"vscode:/vscode.git/clone" did not exist on "58a365086c6076cd7dcabf92cf9df9decd1c7908"
gpu_model_runner.py 119 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

Robert Shaw's avatar
Robert Shaw committed
4
import copy
5
import gc
6
import time
7
import weakref
8
from contextlib import contextmanager
9
from typing import TYPE_CHECKING, Any, Optional, Union
10
11
12
13
14

import numpy as np
import torch
import torch.distributed
import torch.nn as nn
15
from tqdm import tqdm
16

17
import vllm.envs as envs
18
from vllm.attention import AttentionType, get_attn_backend
19
from vllm.attention.backends.abstract import AttentionBackend
20
from vllm.attention.layer import Attention
21
from vllm.compilation.counter import compilation_counter
22
23
from vllm.config import (CompilationLevel, VllmConfig,
                         get_layers_from_vllm_config)
24
from vllm.distributed.eplb.eplb_state import EplbState
25
26
from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group)
Robert Shaw's avatar
Robert Shaw committed
27
from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
28
from vllm.distributed.parallel_state import (
29
    get_pp_group, get_tp_group, graph_capture, is_global_first_rank,
30
    prepare_communication_buffer_for_model)
31
32
from vllm.forward_context import (DPMetadata, get_forward_context,
                                  set_forward_context)
33
from vllm.logger import init_logger
Chen Zhang's avatar
Chen Zhang committed
34
from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
35
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
36
from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
37
38
from vllm.model_executor.models.interfaces import (has_step_pooler,
                                                   is_mixture_of_experts)
39
40
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
41
from vllm.multimodal.utils import group_mm_inputs_by_modality
42
from vllm.pooling_params import PoolingParams
43
from vllm.sampling_params import SamplingType
44
from vllm.sequence import IntermediateTensors
45
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
46
                        GiB_bytes, LazyLoader, async_tensor_h2d, cdiv,
Chen Zhang's avatar
Chen Zhang committed
47
                        check_use_alibi, get_dtype_size,
Woosuk Kwon's avatar
Woosuk Kwon committed
48
                        is_pin_memory_available, round_up)
Chen Zhang's avatar
Chen Zhang committed
49
from vllm.v1.attention.backends.mamba_attn import Mamba2AttentionBackend
50
51
from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
                                              CommonAttentionMetadata)
52
from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
53
from vllm.v1.kv_cache_interface import (AttentionSpec, FullAttentionSpec,
Chen Zhang's avatar
Chen Zhang committed
54
                                        KVCacheConfig, KVCacheSpec, MambaSpec,
55
                                        SlidingWindowSpec)
56
57
from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
                             ModelRunnerOutput)
58
from vllm.v1.pool.metadata import PoolingMetadata
59
from vllm.v1.sample.metadata import SamplingMetadata
60
from vllm.v1.sample.rejection_sampler import RejectionSampler
61
from vllm.v1.sample.sampler import Sampler
62
from vllm.v1.spec_decode.eagle import EagleProposer
63
from vllm.v1.spec_decode.medusa import MedusaProposer
64
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
65
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
66
from vllm.v1.spec_decode.utils import is_spec_decode_supported
67
from vllm.v1.utils import bind_kv_cache
68
from vllm.v1.worker.block_table import BlockTable
69
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
70
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
71

72
73
from .utils import (gather_mm_placeholders, initialize_kv_cache_for_kv_sharing,
                    sanity_check_mm_encoder_outputs, scatter_mm_placeholders)
74

75
if TYPE_CHECKING:
76
    import xgrammar as xgr
77
    import xgrammar.kernels.apply_token_bitmask_inplace_torch_compile as xgr_torch_compile  # noqa: E501
78

79
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
80
    from vllm.v1.core.sched.output import SchedulerOutput
81
82
else:
    xgr = LazyLoader("xgr", globals(), "xgrammar")
83
84
85
    xgr_torch_compile = LazyLoader(
        "xgr_torch_compile", globals(),
        "xgrammar.kernels.apply_token_bitmask_inplace_torch_compile")
86
87
88
89

logger = init_logger(__name__)


90
class GPUModelRunner(LoRAModelRunnerMixin):
91
92
93

    def __init__(
        self,
94
        vllm_config: VllmConfig,
95
        device: torch.device,
96
    ):
97
98
99
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
100
        self.compilation_config = vllm_config.compilation_config
101
102
103
104
105
106
107
        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.prompt_adapter_config = vllm_config.prompt_adapter_config
        self.observability_config = vllm_config.observability_config
108

109
110
111
112
        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
        set_cpu_offload_max_bytes(
            int(self.cache_config.cpu_offload_gb * 1024**3))

113
114
115
116
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
117
        self.device = device
118
119
120
121
122
123
124
125
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
        if cache_config.cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        else:
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                cache_config.cache_dtype]

126
        self.is_multimodal_model = model_config.is_multimodal_model
127
        self.is_pooling_model = model_config.pooler_config is not None
128
129
        self.max_model_len = model_config.max_model_len
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
130
        self.max_num_reqs = scheduler_config.max_num_seqs
131
132

        # Model-related.
133
134
        self.num_query_heads = model_config.get_num_attention_heads(
            parallel_config)
135
        self.hidden_size = model_config.get_hidden_size()
136
        self.attention_chunk_size = model_config.attention_chunk_size
137

138
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
139

140
        # Multi-modal data support
141
        self.mm_registry = MULTIMODAL_REGISTRY
142
        self.uses_mrope = model_config.uses_mrope
143

144
145
146
        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
            model_config=model_config,
            scheduler_config=scheduler_config,
147
            mm_registry=self.mm_registry,
148
149
150
        )
        self.max_num_encoder_input_tokens = encoder_compute_budget
        self.encoder_cache_size = encoder_cache_size
151

152
153
154
        # Sampler
        self.sampler = Sampler()

155
156
157
158
159
160
161
        self.eplb_state: Optional[EplbState] = None
        """
        State of the expert parallelism load balancer.

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

162
        # Lazy initializations
163
        # self.model: nn.Module  # Set after load_model
164
        # Initialize in initialize_kv_cache
165
        self.kv_caches: list[torch.Tensor] = []
166
167
        self.attn_metadata_builders: list[AttentionMetadataBuilder] = []
        self.attn_backends: list[type[AttentionBackend]] = []
168
169
        # self.kv_cache_config: KVCacheConfig

170
        # req_id -> (input_id -> encoder_output)
171
        self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
172

173
        self.use_aux_hidden_state_outputs = False
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
        # 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():
                self.drafter = EagleProposer(self.vllm_config, self.device,
                                             self)  # type: ignore
                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
                    vllm_config=self.vllm_config,
                    device=self.device)  # type: ignore
            else:
                raise ValueError("Unknown speculative decoding method: "
                                 f"{self.speculative_config.method}")
            self.rejection_sampler = RejectionSampler()
194

195
        # Request states.
196
        self.requests: dict[str, CachedRequestState] = {}
197

198
199
200
201
202
203
204
205
206
        # 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.
207
208
209
210
211
212
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
            max_model_len=self.max_model_len,
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
213
            vocab_size=self.model_config.get_vocab_size(),
214
            block_sizes=[self.cache_config.block_size],
215
        )
216

217
218
219
220
221
        self.use_cuda_graph = (
            self.vllm_config.compilation_config.level
            == CompilationLevel.PIECEWISE
            and self.vllm_config.compilation_config.use_cudagraph
            and not self.model_config.enforce_eager)
222
        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
223
224
225
226
        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
        self.cudagraph_batch_sizes = list(
227
228
229
            reversed(self.compilation_config.cudagraph_capture_sizes))

        self.full_cuda_graph = self.compilation_config.full_cuda_graph
230

231
        # Cache the device properties.
232
        self._init_device_properties()
233

234
235
236
237
        # Persistent buffers for CUDA graphs.
        self.input_ids = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int32,
                                     device=self.device)
238
239
240
        self.positions = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int64,
                                     device=self.device)
241
242
243
244
245
246
247
248
249
250
        self.query_start_loc = torch.zeros(self.max_num_reqs + 1,
                                           dtype=torch.int32,
                                           device=self.device)
        self.seq_lens = torch.zeros(self.max_num_reqs,
                                    dtype=torch.int32,
                                    device=self.device)
        self.slot_mapping = torch.zeros(self.max_num_tokens,
                                        dtype=torch.int64,
                                        device=self.device)

251
252
        # None in the first PP rank. The rest are set after load_model.
        self.intermediate_tensors: Optional[IntermediateTensors] = None
253
254

        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
255
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
256
257
258
259
            # 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
260
261
262
263
264
265

            # 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
Roger Wang's avatar
Roger Wang committed
266
            self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1),
267
268
                                               dtype=torch.int64,
                                               device=self.device)
Roger Wang's avatar
Roger Wang committed
269
270
271
272
273
            self.mrope_positions_cpu = torch.zeros(
                (3, self.max_num_tokens + 1),
                dtype=torch.int64,
                device="cpu",
                pin_memory=self.pin_memory)
274
            self.mrope_positions_np = self.mrope_positions_cpu.numpy()
275

276
277
278
        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)

279
280
281
282
        self.inputs_embeds = torch.zeros(
            (self.max_num_tokens, self.hidden_size),
            dtype=self.dtype,
            device=self.device)
283

284
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
285
        # Keep in int64 to avoid overflow with long context
286
        self.arange_np = np.arange(max(self.max_num_reqs + 1,
287
288
                                       self.max_model_len,
                                       self.max_num_tokens),
289
                                   dtype=np.int64)
290
291
292
        # NOTE(woosuk): These tensors are "stateless", i.e., they are literally
        # a faster version of creating a new tensor every time. Thus, we should
        # not make any assumptions about the values in these tensors.
293
294
295
296
297
298
299
300
301
302
303
304
305
306
        self.input_ids_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int32,
                                         device="cpu",
                                         pin_memory=self.pin_memory)
        self.positions_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int64,
                                         device="cpu",
                                         pin_memory=self.pin_memory)
        self.positions_np = self.positions_cpu.numpy()
        self.query_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
                                               dtype=torch.int32,
                                               device="cpu",
                                               pin_memory=self.pin_memory)
        self.query_start_loc_np = self.query_start_loc_cpu.numpy()
307
308
309
310
311
        self.seq_lens_cpu = torch.zeros(self.max_num_reqs,
                                        dtype=torch.int32,
                                        device="cpu",
                                        pin_memory=self.pin_memory)
        self.seq_lens_np = self.seq_lens_cpu.numpy()
312

313
314
315
316
317
318
        # 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] = {}

319
320
321
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> bool:
        """
        Update the order of requests in the batch based on the attention
322
        backend's needs. For example, some attention backends (namely MLA) may
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.

        Returns:
            True if the batch was reordered, False otherwise.
        """
        batch_reordered = self.attn_metadata_builders[0].reorder_batch(
            self.input_batch, scheduler_output)

        # For models with multiple KV cache groups, the groups should agree on
        # the same order of requests. We ensure this by only allowing the first
        # group to reorder the batch and asserting that all other groups do not
        # reorder the batch.
        for i in range(1, len(self.kv_cache_config.kv_cache_groups)):
            assert not self.attn_metadata_builders[i].reorder_batch(
                self.input_batch, scheduler_output)
        return batch_reordered

344
345
346
347
348
349
350
351
352
353
354
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
        """Initialize attributes from torch.cuda.get_device_properties
        """
        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()

355
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
356
357
358
359
360
361
        """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.

362
363
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
364
365
        """
        # Remove finished requests from the cached states.
366
367
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
368
            self.encoder_cache.pop(req_id, None)
369
370
371
372
373
374
        # 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.
375
        removed_req_indices: list[int] = []
376
377
378
379
        for req_id in scheduler_output.finished_req_ids:
            req_index = self.input_batch.remove_request(req_id)
            if req_index is not None:
                removed_req_indices.append(req_index)
380
381
382
383
384
385
386
387

        # Free the cached encoder outputs.
        for req_id, input_id in scheduler_output.free_encoder_input_ids:
            encoder_outputs = self.encoder_cache.get(req_id)
            if encoder_outputs is not None:
                encoder_outputs.pop(input_id, None)
                if not encoder_outputs:
                    self.encoder_cache.pop(req_id, None)
388

389
390
391
392
393
394
395
396
397
398
399
400
401
        # 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:
402
            req_index = self.input_batch.remove_request(req_id)
403
404
            assert req_index is not None
            removed_req_indices.append(req_index)
405

406
        req_ids_to_add: list[str] = []
407
        # Add new requests to the cached states.
408
409
410
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
411
412
413
            pooling_params = new_req_data.pooling_params
            if sampling_params and \
                sampling_params.sampling_type == SamplingType.RANDOM_SEED:
414
415
416
417
418
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

419
420
            self.requests[req_id] = CachedRequestState(
                req_id=req_id,
421
422
423
                prompt_token_ids=new_req_data.prompt_token_ids,
                mm_inputs=new_req_data.mm_inputs,
                mm_positions=new_req_data.mm_positions,
424
                sampling_params=sampling_params,
425
                pooling_params=pooling_params,
426
                generator=generator,
427
428
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
429
                output_token_ids=[],
430
                lora_request=new_req_data.lora_request,
431
            )
432
433

            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
434
            if self.uses_mrope:
435
436
                image_grid_thw = []
                video_grid_thw = []
Roger Wang's avatar
Roger Wang committed
437
                second_per_grid_ts = []
438
439
                audio_feature_lengths = []
                use_audio_in_video = False
440
441
442
443
444
445
446
                for mm_input in self.requests[req_id].mm_inputs:
                    if mm_input.get("image_grid_thw") is not None:
                        image_grid_thw.extend(
                            mm_input["image_grid_thw"].tolist())
                    if mm_input.get("video_grid_thw") is not None:
                        video_grid_thw.extend(
                            mm_input["video_grid_thw"].tolist())
Roger Wang's avatar
Roger Wang committed
447
448
449
                    if mm_input.get("second_per_grid_ts") is not None:
                        second_per_grid_ts.extend(
                            mm_input["second_per_grid_ts"])
450
451
452
453
454
                    if mm_input.get("audio_feature_lengths") is not None:
                        audio_feature_lengths.extend(
                            mm_input["audio_feature_lengths"])
                    if mm_input.get("use_audio_in_video") is True:
                        use_audio_in_video = True
455
456
457
458
459
460
461

                hf_config = self.model_config.hf_config

                self.requests[req_id].mrope_positions, \
                    self.requests[req_id].mrope_position_delta = \
                    MRotaryEmbedding.get_input_positions_tensor(
                        self.requests[req_id].prompt_token_ids,
Roger Wang's avatar
Roger Wang committed
462
                        hf_config=hf_config,
463
464
                        image_grid_thw=image_grid_thw,
                        video_grid_thw=video_grid_thw,
Roger Wang's avatar
Roger Wang committed
465
                        second_per_grid_ts=second_per_grid_ts,
466
467
                        audio_feature_lengths=audio_feature_lengths,
                        use_audio_in_video=use_audio_in_video,
468
469
                    )

470
471
            req_ids_to_add.append(req_id)

472
        # Update the states of the running/resumed requests.
473
        is_last_rank = get_pp_group().is_last_rank
474
475
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
476
            req_state = self.requests[req_id]
477
478
479
            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]
480

481
            # Update the cached states.
482
            req_state.num_computed_tokens = num_computed_tokens
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499

            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.
                num_new_tokens = (num_computed_tokens + len(new_token_ids) -
                                  req_state.num_tokens)
                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
                    req_state.output_token_ids.extend(
                        new_token_ids[-num_new_tokens:])

500
            # Update the block IDs.
501
            if not resumed_from_preemption:
502
                # Append the new blocks to the existing block IDs.
503
504
505
                for block_ids, new_ids in zip(req_state.block_ids,
                                              new_block_ids):
                    block_ids.extend(new_ids)
506
507
508
            else:
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
509
                req_state.block_ids = new_block_ids
510
511
512
513
514
515
516
517
518
519
520

            req_index = self.input_batch.req_id_to_index.get(req_id)
            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.
                req_ids_to_add.append(req_id)
                continue

            # Update the persistent batch.
            self.input_batch.num_computed_tokens_cpu[req_index] = (
521
                num_computed_tokens)
522
            self.input_batch.block_table.append_row(new_block_ids, req_index)
523
524
525
526
527
528
529

            # 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)
530
                self.input_batch.token_ids_cpu[
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
                    req_index,
                    start_token_index:end_token_index] = new_token_ids
                self.input_batch.num_tokens_no_spec[
                    req_index] = end_token_index
                # Add spec_token_ids to token_ids_cpu.
                spec_token_ids = (
                    scheduler_output.scheduled_spec_decode_tokens.get(
                        req_id, ()))
                if spec_token_ids:
                    start_index = end_token_index
                    end_token_index += len(spec_token_ids)
                    self.input_batch.token_ids_cpu[
                        req_index,
                        start_index:end_token_index] = spec_token_ids
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] = end_token_index
547

548
549
        # Check if the batch has changed. If not, we can skip copying the
        # sampling metadata from CPU to GPU.
550
551
        batch_changed = len(removed_req_indices) > 0 or len(req_ids_to_add) > 0

552
553
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
554
        removed_req_indices.sort(reverse=True)
555
556
557
558
559
560
561
562
563
564
565
566
567
        for req_id in req_ids_to_add:
            req_state = self.requests[req_id]
            if removed_req_indices:
                # Fill the empty index.
                req_index = removed_req_indices.pop()
            else:
                # Append to the end.
                req_index = None
            self.input_batch.add_request(req_state, req_index)

        # Condense the batched states if there are empty indices.
        if removed_req_indices:
            self.input_batch.condense(removed_req_indices)
568

569
        batch_reordered = self._may_reorder_batch(scheduler_output)
570
571

        if batch_changed or batch_reordered:
572
            self.input_batch.refresh_sampling_metadata()
573

574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
        cumsum_dtype: Optional[np.dtype] = None,
    ) -> 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

594
    def _prepare_inputs(
595
596
        self,
        scheduler_output: "SchedulerOutput",
597
    ) -> tuple[dict[str, Any], bool, torch.Tensor,
598
               Optional[SpecDecodeMetadata], np.ndarray]:
599
600
601
602
603
604
605
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
            attention_cuda_graphs: whether attention can run in cudagraph
            logits_indices, spec_decode_metadata
        ]
        """
606
607
608
609
610
611
612
        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.
613
        self.input_batch.block_table.commit(num_reqs)
614
615

        # Get the number of scheduled tokens for each request.
616
617
618
619
        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)
620
621
622

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
623
624
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
625

626
627
628
629
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens, arange = self._get_cumsum_and_arange(
            num_scheduled_tokens)
630
631

        # Get positions.
632
        positions_np = self.positions_np[:total_num_scheduled_tokens]
633
634
635
636
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

637
638
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
639
        if self.uses_mrope:
640
641
            self._calc_mrope_positions(scheduler_output)

642
643
644
645
        # 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.
646
647
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
648

649
650
651
652
        # 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.
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
653
                           0,
654
655
                           torch.from_numpy(token_indices),
                           out=self.input_ids_cpu[:total_num_scheduled_tokens])
656

657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
        # Calculate the slot mapping for each KV cache group.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
                self.kv_cache_config.kv_cache_groups):
            block_size = kv_cache_group_spec.kv_cache_spec.block_size
            block_table: BlockTable = self.input_batch.block_table[
                kv_cache_group_id]
            # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
            # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
            # where K is the max_num_blocks_per_req and the block size is 2.
            # NOTE(woosuk): We can't simply use `token_indices // block_size`
            # here because M (max_model_len) is not necessarily divisible by
            # block_size.
            block_table_indices = (
                req_indices * block_table.max_num_blocks_per_req +
                positions_np // block_size)
            block_table_cpu = block_table.get_cpu_tensor()
            block_numbers = block_table_cpu.flatten(
            )[block_table_indices].numpy()
            block_offsets = positions_np % block_size
            np.add(
                block_numbers * block_size,
                block_offsets,
                out=block_table.slot_mapping_np[:total_num_scheduled_tokens])
680
681

        # Prepare the attention metadata.
682
        self.query_start_loc_np[0] = 0
683
        self.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
684

685
686
687
        self.seq_lens_np[:num_reqs] = (
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)
688
689
690
691

        # Copy the tensors to the GPU.
        self.input_ids[:total_num_scheduled_tokens].copy_(
            self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
692
        if self.uses_mrope:
693
694
695
696
697
698
699
700
701
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
            self.mrope_positions[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions_cpu[:, :total_num_scheduled_tokens],
                non_blocking=True)
        else:
            # Common case (1D positions)
            self.positions[:total_num_scheduled_tokens].copy_(
                self.positions_cpu[:total_num_scheduled_tokens],
                non_blocking=True)
702

703
704
705
706
707
708
709
        self.query_start_loc[:num_reqs + 1].copy_(
            self.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
        self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
                                       non_blocking=True)

        # Fill unused with -1. Needed for reshape_and_cache
        self.seq_lens[num_reqs:].fill_(0)
710
711
712
713
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
        self.query_start_loc[num_reqs + 1:].fill_(
            self.query_start_loc_cpu[num_reqs].item())
714
715
716
717

        query_start_loc = self.query_start_loc[:num_reqs + 1]
        seq_lens = self.seq_lens[:num_reqs]

718
        common_attn_metadata = CommonAttentionMetadata(
719
720
721
722
723
724
            query_start_loc=query_start_loc,
            seq_lens=seq_lens,
            num_reqs=num_reqs,
            num_actual_tokens=total_num_scheduled_tokens,
            max_query_len=max_num_scheduled_tokens,
        )
725

726
        attn_metadata: dict[str, Any] = {}
727
728
729
730
731
732
733
        # 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(
                self.kv_cache_config.kv_cache_groups):

            # Prepare for cascade attention if enabled & beneficial.
            common_prefix_len = 0
734
            builder = self.attn_metadata_builders[kv_cache_group_id]
735
736
737
            if self.cascade_attn_enabled:
                common_prefix_len = self._compute_cascade_attn_prefix_len(
                    num_scheduled_tokens,
738
739
740
                    scheduler_output.
                    num_common_prefix_blocks[kv_cache_group_id],
                    kv_cache_group_spec.kv_cache_spec,
741
                    builder,
742
                )
743

744
745
746
747
748
            attn_metadata_i = (builder.build(
                common_prefix_len=common_prefix_len,
                common_attn_metadata=common_attn_metadata,
            ))

749
750
            for layer_name in kv_cache_group_spec.layer_names:
                attn_metadata[layer_name] = attn_metadata_i
751

752
753
754
755
        attention_cuda_graphs = all(
            b.can_run_in_cudagraph(common_attn_metadata)
            for b in self.attn_metadata_builders)

756
757
        use_spec_decode = len(
            scheduler_output.scheduled_spec_decode_tokens) > 0
758
        if not use_spec_decode:
759
760
761
762
763
            # 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.
764
            logits_indices = query_start_loc[1:] - 1
765
766
767
768
769
770
771
772
773
774
775
776
777
778
            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)
            for req_id, draft_token_ids in (
                    scheduler_output.scheduled_spec_decode_tokens.items()):
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)

            spec_decode_metadata = self._calc_spec_decode_metadata(
                num_draft_tokens, cu_num_tokens)
            logits_indices = spec_decode_metadata.logits_indices
779

780
781
782
783
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

784
        return (attn_metadata, attention_cuda_graphs, logits_indices,
785
                spec_decode_metadata, num_scheduled_tokens)
786

787
788
789
790
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
791
792
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
    ) -> 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.
        """
811
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
        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]
849
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
850
851
852
853
854
855
856
857
858
859
        # 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(
            common_prefix_len,
            self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
        # common_prefix_len should be a multiple of the block size.
860
861
862
863
864
865
866
        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))
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
867
868
869
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
870
            num_kv_heads=kv_cache_spec.num_kv_heads,
871
            use_alibi=self.use_alibi,
872
            use_sliding_window=use_sliding_window,
873
874
875
876
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

877
878
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
879
        for index, req_id in enumerate(self.input_batch.req_ids):
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
            req = self.requests[req_id]
            assert req.mrope_positions is not None

            num_computed_tokens = \
                self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = \
                scheduler_output.num_scheduled_tokens[req_id]
            num_prompt_tokens = len(req.prompt_token_ids)

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

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

            if prompt_part_len > 0:
                # prompt's 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

                self.mrope_positions_cpu[:, dst_start:dst_end] = \
                    req.mrope_positions[:,src_start:src_end]

                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

917
918
919
920
921
922
923
                MRotaryEmbedding.get_next_input_positions_tensor(
                    out=self.mrope_positions_np,
                    out_offset=dst_start,
                    mrope_position_delta=req.mrope_position_delta,
                    context_len=num_computed_tokens + prompt_part_len,
                    num_new_tokens=completion_part_len,
                )
924
925
926

                mrope_pos_ptr += completion_part_len

927
928
    def _calc_spec_decode_metadata(
        self,
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
        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
945
946
947
948
949
950

        # 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(
            num_sampled_tokens, cumsum_dtype=np.int32)
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
951
952
        logits_indices = np.repeat(
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
953
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
954
955
956
957
958
959
        logits_indices += arange

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

        # Compute the draft logits indices.
960
961
962
963
        # 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(
            num_draft_tokens, cumsum_dtype=np.int32)
964
965
966
967
968
969
970
971
972
973
974
975
976
977
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens)
        # [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(
            self.device, non_blocking=True)
        logits_indices = torch.from_numpy(logits_indices).to(self.device,
                                                             non_blocking=True)
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
            self.device, non_blocking=True)
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
978
979
            self.device, non_blocking=True)

980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
        draft_token_ids = self.input_ids[logits_indices]
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

        metadata = SpecDecodeMetadata(
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )
        return metadata

995
    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
996
997
998
999
1000
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
1001
1002
        mm_inputs = list[MultiModalKwargs]()
        req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
1003
1004
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1005
1006
1007
1008
1009

            for mm_input_id in encoder_input_ids:
                mm_inputs.append(req_state.mm_inputs[mm_input_id])
                req_ids_pos.append(
                    (req_id, mm_input_id, req_state.mm_positions[mm_input_id]))
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021

        # 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.
        grouped_mm_inputs_list = group_mm_inputs_by_modality(mm_inputs)

        encoder_outputs = []
        for grouped_mm_inputs in grouped_mm_inputs_list:
1022
1023
            batched_mm_inputs = MultiModalKwargs.batch(
                grouped_mm_inputs, pin_memory=self.pin_memory)
1024
1025
1026
1027
            batched_mm_inputs = MultiModalKwargs.as_kwargs(
                batched_mm_inputs,
                device=self.device,
            )
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038

            # 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.
            curr_group_outputs = self.model.get_multimodal_embeddings(
                **batched_mm_inputs)

1039
1040
1041
1042
1043
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
                expected_num_items=len(grouped_mm_inputs),
            )

1044
1045
            for output in curr_group_outputs:
                encoder_outputs.append(output)
1046
1047

        # Cache the encoder outputs.
1048
1049
1050
1051
        for (req_id, input_id, pos_info), output in zip(
                req_ids_pos,
                encoder_outputs,
        ):
1052
1053
1054
            if req_id not in self.encoder_cache:
                self.encoder_cache[req_id] = {}

1055
1056
1057
1058
1059
1060
            self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1061
1062
        self,
        scheduler_output: "SchedulerOutput",
1063
    ) -> list[torch.Tensor]:
1064
        mm_embeds: list[torch.Tensor] = []
1065
        for req_id in self.input_batch.req_ids:
1066
1067
1068
1069
1070
1071
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
            num_computed_tokens = req_state.num_computed_tokens
            mm_positions = req_state.mm_positions
            for i, pos_info in enumerate(mm_positions):
1072
1073
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094

                # 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,
                    num_encoder_tokens)
                assert start_idx < end_idx
                assert req_id in self.encoder_cache
                assert i in self.encoder_cache[req_id]
                encoder_output = self.encoder_cache[req_id][i]
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104

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

                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
                mm_embeds.append(mm_embeds_item)
        return mm_embeds
1105

1106
1107
1108
    def get_model(self) -> nn.Module:
        return self.model

1109
1110
1111
1112
1113
1114
1115
1116
1117
    def apply_grammar_bitmask(
        self,
        scheduler_output: "SchedulerOutput",
        logits: torch.Tensor,
    ):
        grammar_bitmask = scheduler_output.grammar_bitmask
        if grammar_bitmask is None:
            return

1118
1119
1120
1121
1122
1123
1124
1125
1126
        # We receive the structured output bitmask from the scheduler,
        # compacted to contain bitmasks only for structured output requests.
        # The order of the requests in the bitmask is not guaranteed to be the
        # same as the order of the requests in the gpu runner's batch. We need
        # to sort the bitmask to match the order of the requests used here.

        # Get the batch indices of the structured output requests.
        # Keep track of the number of speculative tokens scheduled for every
        # request in the batch, as the logit indices are offset by this amount.
1127
        struct_out_req_batch_indices: dict[str, int] = {}
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
        cumulative_offset = 0
        seq = sorted(self.input_batch.req_id_to_index.items(),
                     key=lambda x: x[1])
        for req_id, batch_index in seq:
            logit_index = batch_index + cumulative_offset
            cumulative_offset += len(
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
            if req_id in scheduler_output.structured_output_request_ids:
                struct_out_req_batch_indices[req_id] = logit_index

        out_indices = []

        # Reorder the bitmask to match the order of the requests in the batch.
        sorted_bitmask = np.zeros_like(grammar_bitmask,
                                       shape=(logits.shape[0],
                                              grammar_bitmask.shape[1]))
        cumulative_index = 0
        seq = sorted(scheduler_output.structured_output_request_ids.items(),
                     key=lambda x: x[1])
        for req_id, _ in seq:
            logit_index = struct_out_req_batch_indices[req_id]
            num_spec_tokens = len(
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
            for i in range(1 + num_spec_tokens):
                sorted_bitmask[logit_index + i] = \
                    grammar_bitmask[cumulative_index + i]
                out_indices.append(logit_index + i)
            cumulative_index += 1 + num_spec_tokens
        grammar_bitmask = sorted_bitmask
1157

1158
1159
        # Serialization of np.ndarray is much more efficient than a tensor,
        # so we receive it in that format.
1160
1161
        grammar_bitmask = torch.from_numpy(grammar_bitmask)

1162
1163
1164
1165
        # Force use of the torch.compile implementation from xgrammar to work
        # around issues with the Triton kernel in concurrent structured output
        # scenarios. See PR #19565 and issues #19493, #18376 for details.
        xgr_torch_compile.apply_token_bitmask_inplace_torch_compile(
1166
1167
            logits,
            grammar_bitmask.to(self.device, non_blocking=True),
1168
            indices=out_indices,
1169
1170
        )

1171
1172
1173
1174
1175
1176
1177
    def sync_and_slice_intermediate_tensors(
            self, num_tokens: int, intermediate_tensors: IntermediateTensors,
            sync_self: bool) -> IntermediateTensors:

        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1178
        enabled_sp = self.compilation_config.pass_config. \
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
            enable_sequence_parallelism
        if enabled_sp:
            # When sequence parallelism is enabled, we always pad num_tokens
            # to be a multiple of tensor_parallel_size (tp) earlier
            assert num_tokens % tp == 0
        is_residual_scattered = tp > 1 and enabled_sp \
            and num_tokens % tp == 0

        # 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():
                is_scattered = "residual" and is_residual_scattered
                copy_len = num_tokens // tp if is_scattered else \
                    num_tokens
                self.intermediate_tensors[k][:copy_len].copy_(
                    v[:copy_len], non_blocking=True)

        return IntermediateTensors({
            k:
            v[:num_tokens // tp]
            if k == "residual" and is_residual_scattered else v[:num_tokens]
            for k, v in self.intermediate_tensors.items()
        })

1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
    def eplb_step(self,
                  is_dummy: bool = False,
                  is_profile: bool = False) -> None:
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
        assert is_mixture_of_experts(self.model)
        self.eplb_state.step(
            self.model,
            is_dummy,
            is_profile,
            log_stats=self.parallel_config.eplb_log_balancedness,
        )

1223
1224
    def get_dp_padding(self,
                       num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
1225
1226
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
1227
1228
1229
1230
1231
1232
1233
1234
1235

        # For DP: Don't pad when setting enforce_eager.
        # 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.
        #
        # TODO(tms) : There are many cases where padding is enabled for
        # prefills, causing unnecessary and excessive padding of activations.

        if dp_size == 1 or self.vllm_config.model_config.enforce_eager:
1236
            # Early exit.
1237
            return 0, None
1238
1239
1240
1241

        num_tokens_across_dp = DPMetadata.num_tokens_across_dp(
            num_tokens, dp_size, dp_rank)
        max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp).item()
1242
1243
1244
1245
1246
        num_tokens_after_padding = torch.tensor([max_tokens_across_dp_cpu] *
                                                dp_size,
                                                device="cpu",
                                                dtype=torch.int32)
        return max_tokens_across_dp_cpu - num_tokens, num_tokens_after_padding
1247

1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
        finished_sending: Optional[set[str]],
        finished_recving: Optional[set[str]],
    ) -> ModelRunnerOutput:
        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"

        extracted_hidden_states = list(
            torch.split(hidden_states[:num_scheduled_tokens],
                        num_scheduled_tokens_np.tolist()))

        pooling_metadata = self.input_batch.pooling_metadata

        raw_pooler_output = self.model.pooler(
            hidden_states=extracted_hidden_states,
            pooling_metadata=pooling_metadata)

        pooler_output: list[Optional[torch.Tensor]] = []
        seq_lens = self.seq_lens[:self.input_batch.num_reqs]
        for raw_output, seq_len, prompt_len in zip(
                raw_pooler_output, seq_lens, pooling_metadata.prompt_lens):

            if seq_len == prompt_len:
                pooler_output.append(raw_output.data.cpu())
            else:
                pooler_output.append(None)

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

1293
1294
1295
1296
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
1297
        intermediate_tensors: Optional[IntermediateTensors] = None,
1298
    ) -> Union[ModelRunnerOutput, IntermediateTensors]:
1299

1300
        self._update_states(scheduler_output)
1301
        if not scheduler_output.total_num_scheduled_tokens:
Robert Shaw's avatar
Robert Shaw committed
1302
1303
1304
1305
1306
            if not has_kv_transfer_group():
                # Return empty ModelRunnerOutput if there's no work to do.
                return EMPTY_MODEL_RUNNER_OUTPUT

            return self.kv_connector_no_forward(scheduler_output)
1307
1308

        # Prepare the decoder inputs.
1309
        (attn_metadata, attention_cuda_graphs, logits_indices,
1310
1311
         spec_decode_metadata,
         num_scheduled_tokens_np) = (self._prepare_inputs(scheduler_output))
1312
1313
1314
1315
1316
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if (self.use_cuda_graph
                and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
1317
            num_input_tokens = self.vllm_config.pad_for_cudagraph(
1318
1319
1320
                num_scheduled_tokens)
        else:
            # Eager mode.
1321
1322
1323
            # Pad tokens to multiple of tensor_parallel_size when
            # enabled collective fusion for SP
            tp_size = self.vllm_config.parallel_config.tensor_parallel_size
1324
            if self.compilation_config.pass_config. \
1325
1326
1327
1328
                enable_sequence_parallelism and tp_size > 1:
                num_input_tokens = round_up(num_scheduled_tokens, tp_size)
            else:
                num_input_tokens = num_scheduled_tokens
1329

1330
        # Padding for DP
1331
1332
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_input_tokens)
        num_input_tokens += num_pad
1333

1334
1335
1336
1337
1338
1339
1340
1341
1342
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
        if self.is_multimodal_model:
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)
        else:
            mm_embeds = []

1343
        if self.is_multimodal_model and get_pp_group().is_first_rank:
1344
1345
1346
1347
            # 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.
            input_ids = self.input_ids[:num_scheduled_tokens]
1348
            if mm_embeds:
1349
                inputs_embeds = self.model.get_input_embeddings(
1350
                    input_ids, mm_embeds)
1351
1352
1353
1354
1355
1356
            else:
                inputs_embeds = self.model.get_input_embeddings(input_ids)
            # TODO(woosuk): Avoid the copy. Optimize.
            self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds)
            inputs_embeds = self.inputs_embeds[:num_input_tokens]
            input_ids = None
1357
        else:
1358
1359
1360
1361
1362
1363
            # 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.
            input_ids = self.input_ids[:num_input_tokens]
            inputs_embeds = None
1364
1365
1366
1367
        if self.uses_mrope:
            positions = self.mrope_positions[:, :num_input_tokens]
        else:
            positions = self.positions[:num_input_tokens]
1368

1369
1370
1371
        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
1372
1373
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                num_input_tokens, intermediate_tensors, True)
1374

1375
1376
1377
1378
1379
        # Some attention backends only support CUDA Graphs in pure decode.
        # If attention doesn't support CUDA Graphs for this batch, but we
        # compiled with full CUDA graphs, we have to skip them entirely.
        skip_cuda_graphs = self.full_cuda_graph and not attention_cuda_graphs

1380
        # Run the model.
1381
        # Use persistent buffers for CUDA graphs.
1382
1383
1384
1385
1386
1387
1388
        with set_forward_context(
                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
                skip_cuda_graphs=skip_cuda_graphs,
        ):
Robert Shaw's avatar
Robert Shaw committed
1389
1390
1391
            self.maybe_setup_kv_connector(scheduler_output)

            model_output = self.model(
1392
                input_ids=input_ids,
1393
                positions=positions,
1394
                intermediate_tensors=intermediate_tensors,
1395
                inputs_embeds=inputs_embeds,
1396
            )
1397

Robert Shaw's avatar
Robert Shaw committed
1398
1399
1400
1401
            self.maybe_wait_for_kv_save()
            finished_sending, finished_recving = (
                self.get_finished_kv_transfers(scheduler_output))

1402
        if self.use_aux_hidden_state_outputs:
Robert Shaw's avatar
Robert Shaw committed
1403
            hidden_states, aux_hidden_states = model_output
1404
        else:
Robert Shaw's avatar
Robert Shaw committed
1405
            hidden_states = model_output
1406
1407
            aux_hidden_states = None

1408
1409
1410
1411
1412
1413
1414
        # 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
        broadcast_pp_output = \
            self.parallel_config.distributed_executor_backend \
            == "external_launcher" and len(get_pp_group().ranks) > 0
1415
        if not get_pp_group().is_last_rank:
1416
            # For mid-pipeline stages, return the hidden states.
1417
1418
1419
1420
1421
1422
1423
            if not broadcast_pp_output:
                return hidden_states
            assert isinstance(hidden_states, IntermediateTensors)
            get_pp_group().send_tensor_dict(hidden_states.tensors,
                                            all_gather_group=get_tp_group())
            logits = None
        else:
1424
1425
1426
1427
1428
            if self.input_batch.pooling_params:
                return self._pool(hidden_states, num_scheduled_tokens,
                                  num_scheduled_tokens_np, finished_sending,
                                  finished_recving)

1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
            sample_hidden_states = hidden_states[logits_indices]
            logits = self.model.compute_logits(sample_hidden_states, None)
        if broadcast_pp_output:
            model_output_broadcast_data = {
                "logits": logits.contiguous(),
            } if logits is not None else {}
            model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
                model_output_broadcast_data, src=len(get_pp_group().ranks) - 1)
            assert model_output_broadcast_data is not None
            logits = model_output_broadcast_data["logits"]
1439

1440
1441
1442
1443
        # Apply structured output bitmasks if present
        if scheduler_output.grammar_bitmask is not None:
            self.apply_grammar_bitmask(scheduler_output, logits)

1444
        # Sample the next token and get logprobs if needed.
1445
        sampling_metadata = self.input_batch.sampling_metadata
1446
        if spec_decode_metadata is None:
1447
            sampler_output = self.sampler(
1448
1449
1450
1451
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
1452
1453
1454
1455
            # When indexing with a tensor (bonus_logits_indices), PyTorch
            # creates a new tensor with separate storage from the original
            # logits tensor. This means any in-place operations on bonus_logits
            # won't affect the original logits tensor.
1456
            assert logits is not None
1457
            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
1458
            sampler_output = self.sampler(
1459
                logits=bonus_logits,
1460
1461
1462
                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
1463

1464
1465
1466
            # Just like `bonus_logits`, `target_logits` is a new tensor with
            # separate storage from the original `logits` tensor. Therefore,
            # it is safe to update `target_logits` in place.
1467
            target_logits = logits[spec_decode_metadata.target_logits_indices]
1468
            output_token_ids = self.rejection_sampler(
1469
                spec_decode_metadata,
1470
                None,  # draft_probs
1471
                target_logits,
1472
                bonus_token_ids,
1473
1474
                sampling_metadata,
            )
1475
            sampler_output.sampled_token_ids = output_token_ids
1476

1477
1478
1479
1480
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

1481
1482
        # TODO(woosuk): The following loop can be slow since it iterates over
        # the requests one by one. Optimize.
1483
1484
        discard_sampled_tokens_req_indices = []
        for i, req_id in enumerate(self.input_batch.req_ids):
1485
1486
1487
            req_state = self.requests[req_id]
            seq_len = (req_state.num_computed_tokens +
                       scheduler_output.num_scheduled_tokens[req_id])
1488
            if seq_len < req_state.num_tokens:
1489
                # Ignore the sampled token for partial prefills.
1490
                # Rewind the generator state as if the token was not sampled.
1491
                # This relies on cuda-specific torch-internal impl details
1492
1493
1494
1495
1496
1497
                generator = self.input_batch.generators.get(i)
                if generator is not None:
                    generator.set_offset(generator.get_offset() - 4)
                # Record the index of the request that should not be sampled,
                # so that we could clear the sampled tokens before returning.
                discard_sampled_tokens_req_indices.append(i)
1498

1499
1500
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
1501
1502
1503
1504
1505
1506
        logprobs_tensors = sampler_output.logprobs_tensors
        logprobs_lists = logprobs_tensors.tolists() \
            if logprobs_tensors is not None else None

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
1507
            hidden_states[:num_scheduled_tokens],
1508
1509
1510
            scheduler_output,
        )

1511
        # Get the valid generated tokens.
1512
1513
1514
        sampled_token_ids = sampler_output.sampled_token_ids
        max_gen_len = sampled_token_ids.shape[-1]
        if max_gen_len == 1:
1515
            # No spec decode tokens.
1516
1517
            valid_sampled_token_ids = sampled_token_ids.tolist()
        else:
1518
            # Includes spec decode tokens.
1519
            valid_sampled_token_ids = self.rejection_sampler.parse_output(
1520
1521
1522
                sampled_token_ids,
                self.input_batch.vocab_size,
            )
1523
1524
1525
        # Mask out the sampled tokens that should not be sampled.
        for i in discard_sampled_tokens_req_indices:
            valid_sampled_token_ids[i].clear()
1526

1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
        # 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.
        for req_idx, sampled_ids in enumerate(valid_sampled_token_ids):
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
            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}")

            self.input_batch.token_ids_cpu[req_idx,
                                           start_idx:end_idx] = sampled_ids
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
            req_id = self.input_batch.req_ids[req_idx]
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

1551
        if not self.speculative_config:
1552
            # Speculative decoding is not enabled.
1553
            spec_token_ids = None
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
        else:
            spec_token_ids = self.propose_draft_token_ids(
                scheduler_output,
                valid_sampled_token_ids,
                sampling_metadata,
                hidden_states,
                sample_hidden_states,
                aux_hidden_states,
                spec_decode_metadata,
                attn_metadata,
            )

        # Clear KVConnector state after all KVs are generated.
        if has_kv_transfer_group():
            get_kv_transfer_group().clear_connector_metadata()

        self.eplb_step()

        return ModelRunnerOutput(
            req_ids=self.input_batch.req_ids,
            req_id_to_index=self.input_batch.req_id_to_index,
            sampled_token_ids=valid_sampled_token_ids,
            spec_token_ids=spec_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
            finished_sending=finished_sending,
            finished_recving=finished_recving,
            num_nans_in_logits=num_nans_in_logits,
        )

    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
        sampled_token_ids: list[list[int]],
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
        aux_hidden_states: Optional[torch.Tensor],
        spec_decode_metadata: Optional[SpecDecodeMetadata],
        attn_metadata: dict[str, Any],
    ) -> list[list[int]]:
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
1598
            assert isinstance(self.drafter, NgramProposer)
1599
1600
            spec_token_ids = self.propose_ngram_draft_token_ids(
                sampled_token_ids)
1601
1602
        elif self.speculative_config.method == "medusa":
            assert isinstance(self.drafter, MedusaProposer)
1603
1604
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
1605
1606
1607
1608
1609
1610
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
                for num_draft, tokens in zip(
                        spec_decode_metadata.num_draft_tokens,
1611
                        sampled_token_ids):
1612
1613
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
1614
                indices = torch.tensor(indices, device=self.device)
1615
1616
1617
1618
1619
1620
                hidden_states = sample_hidden_states[indices]

            spec_token_ids = self.drafter.propose(
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
1621
        elif self.speculative_config.use_eagle():
1622
1623
1624
            assert isinstance(self.drafter, EagleProposer)
            # TODO(woosuk): Refactor the loop.
            next_token_ids: list[int] = []
1625
            for i, token_ids in enumerate(sampled_token_ids):
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
                if token_ids:
                    # Common case.
                    next_token_id = token_ids[-1]
                else:
                    # Partial prefill (rare case).
                    # Get the next token id from the request state.
                    req_id = self.input_batch.req_ids[i]
                    req_state = self.requests[req_id]
                    seq_len = (req_state.num_computed_tokens +
                               scheduler_output.num_scheduled_tokens[req_id])
                    next_token_id = req_state.get_token_id(seq_len)
                next_token_ids.append(next_token_id)
1638
1639
1640
1641
1642
1643
1644
            next_token_ids = torch.tensor(next_token_ids,
                                          dtype=torch.int32,
                                          device=self.device)
            # At this moment, we assume all eagle layers belong to the same KV
            # cache group, thus using the same attention metadata.
            eagle_attn_metadata = attn_metadata[
                self.drafter.attn_layer_names[0]]
1645

Jiayi Yao's avatar
Jiayi Yao committed
1646
1647
1648
1649
1650
1651
            # NOTE: deepseek_mtp uses MLA which does not have `block_table`
            if hasattr(eagle_attn_metadata, "block_table"):
                block_table = eagle_attn_metadata.block_table
            else:
                block_table = None

1652
1653
1654
            if spec_decode_metadata is None:
                # input_ids can be None for multimodal models.
                target_token_ids = self.input_ids[:num_scheduled_tokens]
1655
1656
                # TODO(woosuk): Support M-RoPE.
                target_positions = self.positions[:num_scheduled_tokens]
1657
                if self.use_aux_hidden_state_outputs:
1658
1659
1660
                    target_hidden_states = torch.cat(
                        [h[:num_scheduled_tokens] for h in aux_hidden_states],
                        dim=-1)
1661
1662
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
1663
1664
                target_slot_mapping = eagle_attn_metadata.slot_mapping
                cu_num_tokens = eagle_attn_metadata.query_start_loc
1665
1666
1667
1668
            else:
                # TODO(woosuk): Refactor this.
                num_draft_tokens = spec_decode_metadata.num_draft_tokens
                num_rejected_tokens = [
1669
                    n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
1670
1671
                    for i, n in enumerate(num_draft_tokens)
                ]
1672
                num_rejected_tokens_tensor = async_tensor_h2d(
1673
1674
                    num_rejected_tokens,
                    dtype=torch.int32,
1675
1676
1677
                    target_device=self.device,
                    pin_memory=True)
                num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
1678
                cu_num_tokens, token_indices = self.drafter.prepare_inputs(
1679
                    eagle_attn_metadata.query_start_loc,
1680
1681
                    num_rejected_tokens_tensor,
                    num_tokens,
1682
1683
                )
                target_token_ids = self.input_ids[token_indices]
1684
1685
                # TODO(woosuk): Support M-RoPE.
                target_positions = self.positions[token_indices]
1686
                if self.use_aux_hidden_state_outputs:
1687
1688
                    target_hidden_states = torch.cat(
                        [h[token_indices] for h in aux_hidden_states], dim=-1)
1689
1690
                else:
                    target_hidden_states = hidden_states[token_indices]
1691
1692
                target_slot_mapping = eagle_attn_metadata.slot_mapping[
                    token_indices]
1693
            draft_token_ids = self.drafter.propose(
1694
1695
1696
1697
1698
1699
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                target_slot_mapping=target_slot_mapping,
                next_token_ids=next_token_ids,
                cu_num_tokens=cu_num_tokens,
Jiayi Yao's avatar
Jiayi Yao committed
1700
                block_table=block_table,
1701
1702
1703
                sampling_metadata=sampling_metadata,
            )
            spec_token_ids = draft_token_ids.tolist()
1704
        return spec_token_ids
1705

Robert Shaw's avatar
Robert Shaw committed
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
    def kv_connector_no_forward(
            self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
        # KV send/recv even if no work to do.
        with set_forward_context(None, self.vllm_config):
            self.maybe_setup_kv_connector(scheduler_output)
            finished_sending, finished_recving = (
                self.get_finished_kv_transfers(scheduler_output))

        if not finished_sending and not finished_recving:
            return EMPTY_MODEL_RUNNER_OUTPUT

        output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
        output.finished_sending = finished_sending
        output.finished_recving = finished_recving
        return output

    @staticmethod
    def maybe_setup_kv_connector(scheduler_output: "SchedulerOutput"):
        # Update KVConnector with the KVConnector metadata forward().
        if has_kv_transfer_group():
            kv_connector = get_kv_transfer_group()
            assert isinstance(kv_connector, KVConnectorBase_V1)
            assert scheduler_output.kv_connector_metadata is not None
            kv_connector.bind_connector_metadata(
                scheduler_output.kv_connector_metadata)

            # Background KV cache transfers happen here.
            # These transfers are designed to be async and the requests
            # involved may be disjoint from the running requests.
            # Do this here to save a collective_rpc.
            kv_connector.start_load_kv(get_forward_context())

    @staticmethod
    def maybe_wait_for_kv_save() -> None:
        if has_kv_transfer_group():
            get_kv_transfer_group().wait_for_save()

    @staticmethod
    def get_finished_kv_transfers(
        scheduler_output: "SchedulerOutput",
    ) -> tuple[Optional[set[str]], Optional[set[str]]]:
        if has_kv_transfer_group():
            return get_kv_transfer_group().get_finished(
                scheduler_output.finished_req_ids)
        return None, None

1752
    def propose_ngram_draft_token_ids(
1753
        self,
1754
1755
        sampled_token_ids: list[list[int]],
    ) -> list[list[int]]:
1756
        # TODO(woosuk): Optimize.
1757
        draft_token_ids: list[list[int]] = []
1758
1759
1760
        for i, sampled_ids in enumerate(sampled_token_ids):
            num_sampled_ids = len(sampled_ids)
            if not num_sampled_ids:
1761
1762
1763
1764
                # Skip speculative decoding.
                draft_token_ids.append([])
                continue

1765
1766
            # Skip requests that require sampling parameters that are not
            # supported with speculative decoding.
1767
1768
1769
1770
1771
            req_id = self.input_batch.req_ids[i]
            if not is_spec_decode_supported(req_id, self.input_batch):
                draft_token_ids.append([])
                continue

1772
1773
            num_tokens = self.input_batch.num_tokens_no_spec[i]
            if num_tokens >= self.max_model_len:
1774
1775
1776
1777
                # Skip requests that have already reached the max model length.
                draft_token_ids.append([])
                continue

1778
            drafter_output = self.drafter.propose(
1779
                self.input_batch.token_ids_cpu[i, :num_tokens])
1780
1781
1782
1783
1784
1785
            if drafter_output is None or len(drafter_output) == 0:
                draft_token_ids.append([])
            else:
                draft_token_ids.append(drafter_output.tolist())
        return draft_token_ids

1786
1787
1788
    def load_model(self) -> None:
        logger.info("Starting to load model %s...", self.model_config.model)
        with DeviceMemoryProfiler() as m:  # noqa: SIM117
1789
            time_before_load = time.perf_counter()
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
            model_loader = get_model_loader(self.load_config)
            if not hasattr(self, "model"):
                logger.info("Loading model from scratch...")
                self.model = model_loader.load_model(
                    vllm_config=self.vllm_config,
                    model_config=self.model_config)
            else:
                logger.info(
                    "Model was already initialized. Loading weights inplace..."
                )
                model_loader.load_weights(self.model,
                                          model_config=self.model_config)
1802
1803
            if has_step_pooler(self.model):
                self.input_batch.logits_processing_needs_token_ids = True
1804
1805
1806
1807
1808
1809
            if self.lora_config:
                self.model = self.load_lora_model(self.model,
                                                  self.model_config,
                                                  self.scheduler_config,
                                                  self.lora_config,
                                                  self.device)
1810
1811
1812
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
1813
1814
1815
            if self.use_aux_hidden_state_outputs:
                self.model.set_aux_hidden_state_layers(
                    self.model.get_eagle3_aux_hidden_state_layers())
1816
            time_after_load = time.perf_counter()
1817
        self.model_memory_usage = m.consumed_memory
1818
1819
        logger.info("Model loading took %.4f GiB and %.6f seconds",
                    self.model_memory_usage / GiB_bytes,
1820
                    time_after_load - time_before_load)
1821
        prepare_communication_buffer_for_model(self.model)
1822

1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
        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)
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
            )

1833
1834
1835
1836
1837
1838
1839
1840
1841
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
            self.model,
            tensorizer_config=tensorizer_config,
        )

1842
1843
1844
1845
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
        scheduler_output: "SchedulerOutput",
1846
    ) -> dict[str, Optional[LogprobsTensors]]:
1847
1848
1849
1850
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

1851
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
1852
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866

        # 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():

            num_tokens = scheduler_output.num_scheduled_tokens[req_id]

            # Get metadata for this request.
            request = self.requests[req_id]
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
                self.device, non_blocking=True)

1867
1868
1869
1870
1871
1872
1873
1874
1875
            # 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(
                    num_prompt_tokens - 1, num_prompt_logprobs + 1)
                in_progress_dict[req_id] = logprobs_tensors

1876
            # Determine number of logits to retrieve.
1877
1878
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
1879
            num_remaining_tokens = num_prompt_tokens - start_tok
1880
            if num_tokens <= num_remaining_tokens:
1881
                # This is a chunk, more tokens remain.
1882
1883
1884
                # 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.
1885
1886
1887
1888
1889
                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)
1890
1891
1892
1893
1894
1895
1896
                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
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911

            # 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]
            offset = self.query_start_loc_np[req_idx].item()
            prompt_hidden_states = hidden_states[offset:offset + num_logits]
            logits = self.model.compute_logits(prompt_hidden_states, None)

            # 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.
            tgt_token_ids = prompt_token_ids[start_tok:start_tok + num_logits]

            # Compute prompt logprobs.
1912
1913
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
1914
1915
1916
                logprobs, num_prompt_logprobs, tgt_token_ids)

            # Transfer GPU->CPU async.
1917
1918
1919
1920
1921
1922
1923
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
                token_ids, non_blocking=True)
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs,
                                                         non_blocking=True)
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
                ranks, non_blocking=True)
1924
1925
1926
1927
1928

        # 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]
1929
            del in_progress_dict[req_id]
1930
1931

        # Must synchronize the non-blocking GPU->CPU transfers.
1932
        if prompt_logprobs_dict:
1933
            self._sync_device()
1934
1935
1936

        return prompt_logprobs_dict

1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
    def _get_nans_in_logits(
        self,
        logits: Optional[torch.Tensor],
    ) -> 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])
                    if num_nans_for_index is not None
                    and req_index < logits.shape[0] else 0)
            return num_nans_in_logits
        except IndexError:
            return {}

1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
    @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
         - during DP rank dummy run 
        """
        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(
                    self.input_ids,
                    low=0,
                    high=self.model_config.get_vocab_size(),
                    dtype=input_ids.dtype)

            logger.debug("Randomizing dummy data for DP Rank")
            input_ids.copy_(rand_input_ids()[:input_ids.size(0)],
                            non_blocking=True)
            yield
            input_ids.fill_(0)

1986
1987
1988
1989
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
1990
        capture_attn_cudagraph: bool = False,
1991
1992
        skip_eplb: bool = False,
        is_profile: bool = False,
1993
    ) -> tuple[torch.Tensor, torch.Tensor]:
1994

1995
        # Padding for DP
1996
1997
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
        num_tokens += num_pad
1998

1999
2000
2001
2002
2003
        # 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
2004
        num_reqs = min(num_tokens, max_num_reqs)
2005
2006
2007
2008
2009
2010
2011
        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
        num_scheduled_tokens = np.array(num_scheduled_tokens_list,
                                        dtype=np.int32)
2012

2013
2014
2015
2016
        attn_metadata: Optional[dict[str, Any]] = None
        if capture_attn_cudagraph:
            attn_metadata = {}

2017
            query_start_loc = self.query_start_loc[:num_reqs + 1]
2018
2019
2020
2021
2022
            # Make sure max_model_len is used at the graph capture time.
            self.seq_lens_np[:num_reqs] = self.max_model_len
            self.seq_lens_np[num_reqs:] = 0
            self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
                                           non_blocking=True)
2023
2024
2025
            seq_lens = self.seq_lens[:num_reqs]

            common_attn_metadata = CommonAttentionMetadata(
2026
2027
2028
2029
2030
2031
                query_start_loc=query_start_loc,
                seq_lens=seq_lens,
                num_reqs=num_reqs,
                num_actual_tokens=num_tokens,
                max_query_len=num_tokens,
            )
2032

2033
2034
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
                    self.kv_cache_config.kv_cache_groups):
2035
2036
2037
2038

                attn_metadata_i = self.attn_metadata_builders[
                    kv_cache_group_id].build_for_cudagraph_capture(
                        common_attn_metadata)
2039
2040
                for layer_name in kv_cache_group_spec.layer_names:
                    attn_metadata[layer_name] = attn_metadata_i
2041

2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
        with self.maybe_dummy_run_with_lora(self.lora_config,
                                            num_scheduled_tokens):
            model = self.model
            if self.is_multimodal_model:
                input_ids = None
                inputs_embeds = self.inputs_embeds[:num_tokens]
            else:
                input_ids = self.input_ids[:num_tokens]
                inputs_embeds = None
            if self.uses_mrope:
                positions = self.mrope_positions[:, :num_tokens]
            else:
                positions = self.positions[:num_tokens]

            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,
                            device=self.device))
2065
2066
2067

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                    num_tokens, None, False)
2068

2069
            with self.maybe_randomize_inputs(input_ids), set_forward_context(
2070
2071
2072
2073
                    attn_metadata,
                    self.vllm_config,
                    num_tokens=num_tokens,
                    num_tokens_across_dp=num_tokens_across_dp):
2074
                outputs = model(
2075
2076
2077
2078
2079
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
                )
2080
2081
2082
2083
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
2084

2085
            if self.speculative_config and self.speculative_config.use_eagle():
2086
2087
2088
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
        # 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)

2099
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
2100
        return hidden_states, hidden_states[logit_indices]
2101
2102
2103
2104
2105
2106

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
2107
2108
2109
2110
        # 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)
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137

        logits = self.model.compute_logits(hidden_states, None)
        num_reqs = logits.size(0)

        dummy_tensors = lambda v: torch.full(
            (num_reqs, ), v, device=self.device)

        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),
            min_p=None,
            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)],
            min_tokens={},
            logit_bias=[None for _ in range(num_reqs)],
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
        )
2138
        try:
2139
2140
            sampler_output = self.sampler(logits=logits,
                                          sampling_metadata=dummy_metadata)
2141
2142
2143
2144
2145
2146
2147
2148
2149
        except RuntimeError as e:
            if 'out of memory' in str(e):
                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 "
                    "initializing the engine.") from e
            else:
                raise e
2150
        if self.speculative_config:
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
                draft_token_ids, self.device)

            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
            target_logits = torch.randn(num_tokens,
                                        logits.shape[-1],
                                        device=self.device,
                                        dtype=logits.dtype)
            # NOTE(woosuk): Here, we should use int32 because the sampler uses
            # int32 for bonus_token_ids. If the dtype mismatches, re-compilation
            # will occur at runtime.
            bonus_token_ids = torch.zeros(num_reqs,
                                          device=self.device,
                                          dtype=torch.int32)
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
2177
        return sampler_output
2178

2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:

        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

        hidden_states_list = list(
            torch.split(hidden_states, num_scheduled_tokens_list))

        req_num_tokens = num_tokens // num_reqs

        dummy_metadata = PoolingMetadata(
            prompt_lens=torch.tensor([h.shape[0] for h in hidden_states_list],
                                     device=self.device),
            prompt_token_ids=torch.zeros((num_reqs, req_num_tokens),
                                         dtype=torch.int32,
                                         device=self.device),
            pooling_params=[PoolingParams()] * num_reqs)

        try:
            pooler_output = self.model.pooler(hidden_states=hidden_states_list,
                                              pooling_metadata=dummy_metadata)
        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
                    "CUDA out of memory occurred when warming up pooler with "
                    f"{num_reqs} dummy requests. Please try lowering "
                    "`max_num_seqs` or `gpu_memory_utilization` when "
                    "initializing the engine.") from e
            else:
                raise e
        return pooler_output

2221
    def profile_run(self) -> None:
2222
        # Profile with multimodal encoder & encoder cache.
2223
2224
2225
        # TODO: handle encoder-decoder models once we support them.
        if (self.is_multimodal_model and self.max_num_encoder_input_tokens > 0
                and self.encoder_cache_size > 0):
2226

2227
            # NOTE: Currently model is profiled with a single non-text
2228
2229
            # modality with the max possible input tokens even when
            # it supports multiple.
2230
2231
            max_tokens_by_modality_dict = self.mm_registry \
                .get_max_tokens_per_item_by_nonzero_modality(self.model_config)
2232
2233
2234
2235
            dummy_data_modality, max_tokens_per_mm_item = max(
                max_tokens_by_modality_dict.items(), key=lambda item: item[1])

            # Check how many items of this modality can be supported by
2236
2237
2238
2239
2240
2241
            # the encoder budget.
            encoder_budget = min(self.max_num_encoder_input_tokens,
                                 self.encoder_cache_size)

            max_num_mm_items_encoder_budget = cdiv(encoder_budget,
                                                   max_tokens_per_mm_item)
2242
2243
2244

            # Check how many items of this modality can be supported by
            # the decoder budget.
2245
2246
            max_mm_items_per_req = self.mm_registry.get_mm_limits_per_prompt(
                self.model_config)[dummy_data_modality]
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256

            # NOTE: We do not consider max_num_batched_tokens on purpose
            # because the multimodal embeddings can be generated in advance
            # and chunked prefilled.
            max_num_mm_items_decoder_budget = self.max_num_reqs * \
                max_mm_items_per_req

            max_num_mm_items = min(max_num_mm_items_encoder_budget,
                                   max_num_mm_items_decoder_budget)

2257
2258
2259
2260
2261
2262
            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_num_mm_items, dummy_data_modality)

            # Create dummy batch of multimodal inputs.
2263
            dummy_mm_kwargs = self.mm_registry.get_decoder_dummy_data(
2264
2265
                model_config=self.model_config,
                seq_len=self.max_num_tokens,
2266
2267
2268
2269
                mm_counts={
                    dummy_data_modality: 1
                },
            ).multi_modal_data
2270

2271
            batched_dummy_mm_inputs = MultiModalKwargs.batch(
2272
2273
                [dummy_mm_kwargs] * max_num_mm_items,
                pin_memory=self.pin_memory)
2274
            batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs(
2275
2276
2277
                batched_dummy_mm_inputs,
                device=self.device,
            )
2278
2279
2280
2281

            # Run multimodal encoder.
            dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                **batched_dummy_mm_inputs)
2282
2283
2284
2285
2286

            sanity_check_mm_encoder_outputs(
                dummy_encoder_outputs,
                expected_num_items=max_num_mm_items,
            )
2287
2288
2289
2290

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

2291
        # Add `is_profile` here to pre-allocate communication buffers
2292
        hidden_states, last_hidden_states \
2293
            = self._dummy_run(self.max_num_tokens, is_profile=True)
2294
        if get_pp_group().is_last_rank:
2295
2296
2297
2298
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
2299
        else:
2300
            output = None
2301
        self._sync_device()
2302
        del hidden_states, output
2303
        self.encoder_cache.clear()
2304
        gc.collect()
2305
2306

    def capture_model(self) -> None:
2307
2308
        if not self.use_cuda_graph:
            logger.warning(
2309
2310
2311
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
                "set -O %s and ensure `use_cudagraph` was not manually set to "
                "False", CompilationLevel.PIECEWISE)
2312
2313
            return

2314
2315
        compilation_counter.num_gpu_runner_capture_triggers += 1

2316
2317
2318
        start_time = time.perf_counter()
        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

2319
2320
2321
        # 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.
2322
        with graph_capture(device=self.device):
2323
            full_cg = self.full_cuda_graph
2324
2325
2326
2327
2328
2329
            # Only rank 0 should print progress bar during capture
            compilation_cases = reversed(self.cudagraph_batch_sizes)
            if is_global_first_rank():
                compilation_cases = tqdm(list(compilation_cases),
                                         desc="Capturing CUDA graph shapes")
            for num_tokens in compilation_cases:
2330
                # We skip EPLB here since we don't want to record dummy metrics
2331
2332
                for _ in range(
                        self.compilation_config.cudagraph_num_of_warmups):
2333
2334
2335
2336
2337
2338
                    self._dummy_run(num_tokens,
                                    capture_attn_cudagraph=full_cg,
                                    skip_eplb=True)
                self._dummy_run(num_tokens,
                                capture_attn_cudagraph=full_cg,
                                skip_eplb=True)
2339
2340
2341
2342
2343
2344
2345
2346

        end_time = time.perf_counter()
        end_free_gpu_memory = torch.cuda.mem_get_info()[0]
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
        logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
                    elapsed_time, cuda_graph_size / (1 << 30))
2347

2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
        assert len(self.attn_backends) == 0 and len(
            self.attn_metadata_builders
        ) == 0, "Attention backends are already initialized"
        for i, kv_cache_group_spec in enumerate(
                kv_cache_config.kv_cache_groups):
            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
Chen Zhang's avatar
Chen Zhang committed
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
            if isinstance(kv_cache_spec, AttentionSpec):
                attn_backend_i = get_attn_backend(
                    kv_cache_spec.head_size,
                    self.dtype,
                    kv_cache_spec.dtype,
                    kv_cache_spec.block_size,
                    self.model_config.is_attention_free,
                    use_mla=kv_cache_spec.use_mla,
                )
                if attn_backend_i is None:
                    error_msg = (f"Error with get_attn_backend: "
                                 f"{kv_cache_spec.head_size=}, "
                                 f"{self.dtype=}, {kv_cache_spec.dtype=}, "
                                 f"{kv_cache_spec.block_size=}, "
                                 f"{self.model_config.is_attention_free=}, "
                                 f"{kv_cache_spec.use_mla=}")
                    logger.error(error_msg)
                    raise NotImplementedError(
                        "Non-Attention backend is not supported by V1 "
                        "GPUModelRunner.")
            elif isinstance(kv_cache_spec, MambaSpec):
                attn_backend_i = Mamba2AttentionBackend
            else:
                raise ValueError(
                    f"Unknown KV cache spec type: {type(kv_cache_spec)}")
2383
2384
2385

            block_table_i = self.input_batch.block_table[i]
            attn_metadata_builder_i = attn_backend_i.get_builder_cls()(
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
                weakref.proxy(self),
                kv_cache_spec,
                block_table_i,
            )

            if (self.full_cuda_graph
                    and not attn_metadata_builder_i.full_cudagraph_supported):
                raise ValueError(
                    f"Full CUDAGraph not supported for "
                    f"{attn_backend_i.__name__}. Turn off CompilationConfig."
                    f"full_cuda_graph or use a different attention backend.")

2398
2399
2400
            self.attn_backends.append(attn_backend_i)
            self.attn_metadata_builders.append(attn_metadata_builder_i)

2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
    def may_reinitialize_input_batch(self,
                                     kv_cache_config: KVCacheConfig) -> None:
        """
        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
        ]
        if block_sizes != [self.cache_config.block_size]:
            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
                "for more details.")
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
                max_model_len=self.max_model_len,
                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,
            )

2430
2431
    def _allocate_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
2432
        """
2433
2434
2435
        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.

2436
        Args:
2437
            kv_cache_config: The KV cache config
2438
        Returns:
2439
            dict[str, torch.Tensor]: A map between layer names to their
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
            corresponding memory buffer for KV cache.
         """
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
            tensor = torch.zeros(kv_cache_tensor.size,
                                 dtype=torch.int8,
                                 device=self.device)
            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:
            layer_names.update(group.layer_names)
        assert layer_names == set(kv_cache_raw_tensors.keys(
        )), "Some layers are not correctly initialized"
        return kv_cache_raw_tensors

    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
2462
        """
2463
        Reshape the KV cache tensors to the desired shape and dtype.
2464

2465
        Args:
2466
2467
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
2468
2469
            correct size but uninitialized shape.
        Returns:
2470
            Dict[str, torch.Tensor]: A map between layer names to their
2471
2472
            corresponding memory buffer for KV cache.
        """
2473
        kv_caches: dict[str, torch.Tensor] = {}
2474
2475
2476
2477
2478
2479
2480
2481
        for i, kv_cache_group_spec in enumerate(
                kv_cache_config.kv_cache_groups):
            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
            for layer_name in kv_cache_group_spec.layer_names:
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
                num_blocks = (raw_tensor.numel() //
                              kv_cache_spec.page_size_bytes)
2482
                if isinstance(kv_cache_spec, AttentionSpec):
2483
                    kv_cache_shape = self.attn_backends[i].get_kv_cache_shape(
2484
2485
2486
                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    dtype = kv_cache_spec.dtype
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
                    try:
                        kv_cache_stride_order = self.attn_backends[
                            i].get_kv_cache_stride_order()
                        assert len(kv_cache_stride_order) == len(
                            kv_cache_shape)
                    except (AttributeError, NotImplementedError):
                        kv_cache_stride_order = tuple(
                            range(len(kv_cache_shape)))
                    # 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.
                    kv_cache_shape = tuple(kv_cache_shape[i]
                                           for i in kv_cache_stride_order)
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
2507
2508
2509
                    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
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
                elif isinstance(kv_cache_spec, MambaSpec):
                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    dtype = kv_cache_spec.dtype
                    state_tensors = []
                    start_pos = 0
                    for shape in kv_cache_spec.shapes:
                        target_shape = (num_blocks, *shape)
                        size_in_bytes = np.prod(shape) * get_dtype_size(
                            dtype) * num_blocks
                        tensor = raw_tensor[start_pos:start_pos +
                                            size_in_bytes]
                        tensor = tensor.view(dtype).view(target_shape)
                        state_tensors.append(tensor)
                        start_pos += size_in_bytes
                    assert start_pos == raw_tensor.numel()
                    kv_caches[layer_name] = tuple(state_tensors)
2526
                else:
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
                    raise NotImplementedError
        return kv_caches

    def initialize_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        Returns:
2538
            Dict[str, torch.Tensor]: A map between layer names to their
2539
2540
2541
2542
2543
2544
2545
            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
        kv_caches = self._reshape_kv_cache_tensors(kv_cache_config,
                                                   kv_cache_raw_tensors)
2546

2547
2548
2549
2550
2551
2552
2553
2554
2555
        # Setup `kv_cache_config` and `kv_caches` for models
        # with cross-layer KV sharing
        if self.shared_kv_cache_layers:
            initialize_kv_cache_for_kv_sharing(
                self.shared_kv_cache_layers,
                kv_cache_config.kv_cache_groups,
                kv_caches,
            )

2556
2557
2558
        bind_kv_cache(kv_caches,
                      self.compilation_config.static_forward_context,
                      self.kv_caches)
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
        return kv_caches

    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
        """
        self.kv_cache_config = kv_cache_config
        self.may_reinitialize_input_batch(kv_cache_config)
        self.initialize_attn_backend(kv_cache_config)
        kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)

2573
2574
2575
2576
2577
2578
        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
2579
2580
2581
        if has_kv_transfer_group():
            get_kv_transfer_group().register_kv_caches(kv_caches)

2582
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
2583
        """
2584
        Generates the KVCacheSpec by parsing the kv cache format from each
2585
2586
        Attention module in the static forward context.
        Returns:
2587
            KVCacheSpec: A dictionary mapping layer names to their KV cache
2588
2589
2590
2591
            format. Layers that do not need KV cache are not included.
        """

        block_size = self.vllm_config.cache_config.block_size
2592
        use_mla = self.vllm_config.model_config.use_mla
2593
        kv_cache_spec: dict[str, KVCacheSpec] = {}
Chen Zhang's avatar
Chen Zhang committed
2594
2595
        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
            if (kv_tgt_layer :=
                    attn_module.kv_sharing_target_layer_name) is not None:
                # 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

2608
            # TODO: Support other attention modules, e.g., cross-attention
2609
            if attn_module.attn_type == AttentionType.DECODER:
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
                if attn_module.sliding_window is not None:
                    kv_cache_spec[layer_name] = SlidingWindowSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        sliding_window=attn_module.sliding_window,
                        use_mla=use_mla)
                else:
                    kv_cache_spec[layer_name] = FullAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        use_mla=use_mla)
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
            elif attn_module.attn_type in (AttentionType.ENCODER,
                                           AttentionType.ENCODER_ONLY):
                # encoder-only attention does not need KV cache.
                continue
            elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                raise NotImplementedError
            else:
                raise ValueError(
                    f"Unknown attention type: {attn_module.attn_type}")

Chen Zhang's avatar
Chen Zhang committed
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
        mamba_layers = get_layers_from_vllm_config(self.vllm_config,
                                                   MambaMixer2)
        if len(mamba_layers) > 0:
            if self.vllm_config.speculative_config is not None:
                raise NotImplementedError(
                    "Mamba with speculative decoding is not supported yet.")
            if not self.vllm_config.model_config.enforce_eager:
                raise NotImplementedError(
                    "Mamba with cuda graph is not supported yet.")
            if self.vllm_config.cache_config.enable_prefix_caching:
                raise NotImplementedError(
                    "Prefix caching is not supported for Mamba yet.")
            max_model_len = self.vllm_config.model_config.max_model_len
            # Set block_size to max_model_len, so that mamba model will always
            # have only one block in the KV cache.
            for layer_name, mamba_module in mamba_layers.items():
                kv_cache_spec[layer_name] = MambaSpec(
                    shapes=mamba_module.get_state_shape(),
                    dtype=self.kv_cache_dtype,
                    block_size=max_model_len)
2655
        return kv_cache_spec