[Model] Clean up MiniCPMV (#10751)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/utils.py

@@ -1,7 +1,7 @@
 import itertools
 from dataclasses import dataclass, field
-from typing import (Any, Callable, Dict, Iterable, List, Literal, Mapping,
-                    Optional, Protocol, Set, Tuple, Union, overload)
+from typing import (Callable, Dict, Iterable, List, Literal, Mapping, Optional,
+                    Protocol, Set, Tuple, Union, overload)
 
 import torch
 import torch.nn as nn
@@ -560,30 +560,6 @@ def make_empty_intermediate_tensors_factory(keys: List[str], hidden_size: int):
     return make_empty_intermediate_tensors
 
 
-class LLMWrapper(nn.Module):
-    """
-    To align with the key names of LoRA trained with PEFT, we need to add an
-    additional layer to the llm's implementation.
-    """
-
-    def __init__(self, llm: nn.Module, name: str) -> None:
-        super().__init__()
-        self.model_name = name
-        setattr(self, name, llm)
-
-    def __getattr__(self, key: str):
-        llm = super().__getattr__(self.model_name)
-        if key == self.model_name:
-            return llm
-
-        return getattr(llm, key)
-
-    # We need to explicitly override this
-    def __call__(self, *args: Any, **kwargs: Any) -> Any:
-        llm = super().__getattr__(self.model_name)
-        return llm(*args, **kwargs)
-
-
 def get_vit_attn_backend(support_fa: bool = False) -> _Backend:
     """
     Get the available attention backend for Vision Transformer.