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fix DummyConfig in tests

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Signed-off-by: Xingyu Liu <charlotteliu12x@gmail.com>
This commit is contained in:
Xingyu Liu 2025-12-08 16:48:59 -08:00
parent 1cf506d89e
commit c327dffce1
2 changed files with 16 additions and 6 deletions
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5
tests/models/utils.py
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@ -14,6 +14,9 @@ from vllm.config.model import ModelConfig, ModelDType, RunnerOption
from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
from vllm.multimodal.processing import InputProcessingContext from vllm.multimodal.processing import InputProcessingContext
from vllm.tokenizers import cached_tokenizer_from_config from vllm.tokenizers import cached_tokenizer_from_config
from vllm.transformers_utils.model_arch_config_convertor import (
ModelArchConfigConvertorBase,
)
from .. import ci_envs from .. import ci_envs
from .registry import HF_EXAMPLE_MODELS from .registry import HF_EXAMPLE_MODELS
@ -488,7 +491,7 @@ def dummy_hf_overrides(
# Only set MoE related config when the model has MoE layers. # Only set MoE related config when the model has MoE layers.
# Otherwise all models detected as MoE by _get_transformers_backend_cls. # Otherwise all models detected as MoE by _get_transformers_backend_cls.
if ModelConfig.get_num_experts(DummyConfig) > 0: if ModelArchConfigConvertorBase.get_num_experts(text_config) > 0:
update_dict.update( update_dict.update(
{ {
"num_experts": num_experts, "num_experts": num_experts,

17
vllm/transformers_utils/model_arch_config_convertor.py
View File

@ -1,7 +1,7 @@
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import TYPE_CHECKING from typing import TYPE_CHECKING, final
import torch import torch
from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
@ -88,7 +88,9 @@ class ModelArchConfigConvertorBase:
return self.hf_text_config.num_attention_heads return self.hf_text_config.num_attention_heads
def get_num_experts(self) -> int: @final
@classmethod
def get_num_experts(cls, hf_text_config: PretrainedConfig) -> int:
"""Returns the number of experts in the model.""" """Returns the number of experts in the model."""
num_expert_names = [ num_expert_names = [
"num_experts", # Jamba "num_experts", # Jamba
@ -96,7 +98,7 @@ class ModelArchConfigConvertorBase:
"n_routed_experts", # DeepSeek "n_routed_experts", # DeepSeek
"num_local_experts", # Mixtral "num_local_experts", # Mixtral
] ]
num_experts = getattr_iter(self.hf_text_config, num_expert_names, 0) num_experts = getattr_iter(hf_text_config, num_expert_names, 0)
if isinstance(num_experts, list): if isinstance(num_experts, list):
# Ernie VL's remote code uses list[int]... # Ernie VL's remote code uses list[int]...
# The values are always the same so we just take the first one. # The values are always the same so we just take the first one.
@ -104,8 +106,11 @@ class ModelArchConfigConvertorBase:
# Coerce to 0 if explicitly set to None # Coerce to 0 if explicitly set to None
return num_experts or 0 return num_experts or 0
@final
@classmethod @classmethod
def get_torch_dtype(cls, hf_config, model_id: str, revision: str | None): def get_torch_dtype(
cls, hf_config: PretrainedConfig, model_id: str, revision: str | None
):
# NOTE: getattr(config, "dtype", torch.float32) is not correct # NOTE: getattr(config, "dtype", torch.float32) is not correct
# because config.dtype can be None. # because config.dtype can be None.
config_dtype = getattr(hf_config, "dtype", None) config_dtype = getattr(hf_config, "dtype", None)
@ -139,6 +144,7 @@ class ModelArchConfigConvertorBase:
return config_dtype return config_dtype
@final
@classmethod @classmethod
def _normalize_quantization_config(cls, config: PretrainedConfig): def _normalize_quantization_config(cls, config: PretrainedConfig):
quant_cfg = getattr(config, "quantization_config", None) quant_cfg = getattr(config, "quantization_config", None)
@ -171,6 +177,7 @@ class ModelArchConfigConvertorBase:
return quant_cfg return quant_cfg
@final
@classmethod @classmethod
def get_quantization_config(cls, hf_config: PretrainedConfig): def get_quantization_config(cls, hf_config: PretrainedConfig):
quant_cfg = cls._normalize_quantization_config(hf_config) quant_cfg = cls._normalize_quantization_config(hf_config)
@ -258,7 +265,7 @@ class ModelArchConfigConvertorBase:
head_size=self.get_head_size(), head_size=self.get_head_size(),
vocab_size=self.get_vocab_size(), vocab_size=self.get_vocab_size(),
total_num_kv_heads=self.get_total_num_kv_heads(), total_num_kv_heads=self.get_total_num_kv_heads(),
num_experts=self.get_num_experts(), num_experts=self.get_num_experts(self.hf_text_config),
quantization_config=self.get_quantization_config(self.hf_config), quantization_config=self.get_quantization_config(self.hf_config),
torch_dtype=self.get_torch_dtype(self.hf_config, model_id, revision), torch_dtype=self.get_torch_dtype(self.hf_config, model_id, revision),
is_multimodal_model=self.is_multimodal_model(), is_multimodal_model=self.is_multimodal_model(),