fix DummyConfig in tests

Signed-off-by: Xingyu Liu <charlotteliu12x@gmail.com>

tests/models/utils.py

@@ -14,6 +14,9 @@ from vllm.config.model import ModelConfig, ModelDType, RunnerOption
 from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.multimodal.processing import InputProcessingContext
 from vllm.tokenizers import cached_tokenizer_from_config
+from vllm.transformers_utils.model_arch_config_convertor import (
+    ModelArchConfigConvertorBase,
+)
 
 from .. import ci_envs
 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.
     # 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(
             {
                 "num_experts": num_experts,

vllm/transformers_utils/model_arch_config_convertor.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, final
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
@@ -88,7 +88,9 @@ class ModelArchConfigConvertorBase:
 
         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."""
         num_expert_names = [
             "num_experts",  # Jamba
@@ -96,7 +98,7 @@ class ModelArchConfigConvertorBase:
             "n_routed_experts",  # DeepSeek
             "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):
             # Ernie VL's remote code uses list[int]...
             # 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
         return num_experts or 0
 
+    @final
     @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
         # because config.dtype can be None.
         config_dtype = getattr(hf_config, "dtype", None)
@@ -139,6 +144,7 @@ class ModelArchConfigConvertorBase:
 
         return config_dtype
 
+    @final
     @classmethod
     def _normalize_quantization_config(cls, config: PretrainedConfig):
         quant_cfg = getattr(config, "quantization_config", None)
@@ -171,6 +177,7 @@ class ModelArchConfigConvertorBase:
 
         return quant_cfg
 
+    @final
     @classmethod
     def get_quantization_config(cls, hf_config: PretrainedConfig):
         quant_cfg = cls._normalize_quantization_config(hf_config)
@@ -258,7 +265,7 @@ class ModelArchConfigConvertorBase:
             head_size=self.get_head_size(),
             vocab_size=self.get_vocab_size(),
             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),
             torch_dtype=self.get_torch_dtype(self.hf_config, model_id, revision),
             is_multimodal_model=self.is_multimodal_model(),