gpu_model_runner.py 40.1 KB
Newer Older
1
import gc
2
import time
3
from typing import TYPE_CHECKING, Dict, List, Tuple, cast
4
5
6
7
8
9

import numpy as np
import torch
import torch.distributed
import torch.nn as nn

10
from vllm.config import CompilationLevel, VllmConfig
11
from vllm.distributed.parallel_state import graph_capture
12
from vllm.forward_context import set_forward_context
13
from vllm.inputs import INPUT_REGISTRY
14
15
from vllm.logger import init_logger
from vllm.model_executor.model_loader import get_model
16
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
17
from vllm.sampling_params import SamplingType
18
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
19
20
                        LayerBlockType, bind_kv_cache, cdiv,
                        is_pin_memory_available)
21
22
from vllm.v1.attention.backends.flash_attn import (FlashAttentionBackend,
                                                   FlashAttentionMetadata)
23
from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
24
from vllm.v1.engine.mm_input_mapper import MMInputMapperClient
25
26
from vllm.v1.outputs import ModelRunnerOutput
from vllm.v1.sample.metadata import SamplingMetadata
27
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
28
29
30
31
32
33
34
35
36
37
38

if TYPE_CHECKING:
    from vllm.v1.core.scheduler import SchedulerOutput

logger = init_logger(__name__)


class GPUModelRunner:

    def __init__(
        self,
39
        vllm_config: VllmConfig,
40
        device: torch.device,
41
    ):
42
43
44
45
46
47
48
49
50
51
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.prompt_adapter_config = vllm_config.prompt_adapter_config
        self.observability_config = vllm_config.observability_config
52

53
54
55
56
        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
57
        self.device = device
58
59
60
61
62
63
64
65
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
        if cache_config.cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        else:
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                cache_config.cache_dtype]

66
        self.is_multimodal_model = model_config.is_multimodal_model
67
68
69
70
71
        self.sliding_window = model_config.get_sliding_window()
        self.block_size = cache_config.block_size
        self.max_model_len = model_config.max_model_len
        self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
72
        self.max_num_reqs = scheduler_config.max_num_seqs
73
74

        # Model-related.
75
76
        self.num_attn_layers = model_config.get_num_layers_by_block_type(
            parallel_config, LayerBlockType.attention)
77
78
        self.num_query_heads = model_config.get_num_attention_heads(
            parallel_config)
79
80
        self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
        self.head_size = model_config.get_head_size()
81
82
83
        self.hidden_size = model_config.get_hidden_size()

        # Multi-modal data support
84
85
        self.input_registry = INPUT_REGISTRY
        self.mm_registry = MULTIMODAL_REGISTRY
86

87
88
89
90
        # NOTE: Initialized input mapper is only used for processing dummy
        # multimodal data into multimodal kwargs for GPU memory profiling.
        self.mm_input_mapper_profiling = MMInputMapperClient(self.model_config)
        self.mm_input_mapper_profiling.use_cache = False
91

92
93
94
95
96
97
        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
            model_config=model_config,
            scheduler_config=scheduler_config,
        )
        self.max_num_encoder_input_tokens = encoder_compute_budget
        self.encoder_cache_size = encoder_cache_size
98
99
100
101

        # Lazy initialization
        # self.model: nn.Module  # Set after load_model
        self.kv_caches: List[torch.Tensor] = []
102
103
        # req_id -> (input_id -> encoder_output)
        self.encoder_cache: Dict[str, Dict[int, torch.Tensor]] = {}
104
105
106
107
108

        # Request states.
        self.requests: Dict[str, CachedRequestState] = {}
        # Persistent batch.
        self.input_batch = InputBatch(
109
            max_num_reqs=self.max_num_reqs,
110
111
112
113
            max_model_len=self.max_model_len,
            max_num_blocks_per_req=self.max_num_blocks_per_req,
            device=self.device,
            pin_memory=self.pin_memory,
114
            vocab_size=model_config.get_vocab_size(),
115
116
        )

117
        self.use_cuda_graph = (self.vllm_config.compilation_config.level
118
119
120
                               == CompilationLevel.PIECEWISE
                               and not self.model_config.enforce_eager)
        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
121
122
123
124
125
        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
        self.cudagraph_batch_sizes = list(
            reversed(self.vllm_config.compilation_config.capture_sizes))
126

127
128
129
130
        # Cache the device properties.
        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

131
132
133
134
        # Persistent buffers for CUDA graphs.
        self.input_ids = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int32,
                                     device=self.device)
135
136
137
        self.positions = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int64,
                                     device=self.device)
138
139
140
141
        self.inputs_embeds = torch.zeros(
            (self.max_num_tokens, self.hidden_size),
            dtype=self.dtype,
            device=self.device)
142

143
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
144
145
        self.arange_np = np.arange(max(self.max_num_reqs + 1,
                                       self.max_model_len),
146
147
148
149
                                   dtype=np.int32)
        # NOTE(woosuk): These tensors are "stateless", i.e., they are literally
        # a faster version of creating a new tensor every time. Thus, we should
        # not make any assumptions about the values in these tensors.
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
        self.input_ids_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int32,
                                         device="cpu",
                                         pin_memory=self.pin_memory)
        self.input_ids_np = self.input_ids_cpu.numpy()
        self.positions_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int64,
                                         device="cpu",
                                         pin_memory=self.pin_memory)
        self.positions_np = self.positions_cpu.numpy()
        self.slot_mapping_cpu = torch.zeros(self.max_num_tokens,
                                            dtype=torch.int32,
                                            device="cpu",
                                            pin_memory=self.pin_memory)
        self.slot_mapping_np = self.slot_mapping_cpu.numpy()
        self.query_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
                                               dtype=torch.int32,
                                               device="cpu",
                                               pin_memory=self.pin_memory)
        self.query_start_loc_np = self.query_start_loc_cpu.numpy()
        self.seq_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
                                             dtype=torch.int32,
                                             device="cpu",
                                             pin_memory=self.pin_memory)
        self.seq_start_loc_np = self.seq_start_loc_cpu.numpy()

176
177
178
179
180
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
        # Remove stopped requests from the cached states.
        # Keep the states of the pre-empted requests.
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
181
182
183
184
185
186
187
188
189
            self.encoder_cache.pop(req_id, None)

        # Free the cached encoder outputs.
        for req_id, input_id in scheduler_output.free_encoder_input_ids:
            encoder_outputs = self.encoder_cache.get(req_id)
            if encoder_outputs is not None:
                encoder_outputs.pop(input_id, None)
                if not encoder_outputs:
                    self.encoder_cache.pop(req_id, None)
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218

        # Remove the requests from the persistent batch.
        stopped_req_ids = set().union(
            scheduler_output.preempted_req_ids,
            scheduler_output.finished_req_ids,
        )
        removed_req_indices: List[int] = []
        for req_id in stopped_req_ids:
            req_index = self.input_batch.remove_request(req_id)
            if req_index is not None:
                removed_req_indices.append(req_index)

        # Update the states of the running requests.
        for req_data in scheduler_output.scheduled_running_reqs:
            req_id = req_data.req_id
            req_state = self.requests[req_id]
            req_index = self.input_batch.req_id_to_index[req_id]

            # Update the num_computed_tokens.
            req_state.num_computed_tokens = req_data.num_computed_tokens
            self.input_batch.num_computed_tokens_cpu[req_index] = (
                req_data.num_computed_tokens)

            # Update the block table.
            num_new_blocks = len(req_data.new_block_ids)
            if num_new_blocks == 0:
                continue
            start_index = len(req_state.block_ids)
            req_state.block_ids.extend(req_data.new_block_ids)
219
220
            self.input_batch.block_table.append_row(req_index, start_index,
                                                    req_data.new_block_ids)
221
222
223

        req_ids_to_add: List[str] = []
        # Add new requests to the cached states.
224
225
226
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
227
            if sampling_params.sampling_type == SamplingType.RANDOM_SEED:
228
229
230
231
232
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

233
234
            self.requests[req_id] = CachedRequestState(
                req_id=req_id,
235
236
237
238
                prompt_token_ids=new_req_data.prompt_token_ids,
                prompt=new_req_data.prompt,
                mm_inputs=new_req_data.mm_inputs,
                mm_positions=new_req_data.mm_positions,
239
240
                sampling_params=sampling_params,
                generator=generator,
241
242
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
243
244
245
246
247
                output_token_ids=[],
            )
            req_ids_to_add.append(req_id)

        # Update the cached states of the resumed requests.
248
249
        for res_req_data in scheduler_output.scheduled_resumed_reqs:
            req_id = res_req_data.req_id
250
251
            req_state = self.requests[req_id]

252
253
            req_state.block_ids = res_req_data.block_ids
            req_state.num_computed_tokens = res_req_data.num_computed_tokens
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
            req_ids_to_add.append(req_id)

        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
        removed_req_indices = sorted(removed_req_indices, reverse=True)
        for req_id in req_ids_to_add:
            req_state = self.requests[req_id]
            if removed_req_indices:
                # Fill the empty index.
                req_index = removed_req_indices.pop()
            else:
                # Append to the end.
                req_index = None
            self.input_batch.add_request(req_state, req_index)

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

    def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0

        # OPTIMIZATION: Start copying the block table first.
        # This way, we can overlap the copy with the following CPU operations.
281
        self.input_batch.block_table.commit(num_reqs)
282
283
284
285
286
287

        # Get the number of scheduled tokens for each request.
        # TODO: The Python loop can be slow. Optimize.
        num_scheduled_tokens = []
        max_num_scheduled_tokens = 0
        for req_id in self.input_batch.req_ids[:num_reqs]:
288
            assert req_id is not None
289
290
291
292
293
294
295
296
297
            num_tokens = scheduler_output.num_scheduled_tokens[req_id]
            num_scheduled_tokens.append(num_tokens)
            max_num_scheduled_tokens = max(max_num_scheduled_tokens,
                                           num_tokens)
        num_scheduled_tokens = np.array(num_scheduled_tokens, dtype=np.int32)
        assert max_num_scheduled_tokens > 0

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
298
299
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
300
301
302

        # Get batched arange.
        # E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
303
304
        arange = np.concatenate(
            [self.arange_np[:n] for n in num_scheduled_tokens])
305
306

        # Get positions.
307
        positions_np = self.positions_np[:total_num_scheduled_tokens]
308
309
310
311
312
313
314
315
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

        # Get token indices.
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
        # where M is the max_model_len.
316
317
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
318
319
320
321
        # NOTE(woosuk): We use torch.index_select instead of np.take here
        # because torch.index_select is much faster than np.take for large
        # tensors.
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
322
                           0,
323
324
                           torch.from_numpy(token_indices),
                           out=self.input_ids_cpu[:total_num_scheduled_tokens])
325
326

        # Calculate the slot mapping.
327
328
329
330
331
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
        # where K is the max_num_blocks_per_req and the block size is 2.
        # NOTE(woosuk): We can't simply use `token_indices // block_size` here
        # because M (max_model_len) is not necessarily divisible by block_size.
332
333
334
335
336
        block_table_indices = (req_indices * self.max_num_blocks_per_req +
                               positions_np // self.block_size)
        # NOTE(woosuk): We use torch.index_select instead of np.take here
        # because torch.index_select is much faster than np.take for large
        # tensors.
337
338
        block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
        block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
339
340
341
342
        block_offsets = positions_np % self.block_size
        np.add(block_numbers * self.block_size,
               block_offsets,
               out=self.slot_mapping_np[:total_num_scheduled_tokens])
343
344

        # Prepare the attention metadata.
345
346
347
        self.query_start_loc_np[0] = 0
        np.cumsum(num_scheduled_tokens,
                  out=self.query_start_loc_np[1:num_reqs + 1])
348
349
350
351

        seq_lens = (self.input_batch.num_computed_tokens_cpu[:num_reqs] +
                    num_scheduled_tokens)
        max_seq_len = seq_lens.max()
352
353
354
355
356
357
358
359
360
361
362
363
364
365
        self.seq_start_loc_np[0] = 0
        np.cumsum(seq_lens, out=self.seq_start_loc_np[1:num_reqs + 1])

        # Copy the tensors to the GPU.
        self.input_ids[:total_num_scheduled_tokens].copy_(
            self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
        self.positions[:total_num_scheduled_tokens].copy_(
            self.positions_cpu[:total_num_scheduled_tokens], non_blocking=True)
        query_start_loc = self.query_start_loc_cpu[:num_reqs + 1].to(
            self.device, non_blocking=True)
        seq_start_loc = self.seq_start_loc_cpu[:num_reqs + 1].to(
            self.device, non_blocking=True)
        slot_mapping = self.slot_mapping_cpu[:total_num_scheduled_tokens].to(
            self.device, non_blocking=True).long()
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
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
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447

        # Prepare for cascade attention if needed.
        common_prefix_len = (scheduler_output.num_common_prefix_blocks *
                             self.block_size)
        if common_prefix_len == 0:
            # Common case.
            use_cascade = False
        else:
            # NOTE(woosuk): Cascade attention uses two attention kernels: one
            # for the common prefix and the other for the rest. For the first
            # kernel, we concatenate all the query tokens (possibly from
            # different requests) and treat them as if they are from the same
            # request. Then, we use bi-directional attention to process the
            # common prefix in the KV cache. Importantly, this means that the
            # first kernel does not do any masking.

            # Consider the following example:
            # Request 1's input query: [D, E, X]
            # Request 1's kv cache: [A, B, C, D, E, X]
            # Request 1's num_computed_tokens: 3 (i.e., [A, B, C])
            # Request 2's input query: [E, Y]
            # Request 2's kv cache: [A, B, C, D, E, Y]
            # Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D])

            # If we use [A, B, C, D, E] as the common prefix, then the
            # first kernel will compute the bi-directional attention between
            # input query [D, E, X, E, Y] and common prefix [A, B, C, D, E].
            # However, this is wrong because D in Request 1 should not attend to
            # E in the common prefix (i.e., we need masking).
            # To avoid this, [A, B, C, D] should be the common prefix.
            # That is, the common prefix should be capped by the minimum
            # num_computed_tokens among the requests, and plus one to include
            # the first token of the query.

            # In practice, we use [A, B, C] as the common prefix, instead of
            # [A, B, C, D] (i.e., the common prefix is capped by the minimum
            # num_computed_tokens, without plus one).
            # This is because of an implementation detail: We want to always
            # use two kernels for cascade attention. Let's imagine:
            # Request 3's input query: [D]
            # Request 3's kv cache: [A, B, C, D]
            # Request 3's num_computed_tokens: 4 (i.e., [A, B, C, D])
            # If we use [A, B, C, D] as the common prefix for Request 1-3,
            # then Request 3 will be processed only by the first kernel,
            # and the second kernel will get an empty input. While this is not
            # a fundamental problem, our current implementation does not support
            # this case.
            common_prefix_len = min(
                common_prefix_len,
                self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
            # common_prefix_len should be a multiple of the block size.
            common_prefix_len = (common_prefix_len // self.block_size *
                                 self.block_size)
            use_cascade = FlashAttentionBackend.use_cascade_attention(
                common_prefix_len=common_prefix_len,
                query_lens=num_scheduled_tokens,
                num_query_heads=self.num_query_heads,
                num_kv_heads=self.num_kv_heads,
                use_alibi=False,  # FIXME
                use_sliding_window=self.sliding_window is not None,
                num_sms=self.num_sms,
            )

        if use_cascade:
            # TODO: Optimize.
            cu_prefix_query_lens = torch.tensor(
                [0, total_num_scheduled_tokens],
                dtype=torch.int32,
                device=self.device)
            cu_prefix_kv_lens = torch.tensor([0, common_prefix_len],
                                             dtype=torch.int32,
                                             device=self.device)
            cu_suffix_kv_lens = (
                self.seq_start_loc_np[:num_reqs + 1] -
                self.arange_np[:num_reqs + 1] * common_prefix_len)
            cu_suffix_kv_lens = torch.from_numpy(cu_suffix_kv_lens).to(
                self.device)
        else:
            cu_prefix_query_lens = None
            cu_prefix_kv_lens = None
            cu_suffix_kv_lens = None

448
        attn_metadata = FlashAttentionMetadata(
449
            num_actual_tokens=total_num_scheduled_tokens,
450
451
452
453
            max_query_len=max_num_scheduled_tokens,
            query_start_loc=query_start_loc,
            max_seq_len=max_seq_len,
            seq_start_loc=seq_start_loc,
454
455
            block_table=(
                self.input_batch.block_table.get_device_tensor()[:num_reqs]),
456
            slot_mapping=slot_mapping,
457
458
459
460
461
            use_cascade=use_cascade,
            common_prefix_len=common_prefix_len,
            cu_prefix_query_lens=cu_prefix_query_lens,
            cu_prefix_kv_lens=cu_prefix_kv_lens,
            cu_suffix_kv_lens=cu_suffix_kv_lens,
462
463
464
465
466
467
468
        )
        # NOTE(woosuk): Due to chunked prefills, there can be at most 1 partial
        # request in the batch. While we should not sample any token from this
        # partial request, we do so for simplicity. We will ignore the sampled
        # token from the partial request.
        # TODO: Support prompt logprobs.
        logits_indices = query_start_loc[1:] - 1
469
        return attn_metadata, logits_indices
470
471
472
473
474
475
476
477
478
479
480
481
482

    def _prepare_sampling(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> SamplingMetadata:
        skip_copy = True
        if (scheduler_output.finished_req_ids
                or scheduler_output.preempted_req_ids):
            skip_copy = False
        if (scheduler_output.scheduled_new_reqs
                or scheduler_output.scheduled_resumed_reqs):
            skip_copy = False
        # Create the sampling metadata.
483
484
485
486
487
488
        req_id_output_token_ids: Dict[str, List[int]] = \
            {req_id: req.output_token_ids \
                for req_id, req in self.requests.items()}

        sampling_metadata = self.input_batch.make_sampling_metadata(
            req_id_output_token_ids, skip_copy)
489
490
        return sampling_metadata

491
492
493
494
495
496
497
    def _execute_encoder(self, scheduler_output: "SchedulerOutput"):
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
        mm_inputs: List[MultiModalKwargs] = []
498
        req_input_ids: List[Tuple[str, int]] = []
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
            for input_id in encoder_input_ids:
                mm_inputs.append(req_state.mm_inputs[input_id])
                req_input_ids.append((req_id, input_id))
        batched_mm_inputs = MultiModalKwargs.batch(mm_inputs)
        batched_mm_inputs = MultiModalKwargs.as_kwargs(batched_mm_inputs,
                                                       device=self.device)

        # Run the encoder.
        # `encoder_outputs` is either of the following:
        # 1. A tensor of shape [num_images, feature_size, hidden_size]
        # in case when feature_size is fixed across all images.
        # 2. A list (length: num_images) of tensors, each of shape
        # [feature_size, hidden_size] in case when the feature size is
        # dynamic depending on input images.
515
516
        encoder_outputs = self.model.get_multimodal_embeddings(
            **batched_mm_inputs)
517
518
519
520
521
522
523
524
525
526
527
528
529
530

        # Cache the encoder outputs.
        for (req_id, input_id), output in zip(req_input_ids, encoder_outputs):
            if req_id not in self.encoder_cache:
                self.encoder_cache[req_id] = {}
            self.encoder_cache[req_id][input_id] = output

    def _gather_encoder_outputs(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> List[torch.Tensor]:
        encoder_outputs: List[torch.Tensor] = []
        num_reqs = self.input_batch.num_reqs
        for req_id in self.input_batch.req_ids[:num_reqs]:
531
            assert req_id is not None
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
            num_computed_tokens = req_state.num_computed_tokens
            mm_positions = req_state.mm_positions
            for i, pos_info in enumerate(mm_positions):
                start_pos = pos_info["offset"]
                num_encoder_tokens = pos_info["length"]

                # The encoder output is needed if the two ranges overlap:
                # [num_computed_tokens,
                #  num_computed_tokens + num_scheduled_tokens) and
                # [start_pos, start_pos + num_encoder_tokens)
                if start_pos >= num_computed_tokens + num_scheduled_tokens:
                    # The encoder output is not needed in this step.
                    break
                if start_pos + num_encoder_tokens <= num_computed_tokens:
                    # The encoder output is already processed and stored
                    # in the decoder's KV cache.
                    continue

                start_idx = max(num_computed_tokens - start_pos, 0)
                end_idx = min(
                    num_computed_tokens - start_pos + num_scheduled_tokens,
                    num_encoder_tokens)
                assert start_idx < end_idx
                assert req_id in self.encoder_cache
                assert i in self.encoder_cache[req_id]
                encoder_output = self.encoder_cache[req_id][i]
                encoder_outputs.append(encoder_output[start_idx:end_idx])
        return encoder_outputs

564
565
566
567
568
569
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> ModelRunnerOutput:
        self._update_states(scheduler_output)
570

571
572
573
574
575
576
        if self.is_multimodal_model:
            # Run the multimodal encoder if any.
            self._execute_encoder(scheduler_output)
            encoder_outputs = self._gather_encoder_outputs(scheduler_output)
        else:
            encoder_outputs = []
577
578

        # Prepare the decoder inputs.
579
        attn_metadata, logits_indices = self._prepare_inputs(scheduler_output)
580
581
582
583
584
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if (self.use_cuda_graph
                and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
585
            num_input_tokens = self.vllm_config.pad_for_cudagraph(
586
587
588
589
                num_scheduled_tokens)
        else:
            # Eager mode.
            num_input_tokens = num_scheduled_tokens
590
591
        attn_metadata.num_input_tokens = num_input_tokens

592
593
594
595
596
597
598
599
600
601
602
603
604
605
        if self.is_multimodal_model:
            # NOTE(woosuk): To unify token ids and soft tokens (vision
            # embeddings), we always use embeddings (rather than token ids)
            # as input to the multimodal model, even when the input is text.
            input_ids = self.input_ids[:num_scheduled_tokens]
            if encoder_outputs:
                inputs_embeds = self.model.get_input_embeddings(
                    input_ids, encoder_outputs)
            else:
                inputs_embeds = self.model.get_input_embeddings(input_ids)
            # TODO(woosuk): Avoid the copy. Optimize.
            self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds)
            inputs_embeds = self.inputs_embeds[:num_input_tokens]
            input_ids = None
606
        else:
607
608
609
610
611
612
            # For text-only models, we use token ids as input.
            # While it is possible to use embeddings as input just like the
            # multimodal models, it is not desirable for performance since
            # then the embedding layer is not included in the CUDA graph.
            input_ids = self.input_ids[:num_input_tokens]
            inputs_embeds = None
613
614
615

        # Run the decoder.
        # Use persistent buffers for CUDA graphs.
616
        with set_forward_context(attn_metadata, self.vllm_config):
617
            hidden_states = self.model(
618
                input_ids=input_ids,
619
                positions=self.positions[:num_input_tokens],
620
                kv_caches=self.kv_caches,
621
                attn_metadata=None,
622
                inputs_embeds=inputs_embeds,
623
            )
624
        hidden_states = hidden_states[:num_scheduled_tokens]
625
626
627
628
629
630
631
632
633
634
        hidden_states = hidden_states[logits_indices]
        logits = self.model.compute_logits(hidden_states, None)

        # Sample the next token and get logprobs if needed.
        sampling_metadata = self._prepare_sampling(scheduler_output)
        sampler_output = self.model.sample(
            logits=logits,
            sampling_metadata=sampling_metadata,
        )

635
        sampled_token_ids = sampler_output.sampled_token_ids
636
637
638
639
        # TODO(woosuk): The following loop can be slow since it iterates over
        # the requests one by one. Optimize.
        num_reqs = self.input_batch.num_reqs
        for i, req_id in enumerate(self.input_batch.req_ids[:num_reqs]):
640
            assert req_id is not None
641
642
643
644
645
646
            req_state = self.requests[req_id]
            seq_len = (req_state.num_computed_tokens +
                       scheduler_output.num_scheduled_tokens[req_id])
            assert seq_len <= req_state.num_tokens
            if seq_len == req_state.num_tokens:
                # Append the sampled token to the output token ids.
647
                token_id = sampled_token_ids[i]
648
                self.input_batch.token_ids_cpu[i, seq_len] = token_id
649
                self.input_batch.num_tokens[i] += 1
650
651
652
653
                req_state.output_token_ids.append(token_id)
            else:
                # Ignore the sampled token from the partial request.
                # Rewind the generator state as if the token was not sampled.
654
                generator = self.input_batch.generators.get(i)
655
                if generator is not None:
656
657
                    # This relies on cuda-specific torch-internal impl details
                    generator.set_offset(generator.get_offset() - 4)
658
659
660
661
662
663
664
665
666

        if sampler_output.logprob_token_ids is None:
            logprob_token_ids = None
        else:
            logprob_token_ids = sampler_output.logprob_token_ids.cpu()
        if sampler_output.logprobs is None:
            logprobs = None
        else:
            logprobs = sampler_output.logprobs.cpu()
667
668
669
670
671
672
673

        # num_reqs entries should be non-None
        assert all(
            req_id is not None for req_id in
            self.input_batch.req_ids[:num_reqs]), "req_ids contains None"
        req_ids = cast(List[str], self.input_batch.req_ids[:num_reqs])

674
        model_runner_output = ModelRunnerOutput(
675
            req_ids=req_ids,
676
            req_id_to_index=self.input_batch.req_id_to_index,
677
            sampled_token_ids=sampled_token_ids,
678
679
680
681
682
683
684
685
            logprob_token_ids_cpu=logprob_token_ids,
            logprobs_cpu=logprobs,
        )
        return model_runner_output

    def load_model(self) -> None:
        logger.info("Starting to load model %s...", self.model_config.model)
        with DeviceMemoryProfiler() as m:  # noqa: SIM117
Joe Runde's avatar
Joe Runde committed
686
            self.model = get_model(vllm_config=self.vllm_config)
687
688
689
690
691

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

692
693
694
695
696
697
698
    @torch.inference_mode()
    def _dummy_run(
        self,
        model: nn.Module,
        num_tokens: int,
        kv_caches: List[torch.Tensor],
    ) -> torch.Tensor:
699
700
701
702
703
704
        if self.is_multimodal_model:
            input_ids = None
            inputs_embeds = self.inputs_embeds[:num_tokens]
        else:
            input_ids = self.input_ids[:num_tokens]
            inputs_embeds = None
705
        with set_forward_context(None, self.vllm_config):
706
            hidden_states = model(
707
                input_ids=input_ids,
708
709
710
                positions=self.positions[:num_tokens],
                kv_caches=kv_caches,
                attn_metadata=None,
711
712
                inputs_embeds=inputs_embeds,
            )
713
714
715
        return hidden_states

    def profile_run(self) -> None:
716
717
718
719
720
721
722
723
724
725
726
        # 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).
        # it is important to create tensors inside the loop, rather than
        # multiplying the list, to avoid Dynamo from treating them as
        # tensor aliasing.
        dummy_kv_caches = [
            torch.tensor([], dtype=torch.float32, device=self.device)
            for _ in range(self.num_attn_layers)
        ]
727
728

        # Profile with multimodal encoder & encoder cache.
729
730
731
        # TODO: handle encoder-decoder models once we support them.
        if (self.is_multimodal_model and self.max_num_encoder_input_tokens > 0
                and self.encoder_cache_size > 0):
732

733
            # NOTE: Currently model is profiled with a single non-text
734
735
            # modality with the max possible input tokens even when
            # it supports multiple.
736
            max_tokens_by_modality_dict = MULTIMODAL_REGISTRY.get_max_tokens_per_item_by_nonzero_modality(  # noqa: E501
737
738
739
740
741
                self.model_config)
            dummy_data_modality, max_tokens_per_mm_item = max(
                max_tokens_by_modality_dict.items(), key=lambda item: item[1])

            # Check how many items of this modality can be supported by
742
743
744
745
746
747
            # the encoder budget.
            encoder_budget = min(self.max_num_encoder_input_tokens,
                                 self.encoder_cache_size)

            max_num_mm_items_encoder_budget = cdiv(encoder_budget,
                                                   max_tokens_per_mm_item)
748
749
750

            # Check how many items of this modality can be supported by
            # the decoder budget.
751
752
            max_mm_items_per_req = self.mm_registry.get_mm_limits_per_prompt(
                self.model_config)[dummy_data_modality]
753
754
755
756
757
758
759
760
761
762

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

            max_num_mm_items = min(max_num_mm_items_encoder_budget,
                                   max_num_mm_items_decoder_budget)

763
764
765
766
767
768
769
770
771
772
773
774
775
            logger.info(
                "Encoder cache will be initialized with a budget of %s tokens,"
                " and profiled with %s %s items of the maximum feature size.",
                encoder_budget, max_num_mm_items, dummy_data_modality)

            # Create dummy batch of multimodal inputs.
            dummy_request_data = self.input_registry.dummy_data_for_profiling(
                model_config=self.model_config,
                seq_len=self.max_num_tokens,
                mm_registry=self.mm_registry,
            )
            dummy_mm_data = dummy_request_data.multi_modal_data

776
777
778
779
            # Dummy data definition in V0 may contain multiple multimodal items
            # (e.g, multiple images) for a single request, therefore here we
            # always replicate first item by max_num_mm_items times since in V1
            # they are scheduled to be processed separately.
780
781

            # Case when models have a merged processor, their dummy data is
782
783
            # already batched `MultiModalKwargs`, therefore we take the first
            # `MultiModalKwargsItem` from the desired modality to profile on.
784
            if isinstance(dummy_mm_data, MultiModalKwargs):
785
786
787
                dummy_mm_item = dummy_mm_data.get_item(
                    modality=dummy_data_modality, item_index=0)
                dummy_mm_kwargs = MultiModalKwargs.from_items([dummy_mm_item])
788
789
790
791

            # Case when models have dummy data explicitly defined as
            # `MultiModalDataDict`, so they need to be processed through input
            # mapper.
792
793
            # TODO (ywang96): deprecate this path once merged processor is
            # supported on all models.
794
            else:
795
                mm_kwargs_list = self.mm_input_mapper_profiling.process_inputs(
796
                    mm_data=dummy_mm_data,
797
                    mm_hashes=None,
798
799
800
801
                    mm_processor_kwargs=None,
                    precomputed_mm_inputs=None)
                dummy_mm_kwargs = mm_kwargs_list[0]

802
            batched_dummy_mm_inputs = MultiModalKwargs.batch(
803
                [dummy_mm_kwargs] * max_num_mm_items)
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
            batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs(
                batched_dummy_mm_inputs, device=self.device)

            # Run multimodal encoder.
            dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                **batched_dummy_mm_inputs)
            assert len(dummy_encoder_outputs) == max_num_mm_items, (
                "Expected dimension 0 of encoder outputs to match the number "
                f"of multimodal data items: {max_num_mm_items}, got "
                f"{len(dummy_encoder_outputs)=} instead. This is most likely "
                "due to the 'get_multimodal_embeddings' method of the model "
                "not implemented correctly.")

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

820
821
822
        # Trigger compilation for general shape.
        hidden_states = self._dummy_run(self.model, self.max_num_tokens,
                                        dummy_kv_caches)
823
824
825
        logits = self.model.compute_logits(hidden_states, None)
        logits = logits[:self.max_num_tokens]
        # TODO(woosuk): Consider the memory usage of the sampler.
826
        torch.cuda.synchronize()
827
        del hidden_states, logits
828
        self.encoder_cache.clear()
829
        gc.collect()
830
831

    def capture_model(self) -> None:
832
833
        if not self.use_cuda_graph:
            logger.warning(
834
                "Skipping CUDA graph capture. Please add "
835
                "-O %s to use CUDA graphs.", CompilationLevel.PIECEWISE)
836
837
838
839
840
            return

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

841
842
843
        # Trigger CUDA graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
844
        with graph_capture(device=self.device):
845
            for num_tokens in reversed(self.cudagraph_batch_sizes):
846
847
848
                for _ in range(self.vllm_config.compilation_config.
                               cudagraph_num_of_warmups):
                    self._dummy_run(self.model, num_tokens, self.kv_caches)
849
                self._dummy_run(self.model, num_tokens, self.kv_caches)
850
851
852
853
854
855
856
857

        end_time = time.perf_counter()
        end_free_gpu_memory = torch.cuda.mem_get_info()[0]
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
        logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
                    elapsed_time, cuda_graph_size / (1 << 30))
858
859
860
861
862
863
864
865
866
867

    def initialize_kv_cache(self, num_blocks: int) -> None:
        assert len(self.kv_caches) == 0
        kv_cache_shape = FlashAttentionBackend.get_kv_cache_shape(
            num_blocks, self.block_size, self.num_kv_heads, self.head_size)
        for _ in range(self.num_attn_layers):
            self.kv_caches.append(
                torch.zeros(kv_cache_shape,
                            dtype=self.kv_cache_dtype,
                            device=self.device))
868
869
870
        bind_kv_cache(
            self.vllm_config.compilation_config.static_forward_context,
            [self.kv_caches])