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[Misc] Further clean up some redundant config definitions (#22649)

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Signed-off-by: Isotr0py <2037008807@qq.com>
This commit is contained in:
Isotr0py
2025-08-12 00:22:25 +08:00
committed by GitHub
parent 3fa5b25845
commit 8e13d9fe6d
4 changed files with 34 additions and 85 deletions
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49
vllm/transformers_utils/config.py
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@ -32,11 +32,10 @@ from vllm.logger import init_logger
from vllm.transformers_utils.configs import (ChatGLMConfig, DeepseekVLV2Config,
EAGLEConfig, JAISConfig,
KimiVLConfig, MedusaConfig,
MllamaConfig, MLPSpeculatorConfig,
MLPSpeculatorConfig,
Nemotron_Nano_VL_Config,
NemotronConfig, NVLM_D_Config,
OvisConfig, RWConfig,
SpeculatorsConfig,
NemotronConfig, OvisConfig,
RWConfig, SpeculatorsConfig,
Step3TextConfig, Step3VLConfig,
UltravoxConfig)
# yapf: enable
@ -68,10 +67,6 @@ def _get_hf_token() -> Optional[str]:
return None
_CONFIG_REGISTRY_OVERRIDE_HF: dict[str, type[PretrainedConfig]] = {
"mllama": MllamaConfig
}
_CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = {
"chatglm": ChatGLMConfig,
"deepseek_vl_v2": DeepseekVLV2Config,
@ -85,18 +80,30 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = {
"eagle": EAGLEConfig,
"speculators": SpeculatorsConfig,
"nemotron": NemotronConfig,
"NVLM_D": NVLM_D_Config,
"ovis": OvisConfig,
"ultravox": UltravoxConfig,
"step3_vl": Step3VLConfig,
"step3_text": Step3TextConfig,
**_CONFIG_REGISTRY_OVERRIDE_HF
}
_CONFIG_ATTRS_MAPPING: dict[str, str] = {
"llm_config": "text_config",
}
_AUTO_CONFIG_KWARGS_OVERRIDES: dict[str, dict[str, Any]] = {
"internvl_chat": {
"has_no_defaults_at_init": True
},
# transformers regards mllama as is_encoder_decoder=False
# vllm needs is_encoder_decoder=True to enable cross-attention
"mllama": {
"is_encoder_decoder": True
},
"NVLM_D": {
"has_no_defaults_at_init": True
},
}
class ConfigFormat(str, enum.Enum):
AUTO = "auto"
@ -273,12 +280,13 @@ def thinker_uses_mrope(config: PretrainedConfig) -> bool:
def is_encoder_decoder(config: PretrainedConfig) -> bool:
"""Detect if the model with this config is used as an encoder/decoder."""
text_config = getattr(config, "text_config", None)
if text_config is not None:
return is_encoder_decoder(text_config)
def _is_encoder_decoder(config: PretrainedConfig) -> bool:
return getattr(config, "is_encoder_decoder", False)
return (_is_encoder_decoder(config)
or _is_encoder_decoder(config.get_text_config()))
def is_interleaved(config: PretrainedConfig) -> bool:
"""
@ -291,13 +299,21 @@ def is_interleaved(config: PretrainedConfig) -> bool:
return False
def _maybe_update_auto_config_kwargs(kwargs: dict[str, Any], model_type: str):
"""
Update kwargs for AutoConfig initialization based on model_type
"""
if model_type in _AUTO_CONFIG_KWARGS_OVERRIDES:
kwargs.update(_AUTO_CONFIG_KWARGS_OVERRIDES[model_type])
return kwargs
def _maybe_remap_hf_config_attrs(config: PretrainedConfig) -> PretrainedConfig:
"""Remap config attributes to match the expected names."""
for old_attr, new_attr in _CONFIG_ATTRS_MAPPING.items():
if hasattr(config, old_attr):
if not hasattr(config, new_attr):
config.update({new_attr: getattr(config, old_attr)})
delattr(config, old_attr)
logger.debug("Remapped config attribute '%s' to '%s'", old_attr,
new_attr)
return config
@ -408,15 +424,14 @@ def get_config(
)
else:
try:
kwargs = _maybe_update_auto_config_kwargs(
kwargs, model_type=model_type)
config = AutoConfig.from_pretrained(
model,
trust_remote_code=trust_remote_code,
revision=revision,
code_revision=code_revision,
token=_get_hf_token(),
# some old custom model's config needs
# `has_no_defaults_at_init=True` to work.
has_no_defaults_at_init=trust_remote_code,
**kwargs,
)
except ValueError as e:

6
vllm/transformers_utils/configs/__init__.py
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@ -17,13 +17,11 @@ from vllm.transformers_utils.configs.falcon import RWConfig
from vllm.transformers_utils.configs.jais import JAISConfig
from vllm.transformers_utils.configs.kimi_vl import KimiVLConfig
from vllm.transformers_utils.configs.medusa import MedusaConfig
from vllm.transformers_utils.configs.mllama import MllamaConfig
from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig
from vllm.transformers_utils.configs.moonvit import MoonViTConfig
from vllm.transformers_utils.configs.nemotron import NemotronConfig
from vllm.transformers_utils.configs.nemotron_h import NemotronHConfig
from vllm.transformers_utils.configs.nemotron_vl import Nemotron_Nano_VL_Config
from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
from vllm.transformers_utils.configs.ovis import OvisConfig
from vllm.transformers_utils.configs.speculators.base import SpeculatorsConfig
from vllm.transformers_utils.configs.step3_vl import (Step3TextConfig,
@ -34,18 +32,16 @@ from vllm.transformers_utils.configs.ultravox import UltravoxConfig
__all__ = [
"ChatGLMConfig",
"DeepseekVLV2Config",
"EAGLEConfig",
"RWConfig",
"JAISConfig",
"MedusaConfig",
"EAGLEConfig",
"MllamaConfig",
"MLPSpeculatorConfig",
"MoonViTConfig",
"KimiVLConfig",
"NemotronConfig",
"NemotronHConfig",
"Nemotron_Nano_VL_Config",
"NVLM_D_Config",
"OvisConfig",
"SpeculatorsConfig",
"UltravoxConfig",

31
vllm/transformers_utils/configs/mllama.py
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@ -1,31 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.models.mllama import configuration_mllama as mllama_hf_config
class MllamaTextConfig(mllama_hf_config.MllamaTextConfig):
'''
Use this class to override is_encoder_decoder:
- transformers regards mllama as is_encoder_decoder=False
- vllm needs is_encoder_decoder=True to enable cross-attention
'''
def __init__(
self,
**kwargs,
):
super().__init__(**kwargs)
self.is_encoder_decoder = True
class MllamaConfig(mllama_hf_config.MllamaConfig):
def __init__(
self,
text_config=None,
**kwargs,
):
if isinstance(text_config, dict):
text_config = MllamaTextConfig(**text_config)
super().__init__(text_config=text_config, **kwargs)

31
vllm/transformers_utils/configs/nvlm_d.py
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@ -1,31 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://huggingface.co/nvidia/NVLM-D-72B/blob/main/configuration_nvlm_d.py
# --------------------------------------------------------
# NVLM-D
# Copyright (c) 2024 NVIDIA
# Licensed under Apache 2.0 License [see LICENSE for details]
# --------------------------------------------------------
from transformers import Qwen2Config
from transformers.configuration_utils import PretrainedConfig
class NVLM_D_Config(PretrainedConfig):
model_type = 'NVLM_D'
is_composition = True
def __init__(self, vision_config=None, llm_config=None, **kwargs):
super().__init__(**kwargs)
# Handle vision_config initialization
if vision_config is None:
vision_config = {}
# Handle llm_config initialization
if llm_config is None:
llm_config = {}
self.vision_config = PretrainedConfig(**vision_config)
self.text_config = Qwen2Config(**llm_config)