model_runner.py 83.5 KB
Newer Older
1
import dataclasses
2
import gc
3
import inspect
4
import itertools
5
import time
6
import warnings
7
import weakref
8
from dataclasses import dataclass
9
10
from typing import (TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set,
                    Tuple, Type, TypeVar, Union)
11

12
import numpy as np
13
import torch
14
import torch.distributed
15
import torch.nn as nn
16

17
import vllm.envs as envs
18
from vllm.attention import AttentionMetadata, get_attn_backend
19
from vllm.attention.backends.abstract import AttentionState
20
from vllm.attention.backends.utils import CommonAttentionState
21
22
from vllm.compilation.compile_context import set_compile_context
from vllm.compilation.levels import CompilationLevel
23
from vllm.config import VllmConfig
24
from vllm.core.scheduler import SchedulerOutputs
25
from vllm.distributed import get_pp_group
26
from vllm.distributed.parallel_state import graph_capture
27
from vllm.forward_context import set_forward_context
28
from vllm.inputs import INPUT_REGISTRY, InputRegistry
29
from vllm.logger import init_logger
30
31
32
from vllm.lora.layers import LoRAMapping
from vllm.lora.request import LoRARequest
from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
33
from vllm.model_executor import SamplingMetadata, SamplingMetadataCache
34
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
35
from vllm.model_executor.layers.sampler import SamplerOutput
36
from vllm.model_executor.model_loader import get_model
37
from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
38
from vllm.model_executor.models import supports_lora, supports_multimodal
39
from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
40
from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
41
42
                             MultiModalInputs, MultiModalPlaceholderMap,
                             MultiModalRegistry)
43
from vllm.platforms import current_platform
44
45
46
47
from vllm.prompt_adapter.layers import PromptAdapterMapping
from vllm.prompt_adapter.request import PromptAdapterRequest
from vllm.prompt_adapter.worker_manager import (
    LRUCacheWorkerPromptAdapterManager)
48
from vllm.sampling_params import SamplingParams
49
from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
50
from vllm.transformers_utils.config import uses_mrope
51
from vllm.utils import (DeviceMemoryProfiler, PyObjectCache, async_tensor_h2d,
52
                        flatten_2d_lists, is_pin_memory_available,
53
                        supports_dynamo, weak_ref_tensor)
54
from vllm.worker.model_runner_base import (
55
    ModelRunnerBase, ModelRunnerInputBase, ModelRunnerInputBuilderBase,
56
57
58
    _add_attn_metadata_broadcastable_dict,
    _add_sampling_metadata_broadcastable_dict,
    _init_attn_metadata_from_tensor_dict,
59
    _init_sampling_metadata_from_tensor_dict, dump_input_when_exception)
60
61
62

if TYPE_CHECKING:
    from vllm.attention.backends.abstract import AttentionBackend
63
64
65

logger = init_logger(__name__)

66
LORA_WARMUP_RANK = 8
67
_BATCH_SIZE_ALIGNMENT = 8
68
69
70
71
72
# all the token sizes that **can** be captured by cudagraph.
# they can be arbitrarily large.
# currently it includes: 1, 2, 4, 8, 16, 24, 32, 40, ..., 8192.
# the actual sizes to capture will be determined by the model,
# depending on the model's max_num_seqs.
73
# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
74
_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
75
    _BATCH_SIZE_ALIGNMENT * i for i in range(1, 1025)
76
]
77
_NUM_WARMUP_ITERS = 2
78

79
80
TModelInputForGPU = TypeVar('TModelInputForGPU', bound="ModelInputForGPU")

81
82
83
84
# For now, bump up cache limits for recompilations during CUDA graph warmups.
torch._dynamo.config.cache_size_limit = 128
torch._dynamo.config.accumulated_cache_size_limit = 128

85

86
@dataclass(frozen=True)
87
88
89
90
91
92
93
94
95
96
97
98
99
100
class ModelInputForGPU(ModelRunnerInputBase):
    """
    This base class contains metadata needed for the base model forward pass
    but not metadata for possible additional steps, e.g., sampling. Model
    runners that run additional steps should subclass this method to add
    additional fields.
    """
    input_tokens: Optional[torch.Tensor] = None
    input_positions: Optional[torch.Tensor] = None
    seq_lens: Optional[List[int]] = None
    query_lens: Optional[List[int]] = None
    lora_mapping: Optional["LoRAMapping"] = None
    lora_requests: Optional[Set[LoRARequest]] = None
    attn_metadata: Optional["AttentionMetadata"] = None
101
102
    prompt_adapter_mapping: Optional[PromptAdapterMapping] = None
    prompt_adapter_requests: Optional[Set[PromptAdapterRequest]] = None
103
    multi_modal_kwargs: Optional[BatchedTensorInputs] = None
Mor Zusman's avatar
Mor Zusman committed
104
105
    request_ids_to_seq_ids: Optional[Dict[str, List[int]]] = None
    finished_requests_ids: Optional[List[str]] = None
106
    virtual_engine: int = 0
107
    async_callback: Optional[Callable] = None
108
109
    seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None
    scheduler_outputs: Optional[SchedulerOutputs] = None
110
111
112
113
114
115
116
117

    def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
        tensor_dict = {
            "input_tokens": self.input_tokens,
            "input_positions": self.input_positions,
            "lora_requests": self.lora_requests,
            "lora_mapping": self.lora_mapping,
            "multi_modal_kwargs": self.multi_modal_kwargs,
118
119
            "prompt_adapter_mapping": self.prompt_adapter_mapping,
            "prompt_adapter_requests": self.prompt_adapter_requests,
120
            "virtual_engine": self.virtual_engine,
Mor Zusman's avatar
Mor Zusman committed
121
122
            "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
            "finished_requests_ids": self.finished_requests_ids,
123
124
125
        }
        _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
        return tensor_dict
126
127

    @classmethod
128
129
130
131
132
133
134
135
136
137
138
    def from_broadcasted_tensor_dict(
        cls: Type[TModelInputForGPU],
        tensor_dict: Dict[str, Any],
        attn_backend: Optional["AttentionBackend"] = None,
    ) -> TModelInputForGPU:
        if attn_backend is not None:
            tensor_dict = _init_attn_metadata_from_tensor_dict(
                attn_backend, tensor_dict)
        return cls(**tensor_dict)


139
@dataclass(frozen=True)
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
class ModelInputForGPUWithSamplingMetadata(ModelInputForGPU):
    """
    Used by the ModelRunner.
    """
    sampling_metadata: Optional["SamplingMetadata"] = None
    # Used for speculative decoding. We do not broadcast it because it is only
    # used by the driver worker.
    is_prompt: Optional[bool] = None

    def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
        tensor_dict = {
            "input_tokens": self.input_tokens,
            "input_positions": self.input_positions,
            "lora_requests": self.lora_requests,
            "lora_mapping": self.lora_mapping,
            "multi_modal_kwargs": self.multi_modal_kwargs,
156
157
            "prompt_adapter_mapping": self.prompt_adapter_mapping,
            "prompt_adapter_requests": self.prompt_adapter_requests,
158
            "virtual_engine": self.virtual_engine,
Mor Zusman's avatar
Mor Zusman committed
159
160
            "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
            "finished_requests_ids": self.finished_requests_ids,
161
162
163
164
165
        }
        _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
        _add_sampling_metadata_broadcastable_dict(tensor_dict,
                                                  self.sampling_metadata)
        return tensor_dict
166

167
168
169
170
171
172
173
174
175
176
177
178
179
    @classmethod
    def from_broadcasted_tensor_dict(
        cls,
        tensor_dict: Dict[str, Any],
        attn_backend: Optional["AttentionBackend"] = None,
    ) -> "ModelInputForGPUWithSamplingMetadata":
        tensor_dict = _init_sampling_metadata_from_tensor_dict(tensor_dict)
        if attn_backend is not None:
            tensor_dict = _init_attn_metadata_from_tensor_dict(
                attn_backend, tensor_dict)
        return cls(**tensor_dict)


180
class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
181
182
    """Build ModelInputForGPU from SequenceGroupMetadata."""

183
184
185
    # Note: ideally we would be using a dataclass(kw_only=True)
    # here, so that this can be subclassed easily,
    # but kw_only is not supported in python<3.10.
186
187
    class InterDataForSeqGroup:
        """Intermediate data for the current sequence group."""
188

189
190
191
        def simple_reinit(self):
            self.input_tokens[0].clear()  # type: ignore
            self.input_positions[0].clear()  # type: ignore
192
            self.mrope_input_positions = None  # type: ignore
193
194
195
196
197
198
199
200
201
202
203
            self.seq_lens[0] = 0  # type: ignore
            self.orig_seq_lens[0] = 0  # type: ignore
            self.query_lens[0] = 0  # type: ignore
            self.context_lens[0] = 0  # type: ignore
            self.curr_sliding_window_blocks[0] = 0  # type: ignore
            self.lora_index_mapping.clear()  # type: ignore
            self.lora_prompt_mapping.clear()  # type: ignore
            self.lora_requests.clear()  # type: ignore
            self.prompt_adapter_index_mapping.clear()  # type: ignore
            self.prompt_adapter_prompt_mapping.clear()  # type: ignore

204
205
206
207
208
209
210
211
212
213
214
215
216
217
        def __init__(
            self,
            *,
            # From sequence group metadata.
            request_id: str,
            seq_ids: List[int],
            is_prompt: bool,
            block_tables: Optional[Dict[int, List[int]]],
            computed_block_nums: List[int],
            n_seqs: int = 0,

            # Input tokens and positions.
            input_tokens: Optional[List[List[int]]] = None,
            input_positions: Optional[List[List[int]]] = None,
218
            mrope_input_positions: Optional[List[List[List[int]]]] = None,
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243

            # The sequence length (may be capped to the sliding window).
            seq_lens: Optional[List[int]] = None,
            # The original sequence length (before applying sliding window).
            # This is used to compute slot mapping.
            orig_seq_lens: Optional[List[int]] = None,
            # The query length.
            query_lens: Optional[List[int]] = None,
            # The number of tokens that are already computed.
            context_lens: Optional[List[int]] = None,
            # The current sliding window block.
            curr_sliding_window_blocks: Optional[List[int]] = None,

            # LoRA inputs.
            lora_index_mapping: Optional[List[List[int]]] = None,
            lora_prompt_mapping: Optional[List[List[int]]] = None,
            lora_requests: Optional[Set[LoRARequest]] = None,

            # Prompt adapter inputs.
            prompt_adapter_index_mapping: Optional[List[int]] = None,
            prompt_adapter_prompt_mapping: Optional[List[int]] = None,
            prompt_adapter_request: Optional[PromptAdapterRequest] = None,

            # Multi-modal inputs.
            multi_modal_inputs: Optional[MultiModalInputs] = None,
244
245
            multi_modal_placeholder_maps: Optional[Dict[
                str, MultiModalPlaceholderMap]] = None,
246
247
248

            # Whether the prefix cache is hit (prefill only).
            prefix_cache_hit: bool = False,
249
250
            reinit: bool = False,
            reinit_use_defaults: bool = False,
251
            encoder_seq_len: int = 0,
252
        ):
253
254
255
256
257
258
259
            if reinit:
                assert len(self.seq_ids) == len(seq_ids)  # type: ignore
                for i, seq_id in enumerate(seq_ids):
                    self.seq_ids[i] = seq_id  # type: ignore
            else:
                self.seq_ids = seq_ids

260
261
262
263
264
            self.request_id = request_id
            self.is_prompt = is_prompt
            self.block_tables = block_tables
            self.computed_block_nums = computed_block_nums
            self.n_seqs = n_seqs
265
            self.encoder_seq_len = encoder_seq_len
266

267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
            if reinit:
                if len(self.seq_ids) == 1 and reinit_use_defaults:
                    self.simple_reinit()
                else:
                    if input_tokens:
                        self.input_tokens = input_tokens
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.input_tokens[seq_id].clear()

                    if input_positions:
                        self.input_positions = input_positions
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.input_positions[seq_id].clear()

283
284
                    self.mrope_input_positions = None

285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
                    if seq_lens:
                        self.seq_lens = seq_lens
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.seq_lens[seq_id] = 0

                    if orig_seq_lens:
                        self.orig_seq_lens = orig_seq_lens
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.orig_seq_lens[seq_id] = 0

                    if query_lens:
                        self.query_lens = query_lens
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.query_lens[seq_id] = 0

                    if context_lens:
                        self.context_lens = context_lens
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.context_lens[seq_id] = 0

                    if curr_sliding_window_blocks:
                        self.curr_sliding_window_blocks = \
                            curr_sliding_window_blocks
                    else:
                        for seq_id in range(len(self.seq_ids)):
                            self.curr_sliding_window_blocks[seq_id] = 0

                    if lora_index_mapping:
                        self.lora_index_mapping = lora_index_mapping
                    else:
                        self.lora_index_mapping.clear()

                    if lora_prompt_mapping:
                        self.lora_prompt_mapping = lora_prompt_mapping
                    else:
                        self.lora_prompt_mapping.clear()

                    if lora_requests:
                        self.lora_requests = lora_requests
                    else:
                        self.lora_requests.clear()

                    if prompt_adapter_index_mapping:
                        self.prompt_adapter_index_mapping = \
                            prompt_adapter_index_mapping
                    else:
                        self.prompt_adapter_index_mapping.clear()

                    if prompt_adapter_prompt_mapping:
                        self.prompt_adapter_prompt_mapping = \
                            prompt_adapter_prompt_mapping
                    else:
                        self.prompt_adapter_prompt_mapping.clear()

            else:
                self.input_tokens = input_tokens or []
                self.input_positions = input_positions or []
346
                self.mrope_input_positions = mrope_input_positions or None
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
                self.seq_lens = seq_lens or []
                self.orig_seq_lens = orig_seq_lens or []
                self.query_lens = query_lens or []
                self.context_lens = context_lens or []
                self.curr_sliding_window_blocks = \
                    curr_sliding_window_blocks or []

                self.lora_index_mapping = lora_index_mapping or []
                self.lora_prompt_mapping = lora_prompt_mapping or []
                self.lora_requests = lora_requests or set()

                self.prompt_adapter_index_mapping = (
                    prompt_adapter_index_mapping or [])
                self.prompt_adapter_prompt_mapping = (
                    prompt_adapter_prompt_mapping or [])

            self.prompt_adapter_request = prompt_adapter_request
364
            self.multi_modal_inputs = multi_modal_inputs
365
            self.multi_modal_placeholder_maps = multi_modal_placeholder_maps
366
367
            self.prefix_cache_hit = prefix_cache_hit

368
369
            self.n_seqs = len(self.seq_ids)

370
371
            if not reinit:
                self.__post_init__()
372
373
374
375
376
377

        def __post_init__(self):
            self.n_seqs = len(self.seq_ids)

            self.input_tokens = [[] for _ in range(self.n_seqs)]
            self.input_positions = [[] for _ in range(self.n_seqs)]
378
            self.mrope_input_positions = None
379
380
381
382
383
384
            self.seq_lens = [0] * self.n_seqs
            self.orig_seq_lens = [0] * self.n_seqs
            self.query_lens = [0] * self.n_seqs
            self.context_lens = [0] * self.n_seqs
            self.curr_sliding_window_blocks = [0] * self.n_seqs

385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
            self.lora_index_mapping = []
            self.lora_prompt_mapping = []

    def gen_inter_data_builder(self, num_seqs: int):
        return lambda: ModelInputForGPUBuilder.InterDataForSeqGroup(
            request_id="",
            seq_ids=[0] * num_seqs,
            is_prompt=True,
            block_tables=None,
            computed_block_nums=[])

    def init_cached_inter_data(self, *args, **kwargs):
        assert len(args) == 0
        assert "seq_ids" in kwargs
        seq_ids = kwargs["seq_ids"]
        num_seqs = len(seq_ids)

        # The inter-data cache is per model_runner
        inter_data_cache = self.runner.inter_data_cache
        if num_seqs not in inter_data_cache:
            inter_data_cache[num_seqs] = PyObjectCache(
                self.gen_inter_data_builder(num_seqs))

        obj = inter_data_cache[num_seqs].get_object()
        obj.__init__(*args, **kwargs)
        return obj

    def reset_cached_inter_data(self):
        for cache in self.runner.inter_data_cache.values():
            cache.reset()
415
416
417
418
419

    def __init__(self,
                 runner: "GPUModelRunnerBase",
                 finished_requests_ids: Optional[List[str]] = None):
        super().__init__()
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
        # Compute functions for each sequence in a sequence group.
        # WARNING: The order of the functions matters!
        self.per_seq_compute_fns = [
            self._compute_lens,
            self._compute_for_prefix_cache_hit,
            self._compute_for_sliding_window,
            self._compute_lora_input,
        ]
        # Compute functions for each sequence group.
        # WARNING: The order of the functions matters!
        self.per_seq_group_compute_fns = [
            self._compute_prompt_adapter_input,
            self._compute_multi_modal_input,
        ]

435
436
437
438
439
440
441
442
443
444
445
446
447
        self.runner = runner
        self.model_input_cls = self.runner._model_input_cls
        self.attn_backend = self.runner.attn_backend
        self.scheduler_config = self.runner.scheduler_config
        self.sliding_window = self.runner.sliding_window
        self.block_size = self.runner.block_size
        self.enable_lora = self.runner.lora_config is not None
        self.enable_prompt_adapter = (self.runner.prompt_adapter_config
                                      is not None)
        self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
        self.finished_requests_ids = finished_requests_ids
        self.decode_only = True

448
449
450
451
        # Intermediate data (data in CPU before going to GPU) for
        # the current sequence group.
        self.inter_data_list: List[
            ModelInputForGPUBuilder.InterDataForSeqGroup] = []
452
453
454

        # Attention metadata inputs.
        self.attn_metadata_builder = self.attn_backend.make_metadata_builder(
455
            weakref.proxy(self))
456
457
458
459
460
461
462
463
464
465
466

        # Engine/Model configurations.
        self.chunked_prefill_enabled = (
            self.scheduler_config is not None
            and self.scheduler_config.chunked_prefill_enabled)
        if self.sliding_window is not None:
            self.sliding_window_blocks = (
                self.sliding_window + self.block_size - 1) // self.block_size
            self.block_aligned_sliding_window = \
                self.sliding_window_blocks * self.block_size

467
468
469
470
471
472
473
    def _compute_lens(self, inter_data: InterDataForSeqGroup, seq_idx: int,
                      seq_group_metadata: SequenceGroupMetadata):
        """Compute context length, sequence length and tokens
        for the given sequence data.
        """
        seq_data = seq_group_metadata.seq_data[inter_data.seq_ids[seq_idx]]
        token_chunk_size = seq_group_metadata.token_chunk_size
474

475
476
        # Compute context length (the number of tokens that are
        # already computed) and sequence length (total number of tokens).
477

478
479
480
        seq_len = seq_data.get_len()
        if inter_data.is_prompt:
            context_len = seq_data.get_num_computed_tokens()
481
482
483
            seq_len = min(seq_len, context_len + token_chunk_size)
        elif self.runner.scheduler_config.is_multi_step or \
            self.runner.model_config.is_encoder_decoder_model:
484
            context_len = seq_len - 1
485
486
        else:
            context_len = seq_data.get_num_computed_tokens()
487
488

        # Compute tokens.
489
        tokens = seq_data.get_token_ids()[context_len:seq_len]
490
491
492
493

        inter_data.seq_lens[seq_idx] = seq_len
        inter_data.orig_seq_lens[seq_idx] = seq_len
        inter_data.context_lens[seq_idx] = context_len
494
495
496
        inter_data.input_tokens[seq_idx].extend(tokens)
        inter_data.input_positions[seq_idx].extend(range(context_len, seq_len))
        inter_data.query_lens[seq_idx] = seq_len - context_len
497

498
499
500
501
502
503
504
505
506
507
508
        if seq_data.mrope_position_delta is not None:
            if inter_data.mrope_input_positions is None:
                inter_data.mrope_input_positions = [None] * inter_data.n_seqs

            inter_data.mrope_input_positions[
                seq_idx] = MRotaryEmbedding.get_next_input_positions(
                    seq_data.mrope_position_delta,
                    context_len,
                    seq_len,
                )

509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
    def _compute_for_prefix_cache_hit(
            self, inter_data: InterDataForSeqGroup, seq_idx: int,
            seq_group_metadata: SequenceGroupMetadata):
        """Check if hit prefix cache (i.e., some blocks are already computed).
        If hit, update input tokens and positions to only compute the
        remaining blocks.
        """
        computed_block_nums = inter_data.computed_block_nums

        # Note that prefix caching does not support sliding window.
        prefix_cache_hit = (computed_block_nums is not None
                            and len(computed_block_nums) > 0
                            and self.sliding_window is None
                            and inter_data.is_prompt)
        inter_data.prefix_cache_hit = prefix_cache_hit
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545

        if not prefix_cache_hit:
            return

        assert computed_block_nums is not None
        # The cache hit prompt tokens in this sequence. Note that
        # this may be larger than the sequence length if chunked
        # prefill is enabled.
        prefix_cache_len = len(computed_block_nums) * self.block_size
        # The number of so far computed prompt tokens in this sequence.
        context_len = inter_data.context_lens[seq_idx]
        # The total number of prompt tokens in this sequence.
        # When chunked prefill is enabled, this is the token number of
        # computed chunks + current chunk.
        seq_len = inter_data.seq_lens[seq_idx]
        if prefix_cache_len <= context_len:
            # We already passed the cache hit region,
            # so do normal computation.
            pass
        elif context_len < prefix_cache_len < seq_len:
            # Partial hit. Compute the missing part.
            uncomputed_start = prefix_cache_len - context_len
546
            inter_data.input_tokens[seq_idx] = inter_data.input_tokens[
547
                seq_idx][uncomputed_start:]
548
            inter_data.input_positions[seq_idx] = inter_data.input_positions[
549
550
551
                seq_idx][uncomputed_start:]
            context_len = prefix_cache_len

552
553
554
            inter_data.context_lens[seq_idx] = context_len
            inter_data.query_lens[
                seq_idx] = inter_data.seq_lens[seq_idx] - context_len
555
556
557
558
559
560
561
562
563
564
565
        elif seq_len <= prefix_cache_len:
            # Full hit. Only compute the last token to avoid
            # erroneous behavior. FIXME: Ideally we should directly
            # mark all tokens as computed in the scheduler and do not
            # schedule this sequence, so this case should not happen.
            inter_data.input_tokens[seq_idx] = inter_data.input_tokens[
                seq_idx][-1:]
            inter_data.input_positions[seq_idx] = inter_data.input_positions[
                seq_idx][-1:]
            inter_data.query_lens[seq_idx] = 1
            inter_data.context_lens[seq_idx] = inter_data.seq_lens[seq_idx] - 1
566
567
568
569
570
571
572
573
574
575
576
577
578
579

    def _compute_for_sliding_window(self, inter_data: InterDataForSeqGroup,
                                    seq_idx: int,
                                    seq_group_metadata: SequenceGroupMetadata):
        """Update seq_len and curr_sliding_window_block for the given
        sequence data (only required by decoding) if sliding window is enabled.
        """
        curr_sliding_window_block = 0
        sliding_seq_len = inter_data.seq_lens[seq_idx]
        if not inter_data.is_prompt and self.sliding_window is not None:
            # TODO(sang): This is a hack to make sliding window work with
            # paged attn. We can remove it if we make paged attn kernel
            # to properly handle slinding window attn.
            curr_sliding_window_block = self.sliding_window_blocks
580
581
582
583
584
585
            # number of elements in last block
            suff_len = inter_data.seq_lens[seq_idx] % self.block_size
            sliding_seq_len = min(inter_data.seq_lens[seq_idx],
                                  self.block_aligned_sliding_window + suff_len)
            if suff_len > 0:
                curr_sliding_window_block += 1
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639

        inter_data.curr_sliding_window_blocks[
            seq_idx] = curr_sliding_window_block
        inter_data.seq_lens[seq_idx] = sliding_seq_len

    def _compute_lora_input(self, inter_data: InterDataForSeqGroup,
                            seq_idx: int,
                            seq_group_metadata: SequenceGroupMetadata):
        """If LoRA is enabled, compute LoRA index and prompt mapping."""
        if not self.enable_lora:
            return

        lora_id = seq_group_metadata.lora_int_id
        if lora_id > 0:
            inter_data.lora_requests.add(seq_group_metadata.lora_request)
        query_len = inter_data.query_lens[seq_idx]
        inter_data.lora_index_mapping.append([lora_id] * query_len)
        inter_data.lora_prompt_mapping.append(
            [lora_id] *
            (query_len if seq_group_metadata.sampling_params
             and seq_group_metadata.sampling_params.prompt_logprobs is not None
             else 1))

    def _compute_prompt_adapter_input(
            self, inter_data: InterDataForSeqGroup,
            seq_group_metadata: SequenceGroupMetadata):
        """If prompt adapter is enabled, compute index and prompt mapping.
        """
        # Note that when is_prompt=True, we expect only one sequence
        # in the group.
        if not self.enable_prompt_adapter:
            return

        prompt_adapter_id = seq_group_metadata.prompt_adapter_id
        if prompt_adapter_id <= 0 or not inter_data.is_prompt:
            return

        # We expect only one sequence in the group when is_prompt=True.
        assert inter_data.n_seqs == 1
        query_len = inter_data.query_lens[0]
        inter_data.prompt_adapter_request = (
            seq_group_metadata.prompt_adapter_request)

        num_tokens = seq_group_metadata.prompt_adapter_num_virtual_tokens
        inter_data.prompt_adapter_index_mapping = [
            prompt_adapter_id
        ] * num_tokens + [0] * (query_len - num_tokens)
        inter_data.prompt_adapter_prompt_mapping = [prompt_adapter_id] * (
            query_len if seq_group_metadata.sampling_params
            and seq_group_metadata.sampling_params.prompt_logprobs else 1)

    def _compute_multi_modal_input(self, inter_data: InterDataForSeqGroup,
                                   seq_group_metadata: SequenceGroupMetadata):
        """If multi-modal data is given, add it to the input."""
640
641
642
643
644
645
        # NOTE: mm_data only includes the subset of multi-modal items that
        # intersect with the current prefill positions.
        positions = inter_data.input_positions[0]
        mm_data, placeholder_maps = MultiModalPlaceholderMap.from_seq_group(
            seq_group_metadata,
            range(positions[0], positions[0] + len(positions)))
646
647
648
        if not mm_data:
            return

649
650
651
        mm_kwargs = self.multi_modal_input_mapper(
            mm_data,
            mm_processor_kwargs=seq_group_metadata.mm_processor_kwargs)
652
        inter_data.multi_modal_inputs = mm_kwargs
653
        inter_data.multi_modal_placeholder_maps = placeholder_maps
654

655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
        # special processing for mrope position deltas.
        if self.runner.model_is_mrope:
            image_grid_thw = mm_kwargs.get("image_grid_thw", None)
            video_grid_thw = mm_kwargs.get("video_grid_thw", None)
            assert image_grid_thw is not None or video_grid_thw is not None, (
                "mrope embedding type requires multi-modal input mapper "
                "returns 'image_grid_thw' or 'video_grid_thw'.")

            hf_config = self.runner.model_config.hf_config

            inter_data.mrope_input_positions = [None] * inter_data.n_seqs
            for seq_idx in range(inter_data.n_seqs):
                seq_data = seq_group_metadata.seq_data[
                    inter_data.seq_ids[seq_idx]]
                token_ids = seq_data.get_token_ids()

                mrope_input_positions, mrope_position_delta = \
                    MRotaryEmbedding.get_input_positions(
                        token_ids,
                        image_grid_thw=image_grid_thw,
                        video_grid_thw=video_grid_thw,
                        image_token_id=hf_config.image_token_id,
                        video_token_id=hf_config.video_token_id,
                        vision_start_token_id=hf_config.vision_start_token_id,
                        vision_end_token_id=hf_config.vision_end_token_id,
                        spatial_merge_size=hf_config.vision_config.
                        spatial_merge_size,
                        context_len=inter_data.context_lens[seq_idx],
                    )

                seq_data.mrope_position_delta = mrope_position_delta
                inter_data.mrope_input_positions[
                    seq_idx] = mrope_input_positions

689
    def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata):
690
        """Add a sequence group to the builder."""
691
        seq_ids = seq_group_metadata.seq_data.keys()
692
693
694
695
696
697
698
        n_seqs = len(seq_ids)
        is_prompt = seq_group_metadata.is_prompt

        if is_prompt:
            assert n_seqs == 1
            self.decode_only = False

699
700
701
702
703
        encoder_seq_len = 0

        if self.runner.model_config.is_encoder_decoder_model:
            encoder_seq_len = seq_group_metadata.encoder_seq_data.get_len()

704
        inter_data = self.init_cached_inter_data(
705
706
707
708
            request_id=seq_group_metadata.request_id,
            seq_ids=seq_ids,
            is_prompt=is_prompt,
            block_tables=seq_group_metadata.block_tables,
709
710
            computed_block_nums=seq_group_metadata.computed_block_nums,
            reinit=True,
711
712
            reinit_use_defaults=True,
            encoder_seq_len=encoder_seq_len)
713

714
        self.inter_data_list.append(inter_data)
715

716
717
718
719
720
        for seq_idx in range(n_seqs):
            for per_seq_fn in self.per_seq_compute_fns:
                per_seq_fn(inter_data, seq_idx, seq_group_metadata)
        for per_seq_group_fn in self.per_seq_group_compute_fns:
            per_seq_group_fn(inter_data, seq_group_metadata)
721

722
723
    def _use_captured_graph(self,
                            batch_size: int,
724
                            decode_only: bool,
725
726
                            max_decode_seq_len: int,
                            max_encoder_seq_len: int = 0) -> bool:
727
        return (decode_only and not self.runner.model_config.enforce_eager
728
729
730
731
                and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
                and max_decode_seq_len <= self.runner.max_seq_len_to_capture
                and max_encoder_seq_len <= self.runner.max_seq_len_to_capture
                and batch_size <= self.runner.max_batchsize_to_capture)
732

733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
    def _get_cuda_graph_pad_size(self,
                                 num_seqs: int,
                                 max_decode_seq_len: int,
                                 max_encoder_seq_len: int = 0) -> int:
        """
        Determine the number of padding sequences required for running in
        CUDA graph mode. Returns -1 if CUDA graphs cannot be used.

        In the multi-step + chunked-prefill case, only the first step
        has Prefills (if any). The rest of the steps are guaranteed to be all
        decodes. In this case, we set up the padding as if all the sequences
        are decodes so we may run all steps except the first step in CUDA graph
        mode. The padding is accounted for in the multi-step `advance_step`
        family of functions.

        Args:
749
            num_seqs (int): Number of sequences scheduled to run.
750
751
752
753
754
            max_decode_seq_len (int): Greatest of all the decode sequence
                lengths. Used only in checking the viablility of using
                CUDA graphs.
            max_encoder_seq_len (int, optional): Greatest of all the encode
                sequence lengths. Defaults to 0. Used only in checking the
755
                viability of using CUDA graphs.
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
        Returns:
            int: Returns the determined number of padding sequences. If
                CUDA graphs is not viable, returns -1.
        """
        is_mscp: bool = self.runner.scheduler_config.is_multi_step and \
                    self.runner.scheduler_config.chunked_prefill_enabled
        decode_only = self.decode_only or is_mscp
        if not decode_only:
            # Early exit so we can treat num_seqs as the batch_size below.
            return -1

        # batch_size out of this function refers to the number of input
        # tokens being scheduled. This conflation of num_seqs as batch_size
        # is valid as this is a decode-only case.
        batch_size = num_seqs
        if not self._use_captured_graph(batch_size, decode_only,
                                        max_decode_seq_len,
                                        max_encoder_seq_len):
            return -1

        graph_batch_size = _get_graph_batch_size(batch_size)
        assert graph_batch_size >= batch_size
        return graph_batch_size - batch_size

780
    def build(self) -> ModelInputForGPU:
781
782
783
784
        """Finalize the builder intermediate data and
        create on-device tensors.
        """
        # Combine and flatten intermediate data.
785
786
787
788
789
        input_tokens = []
        for inter_data in self.inter_data_list:
            for cur_input_tokens in inter_data.input_tokens:
                input_tokens.extend(cur_input_tokens)

790
791
792
        if not input_tokens:
            # This may happen when all prefill requests hit
            # prefix caching and there is no decode request.
793
            return self.model_input_cls()
794

795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
        mrope_input_positions: Optional[List[List[int]]] = None
        if any(inter_data.mrope_input_positions is not None
               for inter_data in self.inter_data_list):
            mrope_input_positions = [[] for _ in range(3)]
            for idx in range(3):
                for inter_data in self.inter_data_list:
                    msections = inter_data.mrope_input_positions
                    if msections is None:
                        for _seq_input_positions in inter_data.input_positions:
                            mrope_input_positions[idx].extend(
                                _seq_input_positions)
                    else:
                        for _seq_mrope_input_positions in msections:
                            mrope_input_positions[idx].extend(
                                _seq_mrope_input_positions[idx])
            input_positions = None
        else:
            input_positions = []
            for inter_data in self.inter_data_list:
                for cur_input_positions in inter_data.input_positions:
                    input_positions.extend(cur_input_positions)
816

817
        seq_lens = []
818
        query_lens = []
819
        max_decode_seq_len = 0
820
        max_encoder_seq_len = 0
821
822
        for inter_data in self.inter_data_list:
            seq_lens.extend(inter_data.seq_lens)
823
            query_lens.extend(inter_data.query_lens)
824
825
826
            if not inter_data.is_prompt:
                max_decode_seq_len = max(max_decode_seq_len,
                                         max(inter_data.seq_lens))
827
828
829
                if self.runner.model_config.is_encoder_decoder_model:
                    max_encoder_seq_len = max(max_encoder_seq_len,
                                              inter_data.encoder_seq_len)
830

831
832
833
834
835
836
        # Mapping from request IDs to sequence IDs. Used for Jamba models
        # that manages the cache by itself.
        request_ids_to_seq_ids = {
            data.request_id: data.seq_ids
            for data in self.inter_data_list
        }
837

838
839
        cuda_graph_pad_size = self._get_cuda_graph_pad_size(
            num_seqs=len(seq_lens),
840
            max_decode_seq_len=max_decode_seq_len,
841
            max_encoder_seq_len=max_encoder_seq_len)
842

843
844
845
846
847
848
        batch_size = len(input_tokens)
        if cuda_graph_pad_size != -1:
            # If cuda graph can be used, pad tensors accordingly.
            # See `capture_model` API for more details.
            # vLLM uses cuda graph only for decoding requests.
            batch_size += cuda_graph_pad_size
849
850

        # Tokens and positions.
851
852
        if cuda_graph_pad_size:
            input_tokens.extend(itertools.repeat(0, cuda_graph_pad_size))
853
854
855
856
        assert self.runner.device is not None
        input_tokens_tensor = async_tensor_h2d(input_tokens, torch.long,
                                               self.runner.device,
                                               self.runner.pin_memory)
857
858
859
860
861
862
863
864
865
866
867
868
869
870
        if mrope_input_positions is not None:
            for idx in range(3):
                mrope_input_positions[idx].extend(
                    itertools.repeat(0, cuda_graph_pad_size))
            input_positions_tensor = async_tensor_h2d(mrope_input_positions,
                                                      torch.long,
                                                      self.runner.device,
                                                      self.runner.pin_memory)
        else:
            input_positions.extend(itertools.repeat(0, cuda_graph_pad_size))
            input_positions_tensor = async_tensor_h2d(input_positions,
                                                      torch.long,
                                                      self.runner.device,
                                                      self.runner.pin_memory)
871
        # Sequence and query lengths.
872
873
        if cuda_graph_pad_size:
            seq_lens.extend(itertools.repeat(1, cuda_graph_pad_size))
874
875
876

        # Attention metadata.
        attn_metadata = self.attn_metadata_builder.build(
877
            seq_lens, query_lens, cuda_graph_pad_size, batch_size)
878
879

        # LoRA data.
880
881
        lora_requests = set()
        lora_mapping = None
882
        if self.enable_lora:
883
884
885
886
887
888
            lora_requests = set(r for data in self.inter_data_list
                                for r in data.lora_requests)
            lora_index_mapping = flatten_2d_lists([
                flatten_2d_lists(inter_data.lora_index_mapping)
                for inter_data in self.inter_data_list
            ])
889
890
891
            if cuda_graph_pad_size:
                lora_index_mapping.extend(
                    itertools.repeat(0, cuda_graph_pad_size))
892
893
894
895
            lora_prompt_mapping = flatten_2d_lists([
                flatten_2d_lists(inter_data.lora_prompt_mapping)
                for inter_data in self.inter_data_list
            ])
896

897
            lora_mapping = LoRAMapping(
898
899
900
                **dict(index_mapping=lora_index_mapping,
                       prompt_mapping=lora_prompt_mapping,
                       is_prefill=not self.decode_only))
901
902

        # Prompt adapter data.
903
904
        prompt_adapter_requests: Set[PromptAdapterRequest] = set()
        prompt_adapter_mapping = None
905
        if self.enable_prompt_adapter:
906
907
908
909
910
911
912
            prompt_adapter_requests = set(
                data.prompt_adapter_request for data in self.inter_data_list
                if data.prompt_adapter_request is not None)
            prompt_adapter_index_mapping = flatten_2d_lists([
                inter_data.prompt_adapter_index_mapping
                for inter_data in self.inter_data_list
            ])
913
914
915
            if cuda_graph_pad_size:
                prompt_adapter_index_mapping.extend(
                    itertools.repeat(0, cuda_graph_pad_size))
916
917
918
919
            prompt_adapter_prompt_mapping = flatten_2d_lists([
                inter_data.prompt_adapter_prompt_mapping
                for inter_data in self.inter_data_list
            ])
920
            prompt_adapter_mapping = PromptAdapterMapping(
921
922
                prompt_adapter_index_mapping,
                prompt_adapter_prompt_mapping,
923
924
925
            )

        # Multi-modal data.
926
927
928
929
        multi_modal_inputs_list = [
            data.multi_modal_inputs for data in self.inter_data_list
            if data.multi_modal_inputs is not None
        ]
930
        multi_modal_kwargs = MultiModalInputs.batch(multi_modal_inputs_list)
931
932
933
934
935

        return self.model_input_cls(
            input_tokens=input_tokens_tensor,
            input_positions=input_positions_tensor,
            attn_metadata=attn_metadata,
936
937
            seq_lens=seq_lens,
            query_lens=query_lens,
938
            lora_mapping=lora_mapping,
939
            lora_requests=lora_requests,
940
            multi_modal_kwargs=multi_modal_kwargs,
941
            request_ids_to_seq_ids=request_ids_to_seq_ids,
942
943
            finished_requests_ids=self.finished_requests_ids,
            prompt_adapter_mapping=prompt_adapter_mapping,
944
            prompt_adapter_requests=prompt_adapter_requests)
945
946


947
948
949
950
951
class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
    """
    Helper class for shared methods between GPU model runners.
    """
    _model_input_cls: Type[TModelInputForGPU]
952
    _builder_cls: Type[ModelInputForGPUBuilder]
953
954
955

    def __init__(
        self,
956
        vllm_config: VllmConfig,
957
        kv_cache_dtype: Optional[str] = "auto",
958
        is_driver_worker: bool = False,
959
        return_hidden_states: bool = False,
960
961
        input_registry: InputRegistry = INPUT_REGISTRY,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
962
    ):
963
964
965
966
967

        ModelRunnerBase.__init__(self, vllm_config)
        model_config = self.model_config
        cache_config = self.cache_config

968
        self.is_driver_worker = is_driver_worker
969
        self.return_hidden_states = return_hidden_states
970

971
        self.device = self.device_config.device
972
        self.pin_memory = is_pin_memory_available()
973

974
975
976
977
        self.kv_cache_dtype = kv_cache_dtype
        self.sliding_window = model_config.get_sliding_window()
        self.block_size = cache_config.block_size
        self.max_seq_len_to_capture = self.model_config.max_seq_len_to_capture
978
979
        self.max_batchsize_to_capture = _get_max_graph_batch_size(
            self.scheduler_config.max_num_seqs)
980
981
982
983

        self.graph_runners: List[Dict[int, CUDAGraphRunner]] = [
            {} for _ in range(self.parallel_config.pipeline_parallel_size)
        ]
984
985
        self.graph_memory_pool: Optional[Tuple[
            int, int]] = None  # Set during graph capture.
Mor Zusman's avatar
Mor Zusman committed
986

987
        self.has_inner_state = model_config.has_inner_state
Mor Zusman's avatar
Mor Zusman committed
988

989
        # When using CUDA graph, the input block tables must be padded to
990
        # max_seq_len_to_capture. However, creating the block table in
991
992
993
        # Python can be expensive. To optimize this, we cache the block table
        # in numpy and only copy the actual input content at every iteration.
        # The shape of the cached block table will be
994
        # (max batch size to capture, max seq len to capture / block size).
995
        self.graph_block_tables = np.zeros(
996
            (self.max_batchsize_to_capture, self.get_max_block_per_batch()),
997
            dtype=np.int32)
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008

        # Attention-free but stateful models like Mamba need a placeholder attn
        # backend, as the attention metadata is needed to manage internal state.
        # However we must bypass attention selection altogether for some models
        # used for speculative decoding to avoid a divide-by-zero in
        # model_config.get_head_size()
        num_attn_heads = self.model_config.get_num_attention_heads(
            self.parallel_config)
        needs_attn_backend = (num_attn_heads != 0
                              or self.model_config.is_attention_free)

1009
1010
1011
1012
1013
        self.attn_backend = get_attn_backend(
            self.model_config.get_head_size(),
            self.model_config.dtype,
            self.kv_cache_dtype,
            self.block_size,
1014
            self.model_config.is_attention_free,
1015
1016
1017
1018
1019
1020
        ) if needs_attn_backend else None
        if self.attn_backend:
            self.attn_state = self.attn_backend.get_state_cls()(
                weakref.proxy(self))
        else:
            self.attn_state = CommonAttentionState(weakref.proxy(self))
1021

1022
        # Multi-modal data support
1023
1024
1025
1026
        self.input_registry = input_registry
        self.mm_registry = mm_registry
        self.multi_modal_input_mapper = mm_registry \
            .create_input_mapper(model_config)
1027
        self.mm_registry.init_mm_limits_per_prompt(self.model_config)
1028

1029
        # Lazy initialization
1030
        self.model: nn.Module  # Set after load_model
1031
1032
        # Set after load_model.
        self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None
1033
        self.prompt_adapter_manager: LRUCacheWorkerPromptAdapterManager = None
1034

1035
1036
1037
        set_cpu_offload_max_bytes(
            int(self.cache_config.cpu_offload_gb * 1024**3))

1038
1039
        # Used to cache python objects
        self.inter_data_cache: Dict[int, PyObjectCache] = {}
1040
1041
1042
1043
1044
1045
1046

        # Using the PythonizationCache in Pipeline-Parallel clobbers the
        # SequenceGroupToSample object. In Pipeline-Parallel, we have
        # more than 1 Scheduler, resulting in a potential back-to-back
        # prepare_model_inputs() call. This clobbers the cached
        # SequenceGroupToSample objects, as we reset the cache during
        # every prepare_model_inputs() call.
1047
        self.sampling_metadata_cache: SamplingMetadataCache = \
1048
1049
              SamplingMetadataCache() \
                if self.parallel_config.pipeline_parallel_size == 1 else None
1050

1051
    def load_model(self) -> None:
1052
        logger.info("Starting to load model %s...", self.model_config.model)
1053
        with DeviceMemoryProfiler() as m:
1054
1055
1056
1057
1058
1059
1060
            self.model = get_model(model_config=self.model_config,
                                   device_config=self.device_config,
                                   load_config=self.load_config,
                                   lora_config=self.lora_config,
                                   parallel_config=self.parallel_config,
                                   scheduler_config=self.scheduler_config,
                                   cache_config=self.cache_config)
1061
1062

        self.model_memory_usage = m.consumed_memory
1063
1064
        logger.info("Loading model weights took %.4f GB",
                    self.model_memory_usage / float(2**30))
1065
1066

        if self.lora_config:
1067
            assert supports_lora(
1068
                self.model
1069
            ), f"{self.model.__class__.__name__} does not support LoRA yet."
1070

1071
1072
1073
            if supports_multimodal(self.model):
                logger.warning("Regarding multimodal models, vLLM currently "
                               "only supports adding LoRA to language model.")
1074
1075
1076
1077
1078
1079
1080
            # It's necessary to distinguish between the max_position_embeddings
            # of VLMs and LLMs.
            if hasattr(self.model.config, "max_position_embeddings"):
                max_pos_embeddings = self.model.config.max_position_embeddings
            else:
                max_pos_embeddings = (
                    self.model.config.text_config.max_position_embeddings)
1081

1082
1083
            self.lora_manager = LRUCacheWorkerLoRAManager(
                self.scheduler_config.max_num_seqs,
1084
1085
1086
1087
1088
1089
                self.scheduler_config.max_num_batched_tokens,
                self.vocab_size,
                self.lora_config,
                self.device,
                self.model.embedding_modules,
                self.model.embedding_padding_modules,
1090
                max_position_embeddings=max_pos_embeddings,
1091
            )
1092
            self.model = self.lora_manager.create_lora_manager(self.model)
1093

1094
1095
1096
1097
1098
1099
1100
1101
1102
        if self.prompt_adapter_config:
            self.prompt_adapter_manager = LRUCacheWorkerPromptAdapterManager(
                self.scheduler_config.max_num_seqs,
                self.scheduler_config.max_num_batched_tokens, self.device,
                self.prompt_adapter_config)
            self.model = (
                self.prompt_adapter_manager.create_prompt_adapter_manager(
                    self.model))

1103
        if self.kv_cache_dtype == "fp8" and current_platform.is_rocm():
1104
1105
1106
            # Currently only ROCm accepts kv-cache scaling factors
            # via quantization_param_path and this will be deprecated
            # in the future.
1107
1108
            if self.model_config.quantization_param_path is not None:
                if callable(getattr(self.model, "load_kv_cache_scales", None)):
1109
1110
1111
1112
1113
1114
                    warnings.warn(
                        "Loading kv cache scaling factor from JSON is "
                        "deprecated and will be removed. Please include "
                        "kv cache scaling factors in the model checkpoint.",
                        FutureWarning,
                        stacklevel=2)
1115
1116
                    self.model.load_kv_cache_scales(
                        self.model_config.quantization_param_path)
1117
1118
                    logger.info("Loaded KV cache scaling factors from %s",
                                self.model_config.quantization_param_path)
1119
                else:
1120
1121
1122
1123
                    raise RuntimeError(
                        "Using FP8 KV cache and scaling factors provided but "
                        "model %s does not support loading scaling factors.",
                        self.model.__class__)
1124
            else:
1125
1126
1127
1128
                logger.warning(
                    "Using FP8 KV cache but no scaling factors "
                    "provided. Defaulting to scaling factors of 1.0. "
                    "This may lead to less accurate results!")
1129

1130
1131
        if envs.VLLM_TORCH_COMPILE_LEVEL == CompilationLevel.DYNAMO_AS_IS \
            and supports_dynamo():
1132
            from vllm.plugins import get_torch_compile_backend
1133
            backend = get_torch_compile_backend() or "eager"
1134
1135
1136
            self.model = torch.compile(
                self.model,
                fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
1137
                backend=backend)
1138

1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
    def save_sharded_state(
        self,
        path: str,
        pattern: Optional[str] = None,
        max_size: Optional[int] = None,
    ) -> None:
        from vllm.model_executor.model_loader.loader import ShardedStateLoader
        ShardedStateLoader.save_model(
            self.model,
            path,
            pattern=pattern,
            max_size=max_size,
        )

1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
    def save_tensorized_model(
        self,
        tensorizer_config: TensorizerConfig,
    ) -> None:
        from vllm.model_executor.model_loader.loader import TensorizerLoader
        TensorizerLoader.save_model(
            self.model,
            tensorizer_config=tensorizer_config,
        )

1163
1164
    def get_max_block_per_batch(self) -> int:
        block_size = self.block_size
1165
        return (self.max_seq_len_to_capture + block_size - 1) // block_size
1166

1167
    def _prepare_model_input_tensors(
1168
1169
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
Mor Zusman's avatar
Mor Zusman committed
1170
        finished_requests_ids: Optional[List[str]] = None
1171
1172
1173
1174
    ) -> TModelInputForGPU:
        """Helper method to prepare the model input based on a given sequence
        group. Prepares metadata needed for the base model forward pass but not
        metadata for possible additional steps, e.g., sampling.
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185

        The API assumes seq_group_metadata_list is sorted by prefill -> decode.

        The result tensors and data structure also batches input in prefill
        -> decode order. For example,

        - input_tokens[:num_prefill_tokens] contains prefill tokens.
        - input_tokens[num_prefill_tokens:] contains decode tokens.

        If cuda graph is required, this API automatically pads inputs.
        """
1186
        builder = self._builder_cls(weakref.proxy(self), finished_requests_ids)
1187
        for seq_group_metadata in seq_group_metadata_list:
1188
            builder.add_seq_group(seq_group_metadata)
1189
1190
1191

        builder.reset_cached_inter_data()

1192
        return builder.build()  # type: ignore
1193

1194
1195
1196
    @torch.inference_mode()
    def profile_run(self) -> None:
        # Enable top-k sampling to reflect the accurate memory usage.
1197
        sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
1198
1199
        max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
        max_num_seqs = self.scheduler_config.max_num_seqs
1200
1201
1202
1203
        # This represents the maximum number of different requests
        # that will have unique loras, an therefore the max amount of memory
        # consumption create dummy lora request copies from the lora request
        # passed in, which contains a lora from the lora warmup path.
1204
1205
        dummy_lora_requests: List[LoRARequest] = []
        dummy_lora_requests_per_seq: List[LoRARequest] = []
1206
        if self.lora_config:
1207
            assert self.lora_manager is not None
1208
1209
1210
1211
1212
1213
            with self.lora_manager.dummy_lora_cache():
                for idx in range(self.lora_config.max_loras):
                    lora_id = idx + 1
                    dummy_lora_request = LoRARequest(
                        lora_name=f"warmup_{lora_id}",
                        lora_int_id=lora_id,
1214
                        lora_path="/not/a/real/path",
1215
1216
1217
1218
1219
1220
1221
1222
                    )
                    self.lora_manager.add_dummy_lora(dummy_lora_request,
                                                     rank=LORA_WARMUP_RANK)
                    dummy_lora_requests.append(dummy_lora_request)
                dummy_lora_requests_per_seq = [
                    dummy_lora_requests[idx % len(dummy_lora_requests)]
                    for idx in range(max_num_seqs)
                ]
1223

1224
1225
1226
        # Profile memory usage with max_num_sequences sequences and the total
        # number of tokens equal to max_num_batched_tokens.
        seqs: List[SequenceGroupMetadata] = []
1227
1228
        # Additional GPU memory may be needed for multi-modal encoding, which
        # needs to be accounted for when calculating the GPU blocks for
1229
1230
1231
1232
        # vLLM blocker manager.
        # To exercise the worst scenario for GPU memory consumption,
        # the number of seqs (batch_size) is chosen to maximize the number
        # of images processed.
1233

1234
1235
        max_mm_tokens = self.mm_registry.get_max_multimodal_tokens(
            self.model_config)
1236
        if max_mm_tokens > 0:
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
            max_num_seqs_orig = max_num_seqs
            max_num_seqs = min(max_num_seqs,
                               max_num_batched_tokens // max_mm_tokens)
            if max_num_seqs < 1:
                expr = (f"min({max_num_seqs_orig}, "
                        f"{max_num_batched_tokens} // {max_mm_tokens})")
                logger.warning(
                    "Computed max_num_seqs (%s) to be less than 1. "
                    "Setting it to the minimum value of 1.", expr)
                max_num_seqs = 1

1248
        batch_size = 0
1249
1250
1251
        for group_id in range(max_num_seqs):
            seq_len = (max_num_batched_tokens // max_num_seqs +
                       (group_id < max_num_batched_tokens % max_num_seqs))
1252
            batch_size += seq_len
1253

1254
            dummy_data = self.input_registry \
1255
1256
1257
                .dummy_data_for_profiling(self.model_config,
                                          seq_len,
                                          self.mm_registry)
1258

1259
1260
1261
            seq = SequenceGroupMetadata(
                request_id=str(group_id),
                is_prompt=True,
1262
                seq_data={group_id: dummy_data.seq_data},
1263
1264
                sampling_params=sampling_params,
                block_tables=None,
1265
1266
                lora_request=dummy_lora_requests_per_seq[group_id]
                if dummy_lora_requests_per_seq else None,
1267
1268
                multi_modal_data=dummy_data.multi_modal_data,
                multi_modal_placeholders=dummy_data.multi_modal_placeholders,
1269
1270
1271
1272
1273
            )
            seqs.append(seq)

        # Run the model with the dummy inputs.
        num_layers = self.model_config.get_num_layers(self.parallel_config)
1274
1275
1276
1277
        # use an empty tensor instead of `None`` to force Dynamo to pass
        # it by reference, rather by specializing on the value ``None``.
        # the `dtype` argument does not matter, and we use `float32` as
        # a placeholder (it has wide hardware support).
1278
1279
1280
        # it is important to create tensors inside the loop, rather than
        # multiplying the list, to avoid Dynamo from treating them as
        # tensor aliasing.
1281
1282
        kv_caches = [
            torch.tensor([], dtype=torch.float32, device=self.device)
1283
1284
            for _ in range(num_layers)
        ]
Mor Zusman's avatar
Mor Zusman committed
1285
1286
1287
        finished_requests_ids = [seq.request_id for seq in seqs]
        model_input = self.prepare_model_input(
            seqs, finished_requests_ids=finished_requests_ids)
1288
1289
1290
1291
1292
1293
        intermediate_tensors = None
        if not get_pp_group().is_first_rank:
            intermediate_tensors = self.model.make_empty_intermediate_tensors(
                batch_size=batch_size,
                dtype=self.model_config.dtype,
                device=self.device)
1294
1295
1296
1297
1298
1299
1300
1301
1302

        graph_batch_size = self.max_batchsize_to_capture
        batch_size_capture_list = [
            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
        ]
        if self.model_config.enforce_eager:
            batch_size_capture_list = []
        with set_compile_context(batch_size_capture_list):
            self.execute_model(model_input, kv_caches, intermediate_tensors)
1303
        torch.cuda.synchronize()
1304
1305
        return

1306
    def remove_all_loras(self):
1307
1308
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
1309
        self.lora_manager.remove_all_adapters()
1310

1311
    def set_active_loras(self, lora_requests: Set[LoRARequest],
1312
1313
1314
                         lora_mapping: LoRAMapping) -> None:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
1315
        self.lora_manager.set_active_adapters(lora_requests, lora_mapping)
1316
1317
1318
1319

    def add_lora(self, lora_request: LoRARequest) -> bool:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
1320
        return self.lora_manager.add_adapter(lora_request)
1321
1322
1323
1324

    def remove_lora(self, lora_id: int) -> bool:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
1325
        return self.lora_manager.remove_adapter(lora_id)
1326
1327
1328
1329

    def pin_lora(self, lora_id: int) -> bool:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
1330
        return self.lora_manager.pin_adapter(lora_id)
1331
1332
1333
1334

    def list_loras(self) -> Set[int]:
        if not self.lora_manager:
            raise RuntimeError("LoRA is not enabled.")
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
        return self.lora_manager.list_adapters()

    def remove_all_prompt_adapters(self):
        if not self.prompt_adapter_manager:
            raise RuntimeError("PromptAdapter is not enabled.")
        self.prompt_adapter_manager.remove_all_adapters()

    def set_active_prompt_adapters(
            self, prompt_adapter_requests: Set[PromptAdapterRequest],
            prompt_adapter_mapping: PromptAdapterMapping) -> None:
        if not self.prompt_adapter_manager:
            raise RuntimeError("PromptAdapter is not enabled.")
        self.prompt_adapter_manager.set_active_adapters(
            prompt_adapter_requests, prompt_adapter_mapping)

    def add_prompt_adapter(
            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
        if not self.prompt_adapter_manager:
            raise RuntimeError("PromptAdapter is not enabled.")
        return self.prompt_adapter_manager.add_adapter(prompt_adapter_request)

    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
        if not self.prompt_adapter_manager:
            raise RuntimeError("PromptAdapter is not enabled.")
        return self.prompt_adapter_manager.remove_adapter(prompt_adapter_id)

    def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
        if not self.prompt_adapter_manager:
            raise RuntimeError("PromptAdapter is not enabled.")
        return self.prompt_adapter_manager.pin_adapter(prompt_adapter_id)

    def list_prompt_adapters(self) -> Set[int]:
        if not self.prompt_adapter_manager:
            raise RuntimeError("PromptAdapter is not enabled.")
        return self.prompt_adapter_manager.list_adapters()
1370

1371
1372
1373
1374
    @property
    def model_is_mrope(self) -> bool:
        """Detect if the model has "mrope" rope_scaling type.
        mrope requires keep "rope_deltas" between prompt and decoding phases."""
1375
        return uses_mrope(self.model_config.hf_config)
1376

1377
    @torch.inference_mode()
1378
    def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
        """Cuda graph capture a model.

        Note that CUDA graph's performance gain is negligible if number
        of batched tokens are larger than 200. And since CUDA graph
        requires fixed sized tensors, supporting large/variable batch
        size requires high GPU memory overhead. Thus, vLLM only captures
        decoding requests. Mixed batch (chunked prefill + decoding) or
        prefill requests are not captured.

        Since it is used for decoding-only, it assumes there's only 1 token
        per sequence in the batch.
        """
1391
1392
1393
1394
1395
        assert not self.model_config.enforce_eager
        logger.info("Capturing the model for CUDA graphs. This may lead to "
                    "unexpected consequences if the model is not static. To "
                    "run the model in eager mode, set 'enforce_eager=True' or "
                    "use '--enforce-eager' in the CLI.")
1396
1397
        logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
                    "If you are running out of memory, consider decreasing "
1398
1399
1400
                    "`gpu_memory_utilization` or enforcing eager mode. "
                    "You can also reduce the `max_num_seqs` as needed "
                    "to decrease memory usage.")
1401
1402
1403
        start_time = time.perf_counter()

        # Prepare dummy inputs. These will be reused for all batch sizes.
1404
        max_batch_size = self.max_batchsize_to_capture
1405
1406
        input_tokens = torch.zeros(max_batch_size, dtype=torch.long).cuda()
        input_positions = torch.zeros(max_batch_size, dtype=torch.long).cuda()
1407
1408
        if self.model_is_mrope:
            input_positions = torch.tile(input_positions, (3, 1))
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
        # Prepare dummy previous_hidden_states only if needed by the model.
        # This is used by draft models such as EAGLE.
        previous_hidden_states = None
        if "previous_hidden_states" in inspect.signature(
                self.model.forward).parameters:
            previous_hidden_states = torch.empty(
                [max_batch_size,
                 self.model_config.get_hidden_size()],
                dtype=self.model_config.dtype,
                device=self.device)

1420
1421
1422
1423
1424
1425
        intermediate_inputs = None
        if not get_pp_group().is_first_rank:
            intermediate_inputs = self.model.make_empty_intermediate_tensors(
                batch_size=max_batch_size,
                dtype=self.model_config.dtype,
                device=self.device)
1426

1427
        graph_batch_size = self.max_batchsize_to_capture
1428
1429
1430
1431
        batch_size_capture_list = [
            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
        ]

1432
1433
        with self.attn_state.graph_capture(
                max_batch_size), graph_capture() as graph_capture_context:
1434
1435
            # NOTE: Capturing the largest batch size first may help reduce the
            # memory usage of CUDA graph.
1436
1437
1438
            for virtual_engine in range(
                    self.parallel_config.pipeline_parallel_size):
                for batch_size in reversed(batch_size_capture_list):
1439
1440
                    attn_metadata = (
                        self.attn_state.graph_capture_get_metadata_for_batch(
1441
1442
1443
                            batch_size,
                            is_encoder_decoder_model=self.model_config.
                            is_encoder_decoder_model))
1444
1445
1446

                    if self.lora_config:
                        lora_mapping = LoRAMapping(
1447
1448
1449
                            **dict(index_mapping=[0] * batch_size,
                                   prompt_mapping=[0] * batch_size,
                                   is_prefill=False))
1450
1451
                        self.set_active_loras(set(), lora_mapping)

1452
1453
1454
1455
1456
1457
1458
                    if self.prompt_adapter_config:
                        prompt_adapter_mapping = PromptAdapterMapping(
                            [-1] * batch_size,
                            [-1] * batch_size,
                        )
                        self.set_active_prompt_adapters(
                            set(), prompt_adapter_mapping)
1459
                    graph_runner = CUDAGraphRunner(
1460
                        self.model, self.attn_backend.get_name(),
1461
1462
                        self.attn_state.graph_clone(batch_size),
                        self.model_config.is_encoder_decoder_model)
1463

Mor Zusman's avatar
Mor Zusman committed
1464
1465
                    capture_inputs = {
                        "input_ids":
1466
                        input_tokens[:batch_size],
Mor Zusman's avatar
Mor Zusman committed
1467
                        "positions":
1468
                        input_positions[..., :batch_size],
Mor Zusman's avatar
Mor Zusman committed
1469
                        "intermediate_inputs":
1470
1471
                        intermediate_inputs[:batch_size]
                        if intermediate_inputs is not None else None,
Mor Zusman's avatar
Mor Zusman committed
1472
                        "kv_caches":
1473
                        kv_caches[virtual_engine],
Mor Zusman's avatar
Mor Zusman committed
1474
                        "attn_metadata":
1475
                        attn_metadata,
Mor Zusman's avatar
Mor Zusman committed
1476
1477
1478
1479
1480
                        "memory_pool":
                        self.graph_memory_pool,
                        "stream":
                        graph_capture_context.stream
                    }
1481
1482
1483
1484
1485
                    if previous_hidden_states is not None:
                        capture_inputs[
                            "previous_hidden_states"] = previous_hidden_states[:
                                                                               batch_size]

1486
                    if self.has_inner_state:
Mor Zusman's avatar
Mor Zusman committed
1487
1488
1489
1490
1491
1492
                        # Only used by Mamba-based models CUDA graph atm (Jamba)
                        capture_inputs.update({
                            "seqlen_agnostic_capture_inputs":
                            self.model.get_seqlen_agnostic_capture_inputs(
                                batch_size)
                        })
1493
1494
1495
1496
1497
1498
                    if self.model_config.is_encoder_decoder_model:
                        # add the additional inputs to capture for
                        # encoder-decoder models.
                        self._update_inputs_to_capture_for_enc_dec_model(
                            capture_inputs)

1499
1500
                    with set_forward_context(attn_metadata):
                        graph_runner.capture(**capture_inputs)
1501
1502
1503
                    self.graph_memory_pool = graph_runner.graph.pool()
                    self.graph_runners[virtual_engine][batch_size] = (
                        graph_runner)
1504
1505
1506
1507

        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        # This usually takes < 10 seconds.
1508
        logger.info("Graph capturing finished in %.0f secs.", elapsed_time)
1509

1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
    def _update_inputs_to_capture_for_enc_dec_model(self,
                                                    capture_inputs: Dict[str,
                                                                         Any]):
        """
        Updates the set of input tensors needed for CUDA graph capture in an
        encoder-decoder model.

        This method modifies the provided `capture_inputs` dictionary by
        adding tensors specific to encoder-decoder specific models that
        need to be captured for CUDA Graph replay.
        """
        # During the decode phase encoder_input_ids and encoder_positions are
        # unset. Do the same thing for graph capture.
        capture_inputs["encoder_input_ids"] = torch.tensor(
            [], dtype=torch.long).cuda()
        capture_inputs["encoder_positions"] = torch.tensor(
            [], dtype=torch.long).cuda()

1528
1529
1530
1531
    @property
    def vocab_size(self) -> int:
        return self.model_config.get_vocab_size()

1532

1533
1534
1535
1536
1537
1538
class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
    """
    GPU model runner with sampling step.
    """
    _model_input_cls: Type[ModelInputForGPUWithSamplingMetadata] = (
        ModelInputForGPUWithSamplingMetadata)
1539
    _builder_cls: Type[ModelInputForGPUBuilder] = ModelInputForGPUBuilder
1540
1541
1542
1543
1544

    def make_model_input_from_broadcasted_tensor_dict(
        self,
        tensor_dict: Dict[str, Any],
    ) -> ModelInputForGPUWithSamplingMetadata:
1545
        model_input = \
1546
1547
1548
            ModelInputForGPUWithSamplingMetadata.from_broadcasted_tensor_dict(
                tensor_dict,
                attn_backend=self.attn_backend,
1549
1550
            )
        return model_input
1551
1552
1553
1554

    def prepare_model_input(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
1555
        virtual_engine: int = 0,
1556
        finished_requests_ids: Optional[List[str]] = None,
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
    ) -> ModelInputForGPUWithSamplingMetadata:
        """Prepare the model input based on a given sequence group, including
        metadata for the sampling step.

        The API assumes seq_group_metadata_list is sorted by prefill -> decode.

        The result tensors and data structure also batches input in prefill
        -> decode order. For example,

        - input_tokens[:num_prefill_tokens] contains prefill tokens.
        - input_tokens[num_prefill_tokens:] contains decode tokens.

        If cuda graph is required, this API automatically pads inputs.
        """
        model_input = self._prepare_model_input_tensors(
Mor Zusman's avatar
Mor Zusman committed
1572
            seq_group_metadata_list, finished_requests_ids)
1573
1574
1575
1576
1577
1578
        if get_pp_group().is_last_rank:
            # Sampling metadata is only required for the final pp group
            generators = self.get_generators(finished_requests_ids)
            sampling_metadata = SamplingMetadata.prepare(
                seq_group_metadata_list, model_input.seq_lens,
                model_input.query_lens, self.device, self.pin_memory,
1579
                generators, self.sampling_metadata_cache)
1580
1581
        else:
            sampling_metadata = None
1582
1583
1584
1585
        is_prompt = (seq_group_metadata_list[0].is_prompt
                     if seq_group_metadata_list else None)
        return dataclasses.replace(model_input,
                                   sampling_metadata=sampling_metadata,
1586
1587
                                   is_prompt=is_prompt,
                                   virtual_engine=virtual_engine)
1588
1589

    @torch.inference_mode()
1590
    @dump_input_when_exception(exclude_args=[0], exclude_kwargs=["self"])
1591
1592
1593
1594
    def execute_model(
        self,
        model_input: ModelInputForGPUWithSamplingMetadata,
        kv_caches: List[torch.Tensor],
1595
        intermediate_tensors: Optional[IntermediateTensors] = None,
1596
        num_steps: int = 1,
1597
    ) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]:
1598
1599
1600
        if num_steps > 1:
            raise ValueError("num_steps > 1 is not supported in ModelRunner")

1601
1602
1603
1604
1605
1606
        if self.lora_config:
            assert model_input.lora_requests is not None
            assert model_input.lora_mapping is not None
            self.set_active_loras(model_input.lora_requests,
                                  model_input.lora_mapping)

1607
1608
1609
1610
1611
1612
1613
        if self.prompt_adapter_config:
            assert model_input.prompt_adapter_requests is not None
            assert model_input.prompt_adapter_mapping is not None
            self.set_active_prompt_adapters(
                model_input.prompt_adapter_requests,
                model_input.prompt_adapter_mapping)

1614
        self.attn_state.begin_forward(model_input)
1615

1616
1617
1618
1619
        # Currently cuda graph is only supported by the decode phase.
        assert model_input.attn_metadata is not None
        prefill_meta = model_input.attn_metadata.prefill_metadata
        decode_meta = model_input.attn_metadata.decode_metadata
1620
1621
1622
        # TODO(andoorve): We can remove this once all
        # virtual engines share the same kv cache.
        virtual_engine = model_input.virtual_engine
1623
1624
1625
        if prefill_meta is None and decode_meta.use_cuda_graph:
            assert model_input.input_tokens is not None
            graph_batch_size = model_input.input_tokens.shape[0]
1626
1627
            model_executable = self.graph_runners[virtual_engine][
                graph_batch_size]
1628
1629
1630
1631
        else:
            model_executable = self.model

        multi_modal_kwargs = model_input.multi_modal_kwargs or {}
Mor Zusman's avatar
Mor Zusman committed
1632
1633
1634
        seqlen_agnostic_kwargs = {
            "finished_requests_ids": model_input.finished_requests_ids,
            "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
1635
        } if self.has_inner_state else {}
1636
1637
1638
1639
1640
1641
        if (self.observability_config is not None
                and self.observability_config.collect_model_forward_time):
            model_forward_start = torch.cuda.Event(enable_timing=True)
            model_forward_end = torch.cuda.Event(enable_timing=True)
            model_forward_start.record()

1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
        with set_forward_context(model_input.attn_metadata):
            hidden_or_intermediate_states = model_executable(
                input_ids=model_input.input_tokens,
                positions=model_input.input_positions,
                kv_caches=kv_caches,
                attn_metadata=model_input.attn_metadata,
                intermediate_tensors=intermediate_tensors,
                **MultiModalInputs.as_kwargs(multi_modal_kwargs,
                                             device=self.device),
                **seqlen_agnostic_kwargs)
1652

1653
1654
1655
1656
        if (self.observability_config is not None
                and self.observability_config.collect_model_forward_time):
            model_forward_end.record()

1657
1658
        # Compute the logits in the last pipeline stage.
        if not get_pp_group().is_last_rank:
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
            if (self.is_driver_worker
                    and hidden_or_intermediate_states is not None
                    and isinstance(hidden_or_intermediate_states,
                                   IntermediateTensors)
                    and self.observability_config is not None
                    and self.observability_config.collect_model_forward_time):
                model_forward_end.synchronize()
                model_forward_time = model_forward_start.elapsed_time(
                    model_forward_end)
                orig_model_forward_time = 0.0
                if intermediate_tensors is not None:
                    orig_model_forward_time = intermediate_tensors.tensors.get(
                        "model_forward_time", torch.tensor(0.0)).item()
                hidden_or_intermediate_states.tensors["model_forward_time"] = (
                    torch.tensor(model_forward_time + orig_model_forward_time))
1674
1675
1676
            return hidden_or_intermediate_states

        logits = self.model.compute_logits(hidden_or_intermediate_states,
1677
1678
1679
                                           model_input.sampling_metadata)

        if not self.is_driver_worker:
1680
            return []
1681

1682
1683
        if model_input.async_callback is not None:
            model_input.async_callback()
1684

1685
1686
1687
1688
1689
        # Sample the next token.
        output: SamplerOutput = self.model.sample(
            logits=logits,
            sampling_metadata=model_input.sampling_metadata,
        )
1690
1691
1692
1693
1694
1695
        if (self.observability_config is not None
                and self.observability_config.collect_model_forward_time
                and output is not None):
            model_forward_end.synchronize()
            model_forward_time = model_forward_start.elapsed_time(
                model_forward_end)
1696
1697
1698
1699
            orig_model_forward_time = 0.0
            if intermediate_tensors is not None:
                orig_model_forward_time = intermediate_tensors.tensors.get(
                    "model_forward_time", torch.tensor(0.0)).item()
1700
1701
1702
1703
            # If there are multiple workers, we are still tracking the latency
            # from the start time of the driver worker to the end time of the
            # driver worker. The model forward time will then end up covering
            # the communication time as well.
1704
1705
            output.model_forward_time = (orig_model_forward_time +
                                         model_forward_time)
1706
1707
1708

        if self.return_hidden_states:
            # we only need to pass hidden states of most recent token
1709
1710
            assert model_input.sampling_metadata is not None
            indices = model_input.sampling_metadata.selected_token_indices
1711
            if model_input.is_prompt:
1712
1713
                hidden_states = hidden_or_intermediate_states.index_select(
                    0, indices)
1714
                output.prefill_hidden_states = hidden_or_intermediate_states
1715
            elif decode_meta.use_cuda_graph:
1716
1717
1718
                hidden_states = hidden_or_intermediate_states[:len(indices)]
            else:
                hidden_states = hidden_or_intermediate_states
1719

1720
1721
            output.hidden_states = hidden_states

1722
        return [output]
1723
1724


1725
1726
1727
# NOTE: this is nn.Module so the profiler can properly capture/group
#  kernels calls made within the graph
class CUDAGraphRunner(nn.Module):
1728

1729
    def __init__(self, model: nn.Module, backend_name: str,
1730
                 attn_state: AttentionState, is_encoder_decoder_model: bool):
1731
        super().__init__()
1732
        self.model = model
1733
        self.backend_name = backend_name
1734
        self.attn_state = attn_state
1735

1736
1737
1738
        self.input_buffers: Dict[str, torch.Tensor] = {}
        self.output_buffers: Dict[str, torch.Tensor] = {}

1739
        self._graph: Optional[torch.cuda.CUDAGraph] = None
1740
        self._is_encoder_decoder_model = is_encoder_decoder_model
1741
1742
1743
1744
1745
1746

    @property
    def graph(self):
        assert self._graph is not None
        return self._graph

1747
1748
1749
1750
    def capture(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1751
        intermediate_inputs: Optional[IntermediateTensors],
1752
1753
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
1754
1755
        memory_pool: Optional[Tuple[int, int]],
        stream: torch.cuda.Stream,
1756
        **kwargs,
1757
    ):
1758
        assert self._graph is None
1759
        # Run the model a few times without capturing the graph.
1760
1761
        # This is to make sure that the captured graph does not include the
        # kernel launches for initial benchmarking (e.g., Triton autotune).
1762
1763
1764
        # Note one iteration is not enough for torch.jit.script
        for _ in range(_NUM_WARMUP_ITERS):
            self.model(
1765
1766
1767
1768
1769
                input_ids=input_ids,
                positions=positions,
                kv_caches=kv_caches,
                attn_metadata=attn_metadata,
                intermediate_tensors=intermediate_inputs,
1770
1771
                **kwargs,
            )
1772
1773
        # Wait for the warm up operations to finish before proceeding with
        # Graph Capture.
1774
1775
1776
1777
        torch.cuda.synchronize()
        # Capture the graph.
        self._graph = torch.cuda.CUDAGraph()
        with torch.cuda.graph(self._graph, pool=memory_pool, stream=stream):
1778
            output_hidden_or_intermediate_states = self.model(
1779
1780
1781
1782
1783
                input_ids=input_ids,
                positions=positions,
                kv_caches=kv_caches,
                attn_metadata=attn_metadata,
                intermediate_tensors=intermediate_inputs,
1784
                **kwargs,
1785
            )
1786
1787
1788

            if isinstance(output_hidden_or_intermediate_states, torch.Tensor):
                hidden_or_intermediate_states = weak_ref_tensor(
1789
                    output_hidden_or_intermediate_states)
1790
1791
1792
1793
1794
1795
1796
1797
            elif isinstance(output_hidden_or_intermediate_states,
                            IntermediateTensors):
                hidden_or_intermediate_states = IntermediateTensors(
                    tensors={
                        key: weak_ref_tensor(value)
                        for key, value in
                        output_hidden_or_intermediate_states.tensors.items()
                    })
1798
1799

            del output_hidden_or_intermediate_states
1800
            # make sure `output_hidden_or_intermediate_states` is deleted
1801
1802
            # in the graph's memory pool
            gc.collect()
1803
1804
1805
        torch.cuda.synchronize()

        # Save the input and output buffers.
1806
        self.input_buffers = {
1807
1808
1809
1810
1811
1812
1813
1814
            "input_ids":
            input_ids,
            "positions":
            positions,
            "kv_caches":
            kv_caches,
            **self.attn_state.get_graph_input_buffers(
                attn_metadata, self._is_encoder_decoder_model),
1815
1816
            **kwargs,
        }
1817
1818
1819
1820
1821
1822
1823
1824
        if intermediate_inputs is not None:
            self.input_buffers.update(intermediate_inputs.tensors)
        if get_pp_group().is_last_rank:
            self.output_buffers = {
                "hidden_states": hidden_or_intermediate_states
            }
        else:
            self.output_buffers = hidden_or_intermediate_states
1825
1826
1827
1828
1829

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1830
1831
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
1832
        intermediate_tensors: Optional[IntermediateTensors],
1833
        **kwargs,
1834
1835
1836
1837
1838
    ) -> torch.Tensor:
        # KV caches are fixed tensors, so we don't need to copy them.
        del kv_caches

        # Copy the input tensors to the input buffers.
1839
1840
        self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True)
        self.input_buffers["positions"].copy_(positions, non_blocking=True)
1841

1842
        if self.backend_name != "NO_ATTENTION":
1843
1844
1845
            self.input_buffers["slot_mapping"].copy_(
                attn_metadata.slot_mapping, non_blocking=True)

1846
1847
        self.attn_state.prepare_graph_input_buffers(
            self.input_buffers, attn_metadata, self._is_encoder_decoder_model)
1848

Mor Zusman's avatar
Mor Zusman committed
1849
1850
1851
        if "seqlen_agnostic_capture_inputs" in self.input_buffers:
            self.model.copy_inputs_before_cuda_graphs(self.input_buffers,
                                                      **kwargs)
1852
1853
1854
1855
1856

        if "previous_hidden_states" in self.input_buffers:
            self.input_buffers["previous_hidden_states"].copy_(
                kwargs["previous_hidden_states"], non_blocking=True)

1857
1858
        if intermediate_tensors is not None:
            for key in intermediate_tensors.tensors:
1859
                if key != "model_execute_time" and key != "model_forward_time":
1860
1861
                    self.input_buffers[key].copy_(intermediate_tensors[key],
                                                  non_blocking=True)
1862
1863
1864
1865
1866
1867
        if self._is_encoder_decoder_model:
            self.input_buffers["encoder_input_ids"].copy_(
                kwargs['encoder_input_ids'], non_blocking=True)
            self.input_buffers["encoder_positions"].copy_(
                kwargs['encoder_positions'], non_blocking=True)

1868
1869
1870
        # Run the graph.
        self.graph.replay()
        # Return the output tensor.
1871
1872
1873
1874
        if get_pp_group().is_last_rank:
            return self.output_buffers["hidden_states"]

        return self.output_buffers
1875

1876

1877
def _get_graph_batch_size(batch_size: int) -> int:
1878
1879
1880
1881
1882
    """Returns the padded batch size given actual batch size.

    Batch sizes are 1, 2, 4, _BATCH_SIZE_ALIGNMENT,
    2*_BATCH_SIZE_ALIGNMENT, 3*_BATCH_SIZE_ALIGNMENT...
    """
1883
1884
1885
1886
1887
    if batch_size <= 2:
        return batch_size
    elif batch_size <= 4:
        return 4
    else:
1888
1889
        return ((batch_size + _BATCH_SIZE_ALIGNMENT - 1) //
                _BATCH_SIZE_ALIGNMENT * _BATCH_SIZE_ALIGNMENT)
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908


def _get_max_graph_batch_size(max_num_seqs: int) -> int:
    """
    max_num_seqs: Maximum number of sequences in a batch.
    _BATCH_SIZES_TO_CAPTURE: all the sizes that we want to capture.

    pad the max_num_seqs if necessary by calling _get_graph_batch_size,
    which will deal with some edge cases like 1, 2, 4.

    if the padded size is in _BATCH_SIZES_TO_CAPTURE, return the padded size.
    if not, it means the padded size is larger than the largest size in
    _BATCH_SIZES_TO_CAPTURE, return the largest size in _BATCH_SIZES_TO_CAPTURE.
    """
    padded_size = _get_graph_batch_size(max_num_seqs)
    if padded_size in _BATCH_SIZES_TO_CAPTURE:
        return padded_size
    assert padded_size > _BATCH_SIZES_TO_CAPTURE[-1]
    return _BATCH_SIZES_TO_CAPTURE[-1]