[Model][LoRA]LoRA support added for glm-4v (#10418)

Signed-off-by: B-201 <Joy25810@foxmail.com>

vllm/model_executor/models/chatglm.py

@@ -30,6 +30,7 @@ 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.glm4_vision_encoder import EVA2CLIPModel
+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.inputs import MultiModalData, MultiModalKwargs
@@ -574,25 +575,8 @@ class ChatGLMModel(nn.Module):
         return hidden_states
 
 
-@MULTIMODAL_REGISTRY.register_image_input_mapper(mm_input_mapper_for_glmv)
-@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens)
-@INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv)
-@INPUT_REGISTRY.register_input_processor(input_processor_for_glmv)
-class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
+class ChatGLMBaseModel(nn.Module, SupportsLoRA, SupportsPP,
                        SupportsMultiModal):
-    packed_modules_mapping = {
-        "query_key_value": ["query_key_value"],
-        "dense_h_to_4h": ["dense_h_to_4h"]
-    }
-    # LoRA specific attributes
-    supported_lora_modules = [
-        "query_key_value",
-        "dense",
-        "dense_h_to_4h",
-        "dense_4h_to_h",
-    ]
-    embedding_modules = {}
-    embedding_padding_modules = []
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -692,3 +676,79 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
                 weight_loader(param, combined_weight)
                 loaded_params.add(combined_name)
         return loaded_params
+
+
+class ChatGLM(ChatGLMBaseModel):
+    packed_modules_mapping = {
+        "query_key_value": ["query_key_value"],
+        "dense_h_to_4h": ["dense_h_to_4h"]
+    }
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "query_key_value",
+        "dense",
+        "dense_h_to_4h",
+        "dense_4h_to_h",
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+
+class ChatGLMV(ChatGLMBaseModel):
+    packed_modules_mapping = {
+        "query_key_value": ["query_key_value"],
+        "dense_h_to_4h": ["dense_h_to_4h"],
+        "merged_proj": ["gate_proj", "dense_h_to_4h"]
+    }
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "query_key_value",
+        "dense",
+        "dense_h_to_4h",
+        "dense_4h_to_h",
+        # vision
+        "fc1",
+        "fc2",
+        "merged_proj",
+        "linear_proj"
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="transformer.encoder",
+            connector="transformer.vision.linear_proj",
+            tower_model="transformer.vision.transformer")
+
+
+@MULTIMODAL_REGISTRY.register_image_input_mapper(mm_input_mapper_for_glmv)
+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens)
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_glmv)
+class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
+                         SupportsMultiModal):
+    # Ensure that the LoRA support check passes when the class is not
+    # initialized, but set all these attributes to empty.
+    packed_modules_mapping = {}
+    supported_lora_modules = []
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def __new__(
+        cls,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        config = vllm_config.model_config.hf_config
+        # Initialize VL
+        if hasattr(config, "visual"):
+            return ChatGLM(vllm_config=vllm_config, prefix=prefix)
+        # Initialize LLM
+        else:
+            return ChatGLMV(vllm_config=vllm_config, prefix=prefix)