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- # 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
- from transformers import LongformerTokenizer, LongformerModel # 分词模型最大支持 4096 个token
- import numpy as np
- 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 = 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.fast_inference = config.fast_inference
- self.lora_rank = config.lora_rank
- self.gpu_memory_utilization = config.gpu_memory_utilization
- # 初始化 Longformer 模型和分词器
- self.tokenizer = LongformerTokenizer.from_pretrained(f'../models/allenai/longformer-base-4096')
- self.longformer_model = LongformerModel.from_pretrained(f'../models/allenai/longformer-base-4096')
- 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, # False for LoRA 16bit
- dtype=self.dtype,
- fast_inference=self.fast_inference,
- max_lora_rank=self.lora_rank,
- gpu_memory_utilization=self.gpu_memory_utilization,
- )
- 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, # Choose any number > 0! Suggested 8, 16, 32, 64, 128
- target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
- "gate_proj", "up_proj", "down_proj"], # Remove QKVO if out of memory
- lora_alpha=16,
- lora_dropout=0, # Supports any, but = 0 is optimized
- bias="none", # Supports any, but = "none" is optimized
- use_gradient_checkpointing="unsloth", # True or "unsloth" for very long context
- random_state=3407,
- use_rslora=False, # We support rank stabilized LoRA
- loftq_config=None, # And LoftQ
- )
- 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")
- print("train_dataset -->\n", train_dataset)
- # 打印第一条数据,检查格式是否正确
- print("First example in train_dataset:", train_dataset[0])
- 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()
- print("self.config.learning_rate=", float(self.config.learning_rate))
- training_args = GRPOConfig(
- use_vllm=self.config.use_vllm,
- learning_rate=float(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, # 用于处理输入文本的分词器(tokenizer)
- reward_funcs=[
- self.xmlcount_reward_func, # XML 标签完整性奖励函数
- self.soft_format_reward_func, # 软格式奖励函数
- self.strict_format_reward_func, # 严格格式奖励函数
- self.int_reward_func, # 整数奖励函数
- self.correctness_reward_func, # 正确性奖励函数
- self.semantic_correctness_reward_func, # 语义正确性奖励函数
- self.reasoning_quality_reward_func, # 推理质量奖励函数
- self.combined_reward_func, # 综合奖励函数
- ],
- args=training_args, # 定义的训练超参数
- train_dataset=train_dataset, # 训练数据集
- )
- 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 cosine_similarity(vec1, vec2):
- """
- 计算两个向量的余弦相似度。
- :param vec1: 第一个向量,形状为 (1, 768)
- :param vec2: 第二个向量,形状为 (1, 768)
- :return: 余弦相似度
- """
- vec1 = vec1.squeeze() # 形状从 (1, 768) 变为 (768,)
- vec2 = vec2.squeeze() # 形状从 (1, 768) 变为 (768,)
- return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
- def semantic_correctness_reward_func(self, prompts, completions, answer, **kwargs):
- """
- 使用 Longformer 计算生成答案与标准答案的语义相似度。
- """
- responses = [completion[0]['content'] for completion in completions]
- extracted_responses = [self.extract_xml_answer(r) for r in responses]
- scores = []
- for resp, ans in zip(extracted_responses, answer):
- # 截断文本,确保长度不超过 4096
- resp = self.tokenizer.decode(self.tokenizer.encode(resp, truncation=True, max_length=4096))
- ans = self.tokenizer.decode(self.tokenizer.encode(ans, truncation=True, max_length=4096))
- # 编码生成答案和标准答案
- inputs_resp = self.tokenizer(resp, return_tensors='pt', padding=True, truncation=True, max_length=4096)
- inputs_ans = self.tokenizer(ans, return_tensors='pt', padding=True, truncation=True, max_length=4096)
- with torch.no_grad():
- outputs_resp = self.longformer_model(**inputs_resp).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
- outputs_ans = self.longformer_model(**inputs_ans).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
- # 计算余弦相似度
- similarity = self.cosine_similarity(outputs_resp.numpy(), outputs_ans.numpy())
- scores.append(similarity)
- return scores
- def combined_reward_func(self, prompts, completions, answer, **kwargs):
- """
- 综合多个奖励函数,动态调整权重。
- :param prompts: 输入提示
- :param completions: 模型生成的补全内容
- :param answer: 标准答案
- :return: 综合得分列表
- """
- # 计算各奖励函数的得分
- format_score = self.strict_format_reward_func(completions)
- semantic_score = self.semantic_correctness_reward_func(prompts, completions, answer)
- correctness_score = self.correctness_reward_func(prompts, completions, answer)
- # 动态调整权重
- combined_scores = []
- for fs, ss, cs in zip(format_score, semantic_score, correctness_score):
- if cs == 2.0: # 答案完全正确
- combined_scores.append(fs * 0.2 + ss * 0.3 + cs * 0.5)
- else: # 答案不完全正确
- combined_scores.append(fs * 0.4 + ss * 0.4 + cs * 0.2)
- return combined_scores
- @staticmethod
- def reasoning_quality_reward_func(completions, **kwargs):
- """
- 检查推理过程的质量。
- :param completions: 模型生成的补全内容
- :return: 推理过程质量得分列表
- """
- responses = [completion[0]["content"] for completion in completions]
- scores = []
- for response in responses:
- reasoning_match = re.search(r"<reasoning>\n(.+?)\n</reasoning>", response, re.DOTALL)
- if reasoning_match:
- reasoning_content = reasoning_match.group(1).strip()
- # 简单检查推理内容是否包含关键词
- if "诊断" in reasoning_content and "原因" in reasoning_content:
- scores.append(1.0)
- else:
- scores.append(0.5)
- else:
- scores.append(0.0)
- return scores
- @staticmethod
- def extract_xml_answer(text: str) -> str:
- """
- 从文本中提取 XML 格式的答案。
- :param text: 包含 XML 格式的文本
- :return: 提取的答案
- """
- try:
- print("text -> \n", text)
- if "<answer>" in text and "</answer>" in text:
- answer = text.split("<answer>")[-1]
- answer = answer.split("</answer>")[0]
- return answer.strip()
- else:
- print("Warning: <answer> tag not found in response.")
- # 尝试提取其他有意义的部分
- if "诊断" in text:
- return text.split("诊断")[-1].strip()
- elif "排查建议" in text:
- return text.split("排查建议")[-1].strip()
- else:
- return text.strip() # 返回原始文本作为备用
- except Exception as e:
- print(f"Error extracting XML answer: {e}")
- return "" # 返回空字符串或其他默认值
- @staticmethod
- def count_xml(text) -> float:
- """
- 计算 XML 标签的数量和完整性。
- :param text: 包含 XML 格式的文本
- :return: XML 标签的完整性得分
- """
- 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):
- """
- 计算 XML 标签的完整性得分。
- :param completions: 模型生成的补全内容
- :return: XML 标签的完整性得分列表
- """
- 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):
- """
- 检查补全内容是否符合软格式要求。
- :param completions: 模型生成的补全内容
- :return: 符合软格式要求的得分列表
- """
- 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):
- """
- 检查响应是否符合严格的 XML 格式要求,并确保标签内容非空。
- :param completions: 模型生成的补全内容
- :return: 符合严格格式要求的得分列表
- """
- pattern = r"^<reasoning>\n(.+?)\n</reasoning>\n<answer>\n(.+?)\n</answer>\n$"
- responses = [completion[0]["content"] for completion in completions]
- scores = []
- for response in responses:
- match = re.match(pattern, response, re.DOTALL)
- if match:
- reasoning_content = match.group(1).strip()
- answer_content = match.group(2).strip()
- # 检查内容是否非空
- if reasoning_content and answer_content:
- scores.append(1.0) # 格式和内容均符合要求
- else:
- scores.append(0.5) # 格式符合但内容为空
- else:
- scores.append(0.0) # 格式不符合
- return scores
- @staticmethod
- def int_reward_func(completions, **kwargs):
- """
- 检查补全内容是否包含整数。
- :param completions: 模型生成的补全内容
- :return: 包含整数的得分列表
- """
- 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):
- """
- 检查补全内容是否正确。
- :param prompts: 输入提示
- :param completions: 模型生成的补全内容
- :param answer: 正确答案
- :return: 补全内容正确的得分列表
- """
- print("completions : \n ", completions)
- 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__":
- try:
- # 加载配置文件
- config = load_config(f"../conf/conf_train.yaml")
- print("train config:\n", config)
- # 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)
- print("Training completed.")
- except Exception as e:
- print("exception \n ", e)
- finally:
- print("end")
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