gpu_model_runner.py 230 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

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

import numpy as np
import torch
import torch.distributed
import torch.nn as nn
19
from tqdm import tqdm
20

21
import vllm.envs as envs
22
23
24
from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionMetadata,
25
    AttentionType,
26
27
    MultipleOf,
)
28
from vllm.attention.layer import Attention, MLAAttention
29
from vllm.compilation.counter import compilation_counter
30
31
from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
32
from vllm.config import (
33
    CompilationMode,
34
35
36
37
38
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
39
from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
40
from vllm.distributed.eplb.eplb_state import EplbState
41
from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
42
from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
43
from vllm.distributed.parallel_state import (
44
    get_dcp_group,
45
46
47
48
49
50
    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
51
from vllm.forward_context import BatchDescriptor, set_forward_context
52
from vllm.logger import init_logger
53
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
54
55
56
57
from vllm.model_executor.layers.rotary_embedding import (
    MRotaryEmbedding,
    XDRotaryEmbedding,
)
58
from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
59
from vllm.model_executor.models.interfaces import (
60
    SupportsMRoPE,
61
    SupportsMultiModal,
62
    SupportsXDRoPE,
63
64
65
66
67
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
68
    supports_xdrope,
69
)
70
from vllm.model_executor.models.interfaces_base import (
71
72
73
74
    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
75
from vllm.multimodal import MULTIMODAL_REGISTRY
76
77
78
79
80
from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
81
from vllm.multimodal.utils import group_mm_kwargs_by_modality
82
from vllm.pooling_params import PoolingParams
83
from vllm.sampling_params import SamplingType
84
from vllm.sequence import IntermediateTensors
85
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
86
from vllm.utils import length_from_prompt_token_ids_or_embeds
87
from vllm.utils.jsontree import json_map_leaves
88
from vllm.utils.math_utils import cdiv, round_up
89
90
from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
91
from vllm.utils.platform_utils import is_pin_memory_available
92
93
94
95
96
from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
97
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
98
from vllm.v1.attention.backends.utils import (
99
100
101
    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
102
    create_fast_prefill_custom_backend,
103
    get_dcp_local_seq_lens,
104
105
106
    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
107
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
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,
125
    ECConnectorOutput,
126
    KVConnectorOutput,
127
128
129
130
131
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
132
    make_empty_encoder_model_runner_output,
133
)
134
from vllm.v1.pool.metadata import PoolingMetadata
135
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
136
from vllm.v1.sample.logits_processor.interface import LogitsProcessor
137
from vllm.v1.sample.metadata import SamplingMetadata
138
from vllm.v1.sample.rejection_sampler import RejectionSampler
139
from vllm.v1.sample.sampler import Sampler
140
from vllm.v1.spec_decode.eagle import EagleProposer
141
from vllm.v1.spec_decode.medusa import MedusaProposer
142
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
143
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
144
from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
145
from vllm.v1.structured_output.utils import apply_grammar_bitmask
146
from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
147
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
148
from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
149
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
150
from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
151
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
152
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
153
154
155
156
from vllm.v1.worker.ubatch_utils import (
    UBatchSlices,
    check_ubatch_thresholds,
)
157
from vllm.v1.worker.utils import is_residual_scattered_for_sp
158

159
160
161
162
163
164
165
166
167
from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    gather_mm_placeholders,
    sanity_check_mm_encoder_outputs,
    scatter_mm_placeholders,
)
168

169
if TYPE_CHECKING:
170
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
171
    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
172
173
174

logger = init_logger(__name__)

175
176
AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
177
PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
178

179

180
181
182
183
184
185
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
186
        logprobs_tensors: LogprobsTensors | None,
187
188
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
189
        vocab_size: int,
190
191
192
193
194
    ):
        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.
195
        self.async_copy_ready_event = torch.Event()
196
197
198
199

        # 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
200
        self.vocab_size = vocab_size
201
        self._logprobs_tensors = logprobs_tensors
202
203
204
205
206

        # 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)
207
            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
208
209
                "cpu", non_blocking=True
            )
210
211
212
213
214
            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
215
            self.async_copy_ready_event.record()
216
217
218

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
219

220
221
        This function blocks until the copy is finished.
        """
222
        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
223
        self.async_copy_ready_event.synchronize()
224

225
226
        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
227
        del self._sampled_token_ids
228
        if max_gen_len == 1:
229
            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
230
231
232
            for i in self._invalid_req_indices:
                valid_sampled_token_ids[i].clear()
            cu_num_tokens = None
233
        else:
234
            valid_sampled_token_ids, cu_num_tokens = RejectionSampler.parse_output(
235
236
                self.sampled_token_ids_cpu,
                self.vocab_size,
237
238
                self._invalid_req_indices,
                return_cu_num_tokens=self._logprobs_tensors_cpu is not None,
239
            )
240
241
242

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
243
        if self._logprobs_tensors_cpu:
244
            output.logprobs = self._logprobs_tensors_cpu.tolists(cu_num_tokens)
245
246
247
        return output


248
249
250
251
252
253
254
255
256
257
258
class ExecuteModelState(NamedTuple):
    """Ephemeral cached state transferred between execute_model() and
    sample_tokens(), after execute_model() returns None."""

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
259
    ec_connector_output: ECConnectorOutput | None
260
261


262
263
264
class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
265
266
    def __init__(
        self,
267
        vllm_config: VllmConfig,
268
        device: torch.device,
269
    ):
270
271
272
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
273
        self.compilation_config = vllm_config.compilation_config
274
275
276
277
278
279
        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
280

281
        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
282
283

        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
284

285
286
287
288
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
289
        self.device = device
290
291
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
292
293
294
        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
295

296
        self.is_pooling_model = model_config.runner_type == "pooling"
297
        self.enable_prompt_embeds = model_config.enable_prompt_embeds
298
        self.is_multimodal_raw_input_only_model = (
299
300
            model_config.is_multimodal_raw_input_only_model
        )
301
302
        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
303
        self.max_model_len = model_config.max_model_len
304
305
306

        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
307
        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
308
        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
309
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
310
        self.max_num_reqs = scheduler_config.max_num_seqs
311

312
313
314
315
316
        # 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 = (
317
318
319
            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
320

321
        # Model-related.
322
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
323
        self.hidden_size = model_config.get_hidden_size()
324
        self.attention_chunk_size = model_config.attention_chunk_size
325
        # Only relevant for models using ALiBi (e.g, MPT)
326
        self.use_alibi = model_config.uses_alibi
327

328
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
329

330
        # Multi-modal data support
331
        self.mm_registry = MULTIMODAL_REGISTRY
332
        self.uses_mrope = model_config.uses_mrope
333
        self.uses_xdrope_dim = model_config.uses_xdrope_dim
334
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
335
336
            model_config
        )
337

338
339
340
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
341
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
342
343
344
        else:
            self.max_encoder_len = 0

345
        # Sampler
346
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
347

348
        self.eplb_state: EplbState | None = None
349
350
351
352
353
354
        """
        State of the expert parallelism load balancer.

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

355
        # Lazy initializations
356
        # self.model: nn.Module  # Set after load_model
357
        # Initialize in initialize_kv_cache
358
        self.kv_caches: list[torch.Tensor] = []
359
360
361
        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
362
363
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
364
365
        # self.kv_cache_config: KVCacheConfig

366
367
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
368

369
        self.use_aux_hidden_state_outputs = False
370
371
372
373
374
        # 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:
375
376
377
            self.drafter: (
                NgramProposer | SuffixDecodingProposer | EagleProposer | MedusaProposer
            )
378
379
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
380
381
            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
382
            elif self.speculative_config.use_eagle():
383
                self.drafter = EagleProposer(self.vllm_config, self.device, self)
384
                if self.speculative_config.method == "eagle3":
385
386
387
                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
388
389
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
390
                    vllm_config=self.vllm_config, device=self.device
391
                )
392
            else:
393
394
395
396
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
397
            self.rejection_sampler = RejectionSampler(self.sampler)
398

399
400
401
402
        self.num_spec_tokens = 0
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens

403
        # Request states.
404
        self.requests: dict[str, CachedRequestState] = {}
405
406
407
        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
408
        self.comm_stream = torch.cuda.Stream()
409

410
411
412
413
414
415
416
417
418
        # 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.
419
420
421
422
        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
423
424
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
425
426
427
            # 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),
428
429
430
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
431
            vocab_size=self.model_config.get_vocab_size(),
432
            block_sizes=[self.cache_config.block_size],
433
            kernel_block_sizes=[self.cache_config.block_size],
434
            is_spec_decode=bool(self.vllm_config.speculative_config),
435
            logitsprocs=build_logitsprocs(
436
437
438
                self.vllm_config,
                self.device,
                self.pin_memory,
439
                self.is_pooling_model,
440
                custom_logitsprocs,
441
            ),
442
443
444
            # 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),
445
            is_pooling_model=self.is_pooling_model,
446
            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
447
        )
448

449
        self.use_async_scheduling = self.scheduler_config.async_scheduling
450
451
452
453
454
        # 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.
455
        self.prepare_inputs_event: torch.Event | None = None
456
457
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
458
            self.prepare_inputs_event = torch.Event()
459

460
        # self.cudagraph_batch_sizes sorts in ascending order.
461
462
463
464
        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
465
466
            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
467
            )
468

469
        # Cache the device properties.
470
        self._init_device_properties()
471

472
        # Persistent buffers for CUDA graphs.
473
474
475
476
477
        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
        )
478
        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
479
        self.encoder_seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
480
481
482
483
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
484
485
486
        # 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.
487
488
489
490
        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
491
492
        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
493
494
495
496
497
498
499
        )
        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
        )
500

501
502
        # Only relevant for multimodal models
        if self.supports_mm_inputs:
503
            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
504

505
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
506
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
507
508
509
510
            # 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
511
512
513
514
515
516

            # 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
517
            self.mrope_positions = self._make_buffer(
518
519
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
520

521
522
523
524
525
526
527
        # 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
            )

528
        # None in the first PP rank. The rest are set after load_model.
529
        self.intermediate_tensors: IntermediateTensors | None = None
530

531
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
532
        # Keep in int64 to avoid overflow with long context
533
534
535
536
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
537

538
539
540
541
542
        # 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] = {}
543
544
545
546
547
        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(
548
549
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
550

551
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
552
553
554
555

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

556
557
558
559
560
561
562
563
564
        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
565

566
        self.reorder_batch_threshold: int | None = None
567

568
569
570
571
572
        # 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()

573
        # Cached outputs.
574
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
575
        self.transfer_event = torch.Event()
576
        self.sampled_token_ids_pinned_cpu = torch.empty(
577
            (self.max_num_reqs, 1),
578
579
            dtype=torch.int64,
            device="cpu",
580
581
            pin_memory=self.pin_memory,
        )
582

583
584
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
585
        self.valid_sampled_token_count_event: torch.Event | None = None
586
587
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
        if self.use_async_scheduling and self.num_spec_tokens:
588
            self.valid_sampled_token_count_event = torch.Event()
589
590
591
592
593
594
595
596
            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,
        )

597
598
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
599
        self.kv_connector_output: KVConnectorOutput | None = None
600

601
602
603
604
    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
    @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)

649
650
651
652
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
653
654
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
655
656
657
658
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
659
660
            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
661
662
            return self.positions.gpu[num_tokens]

663
    def _make_buffer(
664
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
665
666
667
668
669
670
671
672
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
673

674
675
676
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

677
        if not self.is_pooling_model:
678
679
            return model_kwargs

680
681
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
682
683
684

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
685
686
687
688
689
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
690
691
692
693
694
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

695
        seq_lens = self.seq_lens.gpu[:num_reqs]
696
697
698
699
700
701
702
703
        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(
704
705
            device=self.device
        )
706
707
        return model_kwargs

708
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
709
710
        """
        Update the order of requests in the batch based on the attention
711
        backend's needs. For example, some attention backends (namely MLA) may
712
713
714
715
716
717
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
718
719
720
721
722
723
724
725
        # 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

726
727
728
729
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
730
731
                decode_threshold=self.reorder_batch_threshold,
            )
732

733
734
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
735
        """Initialize attributes from torch.cuda.get_device_properties"""
736
737
738
739
740
741
742
        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()

743
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
744
745
746
747
748
749
        """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.

750
751
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
752
753
        """
        # Remove finished requests from the cached states.
754
755
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
756
            self.num_prompt_logprobs.pop(req_id, None)
757
758
759
760
761
762
763
        # 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:
764
            self.input_batch.remove_request(req_id)
765
766

        # Free the cached encoder outputs.
767
768
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
769

770
771
772
773
774
775
776
777
778
779
780
781
782
        # 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()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # 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:
783
            self.input_batch.remove_request(req_id)
784

785
        reqs_to_add: list[CachedRequestState] = []
786
        # Add new requests to the cached states.
787
788
789
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
790
            pooling_params = new_req_data.pooling_params
791

792
793
794
795
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
796
797
798
799
800
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

801
802
            if self.is_pooling_model:
                assert pooling_params is not None
803
804
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
805

806
                model = cast(VllmModelForPooling, self.get_model())
807
                to_update = model.pooler.get_pooling_updates(task)
808
809
                to_update.apply(pooling_params)

810
            req_state = CachedRequestState(
811
                req_id=req_id,
812
                prompt_token_ids=new_req_data.prompt_token_ids,
813
                prompt_embeds=new_req_data.prompt_embeds,
814
                mm_features=new_req_data.mm_features,
815
                sampling_params=sampling_params,
816
                pooling_params=pooling_params,
817
                generator=generator,
818
819
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
820
                output_token_ids=[],
821
                lora_request=new_req_data.lora_request,
822
            )
823
824
            self.requests[req_id] = req_state

825
826
827
828
829
830
831
            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
                )

832
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
833
            if self.uses_mrope:
834
                self._init_mrope_positions(req_state)
835

836
837
838
839
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

840
            reqs_to_add.append(req_state)
841

842
        # Update the states of the running/resumed requests.
843
        is_last_rank = get_pp_group().is_last_rank
844
        req_data = scheduler_output.scheduled_cached_reqs
845
846
847
848
849

        # 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()

850
        for i, req_id in enumerate(req_data.req_ids):
851
            req_state = self.requests[req_id]
852
853
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
854
            resumed_from_preemption = req_id in req_data.resumed_req_ids
855
            num_output_tokens = req_data.num_output_tokens[i]
856
            req_index = self.input_batch.req_id_to_index.get(req_id)
857

858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
            # 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.
            if req_state.prev_num_draft_len:
                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)
881

882
            # Update the cached states.
883
            req_state.num_computed_tokens = num_computed_tokens
884
885
886
887
888
889
890
891

            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.
892
893
894
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
895
896
897
898
                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:
899
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
900
901
902
903
904
            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:
905
906
907
908
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
909
910
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
911

912
            # Update the block IDs.
913
            if not resumed_from_preemption:
914
915
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
916
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
917
                        block_ids.extend(new_ids)
918
            else:
919
                assert req_index is None
920
                assert new_block_ids is not None
921
922
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
923
                req_state.block_ids = new_block_ids
924
925
926
927
928

            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.
929
930
931
932
933
934
935

                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:]

936
                reqs_to_add.append(req_state)
937
938
939
                continue

            # Update the persistent batch.
940
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
941
            if new_block_ids is not None:
942
                self.input_batch.block_table.append_row(new_block_ids, req_index)
943
944
945
946
947
948
949

            # For the last rank, we don't need to update the token_ids_cpu
            # because the sampled tokens are already cached.
            if not is_last_rank:
                # Add new_token_ids to token_ids_cpu.
                start_token_index = num_computed_tokens
                end_token_index = num_computed_tokens + len(new_token_ids)
950
                self.input_batch.token_ids_cpu[
951
952
953
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
954
                self.input_batch.num_tokens[req_index] = end_token_index
955

956
            # Add spec_token_ids to token_ids_cpu.
957
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
958
                req_id, []
959
            )
960
961
962
963
964
            num_spec_tokens = len(spec_token_ids)
            # For async scheduling, token_ids_cpu assigned from
            # spec_token_ids are placeholders and will be overwritten in
            # _prepare_input_ids.
            if num_spec_tokens:
965
966
967
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
968
969
                    req_index, start_index:end_token_index
                ] = spec_token_ids
970
971
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
972
973
974
975
976
977

            # When speculative decoding is used with structured output,
            # the scheduler can drop draft tokens that do not
            # conform to the schema. This can result in
            # scheduler_output.scheduled_spec_decode_tokens being empty,
            # even when speculative decoding is enabled.
978
979
            self.input_batch.spec_token_ids[req_index].clear()
            self.input_batch.spec_token_ids[req_index].extend(spec_token_ids)
980

981
982
983
984
985
986
987
988
989
            # there are no draft tokens with async scheduling,
            # we clear the spec_decoding info in scheduler_output and
            # use normal sampling but rejection_sampling.
            if self.use_async_scheduling:
                req_state.prev_num_draft_len = num_spec_tokens
                if num_spec_tokens and self._draft_token_ids is None:
                    scheduler_output.total_num_scheduled_tokens -= num_spec_tokens
                    scheduler_output.num_scheduled_tokens[req_id] -= num_spec_tokens
                    scheduler_output.scheduled_spec_decode_tokens.pop(req_id, None)
990
991
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
992
993
        for request in reqs_to_add:
            self.input_batch.add_request(request)
994

995
996
997
998
999
1000
        # Condense the batched states if there are gaps left by removed requests
        self.input_batch.condense()
        # Allow attention backend to reorder the batch, potentially
        self._may_reorder_batch(scheduler_output)
        # Refresh batch metadata with any pending updates.
        self.input_batch.refresh_metadata()
1001

1002
    def _update_states_after_model_execute(
1003
1004
        self, output_token_ids: torch.Tensor
    ) -> None:
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
        """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.
        """
        if not self.model_config.is_hybrid or not self.speculative_config:
            return

        # Find the number of accepted tokens for each sequence.
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
        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()
        )
1037
1038
1039
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

1040
    def _init_mrope_positions(self, req_state: CachedRequestState):
1041
1042
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
1043
1044
1045
1046
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
1047
1048

        req_state.mrope_positions, req_state.mrope_position_delta = (
1049
            mrope_model.get_mrope_input_positions(
1050
                req_state.prompt_token_ids,
1051
                req_state.mm_features,
1052
            )
1053
        )
1054

1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
    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,
        )

1068
    def _extract_mm_kwargs(
1069
        self,
1070
1071
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
1072
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
1073
            return {}
1074

1075
1076
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
1077
1078
1079
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
1080

1081
        # Input all modalities at once
1082
        model = cast(SupportsMultiModal, self.model)
1083
1084
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1085
1086
1087
1088
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1089
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1090
1091
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
1092

1093
        return mm_kwargs_combined
1094

1095
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1096
        if not self.is_multimodal_raw_input_only_model:
1097
            return {}
1098

1099
1100
1101
1102
1103
        mm_budget = self.mm_budget
        assert mm_budget is not None

        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
1104

1105
1106
1107
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1108
        cumsum_dtype: np.dtype | None = None,
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
    ) -> 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

1125
    def _prepare_input_ids(
1126
1127
1128
1129
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1130
    ) -> None:
1131
        """Prepare the input IDs for the current batch.
1132

1133
1134
1135
1136
1137
1138
1139
        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)
1140
1141
1142
            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)
1143
1144
1145
1146
1147
1148
1149
            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
1150
1151
1152
1153
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1154
1155
        indices_match = True
        max_flattened_index = -1
1156
1157
1158
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1159
1160
1161
1162
1163
        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.
1164
1165
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1166
                flattened_index = cu_num_tokens[cur_index].item() - 1
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
                # 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))
1182
                indices_match &= prev_index == flattened_index
1183
                max_flattened_index = max(max_flattened_index, flattened_index)
1184
1185
1186
        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:
1187
1188
1189
            # 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)
1190
1191
1192
            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)
1193
1194
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1195
            # So input_ids.cpu will have all the input ids.
1196
1197
1198
1199
1200
1201
1202
            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_(
1203
1204
1205
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1206
1207
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1208
            return
1209
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1210
1211
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1212
        ).to(self.device, non_blocking=True)
1213
        prev_common_req_indices_tensor = torch.tensor(
1214
1215
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1216
1217
        self.input_ids.gpu.scatter_(
            dim=0,
1218
            index=sampled_tokens_index_tensor,
1219
            src=self.input_batch.prev_sampled_token_ids[
1220
1221
1222
                prev_common_req_indices_tensor, 0
            ],
        )
1223

1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
        # 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._draft_token_ids = None

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

1247
1248
    def _get_encoder_seq_lens(
        self,
1249
        num_scheduled_tokens: dict[str, int],
1250
1251
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1252
    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
1253
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
1254
            return None, None
1255
1256
1257

        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
1258
        for req_id in num_scheduled_tokens:
1259
            req_index = self.input_batch.req_id_to_index[req_id]
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
            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

        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]
1276

1277
        return encoder_seq_lens, encoder_seq_lens_cpu
1278

1279
    def _prepare_inputs(
1280
1281
1282
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
1283
1284
    ) -> tuple[
        torch.Tensor,
1285
        SpecDecodeMetadata | None,
1286
    ]:
1287
1288
        """
        :return: tuple[
1289
            logits_indices, spec_decode_metadata,
1290
1291
        ]
        """
1292
1293
1294
1295
1296
1297
1298
        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.
1299
        self.input_batch.block_table.commit_block_table(num_reqs)
1300
1301
1302

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1303
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1304

1305
1306
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1307
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1308
1309

        # Get positions.
1310
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1311
1312
1313
1314
1315
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1316

1317
1318
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1319
        if self.uses_mrope:
1320
1321
            self._calc_mrope_positions(scheduler_output)

1322
1323
1324
1325
1326
        # 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)

1327
1328
1329
1330
        # 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.
1331
1332
1333
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1334
        token_indices_tensor = torch.from_numpy(token_indices)
1335

1336
1337
1338
        # 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.
1339
1340
1341
1342
1343
1344
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1345
        if self.enable_prompt_embeds:
1346
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1347
1348
1349
1350
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1351
1352
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385

        # 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:
1386
1387
1388
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1389
1390

                output_idx += num_sched
1391

1392
1393
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1394
1395

        # Prepare the attention metadata.
1396
        self.query_start_loc.np[0] = 0
1397
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1398
1399
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1400
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1401
        self.query_start_loc.copy_to_gpu()
1402
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1403

1404
        self.seq_lens.np[:num_reqs] = (
1405
1406
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1407
        # Fill unused with 0 for full cuda graph mode.
1408
1409
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1410

1411
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1412
1413
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1414
        # Record which requests should not be sampled,
1415
        # so that we could clear the sampled tokens before returning
1416
1417
        self.discard_request_mask.np[:num_reqs] = (
            self.seq_lens.np[:num_reqs] < num_tokens_np
1418
        )
1419
        self.discard_request_mask.copy_to_gpu(num_reqs)
1420

1421
        # Copy the tensors to the GPU.
1422
1423
1424
1425
1426
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1427

1428
        if self.uses_mrope:
1429
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1430
1431
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1432
1433
                non_blocking=True,
            )
1434
1435
1436
1437
1438
1439
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
                non_blocking=True,
            )
1440
1441
        else:
            # Common case (1D positions)
1442
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1443

1444
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1445
1446
1447
1448
1449
1450
1451
        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
1452
            num_draft_tokens = None
1453
            spec_decode_metadata = None
1454
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1455
1456
1457
1458
1459
        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)
1460
1461
1462
            # 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)
1463
1464
1465
1466
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1467
1468
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1469
1470
1471
1472
1473
1474
1475
1476
                num_decode_draft_tokens[req_idx] = (
                    len(draft_token_ids)
                    if (
                        self.input_batch.num_computed_tokens_cpu[req_idx]
                        >= self.input_batch.num_prompt_tokens[req_idx]
                    )
                    else -1
                )
1477
            spec_decode_metadata = self._calc_spec_decode_metadata(
1478
1479
                num_draft_tokens, cu_num_tokens
            )
1480
            logits_indices = spec_decode_metadata.logits_indices
1481
            num_sampled_tokens = num_draft_tokens + 1
1482
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1483
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1484
1485
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1486

1487
1488
1489
1490
1491
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1492
            )
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
1504
        num_tokens: int,
1505
        num_reqs: int,
1506
1507
1508
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
1509
1510
1511
1512
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
1513
        num_scheduled_tokens: dict[str, int] | None = None,
1514
1515
1516
1517
1518
        cascade_attn_prefix_lens: list[list[int]] | None = None,
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
1519
1520
1521
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs

1522
        logits_indices_padded = None
1523
        num_logits_indices = None
1524
1525
1526
1527
1528
1529
        if logits_indices is not None:
            num_logits_indices = logits_indices.size(0)
            if self.cache_config.kv_sharing_fast_prefill:
                logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
                    logits_indices
                )
1530

1531
1532
1533
1534
1535
1536
        # update seq_lens of decode reqs under DCP.
        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,
1537
                self.parallel_config.cp_kv_cache_interleave_size,
1538
            )
1539
1540
            self.dcp_local_seq_lens.cpu[num_reqs:].fill_(0)
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs_padded)
1541

1542
1543
1544
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1545

1546
1547
1548
1549
1550
1551
1552
1553
        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()

1554
1555
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1556
1557
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1558
1559
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1560

1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
        # Used in the below loop, uses padded shapes
        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
        ]

        dcp_local_seq_lens, dcp_local_seq_lens_cpu = None, None
        if self.dcp_world_size > 1:
            dcp_local_seq_lens = self.dcp_local_seq_lens.gpu[:num_reqs_padded]
            dcp_local_seq_lens_cpu = self.dcp_local_seq_lens.cpu[:num_reqs_padded]

        spec_decode_common_attn_metadata = None

1577
1578
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1579
        for kv_cache_gid, kv_cache_group in enumerate(
1580
1581
            self.kv_cache_config.kv_cache_groups
        ):
1582
1583
            encoder_seq_lens, encoder_seq_lens_cpu = self._get_encoder_seq_lens(
                num_scheduled_tokens or {},
1584
                kv_cache_group.kv_cache_spec,
1585
                num_reqs_padded,
1586
            )
1587

1588
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1589
1590
1591
                # Encoder-only layers do not have KV cache, so we need to
                # create a dummy block table and slot mapping for them.
                blk_table_tensor = torch.zeros(
1592
                    (num_reqs_padded, 1),
1593
                    dtype=torch.int32,
1594
1595
1596
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1597
                    (num_tokens_padded,),
1598
1599
1600
                    dtype=torch.int64,
                    device=self.device,
                )
1601
            else:
1602
                blk_table = self.input_batch.block_table[kv_cache_gid]
1603
1604
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
                slot_mapping = blk_table.slot_mapping.gpu[:num_tokens_padded]
1605
1606

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
1607
1608
1609
                # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
                slot_mapping[num_tokens:num_tokens_padded].fill_(-1)
                blk_table_tensor[num_reqs:num_reqs_padded].fill_(-1)
1610

1611
            common_attn_metadata = CommonAttentionMetadata(
1612
1613
1614
1615
1616
                query_start_loc=query_start_loc,
                query_start_loc_cpu=query_start_loc_cpu,
                seq_lens=seq_lens,
                seq_lens_cpu=seq_lens_cpu,
                num_computed_tokens_cpu=num_computed_tokens_cpu,
1617
1618
1619
                num_actual_tokens=num_tokens_padded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
1620
                max_seq_len=max_seq_len,
1621
1622
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1623
                logits_indices_padded=logits_indices_padded,
1624
                num_logits_indices=num_logits_indices,
1625
                causal=True,
1626
                encoder_seq_lens=encoder_seq_lens,
1627
                encoder_seq_lens_cpu=encoder_seq_lens_cpu,
1628
                dcp_local_seq_lens=dcp_local_seq_lens,
1629
                dcp_local_seq_lens_cpu=dcp_local_seq_lens_cpu,
1630
1631
            )

1632
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1633
                if isinstance(self.drafter, EagleProposer):
1634
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1635
1636
1637
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1638

1639
1640
1641
1642
1643
1644
            for attn_gid, attn_group in enumerate(self.attn_groups[kv_cache_gid]):
                cascade_attn_prefix_len = (
                    cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                    if cascade_attn_prefix_lens
                    else 0
                )
1645
                builder = attn_group.get_metadata_builder()
1646

1647
                extra_attn_metadata_args = {}
1648
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1649
                    extra_attn_metadata_args = dict(
1650
1651
1652
                        num_accepted_tokens=self.num_accepted_tokens.gpu[
                            :num_reqs_padded
                        ],
1653
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
1654
                            :num_reqs_padded
1655
                        ],
1656
1657
                    )

1658
1659
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1660
1661
                        ubatch_slices, common_attn_metadata
                    )
1662
                    for ubid, common_attn_metadata in enumerate(
1663
1664
                        common_attn_metadata_list
                    ):
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
                        builder = attn_group.get_metadata_builder(ubatch_id=ubid)
                        if for_cudagraph_capture:
                            attn_metadata_i = builder.build_for_cudagraph_capture(
                                common_attn_metadata
                            )
                        else:
                            attn_metadata_i = builder.build(
                                common_prefix_len=cascade_attn_prefix_len,
                                common_attn_metadata=common_attn_metadata,
                            )
                        for layer_name in kv_cache_group.layer_names:
1676
1677
1678
1679
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
                    if for_cudagraph_capture:
                        attn_metadata_i = builder.build_for_cudagraph_capture(
                            common_attn_metadata
                        )
                    else:
                        attn_metadata_i = builder.build(
                            common_prefix_len=cascade_attn_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                            **extra_attn_metadata_args,
                        )
1690
1691
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1692

1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
        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)
            )

1703
        return attn_metadata, spec_decode_common_attn_metadata
1704

1705
1706
1707
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
1708
        num_computed_tokens: np.ndarray,
1709
1710
1711
1712
1713
1714
1715
        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
        """
1716

1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
        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,
1731
                        num_computed_tokens,
1732
1733
1734
1735
1736
1737
1738
1739
                        num_common_prefix_blocks[kv_cache_gid],
                        attn_group.kv_cache_spec,
                        attn_group.get_metadata_builder(),
                    )
                cascade_attn_prefix_lens[kv_cache_gid].append(cascade_attn_prefix_len)
                use_cascade_attn |= cascade_attn_prefix_len > 0

        return cascade_attn_prefix_lens if use_cascade_attn else None
1740

1741
1742
1743
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
1744
        num_computed_tokens: np.ndarray,
1745
        num_common_prefix_blocks: int,
1746
1747
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
    ) -> 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.
        """
1766

1767
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
        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]
1805
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1806
1807
1808
1809
1810
        # 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.
1811
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
1812
        # common_prefix_len should be a multiple of the block size.
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
        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
        )
1824
1825
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1826
1827
1828
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1829
            num_kv_heads=kv_cache_spec.num_kv_heads,
1830
            use_alibi=self.use_alibi,
1831
            use_sliding_window=use_sliding_window,
1832
            use_local_attention=use_local_attention,
1833
            num_sms=self.num_sms,
1834
            dcp_world_size=self.dcp_world_size,
1835
1836
1837
        )
        return common_prefix_len if use_cascade else 0

1838
1839
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1840
        for index, req_id in enumerate(self.input_batch.req_ids):
1841
1842
1843
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1844
1845
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1846
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1847
1848
                req.prompt_token_ids, req.prompt_embeds
            )
1849
1850

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1851
1852
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
            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

1866
1867
1868
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1869
1870
1871
1872
1873
1874
1875
                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

1876
                assert req.mrope_position_delta is not None
1877
                MRotaryEmbedding.get_next_input_positions_tensor(
1878
                    out=self.mrope_positions.np,
1879
1880
1881
1882
1883
                    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,
                )
1884
1885
1886

                mrope_pos_ptr += completion_part_len

1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
    def _calc_xdrope_positions(self, scheduler_output: "SchedulerOutput"):
        xdrope_pos_ptr = 0
        for index, req_id in enumerate(self.input_batch.req_ids):
            req = self.requests[req_id]
            assert req.xdrope_positions is not None

            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
                req.prompt_token_ids, req.prompt_embeds
            )

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
            else:
                prompt_part_len = num_scheduled_tokens
                completion_part_len = 0

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

            if prompt_part_len > 0:
                # prompt's xdrope_positions are pre-computed
                dst_start = xdrope_pos_ptr
                dst_end = xdrope_pos_ptr + prompt_part_len
                src_start = num_computed_tokens
                src_end = num_computed_tokens + prompt_part_len

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

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

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

                xdrope_pos_ptr += completion_part_len

1934
1935
    def _calc_spec_decode_metadata(
        self,
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
        num_draft_tokens: np.ndarray,
        cu_num_scheduled_tokens: np.ndarray,
    ) -> SpecDecodeMetadata:
        # Inputs:
        # cu_num_scheduled_tokens:  [  4, 104, 107, 207, 209]
        # num_draft_tokens:         [  3,   0,   2,   0,   1]
        # Outputs:
        # cu_num_draft_tokens:      [  3,   3,   5,   5,   6]
        # logits_indices:           [  0,   1,   2,   3, 103, 104, 105, 106,
        #                            206, 207, 208]
        # target_logits_indices:    [  0,   1,   2,   5,   6,   9]
        # bonus_logits_indices:     [  3,   4,   7,   8,  10]

        # Compute the logits indices.
        # [4, 1, 3, 1, 2]
        num_sampled_tokens = num_draft_tokens + 1
1952
1953
1954
1955

        # 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(
1956
1957
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1958
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1959
        logits_indices = np.repeat(
1960
1961
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1962
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1963
1964
1965
1966
1967
1968
        logits_indices += arange

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

        # Compute the draft logits indices.
1969
1970
1971
        # 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(
1972
1973
            num_draft_tokens, cumsum_dtype=np.int32
        )
1974
1975
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1976
1977
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1978
1979
1980
1981
1982
        # [0, 1, 2, 5, 6, 9]
        target_logits_indices += arange

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
1983
1984
            self.device, non_blocking=True
        )
1985
1986
1987
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1988
1989
1990
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1991
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1992
1993
            self.device, non_blocking=True
        )
1994
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1995
1996
            self.device, non_blocking=True
        )
1997

1998
1999
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2000
        draft_token_ids = self.input_ids.gpu[logits_indices]
2001
2002
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2003
        return SpecDecodeMetadata(
2004
2005
2006
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2007
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2008
2009
2010
2011
2012
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2013
2014
2015
2016
2017
2018
2019
    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
2020
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2021
2022
2023
2024
2025
        # 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_(
2026
2027
2028
2029
2030
2031
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
2032
2033
2034
2035
2036
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
            num_logits_padded = self.vllm_config.pad_for_cudagraph(num_logits)
        else:
            num_logits_padded = num_logits
2037
2038
2039
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2040
2041
        return logits_indices_padded

2042
2043
2044
2045
2046
2047
2048
2049
    def _batch_mm_kwargs_from_scheduler(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> tuple[list[MultiModalKwargsItem], list[tuple[str, PlaceholderRange]]]:
        """Batch multimodal kwargs from scheduled encoder inputs.

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2050
                inputs.
2051
2052
2053
2054
2055
2056

        Returns:
            A tuple of (mm_kwargs, req_ids_pos) where:
            - mm_kwargs: List of multimodal kwargs items to be batched
            - mm_hashes_pos: List of (mm_hash, position_info) tuples
        """
2057
2058
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2059
            return [], []
2060
        # Batch the multi-modal inputs.
2061
        mm_kwargs = list[MultiModalKwargsItem]()
2062
2063
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
2064
2065
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2066
2067

            for mm_input_id in encoder_input_ids:
2068
                mm_feature = req_state.mm_features[mm_input_id]
2069
2070
                if mm_feature.data is None:
                    continue
2071
2072
2073
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
2074

2075
2076
        return mm_kwargs, mm_hashes_pos

2077
2078
2079
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2080
2081
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
2082
2083
            scheduler_output
        )
2084
2085

        if not mm_kwargs:
2086
            return []
2087

2088
2089
2090
2091
2092
2093
2094
        # 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.
2095
        model = cast(SupportsMultiModal, self.model)
2096
        encoder_outputs: list[torch.Tensor] = []
2097
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
2098
2099
2100
2101
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
2102
            multimodal_cpu_fields=model.multimodal_cpu_fields,
2103
        ):
2104
            curr_group_outputs: list[torch.Tensor] = []
2105
2106

            # EVS-related change.
2107
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2108
            # processing multimodal data. This solves the issue with scheduler
2109
2110
2111
2112
            # 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)
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
            # 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
            ):
                for video_mm_kwargs_item in filter(
                    lambda item: item.modality == "video", mm_kwargs
                ):
                    _, _, micro_batch_mm_inputs = next(
                        group_mm_kwargs_by_modality(
                            [video_mm_kwargs_item],
                            device=self.device,
                            pin_memory=self.pin_memory,
                            merge_by_field_config=model.merge_by_field_config,
2129
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
2130
                        )
2131
                    )
2132

2133
                    micro_batch_outputs = model.embed_multimodal(
2134
2135
                        **micro_batch_mm_inputs
                    )
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145

                    curr_group_outputs.extend(micro_batch_outputs)
            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.
2146
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)  # type: ignore[assignment]
2147

2148
2149
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
2150
                expected_num_items=num_items,
2151
            )
2152
            encoder_outputs.extend(curr_group_outputs)
2153

2154
2155
2156
        # Cache the encoder outputs by mm_hash
        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
            self.encoder_cache[mm_hash] = scatter_mm_placeholders(
2157
2158
2159
                output,
                is_embed=pos_info.is_embed,
            )
2160
2161
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2162

2163
2164
        return encoder_outputs

2165
    def _gather_mm_embeddings(
2166
2167
        self,
        scheduler_output: "SchedulerOutput",
2168
        shift_computed_tokens: int = 0,
2169
2170
2171
2172
2173
2174
2175
2176
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

        mm_embeds = list[torch.Tensor]()
        is_mm_embed = self.is_mm_embed.cpu
        is_mm_embed[:total_num_scheduled_tokens] = False

        req_start_idx = 0
2177
        should_sync_mrope_positions = False
2178
        should_sync_xdrope_positions = False
2179

2180
        for req_id in self.input_batch.req_ids:
2181
2182
            mm_embeds_req: list[torch.Tensor] = []

2183
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2184
            req_state = self.requests[req_id]
2185
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2186

2187
2188
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2189
2190
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206

                # 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,
2207
2208
                    num_encoder_tokens,
                )
2209
                assert start_idx < end_idx
2210

2211
                mm_hash = mm_feature.identifier
2212
                encoder_output = self.encoder_cache.get(mm_hash, None)
2213
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2214
2215
2216
2217

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]

2218
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2219
2220
2221
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
2222

2223
2224
2225
2226
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
2227
2228
2229
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2230
                assert req_state.mrope_positions is not None
2231
2232
2233
2234
2235
2236
2237
                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,
2238
2239
                    )
                )
2240
2241
2242
2243
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2244
2245
2246
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
2247
2248
2249

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2250
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2251

2252
2253
2254
2255
        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

2256
        return mm_embeds, is_mm_embed
2257

2258
    def get_model(self) -> nn.Module:
2259
        # get raw model out of the cudagraph wrapper.
2260
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2261
            return self.model.unwrap()
2262
2263
        return self.model

2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
    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

2279
2280
2281
2282
2283
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2284
2285
        supported_tasks = list(model.pooler.get_supported_tasks())

2286
        if self.scheduler_config.enable_chunked_prefill:
2287
2288
2289
2290
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
2291

2292
2293
            logger.debug_once(
                "Chunked prefill is not supported with "
2294
2295
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
2296
2297
2298
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
2299
2300
2301
2302
2303

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2304
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2305
2306

        return supported_tasks
2307

2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
    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)

2318
    def sync_and_slice_intermediate_tensors(
2319
2320
        self,
        num_tokens: int,
2321
        intermediate_tensors: IntermediateTensors | None,
2322
2323
        sync_self: bool,
    ) -> IntermediateTensors:
2324
2325
2326
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2327
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2328
2329
2330
2331
2332
2333

        # 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():
2334
                is_scattered = k == "residual" and is_rs
2335
                copy_len = num_tokens // tp if is_scattered else num_tokens
2336
                self.intermediate_tensors[k][:copy_len].copy_(
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
                    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:
2350
2351
2352
2353
2354
2355
2356
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2357
2358
        model = self.get_model()
        assert is_mixture_of_experts(model)
2359
2360
2361
        self.eplb_state.step(
            is_dummy,
            is_profile,
2362
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2363
2364
        )

2365
2366
2367
2368
2369
2370
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2371
2372
2373
        assert self.input_batch.num_reqs == len(self.input_batch.pooling_params), (
            "Either all or none of the requests in a batch must be pooling request"
        )
2374

2375
        hidden_states = hidden_states[:num_scheduled_tokens]
2376
        pooling_metadata = self.input_batch.get_pooling_metadata()
2377
2378
2379
2380
        pooling_metadata.build_pooling_cursor(
            num_scheduled_tokens_np.tolist(), device=hidden_states.device
        )
        seq_lens_cpu = self.seq_lens.cpu[: self.input_batch.num_reqs]
2381

2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
            hidden_states=hidden_states,
            pooling_metadata=pooling_metadata,
        )
        raw_pooler_output = json_map_leaves(
            lambda x: x.to("cpu", non_blocking=True),
            raw_pooler_output,
        )
        self._sync_device()
2392

2393
        pooler_output: list[torch.Tensor | None] = []
2394
        for raw_output, seq_len, prompt_len in zip(
2395
2396
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2397
            output = raw_output if seq_len == prompt_len else None
2398
            pooler_output.append(output)
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408

        return ModelRunnerOutput(
            req_ids=self.input_batch.req_ids,
            req_id_to_index=self.input_batch.req_id_to_index,
            sampled_token_ids=[],
            logprobs=None,
            prompt_logprobs_dict={},
            pooler_output=pooler_output,
        )

2409
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
2410
2411
2412
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2413
2414
2415
2416
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2417
2418
2419
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2420
    def _preprocess(
2421
2422
        self,
        scheduler_output: "SchedulerOutput",
2423
        num_input_tokens: int,  # Padded
2424
        intermediate_tensors: IntermediateTensors | None = None,
2425
    ) -> tuple[
2426
2427
        torch.Tensor | None,
        torch.Tensor | None,
2428
        torch.Tensor,
2429
        IntermediateTensors | None,
2430
        dict[str, Any],
2431
        ECConnectorOutput | None,
2432
    ]:
2433
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2434
        is_first_rank = get_pp_group().is_first_rank
2435

2436
2437
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2438
2439
        ec_connector_output = None

2440
2441
        if (
            self.supports_mm_inputs
2442
            and is_first_rank
2443
2444
            and not self.model_config.is_encoder_decoder
        ):
2445
            # Run the multimodal encoder if any.
2446
2447
2448
2449
2450
2451
            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)
2452

2453
2454
2455
            # 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.
2456
            inputs_embeds_scheduled = self.model.embed_input_ids(
2457
2458
2459
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2460
            )
2461

2462
            # TODO(woosuk): Avoid the copy. Optimize.
2463
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2464

2465
            input_ids = None
2466
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2467
2468
2469
2470
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2471
        elif self.enable_prompt_embeds and is_first_rank:
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
            # 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).
2484
2485
2486
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2487
                .squeeze(1)
2488
            )
2489
2490
2491
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2492
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2493
2494
2495
2496
2497
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
            model_kwargs = self._init_model_kwargs(num_input_tokens)
            input_ids = None
2498
        else:
2499
2500
2501
2502
            # 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.
2503
            input_ids = self.input_ids.gpu[:num_input_tokens]
2504
            inputs_embeds = None
2505
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2506

2507
        if self.uses_mrope:
2508
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2509
2510
        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
2511
        else:
2512
            positions = self.positions.gpu[:num_input_tokens]
2513

2514
        if is_first_rank:
2515
2516
            intermediate_tensors = None
        else:
2517
            assert intermediate_tensors is not None
2518
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2519
2520
                num_input_tokens, intermediate_tensors, True
            )
2521

2522
2523
2524
2525
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2526
2527
2528
2529
2530
2531
2532
            # 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})
2533

2534
2535
2536
2537
2538
2539
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
2540
            ec_connector_output,
2541
        )
2542

2543
    def _sample(
2544
        self,
2545
2546
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2547
    ) -> SamplerOutput:
2548
        # Sample the next token and get logprobs if needed.
2549
        sampling_metadata = self.input_batch.sampling_metadata
2550
        if spec_decode_metadata is None:
2551
2552
2553
            # 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()
2554
            return self.sampler(
2555
2556
2557
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2558

2559
        sampler_output = self.rejection_sampler(
2560
2561
            spec_decode_metadata,
            None,  # draft_probs
2562
            logits,
2563
2564
            sampling_metadata,
        )
2565
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2566
2567
2568
        return sampler_output

    def _bookkeeping_sync(
2569
2570
2571
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2572
        logits: torch.Tensor | None,
2573
2574
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2575
        spec_decode_metadata: SpecDecodeMetadata | None,
2576
    ) -> tuple[
2577
        dict[str, int],
2578
        LogprobsLists | None,
2579
        list[list[int]],
2580
        dict[str, LogprobsTensors | None],
2581
2582
2583
        list[str],
        dict[str, int],
        list[int],
2584
    ]:
2585
2586
2587
2588
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2589
2590
2591
2592
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
2593
2594
2595
2596
        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)
2597

2598
2599
2600
        # 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()
2601
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2602
2603

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2604
        sampled_token_ids = sampler_output.sampled_token_ids
2605
        logprobs_tensors = sampler_output.logprobs_tensors
2606
        invalid_req_indices = []
2607
        cu_num_tokens: list[int] | None = None
2608
2609
2610
2611
2612
2613
        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)
2614
2615
2616
                # 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()
2617
2618
            else:
                # Includes spec decode tokens.
2619
                valid_sampled_token_ids, cu_num_tokens = RejectionSampler.parse_output(
2620
2621
                    sampled_token_ids,
                    self.input_batch.vocab_size,
2622
2623
                    discard_sampled_tokens_req_indices,
                    return_cu_num_tokens=logprobs_tensors is not None,
2624
                )
2625
        else:
2626
            valid_sampled_token_ids = []
2627
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2628
2629
2630
2631
2632
            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.
2633
2634
2635
2636
            # 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
2637
2638
2639
2640
2641
            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
            }
2642

2643
2644
2645
2646
2647
        # 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.
2648
        req_ids = self.input_batch.req_ids
2649
2650
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2651
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2652
2653
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2654

2655
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
2656

2657
            if not sampled_ids:
2658
2659
2660
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
2661
            end_idx = start_idx + num_sampled_ids
2662
2663
2664
2665
            assert end_idx <= self.max_model_len, (
                "Sampled token IDs exceed the max model length. "
                f"Total number of tokens: {end_idx} > max_model_len: "
                f"{self.max_model_len}"
2666
            )
2667

2668
2669
            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
2670
2671
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2672

2673
            req_id = req_ids[req_idx]
2674
2675
2676
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2677
        logprobs_lists = (
2678
            logprobs_tensors.tolists(cu_num_tokens)
2679
            if not self.use_async_scheduling and logprobs_tensors is not None
2680
2681
2682
2683
2684
2685
2686
2687
2688
            else None
        )

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
        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,
        )

2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
    @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()

2714
2715
    def _model_forward(
        self,
2716
2717
2718
2719
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2720
2721
2722
2723
2724
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2725
        Motivation: We can inspect only this method versus
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
        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,
        )

2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
    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,
    ) -> tuple[
        CUDAGraphMode, BatchDescriptor, UBatchSlices | None, torch.Tensor | None
    ]:
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
        uniform_decode = (
            (
                (max_num_scheduled_tokens == self.uniform_decode_query_len)
                and (num_tokens_padded == max_num_scheduled_tokens * num_reqs)
            )
            if force_uniform_decode is None
            else force_uniform_decode
        )

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

        dispatch_cudagraph = (
            lambda num_tokens: self.cudagraph_dispatcher.dispatch(
                num_tokens=num_tokens,
                has_lora=has_lora,
                use_cascade_attn=use_cascade_attn,
                uniform_decode=uniform_decode,
            )
            if not force_eager
            else (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
        )

        cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded)
        num_tokens_padded = batch_descriptor.num_tokens

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
        ubatch_slices, num_tokens_across_dp = None, None
        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
            )

            ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
                num_tokens_unpadded=num_tokens_padded,
                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,
            )

            # Extract DP padding if there is any
            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())

                # Re-dispatch with DP padding
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded)
                # 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

        return cudagraph_mode, batch_descriptor, ubatch_slices, num_tokens_across_dp

2827
2828
2829
2830
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2831
        intermediate_tensors: IntermediateTensors | None = None,
2832
2833
2834
2835
2836
2837
    ) -> ModelRunnerOutput | IntermediateTensors | None:
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852

        # self._draft_token_ids is None when `input_fits_in_drafter=False`
        # and there is no draft tokens scheduled. so it need to update the
        # spec_decoding info in scheduler_output with async_scheduling.
        # use deepcopy to avoid the modification has influence on the
        # scheduler_output in engine core process.
        # TODO(Ronald1995): deepcopy is expensive when there is a large
        # number of requests, optimize it later.
        if (
            self.use_async_scheduling
            and self.num_spec_tokens
            and self._draft_token_ids is None
        ):
            scheduler_output = deepcopy(scheduler_output)

2853
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2854
        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
2855
2856
2857
2858
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

2859
2860
2861
2862
2863
2864
2865
2866
                if has_ec_transfer() and get_ec_transfer().is_producer:
                    with self.maybe_get_ec_connector_output(
                        scheduler_output,
                        encoder_cache=self.encoder_cache,
                    ) as ec_connector_output:
                        self._execute_mm_encoder(scheduler_output)
                        return make_empty_encoder_model_runner_output(scheduler_output)

2867
                if not num_scheduled_tokens:
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
                    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.
2879
                        self._dummy_run(1)
2880
2881
2882
2883
                    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(
2884
2885
                        scheduler_output, self.vllm_config
                    )
2886
                if self.cache_config.kv_sharing_fast_prefill:
2887
                    assert not self.num_prompt_logprobs, (
2888
2889
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2890
2891
                        "it when the requests need prompt logprobs"
                    )
2892

2893
2894
2895
2896
2897
                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())
2898
                num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
2899

2900
2901
2902
                (
                    logits_indices,
                    spec_decode_metadata,
2903
                ) = self._prepare_inputs(
2904
2905
                    scheduler_output,
                    num_scheduled_tokens_np,
2906
2907
2908
2909
                )

                cascade_attn_prefix_lens = None
                # Disable cascade attention when using microbatching (DBO)
2910
                if self.cascade_attn_enabled and not self.parallel_config.enable_dbo:
2911
2912
2913
                    # Pre-compute cascade attention prefix lengths
                    cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
                        num_scheduled_tokens_np,
2914
                        self.input_batch.num_computed_tokens_cpu[:num_reqs],
2915
2916
2917
                        scheduler_output.num_common_prefix_blocks,
                    )

2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
                (
                    cudagraph_mode,
                    batch_desc,
                    ubatch_slices,
                    num_tokens_across_dp,
                ) = 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,
                )

                logger.debug(
                    "Running batch with cudagraph_mode: %s, batch_descriptor: %s, "
                    "ubatch_slices: %s, num_tokens_across_dp: %s",
                    cudagraph_mode,
                    batch_desc,
                    ubatch_slices,
                    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
                )
2944
2945

                use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
2946
2947
2948
                pad_attn = cudagraph_mode == CUDAGraphMode.FULL

                (attn_metadata, spec_decode_common_attn_metadata) = (
2949
                    self._build_attention_metadata(
2950
2951
                        num_tokens=num_tokens_unpadded,
                        num_tokens_padded=num_tokens_padded if pad_attn else None,
2952
                        num_reqs=num_reqs,
2953
2954
                        num_reqs_padded=num_reqs_padded if pad_attn else None,
                        max_query_len=max_num_scheduled_tokens,
2955
2956
2957
                        ubatch_slices=ubatch_slices,
                        logits_indices=logits_indices,
                        use_spec_decode=use_spec_decode,
2958
                        num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
2959
2960
2961
                        cascade_attn_prefix_lens=cascade_attn_prefix_lens,
                    )
                )
2962

2963
2964
2965
2966
2967
2968
2969
2970
2971
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
2972
            )
2973

2974
        # Set cudagraph mode to none if calc_kv_scales is true.
2975
2976
2977
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
2978
            cudagraph_mode = CUDAGraphMode.NONE
2979
2980
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
2981

2982
2983
        # Run the model.
        # Use persistent buffers for CUDA graphs.
2984
2985
        with (
            set_forward_context(
2986
2987
                attn_metadata,
                self.vllm_config,
2988
                num_tokens=num_tokens_padded,
2989
                num_tokens_across_dp=num_tokens_across_dp,
2990
2991
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
2992
                ubatch_slices=ubatch_slices,
2993
            ),
2994
            record_function_or_nullcontext("gpu_model_runner: forward"),
2995
2996
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2997
            model_output = self._model_forward(
2998
2999
3000
3001
3002
3003
3004
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

3005
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
3006
            if self.use_aux_hidden_state_outputs:
3007
                # True when EAGLE 3 is used.
3008
3009
                hidden_states, aux_hidden_states = model_output
            else:
3010
                # Common case.
3011
3012
3013
                hidden_states = model_output
                aux_hidden_states = None

3014
3015
3016
3017
3018
            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)
3019
                    hidden_states.kv_connector_output = kv_connector_output
3020
                    self.kv_connector_output = kv_connector_output
3021
                    return hidden_states
3022

3023
                if self.is_pooling_model:
3024
                    # Return the pooling output.
3025
3026
3027
                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
3028
3029
                    output.kv_connector_output = kv_connector_output
                    return output
3030
3031

                sample_hidden_states = hidden_states[logits_indices]
3032
                logits = self.model.compute_logits(sample_hidden_states)
3033
3034
3035
3036
            else:
                # Rare case.
                assert not self.is_pooling_model

3037
                sample_hidden_states = hidden_states[logits_indices]
3038
                if not get_pp_group().is_last_rank:
3039
                    all_gather_tensors = {
3040
                        "residual": not is_residual_scattered_for_sp(
3041
                            self.vllm_config, num_tokens_padded
3042
                        )
3043
                    }
3044
                    get_pp_group().send_tensor_dict(
3045
3046
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
3047
3048
                        all_gather_tensors=all_gather_tensors,
                    )
3049
3050
                    logits = None
                else:
3051
                    logits = self.model.compute_logits(sample_hidden_states)
3052

3053
                model_output_broadcast_data: dict[str, Any] = {}
3054
3055
3056
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

3057
                broadcasted = get_pp_group().broadcast_tensor_dict(
3058
3059
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
3060
3061
                assert broadcasted is not None
                logits = broadcasted["logits"]
3062

3063
3064
3065
3066
3067
3068
3069
3070
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3071
            ec_connector_output,
3072
        )
3073
        self.kv_connector_output = kv_connector_output
3074
3075
3076
3077
3078
3079
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
3080
3081
3082
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

3083
3084
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
3085
            if not kv_connector_output:
3086
                return None  # type: ignore[return-value]
3087
3088
3089
3090
3091
3092
3093
3094
3095

            # 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
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
3106
            ec_connector_output,
3107
3108
3109
3110
3111
3112
3113
3114
3115
        ) = 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
            )
3116

3117
        with record_function_or_nullcontext("gpu_model_runner: sample"):
3118
3119
            sampler_output = self._sample(logits, spec_decode_metadata)

3120
3121
        self.input_batch.prev_sampled_token_ids = None

3122
        def propose_draft_token_ids(sampled_token_ids):
3123
            assert spec_decode_common_attn_metadata is not None
3124
            with record_function_or_nullcontext("gpu_model_runner: draft"):
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
                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,
                )

3136
        spec_config = self.speculative_config
3137
        use_padded_batch_for_eagle = (
3138
3139
3140
            spec_config is not None
            and spec_config.use_eagle()
            and not spec_config.disable_padded_drafter_batch
3141
        )
3142
3143
3144
        effective_drafter_max_model_len = self.max_model_len
        if effective_drafter_max_model_len is None:
            effective_drafter_max_model_len = self.model_config.max_model_len
3145
        if (
3146
3147
3148
            spec_config is not None
            and spec_config.draft_model_config is not None
            and spec_config.draft_model_config.max_model_len is not None
3149
        ):
3150
            effective_drafter_max_model_len = (
3151
                spec_config.draft_model_config.max_model_len
3152
            )
3153
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
3154
            spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
3155
3156
            <= effective_drafter_max_model_len
        )
3157
        if use_padded_batch_for_eagle:
3158
3159
            assert self.speculative_config is not None
            assert isinstance(self.drafter, EagleProposer)
3160
3161
3162
3163
3164
3165
            sampled_token_ids = sampler_output.sampled_token_ids
            if input_fits_in_drafter:
                # EAGLE speculative decoding can use the GPU sampled tokens
                # as inputs, and does not need to wait for bookkeeping to finish.
                propose_draft_token_ids(sampled_token_ids)
            elif self.valid_sampled_token_count_event is not None:
3166
                assert spec_decode_common_attn_metadata is not None
3167
3168
3169
3170
3171
3172
                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,
3173
                        self.discard_request_mask.gpu,
3174
3175
3176
3177
3178
                    )
                )
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
3179

3180
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
3181
3182
3183
3184
3185
3186
3187
3188
            (
                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,
3189
3190
3191
3192
3193
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
3194
                scheduler_output.total_num_scheduled_tokens,
3195
                spec_decode_metadata,
3196
            )
3197

3198
3199
3200
3201
3202
        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
3203
3204
3205
            # 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)
3206

3207
        with record_function_or_nullcontext("gpu_model_runner: eplb"):
3208
            self.eplb_step()
3209
3210
3211
3212
3213
3214
3215
3216
3217
        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
            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,
                pooler_output=[],
                kv_connector_output=kv_connector_output,
3218
3219
3220
                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
3221
3222
                num_nans_in_logits=num_nans_in_logits,
            )
3223

3224
3225
        if not self.use_async_scheduling:
            return output
3226
3227
3228
3229
3230
3231
3232
3233
3234
        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,
3235
                vocab_size=self.input_batch.vocab_size,
3236
3237
3238
3239
3240
            )
        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
3241
            # any requests with sampling params that require output ids.
3242
3243
3244
3245
            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
3246
3247
3248

        return async_output

3249
    def take_draft_token_ids(self) -> DraftTokenIds | None:
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
        if self._draft_token_ids is None:
            return None
        req_ids = self.input_batch.req_ids
        if isinstance(self._draft_token_ids, torch.Tensor):
            draft_token_ids = self._draft_token_ids.tolist()
        else:
            draft_token_ids = self._draft_token_ids
        self._draft_token_ids = None
        return DraftTokenIds(req_ids, draft_token_ids)

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
    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
            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
        if (
            self.valid_sampled_token_count_event is None
            or prev_sampled_token_ids is None
        ):
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
        self.valid_sampled_token_count_event.synchronize()
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

3291
3292
3293
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
3294
        sampled_token_ids: torch.Tensor | list[list[int]],
3295
3296
3297
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
3298
3299
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3300
        common_attn_metadata: CommonAttentionMetadata,
3301
    ) -> list[list[int]] | torch.Tensor:
3302
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3303
3304
3305
        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
3306
            assert isinstance(sampled_token_ids, list)
3307
            assert isinstance(self.drafter, NgramProposer)
3308
            draft_token_ids = self.drafter.propose(
3309
3310
                sampled_token_ids,
                self.input_batch.req_ids,
3311
3312
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
3313
3314
                self.input_batch.spec_decode_unsupported_reqs,
            )
3315
        elif spec_config.method == "suffix":
3316
3317
3318
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
3319
        elif spec_config.method == "medusa":
3320
            assert isinstance(sampled_token_ids, list)
3321
            assert isinstance(self.drafter, MedusaProposer)
3322

3323
3324
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
3325
3326
3327
3328
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
3329
3330
3331
                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
3332
                for num_draft, tokens in zip(
3333
3334
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
3335
                    indices.append(offset + len(tokens) - 1)
3336
                    offset += num_draft + 1
3337
                indices = torch.tensor(indices, device=self.device)
3338
3339
                hidden_states = sample_hidden_states[indices]

3340
            draft_token_ids = self.drafter.propose(
3341
3342
3343
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
3344
        elif spec_config.use_eagle():
3345
            assert isinstance(self.drafter, EagleProposer)
3346

3347
            if spec_config.disable_padded_drafter_batch:
3348
3349
3350
                # 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.
3351
3352
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
3353
                    "padded-batch is disabled."
3354
                )
3355
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
3356
3357
3358
3359
3360
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
3361
3362
3363
3364
3365
            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.
3366
3367
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
3368
                    "padded-batch is enabled."
3369
3370
                )
                next_token_ids, valid_sampled_tokens_count = (
3371
3372
3373
3374
3375
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
3376
                        self.discard_request_mask.gpu,
3377
                    )
3378
                )
3379
3380
3381
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
3382

3383
            if spec_decode_metadata is None:
3384
                token_indices_to_sample = None
3385
                # input_ids can be None for multimodal models.
3386
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
3387
                target_positions = self._get_positions(num_scheduled_tokens)
3388
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
3389
                    assert aux_hidden_states is not None
3390
                    target_hidden_states = torch.cat(
3391
3392
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
3393
3394
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
3395
            else:
3396
                if spec_config.disable_padded_drafter_batch:
3397
                    token_indices_to_sample = None
3398
3399
3400
3401
3402
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
3403
3404
3405
3406
3407
3408
3409
3410
3411
                    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]
3412
                else:
3413
                    common_attn_metadata, token_indices_to_sample = (
3414
3415
3416
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
3417
3418
3419
                            valid_sampled_tokens_count,
                        )
                    )
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
                    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]
3431

3432
            if self.supports_mm_inputs:
3433
3434
3435
3436
3437
3438
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
3439

3440
            draft_token_ids = self.drafter.propose(
3441
3442
3443
3444
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
3445
                last_token_indices=token_indices_to_sample,
3446
                sampling_metadata=sampling_metadata,
3447
                common_attn_metadata=common_attn_metadata,
3448
                mm_embed_inputs=mm_embed_inputs,
3449
            )
3450

3451
        return draft_token_ids
3452

3453
3454
3455
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
3456
3457
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
3458
                f"Allowed configs: {allowed_config_names}"
3459
            )
3460
3461
3462
3463
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

3464
3465
3466
3467
3468
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
3469
3470
3471
3472
3473
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
3474
3475
3476
3477
3478
        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)
        )
3479

3480
3481
3482
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
3483
        with DeviceMemoryProfiler() as m:
3484
            time_before_load = time.perf_counter()
3485
            model_loader = get_model_loader(self.load_config)
3486
            self.model = model_loader.load_model(
3487
3488
                vllm_config=self.vllm_config, model_config=self.model_config
            )
3489
            if self.lora_config:
3490
3491
3492
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3493
            if hasattr(self, "drafter"):
3494
                logger.info_once("Loading drafter model...")
3495
                self.drafter.load_model(self.model)
3496
3497
3498
3499
3500
                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
3501
3502
3503
                    spec_config = self.vllm_config.speculative_config
                    assert spec_config is not None
                    assert spec_config.draft_model_config is not None
3504
3505
                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
3506
                        spec_config.draft_model_config.model,
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
                    )

                    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,
3523
                        spec_config.draft_model_config,
3524
3525
3526
3527
3528
3529
                        global_expert_load,
                        old_global_expert_indices,
                        rank_mapping,
                    )
                    eplb_models += 1

3530
            if self.use_aux_hidden_state_outputs:
3531
                if not supports_eagle3(self.get_model()):
3532
3533
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
3534
3535
                        "aux_hidden_state_outputs was requested"
                    )
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548

                # 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()

                self.model.set_aux_hidden_state_layers(aux_layers)
3549
            time_after_load = time.perf_counter()
3550
        self.model_memory_usage = m.consumed_memory
3551
        logger.info_once(
3552
            "Model loading took %.4f GiB memory and %.6f seconds",
3553
3554
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3555
            scope="local",
3556
        )
3557
        prepare_communication_buffer_for_model(self.model)
3558
3559
3560
3561
        if (drafter := getattr(self, "drafter", None)) and (
            drafter_model := getattr(drafter, "model", None)
        ):
            prepare_communication_buffer_for_model(drafter_model)
3562
        mm_config = self.model_config.multimodal_config
3563
        self.is_multimodal_pruning_enabled = (
3564
            supports_multimodal_pruning(self.get_model())
3565
3566
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
3567
        )
3568

3569
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
            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(
3581
                self.model,
3582
                self.model_config,
3583
3584
3585
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
3586
            )
3587
3588
            if self.eplb_state.is_async:
                self.eplb_state.start_async_loop(rank_mapping=rank_mapping)
3589

3590
        if (
3591
3592
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3593
            and supports_dynamo()
3594
        ):
3595
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3596
            compilation_counter.stock_torch_compile_count += 1
3597
            self.model.compile(fullgraph=True, backend=backend)
3598
            return
3599
        # for other compilation modes, cudagraph behavior is controlled by
3600
3601
3602
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
3603
3604
3605
        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
        if cudagraph_mode.has_full_cudagraphs() and not self.parallel_config.enable_dbo:
3606
3607
3608
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
3609
        elif self.parallel_config.enable_dbo:
3610
            if cudagraph_mode.has_full_cudagraphs():
3611
3612
3613
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3614
            else:
3615
3616
3617
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3618

3619
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
        """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

3643
    def reload_weights(self) -> None:
3644
        assert getattr(self, "model", None) is not None, (
3645
            "Cannot reload weights before model is loaded."
3646
        )
3647
3648
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3649
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3650

3651
3652
3653
3654
3655
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3656
            self.get_model(),
3657
            tensorizer_config=tensorizer_config,
3658
            model_config=self.model_config,
3659
3660
        )

3661
3662
3663
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3664
        num_scheduled_tokens: dict[str, int],
3665
    ) -> dict[str, LogprobsTensors | None]:
3666
        num_prompt_logprobs_dict = self.num_prompt_logprobs
3667
3668
3669
        if not num_prompt_logprobs_dict:
            return {}

3670
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3671
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3672
3673
3674
3675
3676

        # 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():
3677
3678
3679
3680
            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
3681
3682
3683

            # Get metadata for this request.
            request = self.requests[req_id]
3684
3685
3686
3687
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3688
3689
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3690
3691
                self.device, non_blocking=True
            )
3692

3693
3694
3695
3696
3697
3698
            # 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(
3699
3700
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3701
3702
                in_progress_dict[req_id] = logprobs_tensors

3703
            # Determine number of logits to retrieve.
3704
3705
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3706
            num_remaining_tokens = num_prompt_tokens - start_tok
3707
            if num_tokens <= num_remaining_tokens:
3708
                # This is a chunk, more tokens remain.
3709
3710
3711
                # 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.
3712
3713
3714
3715
3716
                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)
3717
3718
3719
3720
3721
3722
3723
                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
3724
3725
3726
3727
3728

            # 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]
3729
            offset = self.query_start_loc.np[req_idx].item()
3730
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3731
            logits = self.model.compute_logits(prompt_hidden_states)
3732
3733
3734
3735

            # 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.
3736
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3737
3738

            # Compute prompt logprobs.
3739
3740
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3741
3742
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3743
3744

            # Transfer GPU->CPU async.
3745
3746
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3747
3748
3749
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3750
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3751
3752
                ranks, non_blocking=True
            )
3753
3754
3755
3756
3757

        # 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]
3758
            del in_progress_dict[req_id]
3759
3760

        # Must synchronize the non-blocking GPU->CPU transfers.
3761
        if prompt_logprobs_dict:
3762
            self._sync_device()
3763
3764
3765

        return prompt_logprobs_dict

3766
3767
    def _get_nans_in_logits(
        self,
3768
        logits: torch.Tensor | None,
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
    ) -> 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])
3780
3781
3782
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3783
3784
3785
3786
            return num_nans_in_logits
        except IndexError:
            return {}

3787
3788
3789
3790
3791
3792
    @contextmanager
    def maybe_randomize_inputs(self, input_ids: torch.Tensor):
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
3793
         - during DP rank dummy run
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
        """
        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
        else:
            import functools

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
3805
                    self.input_ids.gpu,
3806
3807
                    low=0,
                    high=self.model_config.get_vocab_size(),
3808
3809
                    dtype=input_ids.dtype,
                )
3810

3811
            logger.debug_once("Randomizing dummy data for DP Rank")
3812
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3813
3814
3815
            yield
            input_ids.fill_(0)

3816
3817
3818
3819
3820
3821
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3822
3823
        assert self.mm_budget is not None

3824
3825
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3826
            seq_len=self.max_model_len,
3827
            mm_counts={modality: 1},
3828
            cache=self.mm_budget.cache,
3829
3830
3831
3832
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3833
3834
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3835

3836
        model = cast(SupportsMultiModal, self.model)
3837
3838
3839
3840
3841
3842
3843
        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,
                merge_by_field_config=model.merge_by_field_config,
3844
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3845
3846
            )
        )
3847

3848
3849
3850
3851
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3852
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3853
3854
        force_attention: bool = False,
        uniform_decode: bool = False,
3855
        allow_microbatching: bool = True,
3856
3857
        skip_eplb: bool = False,
        is_profile: bool = False,
3858
        create_mixed_batch: bool = False,
3859
        remove_lora: bool = True,
3860
        activate_lora: bool = False,
Rémi Delacourt's avatar
Rémi Delacourt committed
3861
        is_graph_capturing: bool = False,
3862
    ) -> tuple[torch.Tensor, torch.Tensor]:
3863
3864
3865
3866
3867
3868
3869
        """
        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.
3870
                - if not set will determine the cudagraph mode based on using
3871
                    the self.cudagraph_dispatcher.
3872
3873
3874
3875
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3876
            force_attention: If True, always create attention metadata. Used to
3877
3878
3879
3880
                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.
3881
3882
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3883
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3884
            activate_lora: If False, dummy_run is performed without LoRAs.
3885
        """
3886
3887
3888
3889
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3890

3891
        # If cudagraph_mode.decode_mode() == FULL and
3892
        # cudagraph_mode.separate_routine(). This means that we are using
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
        # 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.
3904
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3905

3906
3907
3908
3909
3910
        # 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
3911
3912
3913
3914
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3915
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3916
3917
3918
3919
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3920
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3921
3922
3923
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3924
            assert not create_mixed_batch
3925
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3926
3927
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3928
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3929
3930
3931
3932
3933
3934
        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

3935
3936
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3937
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3938
3939
        num_tokens_unpadded = int(num_scheduled_tokens.sum())

3940
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3941

3942
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
        _cudagraph_mode, batch_desc, ubatch_slices, num_tokens_across_dp = (
            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,
3961
3962
            )
        )
3963
3964
3965

        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
3966
        else:
3967
3968
3969
3970
3971
3972
3973
3974
3975
            assert cudagraph_runtime_mode == _cudagraph_mode, (
                f"Cudagraph runtime mode mismatch in dummy_run. "
                f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
            )

        num_tokens_padded = batch_desc.num_tokens
        num_reqs_padded = (
            batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
        )
3976

3977
        attn_metadata: PerLayerAttnMetadata | None = None
3978
3979
3980

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3981
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3982
3983
3984
3985
3986
3987
            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:
3988
                seq_lens = max_query_len  # type: ignore[assignment]
3989
            self.seq_lens.np[:num_reqs] = seq_lens
3990
3991
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3992

3993
3994
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3995
3996
            self.query_start_loc.copy_to_gpu()

3997
            attn_metadata, _ = self._build_attention_metadata(
3998
3999
4000
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs_padded,
                max_query_len=max_query_len,
4001
4002
4003
                ubatch_slices=ubatch_slices,
                for_cudagraph_capture=True,
            )
4004

4005
        with self.maybe_dummy_run_with_lora(
4006
4007
4008
4009
4010
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
4011
        ):
4012
            # Make sure padding doesn't exceed max_num_tokens
4013
4014
            assert num_tokens_padded <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_padded)
4015
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
4016
                input_ids = None
4017
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
4018
                model_kwargs = {
4019
                    **model_kwargs,
4020
4021
                    **self._dummy_mm_kwargs(num_reqs),
                }
4022
4023
            elif self.enable_prompt_embeds:
                input_ids = None
4024
4025
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
                model_kwargs = self._init_model_kwargs(num_tokens_padded)
4026
            else:
4027
                input_ids = self.input_ids.gpu[:num_tokens_padded]
4028
                inputs_embeds = None
4029

4030
            if self.uses_mrope:
4031
                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
4032
            elif self.uses_xdrope_dim > 0:
4033
                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
4034
            else:
4035
                positions = self.positions.gpu[:num_tokens_padded]
4036
4037
4038
4039
4040
4041
4042
4043
4044

            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,
4045
4046
4047
                            device=self.device,
                        )
                    )
4048
4049

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
4050
                    num_tokens_padded, None, False
4051
                )
4052

4053
            if ubatch_slices is not None:
4054
4055
4056
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
4057
                num_tokens_padded = ubatch_slices[0].num_tokens
4058
                if num_tokens_across_dp is not None:
4059
                    num_tokens_across_dp[:] = num_tokens_padded
4060

4061
4062
4063
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
4064
4065
                    attn_metadata,
                    self.vllm_config,
4066
                    num_tokens=num_tokens_padded,
4067
4068
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
4069
                    batch_descriptor=batch_desc,
4070
4071
4072
                    ubatch_slices=ubatch_slices,
                ),
            ):
4073
                outputs = self.model(
4074
4075
4076
4077
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
4078
                    **model_kwargs,
4079
                )
4080

4081
4082
4083
4084
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
4085

4086
            if self.speculative_config and self.speculative_config.use_eagle():
4087
                assert isinstance(self.drafter, EagleProposer)
4088
                use_cudagraphs = (
Rémi Delacourt's avatar
Rémi Delacourt committed
4089
                    cudagraph_runtime_mode.has_mode(CUDAGraphMode.PIECEWISE)
4090
4091
                    and not self.speculative_config.enforce_eager
                )
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102

                # 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
4103
                    is_graph_capturing=is_graph_capturing,
4104
                )
4105

4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
        # 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)

4116
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
4117
4118
4119
4120
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
4121
4122
4123
4124
4125
4126

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
4127
4128
4129
4130
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
        hidden_states = torch.rand_like(hidden_states)
4131

4132
        logits = self.model.compute_logits(hidden_states)
4133
4134
        num_reqs = logits.size(0)

4135
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150

        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)],
4151
            spec_token_ids=[[] for _ in range(num_reqs)],
4152
4153
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
4154
            logitsprocs=LogitsProcessors(),
4155
        )
4156
        try:
4157
4158
4159
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
4160
        except RuntimeError as e:
4161
            if "out of memory" in str(e):
4162
4163
4164
4165
                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 "
4166
4167
                    "initializing the engine."
                ) from e
4168
4169
            else:
                raise e
4170
        if self.speculative_config:
4171
4172
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
4173
4174
                draft_token_ids, self.device
            )
4175
4176
4177
4178
4179
4180

            num_tokens = sum(len(ids) for ids in draft_token_ids)
            # draft_probs = torch.randn(
            #     num_tokens, logits.shape[-1], device=self.device,
            #     dtype=logits.dtype)
            draft_probs = None
4181
4182
4183
4184
4185
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
4186
            )
4187
4188
4189
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
4190
                logits,
4191
4192
                dummy_metadata,
            )
4193
        return sampler_output
4194

4195
    def _dummy_pooler_run_task(
4196
4197
        self,
        hidden_states: torch.Tensor,
4198
4199
        task: PoolingTask,
    ) -> PoolerOutput:
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
        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
        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs

        req_num_tokens = num_tokens // num_reqs

4211
        dummy_prompt_lens = torch.tensor(
4212
4213
            num_scheduled_tokens_list,
            device="cpu",
4214
        )
4215
4216
4217
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
4218

4219
        model = cast(VllmModelForPooling, self.get_model())
4220
        dummy_pooling_params = PoolingParams(task=task)
4221
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
4222
        to_update = model.pooler.get_pooling_updates(task)
4223
4224
        to_update.apply(dummy_pooling_params)

4225
        dummy_metadata = PoolingMetadata(
4226
4227
4228
4229
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
4230

4231
4232
4233
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
4234

4235
        try:
4236
4237
4238
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
4239
        except RuntimeError as e:
4240
            if "out of memory" in str(e):
4241
                raise RuntimeError(
4242
4243
4244
                    "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 "
4245
4246
                    "initializing the engine."
                ) from e
4247
4248
            else:
                raise e
4249
4250
4251
4252
4253
4254
4255

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
4256
4257
4258
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
4259
            if self.scheduler_config.enable_chunked_prefill:
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks with chunked prefill enabled. "
                    "Please add --no-enable-chunked-prefill to your "
                    "config or CLI args. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )
            else:
                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."
                )

4276
        output_size = dict[PoolingTask, float]()
4277
        for task in supported_pooling_tasks:
4278
4279
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
4280
            output_size[task] = sum(o.nbytes for o in output)
4281
4282
4283
4284
            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)
4285

4286
    def profile_run(self) -> None:
4287
        # Profile with multimodal encoder & encoder cache.
4288
        if self.supports_mm_inputs:
4289
4290
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
4291
                logger.info(
4292
                    "Skipping memory profiling for multimodal encoder and "
4293
4294
                    "encoder cache."
                )
4295
4296
4297
4298
4299
4300
4301
4302
            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.
4303
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
4304
4305
4306
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
4307
4308
4309
4310
4311
4312
4313
4314
4315

                    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,
                    )
4316

4317
4318
4319
4320
4321
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
4322

4323
                    # Run multimodal encoder.
4324
                    dummy_encoder_outputs = self.model.embed_multimodal(
4325
4326
                        **batched_dummy_mm_inputs
                    )
4327

4328
4329
4330
4331
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
4332

4333
4334
4335
                    # NOTE: This happens when encoder cache needs to store
                    # the embeddings that encoder outputs are scattered onto.
                    # In this case we create dummy embeddings of size
4336
4337
                    # (max_tokens_for_modality, hidden_size) and scatter
                    # encoder output into it.
4338
                    encoder_output_shape = dummy_encoder_outputs[0].shape
4339
4340
4341
4342
4343
                    max_mm_tokens_per_item = mm_budget.max_tokens_by_modality[
                        dummy_modality
                    ]
                    if encoder_output_shape[0] < max_mm_tokens_per_item:
                        encoder_hidden_size = encoder_output_shape[-1]
4344
4345
4346
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
4347
                                (max_mm_tokens_per_item, encoder_hidden_size)
4348
                            )
4349
4350
4351
4352
4353
4354
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

4355
                    # Cache the dummy encoder outputs.
4356
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
4357

4358
        # Add `is_profile` here to pre-allocate communication buffers
4359
4360
4361
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
4362
        if get_pp_group().is_last_rank:
4363
4364
4365
4366
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
4367
        else:
4368
            output = None
4369
        self._sync_device()
4370
        del hidden_states, output
4371
        self.encoder_cache.clear()
4372
        gc.collect()
4373

4374
    def capture_model(self) -> int:
4375
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
4376
            logger.warning(
4377
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
4378
4379
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
4380
            return 0
4381

4382
4383
        compilation_counter.num_gpu_runner_capture_triggers += 1

4384
4385
        start_time = time.perf_counter()

4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
        @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()
4400
                    gc.collect()
4401

4402
4403
4404
        # 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.
4405
        set_cudagraph_capturing_enabled(True)
4406
        with freeze_gc(), graph_capture(device=self.device):
4407
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
4408
            cudagraph_mode = self.compilation_config.cudagraph_mode
4409
            assert cudagraph_mode is not None
4410
4411
4412
4413
4414
4415
4416
4417
4418

            if self.lora_config:
                if self.compilation_config.cudagraph_specialize_lora:
                    lora_cases = [True, False]
                else:
                    lora_cases = [True]
            else:
                lora_cases = [False]

4419
4420
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
4421
                # make sure we capture the largest batch size first
4422
4423
4424
                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
4425
4426
4427
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
4428
4429
                    uniform_decode=False,
                )
4430

4431
4432
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
4433
4434
4435
4436
4437
4438
4439
            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and cudagraph_mode.separate_routine()
            ):
                max_num_tokens = (
                    self.scheduler_config.max_num_seqs * self.uniform_decode_query_len
                )
4440
                decode_cudagraph_batch_sizes = [
4441
4442
                    x
                    for x in self.cudagraph_batch_sizes
4443
                    if max_num_tokens >= x >= self.uniform_decode_query_len
4444
                ]
4445
4446
4447
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
4448
4449
4450
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
4451
4452
                    uniform_decode=True,
                )
4453

4454
4455
4456
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

4457
4458
4459
        # 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
4460
        # we may do lazy capturing in future that still allows capturing
4461
4462
        # after here.
        set_cudagraph_capturing_enabled(False)
4463
4464
4465
4466
4467

        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.
4468
        logger.info_once(
4469
4470
4471
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
4472
            scope="local",
4473
        )
4474
        return cuda_graph_size
4475

4476
4477
    def _capture_cudagraphs(
        self,
4478
        compilation_cases: list[tuple[int, bool]],
4479
4480
4481
4482
4483
4484
4485
        cudagraph_runtime_mode: CUDAGraphMode,
        uniform_decode: bool,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
4486
4487
4488
4489
4490
4491
4492
4493

        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
4494
4495
4496
                    cudagraph_runtime_mode.name,
                ),
            )
4497

4498
        # We skip EPLB here since we don't want to record dummy metrics
4499
        for num_tokens, activate_lora in compilation_cases:
4500
            # We currently only capture ubatched graphs when its a FULL
4501
4502
4503
            # 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
4504
4505
4506
4507
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
4508
4509
4510
4511
4512
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
4513
            )
4514

4515
4516
4517
4518
4519
4520
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # 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.
4521
4522
4523
4524
4525
4526
4527
4528
4529
                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
4530
                    activate_lora=activate_lora,
4531
4532
4533
4534
4535
4536
4537
4538
                )
            self._dummy_run(
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                uniform_decode=uniform_decode,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
4539
                activate_lora=activate_lora,
Rémi Delacourt's avatar
Rémi Delacourt committed
4540
                is_graph_capturing=True,
4541
            )
4542
        self.maybe_remove_all_loras(self.lora_config)
4543

4544
4545
4546
4547
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
4548
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
4549

4550
4551
4552
4553
4554
4555
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
4556
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
4557
            layer_type = cast(type[Any], AttentionLayerBase)
4558
            layers = get_layers_from_vllm_config(
4559
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
4560
            )
4561
4562
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
4563
            # Dedupe based on full class name; this is a bit safer than
4564
4565
4566
4567
            # 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.
4568
            for layer_name in kv_cache_group_spec.layer_names:
4569
                attn_backend = layers[layer_name].get_attn_backend()
4570
4571
4572
4573

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
4574
                        attn_backend,  # type: ignore[arg-type]
4575
4576
                    )

4577
4578
4579
                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):
4580
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
4581
                key = (full_cls_name, layer_kv_cache_spec)
4582
4583
4584
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4585
                attn_backend_layers[key].append(layer_name)
4586
4587
4588
4589
            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()),
            )
4590
4591

        def create_attn_groups(
4592
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4593
            kv_cache_group_id: int,
4594
4595
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4596
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4597
                attn_group = AttentionGroup(
4598
                    attn_backend,
4599
                    layer_names,
4600
                    kv_cache_spec,
4601
                    kv_cache_group_id,
4602
4603
                )

4604
4605
4606
                attn_groups.append(attn_group)
            return attn_groups

4607
        attention_backend_maps = []
4608
        attention_backend_list = []
4609
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4610
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4611
            attention_backend_maps.append(attn_backends[0])
4612
            attention_backend_list.append(attn_backends[1])
4613
4614

        # Resolve cudagraph_mode before actually initialize metadata_builders
4615
4616
4617
        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4618

4619
4620
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4621

4622
4623
4624
4625
4626
4627
4628
4629
4630
4631
4632
4633
4634
4635
4636
4637
4638
4639
    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
                    if not self.parallel_config.enable_dbo
                    else 2,
                )
co63oc's avatar
co63oc committed
4640
        # Calculate reorder batch threshold (if needed)
4641
4642
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
4643
4644
        self.calculate_reorder_batch_threshold()

4645
    def _check_and_update_cudagraph_mode(
4646
4647
4648
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
4649
    ) -> None:
4650
        """
4651
        Resolve the cudagraph_mode when there are multiple attention
4652
        groups with potential conflicting CUDA graph support.
4653
4654
4655
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4656
        min_cg_support = AttentionCGSupport.ALWAYS
4657
        min_cg_backend_name = None
4658

4659
4660
4661
4662
4663
4664
4665
4666
4667
4668
4669
4670
        for attn_backend_set, kv_cache_group in zip(
            attention_backends, kv_cache_groups
        ):
            for attn_backend in attn_backend_set:
                builder_cls = attn_backend.get_builder_cls()

                cg_support = builder_cls.get_cudagraph_support(
                    self.vllm_config, kv_cache_group.kv_cache_spec
                )
                if cg_support.value < min_cg_support.value:
                    min_cg_support = cg_support
                    min_cg_backend_name = attn_backend.__name__
4671
4672
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
4673
        assert cudagraph_mode is not None
4674
        # check cudagraph for mixed batch is supported
4675
4676
4677
4678
4679
4680
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4681
                f"with {min_cg_backend_name} backend (support: "
4682
4683
                f"{min_cg_support})"
            )
4684
4685
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4686
4687
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4688
                    "make sure compilation mode is VLLM_COMPILE"
4689
                )
4690
4691
4692
4693
4694
                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"
4695
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4696
                    CUDAGraphMode.FULL_AND_PIECEWISE
4697
                )
4698
4699
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4700
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4701
                    CUDAGraphMode.FULL_DECODE_ONLY
4702
                )
4703
4704
            logger.warning(msg)

4705
        # check that if we are doing decode full-cudagraphs it is supported
4706
4707
4708
4709
4710
4711
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4712
                f"with {min_cg_backend_name} backend (support: "
4713
4714
                f"{min_cg_support})"
            )
4715
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4716
4717
4718
4719
4720
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4721
                    "attention is compiled piecewise"
4722
4723
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4724
                    CUDAGraphMode.PIECEWISE
4725
                )
4726
            else:
4727
4728
                msg += (
                    "; setting cudagraph_mode=NONE because "
4729
                    "attention is not compiled piecewise"
4730
4731
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4732
                    CUDAGraphMode.NONE
4733
                )
4734
4735
            logger.warning(msg)

4736
4737
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4738
4739
4740
4741
4742
4743
4744
4745
        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 "
4746
                f"{min_cg_backend_name} (support: {min_cg_support})"
4747
            )
4748
4749
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4750
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4751
                    CUDAGraphMode.PIECEWISE
4752
                )
4753
4754
            else:
                msg += "; setting cudagraph_mode=NONE"
4755
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4756
                    CUDAGraphMode.NONE
4757
                )
4758
4759
4760
4761
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4762
4763
4764
4765
4766
4767
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4768
                f"supported with {min_cg_backend_name} backend ("
4769
4770
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4771
                "and make sure compilation mode is VLLM_COMPILE"
4772
            )
4773

4774
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
        # 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
        # Will be removed in the near future when we have seperate cudagraph capture
        # 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
            )
4788
4789
4790
4791
            capture_sizes = self.compilation_config.cudagraph_capture_sizes
            self.cudagraph_batch_sizes = (
                capture_sizes if capture_sizes is not None else []
            )
4792

4793
4794
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4795
        self.compilation_config.cudagraph_mode = cudagraph_mode
4796
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4797
            cudagraph_mode, self.uniform_decode_query_len
4798
        )
4799

4800
4801
    def calculate_reorder_batch_threshold(self) -> None:
        """
4802
4803
4804
4805
        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.
4806
        """
4807
4808
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

4809
        reorder_batch_thresholds: list[int | None] = [
4810
4811
4812
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
4813
4814
4815
4816
4817
        # 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
4818
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
4819

4820
4821
4822
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4823
4824
    ) -> int:
        """
4825
4826
4827
4828
4829
        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.
4830
4831
4832
4833
4834
4835

        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

        Returns:
4836
            The selected block size
4837
4838

        Raises:
4839
            ValueError: If no valid block size found
4840
4841
        """

4842
4843
4844
4845
4846
4847
4848
4849
        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
4850
                for supported_size in backend.get_supported_kernel_block_sizes():
4851
4852
4853
4854
4855
4856
4857
4858
4859
4860
4861
4862
4863
4864
4865
4866
4867
4868
4869
4870
4871
4872
4873
4874
4875
4876
4877
4878
4879
4880
                    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

        backends = [group.backend for group in attn_groups]

        # 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
4881
            for supported_size in backend.get_supported_kernel_block_sizes()
4882
4883
            if isinstance(supported_size, int)
        )
4884

4885
4886
4887
4888
4889
4890
        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}. ")
4891

4892
4893
4894
    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
4895
4896
4897
4898
4899
4900
4901
        """
        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.
4902
            kernel_block_sizes: The kernel block sizes for each KV cache group.
4903
4904
4905
4906
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
4907
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4908
        ]
4909
4910
4911
4912

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
4913
4914
4915
            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
4916
4917
                "for more details."
            )
4918
4919
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
4920
                max_model_len=max(self.max_model_len, self.max_encoder_len),
4921
4922
4923
4924
4925
                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,
4926
                kernel_block_sizes=kernel_block_sizes,
4927
                is_spec_decode=bool(self.vllm_config.speculative_config),
4928
                logitsprocs=self.input_batch.logitsprocs,
4929
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
4930
                is_pooling_model=self.is_pooling_model,
4931
                num_speculative_tokens=self.num_spec_tokens,
4932
4933
            )

4934
    def _allocate_kv_cache_tensors(
4935
4936
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4937
        """
4938
4939
4940
        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.

4941
        Args:
4942
            kv_cache_config: The KV cache config
4943
        Returns:
4944
            dict[str, torch.Tensor]: A map between layer names to their
4945
            corresponding memory buffer for KV cache.
4946
        """
4947
4948
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
4949
4950
4951
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
4952
4953
4954
4955
4956
            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:
4957
4958
4959
4960
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
4961
4962
4963
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
4964
4965
        return kv_cache_raw_tensors

4966
4967
4968
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4969
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4970
4971
        if not self.kv_cache_config.kv_cache_groups:
            return
4972
4973
        for attn_groups in self.attn_groups:
            yield from attn_groups
4974

4975
4976
4977
4978
4979
4980
4981
4982
4983
4984
4985
4986
4987
4988
4989
    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 = []
4990
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
4991
4992
4993
4994
4995
4996
            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):
4997
                continue
4998
            elif isinstance(kv_cache_spec, AttentionSpec):
4999
5000
5001
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
5002
                attn_groups = self.attn_groups[kv_cache_gid]
5003
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
5004
                selected_kernel_size = self.select_common_block_size(
5005
5006
5007
                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
5008
            elif isinstance(kv_cache_spec, MambaSpec):
5009
5010
                # This is likely Mamba or other non-attention cache,
                # no splitting.
5011
                kernel_block_sizes.append(kv_cache_spec.block_size)
5012
5013
5014
5015
5016
5017
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

5018
5019
5020
5021
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
5022
        kernel_block_sizes: list[int],
5023
    ) -> dict[str, torch.Tensor]:
5024
        """
5025
        Reshape the KV cache tensors to the desired shape and dtype.
5026

5027
        Args:
5028
5029
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
5030
                correct size but uninitialized shape.
5031
            kernel_block_sizes: The kernel block sizes for each KV cache group.
5032
        Returns:
5033
            Dict[str, torch.Tensor]: A map between layer names to their
5034
5035
            corresponding memory buffer for KV cache.
        """
5036
        kv_caches: dict[str, torch.Tensor] = {}
5037
        has_attn, has_mamba = False, False
5038
5039
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
5040
            attn_backend = group.backend
5041
5042
5043
5044
            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]
5045
            for layer_name in group.layer_names:
5046
5047
                if layer_name in self.runner_only_attn_layers:
                    continue
5048
5049
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
5050
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
5051
                if isinstance(kv_cache_spec, AttentionSpec):
5052
                    has_attn = True
5053
5054
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
5055
5056
5057
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

5058
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
5059
                        kernel_num_blocks,
5060
                        kernel_block_size,
5061
5062
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
5063
5064
                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
5065
                    dtype = kv_cache_spec.dtype
5066
                    try:
5067
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
5068
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
5069
                    except (AttributeError, NotImplementedError):
5070
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
5071
5072
5073
5074
5075
                    # 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.
5076
5077
5078
                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
5079
5080
5081
5082
5083
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
5084
5085
5086
5087
5088
5089
                    kv_caches[layer_name] = (
                        kv_cache_raw_tensors[layer_name]
                        .view(dtype)
                        .view(kv_cache_shape)
                        .permute(*inv_order)
                    )
Chen Zhang's avatar
Chen Zhang committed
5090
                elif isinstance(kv_cache_spec, MambaSpec):
5091
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
5092
5093
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
5094
                    storage_offset_bytes = 0
5095
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
5096
5097
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
5098
5099
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
5100
                        target_shape = (num_blocks, *shape)
5101
5102
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
5103
                        assert storage_offset_bytes % dtype_size == 0
5104
5105
5106
5107
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
5108
                            storage_offset=storage_offset_bytes // dtype_size,
5109
                        )
Chen Zhang's avatar
Chen Zhang committed
5110
                        state_tensors.append(tensor)
5111
                        storage_offset_bytes += stride[0] * dtype_size
5112
5113

                    kv_caches[layer_name] = state_tensors
5114
                else:
5115
                    raise NotImplementedError
5116
5117

        if has_attn and has_mamba:
5118
            self._update_hybrid_attention_mamba_layout(kv_caches)
5119

5120
5121
        return kv_caches

5122
    def _update_hybrid_attention_mamba_layout(
5123
5124
        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
5125
        """
5126
5127
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
5128
5129

        Args:
5130
            kv_caches: The KV cache buffer of each layer.
5131
5132
        """

5133
5134
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
5135
            for layer_name in group.layer_names:
5136
                kv_cache = kv_caches[layer_name]
5137
5138
5139
5140
                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 "
5141
                        f"a tensor of shape {kv_cache.shape}"
5142
                    )
5143
                    hidden_size = kv_cache.shape[2:].numel()
5144
5145
5146
5147
                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
5148

5149
    def initialize_kv_cache_tensors(
5150
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
5151
    ) -> dict[str, torch.Tensor]:
5152
5153
5154
5155
5156
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
5157
5158
            kernel_block_sizes: The kernel block sizes for each KV cache group.

5159
        Returns:
5160
            Dict[str, torch.Tensor]: A map between layer names to their
5161
5162
            corresponding memory buffer for KV cache.
        """
5163
5164
5165
5166
5167
5168
5169
5170
5171
5172
5173
5174
5175
5176
5177
5178
5179
5180
5181
5182
5183
5184
5185
5186

        # 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
            )
5187

5188
        # Set up cross-layer KV cache sharing
5189
5190
        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)
5191
5192
            kv_caches[layer_name] = kv_caches[target_layer_name]

5193
5194
5195
5196
5197
5198
5199
5200
5201
        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,
        )
5202
5203
5204
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
5205
5206
        self, kv_cache_config: KVCacheConfig
    ) -> None:
5207
5208
5209
5210
5211
5212
5213
5214
5215
5216
5217
5218
5219
5220
5221
5222
5223
5224
        """
        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.
5225
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
5226
5227
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
5228
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
5229
5230
                else:
                    break
5231

5232
5233
5234
5235
5236
5237
5238
    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
        """
5239
        kv_cache_config = deepcopy(kv_cache_config)
5240
        self.kv_cache_config = kv_cache_config
5241
        self.may_add_encoder_only_layers_to_kv_cache_config()
5242
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
5243
        self.initialize_attn_backend(kv_cache_config)
5244
5245
5246
5247
5248
5249
        # 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)
5250
5251
5252
5253

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

5254
        # Reinitialize need to after initialize_attn_backend
5255
5256
5257
5258
        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
5259

5260
5261
5262
5263
5264
5265
        if self.speculative_config and self.speculative_config.use_eagle():
            assert isinstance(self.drafter, EagleProposer)
            # validate all draft model layers belong to the same kv cache
            # group
            self.drafter.validate_same_kv_cache_group(kv_cache_config)

Robert Shaw's avatar
Robert Shaw committed
5266
        if has_kv_transfer_group():
5267
            kv_transfer_group = get_kv_transfer_group()
5268
5269
5270
5271
5272
5273
5274
            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)
5275
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
5276

5277
        if self.dcp_world_size > 1:
5278
5279
            layer_type = cast(type[Any], AttentionLayerBase)
            layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
5280
            for layer in layers.values():
5281
5282
5283
5284
                layer_impl = getattr(layer, "impl", None)
                if layer_impl is None:
                    continue
                assert layer_impl.need_to_return_lse_for_decode, (
5285
5286
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
5287
                    f"{layer_impl.__class__.__name__} "
5288
5289
                    "does not return the softmax lse for decode."
                )
5290

5291
5292
5293
5294
5295
    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
5296
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
5297
5298
5299
        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:
5300
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
5301
5302
5303
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
5304
5305
                    dtype=self.kv_cache_dtype,
                )
5306
5307
5308
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
5309
5310
5311
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
5312
5313
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
5314
5315
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
5316

5317
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
5318
        """
5319
        Generates the KVCacheSpec by parsing the kv cache format from each
5320
5321
        Attention module in the static forward context.
        Returns:
5322
            KVCacheSpec: A dictionary mapping layer names to their KV cache
5323
5324
            format. Layers that do not need KV cache are not included.
        """
5325
5326
        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
5327
        kv_cache_spec: dict[str, KVCacheSpec] = {}
5328
5329
        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
5330
        for layer_name, attn_module in attn_layers.items():
5331
5332
5333
5334
5335
5336
5337
5338
5339
5340
5341
5342
5343
5344
5345
            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
5346

5347
        return kv_cache_spec
5348

5349
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
5350
5351
5352
5353
5354
5355
5356
5357
        # 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.
5358
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
5359
5360
5361
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
5362
        return pinned.tolist()