train_model_grpo_v1.1.py 15 KB

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  1. # train_model_grpo_v1.py
  2. import os
  3. import torch
  4. import torch.distributed as dist
  5. from unsloth import FastLanguageModel
  6. from unsloth import is_bfloat16_supported
  7. from trl import GRPOConfig, GRPOTrainer
  8. from datasets import load_dataset
  9. from conf_train import Config, load_config # 导入配置文件
  10. import re
  11. # from transformers import BertTokenizer, BertModel # 分词模型最大支持 512 个token
  12. from transformers import LongformerTokenizer, LongformerModel # 分词模型最大支持 4096 个token
  13. import numpy as np
  14. class ModelTrainer:
  15. def __init__(self, config: Config):
  16. """
  17. 初始化 ModelTrainer 类,加载配置参数。
  18. :param config: 配置对象,包含模型训练所需的参数
  19. """
  20. self.config: Config = config
  21. self.model_name = config.model_name
  22. self.max_seq_length = config.max_seq_length
  23. self.dtype = torch.float16 if config.dtype == "float16" else torch.bfloat16
  24. self.load_in_4bit = config.load_in_4bit
  25. self.fast_inference = config.fast_inference
  26. self.lora_rank = config.lora_rank
  27. self.gpu_memory_utilization = config.gpu_memory_utilization
  28. # 初始化 BERT 模型和分词器
  29. self.tokenizer = LongformerTokenizer.from_pretrained(f'../models/allenai/longformer-base-4096')
  30. self.longformer_model = LongformerModel.from_pretrained(f'../models/allenai/longformer-base-4096')
  31. def load_model(self):
  32. """
  33. 加载预训练模型和分词器。
  34. :return: 返回加载的模型和分词器
  35. """
  36. model, tokenizer = FastLanguageModel.from_pretrained(
  37. model_name=self.model_name,
  38. max_seq_length=self.max_seq_length,
  39. load_in_4bit=self.load_in_4bit,
  40. dtype=self.dtype,
  41. fast_inference=self.fast_inference,
  42. max_lora_rank=self.lora_rank,
  43. gpu_memory_utilization=self.gpu_memory_utilization,
  44. )
  45. model = model.to_empty(device='cuda')
  46. # 初始化模型的权重
  47. for param in model.parameters():
  48. if param.is_meta:
  49. param.data = torch.randn_like(param)
  50. # 添加 LoRA 适配器
  51. model = FastLanguageModel.get_peft_model(
  52. model,
  53. max_seq_length=self.max_seq_length,
  54. r=self.lora_rank,
  55. target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
  56. "gate_proj", "up_proj", "down_proj"],
  57. lora_alpha=16,
  58. lora_dropout=0,
  59. bias="none",
  60. use_gradient_checkpointing="unsloth",
  61. random_state=3407,
  62. use_rslora=False,
  63. loftq_config=None,
  64. )
  65. return model, tokenizer
  66. def load_data(self, train_data_path):
  67. """
  68. 加载训练数据集。
  69. :param train_data_path: 训练数据路径
  70. :return: 返回加载的训练数据集
  71. """
  72. with open(train_data_path, 'r') as f:
  73. train_dataset = load_dataset("json", data_files={"train": train_data_path}, split="train")
  74. return train_dataset
  75. def train(self, model, tokenizer, train_dataset):
  76. """
  77. 训练模型。
  78. :param model: 预训练模型
  79. :param tokenizer: 分词器
  80. :param train_dataset: 训练数据集
  81. :return: 返回训练后的模型
  82. """
  83. print("is_bfloat16_supported()=", is_bfloat16_supported())
  84. print(f"Reserved memory: {torch.cuda.memory_reserved()}")
  85. print(f"Allocated memory: {torch.cuda.memory_allocated()}")
  86. train_loader = torch.utils.data.DataLoader(
  87. train_dataset, batch_size=1, shuffle=True, pin_memory=True
  88. )
  89. torch.cuda.empty_cache()
  90. print("self.config.learning_rate=", float(self.config.learning_rate))
  91. training_args = GRPOConfig(
  92. use_vllm=self.config.use_vllm,
  93. learning_rate=float(self.config.learning_rate),
  94. adam_beta1=self.config.adam_beta1,
  95. adam_beta2=self.config.adam_beta2,
  96. weight_decay=self.config.weight_decay,
  97. warmup_ratio=self.config.warmup_ratio,
  98. lr_scheduler_type=self.config.lr_scheduler_type,
  99. optim=self.config.optim,
  100. logging_steps=self.config.logging_steps,
  101. bf16=is_bfloat16_supported(),
  102. fp16=not is_bfloat16_supported(),
  103. per_device_train_batch_size=self.config.per_device_train_batch_size,
  104. gradient_accumulation_steps=self.config.gradient_accumulation_steps,
  105. num_generations=self.config.num_generations,
  106. max_prompt_length=self.config.max_prompt_length,
  107. max_completion_length=self.config.max_completion_length,
  108. num_train_epochs=self.config.num_train_epochs,
  109. max_steps=self.config.max_steps,
  110. save_steps=self.config.save_steps,
  111. max_grad_norm=self.config.max_grad_norm,
  112. report_to=self.config.report_to,
  113. output_dir=self.config.output_dir,
  114. )
  115. trainer = GRPOTrainer(
  116. model=model,
  117. processing_class=tokenizer,
  118. reward_funcs=[
  119. self.xmlcount_reward_func,
  120. self.soft_format_reward_func,
  121. # self.strict_format_reward_func,
  122. # self.int_reward_func,
  123. # self.correctness_reward_func,
  124. self.strict_format_reward_func,
  125. self.semantic_correctness_reward_func,
  126. self.reasoning_quality_reward_func,
  127. self.combined_reward_func,
  128. ],
  129. args=training_args,
  130. train_dataset=train_dataset,
  131. )
  132. trainer.train()
  133. return model
  134. def save_model(self, model, tokenizer, save_path):
  135. """
  136. 保存训练后的模型和分词器。
  137. :param model: 训练后的模型
  138. :param tokenizer: 分词器
  139. :param save_path: 保存路径
  140. """
  141. model.save_pretrained(save_path)
  142. tokenizer.save_pretrained(save_path)
  143. print(f"Model saved to {save_path}")
  144. @staticmethod
  145. def cosine_similarity(vec1, vec2):
  146. """
  147. 计算两个向量的余弦相似度。
  148. :param vec1: 第一个向量,形状为 (1, 768)
  149. :param vec2: 第二个向量,形状为 (1, 768)
  150. :return: 余弦相似度
  151. """
  152. # 将 (1, 768) 的矩阵转换为 (768,) 的一维向量
  153. vec1 = vec1.squeeze() # 形状从 (1, 768) 变为 (768,)
  154. vec2 = vec2.squeeze() # 形状从 (1, 768) 变为 (768,)
  155. print(f"vec1 shape: {vec1.shape}, vec2 shape: {vec2.shape}")
  156. # 计算余弦相似度
  157. return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
  158. def semantic_correctness_reward_func(self, prompts, completions, answer, **kwargs):
  159. """
  160. 使用 Longformer 计算生成答案与标准答案的语义相似度。
  161. """
  162. responses = [completion[0]['content'] for completion in completions]
  163. extracted_responses = [self.extract_xml_answer(r) for r in responses]
  164. scores = []
  165. for resp, ans in zip(extracted_responses, answer):
  166. # 截断文本,确保长度不超过 4096
  167. resp = self.tokenizer.decode(self.tokenizer.encode(resp, truncation=True, max_length=4096))
  168. ans = self.tokenizer.decode(self.tokenizer.encode(ans, truncation=True, max_length=4096))
  169. # 编码生成答案和标准答案
  170. inputs_resp = self.tokenizer(resp, return_tensors='pt', padding=True, truncation=True, max_length=4096)
  171. inputs_ans = self.tokenizer(ans, return_tensors='pt', padding=True, truncation=True, max_length=4096)
  172. with torch.no_grad():
  173. outputs_resp = self.longformer_model(**inputs_resp).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
  174. outputs_ans = self.longformer_model(**inputs_ans).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
  175. # 计算余弦相似度
  176. similarity = self.cosine_similarity(outputs_resp.numpy(), outputs_ans.numpy())
  177. scores.append(similarity)
  178. return scores
  179. def combined_reward_func(self, prompts, completions, answer, **kwargs):
  180. """
  181. 综合多个奖励函数,动态调整权重。
  182. :param prompts: 输入提示
  183. :param completions: 模型生成的补全内容
  184. :param answer: 标准答案
  185. :return: 综合得分列表
  186. """
  187. # 计算各奖励函数的得分
  188. format_score = self.strict_format_reward_func(completions)
  189. semantic_score = self.semantic_correctness_reward_func(prompts, completions, answer)
  190. correctness_score = self.correctness_reward_func(prompts, completions, answer)
  191. # 动态调整权重
  192. combined_scores = []
  193. for fs, ss, cs in zip(format_score, semantic_score, correctness_score):
  194. if cs == 2.0: # 答案完全正确
  195. combined_scores.append(fs * 0.2 + ss * 0.3 + cs * 0.5)
  196. else: # 答案不完全正确
  197. combined_scores.append(fs * 0.4 + ss * 0.4 + cs * 0.2)
  198. return combined_scores
  199. @staticmethod
  200. def reasoning_quality_reward_func(completions, **kwargs):
  201. """
  202. 检查推理过程的质量。
  203. :param completions: 模型生成的补全内容
  204. :return: 推理过程质量得分列表
  205. """
  206. responses = [completion[0]["content"] for completion in completions]
  207. scores = []
  208. for response in responses:
  209. reasoning_match = re.search(r"<reasoning>\n(.+?)\n</reasoning>", response, re.DOTALL)
  210. if reasoning_match:
  211. reasoning_content = reasoning_match.group(1).strip()
  212. # 简单检查推理内容是否包含关键词
  213. if "诊断" in reasoning_content and "原因" in reasoning_content:
  214. scores.append(1.0)
  215. else:
  216. scores.append(0.5)
  217. else:
  218. scores.append(0.0)
  219. return scores
  220. @staticmethod
  221. def extract_xml_answer(text: str) -> str:
  222. """
  223. 从文本中提取 XML 格式的答案。
  224. :param text: 包含 XML 格式的文本
  225. :return: 提取的答案
  226. """
  227. answer = text.split("<answer>")[-1]
  228. answer = answer.split("</answer>")[0]
  229. return answer.strip()
  230. @staticmethod
  231. def count_xml(text) -> float:
  232. """
  233. 计算 XML 标签的数量和完整性。
  234. :param text: 包含 XML 格式的文本
  235. :return: XML 标签的完整性得分
  236. """
  237. count = 0.0
  238. if text.count("<reasoning>\n") == 1:
  239. count += 0.125
  240. if text.count("\n</reasoning>\n") == 1:
  241. count += 0.125
  242. if text.count("\n<answer>\n") == 1:
  243. count += 0.125
  244. count -= len(text.split("\n</answer>\n")[-1]) * 0.001
  245. if text.count("\n</answer>") == 1:
  246. count += 0.125
  247. count -= (len(text.split("\n</answer>")[-1]) - 1) * 0.001
  248. return count
  249. @staticmethod
  250. def xmlcount_reward_func(completions, **kwargs):
  251. """
  252. 计算 XML 标签的完整性得分。
  253. :param completions: 模型生成的补全内容
  254. :return: XML 标签的完整性得分列表
  255. """
  256. contents = [completion[0]["content"] for completion in completions]
  257. return [ModelTrainer.count_xml(c) for c in contents]
  258. @staticmethod
  259. def soft_format_reward_func(completions, **kwargs):
  260. """
  261. 检查补全内容是否符合软格式要求。
  262. :param completions: 模型生成的补全内容
  263. :return: 符合软格式要求的得分列表
  264. """
  265. pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
  266. responses = [completion[0]["content"] for completion in completions]
  267. matches = [re.match(pattern, r) for r in responses]
  268. return [0.5 if match else 0.0 for match in matches]
  269. @staticmethod
  270. def strict_format_reward_func(completions, **kwargs):
  271. """
  272. 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。
  273. :param completions: 模型生成的补全内容
  274. :return: 符合严格格式要求的得分列表
  275. """
  276. pattern = r"^<reasoning>\n(.+?)\n</reasoning>\n<answer>\n(.+?)\n</answer>\n$"
  277. responses = [completion[0]["content"] for completion in completions]
  278. scores = []
  279. for response in responses:
  280. match = re.match(pattern, response, re.DOTALL)
  281. if match:
  282. reasoning_content = match.group(1).strip()
  283. answer_content = match.group(2).strip()
  284. # 检查内容是否非空
  285. if reasoning_content and answer_content:
  286. scores.append(1.0) # 格式和内容均符合要求
  287. else:
  288. scores.append(0.5) # 格式符合但内容为空
  289. else:
  290. scores.append(0.0) # 格式不符合
  291. return scores
  292. @staticmethod
  293. def int_reward_func(completions, **kwargs):
  294. """
  295. 检查补全内容是否包含整数。
  296. :param completions: 模型生成的补全内容
  297. :return: 包含整数的得分列表
  298. """
  299. responses = [completion[0]['content'] for completion in completions]
  300. extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
  301. return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]
  302. @staticmethod
  303. def correctness_reward_func(prompts, completions, answer, **kwargs):
  304. """
  305. 检查补全内容是否正确。
  306. :param prompts: 输入提示
  307. :param completions: 模型生成的补全内容
  308. :param answer: 正确答案
  309. :return: 补全内容正确的得分列表
  310. """
  311. responses = [completion[0]['content'] for completion in completions]
  312. q = prompts[0][-1]['content']
  313. extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
  314. print('-' * 20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
  315. return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
  316. if __name__ == "__main__":
  317. # 加载配置文件
  318. config = load_config(f"../conf/conf_train.yaml")
  319. # 设置环境变量
  320. """
  321. # 多机多卡
  322. # export RANK=0 # 第一台机器的 rank
  323. # export WORLD_SIZE=4 # 总共有 4 台机器
  324. # export MASTER_ADDR=<主节点 IP>
  325. # export MASTER_PORT=12345
  326. """
  327. # 单机多卡
  328. os.environ['RANK'] = '0'
  329. os.environ['WORLD_SIZE'] = '1'
  330. os.environ['MASTER_ADDR'] = 'localhost'
  331. os.environ['MASTER_PORT'] = '12345'
  332. # 根据操作系统选择后端
  333. backend = 'gloo' if os.name == 'nt' else 'nccl'
  334. # 使用文件初始化方法 2025-3-11 成功验证支持windows
  335. init_method = f'env://' # env:// # 文件路径需要所有进程都能访问
  336. dist.init_process_group(backend=backend, init_method=init_method)
  337. print(f"Initialized distributed training with backend: {backend}")
  338. # 初始化 ModelTrainer
  339. trainer = ModelTrainer(config)
  340. # 加载模型和分词器
  341. model, tokenizer = trainer.load_model()
  342. # 加载数据集
  343. train_dataset = trainer.load_data(config.train_data_path)
  344. # 训练模型
  345. model = trainer.train(model, tokenizer, train_dataset)
  346. # 保存模型
  347. trainer.save_model(model, tokenizer, config.save_path)
  348. # 确保进程组被销毁
  349. if dist.is_initialized():
  350. dist.destroy_process_group()
  351. print("Training completed.")