111 lines
3.0 KiB
Python
111 lines
3.0 KiB
Python
import os
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import torch
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import inspect
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from datasets import load_from_disk
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from transformers import (
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AutoTokenizer,
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AutoModelForSequenceClassification,
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TrainingArguments,
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Trainer
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)
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from peft import LoraConfig, get_peft_model, TaskType
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from sklearn.metrics import accuracy_score, f1_score
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# ✅ Hugging Face Token
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hf_token = "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"
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# ✅ 检查 TrainingArguments 来源
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from transformers import TrainingArguments
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print("🧠 当前 TrainingArguments 来源:", inspect.getfile(TrainingArguments))
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# ✅ 模型与 LoRA 配置
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base_model = "Qwen/Qwen2-0.5B-Instruct"
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output_dir = "./qwen_lora_checkpoint"
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lora_config = LoraConfig(
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r=8,
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lora_alpha=16,
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lora_dropout=0.05,
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bias="none",
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task_type=TaskType.SEQ_CLS,
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target_modules=["q_proj", "v_proj"]
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)
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# ✅ 加载 tokenizer，并设置 pad_token
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tokenizer = AutoTokenizer.from_pretrained(
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base_model,
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token=hf_token,
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trust_remote_code=True
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)
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tokenizer.pad_token = tokenizer.eos_token
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pad_token_id = tokenizer.pad_token_id
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# ✅ 加载模型（只加载一次），并设置 pad_token_id
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base = AutoModelForSequenceClassification.from_pretrained(
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base_model,
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token=hf_token,
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trust_remote_code=True,
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num_labels=2
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)
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base.config.pad_token_id = pad_token_id
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# ✅ 应用 LoRA
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model = get_peft_model(base, lora_config)
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# ✅ 加载数据
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dataset = load_from_disk("./qwen_classification_dataset")
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def preprocess(example):
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return tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
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tokenized_dataset = dataset.map(preprocess, batched=True)
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tokenized_dataset = tokenized_dataset.rename_column("label", "labels")
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tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
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# ✅ 训练参数（自动使用 GPU / fp16）
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training_args = TrainingArguments(
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output_dir=output_dir,
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per_device_train_batch_size=8,
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per_device_eval_batch_size=8,
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learning_rate=2e-5,
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num_train_epochs=3,
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evaluation_strategy="epoch",
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save_strategy="epoch",
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logging_dir=f"{output_dir}/logs",
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save_total_limit=2,
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load_best_model_at_end=True,
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metric_for_best_model="accuracy",
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remove_unused_columns=False,
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report_to="none",
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fp16=torch.cuda.is_available(), # 自动开启 fp16
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gradient_accumulation_steps=2,
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dataloader_pin_memory=True,
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)
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# ✅ 评估函数
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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preds = torch.argmax(torch.tensor(logits), dim=1)
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acc = accuracy_score(labels, preds)
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f1 = f1_score(labels, preds)
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return {"accuracy": acc, "f1": f1}
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# ✅ 构建 Trainer
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trainer = Trainer(
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model=model,
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tokenizer=tokenizer,
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args=training_args,
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train_dataset=tokenized_dataset["train"],
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eval_dataset=tokenized_dataset["validation"],
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compute_metrics=compute_metrics,
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)
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# ✅ 开始训练
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trainer.train()
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# ✅ 保存模型和 tokenizer
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model.save_pretrained(output_dir)
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tokenizer.save_pretrained(output_dir)
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print(f"✅ 微调完成，模型保存在 {output_dir}")
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