config.py 173 KB
Newer Older
1
2
# SPDX-License-Identifier: Apache-2.0

3
import ast
4
import copy
5
import enum
6
import hashlib
7
import inspect
8
import json
9
import sys
10
import textwrap
11
import warnings
12
13
from collections import Counter
from collections.abc import Mapping
14
from contextlib import contextmanager
15
16
from dataclasses import (MISSING, dataclass, field, fields, is_dataclass,
                         replace)
17
from importlib.util import find_spec
18
from pathlib import Path
19
from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Final, Literal,
20
                    Optional, Protocol, TypeVar, Union)
21
22

import torch
23
from pydantic import BaseModel, Field, PrivateAttr
24
from torch.distributed import ProcessGroup, ReduceOp
25
from transformers import PretrainedConfig
26

27
import vllm.envs as envs
28
from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
Woosuk Kwon's avatar
Woosuk Kwon committed
29
from vllm.logger import init_logger
30
31
from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS,
                                                     get_quantization_config)
32
from vllm.model_executor.models import ModelRegistry
33
from vllm.platforms import CpuArchEnum, current_platform
34
from vllm.sampling_params import GuidedDecodingParams
35
from vllm.tracing import is_otel_available, otel_import_error_traceback
36
37
38
from vllm.transformers_utils.config import (
    ConfigFormat, get_config, get_hf_image_processor_config,
    get_hf_text_config, get_pooling_config,
39
40
    get_sentence_transformer_tokenizer_config, is_encoder_decoder,
    try_get_generation_config, uses_mrope)
41
from vllm.transformers_utils.s3_utils import S3Model
42
from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
43
from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless,
44
45
                        get_cpu_memory, get_open_port, is_torch_equal_or_newer,
                        random_uuid, resolve_obj_by_qualname)
46

47
if TYPE_CHECKING:
48
    from _typeshed import DataclassInstance
49
50
    from ray.util.placement_group import PlacementGroup

51
    from vllm.executor.executor_base import ExecutorBase
52
53
    from vllm.model_executor.layers.quantization.base_config import (
        QuantizationConfig)
54
    from vllm.model_executor.model_loader.loader import BaseModelLoader
55
56
    from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
        BaseTokenizerGroup)
57
58

    Config = TypeVar("Config", bound=DataclassInstance)
59
60
else:
    QuantizationConfig = None
61
    Config = TypeVar("Config")
62

63
64
logger = init_logger(__name__)

65
66
67
# This value is chosen to have a balance between ITL and TTFT. Note it is
# not optimized for throughput.
_DEFAULT_MAX_NUM_BATCHED_TOKENS = 2048
68
_POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
69
_MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120
70

71
TaskOption = Literal["auto", "generate", "embedding", "embed", "classify",
72
                     "score", "reward", "transcription"]
73

74
_ResolvedTask = Literal["generate", "embed", "classify", "score", "reward",
75
                        "draft", "transcription"]
76

77
RunnerType = Literal["generate", "pooling", "draft", "transcription"]
78

79
_RUNNER_TASKS: dict[RunnerType, list[_ResolvedTask]] = {
80
81
82
    "generate": ["generate"],
    "pooling": ["embed", "classify", "score", "reward"],
    "draft": ["draft"],
83
    "transcription": ["transcription"],
84
85
}

86
_TASK_RUNNER: dict[_ResolvedTask, RunnerType] = {
87
    task: runner
88
89
    for runner, tasks in _RUNNER_TASKS.items()
    for task in tasks
90
}
91

92
HfOverrides = Union[dict[str, Any], Callable[[PretrainedConfig],
93
94
                                             PretrainedConfig]]

95

96
97
98
99
100
101
class SupportsHash(Protocol):

    def compute_hash(self) -> str:
        ...


102
103
class SupportsMetricsInfo(Protocol):

104
    def metrics_info(self) -> dict[str, str]:
105
106
107
        ...


108
109
110
111
112
113
class ModelImpl(str, enum.Enum):
    AUTO = "auto"
    VLLM = "vllm"
    TRANSFORMERS = "transformers"


114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
def get_attr_docs(cls: type[Any]) -> dict[str, str]:
    """
    Get any docstrings placed after attribute assignments in a class body.

    https://davidism.com/mit-license/
    """

    def pairwise(iterable):
        """
        Manually implement https://docs.python.org/3/library/itertools.html#itertools.pairwise
        
        Can be removed when Python 3.9 support is dropped.
        """
        iterator = iter(iterable)
        a = next(iterator, None)

        for b in iterator:
            yield a, b
            a = b

    cls_node = ast.parse(textwrap.dedent(inspect.getsource(cls))).body[0]

    if not isinstance(cls_node, ast.ClassDef):
        raise TypeError("Given object was not a class.")

    out = {}

    # Consider each pair of nodes.
    for a, b in pairwise(cls_node.body):
        # Must be an assignment then a constant string.
        if (not isinstance(a, (ast.Assign, ast.AnnAssign))
                or not isinstance(b, ast.Expr)
                or not isinstance(b.value, ast.Constant)
                or not isinstance(b.value.value, str)):
            continue

        doc = inspect.cleandoc(b.value.value)

        # An assignment can have multiple targets (a = b = v), but an
        # annotated assignment only has one target.
        targets = a.targets if isinstance(a, ast.Assign) else [a.target]

        for target in targets:
            # Must be assigning to a plain name.
            if not isinstance(target, ast.Name):
                continue

            out[target.id] = doc

    return out


166
def config(cls: type[Config]) -> type[Config]:
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
    """
    A decorator that ensures all fields in a dataclass have default values
    and that each field has a docstring.
    """
    if not is_dataclass(cls):
        raise TypeError("The decorated class must be a dataclass.")
    attr_docs = get_attr_docs(cls)
    for f in fields(cls):
        if f.init and f.default is MISSING and f.default_factory is MISSING:
            raise ValueError(
                f"Field '{f.name}' in {cls.__name__} must have a default value."
            )
        if f.name not in attr_docs:
            raise ValueError(
                f"Field '{f.name}' in {cls.__name__} must have a docstring.")
    return cls


185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
def get_field(cls: type[Config], name: str) -> Field:
    """Get the default factory field of a dataclass by name. Used for getting
    default factory fields in `EngineArgs`."""
    if not is_dataclass(cls):
        raise TypeError("The given class is not a dataclass.")
    cls_fields = {f.name: f for f in fields(cls)}
    if name not in cls_fields:
        raise ValueError(f"Field '{name}' not found in {cls.__name__}.")
    named_field: Field = cls_fields.get(name)
    if (default_factory := named_field.default_factory) is not MISSING:
        return field(default_factory=default_factory)
    if (default := named_field.default) is not MISSING:
        return field(default=default)
    raise ValueError(
        f"{cls.__name__}.{name} must have a default value or default factory.")


202
class ModelConfig:
203
204
205
206
    """Configuration for the model.

    Args:
        model: Name or path of the huggingface model to use.
207
            It is also used as the content for `model_name` tag in metrics
208
209
210
211
212
            output when `served_model_name` is not specified.
        task: The task to use the model for. Each vLLM instance only supports
            one task, even if the same model can be used for multiple tasks.
            When the model only supports one task, "auto" can be used to select
            it; otherwise, you must specify explicitly which task to use.
213
        tokenizer: Name or path of the huggingface tokenizer to use.
214
        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
215
216
217
            available, "slow" will always use the slow tokenizer,
            "mistral" will always use the tokenizer from `mistral_common`, and
            "custom" will use --tokenizer to select the preregistered tokenizer.
218
219
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
220
221
222
223
        allowed_local_media_path: Allowing API requests to read local images or
            videos from directories specified by the server file system.
            This is a security risk. Should only be enabled in trusted
            environments.
224
225
226
227
        dtype: Data type for model weights and activations. The "auto" option
            will use FP16 precision for FP32 and FP16 models, and BF16 precision
            for BF16 models.
        seed: Random seed for reproducibility.
Jasmond L's avatar
Jasmond L committed
228
229
230
        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id. If unspecified, will use the default
            version.
231
        code_revision: The specific revision to use for the model code on
232
            Hugging Face Hub. It can be a branch name, a tag name, or a
233
            commit id. If unspecified, will use the default version.
234
235
236
        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id. If unspecified, will use
            the default version.
237
238
        max_model_len: Maximum length of a sequence (including prompt and
            output). If None, will be derived from the model.
239
240
        spec_target_max_model_len: Specify the the maximum length for spec
            decoding draft models.
241
242
        quantization: Quantization method that was used to quantize the model
            weights. If None, we assume the model weights are not quantized.
243
244
245
        enforce_eager: Whether to enforce eager execution. If True, we will
            disable CUDA graph and always execute the model in eager mode.
            If False, we will use CUDA graph and eager execution in hybrid.
246
            If None, the user did not specify, so default to False.
247
248
        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
249
250
251
            to eager mode. Additionally for encoder-decoder models, if the
            sequence length of the encoder input is larger than this, we fall
            back to the eager mode.
252
        max_logprobs: Maximum number of log probabilities. Defaults to 20.
253
254
255
256
        disable_sliding_window: Whether to disable sliding window. If True,
            we will disable the sliding window functionality of the model.
            If the model does not support sliding window, this argument is
            ignored.
257
258
        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer.
259
        served_model_name: The model name used in metrics tag `model_name`,
260
261
            matches the model name exposed via the APIs. If multiple model
            names provided, the first name will be used. If not specified,
262
            the model name will be the same as `model`.
263
        limit_mm_per_prompt: Maximum number of data items per modality
264
            per prompt. Only applicable for multimodal models.
265
266
        use_async_output_proc: Whether to use async output processor.
            Defaults to True.
267
268
        config_format: The config format which shall be loaded.
            Defaults to 'auto' which defaults to 'hf'.
269
270
271
        hf_token: The token to use as HTTP bearer authorization for remote files
            . If `True`, will use the token generated when running 
            `huggingface-cli login` (stored in `~/.huggingface`).
272
273
274
        hf_overrides: If a dictionary, contains arguments to be forwarded to the
            HuggingFace config. If a callable, it is called to update the
            HuggingFace config.
275
276
        mm_processor_kwargs: Arguments to be forwarded to the model's processor
            for multi-modal data, e.g., image processor.
277
278
        disable_mm_preprocessor_cache: If true, then disables caching of the
            multi-modal preprocessor/mapper. (not recommended)
279
280
281
282
        override_neuron_config: Initialize non default neuron config or
            override default neuron config that are specific to Neuron devices,
            this argument will be used to configure the neuron config that
            can not be gathered from the vllm arguments.
283
        override_pooler_config: Initialize non default pooling config or
284
            override default pooling config for the pooling model.
285
286
        logits_processor_pattern: Optional regex pattern specifying valid
            logits processor qualified names that can be passed with the
287
            `logits_processors` extra completion argument. Defaults to None,
288
            which allows no processors.
289
        generation_config: Configuration parameter file for generation.
290
291
292
293
294
295
        model_impl: Which implementation of the model to use:
            "auto" will try to use the vLLM implementation if it exists and
                fall back to the Transformers implementation if no vLLM
                implementation is available.
            "vllm" will use the vLLM model implementation.
            "transformers" will use the Transformers model implementation.
296
297
        override_generation_config: Override the generation config with the
            given config.
298
    """
299

300
301
302
303
304
305
306
307
308
309
310
311
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
312
        factors: list[Any] = []
313
314
315
316
317
        factors.append(self.model)
        factors.append(self.dtype)
        factors.append(self.quantization)
        factors.append(self.revision)
        factors.append(self.code_revision)
318
319
320
        factors.append(self.max_model_len)
        factors.append(self.max_logprobs)
        factors.append(self.disable_sliding_window)
321
        factors.append(self.trust_remote_code)
322
323
324
325
        factors.append(self.mm_processor_kwargs)
        factors.append(self.generation_config)
        factors.append(self.model_impl)
        factors.append(self.override_generation_config)
326
327
        factors.append(self.rope_scaling)
        factors.append(self.rope_theta)
328
329
        # hf_config can control how the model looks!
        factors.append(self.hf_config.to_json_string())
330
331
        return hashlib.sha256(str(factors).encode()).hexdigest()

332
333
334
335
336
337
338
339
340
    def __init__(
        self,
        model: str,
        task: Union[TaskOption, Literal["draft"]],
        tokenizer: str,
        tokenizer_mode: str,
        trust_remote_code: bool,
        dtype: Union[str, torch.dtype],
        seed: int,
341
        hf_config_path: Optional[str] = None,
342
343
344
        allowed_local_media_path: str = "",
        revision: Optional[str] = None,
        code_revision: Optional[str] = None,
345
        rope_scaling: Optional[dict[str, Any]] = None,
346
347
348
349
350
351
352
353
354
        rope_theta: Optional[float] = None,
        tokenizer_revision: Optional[str] = None,
        max_model_len: Optional[int] = None,
        spec_target_max_model_len: Optional[int] = None,
        quantization: Optional[str] = None,
        enforce_eager: Optional[bool] = None,
        max_seq_len_to_capture: Optional[int] = None,
        max_logprobs: int = 20,
        disable_sliding_window: bool = False,
355
        disable_cascade_attn: bool = False,
356
        skip_tokenizer_init: bool = False,
357
        served_model_name: Optional[Union[str, list[str]]] = None,
358
359
360
        limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
        use_async_output_proc: bool = True,
        config_format: ConfigFormat = ConfigFormat.AUTO,
361
        hf_token: Optional[Union[bool, str]] = None,
362
        hf_overrides: Optional[HfOverrides] = None,
363
        mm_processor_kwargs: Optional[dict[str, Any]] = None,
364
        disable_mm_preprocessor_cache: bool = False,
365
        override_neuron_config: Optional[dict[str, Any]] = None,
366
367
        override_pooler_config: Optional["PoolerConfig"] = None,
        logits_processor_pattern: Optional[str] = None,
368
        generation_config: str = "auto",
369
        enable_sleep_mode: bool = False,
370
        override_generation_config: Optional[dict[str, Any]] = None,
371
        model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
372
    ) -> None:
373
374
375
        self.model = maybe_model_redirect(model)
        self.tokenizer = maybe_model_redirect(tokenizer)

376
        self.hf_config_path = hf_config_path
377
378
379
        if isinstance(hf_config_path, str):
            self.hf_config_path = maybe_model_redirect(hf_config_path)

380
        self.tokenizer_mode = tokenizer_mode
381
        self.trust_remote_code = trust_remote_code
382
        self.allowed_local_media_path = allowed_local_media_path
383
        self.seed = seed
Jasmond L's avatar
Jasmond L committed
384
        self.revision = revision
385
        self.code_revision = code_revision
386
387
        self.rope_scaling = rope_scaling
        self.rope_theta = rope_theta
388
        self.model_impl = model_impl
389
390
391

        if hf_overrides is None:
            hf_overrides = {}
392
393
394
395
396
397

        if callable(hf_overrides):
            hf_overrides_kw = {}
            hf_overrides_fn = hf_overrides
        else:
            hf_overrides_kw = hf_overrides
398
            hf_overrides_fn = None
399

400
        if rope_scaling is not None:
401
            hf_override: dict[str, Any] = {"rope_scaling": rope_scaling}
402
            hf_overrides_kw.update(hf_override)
403
404
405
406
            hf_overrides_str = json.dumps(hf_overrides)
            msg = (
                "`--rope-scaling` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
407
408
409
            warnings.warn(DeprecationWarning(msg), stacklevel=2)
        if rope_theta is not None:
            hf_override = {"rope_theta": rope_theta}
410
            hf_overrides_kw.update(hf_override)
411
412
413
414
            hf_overrides_str = json.dumps(hf_overrides)
            msg = (
                "`--rope-theta` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
415
416
            warnings.warn(DeprecationWarning(msg), stacklevel=2)

417
418
        self.maybe_pull_model_tokenizer_for_s3(model, tokenizer)

419
420
421
422
        if (backend := envs.VLLM_ATTENTION_BACKEND
            ) and backend == "FLASHINFER" and find_spec("flashinfer") is None:
            raise ValueError(
                "VLLM_ATTENTION_BACKEND is set to FLASHINFER, but flashinfer "
423
424
                "module was not found. See "
                "https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile "  # noqa: E501
425
426
                "for instructions on how to install it.")

427
428
429
430
431
        # The tokenizer version is consistent with the model version by default.
        if tokenizer_revision is None:
            self.tokenizer_revision = revision
        else:
            self.tokenizer_revision = tokenizer_revision
432
        self.quantization = quantization
433
        self.enforce_eager = enforce_eager
434
        self.max_seq_len_to_capture = max_seq_len_to_capture
435
        self.max_logprobs = max_logprobs
436
        self.disable_sliding_window = disable_sliding_window
437
        self.disable_cascade_attn = disable_cascade_attn
438
        self.skip_tokenizer_init = skip_tokenizer_init
439
440
441
442
        self.enable_sleep_mode = enable_sleep_mode

        from vllm.platforms import current_platform

443
444
445
446
        if (self.enable_sleep_mode
                and not current_platform.is_sleep_mode_available()):
            raise ValueError(
                "Sleep mode is not supported on current platform.")
447

448
449
450
        hf_config = get_config(self.hf_config_path or self.model,
                               trust_remote_code, revision, code_revision,
                               config_format)
451
452
453
454
455
456
457
458

        if hf_overrides_kw:
            logger.info("Overriding HF config with %s", hf_overrides_kw)
            hf_config.update(hf_overrides_kw)
        if hf_overrides_fn:
            logger.info("Overriding HF config with %s", hf_overrides_fn)
            hf_config = hf_overrides_fn(hf_config)

459
460
        self.hf_config = hf_config

461
        self.hf_text_config = get_hf_text_config(self.hf_config)
462
463
        self.attention_chunk_size = getattr(self.hf_text_config,
                                            "attention_chunk_size", None)
464
        self.encoder_config = self._get_encoder_config()
465
        self.hf_image_processor_config = get_hf_image_processor_config(
466
            self.model, hf_token=hf_token, revision=revision)
467
        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
468
        self.use_async_output_proc = use_async_output_proc
469
        self.mm_processor_kwargs = mm_processor_kwargs
470
        self.disable_mm_preprocessor_cache = disable_mm_preprocessor_cache
Woosuk Kwon's avatar
Woosuk Kwon committed
471

472
473
        # Set enforce_eager to False if the value is unset.
        if self.enforce_eager is None:
474
475
            self.enforce_eager = False

476
        interleaved_attn_models = ["gemma2", "gemma3_text", "cohere2"]
477
478
479
        sliding_window = getattr(self.hf_text_config, "sliding_window", None)
        has_interleaved_attention = (sliding_window is not None) and (
            isinstance(sliding_window, list) or
480
            (self.hf_text_config.model_type in interleaved_attn_models))
481
482

        if (not self.disable_sliding_window and has_interleaved_attention):
483
484
            if (backend :=
                    envs.VLLM_ATTENTION_BACKEND) in ("XFORMERS", "FLASHINFER"):
485
486
                sliding_window_len_min = get_min_sliding_window(
                    self.hf_text_config.sliding_window)
487

488
                logger.warning_once(
489
490
                    f"{self.hf_text_config.model_type} has interleaved "
                    "attention, which is currently not supported by the "
491
                    f"{backend} backend. Disabling sliding window and capping "
492
493
494
495
496
497
498
499
500
501
502
503
                    "the max length to the sliding window size "
                    f"({sliding_window_len_min}).")
                self.disable_sliding_window = True
            else:
                # for a model with interleaved attention,
                # the scheduler and the model treat it as full attention
                # (i.e., not dropping any tokens outside the window).
                # only the attention layer itself is aware of the sliding
                # window, and use the window size to compute the attention.
                self.hf_text_config.interleaved_sliding_window = sliding_window
                delattr(self.hf_text_config, "sliding_window")
                sliding_window = None
Woosuk Kwon's avatar
Woosuk Kwon committed
504

505
506
507
508
        self.max_model_len = _get_and_verify_max_len(
            hf_config=self.hf_text_config,
            max_model_len=max_model_len,
            disable_sliding_window=self.disable_sliding_window,
509
            sliding_window_len=self.get_hf_config_sliding_window(),
510
511
            spec_target_max_model_len=spec_target_max_model_len,
            encoder_config=self.encoder_config)
512
513
        self.served_model_name = get_served_model_name(model,
                                                       served_model_name)
514
515
        self.multimodal_config = self._init_multimodal_config(
            limit_mm_per_prompt)
516
517
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
518

519
        self.is_attention_free = self._init_attention_free()
520
        self.is_hybrid = self._init_is_hybrid()
521
        self.has_noops = self._init_has_noops()
522
523
        self.has_inner_state = self._init_has_inner_state()

524
525
526
527
        if current_platform.is_neuron():
            self.override_neuron_config = override_neuron_config
        else:
            self.override_neuron_config = None
528

529
        supported_tasks, task = self._resolve_task(task)
530
531
        self.supported_tasks = supported_tasks
        self.task: Final = task
532
533
534
535
        if self.task in ("draft", "generate"):
            self.truncation_side = "left"
        else:
            self.truncation_side = "right"
536

537
        self.pooler_config = self._init_pooler_config(override_pooler_config)
538
        self.logits_processor_pattern = logits_processor_pattern
539

540
        self.generation_config = generation_config
541
        self.override_generation_config = override_generation_config or {}
542

543
        self._verify_quantization()
544
        self._verify_cuda_graph()
545
        self._verify_bnb_config()
546

547
548
549
550
551
552
553
554
    @property
    def registry(self):
        return ModelRegistry

    @property
    def architectures(self) -> list[str]:
        return getattr(self.hf_config, "architectures", [])

555
556
557
    def maybe_pull_model_tokenizer_for_s3(self, model: str,
                                          tokenizer: str) -> None:
        """
558
        Pull the model config or tokenizer to a temporary
559
560
561
562
563
564
565
566
567
        directory in case of S3.

        Args:
            model: The model name or path.
            tokenizer: The tokenizer name or path.

        """
        if is_s3(model) or is_s3(tokenizer):
            if is_s3(model):
568
                s3_model = S3Model()
569
570
                s3_model.pull_files(
                    model, allow_pattern=["*.model", "*.py", "*.json"])
571
                self.model_weights = self.model
572
                self.model = s3_model.dir
573
574

            if is_s3(tokenizer):
575
576
                s3_tokenizer = S3Model()
                s3_tokenizer.pull_files(
577
                    model, ignore_pattern=["*.pt", "*.safetensors", "*.bin"])
578
                self.tokenizer = s3_tokenizer.dir
579

580
581
582
    def _init_multimodal_config(
        self, limit_mm_per_prompt: Optional[Mapping[str, int]]
    ) -> Optional["MultiModalConfig"]:
583
        if self.registry.is_multimodal_model(self.architectures):
584
            return MultiModalConfig(limit_per_prompt=limit_mm_per_prompt or {})
585
586
587
588
589
590

        if limit_mm_per_prompt:
            raise ValueError("`limit_mm_per_prompt` is only supported for "
                             "multimodal models.")

        return None
591

592
593
594
595
    def _get_encoder_config(self):
        return get_sentence_transformer_tokenizer_config(
            self.model, self.revision)

596
597
    def _init_pooler_config(
        self,
598
        override_pooler_config: Optional["PoolerConfig"],
599
    ) -> Optional["PoolerConfig"]:
600

601
        if self.runner_type == "pooling":
602
603
604
605
606
607
608
609
610
            user_config = override_pooler_config or PoolerConfig()

            base_config = get_pooling_config(self.model, self.revision)
            if base_config is not None:
                # Only set values that are not overridden by the user
                for k, v in base_config.items():
                    if getattr(user_config, k) is None:
                        setattr(user_config, k, v)

611
612
613
614
615
616
617
618
619
            if self.is_matryoshka:
                if user_config.normalize is None:
                    user_config.normalize = True
                elif not user_config.normalize:
                    raise ValueError(
                        "`normalize` must be enabled (set to True) "
                        "for models that are compatible with "
                        "Matryoshka Representation.")

620
621
            return user_config

622
623
        return None

624
    def _init_attention_free(self) -> bool:
625
        return self.registry.is_attention_free_model(self.architectures)
626

627
    def _init_is_hybrid(self) -> bool:
628
        return self.registry.is_hybrid_model(self.architectures)
629

630
631
632
633
    def _init_has_noops(self) -> bool:
        architectures = getattr(self.hf_config, "architectures", [])
        return self.registry.is_noops_model(architectures)

634
    def _init_has_inner_state(self) -> bool:
635
        return self.registry.model_has_inner_state(self.architectures)
636

637
638
    def _verify_tokenizer_mode(self) -> None:
        tokenizer_mode = self.tokenizer_mode.lower()
639
        if tokenizer_mode not in ["auto", "slow", "mistral", "custom"]:
640
641
            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
642
                "either 'auto', 'slow', 'mistral' or 'custom'.")
643
        self.tokenizer_mode = tokenizer_mode
644

645
646
    def _get_preferred_task(
        self,
647
648
        architectures: list[str],
        supported_tasks: set[_ResolvedTask],
649
650
651
652
    ) -> Optional[_ResolvedTask]:
        model_id = self.model
        if get_pooling_config(model_id, self.revision):
            return "embed"
653
        if self.registry.is_cross_encoder_model(architectures):
654
            return "score"
655
        if self.registry.is_transcription_model(architectures):
656
            return "transcription"
657

658
        suffix_to_preferred_task: list[tuple[str, _ResolvedTask]] = [
659
660
661
662
663
664
665
666
667
            # Other models follow this pattern
            ("ForCausalLM", "generate"),
            ("ForConditionalGeneration", "generate"),
            ("ForSequenceClassification", "classify"),
            ("ChatModel", "generate"),
            ("LMHeadModel", "generate"),
            ("EmbeddingModel", "embed"),
            ("RewardModel", "reward"),
        ]
668
        _, arch = self.registry.inspect_model_cls(architectures)
669
670
671
672
673
674
675

        for suffix, pref_task in suffix_to_preferred_task:
            if arch.endswith(suffix) and pref_task in supported_tasks:
                return pref_task

        return None

676
677
    def _resolve_task(
        self,
678
        task_option: Union[TaskOption, Literal["draft"]],
679
    ) -> tuple[set[_ResolvedTask], _ResolvedTask]:
680
681
682
        if task_option == "draft":
            return {"draft"}, "draft"

683
684
        registry = self.registry
        architectures = self.architectures
685

686
        runner_support: dict[RunnerType, bool] = {
687
688
            # NOTE: Listed from highest to lowest priority,
            # in case the model supports multiple of them
689
690
691
            "transcription": registry.is_transcription_model(architectures),
            "generate": registry.is_text_generation_model(architectures),
            "pooling": registry.is_pooling_model(architectures),
692
        }
693
        supported_runner_types_lst: list[RunnerType] = [
694
695
696
697
698
            runner_type
            for runner_type, is_supported in runner_support.items()
            if is_supported
        ]

699
        supported_tasks_lst: list[_ResolvedTask] = [
700
701
            task for runner_type in supported_runner_types_lst
            for task in _RUNNER_TASKS[runner_type]
702
703
704
705
706
        ]
        supported_tasks = set(supported_tasks_lst)

        if task_option == "auto":
            selected_task = next(iter(supported_tasks_lst))
707

708
709
710
711
712
            if len(supported_tasks_lst) > 1:
                preferred_task = self._get_preferred_task(
                    architectures, supported_tasks)
                if preferred_task is not None:
                    selected_task = preferred_task
713

714
715
716
                logger.info(
                    "This model supports multiple tasks: %s. "
                    "Defaulting to '%s'.", supported_tasks, selected_task)
717
        else:
718
719
720
721
722
723
724
725
726
727
728
729
730
731
            # Aliases
            if task_option == "embedding":
                preferred_task = self._get_preferred_task(
                    architectures, supported_tasks)
                if preferred_task != "embed":
                    msg = ("The 'embedding' task will be restricted to "
                           "embedding models in a future release. Please "
                           "pass `--task classify`, `--task score`, or "
                           "`--task reward` explicitly for other pooling "
                           "models.")
                    warnings.warn(msg, DeprecationWarning, stacklevel=2)

                task_option = preferred_task or "embed"

732
733
734
735
736
737
738
            if task_option not in supported_tasks:
                msg = (
                    f"This model does not support the '{task_option}' task. "
                    f"Supported tasks: {supported_tasks}")
                raise ValueError(msg)

            selected_task = task_option
739

740
        return supported_tasks, selected_task
741

742
743
744
    def _parse_quant_hf_config(self):
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is None:
745
            # compressed-tensors uses a "compression_config" key
746
            quant_cfg = getattr(self.hf_config, "compression_config", None)
747
748
        return quant_cfg

749
    def _verify_quantization(self) -> None:
750
        supported_quantization = QUANTIZATION_METHODS
751
        optimized_quantization_methods = [
752
753
            "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
            "awq_marlin", "fbgemm_fp8", "compressed_tensors",
754
            "compressed-tensors", "experts_int8", "quark", "nvfp4"
755
        ]
756
757
758
759
        if self.quantization is not None:
            self.quantization = self.quantization.lower()

        # Parse quantization method from the HF model config, if available.
760
761
        quant_cfg = self._parse_quant_hf_config()

762
763
        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()
764
765

            # Detect which checkpoint is it
766
767
            for name in QUANTIZATION_METHODS:
                method = get_quantization_config(name)
768
769
770
771
772
773
                quantization_override = method.override_quantization_method(
                    quant_cfg, self.quantization)
                if quantization_override:
                    quant_method = quantization_override
                    self.quantization = quantization_override
                    break
774

775
            # Verify quantization configurations.
776
            if self.quantization is None:
777
778
                self.quantization = quant_method
            elif self.quantization != quant_method:
779
780
                raise ValueError(
                    "Quantization method specified in the model config "
781
                    f"({quant_method}) does not match the quantization "
782
783
784
785
786
787
788
789
                    f"method specified in the `quantization` argument "
                    f"({self.quantization}).")

        if self.quantization is not None:
            if self.quantization not in supported_quantization:
                raise ValueError(
                    f"Unknown quantization method: {self.quantization}. Must "
                    f"be one of {supported_quantization}.")
790
            from vllm.platforms import current_platform
791
            current_platform.verify_quantization(self.quantization)
792
            if self.quantization not in optimized_quantization_methods:
793
                logger.warning(
794
                    "%s quantization is not fully "
795
                    "optimized yet. The speed can be slower than "
796
                    "non-quantized models.", self.quantization)
797

798
    def _verify_cuda_graph(self) -> None:
799
800
801
802
        if self.max_seq_len_to_capture is None:
            self.max_seq_len_to_capture = self.max_model_len
        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                          self.max_model_len)
803
804
805
806
807
808
809
        ROCM_UNSUPPORTED_MODELS = ['mllama']
        if (self.hf_config.model_type in ROCM_UNSUPPORTED_MODELS
                and not self.enforce_eager and current_platform.is_rocm()):
            logger.warning(
                "CUDA graph is not supported for %s on ROCm yet, fallback "
                "to the eager mode.", self.hf_config.model_type)
            self.enforce_eager = True
810

811
812
    def _verify_bnb_config(self) -> None:
        """
813
        The current version of bitsandbytes (0.45.3) with 8-bit models does not
814
        yet support CUDA graph.
815
        # TODO Remove this when bitsandbytes supports.
816
817
818
819
820
821
822
823
824
825
826
827
828
829
        """
        is_bitsandbytes = self.quantization == "bitsandbytes"
        has_quantization_config = (getattr(self.hf_config,
                                           "quantization_config", None)
                                   is not None)
        is_8bit = (self.hf_config.quantization_config.get(
            "load_in_8bit", False) if has_quantization_config else False)
        if all([
                is_bitsandbytes,
                has_quantization_config,
                is_8bit,
                not self.enforce_eager,
        ]):
            logger.warning(
830
                "CUDA graph is not supported on BitsAndBytes 8bit yet, "
831
                "fallback to the eager mode.")
832

833
834
            self.enforce_eager = True

835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
    def _verify_with_expert_parallelism(self) -> None:
        num_expert_names = [
            "moe_num_experts",  # Dbrx
            "num_experts",  # Jamba
            "n_routed_experts",  # DeepSeek
            "num_local_experts",  # Mixtral
        ]
        num_experts = 0
        for name in num_expert_names:
            num_experts = getattr(self.hf_text_config, name, 0)
            if num_experts > 0:
                break
        if num_experts < 1:
            raise ValueError(
                "Number of experts in the model must be greater than 0 "
                "when expert parallelism is enabled.")

852
853
854
855
856
857
858
859
860
861
    def verify_async_output_proc(self, parallel_config, speculative_config,
                                 device_config) -> None:
        if not self.use_async_output_proc:
            # Nothing to check
            return

        if parallel_config.pipeline_parallel_size > 1:
            self.use_async_output_proc = False
            return

862
        # Reminder: Please update docs/source/features/compatibility_matrix.md
863
        # If the feature combo become valid
864
        from vllm.platforms import current_platform
865
        if not current_platform.is_async_output_supported(self.enforce_eager):
866
867
868
869
870
871
872
            self.use_async_output_proc = False
            return

        if envs.VLLM_USE_RAY_SPMD_WORKER:
            self.use_async_output_proc = False
            return

873
        # Async postprocessor is not necessary for pooling models
874
        # since there is no token generation
875
        if self.runner_type == "pooling":
876
877
            self.use_async_output_proc = False

878
        # Reminder: Please update docs/source/features/compatibility_matrix.md
879
        # If the feature combo become valid
880
881
882
        if speculative_config:
            self.use_async_output_proc = False

883
884
885
886
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
887
888
889
890
891
892

        if parallel_config.distributed_executor_backend == "external_launcher":
            assert self.seed is not None, (
                "Seed must be set when using external launcher backend to "
                "make sure sampling results are the same across workers.")

893
894
        total_num_attention_heads = getattr(self.hf_text_config,
                                            "num_attention_heads", 0)
895
896
897
898
899
900
901
        tensor_parallel_size = parallel_config.tensor_parallel_size
        if total_num_attention_heads % tensor_parallel_size != 0:
            raise ValueError(
                f"Total number of attention heads ({total_num_attention_heads})"
                " must be divisible by tensor parallel size "
                f"({tensor_parallel_size}).")

902
        if parallel_config.enable_expert_parallel:
903
904
            self._verify_with_expert_parallelism()

905
        pipeline_parallel_size = parallel_config.pipeline_parallel_size
906
        if pipeline_parallel_size > 1:
907
            if not self.registry.is_pp_supported_model(self.architectures):
908
909
910
911
912
913
                raise NotImplementedError(
                    "Pipeline parallelism is not supported for this model. "
                    "Supported models implement the `SupportsPP` interface.")

            if self.use_async_output_proc:
                self.use_async_output_proc = False
914

915
    def get_hf_config_sliding_window(
916
            self) -> Union[Optional[int], list[Optional[int]]]:
Woosuk Kwon's avatar
Woosuk Kwon committed
917
        """Get the sliding window size, or None if disabled."""
918
919
920
921

        # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in
        # addition to sliding window size. We check if that field is present
        # and if it's False, return None.
922
923
        if (hasattr(self.hf_text_config, "use_sliding_window")
                and not self.hf_text_config.use_sliding_window):
924
            return None
925
        return getattr(self.hf_text_config, "sliding_window", None)
926

927
    def get_sliding_window(self) -> Optional[Union[int, list[Optional[int]]]]:
928
929
930
931
932
933
934
935
        """Get the sliding window size, or None if disabled.
        """
        # If user disables sliding window, return None.
        if self.disable_sliding_window:
            return None
        # Otherwise get the value from the hf config.
        return self.get_hf_config_sliding_window()

936
    def get_vocab_size(self) -> int:
937
        return self.hf_text_config.vocab_size
938

939
    def get_hidden_size(self) -> int:
940
        return self.hf_text_config.hidden_size
941

942
943
    @property
    def is_deepseek_mla(self) -> bool:
944
945
946
947
948
949
950
951
952
953
954
955
        if not hasattr(self.hf_text_config, "model_type"):
            return False
        elif self.hf_text_config.model_type in \
            ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp'):
            return self.hf_text_config.kv_lora_rank is not None
        elif self.hf_text_config.model_type == 'eagle':
            # if the model is an EAGLE module, check for the
            # underlying architecture
            return self.hf_text_config.model.model_type in \
                    ('deepseek_v2', 'deepseek_v3') \
                and self.hf_text_config.kv_lora_rank is not None
        return False
956

957
    def get_head_size(self) -> int:
wangding zeng's avatar
wangding zeng committed
958
        # TODO remove hard code
959
        if self.is_deepseek_mla:
960
961
            qk_rope_head_dim = getattr(self.hf_text_config, "qk_rope_head_dim",
                                       0)
962
            if self.use_mla:
963
                return self.hf_text_config.kv_lora_rank + qk_rope_head_dim
964
965
966
967
968
            else:
                qk_nope_head_dim = getattr(self.hf_text_config,
                                           "qk_nope_head_dim", 0)
                if qk_rope_head_dim and qk_nope_head_dim:
                    return qk_rope_head_dim + qk_nope_head_dim
969

970
971
972
973
974
        if hasattr(self.hf_text_config,
                   "model_type") and (self.hf_text_config.model_type
                                      == "zamba2"):
            return self.hf_text_config.attention_head_dim

975
976
977
        if self.is_attention_free:
            return 0

978
979
        if hasattr(self.hf_text_config, "head_dim"):
            return self.hf_text_config.head_dim
980
        # FIXME(woosuk): This may not be true for all models.
981
982
        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)
983

984
985
    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
Zhuohan Li's avatar
Zhuohan Li committed
986
        # For GPTBigCode & Falcon:
987
        # NOTE: for falcon, when new_decoder_architecture is True, the
Zhuohan Li's avatar
Zhuohan Li committed
988
989
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
990
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
991
        new_decoder_arch_falcon = (
992
            self.hf_config.model_type in falcon_model_types
993
            and getattr(self.hf_config, "new_decoder_architecture", False))
994
        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
995
                                                   "multi_query", False):
Zhuohan Li's avatar
Zhuohan Li committed
996
            # Multi-query attention, only one KV head.
Woosuk Kwon's avatar
Woosuk Kwon committed
997
            # Currently, tensor parallelism is not supported in this case.
Zhuohan Li's avatar
Zhuohan Li committed
998
            return 1
999

1000
        # For DBRX and MPT
1001
1002
1003
1004
1005
        if self.hf_config.model_type == "mpt":
            if "kv_n_heads" in self.hf_config.attn_config:
                return self.hf_config.attn_config["kv_n_heads"]
            return self.hf_config.num_attention_heads
        if self.hf_config.model_type == "dbrx":
1006
1007
1008
            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

1009
1010
1011
1012
1013
1014
1015
1016
        if self.hf_config.model_type == "nemotron-nas":
            for block in self.hf_config.block_configs:
                if not block.attention.no_op:
                    return self.hf_config.num_attention_heads \
                        // block.attention.n_heads_in_group

            raise RuntimeError("Couldn't determine number of kv heads")

1017
1018
1019
        if self.is_attention_free:
            return 0

1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
        attributes = [
            # For Falcon:
            "n_head_kv",
            "num_kv_heads",
            # For LLaMA-2:
            "num_key_value_heads",
            # For ChatGLM:
            "multi_query_group_num",
        ]
        for attr in attributes:
1030
            num_kv_heads = getattr(self.hf_text_config, attr, None)
1031
1032
1033
1034
1035
            if num_kv_heads is not None:
                return num_kv_heads

        # For non-grouped-query attention models, the number of KV heads is
        # equal to the number of attention heads.
1036
        return self.hf_text_config.num_attention_heads
1037
1038
1039

    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU."""
1040
1041
1042
1043
        if self.use_mla:
            # When using MLA during decode it becomes MQA
            return 1

1044
1045
1046
1047
1048
1049
1050
        total_num_kv_heads = self.get_total_num_kv_heads()
        # If tensor parallelism is used, we divide the number of KV heads by
        # the tensor parallel size. We will replicate the KV heads in the
        # case where the number of KV heads is smaller than the tensor
        # parallel size so each GPU has at least one KV head.
        return max(1,
                   total_num_kv_heads // parallel_config.tensor_parallel_size)
1051

1052
1053
    def get_num_attention_heads(self,
                                parallel_config: "ParallelConfig") -> int:
1054
1055
        num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
        return num_heads // parallel_config.tensor_parallel_size
1056

1057
    def get_layers_start_end_indices(
1058
            self, parallel_config: "ParallelConfig") -> tuple[int, int]:
1059
        from vllm.distributed.utils import get_pp_indices
1060
1061
1062
1063
1064
1065
        if self.hf_text_config.model_type == "deepseek_mtp":
            total_num_hidden_layers = getattr(self.hf_text_config,
                                              "num_nextn_predict_layers", 0)
        else:
            total_num_hidden_layers = getattr(self.hf_text_config,
                                              "num_hidden_layers", 0)
1066
1067
1068
        # the layout order is: DP x PP x TP
        pp_rank = (parallel_config.rank // parallel_config.tensor_parallel_size
                   ) % parallel_config.pipeline_parallel_size
1069
1070
        pp_size = parallel_config.pipeline_parallel_size
        start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
1071
        return start, end
Mor Zusman's avatar
Mor Zusman committed
1072

1073
1074
1075
    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
        start, end = self.get_layers_start_end_indices(parallel_config)
        return end - start
Mor Zusman's avatar
Mor Zusman committed
1076

1077
1078
1079
1080
1081
1082
1083
1084
    def get_num_layers_by_block_type(
        self,
        parallel_config: "ParallelConfig",
        block_type: LayerBlockType = LayerBlockType.attention,
    ) -> int:
        # This function relies on 'layers_block_type' in hf_config,
        # for w/o this attribute, we will need to have workarounds like so
        attn_block_type = block_type == LayerBlockType.attention
1085
1086
1087
        is_transformer = not self.is_hybrid and \
                            not self.has_noops and \
                            not self.is_attention_free
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
        start, end = self.get_layers_start_end_indices(parallel_config)

        if is_transformer:
            # Handle the basic case first
            return end - start if attn_block_type else 0
        elif self.is_attention_free:
            # Attention free
            # Note that this code assumes there
            # is only one type of attention-free block type.
            return 0 if attn_block_type else end - start
1098
1099
1100
1101
        elif self.has_noops:
            block_configs = self.hf_config.block_configs
            return sum(not bc.attention.no_op
                       for bc in block_configs[start:end])
1102
        else:
1103
            # Hybrid model Jamba
1104
1105
            layers_block_type_value = getattr(self.hf_config,
                                              "layers_block_type", None)
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
            if layers_block_type_value is not None:
                if hasattr(self.hf_text_config,
                           "model_type") and (self.hf_text_config.model_type
                                              == "zamba2"):
                    if attn_block_type:
                        return sum(t == "hybrid"
                                   for t in layers_block_type_value[start:end])
                    else:
                        return self.get_num_layers(parallel_config)
                return sum(t == block_type.value
                           for t in layers_block_type_value[start:end])

            # Hybrid model Minimax
            attn_type_list = getattr(self.hf_config, "attn_type_list", None)
            if attn_type_list:
                return sum(t == 1 for t in attn_type_list[start:end])

            if layers_block_type_value is None and attn_type_list is None:
                raise ValueError(
                    "The model is an hybrid without a"
                    "layers_block_type or an attn_type_list in the hf_config,"
                    "cannot determine the num of "
                    f"{block_type.value} layers")

            return sum(t == 1 for t in attn_type_list[start:end])
Mor Zusman's avatar
Mor Zusman committed
1131

1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
    def get_multimodal_config(self) -> "MultiModalConfig":
        """
        Get the multimodal configuration of the model.

        Raises:
            ValueError: If the model is not multimodal.
        """
        if self.multimodal_config is None:
            raise ValueError("The model is not multimodal.")

        return self.multimodal_config

1144
    def try_get_generation_config(self) -> dict[str, Any]:
1145
        if self.generation_config in ("auto", "vllm"):
1146
            config = try_get_generation_config(
1147
                self.hf_config_path or self.model,
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
                trust_remote_code=self.trust_remote_code,
                revision=self.revision,
            )
        else:
            config = try_get_generation_config(
                self.generation_config,
                trust_remote_code=self.trust_remote_code,
            )

        if config is None:
            return {}

        return config.to_diff_dict()

1162
    def get_diff_sampling_param(self) -> dict[str, Any]:
1163
        """
1164
        This method returns a dictionary containing the parameters
1165
1166
        that differ from the default sampling parameters. If
        `generation_config` is `"vllm"`, an empty dictionary is returned.
1167
1168

        Returns:
1169
            dict[str, Any]: A dictionary with the differing sampling
1170
            parameters, if `generation_config` is `"vllm"` an empty dictionary.
1171
        """
1172
        if self.generation_config == "vllm":
1173
1174
1175
1176
1177
1178
1179
            config = {}
        else:
            config = self.try_get_generation_config()

        # Overriding with given generation config
        config.update(self.override_generation_config)

1180
1181
1182
1183
1184
1185
        available_params = [
            "repetition_penalty",
            "temperature",
            "top_k",
            "top_p",
            "min_p",
1186
            "max_new_tokens",
1187
1188
1189
1190
1191
1192
        ]
        if any(p in config for p in available_params):
            diff_sampling_param = {
                p: config.get(p)
                for p in available_params if config.get(p) is not None
            }
1193
1194
1195
1196
1197
            # Huggingface definition of max_new_tokens is equivalent
            # to vLLM's max_tokens
            if "max_new_tokens" in diff_sampling_param:
                diff_sampling_param["max_tokens"] = diff_sampling_param.pop(
                    "max_new_tokens")
1198
1199
        else:
            diff_sampling_param = {}
1200
1201
1202
1203
1204
1205
1206

        if diff_sampling_param:
            logger.warning_once(
                "Default sampling parameters have been overridden by the "
                "model's Hugging Face generation config recommended from the "
                "model creator. If this is not intended, please relaunch "
                "vLLM instance with `--generation-config vllm`.")
1207
1208
        return diff_sampling_param

1209
    @property
1210
    def is_encoder_decoder(self) -> bool:
1211
        """Extract the HF encoder/decoder model flag."""
1212
1213
1214
1215
1216
        return is_encoder_decoder(self.hf_config)

    @property
    def uses_mrope(self) -> bool:
        return uses_mrope(self.hf_config)
1217

1218
1219
1220
1221
    @property
    def is_multimodal_model(self) -> bool:
        return self.multimodal_config is not None

1222
1223
    @property
    def is_cross_encoder(self) -> bool:
1224
        return self.registry.is_cross_encoder_model(self.architectures)
1225

1226
1227
    @property
    def use_mla(self) -> bool:
1228
        return self.is_deepseek_mla and not envs.VLLM_MLA_DISABLE
1229

1230
    @property
1231
    def supported_runner_types(self) -> set[RunnerType]:
1232
1233
1234
1235
1236
1237
        return {_TASK_RUNNER[task] for task in self.supported_tasks}

    @property
    def runner_type(self) -> RunnerType:
        return _TASK_RUNNER[self.task]

1238
1239
1240
1241
1242
    @property
    def is_v1_compatible(self) -> bool:
        architectures = getattr(self.hf_config, "architectures", [])
        return ModelRegistry.is_v1_compatible(architectures)

1243
1244
1245
1246
1247
    @property
    def is_matryoshka(self) -> bool:
        return (hasattr(self.hf_config, "matryoshka_dimensions")
                or getattr(self.hf_config, "is_matryoshka", False))

1248
1249

class CacheConfig:
1250
1251
1252
1253
1254
    """Configuration for the KV cache.

    Args:
        block_size: Size of a cache block in number of tokens.
        gpu_memory_utilization: Fraction of GPU memory to use for the
Woosuk Kwon's avatar
Woosuk Kwon committed
1255
            vLLM execution.
1256
        swap_space: Size of the CPU swap space per GPU (in GiB).
1257
        cache_dtype: Data type for kv cache storage.
1258
        is_attention_free: Whether the model is attention-free.
1259
        num_gpu_blocks_override: Number of GPU blocks to use. This overrides the
1260
            profiled num_gpu_blocks if specified. Does nothing if None.
1261
        sliding_window: Sliding window size for the KV cache.
1262
1263
        enable_prefix_caching: Whether to enable prefix caching.
        cpu_offload_gb: Size of the CPU offload buffer in GiB.
1264
    """
1265

1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
1278
        factors: list[Any] = []
1279
1280
        factors.append(self.cache_dtype)
        # `cpu_offload_gb` does not use `torch.compile` yet.
1281
1282
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1283
1284
        return hash_str

1285
1286
1287
1288
    def __init__(
        self,
        block_size: int,
        gpu_memory_utilization: float,
1289
        swap_space: float,
1290
        cache_dtype: str,
1291
        is_attention_free: bool = False,
1292
        num_gpu_blocks_override: Optional[int] = None,
1293
        sliding_window: Optional[int] = None,
1294
        enable_prefix_caching: bool = False,
1295
        prefix_caching_hash_algo: str = "builtin",
1296
        cpu_offload_gb: float = 0,
1297
        calculate_kv_scales: Optional[bool] = None,
1298
1299
1300
    ) -> None:
        self.block_size = block_size
        self.gpu_memory_utilization = gpu_memory_utilization
1301
        self.swap_space_bytes = swap_space * GiB_bytes
1302
        self.num_gpu_blocks_override = num_gpu_blocks_override
1303
        self.cache_dtype = cache_dtype
1304
        self.is_attention_free = is_attention_free
1305
        self.sliding_window = sliding_window
1306
        self.enable_prefix_caching = enable_prefix_caching
1307
        self.prefix_caching_hash_algo = prefix_caching_hash_algo
1308
        self.cpu_offload_gb = cpu_offload_gb
1309
        self.calculate_kv_scales = calculate_kv_scales
1310
        self._verify_args()
1311
        self._verify_cache_dtype()
1312
        self._verify_prefix_caching()
1313
1314

        # Will be set after profiling.
1315
1316
        self.num_gpu_blocks: Optional[int] = None
        self.num_cpu_blocks: Optional[int] = None
1317

1318
1319
1320
1321
        # Set calculate_kv_scales to False if the value is unset.
        if self.calculate_kv_scales is None:
            self.calculate_kv_scales = False

1322
    def metrics_info(self):
1323
1324
        # convert cache_config to dict(key: str, value: str) for prometheus
        # metrics info
1325
1326
        return {key: str(value) for key, value in self.__dict__.items()}

1327
    def _verify_args(self) -> None:
1328
1329
1330
1331
        if self.cpu_offload_gb < 0:
            raise ValueError("CPU offload space must be non-negative"
                             f", but got {self.cpu_offload_gb}")

1332
1333
1334
1335
1336
        if self.gpu_memory_utilization > 1.0:
            raise ValueError(
                "GPU memory utilization must be less than 1.0. Got "
                f"{self.gpu_memory_utilization}.")

1337
1338
1339
    def _verify_cache_dtype(self) -> None:
        if self.cache_dtype == "auto":
            pass
1340
        elif self.cache_dtype in ("fp8", "fp8_e4m3", "fp8_e5m2"):
1341
            logger.info(
1342
1343
                "Using fp8 data type to store kv cache. It reduces the GPU "
                "memory footprint and boosts the performance. "
1344
1345
                "Meanwhile, it may cause accuracy drop without a proper "
                "scaling factor")
1346
1347
1348
        else:
            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")

1349
1350
1351
1352
    def _verify_prefix_caching(self) -> None:
        if not self.enable_prefix_caching:
            return

1353
        if self.sliding_window is not None and not envs.VLLM_USE_V1:
1354
1355
1356
1357
            raise NotImplementedError(
                "Prefix caching is not supported with sliding window. "
                "Run with --disable-sliding-window to use prefix caching.")

1358
1359
1360
1361
1362
1363
1364
        if self.enable_prefix_caching and self.prefix_caching_hash_algo not in (
                "builtin", "sha256"):
            raise ValueError(
                "Unknown prefix caching hash algorithm: "
                f"{self.prefix_caching_hash_algo}. Must be either "
                "'builtin' or 'sha256'.")

1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_cpu_memory = get_cpu_memory()
        # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel
        # group are in the same node. However, the GPUs may span multiple nodes.
        num_gpus_per_node = parallel_config.tensor_parallel_size
        cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node

1375
1376
1377
        msg = (f"{cpu_memory_usage / GiB_bytes:.2f} GiB out of the "
               f"{total_cpu_memory / GiB_bytes:.2f} GiB total CPU memory "
               "is allocated for the swap space.")
1378
1379
1380
        if cpu_memory_usage > 0.7 * total_cpu_memory:
            raise ValueError("Too large swap space. " + msg)
        elif cpu_memory_usage > 0.4 * total_cpu_memory:
1381
            logger.warning("Possibly too large swap space. %s", msg)
1382

1383

1384
1385
1386
1387
PoolType = Literal["ray"]


@config
1388
1389
@dataclass
class TokenizerPoolConfig:
1390
    """Configuration for the tokenizer pool."""
1391

1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
    pool_size: int = 0
    """Number of tokenizer workers in the pool to use for asynchronous
    tokenization. If 0, will use synchronous tokenization."""

    pool_type: Union[PoolType, type["BaseTokenizerGroup"]] = "ray"
    """Type of tokenizer pool to use for asynchronous tokenization. Ignored if
    tokenizer_pool_size is 0."""

    extra_config: dict = field(default_factory=dict)
    """Additional config for the pool. The way the config will be used depends
    on the pool type. This should be a JSON string that will be parsed into a
    dictionary. Ignored if tokenizer_pool_size is 0."""
1404

1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
1419
        factors: list[Any] = []
1420
1421
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1422
1423
        return hash_str

1424
    def __post_init__(self):
1425
1426
        if self.pool_type not in ("ray", ) and not isinstance(
                self.pool_type, type):
1427
1428
1429
1430
1431
1432
            raise ValueError(f"Unknown pool type: {self.pool_type}")
        if not isinstance(self.extra_config, dict):
            raise ValueError("extra_config must be a dictionary.")

    @classmethod
    def create_config(
1433
        cls, tokenizer_pool_size: int,
1434
        tokenizer_pool_type: Union[PoolType, type["BaseTokenizerGroup"]],
1435
1436
1437
        tokenizer_pool_extra_config: Optional[Union[str, dict]]
    ) -> Optional["TokenizerPoolConfig"]:
        """Create a TokenizerPoolConfig from the given parameters.
1438

1439
        If tokenizer_pool_size is 0, return None.
1440

1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
        Args:
            tokenizer_pool_size: Number of tokenizer workers in the pool.
            tokenizer_pool_type: Type of the pool.
            tokenizer_pool_extra_config: Additional config for the pool.
                The way the config will be used depends on the
                pool type. This can be a JSON string (will be parsed).
        """
        if tokenizer_pool_size:
            if isinstance(tokenizer_pool_extra_config, str):
                tokenizer_pool_extra_config_parsed = json.loads(
                    tokenizer_pool_extra_config)
            else:
                tokenizer_pool_extra_config_parsed = (
                    tokenizer_pool_extra_config or {})
            tokenizer_pool_config = cls(tokenizer_pool_size,
                                        tokenizer_pool_type,
                                        tokenizer_pool_extra_config_parsed)
        else:
            tokenizer_pool_config = None
        return tokenizer_pool_config


1463
1464
1465
1466
1467
1468
1469
class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    TENSORIZER = "tensorizer"
1470
    SHARDED_STATE = "sharded_state"
1471
    GGUF = "gguf"
1472
    BITSANDBYTES = "bitsandbytes"
1473
    MISTRAL = "mistral"
1474
    RUNAI_STREAMER = "runai_streamer"
1475
    FASTSAFETENSORS = "fastsafetensors"
1476
1477


1478
@config
1479
1480
@dataclass
class LoadConfig:
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
    """Configuration for loading the model weights."""

    load_format: Union[str, LoadFormat,
                       "BaseModelLoader"] = LoadFormat.AUTO.value
    """The format of the model weights to load:\n
    - "auto" will try to load the weights in the safetensors format and fall
    back to the pytorch bin format if safetensors format is not available.\n
    - "pt" will load the weights in the pytorch bin format.\n
    - "safetensors" will load the weights in the safetensors format.\n
    - "npcache" will load the weights in pytorch format and store a numpy cache
    to speed up the loading.\n
    - "dummy" will initialize the weights with random values, which is mainly
    for profiling.\n
    - "tensorizer" will use CoreWeave's tensorizer library for fast weight
    loading. See the Tensorize vLLM Model script in the Examples section for
    more information.\n
    - "runai_streamer" will load the Safetensors weights using Run:ai Model
    Streamer.\n
    - "bitsandbytes" will load the weights using bitsandbytes quantization.\n
    - "sharded_state" will load weights from pre-sharded checkpoint files,
    supporting efficient loading of tensor-parallel models.\n
    - "gguf" will load weights from GGUF format files (details specified in
    https://github.com/ggml-org/ggml/blob/master/docs/gguf.md).\n
    - "mistral" will load weights from consolidated safetensors files used by
    Mistral models."""
1506
    download_dir: Optional[str] = None
1507
1508
    """Directory to download and load the weights, default to the default
    cache directory of Hugging Face."""
1509
    model_loader_extra_config: dict = field(default_factory=dict)
1510
1511
1512
    """Extra config for model loader. This will be passed to the model loader
    corresponding to the chosen load_format. This should be a JSON string that
    will be parsed into a dictionary."""
1513
    ignore_patterns: Optional[Union[list[str], str]] = None
1514
1515
    """The list of patterns to ignore when loading the model. Default to
    "original/**/*" to avoid repeated loading of llama's checkpoints."""
1516
    use_tqdm_on_load: bool = True
1517
1518
    """Whether to enable tqdm for showing progress bar when loading model
    weights."""
1519

1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
1534
        factors: list[Any] = []
1535
1536
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1537
1538
        return hash_str

1539
    def __post_init__(self):
1540
1541
1542
        if isinstance(self.load_format, str):
            load_format = self.load_format.lower()
            self.load_format = LoadFormat(load_format)
1543

1544
1545
1546
1547
1548
1549
1550
        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
            logger.info(
                "Ignoring the following patterns when downloading weights: %s",
                self.ignore_patterns)
        else:
            self.ignore_patterns = ["original/**/*"]

1551

1552
1553
1554
DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]


1555
@config
1556
@dataclass
1557
class ParallelConfig:
1558
    """Configuration for the distributed execution."""
1559

1560
1561
1562
1563
1564
1565
1566
1567
1568
    pipeline_parallel_size: int = 1
    """Number of pipeline parallel groups."""
    tensor_parallel_size: int = 1
    """Number of tensor parallel groups."""
    data_parallel_size: int = 1
    """Number of data parallel groups. MoE layers will be sharded according to
    the product of the tensor parallel size and data parallel size."""
    data_parallel_rank: int = 0
    """Rank of the data parallel group."""
1569
    data_parallel_rank_local: Optional[int] = None
1570
    """Local rank of the data parallel group, defaults to global rank."""
1571
    data_parallel_master_ip: str = "127.0.0.1"
1572
1573
1574
1575
1576
    """IP of the data parallel master."""
    data_parallel_master_port: int = 29500
    """Port of the data parallel master."""
    enable_expert_parallel: bool = False
    """Use expert parallelism instead of tensor parallelism for MoE layers."""
1577

1578
    max_parallel_loading_workers: Optional[int] = None
1579
1580
1581
    """Maximum number of parallal loading workers when loading model
    sequentially in multiple batches. To avoid RAM OOM when using tensor
    parallel and large models."""
1582
1583

    disable_custom_all_reduce: bool = False
1584
    """Disable the custom all-reduce kernel and fall back to NCCL."""
1585
1586

    tokenizer_pool_config: Optional[TokenizerPoolConfig] = None
1587
1588
    """Config for the tokenizer pool. If None, will use synchronous
    tokenization."""
1589
1590

    ray_workers_use_nsight: bool = False
1591
    """Whether to profile Ray workers with nsight, see https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler."""
1592
1593

    placement_group: Optional["PlacementGroup"] = None
1594
    """ray distributed model workers placement group."""
1595

1596
    distributed_executor_backend: Optional[Union[DistributedExecutorBackend,
1597
                                                 type["ExecutorBase"]]] = None
1598
1599
1600
1601
1602
1603
1604
    """Backend to use for distributed model
    workers, either "ray" or "mp" (multiprocessing). If the product
    of pipeline_parallel_size and tensor_parallel_size is less than
    or equal to the number of GPUs available, "mp" will be used to
    keep processing on a single host. Otherwise, this will default
    to "ray" if Ray is installed and fail otherwise. Note that tpu
    and hpu only support Ray for distributed inference."""
1605
1606

    worker_cls: str = "auto"
1607
1608
    """The full name of the worker class to use. If "auto", the worker class
    will be determined based on the platform."""
1609
    sd_worker_cls: str = "auto"
1610
1611
    """The full name of the worker class to use for speculative decofing. 
    If "auto", the worker class will be determined based on the platform."""
1612
    worker_extension_cls: str = ""
1613
1614
1615
1616
    """The full name of the worker extension class to use. The worker extension
    class is dynamically inherited by the worker class. This is used to inject
    new attributes and methods to the worker class for use in collective_rpc
    calls."""
1617
1618

    world_size: int = field(init=False)
1619
    """world_size is TPxPP, it affects the number of workers we create."""
1620
    world_size_across_dp: int = field(init=False)
1621
1622
    """world_size_across_dp is TPxPPxDP, it is the size of the world
    including data parallelism."""
1623
1624

    rank: int = 0
1625
    """Global rank in distributed setup."""
1626

1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
    def get_next_dp_init_port(self) -> int:
        """
        We might need to initialize process groups in multiple
        processes that is related to data parallelism,
        e.g. both in the worker and in the engine, which
        can live in different processes. To avoid port conflicts, we
        increment the port number each time we need to initialize a
        new process group related to data parallelism.
        """
        answer = self.data_parallel_master_port
        self.data_parallel_master_port += 1
        return answer

    def stateless_init_dp_group(self) -> "ProcessGroup":
        from vllm.distributed.utils import (
            stateless_init_torch_distributed_process_group)

        # use gloo since the engine process might not have cuda device
        dp_group = stateless_init_torch_distributed_process_group(
            self.data_parallel_master_ip,
            self.get_next_dp_init_port(),
            self.data_parallel_rank,
            self.data_parallel_size,
            backend="gloo")

        return dp_group

    @staticmethod
    def has_unfinished_dp(dp_group: "ProcessGroup",
youkaichao's avatar
youkaichao committed
1656
                          has_unfinished: bool) -> bool:
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
        tensor = torch.tensor([has_unfinished],
                              dtype=torch.int32,
                              device="cpu")
        # dp rank 0: has_unfinished_seqs=True
        # dp rank 1: has_unfinished_seqs=False
        # aggregated: has_unfinished_seqs=True
        # so this is an OR operation, i.e. MAX in integers
        torch.distributed.all_reduce(tensor, op=ReduceOp.MAX, group=dp_group)
        aggregated_has_unfinished = bool(tensor.item())
        return aggregated_has_unfinished

1668
1669
1670
1671
1672
1673
1674
1675
    def compute_hash(self):
        """
        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
1676
        factors: list[Any] = []
1677
1678
1679
1680
        factors.append(self.pipeline_parallel_size)
        factors.append(self.tensor_parallel_size)
        return hashlib.sha256(str(factors).encode()).hexdigest()

1681
1682
1683
    def __post_init__(self) -> None:
        self.world_size = self.pipeline_parallel_size * \
            self.tensor_parallel_size
1684

1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
        if self.data_parallel_size > 1:
            # Data parallel was specified in the engine args.
            self.data_parallel_master_port = get_open_port()
            # TODO multi-node
        else:
            # Otherwise fall back to env vars (e.g. for offline SPMD case).
            self.data_parallel_size = envs.VLLM_DP_SIZE
            self.data_parallel_rank = envs.VLLM_DP_RANK
            self.data_parallel_rank_local = envs.VLLM_DP_RANK_LOCAL
            self.data_parallel_master_ip = envs.VLLM_DP_MASTER_IP
            self.data_parallel_master_port = envs.VLLM_DP_MASTER_PORT

1697
        self.world_size_across_dp = self.world_size * self.data_parallel_size
1698

1699
1700
1701
1702
1703
        if self.distributed_executor_backend == "external_launcher":
            import os
            os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
            logger.info("Disabling V1 multiprocessing for external launcher.")

1704
        ray_only_devices: list[str] = []
1705
        from vllm.platforms import current_platform
1706
1707
        if (current_platform.device_type in ray_only_devices
                and self.world_size > 1):
1708
1709
1710
1711
            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "ray"
            if self.distributed_executor_backend != "ray":
                raise ValueError(
1712
1713
                    f"{current_platform.device_type.upper()} backend only "
                    "supports Ray for distributed inference.")
1714

1715
        if self.distributed_executor_backend is None and self.world_size > 1:
1716
1717
1718
            # We use multiprocessing by default if world_size fits on the
            # current node and we aren't in a ray placement group.

1719
            from vllm.executor import ray_utils
1720
            backend: DistributedExecutorBackend = "mp"
1721
            ray_found = ray_utils.ray_is_available()
1722
1723
1724
1725
1726
            if current_platform.is_neuron():
                # neuron uses single process to control multiple devices
                backend = "uni"
            elif (current_platform.is_cuda()
                  and cuda_device_count_stateless() < self.world_size):
1727
1728
                if not ray_found:
                    raise ValueError("Unable to load Ray which is "
1729
1730
1731
                                     "required for multi-node inference, "
                                     "please install Ray with `pip install "
                                     "ray`.") from ray_utils.ray_import_err
1732
1733
                backend = "ray"
            elif ray_found:
1734
                if self.placement_group:
1735
                    backend = "ray"
1736
1737
1738
1739
1740
1741
                else:
                    from ray import is_initialized as ray_is_initialized
                    if ray_is_initialized():
                        from ray.util import get_current_placement_group
                        if get_current_placement_group():
                            backend = "ray"
1742
1743
1744
            self.distributed_executor_backend = backend
            logger.info("Defaulting to use %s for distributed inference",
                        backend)
1745

1746
1747
1748
        if self.distributed_executor_backend is None and self.world_size == 1:
            self.distributed_executor_backend = "uni"

1749
1750
        self._verify_args()

1751
1752
1753
1754
1755
1756
    @property
    def use_ray(self) -> bool:
        return self.distributed_executor_backend == "ray" or (
            isinstance(self.distributed_executor_backend, type)
            and self.distributed_executor_backend.uses_ray)

1757
    def _verify_args(self) -> None:
1758
1759
        # Lazy import to avoid circular import
        from vllm.executor.executor_base import ExecutorBase
1760
        from vllm.platforms import current_platform
1761
        if self.distributed_executor_backend not in (
1762
1763
                "ray", "mp", "uni",
                "external_launcher", None) and not (isinstance(
1764
1765
                    self.distributed_executor_backend, type) and issubclass(
                        self.distributed_executor_backend, ExecutorBase)):
1766
            raise ValueError(
1767
1768
                "Unrecognized distributed executor backend "
                f"{self.distributed_executor_backend}. Supported "
1769
1770
                "values are 'ray', 'mp' 'uni', 'external_launcher' or"
                " custom ExecutorBase subclass.")
1771
        if self.use_ray:
1772
1773
            from vllm.executor import ray_utils
            ray_utils.assert_ray_available()
1774
1775

        if not current_platform.use_custom_allreduce():
1776
1777
1778
            self.disable_custom_all_reduce = True
            logger.info(
                "Disabled the custom all-reduce kernel because it is not "
1779
                "supported on current platform.")
1780
        if self.ray_workers_use_nsight and not self.use_ray:
1781
1782
            raise ValueError("Unable to use nsight profiling unless workers "
                             "run with Ray.")
1783

1784
1785
1786
        assert isinstance(self.worker_extension_cls, str), (
            "worker_extension_cls must be a string (qualified class name).")

1787

1788
1789
1790
1791
SchedulerPolicy = Literal["fcfs", "priority"]


@config
1792
@dataclass
1793
class SchedulerConfig:
1794
    """Scheduler configuration."""
1795

1796
1797
    runner_type: RunnerType = "generate"
    """The runner type to launch for the model."""
1798

1799
1800
1801
1802
1803
    max_num_batched_tokens: int = None  # type: ignore
    """Maximum number of tokens to be processed in a single iteration.
    
    This config has no static default. If left unspecified by the user, it will
    be set in `EngineArgs.create_engine_config` based on the usage context."""
1804

1805
1806
1807
1808
1809
    max_num_seqs: int = None  # type: ignore
    """Maximum number of sequences to be processed in a single iteration.
    
    This config has no static default. If left unspecified by the user, it will
    be set in `EngineArgs.create_engine_config` based on the usage context."""
1810

1811
1812
1813
1814
    max_model_len: int = None  # type: ignore
    """Maximum length of a sequence (including prompt and generated text). This
    is primarily set in `ModelConfig` and that value should be manually
    duplicated here."""
1815

1816
    max_num_partial_prefills: int = 1
1817
1818
    """For chunked prefill, the maximum number of sequences that can be
    partially prefilled concurrently."""
1819
1820

    max_long_partial_prefills: int = 1
1821
1822
1823
1824
    """For chunked prefill, the maximum number of prompts longer than
    long_prefill_token_threshold that will be prefilled concurrently. Setting
    this less than max_num_partial_prefills will allow shorter prompts to jump
    the queue in front of longer prompts in some cases, improving latency."""
1825
1826

    long_prefill_token_threshold: int = 0
1827
1828
    """For chunked prefill, a request is considered long if the prompt is
    longer than this number of tokens."""
1829

1830
    num_lookahead_slots: int = 0
1831
1832
1833
1834
1835
1836
1837
    """The number of slots to allocate per sequence per
    step, beyond the known token ids. This is used in speculative
    decoding to store KV activations of tokens which may or may not be
    accepted.

    NOTE: This will be replaced by speculative config in the future; it is
    present to enable correctness tests until then."""
1838
1839

    delay_factor: float = 0.0
1840
1841
    """Apply a delay (of delay factor multiplied by previous
    prompt latency) before scheduling next prompt."""
1842

1843
1844
1845
    enable_chunked_prefill: bool = None  # type: ignore
    """If True, prefill requests can be chunked based
    on the remaining max_num_batched_tokens."""
1846
1847

    is_multimodal_model: bool = False
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
    """True if the model is multimodal."""

    # TODO (ywang96): Make this configurable.
    max_num_encoder_input_tokens: int = field(init=False)
    """Multimodal encoder compute budget, only used in V1.
    
    NOTE: This is not currently configurable. It will be overridden by
    max_num_batched_tokens in case max multimodal embedding size is larger."""

    # TODO (ywang96): Make this configurable.
    encoder_cache_size: int = field(init=False)
    """Multimodal encoder cache size, only used in V1.

    NOTE: This is not currently configurable. It will be overridden by
    max_num_batched_tokens in case max multimodal embedding size is larger."""
1863

1864
    preemption_mode: Optional[str] = None
1865
1866
1867
1868
1869
1870
    """Whether to perform preemption by swapping or
    recomputation. If not specified, we determine the mode as follows:
    We use recomputation by default since it incurs lower overhead than
    swapping. However, when the sequence group has multiple sequences
    (e.g., beam search), recomputation is not currently supported. In
    such a case, we use swapping instead."""
1871
1872

    num_scheduler_steps: int = 1
1873
    """Maximum number of forward steps per scheduler call."""
1874

1875
1876
    multi_step_stream_outputs: bool = True
    """If False, then multi-step will stream outputs at the end of all steps"""
1877
1878

    send_delta_data: bool = False
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
    """Private API. If used, scheduler sends delta data to
    workers instead of an entire data. It should be enabled only
    when SPMD worker architecture is enabled. I.e.,
    VLLM_USE_RAY_SPMD_WORKER=1"""

    policy: SchedulerPolicy = "fcfs"
    """The scheduling policy to use:\n
    - "fcfs" means first come first served, i.e. requests are handled in order
    of arrival.\n
    - "priority" means requests are handled based on given priority (lower
    value means earlier handling) and time of arrival deciding any ties)."""
1890
1891

    chunked_prefill_enabled: bool = field(init=False)
1892
    """True if chunked prefill is enabled."""
1893

1894
    disable_chunked_mm_input: bool = False
1895
1896
1897
1898
1899
1900
    """If set to true and chunked prefill is enabled, we do not want to
    partially schedule a multimodal item. Only used in V1
    This ensures that if a request has a mixed prompt
    (like text tokens TTTT followed by image tokens IIIIIIIIII) where only
    some image tokens can be scheduled (like TTTTIIIII, leaving IIIII),
    it will be scheduled as TTTT in one step and IIIIIIIIII in the next."""
1901

1902
    scheduler_cls: Union[str, type[object]] = "vllm.core.scheduler.Scheduler"
1903
1904
1905
    """The scheduler class to use. "vllm.core.scheduler.Scheduler" is the
    default scheduler. Can be a class directly or the path to a class of form
    "mod.custom_class"."""
1906

1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
1921
        factors: list[Any] = []
1922
1923
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1924
1925
        return hash_str

1926
    def __post_init__(self) -> None:
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
        if self.max_model_len is None:
            self.max_model_len = 8192
            logger.warning(
                "max_model_len was is not set. Defaulting to arbitrary value "
                "of %d.", self.max_model_len)

        if self.max_num_seqs is None:
            self.max_num_seqs = 128
            logger.warning(
                "max_num_seqs was is not set. Defaulting to arbitrary value "
                "of %d.", self.max_num_seqs)

1939
1940
1941
        if self.max_num_batched_tokens is None:
            if self.enable_chunked_prefill:
                if self.num_scheduler_steps > 1:
1942
1943
1944
1945
                    # Multi-step Chunked-Prefill doesn't allow prompt-chunking
                    # for now. Have max_num_batched_tokens set to max_model_len
                    # so we don't reject sequences on account of a short
                    # max_num_batched_tokens.
1946
1947
                    self.max_num_batched_tokens = max(
                        self.max_model_len, _DEFAULT_MAX_NUM_BATCHED_TOKENS)
1948
                else:
1949
1950
                    self.max_num_batched_tokens = (
                        _DEFAULT_MAX_NUM_BATCHED_TOKENS)
1951
            else:
1952
1953
                # If max_model_len is too short, use
                # _DEFAULT_MAX_NUM_BATCHED_TOKENS as the default value
1954
                # for higher throughput.
1955
1956
                self.max_num_batched_tokens = max(
                    self.max_model_len, _DEFAULT_MAX_NUM_BATCHED_TOKENS)
1957

1958
1959
            if self.runner_type == "pooling":
                # Choose specific value for higher throughput
1960
1961
                self.max_num_batched_tokens = max(
                    self.max_num_batched_tokens,
1962
                    _POOLING_MODEL_MAX_NUM_BATCHED_TOKENS,
1963
                )
1964
            if self.is_multimodal_model:
1965
                # The value needs to be at least the number of multimodal tokens
1966
1967
                self.max_num_batched_tokens = max(
                    self.max_num_batched_tokens,
1968
1969
1970
                    _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
                )

1971
1972
1973
        self.max_num_encoder_input_tokens = self.max_num_batched_tokens
        self.encoder_cache_size = self.max_num_batched_tokens

1974
        if self.enable_chunked_prefill:
1975
1976
            logger.info(
                "Chunked prefill is enabled with max_num_batched_tokens=%d.",
1977
                self.max_num_batched_tokens)
1978

1979
        self.chunked_prefill_enabled = self.enable_chunked_prefill
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
        if self.max_num_partial_prefills > 1:
            if self.long_prefill_token_threshold == 0:
                self.long_prefill_token_threshold = int(self.max_model_len *
                                                        0.04)

            logger.info(
                "Concurrent partial prefills enabled with "
                "max_num_partial_prefills=%d, max_long_partial_prefills=%d, "
                "long_prefill_token_threshold=%d",
                self.max_num_partial_prefills, self.max_long_partial_prefills,
                self.long_prefill_token_threshold)

1992
1993
1994
        self._verify_args()

    def _verify_args(self) -> None:
1995
1996
        if (self.max_num_batched_tokens < self.max_model_len
                and not self.chunked_prefill_enabled):
1997
1998
1999
2000
2001
2002
2003
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
                f"smaller than max_model_len ({self.max_model_len}). "
                "This effectively limits the maximum sequence length to "
                "max_num_batched_tokens and makes vLLM reject longer "
                "sequences. Please increase max_num_batched_tokens or "
                "decrease max_model_len.")
2004

2005
2006
2007
2008
2009
        if self.max_num_batched_tokens < self.max_num_seqs:
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
                "be greater than or equal to max_num_seqs "
                f"({self.max_num_seqs}).")
2010

2011
2012
2013
2014
2015
2016
        if self.num_lookahead_slots < 0:
            raise ValueError(
                "num_lookahead_slots "
                f"({self.num_lookahead_slots}) must be greater than or "
                "equal to 0.")

2017
2018
2019
2020
2021
2022
        if self.num_scheduler_steps < 1:
            raise ValueError(
                "num_scheduler_steps "
                f"({self.num_scheduler_steps}) must be greater than or "
                "equal to 1.")

2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
        if self.max_num_partial_prefills < 1:
            raise ValueError(
                f"max_num_partial_prefills ({self.max_num_partial_prefills}) "
                "must be greater than or equal to 1.")
        elif self.max_num_partial_prefills > 1:
            if not self.chunked_prefill_enabled:
                raise ValueError("Chunked prefill must be enabled to set "
                                 "max_num_partial_prefills > 1.")

            if self.long_prefill_token_threshold > self.max_model_len:
                raise ValueError(
                    "long_prefill_token_threshold "
                    f"({self.long_prefill_token_threshold}) cannot be greater "
                    f"than the max_model_len ({self.max_model_len}).")

        if (self.max_long_partial_prefills
                < 1) or (self.max_long_partial_prefills
                         > self.max_num_partial_prefills):
            raise ValueError(
                f"max_long_partial_prefills ({self.max_long_partial_prefills}) "
                "must be greater than or equal to 1 and less than or equal to "
                f"max_num_partial_prefills ({self.max_num_partial_prefills}).")

2046
2047
2048
2049
    @property
    def is_multi_step(self) -> bool:
        return self.num_scheduler_steps > 1

2050

2051
2052
2053
2054
2055
Device = Literal["auto", "cuda", "neuron", "cpu", "tpu", "xpu", "hpu"]


@config
@dataclass
2056
class DeviceConfig:
2057
2058
2059
2060
2061
2062
2063
    """Configuration for the device to use for vLLM execution."""

    device: Union[Device, torch.device] = "auto"
    """Device type for vLLM execution."""
    device_type: str = field(init=False)
    """Device type from the current platform. This is set in
    `__post_init__`."""
2064

2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # the device/platform information will be summarized
        # by torch/vllm automatically.
2080
        factors: list[Any] = []
2081
2082
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2083
2084
        return hash_str

2085
2086
    def __post_init__(self):
        if self.device == "auto":
2087
            # Automated device type detection
2088
            from vllm.platforms import current_platform
2089
            self.device_type = current_platform.device_type
2090
            if not self.device_type:
2091
2092
2093
2094
                raise RuntimeError(
                    "Failed to infer device type, please set "
                    "the environment variable `VLLM_LOGGING_LEVEL=DEBUG` "
                    "to turn on verbose logging to help debug the issue.")
2095
2096
        else:
            # Device type is assigned explicitly
2097
            self.device_type = self.device
2098
2099

        # Some device types require processing inputs on CPU
2100
        if self.device_type in ["neuron"]:
2101
            self.device = torch.device("cpu")
2102
2103
        elif self.device_type in ["tpu"]:
            self.device = None
2104
2105
2106
2107
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)

2108

2109
@dataclass
2110
class SpeculativeConfig:
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
    """
    Configuration for speculative decoding.
    Configurable parameters include:
    - General Speculative Decoding Control:
        - num_speculative_tokens (int): The number of speculative
            tokens, if provided. It will default to the number in the draft
            model config if present, otherwise, it is required.
        - model (Optional[str]): The name of the draft model, eagle head,
            or additional weights, if provided.
        - method (Optional[str]): The name of the speculative method to use.
            If users provide and set the `model` param, the speculative method
            type will be detected automatically if possible, if `model` param
            is not provided, the method name must be provided.
            - Possible values:
                - ngram
                    Related additional configuration:
                    - prompt_lookup_max (Optional[int]):
                        Maximum size of ngram token window when using Ngram
                        proposer, required when method is set to ngram.
                    - prompt_lookup_min (Optional[int]):
                        Minimum size of ngram token window when using Ngram
                        proposer, if provided. Defaults to 1.
                - eagle
                - medusa
                - mlp_speculator
                - draft_model
        - acceptance_method (str): The method to use for accepting draft
            tokens. This can take two possible values: 'rejection_sampler' and
            'typical_acceptance_sampler' for RejectionSampler and
            TypicalAcceptanceSampler respectively. If not specified, it
            defaults to 'rejection_sampler'.
            - Possible values:
                - rejection_sampler
                - typical_acceptance_sampler
                    Related additional configuration:
                    - posterior_threshold (Optional[float]):
                        A threshold value that sets a lower bound on the
                        posterior probability of a token in the target model
                        for it to be accepted. This threshold is used only
                        when we use the TypicalAcceptanceSampler for token
                        acceptance.
                    - posterior_alpha (Optional[float]):
                        Scaling factor for entropy-based threshold, applied
                        when using TypicalAcceptanceSampler.
        - draft_tensor_parallel_size (Optional[int]): The degree of the tensor
            parallelism for the draft model. Can only be 1 or the same as the
            target model's tensor parallel size.
        - disable_logprobs (bool): If set to True, token log probabilities are
            not returned during speculative decoding. If set to False, token
            log probabilities are returned according to the log probability
            settings in SamplingParams. If not specified, it defaults to True.

    - Draft Model Configuration:
        - quantization (Optional[str]): Quantization method that was used to
            quantize the draft model weights. If None, we assume the
            model weights are not quantized. Note that it only takes effect
            when using the draft model-based speculative method.
        - max_model_len (Optional[int]): The maximum model length of the
            draft model. Used when testing the ability to skip
            speculation for some sequences.
        - revision: The specific model version to use for the draft model. It
            can be a branch name, a tag name, or a commit id. If unspecified,
            will use the default version.
        - code_revision: The specific revision to use for the draft model code
            on Hugging Face Hub. It can be a branch name, a tag name, or a
            commit id. If unspecified, will use the default version.
2177

2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
    - Advanced Control:
        - disable_mqa_scorer (bool): Disable the MQA scorer and fall back to
            batch expansion for scoring proposals. If not specified, it
            defaults to False.
        - disable_by_batch_size (Optional[int]): Disable speculative decoding
            for new incoming requests when the number of enqueued requests is
            larger than this value, if provided.

    Although the parameters above are structured hierarchically, there is no
    need to nest them during configuration.

    Non-configurable internal parameters include:
    - Model Configuration:
        - target_model_config (ModelConfig): The configuration of the target
            model.
        - draft_model_config (ModelConfig): The configuration of the draft
            model initialized internal.
    - Parallelism Configuration:
        - target_parallel_config (ParallelConfig): The parallel configuration
            for the target model.
        - draft_parallel_config (ParallelConfig): The parallel configuration
            for the draft model initialized internal.
    - Execution Control:
        - enable_chunked_prefill (bool): Whether vLLM is configured to use
            chunked prefill or not. Used for raising an error since it's not
            yet compatible with speculative decode.
        - disable_log_stats (bool): Whether to disable the periodic printing of
            stage times in speculative decoding.
2206
    """
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
    # speculative configs from cli args
    num_speculative_tokens: int = field(default=None,
                                        init=True)  # type: ignore
    method: Optional[str] = None
    acceptance_method: str = "rejection_sampler"
    draft_tensor_parallel_size: Optional[int] = None
    disable_logprobs: bool = True

    model: Optional[str] = None
    quantization: Optional[str] = None
    max_model_len: Optional[int] = None
    revision: Optional[str] = None
    code_revision: Optional[str] = None

    disable_mqa_scorer: bool = False
    disable_by_batch_size: Optional[int] = None
    prompt_lookup_max: Optional[int] = None
    prompt_lookup_min: Optional[int] = None
    posterior_threshold: Optional[float] = None
    posterior_alpha: Optional[float] = None

    # required configuration params passed from engine
    target_model_config: ModelConfig = field(default=None,
                                             init=True)  # type: ignore
    target_parallel_config: ParallelConfig = field(default=None,
                                                   init=True)  # type: ignore
    enable_chunked_prefill: bool = field(default=None,
                                         init=True)  # type: ignore
    disable_log_stats: bool = field(default=None, init=True)  # type: ignore

    # params generated in the post-init stage
    draft_model_config: ModelConfig = field(default=None,
                                            init=True)  # type: ignore
    draft_parallel_config: ParallelConfig = field(default=None,
                                                  init=True)  # type: ignore
2242

2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # spec decode does not use `torch.compile` yet.
2257
        factors: list[Any] = []
2258
2259
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2260
2261
        return hash_str

2262
2263
2264
2265
2266
    @classmethod
    def from_dict(cls, dict_value: dict) -> "SpeculativeConfig":
        """Parse the CLI value for the speculative config."""
        return cls(**dict_value)

2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
    @staticmethod
    def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
        if hf_config.model_type == "deepseek_v3":
            hf_config.model_type = "deepseek_mtp"
        if hf_config.model_type == "deepseek_mtp":
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
            hf_config.update({
                "n_predict": n_predict,
                "architectures": ["DeepSeekMTPModel"]
            })
        return hf_config

2279
    def __post_init__(self):
2280

2281
2282
2283
2284
2285
2286
2287
        # Note: "method" is a new parameter that helps to extend the
        # configuration of non-model-based proposers, and the "model" parameter
        # will be used to set the draft model, eagle head, or additional weight
        # when needed. If users do not specify "method", the speculative method
        # will be detected automatically if possible. If the speculative method
        # can not be detected, it will be considered as the "draft_model" by
        # default.
2288
2289
2290
2291
2292

        if self.model is None and self.num_speculative_tokens is not None:
            # TODO(Shangming): Refactor mtp configuration logic when supporting
            # mtp acceleration for more models besides deepseek_v3
            if self.target_model_config.hf_text_config.model_type \
2293
                        == "deepseek_v3":
2294
2295
2296
2297
                # use the draft model from the same model:
                self.model = self.target_model_config.model
            elif self.method in ("ngram", "[ngram]"):
                self.model = "ngram"
2298
            else:
2299
2300
2301
                raise ValueError("num_speculative_tokens was provided without "
                                 "speculative model.")

2302
2303
        # Automatically configure the method for ngram when "model" is used
        # instead of "method"
2304
2305
2306
2307
2308
2309
2310
        if self.method is None and (self.model is not None
                                    and self.model in ("ngram", "[ngram]")):
            self.method = "ngram"

        if self.method in ("ngram", "[ngram]"):
            # Unified to "ngram" internally
            self.method = "ngram"
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
            # Set default values if not provided
            if (self.prompt_lookup_min is None
                    and self.prompt_lookup_max is None):
                # TODO(woosuk): Tune these values. They are arbitrarily chosen.
                self.prompt_lookup_min = 5
                self.prompt_lookup_max = 5
            elif self.prompt_lookup_min is None:
                assert self.prompt_lookup_max is not None
                self.prompt_lookup_min = self.prompt_lookup_max
            elif self.prompt_lookup_max is None:
                assert self.prompt_lookup_min is not None
                self.prompt_lookup_max = self.prompt_lookup_min

            # Validate values
2325
            if self.prompt_lookup_min < 1:
2326
2327
2328
2329
2330
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must be > 0")
            if self.prompt_lookup_max < 1:
                raise ValueError(
                    f"prompt_lookup_max={self.prompt_lookup_max} must be > 0")
2331
            if self.prompt_lookup_min > self.prompt_lookup_max:
2332
2333
2334
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must "
                    f"be <= prompt_lookup_max={self.prompt_lookup_max}")
2335

2336
2337
2338
            # TODO: current we still need extract vocab_size from target model
            # config, in future, we may try refactor it out, and set
            # draft related config as None here.
2339
2340
            self.draft_model_config = self.target_model_config
            self.draft_parallel_config = self.target_parallel_config
2341
        else:
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
            self.prompt_lookup_max = 0
            self.prompt_lookup_min = 0

            if self.model is not None:
                self.draft_model_config = ModelConfig(
                    model=self.model,
                    task="draft",
                    tokenizer=self.target_model_config.tokenizer,
                    tokenizer_mode=self.target_model_config.tokenizer_mode,
                    trust_remote_code=self.target_model_config.
                    trust_remote_code,
                    allowed_local_media_path=self.target_model_config.
                    allowed_local_media_path,
                    dtype=self.target_model_config.dtype,
                    seed=self.target_model_config.seed,
                    revision=self.revision,
                    code_revision=self.code_revision,
                    tokenizer_revision=self.target_model_config.
                    tokenizer_revision,
                    max_model_len=None,
                    spec_target_max_model_len=self.target_model_config.
                    max_model_len,
                    quantization=self.quantization,
                    enforce_eager=self.target_model_config.enforce_eager,
                    max_seq_len_to_capture=self.target_model_config.
                    max_seq_len_to_capture,
                    max_logprobs=self.target_model_config.max_logprobs,
                    hf_overrides=SpeculativeConfig.hf_config_override,
                )
2371

2372
                # Automatically detect the method
2373
2374
2375
                if self.method == 'eagle':
                    pass
                elif "eagle-" in self.draft_model_config.model.lower():
2376
2377
2378
2379
2380
2381
                    self.method = "eagle"
                elif self.draft_model_config.hf_config.model_type == "medusa":
                    self.method = "medusa"
                elif (self.draft_model_config.hf_config.model_type ==
                      "mlp_speculator"):
                    self.method = "mlp_speculator"
2382
                else:
2383
2384
2385
2386
                    self.method = "draft_model"

                # Replace hf_config for EAGLE draft_model
                if self.method == "eagle":
2387
                    if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
2388
                        raise ValueError(
2389
2390
                            "Chunked prefill and EAGLE are not compatible "
                            "when using V0.")
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426

                    from vllm.transformers_utils.configs.eagle import (
                        EAGLEConfig)
                    if isinstance(self.draft_model_config.hf_config,
                                  EAGLEConfig):
                        pass
                    else:
                        eagle_config = EAGLEConfig(
                            self.draft_model_config.hf_config)
                        self.draft_model_config.hf_config = eagle_config

                if (self.num_speculative_tokens is not None
                        and hasattr(self.draft_model_config.hf_config,
                                    "num_lookahead_tokens")):
                    self.draft_model_config.hf_config.num_lookahead_tokens = \
                    self.num_speculative_tokens

                n_predict = getattr(self.draft_model_config.hf_config,
                                    "n_predict", None)
                if n_predict is not None:
                    if self.num_speculative_tokens is None:
                        # Default to max value defined in draft model config.
                        self.num_speculative_tokens = n_predict
                    elif self.num_speculative_tokens > n_predict and \
                            self.num_speculative_tokens % n_predict != 0:
                        # Ensure divisibility for MTP module reuse.
                        raise ValueError(
                            f"num_speculative_tokens:{self.num_speculative_tokens}"
                            f" must be divisible by {n_predict=}")

                self.draft_tensor_parallel_size = \
                    SpeculativeConfig._verify_and_get_draft_tp(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size,
                        self.draft_model_config.hf_config
                )
2427

2428
2429
2430
2431
2432
2433
                self.draft_model_config.max_model_len = (
                    SpeculativeConfig._maybe_override_draft_max_model_len(
                        self.max_model_len,
                        self.draft_model_config.max_model_len,
                        self.target_model_config.max_model_len,
                    ))
2434

2435
2436
2437
2438
                self.draft_parallel_config = (
                    SpeculativeConfig.create_draft_parallel_config(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size))
2439

2440
2441
2442
2443
2444
        if self.acceptance_method == "typical_acceptance_sampler":
            if self.posterior_threshold is None:
                self.posterior_threshold = 0.09
            if self.posterior_alpha is None:
                self.posterior_alpha = 0.3
2445

2446
        self._verify_args()
2447

2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
    @staticmethod
    def _maybe_override_draft_max_model_len(
        speculative_max_model_len: Optional[int],
        draft_max_model_len: int,
        target_max_model_len: int,
    ) -> int:
        """Determine the max sequence len for the draft model. This is usually
        the draft_max_model_len, but may be the target_max_model_len if it is
        less than the draft_max_model_len, or may be speculative_max_model_len
        if it is specified.

        This is necessary so that sequences do not exceed the capacity of the
        draft model or the target model.

        speculative_max_model_len is mainly used for testing that sequences can
        skip speculation.
        """

        if speculative_max_model_len is not None:

            if speculative_max_model_len > draft_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {draft_max_model_len=}")

            if speculative_max_model_len > target_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {target_max_model_len=}")

            return speculative_max_model_len

        return min(
            draft_max_model_len,
            target_max_model_len,
        )

2483
    @staticmethod
2484
    def _verify_and_get_draft_tp(
2485
2486
2487
2488
2489
2490
            target_parallel_config: ParallelConfig,
            speculative_draft_tensor_parallel_size: Optional[int],
            draft_hf_config: PretrainedConfig) -> int:
        """
        Verifies and adjusts the tensor parallel size for a draft model
        specified using speculative_draft_tensor_parallel_size.
2491
        """
2492
2493
        # If speculative_draft_tensor_parallel_size is unset then set it
        # appropriately else verify that it is set correctly.
2494
        if speculative_draft_tensor_parallel_size is None:
2495
2496
2497
2498
            if draft_hf_config.model_type == "mlp_speculator":
                speculative_draft_tensor_parallel_size = 1
                if target_parallel_config.tensor_parallel_size > 1:
                    logger.warning(
2499
2500
2501
                        "%s cannot currently be run with tp>1; "
                        "setting speculative_draft_tensor_parallel_size=1",
                        draft_hf_config.model_type)
2502
2503
2504
            else:
                speculative_draft_tensor_parallel_size = \
                    target_parallel_config.tensor_parallel_size
2505
2506
        elif speculative_draft_tensor_parallel_size not in (
                1, target_parallel_config.tensor_parallel_size):
2507
            raise ValueError(
2508
                f"{speculative_draft_tensor_parallel_size=} cannot be "
2509
                f"other value than 1 or target model tensor_parallel_size")
2510
        return speculative_draft_tensor_parallel_size
2511

2512
2513
2514
2515
2516
2517
2518
2519
2520
    @staticmethod
    def create_draft_parallel_config(
        target_parallel_config: ParallelConfig,
        speculative_draft_tensor_parallel_size: int,
    ) -> ParallelConfig:
        """Create a parallel config for use by the draft worker.

        This is mostly a copy of the target parallel config, except the tp_size.
        """
2521
2522
2523
        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
2524
            tensor_parallel_size=speculative_draft_tensor_parallel_size,
2525
2526
            distributed_executor_backend=target_parallel_config.
            distributed_executor_backend,
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
            max_parallel_loading_workers=target_parallel_config.
            max_parallel_loading_workers,
            disable_custom_all_reduce=target_parallel_config.
            disable_custom_all_reduce,
            tokenizer_pool_config=target_parallel_config.tokenizer_pool_config,
            ray_workers_use_nsight=target_parallel_config.
            ray_workers_use_nsight,
            placement_group=target_parallel_config.placement_group,
        )

        return draft_parallel_config

    def _verify_args(self) -> None:
2540
2541
2542
2543
2544
2545
        if self.num_speculative_tokens is None:
            raise ValueError(
                "num_speculative_tokens must be provided with "
                "speculative model unless the draft model config contains an "
                "n_predict parameter.")

2546
2547
2548
2549
2550
2551
2552
        if self.num_speculative_tokens <= 0:
            raise ValueError("Expected num_speculative_tokens to be greater "
                             f"than zero ({self.num_speculative_tokens}).")

        if self.draft_model_config:
            self.draft_model_config.verify_with_parallel_config(
                self.draft_parallel_config)
2553
2554
            # Validate and set draft token acceptance related settings.

2555
2556
        if self.acceptance_method is None:
            raise ValueError("acceptance_method is not set. "
2557
2558
2559
                             "Expected values are rejection_sampler or "
                             "typical_acceptance_sampler.")

2560
2561
        if (self.acceptance_method != 'rejection_sampler'
                and self.acceptance_method != 'typical_acceptance_sampler'):
2562
            raise ValueError(
2563
                "Expected acceptance_method to be either "
2564
                "rejection_sampler or typical_acceptance_sampler. Instead it "
2565
                f"is {self.acceptance_method}")
2566

2567
2568
2569
2570
        if self.acceptance_method == "typical_acceptance_sampler" and (
            (self.posterior_threshold is not None
             and self.posterior_threshold < 0) or
            (self.posterior_alpha is not None and self.posterior_alpha < 0)):
2571
            raise ValueError(
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
                "Expected the posterior_threshold and posterior_alpha of "
                "typical_acceptance_sampler to be > 0. "
                "Instead found posterior_threshold = "
                f"{self.posterior_threshold} and posterior_alpha = "
                f"{self.posterior_alpha}")

        if (self.disable_by_batch_size is not None
                and self.disable_by_batch_size < 2):
            raise ValueError("Expect the batch size threshold of disabling "
                             "speculative decoding is > 1, but got "
                             f"{self.disable_by_batch_size=}")
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594

    @property
    def num_lookahead_slots(self) -> int:
        """The number of additional slots the scheduler should allocate per
        step, in addition to the slots allocated for each known token.

        This is equal to the number of speculative tokens, as each speculative
        token must be scored.
        """
        return self.num_speculative_tokens

    def __repr__(self) -> str:
2595
2596
        method = self.method
        model = None if method == "ngram" else self.draft_model_config.model
2597
        num_spec_tokens = self.num_speculative_tokens
2598
        return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
2599
2600


2601
2602
2603
2604
@dataclass
class LoRAConfig:
    max_lora_rank: int
    max_loras: int
2605
    fully_sharded_loras: bool = False
2606
    max_cpu_loras: Optional[int] = None
2607
    lora_dtype: Optional[Union[torch.dtype, str]] = None
2608
2609
2610
    lora_extra_vocab_size: int = 256
    # This is a constant.
    lora_vocab_padding_size: ClassVar[int] = 256
2611
    long_lora_scaling_factors: Optional[tuple[float]] = None
2612
    bias_enabled: bool = False
2613

2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
2626
        factors: list[Any] = []
2627
2628
2629
2630
2631
2632
2633
        factors.append(self.max_lora_rank)
        factors.append(self.max_loras)
        factors.append(self.fully_sharded_loras)
        factors.append(self.lora_dtype)
        factors.append(self.lora_extra_vocab_size)
        factors.append(self.long_lora_scaling_factors)
        factors.append(self.bias_enabled)
2634
2635
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2636
2637
        return hash_str

2638
    def __post_init__(self):
2639
        # Setting the maximum rank to 512 should be able to satisfy the vast
2640
        # majority of applications.
2641
        possible_max_ranks = (8, 16, 32, 64, 128, 256, 320, 512)
2642
        possible_lora_extra_vocab_size = (256, 512)
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
        if self.max_lora_rank not in possible_max_ranks:
            raise ValueError(
                f"max_lora_rank ({self.max_lora_rank}) must be one of "
                f"{possible_max_ranks}.")
        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
            raise ValueError(
                f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
                f"must be one of {possible_lora_extra_vocab_size}.")
        if self.max_loras < 1:
            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
        if self.max_cpu_loras is None:
            self.max_cpu_loras = self.max_loras
        elif self.max_cpu_loras < self.max_loras:
            raise ValueError(
                f"max_cpu_loras ({self.max_cpu_loras}) must be >= "
zspo's avatar
zspo committed
2658
                f"max_loras ({self.max_loras})")
2659

2660
    def verify_with_cache_config(self, cache_config: CacheConfig):
2661
2662
2663
        if cache_config.cpu_offload_gb > 0 and not envs.VLLM_USE_V1:
            raise ValueError(
                "V0 LoRA does not support CPU offload, please use V1.")
2664

2665
2666
2667
2668
2669
2670
2671
    def verify_with_model_config(self, model_config: ModelConfig):
        if self.lora_dtype in (None, "auto"):
            self.lora_dtype = model_config.dtype
        elif isinstance(self.lora_dtype, str):
            self.lora_dtype = getattr(torch, self.lora_dtype)

    def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig):
2672
        # Reminder: Please update docs/source/features/compatibility_matrix.md
2673
        # If the feature combo become valid
2674
        if scheduler_config.chunked_prefill_enabled:
2675
2676
            logger.warning("LoRA with chunked prefill is still experimental "
                           "and may be unstable.")
2677

2678
2679
2680
2681
2682
    def verify_lora_support(self):
        if self.long_lora_scaling_factors is not None and envs.VLLM_USE_V1:
            raise ValueError(
                "V1 LoRA does not support long LoRA, please use V0.")

2683

2684
2685
2686
2687
2688
2689
2690
@dataclass
class PromptAdapterConfig:
    max_prompt_adapters: int
    max_prompt_adapter_token: int
    max_cpu_prompt_adapters: Optional[int] = None
    prompt_adapter_dtype: Optional[torch.dtype] = None

2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
2705
        factors: list[Any] = []
2706
2707
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2708
2709
        return hash_str

2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
    def __post_init__(self):

        if self.max_prompt_adapters < 1:
            raise ValueError(f"max_prompt_adapters "
                             f"({self.max_prompt_adapters}) must be >= 1.")
        if self.max_prompt_adapter_token == 0:
            raise ValueError("max_prompt_adapter_token must be set.")
        if self.max_cpu_prompt_adapters is None:
            self.max_cpu_prompt_adapters = self.max_prompt_adapters

    def verify_with_model_config(self, model_config: ModelConfig):
        if self.prompt_adapter_dtype in (None, "auto"):
            self.prompt_adapter_dtype = model_config.dtype
        elif isinstance(self.prompt_adapter_dtype, str):
            self.prompt_adapter_dtype = getattr(torch,
                                                self.prompt_adapter_dtype)


2728
@dataclass
2729
class MultiModalConfig:
2730
2731
    """Controls the behavior of multimodal models."""

2732
    limit_per_prompt: Mapping[str, int] = field(default_factory=dict)
2733
    """
2734
    The maximum number of input items allowed per prompt for each modality.
2735
2736
    """

2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
2751
        factors: list[Any] = []
2752
2753
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2754
2755
        return hash_str

2756
2757
2758
2759
2760
2761
2762
    def get_default_limit_per_prompt(self) -> int:
        """
        Return the default number of input items allowed per prompt
        for any modality if not specified by the user.
        """
        return 999 if envs.VLLM_USE_V1 else 1

2763
2764
2765
2766
2767
    def get_limit_per_prompt(self, modality: str) -> int:
        """
        Get the maximum number of input items allowed per prompt
        for the given modality.
        """
2768
2769
        default = self.get_default_limit_per_prompt()
        return self.limit_per_prompt.get(modality, default)
2770

2771
    # TODO: Add configs to init vision tower or not.
2772

2773

2774
2775
@dataclass
class PoolerConfig:
2776
    """Controls the behavior of output pooling in pooling models."""
2777
2778

    pooling_type: Optional[str] = None
2779
    """
2780
    The pooling method of the pooling model. This should be a key in
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
    :class:`vllm.model_executor.layers.pooler.PoolingType`.
    """

    normalize: Optional[bool] = None
    """
    Whether to normalize the pooled outputs. Usually, this should be set to
    ``True`` for embedding outputs.
    """

    softmax: Optional[bool] = None
    """
    Whether to apply softmax to the pooled outputs. Usually, this should be set
    to ``True`` for classification outputs.
    """

    step_tag_id: Optional[int] = None
    """
2798
    If set, only the score corresponding to the ``step_tag_id`` in the
2799
2800
2801
2802
    generated sentence should be returned. Otherwise, the scores for all tokens
    are returned.
    """

2803
    returned_token_ids: Optional[list[int]] = None
2804
    """
2805
2806
    A list of indices for the vocabulary dimensions to be extracted,
    such as the token IDs of ``good_token`` and ``bad_token`` in the
2807
2808
2809
    ``math-shepherd-mistral-7b-prm`` model.
    """

2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
2824
        factors: list[Any] = []
2825
2826
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2827
2828
        return hash_str

2829
2830
2831
    @staticmethod
    def from_json(json_str: str) -> "PoolerConfig":
        return PoolerConfig(**json.loads(json_str))
2832
2833


2834
2835
2836
2837
2838
2839
2840
2841
_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

2842
_ROCM_NOT_SUPPORTED_DTYPE: list[str] = []  #
2843

2844
2845
2846

def _get_and_verify_dtype(
    config: PretrainedConfig,
2847
    dtype: Union[str, torch.dtype],
2848
2849
2850
2851
) -> torch.dtype:
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
    config_dtype = getattr(config, "torch_dtype", None)
2852
2853
2854
2855
2856
2857
2858
2859

    # Fallbacks for multi-modal models if the root config
    # does not define torch_dtype
    if config_dtype is None and hasattr(config, "text_config"):
        config_dtype = getattr(config.text_config, "torch_dtype", None)
    if config_dtype is None and hasattr(config, "vision_config"):
        config_dtype = getattr(config.vision_config, "torch_dtype", None)

2860
2861
2862
    if config_dtype is None:
        config_dtype = torch.float32

2863
2864
2865
2866
    if isinstance(dtype, str):
        dtype = dtype.lower()
        if dtype == "auto":
            if config_dtype == torch.float32:
2867
2868
                # Following common practice, we use float16 for float32 models
                torch_dtype = torch.float16
2869
2870
            else:
                torch_dtype = config_dtype
2871

Shinichi Hemmi's avatar
Shinichi Hemmi committed
2872
2873
2874
2875
2876
2877
2878
            if config.model_type == "plamo2":
                logger.info(
                    "For PLaMo2, we cast models to bfloat16 instead of using "
                    "float16 by default. This is because float16 does not work."
                )
                torch_dtype = torch.bfloat16

2879
            from vllm.platforms import current_platform
2880
2881
            if (current_platform.is_cpu()
                    and current_platform.get_cpu_architecture()
2882
                    == CpuArchEnum.POWERPC
2883
2884
2885
2886
2887
2888
2889
2890
                    and (config_dtype == torch.float16
                         or config_dtype == torch.float32)):
                logger.info(
                    "For POWERPC, we cast models to bfloat16 instead of "
                    "using float16 by default. Float16 is not currently "
                    "supported for POWERPC.")
                torch_dtype = torch.bfloat16

2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
            # TODO: change this condition to check if the platform support bf16
            # instead of checking the OS. For instance M2 shall supports bf16
            # already. But we need to modify `cpu_extension.cmake` to activate
            # the feature in the build.
            if (current_platform.is_cpu() and sys.platform.startswith("darwin")
                    and current_platform.get_cpu_architecture()
                    == CpuArchEnum.ARM and config_dtype == torch.bfloat16):
                logger.info("For macOS with Apple Silicon, currently bfloat16 "
                            "is not supported. Setting dtype to float16.")
                torch_dtype = torch.float16

2902
2903
            if current_platform.is_hpu() and config_dtype == torch.float16:
                logger.info(
2904
                    "For HPU, we cast models to bfloat16 instead of "
2905
2906
2907
                    "using float16 by default. Please specify `dtype` if you "
                    "want to use float16.")
                torch_dtype = torch.bfloat16
Shinichi Hemmi's avatar
Shinichi Hemmi committed
2908
2909
2910
2911
2912
        elif dtype == "float16" and config.model_type == "plamo2":
            logger.warning(
                "For PLaMo2, using float16 is unstable and might cause "
                "unexpected behavior. Please use bfloat16 or float32 instead.")
            torch_dtype = torch.float16
2913
        else:
2914
2915
2916
2917
2918
            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
                raise ValueError(f"Unknown dtype: {dtype}")
            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
    elif isinstance(dtype, torch.dtype):
        torch_dtype = dtype
2919
    else:
2920
        raise ValueError(f"Unknown dtype: {dtype}")
2921
2922
2923
2924
2925

    # Verify the dtype.
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
2926
            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
2927
2928
2929
            pass
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
2930
            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
2931
2932
            pass
        else:
Woosuk Kwon's avatar
Woosuk Kwon committed
2933
            # Casting between float16 and bfloat16 is allowed with a warning.
2934
            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
2935
2936

    return torch_dtype
2937
2938
2939
2940
2941


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
    max_model_len: Optional[int],
2942
    disable_sliding_window: bool,
2943
    sliding_window_len: Optional[Union[int, list[Optional[int]]]],
2944
    spec_target_max_model_len: Optional[int] = None,
2945
    encoder_config: Optional[Any] = None,
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
) -> int:
    """Get and verify the model's maximum length."""
    derived_max_model_len = float("inf")
    possible_keys = [
        # OPT
        "max_position_embeddings",
        # GPT-2
        "n_positions",
        # MPT
        "max_seq_len",
2956
2957
        # ChatGLM2
        "seq_length",
2958
2959
        # Command-R
        "model_max_length",
2960
2961
        # Whisper
        "max_target_positions",
2962
2963
2964
2965
2966
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
2967
    # Choose the smallest "max_length" from the possible keys.
2968
    max_len_key = None
2969
    for key in possible_keys:
2970
2971
2972
2973
2974
        max_len = getattr(hf_config, key, None)
        if max_len is not None:
            max_len_key = key if max_len < derived_max_model_len \
                else max_len_key
            derived_max_model_len = min(derived_max_model_len, max_len)
Jennifer Zhao's avatar
Jennifer Zhao committed
2975
2976
2977
2978
    # For Command-R / Cohere, Cohere2 / Aya Vision models
    if tmp_max_len := getattr(hf_config, "model_max_length", None):
        max_len_key = "model_max_length"
        derived_max_model_len = tmp_max_len
2979
2980
2981
2982

    # If sliding window is manually disabled, max_length should be less
    # than the sliding window length in the model config.
    if disable_sliding_window and sliding_window_len is not None:
2983
2984

        sliding_window_len_min = get_min_sliding_window(sliding_window_len)
2985
        max_len_key = "sliding_window" \
2986
2987
2988
            if sliding_window_len_min < derived_max_model_len else max_len_key
        derived_max_model_len = min(derived_max_model_len,
                                    sliding_window_len_min)
2989
2990
2991

    # If none of the keys were found in the config, use a default and
    # log a warning.
2992
    if derived_max_model_len == float("inf"):
2993
2994
2995
2996
        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

2997
2998
2999
3000
3001
        if spec_target_max_model_len is not None:
            # If this is a speculative draft model, we use the max model len
            # from the target model.
            return spec_target_max_model_len

3002
3003
3004
3005
        default_max_len = 2048
        logger.warning(
            "The model's config.json does not contain any of the following "
            "keys to determine the original maximum length of the model: "
3006
            "%s. Assuming the model's maximum length is %d.", possible_keys,
3007
            default_max_len)
3008
        derived_max_model_len = default_max_len
3009

3010
    rope_scaling = getattr(hf_config, "rope_scaling", None)
3011
3012
3013
    # NOTE(woosuk): Gemma3's max_model_len (128K) is already scaled by RoPE
    # scaling, so we skip applying the scaling factor again.
    if rope_scaling is not None and "gemma3" not in hf_config.model_type:
3014
3015
3016
        # No need to consider "type" key because of patch_rope_scaling when
        # loading HF config
        rope_type = rope_scaling["rope_type"]
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026

        if rope_type not in ("su", "longrope", "llama3"):
            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that supports rope_scaling
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "with rope_scaling. Please raise an issue so we can "
                    "investigate.")

3027
3028
3029
3030
            # NOTE: rope_type == "default" does not define factor
            # https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/modeling_rope_utils.py
            scaling_factor = rope_scaling.get("factor", 1.0)

3031
3032
3033
3034
            if rope_type == "yarn":
                derived_max_model_len = rope_scaling[
                    "original_max_position_embeddings"]
            derived_max_model_len *= scaling_factor
3035

3036
3037
3038
    if encoder_config and "max_seq_length" in encoder_config:
        derived_max_model_len = encoder_config["max_seq_length"]

3039
3040
    # If the user specified a max length, make sure it is smaller than the
    # derived length from the HF model config.
3041
    if max_model_len is None:
3042
        max_model_len = int(derived_max_model_len)
3043
    elif max_model_len > derived_max_model_len:
3044
3045
3046
3047
3048
        # Some models might have a separate key for specifying model_max_length
        # that will be bigger than derived_max_model_len. We compare user input
        # with model_max_length and allow this override when it's smaller.
        model_max_length = getattr(hf_config, "model_max_length", None)
        if model_max_length is not None and max_model_len <= model_max_length:
3049
3050
3051
3052
3053
3054
3055
            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that has model_max_length
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "model_max_length in the config. Please raise an issue "
                    "so we can investigate.")
3056
        else:
3057
            msg = (
3058
                f"User-specified max_model_len ({max_model_len}) is greater "
3059
3060
                f"than the derived max_model_len ({max_len_key}="
                f"{derived_max_model_len} or model_max_length="
3061
                f"{model_max_length} in model's config.json). This may lead "
3062
3063
3064
3065
3066
3067
3068
3069
3070
                "to incorrect model outputs or CUDA errors.")
            if envs.VLLM_ALLOW_LONG_MAX_MODEL_LEN:
                logger.warning(
                    "%s Make sure the value is correct and within the "
                    "model context size.", msg)
            else:
                raise ValueError(
                    f"{msg} To allow overriding this maximum, set "
                    "the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN=1")
3071
    return int(max_model_len)
3072
3073


3074
def get_min_sliding_window(
3075
        sliding_window: Union[int, list[Optional[int]]]) -> int:
3076
3077
3078
3079
3080
3081
    if isinstance(sliding_window, list):
        return min(s for s in sliding_window if s is not None)

    return sliding_window


3082
def get_served_model_name(model: str,
3083
                          served_model_name: Optional[Union[str, list[str]]]):
3084
    """
3085
3086
3087
3088
    If the input is a non-empty list, the first model_name in
    `served_model_name` is taken.
    If the input is a non-empty string, it is used directly.
    For cases where the input is either an empty string or an
3089
3090
3091
3092
3093
3094
3095
3096
3097
    empty list, the fallback is to use `self.model`.
    """
    if not served_model_name:
        return model
    if isinstance(served_model_name, list):
        return served_model_name[0]
    return served_model_name


3098
3099
3100
3101
@dataclass
class DecodingConfig:
    """Dataclass which contains the decoding strategy of the engine"""

3102
3103
    # Which guided decoding algo to use.
    # 'outlines' / 'lm-format-enforcer' / 'xgrammar'
3104
    guided_decoding_backend: str = "auto" if envs.VLLM_USE_V1 else "xgrammar"
3105

3106
3107
    reasoning_backend: Optional[str] = None

3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
3122
        factors: list[Any] = []
3123
3124
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3125
3126
        return hash_str

3127
    def __post_init__(self):
3128
        v0_valid_guided_backends = [
3129
            'outlines', 'lm-format-enforcer', 'xgrammar', 'auto'
3130
        ]
3131
        v1_valid_guided_backends = ['xgrammar', 'guidance', 'auto']
3132
3133
3134

        backend = GuidedDecodingParams(
            backend=self.guided_decoding_backend).backend_name
3135
3136
3137
3138
        if envs.VLLM_USE_V1:
            valid_guided_backends = v1_valid_guided_backends
        else:
            valid_guided_backends = v0_valid_guided_backends
3139
        if backend not in valid_guided_backends:
3140
            raise ValueError(f"Invalid guided_decoding_backend '{backend}',"
3141
                             f" must be one of {valid_guided_backends}")
3142
3143


3144
3145
@dataclass
class ObservabilityConfig:
3146
3147
3148
    """Configuration for observability - metrics and tracing."""
    show_hidden_metrics: bool = False

3149
3150
    otlp_traces_endpoint: Optional[str] = None

3151
3152
3153
3154
3155
3156
3157
3158
    # Collecting detailed timing information for each request can be expensive.

    # If set, collects the model forward time for the request.
    collect_model_forward_time: bool = False

    # If set, collects the model execute time for the request.
    collect_model_execute_time: bool = False

3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
3173
        factors: list[Any] = []
3174
3175
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3176
3177
        return hash_str

3178
    def __post_init__(self):
3179
3180
3181
3182
3183
        if not is_otel_available() and self.otlp_traces_endpoint is not None:
            raise ValueError(
                "OpenTelemetry is not available. Unable to configure "
                "'otlp_traces_endpoint'. Ensure OpenTelemetry packages are "
                f"installed. Original error:\n{otel_import_error_traceback}")
3184
3185


3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
class KVTransferConfig(BaseModel):
    """Configuration for distributed KV cache transfer."""

    # The KV connector for vLLM to transmit KV caches between vLLM instances.
    kv_connector: Optional[str] = None

    # The device used by kv connector to buffer the KV cache.
    # Currently only support 'cuda'.
    kv_buffer_device: Optional[str] = "cuda"

    # The buffer size for TorchDistributedConnector. Measured in number of
    # bytes. Recommended value: 1e9 (about 1GB).
    kv_buffer_size: float = 1e9

    # Whether this vLLM instance produces, consumes KV cache, or both. Choices
    # are 'kv_producer', 'kv_consumer', and 'both'.
    kv_role: Optional[str] = None

    # The rank of this vLLM instance in the KV cache transfer. Typical value:
    # 0 for prefill instance, 1 for decode instance.
    # Currently only 1P1D is supported.
    kv_rank: Optional[int] = None

    # The number of parallel instances for KV cache transfer. For
    # PyNcclConnector, this should be 2.
    kv_parallel_size: int = 1

    # The KV connector ip, used to build distributed connection
    kv_ip: str = "127.0.0.1"

    # The KV connector port, used to build distributed connection
    kv_port: int = 14579

3219
3220
3221
    # any extra config that the connector may need
    kv_connector_extra_config: dict[str, Any] = {}

3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
3236
        factors: list[Any] = []
3237
3238
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3239
3240
        return hash_str

3241
3242
    @classmethod
    def from_cli(cls, cli_value: str) -> "KVTransferConfig":
youkaichao's avatar
youkaichao committed
3243
        """Parse the CLI value for the kv cache transfer config."""
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
        return KVTransferConfig.model_validate_json(cli_value)

    def model_post_init(self, __context: Any) -> None:

        if self.kv_role is not None and self.kv_role not in [
                "kv_producer", "kv_consumer", "kv_both"
        ]:
            raise ValueError(
                f"Unsupported kv_role: {self.kv_role}. "
                f"Supported roles are `kv_producer`, `kv_consumer`, "
                f"and `kv_both`")

        if self.kv_connector is not None and self.kv_role is None:
            raise ValueError("Please specify kv_disagg_role when kv_connector "
                             "is set, supported roles are `kv_producer`, "
                             "`kv_consumer`, and `kv_both`")

    @property
    def is_kv_transfer_instance(self) -> bool:
        return self.kv_connector is not None and \
            self.kv_role in ["kv_producer", "kv_consumer", "kv_both"]

    @property
    def is_kv_producer(self) -> bool:
        return self.kv_connector is not None and \
            self.kv_role in ["kv_producer", "kv_both"]

    @property
    def is_kv_consumer(self) -> bool:
        return self.kv_connector is not None and \
            self.kv_role in ["kv_consumer", "kv_both"]

3276
3277
3278
    def get_from_extra_config(self, key, default) -> Any:
        return self.kv_connector_extra_config.get(key, default)

3279

3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
class CompilationLevel:
    # constants for the levels of the compilation process
    NO_COMPILATION = 0
    DYNAMO_AS_IS = 1
    DYNAMO_ONCE = 2
    PIECEWISE = 3


class CompilationConfig(BaseModel):
    """
    Configuration for compilation.
    It has three parts:
    - Top-level Compilation control:
        - level: the level of compilation.
            - 0: no compilation.
            - 1: dynamo as is.
            - 2: dynamo once.
            - 3: piecewise compilation.
3298
        - debug_dump_path: the path to dump the debug information.
3299
3300
3301
        - cache_dir: the directory to store the compiled graph, to
            accelerate Inductor compilation. By default, it will use
            model-related information to generate a cache directory.
3302
3303
3304
3305
3306
3307
3308
        - backend: the backend for compilation. It needs to be a string.
            - "" (empty string): use the default backend.
            - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
            - "full.module.name": a qualified name which can be used to import the backend function.
            We use string to avoid serialization issues when using compilation in a distributed setting.
            When the compilation level is 1 or 2, the backend is used for the compilation directly (it sees the whole graph).
            When the compilation level is 3, the backend is used for the piecewise compilation (it sees a part of the graph).
3309
3310
3311
3312
3313
3314
3315
3316
3317
        - custom_ops: fine-grained control over which custom ops to enable/disable.
            Use 'all' to enable all, 'none' to disable all.
            Also specify a list of custom op names to enable (prefixed with a '+'),
            or disable (prefixed with a '-').
            Examples:
                - 'all,-op1' to enable all except op1
                - 'none,+op1,+op2' to enable only op1 and op2
            By default, all custom ops are enabled when running without Inductor
                and disabled when running with Inductor (compile_level >= Inductor).
3318
        - splitting_ops: a list of ops to split the full graph into subgraphs, used in piecewise compilation.
3319
3320
3321
3322
    - CudaGraph capture:
        - use_cudagraph: whether to use cudagraph inside compilation.
            - False: cudagraph inside compilation is not used.
            - True: cudagraph inside compilation is used. It requires
3323
3324
3325
3326
                that all input buffers have fixed addresses, and all
                splitting ops write their outputs to input buffers.
            Note that this is orthogonal to the cudagraph capture logic
            outside of compilation.
3327
3328
3329
            TODO: move outside cudagraph logic into compilation.
            torch.compile will handle cudagraph capture logic in the future.
        - cudagraph_capture_sizes: sizes to capture cudagraph.
3330
            - None (default): capture sizes are inferred from vllm config.
3331
            - list[int]: capture sizes are specified as given.
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
        - cudagraph_num_of_warmups: number of warmup runs for cudagraph.
            It means the first several runs will be treated as warmup runs.
            Only after that, the execution will be recorded, and the recorded
            cudagraph will be used for subsequent runs.
        - cudagraph_copy_inputs: whether to copy input tensors for
            cudagraph. If the caller can guarantee that the same input buffers
            are always used, it can set this to False. Otherwise, it should
            set this to True, and the compiler will copy the input to an
            internally managed buffer. Default is False.
    - Inductor compilation:
        - use_inductor: whether to use inductor compilation.
            - False: inductor compilation is not used. graph runs in eager.
            - True: inductor compilation is used. one graph for symbolic shape
3345
3346
3347
3348
3349
                is compiled. In addition, compile for compile_sizes,
                using configurations in inductor_compile_config.
        - compile_sizes: sizes to compile for inductor. In addition
            to integers, it also supports "cudagraph_capture_sizes" to
            specify the sizes for cudagraph capture.
3350
3351
3352
3353
3354
3355
3356
        - inductor_compile_config: additional configurations for inductor.
            - None: use default configurations.
        - inductor_passes: additional passes for inductor. It is a dictionary
            from pass name to pass function qualified name. We use function
            name because the config uses json format. If we pass the config
            from Python, functions can also be passed directly via Python object
            constructor, e.g. `CompilationConfig(inductor_passes={"a": func})`
3357
        - custom inductor passes: see PassConfig for more details
3358

3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
    Why we have different sizes for cudagraph and inductor:
    - cudagraph: a cudagraph captured for a specific size can only be used
        for the same size. We need to capture all the sizes we want to use.
    - inductor: a graph compiled by inductor for a general shape can be used
        for different sizes. Inductor can also compile for specific sizes,
        where it can have more information to optimize the graph with fully
        static shapes. However, we find the general shape compilation is
        sufficient for most cases. It might be beneficial to compile for
        certain small batchsizes, where inductor is good at optimizing.
    """ # noqa
    level: int = 0
3370
    debug_dump_path: str = ""
3371
    cache_dir: str = ""
3372
    backend: str = ""
3373
3374
    custom_ops: list[str] = Field(default_factory=list)
    splitting_ops: list[str] = Field(default=None)  # type: ignore
3375
3376

    use_inductor: bool = True
3377
3378
3379
    compile_sizes: Optional[list[Union[int, str]]] = Field(default=None)
    inductor_compile_config: dict = Field(default_factory=dict)
    inductor_passes: dict[str, str] = Field(default_factory=dict)
3380
3381
3382

    use_cudagraph: bool = False
    cudagraph_num_of_warmups: int = 0
3383
    cudagraph_capture_sizes: Optional[list[int]] = None
3384
3385
    cudagraph_copy_inputs: bool = False

3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
    class PassConfig(BaseModel):
        """
        Configuration for custom Inductor passes.
        This is separate from general CompilationConfig so that inductor passes
        don't all have access to full configuration - that would create a cycle
        as the PassManager is set as a property of config.
        - dump_graph_stages: list of stages for which we want to dump the graph.
            Each pass defines its own stages (before, after, maybe in-between).
        - dump_graph_dir: directory to dump the graphs. Default is .
        - enable_fusion: whether to enable the custom fusion pass.
3396
3397
        - enable_noop: whether to enable the custom no-op elimination pass.
            TODO(luka) better pass enabling system.
3398
        """
3399
        dump_graph_stages: list[str] = Field(default_factory=list)
3400
3401
        dump_graph_dir: Path = Field(default=Path("."))
        enable_fusion: bool = True
3402
        enable_noop: bool = True
3403
3404
3405
3406
3407
3408
3409
3410

        def uuid(self):
            """
            Produces a hash unique to the pass configuration.
            Any new fields that affect compilation should be added to the hash.
            Do not include dump_graph_* in the hash - they don't affect
            compilation.
            """
3411
            dict_ = self.model_dump(include={"enable_fusion", "enable_noop"})
3412
            return InductorPass.hash_dict(dict_)
3413
3414

        def model_post_init(self, __context: Any) -> None:
3415
            if not self.enable_noop and self.enable_fusion:
3416
                logger.warning_once(
3417
                    "Fusion enabled but reshape elimination disabled. "
3418
3419
3420
                    "RMSNorm + quant (fp8) fusion might not work")

    pass_config: PassConfig = Field(default_factory=PassConfig)
3421
3422

    # not configurable, computed after init
3423
    max_capture_size: int = PrivateAttr
3424
    local_cache_dir: str = PrivateAttr  # local cache dir for each rank
3425
    # optimization:
3426
    # Intuitively, bs_to_padded_graph_size should be dict[int, int].
3427
    # since we know all keys are in a range [0, max_capture_size],
3428
3429
    # we can optimize it to list[int] for better lookup performance.
    bs_to_padded_graph_size: list[int] = PrivateAttr
3430

3431
3432
3433
    # keep track of enabled and disabled custom ops
    enabled_custom_ops: Counter[str] = PrivateAttr
    disabled_custom_ops: Counter[str] = PrivateAttr
3434
    traced_files: set[str] = PrivateAttr
3435
    compilation_time: float = PrivateAttr
3436

3437
3438
    # Per-model forward context
    # Map from layer name to the attention cls
3439
    static_forward_context: dict[str, Any] = PrivateAttr
3440

3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
3453
        factors: list[Any] = []
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
        factors.append(self.level)
        factors.append(self.backend)
        factors.append(self.custom_ops)
        factors.append(self.splitting_ops)
        factors.append(self.use_inductor)
        factors.append(self.inductor_compile_config)
        factors.append(self.inductor_passes)
        factors.append(self.pass_config.uuid())
        return hashlib.sha256(str(factors).encode()).hexdigest()

3464
3465
3466
3467
3468
3469
3470
3471
    def __repr__(self) -> str:
        exclude = {
            "static_forward_context",
            "enabled_custom_ops",
            "disabled_custom_ops",
            "compilation_time",
            "bs_to_padded_graph_size",
            "pass_config",
3472
            "traced_files",
3473
3474
3475
3476
3477
        }
        return self.model_dump_json(exclude=exclude, exclude_unset=True)

    __str__ = __repr__

3478
3479
3480
3481
3482
    @classmethod
    def from_cli(cls, cli_value: str) -> "CompilationConfig":
        """Parse the CLI value for the compilation config."""
        if cli_value in ["0", "1", "2", "3"]:
            return cls(level=int(cli_value))
3483
3484
3485
        # do not use `eval`, it is dangerous and can execute arbitrary code
        dict_value = ast.literal_eval(cli_value)
        return CompilationConfig.model_validate(dict_value)
3486

3487
3488
3489
3490
3491
3492
    def model_post_init(self, __context: Any) -> None:

        count_none = self.custom_ops.count("none")
        count_all = self.custom_ops.count("all")
        assert count_none + count_all <= 1, "Can only specify 'none' or 'all'"

Michael Goin's avatar
Michael Goin committed
3493
3494
3495
3496
3497
3498
3499
3500
        # TODO(zou3519/luka): There are 2 issues with auto-functionalization V2:
        # 1. A bug in PyTorch, fixed in 2.7:
        #    https://github.com/pytorch/pytorch/issues/147924
        # 2. Custom passes (fusion) rely on auto-functionalization V1 and don't
        #    work with V2. Addressing this will take extra engineering effort
        #    and it is not yet a priority. RFC here:
        #    https://github.com/vllm-project/vllm/issues/14703

3501
        if is_torch_equal_or_newer("2.6"):
Michael Goin's avatar
Michael Goin committed
3502
3503
3504
3505
            KEY = 'enable_auto_functionalized_v2'
            if KEY not in self.inductor_compile_config:
                self.inductor_compile_config[KEY] = False

3506
        if self.splitting_ops is None:
3507
            self.splitting_ops = []
3508

3509
3510
3511
        for k, v in self.inductor_passes.items():
            if not isinstance(v, str):
                assert callable(v), (
3512
3513
3514
                    f"pass {k} should be callable or a qualified name")
                self.inductor_compile_config[k] = v if isinstance(
                    v, InductorPass) else CallableInductorPass(v)
3515
3516
3517
3518
3519
3520
3521
                continue

            # resolve function from qualified name
            names = v.split(".")
            module = ".".join(names[:-1])
            func_name = names[-1]
            func = __import__(module).__dict__[func_name]
3522
3523
            self.inductor_compile_config[k] = func if isinstance(
                func, InductorPass) else CallableInductorPass(func)
3524

3525
3526
        self.enabled_custom_ops = Counter()
        self.disabled_custom_ops = Counter()
3527
        self.traced_files = set()
3528
        self.static_forward_context = {}
3529
        self.compilation_time = 0.0
3530

3531
    def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]:
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
        if self.level == CompilationLevel.NO_COMPILATION:
            raise ValueError("No compilation level is set.")

        from torch._dynamo.backends.registry import list_backends
        torch_backends = list_backends(exclude_tags=tuple())
        if self.level in [
                CompilationLevel.DYNAMO_AS_IS, CompilationLevel.DYNAMO_ONCE
        ]:
            if self.backend == "":
                return "eager"
            if self.backend in torch_backends:
                return self.backend
            return resolve_obj_by_qualname(self.backend)

        # TODO: pass user-specified backend to piecewise compilation
        # merge with the config use_inductor
        assert self.level == CompilationLevel.PIECEWISE
3549

3550
        from vllm.compilation.backends import VllmBackend
3551
        return VllmBackend(vllm_config)
3552

3553
    def init_with_cudagraph_sizes(self,
3554
                                  cudagraph_capture_sizes: list[int]) -> None:
3555
        """To complete the initialization of config,
3556
3557
        we need to know the cudagraph sizes."""

3558
        if self.cudagraph_capture_sizes is None:
3559
            self.cudagraph_capture_sizes = cudagraph_capture_sizes
3560
        else:
3561
3562
3563
            # de-duplicate the sizes provided by the config
            self.cudagraph_capture_sizes = list(
                set(self.cudagraph_capture_sizes))
3564
3565
            logger.info(("cudagraph sizes specified by model runner"
                         " %s is overridden by config %s"),
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
                        cudagraph_capture_sizes, self.cudagraph_capture_sizes)

        computed_compile_sizes = []
        if self.compile_sizes is not None:
            # de-duplicate the sizes provided by the config
            self.compile_sizes = list(set(self.compile_sizes))
            for x in self.compile_sizes:
                if isinstance(x, str):
                    assert x == "cudagraph_capture_sizes", \
                    "Unrecognized size type in compile_sizes, " \
                    f"expect 'cudagraph_capture_sizes', got {x}"
                    computed_compile_sizes.extend(self.cudagraph_capture_sizes)
                else:
                    assert isinstance(x, int)
                    computed_compile_sizes.append(x)
        self.compile_sizes = computed_compile_sizes  # type: ignore
3582

3583
        # sort to make sure cudagraph capture sizes are in descending order
3584
3585
3586
        self.cudagraph_capture_sizes.sort(reverse=True)
        self.max_capture_size = self.cudagraph_capture_sizes[
            0] if self.cudagraph_capture_sizes else 0
3587

3588
3589
3590
3591
        # pre-compute the mapping from batch size to padded graph size
        self.bs_to_padded_graph_size = [
            0 for i in range(self.max_capture_size + 1)
        ]
3592
3593
        for end, start in zip(self.cudagraph_capture_sizes,
                              self.cudagraph_capture_sizes[1:] + [0]):
3594
3595
3596
3597
3598
3599
3600
            for bs in range(start, end):
                if bs == start:
                    self.bs_to_padded_graph_size[bs] = start
                else:
                    self.bs_to_padded_graph_size[bs] = end
        self.bs_to_padded_graph_size[
            self.max_capture_size] = self.max_capture_size
3601

3602
3603
3604
3605
3606
3607
3608
3609
3610
    def set_splitting_ops_for_v1(self):
        # If default, override splitting ops for piecewise cudagraph on V1.
        # NOTE: this function needs to be called
        if not self.splitting_ops:
            self.splitting_ops = [
                "vllm.unified_attention",
                "vllm.unified_attention_with_output",
            ]

3611

3612
3613
3614
@dataclass
class VllmConfig:
    """Dataclass which contains all vllm-related configuration. This
3615
3616
3617
    simplifies passing around the distinct configurations in the codebase.
    """

3618
3619
    model_config: ModelConfig = field(default=None, init=True)  # type: ignore
    cache_config: CacheConfig = field(default=None, init=True)  # type: ignore
3620
3621
3622
3623
    parallel_config: ParallelConfig = field(default_factory=ParallelConfig,
                                            init=True)
    scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig,
                                              init=True)
3624
3625
3626
    device_config: DeviceConfig = field(default=None,
                                        init=True)  # type: ignore
    load_config: LoadConfig = field(default=None, init=True)  # type: ignore
3627
    lora_config: Optional[LoRAConfig] = None
3628
3629
    speculative_config: SpeculativeConfig = field(default=None,
                                                  init=True)  # type: ignore
3630
3631
3632
    decoding_config: Optional[DecodingConfig] = None
    observability_config: Optional[ObservabilityConfig] = None
    prompt_adapter_config: Optional[PromptAdapterConfig] = None
3633
    quant_config: Optional[QuantizationConfig] = None
3634
3635
    compilation_config: CompilationConfig = field(default=None,
                                                  init=True)  # type: ignore
3636
3637
    kv_transfer_config: KVTransferConfig = field(default=None,
                                                 init=True)  # type: ignore
3638
    # some opaque config, only used to provide additional information
3639
3640
    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
3641
3642
    additional_config: SupportsHash = field(default=None,
                                            init=True)  # type: ignore
3643
    instance_id: str = ""
3644

3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
3657
        factors: list[Any] = []
3658
3659

        # summarize vllm config
3660
        vllm_factors: list[Any] = []
3661
3662
        from vllm import __version__
        vllm_factors.append(__version__)
3663
        vllm_factors.append(envs.VLLM_USE_V1)
3664
3665
        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
3666
3667
        else:
            vllm_factors.append("None")
3668
3669
        if self.cache_config:
            vllm_factors.append(self.cache_config.compute_hash())
3670
3671
        else:
            vllm_factors.append("None")
3672
3673
        if self.parallel_config:
            vllm_factors.append(self.parallel_config.compute_hash())
3674
3675
        else:
            vllm_factors.append("None")
3676
3677
        if self.scheduler_config:
            vllm_factors.append(self.scheduler_config.compute_hash())
3678
3679
        else:
            vllm_factors.append("None")
3680
3681
        if self.device_config:
            vllm_factors.append(self.device_config.compute_hash())
3682
3683
        else:
            vllm_factors.append("None")
3684
3685
        if self.load_config:
            vllm_factors.append(self.load_config.compute_hash())
3686
3687
        else:
            vllm_factors.append("None")
3688
3689
        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
3690
3691
3692
3693
3694
            # LoRA creates static buffers based on max_num_batched_tokens.
            # The tensor sizes and strides get captured in the torch.compile
            # graph explicitly.
            vllm_factors.append(
                str(self.scheduler_config.max_num_batched_tokens))
3695
3696
        else:
            vllm_factors.append("None")
3697
3698
        if self.speculative_config:
            vllm_factors.append(self.speculative_config.compute_hash())
3699
3700
        else:
            vllm_factors.append("None")
3701
3702
        if self.decoding_config:
            vllm_factors.append(self.decoding_config.compute_hash())
3703
3704
        else:
            vllm_factors.append("None")
3705
3706
        if self.observability_config:
            vllm_factors.append(self.observability_config.compute_hash())
3707
3708
        else:
            vllm_factors.append("None")
3709
3710
        if self.prompt_adapter_config:
            vllm_factors.append(self.prompt_adapter_config.compute_hash())
3711
3712
        else:
            vllm_factors.append("None")
3713
3714
3715
3716
        if self.quant_config:
            pass  # should be captured by model_config.quantization
        if self.compilation_config:
            vllm_factors.append(self.compilation_config.compute_hash())
3717
3718
        else:
            vllm_factors.append("None")
3719
3720
        if self.kv_transfer_config:
            vllm_factors.append(self.kv_transfer_config.compute_hash())
3721
3722
3723
3724
3725
3726
        else:
            vllm_factors.append("None")
        if self.additional_config:
            vllm_factors.append(self.additional_config.compute_hash())
        else:
            vllm_factors.append("None")
3727
3728
        factors.append(vllm_factors)

3729
3730
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()[:10]
3731
3732
        return hash_str

3733
3734
3735
3736
3737
3738
    def pad_for_cudagraph(self, batch_size: int) -> int:
        # if batch_size > self.compilation_config.max_capture_size,
        # it should raise an IndexError.
        # the caller should make sure the batch_size is within the range,
        # i.e., batch_size <= self.compilation_config.max_capture_size
        return self.compilation_config.bs_to_padded_graph_size[batch_size]
3739

3740
3741
3742
3743
3744
    @staticmethod
    def _get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        """Get the quantization config."""
3745
        from vllm.platforms import current_platform
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
        if model_config.quantization is not None:
            from vllm.model_executor.model_loader.weight_utils import (
                get_quant_config)
            quant_config = get_quant_config(model_config, load_config)
            capability_tuple = current_platform.get_device_capability()

            if capability_tuple is not None:
                capability = capability_tuple.to_int()
                if capability < quant_config.get_min_capability():
                    raise ValueError(
                        f"The quantization method {model_config.quantization} "
                        "is not supported for the current GPU. Minimum "
                        f"capability: {quant_config.get_min_capability()}. "
                        f"Current capability: {capability}.")
            supported_dtypes = quant_config.get_supported_act_dtypes()
            if model_config.dtype not in supported_dtypes:
                raise ValueError(
                    f"{model_config.dtype} is not supported for quantization "
                    f"method {model_config.quantization}. Supported dtypes: "
                    f"{supported_dtypes}")
            return quant_config
        return None
3768

3769
3770
3771
3772
3773
3774
3775
3776
3777
    def with_hf_config(
        self,
        hf_config: PretrainedConfig,
        architectures: Optional[list[str]] = None,
    ) -> "VllmConfig":
        if architectures is not None:
            hf_config = copy.deepcopy(hf_config)
            hf_config.architectures = architectures

3778
3779
3780
3781
3782
        model_config = copy.deepcopy(self.model_config)
        model_config.hf_config = hf_config

        return replace(self, model_config=model_config)

3783
3784
3785
    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """
3786
3787
3788
3789
3790
3791
3792
3793
        if self.model_config is not None:
            self.model_config.verify_async_output_proc(self.parallel_config,
                                                       self.speculative_config,
                                                       self.device_config)
            self.model_config.verify_with_parallel_config(self.parallel_config)

        if self.cache_config is not None:
            self.cache_config.verify_with_parallel_config(self.parallel_config)
3794
3795

        if self.lora_config:
3796
            self.lora_config.verify_with_cache_config(self.cache_config)
3797
3798
3799
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
3800
            self.lora_config.verify_lora_support()
3801
3802
3803
        if self.prompt_adapter_config:
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)
3804
3805
3806
3807
3808

        if self.quant_config is None and \
            self.model_config is not None and self.load_config is not None:
            self.quant_config = VllmConfig._get_quantization_config(
                self.model_config, self.load_config)
3809

3810
        from vllm.platforms import current_platform
3811
3812
3813
3814
3815
        if self.scheduler_config is not None and \
            self.model_config is not None and \
            self.scheduler_config.chunked_prefill_enabled and \
            self.model_config.dtype == torch.float32 and \
            current_platform.get_device_capability() == (7, 5):
3816
            logger.warning_once(
3817
3818
3819
3820
                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
                "precision for chunked prefill triton kernels.")

3821
        if self.compilation_config is None:
3822
            self.compilation_config = CompilationConfig()
3823
3824
        if envs.VLLM_USE_V1 and self.model_config is not None and \
            not self.model_config.enforce_eager:
3825
3826
3827
3828
            # NOTE(woosuk): Currently, we use inductor because the piecewise
            # CUDA graphs do not work properly with the custom CUDA kernels.
            # FIXME(woosuk): Disable inductor to reduce the compilation time
            # and avoid any potential issues with the inductor.
3829
            # FIXME(rob): Add function to set all of these.
3830
3831
3832
            self.compilation_config.custom_ops = ["none"]
            self.compilation_config.use_cudagraph = True
            self.compilation_config.use_inductor = True
3833
            self.compilation_config.cudagraph_num_of_warmups = 1
3834
            self.compilation_config.pass_config.enable_fusion = False
3835
            self.compilation_config.pass_config.enable_noop = False
3836
            self.compilation_config.level = CompilationLevel.PIECEWISE
3837
            self.compilation_config.set_splitting_ops_for_v1()
3838

3839
        self._set_cudagraph_sizes()
3840

3841
3842
        if self.cache_config is not None and \
            self.cache_config.cpu_offload_gb > 0 and \
3843
3844
            self.compilation_config.level != CompilationLevel.NO_COMPILATION \
                and not envs.VLLM_USE_V1:
3845
            logger.warning(
3846
                "CPU offload is not supported with `torch.compile` in v0 yet."
3847
3848
3849
                " Disabling `torch.compile`.")
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

3850
3851
3852
3853
3854
3855
        if ((not envs.VLLM_USE_V1) and self.lora_config is not None
                and self.compilation_config.level
                != CompilationLevel.NO_COMPILATION):
            logger.warning(
                "LoRA for V0 is not supported with `torch.compile` yet. "
                "Disabling `torch.compile`.")
3856
3857
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

3858

3859
        if self.model_config and self.model_config.use_mla and \
3860
            not (current_platform.is_cuda() or current_platform.is_rocm()):
3861
            logger.info(
3862
                "MLA is enabled on a non-GPU platform; forcing chunked "
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
                "prefill and prefix caching to be disabled.")
            self.scheduler_config.enable_chunked_prefill = False
            self.scheduler_config.chunked_prefill_enabled = False
            self.scheduler_config.max_num_batched_tokens = max(
                self.scheduler_config.max_model_len,
                _DEFAULT_MAX_NUM_BATCHED_TOKENS)

            if self.cache_config is not None:
                self.cache_config.enable_prefix_caching = False

3873
3874
        current_platform.check_and_update_config(self)

3875
3876
3877
        if not self.instance_id:
            self.instance_id = random_uuid()[:5]

3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
    def _set_cudagraph_sizes(self):
        """
        cudagraph batchsize padding logic:

        `[1, 2, 4] + [8 * i for i in range(1, 1025)]` is a list of all possible
        batch sizes that cudagraph will capture.

        Depending on the engine's configuration of `max_num_seqs`, the
        candidate batch sizes to capture cudagraph will shrink to the subset
        which just cover the range of `[1, max_num_seqs]`. In the common case,
        `max_num_seqs` is 256, and the cudagraph batch sizes will be
        `[1, 2, 4, 8, 16, 24, 32, 40, ..., 256]`.

        However, if users specify the cudagraph capture sizes through
        compilation config, we will use the specified sizes instead.

3894
3895
        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
        will be the final sizes to capture cudagraph (in descending order).
3896
3897

        During runtime, if batchsize is larger than
3898
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
3899
3900
        no cudagraph will be used.
        If the batch size is no larger than
3901
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
3935
3936
3937
        we can quickly find the padded graph size for a given batch size by
        looking up `vllm_config.compilation_config.bs_to_padded_graph_size`.
        """

        # calculate the default `batch_size_capture_list`
        if not envs.VLLM_USE_V1:
            batch_size_capture_list = []
            max_batchsize_to_capture = 0
            if self.scheduler_config is not None and \
                self.model_config is not None and \
                    not self.model_config.enforce_eager:

                possible_sizes = [1, 2, 4] + [8 * i for i in range(1, 1025)]
                # find the minimum size that is larger than max_num_seqs,
                # which then becomes the max_batchsize_to_capture
                larger_sizes = [
                    x for x in possible_sizes
                    if x >= self.scheduler_config.max_num_seqs
                ]
                if larger_sizes:
                    max_batchsize_to_capture = larger_sizes[0]
                else:
                    max_batchsize_to_capture = possible_sizes[-1]

                # filter out the sizes that are
                # larger than max_batchsize_to_capture
                batch_size_capture_list = [
                    size for size in possible_sizes
                    if size <= max_batchsize_to_capture
                ]
        else:
            batch_size_capture_list = []
            if self.model_config is not None and \
                not self.model_config.enforce_eager:
                batch_size_capture_list = [1, 2, 4
                                           ] + [i for i in range(8, 513, 8)]
3938
3939
3940
3941
3942
                max_num_tokens = self.scheduler_config.max_num_batched_tokens
                batch_size_capture_list = [
                    size for size in batch_size_capture_list
                    if size <= max_num_tokens
                ]
3943
3944
3945
3946

        self.compilation_config.init_with_cudagraph_sizes(
            batch_size_capture_list)

3947
    def __str__(self):
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
        return (
            f"model={self.model_config.model!r},"
            f" speculative_config={self.speculative_config!r},"
            f" tokenizer={self.model_config.tokenizer!r}, "
            f"skip_tokenizer_init={self.model_config.skip_tokenizer_init},"
            f" tokenizer_mode={self.model_config.tokenizer_mode}, "
            f"revision={self.model_config.revision}, "
            f"override_neuron_config={self.model_config.override_neuron_config},"
            f" tokenizer_revision={self.model_config.tokenizer_revision}, "
            f"trust_remote_code={self.model_config.trust_remote_code}, "
            f"dtype={self.model_config.dtype}, "
            f"max_seq_len={self.model_config.max_model_len},"
            f" download_dir={self.load_config.download_dir!r}, "
            f"load_format={self.load_config.load_format}, "
            f"tensor_parallel_size={self.parallel_config.tensor_parallel_size},"
            f" pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, "  # noqa
            f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, "  # noqa
            f"quantization={self.model_config.quantization}, "
            f"enforce_eager={self.model_config.enforce_eager}, "
            f"kv_cache_dtype={self.cache_config.cache_dtype}, "
            f" device_config={self.device_config.device}, "
            f"decoding_config={self.decoding_config!r}, "
            f"observability_config={self.observability_config!r}, "
            f"seed={self.model_config.seed}, "
            f"served_model_name={self.model_config.served_model_name}, "
            f"num_scheduler_steps={self.scheduler_config.num_scheduler_steps}, "
            f"multi_step_stream_outputs={self.scheduler_config.multi_step_stream_outputs}, "  # noqa
            f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
            f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, "  # noqa
            f"use_async_output_proc={self.model_config.use_async_output_proc}, "
3978
            f"disable_mm_preprocessor_cache={self.model_config.disable_mm_preprocessor_cache!r}, "  # noqa
3979
            f"mm_processor_kwargs={self.model_config.mm_processor_kwargs}, "
3980
3981
            f"pooler_config={self.model_config.pooler_config!r}, "
            f"compilation_config={self.compilation_config!r}")
3982
3983
3984
3985
3986
3987


_current_vllm_config: Optional[VllmConfig] = None


@contextmanager
3988
def set_current_vllm_config(vllm_config: VllmConfig, check_compile=False):
3989
    """
3990
    Temporarily set the current vLLM config.
3991
    Used during model initialization.
3992
    We save the current vLLM config in a global variable,
3993
    so that all modules can access it, e.g. custom ops
3994
    can access the vLLM config to determine how to dispatch.
3995
3996
3997
3998
3999
4000
4001
4002
    """
    global _current_vllm_config
    old_vllm_config = _current_vllm_config
    from vllm.compilation.counter import compilation_counter
    num_models_seen = compilation_counter.num_models_seen
    try:
        _current_vllm_config = vllm_config
        yield
4003
4004
4005
    except Exception:
        raise
    else:
4006
4007
4008
4009
        logger.debug("enabled custom ops: %s",
                     vllm_config.compilation_config.enabled_custom_ops)
        logger.debug("disabled custom ops: %s",
                     vllm_config.compilation_config.disabled_custom_ops)
4010
4011
        if check_compile and \
            vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \
4012
4013
4014
4015
4016
4017
4018
4019
4020
            and compilation_counter.num_models_seen == num_models_seen:
            # If the model supports compilation,
            # compilation_counter.num_models_seen should be increased
            # by at least 1.
            # If it is not increased, it means the model does not support
            # compilation (does not have @support_torch_compile decorator).
            logger.warning(
                "`torch.compile` is turned on, but the model %s"
                " does not support it. Please open an issue on GitHub"
4021
                " if you want it to be supported.",
4022
                vllm_config.model_config.model)
4023
    finally:
4024
4025
4026
4027
4028
4029
4030
4031
        _current_vllm_config = old_vllm_config


def get_current_vllm_config() -> VllmConfig:
    if _current_vllm_config is None:
        # in ci, usually when we test custom ops/modules directly,
        # we don't set the vllm config. In that case, we set a default
        # config.
4032
        logger.warning("Current vLLM config is not set.")
4033
4034
4035
        from vllm.config import VllmConfig
        return VllmConfig()
    return _current_vllm_config