unsloth_deepseek/src/train_model_grpo_v1.py

import os
import torch
import torch.distributed as dist
from unsloth import FastLanguageModel
from unsloth import is_bfloat16_supported
from trl import GRPOConfig, GRPOTrainer
from datasets import load_dataset
from conf_train import Config ,load_config # 导入配置文件
import re

class ModelTrainer:
    def __init__(self, config:Config):
        """
        初始化 ModelTrainer 类，加载配置参数。
        :param config: 配置对象，包含模型训练所需的参数
        """
        self.config = config
        self.model_name = config.model_name
        self.max_seq_length = config.max_seq_length
        self.dtype = torch.float16 if config.dtype == "float16" else torch.bfloat16
        self.load_in_4bit = config.load_in_4bit
        self.lora_rank = config.lora_rank
        self.gpu_memory_utilization=config.gpu_memory_utilization

    def load_model(self):
        """
        加载预训练模型和分词器。
        :return: 返回加载的模型和分词器
        """
        model, tokenizer = FastLanguageModel.from_pretrained(
            model_name=self.model_name,
            max_seq_length=self.max_seq_length,
            load_in_4bit=self.load_in_4bit,
            dtype=self.dtype,
            fast_inference=False,
            max_lora_rank=self.lora_rank,
            gpu_memory_utilization=0.6,
        )

        model = model.to_empty(device='cuda')

        # 初始化模型的权重
        for param in model.parameters():
            if param.is_meta:
                param.data = torch.randn_like(param)

        # 添加 LoRA 适配器
        model = FastLanguageModel.get_peft_model(
            model,
            max_seq_length=self.max_seq_length,
            r=self.lora_rank,
            target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                          "gate_proj", "up_proj", "down_proj"],
            lora_alpha=16,
            lora_dropout=0,
            bias="none",
            use_gradient_checkpointing="unsloth",
            random_state=3407,
            use_rslora=False,
            loftq_config=None,
        )

        return model, tokenizer

    def load_data(self, train_data_path):
        """
        加载训练数据集。
        :param train_data_path: 训练数据路径
        :return: 返回加载的训练数据集
        """
        with open(train_data_path, 'r') as f:
            train_dataset = load_dataset("json", data_files={"train": train_data_path}, split="train")
        return train_dataset

    def train(self, model, tokenizer, train_dataset):
        """
        训练模型。
        :param model: 预训练模型
        :param tokenizer: 分词器
        :param train_dataset: 训练数据集
        :return: 返回训练后的模型
        """
        print("is_bfloat16_supported()=", is_bfloat16_supported())
        print(f"Reserved memory: {torch.cuda.memory_reserved()}")
        print(f"Allocated memory: {torch.cuda.memory_allocated()}")

        train_loader = torch.utils.data.DataLoader(
            train_dataset, batch_size=1, shuffle=True, pin_memory=True
        )

        torch.cuda.empty_cache()

        training_args = GRPOConfig(
            use_vllm=False,
            learning_rate=self.config.learning_rate,
            adam_beta1=self.config.adam_beta1,
            adam_beta2=self.config.adam_beta2,
            weight_decay=self.config.weight_decay,
            warmup_ratio=self.config.warmup_ratio,
            lr_scheduler_type=self.config.lr_scheduler_type,
            optim=self.config.optim,
            logging_steps=self.config.logging_steps,
            bf16=is_bfloat16_supported(),
            fp16=not is_bfloat16_supported(),
            per_device_train_batch_size=self.config.per_device_train_batch_size,
            gradient_accumulation_steps=self.config.gradient_accumulation_steps,
            num_generations=self.config.num_generations,
            max_prompt_length=self.config.max_prompt_length,
            max_completion_length=self.config.max_completion_length,
            num_train_epochs=self.config.num_train_epochs,
            max_steps=self.config.max_steps,
            save_steps=self.config.save_steps,
            max_grad_norm=self.config.max_grad_norm,
            report_to=self.config.report_to,
            output_dir=self.config.output_dir,
        )

        trainer = GRPOTrainer(
            model=model,
            processing_class=tokenizer,
            reward_funcs=[
                self.xmlcount_reward_func,
                self.soft_format_reward_func,
                self.strict_format_reward_func,
                self.int_reward_func,
                self.correctness_reward_func,
            ],
            args=training_args,
            train_dataset=train_dataset,
        )

        trainer.train()
        return model

    def save_model(self, model, tokenizer, save_path):
        """
        保存训练后的模型和分词器。
        :param model: 训练后的模型
        :param tokenizer: 分词器
        :param save_path: 保存路径
        """
        model.save_pretrained(save_path)
        tokenizer.save_pretrained(save_path)
        print(f"Model saved to {save_path}")

    @staticmethod
    def extract_xml_answer(text: str) -> str:
        answer = text.split("<answer>")[-1]
        answer = answer.split("</answer>")[0]
        return answer.strip()
    
    @staticmethod
    def count_xml(text) -> float:
        count = 0.0
        if text.count("<reasoning>\n") == 1:
            count += 0.125
        if text.count("\n</reasoning>\n") == 1:
            count += 0.125
        if text.count("\n<answer>\n") == 1:
            count += 0.125
            count -= len(text.split("\n</answer>\n")[-1])*0.001
        if text.count("\n</answer>") == 1:
            count += 0.125
            count -= (len(text.split("\n</answer>")[-1]) - 1)*0.001
        return count


    @staticmethod
    def xmlcount_reward_func(completions, **kwargs):
        """
        Reward function that counts XML tags in the completion.
        """
        contents = [completion[0]["content"] for completion in completions]
        return [ModelTrainer.count_xml(c) for c in contents]

    @staticmethod
    def soft_format_reward_func(completions, **kwargs):
        """
        Reward function that checks if the completion has a specific format.
        """
        pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
        responses = [completion[0]["content"] for completion in completions]
        matches = [re.match(pattern, r) for r in responses]
        return [0.5 if match else 0.0 for match in matches]

    @staticmethod
    def strict_format_reward_func(completions, **kwargs):
        """
        Reward function that checks if the completion has a specific format.
        """
        pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
        responses = [completion[0]["content"] for completion in completions]
        matches = [re.match(pattern, r) for r in responses]
        return [0.5 if match else 0.0 for match in matches]

    @staticmethod
    def int_reward_func(completions, **kwargs):
        """
        Reward function that checks if the completion contains an integer.
        """
        responses = [completion[0]['content'] for completion in completions]
        extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
        return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]

    @staticmethod
    def correctness_reward_func(prompts, completions, answer, **kwargs):
        """
        Reward function that checks if the completion matches the correct answer.
        """
        responses = [completion[0]['content'] for completion in completions]
        q = prompts[0][-1]['content']
        extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
        print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
        return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]

if __name__ == "__main__":
    # 加载配置文件
    config = load_config()

    # 设置环境变量
    """
        # 多机多卡
        # export RANK=0  # 第一台机器的 rank
        # export WORLD_SIZE=4  # 总共有 4 台机器
        # export MASTER_ADDR=<主节点 IP>
        # export MASTER_PORT=12345

    """
    # 单机多卡
    os.environ['RANK'] = '0'
    os.environ['WORLD_SIZE'] = '1'
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12345'
    

    # 初始化进程组
    dist.init_process_group(backend='nccl', init_method='env://')

    # 初始化 ModelTrainer
    trainer = ModelTrainer(config)

    # 加载模型和分词器
    model, tokenizer = trainer.load_model()

    # 加载数据集
    train_dataset = trainer.load_data(config.train_data_path)

    # 训练模型
    model = trainer.train(model, tokenizer, train_dataset)

    # 保存模型
    trainer.save_model(model, tokenizer, config.save_path)

    # 确保进程组被销毁
    if dist.is_initialized():
        dist.destroy_process_group()
    print("Training completed.")