__init__.py 166 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
# ruff: noqa: F401
5
import ast
6
import copy
7
import enum
8
import hashlib
9
import inspect
10
import json
11
import textwrap
Robert Shaw's avatar
Robert Shaw committed
12
import uuid
13
import warnings
14
from collections.abc import Mapping
15
from contextlib import contextmanager
16
from dataclasses import MISSING, Field, field, fields, is_dataclass, replace
17
from functools import cached_property, lru_cache
18
from importlib.util import find_spec
19
from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Literal, Optional,
20
                    Protocol, TypeVar, Union, cast, get_args)
21

22
import regex as re
23
import torch
24
from pydantic import (ConfigDict, SkipValidation, field_validator,
25
26
                      model_validator)
from pydantic.dataclasses import dataclass
27
from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
28
from typing_extensions import Self, assert_never, runtime_checkable
29

30
import vllm.envs as envs
31
from vllm import version
32
33
from vllm.config.cache import (BlockSize, CacheConfig, CacheDType,
                               PrefixCachingHashAlgo)
34
35
from vllm.config.compilation import (CompilationConfig, CompilationLevel,
                                     PassConfig)
36
from vllm.config.parallel import DistributedExecutorBackend, ParallelConfig
37
from vllm.config.scheduler import SchedulerConfig, SchedulerPolicy
38
from vllm.config.utils import ConfigType, config
Woosuk Kwon's avatar
Woosuk Kwon committed
39
from vllm.logger import init_logger
40
from vllm.model_executor.layers.quantization import QuantizationMethods
41
from vllm.platforms import current_platform
42
43
44
from vllm.transformers_utils.config import (
    ConfigFormat, get_config, get_hf_image_processor_config,
    get_hf_text_config, get_pooling_config,
45
    get_sentence_transformer_tokenizer_config, is_encoder_decoder,
46
47
48
    is_interleaved, maybe_override_with_speculators_target_model,
    try_get_generation_config, try_get_safetensors_metadata,
    try_get_tokenizer_config, uses_mrope)
49
from vllm.transformers_utils.s3_utils import S3Model
50
from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
51
from vllm.utils import (DEFAULT_MAX_NUM_BATCHED_TOKENS, LayerBlockType,
52
                        LazyLoader, common_broadcastable_dtype, random_uuid)
53

54
if TYPE_CHECKING:
55
    from _typeshed import DataclassInstance
56
    from transformers.configuration_utils import PretrainedConfig
57

58
59
60
    import vllm.model_executor.layers.quantization as me_quant
    import vllm.model_executor.models as me_models
    from vllm.model_executor.layers.quantization import QuantizationMethods
61
62
    from vllm.model_executor.layers.quantization.base_config import (
        QuantizationConfig)
63
    from vllm.model_executor.model_loader import LoadFormats
64
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
65

66
    HfOverrides = Union[dict, Callable[[type], type]]
67
else:
68
    DataclassInstance = Any
69
    PretrainedConfig = Any
70
    QuantizationConfig = Any
71
    QuantizationMethods = Any
72
    BaseModelLoader = Any
73
    LoadFormats = Any
74
    TensorizerConfig = Any
75
76
77
78
79
80
    HfOverrides = Union[dict[str, Any], Callable[[type], type]]

    me_quant = LazyLoader("model_executor", globals(),
                          "vllm.model_executor.layers.quantization")
    me_models = LazyLoader("model_executor", globals(),
                           "vllm.model_executor.models")
81

82
logger = init_logger(__name__)
83
DataclassInstanceT = TypeVar("DataclassInstanceT", bound=DataclassInstance)
84

85
TaskOption = Literal["auto", "generate", "embedding", "embed", "classify",
86
                     "score", "reward", "transcription", "draft"]
87

88
_ResolvedTask = Literal["generate", "transcription", "encode", "embed",
89
                        "classify", "reward", "draft"]
90

91
RunnerOption = Literal["auto", "generate", "pooling", "draft"]
92

93
RunnerType = Literal["generate", "pooling", "draft"]
94

95
96
97
98
99
ConvertOption = Literal["auto", "none", "embed", "classify", "reward"]

ConvertType = Literal["none", "embed", "classify", "reward"]

_RUNNER_TASKS: dict[RunnerType, list[TaskOption]] = {
100
    "generate": ["generate", "transcription"],
101
102
103
104
105
106
107
    "pooling": ["embedding", "embed", "classify", "score", "reward"],
    "draft": ["draft"],
}

_RUNNER_CONVERTS: dict[RunnerType, list[ConvertType]] = {
    "generate": [],
    "pooling": ["embed", "classify", "reward"],
108
    "draft": [],
109
}
110

111
112
113
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
# Some model suffixes are based on auto classes from Transformers:
# https://huggingface.co/docs/transformers/en/model_doc/auto
# NOTE: Items higher on this list priority over lower ones
_SUFFIX_TO_DEFAULTS: list[tuple[str, tuple[RunnerType, ConvertType]]] = [
    ("ForCausalLM", ("generate", "none")),
    ("ForConditionalGeneration", ("generate", "none")),
    ("ChatModel", ("generate", "none")),
    ("LMHeadModel", ("generate", "none")),
    ("ForTextEncoding", ("pooling", "embed")),
    ("EmbeddingModel", ("pooling", "embed")),
    ("ForSequenceClassification", ("pooling", "classify")),
    ("ForAudioClassification", ("pooling", "classify")),
    ("ForImageClassification", ("pooling", "classify")),
    ("ForVideoClassification", ("pooling", "classify")),
    ("ClassificationModel", ("pooling", "classify")),
    ("ForRewardModeling", ("pooling", "reward")),
    ("RewardModel", ("pooling", "reward")),
    # Let other `*Model`s take priority
    ("Model", ("pooling", "embed")),
]


def iter_architecture_defaults():
    yield from _SUFFIX_TO_DEFAULTS


def try_match_architecture_defaults(
    architecture: str,
    *,
    runner_type: Optional[RunnerType] = None,
    convert_type: Optional[ConvertType] = None,
) -> Optional[tuple[str, tuple[RunnerType, ConvertType]]]:
    for suffix, (default_runner_type,
                 default_convert_type) in iter_architecture_defaults():
        if ((runner_type is None or runner_type == default_runner_type) and
            (convert_type is None or convert_type == default_convert_type)
                and architecture.endswith(suffix)):
            return suffix, (default_runner_type, default_convert_type)

    return None

152

153
@runtime_checkable
154
155
156
157
158
159
class SupportsHash(Protocol):

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


160
161
class SupportsMetricsInfo(Protocol):

162
    def metrics_info(self) -> dict[str, str]:
163
164
165
        ...


166
167
168
169
170
171
class ModelImpl(str, enum.Enum):
    AUTO = "auto"
    VLLM = "vllm"
    TRANSFORMERS = "transformers"


172
173
174
175
176
177
178
179
180
181
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
182

183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
        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


224
def get_field(cls: ConfigType, name: str) -> Field:
225
226
227
228
229
230
231
    """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__}.")
232
    named_field: Field = cls_fields[name]
233
234
235
236
237
238
239
240
    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.")


241
242
243
244
def is_init_field(cls: ConfigType, name: str) -> bool:
    return next(f for f in fields(cls) if f.name == name).init


245
246
TokenizerMode = Literal["auto", "slow", "mistral", "custom"]
ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
247
248
LogprobsMode = Literal["raw_logprobs", "raw_logits", "processed_logprobs",
                       "processed_logits"]
249
250
251


@config
252
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
253
class ModelConfig:
254
255
    """Configuration for the model."""

256
    model: str = "Qwen/Qwen3-0.6B"
257
258
259
    """Name or path of the Hugging Face model to use. It is also used as the
    content for `model_name` tag in metrics output when `served_model_name` is
    not specified."""
260
261
262
    runner: RunnerOption = "auto"
    """The type of model runner to use. Each vLLM instance only supports one
    model runner, even if the same model can be used for multiple types."""
263
264
265
266
267
268
269
270
271
272
    convert: ConvertOption = "auto"
    """Convert the model using adapters defined in
    [vllm.model_executor.models.adapters][]. The most common use case is to
    adapt a text generation model to be used for pooling tasks."""
    task: Optional[TaskOption] = None
    """[DEPRECATED] The task to use the model for. If the model supports more
    than one model runner, this is used to select which model runner to run.

    Note that the model may support other tasks using the same model runner.
    """
273
    tokenizer: SkipValidation[str] = None  # type: ignore
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
    """Name or path of the Hugging Face tokenizer to use. If unspecified, model
    name or path will be used."""
    tokenizer_mode: TokenizerMode = "auto"
    """Tokenizer mode:\n
    - "auto" will use the fast tokenizer if available.\n
    - "slow" will always use the slow tokenizer.\n
    - "mistral" will always use the tokenizer from `mistral_common`.\n
    - "custom" will use --tokenizer to select the preregistered tokenizer."""
    trust_remote_code: bool = False
    """Trust remote code (e.g., from HuggingFace) when downloading the model
    and tokenizer."""
    dtype: Union[ModelDType, torch.dtype] = "auto"
    """Data type for model weights and activations:\n
    - "auto" will use FP16 precision for FP32 and FP16 models, and BF16
    precision for BF16 models.\n
    - "half" for FP16. Recommended for AWQ quantization.\n
    - "float16" is the same as "half".\n
    - "bfloat16" for a balance between precision and range.\n
    - "float" is shorthand for FP32 precision.\n
    - "float32" for FP32 precision."""
    seed: Optional[int] = None
295
296
    """Random seed for reproducibility. Initialized to None in V0, but
    initialized to 0 in V1."""
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
    hf_config_path: Optional[str] = None
    """Name or path of the Hugging Face config to use. If unspecified, model
    name or path will be used."""
    allowed_local_media_path: str = ""
    """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."""
    revision: Optional[str] = None
    """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."""
    code_revision: Optional[str] = None
    """The specific revision to use for the model code on the Hugging Face Hub.
    It can be a branch name, a tag name, or a commit id. If unspecified, will
    use the default version."""
    rope_scaling: dict[str, Any] = field(default_factory=dict)
312
    """RoPE scaling configuration. For example,
313
314
315
316
317
318
319
320
    `{"rope_type":"dynamic","factor":2.0}`."""
    rope_theta: Optional[float] = None
    """RoPE theta. Use with `rope_scaling`. In some cases, changing the RoPE
    theta improves the performance of the scaled model."""
    tokenizer_revision: Optional[str] = None
    """The specific revision to use for the tokenizer on the Hugging Face Hub.
    It can be a branch name, a tag name, or a commit id. If unspecified, will
    use the default version."""
321
    max_model_len: SkipValidation[int] = None  # type: ignore
322
323
    """Model context length (prompt and output). If unspecified, will be
    automatically derived from the model config.
324

325
326
327
328
329
330
    When passing via `--max-model-len`, supports k/m/g/K/M/G in human-readable
    format. Examples:\n
    - 1k -> 1000\n
    - 1K -> 1024\n
    - 25.6k -> 25,600"""
    spec_target_max_model_len: Optional[int] = None
omahs's avatar
omahs committed
331
    """Specify the maximum length for spec decoding draft models."""
332
    quantization: SkipValidation[Optional[QuantizationMethods]] = None
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
    """Method used to quantize the weights. If `None`, we first check the
    `quantization_config` attribute in the model config file. If that is
    `None`, we assume the model weights are not quantized and use `dtype` to
    determine the data type of the weights."""
    enforce_eager: bool = False
    """Whether to always use eager-mode PyTorch. 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 for maximal performance and
    flexibility."""
    max_seq_len_to_capture: int = 8192
    """Maximum sequence len covered by CUDA graphs. When a sequence has context
    length larger than this, we fall back 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."""
    max_logprobs: int = 20
    """Maximum number of log probabilities to return when `logprobs` is
    specified in `SamplingParams`. The default value comes the default for the
350
351
    OpenAI Chat Completions API. -1 means no cap, i.e. all (output_length *
    vocab_size) logprobs are allowed to be returned and it may cause OOM."""
352
353
354
355
356
357
358
    logprobs_mode: LogprobsMode = "raw_logprobs"
    """Indicates the content returned in the logprobs and prompt_logprobs.
    Supported mode:
    1) raw_logprobs, 2) processed_logprobs, 3) raw_logits, 4) processed_logits.
    Raw means the values before applying logit processors, like bad words.
    Processed means the values after applying such processors.
    """
359
360
361
362
363
364
365
366
367
368
369
370
371
372
    disable_sliding_window: bool = False
    """Whether to disable sliding window. If True, we will disable the sliding
    window functionality of the model, capping to sliding window size. If the
    model does not support sliding window, this argument is ignored."""
    disable_cascade_attn: bool = False
    """Disable cascade attention for V1. While cascade attention does not
    change the mathematical correctness, disabling it could be useful for
    preventing potential numerical issues. Note that even if this is set to
    False, cascade attention will be only used when the heuristic tells that
    it's beneficial."""
    skip_tokenizer_init: bool = False
    """Skip initialization of tokenizer and detokenizer. Expects valid
    `prompt_token_ids` and `None` for prompt from the input. The generated
    output will contain token ids."""
373
374
375
376
    enable_prompt_embeds: bool = False
    """If `True`, enables passing text embeddings as inputs via the
    `prompt_embeds` key. Note that enabling this will double the time required
    for graph compilation."""
377
378
379
380
381
382
383
384
385
386
387
    served_model_name: Optional[Union[str, list[str]]] = None
    """The model name(s) used in the API. If multiple names are provided, the
    server will respond to any of the provided names. The model name in the
    model field of a response will be the first name in this list. If not
    specified, the model name will be the same as the `--model` argument. Noted
    that this name(s) will also be used in `model_name` tag content of
    prometheus metrics, if multiple names provided, metrics tag will take the
    first one."""
    limit_mm_per_prompt: dict[str, int] = field(default_factory=dict)
    """Maximum number of data items per modality per prompt. Only applicable
    for multimodal models."""
388
    interleave_mm_strings: bool = False
389
    """Enable fully interleaved support for multimodal prompts, while using
390
    --chat-template-content-format=string. Defaults to False."""
391
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
392
393
    """Additional args passed to process media inputs, keyed by modalities.
    For example, to set num_frames for video, set
394
    `--media-io-kwargs '{"video": {"num_frames": 40} }'` """
395
396
397
398
399
400
401
402
403
404
405
406
407
408
    use_async_output_proc: bool = True
    """Whether to use async output processor."""
    config_format: Union[str, ConfigFormat] = ConfigFormat.AUTO.value
    """The format of the model config to load:\n
    - "auto" will try to load the config in hf format if available else it
    will try to load in mistral format.\n
    - "hf" will load the config in hf format.\n
    - "mistral" will load the config in mistral format."""
    hf_token: Optional[Union[bool, str]] = None
    """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`)."""
    hf_overrides: HfOverrides = field(default_factory=dict)
    """If a dictionary, contains arguments to be forwarded to the Hugging Face
409
    config. If a callable, it is called to update the HuggingFace config."""
410
411
412
413
414
    mm_processor_kwargs: Optional[dict[str, Any]] = None
    """Arguments to be forwarded to the model's processor for multi-modal data,
    e.g., image processor. Overrides for the multi-modal processor obtained
    from `AutoProcessor.from_pretrained`. The available overrides depend on the
    model that is being run. For example, for Phi-3-Vision: `{"num_crops": 4}`.
415
    """
416
417
418
419
420
421
422
423
424
    mm_processor_cache_gb: int = 4
    """The size (in GiB) of the multi-modal processor cache, which is used to
    avoid re-processing past multi-modal inputs.

    This cache is duplicated for each API process and engine core process,
    resulting in a total memory usage of
    `mm_processor_cache_gb * (api_server_count + data_parallel_size)`.

    Set to `0` to disable this cache completely (not recommended)."""
425
426
427
428
    override_neuron_config: dict[str, Any] = field(default_factory=dict)
    """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
429
    arguments. e.g. `{"cast_logits_dtype": "bfloat16"}`."""
430
431
432
433
434
435
    pooler_config: Optional["PoolerConfig"] = field(init=False)
    """Pooler config which controls the behaviour of output pooling in pooling
    models."""
    override_pooler_config: Optional[Union[dict, "PoolerConfig"]] = None
    """Initialize non-default pooling config or override default pooling config
    for the pooling model. e.g. `{"pooling_type": "mean", "normalize": false}`.
436
    """
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
    logits_processor_pattern: Optional[str] = None
    """Optional regex pattern specifying valid logits processor qualified names
    that can be passed with the `logits_processors` extra completion argument.
    Defaults to `None`, which allows no processors."""
    generation_config: str = "auto"
    """The folder path to the generation config. Defaults to `"auto"`, the
    generation config will be loaded from model path. If set to `"vllm"`, no
    generation config is loaded, vLLM defaults will be used. If set to a folder
    path, the generation config will be loaded from the specified folder path.
    If `max_new_tokens` is specified in generation config, then it sets a
    server-wide limit on the number of output tokens for all requests."""
    override_generation_config: dict[str, Any] = field(default_factory=dict)
    """Overrides or sets generation config. e.g. `{"temperature": 0.5}`. If
    used with `--generation-config auto`, the override parameters will be
    merged with the default config from the model. If used with
452
    `--generation-config vllm`, only the override parameters are used."""
453
454
455
456
457
458
459
460
461
    enable_sleep_mode: bool = False
    """Enable sleep mode for the engine (only cuda platform is supported)."""
    model_impl: Union[str, ModelImpl] = ModelImpl.AUTO.value
    """Which implementation of the model to use:\n
    - "auto" will try to use the vLLM implementation, if it exists, and fall
    back to the Transformers implementation if no vLLM implementation is
    available.\n
    - "vllm" will use the vLLM model implementation.\n
    - "transformers" will use the Transformers model implementation."""
462
463
    override_attention_dtype: Optional[str] = None
    """Override dtype for attention"""
464

465
466
467
468
469
470
471
472
473
474
475
476
    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.
        """
477
        factors: list[Any] = []
478
479
480
481
482
        factors.append(self.model)
        factors.append(self.dtype)
        factors.append(self.quantization)
        factors.append(self.revision)
        factors.append(self.code_revision)
483
484
485
        factors.append(self.max_model_len)
        factors.append(self.max_logprobs)
        factors.append(self.disable_sliding_window)
486
        factors.append(self.trust_remote_code)
487
488
489
        factors.append(self.generation_config)
        factors.append(self.model_impl)
        factors.append(self.override_generation_config)
490
491
        factors.append(self.rope_scaling)
        factors.append(self.rope_theta)
492
493
        # hf_config can control how the model looks!
        factors.append(self.hf_config.to_json_string())
494
495
        str_factors = str(factors)
        assert_hashable(str_factors)
496
497
        return hashlib.sha256(str(factors).encode()).hexdigest()

498
    def __post_init__(self) -> None:
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
        # Set the default seed to 0 in V1.
        # NOTE(woosuk): In V0, we set the default seed to None because the
        # driver worker shares the same process as the user process, and thus
        # setting a seed affects the user process as well.
        # In V1, we use separate processes for workers (unless
        # VLLM_ENABLE_V1_MULTIPROCESSING=0), so setting a seed here
        # doesn't affect the user process. However, without a consistent seed,
        # different tensor parallel workers would sample different tokens,
        # leading to inconsistent results.
        if envs.VLLM_USE_V1 and self.seed is None:
            self.seed = 0
            if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
                logger.warning(
                    "The global random seed is set to %d. Since "
                    "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
                    "affect the random state of the Python process that "
                    "launched vLLM.", self.seed)

517
518
519
520
521
522
523
524
525
        if self.runner != "draft":
            # If we're not running the draft model, check for speculators config
            # If speculators config, set model / tokenizer to be target model
            self.model, self.tokenizer = maybe_override_with_speculators_target_model(  # noqa: E501
                model=self.model,
                tokenizer=self.tokenizer,
                revision=self.revision,
                trust_remote_code=self.trust_remote_code)

526
527
528
        # Keep set served_model_name before maybe_model_redirect(self.model)
        self.served_model_name = get_served_model_name(self.model,
                                                       self.served_model_name)
529
530
531
532
533
534
535
536
537
538
539
540
        self.model = maybe_model_redirect(self.model)
        # The tokenizer is consistent with the model by default.
        if self.tokenizer is None:
            self.tokenizer = self.model
        if self.tokenizer_revision is None:
            self.tokenizer_revision = self.revision
        self.tokenizer = maybe_model_redirect(self.tokenizer)

        if isinstance(self.hf_config_path, str):
            self.hf_config_path = maybe_model_redirect(self.hf_config_path)

        if callable(self.hf_overrides):
541
            hf_overrides_kw = {}
542
            hf_overrides_fn = self.hf_overrides
543
        else:
544
            hf_overrides_kw = self.hf_overrides
545
            hf_overrides_fn = None
546

547
548
        if self.rope_scaling:
            hf_override: dict[str, Any] = {"rope_scaling": self.rope_scaling}
549
            hf_overrides_kw.update(hf_override)
550
            hf_overrides_str = json.dumps(hf_overrides_kw)
551
552
553
            msg = (
                "`--rope-scaling` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
554
            warnings.warn(DeprecationWarning(msg), stacklevel=2)
555
556
        if self.rope_theta is not None:
            hf_override = {"rope_theta": self.rope_theta}
557
            hf_overrides_kw.update(hf_override)
558
            hf_overrides_str = json.dumps(hf_overrides_kw)
559
560
561
            msg = (
                "`--rope-theta` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
562
563
            warnings.warn(DeprecationWarning(msg), stacklevel=2)

564
        self.maybe_pull_model_tokenizer_for_s3(self.model, self.tokenizer)
565

566
567
568
569
        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 "
570
571
                "module was not found. See "
                "https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile "  # noqa: E501
572
573
                "for instructions on how to install it.")

574
575
        from vllm.platforms import current_platform

576
577
578
579
580
581
        if (self.override_attention_dtype is not None
                and not current_platform.is_rocm()):
            warnings.warn(
                "override-attention-dtype is set but not using ROCm platform",
                stacklevel=2)

582
583
584
585
        if (self.enable_sleep_mode
                and not current_platform.is_sleep_mode_available()):
            raise ValueError(
                "Sleep mode is not supported on current platform.")
586

587
588
589
        if isinstance(self.config_format, str):
            self.config_format = ConfigFormat(self.config_format)

590
        hf_config = get_config(self.hf_config_path or self.model,
591
592
593
594
595
596
                               self.trust_remote_code,
                               self.revision,
                               self.code_revision,
                               self.config_format,
                               hf_overrides_kw=hf_overrides_kw,
                               hf_overrides_fn=hf_overrides_fn)
597

598
        self.hf_config = hf_config
599
        self.hf_text_config = get_hf_text_config(self.hf_config)
600
601
        self.attention_chunk_size = getattr(self.hf_text_config,
                                            "attention_chunk_size", None)
602
        self.encoder_config = self._get_encoder_config()
603
        self.hf_image_processor_config = get_hf_image_processor_config(
604
            self.model, hf_token=self.hf_token, revision=self.revision)
605

606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
        architectures = self.architectures
        registry = self.registry
        is_generative_model = registry.is_text_generation_model(
            architectures, self)
        is_pooling_model = registry.is_pooling_model(architectures, self)

        def _task_to_convert(task: TaskOption) -> ConvertType:
            if task == "embedding" or task == "embed":
                return "embed"
            if task == "classify":
                return "classify"
            if task == "reward":
                return "reward"
            if task == "score":
                new_task = self._get_default_pooling_task(architectures)
                return "classify" if new_task == "classify" else "embed"

            return "none"

        if self.task is not None:
            runner: RunnerOption = "auto"
            convert: ConvertOption = "auto"
            msg_prefix = ("The 'task' option has been deprecated and will be "
                          "removed in v0.13.0 or v1.0, whichever comes first.")
            msg_hint = "Please remove this option."

            is_generative_task = self.task in _RUNNER_TASKS["generate"]
            is_pooling_task = self.task in _RUNNER_TASKS["pooling"]

            if is_generative_model and is_pooling_model:
                if is_generative_task:
                    runner = "generate"
                    convert = "auto"
                    msg_hint = ("Please replace this option with `--runner "
                                "generate` to continue using this model "
                                "as a generative model.")
                elif is_pooling_task:
                    runner = "pooling"
                    convert = "auto"
                    msg_hint = ("Please replace this option with `--runner "
                                "pooling` to continue using this model "
                                "as a pooling model.")
                else:  # task == "auto"
                    pass
            elif is_generative_model or is_pooling_model:
                if is_generative_task:
                    runner = "generate"
                    convert = "auto"
                    msg_hint = "Please remove this option"
                elif is_pooling_task:
                    runner = "pooling"
                    convert = _task_to_convert(self.task)
                    msg_hint = ("Please replace this option with `--convert "
                                f"{convert}` to continue using this model "
                                "as a pooling model.")
                else:  # task == "auto"
                    pass
663
            else:
664
665
666
667
668
669
670
671
                raise AssertionError("The model should be a generative or "
                                     "pooling model when task is set to "
                                     f"{self.task!r}.")

            self.runner = runner
            self.convert = convert

            msg = f"{msg_prefix} {msg_hint}"
672
673
            warnings.warn(msg, DeprecationWarning, stacklevel=2)

674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
        self.runner_type = self._get_runner_type(architectures, self.runner)
        self.convert_type = self._get_convert_type(architectures,
                                                   self.runner_type,
                                                   self.convert)

        if self.runner_type == "generate" and not is_generative_model:
            generate_converts = _RUNNER_CONVERTS["generate"]
            if self.convert_type not in generate_converts:
                # Currently we don't have any converters for generative models
                raise ValueError(
                    "This model does not support `--runner generate`.")
        if self.runner_type == "pooling" and not is_pooling_model:
            pooling_converts = _RUNNER_CONVERTS["pooling"]
            if self.convert_type not in pooling_converts:
                convert_option = "<" + "|".join(pooling_converts) + ">"
                raise ValueError(
                    "This model does not support `--runner pooling`. "
                    f"You can pass `--convert {convert_option} to adapt "
                    "it into a pooling model.")
693

694
695
696
697
698
699
        self.supported_tasks = self._get_supported_tasks(
            architectures, self.runner_type, self.convert_type)

        # Note: Initialize these attributes early because transformers fallback
        # may fail to load dynamic modules in child processes
        model_info, arch = registry.inspect_model_cls(architectures, self)
700
701
        self._model_info = model_info
        self._architecture = arch
702
        logger.info("Resolved architecture: %s", arch)
703
704
705
706
707
708
709
710
711
712

        self.pooler_config = self._init_pooler_config()

        self.dtype = _get_and_verify_dtype(
            self.model,
            self.hf_config,
            self.dtype,
            is_pooling_model=self.runner_type == "pooling",
            revision=self.revision,
        )
713

714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
        # Interleaved attention is not supported by some backends in V0
        if (not self.disable_sliding_window
                and is_interleaved(self.hf_text_config)
                and not envs.VLLM_USE_V1
                and (backend := envs.VLLM_ATTENTION_BACKEND)
                in ("XFORMERS", "FLASHINFER")):
            logger.warning_once(
                "%s has interleaved attention, which is currently not "
                "supported by the %s backend. Disabling sliding window and "
                "capping the max length to the sliding window size (%d).",
                self.hf_text_config.model_type,
                backend,
                self.hf_text_config.sliding_window,
            )
            self.disable_sliding_window = True
Woosuk Kwon's avatar
Woosuk Kwon committed
729

730
        self.original_max_model_len = self.max_model_len
731
        self.max_model_len = self.get_and_verify_max_len(self.max_model_len)
732
        self.multimodal_config = self._init_multimodal_config()
733

734
735
736
737
738
        if self.disable_sliding_window:
            # Set after get_and_verify_max_len to ensure that max_model_len
            # can be correctly capped to sliding window size
            self.hf_text_config.sliding_window = None

739
740
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
741

742
743
744
        if (not current_platform.is_neuron() and self.override_neuron_config):
            raise ValueError(
                "`override_neuron_config` is only supported on Neuron.")
745

746
747
748
        # Avoid running try_verify_and_update_config multiple times
        self.config_updated = False

749
        self._verify_quantization()
750
        self._verify_cuda_graph()
751
        self._verify_bnb_config()
752

753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
    @field_validator("quantization", mode="before")
    @classmethod
    def validate_quantization_before(cls, value: Any) -> Any:
        if isinstance(value, str):
            return value.lower()
        return value

    @model_validator(mode="after")
    def validate_model_config_after(self: "ModelConfig") -> "ModelConfig":
        if not isinstance(self.tokenizer, str):
            raise ValueError("tokenizer must be a string after __post_init__.")
        if not isinstance(self.max_model_len, int):
            raise ValueError(
                "max_model_len must be an integer after __post_init__.")
        return self

769
770
771
    def _get_transformers_backend_cls(self) -> str:
        """Determine which Transformers backend class will be used if
        `model_impl` is set to `transformers` or `auto`."""
772
773
        if getattr(self, "runner_type", self.runner) == "pooling":
            return "TransformersModel"
774
775
776
777
        if self.hf_config != self.hf_text_config:
            # If 'hf_text_config' is the same as 'hf_config'. If not, it is
            # probably a composite config, i.e. multimodal
            return "TransformersForMultimodalLM"
778
779
780
781
782
        return "TransformersForCausalLM"

    def using_transformers_backend(self) -> bool:
        """Check if the model is using the Transformers backend class."""
        return self.architecture == self._get_transformers_backend_cls()
783

784
785
    @property
    def registry(self):
786
        return me_models.ModelRegistry
787
788
789

    @property
    def architectures(self) -> list[str]:
790
        return getattr(self.hf_config, "architectures", [])
791

792
793
    @property
    def architecture(self) -> str:
794
        """The architecture vllm actually used."""
795
796
        return self._architecture

797
798
    def maybe_pull_model_tokenizer_for_s3(self, model: str,
                                          tokenizer: str) -> None:
799
        """Pull model/tokenizer from S3 to temporary directory when needed.
800

801
        Args:
802
803
            model: Model name or path
            tokenizer: Tokenizer name or path
804
        """
805
806
807
808
809
810
811
812
813
814
815
816
        if not (is_s3(model) or is_s3(tokenizer)):
            return

        if is_s3(model):
            s3_model = S3Model()
            s3_model.pull_files(model,
                                allow_pattern=["*.model", "*.py", "*.json"])
            self.model_weights = model
            self.model = s3_model.dir

            # If tokenizer is same as model, download to same directory
            if model == tokenizer:
817
818
819
820
821
                s3_model.pull_files(model,
                                    ignore_pattern=[
                                        "*.pt", "*.safetensors", "*.bin",
                                        "*.tensors"
                                    ])
822
823
824
825
826
827
828
                self.tokenizer = s3_model.dir
                return

        # Only download tokenizer if needed and not already handled
        if is_s3(tokenizer):
            s3_tokenizer = S3Model()
            s3_tokenizer.pull_files(
829
830
                model,
                ignore_pattern=["*.pt", "*.safetensors", "*.bin", "*.tensors"])
831
            self.tokenizer = s3_tokenizer.dir
832

833
    def _init_multimodal_config(self) -> Optional["MultiModalConfig"]:
834
        if self._model_info.supports_multimodal:
835
            return MultiModalConfig(
836
                limit_per_prompt=self.limit_mm_per_prompt,
837
                media_io_kwargs=self.media_io_kwargs,
838
                mm_processor_kwargs=self.mm_processor_kwargs,
839
                mm_processor_cache_gb=self.mm_processor_cache_gb,
840
                interleave_mm_strings=self.interleave_mm_strings)
841
842

        return None
843

844
    def set_mm_processor_cache_gb(self, value: int) -> None:
845
846
        mm_config = self.get_multimodal_config()

847
848
        self.mm_processor_cache_gb = value
        mm_config.mm_processor_cache_gb = value
849

850
851
852
853
    def _get_encoder_config(self):
        return get_sentence_transformer_tokenizer_config(
            self.model, self.revision)

854
    def _init_pooler_config(self) -> Optional["PoolerConfig"]:
855
        if self.runner_type == "pooling":
856
857
858
859
860
            if isinstance(self.override_pooler_config, dict):
                self.override_pooler_config = PoolerConfig(
                    **self.override_pooler_config)

            pooler_config = self.override_pooler_config or PoolerConfig()
861
862
863
864
865

            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():
866
867
                    if getattr(pooler_config, k) is None:
                        setattr(pooler_config, k, v)
868

869
870
871
872
            default_pooling_type = self._model_info.default_pooling_type
            if pooler_config.pooling_type is None:
                pooler_config.pooling_type = default_pooling_type

873
            return pooler_config
874

875
876
        return None

877
    def _verify_tokenizer_mode(self) -> None:
878
879
        tokenizer_mode = cast(TokenizerMode, self.tokenizer_mode.lower())
        if tokenizer_mode not in get_args(TokenizerMode):
880
881
            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
882
                f"one of {get_args(TokenizerMode)}.")
883
        self.tokenizer_mode = tokenizer_mode
884

885
886
887
888
889
890
891
892
893
894
    def _get_default_runner_type(
        self,
        architectures: list[str],
    ) -> RunnerType:
        registry = self.registry

        # Some Sentence Transformers models use *ForCausalLM archs
        if get_pooling_config(self.model, self.revision):
            return "pooling"

895
        for arch in architectures:
896
897
898
899
900
901
902
903
904
905
906
907
            if arch in registry.get_supported_archs():
                if registry.is_pooling_model(architectures, self):
                    return "pooling"
                if registry.is_text_generation_model(architectures, self):
                    return "generate"

            match = try_match_architecture_defaults(arch)
            if match:
                _, (runner_type, _) = match
                return runner_type

        return "generate"
908

909
    def _get_runner_type(
910
        self,
911
        architectures: list[str],
912
913
914
915
916
917
918
        runner: RunnerOption,
    ) -> RunnerType:
        if runner != "auto":
            return runner

        runner_type = self._get_default_runner_type(architectures)

919
920
921
922
923
924
        # Don't log the most common case
        if runner_type != "generate":
            logger.info(
                "Resolved `--runner auto` to `--runner %s`. "
                "Pass the value explicitly to silence this message.",
                runner_type)
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954

        return runner_type

    def _get_default_convert_type(
        self,
        architectures: list[str],
        runner_type: RunnerType,
    ) -> ConvertType:
        registry = self.registry

        for arch in architectures:
            if arch in registry.get_supported_archs():
                if (runner_type == "generate"
                        and registry.is_text_generation_model(
                            architectures, self)):
                    return "none"
                if (runner_type == "pooling"
                        and registry.is_pooling_model(architectures, self)):
                    return "none"

            match = try_match_architecture_defaults(arch,
                                                    runner_type=runner_type)
            if match:
                _, (_, convert_type) = match
                return convert_type

        # This is to handle Sentence Transformers models that use *ForCausalLM
        # and also multi-modal pooling models which are not defined as
        # Sentence Transformers models
        if runner_type == "pooling":
955
956
            return "embed"

957
958
959
960
961
962
963
964
965
966
        return "none"

    def _get_convert_type(
        self,
        architectures: list[str],
        runner_type: RunnerType,
        convert: ConvertOption,
    ) -> ConvertType:
        if convert != "auto":
            return convert
967

968
969
        convert_type = self._get_default_convert_type(architectures,
                                                      runner_type)
970

971
972
973
974
975
976
        # Don't log the most common case
        if convert_type != "none":
            logger.info(
                "Resolved `--convert auto` to `--convert %s`. "
                "Pass the value explicitly to silence this message.",
                convert_type)
977
978

        return convert_type
979

980
    def _get_supported_generation_tasks(
981
        self,
982
983
        architectures: list[str],
        convert_type: ConvertType,
984
985
986
    ) -> list[_ResolvedTask]:
        registry = self.registry

987
        if registry.is_transcription_only_model(architectures, self):
988
989
            return ["transcription"]

990
        # TODO: Use get_supported_generation_tasks once V0 is removed
991
        supported_tasks = list[_ResolvedTask]()
992
993
        if (registry.is_text_generation_model(architectures, self)
                or convert_type in _RUNNER_CONVERTS["generate"]):
994
995
            supported_tasks.append("generate")

996
997
        if registry.is_transcription_model(architectures, self):
            supported_tasks.append("transcription")
998
999

        return supported_tasks
1000

1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
    def _get_default_pooling_task(
        self,
        architectures: list[str],
    ) -> Literal["embed", "classify", "reward"]:
        if self.registry.is_cross_encoder_model(architectures, self):
            return "classify"

        for arch in architectures:
            match = try_match_architecture_defaults(arch,
                                                    runner_type="pooling")
            if match:
                _, (_, convert_type) = match
                assert convert_type != "none"
                return convert_type

        return "embed"

1018
1019
    def _get_supported_pooling_tasks(
        self,
1020
1021
        architectures: list[str],
        convert_type: ConvertType,
1022
    ) -> list[_ResolvedTask]:
1023
        registry = self.registry
1024

1025
        # TODO: Use get_supported_pooling_tasks once V0 is removed
1026
        supported_tasks = list[_ResolvedTask]()
1027
1028
        if (registry.is_pooling_model(architectures, self)
                or convert_type in _RUNNER_CONVERTS["pooling"]):
1029
            supported_tasks.append("encode")
1030

1031
1032
1033
            extra_task = (self._get_default_pooling_task(architectures)
                          if convert_type == "none" else convert_type)
            supported_tasks.append(extra_task)
1034
1035
1036
1037
1038

        return supported_tasks

    def _get_supported_tasks(
        self,
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
        architectures: list[str],
        runner_type: RunnerType,
        convert_type: ConvertType,
    ) -> list[_ResolvedTask]:
        if runner_type == "generate":
            return self._get_supported_generation_tasks(
                architectures, convert_type)
        if runner_type == "pooling":
            return self._get_supported_pooling_tasks(architectures,
                                                     convert_type)
        if runner_type == "draft":
            return ["draft"]
1051

1052
        assert_never(runner_type)
1053

1054
1055
1056
    def _parse_quant_hf_config(self):
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is None:
1057
            # compressed-tensors uses a "compression_config" key
1058
            quant_cfg = getattr(self.hf_config, "compression_config", None)
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073

        else:
            # Set quant_method for ModelOpt models.
            producer_name = quant_cfg.get("producer", {}).get("name")
            if producer_name == "modelopt":
                quant_algo = quant_cfg.get("quantization",
                                           {}).get("quant_algo")
                if quant_algo == "FP8":
                    quant_cfg["quant_method"] = "modelopt"
                elif quant_algo == "NVFP4":
                    quant_cfg["quant_method"] = "modelopt_fp4"
                elif quant_algo is not None:
                    raise ValueError(
                        f"Unknown ModelOpt quant algo: {quant_algo}")

1074
1075
        return quant_cfg

1076
    def _verify_quantization(self) -> None:
1077
        supported_quantization = me_quant.QUANTIZATION_METHODS
1078
        optimized_quantization_methods = [
1079
            "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
1080
            "awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
1081
            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas", "inc"
1082
        ]
1083
        if self.quantization is not None:
1084
1085
            self.quantization = cast(me_quant.QuantizationMethods,
                                     self.quantization)
1086
1087

        # Parse quantization method from the HF model config, if available.
1088
1089
        quant_cfg = self._parse_quant_hf_config()

1090
        if quant_cfg is not None:
1091
            # Use the community standard 'quant_method'
1092
            quant_method = quant_cfg.get("quant_method", "").lower()
1093
1094

            # Normalize library names
1095
1096
            quant_method = quant_method.replace("compressed_tensors",
                                                "compressed-tensors")
1097

1098
            quant_cfg["quant_method"] = quant_method
1099

1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
            # Quantization methods which are overrides (i.e. they have a
            # `override_quantization_method` method) must be checked in order
            # of preference (this is particularly important for GPTQ).
            overrides = [
                "marlin",
                "bitblas",
                "gptq_marlin_24",
                "gptq_marlin",
                "gptq_bitblas",
                "awq_marlin",
                "ipex",
                "moe_wna16",
1112
1113
                "modelopt",
                "modelopt_fp4",
1114
1115
1116
1117
1118
1119
1120
1121
1122
            ]
            quantization_methods = [
                q for q in supported_quantization if q not in overrides
            ]
            # Any custom overrides will be in quantization_methods so we place
            # them at the start of the list so custom overrides have preference
            # over the built in ones.
            quantization_methods = quantization_methods + overrides

1123
            # Detect which checkpoint is it
1124
            for name in quantization_methods:
1125
                method = me_quant.get_quantization_config(name)
1126
1127
                quantization_override = method.override_quantization_method(
                    quant_cfg, self.quantization)
1128
1129
1130
1131
                if quantization_override is not None:
                    # Raise error if the override is not custom (custom would
                    # be in QUANTIZATION_METHODS but not QuantizationMethods)
                    # and hasn't been added to the overrides list.
1132
                    if (name in get_args(me_quant.QuantizationMethods)
1133
1134
1135
1136
1137
1138
                            and name not in overrides):
                        raise ValueError(
                            f"Quantization method {name} is an override but "
                            "is has not been added to the `overrides` list "
                            "above. This is necessary to ensure that the "
                            "overrides are checked in order of preference.")
1139
1140
1141
                    quant_method = quantization_override
                    self.quantization = quantization_override
                    break
1142

1143
            # Verify quantization configurations.
1144
            if self.quantization is None:
1145
1146
                self.quantization = quant_method
            elif self.quantization != quant_method:
1147
1148
                raise ValueError(
                    "Quantization method specified in the model config "
1149
                    f"({quant_method}) does not match the quantization "
1150
1151
1152
1153
1154
1155
1156
1157
                    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}.")
1158
            from vllm.platforms import current_platform
1159
            current_platform.verify_quantization(self.quantization)
1160
            if self.quantization not in optimized_quantization_methods:
1161
                logger.warning(
1162
                    "%s quantization is not fully "
1163
                    "optimized yet. The speed can be slower than "
1164
                    "non-quantized models.", self.quantization)
1165

1166
    def _verify_cuda_graph(self) -> None:
1167
1168
        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                          self.max_model_len)
1169
        # CUDAGraph capture not supported for enc-dec models and mllama on ROCm
1170
        ROCM_UNSUPPORTED_MODELS = ['mllama']
1171
1172
1173
1174
1175
1176
        unsupported_rocm = (self.hf_config.model_type
                            in ROCM_UNSUPPORTED_MODELS
                            or self.is_encoder_decoder)

        if (unsupported_rocm and not self.enforce_eager
                and current_platform.is_rocm()):
1177
1178
            logger.warning(
                "CUDA graph is not supported for %s on ROCm yet, fallback "
1179
                "to eager mode.", self.hf_config.model_type)
1180
            self.enforce_eager = True
1181

1182
1183
    def _verify_bnb_config(self) -> None:
        """
1184
        The current version of bitsandbytes (0.46.1) with 8-bit models does not
1185
        yet support CUDA graph.
1186
        # TODO Remove this when bitsandbytes supports.
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
        """
        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(
1201
                "CUDA graph is not supported on BitsAndBytes 8bit yet, "
1202
                "fallback to the eager mode.")
1203

1204
1205
            self.enforce_eager = True

1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
    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.")

1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
    def verify_dual_chunk_attention_config(
        self,
        load_config: "LoadConfig",
    ) -> None:
        if hasattr(self.hf_config, "dual_chunk_attention_config"):
            # Try loading the sparse attention config
            from vllm.model_executor.model_loader.weight_utils import (
                get_sparse_attention_config)
            sparse_attn_config = get_sparse_attention_config(self, load_config)
            if sparse_attn_config:
                self.hf_config.dual_chunk_attention_config[
                    "sparse_attention_config"] = sparse_attn_config
                if "sparse_attention_enabled" not in \
                        self.hf_config.dual_chunk_attention_config:
                    self.hf_config.dual_chunk_attention_config[
                        "sparse_attention_enabled"] = True

1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
    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

1250
        # Reminder: Please update docs/features/compatibility_matrix.md
1251
        # If the feature combo become valid
1252
        from vllm.platforms import current_platform
1253
        if not current_platform.is_async_output_supported(self.enforce_eager):
1254
1255
1256
1257
1258
1259
1260
            self.use_async_output_proc = False
            return

        if envs.VLLM_USE_RAY_SPMD_WORKER:
            self.use_async_output_proc = False
            return

1261
        # Async postprocessor is not necessary for pooling models
1262
        # since there is no token generation
1263
        if self.runner_type == "pooling":
1264
1265
            self.use_async_output_proc = False

1266
        # Reminder: Please update docs/features/compatibility_matrix.md
1267
        # If the feature combo become valid
1268
1269
1270
        if speculative_config:
            self.use_async_output_proc = False

1271
1272
1273
1274
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
1275
1276
1277
1278
1279
1280

        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.")

1281
1282
        total_num_attention_heads = getattr(self.hf_text_config,
                                            "num_attention_heads", 0)
1283
1284
1285
1286
1287
1288
1289
        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}).")

1290
        if parallel_config.enable_expert_parallel:
1291
1292
            self._verify_with_expert_parallelism()

1293
        pipeline_parallel_size = parallel_config.pipeline_parallel_size
1294
        if pipeline_parallel_size > 1:
1295
1296
            if not self.registry.is_pp_supported_model(self.architectures,
                                                       self):
1297
1298
1299
1300
1301
1302
                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
1303

1304
1305
    def get_sliding_window(self) -> Optional[int]:
        """Get the sliding window size from the HF text config if present."""
1306
        return getattr(self.hf_text_config, "sliding_window", None)
1307
1308

    def get_vocab_size(self) -> int:
1309
        return getattr(self.hf_text_config, "vocab_size", 0)
1310

1311
    def get_hidden_size(self) -> int:
1312
        return getattr(self.hf_text_config, "hidden_size", 0)
1313

1314
1315
    @property
    def is_deepseek_mla(self) -> bool:
1316
1317
1318
        if not hasattr(self.hf_text_config, "model_type"):
            return False
        elif self.hf_text_config.model_type in \
bigmoyan's avatar
bigmoyan committed
1319
            ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp', 'kimi_k2'):
1320
1321
1322
1323
1324
1325
1326
1327
            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
1328

1329
    def get_head_size(self) -> int:
wangding zeng's avatar
wangding zeng committed
1330
        # TODO remove hard code
1331
        if self.is_deepseek_mla:
1332
1333
            qk_rope_head_dim = getattr(self.hf_text_config, "qk_rope_head_dim",
                                       0)
1334
            if self.use_mla:
1335
                return self.hf_text_config.kv_lora_rank + qk_rope_head_dim
1336
1337
1338
1339
1340
            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
1341

1342
1343
1344
1345
1346
        if hasattr(self.hf_text_config,
                   "model_type") and (self.hf_text_config.model_type
                                      == "zamba2"):
            return self.hf_text_config.attention_head_dim

1347
1348
1349
        if self.is_attention_free:
            return 0

1350
1351
        # NOTE: Some configs may set head_dim=None in the config
        if getattr(self.hf_text_config, "head_dim", None) is not None:
1352
            return self.hf_text_config.head_dim
1353

1354
        # FIXME(woosuk): This may not be true for all models.
1355
1356
        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)
1357

1358
1359
    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
Zhuohan Li's avatar
Zhuohan Li committed
1360
        # For GPTBigCode & Falcon:
1361
        # NOTE: for falcon, when new_decoder_architecture is True, the
Zhuohan Li's avatar
Zhuohan Li committed
1362
1363
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
1364
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
1365
        new_decoder_arch_falcon = (
1366
            self.hf_config.model_type in falcon_model_types
1367
            and getattr(self.hf_config, "new_decoder_architecture", False))
1368
        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
1369
                                                   "multi_query", False):
Zhuohan Li's avatar
Zhuohan Li committed
1370
            # Multi-query attention, only one KV head.
Woosuk Kwon's avatar
Woosuk Kwon committed
1371
            # Currently, tensor parallelism is not supported in this case.
Zhuohan Li's avatar
Zhuohan Li committed
1372
            return 1
1373

1374
        # For DBRX and MPT
1375
1376
1377
1378
1379
        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":
1380
1381
1382
            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

1383
1384
1385
1386
1387
1388
1389
1390
        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")

1391
1392
1393
        if self.is_attention_free:
            return 0

1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
        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:
1404
            num_kv_heads = getattr(self.hf_text_config, attr, None)
1405
1406
1407
1408
1409
            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.
1410
        return self.hf_text_config.num_attention_heads
1411
1412
1413

    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU."""
1414
1415
1416
1417
        if self.use_mla:
            # When using MLA during decode it becomes MQA
            return 1

1418
1419
1420
1421
1422
1423
1424
        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)
1425

1426
1427
    def get_num_attention_heads(self,
                                parallel_config: "ParallelConfig") -> int:
1428
1429
        num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
        return num_heads // parallel_config.tensor_parallel_size
1430

1431
    def get_layers_start_end_indices(
1432
            self, parallel_config: "ParallelConfig") -> tuple[int, int]:
1433
        from vllm.distributed.utils import get_pp_indices
1434
        if (self.hf_text_config.model_type == "deepseek_mtp"
Yuxuan Zhang's avatar
Yuxuan Zhang committed
1435
1436
                or self.hf_config.model_type == "mimo_mtp"
                or self.hf_config.model_type == "glm4_moe_mtp"):
1437
1438
1439
1440
1441
            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)
1442
1443
1444
        # the layout order is: DP x PP x TP
        pp_rank = (parallel_config.rank // parallel_config.tensor_parallel_size
                   ) % parallel_config.pipeline_parallel_size
1445
1446
        pp_size = parallel_config.pipeline_parallel_size
        start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
1447
        return start, end
Mor Zusman's avatar
Mor Zusman committed
1448

1449
1450
1451
    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
1452

1453
1454
1455
1456
1457
1458
1459
1460
    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
1461
1462
1463
        is_transformer = not self.is_hybrid and \
                            not self.has_noops and \
                            not self.is_attention_free
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
        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
1474
1475
1476
1477
        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])
1478
        else:
1479
            # Hybrid model Jamba
1480
1481
            layers_block_type_value = getattr(self.hf_config,
                                              "layers_block_type", None)
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
            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
1507

1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
    def get_mamba_chunk_size(self) -> Optional[int]:
        """
        Returns the mamba chunk size if it exists
        """
        # used by e.g. Bamba, FalconH1, Granite, PLaMo2
        chunk_size = getattr(self.hf_text_config, "mamba_chunk_size", None)
        if chunk_size is None:
            # used by e.g. Mamba2, NemotronH, Zamba
            chunk_size = getattr(self.hf_text_config, "chunk_size", None)
        return chunk_size

1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
    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

1531
    def try_get_generation_config(self) -> dict[str, Any]:
1532
1533
1534
        """
        This method attempts to retrieve the non-default values of the
        generation config for this model.
1535

1536
1537
1538
1539
1540
1541
1542
1543
        The generation config can contain information about special tokens, as
        well as sampling parameters. Which is why this method exists separately
        to `get_diff_sampling_param`.

        Returns:
            A dictionary containing the non-default generation config.
        """
        if self.generation_config in {"auto", "vllm"}:
1544
            config = try_get_generation_config(
1545
                self.hf_config_path or self.model,
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
                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()

1560
    def get_diff_sampling_param(self) -> dict[str, Any]:
1561
        """
1562
1563
1564
1565
1566
1567
1568
1569
1570
        This method returns a dictionary containing the non-default sampling
        parameters with `override_generation_config` applied.

        The default sampling parameters are:

        - vLLM's neutral defaults if `self.generation_config="vllm"`
        - the model's defaults if `self.generation_config="auto"`
        - as defined in `generation_config.json` if
            `self.generation_config="path/to/generation_config/dir"`
1571
1572

        Returns:
1573
            A dictionary containing the non-default sampling parameters.
1574
        """
1575
        if self.generation_config == "vllm":
1576
1577
1578
1579
1580
1581
1582
            config = {}
        else:
            config = self.try_get_generation_config()

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

1583
1584
1585
1586
1587
1588
        available_params = [
            "repetition_penalty",
            "temperature",
            "top_k",
            "top_p",
            "min_p",
1589
            "max_new_tokens",
1590
1591
1592
1593
1594
1595
        ]
        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
            }
1596
1597
1598
1599
1600
            # 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")
1601
1602
        else:
            diff_sampling_param = {}
1603
1604
1605
1606
1607
1608
1609

        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`.")
1610
1611
        return diff_sampling_param

1612
    @property
1613
    def is_encoder_decoder(self) -> bool:
1614
        """Extract the HF encoder/decoder model flag."""
1615
        """
1616
        For Mllama, VLLM overrides HF's is_encoder_decoder flag and sets it to
1617
        True to enable cross-attention
1618
        Neuron needs all multimodal data to be in the decoder and does not
1619
1620
1621
1622
1623
1624
        need to explicitly enable cross-attention
        """
        if (current_platform.is_neuron()
                and self.hf_config.model_type == "mllama"):
            return False

1625
1626
1627
1628
1629
        return is_encoder_decoder(self.hf_config)

    @property
    def uses_mrope(self) -> bool:
        return uses_mrope(self.hf_config)
1630

1631
1632
1633
1634
    @property
    def is_multimodal_model(self) -> bool:
        return self.multimodal_config is not None

1635
1636
1637
1638
1639
1640
1641
    @property
    def processor_return_mm_hashes(self) -> bool:
        """Whether the multi-modal processor should output hashes."""
        mm_config = self.multimodal_config
        if mm_config is None:
            return False

1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
        return mm_config.mm_processor_cache_gb > 0

    @property
    def enable_mm_processor_cache(self) -> bool:
        """Whether the multi-modal processor cache should be enabled."""
        mm_config = self.multimodal_config
        if mm_config is None:
            return False

        return mm_config.mm_processor_cache_gb > 0
1652
1653
1654
1655
1656
1657
1658
1659

    def get_mm_input_cache_gb(self) -> int:
        mm_config = self.multimodal_config
        if mm_config is None:
            return 0

        return envs.VLLM_MM_INPUT_CACHE_GIB

1660
1661
    @property
    def is_cross_encoder(self) -> bool:
1662
1663
        return (self._model_info.supports_cross_encoding
                or self.convert_type == "classify")
1664

1665
    @property
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
    def is_pp_supported(self) -> bool:
        return self._model_info.supports_pp

    @property
    def is_multimodal_raw_input_supported(self) -> bool:
        return self._model_info.supports_multimodal_raw_input

    @property
    def is_attention_free(self) -> bool:
        return self._model_info.is_attention_free

    @property
    def is_hybrid(self) -> bool:
        return self._model_info.is_hybrid

    @property
    def has_noops(self) -> bool:
        return self._model_info.has_noops

    @property
    def has_inner_state(self):
        return self._model_info.has_inner_state
1688

1689
1690
    @property
    def is_v1_compatible(self) -> bool:
1691
1692
1693
1694
1695
        return not self._model_info.supports_v0_only

    @property
    def use_mla(self) -> bool:
        return self.is_deepseek_mla and not envs.VLLM_MLA_DISABLE
1696

1697
1698
    @property
    def is_matryoshka(self) -> bool:
1699
        return (bool(getattr(self.hf_config, "matryoshka_dimensions", None))
1700
1701
                or getattr(self.hf_config, "is_matryoshka", False))

1702
1703
1704
1705
    @property
    def matryoshka_dimensions(self):
        return getattr(self.hf_config, "matryoshka_dimensions", None)

1706
1707
1708
1709
1710
1711
    @property
    def use_pad_token(self) -> bool:
        # cross_encoder models defaults to using pad_token.
        # `llm as reranker` models defaults to not using pad_token.
        return getattr(self.hf_config, "use_pad_token", True)

1712
    def get_and_verify_max_len(self, max_model_len: int):
1713
1714
        # Consider max_model_len in tokenizer_config only when
        # pooling models use absolute position_embedding.
1715
        tokenizer_config = None
1716
1717
        if (self.runner_type == "pooling" and getattr(
                self.hf_config, "position_embedding_type", "") == "absolute"):
1718
1719
1720
1721
            tokenizer_config = try_get_tokenizer_config(
                self.tokenizer,
                trust_remote_code=self.trust_remote_code,
                revision=self.tokenizer_revision)
1722
1723
        max_model_len = _get_and_verify_max_len(
            hf_config=self.hf_text_config,
1724
            tokenizer_config=tokenizer_config,
1725
1726
            max_model_len=max_model_len,
            disable_sliding_window=self.disable_sliding_window,
1727
            sliding_window=self.get_sliding_window(),
1728
1729
            spec_target_max_model_len=self.spec_target_max_model_len,
            encoder_config=self.encoder_config)
1730
        logger.info("Using max model len %s", max_model_len)
1731
1732
        return max_model_len

1733

1734
@config
1735
1736
@dataclass
class LoadConfig:
1737
1738
    """Configuration for loading the model weights."""

1739
    load_format: Union[str, LoadFormats] = "auto"
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
    """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
1760
1761
    Mistral models.
    - Other custom values can be supported via plugins."""
1762
    download_dir: Optional[str] = None
1763
1764
    """Directory to download and load the weights, default to the default
    cache directory of Hugging Face."""
1765
1766
    model_loader_extra_config: Union[dict, TensorizerConfig] = field(
        default_factory=dict)
1767
    """Extra config for model loader. This will be passed to the model loader
1768
    corresponding to the chosen load_format."""
1769
1770
1771
    device: Optional[str] = None
    """Device to which model weights will be loaded, default to
    device_config.device"""
1772
    ignore_patterns: Optional[Union[list[str], str]] = None
1773
1774
    """The list of patterns to ignore when loading the model. Default to
    "original/**/*" to avoid repeated loading of llama's checkpoints."""
1775
    use_tqdm_on_load: bool = True
1776
1777
    """Whether to enable tqdm for showing progress bar when loading model
    weights."""
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
    pt_load_map_location: Union[str, dict[str, str]] = "cpu"
    """
    pt_load_map_location: the map location for loading pytorch checkpoint, to
    support loading checkpoints can only be loaded on certain devices like
    "cuda", this is equivalent to {"": "cuda"}. Another supported format is
    mapping from different devices like from GPU 1 to GPU 0:
    {"cuda:1": "cuda:0"}. Note that when passed from command line, the strings
    in dictionary needs to be double quoted for json parsing. For more details,
    see original doc for `map_location` in https://pytorch.org/docs/stable/generated/torch.load.html
    """
1788

1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
    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.
1803
        factors: list[Any] = []
1804
1805
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1806
1807
        return hash_str

1808
    def __post_init__(self):
1809
        self.load_format = self.load_format.lower()
1810
1811
1812
1813
1814
1815
1816
        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/**/*"]

1817

1818
Device = Literal["auto", "cuda", "neuron", "cpu", "tpu", "xpu"]
1819
1820
1821


@config
1822
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
1823
class DeviceConfig:
1824
1825
    """Configuration for the device to use for vLLM execution."""

1826
    device: SkipValidation[Optional[Union[Device, torch.device]]] = "auto"
1827
    """Device type for vLLM execution.
1828
1829
1830
    This parameter is deprecated and will be
    removed in a future release.
    It will now be set automatically based
1831
    on the current platform."""
1832
1833
1834
    device_type: str = field(init=False)
    """Device type from the current platform. This is set in
    `__post_init__`."""
1835

1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
    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.
1851
        factors: list[Any] = []
1852
1853
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1854
1855
        return hash_str

1856
1857
    def __post_init__(self):
        if self.device == "auto":
1858
            # Automated device type detection
1859
            from vllm.platforms import current_platform
1860
            self.device_type = current_platform.device_type
1861
            if not self.device_type:
1862
1863
1864
1865
                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.")
1866
1867
        else:
            # Device type is assigned explicitly
1868
1869
1870
1871
            if isinstance(self.device, str):
                self.device_type = self.device
            elif isinstance(self.device, torch.device):
                self.device_type = self.device.type
1872
1873

        # Some device types require processing inputs on CPU
1874
        if self.device_type in ["neuron"]:
1875
            self.device = torch.device("cpu")
1876
1877
        elif self.device_type in ["tpu"]:
            self.device = None
1878
1879
1880
1881
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)

1882

1883
1884
SpeculativeMethod = Literal["ngram", "eagle", "eagle3", "medusa",
                            "mlp_speculator", "draft_model", "deepseek_mtp"]
1885
1886
1887


@config
1888
@dataclass
1889
class SpeculativeConfig:
1890
    """Configuration for speculative decoding."""
1891

1892
    # General speculative decoding control
1893
    num_speculative_tokens: SkipValidation[int] = None  # type: ignore
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
    """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] = None
    """The name of the draft model, eagle head, or additional weights, if
    provided."""
    method: Optional[SpeculativeMethod] = None
    """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.

    If using `ngram` method, the related configuration `prompt_lookup_max` and
    `prompt_lookup_min` should be considered."""
1907
    draft_tensor_parallel_size: Optional[int] = None
1908
1909
    """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."""
1910
    disable_logprobs: bool = True
1911
1912
1913
    """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."""
1914

1915
    # Draft model configuration
1916
    quantization: Optional[me_quant.QuantizationMethods] = None
1917
1918
1919
    """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."""
1920
    max_model_len: Optional[int] = None
1921
1922
    """The maximum model length of the draft model. Used when testing the
    ability to skip speculation for some sequences."""
1923
    revision: Optional[str] = None
1924
1925
1926
    """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."""
1927
    code_revision: Optional[str] = None
1928
1929
1930
    """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."""
1931

1932
    # Advanced control
1933
    disable_by_batch_size: Optional[int] = None
1934
1935
1936
1937
    """Disable speculative decoding for new incoming requests when the number
    of enqueued requests is larger than this value, if provided."""

    # Ngram proposer configuration
1938
    prompt_lookup_max: Optional[int] = None
1939
1940
    """Maximum size of ngram token window when using Ngram proposer, required
    when method is set to ngram."""
1941
    prompt_lookup_min: Optional[int] = None
1942
1943
1944
    """Minimum size of ngram token window when using Ngram proposer, if
    provided. Defaults to 1."""

1945
    speculative_token_tree: Optional[str] = None
1946
    """Specifies the tree structure for speculative token generation.
1947
    """
1948
    # required configuration params passed from engine
1949
    target_model_config: SkipValidation[ModelConfig] = None  # type: ignore
1950
    """The configuration of the target model."""
1951
1952
    target_parallel_config: SkipValidation[
        ParallelConfig] = None  # type: ignore
1953
    """The parallel configuration for the target model."""
1954
    enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
1955
1956
    """Whether vLLM is configured to use chunked prefill or not. Used for
    raising an error since it's not yet compatible with speculative decode."""
1957
    disable_log_stats: SkipValidation[bool] = None  # type: ignore
1958
1959
    """Whether to disable the periodic printing of stage times in speculative
    decoding."""
1960
1961

    # params generated in the post-init stage
1962
    draft_model_config: SkipValidation[ModelConfig] = None  # type: ignore
1963
    """The configuration of the draft model initialized internal."""
1964
1965
    draft_parallel_config: SkipValidation[
        ParallelConfig] = None  # type: ignore
1966
    """The parallel configuration for the draft model initialized internal."""
1967

1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
    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.
        """
1980
        factors: list[Any] = []
1981
1982
1983
        # Eagle3 affects the computation graph because it returns intermediate
        # hidden states in addition to the final hidden state.
        factors.append(self.method == "eagle3")
1984
1985
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1986
1987
        return hash_str

1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
    @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"]
            })
1998
1999
2000
2001
2002
2003
2004
2005
2006

        if hf_config.architectures[0] == "MiMoForCausalLM":
            hf_config.model_type = "mimo_mtp"
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
            hf_config.update({
                "num_hidden_layers": 0,
                "n_predict": n_predict,
                "architectures": ["MiMoMTPModel"]
            })
Yuxuan Zhang's avatar
Yuxuan Zhang committed
2007
2008
2009
2010
2011
2012
2013
2014
2015

        if hf_config.architectures[0] == "Glm4MoeForCausalLM":
            hf_config.model_type = "glm4_moe_mtp"
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
            hf_config.update({
                "num_hidden_layers": 0,
                "n_predict": n_predict,
                "architectures": ["Glm4MoeMTPModel"]
            })
2016

2017
2018
        return hf_config

2019
    def __post_init__(self):
2020

2021
2022
2023
2024
2025
2026
2027
        # 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.
2028
2029
2030
2031

        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
2032
            if self.target_model_config and \
2033
2034
2035
2036
                (self.target_model_config.hf_text_config.model_type \
                        == "deepseek_v3" or
                    self.target_model_config.hf_text_config.model_type \
                        == "mimo"):
2037
2038
2039
2040
                # use the draft model from the same model:
                self.model = self.target_model_config.model
            elif self.method in ("ngram", "[ngram]"):
                self.model = "ngram"
2041
            else:
2042
2043
2044
                raise ValueError("num_speculative_tokens was provided without "
                                 "speculative model.")

2045
2046
        # Automatically configure the method for ngram when "model" is used
        # instead of "method"
2047
2048
2049
2050
2051
2052
2053
        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"
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
            # 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
2068
            if self.prompt_lookup_min < 1:
2069
2070
2071
2072
2073
                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")
2074
            if self.prompt_lookup_min > self.prompt_lookup_max:
2075
2076
2077
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must "
                    f"be <= prompt_lookup_max={self.prompt_lookup_max}")
2078

2079
2080
2081
            # 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.
2082
2083
            self.draft_model_config = self.target_model_config
            self.draft_parallel_config = self.target_parallel_config
2084
        else:
2085
2086
2087
2088
2089
2090
            self.prompt_lookup_max = 0
            self.prompt_lookup_min = 0

            if self.model is not None:
                self.draft_model_config = ModelConfig(
                    model=self.model,
2091
                    runner="draft",
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
                    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,
                    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,
                )
2113

2114
                # Automatically detect the method
2115
                if self.method in ('eagle', 'eagle3'):
2116
                    pass
2117
2118
                elif "eagle-" in self.draft_model_config.model.lower() or \
                        "eagle3-" in self.draft_model_config.model.lower():
2119
2120
2121
2122
2123
2124
                    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"
2125
                elif (self.draft_model_config.hf_config.model_type
Yuxuan Zhang's avatar
Yuxuan Zhang committed
2126
                      in ("deepseek_mtp", "mimo_mtp", "glm4_moe_mtp")):
Jiayi Yao's avatar
Jiayi Yao committed
2127
2128
2129
2130
2131
2132
2133
                    self.method = "deepseek_mtp"
                    if self.num_speculative_tokens > 1:
                        logger.warning(
                                "All Deepseek MTP models only have " \
                                "one layer. Might need some code changes " \
                                "to support multiple layers."
                            )
2134
                else:
2135
                    self.method = "draft_model"
2136
2137
2138
2139
2140
                    raise NotImplementedError(
                        "Speculative decoding with draft model is not "
                        "supported yet. Please consider using other "
                        "speculative decoding methods such as ngram, medusa, "
                        "eagle, or deepseek_mtp.")
2141
2142

                # Replace hf_config for EAGLE draft_model
2143
                if self.method in ("eagle", "eagle3"):
2144
                    if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
2145
                        raise ValueError(
2146
2147
                            "Chunked prefill and EAGLE are not compatible "
                            "when using V0.")
2148

2149
2150
                    from vllm.transformers_utils.configs import (
                        SpeculatorsConfig)
2151
2152
                    from vllm.transformers_utils.configs.eagle import (
                        EAGLEConfig)
2153

2154
                    if isinstance(self.draft_model_config.hf_config,
2155
                                  (EAGLEConfig, SpeculatorsConfig)):
2156
2157
2158
                        pass
                    else:
                        eagle_config = EAGLEConfig(
2159
                            self.draft_model_config.hf_config,
2160
2161
                            method=self.method,
                            model_type="eagle")
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
                        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=}")

2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
                if self.speculative_token_tree is None:
                    # Generate chain of tokens.
                    self.speculative_token_tree = str([
                        (i + 1) * (0, )
                        for i in range(self.num_speculative_tokens)
                    ])
                else:
                    # Sort the token tree breadth-first.
                    tree_choices = ast.literal_eval(
                        self.speculative_token_tree)
                    self.speculative_token_tree = str(
                        sorted(tree_choices, key=lambda t: (len(t), t)))

2196
2197
2198
2199
2200
2201
                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
                )
2202

2203
2204
2205
2206
2207
2208
                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,
                    ))
2209

2210
2211
2212
2213
                self.draft_parallel_config = (
                    SpeculativeConfig.create_draft_parallel_config(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size))
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
2242
2243
2244
2245
2246
2247
2248
2249
    @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,
        )

2250
    @staticmethod
2251
    def _verify_and_get_draft_tp(
2252
2253
2254
2255
2256
2257
            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.
2258
        """
2259
2260
        # If speculative_draft_tensor_parallel_size is unset then set it
        # appropriately else verify that it is set correctly.
2261
        if speculative_draft_tensor_parallel_size is None:
2262
2263
2264
2265
            if draft_hf_config.model_type == "mlp_speculator":
                speculative_draft_tensor_parallel_size = 1
                if target_parallel_config.tensor_parallel_size > 1:
                    logger.warning(
2266
2267
2268
                        "%s cannot currently be run with tp>1; "
                        "setting speculative_draft_tensor_parallel_size=1",
                        draft_hf_config.model_type)
2269
2270
2271
            else:
                speculative_draft_tensor_parallel_size = \
                    target_parallel_config.tensor_parallel_size
2272
2273
        elif speculative_draft_tensor_parallel_size not in (
                1, target_parallel_config.tensor_parallel_size):
2274
            raise ValueError(
2275
                f"{speculative_draft_tensor_parallel_size=} cannot be "
2276
                f"other value than 1 or target model tensor_parallel_size")
2277
        return speculative_draft_tensor_parallel_size
2278

2279
2280
2281
2282
2283
2284
2285
2286
2287
    @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.
        """
2288
2289
2290
        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
2291
            tensor_parallel_size=speculative_draft_tensor_parallel_size,
2292
2293
            distributed_executor_backend=target_parallel_config.
            distributed_executor_backend,
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
            max_parallel_loading_workers=target_parallel_config.
            max_parallel_loading_workers,
            disable_custom_all_reduce=target_parallel_config.
            disable_custom_all_reduce,
            ray_workers_use_nsight=target_parallel_config.
            ray_workers_use_nsight,
            placement_group=target_parallel_config.placement_group,
        )

        return draft_parallel_config

2305
2306
    @model_validator(mode='after')
    def _verify_args(self) -> Self:
2307
2308
2309
2310
2311
2312
        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.")

2313
2314
2315
2316
2317
2318
2319
        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)
2320
2321
2322
2323
2324
2325

        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=}")
2326

2327
        eagle3_target_supported = ["llama", "qwen"]
2328
2329
2330
2331
        if self.method == "eagle3" and self.target_model_config and not any(
                supported_model in
                self.target_model_config.hf_text_config.model_type
                for supported_model in eagle3_target_supported):
2332
            raise ValueError(
2333
                f"Eagle3 is only supported for {eagle3_target_supported} models. "  # noqa: E501
2334
2335
                f"Got {self.target_model_config.hf_text_config.model_type=}")

2336
2337
        return self

2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
    @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

2348
    def use_eagle(self) -> bool:
Jiayi Yao's avatar
Jiayi Yao committed
2349
        return self.method in ("eagle", "eagle3", "deepseek_mtp")
2350

2351
    def __repr__(self) -> str:
2352
2353
        method = self.method
        model = None if method == "ngram" else self.draft_model_config.model
2354
        num_spec_tokens = self.num_speculative_tokens
2355
        return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
2356
2357


2358
2359
2360
2361
LoRADType = Literal["auto", "float16", "bfloat16"]


@config
2362
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
2363
class LoRAConfig:
2364
2365
2366
2367
2368
2369
    """Configuration for LoRA."""

    max_lora_rank: int = 16
    """Max LoRA rank."""
    max_loras: int = 1
    """Max number of LoRAs in a single batch."""
2370
    fully_sharded_loras: bool = False
2371
2372
2373
2374
    """By default, only half of the LoRA computation is sharded with tensor
    parallelism. Enabling this will use the fully sharded layers. At high
    sequence length, max rank or tensor parallel size, this is likely faster.
    """
2375
    max_cpu_loras: Optional[int] = None
2376
2377
2378
2379
    """Maximum number of LoRAs to store in CPU memory. Must be >= than
    `max_loras`."""
    lora_dtype: Union[torch.dtype, LoRADType] = "auto"
    """Data type for LoRA. If auto, will default to base model dtype."""
2380
    lora_extra_vocab_size: int = 256
2381
2382
    """Maximum size of extra vocabulary that can be present in a LoRA adapter
    (added to the base model vocabulary)."""
2383
2384
    lora_vocab_padding_size: ClassVar[int] = current_platform\
        .get_lora_vocab_padding_size()
2385

2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
    default_mm_loras: Optional[dict[str, str]] = None
    """Dictionary mapping specific modalities to LoRA model paths; this field
    is only applicable to multimodal models and should be leveraged when a
    model always expects a LoRA to be active when a given modality is present.
    Note that currently, if a request provides multiple additional
    modalities, each of which have their own LoRA, we do NOT apply
    default_mm_loras because we currently only support one lora adapter
    per prompt. When run in offline mode, the lora IDs for n modalities
    will be automatically assigned to 1-n with the names of the modalities
    in alphabetic order."""
2396
    bias_enabled: bool = False
2397
    """Enable bias for LoRA adapters."""
2398

2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
    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.
        """
2411
        factors: list[Any] = []
2412
2413
2414
2415
2416
        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)
2417
        factors.append(self.lora_vocab_padding_size)
2418
        factors.append(self.bias_enabled)
2419
2420
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2421
2422
        return hash_str

2423
    def __post_init__(self):
2424
        # Setting the maximum rank to 512 should be able to satisfy the vast
2425
        # majority of applications.
2426
        possible_max_ranks = (8, 16, 32, 64, 128, 256, 320, 512)
2427
        possible_lora_extra_vocab_size = (256, 512)
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
        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
2443
                f"max_loras ({self.max_loras})")
2444

2445
    def verify_with_cache_config(self, cache_config: CacheConfig):
2446
2447
2448
        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.")
2449

2450
2451
2452
2453
2454
2455
2456
    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)


2457
@config
2458
@dataclass
2459
class MultiModalConfig:
2460
2461
    """Controls the behavior of multimodal models."""

2462
2463
    limit_per_prompt: dict[str, int] = \
        cast(dict[str, int], get_field(ModelConfig, "limit_mm_per_prompt"))
2464
    """
2465
    The maximum number of input items allowed per prompt for each modality.
2466
    Defaults to 1 (V0) or 999 (V1) for each modality.
2467
2468

    For example, to allow up to 16 images and 2 videos per prompt:
2469
    `{"image": 16, "video": 2}`
2470
2471
    """

2472
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
2473
2474
    """Additional args passed to process media inputs, keyed by modalities.
    For example, to set num_frames for video, set
2475
2476
    `--media-io-kwargs '{"video": {"num_frames": 40} }'` """

2477
2478
2479
    mm_processor_kwargs: Optional[dict[str, object]] = None
    """
    Overrides for the multi-modal processor obtained from
2480
    `transformers.AutoProcessor.from_pretrained`.
2481
2482
2483
2484

    The available overrides depend on the model that is being run.

    For example, for Phi-3-Vision:
2485
    `{"num_crops": 4}`.
2486
2487
    """

2488
    mm_processor_cache_gb: int = 4
2489
    """
2490
2491
2492
2493
2494
2495
2496
    The size (in GiB) of the multi-modal processor cache, which is used to

    This cache is duplicated for each API process and engine core process,
    resulting in a total memory usage of
    `mm_processor_cache_gb * (api_server_count + data_parallel_size)`.

    Set to `0` to disable this cache completely (not recommended).
2497
2498
    """

2499
2500
2501
2502
2503
    interleave_mm_strings: bool = False
    """
    Enable fully interleaved support for multimodal prompts.
    """

2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
    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.
2518
        factors: list[Any] = []
2519
2520
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2521
2522
        return hash_str

2523
2524
2525
2526
2527
    def get_limit_per_prompt(self, modality: str) -> int:
        """
        Get the maximum number of input items allowed per prompt
        for the given modality.
        """
2528
2529
2530
2531
        return self.limit_per_prompt.get(
            modality,
            999 if envs.VLLM_USE_V1 else 1,
        )
2532

2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
    def merge_mm_processor_kwargs(
        self,
        inference_kwargs: Mapping[str, object],
    ) -> dict[str, object]:
        """
        Get the keyword arguments to pass to the multi-modal processor
        according to the extra arguments passed during inference.
        """
        kwargs = self.mm_processor_kwargs or {}
        return kwargs | dict(inference_kwargs)
2543

2544

2545
@config
2546
2547
@dataclass
class PoolerConfig:
2548
    """Controls the behavior of output pooling in pooling models."""
2549
2550

    pooling_type: Optional[str] = None
2551
    """
2552
    The pooling method of the pooling model. This should be a key in
2553
    [`vllm.model_executor.layers.pooler.PoolingType`][].
2554
2555
    """

2556
    ## for embeddings models
2557
2558
    normalize: Optional[bool] = None
    """
2559
2560
2561
2562
2563
2564
    Whether to normalize the embeddings outputs. 
    """
    dimensions: Optional[int] = None
    """
    Reduce the dimensions of embeddings if model 
    support matryoshka representation.
2565
2566
    """

2567
2568
    ## for classification models
    activation: Optional[bool] = None
2569
    """
2570
    Whether to apply activation function to the classification outputs. 
2571
2572
    """

2573
2574
2575
2576
2577
    ## for reward models
    softmax: Optional[bool] = None
    """
    Whether to apply softmax to the reward outputs. 
    """
2578
2579
    step_tag_id: Optional[int] = None
    """
2580
    If set, only the score corresponding to the ``step_tag_id`` in the
2581
2582
2583
    generated sentence should be returned. Otherwise, the scores for all tokens
    are returned.
    """
2584
    returned_token_ids: Optional[list[int]] = None
2585
    """
2586
2587
    A list of indices for the vocabulary dimensions to be extracted,
    such as the token IDs of ``good_token`` and ``bad_token`` in the
2588
2589
2590
    ``math-shepherd-mistral-7b-prm`` model.
    """

2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
    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.
2605
        factors: list[Any] = []
2606
2607
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2608
2609
        return hash_str

2610

2611
2612
2613
2614
2615
2616
2617
2618
_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

2619
2620
2621
2622
2623
2624
2625
# model_type -> reason
_FLOAT16_NOT_SUPPORTED_MODELS = {
    "gemma2": "Numerical instability. Please use bfloat16 or float32 instead.",
    "gemma3": "Numerical instability. Please use bfloat16 or float32 instead.",
    "plamo2": "Numerical instability. Please use bfloat16 or float32 instead.",
    "glm4": "Numerical instability. Please use bfloat16 or float32 instead.",
}
2626

2627

2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
def _is_valid_dtype(model_type: str, dtype: torch.dtype):
    if model_type in _FLOAT16_NOT_SUPPORTED_MODELS and dtype == torch.float16:  # noqa: E501, SIM103
        return False

    return True


def _check_valid_dtype(model_type: str, dtype: torch.dtype):
    if model_type in _FLOAT16_NOT_SUPPORTED_MODELS and dtype == torch.float16:
        reason = _FLOAT16_NOT_SUPPORTED_MODELS[model_type]
        raise ValueError(f"The model type {model_type!r} "
                         f"does not support float16. Reason: {reason}")

    return True


def _find_dtype(
    model_id: str,
2646
    config: PretrainedConfig,
2647
2648
2649
    *,
    revision: Optional[str],
):
2650
2651
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
2652
    config_dtype = getattr(config, "torch_dtype", None)
2653

2654
    # Fallbacks for multi-modal models if the root config
2655
    # does not define torch_dtype
2656
2657
    if config_dtype is None:
        config_dtype = getattr(config.get_text_config(), "torch_dtype", None)
2658
2659
    if config_dtype is None and hasattr(config, "vision_config"):
        config_dtype = getattr(config.vision_config, "torch_dtype", None)
2660
2661
    if config_dtype is None and hasattr(config, "encoder_config"):
        config_dtype = getattr(config.encoder_config, "torch_dtype", None)
2662

2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
    # Try to read the dtype of the weights if they are in safetensors format
    if config_dtype is None:
        repo_mt = try_get_safetensors_metadata(model_id, revision=revision)

        if repo_mt and (files_mt := repo_mt.files_metadata):
            param_dtypes: set[torch.dtype] = {
                _SAFETENSORS_TO_TORCH_DTYPE[dtype_str]
                for file_mt in files_mt.values()
                for dtype_str in file_mt.parameter_count
                if dtype_str in _SAFETENSORS_TO_TORCH_DTYPE
            }

            if param_dtypes:
                return common_broadcastable_dtype(param_dtypes)

2678
2679
2680
    if config_dtype is None:
        config_dtype = torch.float32

2681
    return config_dtype
2682

Shinichi Hemmi's avatar
Shinichi Hemmi committed
2683

2684
2685
2686
2687
2688
2689
2690
def _resolve_auto_dtype(
    model_type: str,
    config_dtype: torch.dtype,
    *,
    is_pooling_model: bool,
):
    from vllm.platforms import current_platform
2691

2692
2693
2694
2695
    supported_dtypes = [
        dtype for dtype in current_platform.supported_dtypes
        if _is_valid_dtype(model_type, dtype)
    ]
2696

2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
    if is_pooling_model and torch.float16 in supported_dtypes:
        preferred_dtype = torch.float16
    else:
        preferred_dtype = supported_dtypes[0]

    # Downcast for float32 models
    if config_dtype == torch.float32:
        config_dtype = preferred_dtype

    if config_dtype in supported_dtypes:
        return config_dtype

    # Ensure device compatibility
    device_name = current_platform.get_device_name()
    device_capability = current_platform.get_device_capability()

    if device_capability is None:
        device_str = f"{device_name!r}"
    else:
        version_str = device_capability.as_version_str()
        device_str = f"{device_name!r} (with compute capability {version_str})"

    logger.warning(
        "Your device %s doesn't support %s. "
        "Falling back to %s for compatibility.",
        device_str,
        config_dtype,
        preferred_dtype,
    )

    return preferred_dtype


def _get_and_verify_dtype(
    model_id: str,
    config: PretrainedConfig,
    dtype: Union[str, torch.dtype],
    *,
    is_pooling_model: bool,
    revision: Optional[str] = None,
) -> torch.dtype:
    config_dtype = _find_dtype(model_id, config, revision=revision)
    model_type = config.model_type

    if isinstance(dtype, str):
        dtype = dtype.lower()
        if dtype == "auto":
            # Set default dtype from model config
            torch_dtype = _resolve_auto_dtype(
                model_type,
                config_dtype,
                is_pooling_model=is_pooling_model,
            )
2750
        else:
2751
            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
2752
                raise ValueError(f"Unknown dtype: {dtype!r}")
2753
2754
2755
            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
    elif isinstance(dtype, torch.dtype):
        torch_dtype = dtype
2756
    else:
2757
        raise ValueError(f"Unknown dtype: {dtype}")
2758

2759
2760
    _check_valid_dtype(model_type, torch_dtype)

2761
2762
2763
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
2764
            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
2765
2766
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
2767
            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
2768
        else:
Woosuk Kwon's avatar
Woosuk Kwon committed
2769
            # Casting between float16 and bfloat16 is allowed with a warning.
2770
            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
2771
2772

    return torch_dtype
2773
2774
2775
2776


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
2777
    tokenizer_config: Optional[dict],
2778
    max_model_len: Optional[int],
2779
    disable_sliding_window: bool,
2780
    sliding_window: Optional[int],
2781
    spec_target_max_model_len: Optional[int] = None,
2782
    encoder_config: Optional[Any] = None,
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
) -> 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",
2793
2794
        # ChatGLM2
        "seq_length",
2795
2796
        # Command-R
        "model_max_length",
2797
2798
        # Whisper
        "max_target_positions",
2799
2800
2801
2802
2803
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
2804
    # Choose the smallest "max_length" from the possible keys
2805
    max_len_key = None
2806
    for key in possible_keys:
2807
2808
2809
2810
2811
        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
2812
2813
2814
2815
    # 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
2816
2817
2818

    # If sliding window is manually disabled, max_length should be less
    # than the sliding window length in the model config.
2819
2820
2821
2822
    if (disable_sliding_window and sliding_window is not None
            and sliding_window < derived_max_model_len):
        max_len_key = "sliding_window"
        derived_max_model_len = sliding_window
2823

2824
2825
2826
2827
2828
2829
2830
    # Consider model_max_length in tokenizer_config
    if tokenizer_config:
        tokenizer_model_max_length = tokenizer_config.get(
            "model_max_length", derived_max_model_len)
        derived_max_model_len = min(derived_max_model_len,
                                    tokenizer_model_max_length)

2831
2832
    # If none of the keys were found in the config, use a default and
    # log a warning.
2833
    if derived_max_model_len == float("inf"):
2834
2835
2836
2837
        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

2838
2839
2840
2841
2842
        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

2843
2844
2845
2846
        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: "
2847
            "%s. Assuming the model's maximum length is %d.", possible_keys,
2848
            default_max_len)
2849
        derived_max_model_len = default_max_len
2850

2851
    rope_scaling = getattr(hf_config, "rope_scaling", None)
2852
2853
2854
    # 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:
2855
2856
2857
        # No need to consider "type" key because of patch_rope_scaling when
        # loading HF config
        rope_type = rope_scaling["rope_type"]
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867

        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.")

2868
2869
2870
2871
            # 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)

2872
2873
2874
2875
            if rope_type == "yarn":
                derived_max_model_len = rope_scaling[
                    "original_max_position_embeddings"]
            derived_max_model_len *= scaling_factor
2876

2877
2878
2879
    if encoder_config and "max_seq_length" in encoder_config:
        derived_max_model_len = encoder_config["max_seq_length"]

2880
2881
    # If the user specified a max length, make sure it is smaller than the
    # derived length from the HF model config.
2882
    if max_model_len is None:
2883
        max_model_len = int(derived_max_model_len)
2884
2885
2886
2887
2888
2889
2890
2891
        if current_platform.is_tpu():
            logger.warning(
                "--max-model-len is not specified, "
                "it's currently using model's default length %s, "
                "which might be too large."
                "Please input with --max-model-len based on your "
                "request input length and output length, to avoid "
                "unnecessary degradation.", max_model_len)
2892
    elif max_model_len > derived_max_model_len:
2893
2894
2895
2896
2897
        # 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:
2898
2899
2900
2901
2902
2903
2904
            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.")
2905
        else:
2906
            msg = (
2907
                f"User-specified max_model_len ({max_model_len}) is greater "
2908
2909
                f"than the derived max_model_len ({max_len_key}="
                f"{derived_max_model_len} or model_max_length="
2910
                f"{model_max_length} in model's config.json). This may lead "
2911
2912
2913
2914
2915
2916
2917
2918
2919
                "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")
2920
    return int(max_model_len)
2921
2922


2923
def get_served_model_name(model: str,
2924
                          served_model_name: Optional[Union[str, list[str]]]):
2925
    """
2926
2927
2928
2929
    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
2930
2931
2932
2933
2934
2935
2936
2937
2938
    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


2939
GuidedDecodingBackend = Literal["auto", "xgrammar", "guidance", "outlines"]
2940
2941
2942


@config
2943
2944
@dataclass
class DecodingConfig:
2945
    """Dataclass which contains the decoding strategy of the engine."""
2946

2947
    backend: GuidedDecodingBackend = "auto"
2948
2949
2950
2951
    """Which engine will be used for guided decoding (JSON schema / regex etc)
    by default. With "auto", we will make opinionated choices based on request
    contents and what the backend libraries currently support, so the behavior
    is subject to change in each release."""
2952

2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
    disable_fallback: bool = False
    """If `True`, vLLM will not fallback to a different backend on error."""

    disable_any_whitespace: bool = False
    """If `True`, the model will not generate any whitespace during guided
    decoding. This is only supported for xgrammar and guidance backends."""

    disable_additional_properties: bool = False
    """If `True`, the `guidance` backend will not use `additionalProperties`
    in the JSON schema. This is only supported for the `guidance` backend and
    is used to better align its behaviour with `outlines` and `xgrammar`."""

2965
    reasoning_backend: str = ""
2966
    """Select the reasoning parser depending on the model that you're using.
2967
    This is used to parse the reasoning content into OpenAI API format."""
2968

2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
    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.
2983
        factors: list[Any] = []
2984
2985
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2986
2987
        return hash_str

2988
    def __post_init__(self):
2989
2990
2991
2992
2993
2994
2995
2996
        if (self.disable_any_whitespace
                and self.backend not in ("xgrammar", "guidance")):
            raise ValueError("disable_any_whitespace is only supported for "
                             "xgrammar and guidance backends.")
        if (self.disable_additional_properties and self.backend != "guidance"):
            raise ValueError("disable_additional_properties is only supported "
                             "for the guidance backend.")

2997

2998
2999
3000
3001
DetailedTraceModules = Literal["model", "worker", "all"]


@config
3002
3003
@dataclass
class ObservabilityConfig:
3004
    """Configuration for observability - metrics and tracing."""
3005

3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
    show_hidden_metrics_for_version: Optional[str] = None
    """Enable deprecated Prometheus metrics that have been hidden since the
    specified version. For example, if a previously deprecated metric has been
    hidden since the v0.7.0 release, you use
    `--show-hidden-metrics-for-version=0.7` as a temporary escape hatch while
    you migrate to new metrics. The metric is likely to be removed completely
    in an upcoming release."""

    @cached_property
    def show_hidden_metrics(self) -> bool:
        """Check if the hidden metrics should be shown."""
        if self.show_hidden_metrics_for_version is None:
            return False
        return version._prev_minor_version_was(
            self.show_hidden_metrics_for_version)
3021

3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
    otlp_traces_endpoint: Optional[str] = None
    """Target URL to which OpenTelemetry traces will be sent."""

    collect_detailed_traces: Optional[list[DetailedTraceModules]] = None
    """It makes sense to set this only if `--otlp-traces-endpoint` is set. If
    set, it will collect detailed traces for the specified modules. This
    involves use of possibly costly and or blocking operations and hence might
    have a performance impact.

    Note that collecting detailed timing information for each request can be
    expensive."""

    @cached_property
    def collect_model_forward_time(self) -> bool:
        """Whether to collect model forward time for the request."""
        return (self.collect_detailed_traces is not None
                and ("model" in self.collect_detailed_traces
                     or "all" in self.collect_detailed_traces))

    @cached_property
    def collect_model_execute_time(self) -> bool:
        """Whether to collect model execute time for the request."""
        return (self.collect_detailed_traces is not None
                and ("worker" in self.collect_detailed_traces
                     or "all" in self.collect_detailed_traces))
3047

3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
    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.
3062
        factors: list[Any] = []
3063
3064
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3065
3066
        return hash_str

3067
    def __post_init__(self):
3068
3069
3070
3071
3072
        if (self.collect_detailed_traces is not None
                and len(self.collect_detailed_traces) == 1
                and "," in self.collect_detailed_traces[0]):
            self._parse_collect_detailed_traces()

3073
        from vllm.tracing import is_otel_available, otel_import_error_traceback
3074
3075
3076
3077
3078
        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}")
3079

3080
3081
3082
3083
3084
3085
    def _parse_collect_detailed_traces(self):
        assert isinstance(self.collect_detailed_traces, list)
        self.collect_detailed_traces = cast(
            list[DetailedTraceModules],
            self.collect_detailed_traces[0].split(","))

3086

3087
3088
3089
3090
3091
3092
3093
3094
KVProducer = Literal["kv_producer", "kv_both"]
KVConsumer = Literal["kv_consumer", "kv_both"]
KVRole = Literal[KVProducer, KVConsumer]


@config
@dataclass
class KVTransferConfig:
3095
3096
3097
    """Configuration for distributed KV cache transfer."""

    kv_connector: Optional[str] = None
3098
3099
    """The KV connector for vLLM to transmit KV caches between vLLM instances.
    """
3100

3101
    engine_id: Optional[str] = None
Robert Shaw's avatar
Robert Shaw committed
3102
3103
    """The engine id for KV transfers."""

3104
    kv_buffer_device: Optional[str] = "cuda"
3105
3106
    """The device used by kv connector to buffer the KV cache.
    Currently only support 'cuda'."""
3107
3108

    kv_buffer_size: float = 1e9
3109
3110
    """The buffer size for TorchDistributedConnector. Measured in number of
    bytes. Recommended value: 1e9 (about 1GB)."""
3111

3112
3113
    kv_role: Optional[KVRole] = None
    """Whether this vLLM instance produces, consumes KV cache, or both. Choices
Robert Shaw's avatar
Robert Shaw committed
3114
    are 'kv_producer', 'kv_consumer', and 'kv_both'."""
3115
3116

    kv_rank: Optional[int] = None
3117
3118
3119
    """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."""
3120
3121

    kv_parallel_size: int = 1
3122
3123
    """The number of parallel instances for KV cache transfer. For
    PyNcclConnector, this should be 2."""
3124
3125

    kv_ip: str = "127.0.0.1"
3126
    """The KV connector ip, used to build distributed connection."""
3127
3128

    kv_port: int = 14579
3129
    """The KV connector port, used to build distributed connection."""
3130

3131
3132
    kv_connector_extra_config: dict[str, Any] = field(default_factory=dict)
    """any extra config that the connector may need."""
3133

3134
3135
3136
3137
    kv_connector_module_path: Optional[str] = None
    """The Python module path to dynamically load the KV connector from.
    Only supported in V1."""

3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
    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.
3152
        factors: list[Any] = []
3153
3154
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3155
3156
        return hash_str

3157
    def __post_init__(self) -> None:
3158
3159
3160
        if self.engine_id is None:
            self.engine_id = str(uuid.uuid4())

3161
3162
3163
        if self.kv_role is not None and self.kv_role not in get_args(KVRole):
            raise ValueError(f"Unsupported kv_role: {self.kv_role}. "
                             f"Supported roles are {get_args(KVRole)}")
3164
3165
3166

        if self.kv_connector is not None and self.kv_role is None:
            raise ValueError("Please specify kv_disagg_role when kv_connector "
3167
                             f"is set, supported roles are {get_args(KVRole)}")
3168
3169
3170
3171

    @property
    def is_kv_transfer_instance(self) -> bool:
        return self.kv_connector is not None and \
3172
            self.kv_role in get_args(KVRole)
3173
3174
3175
3176

    @property
    def is_kv_producer(self) -> bool:
        return self.kv_connector is not None and \
3177
            self.kv_role in get_args(KVProducer)
3178
3179
3180
3181

    @property
    def is_kv_consumer(self) -> bool:
        return self.kv_connector is not None and \
3182
            self.kv_role in get_args(KVConsumer)
3183

3184
3185
3186
    def get_from_extra_config(self, key, default) -> Any:
        return self.kv_connector_extra_config.get(key, default)

3187

3188
3189
3190
@config
@dataclass
class KVEventsConfig:
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
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
    """Configuration for KV event publishing."""

    enable_kv_cache_events: bool = False
    """If True, enable KV cache events for tracking block storage and removal.
    Events can be published externally by zmq using the event publisher config.
    """

    publisher: str = "null"
    """The publisher to use for publishing kv events. Can be "null", "zmq".
    """

    endpoint: str = "tcp://*:5557"
    """The zmq endpoint to use for publishing kv events.
    """

    replay_endpoint: Optional[str] = None
    """The zmq endpoint to use for replaying kv events.
    """

    buffer_steps: int = 10_000
    """The number of steps to cache for replay endpoint. Will only save
    events from the last N steps for the replay endpoint.
    """

    hwm: int = 100_000
    """The zmq high water mark for the event publisher. After queueing N events,
    events will start dropping if the consumer is not keeping up.
    """

    max_queue_size: int = 100_000
    """The maximum number of events to queue while waiting for publishing.
    """

    topic: str = ""
    """The topic to use for the event publisher. Consumers can subscribe to
    this topic to receive events.
    """


3230
@config
3231
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
3232
3233
class VllmConfig:
    """Dataclass which contains all vllm-related configuration. This
3234
3235
3236
    simplifies passing around the distinct configurations in the codebase.
    """

3237
3238
3239
    # TODO: use default_factory once default constructing ModelConfig doesn't
    # try to download a model
    model_config: ModelConfig = None  # type: ignore
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
    """Model configuration."""
    cache_config: CacheConfig = field(default_factory=CacheConfig)
    """Cache configuration."""
    parallel_config: ParallelConfig = field(default_factory=ParallelConfig)
    """Parallel configuration."""
    scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig)
    """Scheduler configuration."""
    device_config: DeviceConfig = field(default_factory=DeviceConfig)
    """Device configuration."""
    load_config: LoadConfig = field(default_factory=LoadConfig)
    """Load configuration."""
3251
    lora_config: Optional[LoRAConfig] = None
3252
3253
3254
    """LoRA configuration."""
    speculative_config: Optional[SpeculativeConfig] = None
    """Speculative decoding configuration."""
3255
    decoding_config: DecodingConfig = field(default_factory=DecodingConfig)
3256
    """Decoding configuration."""
3257
    observability_config: Optional[ObservabilityConfig] = None
3258
    """Observability configuration."""
3259
    quant_config: Optional[QuantizationConfig] = None
3260
3261
3262
    """Quantization configuration."""
    compilation_config: CompilationConfig = field(
        default_factory=CompilationConfig)
3263
    """`torch.compile` and cudagraph capture configuration for the model.
3264

3265
3266
    As a shorthand, `-O<n>` can be used to directly specify the compilation
    level `n`: `-O3` is equivalent to `-O.level=3` (same as `-O='{"level":3}'`).
3267
    Currently, -O <n> and -O=<n> are supported as well but this will likely be
3268
    removed in favor of clearer -O<n> syntax in the future.
3269
3270
3271

    NOTE: level 0 is the default level without any optimization. level 1 and 2
    are for internal testing only. level 3 is the recommended level for
3272
    production, also default in V1.
3273
3274
3275
3276
3277
3278

    You can specify the full compilation config like so:
    `{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
    """
    kv_transfer_config: Optional[KVTransferConfig] = None
    """The configurations for distributed KV cache transfer."""
3279
    kv_events_config: Optional[KVEventsConfig] = None
3280
    """The configurations for event publishing."""
3281
    # some opaque config, only used to provide additional information
3282
3283
    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
3284
3285
3286
3287
    additional_config: Union[dict, SupportsHash] = field(default_factory=dict)
    """Additional config for specified platform. Different platforms may
    support different configs. Make sure the configs are valid for the platform
    you are using. Contents must be hashable."""
3288
    instance_id: str = ""
3289
    """The ID of the vLLM instance."""
3290

3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
    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.
        """
3303
        factors: list[Any] = []
3304
3305

        # summarize vllm config
3306
        vllm_factors: list[Any] = []
3307
3308
        from vllm import __version__
        vllm_factors.append(__version__)
3309
        vllm_factors.append(envs.VLLM_USE_V1)
3310
3311
        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
3312
3313
        else:
            vllm_factors.append("None")
3314
3315
        if self.cache_config:
            vllm_factors.append(self.cache_config.compute_hash())
3316
3317
        else:
            vllm_factors.append("None")
3318
3319
        if self.parallel_config:
            vllm_factors.append(self.parallel_config.compute_hash())
3320
3321
        else:
            vllm_factors.append("None")
3322
3323
        if self.scheduler_config:
            vllm_factors.append(self.scheduler_config.compute_hash())
3324
3325
        else:
            vllm_factors.append("None")
3326
3327
        if self.device_config:
            vllm_factors.append(self.device_config.compute_hash())
3328
3329
        else:
            vllm_factors.append("None")
3330
3331
        if self.load_config:
            vllm_factors.append(self.load_config.compute_hash())
3332
3333
        else:
            vllm_factors.append("None")
3334
3335
        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
3336
3337
3338
3339
3340
            # 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))
3341
3342
        else:
            vllm_factors.append("None")
3343
3344
        if self.speculative_config:
            vllm_factors.append(self.speculative_config.compute_hash())
3345
3346
        else:
            vllm_factors.append("None")
3347
3348
        if self.decoding_config:
            vllm_factors.append(self.decoding_config.compute_hash())
3349
3350
        else:
            vllm_factors.append("None")
3351
3352
        if self.observability_config:
            vllm_factors.append(self.observability_config.compute_hash())
3353
3354
        else:
            vllm_factors.append("None")
3355
3356
3357
3358
        if self.quant_config:
            pass  # should be captured by model_config.quantization
        if self.compilation_config:
            vllm_factors.append(self.compilation_config.compute_hash())
3359
3360
        else:
            vllm_factors.append("None")
3361
3362
        if self.kv_transfer_config:
            vllm_factors.append(self.kv_transfer_config.compute_hash())
3363
3364
3365
        else:
            vllm_factors.append("None")
        if self.additional_config:
3366
3367
3368
3369
3370
3371
3372
3373
            if isinstance(additional_config := self.additional_config, dict):
                additional_config_hash = hashlib.md5(
                    json.dumps(additional_config, sort_keys=True).encode(),
                    usedforsecurity=False,
                ).hexdigest()
            else:
                additional_config_hash = additional_config.compute_hash()
            vllm_factors.append(additional_config_hash)
3374
3375
        else:
            vllm_factors.append("None")
3376
3377
        factors.append(vllm_factors)

3378
3379
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()[:10]
3380
3381
        return hash_str

3382
3383
3384
3385
3386
3387
    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]
3388

3389
3390
3391
3392
3393
    @staticmethod
    def _get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        """Get the quantization config."""
3394
        from vllm.platforms import current_platform
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
        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
3417

3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
    @staticmethod
    def get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        import copy

        # For some reason, the _ version of this modifies the model_config
        # object, so using deepcopy to avoid this problem.
        return VllmConfig._get_quantization_config(copy.deepcopy(model_config),
                                                   load_config)

3429
3430
3431
3432
3433
3434
3435
3436
3437
    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

3438
3439
3440
3441
3442
        model_config = copy.deepcopy(self.model_config)
        model_config.hf_config = hf_config

        return replace(self, model_config=model_config)

3443
3444
3445
    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """
3446
3447
3448

        self.try_verify_and_update_config()

3449
3450
3451
3452
3453
        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)
3454
3455
            self.model_config.verify_dual_chunk_attention_config(
                self.load_config)
3456

3457
        self.cache_config.verify_with_parallel_config(self.parallel_config)
3458

3459
        if self.lora_config is not None:
3460
            self.lora_config.verify_with_cache_config(self.cache_config)
3461
            self.lora_config.verify_with_model_config(self.model_config)
3462

3463
        if self.quant_config is None and self.model_config is not None:
3464
3465
            self.quant_config = VllmConfig._get_quantization_config(
                self.model_config, self.load_config)
3466

3467
        from vllm.platforms import current_platform
3468
        if self.model_config is not None and \
3469
3470
3471
            self.scheduler_config.chunked_prefill_enabled and \
            self.model_config.dtype == torch.float32 and \
            current_platform.get_device_capability() == (7, 5):
3472
            logger.warning_once(
3473
3474
3475
3476
                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
                "precision for chunked prefill triton kernels.")

3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
        # If the user does not explicitly set a compilation level, then
        # we use the default level. The default level depends on other
        # settings (see the below code).
        if self.compilation_config.level is None:
            if envs.VLLM_USE_V1:
                if (self.model_config is not None
                        and not self.model_config.enforce_eager):
                    self.compilation_config.level = CompilationLevel.PIECEWISE
                else:
                    self.compilation_config.level = \
                            CompilationLevel.NO_COMPILATION
            else:
                # NB: Passing both --enforce-eager and a compilation level
                # in V0 means the compilation level wins out.
                self.compilation_config.level = CompilationLevel.NO_COMPILATION

3493
3494
3495
3496
3497
        # async tp is built on top of sequence parallelism
        # and requires it to be enabled.
        if self.compilation_config.pass_config.enable_async_tp:
            self.compilation_config.pass_config.enable_sequence_parallelism = \
                True
3498
3499
        if self.compilation_config.pass_config.enable_sequence_parallelism:
            self.compilation_config.custom_ops.append("+rms_norm")
3500
3501
        if envs.VLLM_USE_V1 and self.model_config is not None and \
            not self.model_config.enforce_eager:
3502
3503
            # By default, V1 uses piecewise CUDA graphs. If full_cuda_graph
            # is set to True, full CUDA graphs will be used.
3504
            self.compilation_config.cudagraph_num_of_warmups = 1
3505
            self.compilation_config.set_splitting_ops_for_v1()
3506

3507
        self._set_cudagraph_sizes()
3508

3509
        if self.cache_config.cpu_offload_gb > 0 and \
3510
3511
            self.compilation_config.level != CompilationLevel.NO_COMPILATION \
                and not envs.VLLM_USE_V1:
3512
            logger.warning(
3513
                "CPU offload is not supported with `torch.compile` in v0 yet."
3514
3515
3516
                " Disabling `torch.compile`.")
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

3517
3518
3519
3520
3521
3522
        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`.")
3523
3524
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

3525
3526
        if self.compilation_config.full_cuda_graph and \
            not self.model_config.disable_cascade_attn:
3527
3528
            logger.info("full_cuda_graph is not supported with "
                        "cascade attention. Disabling cascade attention.")
3529
            self.model_config.disable_cascade_attn = True
3530

3531
3532
3533
3534
3535
3536
3537
3538
        disable_chunked_prefill_reasons: list[str] = []

        if self.model_config and self.model_config.pooler_config:
            pooling_type = self.model_config.pooler_config.pooling_type
            if pooling_type is None or pooling_type.lower() != "last":
                disable_chunked_prefill_reasons.append(
                    "Only \"last\" pooling supports chunked "
                    "prefill and prefix caching; disabling both.")
3539
3540
3541
3542
            elif not getattr(self.model_config.hf_config, "is_causal", True):
                disable_chunked_prefill_reasons.append(
                    "Only models using causal attention supports chunked "
                    "prefill and prefix caching; disabling both.")
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555

        if disable_chunked_prefill_reasons:
            for reason in disable_chunked_prefill_reasons:
                logger.info(reason)
            self.scheduler_config.chunked_prefill_enabled = False
            self.scheduler_config.long_prefill_token_threshold = 0
            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

3556
        if (self.kv_events_config is not None
3557
3558
3559
3560
3561
                and self.kv_events_config.enable_kv_cache_events
                and not self.cache_config.enable_prefix_caching):
            logger.warning(
                "KV cache events are on, but prefix caching is not enabled."
                "Use --enable-prefix-caching to enable.")
3562
3563
        if (self.kv_events_config is not None
                and self.kv_events_config.publisher != "null"
3564
3565
3566
3567
3568
                and not self.kv_events_config.enable_kv_cache_events):
            logger.warning("KV cache events are disabled,"
                           "but the scheduler is configured to publish them."
                           "Modify KVEventsConfig.enable_kv_cache_events"
                           "to True to enable.")
3569
3570
        current_platform.check_and_update_config(self)

3571
3572
3573
        if not self.instance_id:
            self.instance_id = random_uuid()[:5]

3574
3575
3576
3577
3578
3579
3580
        if (envs.VLLM_USE_V1
                and not self.scheduler_config.disable_hybrid_kv_cache_manager):
            # logger should only print warning message for hybrid models. As we
            # can't know whether the model is hybrid or not now, so we don't log
            # warning message here and will log it later.
            if not (current_platform.is_cuda() or current_platform.is_rocm()):
                # Hybrid KV cache manager is not supported on non-GPU platforms.
3581
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
3582
3583
            if self.kv_transfer_config is not None:
                # Hybrid KV cache manager is not compatible with KV transfer.
3584
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
3585
3586
            if self.kv_events_config is not None:
                # Hybrid KV cache manager is not compatible with KV events.
3587
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
3588
            if self.model_config is not None and \
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
                self.model_config.attention_chunk_size is not None:
                if self.speculative_config is not None and \
                    self.speculative_config.use_eagle():
                    # Hybrid KV cache manager is not yet supported with chunked
                    # local attention + eagle.
                    self.scheduler_config.disable_hybrid_kv_cache_manager = True
                elif \
                    not envs.VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE:
                    logger.warning(
                        "There is a latency regression when using chunked local"
                        " attention with the hybrid KV cache manager. Disabling"
                        " it, by default. To enable it, set the environment "
                        "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1."
                    )
                    # Hybrid KV cache manager is not yet supported with chunked
                    # local attention.
                    self.scheduler_config.disable_hybrid_kv_cache_manager = True
3606

3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
    def update_sizes_for_sequence_parallelism(self,
                                              possible_sizes: list) -> list:
        # remove the sizes that not multiple of tp_size when
        # enable sequence parallelism
        removed_sizes = [
            size for size in possible_sizes
            if size % self.parallel_config.tensor_parallel_size != 0
        ]
        if removed_sizes:
            logger.warning(
                "Batch sizes %s are removed because they are not "
                "multiple of tp_size %d when "
                "sequence parallelism is enabled", removed_sizes,
                self.parallel_config.tensor_parallel_size)

        return [
            size for size in possible_sizes
            if size % self.parallel_config.tensor_parallel_size == 0
        ]

3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
    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.

3643
3644
        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
        will be the final sizes to capture cudagraph (in descending order).
3645
3646

        During runtime, if batchsize is larger than
3647
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
3648
3649
        no cudagraph will be used.
        If the batch size is no larger than
3650
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
        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 = []
            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)]
3663
3664
3665
3666
3667
                if self.parallel_config.tensor_parallel_size > 1 and \
                    self.compilation_config.pass_config.enable_sequence_parallelism:
                    possible_sizes = self.update_sizes_for_sequence_parallelism(
                        possible_sizes)

3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
                # 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:
3689
3690
3691
3692
3693
3694
3695
3696
                cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes
                if len(cuda_graph_sizes) == 1:
                    batch_size_capture_list = [1, 2, 4] + [
                        i for i in range(8, cuda_graph_sizes[0] + 1, 8)
                    ]
                elif len(cuda_graph_sizes) > 1:
                    batch_size_capture_list = sorted(cuda_graph_sizes)
                else:
Cyrus Leung's avatar
Cyrus Leung committed
3697
                    raise TypeError(f"Invalid value for {cuda_graph_sizes=}.")
3698
3699
3700
3701
                if self.parallel_config.tensor_parallel_size > 1 and \
                    self.compilation_config.pass_config.enable_sequence_parallelism:
                    batch_size_capture_list = \
                        self.update_sizes_for_sequence_parallelism(batch_size_capture_list)
3702
3703
3704
3705
3706
                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
                ]
3707
3708
3709
3710

        self.compilation_config.init_with_cudagraph_sizes(
            batch_size_capture_list)

3711
    def recalculate_max_model_len(self, max_model_len: int):
3712
        # Can only be called in try_verify_and_update_config
3713
        model_config = self.model_config
3714
        max_model_len = model_config.get_and_verify_max_len(max_model_len)
3715
3716
        self.model_config.max_model_len = max_model_len
        self.scheduler_config.max_model_len = max_model_len
3717
3718

    def try_verify_and_update_config(self):
3719
3720
3721
        if self.model_config is None:
            return

3722
3723
3724
3725
3726
        # Avoid running try_verify_and_update_config multiple times
        if getattr(self.model_config, "config_updated", False):
            return
        self.model_config.config_updated = True

3727
        architecture = self.model_config.architecture
3728
3729
3730
        if architecture is None:
            return

3731
3732
        from vllm.model_executor.models.config import (
            MODELS_CONFIG_MAP, HybridAttentionMambaModelConfig)
3733
3734
3735
        cls = MODELS_CONFIG_MAP.get(architecture, None)
        if cls is not None:
            cls.verify_and_update_config(self)
3736

3737
3738
3739
        if self.model_config.is_hybrid:
            HybridAttentionMambaModelConfig.verify_and_update_config(self)

3740
        if self.model_config.convert_type == "classify":
3741
3742
3743
3744
3745
            # Maybe convert ForCausalLM into ForSequenceClassification model.
            from vllm.model_executor.models.adapters import (
                SequenceClassificationConfig)
            SequenceClassificationConfig.verify_and_update_config(self)

3746
    def __str__(self):
3747
        return (
3748
3749
3750
3751
3752
            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}, "
3753
            f"revision={self.model_config.revision}, "
3754
3755
            f"override_neuron_config={self.model_config.override_neuron_config}, "  # noqa
            f"tokenizer_revision={self.model_config.tokenizer_revision}, "
3756
3757
            f"trust_remote_code={self.model_config.trust_remote_code}, "
            f"dtype={self.model_config.dtype}, "
3758
3759
            f"max_seq_len={self.model_config.max_model_len}, "
            f"download_dir={self.load_config.download_dir!r}, "
3760
            f"load_format={self.load_config.load_format}, "
3761
3762
            f"tensor_parallel_size={self.parallel_config.tensor_parallel_size}, "  # noqa
            f"pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, "  # noqa
3763
3764
3765
3766
            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}, "
3767
            f"device_config={self.device_config.device}, "
3768
3769
3770
3771
3772
3773
3774
3775
3776
            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}, "
3777
3778
            f"pooler_config={self.model_config.pooler_config!r}, "
            f"compilation_config={self.compilation_config!r}")
3779
3780
3781


_current_vllm_config: Optional[VllmConfig] = None
3782
_current_prefix: Optional[str] = None
3783
3784
3785


@contextmanager
3786
3787
3788
def set_current_vllm_config(vllm_config: VllmConfig,
                            check_compile=False,
                            prefix: Optional[str] = None):
3789
    """
3790
    Temporarily set the current vLLM config.
3791
    Used during model initialization.
3792
    We save the current vLLM config in a global variable,
3793
    so that all modules can access it, e.g. custom ops
3794
    can access the vLLM config to determine how to dispatch.
3795
    """
3796
    global _current_vllm_config, _current_prefix
3797
    old_vllm_config = _current_vllm_config
3798
    old_prefix = _current_prefix
3799
3800
3801
3802
    from vllm.compilation.counter import compilation_counter
    num_models_seen = compilation_counter.num_models_seen
    try:
        _current_vllm_config = vllm_config
3803
        _current_prefix = prefix
3804
        yield
3805
3806
3807
    except Exception:
        raise
    else:
3808
3809
3810
3811
        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)
3812
3813
        if check_compile and \
            vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \
3814
3815
3816
3817
3818
3819
3820
3821
3822
            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"
3823
                " if you want it to be supported.",
3824
                vllm_config.model_config.model)
3825
    finally:
3826
        _current_vllm_config = old_vllm_config
3827
        _current_prefix = old_prefix
3828
3829
3830
3831
3832
3833
3834
3835
        # Clear the compilation config cache when context changes
        get_cached_compilation_config.cache_clear()


@lru_cache(maxsize=1)
def get_cached_compilation_config():
    """Cache config to avoid repeated calls to get_current_vllm_config()"""
    return get_current_vllm_config().compilation_config
3836
3837
3838
3839
3840
3841
3842


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.
3843
        logger.warning("Current vLLM config is not set.")
3844
3845
3846
        from vllm.config import VllmConfig
        return VllmConfig()
    return _current_vllm_config
3847
3848


3849
3850
3851
3852
3853
3854
3855
3856
3857
def get_current_model_prefix() -> str:
    """
    Get the prefix of the model that's currently being initialized.
    """
    assert _current_prefix is not None, \
        "Current model prefix is not set. "
    return _current_prefix


3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
def contains_object_print(text):
    """
    Check if the text looks like a printed Python object, e.g.
    contains any substring matching the pattern: "at 0xFFFFFFF>"
    We match against 0x followed by 2-16 hex chars (there's
    a max of 16 on a 64 bit system).

    Args:
        text (str): The text to check

    Returns:
3869
        result (bool): `True` if a match is found, `False` otherwise.
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
    """
    pattern = r'at 0x[a-fA-F0-9]{2,16}>'
    match = re.search(pattern, text)
    return match is not None


def assert_hashable(text):
    if not contains_object_print(text):
        return True
    raise AssertionError(
        f"vLLM tried to hash some configs that may have Python objects ids "
        f"in them. This is a bug, please file an issue. "
        f"Text being hashed: {text}")
3883
3884
3885
3886
3887


T = TypeVar("T")


3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
def get_layers_from_vllm_config(
        vllm_config: VllmConfig,
        layer_type: type[T],
        layer_names: Optional[list[str]] = None) -> dict[str, T]:
    """
    Get layers from the vLLM config.

    Args:
        vllm_config: The vLLM config.
        layer_type: The type of the layer to get.
        layer_names: The names of the layers to get. If None, return all layers.
    """

    if layer_names is None:
        layer_names = list(
            vllm_config.compilation_config.static_forward_context.keys())

    forward_context = vllm_config.compilation_config.static_forward_context

3907
    return {
3908
3909
3910
        layer_name: forward_context[layer_name]
        for layer_name in layer_names
        if isinstance(forward_context[layer_name], layer_type)
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
3938
3939
3940
3941


@config
@dataclass
class SpeechToTextConfig:
    """Configuration for speech-to-text models."""

    sample_rate: float = 16_000
    """Sample rate (Hz) to resample input audio to. Most speech models expect
    16kHz audio input. The input audio will be automatically resampled to this
    rate before processing."""

    max_audio_clip_s: int = 30
    """Maximum duration in seconds for a single audio clip without chunking.
    Audio longer than this will be split into smaller chunks if
    `allow_audio_chunking` evaluates to True, otherwise it will be rejected."""

    overlap_chunk_second: int = 1
    """Overlap duration in seconds between consecutive audio chunks when
    splitting long audio. This helps maintain context across chunk boundaries
    and improves transcription quality at split points."""

    min_energy_split_window_size: Optional[int] = 1600
    """Window size in samples for finding low-energy (quiet) regions to split
    audio chunks. The algorithm looks for the quietest moment within this
    window to minimize cutting through speech. Default 1600 samples ≈ 100ms
    at 16kHz. If None, no chunking will be done."""

    @property
    def allow_audio_chunking(self) -> bool:
3942
        return self.min_energy_split_window_size is not None
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960


def update_config(config: DataclassInstanceT,
                  overrides: dict[str, Any]) -> DataclassInstanceT:
    processed_overrides = {}
    for field_name, value in overrides.items():
        assert hasattr(
            config, field_name), f"{type(config)} has no field `{field_name}`"
        current_value = getattr(config, field_name)
        if is_dataclass(current_value) and not is_dataclass(value):
            assert isinstance(value, dict), (
                f"Overrides to {type(config)}.{field_name} must be a dict"
                f"  or {type(current_value)}, but got {type(value)}")
            value = update_config(
                current_value,  # type: ignore[type-var]
                value)
        processed_overrides[field_name] = value
    return replace(config, **processed_overrides)