[Model][LoRA]LoRA support added for MiniCPMV2.5 (#7199)

vllm/model_executor/models/utils.py

@@ -1,7 +1,7 @@
 import itertools
 from collections import UserDict
-from typing import (Dict, Iterable, List, Literal, Optional, Protocol, Tuple,
-                    Union, overload)
+from typing import (Any, Dict, Iterable, List, Literal, Optional, Protocol,
+                    Tuple, Union, overload)
 
 import torch
 import torch.nn as nn
@@ -329,3 +329,21 @@ 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 forward(self, *args, **kwargs) -> Any:
+        return getattr(self, self.model_name)(*args, **kwargs)
+
+    def embed_tokens(self, *args, **kwargs) -> Any:
+        return getattr(self, self.model_name).embed_tokens(*args, **kwargs)