refactor: 优化代码结构，提升可读性和一致性，移除不必要的文件

src/.gitignore

@@ -1,2 +1,3 @@
 checkpoint/*
-wandb/*
+wandb/*
+test.py

src/test.py

@@ -1,35 +0,0 @@
-# import sys
-
-# sys.path.insert(0, "transformers_repo/src/")
-# sys.path.insert(0, "peft_repo/src/")
-
-# from calflops import calculate_flops_hf
-
-# batch_size, max_seq_length = 1, 128
-# model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
-
-# flops, macs, params = calculate_flops_hf(model_name=model_name, input_shape=(batch_size, max_seq_length), access_token="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")
-# print("%s FLOPs:%s  MACs:%s  Params:%s \n" %(model_name, flops, macs, params))
-
-# Transformers Model, such as bert.
-from calflops import calculate_flops
-from transformers import AutoModel
-from transformers import AutoTokenizer
-
-batch_size, max_seq_length = 1, 128
-# Load model directly
-from transformers import AutoProcessor, AutoModelForImageTextToText
-
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
-model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
-
-flops, macs, params = calculate_flops(
-    model=model,
-    input_shape=(batch_size, max_seq_length),
-    transformer_tokenizer=tokenizer,
-)
-print(
-    "Bert(hfl/chinese-roberta-wwm-ext) FLOPs:%s   MACs:%s   Params:%s \n"
-    % (flops, macs, params)
-)
-# Bert(hfl/chinese-roberta-wwm-ext) FLOPs:67.1 GFLOPS   MACs:33.52 GMACs   Params:102.27 M

src/train.py

@@ -13,7 +13,11 @@ from trl import (
     TrlParser,
 )
 from utils.trainer import ContinualTrainer
-from utils.args import ContinualScriptArguments, ContinualModelConfig, ContiunalRegularizationArguments
+from utils.args import (
+    ContinualScriptArguments,
+    ContinualModelConfig,
+    ContiunalRegularizationArguments,
+)
 import logging
 from typing import TYPE_CHECKING
 
@@ -23,19 +27,23 @@ logging.basicConfig(level=logging.INFO)
 
 if __name__ == "__main__":
     parser = TrlParser(
-        (ContinualScriptArguments, TrainingArguments, ContinualModelConfig, ContiunalRegularizationArguments)
+        (
+            ContinualScriptArguments,
+            TrainingArguments,
+            ContinualModelConfig,
+            ContiunalRegularizationArguments,
+        ) # type: ignore
     )
     script_args, training_args, model_args, reg_args = parser.parse_args_and_config()
     # for type hint
-    if 1 == 0:
-        script_args = ContinualScriptArguments
-        training_args = TrainingArguments
-        model_args = ContinualModelConfig
-        reg_args = ContiunalRegularizationArguments
+    if TYPE_CHECKING:
+        script_args = ContinualScriptArguments()
+        training_args = TrainingArguments()
+        model_args = ContinualModelConfig()
+        reg_args = ContiunalRegularizationArguments()
+
     training_args.gradient_checkpointing_kwargs = dict(use_reentrant=False)
     training_args.remove_unused_columns = False
-    training_args.dataset_kwargs = {"skip_prepare_dataset": True}
-
 
     from model_library.factory import get_model
 
@@ -85,9 +93,9 @@ if __name__ == "__main__":
             model=model,
             args=training_args,
             data_collator=collate_fn_for_train,
-            train_dataset=dataset[script_args.dataset_train_split],
+            train_dataset=dataset[script_args.dataset_train_split], # type: ignore
             eval_dataset=(
-                dataset[script_args.dataset_test_split]
+                dataset[script_args.dataset_test_split] # type: ignore
                 if training_args.eval_strategy != "no"
                 else None
             ),