"vllm/vscode:/vscode.git/clone" did not exist on "db6f28d89832326520afecfab4672c7eca8c1147"
gpu_model_runner.py 198 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
8
from collections import defaultdict
from collections.abc import Iterator
9
from contextlib import contextmanager
10
from copy import deepcopy
11
from itertools import product
12
from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
13
14
15
16
17

import numpy as np
import torch
import torch.distributed
import torch.nn as nn
18
from tqdm import tqdm
19

20
import vllm.envs as envs
21
from vllm.attention import Attention, AttentionType
22
from vllm.attention.backends.abstract import AttentionBackend, MultipleOf
23
from vllm.compilation.counter import compilation_counter
24
25
from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
26
from vllm.config import (
27
    CompilationMode,
28
29
30
31
32
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
33
from vllm.distributed.eplb.eplb_state import EplbState
34
from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
35
from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
36
from vllm.distributed.parallel_state import (
37
38
39
40
41
42
    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
43
from vllm.forward_context import BatchDescriptor, set_forward_context
44
from vllm.logger import init_logger
45
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
46
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
47
from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
48
49
50
51
52
53
54
55
from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
)
56
from vllm.model_executor.models.interfaces_base import (
57
58
59
60
    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
61
from vllm.multimodal import MULTIMODAL_REGISTRY
62
63
64
65
66
from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
67
from vllm.multimodal.utils import group_mm_kwargs_by_modality
68
from vllm.pooling_params import PoolingParams
69
from vllm.sampling_params import SamplingType
70
from vllm.sequence import IntermediateTensors
71
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
72
from vllm.utils import length_from_prompt_token_ids_or_embeds
73
from vllm.utils.jsontree import json_map_leaves
74
from vllm.utils.math_utils import cdiv, round_up
75
76
from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
77
from vllm.utils.platform_utils import is_pin_memory_available
78
79
80
81
82
from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
83
from vllm.v1.attention.backends.flash_attn import AttentionMetadata
84
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
85
from vllm.v1.attention.backends.utils import (
86
87
88
    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
89
    create_fast_prefill_custom_backend,
90
91
92
    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
93
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
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,
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
)
117
from vllm.v1.pool.metadata import PoolingMetadata
118
from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
119
from vllm.v1.sample.metadata import SamplingMetadata
120
from vllm.v1.sample.rejection_sampler import RejectionSampler
121
from vllm.v1.sample.sampler import Sampler
122
from vllm.v1.spec_decode.eagle import EagleProposer
123
from vllm.v1.spec_decode.medusa import MedusaProposer
124
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
125
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
126
from vllm.v1.structured_output.utils import apply_grammar_bitmask
127
from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
128
from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
129
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
130
from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
131
from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
132
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
133
134
135
136
137
from vllm.v1.worker.ubatch_utils import (
    UBatchSlice,
    UBatchSlices,
    check_ubatch_thresholds,
)
138
from vllm.v1.worker.utils import is_residual_scattered_for_sp
139

140
141
142
143
144
145
146
147
148
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,
)
149

150
if TYPE_CHECKING:
151
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
152
    from vllm.v1.core.sched.output import SchedulerOutput
153
154
155

logger = init_logger(__name__)

156
157
AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
158
PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
159

160

161
162
163
164
165
166
# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
167
        logprobs_tensors: torch.Tensor | None,
168
169
170
171
172
173
174
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        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.
175
        self.async_copy_ready_event = torch.cuda.Event()
176
177
178
179

        # 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
180
        self._logprobs_tensors = logprobs_tensors
181
182
183
184
185

        # 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)
186
            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
187
188
                "cpu", non_blocking=True
            )
189
190
191
192
193
            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
194
            self.async_copy_ready_event.record()
195
196
197

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
198

199
200
        This function blocks until the copy is finished.
        """
201
        self.async_copy_ready_event.synchronize()
202

203
204
        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
205
206
        del self._sampled_token_ids

207
        valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
208
209
210
211
212
        for i in self._invalid_req_indices:
            valid_sampled_token_ids[i].clear()

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
213
214
215
216
        if self._logprobs_tensors_cpu:
            # NOTE(nick): this will need to be updated to use cu_num_accepted_tokens
            # for async sched + spec decode + logprobs compatibility.
            output.logprobs = self._logprobs_tensors_cpu.tolists()
217
218
219
        return output


220
class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
221
222
    def __init__(
        self,
223
        vllm_config: VllmConfig,
224
        device: torch.device,
225
    ):
226
227
228
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
229
        self.compilation_config = vllm_config.compilation_config
230
231
232
233
234
235
        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
236

237
        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
238
239

        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
240

241
242
243
244
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
245
        self.device = device
246
247
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
248
249
250
        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
251

252
        self.is_pooling_model = model_config.runner_type == "pooling"
253
        self.enable_prompt_embeds = model_config.enable_prompt_embeds
254
        self.is_multimodal_raw_input_only_model = (
255
256
            model_config.is_multimodal_raw_input_only_model
        )
257
258
        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
259
        self.max_model_len = model_config.max_model_len
260
        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
261
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
262
        self.max_num_reqs = scheduler_config.max_num_seqs
263

264
265
266
267
268
        # 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 = (
269
270
271
            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
272

273
        # Model-related.
274
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
275
        self.hidden_size = model_config.get_hidden_size()
276
        self.attention_chunk_size = model_config.attention_chunk_size
277
        # Only relevant for models using ALiBi (e.g, MPT)
278
        self.use_alibi = model_config.uses_alibi
279

280
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
281

282
        # Multi-modal data support
283
        self.mm_registry = MULTIMODAL_REGISTRY
284
        self.uses_mrope = model_config.uses_mrope
285
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
286
287
            model_config
        )
288

289
290
291
        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
292
            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
293
294
295
        else:
            self.max_encoder_len = 0

296
        # Sampler
297
        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
298

299
        self.eplb_state: EplbState | None = None
300
301
302
303
304
305
        """
        State of the expert parallelism load balancer.

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

306
        # Lazy initializations
307
        # self.model: nn.Module  # Set after load_model
308
        # Initialize in initialize_kv_cache
309
        self.kv_caches: list[torch.Tensor] = []
310
311
        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
312
313
        # self.kv_cache_config: KVCacheConfig

314
315
        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
316

317
        self.use_aux_hidden_state_outputs = False
318
319
320
321
322
323
324
325
        # 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:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
326
                self.drafter = EagleProposer(self.vllm_config, self.device, self)  # type: ignore
327
328
329
330
                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
331
332
                    vllm_config=self.vllm_config, device=self.device
                )  # type: ignore
333
            else:
334
335
336
337
                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
338
            self.rejection_sampler = RejectionSampler(self.sampler)
339

340
        # Request states.
341
        self.requests: dict[str, CachedRequestState] = {}
342
        self.comm_stream = torch.cuda.Stream()
343

344
345
346
347
348
349
350
351
352
        # 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.
353
        custom_logitsprocs = model_config.logits_processors
354
355
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
356
357
358
            # 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),
359
360
361
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
362
            vocab_size=self.model_config.get_vocab_size(),
363
            block_sizes=[self.cache_config.block_size],
364
            kernel_block_sizes=[self.cache_config.block_size],
365
            is_spec_decode=bool(self.vllm_config.speculative_config),
366
            logitsprocs=build_logitsprocs(
367
368
369
                self.vllm_config,
                self.device,
                self.pin_memory,
370
                self.is_pooling_model,
371
                custom_logitsprocs,
372
            ),
373
374
375
            # 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),
376
            is_pooling_model=self.is_pooling_model,
377
        )
378

379
        self.use_async_scheduling = self.scheduler_config.async_scheduling
380
381
382
383
384
385
386
387
388
        # 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.
        self.prepare_inputs_event: torch.cuda.Event | None = None
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
            self.prepare_inputs_event = torch.cuda.Event()
389

390
        # self.cudagraph_batch_sizes sorts in ascending order.
391
392
393
394
        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
395
396
            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
397
            )
398

399
        # Cache the device properties.
400
        self._init_device_properties()
401

402
        # Persistent buffers for CUDA graphs.
403
404
405
406
407
        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
        )
408
        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
409
410
411
412
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
413
414
415
        # 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.
416
417
418
419
420
421
422
        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)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
423
424
        self.num_discarded_requests = 0

425
426
427
428
429
430
        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
        )
431

432
433
        # Only relevant for multimodal models
        if self.supports_mm_inputs:
434
            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
435

436
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
437
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
438
439
440
441
            # 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
442
443
444
445
446
447

            # 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
448
            self.mrope_positions = self._make_buffer(
449
450
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
451

452
        # None in the first PP rank. The rest are set after load_model.
453
        self.intermediate_tensors: IntermediateTensors | None = None
454

455
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
456
        # Keep in int64 to avoid overflow with long context
457
458
459
460
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
461

462
463
464
465
466
        # 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] = {}
467
468
469
470
471
        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(
472
473
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
474

475
476
477
478
479
        self.uniform_decode_query_len = (
            1
            if not self.speculative_config
            else 1 + self.speculative_config.num_speculative_tokens
        )
480
481
482
483

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

484
485
486
487
488
489
490
491
492
        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
493

494
        self.reorder_batch_threshold: int | None = None
495

496
497
498
499
500
        # 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()

501
        # Cached outputs.
502
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
503
504
505
506
507
        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
508
509
            pin_memory=self.pin_memory,
        )
510

511
512
513
514
    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

515
516
517
518
519
520
521
522
523
524
    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
            return self.positions.gpu[num_tokens]

525
    def _make_buffer(
526
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
527
528
529
530
531
532
533
534
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
535

536
537
538
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

539
        if not self.is_pooling_model:
540
541
            return model_kwargs

542
543
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
544
545
546

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
547
548
549
550
551
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
552
553
554
555
556
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

557
        seq_lens = self.seq_lens.gpu[:num_reqs]
558
559
560
561
562
563
564
565
        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(
566
567
            device=self.device
        )
568
569
        return model_kwargs

570
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
571
572
        """
        Update the order of requests in the batch based on the attention
573
        backend's needs. For example, some attention backends (namely MLA) may
574
575
576
577
578
579
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
580
581
582
583
584
585
586
587
        # 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

588
        if self.reorder_batch_threshold is not None:
589
590
591
            # NOTE(lucas): currently no backend supports the custom masking
            #  required for DCP with q_len > 1, so we assert here. Remove this
            #  assert once the custom mask is support is added to FA3.
592
593
594
595
            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
596
                assert self.reorder_batch_threshold == 1, (
597
                    "DCP not support reorder_batch_threshold > 1 now."
598
                )
599
600
601
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
602
603
                decode_threshold=self.reorder_batch_threshold,
            )
604

605
606
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
607
        """Initialize attributes from torch.cuda.get_device_properties"""
608
609
610
611
612
613
614
        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()

615
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
616
617
618
619
620
621
        """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.

622
623
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
624
625
        """
        # Remove finished requests from the cached states.
626
627
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
628
629
630
631
632
633
634
        # 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:
635
            self.input_batch.remove_request(req_id)
636
637

        # Free the cached encoder outputs.
638
639
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
640

641
642
643
644
645
646
647
648
649
650
651
652
653
        # 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:
654
            self.input_batch.remove_request(req_id)
655

656
        reqs_to_add: list[CachedRequestState] = []
657
        # Add new requests to the cached states.
658
659
660
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
661
            pooling_params = new_req_data.pooling_params
662

663
664
665
666
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
667
668
669
670
671
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

672
673
            if self.is_pooling_model:
                assert pooling_params is not None
674
675
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
676

677
                model = cast(VllmModelForPooling, self.get_model())
678
                to_update = model.pooler.get_pooling_updates(task)
679
680
                to_update.apply(pooling_params)

681
            req_state = CachedRequestState(
682
                req_id=req_id,
683
                prompt_token_ids=new_req_data.prompt_token_ids,
684
                prompt_embeds=new_req_data.prompt_embeds,
685
                mm_features=new_req_data.mm_features,
686
                sampling_params=sampling_params,
687
                pooling_params=pooling_params,
688
                generator=generator,
689
690
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
691
                output_token_ids=[],
692
                lora_request=new_req_data.lora_request,
693
            )
694
695
            self.requests[req_id] = req_state

696
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
697
            if self.uses_mrope:
698
                self._init_mrope_positions(req_state)
699

700
            reqs_to_add.append(req_state)
701

702
        # Update the states of the running/resumed requests.
703
        is_last_rank = get_pp_group().is_last_rank
704
705
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
706
            req_state = self.requests[req_id]
707
708
709
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
            resumed_from_preemption = req_data.resumed_from_preemption[i]
710
            num_output_tokens = req_data.num_output_tokens[i]
711

712
            # Update the cached states.
713

714
            req_state.num_computed_tokens = num_computed_tokens
715
            req_index = self.input_batch.req_id_to_index.get(req_id)
716
717
718
719
720
721
722
723

            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.
724
725
726
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
727
728
729
730
                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:
731
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
732
733
734
735
736
            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:
737
738
739
740
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
741
742
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
743

744
            # Update the block IDs.
745
            if not resumed_from_preemption:
746
747
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
748
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
749
                        block_ids.extend(new_ids)
750
            else:
751
                assert req_index is None
752
                assert new_block_ids is not None
753
754
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
755
                req_state.block_ids = new_block_ids
756

757
758
759
760
761
762
                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.resumed_req_token_ids[i]
                    assert resumed_token_ids is not None
                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]
763
764
765
766
            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.
767
                reqs_to_add.append(req_state)
768
769
770
                continue

            # Update the persistent batch.
771
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
772
            if new_block_ids is not None:
773
                self.input_batch.block_table.append_row(new_block_ids, req_index)
774
775
776
777
778
779
780

            # 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)
781
                self.input_batch.token_ids_cpu[
782
783
784
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
785
                self.input_batch.num_tokens[req_index] = end_token_index
786

787
            # Add spec_token_ids to token_ids_cpu.
788
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
789
                req_id, []
790
            )
791
792
793
794
795
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                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[
796
797
                    req_index, start_index:end_token_index
                ] = spec_token_ids
798
799
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
800
801
802
803
804
805
806

            # 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.
            self.input_batch.spec_token_ids[req_index] = spec_token_ids
807

808
809
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
810
811
        for request in reqs_to_add:
            self.input_batch.add_request(request)
812

813
814
815
816
817
818
        # 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()
819

820
    def _update_states_after_model_execute(
821
822
        self, output_token_ids: torch.Tensor
    ) -> None:
823
824
825
826
827
828
829
830
831
832
833
834
        """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.
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
        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()
        )
855
856
857
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

858
859
860
861
862
863
    def _init_mrope_positions(self, req_state: CachedRequestState):
        image_grid_thw = []
        video_grid_thw = []
        second_per_grid_ts = []
        audio_feature_lengths = []
        use_audio_in_video = False
864
865
866
867
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
868
869
870
871
872
873
874
875
876
877
878
879
            mm_input = mm_item.get_data()
            if (t := mm_input.get("image_grid_thw")) is not None:
                image_grid_thw.append(t.tolist())
            if (t := mm_input.get("video_grid_thw")) is not None:
                video_grid_thw.append(t.tolist())
            if (t := mm_input.get("second_per_grid_ts")) is not None:
                second_per_grid_ts.append(t)
            if (t := mm_input.get("audio_feature_lengths")) is not None:
                audio_feature_lengths.append(t)
            if mm_input.get("use_audio_in_video") is True:
                use_audio_in_video = True

880
881
882
883
884
885
886
887
888
889
890
        assert supports_mrope(self.get_model()), "M-RoPE support is not implemented."

        req_state.mrope_positions, req_state.mrope_position_delta = (
            self.model.get_mrope_input_positions(
                req_state.prompt_token_ids,
                hf_config=self.model_config.hf_config,
                image_grid_thw=image_grid_thw,
                video_grid_thw=video_grid_thw,
                second_per_grid_ts=second_per_grid_ts,
                audio_feature_lengths=audio_feature_lengths,
                use_audio_in_video=use_audio_in_video,
891
            )
892
        )
893

894
    def _extract_mm_kwargs(
895
        self,
896
897
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
898
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
899
            return {}
900

901
902
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
903
904
905
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
906

907
        # Input all modalities at once
908
        model = cast(SupportsMultiModal, self.model)
909
910
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
911
912
913
914
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
915
916
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
917

918
        return mm_kwargs_combined
919

920
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
921
        if not self.is_multimodal_raw_input_only_model:
922
            return {}
923

924
925
926
927
928
        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)
929

930
931
932
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
933
        cumsum_dtype: np.dtype | None = None,
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
    ) -> 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

950
951
952
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
953
        """Prepare the input IDs for the current batch.
954

955
956
957
958
959
960
961
        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)
962
963
964
            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)
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
            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
        flattened_indices = []
        prev_common_req_indices = []
        indices_match = True
        max_flattened_index = -1
        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.
                flattened_index = cu_num_tokens[cur_index].item() - 1
                flattened_indices.append(flattened_index)
983
                indices_match &= prev_index == flattened_index
984
985
986
987
988
989
                max_flattened_index = max(max_flattened_index, flattened_index)
        num_commmon_tokens = len(flattened_indices)
        if num_commmon_tokens < total_num_scheduled_tokens:
            # 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)
990
991
992
            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)
993
994
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
995
            # So input_ids.cpu will have all the input ids.
996
997
998
999
1000
1001
1002
            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_(
1003
1004
1005
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1006
1007
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1008
            return
1009
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1010
1011
1012
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1013
        prev_common_req_indices_tensor = torch.tensor(
1014
1015
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1016
1017
1018
1019
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1020
1021
1022
                prev_common_req_indices_tensor, 0
            ],
        )
1023

1024
1025
1026
1027
1028
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1029
    ) -> np.ndarray | None:
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
            return None

        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
        encoder_seq_lens = np.zeros(num_reqs, dtype=np.int32)
        for req_id in scheduler_output.scheduled_encoder_inputs:
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1042
    def _prepare_inputs(
1043
        self, scheduler_output: "SchedulerOutput"
1044
1045
1046
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
1047
        SpecDecodeMetadata | None,
1048
        np.ndarray,
1049
        CommonAttentionMetadata | None,
1050
        int,
1051
1052
        UBatchSlices | None,
        torch.Tensor | None,
1053
1054
        bool,
    ]:
1055
1056
1057
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1058
1059
1060
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1061
1062
        ]
        """
1063
1064
1065
1066
1067
1068
1069
        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.
1070
        self.input_batch.block_table.commit_block_table(num_reqs)
1071
1072

        # Get the number of scheduled tokens for each request.
1073
1074
1075
1076
        req_ids = self.input_batch.req_ids
        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
        max_num_scheduled_tokens = max(tokens)
1077
1078
1079

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1080
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1081

1082
1083
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1084
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1085
1086

        # Get positions.
1087
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1088
1089
1090
1091
1092
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1093

1094
1095
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1096
        if self.uses_mrope:
1097
1098
            self._calc_mrope_positions(scheduler_output)

1099
1100
1101
1102
        # 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.
1103
1104
1105
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1106
        token_indices_tensor = torch.from_numpy(token_indices)
1107

1108
1109
1110
        # 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.
1111
1112
1113
1114
1115
1116
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1117
        if self.enable_prompt_embeds:
1118
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1119
1120
1121
1122
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1123
1124
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157

        # 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:
1158
1159
1160
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1161
1162

                output_idx += num_sched
1163

1164
1165
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1166
1167

        # Prepare the attention metadata.
1168
        self.query_start_loc.np[0] = 0
1169
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1170
1171
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1172
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1173
        self.query_start_loc.copy_to_gpu()
1174
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1175

1176
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1177
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1178
1179
1180
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1181
1182
1183
1184
1185
1186
1187

        # Disable DP padding when running eager to avoid excessive padding when
        # running prefills. This lets us set enforce_eager 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

1188
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1189
1190
1191
1192
1193
1194
1195
            num_tokens_unpadded=num_tokens_unpadded,
            parallel_config=self.parallel_config,
            allow_microbatching=True,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=num_tokens_padded,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
1196
        )
1197

1198
        self.seq_lens.np[:num_reqs] = (
1199
1200
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1201
        # Fill unused with 0 for full cuda graph mode.
1202
1203
1204
1205
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
        seq_lens = self.seq_lens.gpu[:num_reqs]
        max_seq_len = self.seq_lens.np[:num_reqs].max().item()
1206

1207
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1208
1209
1210
1211
1212
1213
1214
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

        # Record the index of requests that should not be sampled,
        # so that we could clear the sampled tokens before returning
        discard_requests_mask = self.seq_lens.np[:num_reqs] < num_tokens_np
        discard_request_indices = np.nonzero(discard_requests_mask)[0]
        self.num_discarded_requests = len(discard_request_indices)
1215
1216
1217
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1218
1219
1220

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1221
        # Copy the tensors to the GPU.
1222
1223
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1224
        if self.uses_mrope:
1225
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1226
1227
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1228
1229
                non_blocking=True,
            )
1230
1231
        else:
            # Common case (1D positions)
1232
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1233

1234
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1235
1236
1237
1238
1239
1240
1241
        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
1242
            num_draft_tokens = None
1243
1244
1245
1246
1247
1248
            spec_decode_metadata = None
        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)
1249
1250
1251
            # 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)
1252
1253
1254
1255
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1256
1257
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1258
1259
1260
1261
1262
1263
1264
1265
                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
                )
1266
            spec_decode_metadata = self._calc_spec_decode_metadata(
1267
1268
                num_draft_tokens, cu_num_tokens
            )
1269
            logits_indices = spec_decode_metadata.logits_indices
1270
1271

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1272
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1273
1274
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1275
1276
1277

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1278
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1279
1280
                logits_indices
            )
1281

1282
1283
1284
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1285
        use_cascade_attn = False
1286

1287
        # Used in the below loop.
1288
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1289
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1290
1291
1292
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1293
        spec_decode_common_attn_metadata = None
1294
1295
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1296
1297
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1298
1299
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1300

1301
1302
1303
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
1304
1305
            self.kv_cache_config.kv_cache_groups
        ):
1306
            encoder_seq_lens = self._get_encoder_seq_lens(
1307
1308
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1309

1310
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1311
1312
1313
1314
1315
                # 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(
                    (num_reqs, 1),
                    dtype=torch.int32,
1316
1317
1318
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1319
                    (total_num_scheduled_tokens,),
1320
1321
1322
                    dtype=torch.int64,
                    device=self.device,
                )
1323
1324
1325
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1326
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1327
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1328
1329
1330

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1331
1332
1333
1334
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
                num_common_prefix_blocks = scheduler_output.num_common_prefix_blocks[
                    kv_cache_group_id
                ]
1335

1336
            common_attn_metadata = CommonAttentionMetadata(
1337
1338
1339
1340
1341
                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,
1342
1343
1344
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1345
                max_seq_len=max_seq_len,
1346
1347
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1348
1349
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1350
                causal=True,
1351
                encoder_seq_lens=encoder_seq_lens,
1352
1353
1354
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1355
1356
            )

1357
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1358
                if isinstance(self.drafter, EagleProposer):
1359
1360
1361
1362
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1363
1364
1365
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1366

1367
1368
1369
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1370
                builder = attn_group.get_metadata_builder()
1371
1372
1373
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1374
                        num_common_prefix_blocks,
1375
                        attn_group.kv_cache_spec,
1376
1377
                        builder,
                    )
1378

1379
                extra_attn_metadata_args = {}
1380
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1381
                    extra_attn_metadata_args = dict(
1382
1383
1384
1385
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1386
1387
                    )

1388
1389
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1390
1391
                        ubatch_slices, common_attn_metadata
                    )
1392
                    for ubid, common_attn_metadata in enumerate(
1393
1394
1395
1396
1397
1398
1399
1400
                        common_attn_metadata_list
                    ):
                        attn_metadata_i = attn_group.get_metadata_builder(
                            ubatch_id=ubid
                        ).build(
                            common_prefix_len=common_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                        )
1401
1402
1403
1404
1405
1406
1407
1408
                        for layer_name in kv_cache_group_spec.layer_names:
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
                    attn_metadata_i = builder.build(
                        common_prefix_len=common_prefix_len,
                        common_attn_metadata=common_attn_metadata,
1409
1410
1411
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1412
1413
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1414

1415
1416
1417
1418
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1419
1420
1421
1422
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1423
1424
1425
1426
1427
1428
1429
1430
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1431
            num_tokens_across_dp,
1432
1433
            use_cascade_attn,
        )
1434

1435
1436
1437
1438
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1439
1440
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
    ) -> 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.
        """
1459
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
        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]
1497
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1498
1499
1500
1501
1502
1503
1504
        # 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.
        num_reqs = len(num_scheduled_tokens)
        common_prefix_len = min(
1505
1506
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1507
        # common_prefix_len should be a multiple of the block size.
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
        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
        )
1519
1520
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1521
1522
1523
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1524
            num_kv_heads=kv_cache_spec.num_kv_heads,
1525
            use_alibi=self.use_alibi,
1526
            use_sliding_window=use_sliding_window,
1527
            use_local_attention=use_local_attention,
1528
            num_sms=self.num_sms,
1529
            dcp_world_size=self.dcp_world_size,
1530
1531
1532
        )
        return common_prefix_len if use_cascade else 0

1533
1534
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1535
        for index, req_id in enumerate(self.input_batch.req_ids):
1536
1537
1538
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1539
1540
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1541
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1542
1543
                req.prompt_token_ids, req.prompt_embeds
            )
1544
1545

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1546
1547
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
            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

1561
1562
1563
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1564
1565
1566
1567
1568
1569
1570
                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

1571
                MRotaryEmbedding.get_next_input_positions_tensor(
1572
                    out=self.mrope_positions.np,
1573
1574
1575
1576
1577
                    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,
                )
1578
1579
1580

                mrope_pos_ptr += completion_part_len

1581
1582
    def _calc_spec_decode_metadata(
        self,
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
        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
1599
1600
1601
1602

        # 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(
1603
1604
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1605
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1606
        logits_indices = np.repeat(
1607
1608
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1609
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1610
1611
1612
1613
1614
1615
        logits_indices += arange

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

        # Compute the draft logits indices.
1616
1617
1618
        # 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(
1619
1620
            num_draft_tokens, cumsum_dtype=np.int32
        )
1621
1622
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1623
1624
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1625
1626
1627
1628
1629
        # [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(
1630
1631
            self.device, non_blocking=True
        )
1632
1633
1634
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1635
1636
1637
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1638
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1639
1640
            self.device, non_blocking=True
        )
1641
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1642
1643
            self.device, non_blocking=True
        )
1644

1645
1646
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1647
        draft_token_ids = self.input_ids.gpu[logits_indices]
1648
1649
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1650
        return SpecDecodeMetadata(
1651
1652
1653
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1654
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1655
1656
1657
1658
1659
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1660
1661
1662
1663
1664
1665
1666
    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
1667
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1668
1669
1670
1671
1672
        # 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_(
1673
1674
1675
1676
1677
1678
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1679
1680
1681
1682
1683
            # 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
1684
1685
1686
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1687
1688
        return logits_indices_padded

1689
1690
1691
1692
1693
1694
1695
1696
    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
1697
                inputs.
1698
1699
1700
1701
1702
1703

        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
        """
1704
1705
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1706
            return [], []
1707
        # Batch the multi-modal inputs.
1708
        mm_kwargs = list[MultiModalKwargsItem]()
1709
1710
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1711
1712
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1713
1714

            for mm_input_id in encoder_input_ids:
1715
1716
1717
1718
                mm_feature = req_state.mm_features[mm_input_id]
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
1719

1720
1721
1722
1723
1724
        return mm_kwargs, mm_hashes_pos

    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
1725
1726
            scheduler_output
        )
1727
1728
1729
1730

        if not mm_kwargs:
            return

1731
1732
1733
1734
1735
1736
1737
        # 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.
1738
        model = cast(SupportsMultiModal, self.model)
1739
        encoder_outputs = []
1740
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1741
1742
1743
1744
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1745
        ):
1746
1747
1748
            curr_group_outputs = []

            # EVS-related change.
1749
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1750
            # processing multimodal data. This solves the issue with scheduler
1751
1752
1753
1754
            # 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)
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
            # 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,
                        )
1772
                    )
1773
1774

                    micro_batch_outputs = model.get_multimodal_embeddings(
1775
1776
                        **micro_batch_mm_inputs
                    )
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786

                    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.
1787
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1788

1789
1790
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1791
                expected_num_items=num_items,
1792
            )
1793
            encoder_outputs.extend(curr_group_outputs)
1794

1795
1796
1797
        # 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(
1798
1799
1800
1801
1802
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1803
1804
        self,
        scheduler_output: "SchedulerOutput",
1805
        shift_computed_tokens: int = 0,
1806
1807
1808
1809
1810
1811
1812
1813
    ) -> 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
1814
        should_sync_mrope_positions = False
1815

1816
        for req_id in self.input_batch.req_ids:
1817
1818
            mm_embeds_req: list[torch.Tensor] = []

1819
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1820
            req_state = self.requests[req_id]
1821
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1822

1823
1824
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1825
1826
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842

                # 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,
1843
1844
                    num_encoder_tokens,
                )
1845
                assert start_idx < end_idx
1846

1847
                mm_hash = mm_feature.identifier
1848
                encoder_output = self.encoder_cache.get(mm_hash, None)
1849
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1850
1851
1852
1853

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

1854
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1855
1856
1857
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1858

1859
1860
1861
1862
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1863
1864
1865
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1866
                assert req_state.mrope_positions is not None
1867
1868
1869
1870
1871
1872
1873
                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,
1874
1875
                    )
                )
1876
1877
1878
1879
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1880
1881
1882
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1883
1884
1885

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1886
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1887

1888
        return mm_embeds, is_mm_embed
1889

1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
    def _extract_encoder_inputs(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> dict[str, torch.Tensor]:
        """Extract encoder inputs for encoder-decoder models.

        This method extracts multimodal input features from scheduled encoder
        inputs and formats them for the encoder-decoder model forward pass.
        """
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, _ = self._batch_mm_kwargs_from_scheduler(scheduler_output)

        if not mm_kwargs:
            return {}

        # Group MM kwargs by modality and extract features
1906
        model = cast(SupportsMultiModal, self.model)
1907
1908
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1909
1910
1911
1912
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1913
1914
1915
1916
1917
1918
1919
1920
        ):
            # Add the grouped features to encoder_features dict
            # This allows the model to receive them as kwargs (e.g.,
            # input_features=...)
            encoder_features.update(mm_kwargs_group)

        return encoder_features

1921
    def get_model(self) -> nn.Module:
1922
        # get raw model out of the cudagraph wrapper.
1923
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1924
            return self.model.unwrap()
1925
1926
        return self.model

1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
    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

1942
1943
1944
1945
1946
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1947
1948
        supported_tasks = list(model.pooler.get_supported_tasks())

1949
1950
1951
1952
1953
        if self.scheduler_config.chunked_prefill_enabled:
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
1954

1955
1956
            logger.debug_once(
                "Chunked prefill is not supported with "
1957
1958
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
1959
1960
1961
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1962
1963
1964
1965
1966

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1967
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1968
1969

        return supported_tasks
1970

1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
    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)

1981
    def sync_and_slice_intermediate_tensors(
1982
1983
1984
1985
1986
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1987
1988
1989
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1990
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1991
1992
1993
1994
1995
1996

        # 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():
1997
                is_scattered = k == "residual" and is_rs
1998
                copy_len = num_tokens // tp if is_scattered else num_tokens
1999
                self.intermediate_tensors[k][:copy_len].copy_(
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
                    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:
2013
2014
2015
2016
2017
2018
2019
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2020
2021
        model = self.get_model()
        assert is_mixture_of_experts(model)
2022
        self.eplb_state.step(
2023
            model,
2024
2025
            is_dummy,
            is_profile,
2026
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2027
2028
        )

2029
2030
2031
2032
    # This is where the second ubatch is adjusted to account for the padding.
    # Should be called after attention metadata creation. This just pads
    # the second ubatch slice out to the total number of tokens
    # (num_tokens + padding)
2033
2034
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2035
2036
2037
2038
2039
2040
        padded_second_ubatch_slice = slice(
            ubatch_slices[1].token_slice.start, num_total_tokens
        )
        ubatch_slices[1] = UBatchSlice(
            padded_second_ubatch_slice, padded_second_ubatch_slice
        )
2041

2042
2043
2044
2045
2046
2047
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2048
2049
2050
        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"
        )
2051

2052
        hidden_states = hidden_states[:num_scheduled_tokens]
2053
        pooling_metadata = self.input_batch.get_pooling_metadata()
2054
2055
2056
2057
        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]
2058

2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
        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()
2069

2070
        pooler_output: list[torch.Tensor | None] = []
2071
        for raw_output, seq_len, prompt_len in zip(
2072
2073
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2074
            output = raw_output if seq_len == prompt_len else None
2075
            pooler_output.append(output)
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085

        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,
        )

2086
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2087
2088
2089
2090
2091
2092
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2093
2094
2095
2096
2097
2098
2099
2100
            # Use CUDA graphs.
            # Add padding to the batch size.
            return self.vllm_config.pad_for_cudagraph(num_scheduled_tokens)

        # Eager mode.
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2101
2102
2103
2104
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2105
2106
2107
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2108
    def _preprocess(
2109
2110
        self,
        scheduler_output: "SchedulerOutput",
2111
        num_input_tokens: int,  # Padded
2112
        intermediate_tensors: IntermediateTensors | None = None,
2113
2114
    ) -> tuple[
        int,
2115
2116
        torch.Tensor | None,
        torch.Tensor | None,
2117
        torch.Tensor,
2118
        IntermediateTensors | None,
2119
2120
        dict[str, Any],
    ]:
2121
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2122
        is_first_rank = get_pp_group().is_first_rank
2123

2124
2125
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2126
2127
        if (
            self.supports_mm_inputs
2128
            and is_first_rank
2129
2130
            and not self.model_config.is_encoder_decoder
        ):
2131
2132
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2133
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2134

2135
2136
2137
            # 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.
2138
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2139
2140
2141
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2142
            )
2143

2144
            # TODO(woosuk): Avoid the copy. Optimize.
2145
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2146

2147
            input_ids = None
2148
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2149
2150
2151
2152
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2153
        elif self.enable_prompt_embeds and is_first_rank:
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
            # 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).
2166
2167
2168
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2169
                .squeeze(1)
2170
            )
2171
2172
2173
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2174
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2175
2176
2177
2178
2179
                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
2180
        else:
2181
2182
2183
2184
            # 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.
2185
            input_ids = self.input_ids.gpu[:num_input_tokens]
2186
            inputs_embeds = None
2187
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2188
        if self.uses_mrope:
2189
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2190
        else:
2191
            positions = self.positions.gpu[:num_input_tokens]
2192

2193
        if is_first_rank:
2194
2195
            intermediate_tensors = None
        else:
2196
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2197
2198
                num_input_tokens, intermediate_tensors, True
            )
2199

2200
2201
2202
2203
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2204
2205
2206
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2207
2208
2209
2210
2211
2212
2213
2214
        return (
            num_scheduled_tokens,
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2215

2216
    def _sample(
2217
        self,
2218
2219
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2220
    ) -> SamplerOutput:
2221
        # Sample the next token and get logprobs if needed.
2222
        sampling_metadata = self.input_batch.sampling_metadata
2223
        if spec_decode_metadata is None:
2224
2225
2226
            # 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()
2227
            return self.sampler(
2228
2229
2230
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2231

2232
        sampler_output = self.rejection_sampler(
2233
2234
            spec_decode_metadata,
            None,  # draft_probs
2235
            logits,
2236
2237
            sampling_metadata,
        )
2238
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2239
2240
2241
        return sampler_output

    def _bookkeeping_sync(
2242
2243
2244
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2245
        logits: torch.Tensor | None,
2246
2247
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2248
        spec_decode_metadata: SpecDecodeMetadata | None,
2249
    ) -> tuple[
2250
        dict[str, int],
2251
        LogprobsLists | None,
2252
        list[list[int]],
2253
        dict[str, LogprobsTensors | None],
2254
2255
2256
        list[str],
        dict[str, int],
        list[int],
2257
    ]:
2258
2259
2260
2261
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2262
2263
2264
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2265
2266
2267
2268
        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)
2269

2270
2271
2272
        # 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()
2273
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2274
2275

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2276
        sampled_token_ids = sampler_output.sampled_token_ids
2277
        invalid_req_indices = []
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
        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)
            else:
                # Includes spec decode tokens.
                valid_sampled_token_ids = self.rejection_sampler.parse_output(
                    sampled_token_ids,
                    self.input_batch.vocab_size,
                )
            # Mask out the sampled tokens that should not be sampled.
            for i in discard_sampled_tokens_req_indices:
2292
                valid_sampled_token_ids[int(i)].clear()
2293
        else:
2294
            valid_sampled_token_ids = []
2295
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2296
2297
2298
2299
2300
2301
            invalid_req_indices_set = set(invalid_req_indices)
            assert sampled_token_ids.shape[-1] == 1

            # 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.
2302
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2303
2304
2305
2306
2307
            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
            }
2308

2309
2310
2311
2312
2313
        # 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.
2314
        req_ids = self.input_batch.req_ids
2315
2316
2317
2318
        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
2319
2320
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2321
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2322
2323
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2324
2325
2326
2327
2328
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2329
2330
2331
2332
            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}"
2333
            )
2334

2335
2336
            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
2337
2338
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2339

2340
            req_id = req_ids[req_idx]
2341
2342
2343
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2344
2345
2346
2347
2348
2349
2350
            if cu_num_accepted_tokens is not None:
                cu_num_accepted_tokens.append(
                    cu_num_accepted_tokens[-1] + len(sampled_ids)
                )

        logprobs_lists = (
            logprobs_tensors.tolists(cu_num_accepted_tokens)
2351
            if not self.use_async_scheduling and logprobs_tensors is not None
2352
2353
2354
2355
2356
2357
2358
2359
2360
            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,
        )

2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
        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,
        )

2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
    @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()

2386
2387
    def _model_forward(
        self,
2388
2389
2390
2391
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2392
2393
2394
2395
2396
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2397
        Motivation: We can inspect only this method versus
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
        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,
        )

2418
2419
2420
2421
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2422
2423
        intermediate_tensors: IntermediateTensors | None = None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
2424
        with record_function_or_nullcontext("Preprocess"):
2425
2426
2427
2428
2429
2430
2431
2432
2433
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

                if not scheduler_output.total_num_scheduled_tokens:
                    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(
2434
2435
                        scheduler_output, self.vllm_config
                    )
2436
2437
2438
2439
                if self.cache_config.kv_sharing_fast_prefill:
                    assert not self.input_batch.num_prompt_logprobs, (
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2440
2441
                        "it when the requests need prompt logprobs"
                    )
2442

2443
                # Prepare the decoder inputs.
2444
2445
2446
2447
2448
2449
2450
2451
                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
2452
                    num_tokens_across_dp,
2453
2454
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2455

2456
            dp_rank = self.parallel_config.data_parallel_rank
2457
2458
            if ubatch_slices:
                assert num_tokens_across_dp is not None
2459
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
2460
2461
                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
2462
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
2463
2464
2465
2466
2467
            else:
                num_input_tokens = self._get_num_input_tokens(
                    scheduler_output.total_num_scheduled_tokens
                )

2468
2469
2470
2471
2472
2473
2474
            (
                num_scheduled_tokens,
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
2475
            ) = self._preprocess(
2476
                scheduler_output, num_input_tokens, intermediate_tensors
2477
2478
2479
2480
2481
2482
            )

            uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
                num_scheduled_tokens == self.input_batch.num_reqs * max_query_len
            )
            batch_descriptor = BatchDescriptor(
2483
2484
2485
                num_tokens=num_input_tokens,
                uniform_decode=uniform_decode,
                has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
2486
2487
2488
2489
            )
            cudagraph_runtime_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn)
            )
2490

2491
2492
        # Set cudagraph mode to none if calc_kv_scales is true.
        if attn_metadata is not None:
2493
2494
2495
2496
2497
            metadata_list = (
                attn_metadata.values()
                if isinstance(attn_metadata, dict)
                else [attn_metadata]
            )
2498
            if any(
2499
2500
                getattr(m, "enable_kv_scales_calculation", False) for m in metadata_list
            ):
2501
2502
                cudagraph_runtime_mode = CUDAGraphMode.NONE

2503
2504
        # Run the model.
        # Use persistent buffers for CUDA graphs.
2505
2506
        with (
            set_forward_context(
2507
2508
2509
2510
2511
2512
                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                batch_descriptor=batch_descriptor,
2513
                ubatch_slices=ubatch_slices,
2514
2515
2516
2517
            ),
            record_function_or_nullcontext("Forward"),
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2518
            model_output = self._model_forward(
2519
2520
2521
2522
2523
2524
2525
2526
2527
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

        with record_function_or_nullcontext("Postprocess"):
            if self.use_aux_hidden_state_outputs:
2528
                # True when EAGLE 3 is used.
2529
2530
                hidden_states, aux_hidden_states = model_output
            else:
2531
                # Common case.
2532
2533
2534
                hidden_states = model_output
                aux_hidden_states = None

2535
2536
2537
2538
2539
            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)
2540
2541
                    hidden_states.kv_connector_output = kv_connector_output
                    return hidden_states
2542

2543
                if self.is_pooling_model:
2544
                    # Return the pooling output.
2545
2546
2547
                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
2548
2549
                    output.kv_connector_output = kv_connector_output
                    return output
2550
2551

                sample_hidden_states = hidden_states[logits_indices]
2552
                logits = self.model.compute_logits(sample_hidden_states)
2553
2554
2555
2556
2557
            else:
                # Rare case.
                assert not self.is_pooling_model

                if not get_pp_group().is_last_rank:
2558
                    all_gather_tensors = {
2559
2560
2561
                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2562
                    }
2563
                    get_pp_group().send_tensor_dict(
2564
2565
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
2566
2567
                        all_gather_tensors=all_gather_tensors,
                    )
2568
2569
2570
                    logits = None
                else:
                    sample_hidden_states = hidden_states[logits_indices]
2571
                    logits = self.model.compute_logits(sample_hidden_states)
2572
2573
2574
2575
2576

                model_output_broadcast_data = {}
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

2577
2578
2579
                model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
2580
2581
2582
2583
                assert model_output_broadcast_data is not None
                logits = model_output_broadcast_data["logits"]

            # Apply structured output bitmasks if present
2584
2585
            if scheduler_output.structured_output_request_ids:
                apply_grammar_bitmask(scheduler_output, self.input_batch, logits)
2586
2587
2588
2589

        with record_function_or_nullcontext("Sample"):
            sampler_output = self._sample(logits, spec_decode_metadata)

2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
        def propose_draft_token_ids(sampled_token_ids):
            assert spec_decode_common_attn_metadata is not None
            with record_function_or_nullcontext("Draft"):
                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,
                )

2604
2605
2606
2607
2608
        use_padded_batch_for_eagle = (
            self.speculative_config
            and self.speculative_config.use_eagle()
            and not self.speculative_config.disable_padded_drafter_batch
        )
2609
2610
2611
        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
2612
2613
2614
2615
2616
        if (
            self.speculative_config
            and self.speculative_config.draft_model_config is not None
            and self.speculative_config.draft_model_config.max_model_len is not None
        ):
2617
            effective_drafter_max_model_len = (
2618
2619
                self.speculative_config.draft_model_config.max_model_len
            )
2620
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
2621
2622
2623
2624
            spec_decode_common_attn_metadata.max_seq_len
            + self.speculative_config.num_speculative_tokens
            <= effective_drafter_max_model_len
        )
2625
        if use_padded_batch_for_eagle and input_fits_in_drafter:
2626
2627
2628
2629
            # 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(sampler_output.sampled_token_ids)

2630
2631
2632
2633
2634
2635
2636
2637
2638
        with record_function_or_nullcontext("Bookkeep"):
            (
                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,
2639
2640
2641
2642
2643
2644
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
2645
                spec_decode_metadata,
2646
            )
2647

2648
2649
2650
2651
2652
        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
2653
2654
2655
            # 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)
2656

2657
2658
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2659

2660
2661
2662
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
2663
2664
2665
2666
            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
2667
            kv_connector_output=kv_connector_output,
2668
2669
2670
            num_nans_in_logits=num_nans_in_logits,
        )

2671
2672
2673
        if not self.use_async_scheduling:
            return output

2674
        async_output = AsyncGPUModelRunnerOutput(
2675
            model_runner_output=output,
2676
            sampled_token_ids=sampler_output.sampled_token_ids,
2677
            logprobs_tensors=sampler_output.logprobs_tensors,
2678
2679
2680
2681
            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

2682
2683
2684
2685
2686
2687
2688
2689
2690
        # Save ref of sampled_token_ids CPU tensor if the batch contains
        # any requests with sampling params that that require output ids.
        self.input_batch.set_async_sampled_token_ids(
            async_output.sampled_token_ids_cpu,
            async_output.async_copy_ready_event,
        )

        return async_output

2691
    def take_draft_token_ids(self) -> DraftTokenIds | None:
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
        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)

2702
2703
2704
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2705
        sampled_token_ids: torch.Tensor | list[list[int]],
2706
2707
2708
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
2709
2710
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2711
        common_attn_metadata: CommonAttentionMetadata,
2712
    ) -> list[list[int]] | torch.Tensor:
2713
2714
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2715
            assert isinstance(sampled_token_ids, list)
2716
            assert isinstance(self.drafter, NgramProposer)
2717
            draft_token_ids = self.drafter.propose(
2718
2719
                sampled_token_ids,
                self.input_batch.req_ids,
2720
2721
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2722
2723
                self.input_batch.spec_decode_unsupported_reqs,
            )
2724
        elif self.speculative_config.method == "medusa":
2725
            assert isinstance(sampled_token_ids, list)
2726
            assert isinstance(self.drafter, MedusaProposer)
2727

2728
2729
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2730
2731
2732
2733
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
Wentao Ye's avatar
Wentao Ye committed
2734
                assert spec_decode_metadata is not None
2735
                for num_draft, tokens in zip(
2736
2737
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
2738
2739
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2740
                indices = torch.tensor(indices, device=self.device)
2741
2742
                hidden_states = sample_hidden_states[indices]

2743
            draft_token_ids = self.drafter.propose(
2744
2745
2746
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2747
        elif self.speculative_config.use_eagle():
2748
            assert isinstance(self.drafter, EagleProposer)
2749
2750
2751
2752
2753

            if self.speculative_config.disable_padded_drafter_batch:
                # 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.
2754
2755
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2756
                    "padded-batch is disabled."
2757
                )
2758
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
2759
2760
2761
2762
2763
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
2764
2765
2766
2767
2768
            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.
2769
2770
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2771
                    "padded-batch is enabled."
2772
2773
                )
                next_token_ids, valid_sampled_tokens_count = (
2774
2775
2776
2777
2778
2779
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2780
                        self.num_discarded_requests,
2781
                    )
2782
                )
Jiayi Yao's avatar
Jiayi Yao committed
2783

2784
            if spec_decode_metadata is None:
2785
                token_indices_to_sample = None
2786
                # input_ids can be None for multimodal models.
2787
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2788
                target_positions = self._get_positions(num_scheduled_tokens)
2789
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
2790
                    assert aux_hidden_states is not None
2791
                    target_hidden_states = torch.cat(
2792
2793
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
2794
2795
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2796
            else:
2797
2798
                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
2799
2800
2801
2802
2803
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
2804
                else:
2805
                    common_attn_metadata, token_indices, token_indices_to_sample = (
2806
2807
2808
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
2809
2810
2811
                            valid_sampled_tokens_count,
                        )
                    )
2812

2813
                target_token_ids = self.input_ids.gpu[token_indices]
2814
                target_positions = self._get_positions(token_indices)
2815
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
2816
                    assert aux_hidden_states is not None
2817
                    target_hidden_states = torch.cat(
2818
2819
                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
2820
2821
                else:
                    target_hidden_states = hidden_states[token_indices]
2822

2823
            if self.supports_mm_inputs:
2824
2825
2826
2827
2828
2829
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
2830

2831
            draft_token_ids = self.drafter.propose(
2832
2833
2834
2835
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2836
                last_token_indices=token_indices_to_sample,
2837
                sampling_metadata=sampling_metadata,
2838
                common_attn_metadata=common_attn_metadata,
2839
                mm_embed_inputs=mm_embed_inputs,
2840
            )
2841

2842
        return draft_token_ids
2843

2844
2845
2846
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2847
2848
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2849
                f"Allowed configs: {allowed_config_names}"
2850
            )
2851
2852
2853
2854
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2855
2856
2857
2858
2859
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2860
2861
2862
2863
2864
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
2865
2866
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
2867
2868
2869
2870
2871

            num_local_physical_experts = torch.empty(1, dtype=torch.int32, device="cpu")
            torch.distributed.broadcast(
                num_local_physical_experts, group=get_ep_group().cpu_group, group_src=0
            )
2872
2873
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2874
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2875
            num_logical_experts = global_expert_load.shape[1]
2876
            self.parallel_config.eplb_config.num_redundant_experts = (
2877
2878
2879
2880
2881
2882
                num_local_physical_experts * new_ep_size - num_logical_experts
            )
            assert old_global_expert_indices.shape[1] % num_local_physical_experts == 0
            old_ep_size = (
                old_global_expert_indices.shape[1] // num_local_physical_experts
            )
2883
            rank_mapping = {
2884
                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
2885
2886
2887
2888
2889
2890
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2891
        with DeviceMemoryProfiler() as m:
2892
            time_before_load = time.perf_counter()
2893
            model_loader = get_model_loader(self.load_config)
2894
            self.model = model_loader.load_model(
2895
2896
                vllm_config=self.vllm_config, model_config=self.model_config
            )
2897
            if self.lora_config:
2898
2899
2900
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2901
2902
2903
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2904
            if self.use_aux_hidden_state_outputs:
2905
                if not supports_eagle3(self.get_model()):
2906
2907
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2908
2909
                        "aux_hidden_state_outputs was requested"
                    )
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922

                # 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)
2923
            time_after_load = time.perf_counter()
2924
        self.model_memory_usage = m.consumed_memory
2925
        logger.info_once(
2926
2927
2928
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
2929
            scope="local",
2930
        )
2931
        prepare_communication_buffer_for_model(self.model)
2932

2933
        self.is_multimodal_pruning_enabled = (
2934
            supports_multimodal_pruning(self.get_model())
2935
2936
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2937

2938
2939
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
            logger.info("EPLB is enabled for model %s.", self.model_config.model)
2940
2941
2942
2943
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2944
2945
2946
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2947
2948
            )

2949
        if (
2950
2951
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
2952
            and supports_dynamo()
2953
        ):
2954
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2955
            compilation_counter.stock_torch_compile_count += 1
2956
            self.model.compile(fullgraph=True, backend=backend)
2957
            return
2958
        # for other compilation modes, cudagraph behavior is controlled by
2959
2960
2961
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
2962
2963
2964
2965
2966
2967
2968
        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
2969
2970
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
2971
2972
2973
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
2974
            else:
2975
2976
2977
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
2978

2979
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
        """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

3003
    def reload_weights(self) -> None:
3004
        assert getattr(self, "model", None) is not None, (
3005
            "Cannot reload weights before model is loaded."
3006
        )
3007
3008
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3009
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3010

3011
3012
3013
3014
3015
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3016
            self.get_model(),
3017
            tensorizer_config=tensorizer_config,
3018
            model_config=self.model_config,
3019
3020
        )

3021
3022
3023
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3024
        num_scheduled_tokens: dict[str, int],
3025
    ) -> dict[str, LogprobsTensors | None]:
3026
3027
3028
3029
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3030
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3031
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3032
3033
3034
3035
3036

        # 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():
3037
            num_tokens = num_scheduled_tokens[req_id]
3038
3039
3040

            # Get metadata for this request.
            request = self.requests[req_id]
3041
3042
3043
3044
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3045
3046
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3047
3048
                self.device, non_blocking=True
            )
3049

3050
3051
3052
3053
3054
3055
            # 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(
3056
3057
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3058
3059
                in_progress_dict[req_id] = logprobs_tensors

3060
            # Determine number of logits to retrieve.
3061
3062
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3063
            num_remaining_tokens = num_prompt_tokens - start_tok
3064
            if num_tokens <= num_remaining_tokens:
3065
                # This is a chunk, more tokens remain.
3066
3067
3068
                # 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.
3069
3070
3071
3072
3073
                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)
3074
3075
3076
3077
3078
3079
3080
                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
3081
3082
3083
3084
3085

            # 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]
3086
            offset = self.query_start_loc.np[req_idx].item()
3087
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3088
            logits = self.model.compute_logits(prompt_hidden_states)
3089
3090
3091
3092

            # 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.
3093
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3094
3095

            # Compute prompt logprobs.
3096
3097
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3098
3099
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3100
3101

            # Transfer GPU->CPU async.
3102
3103
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3104
3105
3106
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3107
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3108
3109
                ranks, non_blocking=True
            )
3110
3111
3112
3113
3114

        # 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]
3115
            del in_progress_dict[req_id]
3116
3117

        # Must synchronize the non-blocking GPU->CPU transfers.
3118
        if prompt_logprobs_dict:
3119
            self._sync_device()
3120
3121
3122

        return prompt_logprobs_dict

3123
3124
    def _get_nans_in_logits(
        self,
3125
        logits: torch.Tensor | None,
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
    ) -> 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])
3137
3138
3139
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3140
3141
3142
3143
            return num_nans_in_logits
        except IndexError:
            return {}

3144
3145
3146
3147
3148
3149
    @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
3150
         - during DP rank dummy run
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
        """
        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(
3162
                    self.input_ids.gpu,
3163
3164
                    low=0,
                    high=self.model_config.get_vocab_size(),
3165
3166
                    dtype=input_ids.dtype,
                )
3167

3168
            logger.debug_once("Randomizing dummy data for DP Rank")
3169
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3170
3171
3172
            yield
            input_ids.fill_(0)

3173
3174
3175
3176
3177
3178
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3179
3180
        assert self.mm_budget is not None

3181
3182
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3183
            seq_len=self.max_model_len,
3184
            mm_counts={modality: 1},
3185
            cache=self.mm_budget.cache,
3186
3187
3188
3189
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3190
3191
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3192

3193
        model = cast(SupportsMultiModal, self.model)
3194
3195
3196
3197
3198
3199
3200
3201
3202
        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,
            )
        )
3203

3204
3205
3206
3207
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3208
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3209
3210
        force_attention: bool = False,
        uniform_decode: bool = False,
3211
        allow_microbatching: bool = True,
3212
3213
        skip_eplb: bool = False,
        is_profile: bool = False,
3214
        create_mixed_batch: bool = False,
3215
        remove_lora: bool = True,
3216
        activate_lora: bool = False,
3217
    ) -> tuple[torch.Tensor, torch.Tensor]:
3218
3219
3220
3221
3222
3223
3224
        """
        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.
3225
                - if not set will determine the cudagraph mode based on using
3226
                    the self.cudagraph_dispatcher.
3227
3228
3229
3230
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3231
            force_attention: If True, always create attention metadata. Used to
3232
3233
3234
3235
                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.
3236
3237
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3238
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3239
            activate_lora: If False, dummy_run is performed without LoRAs.
3240
        """
3241
3242
3243
3244
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3245

3246
        # If cudagraph_mode.decode_mode() == FULL and
3247
        # cudagraph_mode.separate_routine(). This means that we are using
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
        # 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.
3259
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3260

3261
3262
3263
3264
3265
        # 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
3266
3267
3268
3269
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3270
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3271
3272
3273
3274
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3275
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3276
3277
3278
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3279
            assert not create_mixed_batch
3280
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3281
3282
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3283
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3284
3285
3286
3287
3288
3289
        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

3290
3291
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3292
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3293
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3294

3295
3296
3297
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3298
3299
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3300
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3301
3302
3303
3304
3305
3306
3307
            num_tokens_unpadded=total_num_scheduled_tokens,
            parallel_config=self.vllm_config.parallel_config,
            allow_microbatching=allow_microbatching,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=total_num_scheduled_tokens,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
3308
3309
3310
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3311
3312
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3313

3314
        attn_metadata: PerLayerAttnMetadata | None = None
3315
3316
3317

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3318
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3319
            attn_metadata = {}
3320
3321
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3322

3323
3324
3325
3326
3327
3328
            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:
3329
                seq_lens = max_query_len
3330
            self.seq_lens.np[:num_reqs] = seq_lens
3331
3332
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3333

3334
3335
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3336
3337
            self.query_start_loc.copy_to_gpu()

3338
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3339
3340
                self.kv_cache_config.kv_cache_groups
            ):
3341
                common_attn_metadata = CommonAttentionMetadata(
3342
3343
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3344
3345
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3346
3347
3348
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3349
3350
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3351
                    max_query_len=max_query_len,
3352
                    max_seq_len=self.max_model_len,
3353
3354
3355
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3356
                    slot_mapping=self.input_batch.block_table[
3357
3358
3359
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3360
3361
3362
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3363
                )
3364
                for attn_group in self.attn_groups[kv_cache_group_id]:
3365
3366
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3367
3368
                            ubatch_slices, common_attn_metadata
                        )
3369
                        for ubid, common_attn_metadata in enumerate(
3370
3371
                            common_attn_metadata_list
                        ):
3372
                            assert common_attn_metadata.max_query_len == 1
3373
3374
3375
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3376
                            for layer_name in attn_group.layer_names:
3377
                                assert type(attn_metadata) is list
3378
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3379
3380
                    else:
                        assert type(attn_metadata) is dict
3381
3382
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3383
3384
                            common_attn_metadata
                        )
3385
                        for layer_name in attn_group.layer_names:
3386
                            attn_metadata[layer_name] = attn_metadata_i
3387

3388
        with self.maybe_dummy_run_with_lora(
3389
            self.lora_config, num_scheduled_tokens, activate_lora, remove_lora
3390
        ):
3391
3392
3393
            # Make sure padding doesn't exceed max_num_tokens
            assert num_tokens_after_padding <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3394
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3395
                input_ids = None
3396
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3397
                model_kwargs = {
3398
                    **model_kwargs,
3399
3400
                    **self._dummy_mm_kwargs(num_reqs),
                }
3401
3402
            elif self.enable_prompt_embeds:
                input_ids = None
3403
3404
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3405
            else:
3406
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3407
                inputs_embeds = None
3408

3409
            if self.uses_mrope:
3410
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3411
            else:
3412
                positions = self.positions.gpu[:num_tokens_after_padding]
3413
3414
3415
3416
3417
3418
3419
3420
3421

            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,
3422
3423
3424
                            device=self.device,
                        )
                    )
3425
3426

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3427
                    num_tokens_after_padding, None, False
3428
                )
3429
3430

            # filter out the valid batch descriptor
3431
3432
3433
3434
3435
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3436
                        has_lora=activate_lora and self.lora_config is not None,
3437
3438
3439
3440
3441
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3442
3443
3444
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3445
3446
3447
3448
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3449
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3450
3451
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3452
3453
            else:
                cudagraph_runtime_mode = _cg_mode
3454

3455
            if ubatch_slices is not None:
3456
3457
3458
3459
3460
3461
3462
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
                num_tokens_after_padding = ubatch_slices[0].num_tokens
                if num_tokens_across_dp is not None:
                    num_tokens_across_dp[:] = num_tokens_after_padding

3463
3464
3465
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3466
3467
                    attn_metadata,
                    self.vllm_config,
3468
                    num_tokens=num_tokens_after_padding,
3469
3470
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3471
                    batch_descriptor=batch_descriptor,
3472
3473
3474
                    ubatch_slices=ubatch_slices,
                ),
            ):
3475
                outputs = self.model(
3476
3477
3478
3479
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3480
                    **model_kwargs,
3481
                )
3482

3483
3484
3485
3486
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3487

3488
            if self.speculative_config and self.speculative_config.use_eagle():
3489
                assert isinstance(self.drafter, EagleProposer)
3490
3491
3492
3493
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3494
                self.drafter.dummy_run(num_tokens, use_cudagraphs=use_cudagraphs)
3495

3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
        # 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)

3506
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3507
3508
3509
3510
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3511
3512
3513
3514
3515
3516

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3517
3518
3519
3520
        # 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)
3521

3522
        logits = self.model.compute_logits(hidden_states)
3523
3524
        num_reqs = logits.size(0)

3525
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540

        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)],
3541
            spec_token_ids=[[] for _ in range(num_reqs)],
3542
3543
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3544
            logitsprocs=LogitsProcessors(),
3545
        )
3546
        try:
3547
3548
3549
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3550
        except RuntimeError as e:
3551
            if "out of memory" in str(e):
3552
3553
3554
3555
                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 "
3556
3557
                    "initializing the engine."
                ) from e
3558
3559
            else:
                raise e
3560
        if self.speculative_config:
3561
3562
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3563
3564
                draft_token_ids, self.device
            )
3565
3566
3567
3568
3569
3570

            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
3571
3572
3573
3574
3575
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3576
            )
3577
3578
3579
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3580
                logits,
3581
3582
                dummy_metadata,
            )
3583
        return sampler_output
3584

3585
    def _dummy_pooler_run_task(
3586
3587
        self,
        hidden_states: torch.Tensor,
3588
3589
        task: PoolingTask,
    ) -> PoolerOutput:
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
        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

3601
        dummy_prompt_lens = torch.tensor(
3602
3603
            num_scheduled_tokens_list,
            device="cpu",
3604
        )
3605
3606
3607
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3608

3609
        model = cast(VllmModelForPooling, self.get_model())
3610
        dummy_pooling_params = PoolingParams(task=task)
3611
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3612
        to_update = model.pooler.get_pooling_updates(task)
3613
3614
        to_update.apply(dummy_pooling_params)

3615
        dummy_metadata = PoolingMetadata(
3616
3617
3618
3619
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3620

3621
3622
3623
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3624

3625
        try:
3626
3627
3628
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3629
        except RuntimeError as e:
3630
            if "out of memory" in str(e):
3631
                raise RuntimeError(
3632
3633
3634
                    "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 "
3635
3636
                    "initializing the engine."
                ) from e
3637
3638
            else:
                raise e
3639
3640
3641
3642
3643
3644
3645

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
            if self.scheduler_config.chunked_prefill_enabled:
                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."
                )

3666
        output_size = dict[PoolingTask, float]()
3667
        for task in supported_pooling_tasks:
3668
3669
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3670
            output_size[task] = sum(o.nbytes for o in output)
3671
3672
3673
3674
            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)
3675

3676
    def profile_run(self) -> None:
3677
        # Profile with multimodal encoder & encoder cache.
3678
        if self.supports_mm_inputs:
3679
            if self.model_config.multimodal_config.skip_mm_profiling:
3680
                logger.info(
3681
                    "Skipping memory profiling for multimodal encoder and "
3682
3683
                    "encoder cache."
                )
3684
3685
3686
3687
3688
3689
3690
3691
            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.
3692
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3693
3694
3695
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3696
3697
3698
3699
3700
3701
3702
3703
3704

                    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,
                    )
3705

3706
3707
3708
3709
3710
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3711

3712
                    # Run multimodal encoder.
3713
3714
3715
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3716

3717
3718
3719
3720
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3721

3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
                    # 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
                    # (encode_budget, hidden_size) and scatter encoder
                    # output into it.
                    encoder_output_shape = dummy_encoder_outputs[0].shape
                    if encoder_output_shape[0] < encoder_budget:
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
3732
3733
                                (encoder_budget, encoder_output_shape[-1])
                            )
3734
3735
3736
3737
3738
3739
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3740
                    # Cache the dummy encoder outputs.
3741
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3742

3743
        # Add `is_profile` here to pre-allocate communication buffers
3744
3745
3746
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3747
        if get_pp_group().is_last_rank:
3748
3749
3750
3751
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3752
        else:
3753
            output = None
3754
        self._sync_device()
3755
        del hidden_states, output
3756
        self.encoder_cache.clear()
3757
        gc.collect()
3758

3759
    def capture_model(self) -> int:
3760
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3761
            logger.warning(
3762
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3763
3764
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3765
            return 0
3766

3767
3768
        compilation_counter.num_gpu_runner_capture_triggers += 1

3769
3770
        start_time = time.perf_counter()

3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
        @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()
3785
                    gc.collect()
3786

3787
3788
3789
        # 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.
3790
        set_cudagraph_capturing_enabled(True)
3791
        with freeze_gc(), graph_capture(device=self.device):
3792
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3793
            cudagraph_mode = self.compilation_config.cudagraph_mode
3794
            assert cudagraph_mode is not None
3795
3796
3797
3798
3799
3800
3801
3802
3803

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

3804
3805
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
3806
                # make sure we capture the largest batch size first
3807
3808
3809
                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
3810
3811
3812
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3813
3814
                    uniform_decode=False,
                )
3815

3816
3817
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3818
3819
3820
3821
3822
3823
3824
            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
                )
3825
                decode_cudagraph_batch_sizes = [
3826
3827
                    x
                    for x in self.cudagraph_batch_sizes
3828
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3829
                ]
3830
3831
3832
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
3833
3834
3835
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3836
3837
                    uniform_decode=True,
                )
3838

3839
3840
3841
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3842
3843
3844
        # 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
3845
        # we may do lazy capturing in future that still allows capturing
3846
3847
        # after here.
        set_cudagraph_capturing_enabled(False)
3848
3849
3850
3851
3852

        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.
3853
        logger.info_once(
3854
3855
3856
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
3857
            scope="local",
3858
        )
3859
        return cuda_graph_size
3860

3861
3862
    def _capture_cudagraphs(
        self,
3863
        compilation_cases: list[tuple[int, bool]],
3864
3865
3866
3867
3868
3869
3870
        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}"
3871
3872
3873
3874
3875
3876
3877
3878

        # 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",
3879
3880
3881
                    cudagraph_runtime_mode.name,
                ),
            )
3882

3883
        # We skip EPLB here since we don't want to record dummy metrics
3884
        for num_tokens, activate_lora in compilation_cases:
3885
            # We currently only capture ubatched graphs when its a FULL
3886
3887
3888
            # 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
3889
3890
3891
3892
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3893
3894
3895
3896
3897
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3898
            )
3899

3900
3901
3902
3903
3904
3905
            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.
3906
3907
3908
3909
3910
3911
3912
3913
3914
                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,
3915
                    activate_lora=activate_lora,
3916
3917
3918
3919
3920
3921
3922
3923
                )
            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,
3924
                activate_lora=activate_lora,
3925
            )
3926
        self.maybe_remove_all_loras(self.lora_config)
3927

3928
3929
3930
3931
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3932
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
3933

3934
3935
3936
3937
3938
3939
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
3940
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
3941
            layers = get_layers_from_vllm_config(
3942
3943
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
3944
3945
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
3946
            # Dedupe based on full class name; this is a bit safer than
3947
3948
3949
3950
            # 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.
3951
            for layer_name in kv_cache_group_spec.layer_names:
3952
                attn_backend = layers[layer_name].get_attn_backend()
3953
3954
3955
3956
3957
3958
3959

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

3960
3961
3962
                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):
3963
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
3964
                key = (full_cls_name, layer_kv_cache_spec)
3965
3966
3967
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
3968
                attn_backend_layers[key].append(layer_name)
3969
3970
3971
3972
            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()),
            )
3973
3974

        def create_attn_groups(
3975
            attn_backends_map: dict[AttentionGroupKey, list[str]],
3976
3977
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
3978
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
3979
3980
                attn_group = AttentionGroup.create_with_metadata_builders(
                    attn_backend,
3981
                    layer_names,
3982
                    kv_cache_spec,
3983
3984
                    self.vllm_config,
                    self.device,
3985
                    num_metadata_builders=1
3986
3987
                    if not self.parallel_config.enable_dbo
                    else 2,
3988
3989
                )

3990
3991
3992
                attn_groups.append(attn_group)
            return attn_groups

3993
3994
        attention_backend_maps = []
        attention_backend_set: set[type[AttentionBackend]] = set()
3995
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
3996
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
3997
3998
3999
4000
4001
4002
4003
4004
            attention_backend_maps.append(attn_backends[0])
            attention_backend_set.update(attn_backends[1])

        # Resolve cudagraph_mode before actually initialize metadata_builders
        self._check_and_update_cudagraph_mode(attention_backend_set)

        for attn_backends_map in attention_backend_maps:
            self.attn_groups.append(create_attn_groups(attn_backends_map))
4005

co63oc's avatar
co63oc committed
4006
        # Calculate reorder batch threshold (if needed)
4007
4008
        self.calculate_reorder_batch_threshold()

4009
4010
4011
    def _check_and_update_cudagraph_mode(
        self, attention_backends: set[type[AttentionBackend]]
    ) -> None:
4012
        """
4013
        Resolve the cudagraph_mode when there are multiple attention
4014
4015
4016
4017
        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4018
        min_cg_support = AttentionCGSupport.ALWAYS
4019
        min_cg_backend_name = None
4020

4021
4022
4023
4024
4025
        for attn_backend in attention_backends:
            builder_cls = attn_backend.get_builder_cls()
            if builder_cls.cudagraph_support.value < min_cg_support.value:
                min_cg_support = builder_cls.cudagraph_support
                min_cg_backend_name = attn_backend.__name__
4026
4027
4028
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
4029
4030
4031
4032
4033
4034
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4035
                f"with {min_cg_backend_name} backend (support: "
4036
4037
                f"{min_cg_support})"
            )
4038
4039
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4040
4041
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4042
                    "make sure compilation mode is VLLM_COMPILE"
4043
                )
4044
4045
4046
4047
4048
                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"
4049
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4050
                    CUDAGraphMode.FULL_AND_PIECEWISE
4051
                )
4052
4053
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4054
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4055
                    CUDAGraphMode.FULL_DECODE_ONLY
4056
                )
4057
4058
            logger.warning(msg)

4059
        # check that if we are doing decode full-cudagraphs it is supported
4060
4061
4062
4063
4064
4065
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4066
                f"with {min_cg_backend_name} backend (support: "
4067
4068
                f"{min_cg_support})"
            )
4069
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4070
4071
4072
4073
4074
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4075
                    "attention is compiled piecewise"
4076
4077
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4078
                    CUDAGraphMode.PIECEWISE
4079
                )
4080
            else:
4081
4082
                msg += (
                    "; setting cudagraph_mode=NONE because "
4083
                    "attention is not compiled piecewise"
4084
4085
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4086
                    CUDAGraphMode.NONE
4087
                )
4088
4089
            logger.warning(msg)

4090
4091
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4092
4093
4094
4095
4096
4097
4098
4099
        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 "
4100
                f"{min_cg_backend_name} (support: {min_cg_support})"
4101
            )
4102
4103
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4104
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4105
                    CUDAGraphMode.PIECEWISE
4106
                )
4107
4108
            else:
                msg += "; setting cudagraph_mode=NONE"
4109
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4110
                    CUDAGraphMode.NONE
4111
                )
4112
4113
4114
4115
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4116
4117
4118
4119
4120
4121
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4122
                f"supported with {min_cg_backend_name} backend ("
4123
4124
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4125
                "and make sure compilation mode is VLLM_COMPILE"
4126
            )
4127

4128
4129
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4130
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4131
4132
            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4133

4134
4135
4136
4137
4138
    def calculate_reorder_batch_threshold(self) -> None:
        """
        Check that if any backends reorder batches; that the reordering
        is compatible (e.g., decode threshold is the same)
        """
4139
        for group in self._attn_group_iterator():
4140
            attn_metadata_builder_i = group.get_metadata_builder()
4141

4142
4143
            # check that if any backends reorder batches; that the reordering
            # is compatible (e.g., decode threshold is the same)
4144
            reorder_batch_threshold_i = attn_metadata_builder_i.reorder_batch_threshold
4145
4146
            if reorder_batch_threshold_i is not None:
                if self.reorder_batch_threshold is not None:
4147
                    if reorder_batch_threshold_i != self.reorder_batch_threshold:
4148
4149
4150
4151
                        raise ValueError(
                            f"Attention backend reorders decodes with "
                            f"threshold {reorder_batch_threshold_i} but other "
                            f"backend uses threshold "
4152
4153
                            f"{self.reorder_batch_threshold}"
                        )
4154
4155
4156
                else:
                    self.reorder_batch_threshold = reorder_batch_threshold_i

4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
    def _find_compatible_block_sizes(
        self,
        kv_manager_block_size: int,
        backend_cls: type[AttentionBackend],
        return_all: bool = False,
    ) -> list[int]:
        """
        Find compatible block sizes for a backend.

        Args:
            kv_manager_block_size: Physical block size of KV cache
            backend_cls: Attention backend class
            return_all: Return all compatible sizes if True, max size if False

        Returns:
            Compatible block size(s) based on return_all parameter

        Raises:
            ValueError: If no compatible block size found
        """
        supported_block_size = backend_cls.get_supported_kernel_block_size()
        compatible_sizes = []

        for block_size in supported_block_size:
            if isinstance(block_size, int):
                if kv_manager_block_size % block_size == 0:
                    compatible_sizes.append(block_size)
            elif (
                isinstance(block_size, MultipleOf)
                and kv_manager_block_size % block_size.base == 0
            ):
                compatible_sizes.append(kv_manager_block_size)

        if not compatible_sizes:
            raise ValueError(f"No compatible block size for {kv_manager_block_size}")

        return compatible_sizes if return_all else [max(compatible_sizes)]

    def _select_common_block_size(
        self, kv_manager_block_size: int, attn_groups: list[AttentionGroup]
    ) -> int:
        """
        Select common block size for all backends.

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

        Returns:
            Block size supported by all backends,
            prioritizing cache_config.block_size

        Raises:
            ValueError: If no common block size found
        """
        all_backend_supports = []

        for attn_group in attn_groups:
            compatible_sizes = self._find_compatible_block_sizes(
                kv_manager_block_size, attn_group.backend, return_all=True
            )
            supported_sizes = sorted(list(set(compatible_sizes)), reverse=True)
            all_backend_supports.append(set(supported_sizes))

        common_supported_sizes = set.intersection(*all_backend_supports)

        if not common_supported_sizes:
            error_msg = f"No common block size for {kv_manager_block_size}. "
            for i, attn_group in enumerate(attn_groups):
                supported = all_backend_supports[i]
                error_msg += (
                    f"Backend {attn_group.backend} supports: {sorted(supported)}. "
                )
            raise ValueError(error_msg)

        if self.cache_config.block_size in common_supported_sizes:
            return self.cache_config.block_size

        return max(common_supported_sizes)

4237
    def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None:
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
        """
        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.
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
4249
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4250
        ]
4251
4252
4253
4254
4255
4256
4257

        # Generate kernel_block_sizes that matches each block_size
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
4258
4259
4260
            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
4261
4262
                "for more details."
            )
4263
4264
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
4265
                max_model_len=max(self.max_model_len, self.max_encoder_len),
4266
4267
4268
4269
4270
                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,
4271
                kernel_block_sizes=kernel_block_sizes,
4272
                is_spec_decode=bool(self.vllm_config.speculative_config),
4273
                logitsprocs=self.input_batch.logitsprocs,
4274
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
4275
                is_pooling_model=self.is_pooling_model,
4276
4277
                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
4278
4279
4280
                    if self.vllm_config.speculative_config
                    else 0
                ),
4281
4282
            )

4283
    def _allocate_kv_cache_tensors(
4284
4285
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4286
        """
4287
4288
4289
        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.

4290
        Args:
4291
            kv_cache_config: The KV cache config
4292
        Returns:
4293
            dict[str, torch.Tensor]: A map between layer names to their
4294
            corresponding memory buffer for KV cache.
4295
        """
4296
4297
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
4298
4299
4300
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
4301
4302
4303
4304
4305
            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:
4306
4307
4308
4309
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
4310
4311
4312
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
4313
4314
        return kv_cache_raw_tensors

4315
4316
4317
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4318
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4319
4320
        if not self.kv_cache_config.kv_cache_groups:
            return
4321
4322
        for attn_groups in self.attn_groups:
            yield from attn_groups
4323

4324
4325
4326
4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
    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 = []
        for kv_cache_group_id, kv_cache_group in enumerate(
            kv_cache_config.kv_cache_groups
        ):
4342
4343
4344
4345
4346
4347
            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):
4348
                continue
4349
            elif isinstance(kv_cache_spec, AttentionSpec):
4350
4351
4352
4353
4354
4355
4356
4357
4358
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
                attn_groups = self.attn_groups[kv_cache_group_id]
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
                selected_kernel_size = self._select_common_block_size(
                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
4359
            elif isinstance(kv_cache_spec, MambaSpec):
4360
4361
                # This is likely Mamba or other non-attention cache,
                # no splitting.
4362
                kernel_block_sizes.append(kv_cache_spec.block_size)
4363
4364
4365
4366
4367
4368
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

4369
4370
4371
4372
4373
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
4374
        """
4375
        Reshape the KV cache tensors to the desired shape and dtype.
4376

4377
        Args:
4378
4379
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
4380
                correct size but uninitialized shape.
4381
        Returns:
4382
            Dict[str, torch.Tensor]: A map between layer names to their
4383
4384
            corresponding memory buffer for KV cache.
        """
4385
        kv_caches: dict[str, torch.Tensor] = {}
4386
        has_attn, has_mamba = False, False
4387
4388
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
4389
4390
            attn_backend = group.backend
            for layer_name in group.layer_names:
4391
4392
                if layer_name in self.runner_only_attn_layers:
                    continue
4393
4394
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
4395
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4396
                if isinstance(kv_cache_spec, AttentionSpec):
4397
                    has_attn = True
4398
4399
4400
4401
4402
4403
4404
4405
                    kv_manager_block_size = kv_cache_spec.block_size
                    kernel_size_list = self._find_compatible_block_sizes(
                        kv_manager_block_size, attn_backend, return_all=False
                    )
                    kernel_size = kernel_size_list[0]
                    num_blocks_per_kv_block = kv_manager_block_size // kernel_size
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

4406
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
4407
4408
                        kernel_num_blocks,
                        kernel_size,
4409
4410
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
4411
4412
                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
4413
                    dtype = kv_cache_spec.dtype
4414
                    try:
4415
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()  # noqa: E501
4416
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
4417
                    except (AttributeError, NotImplementedError):
4418
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
4419
4420
4421
4422
4423
                    # 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.
4424
4425
4426
                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
4427
4428
4429
4430
4431
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
4432
4433
4434
4435
4436
4437
                    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
4438
                elif isinstance(kv_cache_spec, MambaSpec):
4439
                    has_mamba = True
Chen Zhang's avatar
Chen Zhang committed
4440
4441
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
4442
                    storage_offset_bytes = 0
4443
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
4444
4445
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
4446
4447
                            kv_cache_spec.page_size_bytes // dtype_size
                        )
Chen Zhang's avatar
Chen Zhang committed
4448
                        target_shape = (num_blocks, *shape)
4449
4450
                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
4451
                        assert storage_offset_bytes % dtype_size == 0
4452
4453
4454
4455
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
4456
                            storage_offset=storage_offset_bytes // dtype_size,
4457
                        )
Chen Zhang's avatar
Chen Zhang committed
4458
                        state_tensors.append(tensor)
4459
                        storage_offset_bytes += stride[0] * dtype_size
4460
4461

                    kv_caches[layer_name] = state_tensors
4462
                else:
4463
                    raise NotImplementedError
4464
4465

        if has_attn and has_mamba:
4466
            self._update_hybrid_attention_mamba_layout(kv_caches)
4467

4468
4469
        return kv_caches

4470
    def _update_hybrid_attention_mamba_layout(
4471
4472
        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
4473
        """
4474
4475
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
4476
4477

        Args:
4478
            kv_caches: The KV cache buffer of each layer.
4479
4480
        """

4481
4482
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
4483
            for layer_name in group.layer_names:
4484
                kv_cache = kv_caches[layer_name]
4485
4486
4487
4488
                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 "
4489
                        f"a tensor of shape {kv_cache.shape}"
4490
                    )
4491
                    hidden_size = kv_cache.shape[2:].numel()
4492
4493
4494
4495
                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
4496

4497
    def initialize_kv_cache_tensors(
4498
4499
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4500
4501
4502
4503
4504
4505
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        Returns:
4506
            Dict[str, torch.Tensor]: A map between layer names to their
4507
4508
4509
4510
4511
            corresponding memory buffer for KV cache.
        """
        # 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
4512
4513
4514
        kv_caches = self._reshape_kv_cache_tensors(
            kv_cache_config, kv_cache_raw_tensors
        )
4515

4516
        # Set up cross-layer KV cache sharing
4517
4518
        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)
4519
4520
            kv_caches[layer_name] = kv_caches[target_layer_name]

4521
4522
4523
4524
4525
4526
4527
4528
4529
        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,
        )
4530
4531
4532
        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
4533
4534
        self, kv_cache_config: KVCacheConfig
    ) -> None:
4535
4536
4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
4552
        """
        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.
4553
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
4554
4555
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
4556
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
4557
4558
                else:
                    break
4559

4560
4561
4562
4563
4564
4565
4566
    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
        """
4567
        kv_cache_config = deepcopy(kv_cache_config)
4568
        self.kv_cache_config = kv_cache_config
4569
        self.may_add_encoder_only_layers_to_kv_cache_config()
4570
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
4571
        self.initialize_attn_backend(kv_cache_config)
4572
4573
        # Reinitialize need to after initialize_attn_backend
        self.may_reinitialize_input_batch(kv_cache_config)
4574
4575
        kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)

4576
4577
4578
4579
4580
4581
        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
4582
        if has_kv_transfer_group():
4583
4584
4585
            kv_transfer_group = get_kv_transfer_group()
            kv_transfer_group.register_kv_caches(kv_caches)
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
4586

4587
        if self.dcp_world_size > 1:
4588
            layer_names = self.attn_groups[0][0].layer_names
4589
4590
4591
            layers = get_layers_from_vllm_config(
                self.vllm_config, AttentionLayerBase, layer_names
            )
4592
4593
4594
4595
4596
            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
4597
4598
                    "does not return the softmax lse for decode."
                )
4599

4600
4601
4602
4603
4604
    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
4605
        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
4606
4607
4608
        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:
4609
                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
4610
4611
4612
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
4613
4614
                    dtype=self.kv_cache_dtype,
                )
4615
4616
4617
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
4618
4619
4620
            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
4621
4622
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
4623
4624
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
4625

4626
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
4627
        """
4628
        Generates the KVCacheSpec by parsing the kv cache format from each
4629
4630
        Attention module in the static forward context.
        Returns:
4631
            KVCacheSpec: A dictionary mapping layer names to their KV cache
4632
4633
4634
            format. Layers that do not need KV cache are not included.
        """

4635
        kv_cache_spec: dict[str, KVCacheSpec] = {}
4636
        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
Chen Zhang's avatar
Chen Zhang committed
4637
        for layer_name, attn_module in attn_layers.items():
4638
4639
4640
4641
4642
4643
4644
4645
4646
4647
4648
4649
4650
4651
4652
            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
4653

4654
        return kv_cache_spec
4655
4656
4657
4658
4659
4660
4661
4662
4663
4664

    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
4665
        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
4666
4667
4668
4669
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()