Delete redundant code

Signed-off-by: Jee Jee Li <[email protected]>

vllm/lora/models.py

@@ -26,8 +26,7 @@
                              is_regex_target_modules,
                              parse_fine_tuned_lora_name, replace_submodule)
 from vllm.model_executor.models import SupportsLoRA, supports_multimodal
-from vllm.model_executor.models.module_mapping import (ModelComposeMethod,
-                                                       MultiModelKeys)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.utils import PPMissingLayer
 from vllm.utils import is_pin_memory_available
 
@@ -578,29 +577,11 @@ def _filter_unsupported_mm_module(self, module_name: str) -> bool:
         language model. LoRA for other modules, such as the vision tower, will 
         be filtered out.
         """
-        module_mapping: MultiModelKeys = self.model.get_mm_mapping()
-
-        def _verify_decoupled_model():
-            """
-            Suitable for MiniCPMV, InternVL, etc.
-            """
-            prefix = module_name.split(".")[0]
-            return (prefix in module_mapping.connector
-                    or prefix in module_mapping.tower_model)
-
-        def _verify_coupled_model():
-            """
-            Suitable for QWenVL, GLM4V, etc.
-            """
+        if self.supports_mm:
+            module_mapping: MultiModelKeys = self.model.get_mm_mapping()
             prefix_lst = module_mapping.connector + module_mapping.tower_model
             return any(
                 [module_name.startswith(prefix) for prefix in prefix_lst])
-
-        if self.supports_mm:
-            if module_mapping.compose_type == ModelComposeMethod.Decoupled:
-                return _verify_decoupled_model()
-            else:
-                return _verify_coupled_model()
         return False
 
     def _register_packed_modules(self, module_full_name: str) -> None:

vllm/model_executor/models/minicpmv.py

@@ -48,8 +48,7 @@
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.models.minicpm import MiniCPMModel
-from vllm.model_executor.models.module_mapping import (ModelComposeMethod,
-                                                       MultiModelKeys)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.qwen2 import Qwen2Model
 from vllm.model_executor.models.utils import LLMWrapper
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -636,11 +635,9 @@ def get_mm_mapping(self) -> MultiModelKeys:
         """
         Get the module prefix in multimodal models
         """
-        return MultiModelKeys.from_string_field(
-            language_model="llm",
-            connector="resampler",
-            tower_model="vpm",
-            compose_type=ModelComposeMethod.Decoupled)
+        return MultiModelKeys.from_string_field(language_model="llm",
+                                                connector="resampler",
+                                                tower_model="vpm")
 
     def init_llm(
         self,

vllm/model_executor/models/module_mapping.py

@@ -2,46 +2,9 @@
 #  https://github.com/modelscope/ms-swift/blob/v2.4.2/swift/utils/module_mapping.py
 
 from dataclasses import dataclass, field
-from enum import IntEnum
 from typing import List, Union
 
 
-class ModelComposeMethod(IntEnum):
-    """
-    `ModelComposeMethod` distinguishes between two architectural patterns in 
-    multi-modal models, focusing on how vision model, language model, and 
-    projector are implemented:
-    1. Decoupled Architecture (like mllama, InternVL, miniCPMV), complete 
-    independent implementation with its own layers, for example:
-    ```
-    InternVLChatModel
-    ├── vision_model (visual encoder)
-    │   ├── embeddings
-    │   └── encoder
-    ├── language_model (language model)
-    │   ├── tok_embeddings
-    │   └── layers
-    └── mlp1 (projector)
-    ```
-    2. Coupled Architecture (like QWenVL, GLM4V), Integrated as a sub-module 
-    with shared architectural patterns , for example: 
-    
-    ```
-    QWenVL
-    └── transformer
-        ├── wte
-        ├── h (language model)
-        ├── ln_f
-        └── visual (visual encoder)
-            ├── conv1
-            ├── transformer
-            └── attn_pool (projector)
-    ```
-    """
-    Decoupled = 0
-    Coupled = 1
-
-
 @dataclass
 class ModelKeys:
     model_type: str = None
@@ -78,8 +41,6 @@ class ModelKeys:
 
     output: str = None
 
-    compose_type: str = None
-
 
 @dataclass
 class MultiModelKeys(ModelKeys):
@@ -94,9 +55,7 @@ def from_string_field(language_model: Union[str, List[str]] = None,
                           connector: Union[str, List[str]] = None,
                           tower_model: Union[str, List[str]] = None,
                           generator: Union[str, List[str]] = None,
-                          compose_type: str = None,
                           **kwargs) -> 'MultiModelKeys':
-        assert compose_type, "compose_type is not allowed to be None"
 
         def to_list(value):
             if value is None:
@@ -107,5 +66,4 @@ def to_list(value):
                               connector=to_list(connector),
                               tower_model=to_list(tower_model),
                               generator=to_list(generator),
-                              compose_type=compose_type,
                               **kwargs)

vllm/model_executor/models/qwen.py

@@ -40,8 +40,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.models.module_mapping import (ModelComposeMethod,
-                                                       MultiModelKeys)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
@@ -1042,8 +1041,7 @@ def get_mm_mapping(self) -> MultiModelKeys:
         return MultiModelKeys.from_string_field(
             language_model="transformer.h",
             connector="transformer.visual.attn_pool",
-            tower_model="transformer.visual.transformer",
-            compose_type=ModelComposeMethod.Coupled)
+            tower_model="transformer.visual.transformer")
 
 
 @MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_qwen)