[misc] fix lora regex (#6944)

* fix lora regex

* fix

Former-commit-id: 1ada3ae5a3a14057341540c6d6ba985adf95f348

src/llamafactory/data/mm_plugin.py

@@ -1147,7 +1147,7 @@ class Qwen2vlPlugin(BasePlugin):
         mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         fps_per_video = mm_inputs.pop("fps_per_video", [])
         image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
-        if "second_per_grid_ts" in getattr(image_processor, "model_input_names", []) and fps_per_video:
+        if "second_per_grid_ts" in processor.model_input_names and fps_per_video:
             mm_inputs["second_per_grid_ts"] = [image_processor.temporal_patch_size / fps for fps in fps_per_video]
 
         return mm_inputs

src/llamafactory/extras/constants.py

@@ -1210,7 +1210,7 @@ register_model_group(
             DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-o-2_6",
         },
     },
-    template="minicpm_v",
+    template="minicpm_o",
     multimodal=True,
 )
 

src/llamafactory/model/adapter.py

@@ -201,7 +201,7 @@ def _setup_lora_tuning(
         if finetuning_args.use_llama_pro:
             target_modules = find_expanded_modules(model, target_modules, finetuning_args.freeze_trainable_layers)
 
-        target_modules = patch_target_modules(model.config, finetuning_args, target_modules)
+        target_modules = patch_target_modules(model, finetuning_args, target_modules)
 
         if (
             finetuning_args.use_dora

src/llamafactory/model/model_utils/misc.py

@@ -77,7 +77,7 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
         ):
             module_names.append(name)
 
-    logger.info_rank0("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
+    logger.info_rank0("Apply lora to layers: {}.".format(",".join(map(str, trainable_layer_ids))))
     return module_names
 
 

src/llamafactory/model/model_utils/visual.py

@@ -16,7 +16,7 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, Tuple
 
 import torch
 import transformers
@@ -42,6 +42,7 @@ class CompositeModel:
     projector_key: str
     vision_model_keys: List[str]
     language_model_keys: List[str]
+    lora_conflict_keys: List[str]
 
     def get_projector(self, module: "torch.nn.Module") -> "torch.nn.Module":
         for key in self.projector_key.split("."):
@@ -58,15 +59,14 @@ def _register_composite_model(
     projector_key: Optional[str] = None,
     vision_model_keys: Optional[List[str]] = None,
     language_model_keys: Optional[List[str]] = None,
+    lora_conflict_keys: Optional[List[str]] = None,
 ):
-    projector_key = projector_key or "multi_modal_projector"
-    vision_model_keys = vision_model_keys or ["vision_tower"]
-    language_model_keys = language_model_keys or ["language_model"]
     COMPOSITE_MODELS[model_type] = CompositeModel(
         model_type=model_type,
-        projector_key=projector_key,
-        vision_model_keys=vision_model_keys,
-        language_model_keys=language_model_keys,
+        projector_key=projector_key or "multi_modal_projector",
+        vision_model_keys=vision_model_keys or ["vision_tower"],
+        language_model_keys=language_model_keys or ["language_model"],
+        lora_conflict_keys=lora_conflict_keys or [],
     )
 
 
@@ -210,29 +210,25 @@ def get_vision_feature_select_strategy(config: "PretrainedConfig", processor: "P
 
 
 def patch_target_modules(
-    config: "PretrainedConfig", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
-) -> Union[str, List[str]]:
+    model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
+) -> List[str]:
     r"""
     Freezes vision tower for VLM LoRA tuning.
     """
-    model_type = getattr(config, "model_type", None)
-    vit_model_type = getattr(getattr(config, "vision_config", None), "model_type", None)
-    if finetuning_args.freeze_vision_tower:
-        if model_type in COMPOSITE_MODELS:
-            vision_model_keys = COMPOSITE_MODELS[model_type].vision_model_keys
-            logger.info_rank0(f"Set vision model not trainable: {vision_model_keys}.")
-            vision_model_keys = "|".join(vision_model_keys)
-            target_modules = "|".join(target_modules)
-            return f"^(?!.*{vision_model_keys}).*(?:{target_modules}).*"
-        else:
-            return target_modules
+    model_type = getattr(model.config, "model_type", None)
+    if model_type in COMPOSITE_MODELS:
+        forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
+        forbidden_modules.update(COMPOSITE_MODELS[model_type].lora_conflict_keys)
+        module_names = []
+        for name, _ in model.named_modules():
+            if any(target_module in name for target_module in target_modules) and not any(
+                forbidden_module in name for forbidden_module in forbidden_modules
+            ):
+                module_names.append(name)
+
+        return module_names
     else:
-        if model_type == "qwen2_vl":  # avoid attaching lora to Conv3D layer
-            return "^(?!.*patch_embed).*(?:{}).*".format("|".join(target_modules))
-        elif vit_model_type == "pixtral":
-            return "^(?!.*patch_conv).*(?:{}).*".format("|".join(target_modules))
-        else:
-            return target_modules
+        return target_modules
 
 
 _register_composite_model(
@@ -252,6 +248,7 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="minicpmv",
+    projector_key="resampler",
     vision_model_keys=["vpm"],
     language_model_keys=["llm"],
 )
@@ -259,8 +256,10 @@ _register_composite_model(
 
 _register_composite_model(
     model_type="minicpmo",
-    vision_model_keys=["vpm", "apm", "resampler", "tts"],
+    projector_key="resampler",
+    vision_model_keys=["vpm", "apm", "audio_avg_pooler", "audio_projection_layer", "tts"],
     language_model_keys=["llm"],
+    lora_conflict_keys=["audio_projection_layer"],
 )
 
 
@@ -291,6 +290,7 @@ _register_composite_model(
     projector_key="visual.merger",
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
 )
 
 
@@ -299,4 +299,5 @@ _register_composite_model(
     projector_key="visual.merger",
     vision_model_keys=["visual.patch_embed", "visual.blocks"],
     language_model_keys=["model", "lm_head"],
+    lora_conflict_keys=["patch_embed"],
 )