gpu_model_runner.py 106 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
22
from vllm.config import (CompilationLevel, VllmConfig,
                         get_layers_from_vllm_config)
23
24
from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group)
Robert Shaw's avatar
Robert Shaw committed
25
from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
26
from vllm.distributed.parallel_state import (
27
28
    get_pp_group, get_tp_group, graph_capture,
    prepare_communication_buffer_for_model)
29
30
from vllm.forward_context import (DPMetadata, get_forward_context,
                                  set_forward_context)
31
from vllm.logger import init_logger
32
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
33
from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
34
35
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
36
from vllm.multimodal.utils import group_mm_inputs_by_modality
37
from vllm.sampling_params import SamplingType
38
from vllm.sequence import IntermediateTensors
39
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
40
41
                        GiB_bytes, LazyLoader, async_tensor_h2d, cdiv,
                        check_use_alibi, is_pin_memory_available)
42
43
from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
                                              CommonAttentionMetadata)
44
from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
45
46
47
from vllm.v1.kv_cache_interface import (AttentionSpec, FullAttentionSpec,
                                        KVCacheConfig, KVCacheSpec,
                                        SlidingWindowSpec)
48
49
from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
                             ModelRunnerOutput)
50
from vllm.v1.sample.metadata import SamplingMetadata
51
from vllm.v1.sample.rejection_sampler import RejectionSampler
52
from vllm.v1.sample.sampler import Sampler
53
from vllm.v1.spec_decode.eagle import EagleProposer
54
from vllm.v1.spec_decode.medusa import MedusaProposer
55
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
56
from vllm.v1.spec_decode.ngram_proposer import NgramProposer
57
from vllm.v1.spec_decode.utils import is_spec_decode_supported
58
from vllm.v1.utils import bind_kv_cache
59
from vllm.v1.worker.block_table import BlockTable
60
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
61
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
62

63
64
from .utils import (gather_mm_placeholders, initialize_kv_cache_for_kv_sharing,
                    sanity_check_mm_encoder_outputs, scatter_mm_placeholders)
65

66
if TYPE_CHECKING:
67
    import xgrammar as xgr
68
    import xgrammar.kernels.apply_token_bitmask_inplace_torch_compile as xgr_torch_compile  # noqa: E501
69

70
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
71
    from vllm.v1.core.sched.output import SchedulerOutput
72
73
else:
    xgr = LazyLoader("xgr", globals(), "xgrammar")
74
75
76
    xgr_torch_compile = LazyLoader(
        "xgr_torch_compile", globals(),
        "xgrammar.kernels.apply_token_bitmask_inplace_torch_compile")
77
78
79
80

logger = init_logger(__name__)


81
class GPUModelRunner(LoRAModelRunnerMixin):
82
83
84

    def __init__(
        self,
85
        vllm_config: VllmConfig,
86
        device: torch.device,
87
    ):
88
89
90
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
91
        self.compilation_config = vllm_config.compilation_config
92
93
94
95
96
97
98
        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
99

100
101
102
103
        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))

104
105
106
107
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
108
        self.device = device
109
110
111
112
113
114
115
116
        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]

117
        self.is_multimodal_model = model_config.is_multimodal_model
118
119
        self.max_model_len = model_config.max_model_len
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
120
        self.max_num_reqs = scheduler_config.max_num_seqs
121
122

        # Model-related.
123
124
        self.num_query_heads = model_config.get_num_attention_heads(
            parallel_config)
125
        self.hidden_size = model_config.get_hidden_size()
126
        self.attention_chunk_size = model_config.attention_chunk_size
127

128
        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
129

130
        # Multi-modal data support
131
        self.mm_registry = MULTIMODAL_REGISTRY
132
        self.uses_mrope = model_config.uses_mrope
133

134
135
136
        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
            model_config=model_config,
            scheduler_config=scheduler_config,
137
            mm_registry=self.mm_registry,
138
139
140
        )
        self.max_num_encoder_input_tokens = encoder_compute_budget
        self.encoder_cache_size = encoder_cache_size
141

142
143
144
        # Sampler
        self.sampler = Sampler()

145
        # Lazy initializations
146
        # self.model: nn.Module  # Set after load_model
147
        # Initialize in initialize_kv_cache
148
        self.kv_caches: list[torch.Tensor] = []
149
150
        self.attn_metadata_builders: list[AttentionMetadataBuilder] = []
        self.attn_backends: list[type[AttentionBackend]] = []
151
152
        # self.kv_cache_config: KVCacheConfig

153
        # req_id -> (input_id -> encoder_output)
154
        self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
155

156
        self.use_aux_hidden_state_outputs = False
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
        # 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()
177

178
        # Request states.
179
        self.requests: dict[str, CachedRequestState] = {}
180

181
182
183
184
185
186
187
188
189
        # 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.
190
191
192
193
194
195
        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,
196
            vocab_size=self.model_config.get_vocab_size(),
197
            block_sizes=[self.cache_config.block_size],
198
        )
199

200
        self.use_cuda_graph = (self.compilation_config.level
201
202
203
                               == CompilationLevel.PIECEWISE
                               and not self.model_config.enforce_eager)
        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
204
205
206
207
        # 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(
208
209
210
            reversed(self.compilation_config.cudagraph_capture_sizes))

        self.full_cuda_graph = self.compilation_config.full_cuda_graph
211

212
        # Cache the device properties.
213
        self._init_device_properties()
214

215
216
217
218
        # Persistent buffers for CUDA graphs.
        self.input_ids = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int32,
                                     device=self.device)
219
220
221
        self.positions = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int64,
                                     device=self.device)
222
223
224
225
226
227
228
229
230
231
        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)

232
233
        # None in the first PP rank. The rest are set after load_model.
        self.intermediate_tensors: Optional[IntermediateTensors] = None
234
235

        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
236
        if self.uses_mrope:
Roger Wang's avatar
Roger Wang committed
237
238
239
240
            # 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
241
242
243
244
245
246

            # 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
247
            self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1),
248
249
                                               dtype=torch.int64,
                                               device=self.device)
Roger Wang's avatar
Roger Wang committed
250
251
252
253
254
            self.mrope_positions_cpu = torch.zeros(
                (3, self.max_num_tokens + 1),
                dtype=torch.int64,
                device="cpu",
                pin_memory=self.pin_memory)
255

256
257
258
        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)

259
260
261
262
        self.inputs_embeds = torch.zeros(
            (self.max_num_tokens, self.hidden_size),
            dtype=self.dtype,
            device=self.device)
263

264
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
265
        # Keep in int64 to avoid overflow with long context
266
        self.arange_np = np.arange(max(self.max_num_reqs + 1,
267
268
                                       self.max_model_len,
                                       self.max_num_tokens),
269
                                   dtype=np.int64)
270
271
272
        # 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.
273
274
275
276
277
278
279
280
281
282
283
284
285
286
        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()
287
288
289
290
291
        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()
292

293
294
295
296
297
298
        # 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] = {}

299
300
301
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> bool:
        """
        Update the order of requests in the batch based on the attention
302
        backend's needs. For example, some attention backends (namely MLA) may
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
        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

324
325
326
327
328
329
330
331
332
333
334
    # 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()

335
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
336
337
338
339
340
341
        """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.

342
343
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
344
345
        """
        # Remove finished requests from the cached states.
346
347
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
348
            self.encoder_cache.pop(req_id, None)
349
350
351
352
353
354
        # 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.
355
        removed_req_indices: list[int] = []
356
357
358
359
        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)
360
361
362
363
364
365
366
367

        # 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)
368

369
370
371
372
373
374
375
376
377
378
379
380
381
        # 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:
382
            req_index = self.input_batch.remove_request(req_id)
383
384
            assert req_index is not None
            removed_req_indices.append(req_index)
385

386
        req_ids_to_add: list[str] = []
387
        # Add new requests to the cached states.
388
389
390
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
391
            if sampling_params.sampling_type == SamplingType.RANDOM_SEED:
392
393
394
395
396
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

397
398
            self.requests[req_id] = CachedRequestState(
                req_id=req_id,
399
400
401
                prompt_token_ids=new_req_data.prompt_token_ids,
                mm_inputs=new_req_data.mm_inputs,
                mm_positions=new_req_data.mm_positions,
402
403
                sampling_params=sampling_params,
                generator=generator,
404
405
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
406
                output_token_ids=[],
407
                lora_request=new_req_data.lora_request,
408
            )
409
410

            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
411
            if self.uses_mrope:
412
413
                image_grid_thw = []
                video_grid_thw = []
Roger Wang's avatar
Roger Wang committed
414
                second_per_grid_ts = []
415
416
                audio_feature_lengths = []
                use_audio_in_video = False
417
418
419
420
421
422
423
                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
424
425
426
                    if mm_input.get("second_per_grid_ts") is not None:
                        second_per_grid_ts.extend(
                            mm_input["second_per_grid_ts"])
427
428
429
430
431
                    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
432
433
434
435
436
437
438

                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
439
                        hf_config=hf_config,
440
441
                        image_grid_thw=image_grid_thw,
                        video_grid_thw=video_grid_thw,
Roger Wang's avatar
Roger Wang committed
442
                        second_per_grid_ts=second_per_grid_ts,
443
444
                        audio_feature_lengths=audio_feature_lengths,
                        use_audio_in_video=use_audio_in_video,
445
446
                    )

447
448
            req_ids_to_add.append(req_id)

449
450
451
        # Update the states of the running/resumed requests.
        for req_data in scheduler_output.scheduled_cached_reqs:
            req_id = req_data.req_id
452
453
            req_state = self.requests[req_id]

454
            # Update the cached states.
455
456
457
458
459
460
461
            num_computed_tokens = req_data.num_computed_tokens
            req_state.num_computed_tokens = num_computed_tokens
            # Add the sampled token(s) from the previous step (if any).
            # This doesn't include "unverified" tokens like spec decode tokens.
            num_new_tokens = (num_computed_tokens +
                              len(req_data.new_token_ids) -
                              req_state.num_tokens)
462
463
464
465
466
467
            if num_new_tokens == 1:
                # Avoid slicing list in most common case.
                req_state.output_token_ids.append(req_data.new_token_ids[-1])
            elif num_new_tokens > 0:
                req_state.output_token_ids.extend(
                    req_data.new_token_ids[-num_new_tokens:])
468
            # Update the block IDs.
469
470
            if not req_data.resumed_from_preemption:
                # Append the new blocks to the existing block IDs.
471
472
                for block_ids, new_block_ids in zip(req_state.block_ids,
                                                    req_data.new_block_ids):
473
                    block_ids.extend(new_block_ids)
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
            else:
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
                req_state.block_ids = req_data.new_block_ids

            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] = (
489
                num_computed_tokens)
490
491
            self.input_batch.block_table.append_row(req_data.new_block_ids,
                                                    req_index)
492
493
494
495
496
497
            # Add new_token_ids to token_ids_cpu.
            start_token_index = num_computed_tokens
            end_token_index = num_computed_tokens + len(req_data.new_token_ids)
            self.input_batch.token_ids_cpu[
                req_index,
                start_token_index:end_token_index] = req_data.new_token_ids
498
            self.input_batch.num_tokens_no_spec[req_index] = end_token_index
499
500
            # Add spec_token_ids to token_ids_cpu.
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
501
                req_id, ())
502
503
504
505
506
507
508
            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 decode tokens.
            self.input_batch.num_tokens[req_index] = end_token_index
509

510
511
        # Check if the batch has changed. If not, we can skip copying the
        # sampling metadata from CPU to GPU.
512
513
        batch_changed = len(removed_req_indices) > 0 or len(req_ids_to_add) > 0

514
515
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
516
        removed_req_indices.sort(reverse=True)
517
518
519
520
521
522
523
524
525
526
527
528
529
        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)
530

531
        batch_reordered = self._may_reorder_batch(scheduler_output)
532
533

        if batch_changed or batch_reordered:
534
            self.input_batch.refresh_sampling_metadata()
535

536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
    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

556
    def _prepare_inputs(
557
558
        self,
        scheduler_output: "SchedulerOutput",
559
560
561
562
563
564
565
566
567
    ) -> tuple[dict[str, Any], bool, torch.Tensor,
               Optional[SpecDecodeMetadata]]:
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
            attention_cuda_graphs: whether attention can run in cudagraph
            logits_indices, spec_decode_metadata
        ]
        """
568
569
570
571
572
573
574
        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.
575
        self.input_batch.block_table.commit(num_reqs)
576
577

        # Get the number of scheduled tokens for each request.
578
579
580
581
        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)
582
583
584

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
585
586
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
587

588
589
590
591
        # 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)
592
593

        # Get positions.
594
        positions_np = self.positions_np[:total_num_scheduled_tokens]
595
596
597
598
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

599
600
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
601
        if self.uses_mrope:
602
603
            self._calc_mrope_positions(scheduler_output)

604
605
606
607
        # 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.
608
609
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
610

611
612
613
614
        # 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(),
615
                           0,
616
617
                           torch.from_numpy(token_indices),
                           out=self.input_ids_cpu[:total_num_scheduled_tokens])
618

619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
        # 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])
642
643

        # Prepare the attention metadata.
644
        self.query_start_loc_np[0] = 0
645
        self.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
646

647
648
649
        self.seq_lens_np[:num_reqs] = (
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)
650
651
652
653

        # 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)
654
        if self.uses_mrope:
655
656
657
658
659
660
661
662
663
            # 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)
664

665
666
667
668
669
670
671
        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)
672
673
674
675
        # 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())
676
677
678
679

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

680
        common_attn_metadata = CommonAttentionMetadata(
681
682
683
684
685
686
            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,
        )
687

688
        attn_metadata: dict[str, Any] = {}
689
690
691
692
693
694
695
        # 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
696
            builder = self.attn_metadata_builders[kv_cache_group_id]
697
698
699
            if self.cascade_attn_enabled:
                common_prefix_len = self._compute_cascade_attn_prefix_len(
                    num_scheduled_tokens,
700
701
702
                    scheduler_output.
                    num_common_prefix_blocks[kv_cache_group_id],
                    kv_cache_group_spec.kv_cache_spec,
703
                    builder,
704
                )
705

706
707
708
709
710
            attn_metadata_i = (builder.build(
                common_prefix_len=common_prefix_len,
                common_attn_metadata=common_attn_metadata,
            ))

711
712
            for layer_name in kv_cache_group_spec.layer_names:
                attn_metadata[layer_name] = attn_metadata_i
713

714
715
716
717
        attention_cuda_graphs = all(
            b.can_run_in_cudagraph(common_attn_metadata)
            for b in self.attn_metadata_builders)

718
719
        use_spec_decode = len(
            scheduler_output.scheduled_spec_decode_tokens) > 0
720
        if not use_spec_decode:
721
722
723
724
725
            # 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.
726
            logits_indices = query_start_loc[1:] - 1
727
728
729
730
731
732
733
734
735
736
737
738
739
740
            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
741

742
743
744
745
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

746
747
        return (attn_metadata, attention_cuda_graphs, logits_indices,
                spec_decode_metadata)
748

749
750
751
752
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
753
754
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
    ) -> 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.
        """
773
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
        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]
811
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
812
813
814
815
816
817
818
819
820
821
        # 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.
822
823
824
825
826
827
828
        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(
829
830
831
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
832
            num_kv_heads=kv_cache_spec.num_kv_heads,
833
            use_alibi=self.use_alibi,
834
            use_sliding_window=use_sliding_window,
835
836
837
838
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

839
840
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
841
        for index, req_id in enumerate(self.input_batch.req_ids):
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
            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

                self.mrope_positions_cpu[:, dst_start:dst_end] = \
                    MRotaryEmbedding.get_next_input_positions_tensor(
                        req.mrope_position_delta,
                        context_len=num_computed_tokens +
                        prompt_part_len,
                        seq_len=num_computed_tokens +
                        prompt_part_len +
                        completion_part_len,
                    )

                mrope_pos_ptr += completion_part_len

891
892
    def _calc_spec_decode_metadata(
        self,
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
        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
909
910
911
912
913
914

        # 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]
915
916
        logits_indices = np.repeat(
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
917
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
918
919
920
921
922
923
        logits_indices += arange

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

        # Compute the draft logits indices.
924
925
926
927
        # 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)
928
929
930
931
932
933
934
935
936
937
938
939
940
941
        # [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(
942
943
            self.device, non_blocking=True)

944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
        # 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

959
    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
960
961
962
963
964
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
965
966
        mm_inputs = list[MultiModalKwargs]()
        req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
967
968
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
969
970
971
972
973

            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]))
974
975
976
977
978
979
980
981
982
983
984
985

        # 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:
986
987
            batched_mm_inputs = MultiModalKwargs.batch(
                grouped_mm_inputs, pin_memory=self.pin_memory)
988
989
990
991
            batched_mm_inputs = MultiModalKwargs.as_kwargs(
                batched_mm_inputs,
                device=self.device,
            )
992
993
994
995
996
997
998
999
1000
1001
1002

            # 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)

1003
1004
1005
1006
1007
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
                expected_num_items=len(grouped_mm_inputs),
            )

1008
1009
            for output in curr_group_outputs:
                encoder_outputs.append(output)
1010
1011

        # Cache the encoder outputs.
1012
1013
1014
1015
        for (req_id, input_id, pos_info), output in zip(
                req_ids_pos,
                encoder_outputs,
        ):
1016
1017
1018
            if req_id not in self.encoder_cache:
                self.encoder_cache[req_id] = {}

1019
1020
1021
1022
1023
1024
            self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1025
1026
        self,
        scheduler_output: "SchedulerOutput",
1027
    ) -> list[torch.Tensor]:
1028
        mm_embeds: list[torch.Tensor] = []
1029
        for req_id in self.input_batch.req_ids:
1030
1031
1032
1033
1034
1035
            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):
1036
1037
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058

                # 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]
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068

                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
1069

1070
1071
1072
    def get_model(self) -> nn.Module:
        return self.model

1073
1074
1075
1076
1077
1078
1079
1080
1081
    def apply_grammar_bitmask(
        self,
        scheduler_output: "SchedulerOutput",
        logits: torch.Tensor,
    ):
        grammar_bitmask = scheduler_output.grammar_bitmask
        if grammar_bitmask is None:
            return

1082
1083
1084
1085
1086
1087
1088
1089
1090
        # 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.
1091
        struct_out_req_batch_indices: dict[str, int] = {}
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
        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
1121

1122
1123
        # Serialization of np.ndarray is much more efficient than a tensor,
        # so we receive it in that format.
1124
1125
        grammar_bitmask = torch.from_numpy(grammar_bitmask)

1126
1127
1128
1129
        # 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(
1130
1131
            logits,
            grammar_bitmask.to(self.device, non_blocking=True),
1132
            indices=out_indices,
1133
1134
        )

1135
1136
1137
1138
1139
1140
1141
    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
1142
        enabled_sp = self.compilation_config.pass_config. \
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
            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()
        })

1169
1170
    def get_dp_padding(self,
                       num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
1171
1172
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
1173
1174
1175
1176
1177
1178
1179
1180
1181

        # 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:
1182
            # Early exit.
1183
            return 0, None
1184
1185
1186
1187

        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()
1188
1189
1190
1191
1192
        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
1193

1194
1195
1196
1197
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
1198
        intermediate_tensors: Optional[IntermediateTensors] = None,
1199
    ) -> Union[ModelRunnerOutput, IntermediateTensors]:
1200

1201
        self._update_states(scheduler_output)
1202
        if not scheduler_output.total_num_scheduled_tokens:
Robert Shaw's avatar
Robert Shaw committed
1203
1204
1205
1206
1207
            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)
1208
1209

        # Prepare the decoder inputs.
1210
1211
        (attn_metadata, attention_cuda_graphs, logits_indices,
         spec_decode_metadata) = (self._prepare_inputs(scheduler_output))
1212
1213
1214
1215
1216
        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.
1217
            num_input_tokens = self.vllm_config.pad_for_cudagraph(
1218
1219
1220
                num_scheduled_tokens)
        else:
            # Eager mode.
1221
1222
1223
            # Pad tokens to multiple of tensor_parallel_size when
            # enabled collective fusion for SP
            tp_size = self.vllm_config.parallel_config.tensor_parallel_size
1224
            if self.compilation_config.pass_config. \
1225
1226
1227
1228
1229
                enable_sequence_parallelism and tp_size > 1:
                from vllm.utils import round_up
                num_input_tokens = round_up(num_scheduled_tokens, tp_size)
            else:
                num_input_tokens = num_scheduled_tokens
1230

1231
        # Padding for DP
1232
1233
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_input_tokens)
        num_input_tokens += num_pad
1234

1235
1236
1237
1238
1239
1240
1241
1242
1243
        # _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 = []

1244
        if self.is_multimodal_model and get_pp_group().is_first_rank:
1245
1246
1247
1248
            # 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]
1249
            if mm_embeds:
1250
                inputs_embeds = self.model.get_input_embeddings(
1251
                    input_ids, mm_embeds)
1252
1253
1254
1255
1256
1257
            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
1258
        else:
1259
1260
1261
1262
1263
1264
            # 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
1265
1266
1267
1268
        if self.uses_mrope:
            positions = self.mrope_positions[:, :num_input_tokens]
        else:
            positions = self.positions[:num_input_tokens]
1269

1270
1271
1272
        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
1273
1274
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                num_input_tokens, intermediate_tensors, True)
1275

1276
1277
1278
1279
1280
        # 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

1281
1282
        # Run the decoder.
        # Use persistent buffers for CUDA graphs.
1283
1284
1285
1286
1287
1288
1289
        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
1290
1291
1292
            self.maybe_setup_kv_connector(scheduler_output)

            model_output = self.model(
1293
                input_ids=input_ids,
1294
                positions=positions,
1295
                intermediate_tensors=intermediate_tensors,
1296
                inputs_embeds=inputs_embeds,
1297
            )
1298

Robert Shaw's avatar
Robert Shaw committed
1299
1300
1301
1302
            self.maybe_wait_for_kv_save()
            finished_sending, finished_recving = (
                self.get_finished_kv_transfers(scheduler_output))

1303
        if self.use_aux_hidden_state_outputs:
Robert Shaw's avatar
Robert Shaw committed
1304
            hidden_states, aux_hidden_states = model_output
1305
        else:
Robert Shaw's avatar
Robert Shaw committed
1306
            hidden_states = model_output
1307
1308
1309
1310
1311
1312
1313
        # 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
1314
        if not get_pp_group().is_last_rank:
1315
            # For mid-pipeline stages, return the hidden states.
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
            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:
            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"]
1333

1334
1335
1336
1337
        # Apply structured output bitmasks if present
        if scheduler_output.grammar_bitmask is not None:
            self.apply_grammar_bitmask(scheduler_output, logits)

1338
        # Sample the next token and get logprobs if needed.
1339
        sampling_metadata = self.input_batch.sampling_metadata
1340
        if spec_decode_metadata is None:
1341
            sampler_output = self.sampler(
1342
1343
1344
1345
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
1346
1347
1348
1349
            # 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.
1350
            assert logits is not None
1351
            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
1352
            sampler_output = self.sampler(
1353
                logits=bonus_logits,
1354
1355
1356
                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
1357

1358
1359
1360
            # 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.
1361
            target_logits = logits[spec_decode_metadata.target_logits_indices]
1362
            output_token_ids = self.rejection_sampler(
1363
                spec_decode_metadata,
1364
                None,  # draft_probs
1365
                target_logits,
1366
                bonus_token_ids,
1367
1368
                sampling_metadata,
            )
1369
            sampler_output.sampled_token_ids = output_token_ids
1370
1371
1372

        # TODO(woosuk): The following loop can be slow since it iterates over
        # the requests one by one. Optimize.
1373
1374
        discard_sampled_tokens_req_indices = []
        for i, req_id in enumerate(self.input_batch.req_ids):
1375
1376
1377
            req_state = self.requests[req_id]
            seq_len = (req_state.num_computed_tokens +
                       scheduler_output.num_scheduled_tokens[req_id])
1378
            if seq_len < req_state.num_tokens:
1379
                # Ignore the sampled token for partial prefills.
1380
                # Rewind the generator state as if the token was not sampled.
1381
                # This relies on cuda-specific torch-internal impl details
1382
1383
1384
1385
1386
1387
                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)
1388

1389
1390
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
1391
1392
1393
1394
1395
1396
        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(
1397
            hidden_states[:num_scheduled_tokens],
1398
1399
1400
            scheduler_output,
        )

1401
        # Get the valid generated tokens.
1402
1403
1404
        sampled_token_ids = sampler_output.sampled_token_ids
        max_gen_len = sampled_token_ids.shape[-1]
        if max_gen_len == 1:
1405
            # No spec decode tokens.
1406
1407
            valid_sampled_token_ids = sampled_token_ids.tolist()
        else:
1408
            # Includes spec decode tokens.
1409
            valid_sampled_token_ids = self.rejection_sampler.parse_output(
1410
1411
1412
                sampled_token_ids,
                self.input_batch.vocab_size,
            )
1413
1414
1415
        # Mask out the sampled tokens that should not be sampled.
        for i in discard_sampled_tokens_req_indices:
            valid_sampled_token_ids[i].clear()
1416

1417
        if not self.speculative_config:
1418
            # Speculative decoding is not enabled.
1419
            spec_token_ids = None
1420
1421
        elif self.speculative_config.method == "ngram":
            assert isinstance(self.drafter, NgramProposer)
1422
            spec_token_ids = self.generate_draft_token_ids(
1423
                valid_sampled_token_ids, sampling_metadata)
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
        elif self.speculative_config.method == "medusa":
            assert isinstance(self.drafter, MedusaProposer)
            if max_gen_len == 1:
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
                for num_draft, tokens in zip(
                        spec_decode_metadata.num_draft_tokens,
                        valid_sampled_token_ids):
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1

                indices = torch.tensor(indices,
                                       device=sample_hidden_states.device)
                hidden_states = sample_hidden_states[indices]

            spec_token_ids = self.drafter.propose(
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
1445
        elif self.speculative_config.use_eagle():
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
            assert isinstance(self.drafter, EagleProposer)
            # TODO(woosuk): Refactor the loop.
            next_token_ids: list[int] = []
            for i, token_ids in enumerate(valid_sampled_token_ids):
                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)
1462
1463
1464
1465
1466
1467
1468
            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]]
1469

Jiayi Yao's avatar
Jiayi Yao committed
1470
1471
1472
1473
1474
1475
            # 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

1476
1477
1478
            if spec_decode_metadata is None:
                # input_ids can be None for multimodal models.
                target_token_ids = self.input_ids[:num_scheduled_tokens]
1479
                target_positions = positions[:num_scheduled_tokens]
1480
                if self.use_aux_hidden_state_outputs:
1481
1482
1483
                    target_hidden_states = torch.cat(
                        [h[:num_scheduled_tokens] for h in aux_hidden_states],
                        dim=-1)
1484
1485
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
1486
1487
                target_slot_mapping = eagle_attn_metadata.slot_mapping
                cu_num_tokens = eagle_attn_metadata.query_start_loc
1488
1489
1490
1491
1492
1493
1494
            else:
                # TODO(woosuk): Refactor this.
                num_draft_tokens = spec_decode_metadata.num_draft_tokens
                num_rejected_tokens = [
                    n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0
                    for i, n in enumerate(num_draft_tokens)
                ]
1495
                num_rejected_tokens_tensor = async_tensor_h2d(
1496
1497
                    num_rejected_tokens,
                    dtype=torch.int32,
1498
1499
1500
                    target_device=self.device,
                    pin_memory=True)
                num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
1501
                cu_num_tokens, token_indices = self.drafter.prepare_inputs(
1502
                    eagle_attn_metadata.query_start_loc,
1503
1504
                    num_rejected_tokens_tensor,
                    num_tokens,
1505
1506
1507
                )
                target_token_ids = self.input_ids[token_indices]
                target_positions = positions[token_indices]
1508
                if self.use_aux_hidden_state_outputs:
1509
1510
                    target_hidden_states = torch.cat(
                        [h[token_indices] for h in aux_hidden_states], dim=-1)
1511
1512
                else:
                    target_hidden_states = hidden_states[token_indices]
1513
1514
                target_slot_mapping = eagle_attn_metadata.slot_mapping[
                    token_indices]
1515
            draft_token_ids = self.drafter.propose(
1516
1517
1518
1519
1520
1521
                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
1522
                block_table=block_table,
1523
1524
1525
                sampling_metadata=sampling_metadata,
            )
            spec_token_ids = draft_token_ids.tolist()
1526

1527
1528
1529
1530
        # Clear KVConnector state after all KVs are generated.
        if has_kv_transfer_group():
            get_kv_transfer_group().clear_connector_metadata()

1531
        return ModelRunnerOutput(
1532
            req_ids=self.input_batch.req_ids,
1533
            req_id_to_index=self.input_batch.req_id_to_index,
1534
            sampled_token_ids=valid_sampled_token_ids,
1535
            spec_token_ids=spec_token_ids,
1536
1537
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
Robert Shaw's avatar
Robert Shaw committed
1538
1539
            finished_sending=finished_sending,
            finished_recving=finished_recving,
1540
1541
        )

Robert Shaw's avatar
Robert Shaw committed
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
    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

1588
1589
    def generate_draft_token_ids(
        self,
1590
        sampled_token_ids: list[list[int]],
1591
        sampling_metadata: SamplingMetadata,
1592
    ) -> list[list[int]]:
1593
        # TODO(woosuk): Optimize.
1594
        draft_token_ids: list[list[int]] = []
1595
1596
1597
        for i, sampled_ids in enumerate(sampled_token_ids):
            num_sampled_ids = len(sampled_ids)
            if not num_sampled_ids:
1598
1599
1600
1601
                # Skip speculative decoding.
                draft_token_ids.append([])
                continue

1602
1603
            # Skip requests that require sampling parameters that are not
            # supported with speculative decoding.
1604
1605
1606
1607
1608
            req_id = self.input_batch.req_ids[i]
            if not is_spec_decode_supported(req_id, self.input_batch):
                draft_token_ids.append([])
                continue

1609
1610
            # Add sampled_token_ids to token_ids_cpu.
            start_idx = self.input_batch.num_tokens_no_spec[i]
1611
            end_idx = start_idx + num_sampled_ids
1612
1613
1614
1615
1616
            if end_idx >= self.max_model_len:
                # Skip requests that have already reached the max model length.
                draft_token_ids.append([])
                continue

1617
            self.input_batch.token_ids_cpu[i, start_idx:end_idx] = sampled_ids
1618
            drafter_output = self.drafter.propose(
1619
                self.input_batch.token_ids_cpu[i, :end_idx])
1620
1621
1622
1623
1624
1625
            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

1626
1627
1628
    def load_model(self) -> None:
        logger.info("Starting to load model %s...", self.model_config.model)
        with DeviceMemoryProfiler() as m:  # noqa: SIM117
1629
            time_before_load = time.perf_counter()
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
            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)
1642
1643
1644
1645
1646
1647
            if self.lora_config:
                self.model = self.load_lora_model(self.model,
                                                  self.model_config,
                                                  self.scheduler_config,
                                                  self.lora_config,
                                                  self.device)
1648
1649
1650
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
1651
1652
1653
            if self.use_aux_hidden_state_outputs:
                self.model.set_aux_hidden_state_layers(
                    self.model.get_eagle3_aux_hidden_state_layers())
1654
            time_after_load = time.perf_counter()
1655
        self.model_memory_usage = m.consumed_memory
1656
1657
        logger.info("Model loading took %.4f GiB and %.6f seconds",
                    self.model_memory_usage / GiB_bytes,
1658
                    time_after_load - time_before_load)
1659
        prepare_communication_buffer_for_model(self.model)
1660

1661
1662
1663
1664
1665
1666
1667
1668
1669
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
            self.model,
            tensorizer_config=tensorizer_config,
        )

1670
1671
1672
1673
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
        scheduler_output: "SchedulerOutput",
1674
    ) -> dict[str, Optional[LogprobsTensors]]:
1675
1676
1677
1678
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

1679
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
1680
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694

        # 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)

1695
1696
1697
1698
1699
1700
1701
1702
1703
            # 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

1704
            # Determine number of logits to retrieve.
1705
1706
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
1707
            num_remaining_tokens = num_prompt_tokens - start_tok
1708
            if num_tokens <= num_remaining_tokens:
1709
                # This is a chunk, more tokens remain.
1710
1711
1712
                # 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.
1713
1714
1715
1716
1717
                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)
1718
1719
1720
1721
1722
1723
1724
                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
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739

            # 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.
1740
1741
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
1742
1743
1744
                logprobs, num_prompt_logprobs, tgt_token_ids)

            # Transfer GPU->CPU async.
1745
1746
1747
1748
1749
1750
1751
            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)
1752
1753
1754
1755
1756

        # 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]
1757
            del in_progress_dict[req_id]
1758
1759

        # Must synchronize the non-blocking GPU->CPU transfers.
1760
        if prompt_logprobs_dict:
1761
            self._sync_device()
1762
1763
1764

        return prompt_logprobs_dict

1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
    @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)

1794
1795
1796
1797
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
1798
        capture_attn_cudagraph: bool = False,
1799
    ) -> torch.Tensor:
1800

1801
        # Padding for DP
1802
1803
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
        num_tokens += num_pad
1804

1805
1806
1807
1808
1809
        # 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
1810
        num_reqs = min(num_tokens, max_num_reqs)
1811
1812
1813
1814
1815
1816
1817
        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)
1818

1819
1820
1821
1822
        attn_metadata: Optional[dict[str, Any]] = None
        if capture_attn_cudagraph:
            attn_metadata = {}

1823
            query_start_loc = self.query_start_loc[:num_reqs + 1]
1824
1825
1826
1827
1828
            # 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)
1829
1830
1831
            seq_lens = self.seq_lens[:num_reqs]

            common_attn_metadata = CommonAttentionMetadata(
1832
1833
1834
1835
1836
1837
                query_start_loc=query_start_loc,
                seq_lens=seq_lens,
                num_reqs=num_reqs,
                num_actual_tokens=num_tokens,
                max_query_len=num_tokens,
            )
1838

1839
1840
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
                    self.kv_cache_config.kv_cache_groups):
1841
1842
1843
1844

                attn_metadata_i = self.attn_metadata_builders[
                    kv_cache_group_id].build_for_cudagraph_capture(
                        common_attn_metadata)
1845
1846
                for layer_name in kv_cache_group_spec.layer_names:
                    attn_metadata[layer_name] = attn_metadata_i
1847

1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
        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))
1871
1872
1873

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                    num_tokens, None, False)
1874

1875
            with self.maybe_randomize_inputs(input_ids), set_forward_context(
1876
1877
1878
1879
                    attn_metadata,
                    self.vllm_config,
                    num_tokens=num_tokens,
                    num_tokens_across_dp=num_tokens_across_dp):
1880
                outputs = model(
1881
1882
1883
1884
1885
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
                )
1886
1887
1888
1889
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
1890

1891
            if self.speculative_config and self.speculative_config.use_eagle():
1892
1893
1894
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

1895
1896
1897
1898
1899
1900
1901
1902
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
        return hidden_states[logit_indices]

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
1903
1904
1905
1906
        # 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)
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933

        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={},
        )
1934
        try:
1935
1936
            sampler_output = self.sampler(logits=logits,
                                          sampling_metadata=dummy_metadata)
1937
1938
1939
1940
1941
1942
1943
1944
1945
        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
1946
        if self.speculative_config:
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
            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,
            )
1973
        return sampler_output
1974
1975

    def profile_run(self) -> None:
1976
        # Profile with multimodal encoder & encoder cache.
1977
1978
1979
        # 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):
1980

1981
            # NOTE: Currently model is profiled with a single non-text
1982
1983
            # modality with the max possible input tokens even when
            # it supports multiple.
1984
1985
            max_tokens_by_modality_dict = self.mm_registry \
                .get_max_tokens_per_item_by_nonzero_modality(self.model_config)
1986
1987
1988
1989
            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
1990
1991
1992
1993
1994
1995
            # 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)
1996
1997
1998

            # Check how many items of this modality can be supported by
            # the decoder budget.
1999
2000
            max_mm_items_per_req = self.mm_registry.get_mm_limits_per_prompt(
                self.model_config)[dummy_data_modality]
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

            # 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)

2011
2012
2013
2014
2015
2016
            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.
2017
            dummy_mm_kwargs = self.mm_registry.get_decoder_dummy_data(
2018
2019
                model_config=self.model_config,
                seq_len=self.max_num_tokens,
2020
2021
2022
2023
                mm_counts={
                    dummy_data_modality: 1
                },
            ).multi_modal_data
2024

2025
            batched_dummy_mm_inputs = MultiModalKwargs.batch(
2026
2027
                [dummy_mm_kwargs] * max_num_mm_items,
                pin_memory=self.pin_memory)
2028
            batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs(
2029
2030
2031
                batched_dummy_mm_inputs,
                device=self.device,
            )
2032
2033
2034
2035

            # Run multimodal encoder.
            dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                **batched_dummy_mm_inputs)
2036
2037
2038
2039
2040

            sanity_check_mm_encoder_outputs(
                dummy_encoder_outputs,
                expected_num_items=max_num_mm_items,
            )
2041
2042
2043
2044

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

2045
2046
2047
2048
2049
        hidden_states = self._dummy_run(self.max_num_tokens)
        if get_pp_group().is_last_rank:
            sampler_output = self._dummy_sampler_run(hidden_states)
        else:
            sampler_output = None
2050
        self._sync_device()
2051
2052
        del hidden_states, sampler_output
        self.encoder_cache.clear()
2053
        gc.collect()
2054
2055

    def capture_model(self) -> None:
2056
2057
        if not self.use_cuda_graph:
            logger.warning(
2058
                "Skipping CUDA graph capture. Please add "
2059
                "-O %s to use CUDA graphs.", CompilationLevel.PIECEWISE)
2060
2061
2062
2063
2064
            return

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

2065
2066
2067
        # 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.
2068
        with graph_capture(device=self.device):
2069
            full_cg = self.full_cuda_graph
2070
2071
2072
            for num_tokens in tqdm(reversed(self.cudagraph_batch_sizes),
                                   desc="Capturing CUDA graphs",
                                   total=len(self.cudagraph_batch_sizes)):
2073
2074
2075
2076
                for _ in range(
                        self.compilation_config.cudagraph_num_of_warmups):
                    self._dummy_run(num_tokens, capture_attn_cudagraph=full_cg)
                self._dummy_run(num_tokens, capture_attn_cudagraph=full_cg)
2077
2078
2079
2080
2081
2082
2083
2084

        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))
2085

2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
    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
            if not isinstance(kv_cache_spec, AttentionSpec):
                raise NotImplementedError(
                    "Only AttentionSpec is supported for now.")
            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: {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.")

            block_table_i = self.input_batch.block_table[i]
            attn_metadata_builder_i = attn_backend_i.get_builder_cls()(
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
                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.")

2133
2134
2135
            self.attn_backends.append(attn_backend_i)
            self.attn_metadata_builders.append(attn_metadata_builder_i)

2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
    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,
            )

2165
2166
    def _allocate_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
2167
        """
2168
2169
2170
        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.

2171
        Args:
2172
            kv_cache_config: The KV cache config
2173
        Returns:
2174
            dict[str, torch.Tensor]: A map between layer names to their
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
            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]:
2197
        """
2198
        Reshape the KV cache tensors to the desired shape and dtype.
2199

2200
        Args:
2201
2202
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
2203
2204
            correct size but uninitialized shape.
        Returns:
2205
            Dict[str, torch.Tensor]: A map between layer names to their
2206
2207
            corresponding memory buffer for KV cache.
        """
2208
        kv_caches: dict[str, torch.Tensor] = {}
2209
2210
2211
2212
2213
2214
2215
2216
        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)
2217
                if isinstance(kv_cache_spec, AttentionSpec):
2218
                    kv_cache_shape = self.attn_backends[i].get_kv_cache_shape(
2219
2220
2221
                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    dtype = kv_cache_spec.dtype
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
                    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))
                    ]
2242
2243
2244
                    kv_caches[layer_name] = kv_cache_raw_tensors[
                        layer_name].view(dtype).view(kv_cache_shape).permute(
                            *inv_order)
2245
                else:
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
                    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:
2257
            Dict[str, torch.Tensor]: A map between layer names to their
2258
2259
2260
2261
2262
2263
2264
            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)
2265

2266
2267
2268
2269
2270
2271
2272
2273
2274
        # 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,
            )

2275
2276
2277
        bind_kv_cache(kv_caches,
                      self.compilation_config.static_forward_context,
                      self.kv_caches)
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
        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)

2292
2293
2294
2295
2296
2297
        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
2298
2299
2300
        if has_kv_transfer_group():
            get_kv_transfer_group().register_kv_caches(kv_caches)

2301
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
2302
        """
2303
        Generates the KVCacheSpec by parsing the kv cache format from each
2304
2305
        Attention module in the static forward context.
        Returns:
2306
            KVCacheSpec: A dictionary mapping layer names to their KV cache
2307
2308
2309
            format. Layers that do not need KV cache are not included.
        """

2310
        layers = get_layers_from_vllm_config(self.vllm_config, Attention)
2311
        block_size = self.vllm_config.cache_config.block_size
2312
        use_mla = self.vllm_config.model_config.use_mla
2313
        kv_cache_spec: dict[str, KVCacheSpec] = {}
2314
        for layer_name, attn_module in layers.items():
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
            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

2327
            # TODO: Support other attention modules, e.g., cross-attention
2328
            if attn_module.attn_type == AttentionType.DECODER:
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
                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)
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
            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}")

        return kv_cache_spec