feat(train): 添加训练过程中的日志记录和 loss 可视化功能

- 新增 LossCallback 类，用于在训练过程中记录 loss 数据
- 在训练模型函数中添加回调，实现日志记录和 loss 可视化
- 优化训练过程中的输出信息，增加当前步数和 loss 值的打印

frontend/train_page.py

@@ -1,8 +1,8 @@
 import gradio as gr
 import sys
-import torch
 from tinydb import Query
 from pathlib import Path
+from transformers import TrainerCallback
 
 sys.path.append(str(Path(__file__).resolve().parent.parent))
 from global_var import get_model, get_tokenizer, get_datasets, get_workdir
@@ -45,7 +45,31 @@ def train_page():
             # 加载数据集
             dataset = get_datasets().get(Query().name == dataset_name)
             dataset = [ds["message"][0] for ds in dataset["dataset_items"]] 
-            train_model(get_model(), get_tokenizer(), dataset, get_workdir(), learning_rate, per_device_train_batch_size, epoch, save_steps, lora_rank)
+            
+            class LossCallback(TrainerCallback):
+                def __init__(self):
+                    self.loss_data = []
+                    self.log_text = ""
+                    self.last_output = {"text": "", "plot": None}
+                def on_log(self, args, state, control, logs=None, **kwargs):
+                    if "loss" in logs:
+                        self.loss_data.append({
+                            "step": state.global_step,
+                            "loss": float(logs["loss"])
+                        })
+                    self.log_text += f"Step {state.global_step}: loss={logs['loss']:.4f}\n"
+                    # 添加以下两行print语句
+                    print(f"Current Step: {state.global_step}")
+                    print(f"Loss Value: {logs['loss']:.4f}")
+                    self.last_output = {
+                        "text": self.log_text,
+                    }
+                    # 不返回 control，避免干预训练过程
+
+            train_model(get_model(), get_tokenizer(),
+                         dataset, get_workdir()+"/checkpoint", 
+                         learning_rate, per_device_train_batch_size, epoch,
+                           save_steps, lora_rank, LossCallback)
 
 
         train_button.click(

tools/model.py

@@ -31,8 +31,18 @@ def formatting_prompts(examples,tokenizer):
     return {"text": texts}
 
 
-def train_model(model, tokenizer, dataset, output_dir, learning_rate, 
-                per_device_train_batch_size, epoch, save_steps, lora_rank):
+def train_model(
+    model, 
+    tokenizer, 
+    dataset: list, 
+    output_dir: str, 
+    learning_rate: float, 
+    per_device_train_batch_size: int, 
+    epoch: int, 
+    save_steps: int, 
+    lora_rank: int,
+    trainer_callback
+) -> None:
     # 模型配置参数
     dtype = None  # 数据类型，None表示自动选择
     load_in_4bit = False  # 使用4bit量化加载模型以节省显存
@@ -75,8 +85,8 @@ def train_model(model, tokenizer, dataset, output_dir, learning_rate,
         chat_template="qwen-2.5",
     )
 
-    dataset = HFDataset.from_list(dataset)
-    dataset = dataset.map(formatting_prompts, 
+    train_dataset = HFDataset.from_list(dataset)
+    train_dataset = train_dataset.map(formatting_prompts, 
                         fn_kwargs={"tokenizer": tokenizer},
                         batched=True)
 
@@ -84,7 +94,7 @@ def train_model(model, tokenizer, dataset, output_dir, learning_rate,
     trainer = SFTTrainer(
         model=model, # 待训练的模型
         tokenizer=tokenizer,  # 分词器
-        train_dataset=dataset,  # 训练数据集
+        train_dataset=train_dataset,  # 训练数据集
         dataset_text_field="text",  # 数据集字段的名称
         max_seq_length=model.max_seq_length,  # 最大序列长度
         data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer),
@@ -96,7 +106,7 @@ def train_model(model, tokenizer, dataset, output_dir, learning_rate,
             warmup_steps=int(epoch * 0.1),  # 预热步数,逐步增加学习率
             learning_rate=learning_rate,  # 学习率
             lr_scheduler_type="linear", # 线性学习率调度器
-            max_steps=int(epoch * len(dataset)/per_device_train_batch_size), # 最大训练步数（一步 = 处理一个batch的数据）
+            max_steps=int(epoch * len(train_dataset)/per_device_train_batch_size), # 最大训练步数（一步 = 处理一个batch的数据）
             fp16=not is_bfloat16_supported(),  # 如果不支持bf16则使用fp16
             bf16=is_bfloat16_supported(),  # 如果支持则使用bf16
             logging_steps=1,  # 每1步记录一次日志
@@ -106,10 +116,12 @@ def train_model(model, tokenizer, dataset, output_dir, learning_rate,
             output_dir=output_dir,  # 保存模型检查点和训练日志
             save_strategy="steps",  # 按步保存中间权重
             save_steps=save_steps,  # 使用动态传入的保存步数
-            # report_to="tensorboard",  # 将信息输出到tensorboard
+            report_to="none",
         ),
     )
 
+    trainer.add_callback(trainer_callback)
+
     trainer = train_on_responses_only(
         trainer,
         instruction_part = "<|im_start|>user\n",