[Misc] Clean up duplicated hf overrides (#22311)

Signed-off-by: Isotr0py <2037008807@qq.com>

tests/models/multimodal/test_tensor_schema.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from functools import partial
-from typing import Any
 from unittest.mock import patch
 
 import pytest
-from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig
 from vllm.engine.llm_engine import LLMEngine as V0LLMEngine
@@ -19,6 +17,7 @@ from vllm.v1.engine.core import EngineCore as V1EngineCore
 
 from ...conftest import VllmRunner
 from ..registry import _MULTIMODAL_EXAMPLE_MODELS, HF_EXAMPLE_MODELS
+from ..utils import dummy_hf_overrides
 
 ARCH_TO_SKIP = {
     "MolmoForCausalLM": "incompatible requirements",
@@ -51,51 +50,6 @@ def create_batched_mm_kwargs(
     return mm_kwargs
 
 
-# Avoid OOM and reduce initialization time by only using 1 layer
-def hf_overrides(hf_config: PretrainedConfig,
-                 exist_overrides: dict[str, Any]) -> PretrainedConfig:
-    hf_config.update(exist_overrides)
-    text_config = hf_config.get_text_config()
-    # Ensure at least 2 expert per group
-    # Since `grouped_topk` assumes top-2
-    n_group = getattr(text_config, 'n_group', None)
-    num_experts = n_group * 2 if n_group is not None else 2
-    # we use three layers for Gemma-3n to check
-    # both normal layer and kv_shared_layer
-    text_config.update({
-        "num_layers": 1,
-        "num_hidden_layers": 1,
-        "num_experts": num_experts,
-        "num_experts_per_tok": 2,
-        "num_local_experts": num_experts,
-        # Otherwise there will not be any expert layers
-        "first_k_dense_replace": 0,
-        # To avoid OOM on DeepSeek-V3
-        "n_routed_experts": num_experts,
-        # For Gemma-3n
-        "num_kv_shared_layers": 1,
-    })
-    if hasattr(hf_config, "vision_config"):
-        hf_config.vision_config.update({
-            "num_layers": 1,
-            "num_hidden_layers": 1,
-        })
-    # e.g.: ibm-granite/granite-speech-3.3-2b
-    if hasattr(hf_config, "encoder_config"):
-        hf_config.encoder_config.update({
-            "num_layers": 1,
-            "num_hidden_layers": 1,
-        })
-    # e.g.: Qwen/Qwen2-Audio-7B-Instruct
-    if hasattr(hf_config, "audio_config"):
-        hf_config.audio_config.update({
-            "num_layers": 1,
-            "num_hidden_layers": 1,
-            "encoder_layers": 1,
-        })
-    return hf_config
-
-
 @pytest.mark.core_model
 @pytest.mark.parametrize("model_arch", list(_MULTIMODAL_EXAMPLE_MODELS.keys()))
 def test_model_tensor_schema(model_arch: str, vllm_runner: type[VllmRunner],
@@ -110,7 +64,8 @@ def test_model_tensor_schema(model_arch: str, vllm_runner: type[VllmRunner],
 
     model_id = model_info.default
 
-    hf_overrides_fn = partial(hf_overrides,
+    hf_overrides_fn = partial(dummy_hf_overrides,
+                              model_arch=model_arch,
                               exist_overrides=model_info.hf_overrides)
 
     model_config = ModelConfig(

tests/models/test_initialization.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from functools import partial
 from unittest.mock import patch
 
 import pytest
-from transformers import PretrainedConfig
 
 from vllm import LLM
 from vllm.config import ModelImpl
@@ -16,6 +16,7 @@ from vllm.v1.engine.core import EngineCore as V1EngineCore
 from ..utils import create_new_process_for_each_test
 from .registry import (_TRANSFORMERS_BACKEND_MODELS, AUTO_EXAMPLE_MODELS,
                        HF_EXAMPLE_MODELS, HfExampleModels)
+from .utils import dummy_hf_overrides
 
 
 @create_new_process_for_each_test()
@@ -33,64 +34,15 @@ def can_initialize(model_arch: str, monkeypatch: pytest.MonkeyPatch,
     model_info.check_available_online(on_fail="skip")
     model_info.check_transformers_version(on_fail="skip")
 
+    hf_overrides_fn = partial(dummy_hf_overrides,
+                              model_arch=model_arch,
+                              exist_overrides=model_info.hf_overrides)
+
     if model_arch in ("Llama4ForCausalLM", "EagleLlama4ForCausalLM"):
         from vllm.model_executor.models.llama4 import Llama4ForCausalLM
         from vllm.model_executor.models.registry import ModelRegistry
         ModelRegistry.register_model("Llama4ForCausalLM", Llama4ForCausalLM)
 
-    # Avoid OOM and reduce initialization time by only using 1 layer
-    def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
-        hf_config.update(model_info.hf_overrides)
-
-        text_config = hf_config.get_text_config()
-
-        # Ensure at least 2 expert per group
-        # Since `grouped_topk` assumes top-2
-        n_group = getattr(text_config, 'n_group', None)
-        num_experts = n_group * 2 if n_group is not None else 2
-
-        # we use three layers for Gemma-3n to check
-        # both normal layer and kv_shared_layer
-        num_hidden_layers = (3 if model_arch
-                             == "Gemma3nForConditionalGeneration" else 1)
-
-        text_config.update({
-            "num_layers": 1,
-            "num_hidden_layers": num_hidden_layers,
-            "num_experts": num_experts,
-            "num_experts_per_tok": 2,
-            "num_local_experts": num_experts,
-            # Otherwise there will not be any expert layers
-            "first_k_dense_replace": 0,
-            # To avoid OOM on DeepSeek-V3
-            "n_routed_experts": num_experts,
-            # For Gemma-3n
-            "num_kv_shared_layers": 1,
-        })
-
-        if hasattr(hf_config, "vision_config"):
-            hf_config.vision_config.update({
-                "num_layers": 1,
-                "num_hidden_layers": 1,
-            })
-
-        # e.g.: ibm-granite/granite-speech-3.3-2b
-        if hasattr(hf_config, "encoder_config"):
-            hf_config.encoder_config.update({
-                "num_layers": 1,
-                "num_hidden_layers": 1,
-            })
-
-        # e.g.: Qwen/Qwen2-Audio-7B-Instruct
-        if hasattr(hf_config, "audio_config"):
-            hf_config.audio_config.update({
-                "num_layers": 1,
-                "num_hidden_layers": 1,
-                "encoder_layers": 1,
-            })
-
-        return hf_config
-
     # Avoid calling model.forward()
     def _initialize_kv_caches_v0(self) -> None:
         self.cache_config.num_gpu_blocks = 0
@@ -132,7 +84,7 @@ def can_initialize(model_arch: str, monkeypatch: pytest.MonkeyPatch,
             load_format="dummy",
             model_impl=ModelImpl.TRANSFORMERS
             if model_arch in _TRANSFORMERS_BACKEND_MODELS else ModelImpl.VLLM,
-            hf_overrides=hf_overrides,
+            hf_overrides=hf_overrides_fn,
         )
 
 

tests/models/utils.py

@@ -7,6 +7,7 @@ from typing import Any, NamedTuple, Optional, Union
 
 import torch
 import torch.nn.functional as F
+from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig, RunnerOption
 from vllm.inputs import InputContext
@@ -351,3 +352,63 @@ class RerankModelInfo(NamedTuple):
     architecture: str = ""
     dtype: str = "auto"
     enable_test: bool = True
+
+
+def dummy_hf_overrides(
+    hf_config: PretrainedConfig,
+    model_arch: str,
+    exist_overrides: Optional[dict[str, Any]] = None,
+) -> PretrainedConfig:
+    """
+    Dummy HF overrides function used to create dummy model
+    with only minimum nums of layer.
+    """
+    hf_config.update(exist_overrides or {})
+
+    text_config = hf_config.get_text_config()
+
+    # Ensure at least 2 expert per group
+    # Since `grouped_topk` assumes top-2
+    n_group = getattr(text_config, 'n_group', None)
+    num_experts = n_group * 2 if n_group is not None else 2
+
+    # we use three layers for Gemma-3n to check
+    # both normal layer and kv_shared_layer
+    num_hidden_layers = (3 if model_arch == "Gemma3nForConditionalGeneration"
+                         else 1)
+    text_config.update({
+        "num_layers": 1,
+        "num_hidden_layers": num_hidden_layers,
+        "num_experts": num_experts,
+        "num_experts_per_tok": 2,
+        "num_local_experts": num_experts,
+        # Otherwise there will not be any expert layers
+        "first_k_dense_replace": 0,
+        # To avoid OOM on DeepSeek-V3
+        "n_routed_experts": num_experts,
+        # For Gemma-3n
+        "num_kv_shared_layers": 1,
+    })
+
+    if hasattr(hf_config, "vision_config"):
+        hf_config.vision_config.update({
+            "num_layers": 1,
+            "num_hidden_layers": 1,
+        })
+
+    # e.g.: ibm-granite/granite-speech-3.3-2b
+    if hasattr(hf_config, "encoder_config"):
+        hf_config.encoder_config.update({
+            "num_layers": 1,
+            "num_hidden_layers": 1,
+        })
+
+    # e.g.: Qwen/Qwen2-Audio-7B-Instruct
+    if hasattr(hf_config, "audio_config"):
+        hf_config.audio_config.update({
+            "num_layers": 1,
+            "num_hidden_layers": 1,
+            "encoder_layers": 1,
+        })
+
+    return hf_config