config.py 207 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import ast
5
import copy
6
import enum
7
import hashlib
8
import inspect
9
import json
10
import textwrap
Robert Shaw's avatar
Robert Shaw committed
11
import uuid
12
import warnings
13
from collections import Counter
14
from contextlib import contextmanager
15
16
from dataclasses import (MISSING, Field, asdict, field, fields, is_dataclass,
                         replace)
17
from functools import cached_property
18
from importlib.util import find_spec
19
from pathlib import Path
20
21
from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Literal, Optional,
                    Protocol, TypeVar, Union, cast, get_args, get_origin)
22

23
import regex as re
24
import torch
25
26
27
from pydantic import (ConfigDict, SkipValidation, TypeAdapter, field_validator,
                      model_validator)
from pydantic.dataclasses import dataclass
28
from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
29
from torch.distributed import ProcessGroup, ReduceOp
30
from typing_extensions import Self, deprecated, runtime_checkable
31

32
import vllm.envs as envs
33
from vllm import version
34
from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
Woosuk Kwon's avatar
Woosuk Kwon committed
35
from vllm.logger import init_logger
36
from vllm.platforms import current_platform
37
38
39
from vllm.transformers_utils.config import (
    ConfigFormat, get_config, get_hf_image_processor_config,
    get_hf_text_config, get_pooling_config,
40
    get_sentence_transformer_tokenizer_config, is_encoder_decoder,
41
42
    try_get_generation_config, try_get_safetensors_metadata,
    try_get_tokenizer_config, uses_mrope)
43
from vllm.transformers_utils.s3_utils import S3Model
44
from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
45
46
# yapf conflicts with isort for this block
# yapf: disable
47
48
49
from vllm.utils import (DEFAULT_MAX_NUM_BATCHED_TOKENS,
                        MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
                        POOLING_MODEL_MAX_NUM_BATCHED_TOKENS, GiB_bytes,
50
                        LayerBlockType, LazyLoader, common_broadcastable_dtype,
51
52
53
                        cuda_device_count_stateless, get_cpu_memory,
                        get_open_port, is_torch_equal_or_newer, random_uuid,
                        resolve_obj_by_qualname)
54

55
56
# yapf: enable

57
if TYPE_CHECKING:
58
    from _typeshed import DataclassInstance
59
    from ray.util.placement_group import PlacementGroup
60
    from transformers.configuration_utils import PretrainedConfig
61

62
63
    import vllm.model_executor.layers.quantization as me_quant
    import vllm.model_executor.models as me_models
64
    from vllm.executor.executor_base import ExecutorBase
65
    from vllm.model_executor.layers.quantization import QuantizationMethods
66
67
    from vllm.model_executor.layers.quantization.base_config import (
        QuantizationConfig)
68
    from vllm.model_executor.model_loader import BaseModelLoader
69
    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
70

71
    ConfigType = type[DataclassInstance]
72
    HfOverrides = Union[dict, Callable[[type], type]]
73
else:
74
    PlacementGroup = Any
75
    PretrainedConfig = Any
76
    ExecutorBase = Any
77
    QuantizationConfig = Any
78
    QuantizationMethods = Any
79
80
    BaseModelLoader = Any
    TensorizerConfig = Any
81
    ConfigType = type
82
83
84
85
86
87
    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")
88

89
90
logger = init_logger(__name__)

91
92
ConfigT = TypeVar("ConfigT", bound=ConfigType)

93
TaskOption = Literal["auto", "generate", "embedding", "embed", "classify",
94
                     "score", "reward", "transcription"]
95

96
_ResolvedTask = Literal["generate", "embed", "classify", "score", "reward",
97
                        "draft", "transcription"]
98

99
RunnerType = Literal["generate", "pooling", "draft", "transcription"]
100

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

108
_TASK_RUNNER: dict[_ResolvedTask, RunnerType] = {
109
    task: runner
110
111
    for runner, tasks in _RUNNER_TASKS.items()
    for task in tasks
112
}
113

114

115
@runtime_checkable
116
117
118
119
120
121
class SupportsHash(Protocol):

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


122
123
class SupportsMetricsInfo(Protocol):

124
    def metrics_info(self) -> dict[str, str]:
125
126
127
        ...


128
129
130
131
132
133
class ModelImpl(str, enum.Enum):
    AUTO = "auto"
    VLLM = "vllm"
    TRANSFORMERS = "transformers"


134
135
136
137
138
139
140
141
142
143
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
144

145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
        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


186
def config(cls: ConfigT) -> ConfigT:
187
188
189
    """
    A decorator that ensures all fields in a dataclass have default values
    and that each field has a docstring.
190
191
192
193
194
195

    If a `ConfigT` is used as a CLI argument itself, the default value provided
    by `get_kwargs` will be the result parsing a JSON string as the kwargs
    (i.e. `ConfigT(**json.loads(cli_arg))`). However, if a particular `ConfigT`
    requires custom construction from CLI (i.e. `CompilationConfig`), it can
    have a `from_cli` method, which will be called instead.
196
197
198
199
200
201
202
203
204
    """
    if not is_dataclass(cls):
        raise TypeError("The decorated class must be a dataclass.")
    attr_docs = get_attr_docs(cls)
    for f in fields(cls):
        if f.init and f.default is MISSING and f.default_factory is MISSING:
            raise ValueError(
                f"Field '{f.name}' in {cls.__name__} must have a default value."
            )
205

206
207
208
        if f.name not in attr_docs:
            raise ValueError(
                f"Field '{f.name}' in {cls.__name__} must have a docstring.")
209
210
211
212
213
214
215
216
217

        if get_origin(f.type) is Union:
            args = get_args(f.type)
            literal_args = [arg for arg in args if get_origin(arg) is Literal]
            if len(literal_args) > 1:
                raise ValueError(
                    f"Field '{f.name}' in {cls.__name__} must use a single "
                    "Literal type. Please use 'Literal[Literal1, Literal2]' "
                    "instead of 'Union[Literal1, Literal2]'.")
218
219
220
    return cls


221
def get_field(cls: ConfigType, name: str) -> Field:
222
223
224
225
226
227
228
    """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__}.")
229
    named_field: Field = cls_fields[name]
230
231
232
233
234
235
236
237
    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.")


238
239
240
241
def is_init_field(cls: ConfigType, name: str) -> bool:
    return next(f for f in fields(cls) if f.name == name).init


242
243
244
245
246
TokenizerMode = Literal["auto", "slow", "mistral", "custom"]
ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]


@config
247
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
248
class ModelConfig:
249
250
251
252
253
254
255
256
257
258
259
    """Configuration for the model."""

    model: str = "facebook/opt-125m"
    """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."""
    task: Literal[TaskOption, Literal["draft"]] = "auto"
    """The task to use the model for. Each vLLM instance only supports one
    task, even if the same model can be used for multiple tasks. When the model
    only supports one task, "auto" can be used to select it; otherwise, you
    must specify explicitly which task to use."""
260
    tokenizer: SkipValidation[str] = None  # type: ignore
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
    """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
282
283
    """Random seed for reproducibility. Initialized to None in V0, but
    initialized to 0 in V1."""
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
    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)
299
    """RoPE scaling configuration. For example,
300
301
302
303
304
305
306
307
    `{"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."""
308
    max_model_len: SkipValidation[int] = None  # type: ignore
309
310
    """Model context length (prompt and output). If unspecified, will be
    automatically derived from the model config.
311

312
313
314
315
316
317
    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
318
    """Specify the maximum length for spec decoding draft models."""
319
    quantization: SkipValidation[Optional[QuantizationMethods]] = None
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
    """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
    OpenAI Chat Completions API."""
    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."""
352
353
354
355
    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."""
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
    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."""
    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
381
    config. If a callable, it is called to update the HuggingFace config."""
382
383
384
385
386
    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}`.
387
    """
388
389
390
391
392
393
394
    disable_mm_preprocessor_cache: bool = False
    """If `True`, disable caching of the multi-modal preprocessor/mapper (not
    recommended)."""
    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
395
    arguments. e.g. `{"cast_logits_dtype": "bfloat16"}`."""
396
397
398
399
400
401
    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}`.
402
    """
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
    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
418
    `--generation-config vllm`, only the override parameters are used."""
419
420
421
422
423
424
425
426
427
    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."""
428
429
    override_attention_dtype: Optional[str] = None
    """Override dtype for attention"""
430

431
432
433
434
435
436
437
438
439
440
441
442
    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.
        """
443
        factors: list[Any] = []
444
445
446
447
448
        factors.append(self.model)
        factors.append(self.dtype)
        factors.append(self.quantization)
        factors.append(self.revision)
        factors.append(self.code_revision)
449
450
451
        factors.append(self.max_model_len)
        factors.append(self.max_logprobs)
        factors.append(self.disable_sliding_window)
452
        factors.append(self.trust_remote_code)
453
454
455
        factors.append(self.generation_config)
        factors.append(self.model_impl)
        factors.append(self.override_generation_config)
456
457
        factors.append(self.rope_scaling)
        factors.append(self.rope_theta)
458
459
        # hf_config can control how the model looks!
        factors.append(self.hf_config.to_json_string())
460
461
        str_factors = str(factors)
        assert_hashable(str_factors)
462
463
        return hashlib.sha256(str(factors).encode()).hexdigest()

464
    def __post_init__(self) -> None:
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
        # 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)

483
484
485
486
487
488
489
490
491
492
493
494
        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):
495
            hf_overrides_kw = {}
496
            hf_overrides_fn = self.hf_overrides
497
        else:
498
            hf_overrides_kw = self.hf_overrides
499
            hf_overrides_fn = None
500

501
502
        if self.rope_scaling:
            hf_override: dict[str, Any] = {"rope_scaling": self.rope_scaling}
503
            hf_overrides_kw.update(hf_override)
504
            hf_overrides_str = json.dumps(hf_overrides_kw)
505
506
507
            msg = (
                "`--rope-scaling` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
508
            warnings.warn(DeprecationWarning(msg), stacklevel=2)
509
510
        if self.rope_theta is not None:
            hf_override = {"rope_theta": self.rope_theta}
511
            hf_overrides_kw.update(hf_override)
512
            hf_overrides_str = json.dumps(hf_overrides_kw)
513
514
515
            msg = (
                "`--rope-theta` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
516
517
            warnings.warn(DeprecationWarning(msg), stacklevel=2)

518
        self.maybe_pull_model_tokenizer_for_s3(self.model, self.tokenizer)
519

520
521
522
523
        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 "
524
525
                "module was not found. See "
                "https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile "  # noqa: E501
526
527
                "for instructions on how to install it.")

528
529
        from vllm.platforms import current_platform

530
531
532
533
534
535
        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)

536
537
538
539
        if (self.enable_sleep_mode
                and not current_platform.is_sleep_mode_available()):
            raise ValueError(
                "Sleep mode is not supported on current platform.")
540

541
542
543
        if isinstance(self.config_format, str):
            self.config_format = ConfigFormat(self.config_format)

544
        hf_config = get_config(self.hf_config_path or self.model,
545
546
                               self.trust_remote_code, self.revision,
                               self.code_revision, self.config_format)
547
548

        if hf_overrides_kw:
549
            logger.debug("Overriding HF config with %s", hf_overrides_kw)
550
551
            hf_config.update(hf_overrides_kw)
        if hf_overrides_fn:
552
            logger.debug("Overriding HF config with %s", hf_overrides_fn)
553
554
            hf_config = hf_overrides_fn(hf_config)

555
556
        self.hf_config = hf_config

557
        self.hf_text_config = get_hf_text_config(self.hf_config)
558
559
        self.attention_chunk_size = getattr(self.hf_text_config,
                                            "attention_chunk_size", None)
560
        self.encoder_config = self._get_encoder_config()
561
        self.hf_image_processor_config = get_hf_image_processor_config(
562
            self.model, hf_token=self.hf_token, revision=self.revision)
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580

        supported_tasks, task = self._resolve_task(self.task)
        self.supported_tasks = supported_tasks
        self.task = task
        if self.task in ("draft", "generate"):
            self.truncation_side = "left"
        else:
            self.truncation_side = "right"

        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,
        )
581

582
583
584
585
586
587
        # Workaround for Gemma 2 which uses interleaved sliding window
        # attention, but it's not specified in its config. TODO: remove this
        # when Gemma 2 is fixed in Transformers.
        if self.hf_text_config.model_type == "gemma2":
            self.hf_text_config.sliding_window_pattern = 2

588
        sliding_window = getattr(self.hf_text_config, "sliding_window", None)
589
590
        sliding_window_pattern = getattr(self.hf_text_config,
                                         "sliding_window_pattern", None)
591
592
        has_interleaved_attention = sliding_window_pattern is not None or (
            isinstance(sliding_window, list))
593

594
        if not self.disable_sliding_window and has_interleaved_attention:
595
596
            if (backend :=
                    envs.VLLM_ATTENTION_BACKEND) in ("XFORMERS", "FLASHINFER"):
597
598
                sliding_window_len_min = get_min_sliding_window(
                    self.hf_text_config.sliding_window)
599

600
                logger.warning_once(
601
602
603
604
605
                    "%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).",  # noqa: E501
                    self.hf_text_config.model_type,
                    backend,
                    sliding_window_len_min,
                )
606
607
608
609
610
611
612
613
                self.disable_sliding_window = True
            else:
                # for a model with interleaved attention,
                # the scheduler and the model treat it as full attention
                # (i.e., not dropping any tokens outside the window).
                # only the attention layer itself is aware of the sliding
                # window, and use the window size to compute the attention.
                self.hf_text_config.interleaved_sliding_window = sliding_window
614
615
616
617

                if hasattr(self.hf_text_config, "sliding_window"):
                    delattr(self.hf_text_config, "sliding_window")

618
                sliding_window = None
Woosuk Kwon's avatar
Woosuk Kwon committed
619

620
        self.original_max_model_len = self.max_model_len
621
        self.max_model_len = self.get_and_verify_max_len(self.max_model_len)
622
623
624
        self.served_model_name = get_served_model_name(self.model,
                                                       self.served_model_name)
        self.multimodal_config = self._init_multimodal_config()
625
626
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
627

628
        self.is_attention_free = self._init_attention_free()
629
        self.is_hybrid = self._init_is_hybrid()
630
        self.has_noops = self._init_has_noops()
631
632
        self.has_inner_state = self._init_has_inner_state()

633
634
635
        if (not current_platform.is_neuron() and self.override_neuron_config):
            raise ValueError(
                "`override_neuron_config` is only supported on Neuron.")
636

637
        self._verify_quantization()
638
        self._verify_cuda_graph()
639
        self._verify_bnb_config()
640

641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
    @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

657
658
    @property
    def registry(self):
659
        return me_models.ModelRegistry
660
661
662
663
664

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

665
666
    def maybe_pull_model_tokenizer_for_s3(self, model: str,
                                          tokenizer: str) -> None:
667
        """Pull model/tokenizer from S3 to temporary directory when needed.
668

669
        Args:
670
671
            model: Model name or path
            tokenizer: Tokenizer name or path
672
        """
673
674
675
676
677
678
679
680
681
682
683
684
        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:
685
                s3_model.pull_files(
686
                    model, ignore_pattern=["*.pt", "*.safetensors", "*.bin"])
687
688
689
690
691
692
693
694
695
                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(
                model, ignore_pattern=["*.pt", "*.safetensors", "*.bin"])
            self.tokenizer = s3_tokenizer.dir
696

697
    def _init_multimodal_config(self) -> Optional["MultiModalConfig"]:
698
        if self.registry.is_multimodal_model(self.architectures):
699
            return MultiModalConfig(
700
701
702
703
                limit_per_prompt=self.limit_mm_per_prompt,
                mm_processor_kwargs=self.mm_processor_kwargs,
                disable_mm_preprocessor_cache=self.
                disable_mm_preprocessor_cache)
704

705
        if self.limit_mm_per_prompt:
706
707
            raise ValueError("`limit_mm_per_prompt` is only supported for "
                             "multimodal models.")
708
        if self.mm_processor_kwargs:
709
710
            raise ValueError("`mm_processor_kwargs` is only supported for "
                             "multimodal models.")
711
        if self.disable_mm_preprocessor_cache:
712
713
            raise ValueError("`disable_mm_preprocessor_cache` is only "
                             "supported for multimodal models.")
714
715

        return None
716

717
718
719
720
    def _get_encoder_config(self):
        return get_sentence_transformer_tokenizer_config(
            self.model, self.revision)

721
    def _init_pooler_config(self) -> Optional["PoolerConfig"]:
722
        if self.runner_type == "pooling":
723
724
725
726
727
            if isinstance(self.override_pooler_config, dict):
                self.override_pooler_config = PoolerConfig(
                    **self.override_pooler_config)

            pooler_config = self.override_pooler_config or PoolerConfig()
728
729
730
731
732

            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():
733
734
                    if getattr(pooler_config, k) is None:
                        setattr(pooler_config, k, v)
735

736
            if self.is_matryoshka:
737
738
739
                if pooler_config.normalize is None:
                    pooler_config.normalize = True
                elif not pooler_config.normalize:
740
741
742
743
744
                    raise ValueError(
                        "`normalize` must be enabled (set to True) "
                        "for models that are compatible with "
                        "Matryoshka Representation.")

745
            return pooler_config
746

747
748
        return None

749
    def _init_attention_free(self) -> bool:
750
        return self.registry.is_attention_free_model(self.architectures)
751

752
    def _init_is_hybrid(self) -> bool:
753
        return self.registry.is_hybrid_model(self.architectures)
754

755
756
757
758
    def _init_has_noops(self) -> bool:
        architectures = getattr(self.hf_config, "architectures", [])
        return self.registry.is_noops_model(architectures)

759
    def _init_has_inner_state(self) -> bool:
760
        return self.registry.model_has_inner_state(self.architectures)
761

762
    def _verify_tokenizer_mode(self) -> None:
763
764
        tokenizer_mode = cast(TokenizerMode, self.tokenizer_mode.lower())
        if tokenizer_mode not in get_args(TokenizerMode):
765
766
            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
767
                f"one of {get_args(TokenizerMode)}.")
768
        self.tokenizer_mode = tokenizer_mode
769

770
771
    def _get_preferred_task(
        self,
772
773
        architectures: list[str],
        supported_tasks: set[_ResolvedTask],
774
775
776
777
    ) -> Optional[_ResolvedTask]:
        model_id = self.model
        if get_pooling_config(model_id, self.revision):
            return "embed"
778
        if self.registry.is_cross_encoder_model(architectures):
779
            return "score"
780
        if self.registry.is_transcription_model(architectures):
781
            return "transcription"
782

783
        suffix_to_preferred_task: list[tuple[str, _ResolvedTask]] = [
784
785
786
787
788
789
790
791
792
            # Other models follow this pattern
            ("ForCausalLM", "generate"),
            ("ForConditionalGeneration", "generate"),
            ("ForSequenceClassification", "classify"),
            ("ChatModel", "generate"),
            ("LMHeadModel", "generate"),
            ("EmbeddingModel", "embed"),
            ("RewardModel", "reward"),
        ]
793
        _, arch = self.registry.inspect_model_cls(architectures)
794
795
796
797
798
799
800

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

        return None

801
802
    def _resolve_task(
        self,
803
        task_option: Literal[TaskOption, Literal["draft"]],
804
    ) -> tuple[set[_ResolvedTask], _ResolvedTask]:
805
806
807
        if task_option == "draft":
            return {"draft"}, "draft"

808
809
        registry = self.registry
        architectures = self.architectures
810

811
        runner_support: dict[RunnerType, bool] = {
812
813
            # NOTE: Listed from highest to lowest priority,
            # in case the model supports multiple of them
814
815
816
            "transcription": registry.is_transcription_model(architectures),
            "generate": registry.is_text_generation_model(architectures),
            "pooling": registry.is_pooling_model(architectures),
817
        }
818
        supported_runner_types_lst: list[RunnerType] = [
819
820
821
822
823
            runner_type
            for runner_type, is_supported in runner_support.items()
            if is_supported
        ]

824
        supported_tasks_lst: list[_ResolvedTask] = [
825
826
            task for runner_type in supported_runner_types_lst
            for task in _RUNNER_TASKS[runner_type]
827
828
829
830
831
        ]
        supported_tasks = set(supported_tasks_lst)

        if task_option == "auto":
            selected_task = next(iter(supported_tasks_lst))
832

833
834
835
836
837
            if len(supported_tasks_lst) > 1:
                preferred_task = self._get_preferred_task(
                    architectures, supported_tasks)
                if preferred_task is not None:
                    selected_task = preferred_task
838

839
840
841
                logger.info(
                    "This model supports multiple tasks: %s. "
                    "Defaulting to '%s'.", supported_tasks, selected_task)
842
        else:
843
844
            # Aliases
            if task_option == "embedding":
845
846
847
848
849
850
                msg = ("The 'embedding' task has been renamed to "
                       "'embed', please use the new name. The old name "
                       "will be removed in v1.0.")
                warnings.warn(msg, DeprecationWarning, stacklevel=2)

                task_option = "embed"
851

852
853
854
855
856
857
858
            if task_option not in supported_tasks:
                msg = (
                    f"This model does not support the '{task_option}' task. "
                    f"Supported tasks: {supported_tasks}")
                raise ValueError(msg)

            selected_task = task_option
859

860
        return supported_tasks, selected_task
861

862
863
864
    def _parse_quant_hf_config(self):
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is None:
865
            # compressed-tensors uses a "compression_config" key
866
            quant_cfg = getattr(self.hf_config, "compression_config", None)
867
868
        return quant_cfg

869
    def _verify_quantization(self) -> None:
870
        supported_quantization = me_quant.QUANTIZATION_METHODS
871
        optimized_quantization_methods = [
872
            "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
873
            "awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
874
            "quark", "modelopt_fp4", "bitblas", "gptq_bitblas"
875
        ]
876
        if self.quantization is not None:
877
878
            self.quantization = cast(me_quant.QuantizationMethods,
                                     self.quantization)
879
880

        # Parse quantization method from the HF model config, if available.
881
882
        quant_cfg = self._parse_quant_hf_config()

883
884
        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()
885
886
887
            quant_method = quant_method.replace("compressed_tensors",
                                                "compressed-tensors")
            quant_cfg["quant_method"] = quant_method
888

889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
            # 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",
            ]
            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

910
            # Detect which checkpoint is it
911
            for name in quantization_methods:
912
                method = me_quant.get_quantization_config(name)
913
914
                quantization_override = method.override_quantization_method(
                    quant_cfg, self.quantization)
915
916
917
918
                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.
919
                    if (name in get_args(me_quant.QuantizationMethods)
920
921
922
923
924
925
                            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.")
926
927
928
                    quant_method = quantization_override
                    self.quantization = quantization_override
                    break
929

930
            # Verify quantization configurations.
931
            if self.quantization is None:
932
933
                self.quantization = quant_method
            elif self.quantization != quant_method:
934
935
                raise ValueError(
                    "Quantization method specified in the model config "
936
                    f"({quant_method}) does not match the quantization "
937
938
939
940
941
942
943
944
                    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}.")
945
            from vllm.platforms import current_platform
946
            current_platform.verify_quantization(self.quantization)
947
            if self.quantization not in optimized_quantization_methods:
948
                logger.warning(
949
                    "%s quantization is not fully "
950
                    "optimized yet. The speed can be slower than "
951
                    "non-quantized models.", self.quantization)
952

953
    def _verify_cuda_graph(self) -> None:
954
955
        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                          self.max_model_len)
956
        # CUDAGraph capture not supported for enc-dec models and mllama on ROCm
957
        ROCM_UNSUPPORTED_MODELS = ['mllama']
958
959
960
961
962
963
        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()):
964
965
            logger.warning(
                "CUDA graph is not supported for %s on ROCm yet, fallback "
966
                "to eager mode.", self.hf_config.model_type)
967
            self.enforce_eager = True
968

969
970
    def _verify_bnb_config(self) -> None:
        """
971
        The current version of bitsandbytes (0.45.3) with 8-bit models does not
972
        yet support CUDA graph.
973
        # TODO Remove this when bitsandbytes supports.
974
975
976
977
978
979
980
981
982
983
984
985
986
987
        """
        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(
988
                "CUDA graph is not supported on BitsAndBytes 8bit yet, "
989
                "fallback to the eager mode.")
990

991
992
            self.enforce_eager = True

993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
    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.")

1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
    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

1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
    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

1037
        # Reminder: Please update docs/features/compatibility_matrix.md
1038
        # If the feature combo become valid
1039
        from vllm.platforms import current_platform
1040
        if not current_platform.is_async_output_supported(self.enforce_eager):
1041
1042
1043
1044
1045
1046
1047
            self.use_async_output_proc = False
            return

        if envs.VLLM_USE_RAY_SPMD_WORKER:
            self.use_async_output_proc = False
            return

1048
        # Async postprocessor is not necessary for pooling models
1049
        # since there is no token generation
1050
        if self.runner_type == "pooling":
1051
1052
            self.use_async_output_proc = False

1053
        # Reminder: Please update docs/features/compatibility_matrix.md
1054
        # If the feature combo become valid
1055
1056
1057
        if speculative_config:
            self.use_async_output_proc = False

1058
1059
1060
1061
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
1062
1063
1064
1065
1066
1067

        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.")

1068
1069
        total_num_attention_heads = getattr(self.hf_text_config,
                                            "num_attention_heads", 0)
1070
1071
1072
1073
1074
1075
1076
        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}).")

1077
        if parallel_config.enable_expert_parallel:
1078
1079
            self._verify_with_expert_parallelism()

1080
        pipeline_parallel_size = parallel_config.pipeline_parallel_size
1081
        if pipeline_parallel_size > 1:
1082
            if not self.registry.is_pp_supported_model(self.architectures):
1083
1084
1085
1086
1087
1088
                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
1089

1090
1091
    def get_hf_config_sliding_window(
            self) -> Union[Optional[int], list[Optional[int]]]:
Woosuk Kwon's avatar
Woosuk Kwon committed
1092
        """Get the sliding window size, or None if disabled."""
1093
1094
1095
1096

        # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in
        # addition to sliding window size. We check if that field is present
        # and if it's False, return None.
1097
1098
        if (hasattr(self.hf_text_config, "use_sliding_window")
                and not self.hf_text_config.use_sliding_window):
1099
            return None
1100
        return getattr(self.hf_text_config, "sliding_window", None)
1101

1102
    def get_sliding_window(self) -> Optional[Union[int, list[Optional[int]]]]:
1103
1104
1105
1106
1107
1108
1109
1110
        """Get the sliding window size, or None if disabled.
        """
        # If user disables sliding window, return None.
        if self.disable_sliding_window:
            return None
        # Otherwise get the value from the hf config.
        return self.get_hf_config_sliding_window()

1111
    def get_vocab_size(self) -> int:
1112
        return self.hf_text_config.vocab_size
1113

1114
    def get_hidden_size(self) -> int:
1115
        return self.hf_text_config.hidden_size
1116

1117
1118
    @property
    def is_deepseek_mla(self) -> bool:
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
        if not hasattr(self.hf_text_config, "model_type"):
            return False
        elif self.hf_text_config.model_type in \
            ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp'):
            return self.hf_text_config.kv_lora_rank is not None
        elif self.hf_text_config.model_type == 'eagle':
            # if the model is an EAGLE module, check for the
            # underlying architecture
            return self.hf_text_config.model.model_type in \
                    ('deepseek_v2', 'deepseek_v3') \
                and self.hf_text_config.kv_lora_rank is not None
        return False
1131

1132
    def get_head_size(self) -> int:
wangding zeng's avatar
wangding zeng committed
1133
        # TODO remove hard code
1134
        if self.is_deepseek_mla:
1135
1136
            qk_rope_head_dim = getattr(self.hf_text_config, "qk_rope_head_dim",
                                       0)
1137
            if self.use_mla:
1138
                return self.hf_text_config.kv_lora_rank + qk_rope_head_dim
1139
1140
1141
1142
1143
            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
1144

1145
1146
1147
1148
1149
        if hasattr(self.hf_text_config,
                   "model_type") and (self.hf_text_config.model_type
                                      == "zamba2"):
            return self.hf_text_config.attention_head_dim

1150
1151
1152
        if self.is_attention_free:
            return 0

1153
1154
        # NOTE: Some configs may set head_dim=None in the config
        if getattr(self.hf_text_config, "head_dim", None) is not None:
1155
            return self.hf_text_config.head_dim
1156

1157
        # FIXME(woosuk): This may not be true for all models.
1158
1159
        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)
1160

1161
1162
    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
Zhuohan Li's avatar
Zhuohan Li committed
1163
        # For GPTBigCode & Falcon:
1164
        # NOTE: for falcon, when new_decoder_architecture is True, the
Zhuohan Li's avatar
Zhuohan Li committed
1165
1166
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
1167
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
1168
        new_decoder_arch_falcon = (
1169
            self.hf_config.model_type in falcon_model_types
1170
            and getattr(self.hf_config, "new_decoder_architecture", False))
1171
        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
1172
                                                   "multi_query", False):
Zhuohan Li's avatar
Zhuohan Li committed
1173
            # Multi-query attention, only one KV head.
Woosuk Kwon's avatar
Woosuk Kwon committed
1174
            # Currently, tensor parallelism is not supported in this case.
Zhuohan Li's avatar
Zhuohan Li committed
1175
            return 1
1176

1177
        # For DBRX and MPT
1178
1179
1180
1181
1182
        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":
1183
1184
1185
            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

1186
1187
1188
1189
1190
1191
1192
1193
        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")

1194
1195
1196
        if self.is_attention_free:
            return 0

1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
        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:
1207
            num_kv_heads = getattr(self.hf_text_config, attr, None)
1208
1209
1210
1211
1212
            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.
1213
        return self.hf_text_config.num_attention_heads
1214
1215
1216

    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU."""
1217
1218
1219
1220
        if self.use_mla:
            # When using MLA during decode it becomes MQA
            return 1

1221
1222
1223
1224
1225
1226
1227
        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)
1228

1229
1230
    def get_num_attention_heads(self,
                                parallel_config: "ParallelConfig") -> int:
1231
1232
        num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
        return num_heads // parallel_config.tensor_parallel_size
1233

1234
    def get_layers_start_end_indices(
1235
            self, parallel_config: "ParallelConfig") -> tuple[int, int]:
1236
        from vllm.distributed.utils import get_pp_indices
1237
1238
        if (self.hf_text_config.model_type == "deepseek_mtp"
                or self.hf_config.model_type == "mimo_mtp"):
1239
1240
1241
1242
1243
            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)
1244
1245
1246
        # the layout order is: DP x PP x TP
        pp_rank = (parallel_config.rank // parallel_config.tensor_parallel_size
                   ) % parallel_config.pipeline_parallel_size
1247
1248
        pp_size = parallel_config.pipeline_parallel_size
        start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
1249
        return start, end
Mor Zusman's avatar
Mor Zusman committed
1250

1251
1252
1253
    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
1254

1255
1256
1257
1258
1259
1260
1261
1262
    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
1263
1264
1265
        is_transformer = not self.is_hybrid and \
                            not self.has_noops and \
                            not self.is_attention_free
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
        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
1276
1277
1278
1279
        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])
1280
        else:
1281
            # Hybrid model Jamba
1282
1283
            layers_block_type_value = getattr(self.hf_config,
                                              "layers_block_type", None)
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
            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
1309

1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
    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

1322
    def try_get_generation_config(self) -> dict[str, Any]:
1323
        if self.generation_config in ("auto", "vllm"):
1324
            config = try_get_generation_config(
1325
                self.hf_config_path or self.model,
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
                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()

1340
    def get_diff_sampling_param(self) -> dict[str, Any]:
1341
        """
1342
        This method returns a dictionary containing the parameters
1343
1344
        that differ from the default sampling parameters. If
        `generation_config` is `"vllm"`, an empty dictionary is returned.
1345
1346

        Returns:
1347
            dict[str, Any]: A dictionary with the differing sampling
1348
            parameters, if `generation_config` is `"vllm"` an empty dictionary.
1349
        """
1350
        if self.generation_config == "vllm":
1351
1352
1353
1354
1355
1356
1357
            config = {}
        else:
            config = self.try_get_generation_config()

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

1358
1359
1360
1361
1362
1363
        available_params = [
            "repetition_penalty",
            "temperature",
            "top_k",
            "top_p",
            "min_p",
1364
            "max_new_tokens",
1365
1366
1367
1368
1369
1370
        ]
        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
            }
1371
1372
1373
1374
1375
            # 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")
1376
1377
        else:
            diff_sampling_param = {}
1378
1379
1380
1381
1382
1383
1384

        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`.")
1385
1386
        return diff_sampling_param

1387
    @property
1388
    def is_encoder_decoder(self) -> bool:
1389
        """Extract the HF encoder/decoder model flag."""
1390
        """
1391
        For Mllama, VLLM overrides HF's is_encoder_decoder flag and sets it to
1392
        True to enable cross-attention
1393
        Neuron needs all multimodal data to be in the decoder and does not
1394
1395
1396
1397
1398
1399
        need to explicitly enable cross-attention
        """
        if (current_platform.is_neuron()
                and self.hf_config.model_type == "mllama"):
            return False

1400
1401
1402
1403
1404
        return is_encoder_decoder(self.hf_config)

    @property
    def uses_mrope(self) -> bool:
        return uses_mrope(self.hf_config)
1405

1406
1407
1408
1409
    @property
    def is_multimodal_model(self) -> bool:
        return self.multimodal_config is not None

1410
1411
    @property
    def is_cross_encoder(self) -> bool:
1412
        return self.registry.is_cross_encoder_model(self.architectures)
1413

1414
1415
    @property
    def use_mla(self) -> bool:
1416
        return self.is_deepseek_mla and not envs.VLLM_MLA_DISABLE
1417

1418
    @property
1419
    def supported_runner_types(self) -> set[RunnerType]:
1420
1421
1422
1423
        return {_TASK_RUNNER[task] for task in self.supported_tasks}

    @property
    def runner_type(self) -> RunnerType:
1424
        return _TASK_RUNNER[cast(_ResolvedTask, self.task)]
1425

1426
1427
1428
    @property
    def is_v1_compatible(self) -> bool:
        architectures = getattr(self.hf_config, "architectures", [])
1429
        return me_models.ModelRegistry.is_v1_compatible(architectures)
1430

1431
1432
1433
1434
1435
    @property
    def is_matryoshka(self) -> bool:
        return (hasattr(self.hf_config, "matryoshka_dimensions")
                or getattr(self.hf_config, "is_matryoshka", False))

1436
1437
1438
1439
    @property
    def matryoshka_dimensions(self):
        return getattr(self.hf_config, "matryoshka_dimensions", None)

1440
    def get_and_verify_max_len(self, max_model_len: int):
1441
1442
1443
1444
        tokenizer_config = try_get_tokenizer_config(
            self.tokenizer,
            trust_remote_code=self.trust_remote_code,
            revision=self.tokenizer_revision)
1445
1446
        max_model_len = _get_and_verify_max_len(
            hf_config=self.hf_text_config,
1447
            tokenizer_config=tokenizer_config,
1448
1449
1450
1451
1452
            max_model_len=max_model_len,
            disable_sliding_window=self.disable_sliding_window,
            sliding_window_len=self.get_hf_config_sliding_window(),
            spec_target_max_model_len=self.spec_target_max_model_len,
            encoder_config=self.encoder_config)
1453
        logger.info("Using max model len %s", max_model_len)
1454
1455
        return max_model_len

1456

1457
BlockSize = Literal[1, 8, 16, 32, 64, 128]
1458
1459
1460
1461
1462
1463
CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2"]
PrefixCachingHashAlgo = Literal["builtin", "sha256"]


@config
@dataclass
1464
class CacheConfig:
1465
    """Configuration for the KV cache."""
1466

1467
    block_size: SkipValidation[BlockSize] = None  # type: ignore
1468
1469
1470
    """Size of a contiguous cache block in number of tokens. This is ignored on
    neuron devices and set to `--max-model-len`. On CUDA devices, only block
    sizes up to 32 are supported. On HPU devices, block size defaults to 128.
1471
1472
1473
1474

    This config has no static default. If left unspecified by the user, it will
    be set in `Platform.check_and_update_configs()` based on the current
    platform."""
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
    gpu_memory_utilization: float = 0.9
    """The fraction of GPU memory to be used for the model executor, which can
    range from 0 to 1. For example, a value of 0.5 would imply 50% GPU memory
    utilization. If unspecified, will use the default value of 0.9. This is a
    per-instance limit, and only applies to the current vLLM instance. It does
    not matter if you have another vLLM instance running on the same GPU. For
    example, if you have two vLLM instances running on the same GPU, you can
    set the GPU memory utilization to 0.5 for each instance."""
    swap_space: float = 4
    """Size of the CPU swap space per GPU (in GiB)."""
    cache_dtype: CacheDType = "auto"
    """Data type for kv cache storage. If "auto", will use model data type.
    CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ROCm (AMD GPU) supports
    fp8 (=fp8_e4m3)."""
    is_attention_free: bool = False
    """Whether the model is attention-free. This is primarily set in
    `ModelConfig` and that value should be manually duplicated here."""
    num_gpu_blocks_override: Optional[int] = None
    """Number of GPU blocks to use. This overrides the profiled `num_gpu_blocks`
    if specified. Does nothing if `None`. Used for testing preemption."""
    sliding_window: Optional[int] = None
    """Sliding window size for the KV cache. This is primarily set in
    `ModelConfig` and that value should be manually duplicated here."""
    enable_prefix_caching: Optional[bool] = None
    """Whether to enable prefix caching. Disabled by default for V0. Enabled by
    default for V1."""
    prefix_caching_hash_algo: PrefixCachingHashAlgo = "builtin"
    """Set the hash algorithm for prefix caching:\n
    - "builtin" is Python's built-in hash.\n
    - "sha256" is collision resistant but with certain overheads."""
    cpu_offload_gb: float = 0
    """The space in GiB to offload to CPU, per GPU. Default is 0, which means
    no offloading. Intuitively, this argument can be seen as a virtual way to
    increase the GPU memory size. For example, if you have one 24 GB GPU and
    set this to 10, virtually you can think of it as a 34 GB GPU. Then you can
    load a 13B model with BF16 weight, which requires at least 26GB GPU memory.
    Note that this requires fast CPU-GPU interconnect, as part of the model is
    loaded from CPU memory to GPU memory on the fly in each model forward pass.
    """
    calculate_kv_scales: bool = False
    """This enables dynamic calculation of `k_scale` and `v_scale` when
    kv_cache_dtype is fp8. If `False`, the scales will be loaded from the model
    checkpoint if available. Otherwise, the scales will default to 1.0."""
1518
1519
    cpu_kvcache_space_bytes: Optional[int] = None
    """(CPU backend only) CPU key-value cache space."""
1520
1521
1522
1523
1524
1525

    # Will be set after profiling.
    num_gpu_blocks: Optional[int] = field(default=None, init=False)
    """The number of blocks to allocate for GPU memory."""
    num_cpu_blocks: Optional[int] = field(default=None, init=False)
    """The number of blocks to allocate for CPU memory."""
1526

1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
    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.
        """
1539
        factors: list[Any] = []
1540
1541
        factors.append(self.cache_dtype)
        # `cpu_offload_gb` does not use `torch.compile` yet.
1542
1543
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1544
1545
        return hash_str

1546
1547
1548
    def __post_init__(self) -> None:
        self.swap_space_bytes = self.swap_space * GiB_bytes

1549
        self._verify_cache_dtype()
1550
        self._verify_prefix_caching()
1551

1552
    def metrics_info(self):
1553
1554
        # convert cache_config to dict(key: str, value: str) for prometheus
        # metrics info
1555
1556
        return {key: str(value) for key, value in self.__dict__.items()}

1557
1558
    @model_validator(mode='after')
    def _verify_args(self) -> Self:
1559
1560
1561
1562
        if self.cpu_offload_gb < 0:
            raise ValueError("CPU offload space must be non-negative"
                             f", but got {self.cpu_offload_gb}")

1563
1564
1565
1566
1567
        if self.gpu_memory_utilization > 1.0:
            raise ValueError(
                "GPU memory utilization must be less than 1.0. Got "
                f"{self.gpu_memory_utilization}.")

1568
1569
        return self

1570
1571
1572
    def _verify_cache_dtype(self) -> None:
        if self.cache_dtype == "auto":
            pass
1573
        elif self.cache_dtype in get_args(CacheDType):
1574
            logger.info(
1575
1576
                "Using fp8 data type to store kv cache. It reduces the GPU "
                "memory footprint and boosts the performance. "
1577
1578
                "Meanwhile, it may cause accuracy drop without a proper "
                "scaling factor")
1579
1580
1581
        else:
            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")

1582
1583
1584
1585
    def _verify_prefix_caching(self) -> None:
        if not self.enable_prefix_caching:
            return

1586
        if self.sliding_window is not None and not envs.VLLM_USE_V1:
1587
1588
1589
1590
            raise NotImplementedError(
                "Prefix caching is not supported with sliding window. "
                "Run with --disable-sliding-window to use prefix caching.")

1591
1592
        if (self.enable_prefix_caching and self.prefix_caching_hash_algo
                not in get_args(PrefixCachingHashAlgo)):
1593
1594
            raise ValueError(
                "Unknown prefix caching hash algorithm: "
1595
1596
                f"{self.prefix_caching_hash_algo}. Must be one of "
                f"{get_args(PrefixCachingHashAlgo)}.")
1597

1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_cpu_memory = get_cpu_memory()
        # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel
        # group are in the same node. However, the GPUs may span multiple nodes.
        num_gpus_per_node = parallel_config.tensor_parallel_size
        cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node

1608
1609
1610
        msg = (f"{cpu_memory_usage / GiB_bytes:.2f} GiB out of the "
               f"{total_cpu_memory / GiB_bytes:.2f} GiB total CPU memory "
               "is allocated for the swap space.")
1611
1612
1613
        if cpu_memory_usage > 0.7 * total_cpu_memory:
            raise ValueError("Too large swap space. " + msg)
        elif cpu_memory_usage > 0.4 * total_cpu_memory:
1614
            logger.warning("Possibly too large swap space. %s", msg)
1615

1616

1617
@config
1618
1619
@dataclass
class TokenizerPoolConfig:
1620
    """This config is deprecated and will be removed in a future release.
1621

1622
1623
1624
    Passing these parameters will have no effect. Please remove them from your
    configurations.
    """
1625

1626
1627
1628
1629
1630
1631
1632
1633
    pool_size: int = 0
    """This parameter is deprecated and will be removed in a future release.
    Passing this parameter will have no effect. Please remove it from your
    configurations."""
    pool_type: str = "ray"
    """This parameter is deprecated and will be removed in a future release.
    Passing this parameter will have no effect. Please remove it from your
    configurations."""
1634
    extra_config: dict = field(default_factory=dict)
1635
1636
1637
    """This parameter is deprecated and will be removed in a future release.
    Passing this parameter will have no effect. Please remove it from your
    configurations."""
1638

1639
1640
1641
1642
1643
    def __post_init__(self) -> None:
        logger.warning_once(
            "TokenizerPoolConfig is deprecated and will be removed in a "
            "future release. Passing this parameter will have no effect. "
            "Please remove it from your configurations.")
1644
1645


1646
1647
1648
1649
1650
1651
1652
class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    TENSORIZER = "tensorizer"
1653
    SHARDED_STATE = "sharded_state"
1654
    GGUF = "gguf"
1655
    BITSANDBYTES = "bitsandbytes"
1656
    MISTRAL = "mistral"
1657
    RUNAI_STREAMER = "runai_streamer"
1658
    RUNAI_STREAMER_SHARDED = "runai_streamer_sharded"
1659
    FASTSAFETENSORS = "fastsafetensors"
1660
1661


1662
@config
1663
1664
@dataclass
class LoadConfig:
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
    """Configuration for loading the model weights."""

    load_format: Union[str, LoadFormat,
                       "BaseModelLoader"] = LoadFormat.AUTO.value
    """The format of the model weights to load:\n
    - "auto" will try to load the weights in the safetensors format and fall
    back to the pytorch bin format if safetensors format is not available.\n
    - "pt" will load the weights in the pytorch bin format.\n
    - "safetensors" will load the weights in the safetensors format.\n
    - "npcache" will load the weights in pytorch format and store a numpy cache
    to speed up the loading.\n
    - "dummy" will initialize the weights with random values, which is mainly
    for profiling.\n
    - "tensorizer" will use CoreWeave's tensorizer library for fast weight
    loading. See the Tensorize vLLM Model script in the Examples section for
    more information.\n
    - "runai_streamer" will load the Safetensors weights using Run:ai Model
    Streamer.\n
    - "bitsandbytes" will load the weights using bitsandbytes quantization.\n
    - "sharded_state" will load weights from pre-sharded checkpoint files,
    supporting efficient loading of tensor-parallel models.\n
    - "gguf" will load weights from GGUF format files (details specified in
    https://github.com/ggml-org/ggml/blob/master/docs/gguf.md).\n
    - "mistral" will load weights from consolidated safetensors files used by
    Mistral models."""
1690
    download_dir: Optional[str] = None
1691
1692
    """Directory to download and load the weights, default to the default
    cache directory of Hugging Face."""
1693
1694
    model_loader_extra_config: Union[dict, TensorizerConfig] = field(
        default_factory=dict)
1695
    """Extra config for model loader. This will be passed to the model loader
1696
    corresponding to the chosen load_format."""
1697
    ignore_patterns: Optional[Union[list[str], str]] = None
1698
1699
    """The list of patterns to ignore when loading the model. Default to
    "original/**/*" to avoid repeated loading of llama's checkpoints."""
1700
    use_tqdm_on_load: bool = True
1701
1702
    """Whether to enable tqdm for showing progress bar when loading model
    weights."""
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
    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
    """
1713

1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
    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.
1728
        factors: list[Any] = []
1729
1730
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
1731
1732
        return hash_str

1733
    def __post_init__(self):
1734
1735
1736
        if isinstance(self.load_format, str):
            load_format = self.load_format.lower()
            self.load_format = LoadFormat(load_format)
1737

1738
1739
1740
1741
1742
1743
1744
        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/**/*"]

1745

1746
1747
1748
DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]


1749
@config
1750
@dataclass
1751
class ParallelConfig:
1752
    """Configuration for the distributed execution."""
1753

1754
1755
1756
1757
1758
1759
1760
    pipeline_parallel_size: int = 1
    """Number of pipeline parallel groups."""
    tensor_parallel_size: int = 1
    """Number of tensor parallel groups."""
    data_parallel_size: int = 1
    """Number of data parallel groups. MoE layers will be sharded according to
    the product of the tensor parallel size and data parallel size."""
1761
1762
    data_parallel_size_local: int = 1
    """Number of local data parallel groups."""
1763
1764
    data_parallel_rank: int = 0
    """Rank of the data parallel group."""
1765
1766
1767
    data_parallel_rank_local: Optional[int] = None
    """Local rank of the data parallel group,
    set only in SPMD mode."""
1768
    data_parallel_master_ip: str = "127.0.0.1"
1769
    """IP of the data parallel master."""
1770
1771
    data_parallel_rpc_port: int = 29550
    """Port for data parallel messaging."""
1772
1773
    data_parallel_master_port: int = 29500
    """Port of the data parallel master."""
Rui Qiao's avatar
Rui Qiao committed
1774
1775
    data_parallel_backend: str = "mp"
    """Backend to use for data parallel, either "mp" or "ray"."""
1776
1777
    enable_expert_parallel: bool = False
    """Use expert parallelism instead of tensor parallelism for MoE layers."""
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
    enable_eplb: bool = False
    """Enable expert parallelism load balancing for MoE layers."""
    num_redundant_experts: int = 0
    """Number of redundant experts to use for expert parallelism."""
    eplb_window_size: int = 1000
    """Window size for expert load recording."""
    eplb_step_interval: int = 3000
    """
    Interval for rearranging experts in expert parallelism.
    
    Note that if this is greater than the EPLB window size, only the metrics
    of the last `eplb_window_size` steps will be used for rearranging experts.
    """
    eplb_log_balancedness: bool = False
    """
    Log the balancedness each step of expert parallelism.
    This is turned off by default since it will cause communication overhead.
    """

1797
    max_parallel_loading_workers: Optional[int] = None
1798
    """Maximum number of parallel loading workers when loading model
1799
1800
    sequentially in multiple batches. To avoid RAM OOM when using tensor
    parallel and large models."""
1801
1802

    disable_custom_all_reduce: bool = False
1803
    """Disable the custom all-reduce kernel and fall back to NCCL."""
1804
1805

    tokenizer_pool_config: Optional[TokenizerPoolConfig] = None
1806
1807
    """This parameter is deprecated and will be removed in a future release.
    Please remove it from your configs"""
1808
1809

    ray_workers_use_nsight: bool = False
1810
    """Whether to profile Ray workers with nsight, see https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler."""
1811
1812

    placement_group: Optional["PlacementGroup"] = None
1813
    """ray distributed model workers placement group."""
1814

1815
    distributed_executor_backend: Optional[Union[DistributedExecutorBackend,
1816
                                                 type["ExecutorBase"]]] = None
1817
1818
1819
1820
1821
1822
1823
    """Backend to use for distributed model
    workers, either "ray" or "mp" (multiprocessing). If the product
    of pipeline_parallel_size and tensor_parallel_size is less than
    or equal to the number of GPUs available, "mp" will be used to
    keep processing on a single host. Otherwise, this will default
    to "ray" if Ray is installed and fail otherwise. Note that tpu
    and hpu only support Ray for distributed inference."""
1824
1825

    worker_cls: str = "auto"
1826
1827
    """The full name of the worker class to use. If "auto", the worker class
    will be determined based on the platform."""
1828
    sd_worker_cls: str = "auto"
Ning Xie's avatar
Ning Xie committed
1829
    """The full name of the worker class to use for speculative decoding.
1830
    If "auto", the worker class will be determined based on the platform."""
1831
    worker_extension_cls: str = ""
1832
1833
1834
1835
    """The full name of the worker extension class to use. The worker extension
    class is dynamically inherited by the worker class. This is used to inject
    new attributes and methods to the worker class for use in collective_rpc
    calls."""
1836
1837

    world_size: int = field(init=False)
1838
    """world_size is TPxPP, it affects the number of workers we create."""
1839
1840

    rank: int = 0
1841
    """Global rank in distributed setup."""
1842

1843
    enable_multimodal_encoder_data_parallel: bool = False
1844
    """ Use data parallelism instead of tensor parallelism for vision encoder.
1845
1846
    Only support LLama4 for now"""

1847
1848
1849
1850
1851
1852
    @property
    def world_size_across_dp(self) -> int:
        """world_size_across_dp is TPxPPxDP, it is the size of the world
        including data parallelism."""
        return self.world_size * self.data_parallel_size

1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
    def get_next_dp_init_port(self) -> int:
        """
        We might need to initialize process groups in multiple
        processes that is related to data parallelism,
        e.g. both in the worker and in the engine, which
        can live in different processes. To avoid port conflicts, we
        increment the port number each time we need to initialize a
        new process group related to data parallelism.
        """
        answer = self.data_parallel_master_port
        self.data_parallel_master_port += 1
        return answer

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

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

        return dp_group

    @staticmethod
    def has_unfinished_dp(dp_group: "ProcessGroup",
youkaichao's avatar
youkaichao committed
1882
                          has_unfinished: bool) -> bool:
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
        tensor = torch.tensor([has_unfinished],
                              dtype=torch.int32,
                              device="cpu")
        # dp rank 0: has_unfinished_seqs=True
        # dp rank 1: has_unfinished_seqs=False
        # aggregated: has_unfinished_seqs=True
        # so this is an OR operation, i.e. MAX in integers
        torch.distributed.all_reduce(tensor, op=ReduceOp.MAX, group=dp_group)
        aggregated_has_unfinished = bool(tensor.item())
        return aggregated_has_unfinished

1894
1895
1896
1897
1898
1899
1900
1901
    def compute_hash(self):
        """
        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
1902
        factors: list[Any] = []
1903
1904
        factors.append(self.pipeline_parallel_size)
        factors.append(self.tensor_parallel_size)
1905
        factors.append(self.enable_expert_parallel)
1906
1907
        factors.append(self.data_parallel_size)
        factors.append(envs.VLLM_ALL2ALL_BACKEND)
1908
1909
        return hashlib.sha256(str(factors).encode()).hexdigest()

1910
1911
1912
    def __post_init__(self) -> None:
        self.world_size = self.pipeline_parallel_size * \
            self.tensor_parallel_size
1913

1914
1915
1916
1917
1918
1919
        if self.data_parallel_size_local > self.data_parallel_size:
            raise ValueError(
                f"data_parallel_size_local ({self.data_parallel_size_local}) "
                f"must be <= data_parallel_size ({self.data_parallel_size})")

        if self.data_parallel_size > 1 or self.data_parallel_size_local == 0:
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
            # Data parallel was specified in the engine args.
            self.data_parallel_master_port = get_open_port()
        else:
            # Otherwise fall back to env vars (e.g. for offline SPMD case).
            self.data_parallel_size = envs.VLLM_DP_SIZE
            self.data_parallel_rank = envs.VLLM_DP_RANK
            self.data_parallel_rank_local = envs.VLLM_DP_RANK_LOCAL
            self.data_parallel_master_ip = envs.VLLM_DP_MASTER_IP
            self.data_parallel_master_port = envs.VLLM_DP_MASTER_PORT

1930
1931
1932
1933
1934
        if self.distributed_executor_backend == "external_launcher":
            import os
            os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
            logger.info("Disabling V1 multiprocessing for external launcher.")

1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
        if self.enable_eplb:
            if not current_platform.is_cuda():
                raise ValueError(
                    "Expert parallelism load balancing is only supported on "
                    "CUDA devices now.")
            if self.num_redundant_experts < 0:
                raise ValueError(
                    "num_redundant_experts must be non-negative, but got "
                    f"{self.num_redundant_experts}.")
        else:
            if self.num_redundant_experts != 0:
                raise ValueError(
                    "num_redundant_experts should be used with EPLB."
                    f"{self.num_redundant_experts}.")
1949
        if self.distributed_executor_backend is None and self.world_size > 1:
1950
1951
1952
            # We use multiprocessing by default if world_size fits on the
            # current node and we aren't in a ray placement group.

1953
            from vllm.executor import ray_utils
1954
            backend: DistributedExecutorBackend = "mp"
1955
            ray_found = ray_utils.ray_is_available()
1956
1957
            if current_platform.is_neuron():
                # neuron uses single process to control multiple devices
1958
1959
                backend = "uni"
            elif current_platform.is_tpu() and envs.VLLM_XLA_USE_SPMD:
1960
1961
1962
                backend = "uni"
            elif (current_platform.is_cuda()
                  and cuda_device_count_stateless() < self.world_size):
1963
1964
                if not ray_found:
                    raise ValueError("Unable to load Ray which is "
1965
1966
1967
                                     "required for multi-node inference, "
                                     "please install Ray with `pip install "
                                     "ray`.") from ray_utils.ray_import_err
1968
                backend = "ray"
Rui Qiao's avatar
Rui Qiao committed
1969
1970
1971
1972
            elif self.data_parallel_backend == "ray":
                logger.info("Using ray distributed inference because "
                            "data_parallel_backend is ray")
                backend = "ray"
1973
            elif ray_found:
1974
                if self.placement_group:
1975
                    backend = "ray"
1976
1977
1978
1979
1980
1981
                else:
                    from ray import is_initialized as ray_is_initialized
                    if ray_is_initialized():
                        from ray.util import get_current_placement_group
                        if get_current_placement_group():
                            backend = "ray"
1982
            self.distributed_executor_backend = backend
1983
1984
            logger.debug("Defaulting to use %s for distributed inference",
                         backend)
1985

1986
1987
1988
        if self.distributed_executor_backend is None and self.world_size == 1:
            self.distributed_executor_backend = "uni"

1989
1990
1991
1992
1993
1994
    @property
    def use_ray(self) -> bool:
        return self.distributed_executor_backend == "ray" or (
            isinstance(self.distributed_executor_backend, type)
            and self.distributed_executor_backend.uses_ray)

1995
1996
    @model_validator(mode='after')
    def _verify_args(self) -> Self:
1997
1998
        # Lazy import to avoid circular import
        from vllm.executor.executor_base import ExecutorBase
1999
        from vllm.platforms import current_platform
2000
        if self.distributed_executor_backend not in (
2001
2002
                "ray", "mp", "uni",
                "external_launcher", None) and not (isinstance(
2003
2004
                    self.distributed_executor_backend, type) and issubclass(
                        self.distributed_executor_backend, ExecutorBase)):
2005
            raise ValueError(
2006
2007
                "Unrecognized distributed executor backend "
                f"{self.distributed_executor_backend}. Supported "
2008
2009
                "values are 'ray', 'mp' 'uni', 'external_launcher' or"
                " custom ExecutorBase subclass.")
2010
        if self.use_ray:
2011
2012
            from vllm.executor import ray_utils
            ray_utils.assert_ray_available()
2013
2014

        if not current_platform.use_custom_allreduce():
2015
            self.disable_custom_all_reduce = True
Aaron Pham's avatar
Aaron Pham committed
2016
            logger.debug(
2017
                "Disabled the custom all-reduce kernel because it is not "
2018
                "supported on current platform.")
2019
        if self.ray_workers_use_nsight and not self.use_ray:
2020
2021
            raise ValueError("Unable to use nsight profiling unless workers "
                             "run with Ray.")
2022

2023
        return self
2024

2025

2026
PreemptionMode = Literal["swap", "recompute"]
2027
2028
2029
2030
SchedulerPolicy = Literal["fcfs", "priority"]


@config
2031
@dataclass
2032
class SchedulerConfig:
2033
    """Scheduler configuration."""
2034

2035
2036
    runner_type: RunnerType = "generate"
    """The runner type to launch for the model."""
2037

2038
    max_num_batched_tokens: SkipValidation[int] = None  # type: ignore
2039
    """Maximum number of tokens to be processed in a single iteration.
2040

2041
2042
    This config has no static default. If left unspecified by the user, it will
    be set in `EngineArgs.create_engine_config` based on the usage context."""
2043

2044
    max_num_seqs: SkipValidation[int] = None  # type: ignore
2045
    """Maximum number of sequences to be processed in a single iteration.
2046

2047
2048
    This config has no static default. If left unspecified by the user, it will
    be set in `EngineArgs.create_engine_config` based on the usage context."""
2049

2050
    max_model_len: SkipValidation[int] = None  # type: ignore
2051
2052
2053
    """Maximum length of a sequence (including prompt and generated text). This
    is primarily set in `ModelConfig` and that value should be manually
    duplicated here."""
2054

2055
    max_num_partial_prefills: int = 1
2056
2057
    """For chunked prefill, the maximum number of sequences that can be
    partially prefilled concurrently."""
2058
2059

    max_long_partial_prefills: int = 1
2060
2061
2062
2063
    """For chunked prefill, the maximum number of prompts longer than
    long_prefill_token_threshold that will be prefilled concurrently. Setting
    this less than max_num_partial_prefills will allow shorter prompts to jump
    the queue in front of longer prompts in some cases, improving latency."""
2064
2065

    long_prefill_token_threshold: int = 0
2066
2067
    """For chunked prefill, a request is considered long if the prompt is
    longer than this number of tokens."""
2068

2069
    num_lookahead_slots: int = 0
2070
2071
2072
2073
2074
2075
2076
    """The number of slots to allocate per sequence per
    step, beyond the known token ids. This is used in speculative
    decoding to store KV activations of tokens which may or may not be
    accepted.

    NOTE: This will be replaced by speculative config in the future; it is
    present to enable correctness tests until then."""
2077

2078
2079
2080
    cuda_graph_sizes: list[int] = field(default_factory=lambda: [512])
    """Cuda graph capture sizes, default is 512.
    1. if one value is provided, then the capture list would follow the
2081
    pattern: [1, 2, 4] + [i for i in range(8, cuda_graph_sizes + 1, 8)]
2082
    2. more than one value (e.g. 1 2 128) is provided, then the capture list
2083
    will follow the provided list."""
2084

2085
    delay_factor: float = 0.0
2086
2087
    """Apply a delay (of delay factor multiplied by previous
    prompt latency) before scheduling next prompt."""
2088

2089
    enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
2090
2091
    """If True, prefill requests can be chunked based
    on the remaining max_num_batched_tokens."""
2092
2093

    is_multimodal_model: bool = False
2094
2095
2096
2097
2098
    """True if the model is multimodal."""

    # TODO (ywang96): Make this configurable.
    max_num_encoder_input_tokens: int = field(init=False)
    """Multimodal encoder compute budget, only used in V1.
2099

2100
2101
2102
2103
2104
2105
2106
2107
2108
    NOTE: This is not currently configurable. It will be overridden by
    max_num_batched_tokens in case max multimodal embedding size is larger."""

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

    NOTE: This is not currently configurable. It will be overridden by
    max_num_batched_tokens in case max multimodal embedding size is larger."""
2109

2110
    preemption_mode: Optional[PreemptionMode] = None
2111
2112
2113
2114
2115
2116
    """Whether to perform preemption by swapping or
    recomputation. If not specified, we determine the mode as follows:
    We use recomputation by default since it incurs lower overhead than
    swapping. However, when the sequence group has multiple sequences
    (e.g., beam search), recomputation is not currently supported. In
    such a case, we use swapping instead."""
2117
2118

    num_scheduler_steps: int = 1
2119
    """Maximum number of forward steps per scheduler call."""
2120

2121
2122
    multi_step_stream_outputs: bool = True
    """If False, then multi-step will stream outputs at the end of all steps"""
2123
2124

    send_delta_data: bool = False
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
    """Private API. If used, scheduler sends delta data to
    workers instead of an entire data. It should be enabled only
    when SPMD worker architecture is enabled. I.e.,
    VLLM_USE_RAY_SPMD_WORKER=1"""

    policy: SchedulerPolicy = "fcfs"
    """The scheduling policy to use:\n
    - "fcfs" means first come first served, i.e. requests are handled in order
    of arrival.\n
    - "priority" means requests are handled based on given priority (lower
    value means earlier handling) and time of arrival deciding any ties)."""
2136
2137

    chunked_prefill_enabled: bool = field(init=False)
2138
    """True if chunked prefill is enabled."""
2139

2140
    disable_chunked_mm_input: bool = False
2141
2142
2143
2144
2145
2146
    """If set to true and chunked prefill is enabled, we do not want to
    partially schedule a multimodal item. Only used in V1
    This ensures that if a request has a mixed prompt
    (like text tokens TTTT followed by image tokens IIIIIIIIII) where only
    some image tokens can be scheduled (like TTTTIIIII, leaving IIIII),
    it will be scheduled as TTTT in one step and IIIIIIIIII in the next."""
2147

2148
2149
    # scheduler class or path. "vllm.core.scheduler.Scheduler" (default)
    # or "mod.custom_class".
2150
    scheduler_cls: Union[str, type[object]] = "vllm.core.scheduler.Scheduler"
2151
2152
2153
    """The scheduler class to use. "vllm.core.scheduler.Scheduler" is the
    default scheduler. Can be a class directly or the path to a class of form
    "mod.custom_class"."""
2154

2155
2156
2157
2158
2159
2160
    disable_hybrid_kv_cache_manager: bool = False
    """If set to True, KV cache manager will allocate the same size of KV cache
    for all attention layers even if there are multiple type of attention layers
    like full attention and sliding window attention.
    """

2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
    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.
2175
        factors: list[Any] = []
2176
2177
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2178
2179
        return hash_str

2180
    def __post_init__(self) -> None:
2181
2182
2183
2184
2185
2186
        if self.max_model_len is None:
            self.max_model_len = 8192

        if self.max_num_seqs is None:
            self.max_num_seqs = 128

2187
2188
2189
        if self.max_num_batched_tokens is None:
            if self.enable_chunked_prefill:
                if self.num_scheduler_steps > 1:
2190
2191
2192
2193
                    # Multi-step Chunked-Prefill doesn't allow prompt-chunking
                    # for now. Have max_num_batched_tokens set to max_model_len
                    # so we don't reject sequences on account of a short
                    # max_num_batched_tokens.
2194
                    self.max_num_batched_tokens = max(
2195
                        self.max_model_len, DEFAULT_MAX_NUM_BATCHED_TOKENS)
2196
                else:
2197
                    self.max_num_batched_tokens = (
2198
                        DEFAULT_MAX_NUM_BATCHED_TOKENS)
2199
            else:
2200
                # If max_model_len is too short, use
2201
                # DEFAULT_MAX_NUM_BATCHED_TOKENS as the default value
2202
                # for higher throughput.
2203
                self.max_num_batched_tokens = max(
2204
                    self.max_model_len, DEFAULT_MAX_NUM_BATCHED_TOKENS)
2205

2206
2207
            if self.runner_type == "pooling":
                # Choose specific value for higher throughput
2208
2209
                self.max_num_batched_tokens = max(
                    self.max_num_batched_tokens,
2210
                    POOLING_MODEL_MAX_NUM_BATCHED_TOKENS,
2211
                )
2212
            if self.is_multimodal_model:
2213
                # The value needs to be at least the number of multimodal tokens
2214
2215
                self.max_num_batched_tokens = max(
                    self.max_num_batched_tokens,
2216
                    MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
2217
2218
                )

2219
2220
2221
2222
2223
2224
2225
            # When using default settings,
            # Ensure max_num_batched_tokens does not exceed model limit.
            # Some models (e.g., Whisper) have embeddings tied to max length.
            self.max_num_batched_tokens = min(
                self.max_num_seqs * self.max_model_len,
                self.max_num_batched_tokens)

2226
2227
2228
        self.max_num_encoder_input_tokens = self.max_num_batched_tokens
        self.encoder_cache_size = self.max_num_batched_tokens

2229
        if self.enable_chunked_prefill:
2230
2231
            logger.info(
                "Chunked prefill is enabled with max_num_batched_tokens=%d.",
2232
                self.max_num_batched_tokens)
2233

2234
        self.chunked_prefill_enabled = self.enable_chunked_prefill
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
        if self.max_num_partial_prefills > 1:
            if self.long_prefill_token_threshold == 0:
                self.long_prefill_token_threshold = int(self.max_model_len *
                                                        0.04)

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

2247
2248
    @model_validator(mode='after')
    def _verify_args(self) -> Self:
2249
2250
        if (self.max_num_batched_tokens < self.max_model_len
                and not self.chunked_prefill_enabled):
2251
2252
2253
2254
2255
2256
2257
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
                f"smaller than max_model_len ({self.max_model_len}). "
                "This effectively limits the maximum sequence length to "
                "max_num_batched_tokens and makes vLLM reject longer "
                "sequences. Please increase max_num_batched_tokens or "
                "decrease max_model_len.")
2258

2259
2260
2261
2262
2263
        if self.max_num_batched_tokens < self.max_num_seqs:
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
                "be greater than or equal to max_num_seqs "
                f"({self.max_num_seqs}).")
2264

2265
2266
2267
2268
2269
2270
2271
        if self.max_num_batched_tokens > self.max_num_seqs * self.max_model_len:
            logger.warning(
                "max_num_batched_tokens (%d) exceeds max_num_seqs"
                "* max_model_len (%d). This may lead to unexpected behavior.",
                self.max_num_batched_tokens,
                self.max_num_seqs * self.max_model_len)

2272
2273
2274
2275
2276
2277
        if self.num_lookahead_slots < 0:
            raise ValueError(
                "num_lookahead_slots "
                f"({self.num_lookahead_slots}) must be greater than or "
                "equal to 0.")

2278
2279
2280
2281
2282
2283
        if self.num_scheduler_steps < 1:
            raise ValueError(
                "num_scheduler_steps "
                f"({self.num_scheduler_steps}) must be greater than or "
                "equal to 1.")

2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
        if self.max_num_partial_prefills < 1:
            raise ValueError(
                f"max_num_partial_prefills ({self.max_num_partial_prefills}) "
                "must be greater than or equal to 1.")
        elif self.max_num_partial_prefills > 1:
            if not self.chunked_prefill_enabled:
                raise ValueError("Chunked prefill must be enabled to set "
                                 "max_num_partial_prefills > 1.")

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

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

2307
2308
        return self

2309
2310
2311
2312
    @property
    def is_multi_step(self) -> bool:
        return self.num_scheduler_steps > 1

2313

2314
2315
2316
2317
Device = Literal["auto", "cuda", "neuron", "cpu", "tpu", "xpu", "hpu"]


@config
2318
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
2319
class DeviceConfig:
2320
2321
    """Configuration for the device to use for vLLM execution."""

2322
    device: SkipValidation[Optional[Union[Device, torch.device]]] = "auto"
2323
    """Device type for vLLM execution.
2324
2325
2326
    This parameter is deprecated and will be
    removed in a future release.
    It will now be set automatically based
2327
    on the current platform."""
2328
2329
2330
    device_type: str = field(init=False)
    """Device type from the current platform. This is set in
    `__post_init__`."""
2331

2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
    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.
2347
        factors: list[Any] = []
2348
2349
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2350
2351
        return hash_str

2352
2353
    def __post_init__(self):
        if self.device == "auto":
2354
            # Automated device type detection
2355
            from vllm.platforms import current_platform
2356
            self.device_type = current_platform.device_type
2357
            if not self.device_type:
2358
2359
2360
2361
                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.")
2362
2363
        else:
            # Device type is assigned explicitly
2364
2365
2366
2367
            if isinstance(self.device, str):
                self.device_type = self.device
            elif isinstance(self.device, torch.device):
                self.device_type = self.device.type
2368
2369

        # Some device types require processing inputs on CPU
2370
        if self.device_type in ["neuron"]:
2371
            self.device = torch.device("cpu")
2372
2373
        elif self.device_type in ["tpu"]:
            self.device = None
2374
2375
2376
2377
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)

2378

2379
2380
SpeculativeMethod = Literal["ngram", "eagle", "eagle3", "medusa",
                            "mlp_speculator", "draft_model", "deepseek_mtp"]
2381
2382
2383
2384
2385
SpeculativeAcceptanceMethod = Literal["rejection_sampler",
                                      "typical_acceptance_sampler"]


@config
2386
@dataclass
2387
class SpeculativeConfig:
2388
    """Configuration for speculative decoding."""
2389

2390
    # General speculative decoding control
2391
    num_speculative_tokens: SkipValidation[int] = None  # type: ignore
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
    """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."""
    acceptance_method: SpeculativeAcceptanceMethod = "rejection_sampler"
    """The method to use for accepting draft tokens:\n
    - "rejection_sampler" maps to `RejectionSampler`.\n
    - "typical_acceptance_sampler" maps to `TypicalAcceptanceSampler`.

    If using `typical_acceptance_sampler`, the related configuration
    `posterior_threshold` and `posterior_alpha` should be considered."""
2412
    draft_tensor_parallel_size: Optional[int] = None
2413
2414
    """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."""
2415
    disable_logprobs: bool = True
2416
2417
2418
    """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."""
2419

2420
    # Draft model configuration
2421
    quantization: Optional[me_quant.QuantizationMethods] = None
2422
2423
2424
    """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."""
2425
    max_model_len: Optional[int] = None
2426
2427
    """The maximum model length of the draft model. Used when testing the
    ability to skip speculation for some sequences."""
2428
    revision: Optional[str] = None
2429
2430
2431
    """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."""
2432
    code_revision: Optional[str] = None
2433
2434
2435
    """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."""
2436

2437
    # Advanced control
2438
    disable_mqa_scorer: bool = False
2439
2440
    """Disable the MQA scorer and fall back to batch expansion for scoring
    proposals."""
2441
    disable_by_batch_size: Optional[int] = None
2442
2443
2444
2445
    """Disable speculative decoding for new incoming requests when the number
    of enqueued requests is larger than this value, if provided."""

    # Ngram proposer configuration
2446
    prompt_lookup_max: Optional[int] = None
2447
2448
    """Maximum size of ngram token window when using Ngram proposer, required
    when method is set to ngram."""
2449
    prompt_lookup_min: Optional[int] = None
2450
2451
2452
2453
    """Minimum size of ngram token window when using Ngram proposer, if
    provided. Defaults to 1."""

    # Typical acceptance sampler configuration
2454
    posterior_threshold: Optional[float] = None
2455
2456
2457
2458
    """A threshold value that sets a lower bound on the posterior probability
    of a token in the target model for it to be accepted. This threshold is
    used only when we use the `TypicalAcceptanceSampler` for token acceptance.
    """
2459
    posterior_alpha: Optional[float] = None
2460
2461
    """Scaling factor for entropy-based threshold, applied when using
    `TypicalAcceptanceSampler`."""
2462

2463
    speculative_token_tree: Optional[str] = None
2464
    """Specifies the tree structure for speculative token generation.
2465
    """
2466
    # required configuration params passed from engine
2467
    target_model_config: SkipValidation[ModelConfig] = None  # type: ignore
2468
    """The configuration of the target model."""
2469
2470
    target_parallel_config: SkipValidation[
        ParallelConfig] = None  # type: ignore
2471
    """The parallel configuration for the target model."""
2472
    enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
2473
2474
    """Whether vLLM is configured to use chunked prefill or not. Used for
    raising an error since it's not yet compatible with speculative decode."""
2475
    disable_log_stats: SkipValidation[bool] = None  # type: ignore
2476
2477
    """Whether to disable the periodic printing of stage times in speculative
    decoding."""
2478
2479

    # params generated in the post-init stage
2480
    draft_model_config: SkipValidation[ModelConfig] = None  # type: ignore
2481
    """The configuration of the draft model initialized internal."""
2482
2483
    draft_parallel_config: SkipValidation[
        ParallelConfig] = None  # type: ignore
2484
    """The parallel configuration for the draft model initialized internal."""
2485

2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
    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.
        """
2498
        factors: list[Any] = []
2499
2500
2501
        # Eagle3 affects the computation graph because it returns intermediate
        # hidden states in addition to the final hidden state.
        factors.append(self.method == "eagle3")
2502
2503
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2504
2505
        return hash_str

2506
2507
2508
2509
2510
    @classmethod
    def from_dict(cls, dict_value: dict) -> "SpeculativeConfig":
        """Parse the CLI value for the speculative config."""
        return cls(**dict_value)

2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
    @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"]
            })
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531

        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"]
            })
            return hf_config

2532
2533
        return hf_config

2534
    def __post_init__(self):
2535

2536
2537
2538
2539
2540
2541
2542
        # 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.
2543
2544
2545
2546

        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
2547
            if self.target_model_config and \
2548
2549
2550
2551
                (self.target_model_config.hf_text_config.model_type \
                        == "deepseek_v3" or
                    self.target_model_config.hf_text_config.model_type \
                        == "mimo"):
2552
2553
2554
2555
                # use the draft model from the same model:
                self.model = self.target_model_config.model
            elif self.method in ("ngram", "[ngram]"):
                self.model = "ngram"
2556
            else:
2557
2558
2559
                raise ValueError("num_speculative_tokens was provided without "
                                 "speculative model.")

2560
2561
        # Automatically configure the method for ngram when "model" is used
        # instead of "method"
2562
2563
2564
2565
2566
2567
2568
        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"
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
            # 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
2583
            if self.prompt_lookup_min < 1:
2584
2585
2586
2587
2588
                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")
2589
            if self.prompt_lookup_min > self.prompt_lookup_max:
2590
2591
2592
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must "
                    f"be <= prompt_lookup_max={self.prompt_lookup_max}")
2593

2594
2595
2596
            # 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.
2597
2598
            self.draft_model_config = self.target_model_config
            self.draft_parallel_config = self.target_parallel_config
2599
        else:
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
            self.prompt_lookup_max = 0
            self.prompt_lookup_min = 0

            if self.model is not None:
                self.draft_model_config = ModelConfig(
                    model=self.model,
                    task="draft",
                    tokenizer=self.target_model_config.tokenizer,
                    tokenizer_mode=self.target_model_config.tokenizer_mode,
                    trust_remote_code=self.target_model_config.
                    trust_remote_code,
                    allowed_local_media_path=self.target_model_config.
                    allowed_local_media_path,
                    dtype=self.target_model_config.dtype,
                    seed=self.target_model_config.seed,
                    revision=self.revision,
                    code_revision=self.code_revision,
                    tokenizer_revision=self.target_model_config.
                    tokenizer_revision,
                    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,
                )
2628

2629
                # Automatically detect the method
2630
                if self.method in ('eagle', 'eagle3'):
2631
                    pass
2632
2633
                elif "eagle-" in self.draft_model_config.model.lower() or \
                        "eagle3-" in self.draft_model_config.model.lower():
2634
2635
2636
2637
2638
2639
                    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"
Jiayi Yao's avatar
Jiayi Yao committed
2640
2641
2642
2643
2644
2645
2646
2647
2648
                elif (self.draft_model_config.hf_config.model_type ==
                      "deepseek_mtp"):
                    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."
                            )
2649
                else:
2650
2651
2652
                    self.method = "draft_model"

                # Replace hf_config for EAGLE draft_model
2653
                if self.method in ("eagle", "eagle3"):
2654
                    if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
2655
                        raise ValueError(
2656
2657
                            "Chunked prefill and EAGLE are not compatible "
                            "when using V0.")
2658
2659
2660
2661

                    from vllm.transformers_utils.configs.eagle import (
                        EAGLEConfig)
                    if isinstance(self.draft_model_config.hf_config,
2662
                                  EAGLEConfig):
2663
2664
2665
                        pass
                    else:
                        eagle_config = EAGLEConfig(
2666
                            self.draft_model_config.hf_config,
2667
2668
                            method=self.method,
                            model_type="eagle")
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
                        self.draft_model_config.hf_config = eagle_config

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

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

                self.draft_tensor_parallel_size = \
                    SpeculativeConfig._verify_and_get_draft_tp(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size,
                        self.draft_model_config.hf_config
                )
2696

2697
2698
2699
2700
2701
2702
                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,
                    ))
2703

2704
2705
2706
2707
                self.draft_parallel_config = (
                    SpeculativeConfig.create_draft_parallel_config(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size))
2708

2709
2710
2711
2712
2713
        if self.acceptance_method == "typical_acceptance_sampler":
            if self.posterior_threshold is None:
                self.posterior_threshold = 0.09
            if self.posterior_alpha is None:
                self.posterior_alpha = 0.3
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
    @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,
        )

2750
    @staticmethod
2751
    def _verify_and_get_draft_tp(
2752
2753
2754
2755
2756
2757
            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.
2758
        """
2759
2760
        # If speculative_draft_tensor_parallel_size is unset then set it
        # appropriately else verify that it is set correctly.
2761
        if speculative_draft_tensor_parallel_size is None:
2762
2763
2764
2765
            if draft_hf_config.model_type == "mlp_speculator":
                speculative_draft_tensor_parallel_size = 1
                if target_parallel_config.tensor_parallel_size > 1:
                    logger.warning(
2766
2767
2768
                        "%s cannot currently be run with tp>1; "
                        "setting speculative_draft_tensor_parallel_size=1",
                        draft_hf_config.model_type)
2769
2770
2771
            else:
                speculative_draft_tensor_parallel_size = \
                    target_parallel_config.tensor_parallel_size
2772
2773
        elif speculative_draft_tensor_parallel_size not in (
                1, target_parallel_config.tensor_parallel_size):
2774
            raise ValueError(
2775
                f"{speculative_draft_tensor_parallel_size=} cannot be "
2776
                f"other value than 1 or target model tensor_parallel_size")
2777
        return speculative_draft_tensor_parallel_size
2778

2779
2780
2781
2782
2783
2784
2785
2786
2787
    @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.
        """
2788
2789
2790
        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
2791
            tensor_parallel_size=speculative_draft_tensor_parallel_size,
2792
2793
            distributed_executor_backend=target_parallel_config.
            distributed_executor_backend,
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
            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

2805
2806
    @model_validator(mode='after')
    def _verify_args(self) -> Self:
2807
2808
2809
2810
2811
2812
        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.")

2813
2814
2815
2816
2817
2818
2819
        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)
2820
2821
            # Validate and set draft token acceptance related settings.

2822
2823
        if self.acceptance_method is None:
            raise ValueError("acceptance_method is not set. "
2824
2825
2826
                             "Expected values are rejection_sampler or "
                             "typical_acceptance_sampler.")

2827
2828
        if (self.acceptance_method != 'rejection_sampler'
                and self.acceptance_method != 'typical_acceptance_sampler'):
2829
            raise ValueError(
2830
                "Expected acceptance_method to be either "
2831
                "rejection_sampler or typical_acceptance_sampler. Instead it "
2832
                f"is {self.acceptance_method}")
2833

2834
2835
2836
2837
        if self.acceptance_method == "typical_acceptance_sampler" and (
            (self.posterior_threshold is not None
             and self.posterior_threshold < 0) or
            (self.posterior_alpha is not None and self.posterior_alpha < 0)):
2838
            raise ValueError(
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
                "Expected the posterior_threshold and posterior_alpha of "
                "typical_acceptance_sampler to be > 0. "
                "Instead found posterior_threshold = "
                f"{self.posterior_threshold} and posterior_alpha = "
                f"{self.posterior_alpha}")

        if (self.disable_by_batch_size is not None
                and self.disable_by_batch_size < 2):
            raise ValueError("Expect the batch size threshold of disabling "
                             "speculative decoding is > 1, but got "
                             f"{self.disable_by_batch_size=}")
2850

2851
2852
2853
2854
2855
2856
        if self.method == "eagle3" and self.target_model_config and \
            "llama" not in self.target_model_config.hf_text_config.model_type:
            raise ValueError(
                "Eagle3 is only supported for Llama models. "
                f"Got {self.target_model_config.hf_text_config.model_type=}")

2857
2858
        return self

2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
    @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

2869
    def use_eagle(self) -> bool:
Jiayi Yao's avatar
Jiayi Yao committed
2870
        return self.method in ("eagle", "eagle3", "deepseek_mtp")
2871

2872
    def __repr__(self) -> str:
2873
2874
        method = self.method
        model = None if method == "ngram" else self.draft_model_config.model
2875
        num_spec_tokens = self.num_speculative_tokens
2876
        return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
2877
2878


2879
2880
2881
2882
LoRADType = Literal["auto", "float16", "bfloat16"]


@config
2883
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
2884
class LoRAConfig:
2885
2886
2887
2888
2889
2890
    """Configuration for LoRA."""

    max_lora_rank: int = 16
    """Max LoRA rank."""
    max_loras: int = 1
    """Max number of LoRAs in a single batch."""
2891
    fully_sharded_loras: bool = False
2892
2893
2894
2895
    """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.
    """
2896
    max_cpu_loras: Optional[int] = None
2897
2898
2899
2900
    """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."""
2901
    lora_extra_vocab_size: int = 256
2902
2903
    """Maximum size of extra vocabulary that can be present in a LoRA adapter
    (added to the base model vocabulary)."""
2904
2905
    lora_vocab_padding_size: ClassVar[int] = current_platform\
        .get_lora_vocab_padding_size()
2906
2907
2908
2909
2910
2911
    long_lora_scaling_factors: Optional[tuple[float, ...]] = None
    """Specify multiple scaling factors (which can be different from base model
    scaling factor - see eg. Long LoRA) to allow for multiple LoRA adapters
    trained with those scaling factors to be used at the same time. If not
    specified, only adapters trained with the base model scaling factor are
    allowed."""
2912
    bias_enabled: bool = False
2913
    """Enable bias for LoRA adapters."""
2914

2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
    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.
        """
2927
        factors: list[Any] = []
2928
2929
2930
2931
2932
        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)
2933
        factors.append(self.lora_vocab_padding_size)
2934
2935
        factors.append(self.long_lora_scaling_factors)
        factors.append(self.bias_enabled)
2936
2937
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2938
2939
        return hash_str

2940
    def __post_init__(self):
2941
        # Setting the maximum rank to 512 should be able to satisfy the vast
2942
        # majority of applications.
2943
        possible_max_ranks = (8, 16, 32, 64, 128, 256, 320, 512)
2944
        possible_lora_extra_vocab_size = (256, 512)
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
        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
2960
                f"max_loras ({self.max_loras})")
2961

2962
    def verify_with_cache_config(self, cache_config: CacheConfig):
2963
2964
2965
        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.")
2966

2967
2968
2969
2970
2971
2972
    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)

2973
2974
2975
2976
2977
    def verify_lora_support(self):
        if self.long_lora_scaling_factors is not None and envs.VLLM_USE_V1:
            raise ValueError(
                "V1 LoRA does not support long LoRA, please use V0.")

2978

2979
@config
2980
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
2981
class PromptAdapterConfig:
2982
2983
    """Configuration for PromptAdapters."""

2984
2985
2986
2987
    max_prompt_adapters: int = 1
    """Max number of PromptAdapters in a batch."""
    max_prompt_adapter_token: int = 0
    """Max number of PromptAdapters tokens."""
2988
    max_cpu_prompt_adapters: Optional[int] = None
2989
2990
2991
2992
2993
    """Maximum number of PromptAdapters to store in CPU memory. Must be >= than
    `max_prompt_adapters`."""
    prompt_adapter_dtype: Union[torch.dtype, str] = "auto"
    """Data type for PromptAdapter. If auto, will default to base model dtype.
    """
2994

2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
    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.
3009
        factors: list[Any] = []
3010
3011
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3012
3013
        return hash_str

3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
    def __post_init__(self):

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

    def verify_with_model_config(self, model_config: ModelConfig):
3025
        if self.prompt_adapter_dtype == "auto":
3026
3027
3028
3029
3030
3031
            self.prompt_adapter_dtype = model_config.dtype
        elif isinstance(self.prompt_adapter_dtype, str):
            self.prompt_adapter_dtype = getattr(torch,
                                                self.prompt_adapter_dtype)


3032
@config
3033
@dataclass
3034
class MultiModalConfig:
3035
3036
    """Controls the behavior of multimodal models."""

3037
3038
    limit_per_prompt: dict[str, int] = \
        cast(dict[str, int], get_field(ModelConfig, "limit_mm_per_prompt"))
3039
    """
3040
    The maximum number of input items allowed per prompt for each modality.
3041
    Defaults to 1 (V0) or 999 (V1) for each modality.
3042
3043

    For example, to allow up to 16 images and 2 videos per prompt:
3044
    `{"images": 16, "videos": 2}`
3045
3046
3047
3048
3049
    """

    mm_processor_kwargs: Optional[dict[str, object]] = None
    """
    Overrides for the multi-modal processor obtained from
3050
    `transformers.AutoProcessor.from_pretrained`.
3051
3052
3053
3054

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

    For example, for Phi-3-Vision:
3055
    `{"num_crops": 4}`.
3056
3057
3058
3059
    """

    disable_mm_preprocessor_cache: bool = False
    """
3060
    If `True`, disable caching of the processed multi-modal inputs.
3061
3062
    """

3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
    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.
3077
        factors: list[Any] = []
3078
3079
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3080
3081
        return hash_str

3082
3083
3084
3085
3086
    def get_limit_per_prompt(self, modality: str) -> int:
        """
        Get the maximum number of input items allowed per prompt
        for the given modality.
        """
3087
3088
3089
3090
        return self.limit_per_prompt.get(
            modality,
            999 if envs.VLLM_USE_V1 else 1,
        )
3091

3092
    # TODO: Add configs to init vision tower or not.
3093

3094

3095
@config
3096
3097
@dataclass
class PoolerConfig:
3098
    """Controls the behavior of output pooling in pooling models."""
3099
3100

    pooling_type: Optional[str] = None
3101
    """
3102
    The pooling method of the pooling model. This should be a key in
3103
    [`vllm.model_executor.layers.pooler.PoolingType`][].
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
    """

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

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

    step_tag_id: Optional[int] = None
    """
3120
    If set, only the score corresponding to the ``step_tag_id`` in the
3121
3122
3123
3124
    generated sentence should be returned. Otherwise, the scores for all tokens
    are returned.
    """

3125
    returned_token_ids: Optional[list[int]] = None
3126
    """
3127
3128
    A list of indices for the vocabulary dimensions to be extracted,
    such as the token IDs of ``good_token`` and ``bad_token`` in the
3129
3130
3131
    ``math-shepherd-mistral-7b-prm`` model.
    """

3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
    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.
3146
        factors: list[Any] = []
3147
3148
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3149
3150
        return hash_str

3151

3152
3153
3154
3155
3156
3157
3158
3159
_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

3160
3161
3162
3163
3164
3165
3166
# 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.",
}
3167

3168

3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
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,
3187
    config: PretrainedConfig,
3188
3189
3190
    *,
    revision: Optional[str],
):
3191
3192
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
3193
    config_dtype = getattr(config, "torch_dtype", None)
3194

3195
    # Fallbacks for multi-modal models if the root config
3196
    # does not define torch_dtype
3197
3198
    if config_dtype is None:
        config_dtype = getattr(config.get_text_config(), "torch_dtype", None)
3199
3200
    if config_dtype is None and hasattr(config, "vision_config"):
        config_dtype = getattr(config.vision_config, "torch_dtype", None)
3201
3202
    if config_dtype is None and hasattr(config, "encoder_config"):
        config_dtype = getattr(config.encoder_config, "torch_dtype", None)
3203

3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
    # 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)

3219
3220
3221
    if config_dtype is None:
        config_dtype = torch.float32

3222
    return config_dtype
3223

Shinichi Hemmi's avatar
Shinichi Hemmi committed
3224

3225
3226
3227
3228
3229
3230
3231
def _resolve_auto_dtype(
    model_type: str,
    config_dtype: torch.dtype,
    *,
    is_pooling_model: bool,
):
    from vllm.platforms import current_platform
3232

3233
3234
3235
3236
    supported_dtypes = [
        dtype for dtype in current_platform.supported_dtypes
        if _is_valid_dtype(model_type, dtype)
    ]
3237

3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
    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,
            )
3291
        else:
3292
            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
3293
                raise ValueError(f"Unknown dtype: {dtype!r}")
3294
3295
3296
            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
    elif isinstance(dtype, torch.dtype):
        torch_dtype = dtype
3297
    else:
3298
        raise ValueError(f"Unknown dtype: {dtype}")
3299

3300
3301
    _check_valid_dtype(model_type, torch_dtype)

3302
3303
3304
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
3305
            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
3306
3307
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
3308
            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
3309
        else:
Woosuk Kwon's avatar
Woosuk Kwon committed
3310
            # Casting between float16 and bfloat16 is allowed with a warning.
3311
            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
3312
3313

    return torch_dtype
3314
3315
3316
3317


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
3318
    tokenizer_config: Optional[dict],
3319
    max_model_len: Optional[int],
3320
    disable_sliding_window: bool,
3321
    sliding_window_len: Optional[Union[int, list[Optional[int]]]],
3322
    spec_target_max_model_len: Optional[int] = None,
3323
    encoder_config: Optional[Any] = None,
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
) -> 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",
3334
3335
        # ChatGLM2
        "seq_length",
3336
3337
        # Command-R
        "model_max_length",
3338
3339
        # Whisper
        "max_target_positions",
3340
3341
3342
3343
3344
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
3345
    # Choose the smallest "max_length" from the possible keys
3346
    max_len_key = None
3347
    for key in possible_keys:
3348
3349
3350
3351
3352
        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
3353
3354
3355
3356
    # 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
3357
3358
3359
3360

    # If sliding window is manually disabled, max_length should be less
    # than the sliding window length in the model config.
    if disable_sliding_window and sliding_window_len is not None:
3361
3362

        sliding_window_len_min = get_min_sliding_window(sliding_window_len)
3363
        max_len_key = "sliding_window" \
3364
3365
3366
            if sliding_window_len_min < derived_max_model_len else max_len_key
        derived_max_model_len = min(derived_max_model_len,
                                    sliding_window_len_min)
3367

3368
3369
3370
3371
3372
3373
3374
    # 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)

3375
3376
    # If none of the keys were found in the config, use a default and
    # log a warning.
3377
    if derived_max_model_len == float("inf"):
3378
3379
3380
3381
        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

3382
3383
3384
3385
3386
        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

3387
3388
3389
3390
        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: "
3391
            "%s. Assuming the model's maximum length is %d.", possible_keys,
3392
            default_max_len)
3393
        derived_max_model_len = default_max_len
3394

3395
    rope_scaling = getattr(hf_config, "rope_scaling", None)
3396
3397
3398
    # 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:
3399
3400
3401
        # No need to consider "type" key because of patch_rope_scaling when
        # loading HF config
        rope_type = rope_scaling["rope_type"]
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411

        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.")

3412
3413
3414
3415
            # 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)

3416
3417
3418
3419
            if rope_type == "yarn":
                derived_max_model_len = rope_scaling[
                    "original_max_position_embeddings"]
            derived_max_model_len *= scaling_factor
3420

3421
3422
3423
    if encoder_config and "max_seq_length" in encoder_config:
        derived_max_model_len = encoder_config["max_seq_length"]

3424
3425
    # If the user specified a max length, make sure it is smaller than the
    # derived length from the HF model config.
3426
    if max_model_len is None:
3427
        max_model_len = int(derived_max_model_len)
3428
3429
3430
3431
3432
3433
3434
3435
        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)
3436
    elif max_model_len > derived_max_model_len:
3437
3438
3439
3440
3441
        # 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:
3442
3443
3444
3445
3446
3447
3448
            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.")
3449
        else:
3450
            msg = (
3451
                f"User-specified max_model_len ({max_model_len}) is greater "
3452
3453
                f"than the derived max_model_len ({max_len_key}="
                f"{derived_max_model_len} or model_max_length="
3454
                f"{model_max_length} in model's config.json). This may lead "
3455
3456
3457
3458
3459
3460
3461
3462
3463
                "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")
3464
    return int(max_model_len)
3465
3466


3467
def get_min_sliding_window(
3468
        sliding_window: Union[int, list[Optional[int]]]) -> int:
3469
3470
3471
3472
3473
3474
    if isinstance(sliding_window, list):
        return min(s for s in sliding_window if s is not None)

    return sliding_window


3475
def get_served_model_name(model: str,
3476
                          served_model_name: Optional[Union[str, list[str]]]):
3477
    """
3478
3479
3480
3481
    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
3482
3483
3484
3485
3486
3487
3488
3489
3490
    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


3491
GuidedDecodingBackendV0 = Literal["auto", "outlines", "lm-format-enforcer",
3492
                                  "xgrammar", "guidance"]
3493
GuidedDecodingBackendV1 = Literal["auto", "xgrammar", "guidance"]
3494
3495
GuidedDecodingBackend = Literal[GuidedDecodingBackendV0,
                                GuidedDecodingBackendV1]
3496
3497
3498


@config
3499
3500
@dataclass
class DecodingConfig:
3501
    """Dataclass which contains the decoding strategy of the engine."""
3502

3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
    @property
    @deprecated(
        "`guided_decoding_backend` is deprecated and has been renamed to "
        "`backend`. This will be removed in v0.10.0. Please use the "
        "`backend` argument instead.")
    def guided_decoding_backend(self) -> GuidedDecodingBackend:
        return self.backend

    @guided_decoding_backend.setter
    def guided_decoding_backend(self, value: GuidedDecodingBackend):
        self.backend = value

    backend: GuidedDecodingBackend = "auto" if envs.VLLM_USE_V1 else "xgrammar"
3516
3517
3518
3519
    """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."""
3520

3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
    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`."""

3533
    reasoning_backend: str = ""
3534
    """Select the reasoning parser depending on the model that you're using.
3535
    This is used to parse the reasoning content into OpenAI API format."""
3536

3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
    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.
3551
        factors: list[Any] = []
3552
3553
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3554
3555
        return hash_str

3556
    def __post_init__(self):
3557
3558
3559
        if ":" in self.backend:
            self._extract_backend_options()

3560
        if envs.VLLM_USE_V1:
3561
            valid_guided_backends = get_args(GuidedDecodingBackendV1)
3562
        else:
3563
            valid_guided_backends = get_args(GuidedDecodingBackendV0)
3564
3565
        if self.backend not in valid_guided_backends:
            raise ValueError(f"Invalid backend '{self.backend}',"
3566
                             f" must be one of {valid_guided_backends}")
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
        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.")

    @deprecated(
        "Passing guided decoding backend options inside backend in the format "
        "'backend:...' is deprecated. This will be removed in v0.10.0. Please "
        "use the dedicated arguments '--disable-fallback', "
        "'--disable-any-whitespace' and '--disable-additional-properties' "
        "instead.")
    def _extract_backend_options(self):
        """Extract backend options from the backend string."""
        backend, options = self.backend.split(":")
        self.backend = cast(GuidedDecodingBackend, backend)
        options_set = set(options.strip().split(","))
        if "no-fallback" in options_set:
            self.disable_fallback = True
        if "disable-any-whitespace" in options_set:
            self.disable_any_whitespace = True
        if "no-additional-properties" in options_set:
            self.disable_additional_properties = True
3592
3593


3594
3595
3596
3597
DetailedTraceModules = Literal["model", "worker", "all"]


@config
3598
3599
@dataclass
class ObservabilityConfig:
3600
    """Configuration for observability - metrics and tracing."""
3601

3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
    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)
3617

3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
    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))
3643

3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
    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.
3658
        factors: list[Any] = []
3659
3660
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3661
3662
        return hash_str

3663
    def __post_init__(self):
3664
3665
3666
3667
3668
        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()

3669
        from vllm.tracing import is_otel_available, otel_import_error_traceback
3670
3671
3672
3673
3674
        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}")
3675

3676
3677
3678
3679
3680
3681
    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(","))

3682

3683
3684
3685
3686
3687
3688
3689
3690
KVProducer = Literal["kv_producer", "kv_both"]
KVConsumer = Literal["kv_consumer", "kv_both"]
KVRole = Literal[KVProducer, KVConsumer]


@config
@dataclass
class KVTransferConfig:
3691
3692
3693
    """Configuration for distributed KV cache transfer."""

    kv_connector: Optional[str] = None
3694
3695
    """The KV connector for vLLM to transmit KV caches between vLLM instances.
    """
3696

3697
    engine_id: Optional[str] = None
Robert Shaw's avatar
Robert Shaw committed
3698
3699
    """The engine id for KV transfers."""

3700
    kv_buffer_device: Optional[str] = "cuda"
3701
3702
    """The device used by kv connector to buffer the KV cache.
    Currently only support 'cuda'."""
3703
3704

    kv_buffer_size: float = 1e9
3705
3706
    """The buffer size for TorchDistributedConnector. Measured in number of
    bytes. Recommended value: 1e9 (about 1GB)."""
3707

3708
3709
    kv_role: Optional[KVRole] = None
    """Whether this vLLM instance produces, consumes KV cache, or both. Choices
Robert Shaw's avatar
Robert Shaw committed
3710
    are 'kv_producer', 'kv_consumer', and 'kv_both'."""
3711
3712

    kv_rank: Optional[int] = None
3713
3714
3715
    """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."""
3716
3717

    kv_parallel_size: int = 1
3718
3719
    """The number of parallel instances for KV cache transfer. For
    PyNcclConnector, this should be 2."""
3720
3721

    kv_ip: str = "127.0.0.1"
3722
    """The KV connector ip, used to build distributed connection."""
3723
3724

    kv_port: int = 14579
3725
    """The KV connector port, used to build distributed connection."""
3726

3727
3728
    kv_connector_extra_config: dict[str, Any] = field(default_factory=dict)
    """any extra config that the connector may need."""
3729

3730
3731
3732
3733
    kv_connector_module_path: Optional[str] = None
    """The Python module path to dynamically load the KV connector from.
    Only supported in V1."""

3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
    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.
3748
        factors: list[Any] = []
3749
3750
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
3751
3752
        return hash_str

3753
    def __post_init__(self) -> None:
3754
3755
3756
        if self.engine_id is None:
            self.engine_id = str(uuid.uuid4())

3757
3758
3759
        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)}")
3760
3761
3762

        if self.kv_connector is not None and self.kv_role is None:
            raise ValueError("Please specify kv_disagg_role when kv_connector "
3763
                             f"is set, supported roles are {get_args(KVRole)}")
3764
3765
3766
3767

    @property
    def is_kv_transfer_instance(self) -> bool:
        return self.kv_connector is not None and \
3768
            self.kv_role in get_args(KVRole)
3769
3770
3771
3772

    @property
    def is_kv_producer(self) -> bool:
        return self.kv_connector is not None and \
3773
            self.kv_role in get_args(KVProducer)
3774
3775
3776
3777

    @property
    def is_kv_consumer(self) -> bool:
        return self.kv_connector is not None and \
3778
            self.kv_role in get_args(KVConsumer)
3779

3780
3781
3782
    def get_from_extra_config(self, key, default) -> Any:
        return self.kv_connector_extra_config.get(key, default)

3783

3784
3785
3786
@config
@dataclass
class KVEventsConfig:
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
    """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.
    """


3826
3827
3828
3829
3830
3831
3832
3833
class CompilationLevel:
    # constants for the levels of the compilation process
    NO_COMPILATION = 0
    DYNAMO_AS_IS = 1
    DYNAMO_ONCE = 2
    PIECEWISE = 3


3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
@config
@dataclass
class PassConfig:
    """Configuration for custom Inductor passes.

    This is separate from general `CompilationConfig` so that inductor passes
    don't all have access to full configuration - that would create a cycle as
    the `PassManager` is set as a property of config."""

    dump_graph_stages: list[str] = field(default_factory=list)
    """List of stages for which we want to dump the graph. Each pass defines
    its own stages (before, after, maybe in-between)."""
    dump_graph_dir: Path = Path(".")
    """Directory to dump the graphs."""
3848
    enable_fusion: bool = field(default_factory=lambda: not envs.VLLM_USE_V1)
3849
3850
3851
    """Whether to enable the custom fusion (RMSNorm/SiluMul+quant) pass."""
    enable_attn_fusion: bool = False
    """Whether to enable the custom attention+quant fusion pass."""
3852
    enable_noop: bool = field(default_factory=lambda: not envs.VLLM_USE_V1)
3853
3854
3855
    """Whether to enable the custom no-op elimination pass."""
    enable_sequence_parallelism: bool = False
    """Whether to enable sequence parallelism."""
3856
3857
    enable_async_tp: bool = False
    """Whether to enable async TP."""
3858

3859
3860
    # TODO(luka) better pass enabling system.

3861
3862
3863
3864
3865
3866
3867
    def uuid(self):
        """
        Produces a hash unique to the pass configuration.
        Any new fields that affect compilation should be added to the hash.
        Do not include dump_graph_* in the hash - they don't affect
        compilation.
        """
3868
3869
        exclude = {"dump_graph_stages", "dump_graph_dir"}
        dict_ = {k: v for k, v in asdict(self).items() if k not in exclude}
3870
3871
3872
        return InductorPass.hash_dict(dict_)

    def __post_init__(self) -> None:
3873
3874
3875
3876
3877
3878
3879
3880
3881
        if not self.enable_noop:
            if self.enable_fusion:
                logger.warning_once(
                    "Fusion enabled but reshape elimination disabled. "
                    "RMSNorm/SiluMul + quant (fp8) fusion might not work")
            if self.enable_attn_fusion:
                logger.warning_once(
                    "Fusion enabled but reshape elimination disabled. "
                    "Attention + quant (fp8) fusion might not work")
3882
3883
3884
3885
3886
3887
3888


@config
@dataclass
class CompilationConfig:
    """Configuration for compilation. It has three parts:

3889
    - Top-level Compilation control:
3890
3891
3892
3893
3894
3895
        - [`level`][vllm.config.CompilationConfig.level]
        - [`debug_dump_path`][vllm.config.CompilationConfig.debug_dump_path]
        - [`cache_dir`][vllm.config.CompilationConfig.cache_dir]
        - [`backend`][vllm.config.CompilationConfig.backend]
        - [`custom_ops`][vllm.config.CompilationConfig.custom_ops]
        - [`splitting_ops`][vllm.config.CompilationConfig.splitting_ops]
3896
    - CudaGraph capture:
3897
3898
3899
3900
3901
3902
3903
3904
        - [`use_cudagraph`][vllm.config.CompilationConfig.use_cudagraph]
        - [`cudagraph_capture_sizes`]
        [vllm.config.CompilationConfig.cudagraph_capture_sizes]
        - [`cudagraph_num_of_warmups`]
        [vllm.config.CompilationConfig.cudagraph_num_of_warmups]
        - [`cudagraph_copy_inputs`]
        [vllm.config.CompilationConfig.cudagraph_copy_inputs]
        - [`full_cuda_graph`][vllm.config.CompilationConfig.full_cuda_graph]
3905
    - Inductor compilation:
3906
3907
3908
3909
3910
        - [`use_inductor`][vllm.config.CompilationConfig.use_inductor]
        - [`compile_sizes`][vllm.config.CompilationConfig.compile_sizes]
        - [`inductor_compile_config`]
        [vllm.config.CompilationConfig.inductor_compile_config]
        - [`inductor_passes`][vllm.config.CompilationConfig.inductor_passes]
3911
        - custom inductor passes
3912

3913
3914
3915
3916
3917
3918
3919
3920
3921
    Why we have different sizes for cudagraph and inductor:
    - cudagraph: a cudagraph captured for a specific size can only be used
        for the same size. We need to capture all the sizes we want to use.
    - inductor: a graph compiled by inductor for a general shape can be used
        for different sizes. Inductor can also compile for specific sizes,
        where it can have more information to optimize the graph with fully
        static shapes. However, we find the general shape compilation is
        sufficient for most cases. It might be beneficial to compile for
        certain small batchsizes, where inductor is good at optimizing.
3922
3923
    """
    # Top-level Compilation control
3924
    level: int = 0
3925
3926
3927
3928
3929
3930
    """The level of compilation:

    - 0: no compilation.
    - 1: dynamo as is.
    - 2: dynamo once.
    - 3: piecewise compilation."""
3931
    debug_dump_path: str = ""
3932
    """The path to dump the debug information."""
3933
    cache_dir: str = ""
3934
3935
3936
    """The directory to store the compiled graph, to accelerate Inductor
    compilation. By default, it will use model-related information to generate
    a cache directory."""
3937
    backend: str = ""
3938
3939
3940
3941
3942
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
    """The backend for compilation. It needs to be a string:

    - "" (empty string): use the default backend.
    - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
    - "full.module.name": a qualified name which can be used to import the

    backend function.
    We use string to avoid serialization issues when using compilation in a
    distributed setting. When the compilation level is 1 or 2, the backend is
    used for the compilation directly (it sees the whole graph). When the
    compilation level is 3, the backend is used for the piecewise compilation
    (it sees a part of the graph)."""
    custom_ops: list[str] = field(default_factory=list)
    """Fine-grained control over which custom ops to enable/disable. Use 'all'
    to enable all, 'none' to disable all. Also specify a list of custom op
    names to enable (prefixed with a '+'), or disable (prefixed with a '-').
    Examples:

    - 'all,-op1' to enable all except op1
    - 'none,+op1,+op2' to enable only op1 and op2

    By default, all custom ops are enabled when running without Inductor and
    disabled when running with Inductor (compile_level >= Inductor)."""
    splitting_ops: list[str] = field(default_factory=list)
    """A list of ops to split the full graph into subgraphs, used in piecewise
    compilation."""

    # Inductor capture
3966
    use_inductor: bool = True
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
3987
    """Whether to use inductor compilation:

    - False: inductor compilation is not used. graph runs in eager.
    - True: inductor compilation is used. one graph for symbolic shape
        is compiled. In addition, compile for compile_sizes,
        using configurations in inductor_compile_config."""
    compile_sizes: Optional[list[Union[int, str]]] = None
    """Sizes to compile for inductor. In addition
    to integers, it also supports "cudagraph_capture_sizes" to
    specify the sizes for cudagraph capture."""
    inductor_compile_config: dict = field(default_factory=dict)
    """Additional configurations for inductor.
    - None: use default configurations."""
    inductor_passes: dict[str, str] = field(default_factory=dict)
    """Additional passes for inductor. It is a dictionary
    from pass name to pass function qualified name. We use function
    name because the config uses JSON format. If we pass the config
    from Python, functions can also be passed directly via Python object
    constructor, e.g. `CompilationConfig(inductor_passes={"a": func})`."""

    # CudaGraph compilation
3988
    use_cudagraph: bool = field(default_factory=lambda: envs.VLLM_USE_V1)
3989
3990
3991
3992
3993
    """Whether to use cudagraph inside compilation.
    - False: cudagraph inside compilation is not used.
    - True: cudagraph inside compilation is used. It requires
        that all input buffers have fixed addresses, and all
        splitting ops write their outputs to input buffers.
3994
3995
    In the vLLM V1 Engine, this flag only applies for
    CompilationLevel.PIECEWISE (aka -O3).
3996
3997
3998
3999
    Note that this is orthogonal to the cudagraph capture logic
    outside of compilation.
    TODO: move outside cudagraph logic into compilation.
    torch.compile will handle cudagraph capture logic in the future."""
4000
    cudagraph_num_of_warmups: int = 0
4001
4002
4003
4004
    """Number of warmup runs for cudagraph.
    It means the first several runs will be treated as warmup runs.
    Only after that, the execution will be recorded, and the recorded
    cudagraph will be used for subsequent runs."""
4005
    cudagraph_capture_sizes: Optional[list[int]] = None
4006
4007
4008
    """Sizes to capture cudagraph.
    - None (default): capture sizes are inferred from vllm config.
    - list[int]: capture sizes are specified as given."""
4009
    cudagraph_copy_inputs: bool = False
4010
4011
4012
4013
4014
    """Whether to copy input tensors for
    cudagraph. If the caller can guarantee that the same input buffers
    are always used, it can set this to False. Otherwise, it should
    set this to True, and the compiler will copy the input to an
    internally managed buffer. Default is False."""
4015
    full_cuda_graph: bool = False
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
    """whether to use a full cuda graph for the entire forward pass rather than
    splitting certain operations such as attention into subgraphs. Thus this
    flag cannot be used together with splitting_ops. This may provide
    performance benefits for smaller models."""

    pass_config: PassConfig = field(default_factory=PassConfig)
    """Custom inductor passes, see PassConfig for more details"""

    max_capture_size: int = field(default=None, init=False)  # type: ignore
    """not configurable, computed after init"""
    local_cache_dir: str = field(default=None, init=False)  # type: ignore
    """local cache dir for each rank"""
    bs_to_padded_graph_size: list[int] = field(
        default=None,  # type: ignore
        init=False)
    """optimization:
    Intuitively, bs_to_padded_graph_size should be dict[int, int].
    since we know all keys are in a range [0, max_capture_size],
    we can optimize it to list[int] for better lookup performance."""
4035

4036
    # keep track of enabled and disabled custom ops
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
4051
4052
    enabled_custom_ops: Counter[str] = field(default_factory=Counter,
                                             init=False)
    """custom ops that are enabled"""
    disabled_custom_ops: Counter[str] = field(default_factory=Counter,
                                              init=False)
    """custom ops that are disabled"""
    traced_files: set[str] = field(default_factory=set, init=False)
    """files that are traced for compilation"""
    compilation_time: float = field(default=0.0, init=False)
    """time taken for compilation"""

    static_forward_context: dict[str, Any] = field(default_factory=dict,
                                                   init=False)
    """Per-model forward context
    Map from layer name to layer objects that need to be accessed outside
    model code, e.g., Attention, FusedMOE when dp_size>1."""
4053

4054
4055
4056
4057
4058
4059
4060
4061
4062
4063
4064
4065
    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.
        """
4066
        factors: list[Any] = []
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
        factors.append(self.level)
        factors.append(self.backend)
        factors.append(self.custom_ops)
        factors.append(self.splitting_ops)
        factors.append(self.use_inductor)
        factors.append(self.inductor_compile_config)
        factors.append(self.inductor_passes)
        factors.append(self.pass_config.uuid())
        return hashlib.sha256(str(factors).encode()).hexdigest()

4077
4078
    def __repr__(self) -> str:
        exclude = {
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
            "static_forward_context": True,
            "enabled_custom_ops": True,
            "disabled_custom_ops": True,
            "compilation_time": True,
            "bs_to_padded_graph_size": True,
            "pass_config": True,
            "traced_files": True,
            "inductor_compile_config": {
                "post_grad_custom_post_pass": True,
            },
4089
        }
4090
4091
4092
4093
        # The cast to string is necessary because Pydantic is mocked in docs
        # builds and sphinx-argparse doesn't know the return type of decode()
        return str(
            TypeAdapter(CompilationConfig).dump_json(
4094
4095
4096
                self,
                exclude=exclude,  # type: ignore[arg-type]
                exclude_unset=True).decode())
4097
4098
4099

    __str__ = __repr__

4100
4101
4102
4103
4104
    @classmethod
    def from_cli(cls, cli_value: str) -> "CompilationConfig":
        """Parse the CLI value for the compilation config."""
        if cli_value in ["0", "1", "2", "3"]:
            return cls(level=int(cli_value))
4105
        return TypeAdapter(CompilationConfig).validate_json(cli_value)
4106

4107
    def __post_init__(self) -> None:
4108
4109
4110
4111
        count_none = self.custom_ops.count("none")
        count_all = self.custom_ops.count("all")
        assert count_none + count_all <= 1, "Can only specify 'none' or 'all'"

Michael Goin's avatar
Michael Goin committed
4112
4113
4114
4115
4116
4117
4118
4119
        # TODO(zou3519/luka): There are 2 issues with auto-functionalization V2:
        # 1. A bug in PyTorch, fixed in 2.7:
        #    https://github.com/pytorch/pytorch/issues/147924
        # 2. Custom passes (fusion) rely on auto-functionalization V1 and don't
        #    work with V2. Addressing this will take extra engineering effort
        #    and it is not yet a priority. RFC here:
        #    https://github.com/vllm-project/vllm/issues/14703

4120
        if is_torch_equal_or_newer("2.6"):
Michael Goin's avatar
Michael Goin committed
4121
4122
4123
4124
            KEY = 'enable_auto_functionalized_v2'
            if KEY not in self.inductor_compile_config:
                self.inductor_compile_config[KEY] = False

4125
4126
4127
        for k, v in self.inductor_passes.items():
            if not isinstance(v, str):
                assert callable(v), (
4128
4129
4130
                    f"pass {k} should be callable or a qualified name")
                self.inductor_compile_config[k] = v if isinstance(
                    v, InductorPass) else CallableInductorPass(v)
4131
4132
4133
4134
4135
4136
4137
                continue

            # resolve function from qualified name
            names = v.split(".")
            module = ".".join(names[:-1])
            func_name = names[-1]
            func = __import__(module).__dict__[func_name]
4138
4139
            self.inductor_compile_config[k] = func if isinstance(
                func, InductorPass) else CallableInductorPass(func)
4140

4141
4142
        if isinstance(self.pass_config, dict):
            self.pass_config = PassConfig(**self.pass_config)
4143

4144
    def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]:
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
        if self.level == CompilationLevel.NO_COMPILATION:
            raise ValueError("No compilation level is set.")

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

        # TODO: pass user-specified backend to piecewise compilation
        # merge with the config use_inductor
        assert self.level == CompilationLevel.PIECEWISE
4162

4163
        from vllm.compilation.backends import VllmBackend
4164
        return VllmBackend(vllm_config)
4165

4166
    def init_with_cudagraph_sizes(self,
4167
                                  cudagraph_capture_sizes: list[int]) -> None:
4168
        """To complete the initialization of config,
4169
4170
        we need to know the cudagraph sizes."""

4171
        if self.cudagraph_capture_sizes is None:
4172
            self.cudagraph_capture_sizes = cudagraph_capture_sizes
4173
        else:
4174
            # de-duplicate the sizes provided by the config
4175
4176
4177
4178
4179
4180
            dedup_sizes = list(set(self.cudagraph_capture_sizes))
            if len(dedup_sizes) < len(self.cudagraph_capture_sizes):
                logger.info(("cudagraph sizes specified by model runner"
                             " %s is overridden by config %s"),
                            cudagraph_capture_sizes, dedup_sizes)
            self.cudagraph_capture_sizes = dedup_sizes
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195

        computed_compile_sizes = []
        if self.compile_sizes is not None:
            # de-duplicate the sizes provided by the config
            self.compile_sizes = list(set(self.compile_sizes))
            for x in self.compile_sizes:
                if isinstance(x, str):
                    assert x == "cudagraph_capture_sizes", \
                    "Unrecognized size type in compile_sizes, " \
                    f"expect 'cudagraph_capture_sizes', got {x}"
                    computed_compile_sizes.extend(self.cudagraph_capture_sizes)
                else:
                    assert isinstance(x, int)
                    computed_compile_sizes.append(x)
        self.compile_sizes = computed_compile_sizes  # type: ignore
4196

4197
        # sort to make sure cudagraph capture sizes are in descending order
4198
4199
4200
        self.cudagraph_capture_sizes.sort(reverse=True)
        self.max_capture_size = self.cudagraph_capture_sizes[
            0] if self.cudagraph_capture_sizes else 0
4201

4202
4203
4204
4205
        # pre-compute the mapping from batch size to padded graph size
        self.bs_to_padded_graph_size = [
            0 for i in range(self.max_capture_size + 1)
        ]
4206
4207
        for end, start in zip(self.cudagraph_capture_sizes,
                              self.cudagraph_capture_sizes[1:] + [0]):
4208
4209
4210
4211
4212
4213
4214
            for bs in range(start, end):
                if bs == start:
                    self.bs_to_padded_graph_size[bs] = start
                else:
                    self.bs_to_padded_graph_size[bs] = end
        self.bs_to_padded_graph_size[
            self.max_capture_size] = self.max_capture_size
4215

4216
4217
    def set_splitting_ops_for_v1(self):
        # NOTE: this function needs to be called
4218
4219
4220
4221
4222
        if self.splitting_ops and self.full_cuda_graph:
            raise ValueError("full_cuda_graph cannot be used together with "
                             "splitting_ops, as Full CUDA graph will override "
                             f"the splitting_ops: {self.splitting_ops}")

4223
        if not self.splitting_ops:
4224
            self.splitting_ops = [] if self.full_cuda_graph else [
4225
4226
4227
4228
                "vllm.unified_attention",
                "vllm.unified_attention_with_output",
            ]

4229

4230
@config
4231
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
4232
4233
class VllmConfig:
    """Dataclass which contains all vllm-related configuration. This
4234
4235
4236
    simplifies passing around the distinct configurations in the codebase.
    """

4237
4238
4239
    # TODO: use default_factory once default constructing ModelConfig doesn't
    # try to download a model
    model_config: ModelConfig = None  # type: ignore
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
    """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."""
4251
    lora_config: Optional[LoRAConfig] = None
4252
4253
4254
    """LoRA configuration."""
    speculative_config: Optional[SpeculativeConfig] = None
    """Speculative decoding configuration."""
4255
    decoding_config: DecodingConfig = field(default_factory=DecodingConfig)
4256
    """Decoding configuration."""
4257
    observability_config: Optional[ObservabilityConfig] = None
4258
    """Observability configuration."""
4259
    prompt_adapter_config: Optional[PromptAdapterConfig] = None
4260
    """Prompt adapter configuration."""
4261
    quant_config: Optional[QuantizationConfig] = None
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
4280
4281
    """Quantization configuration."""
    compilation_config: CompilationConfig = field(
        default_factory=CompilationConfig)
    """`torch.compile` configuration for the model.

    When it is a number (0, 1, 2, 3), it will be interpreted as the
    optimization level.

    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
    production.

    Following the convention of traditional compilers, using `-O` without space
    is also supported. `-O3` is equivalent to `-O 3`.

    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."""
4282
    kv_events_config: Optional[KVEventsConfig] = None
4283
    """The configurations for event publishing."""
4284
    # some opaque config, only used to provide additional information
4285
4286
    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
4287
4288
4289
4290
    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."""
4291
    instance_id: str = ""
4292
    """The ID of the vLLM instance."""
4293

4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
    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.
        """
4306
        factors: list[Any] = []
4307
4308

        # summarize vllm config
4309
        vllm_factors: list[Any] = []
4310
4311
        from vllm import __version__
        vllm_factors.append(__version__)
4312
        vllm_factors.append(envs.VLLM_USE_V1)
4313
4314
        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
4315
4316
        else:
            vllm_factors.append("None")
4317
4318
        if self.cache_config:
            vllm_factors.append(self.cache_config.compute_hash())
4319
4320
        else:
            vllm_factors.append("None")
4321
4322
        if self.parallel_config:
            vllm_factors.append(self.parallel_config.compute_hash())
4323
4324
        else:
            vllm_factors.append("None")
4325
4326
        if self.scheduler_config:
            vllm_factors.append(self.scheduler_config.compute_hash())
4327
4328
        else:
            vllm_factors.append("None")
4329
4330
        if self.device_config:
            vllm_factors.append(self.device_config.compute_hash())
4331
4332
        else:
            vllm_factors.append("None")
4333
4334
        if self.load_config:
            vllm_factors.append(self.load_config.compute_hash())
4335
4336
        else:
            vllm_factors.append("None")
4337
4338
        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
4339
4340
4341
4342
4343
            # 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))
4344
4345
        else:
            vllm_factors.append("None")
4346
4347
        if self.speculative_config:
            vllm_factors.append(self.speculative_config.compute_hash())
4348
4349
        else:
            vllm_factors.append("None")
4350
4351
        if self.decoding_config:
            vllm_factors.append(self.decoding_config.compute_hash())
4352
4353
        else:
            vllm_factors.append("None")
4354
4355
        if self.observability_config:
            vllm_factors.append(self.observability_config.compute_hash())
4356
4357
        else:
            vllm_factors.append("None")
4358
4359
        if self.prompt_adapter_config:
            vllm_factors.append(self.prompt_adapter_config.compute_hash())
4360
4361
        else:
            vllm_factors.append("None")
4362
4363
4364
4365
        if self.quant_config:
            pass  # should be captured by model_config.quantization
        if self.compilation_config:
            vllm_factors.append(self.compilation_config.compute_hash())
4366
4367
        else:
            vllm_factors.append("None")
4368
4369
        if self.kv_transfer_config:
            vllm_factors.append(self.kv_transfer_config.compute_hash())
4370
4371
4372
        else:
            vllm_factors.append("None")
        if self.additional_config:
4373
4374
4375
4376
4377
4378
4379
4380
            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)
4381
4382
        else:
            vllm_factors.append("None")
4383
4384
        factors.append(vllm_factors)

4385
4386
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()[:10]
4387
4388
        return hash_str

4389
4390
4391
4392
4393
4394
    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]
4395

4396
4397
4398
4399
4400
    @staticmethod
    def _get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        """Get the quantization config."""
4401
        from vllm.platforms import current_platform
4402
4403
4404
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417
4418
4419
4420
4421
4422
4423
        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
4424

4425
4426
4427
4428
4429
4430
4431
4432
4433
4434
4435
    @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)

4436
4437
4438
4439
4440
4441
4442
4443
4444
    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

4445
4446
4447
4448
4449
        model_config = copy.deepcopy(self.model_config)
        model_config.hf_config = hf_config

        return replace(self, model_config=model_config)

4450
4451
4452
    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """
4453
4454
4455
4456
4457
        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)
4458
4459
            self.model_config.verify_dual_chunk_attention_config(
                self.load_config)
4460

4461
        self.cache_config.verify_with_parallel_config(self.parallel_config)
4462

4463
        if self.lora_config is not None:
4464
            self.lora_config.verify_with_cache_config(self.cache_config)
4465
            self.lora_config.verify_with_model_config(self.model_config)
4466
            self.lora_config.verify_lora_support()
4467
        if self.prompt_adapter_config is not None:
4468
4469
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)
4470

4471
        if self.quant_config is None and self.model_config is not None:
4472
4473
            self.quant_config = VllmConfig._get_quantization_config(
                self.model_config, self.load_config)
4474

4475
        from vllm.platforms import current_platform
4476
        if self.model_config is not None and \
4477
4478
4479
            self.scheduler_config.chunked_prefill_enabled and \
            self.model_config.dtype == torch.float32 and \
            current_platform.get_device_capability() == (7, 5):
4480
            logger.warning_once(
4481
4482
4483
4484
                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
                "precision for chunked prefill triton kernels.")

4485
4486
4487
4488
4489
        # 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
4490
4491
        if self.compilation_config.pass_config.enable_sequence_parallelism:
            self.compilation_config.custom_ops.append("+rms_norm")
4492
4493
        if envs.VLLM_USE_V1 and self.model_config is not None and \
            not self.model_config.enforce_eager:
4494
4495
            # By default, V1 uses piecewise CUDA graphs. If full_cuda_graph
            # is set to True, full CUDA graphs will be used.
4496
            self.compilation_config.cudagraph_num_of_warmups = 1
4497
            self.compilation_config.level = CompilationLevel.PIECEWISE
4498
            self.compilation_config.set_splitting_ops_for_v1()
4499

4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
            # The behavior of custom ops with inductor depends on the config:
            # - If use_inductor=True and custom_ops is empty:
            #   Inductor generates Triton kernels for all registered custom ops
            #   (default behavior)
            # - If use_inductor=True and custom_ops is non-empty:
            #   Custom CUDA kernels are used for specified ops while inductor
            #   generates Triton kernels for remaining ops, including misc torch
            #   ops in the model.
            if (not self.compilation_config.custom_ops
                    and self.compilation_config.use_inductor):
                # Let inductor generate Triton kernels for the custom ops.
                self.compilation_config.custom_ops = ["none"]

4513
        self._set_cudagraph_sizes()
4514

4515
        if self.cache_config.cpu_offload_gb > 0 and \
4516
4517
            self.compilation_config.level != CompilationLevel.NO_COMPILATION \
                and not envs.VLLM_USE_V1:
4518
            logger.warning(
4519
                "CPU offload is not supported with `torch.compile` in v0 yet."
4520
4521
4522
                " Disabling `torch.compile`.")
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

4523
4524
4525
4526
4527
4528
        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`.")
4529
4530
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

4531
4532
        if self.compilation_config.full_cuda_graph and \
            not self.model_config.disable_cascade_attn:
4533
4534
            logger.info("full_cuda_graph is not supported with "
                        "cascade attention. Disabling cascade attention.")
4535
            self.model_config.disable_cascade_attn = True
4536

4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
4552
4553
4554
4555
4556
4557
        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.")

        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

4558
        if (self.kv_events_config is not None
4559
4560
4561
4562
4563
                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.")
4564
4565
        if (self.kv_events_config is not None
                and self.kv_events_config.publisher != "null"
4566
4567
4568
4569
4570
                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.")
4571
4572
        current_platform.check_and_update_config(self)

4573
4574
4575
        if not self.instance_id:
            self.instance_id = random_uuid()[:5]

4576
4577
4578
4579
4580
4581
4582
        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.
4583
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
4584
4585
            if self.kv_transfer_config is not None:
                # Hybrid KV cache manager is not compatible with KV transfer.
4586
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
4587
4588
            if self.kv_events_config is not None:
                # Hybrid KV cache manager is not compatible with KV events.
4589
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
4590

4591
4592
4593
4594
4595
4596
4597
4598
4599
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
    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
        ]

4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
4626
    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.

4627
4628
        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
        will be the final sizes to capture cudagraph (in descending order).
4629
4630

        During runtime, if batchsize is larger than
4631
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
4632
4633
        no cudagraph will be used.
        If the batch size is no larger than
4634
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
4635
4636
4637
4638
4639
4640
4641
4642
4643
4644
4645
4646
4647
        we can quickly find the padded graph size for a given batch size by
        looking up `vllm_config.compilation_config.bs_to_padded_graph_size`.
        """

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

                possible_sizes = [1, 2, 4] + [8 * i for i in range(1, 1025)]
4648
4649
4650
4651
4652
                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)

4653
4654
4655
4656
4657
4658
4659
4660
4661
4662
4663
4664
4665
4666
4667
4668
4669
4670
4671
4672
4673
                # 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:
4674
4675
4676
4677
4678
4679
4680
4681
                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
4682
                    raise TypeError(f"Invalid value for {cuda_graph_sizes=}.")
4683
4684
4685
4686
                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)
4687
4688
4689
4690
4691
                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
                ]
4692
4693
4694
4695

        self.compilation_config.init_with_cudagraph_sizes(
            batch_size_capture_list)

4696
4697
    def recalculate_max_model_len(self, max_model_len: int):
        model_config = self.model_config
4698
        max_model_len = model_config.get_and_verify_max_len(max_model_len)
4699
4700
4701
4702
        self.model_config.max_model_len = max_model_len
        self.scheduler_config.max_model_len = max_model_len
        self.compute_hash()

4703
    def __str__(self):
4704
4705
4706
4707
4708
4709
4710
4711
4712
4713
4714
4715
4716
4717
4718
4719
4720
4721
4722
4723
4724
4725
4726
4727
4728
4729
4730
4731
4732
4733
        return (
            f"model={self.model_config.model!r},"
            f" speculative_config={self.speculative_config!r},"
            f" tokenizer={self.model_config.tokenizer!r}, "
            f"skip_tokenizer_init={self.model_config.skip_tokenizer_init},"
            f" tokenizer_mode={self.model_config.tokenizer_mode}, "
            f"revision={self.model_config.revision}, "
            f"override_neuron_config={self.model_config.override_neuron_config},"
            f" tokenizer_revision={self.model_config.tokenizer_revision}, "
            f"trust_remote_code={self.model_config.trust_remote_code}, "
            f"dtype={self.model_config.dtype}, "
            f"max_seq_len={self.model_config.max_model_len},"
            f" download_dir={self.load_config.download_dir!r}, "
            f"load_format={self.load_config.load_format}, "
            f"tensor_parallel_size={self.parallel_config.tensor_parallel_size},"
            f" pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, "  # noqa
            f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, "  # noqa
            f"quantization={self.model_config.quantization}, "
            f"enforce_eager={self.model_config.enforce_eager}, "
            f"kv_cache_dtype={self.cache_config.cache_dtype}, "
            f" device_config={self.device_config.device}, "
            f"decoding_config={self.decoding_config!r}, "
            f"observability_config={self.observability_config!r}, "
            f"seed={self.model_config.seed}, "
            f"served_model_name={self.model_config.served_model_name}, "
            f"num_scheduler_steps={self.scheduler_config.num_scheduler_steps}, "
            f"multi_step_stream_outputs={self.scheduler_config.multi_step_stream_outputs}, "  # noqa
            f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
            f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, "  # noqa
            f"use_async_output_proc={self.model_config.use_async_output_proc}, "
4734
4735
            f"pooler_config={self.model_config.pooler_config!r}, "
            f"compilation_config={self.compilation_config!r}")
4736
4737
4738


_current_vllm_config: Optional[VllmConfig] = None
4739
_current_prefix: Optional[str] = None
4740
4741
4742


@contextmanager
4743
4744
4745
def set_current_vllm_config(vllm_config: VllmConfig,
                            check_compile=False,
                            prefix: Optional[str] = None):
4746
    """
4747
    Temporarily set the current vLLM config.
4748
    Used during model initialization.
4749
    We save the current vLLM config in a global variable,
4750
    so that all modules can access it, e.g. custom ops
4751
    can access the vLLM config to determine how to dispatch.
4752
    """
4753
    global _current_vllm_config, _current_prefix
4754
    old_vllm_config = _current_vllm_config
4755
    old_prefix = _current_prefix
4756
4757
4758
4759
    from vllm.compilation.counter import compilation_counter
    num_models_seen = compilation_counter.num_models_seen
    try:
        _current_vllm_config = vllm_config
4760
        _current_prefix = prefix
4761
        yield
4762
4763
4764
    except Exception:
        raise
    else:
4765
4766
4767
4768
        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)
4769
4770
        if check_compile and \
            vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \
4771
4772
4773
4774
4775
4776
4777
4778
4779
            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"
4780
                " if you want it to be supported.",
4781
                vllm_config.model_config.model)
4782
    finally:
4783
        _current_vllm_config = old_vllm_config
4784
        _current_prefix = old_prefix
4785
4786
4787
4788
4789
4790
4791


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.
4792
        logger.warning("Current vLLM config is not set.")
4793
4794
4795
        from vllm.config import VllmConfig
        return VllmConfig()
    return _current_vllm_config
4796
4797


4798
4799
4800
4801
4802
4803
4804
4805
4806
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


4807
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
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:
4818
        result (bool): `True` if a match is found, `False` otherwise.
4819
4820
4821
4822
4823
4824
4825
4826
4827
4828
4829
4830
4831
    """
    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}")
4832
4833
4834
4835
4836
4837
4838
4839
4840
4841
4842
4843
4844


T = TypeVar("T")


def get_layers_from_vllm_config(vllm_config: VllmConfig,
                                layer_type: type[T]) -> dict[str, T]:
    return {
        layer_name: layer
        for layer_name, layer in
        vllm_config.compilation_config.static_forward_context.items()
        if isinstance(layer, layer_type)
    }