Improve configs - ModelConfig (#17130)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/conftest.py

@@ -738,7 +738,7 @@ class VllmRunner:
     - `block_size`: Set to `16` instead of `None` to reduce memory usage.
     - `enable_chunked_prefill`: Set to `False` instead of `None` for
       test reproducibility.
-    - `enforce_eager`: Set to `False` instead of `None` to test CUDA graph.
+    - `enforce_eager`: Set to `False` to test CUDA graph.
     """
 
     def __init__(

tests/engine/test_arg_utils.py

@@ -8,7 +8,7 @@ from typing import Literal, Optional
 
 import pytest
 
-from vllm.config import PoolerConfig, config
+from vllm.config import config
 from vllm.engine.arg_utils import (EngineArgs, contains_type, get_kwargs,
                                    get_type, is_not_builtin, is_type,
                                    literal_to_kwargs, nullable_kvs,
@@ -222,17 +222,6 @@ def test_prefix_cache_default():
     assert not engine_args.enable_prefix_caching
 
 
-def test_valid_pooling_config():
-    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
-    args = parser.parse_args([
-        '--override-pooler-config',
-        '{"pooling_type": "MEAN"}',
-    ])
-    engine_args = EngineArgs.from_cli_args(args=args)
-    assert engine_args.override_pooler_config == PoolerConfig(
-        pooling_type="MEAN", )
-
-
 @pytest.mark.parametrize(
     ("arg"),
     [

tests/quantization/test_register_quantization_config.py

@@ -14,7 +14,7 @@ import torch.nn.functional as F
 from vllm.model_executor.layers.linear import LinearBase  # noqa: E501
 from vllm.model_executor.layers.linear import UnquantizedLinearMethod
 from vllm.model_executor.layers.quantization import (
-    get_quantization_config, register_quantization_config)
+    QuantizationMethods, get_quantization_config, register_quantization_config)
 from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
     QuantizationConfig)
 
@@ -54,7 +54,7 @@ class CustomQuantConfig(QuantizationConfig):
         """Initialize the quantization config."""
         self.num_bits = num_bits
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         """Name of the quantization method."""
         return "custom_quant"
 

tests/test_config.py

@@ -185,7 +185,7 @@ def test_get_pooling_config():
         revision=None,
     )
 
-    pooling_config = model_config._init_pooler_config(None)
+    pooling_config = model_config._init_pooler_config()
     assert pooling_config is not None
 
     assert pooling_config.normalize
@@ -205,11 +205,12 @@ def test_get_pooling_config_from_args():
                                dtype="float16",
                                revision=None)
 
-    override_config = PoolerConfig(pooling_type='CLS', normalize=True)
+    override_pooler_config = PoolerConfig(pooling_type='CLS', normalize=True)
+    model_config.override_pooler_config = override_pooler_config
 
-    pooling_config = model_config._init_pooler_config(override_config)
+    pooling_config = model_config._init_pooler_config()
     assert pooling_config is not None
-    assert asdict(pooling_config) == asdict(override_config)
+    assert asdict(pooling_config) == asdict(override_pooler_config)
 
 
 @pytest.mark.skipif(current_platform.is_rocm(),

vllm/config.py

@@ -16,9 +16,8 @@ from dataclasses import (MISSING, dataclass, field, fields, is_dataclass,
                          replace)
 from importlib.util import find_spec
 from pathlib import Path
-from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Final, Literal,
-                    Optional, Protocol, TypeVar, Union, cast, get_args,
-                    get_origin)
+from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Literal, Optional,
+                    Protocol, TypeVar, Union, cast, get_args, get_origin)
 
 import torch
 from pydantic import BaseModel, Field, PrivateAttr
@@ -211,103 +210,190 @@ def get_field(cls: ConfigType, name: str) -> Field:
         f"{cls.__name__}.{name} must have a default value or default factory.")
 
 
-class ModelConfig:
-    """Configuration for the model.
+TokenizerMode = Literal["auto", "slow", "mistral", "custom"]
+ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 
-    Args:
-        model: Name or path of the huggingface 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: 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.
-        tokenizer: Name or path of the huggingface tokenizer to use.
-        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
-            available, "slow" will always use the slow tokenizer,
-            "mistral" will always use the tokenizer from `mistral_common`, and
-            "custom" will use --tokenizer to select the preregistered tokenizer.
-        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
-            downloading the model and tokenizer.
-        allowed_local_media_path: Allowing API requests to read local images or
-            videos from directories specified by the server file system.
-            This is a security risk. Should only be enabled in trusted
-            environments.
-        dtype: Data type for model weights and activations. The "auto" option
-            will use FP16 precision for FP32 and FP16 models, and BF16 precision
-            for BF16 models.
-        seed: Random seed for reproducibility.
-        revision: The specific model version to use. It can be a branch name,
-            a tag name, or a commit id. If unspecified, will use the default
-            version.
-        code_revision: The specific revision to use for the 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.
-        tokenizer_revision: The specific tokenizer version to use. It can be a
-            branch name, a tag name, or a commit id. If unspecified, will use
-            the default version.
-        max_model_len: Maximum length of a sequence (including prompt and
-            output). If None, will be derived from the model.
-        spec_target_max_model_len: Specify the the maximum length for spec
-            decoding draft models.
-        quantization: Quantization method that was used to quantize the model
-            weights. If None, we assume the model weights are not quantized.
-        enforce_eager: Whether to enforce eager execution. If True, we will
-            disable CUDA graph and always execute the model in eager mode.
-            If False, we will use CUDA graph and eager execution in hybrid.
-            If None, the user did not specify, so default to False.
-        max_seq_len_to_capture: 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: Maximum number of log probabilities. Defaults to 20.
-        disable_sliding_window: Whether to disable sliding window. If True,
-            we will disable the sliding window functionality of the model.
-            If the model does not support sliding window, this argument is
-            ignored.
-        skip_tokenizer_init: If true, skip initialization of tokenizer and
-            detokenizer.
-        served_model_name: The model name used in metrics tag `model_name`,
-            matches the model name exposed via the APIs. If multiple model
-            names provided, the first name will be used. If not specified,
-            the model name will be the same as `model`.
-        limit_mm_per_prompt: Maximum number of data items per modality
-            per prompt. Only applicable for multimodal models.
-        mm_processor_kwargs: Overrides for the multi-modal processor obtained
-            from `AutoProcessor.from_pretrained`.
-        disable_mm_preprocessor_cache: If True, disable caching of the
-            processed multi-modal inputs.
-        use_async_output_proc: Whether to use async output processor.
-            Defaults to True.
-        config_format: The config format which shall be loaded.
-            Defaults to 'auto' which defaults to 'hf'.
-        hf_token: The token to use as HTTP bearer authorization for remote files
-            . If `True`, will use the token generated when running
-            `huggingface-cli login` (stored in `~/.huggingface`).
-        hf_overrides: If a dictionary, contains arguments to be forwarded to the
-            HuggingFace config. If a callable, it is called to update the
-            HuggingFace config.
-        override_neuron_config: Initialize non default neuron config or
-            override default neuron config that are specific to Neuron devices,
-            this argument will be used to configure the neuron config that
-            can not be gathered from the vllm arguments.
-        override_pooler_config: Initialize non default pooling config or
-            override default pooling config for the pooling model.
-        logits_processor_pattern: 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: Configuration parameter file for generation.
-        model_impl: Which implementation of the model to use:
-            "auto" will try to use the vLLM implementation if it exists and
-                fall back to the Transformers implementation if no vLLM
-                implementation is available.
-            "vllm" will use the vLLM model implementation.
-            "transformers" will use the Transformers model implementation.
-        override_generation_config: Override the generation config with the
-            given config.
-    """
+
+@config
+@dataclass
+class ModelConfig:
+    """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."""
+    tokenizer: str = None  # type: ignore
+    """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
+    """Random seed for reproducibility."""
+    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)
+    """RoPE scaling configuration in JSON format. For example,
+    `{"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."""
+    max_model_len: int = None  # type: ignore
+    """Model context length (prompt and output). If unspecified, will be
+    automatically derived from the model config.
+    
+    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
+    """Specify the the maximum length for spec decoding draft models."""
+    quantization: Optional[QuantizationMethods] = None
+    """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."""
+    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
+    config. If a callable, it is called to update the HuggingFace config. When
+    specified via CLI, the argument must be a valid JSON string."""
+    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}`.
+    When specified via CLI, the argument must be a valid JSON string."""
+    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
+    arguments. e.g. `{"cast_logits_dtype": "bloat16"}`. When specified via CLI,
+    the argument must be a valid JSON string."""
+    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}`.
+    When specified via CLI, the argument must be a valid JSON string."""
+    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
+    `--generation-config vllm`, only the override parameters are used.
+    When specified via CLI, the argument must be a valid JSON string."""
+    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."""
 
     def compute_hash(self) -> str:
         """
@@ -342,92 +428,43 @@ class ModelConfig:
         assert_hashable(str_factors)
         return hashlib.sha256(str(factors).encode()).hexdigest()
 
-    def __init__(
-        self,
-        model: str,
-        task: Literal[TaskOption, Literal["draft"]],
-        tokenizer: str,
-        tokenizer_mode: str,
-        trust_remote_code: bool,
-        dtype: Union[str, torch.dtype],
-        seed: int,
-        hf_config_path: Optional[str] = None,
-        allowed_local_media_path: str = "",
-        revision: Optional[str] = None,
-        code_revision: Optional[str] = None,
-        rope_scaling: Optional[dict[str, Any]] = None,
-        rope_theta: Optional[float] = None,
-        tokenizer_revision: Optional[str] = None,
-        max_model_len: Optional[int] = None,
-        spec_target_max_model_len: Optional[int] = None,
-        quantization: Optional[str] = None,
-        enforce_eager: Optional[bool] = None,
-        max_seq_len_to_capture: Optional[int] = None,
-        max_logprobs: int = 20,
-        disable_sliding_window: bool = False,
-        disable_cascade_attn: bool = False,
-        skip_tokenizer_init: bool = False,
-        served_model_name: Optional[Union[str, list[str]]] = None,
-        limit_mm_per_prompt: Optional[dict[str, int]] = None,
-        mm_processor_kwargs: Optional[dict[str, Any]] = None,
-        disable_mm_preprocessor_cache: bool = False,
-        use_async_output_proc: bool = True,
-        config_format: ConfigFormat = ConfigFormat.AUTO,
-        hf_token: Optional[Union[bool, str]] = None,
-        hf_overrides: Optional[HfOverrides] = None,
-        override_neuron_config: Optional[dict[str, Any]] = None,
-        override_pooler_config: Optional["PoolerConfig"] = None,
-        logits_processor_pattern: Optional[str] = None,
-        generation_config: str = "auto",
-        enable_sleep_mode: bool = False,
-        override_generation_config: Optional[dict[str, Any]] = None,
-        model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
-    ) -> None:
-        self.model = maybe_model_redirect(model)
-        self.tokenizer = maybe_model_redirect(tokenizer)
+    def __post_init__(self) -> None:
+        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)
 
-        self.hf_config_path = hf_config_path
-        if isinstance(hf_config_path, str):
-            self.hf_config_path = maybe_model_redirect(hf_config_path)
+        if isinstance(self.hf_config_path, str):
+            self.hf_config_path = maybe_model_redirect(self.hf_config_path)
 
-        self.tokenizer_mode = tokenizer_mode
-        self.trust_remote_code = trust_remote_code
-        self.allowed_local_media_path = allowed_local_media_path
-        self.seed = seed
-        self.revision = revision
-        self.code_revision = code_revision
-        self.rope_scaling = rope_scaling
-        self.rope_theta = rope_theta
-        self.model_impl = model_impl
-
-        if hf_overrides is None:
-            hf_overrides = {}
-
-        if callable(hf_overrides):
+        if callable(self.hf_overrides):
             hf_overrides_kw = {}
-            hf_overrides_fn = hf_overrides
+            hf_overrides_fn = self.hf_overrides
         else:
-            hf_overrides_kw = hf_overrides
+            hf_overrides_kw = self.hf_overrides
             hf_overrides_fn = None
 
-        if rope_scaling is not None:
-            hf_override: dict[str, Any] = {"rope_scaling": rope_scaling}
+        if self.rope_scaling:
+            hf_override: dict[str, Any] = {"rope_scaling": self.rope_scaling}
             hf_overrides_kw.update(hf_override)
-            hf_overrides_str = json.dumps(hf_overrides)
+            hf_overrides_str = json.dumps(hf_overrides_kw)
             msg = (
                 "`--rope-scaling` will be removed in a future release. "
                 f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
             warnings.warn(DeprecationWarning(msg), stacklevel=2)
-        if rope_theta is not None:
-            hf_override = {"rope_theta": rope_theta}
+        if self.rope_theta is not None:
+            hf_override = {"rope_theta": self.rope_theta}
             hf_overrides_kw.update(hf_override)
-            hf_overrides_str = json.dumps(hf_overrides)
+            hf_overrides_str = json.dumps(hf_overrides_kw)
             msg = (
                 "`--rope-theta` will be removed in a future release. "
                 f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
             warnings.warn(DeprecationWarning(msg), stacklevel=2)
 
-        self.maybe_pull_model_tokenizer_for_s3(model, tokenizer)
+        self.maybe_pull_model_tokenizer_for_s3(self.model, self.tokenizer)
 
         if (backend := envs.VLLM_ATTENTION_BACKEND
             ) and backend == "FLASHINFER" and find_spec("flashinfer") is None:
@@ -437,20 +474,6 @@ class ModelConfig:
                 "https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile "  # noqa: E501
                 "for instructions on how to install it.")
 
-        # The tokenizer version is consistent with the model version by default.
-        if tokenizer_revision is None:
-            self.tokenizer_revision = revision
-        else:
-            self.tokenizer_revision = tokenizer_revision
-        self.quantization = quantization
-        self.enforce_eager = enforce_eager
-        self.max_seq_len_to_capture = max_seq_len_to_capture
-        self.max_logprobs = max_logprobs
-        self.disable_sliding_window = disable_sliding_window
-        self.disable_cascade_attn = disable_cascade_attn
-        self.skip_tokenizer_init = skip_tokenizer_init
-        self.enable_sleep_mode = enable_sleep_mode
-
         from vllm.platforms import current_platform
 
         if (self.enable_sleep_mode
@@ -458,9 +481,12 @@ class ModelConfig:
             raise ValueError(
                 "Sleep mode is not supported on current platform.")
 
+        if isinstance(self.config_format, str):
+            self.config_format = ConfigFormat(self.config_format)
+
         hf_config = get_config(self.hf_config_path or self.model,
-                               trust_remote_code, revision, code_revision,
-                               config_format)
+                               self.trust_remote_code, self.revision,
+                               self.code_revision, self.config_format)
 
         if hf_overrides_kw:
             logger.info("Overriding HF config with %s", hf_overrides_kw)
@@ -476,13 +502,8 @@ class ModelConfig:
                                             "attention_chunk_size", None)
         self.encoder_config = self._get_encoder_config()
         self.hf_image_processor_config = get_hf_image_processor_config(
-            self.model, hf_token=hf_token, revision=revision)
-        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
-        self.use_async_output_proc = use_async_output_proc
-
-        # Set enforce_eager to False if the value is unset.
-        if self.enforce_eager is None:
-            self.enforce_eager = False
+            self.model, hf_token=self.hf_token, revision=self.revision)
+        self.dtype = _get_and_verify_dtype(self.hf_config, self.dtype)
 
         interleaved_attn_models = ["gemma2", "gemma3_text", "cohere2"]
         sliding_window = getattr(self.hf_text_config, "sliding_window", None)
@@ -515,18 +536,14 @@ class ModelConfig:
 
         self.max_model_len = _get_and_verify_max_len(
             hf_config=self.hf_text_config,
-            max_model_len=max_model_len,
+            max_model_len=self.max_model_len,
             disable_sliding_window=self.disable_sliding_window,
             sliding_window_len=self.get_hf_config_sliding_window(),
-            spec_target_max_model_len=spec_target_max_model_len,
+            spec_target_max_model_len=self.spec_target_max_model_len,
             encoder_config=self.encoder_config)
-        self.served_model_name = get_served_model_name(model,
-                                                       served_model_name)
-        self.multimodal_config = self._init_multimodal_config(
-            limit_mm_per_prompt=limit_mm_per_prompt,
-            mm_processor_kwargs=mm_processor_kwargs,
-            disable_mm_preprocessor_cache=disable_mm_preprocessor_cache,
-        )
+        self.served_model_name = get_served_model_name(self.model,
+                                                       self.served_model_name)
+        self.multimodal_config = self._init_multimodal_config()
         if not self.skip_tokenizer_init:
             self._verify_tokenizer_mode()
 
@@ -535,24 +552,19 @@ class ModelConfig:
         self.has_noops = self._init_has_noops()
         self.has_inner_state = self._init_has_inner_state()
 
-        if current_platform.is_neuron():
-            self.override_neuron_config = override_neuron_config
-        else:
-            self.override_neuron_config = None
+        if (not current_platform.is_neuron() and self.override_neuron_config):
+            raise ValueError(
+                "`override_neuron_config` is only supported on Neuron.")
 
-        supported_tasks, task = self._resolve_task(task)
+        supported_tasks, task = self._resolve_task(self.task)
         self.supported_tasks = supported_tasks
-        self.task: Final = task
+        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(override_pooler_config)
-        self.logits_processor_pattern = logits_processor_pattern
-
-        self.generation_config = generation_config
-        self.override_generation_config = override_generation_config or {}
+        self.pooler_config = self._init_pooler_config()
 
         self._verify_quantization()
         self._verify_cuda_graph()
@@ -591,26 +603,21 @@ class ModelConfig:
                     model, ignore_pattern=["*.pt", "*.safetensors", "*.bin"])
                 self.tokenizer = s3_tokenizer.dir
 
-    def _init_multimodal_config(
-        self,
-        limit_mm_per_prompt: Optional[dict[str, int]],
-        mm_processor_kwargs: Optional[dict[str, Any]],
-        disable_mm_preprocessor_cache: bool,
-    ) -> Optional["MultiModalConfig"]:
+    def _init_multimodal_config(self) -> Optional["MultiModalConfig"]:
         if self.registry.is_multimodal_model(self.architectures):
             return MultiModalConfig(
-                limit_per_prompt=limit_mm_per_prompt or {},
-                mm_processor_kwargs=mm_processor_kwargs or {},
-                disable_mm_preprocessor_cache=disable_mm_preprocessor_cache,
-            )
+                limit_per_prompt=self.limit_mm_per_prompt,
+                mm_processor_kwargs=self.mm_processor_kwargs,
+                disable_mm_preprocessor_cache=self.
+                disable_mm_preprocessor_cache)
 
-        if limit_mm_per_prompt:
+        if self.limit_mm_per_prompt:
             raise ValueError("`limit_mm_per_prompt` is only supported for "
                              "multimodal models.")
-        if mm_processor_kwargs:
+        if self.mm_processor_kwargs:
             raise ValueError("`mm_processor_kwargs` is only supported for "
                              "multimodal models.")
-        if disable_mm_preprocessor_cache:
+        if self.disable_mm_preprocessor_cache:
             raise ValueError("`disable_mm_preprocessor_cache` is only "
                              "supported for multimodal models.")
 
@@ -620,31 +627,32 @@ class ModelConfig:
         return get_sentence_transformer_tokenizer_config(
             self.model, self.revision)
 
-    def _init_pooler_config(
-        self,
-        override_pooler_config: Optional["PoolerConfig"],
-    ) -> Optional["PoolerConfig"]:
+    def _init_pooler_config(self) -> Optional["PoolerConfig"]:
 
         if self.runner_type == "pooling":
-            user_config = override_pooler_config or PoolerConfig()
+            if isinstance(self.override_pooler_config, dict):
+                self.override_pooler_config = PoolerConfig(
+                    **self.override_pooler_config)
+
+            pooler_config = self.override_pooler_config or PoolerConfig()
 
             base_config = get_pooling_config(self.model, self.revision)
             if base_config is not None:
                 # Only set values that are not overridden by the user
                 for k, v in base_config.items():
-                    if getattr(user_config, k) is None:
-                        setattr(user_config, k, v)
+                    if getattr(pooler_config, k) is None:
+                        setattr(pooler_config, k, v)
 
             if self.is_matryoshka:
-                if user_config.normalize is None:
-                    user_config.normalize = True
-                elif not user_config.normalize:
+                if pooler_config.normalize is None:
+                    pooler_config.normalize = True
+                elif not pooler_config.normalize:
                     raise ValueError(
                         "`normalize` must be enabled (set to True) "
                         "for models that are compatible with "
                         "Matryoshka Representation.")
 
-            return user_config
+            return pooler_config
 
         return None
 
@@ -662,11 +670,11 @@ class ModelConfig:
         return self.registry.model_has_inner_state(self.architectures)
 
     def _verify_tokenizer_mode(self) -> None:
-        tokenizer_mode = self.tokenizer_mode.lower()
-        if tokenizer_mode not in ["auto", "slow", "mistral", "custom"]:
+        tokenizer_mode = cast(TokenizerMode, self.tokenizer_mode.lower())
+        if tokenizer_mode not in get_args(TokenizerMode):
             raise ValueError(
                 f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
-                "either 'auto', 'slow', 'mistral' or 'custom'.")
+                f"one of {get_args(TokenizerMode)}.")
         self.tokenizer_mode = tokenizer_mode
 
     def _get_preferred_task(
@@ -781,7 +789,8 @@ class ModelConfig:
             "quark", "nvfp4", "bitblas", "gptq_bitblas"
         ]
         if self.quantization is not None:
-            self.quantization = self.quantization.lower()
+            self.quantization = cast(QuantizationMethods,
+                                     self.quantization.lower())
 
         # Parse quantization method from the HF model config, if available.
         quant_cfg = self._parse_quant_hf_config()
@@ -857,8 +866,6 @@ class ModelConfig:
                     "non-quantized models.", self.quantization)
 
     def _verify_cuda_graph(self) -> None:
-        if self.max_seq_len_to_capture is None:
-            self.max_seq_len_to_capture = self.max_model_len
         self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                           self.max_model_len)
         ROCM_UNSUPPORTED_MODELS = ['mllama']
@@ -1294,7 +1301,7 @@ class ModelConfig:
 
     @property
     def runner_type(self) -> RunnerType:
-        return _TASK_RUNNER[self.task]
+        return _TASK_RUNNER[cast(_ResolvedTask, self.task)]
 
     @property
     def is_v1_compatible(self) -> bool:
@@ -2201,7 +2208,7 @@ class SpeculativeConfig:
     according to the log probability settings in SamplingParams."""
 
     # Draft model configuration
-    quantization: Optional[str] = None
+    quantization: Optional[QuantizationMethods] = None
     """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."""
@@ -2386,7 +2393,6 @@ class SpeculativeConfig:
                     code_revision=self.code_revision,
                     tokenizer_revision=self.target_model_config.
                     tokenizer_revision,
-                    max_model_len=None,
                     spec_target_max_model_len=self.target_model_config.
                     max_model_len,
                     quantization=self.quantization,
@@ -2793,30 +2799,31 @@ class PromptAdapterConfig:
 class MultiModalConfig:
     """Controls the behavior of multimodal models."""
 
-    limit_per_prompt: dict[str, int] = field(default_factory=dict)
+    limit_per_prompt: dict[str, int] = get_field(ModelConfig,
+                                                 "limit_mm_per_prompt")
     """
     The maximum number of input items allowed per prompt for each modality.
     This should be a JSON string that will be parsed into a dictionary.
     Defaults to 1 (V0) or 999 (V1) for each modality.
 
     For example, to allow up to 16 images and 2 videos per prompt:
-    :code:`{"images": 16, "videos": 2}`
+    `{"images": 16, "videos": 2}`
     """
 
     mm_processor_kwargs: Optional[dict[str, object]] = None
     """
     Overrides for the multi-modal processor obtained from
-    :meth:`transformers.AutoProcessor.from_pretrained`.
+    `transformers.AutoProcessor.from_pretrained`.
 
     The available overrides depend on the model that is being run.
 
     For example, for Phi-3-Vision:
-    :code:`{"num_crops": 4}`.
+    `{"num_crops": 4}`.
     """
 
     disable_mm_preprocessor_cache: bool = False
     """
-    If :code:`True`, disable caching of the processed multi-modal inputs.
+    If `True`, disable caching of the processed multi-modal inputs.
     """
 
     def compute_hash(self) -> str:
@@ -2907,10 +2914,6 @@ class PoolerConfig:
                                usedforsecurity=False).hexdigest()
         return hash_str
 
-    @staticmethod
-    def from_json(json_str: str) -> "PoolerConfig":
-        return PoolerConfig(**json.loads(json_str))
-
 
 _STR_DTYPE_TO_TORCH_DTYPE = {
     "half": torch.float16,

vllm/engine/arg_utils.py

@@ -20,15 +20,16 @@ from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
                          DeviceConfig, DistributedExecutorBackend,
                          GuidedDecodingBackend, GuidedDecodingBackendV1,
                          HfOverrides, KVTransferConfig, LoadConfig, LoadFormat,
-                         LoRAConfig, ModelConfig, ModelImpl, MultiModalConfig,
-                         ObservabilityConfig, ParallelConfig, PoolerConfig,
-                         PrefixCachingHashAlgo, PromptAdapterConfig,
-                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
-                         TaskOption, TokenizerPoolConfig, VllmConfig,
-                         get_attr_docs, get_field)
+                         LoRAConfig, ModelConfig, ModelDType, ModelImpl,
+                         MultiModalConfig, ObservabilityConfig, ParallelConfig,
+                         PoolerConfig, PrefixCachingHashAlgo,
+                         PromptAdapterConfig, SchedulerConfig, SchedulerPolicy,
+                         SpeculativeConfig, TaskOption, TokenizerMode,
+                         TokenizerPoolConfig, VllmConfig, get_attr_docs,
+                         get_field)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.plugins import load_general_plugins
 from vllm.reasoning import ReasoningParserManager
 from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
@@ -183,6 +184,9 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
             kwargs[name]["nargs"] = "+"
         elif contains_type(type_hints, int):
             kwargs[name]["type"] = int
+            # Special case for large integers
+            if name in {"max_model_len"}:
+                kwargs[name]["type"] = human_readable_int
         elif contains_type(type_hints, float):
             kwargs[name]["type"] = float
         elif contains_type(type_hints, dict):
@@ -212,22 +216,23 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
 @dataclass
 class EngineArgs:
     """Arguments for vLLM engine."""
-    model: str = 'facebook/opt-125m'
-    served_model_name: Optional[Union[str, List[str]]] = None
-    tokenizer: Optional[str] = None
-    hf_config_path: Optional[str] = None
-    task: TaskOption = "auto"
-    skip_tokenizer_init: bool = False
-    tokenizer_mode: str = 'auto'
-    trust_remote_code: bool = False
-    allowed_local_media_path: str = ""
+    model: str = ModelConfig.model
+    served_model_name: Optional[Union[
+        str, List[str]]] = ModelConfig.served_model_name
+    tokenizer: Optional[str] = ModelConfig.tokenizer
+    hf_config_path: Optional[str] = ModelConfig.hf_config_path
+    task: TaskOption = ModelConfig.task
+    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
+    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
+    trust_remote_code: bool = ModelConfig.trust_remote_code
+    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
     download_dir: Optional[str] = LoadConfig.download_dir
     load_format: str = LoadConfig.load_format
-    config_format: ConfigFormat = ConfigFormat.AUTO
-    dtype: str = 'auto'
+    config_format: str = ModelConfig.config_format
+    dtype: ModelDType = ModelConfig.dtype
     kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
-    seed: Optional[int] = None
-    max_model_len: Optional[int] = None
+    seed: Optional[int] = ModelConfig.seed
+    max_model_len: Optional[int] = ModelConfig.max_model_len
     # Note: Specifying a custom executor backend by passing a class
     # is intended for expert use only. The API may change without
     # notice.
@@ -245,8 +250,8 @@ class EngineArgs:
     enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
     prefix_caching_hash_algo: PrefixCachingHashAlgo = \
         CacheConfig.prefix_caching_hash_algo
-    disable_sliding_window: bool = False
-    disable_cascade_attn: bool = False
+    disable_sliding_window: bool = ModelConfig.disable_sliding_window
+    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
     use_v2_block_manager: bool = True
     swap_space: float = CacheConfig.swap_space
     cpu_offload_gb: float = CacheConfig.cpu_offload_gb
@@ -258,18 +263,19 @@ class EngineArgs:
     long_prefill_token_threshold: int = \
         SchedulerConfig.long_prefill_token_threshold
     max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
-    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
+    max_logprobs: int = ModelConfig.max_logprobs
     disable_log_stats: bool = False
-    revision: Optional[str] = None
-    code_revision: Optional[str] = None
-    rope_scaling: Optional[Dict[str, Any]] = None
-    rope_theta: Optional[float] = None
-    hf_token: Optional[Union[bool, str]] = None
-    hf_overrides: Optional[HfOverrides] = None
-    tokenizer_revision: Optional[str] = None
-    quantization: Optional[str] = None
-    enforce_eager: Optional[bool] = None
-    max_seq_len_to_capture: int = 8192
+    revision: Optional[str] = ModelConfig.revision
+    code_revision: Optional[str] = ModelConfig.code_revision
+    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
+    rope_theta: Optional[float] = ModelConfig.rope_theta
+    hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
+    hf_overrides: Optional[HfOverrides] = \
+        get_field(ModelConfig, "hf_overrides")
+    tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
+    quantization: Optional[QuantizationMethods] = ModelConfig.quantization
+    enforce_eager: bool = ModelConfig.enforce_eager
+    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
     disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
     # The following three fields are deprecated and will be removed in a future
     # release. Setting them will have no effect. Please remove them from your
@@ -280,8 +286,10 @@ class EngineArgs:
         get_field(TokenizerPoolConfig, "extra_config")
     limit_mm_per_prompt: dict[str, int] = \
         get_field(MultiModalConfig, "limit_per_prompt")
-    mm_processor_kwargs: Optional[Dict[str, Any]] = None
-    disable_mm_preprocessor_cache: bool = False
+    mm_processor_kwargs: Optional[Dict[str, Any]] = \
+        MultiModalConfig.mm_processor_kwargs
+    disable_mm_preprocessor_cache: bool = \
+        MultiModalConfig.disable_mm_preprocessor_cache
     # LoRA fields
     enable_lora: bool = False
     enable_lora_bias: bool = LoRAConfig.bias_enabled
@@ -323,7 +331,8 @@ class EngineArgs:
         DecodingConfig.disable_any_whitespace
     guided_decoding_disable_additional_properties: bool = \
         DecodingConfig.disable_additional_properties
-    logits_processor_pattern: Optional[str] = None
+    logits_processor_pattern: Optional[
+        str] = ModelConfig.logits_processor_pattern
 
     speculative_config: Optional[Dict[str, Any]] = None
 
@@ -331,22 +340,25 @@ class EngineArgs:
     show_hidden_metrics_for_version: Optional[str] = None
     otlp_traces_endpoint: Optional[str] = None
     collect_detailed_traces: Optional[str] = None
-    disable_async_output_proc: bool = False
+    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
     scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
     scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
 
-    override_neuron_config: Optional[Dict[str, Any]] = None
-    override_pooler_config: Optional[PoolerConfig] = None
+    override_neuron_config: dict[str, Any] = \
+        get_field(ModelConfig, "override_neuron_config")
+    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
+        ModelConfig.override_pooler_config
     compilation_config: Optional[CompilationConfig] = None
     worker_cls: str = ParallelConfig.worker_cls
     worker_extension_cls: str = ParallelConfig.worker_extension_cls
 
     kv_transfer_config: Optional[KVTransferConfig] = None
 
-    generation_config: Optional[str] = "auto"
-    override_generation_config: Optional[Dict[str, Any]] = None
-    enable_sleep_mode: bool = False
-    model_impl: str = "auto"
+    generation_config: str = ModelConfig.generation_config
+    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
+    override_generation_config: dict[str, Any] = \
+        get_field(ModelConfig, "override_generation_config")
+    model_impl: str = ModelConfig.model_impl
 
     calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
 
@@ -356,9 +368,6 @@ class EngineArgs:
     use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
 
     def __post_init__(self):
-        if not self.tokenizer:
-            self.tokenizer = self.model
-
         # support `EngineArgs(compilation_config={...})`
         # without having to manually construct a
         # CompilationConfig object
@@ -375,80 +384,87 @@ class EngineArgs:
         """Shared CLI arguments for vLLM engine."""
 
         # Model arguments
-        parser.add_argument(
-            '--model',
-            type=str,
-            default=EngineArgs.model,
-            help='Name or path of the huggingface model to use.')
-        parser.add_argument(
-            '--task',
-            default=EngineArgs.task,
-            choices=get_args(TaskOption),
-            help='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.')
-        parser.add_argument(
-            '--tokenizer',
-            type=optional_type(str),
-            default=EngineArgs.tokenizer,
-            help='Name or path of the huggingface tokenizer to use. '
-            'If unspecified, model name or path will be used.')
-        parser.add_argument(
-            "--hf-config-path",
-            type=optional_type(str),
-            default=EngineArgs.hf_config_path,
-            help='Name or path of the huggingface config to use. '
-            'If unspecified, model name or path will be used.')
-        parser.add_argument(
-            '--skip-tokenizer-init',
-            action='store_true',
-            help='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.')
-        parser.add_argument(
-            '--revision',
-            type=optional_type(str),
-            default=None,
-            help='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.')
-        parser.add_argument(
-            '--code-revision',
-            type=optional_type(str),
-            default=None,
-            help='The specific revision to use for the 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.')
-        parser.add_argument(
-            '--tokenizer-revision',
-            type=optional_type(str),
-            default=None,
-            help='Revision of the huggingface tokenizer to use. '
-            'It can be a branch name, a tag name, or a commit id. '
-            'If unspecified, will use the default version.')
-        parser.add_argument(
-            '--tokenizer-mode',
-            type=str,
-            default=EngineArgs.tokenizer_mode,
-            choices=['auto', 'slow', 'mistral', 'custom'],
-            help='The tokenizer mode.\n\n* "auto" will use the '
-            'fast tokenizer if available.\n* "slow" will '
-            'always use the slow tokenizer. \n* '
-            '"mistral" will always use the `mistral_common` tokenizer. \n* '
-            '"custom" will use --tokenizer to select the '
-            'preregistered tokenizer.')
-        parser.add_argument('--trust-remote-code',
-                            action='store_true',
-                            help='Trust remote code from huggingface.')
-        parser.add_argument(
-            '--allowed-local-media-path',
-            type=str,
-            help="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.")
+        model_kwargs = get_kwargs(ModelConfig)
+        model_group = parser.add_argument_group(
+            title="ModelConfig",
+            description=ModelConfig.__doc__,
+        )
+        model_group.add_argument("--model", **model_kwargs["model"])
+        model_group.add_argument("--task", **model_kwargs["task"])
+        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
+        model_group.add_argument("--tokenizer-mode",
+                                 **model_kwargs["tokenizer_mode"])
+        model_group.add_argument("--trust-remote-code",
+                                 **model_kwargs["trust_remote_code"])
+        model_group.add_argument("--dtype", **model_kwargs["dtype"])
+        model_group.add_argument("--seed", **model_kwargs["seed"])
+        model_group.add_argument("--hf-config-path",
+                                 **model_kwargs["hf_config_path"])
+        model_group.add_argument("--allowed-local-media-path",
+                                 **model_kwargs["allowed_local_media_path"])
+        model_group.add_argument("--revision", **model_kwargs["revision"])
+        model_group.add_argument("--code-revision",
+                                 **model_kwargs["code_revision"])
+        model_group.add_argument("--rope-scaling",
+                                 **model_kwargs["rope_scaling"])
+        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
+        model_group.add_argument("--tokenizer-revision",
+                                 **model_kwargs["tokenizer_revision"])
+        model_group.add_argument("--max-model-len",
+                                 **model_kwargs["max_model_len"])
+        model_group.add_argument("--quantization", "-q",
+                                 **model_kwargs["quantization"])
+        model_group.add_argument("--enforce-eager",
+                                 **model_kwargs["enforce_eager"])
+        model_group.add_argument("--max-seq-len-to-capture",
+                                 **model_kwargs["max_seq_len_to_capture"])
+        model_group.add_argument("--max-logprobs",
+                                 **model_kwargs["max_logprobs"])
+        model_group.add_argument("--disable-sliding-window",
+                                 **model_kwargs["disable_sliding_window"])
+        model_group.add_argument("--disable-cascade-attn",
+                                 **model_kwargs["disable_cascade_attn"])
+        model_group.add_argument("--skip-tokenizer-init",
+                                 **model_kwargs["skip_tokenizer_init"])
+        model_group.add_argument("--served-model-name",
+                                 **model_kwargs["served_model_name"])
+        # This one is a special case because it is the
+        # opposite of ModelConfig.use_async_output_proc
+        model_group.add_argument(
+            "--disable-async-output-proc",
+            action="store_true",
+            default=EngineArgs.disable_async_output_proc,
+            help="Disable async output processing. This may result in "
+            "lower performance.")
+        model_group.add_argument("--config-format",
+                                 choices=[f.value for f in ConfigFormat],
+                                 **model_kwargs["config_format"])
+        # This one is a special case because it can bool
+        # or str. TODO: Handle this in get_kwargs
+        model_group.add_argument("--hf-token",
+                                 type=str,
+                                 nargs="?",
+                                 const=True,
+                                 default=model_kwargs["hf_token"]["default"],
+                                 help=model_kwargs["hf_token"]["help"])
+        model_group.add_argument("--hf-overrides",
+                                 **model_kwargs["hf_overrides"])
+        model_group.add_argument("--override-neuron-config",
+                                 **model_kwargs["override_neuron_config"])
+        model_group.add_argument("--override-pooler-config",
+                                 **model_kwargs["override_pooler_config"])
+        model_group.add_argument("--logits-processor-pattern",
+                                 **model_kwargs["logits_processor_pattern"])
+        model_group.add_argument("--generation-config",
+                                 **model_kwargs["generation_config"])
+        model_group.add_argument("--override-generation-config",
+                                 **model_kwargs["override_generation_config"])
+        model_group.add_argument("--enable-sleep-mode",
+                                 **model_kwargs["enable_sleep_mode"])
+        model_group.add_argument("--model-impl",
+                                 choices=[f.value for f in ModelImpl],
+                                 **model_kwargs["model_impl"])
+
         # Model loading arguments
         load_kwargs = get_kwargs(LoadConfig)
         load_group = parser.add_argument_group(
@@ -465,38 +481,6 @@ class EngineArgs:
         load_group.add_argument('--use-tqdm-on-load',
                                 **load_kwargs["use_tqdm_on_load"])
 
-        parser.add_argument(
-            '--config-format',
-            default=EngineArgs.config_format,
-            choices=[f.value for f in ConfigFormat],
-            help='The format of the model config to load.\n\n'
-            '* "auto" will try to load the config in hf format '
-            'if available else it will try to load in mistral format ')
-        parser.add_argument(
-            '--dtype',
-            type=str,
-            default=EngineArgs.dtype,
-            choices=[
-                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
-            ],
-            help='Data type for model weights and activations.\n\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.')
-        parser.add_argument('--max-model-len',
-                            type=human_readable_int,
-                            default=EngineArgs.max_model_len,
-                            help='Model context length. If unspecified, will '
-                            'be automatically derived from the model config. '
-                            'Supports k/m/g/K/M/G in human-readable format.\n'
-                            'Examples:\n'
-                            '- 1k → 1000\n'
-                            '- 1K → 1024\n')
-
         # Guided decoding arguments
         guided_decoding_kwargs = get_kwargs(DecodingConfig)
         guided_decoding_group = parser.add_argument_group(
@@ -520,26 +504,6 @@ class EngineArgs:
             choices=list(ReasoningParserManager.reasoning_parsers),
             **guided_decoding_kwargs["reasoning_backend"])
 
-        parser.add_argument(
-            '--logits-processor-pattern',
-            type=optional_type(str),
-            default=None,
-            help='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.')
-        parser.add_argument(
-            '--model-impl',
-            type=str,
-            default=EngineArgs.model_impl,
-            choices=[f.value for f in ModelImpl],
-            help='Which implementation of the model to use.\n\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.\n')
         # Parallel arguments
         parallel_kwargs = get_kwargs(ParallelConfig)
         parallel_group = parser.add_argument_group(
@@ -592,10 +556,6 @@ class EngineArgs:
         cache_group.add_argument('--calculate-kv-scales',
                                  **cache_kwargs["calculate_kv_scales"])
 
-        parser.add_argument('--disable-sliding-window',
-                            action='store_true',
-                            help='Disables sliding window, '
-                            'capping to sliding window size.')
         parser.add_argument('--use-v2-block-manager',
                             action='store_true',
                             default=True,
@@ -605,73 +565,9 @@ class EngineArgs:
                             'Setting this flag to True or False'
                             ' has no effect on vLLM behavior.')
 
-        parser.add_argument('--seed',
-                            type=int,
-                            default=EngineArgs.seed,
-                            help='Random seed for operations.')
-        parser.add_argument(
-            '--max-logprobs',
-            type=int,
-            default=EngineArgs.max_logprobs,
-            help=('Max number of log probs to return logprobs is specified in'
-                  ' SamplingParams.'))
         parser.add_argument('--disable-log-stats',
                             action='store_true',
                             help='Disable logging statistics.')
-        # Quantization settings.
-        parser.add_argument('--quantization',
-                            '-q',
-                            type=optional_type(str),
-                            choices=[*QUANTIZATION_METHODS, None],
-                            default=EngineArgs.quantization,
-                            help='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.')
-        parser.add_argument(
-            '--rope-scaling',
-            default=None,
-            type=json.loads,
-            help='RoPE scaling configuration in JSON format. '
-            'For example, ``{"rope_type":"dynamic","factor":2.0}``')
-        parser.add_argument('--rope-theta',
-                            default=None,
-                            type=float,
-                            help='RoPE theta. Use with `rope_scaling`. In '
-                            'some cases, changing the RoPE theta improves the '
-                            'performance of the scaled model.')
-        parser.add_argument(
-            '--hf-token',
-            type=str,
-            nargs='?',
-            const=True,
-            default=None,
-            help='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`).')
-        parser.add_argument('--hf-overrides',
-                            type=json.loads,
-                            default=EngineArgs.hf_overrides,
-                            help='Extra arguments for the HuggingFace config. '
-                            'This should be a JSON string that will be '
-                            'parsed into a dictionary.')
-        parser.add_argument('--enforce-eager',
-                            action='store_true',
-                            help='Always use eager-mode PyTorch. If False, '
-                            'will use eager mode and CUDA graph in hybrid '
-                            'for maximal performance and flexibility.')
-        parser.add_argument('--max-seq-len-to-capture',
-                            type=int,
-                            default=EngineArgs.max_seq_len_to_capture,
-                            help='Maximum sequence length 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.')
 
         # Tokenizer arguments
         tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
@@ -775,20 +671,6 @@ class EngineArgs:
             "Default to `original/**/*` to avoid repeated loading of llama's "
             "checkpoints.")
 
-        parser.add_argument(
-            "--served-model-name",
-            nargs="+",
-            type=str,
-            default=None,
-            help="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.")
         parser.add_argument('--qlora-adapter-name-or-path',
                             type=str,
                             default=None,
@@ -822,13 +704,6 @@ class EngineArgs:
             "modules. This involves use of possibly costly and or blocking "
             "operations and hence might have a performance impact.")
 
-        parser.add_argument(
-            '--disable-async-output-proc',
-            action='store_true',
-            default=EngineArgs.disable_async_output_proc,
-            help="Disable async output processing. This may result in "
-            "lower performance.")
-
         # Scheduler arguments
         scheduler_kwargs = get_kwargs(SchedulerConfig)
         scheduler_group = parser.add_argument_group(
@@ -871,19 +746,6 @@ class EngineArgs:
         parser.add_argument('--scheduler-cls',
                             **scheduler_kwargs["scheduler_cls"])
 
-        parser.add_argument(
-            '--override-neuron-config',
-            type=json.loads,
-            default=None,
-            help="Override or set neuron device configuration. "
-            "e.g. ``{\"cast_logits_dtype\": \"bloat16\"}``.")
-        parser.add_argument(
-            '--override-pooler-config',
-            type=PoolerConfig.from_json,
-            default=None,
-            help="Override or set the pooling method for pooling models. "
-            "e.g. ``{\"pooling_type\": \"mean\", \"normalize\": false}``.")
-
         parser.add_argument('--compilation-config',
                             '-O',
                             type=CompilationConfig.from_cli,
@@ -920,34 +782,6 @@ class EngineArgs:
             help='The worker extension class on top of the worker cls, '
             'it is useful if you just want to add new functions to the worker '
             'class without changing the existing functions.')
-        parser.add_argument(
-            "--generation-config",
-            type=optional_type(str),
-            default="auto",
-            help="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.")
-
-        parser.add_argument(
-            "--override-generation-config",
-            type=json.loads,
-            default=None,
-            help="Overrides or sets generation config in JSON format. "
-            "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 generation-config is "
-            "None, only the override parameters are used.")
-
-        parser.add_argument("--enable-sleep-mode",
-                            action="store_true",
-                            default=False,
-                            help="Enable sleep mode for the engine. "
-                            "(only cuda platform is supported)")
 
         parser.add_argument(
             "--additional-config",
@@ -966,16 +800,6 @@ class EngineArgs:
             "If enabled, the model will be able to generate reasoning content."
         )
 
-        parser.add_argument(
-            "--disable-cascade-attn",
-            action="store_true",
-            default=False,
-            help="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.")
-
         return parser
 
     @classmethod
@@ -1002,8 +826,7 @@ class EngineArgs:
             model=self.model,
             hf_config_path=self.hf_config_path,
             task=self.task,
-            # We know this is not None because we set it in __post_init__
-            tokenizer=cast(str, self.tokenizer),
+            tokenizer=self.tokenizer,
             tokenizer_mode=self.tokenizer_mode,
             trust_remote_code=self.trust_remote_code,
             allowed_local_media_path=self.allowed_local_media_path,

vllm/entrypoints/llm.py

@@ -13,7 +13,7 @@ from typing_extensions import TypeVar, deprecated
 
 from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
                               BeamSearchSequence, get_beam_search_score)
-from vllm.config import CompilationConfig
+from vllm.config import CompilationConfig, ModelDType, TokenizerMode
 from vllm.engine.arg_utils import (EngineArgs, HfOverrides, PoolerConfig,
                                    TaskOption)
 from vllm.engine.llm_engine import LLMEngine
@@ -32,6 +32,7 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding.guided_fields import (
     GuidedDecodingRequest, LLMGuidedOptions)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.outputs import (ClassificationRequestOutput, EmbeddingRequestOutput,
                           PoolingRequestOutput, RequestOutput,
                           ScoringRequestOutput)
@@ -163,20 +164,20 @@ class LLM:
         self,
         model: str,
         tokenizer: Optional[str] = None,
-        tokenizer_mode: str = "auto",
+        tokenizer_mode: TokenizerMode = "auto",
         skip_tokenizer_init: bool = False,
         trust_remote_code: bool = False,
         allowed_local_media_path: str = "",
         tensor_parallel_size: int = 1,
-        dtype: str = "auto",
-        quantization: Optional[str] = None,
+        dtype: ModelDType = "auto",
+        quantization: Optional[QuantizationMethods] = None,
         revision: Optional[str] = None,
         tokenizer_revision: Optional[str] = None,
         seed: Optional[int] = None,
         gpu_memory_utilization: float = 0.9,
         swap_space: float = 4,
         cpu_offload_gb: float = 0,
-        enforce_eager: Optional[bool] = None,
+        enforce_eager: bool = False,
         max_seq_len_to_capture: int = 8192,
         disable_custom_all_reduce: bool = False,
         disable_async_output_proc: bool = False,
@@ -189,12 +190,7 @@ class LLM:
         compilation_config: Optional[Union[int, dict[str, Any]]] = None,
         **kwargs,
     ) -> None:
-        '''
-        LLM constructor.
-
-        Note: if enforce_eager is unset (enforce_eager is None)
-        it defaults to False.
-        '''
+        """LLM constructor."""
 
         if "disable_log_stats" not in kwargs:
             kwargs["disable_log_stats"] = True

vllm/model_executor/layers/quantization/aqlm.py

@@ -12,6 +12,7 @@ from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.utils import set_weight_attrs
@@ -186,7 +187,7 @@ class AQLMConfig(QuantizationConfig):
                 f"out_group_size={self.out_group_size})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "aqlm"
 
     @classmethod

vllm/model_executor/layers/quantization/awq.py

@@ -7,6 +7,7 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
@@ -44,7 +45,7 @@ class AWQConfig(QuantizationConfig):
                 f"zero_point={self.zero_point}, "
                 f"modules_to_not_convert={self.modules_to_not_convert})")
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "awq"
 
     def get_supported_act_dtypes(self) -> List[torch.dtype]:

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -13,6 +13,7 @@ from vllm.model_executor.layers.fused_moe.layer import (
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod,
                                                set_weight_attrs)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.awq import (AWQConfig,
                                                          is_layer_skipped_awq)
 from vllm.model_executor.layers.quantization.base_config import (
@@ -73,7 +74,7 @@ class AWQMarlinConfig(QuantizationConfig):
                 f"modules_to_not_convert={self.modules_to_not_convert})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "awq_marlin"
 
     @classmethod
@@ -101,8 +102,8 @@ class AWQMarlinConfig(QuantizationConfig):
                    modules_to_not_convert, config)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg)
         is_valid_user_quant = (user_quant is None or user_quant == "marlin"
                                or user_quant == "awq_marlin")

vllm/model_executor/layers/quantization/base_config.py

@@ -2,11 +2,16 @@
 
 import inspect
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional, Type
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type
 
 import torch
 from torch import nn
 
+if TYPE_CHECKING:
+    from vllm.model_executor.layers.quantization import QuantizationMethods
+else:
+    QuantizationMethods = str
+
 
 class QuantizeMethodBase(ABC):
     """Base class for different quantized methods."""
@@ -66,7 +71,7 @@ class QuantizationConfig(ABC):
         self.packed_modules_mapping: Dict[str, List[str]] = dict()
 
     @abstractmethod
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         """Name of the quantization method."""
         raise NotImplementedError
 
@@ -99,8 +104,8 @@ class QuantizationConfig(ABC):
         raise NotImplementedError
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         """
            Detects if this quantization method can support a given checkpoint
            format by overriding the user specified quantization method -- 

vllm/model_executor/layers/quantization/bitblas.py

@@ -5,6 +5,7 @@ import torch
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
@@ -100,7 +101,7 @@ class BitBLASConfig(QuantizationConfig):
                 f"quant_method={self.quant_method})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "bitblas"
 
     @classmethod
@@ -139,8 +140,8 @@ class BitBLASConfig(QuantizationConfig):
                    lm_head_quantized)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         # compat: autogptq >=0.8.0 use checkpoint_format: str
         # compat: autogptq <=0.7.1 is_bitblas_format: bool
         is_bitblas_format = (hf_quant_cfg.get("checkpoint_format") == "bitblas"

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -7,6 +7,7 @@ import torch
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod,
                                                set_weight_attrs)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.utils import direct_register_custom_op
@@ -56,7 +57,7 @@ class BitsAndBytesConfig(QuantizationConfig):
                 f"llm_int8_skip_modules={self.llm_int8_skip_modules})")
 
     @classmethod
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "bitsandbytes"
 
     @classmethod

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -16,6 +16,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
@@ -71,7 +72,7 @@ class CompressedTensorsConfig(QuantizationConfig):
     def get_min_capability(cls) -> int:
         return 70
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "compressed-tensors"
 
     def get_quant_method(

vllm/model_executor/layers/quantization/deepspeedfp.py

@@ -7,6 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.utils import set_weight_attrs
@@ -41,8 +42,8 @@ class DeepSpeedFPConfig(QuantizationConfig):
                 f"group_size={self.group_size}")
 
     @classmethod
-    def get_name(cls) -> str:
-        return "DeepSpeedFP"
+    def get_name(cls) -> QuantizationMethods:
+        return "deepspeedfp"
 
     @classmethod
     def from_config(cls, config: Dict[str, Any]) -> "DeepSpeedFPConfig":

vllm/model_executor/layers/quantization/experts_int8.py

@@ -8,6 +8,7 @@ from vllm.distributed import get_tensor_model_parallel_rank, get_tp_group
 from vllm.model_executor.layers.fused_moe import FusedMoE, FusedMoEMethodBase
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
@@ -20,7 +21,7 @@ class ExpertsInt8Config(QuantizationConfig):
         super().__init__()
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "experts_int8"
 
     @classmethod

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -9,6 +9,7 @@ from torch.nn.parameter import Parameter
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
@@ -38,7 +39,7 @@ class FBGEMMFp8Config(QuantizationConfig):
         self.fp8_linear = Fp8LinearOp()
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "fbgemm_fp8"
 
     @classmethod

vllm/model_executor/layers/quantization/fp8.py

@@ -16,6 +16,7 @@ from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
@@ -83,7 +84,7 @@ class Fp8Config(QuantizationConfig):
         self.weight_block_size = weight_block_size
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "fp8"
 
     @classmethod

vllm/model_executor/layers/quantization/gguf.py

@@ -13,6 +13,7 @@ from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe.layer import (FusedMoE,
                                                         FusedMoEMethodBase)
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -31,7 +32,7 @@ class GGUFConfig(QuantizationConfig):
     def __repr__(self) -> str:
         return ("GGUFConfig()")
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "gguf"
 
     def get_supported_act_dtypes(self) -> List[torch.dtype]:

vllm/model_executor/layers/quantization/gptq.py

@@ -10,6 +10,7 @@ from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.linear import LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.quantization.utils.gptq_utils import (
@@ -79,7 +80,7 @@ class GPTQConfig(QuantizationConfig):
                 f"dynamic={self.dynamic}")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "gptq"
 
     @classmethod

vllm/model_executor/layers/quantization/gptq_bitblas.py

@@ -7,6 +7,7 @@ from torch.nn.parameter import Parameter
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                set_weight_attrs)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
@@ -123,7 +124,7 @@ class GPTQBitBLASConfig(QuantizationConfig):
                 f"quant_method={self.quant_method})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "gptq_bitblas"
 
     @classmethod
@@ -151,8 +152,8 @@ class GPTQBitBLASConfig(QuantizationConfig):
                    lm_head_quantized)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         can_convert = cls.is_gptq_bitblas_compatible(hf_quant_cfg)
 
         is_valid_user_quant = (user_quant is None or user_quant == "bitblas"

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -11,6 +11,7 @@ from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearMethodBase,
                                                set_weight_attrs)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
@@ -100,7 +101,7 @@ class GPTQMarlinConfig(QuantizationConfig):
                 f"dynamic={self.dynamic}")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "gptq_marlin"
 
     @classmethod
@@ -130,8 +131,8 @@ class GPTQMarlinConfig(QuantizationConfig):
                    lm_head_quantized, dynamic, config)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         can_convert = cls.is_gptq_marlin_compatible(hf_quant_cfg)
 
         is_valid_user_quant = (user_quant is None or user_quant == "marlin"

vllm/model_executor/layers/quantization/gptq_marlin_24.py

@@ -8,6 +8,7 @@ from torch.nn.parameter import Parameter
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.parameter import (BasevLLMParameter,
@@ -85,7 +86,7 @@ class GPTQMarlin24Config(QuantizationConfig):
             self.quant_type, self.group_size)
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "gptq_marlin_24"
 
     @classmethod
@@ -108,8 +109,8 @@ class GPTQMarlin24Config(QuantizationConfig):
         return cls(weight_bits, group_size)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         is_marlin_24_format = (
             hf_quant_cfg.get("checkpoint_format") == "marlin_24")
 

vllm/model_executor/layers/quantization/hqq_marlin.py

@@ -8,6 +8,7 @@ from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
@@ -50,7 +51,7 @@ class HQQMarlinConfig(QuantizationConfig):
                 f"group_size={self.group_size})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "hqq"
 
     @classmethod

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -6,6 +6,7 @@ import torch
 
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.awq import (AWQLinearMethod,
                                                          is_layer_skipped_awq)
 from vllm.model_executor.layers.quantization.base_config import (
@@ -58,7 +59,7 @@ class IPEXConfig(QuantizationConfig):
                 f"group_size={self.group_size})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "ipex"
 
     @classmethod
@@ -97,8 +98,8 @@ class IPEXConfig(QuantizationConfig):
                    lm_head_quantized)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         if not current_platform.is_cpu() and not current_platform.is_xpu():
             return None
 

vllm/model_executor/layers/quantization/marlin.py

@@ -8,6 +8,7 @@ from torch.nn.parameter import Parameter
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
@@ -63,7 +64,7 @@ class MarlinConfig(QuantizationConfig):
                 f"lm_head_quantized={self.lm_head_quantized})")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "marlin"
 
     @classmethod
@@ -87,8 +88,8 @@ class MarlinConfig(QuantizationConfig):
         return cls(group_size, lm_head_quantized)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         # compat: autogptq >=0.8.0 use checkpoint_format: str
         # compat: autogptq <=0.7.1 is_marlin_format: bool
         is_marlin_format = (hf_quant_cfg.get("checkpoint_format") == "marlin"

vllm/model_executor/layers/quantization/modelopt.py

@@ -11,6 +11,7 @@ from vllm._custom_ops import (cutlass_scaled_fp4_mm,
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
@@ -42,7 +43,7 @@ class ModelOptFp8Config(QuantizationConfig):
                            " the format is experimental and could change.")
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "modelopt"
 
     @classmethod
@@ -184,8 +185,8 @@ class ModelOptNvFp4Config(QuantizationConfig):
             self.exclude_modules = exclude_modules
 
     @classmethod
-    def get_name(cls) -> str:
-        return "modelopt_nvfp4"
+    def get_name(cls) -> QuantizationMethods:
+        return "nvfp4"
 
     @classmethod
     def get_supported_act_dtypes(cls) -> List[torch.dtype]:

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -9,6 +9,7 @@ from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
@@ -64,7 +65,7 @@ class MoeWNA16Config(QuantizationConfig):
             self.modules_to_not_convert = modules_to_not_convert
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "moe_wna16"
 
     @classmethod
@@ -100,8 +101,8 @@ class MoeWNA16Config(QuantizationConfig):
                    lm_head_quantized, modules_to_not_convert, config)
 
     @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
+    def override_quantization_method(
+            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
         can_convert = cls.is_moe_wna16_compatible(hf_quant_cfg)
         if can_convert and user_quant == "moe_wna16":
             return cls.get_name()

vllm/model_executor/layers/quantization/neuron_quant.py

@@ -6,6 +6,7 @@ from typing import Any, Dict, List, Optional
 
 from torch.nn import Module
 
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 
@@ -30,7 +31,7 @@ class NeuronQuantConfig(QuantizationConfig):
         self.dequant_dtype = dequant_dtype
         self.quantize_method = quantize_method
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "neuron_quant"
 
     def get_supported_act_dtypes(self) -> List[str]:

vllm/model_executor/layers/quantization/ptpc_fp8.py

@@ -9,6 +9,7 @@ from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.fp8 import (Fp8Config,
@@ -50,7 +51,7 @@ class PTPCFp8Config(Fp8Config):
                          ignored_layers=ignored_layers)
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "ptpc_fp8"
 
     @classmethod

vllm/model_executor/layers/quantization/qqq.py

@@ -8,6 +8,7 @@ from torch.nn.parameter import Parameter
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.parameter import (BasevLLMParameter,
@@ -84,7 +85,7 @@ class QQQConfig(QuantizationConfig):
             self.weight_bits, self.group_size)
 
     @classmethod
-    def get_name(cls) -> str:
+    def get_name(cls) -> QuantizationMethods:
         return "qqq"
 
     @classmethod

vllm/model_executor/layers/quantization/quark/quark.py

@@ -8,6 +8,7 @@ import torch
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
@@ -47,7 +48,7 @@ class QuarkConfig(QuantizationConfig):
     def get_min_capability(cls) -> int:
         return 70
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "quark"
 
     def get_quant_method(self, layer: torch.nn.Module,

vllm/model_executor/layers/quantization/torchao.py

@@ -6,6 +6,7 @@ import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.utils import set_weight_attrs
@@ -20,7 +21,7 @@ class TorchAOConfig(QuantizationConfig):
     def __repr__(self) -> str:
         return f"TorchAOConfig({self.torchao_config})"
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "torchao"
 
     def get_supported_act_dtypes(self) -> List[torch.dtype]:

vllm/model_executor/layers/quantization/tpu_int8.py

@@ -7,6 +7,7 @@ from torch.nn import Module
 from torch.nn.parameter import Parameter
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.parameter import ModelWeightParameter
@@ -27,7 +28,7 @@ class Int8TpuConfig(QuantizationConfig):
                 f"Unsupported activation scheme {activation_scheme}")
         self.activation_scheme = activation_scheme
 
-    def get_name(self) -> str:
+    def get_name(self) -> QuantizationMethods:
         return "tpu_int8"
 
     def get_supported_act_dtypes(self) -> List[torch.dtype]:

vllm/model_executor/layers/rotary_embedding.py

@@ -1496,7 +1496,7 @@ def get_rope(
     if key in _ROPE_DICT:
         return _ROPE_DICT[key]
 
-    if rope_scaling is None:
+    if not rope_scaling:
         rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
                                      is_neox_style, dtype)
     else:

vllm/model_executor/model_loader/neuron.py

@@ -180,7 +180,6 @@ def _get_neuron_config_after_override(default_neuron_config,
                                              NeuronConfig, QuantizationConfig,
                                              SparseAttnConfig)
 
-    overridden_neuron_config = overridden_neuron_config or {}
     sparse_attn = overridden_neuron_config.pop("sparse_attn", {})
     if sparse_attn:
         overridden_neuron_config["sparse_attn"] = SparseAttnConfig(