[misc] lint code (#9395)

src/llamafactory/train/mca/__init__.py

@@ -16,4 +16,3 @@ from .workflow import run_dpo, run_pt, run_sft
 
 
 __all__ = ["run_dpo", "run_pt", "run_sft"]
-

src/llamafactory/train/mca/workflow.py

@@ -75,12 +75,17 @@ def _data_collator_wrapper(data_collator: Any):
 
     return wrapper
 
+
 def _check_model_support(model_args: ModelArguments):
     from transformers import AutoConfig as HfAutoConfig
-    config = HfAutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code)
+
+    config = HfAutoConfig.from_pretrained(
+        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+    )
     if config.model_type not in MCA_SUPPORTED_MODELS:
         raise ValueError(f"Model {config.model_type} is not supported by MCA.")
 
+
 def run_pt(
     model_args: ModelArguments,
     data_args: DataArguments,
@@ -161,22 +166,23 @@ def run_sft(
     model = AutoModel.from_pretrained(model_args.model_name_or_path, training_args)
 
     # optional freezing for qwen2_vl, qwen2_5_vl
-    if getattr(model.config, "hf_model_type", None) in ["qwen2_vl", "qwen2_5_vl"] and finetuning_args.freeze_vision_tower:
-        for name, p in model.named_parameters():
-            if any(name.startswith(k) for k in ["vision_model.blocks", "vision_model.patch_embed"]):
-                p.requires_grad_(False)
-    if getattr(model.config, "hf_model_type", None) in ["qwen2_vl", "qwen2_5_vl"] and finetuning_args.freeze_multi_modal_projector:
-        for name, p in model.named_parameters():
-            if any(name.startswith(k) for k in ["multi_modal_projector"]):
-                p.requires_grad_(False)
-    if getattr(model.config, "hf_model_type", None) in ["qwen2_vl", "qwen2_5_vl"] and finetuning_args.freeze_language_model:
-        for name, p in model.named_parameters():
-            if any(name.startswith(k) for k in ["embedding", "decoder", "output_layer"]):
-                p.requires_grad_(False)
+    if getattr(model.config, "hf_model_type", None) in ["qwen2_vl", "qwen2_5_vl"]:
+        params_to_freeze = []
+        if finetuning_args.freeze_vision_tower:
+            params_to_freeze.extend(["vision_model.blocks", "vision_model.patch_embed"])
 
-    pad_to_max = (
-        training_args.expert_model_parallel_size is not None and training_args.expert_model_parallel_size > 1
-    )
+        if finetuning_args.freeze_multi_modal_projector:
+            params_to_freeze.extend(["multi_modal_projector"])
+
+        if finetuning_args.freeze_language_model:
+            params_to_freeze.extend(["embedding", "decoder", "output_layer"])
+
+        if params_to_freeze:
+            for name, p in model.named_parameters():
+                if any(name.startswith(k) for k in params_to_freeze):
+                    p.requires_grad_(False)
+
+    pad_to_max = training_args.expert_model_parallel_size is not None and training_args.expert_model_parallel_size > 1
     data_collator = SFTDataCollatorWith4DAttentionMask(
         template=template,
         padding="max_length" if pad_to_max else "longest",
@@ -239,9 +245,7 @@ def run_dpo(
     dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module)
     data_args.cutoff_len -= 1
 
-    pad_to_max = (
-        training_args.expert_model_parallel_size is not None and training_args.expert_model_parallel_size > 1
-    )
+    pad_to_max = training_args.expert_model_parallel_size is not None and training_args.expert_model_parallel_size > 1
     dpo_config = DPOConfig(
         beta=finetuning_args.pref_beta,
         pref_loss=finetuning_args.pref_loss,
@@ -289,4 +293,3 @@ def run_dpo(
             keys += ["eval_loss"]
 
         plot_loss(training_args.output_dir, keys=keys)
-

src/llamafactory/train/tuner.py

@@ -71,13 +71,17 @@ def _training_function(config: dict[str, Any]) -> None:
             raise ImportError("mcore_adapter is not installed. Please install it with `pip install mcore-adapter`.")
         if finetuning_args.stage == "pt":
             from .mca import run_pt as run_pt_mca
+
             run_pt_mca(model_args, data_args, training_args, finetuning_args, callbacks)
         elif finetuning_args.stage == "sft":
             from .mca import run_sft as run_sft_mca
+
             run_sft_mca(model_args, data_args, training_args, finetuning_args, callbacks)
-        else:  # dpo
+        elif finetuning_args.stage == "dpo":
             from .mca import run_dpo as run_dpo_mca
+
             run_dpo_mca(model_args, data_args, training_args, finetuning_args, callbacks)
+
     elif finetuning_args.stage == "pt":
         run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
     elif finetuning_args.stage == "sft":