2900

2901
2902
2903
2904
2905
2906
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
2907
            ec_connector_output,
2908
        )
2909

2910
    def _sample(
2911
        self,
2912
2913
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2914
    ) -> SamplerOutput:
2915
        # Sample the next token and get logprobs if needed.
2916
        sampling_metadata = self.input_batch.sampling_metadata
2917
2918
2919
        # 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()
2920
        if spec_decode_metadata is None:
2921
            return self.sampler(
2922
2923
2924
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2925

2926
2927
2928
2929
2930
2931
        # 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)

2932
        sampler_output = self.rejection_sampler(
2933
            spec_decode_metadata,
王敏's avatar
王敏 committed
2934
2935
            None if self.draft_probs is None else \
                self.draft_probs.get_probs(spec_decode_metadata.spec_decode_ids),  # draft_probs
2936
            logits,
2937
2938
            sampling_metadata,
        )
2939
2940
2941
        return sampler_output

    def _bookkeeping_sync(
2942
2943
2944
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2945
        logits: torch.Tensor | None,
2946
2947
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2948
        spec_decode_metadata: SpecDecodeMetadata | None,
2949
    ) -> tuple[
2950
        dict[str, int],
2951
        LogprobsLists | None,
2952
        list[list[int]],
2953
        dict[str, LogprobsTensors | None],
2954
2955
2956
        list[str],
        dict[str, int],
        list[int],
2957
    ]:
2958
2959
2960
2961
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2962
2963
2964
2965
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
2966
2967
2968
2969
        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)
2970

2971
2972
2973
        # 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()
2974
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2975

2976
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2977
        sampled_token_ids = sampler_output.sampled_token_ids
2978
        logprobs_tensors = sampler_output.logprobs_tensors
2979
        invalid_req_indices = []
2980
        logprobs_lists = None
2981
2982
2983
2984
2985
2986
        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)
2987
2988
2989
                # 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()
2990
2991
2992

                if logprobs_tensors is not None:
                    logprobs_lists = logprobs_tensors.tolists()
2993
2994
            else:
                # Includes spec decode tokens.
2995
                valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
2996
2997
                    sampled_token_ids,
                    self.input_batch.vocab_size,
2998
                    discard_sampled_tokens_req_indices,
2999
                    logprobs_tensors=logprobs_tensors,
3000
                )
3001
        else:
3002
            valid_sampled_token_ids = []
3003
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
3004
3005
3006
3007
3008
            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.
3009
3010
3011
3012
            # 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
3013
3014
3015
3016
3017
            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
            }
3018

3019
3020
3021
3022
3023
        # 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.
3024
        req_ids = self.input_batch.req_ids
3025
3026
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
3027
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
3028
3029
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
3030

3031
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
3032

3033
3034
3035
3036
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
3037
            end_idx = start_idx + num_sampled_ids
3038
3039
3040
            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: "
3041
                f"{self.max_model_len}"
3042
            )
3043

3044
            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
3045
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
3046
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
3047

3048
            req_id = req_ids[req_idx]
3049
3050
3051
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

3052
3053
3054
3055
3056
3057
        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
        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,
        )

3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
    @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()

3083
3084
    def _model_forward(
        self,
3085
3086
3087
3088
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
3089
3090
3091
3092
3093
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
3094
        Motivation: We can inspect only this method versus
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
        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,
        )

3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
    @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
        )

3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
    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,
3149
        num_encoder_reqs: int = 0,
3150
    ) -> tuple[
3151
3152
        CUDAGraphMode,
        BatchDescriptor,
3153
        bool,
3154
3155
        torch.Tensor | None,
        CUDAGraphStat | None,
3156
    ]:
3157
3158
3159
3160
3161
3162
        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,
3163
        )
3164
3165
3166
3167
3168
        # 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
        )
3169
3170
3171
3172
3173
3174
3175

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

3176
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
3177
        dispatch_cudagraph = (
3178
            lambda num_tokens, disable_full: self.cudagraph_dispatcher.dispatch(
3179
3180
3181
                num_tokens=num_tokens,
                has_lora=has_lora,
                uniform_decode=uniform_decode,
3182
                disable_full=disable_full,
3183
3184
3185
3186
3187
            )
            if not force_eager
            else (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
        )

3188
        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
3189
            num_tokens_padded, use_cascade_attn or has_encoder_output
3190
        )
3191
        num_tokens_padded = batch_descriptor.num_tokens
3192
3193
3194
3195
3196
3197
3198
3199
3200
        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"
            )
3201
3202
3203

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
3204
        should_ubatch, num_tokens_across_dp = False, None
3205
3206
3207
3208
3209
3210
3211
3212
3213
        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
            )

3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
            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,
                )
3225
3226
            )

3227
            # Extract DP-synced values
3228
3229
3230
            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())
3231
3232
3233
3234
3235
                # 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,
                )
3236
3237
3238
3239
                # 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

3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
        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,
3252
            should_ubatch,
3253
3254
3255
            num_tokens_across_dp,
            cudagraph_stats,
        )
3256

3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
    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
3292

3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
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
    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

3367
3368
3369
3370
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
3371
        intermediate_tensors: IntermediateTensors | None = None,
3372
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
3373
3374
3375
3376
3377
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
3378

3379
3380
3381
3382
3383
3384
        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.")
3385

3386
3387
3388
3389
        if scheduler_output.preempted_req_ids and has_kv_transfer_group():
            get_kv_transfer_group().handle_preemptions(
                scheduler_output.preempted_req_ids
            )
3390

3391
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3392
3393
3394
3395
3396
3397
        with (
            record_function_or_nullcontext("gpu_model_runner: preprocess"),
            self.synchronize_input_prep(),
        ):
            # Update persistent batch states.
            self._update_states(scheduler_output)
3398

3399
3400
            if has_ec_transfer() and get_ec_transfer().is_producer:
                with self.maybe_get_ec_connector_output(
3401
                    scheduler_output,
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
                    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"
3430
                )
3431

3432
3433
3434
3435
3436
3437
            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
3438

3439
3440
3441
3442
            logits_indices, spec_decode_metadata = self._prepare_inputs(
                scheduler_output,
                num_scheduled_tokens_np,
            )
3443

3444
3445
3446
3447
3448
            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(
3449
                    num_scheduled_tokens_np,
3450
3451
                    self.input_batch.num_computed_tokens_cpu[:num_reqs],
                    scheduler_output.num_common_prefix_blocks,
3452
                )
3453

3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
            (
                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),
            )
3468

3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
            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,
            )

3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
            # 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)
            )
3507
3508
            pad_attn = cudagraph_mode == CUDAGraphMode.FULL

3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
            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(),
                )

3521
3522
3523
            use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
            ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices

3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
            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,
            )

3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
            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,
3547
                    slot_mappings=slot_mappings_by_group,
3548
                )
3549
            )
3550
3551
3552
3553
3554
3555
3556

            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
3557
3558
3559
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
3560
            )
3561

3562
        # Set cudagraph mode to none if calc_kv_scales is true.
3563
3564
3565
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
3566
            cudagraph_mode = CUDAGraphMode.NONE
3567
3568
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
3569

3570
3571
3572
3573
3574
3575
3576
        # 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
        )

3577
3578
        # Run the model.
        # Use persistent buffers for CUDA graphs.
3579
3580
        with (
            set_forward_context(
3581
3582
                attn_metadata,
                self.vllm_config,
3583
                num_tokens=num_tokens_padded,
3584
                num_tokens_across_dp=num_tokens_across_dp,
3585
3586
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
3587
                ubatch_slices=ubatch_slices_padded,
3588
                slot_mapping=slot_mappings,
3589
                skip_compiled=has_encoder_input,
3590
            ),
3591
            record_function_or_nullcontext("gpu_model_runner: forward"),
3592
3593
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
3594
            model_output = self._model_forward(
3595
3596
3597
3598
3599
3600
3601
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

3602
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
3603
            if self.use_aux_hidden_state_outputs:
3604
                # True when EAGLE 3 is used.
3605
3606
                hidden_states, aux_hidden_states = model_output
            else:
3607
                # Common case.
3608
3609
3610
                hidden_states = model_output
                aux_hidden_states = None

3611
3612
3613
3614
3615
            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)
3616
                    hidden_states.kv_connector_output = kv_connector_output
3617
                    self.kv_connector_output = kv_connector_output
3618
                    return hidden_states
3619

3620
                if self.is_pooling_model:
3621
                    # Return the pooling output.
3622
3623
3624
3625
3626
                    return self._pool(
                        hidden_states,
                        num_scheduled_tokens,
                        num_scheduled_tokens_np,
                        kv_connector_output,
3627
                    )
3628
3629

                sample_hidden_states = hidden_states[logits_indices]
3630
                logits = self.model.compute_logits(sample_hidden_states)
3631
3632
3633
3634
            else:
                # Rare case.
                assert not self.is_pooling_model

3635
                sample_hidden_states = hidden_states[logits_indices]
3636
                if not get_pp_group().is_last_rank:
3637
                    all_gather_tensors = {
3638
                        "residual": not is_residual_scattered_for_sp(
3639
                            self.vllm_config, num_tokens_padded
3640
                        )
3641
                    }
3642
                    get_pp_group().send_tensor_dict(
3643
3644
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
3645
3646
                        all_gather_tensors=all_gather_tensors,
                    )
3647
3648
                    logits = None
                else:
3649
                    logits = self.model.compute_logits(sample_hidden_states)
3650

3651
                model_output_broadcast_data: dict[str, Any] = {}
3652
3653
3654
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

3655
                broadcasted = get_pp_group().broadcast_tensor_dict(
3656
3657
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
3658
3659
                assert broadcasted is not None
                logits = broadcasted["logits"]
3660

3661
3662
3663
3664
3665
3666
3667
3668
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3669
            ec_connector_output,
3670
            cudagraph_stats,
3671
            slot_mappings,
3672
        )
3673
        self.kv_connector_output = kv_connector_output
3674
3675
3676
3677
3678
3679
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
3680
3681
3682
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

3683
3684
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
3685
            if not kv_connector_output:
3686
                return None  # type: ignore[return-value]
3687
3688
3689
3690
3691
3692
3693
3694
3695

            # 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
3696

3697
3698
3699
3700
3701
3702
3703
3704
3705
        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3706
            ec_connector_output,
3707
            cudagraph_stats,
3708
            slot_mappings,
3709
3710
3711
3712
3713
3714
3715
3716
3717
        ) = 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
            )
3718

3719
        with record_function_or_nullcontext("gpu_model_runner: sample"):
3720
3721
            sampler_output = self._sample(logits, spec_decode_metadata)

3722
3723
3724
3725
        self._update_states_after_model_execute(
            sampler_output.sampled_token_ids, scheduler_output
        )

3726
3727
        self._draft_token_ids = None
        self._draft_token_req_ids = None
3728
3729
        self.input_batch.prev_sampled_token_ids = None

3730
3731
        def propose_draft_token_ids(sampled_token_ids):
            assert spec_decode_common_attn_metadata is not None
3732
            with record_function_or_nullcontext("gpu_model_runner: draft"):
3733
3734
3735
3736
3737
3738
3739
3740
3741
                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,
3742
                    slot_mappings,
3743
                )
3744
                self._copy_draft_token_ids_to_cpu(scheduler_output)
3745

3746
        spec_config = self.speculative_config
3747
3748
3749
3750
3751
        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
3752
            )
3753
3754
3755
3756
3757
            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
3758
                # as inputs, and does not need to wait for bookkeeping to finish.
3759
                assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
                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,
                        )
3773
                    )
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
                    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
3785

3786
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
3787
3788
3789
3790
3791
3792
3793
3794
            (
                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,
3795
3796
3797
3798
3799
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
3800
                scheduler_output.total_num_scheduled_tokens,
3801
                spec_decode_metadata,
3802
            )
3803

3804
        if propose_drafts_after_bookkeeping:
3805
3806
3807
            # 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)
3808

3809
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
3810
            self.eplb_step()
3811

3812
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
3813
3814
3815
3816
3817
3818
3819
            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.")

3820
3821
3822
3823
3824
3825
3826
            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,
3827
3828
3829
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
3830
                num_nans_in_logits=num_nans_in_logits,
3831
                cudagraph_stats=cudagraph_stats,
3832
            )
3833

3834
3835
        if not self.use_async_scheduling:
            return output
3836

3837
3838
3839
3840
3841
3842
3843
3844
3845
        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,
3846
                vocab_size=self.input_batch.vocab_size,
3847
3848
3849
3850
3851
            )
        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
3852
            # any requests with sampling params that require output ids.
3853
3854
3855
3856
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
3857

3858
        return async_output
3859

3860
    def take_draft_token_ids(self) -> DraftTokenIds | None:
3861
        if not self.num_spec_tokens or not self._draft_token_req_ids:
3862
            return None
3863
        draft_token_ids, req_ids = self._get_draft_token_ids_cpu()
3864
3865
        return DraftTokenIds(req_ids, draft_token_ids)

3866
3867
3868
    def _copy_draft_token_ids_to_cpu(
        self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
    ) -> None:
3869
3870
3871
3872
3873
3874
        # 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
        ):
3875
3876
3877
            return
        # We must also set the corresponding request ids.
        self._draft_token_req_ids = self.input_batch.req_ids.copy()
3878

3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
        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()

3899
    def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
3900
        if isinstance(self._draft_token_ids, list):
3901
3902
3903
3904
            return self._draft_token_ids, self.input_batch.req_ids
        req_ids = self._draft_token_req_ids
        if req_ids is None:
            return [], []
3905
3906
3907
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_cpu is not None
        self.draft_token_ids_event.synchronize()
3908
        return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
3909

3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
    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
3923
            assert counts_cpu is not None
3924
3925
3926
3927
3928
3929
3930
3931
            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
3932
3933
        sampled_count_event = self.valid_sampled_token_count_event
        if sampled_count_event is None or prev_sampled_token_ids is None:
3934
3935
3936
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
3937
3938
        assert counts_cpu is not None
        sampled_count_event.synchronize()
3939
3940
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

3941
3942
3943
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
3944
        sampled_token_ids: torch.Tensor | list[list[int]],
3945
3946
3947
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
3948
3949
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3950
        common_attn_metadata: CommonAttentionMetadata,
3951
        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
3952
    ) -> list[list[int]] | torch.Tensor:
3953
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3954
3955
3956
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
3957
            assert isinstance(sampled_token_ids, list)
3958
            assert isinstance(self.drafter, NgramProposer)
3959
            draft_token_ids = self.drafter.propose(
3960
                sampled_token_ids,
3961
3962
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
3963
                slot_mappings=slot_mappings,
3964
            )
3965
        elif spec_config.method == "suffix":
3966
3967
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
3968
3969
3970
            draft_token_ids = self.drafter.propose(
                self.input_batch, sampled_token_ids, slot_mappings=slot_mappings
            )
3971
        elif spec_config.method == "medusa":
3972
            assert isinstance(sampled_token_ids, list)
3973
            assert isinstance(self.drafter, MedusaProposer)
3974

3975
3976
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
3977
3978
3979
3980
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
3981
3982
3983
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
3984
                for num_draft, tokens in zip(
3985
3986
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
3987
3988
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
3989
                indices = torch.tensor(indices, device=self.device)
3990
3991
                hidden_states = sample_hidden_states[indices]

3992
            draft_token_ids = self.drafter.propose(
3993
3994
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
3995
                slot_mappings=slot_mappings,
3996
            )
3997
3998
        elif spec_config.use_eagle() or spec_config.uses_draft_model():
            assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
3999

4000
            if spec_config.disable_padded_drafter_batch:
4001
4002
4003
                # 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.
4004
4005
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
4006
                    "padded-batch is disabled."
4007
                )
4008
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
4009
4010
4011
4012
4013
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
4014
4015
4016
4017
4018
            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.
4019
4020
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
4021
                    "padded-batch is enabled."
4022
4023
                )
                next_token_ids, valid_sampled_tokens_count = (
4024
4025
4026
4027
4028
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
4029
                        self.discard_request_mask.gpu,
4030
                    )
4031
                )
4032
4033
4034
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
4035

4036
            num_rejected_tokens_gpu = None
4037
            if spec_decode_metadata is None:
4038
                token_indices_to_sample = None
4039
                # input_ids can be None for multimodal models.
4040
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
4041
                target_positions = self._get_positions(num_scheduled_tokens)
4042
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
4043
                    assert aux_hidden_states is not None
4044
                    target_hidden_states = torch.cat(
4045
4046
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
4047
4048
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
4049
            else:
4050
                if spec_config.disable_padded_drafter_batch:
4051
                    token_indices_to_sample = None
4052
4053
4054
4055
4056
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
4057
4058
4059
4060
4061
4062
4063
4064
4065
                    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]
4066
                else:
4067
4068
4069
4070
4071
4072
4073
4074
                    (
                        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,
4075
                    )
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
                    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]
4087

4088
            if self.supports_mm_inputs:
4089
4090
4091
4092
4093
4094
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
4095

王敏's avatar
王敏 committed
4096
            draft_result = self.drafter.propose(
4097
4098
4099
4100
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
4101
                last_token_indices=token_indices_to_sample,
4102
                sampling_metadata=sampling_metadata,
4103
                common_attn_metadata=common_attn_metadata,
4104
                mm_embed_inputs=mm_embed_inputs,
4105
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
4106
                slot_mappings=slot_mappings,
4107
            )
4108

王敏's avatar
王敏 committed
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
            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)

4122
        return draft_token_ids
4123

4124
4125
4126
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
4127
4128
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
4129
                f"Allowed configs: {allowed_config_names}"
4130
            )
4131
4132
4133
4134
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

4135
4136
4137
4138
4139
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
4140
4141
4142
4143
4144
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
4145
4146
4147
4148
4149
        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)
        )
4150

4151
4152
4153
4154
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0

4155
4156
4157
4158
4159
4160
        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
4161
                )
4162
4163
4164
                if self.lora_config:
                    self.model = self.load_lora_model(
                        self.model, self.vllm_config, self.device
4165
                    )
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
                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,
                        )
4181

4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
                        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
4204

4205
4206
4207
4208
4209
4210
                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"
                        )
4211

4212
4213
4214
4215
4216
4217
4218
4219
4220
4221
                    # 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()
4222

4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
                    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
4237
        logger.info_once(
4238
4239
            "Model loading took %s GiB memory and %.6f seconds",
            format_gib(self.model_memory_usage),
4240
            time_after_load - time_before_load,
4241
            scope="local",
4242
        )
4243
        prepare_communication_buffer_for_model(self.model)
4244
4245
4246
4247
        if (drafter := getattr(self, "drafter", None)) and (
            drafter_model := getattr(drafter, "model", None)
        ):
            prepare_communication_buffer_for_model(drafter_model)
4248
        mm_config = self.model_config.multimodal_config
4249
        self.is_multimodal_pruning_enabled = (
4250
            supports_multimodal_pruning(self.get_model())
4251
4252
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
4253
        )
4254

4255
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
            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(
4267
                self.model,
4268
                self.model_config,
4269
4270
4271
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
4272
            )
4273
4274
            if self.eplb_state.is_async:
                self.eplb_state.start_async_loop(rank_mapping=rank_mapping)
4275

4276
        if (
4277
4278
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
4279
        ):
4280
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
4281
            compilation_counter.stock_torch_compile_count += 1
4282
            self.model.compile(fullgraph=True, backend=backend)
4283
            return
4284
        # for other compilation modes, cudagraph behavior is controlled by
4285
4286
4287
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
4288
4289
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
4290
4291
4292
4293
        if (
            cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.use_ubatching
        ):
4294
4295
4296
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
4297
        elif self.parallel_config.use_ubatching:
4298
            if cudagraph_mode.has_full_cudagraphs():
4299
4300
4301
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
4302
            else:
4303
4304
4305
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
4306

4307
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
4308
4309
4310
4311
4312
4313
4314
4315
4316
4317
4318
4319
4320
4321
4322
4323
4324
4325
4326
4327
4328
4329
        """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
4330

4331
    def reload_weights(self) -> None:
4332
        assert getattr(self, "model", None) is not None, (
4333
            "Cannot reload weights before model is loaded."
4334
        )
4335
4336
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
4337
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
4338

4339
4340
4341
4342
4343
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
4344
            self.get_model(),
4345
            tensorizer_config=tensorizer_config,
4346
            model_config=self.model_config,
4347
4348
        )

4349
4350
4351
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
4352
        num_scheduled_tokens: dict[str, int],
4353
    ) -> dict[str, LogprobsTensors | None]:
4354
        num_prompt_logprobs_dict = self.num_prompt_logprobs
4355
4356
4357
        if not num_prompt_logprobs_dict:
            return {}

4358
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
4359
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
4360
4361
4362
4363
4364

        # 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():
4365
4366
4367
4368
            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
4369
4370
4371

            # Get metadata for this request.
            request = self.requests[req_id]
4372
4373
4374
4375
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

4376
4377
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
4378
4379
                self.device, non_blocking=True
            )
4380

4381
4382
4383
4384
4385
4386
            # 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(
4387
4388
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
4389
4390
                in_progress_dict[req_id] = logprobs_tensors

4391
            # Determine number of logits to retrieve.
4392
4393
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
4394
            num_remaining_tokens = num_prompt_tokens - start_tok
4395
            if num_tokens <= num_remaining_tokens:
4396
                # This is a chunk, more tokens remain.
4397
4398
4399
                # 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.
4400
4401
4402
4403
4404
                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)
4405
4406
4407
4408
4409
4410
4411
                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
4412
4413
4414
4415
4416

            # 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]
4417
            offset = self.query_start_loc.np[req_idx].item()
4418
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
4419
            logits = self.model.compute_logits(prompt_hidden_states)
4420
4421
4422
4423

            # 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.
4424
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
4425
4426

            # Compute prompt logprobs.
4427
4428
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
4429
4430
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
4431
4432

            # Transfer GPU->CPU async.
4433
4434
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
4435
4436
4437
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
4438
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
4439
4440
                ranks, non_blocking=True
            )
4441
4442
4443
4444
4445

        # 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]
4446
            del in_progress_dict[req_id]
4447
4448

        # Must synchronize the non-blocking GPU->CPU transfers.
4449
        if prompt_logprobs_dict:
4450
            self._sync_device()
4451
4452
4453

        return prompt_logprobs_dict

4454
4455
    def _get_nans_in_logits(
        self,
4456
        logits: torch.Tensor | None,
4457
4458
4459
4460
4461
4462
4463
4464
4465
4466
4467
    ) -> 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])
4468
4469
4470
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
4471
4472
4473
4474
            return num_nans_in_logits
        except IndexError:
            return {}

4475
    @contextmanager
4476
4477
4478
    def maybe_randomize_inputs(
        self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None
    ):
4479
4480
4481
4482
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
4483
         - during DP rank dummy run
4484
        """
4485

4486
4487
4488
4489
        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
4490
        elif input_ids is not None:
4491
4492
4493
4494

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
4495
                    self.input_ids.gpu,
4496
4497
                    low=0,
                    high=self.model_config.get_vocab_size(),
4498
                )
4499

4500
            logger.debug_once("Randomizing dummy input_ids for DP Rank")
4501
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
4502
4503
            yield
            input_ids.fill_(0)
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
        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)
4519

4520
4521
4522
4523
4524
4525
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
4526
4527
        assert self.mm_budget is not None

4528
4529
4530
        # 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,
4531
            mm_counts={modality: 1},
4532
            cache=self.mm_budget.cache,
4533
        )
4534
4535
4536
4537
4538
        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"
4539

4540
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
4541

4542
4543
4544
4545
4546
4547
4548
4549
        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,
            )
        )
4550

4551
4552
4553
4554
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
4555
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
4556
4557
        force_attention: bool = False,
        uniform_decode: bool = False,
4558
        allow_microbatching: bool = True,
4559
4560
        skip_eplb: bool = False,
        is_profile: bool = False,
4561
        create_mixed_batch: bool = False,
4562
        remove_lora: bool = True,
4563
        activate_lora: bool = False,
Rémi Delacourt's avatar
Rémi Delacourt committed
4564
        is_graph_capturing: bool = False,
4565
    ) -> tuple[torch.Tensor, torch.Tensor]:
4566
4567
4568
4569
4570
4571
4572
        """
        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.
4573
                - if not set will determine the cudagraph mode based on using
4574
                    the self.cudagraph_dispatcher.
4575
4576
4577
4578
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
4579
            force_attention: If True, always create attention metadata. Used to
4580
4581
4582
4583
                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.
4584
4585
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
4586
            remove_lora: If False, dummy LoRAs are not destroyed after the run
4587
            activate_lora: If False, dummy_run is performed without LoRAs.
4588
        """
4589
4590
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
4591
4592
4593
4594
            # 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([])

4595
4596
4597
4598
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
4599

4600
        # If cudagraph_mode.decode_mode() == FULL and
4601
        # cudagraph_mode.separate_routine(). This means that we are using
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
4612
        # 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.
4613
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
4614

4615
4616
4617
4618
4619
        # 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
4620
4621
4622
4623
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
4624
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
4625
4626
4627
4628
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
4629
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
4630
4631
4632
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
4633
            assert not create_mixed_batch
4634
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
4635
4636
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
4637
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
4638
4639
4640
4641
4642
4643
        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

4644
4645
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
4646
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
4647
4648
        num_tokens_unpadded = int(num_scheduled_tokens.sum())

4649
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
4650

4651
        _cudagraph_mode, batch_desc, should_ubatch, num_tokens_across_dp, _ = (
4652
4653
4654
4655
4656
4657
4658
4659
4660
4661
4662
4663
4664
4665
4666
4667
4668
4669
            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,
4670
            )
4671
        )
4672
4673
4674

        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
4675
        else:
4676
4677
4678
4679
            assert cudagraph_runtime_mode == _cudagraph_mode, (
                f"Cudagraph runtime mode mismatch in dummy_run. "
                f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
            )
4680

4681
4682
4683
4684
        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
        )
4685
        ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
            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,
4696
        )
4697

4698
        attn_metadata: PerLayerAttnMetadata | None = None
4699

4700
4701
4702
4703
4704
4705
4706
        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,
        )

4707
4708
        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
4709
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
4710
4711
4712
4713
4714
4715
            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:
4716
4717
4718
4719
4720
                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
4721
            self.seq_lens.np[:num_reqs] = seq_lens
4722
4723
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
4724

4725
4726
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
4727
4728
            self.query_start_loc.copy_to_gpu()

4729
            pad_attn = cudagraph_runtime_mode == CUDAGraphMode.FULL
4730
            attn_metadata, _ = self._build_attention_metadata(
4731
4732
4733
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
4734
                ubatch_slices=ubatch_slices_padded if pad_attn else ubatch_slices,
4735
                for_cudagraph_capture=is_graph_capturing,
4736
                slot_mappings=slot_mappings_by_group,
4737
            )
4738

4739
        with self.maybe_dummy_run_with_lora(
4740
4741
4742
4743
4744
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
4745
        ):
4746
            # Make sure padding doesn't exceed max_num_tokens
4747
            assert num_tokens_padded <= self.max_num_tokens
4748
            model_kwargs = self._init_model_kwargs()
4749
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
Patrick von Platen's avatar
Patrick von Platen committed
4750
4751
                input_ids, inputs_embeds = self._prepare_mm_inputs(num_tokens_padded)

4752
                model_kwargs = {
4753
                    **model_kwargs,
4754
4755
                    **self._dummy_mm_kwargs(num_reqs),
                }
4756
4757
            elif self.enable_prompt_embeds:
                input_ids = None
4758
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
4759
                model_kwargs = self._init_model_kwargs()
4760
            else:
王敏's avatar
王敏 committed
4761
4762
4763
                self.input_ids.gpu[:num_tokens_padded] = torch.randint(0, self.model_config.get_vocab_size(),
                                                                        (num_tokens_padded,),
                                                                        dtype=torch.int32)
4764
                input_ids = self.input_ids.gpu[:num_tokens_padded]
4765
                inputs_embeds = None
4766

guanyu1's avatar
guanyu1 committed
4767
4768
4769
4770
4771
4772
4773
            # 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)
4774
4775
4776
4777
4778
4779
4780
4781
            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,
4782
4783
4784
                            device=self.device,
                        )
                    )
4785
4786

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
4787
                    num_tokens_padded, None, False
4788
                )
4789

4790
            if ubatch_slices_padded is not None:
4791
4792
4793
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
4794
                num_tokens_padded = ubatch_slices_padded[0].num_tokens
4795
                if num_tokens_across_dp is not None:
4796
                    num_tokens_across_dp[:] = num_tokens_padded
4797

4798
            with (
4799
                self.maybe_randomize_inputs(input_ids, inputs_embeds),
4800
                set_forward_context(
4801
4802
                    attn_metadata,
                    self.vllm_config,
4803
                    num_tokens=num_tokens_padded,
4804
4805
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
4806
                    batch_descriptor=batch_desc,
4807
                    ubatch_slices=ubatch_slices_padded,
4808
                    slot_mapping=slot_mappings,
4809
4810
                ),
            ):
4811
                outputs = self.model(
4812
4813
4814
4815
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
4816
                    **model_kwargs,
4817
                )
4818

4819
4820
4821
4822
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
4823

4824
4825
4826
4827
4828
4829
            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
4830
4831
4832
                # 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.
4833
                use_cudagraphs = (
4834
4835
4836
4837
4838
4839
4840
4841
4842
                    (
                        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
4843
4844
4845
4846
4847
4848
4849
4850
4851
4852
4853

                # 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
4854
                    is_graph_capturing=is_graph_capturing,
4855
                    slot_mappings=slot_mappings,
4856
                )
4857

4858
4859
4860
4861
4862
4863
4864
4865
4866
4867
4868
        # 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()

4869
4870
4871
4872
4873
4874
4875
4876
4877
4878
        # 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)

4879
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
4880
4881
4882
4883
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
4884
4885
4886
4887
4888
4889

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
4890
4891
4892
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
4893

4894
4895
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
4896
4897
4898
            # MM Encoder only model no need to run sampler.
            return torch.tensor([])

4899
        hidden_states = torch.rand_like(hidden_states)
4900

4901
        logits = self.model.compute_logits(hidden_states)
4902
4903
        num_reqs = logits.size(0)

4904
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
4905
4906
4907
4908
4909
4910
4911
4912
4913
4914
4915
4916
4917
4918
4919

        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)],
4920
            spec_token_ids=[[] for _ in range(num_reqs)],
4921
4922
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
4923
            logitsprocs=LogitsProcessors(),
4924
        )
4925
        try:
4926
4927
4928
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
4929
        except RuntimeError as e:
4930
            if "out of memory" in str(e):
4931
4932
4933
4934
                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 "
4935
4936
                    "initializing the engine."
                ) from e
4937
4938
            else:
                raise e
4939
        if self.speculative_config:
4940
4941
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
4942
4943
                draft_token_ids, self.device
            )
4944
4945

            num_tokens = sum(len(ids) for ids in draft_token_ids)
4946
4947
4948
            # draft_probs = torch.randn(
            #     num_tokens, logits.shape[-1], device=self.device,
            #     dtype=logits.dtype)
王敏's avatar
王敏 committed
4949
4950
4951
4952
4953
4954
4955
            
            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)
4956
                dummy_metadata.all_greedy = True
王敏's avatar
王敏 committed
4957

4958
4959
4960
4961
4962
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
4963
            )
4964
4965
4966
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
4967
                logits,
4968
4969
                dummy_metadata,
            )
4970
        return sampler_output
4971

4972
    def _dummy_pooler_run_task(
4973
4974
        self,
        hidden_states: torch.Tensor,
4975
4976
        task: PoolingTask,
    ) -> PoolerOutput:
4977
4978
4979
4980
        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
4981
4982
4983
4984
        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
4985
4986
4987

        req_num_tokens = num_tokens // num_reqs

4988
        dummy_prompt_lens = torch.from_numpy(num_scheduled_tokens_np)
4989
4990
4991
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
4992

4993
        model = cast(VllmModelForPooling, self.get_model())
4994
        dummy_pooling_params = PoolingParams(task=task)
4995
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
4996
        to_update = model.pooler.get_pooling_updates(task)
4997
4998
        to_update.apply(dummy_pooling_params)

4999
        dummy_metadata = PoolingMetadata(
5000
5001
5002
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
5003
            pooling_states=[PoolingStates() for i in range(num_reqs)],
5004
        )
5005

5006
        dummy_metadata.build_pooling_cursor(
5007
            num_scheduled_tokens_np,
5008
5009
            seq_lens_cpu=dummy_prompt_lens,
            device=hidden_states.device,
5010
        )
5011

5012
        try:
5013
5014
5015
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
5016
        except RuntimeError as e:
5017
            if "out of memory" in str(e):
5018
                raise RuntimeError(
5019
5020
5021
                    "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 "
5022
5023
                    "initializing the engine."
                ) from e
5024
5025
            else:
                raise e
5026
5027
5028
5029
5030
5031

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
5032
5033
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5034
5035
5036
            # MM Encoder only model not need to run pooler.
            return torch.tensor([])

5037
        # Find the task that has the largest output for subsequent steps
5038
5039
5040
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
5041
5042
5043
5044
5045
5046
            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."
            )
5047

5048
        output_size = dict[PoolingTask, float]()
5049
        for task in supported_pooling_tasks:
5050
5051
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
5052
            output_size[task] = sum(o.nbytes for o in output if o is not None)
5053
5054
5055
5056
            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)
5057

5058
    def profile_run(self) -> None:
5059
        # Profile with multimodal encoder & encoder cache.
5060
        if self.supports_mm_inputs:
5061
5062
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
5063
                logger.info(
5064
                    "Skipping memory profiling for multimodal encoder and "
5065
5066
                    "encoder cache."
                )
5067
5068
5069
5070
5071
5072
5073
5074
            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.
5075
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
5076
5077
5078
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
5079
5080
5081
5082
5083
5084
5085
5086
5087

                    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,
                    )
5088

5089
5090
5091
5092
5093
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
5094

5095
                    # Run multimodal encoder.
5096
                    dummy_encoder_outputs = self.model.embed_multimodal(
5097
5098
                        **batched_dummy_mm_inputs
                    )
5099

5100
5101
5102
5103
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
5104
5105
                    for i, output in enumerate(dummy_encoder_outputs):
                        self.encoder_cache[f"tmp_{i}"] = output
5106

5107
        # Add `is_profile` here to pre-allocate communication buffers
5108
5109
5110
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
5111
        if get_pp_group().is_last_rank:
5112
5113
5114
5115
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
5116
        else:
5117
            output = None
5118
        self._sync_device()
5119
        del hidden_states, output
5120
        self.encoder_cache.clear()
5121
        gc.collect()
5122

5123
    def capture_model(self) -> int:
5124
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
5125
            logger.warning(
5126
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
5127
5128
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
5129
            return 0
5130

5131
5132
        compilation_counter.num_gpu_runner_capture_triggers += 1

5133
5134
        start_time = time.perf_counter()

5135
5136
5137
5138
5139
5140
5141
5142
5143
5144
5145
5146
5147
5148
        @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()
5149
                    gc.collect()
5150

5151
5152
5153
        # 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.
5154
        set_cudagraph_capturing_enabled(True)
5155
        with freeze_gc(), graph_capture(device=self.device):
5156
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
5157

5158
5159
5160
5161
            for (
                runtime_mode,
                batch_descs,
            ) in self.cudagraph_dispatcher.get_capture_descs():
5162
                self._capture_cudagraphs(
5163
5164
                    batch_descriptors=batch_descs,
                    cudagraph_runtime_mode=runtime_mode,
5165
                )
5166

5167
5168
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]
5169
5170
5171
5172

        # 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
5173
        # we may do lazy capturing in future that still allows capturing
5174
5175
        # after here.
        set_cudagraph_capturing_enabled(False)
5176

5177
5178
5179
5180
        # Lock workspace to prevent resizing during execution.
        # Max workspace sizes should have been captured during warmup/profiling.
        lock_workspace()

5181
5182
5183
5184
        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.
5185
        logger.info_once(
5186
5187
5188
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
5189
            scope="local",
5190
        )
5191
        return cuda_graph_size
5192

5193
5194
    def _capture_cudagraphs(
        self,
5195
        batch_descriptors: list[BatchDescriptor],
5196
5197
5198
5199
5200
5201
        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}"
5202

5203
5204
5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
5216
        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,
        )

5217
5218
        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
5219
5220
            batch_descriptors = tqdm(
                batch_descriptors,
5221
5222
5223
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
5224
5225
5226
                    cudagraph_runtime_mode.name,
                ),
            )
5227

5228
        # We skip EPLB here since we don't want to record dummy metrics
5229
5230
5231
5232
        for batch_desc in batch_descriptors:
            num_tokens = batch_desc.num_tokens
            activate_lora = batch_desc.has_lora

5233
            # We currently only capture ubatched graphs when its a FULL
5234
5235
5236
            # 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
5237
            allow_microbatching = (
5238
                self.parallel_config.use_ubatching
5239
5240
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
5241
5242
5243
5244
5245
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
5246
            )
5247

5248
5249
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
5250
                # But be careful, warm up with `NONE` is orthogonal to
5251
5252
5253
                # 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.
5254
                dummy_run(
5255
5256
5257
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    allow_microbatching=allow_microbatching,
5258
                    activate_lora=activate_lora,
5259
                )
5260
5261
5262

            # Capture run
            dummy_run(
5263
5264
5265
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                allow_microbatching=allow_microbatching,
5266
                activate_lora=activate_lora,
Rémi Delacourt's avatar
Rémi Delacourt committed
5267
                is_graph_capturing=True,
5268
            )
5269
        self.maybe_remove_all_loras(self.lora_config)
5270

5271
5272
5273
5274
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
5275
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
5276

5277
5278
5279
5280
5281
5282
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
5283
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
5284
            layer_type = cast(type[Any], AttentionLayerBase)
5285
            layers = get_layers_from_vllm_config(
5286
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
5287
            )
5288
5289
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
5290
            # Dedupe based on full class name; this is a bit safer than
5291
5292
5293
5294
            # 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.
5295
            for layer_name in kv_cache_group_spec.layer_names:
5296
                attn_backend = layers[layer_name].get_attn_backend()
5297
5298
5299
5300

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
5301
                        attn_backend,  # type: ignore[arg-type]
5302
5303
                    )

5304
5305
5306
                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):
5307
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
5308
                key = (full_cls_name, layer_kv_cache_spec)
5309
5310
5311
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
5312
                attn_backend_layers[key].append(layer_name)
5313
5314
5315
5316
            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()),
            )
5317
5318

        def create_attn_groups(
5319
            attn_backends_map: dict[AttentionGroupKey, list[str]],
5320
            kv_cache_group_id: int,
5321
5322
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
5323
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
5324
                attn_group = AttentionGroup(
5325
                    attn_backend,
5326
                    layer_names,
5327
                    kv_cache_spec,
5328
                    kv_cache_group_id,
5329
                )
5330

5331
5332
5333
                attn_groups.append(attn_group)
            return attn_groups

5334
        attention_backend_maps = []
5335
        attention_backend_list = []
5336
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
5337
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
5338
            attention_backend_maps.append(attn_backends[0])
5339
            attention_backend_list.append(attn_backends[1])
5340
5341

        # Resolve cudagraph_mode before actually initialize metadata_builders
5342
5343
5344
        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
5345

5346
5347
5348
        # Check if attention backend supports PCP&DCP and related features.
        check_attention_cp_compatibility(self.vllm_config)

5349
5350
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
5351

5352
5353
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363
5364
5365
5366
    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
5367
5368
                    if not self.parallel_config.use_ubatching
                    else self.parallel_config.num_ubatches,
5369
                )
co63oc's avatar
co63oc committed
5370
        # Calculate reorder batch threshold (if needed)
5371
5372
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
5373
5374
        self.calculate_reorder_batch_threshold()

5375
    def _check_and_update_cudagraph_mode(
5376
5377
5378
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
5379
    ) -> None:
5380
        """
5381
        Resolve the cudagraph_mode when there are multiple attention
5382
        groups with potential conflicting CUDA graph support.
5383
5384
5385
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
5386
        min_cg_support = AttentionCGSupport.ALWAYS
5387
        min_cg_backend_name = None
5388

5389
5390
5391
5392
5393
        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()
5394

5395
5396
5397
5398
5399
5400
                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__
5401
5402
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
5403
        assert cudagraph_mode is not None
5404
        # check cudagraph for mixed batch is supported
5405
5406
5407
5408
5409
5410
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
5411
                f"with {min_cg_backend_name} backend (support: "
5412
5413
                f"{min_cg_support})"
            )
5414
5415
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
5416
5417
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
5418
                    "make sure compilation mode is VLLM_COMPILE"
5419
                )
5420
5421
5422
5423
5424
                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"
5425
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5426
                    CUDAGraphMode.FULL_AND_PIECEWISE
5427
                )
5428
5429
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
5430
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5431
                    CUDAGraphMode.FULL_DECODE_ONLY
5432
                )
5433
5434
            logger.warning(msg)

5435
        # check that if we are doing decode full-cudagraphs it is supported
5436
5437
5438
5439
        if not envs.VLLM_USE_PIECEWISE:
            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and min_cg_support == AttentionCGSupport.NEVER
5440
            ):
5441
5442
5443
5444
                msg = (
                    f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                    f"with {min_cg_backend_name} backend (support: "
                    f"{min_cg_support})"
5445
                )
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
5456
5457
5458
5459
5460
5461
5462
5463
5464
5465
                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)
5466

5467
5468
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
5469
5470
5471
5472
5473
5474
5475
5476
        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 "
5477
                f"{min_cg_backend_name} (support: {min_cg_support})"
5478
            )
5479
5480
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
5481
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5482
                    CUDAGraphMode.PIECEWISE
5483
                )
5484
5485
            else:
                msg += "; setting cudagraph_mode=NONE"
5486
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
5487
                    CUDAGraphMode.NONE
5488
                )
5489
5490
5491
5492
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
5493
5494
5495
5496
5497
5498
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
5499
                f"supported with {min_cg_backend_name} backend ("
5500
5501
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
5502
                "and make sure compilation mode is VLLM_COMPILE"
5503
            )
5504

5505
5506
5507
5508
        # 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
5509
        # Will be removed in the near future when we have separate cudagraph capture
5510
5511
5512
5513
5514
5515
5516
5517
5518
        # 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
            )
5519
5520
5521
5522
            capture_sizes = self.compilation_config.cudagraph_capture_sizes
            self.cudagraph_batch_sizes = (
                capture_sizes if capture_sizes is not None else []
            )
5523

5524
5525
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
5526
        self.compilation_config.cudagraph_mode = cudagraph_mode
5527
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
5528
            cudagraph_mode, self.uniform_decode_query_len
5529
        )
5530

5531
5532
5533
5534
5535
        # 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)

5536
5537
    def calculate_reorder_batch_threshold(self) -> None:
        """
5538
5539
5540
5541
        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.
5542
        """
5543
5544
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

5545
        reorder_batch_thresholds: list[int | None] = [
5546
5547
5548
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
5549
5550
5551
5552
5553
        # 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
5554
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
5555

5556
5557
5558
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
5559
5560
    ) -> int:
        """
5561
5562
5563
5564
5565
        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.
5566

5567
5568
5569
5570
5571
        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

        Returns:
5572
            The selected block size
5573
5574

        Raises:
5575
            ValueError: If no valid block size found
5576
5577
        """

王敏's avatar
王敏 committed
5578
5579
5580
5581
        #exclude indexer backend
        def _participates_in_block_size_selection(backend: type[AttentionBackend]) -> bool:
            return not getattr(backend, "exclude_from_block_size_selection", False)

5582
5583
5584
5585
5586
5587
5588
5589
        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
5590
                for supported_size in backend.get_supported_kernel_block_sizes():
5591
5592
5593
5594
5595
5596
5597
5598
5599
5600
5601
5602
                    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
5603
5604
5605
5606
        all_backends = [group.backend for group in attn_groups]
        backends = [
            b for b in all_backends
            if _participates_in_block_size_selection(b)
5607
            ]
zhuwenwen's avatar
zhuwenwen committed
5608

5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
5622
5623
5624
5625

        # 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
5626
            for supported_size in backend.get_supported_kernel_block_sizes()
5627
5628
            if isinstance(supported_size, int)
        )
5629

5630
5631
5632
5633
5634
5635
        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}. ")
5636

5637
5638
5639
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
5640
5641
5642
5643
5644
5645
5646
        """
        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.
5647
            kernel_block_sizes: The kernel block sizes for each KV cache group.
5648
5649
5650
5651
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
5652
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
5653
        ]
5654
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
5666
5667
5668
5669
5670
5671
        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)
5672
5673
5674
5675

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
5676
5677
5678
            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
5679
5680
                "for more details."
            )
5681
5682
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
5683
                max_model_len=max_model_len,
5684
5685
5686
5687
5688
                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,
5689
                kernel_block_sizes=kernel_block_sizes,
5690
                max_num_blocks_per_req=max_num_blocks,
5691
                is_spec_decode=bool(self.vllm_config.speculative_config),
5692
                logitsprocs=self.input_batch.logitsprocs,
5693
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
5694
                is_pooling_model=self.is_pooling_model,
5695
5696
            )

5697
    def _allocate_kv_cache_tensors(
5698
5699
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
5700
        """
5701
5702
5703
        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.

5704
        Args:
5705
            kv_cache_config: The KV cache config
5706
        Returns:
5707
            dict[str, torch.Tensor]: A map between layer names to their
5708
            corresponding memory buffer for KV cache.
5709
        """
5710
5711
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
5712
5713
5714
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
5715
5716
5717
5718
5719
            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:
5720
5721
5722
5723
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
5724
5725
5726
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
5727
5728
        return kv_cache_raw_tensors

5729
5730
5731
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

5732
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
5733
5734
        if not self.kv_cache_config.kv_cache_groups:
            return
5735
5736
        for attn_groups in self.attn_groups:
            yield from attn_groups
5737

5738
5739
5740
5741
5742
5743
5744
5745
5746
5747
5748
5749
5750
5751
5752
    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 = []
5753
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
5754
5755
5756
5757
5758
5759
            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):
5760
                continue
5761
            elif isinstance(kv_cache_spec, AttentionSpec):
5762
5763
5764
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
5765
                attn_groups = self.attn_groups[kv_cache_gid]
5766
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
5767
                selected_kernel_size = self.select_common_block_size(
5768
5769
5770
                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
5771
            elif isinstance(kv_cache_spec, MambaSpec):
5772
5773
                # This is likely Mamba or other non-attention cache,
                # no splitting.
5774
                kernel_block_sizes.append(kv_cache_spec.block_size)
5775
5776
5777
5778
5779
5780
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

5781
5782
5783
5784
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
5785
        kernel_block_sizes: list[int],
5786
    ) -> dict[str, torch.Tensor]:
5787
        """
5788
        Reshape the KV cache tensors to the desired shape and dtype.
5789

5790
        Args:
5791
5792
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
5793
                correct size but uninitialized shape.
5794
            kernel_block_sizes: The kernel block sizes for each KV cache group.
5795
        Returns:
5796
            Dict[str, torch.Tensor]: A map between layer names to their
5797
5798
            corresponding memory buffer for KV cache.
        """
5799
        kv_caches: dict[str, torch.Tensor] = {}
5800
        has_attn, has_mamba = False, False
5801
5802
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
5803
            attn_backend = group.backend
5804
5805
5806
5807
            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]
5808
            for layer_name in group.layer_names:
5809
5810
                if layer_name in self.runner_only_attn_layers:
                    continue
5811
5812
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
5813
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
5814
                if isinstance(kv_cache_spec, AttentionSpec):
5815
                    has_attn = True
5816
5817
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
5818
5819
5820
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

5821
                    if envs.VLLM_USE_FLASH_ATTN_PA and not self.vllm_config.model_config.use_mla:
5822
                        key_cache_shape, value_cache_shape = attn_backend.get_kv_cache_shape(
5823
5824
                            kernel_num_blocks,
                            kernel_block_size,
5825
5826
                            kv_cache_spec.num_kv_heads,
                            kv_cache_spec.head_size,
5827
5828
                            cache_dtype_str=self.cache_config.cache_dtype,
                        )
5829
5830
5831
                        dtype = kv_cache_spec.dtype
                        try:
                            key_stride_order, value_stride_order = attn_backend.get_kv_cache_stride_order()
5832
5833
                            assert len(key_stride_order) == len(key_stride_order)
                            assert len(value_stride_order) == len(value_cache_shape)
5834
                        except (AttributeError, NotImplementedError):
5835
5836
                            key_stride_order = tuple(range(len(key_cache_shape)))
                            value_stride_order = tuple(range(len(value_cache_shape)))
5837
5838
5839
5840
5841
                        # 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.
5842
5843
5844
5845
                        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)
5846
5847
5848
5849
5850
5851
5852
5853
5854
5855
5856
5857
5858
5859
5860
5861
5862
5863
5864
5865
5866
5867
5868
5869
5870
5871
5872
                        # 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(
5873
5874
                            kernel_num_blocks,
                            kernel_block_size,
5875
5876
                            kv_cache_spec.num_kv_heads,
                            kv_cache_spec.head_size,
5877
5878
                            cache_dtype_str=self.cache_config.cache_dtype,
                        )
5879
5880
                        dtype = kv_cache_spec.dtype
                        try:
5881
5882
                            kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
                            assert len(kv_cache_stride_order) == len(kv_cache_shape)
5883
                        except (AttributeError, NotImplementedError):
5884
                            kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
5885
5886
5887
5888
5889
                        # 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.
5890
5891
5892
                        kv_cache_shape = tuple(
                            kv_cache_shape[i] for i in kv_cache_stride_order
                        )
5893
5894
5895
5896
5897
                        # Maintain original KV shape view.
                        inv_order = [
                            kv_cache_stride_order.index(i)
                            for i in range(len(kv_cache_stride_order))
                        ]
5898
5899
5900
5901
5902
5903
                        kv_caches[layer_name] = (
                            kv_cache_raw_tensors[layer_name]
                            .view(dtype)
                            .view(kv_cache_shape)
                            .permute(*inv_order)
                        )
5904

Chen Zhang's avatar
Chen Zhang committed
5905
                elif isinstance(kv_cache_spec, MambaSpec):
5906
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
5907
5908
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
5909
                    storage_offset_bytes = 0
5910
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
5911
5912
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
5913
5914
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
5915
                        target_shape = (num_blocks, *shape)
5916
5917
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
5918
                        assert storage_offset_bytes % dtype_size == 0
5919
5920
5921
5922
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
5923
                            storage_offset=storage_offset_bytes // dtype_size,
5924
                        )
Chen Zhang's avatar
Chen Zhang committed
5925
                        state_tensors.append(tensor)
5926
                        storage_offset_bytes += stride[0] * dtype_size
5927
5928

                    kv_caches[layer_name] = state_tensors
5929
                else:
5930
                    raise NotImplementedError
5931
5932

        if has_attn and has_mamba:
5933
            self._update_hybrid_attention_mamba_layout(kv_caches)
5934

5935
5936
        return kv_caches

5937
    def _update_hybrid_attention_mamba_layout(
5938
5939
        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
5940
        """
5941
5942
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
5943
5944

        Args:
5945
            kv_caches: The KV cache buffer of each layer.
5946
5947
        """

5948
5949
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
5950
            for layer_name in group.layer_names:
5951
                kv_cache = kv_caches[layer_name]
5952
5953
5954
5955
                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 "
5956
                        f"a tensor of shape {kv_cache.shape}"
5957
                    )
5958
                    hidden_size = kv_cache.shape[2:].numel()
5959
5960
5961
5962
                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
5963

5964
    def initialize_kv_cache_tensors(
5965
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
5966
    ) -> dict[str, torch.Tensor]:
5967
5968
5969
5970
5971
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
5972
5973
            kernel_block_sizes: The kernel block sizes for each KV cache group.

5974
        Returns:
5975
            Dict[str, torch.Tensor]: A map between layer names to their
5976
5977
            corresponding memory buffer for KV cache.
        """
5978
5979
5980
5981
5982
5983
5984
5985
5986
5987
5988
5989
5990
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001

        # 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
            )
6002

6003
        # Set up cross-layer KV cache sharing
6004
6005
        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)
6006
6007
            kv_caches[layer_name] = kv_caches[target_layer_name]

6008
6009
6010
6011
6012
6013
6014
6015
6016
        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,
        )
6017
6018
6019
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
6020
6021
        self, kv_cache_config: KVCacheConfig
    ) -> None:
6022
6023
6024
6025
6026
6027
6028
6029
6030
6031
6032
6033
6034
6035
6036
6037
6038
6039
        """
        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.
6040
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
6041
6042
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
6043
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
6044
6045
                else:
                    break
6046

6047
6048
6049
6050
6051
6052
6053
    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
        """
6054
        kv_cache_config = deepcopy(kv_cache_config)
6055
        self.kv_cache_config = kv_cache_config
6056
        self.may_add_encoder_only_layers_to_kv_cache_config()
6057
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
6058
        self.initialize_attn_backend(kv_cache_config)
6059
6060
6061
6062
6063
6064
        # 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)
6065
6066
6067
6068

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

6069
        # Reinitialize need to after initialize_attn_backend
6070
6071
6072
6073
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
6074

6075
6076
6077
6078
6079
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_draft_model()
        ):
            assert isinstance(self.drafter, EagleProposer | DraftModelProposer)
6080
6081
            # validate all draft model layers belong to the same kv cache
            # group
zhuwenwen's avatar
zhuwenwen committed
6082
            self.drafter.validate_same_kv_cache_group(kv_cache_config)
6083

Robert Shaw's avatar
Robert Shaw committed
6084
        if has_kv_transfer_group():
6085
            kv_transfer_group = get_kv_transfer_group()
6086
6087
6088
6089
6090
6091
6092
            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)
6093
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
6094

6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
6105
6106
6107
6108
6109
6110
6111
        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,
6112
            vllm_config=self.vllm_config,
6113
6114
        )

6115
6116
6117
6118
6119
    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
6120
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
6121
6122
6123
        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:
6124
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
6125
6126
6127
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
6128
6129
                    dtype=self.kv_cache_dtype,
                )
6130
6131
6132
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
6133
6134
6135
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
6136
6137
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
6138
6139
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
6140

6141
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
6142
        """
6143
        Generates the KVCacheSpec by parsing the kv cache format from each
6144
6145
        Attention module in the static forward context.
        Returns:
6146
            KVCacheSpec: A dictionary mapping layer names to their KV cache
6147
6148
            format. Layers that do not need KV cache are not included.
        """
6149
6150
        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
6151
        kv_cache_spec: dict[str, KVCacheSpec] = {}
6152
6153
        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
6154
        for layer_name, attn_module in attn_layers.items():
6155
6156
6157
            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
6158
6159
6160
6161
6162
6163
6164
6165
6166
                # 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
6167
6168
6169
            # 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
6170

6171
        return kv_cache_spec
6172

6173
6174
6175
6176
6177
6178
6179
6180
6181
    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.
6182
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
6183
6184
6185
6186
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()
6187
6188
6189
6190
6191
6192
6193
6194
6195
6196
6197
6198
6199
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

    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,
        }