train_model_grpo_v1.2.py 16 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384
  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 LongformerTokenizer, LongformerModel # 分词模型最大支持 4096 个token
  12. import numpy as np
  13. class ModelTrainer:
  14. def __init__(self, config: Config):
  15. """
  16. 初始化 ModelTrainer 类,加载配置参数。
  17. :param config: 配置对象,包含模型训练所需的参数
  18. """
  19. self.config: Config = config
  20. self.model_name = config.model_name
  21. self.max_seq_length = config.max_seq_length
  22. self.dtype = torch.float16 if config.dtype == "float16" else torch.bfloat16
  23. self.load_in_4bit = config.load_in_4bit
  24. self.fast_inference = config.fast_inference
  25. self.lora_rank = config.lora_rank
  26. self.gpu_memory_utilization = config.gpu_memory_utilization
  27. # 初始化 Longformer 模型和分词器
  28. self.tokenizer = LongformerTokenizer.from_pretrained(f'../models/allenai/longformer-base-4096')
  29. self.longformer_model = LongformerModel.from_pretrained(f'../models/allenai/longformer-base-4096')
  30. def load_model(self):
  31. """
  32. 加载预训练模型和分词器。
  33. :return: 返回加载的模型和分词器
  34. """
  35. model, tokenizer = FastLanguageModel.from_pretrained(
  36. model_name=self.model_name,
  37. max_seq_length=self.max_seq_length,
  38. load_in_4bit=self.load_in_4bit, # False for LoRA 16bit
  39. dtype=self.dtype,
  40. fast_inference=self.fast_inference,
  41. max_lora_rank=self.lora_rank,
  42. gpu_memory_utilization=self.gpu_memory_utilization,
  43. )
  44. model = model.to_empty(device='cuda')
  45. # 初始化模型的权重
  46. for param in model.parameters():
  47. if param.is_meta:
  48. param.data = torch.randn_like(param)
  49. # 添加 LoRA 适配器
  50. model = FastLanguageModel.get_peft_model(
  51. model,
  52. max_seq_length=self.max_seq_length,
  53. r=self.lora_rank, # Choose any number > 0! Suggested 8, 16, 32, 64, 128
  54. target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
  55. "gate_proj", "up_proj", "down_proj"], # Remove QKVO if out of memory
  56. lora_alpha=16,
  57. lora_dropout=0, # Supports any, but = 0 is optimized
  58. bias="none", # Supports any, but = "none" is optimized
  59. use_gradient_checkpointing="unsloth", # True or "unsloth" for very long context
  60. random_state=3407,
  61. use_rslora=False, # We support rank stabilized LoRA
  62. loftq_config=None, # And LoftQ
  63. )
  64. return model, tokenizer
  65. def load_data(self, train_data_path):
  66. """
  67. 加载训练数据集。
  68. :param train_data_path: 训练数据路径
  69. :return: 返回加载的训练数据集
  70. """
  71. with open(train_data_path, 'r') as f:
  72. train_dataset = load_dataset("json", data_files={"train": train_data_path}, split="train")
  73. print("train_dataset -->\n", train_dataset)
  74. # 打印第一条数据,检查格式是否正确
  75. print("First example in train_dataset:", train_dataset[0])
  76. return train_dataset
  77. def train(self, model, tokenizer, train_dataset):
  78. """
  79. 训练模型。
  80. :param model: 预训练模型
  81. :param tokenizer: 分词器
  82. :param train_dataset: 训练数据集
  83. :return: 返回训练后的模型
  84. """
  85. print("is_bfloat16_supported()=", is_bfloat16_supported())
  86. print(f"Reserved memory: {torch.cuda.memory_reserved()}")
  87. print(f"Allocated memory: {torch.cuda.memory_allocated()}")
  88. train_loader = torch.utils.data.DataLoader(
  89. train_dataset, batch_size=1, shuffle=True, pin_memory=True
  90. )
  91. torch.cuda.empty_cache()
  92. print("self.config.learning_rate=", float(self.config.learning_rate))
  93. training_args = GRPOConfig(
  94. use_vllm=self.config.use_vllm,
  95. learning_rate=float(self.config.learning_rate),
  96. adam_beta1=self.config.adam_beta1,
  97. adam_beta2=self.config.adam_beta2,
  98. weight_decay=self.config.weight_decay,
  99. warmup_ratio=self.config.warmup_ratio,
  100. lr_scheduler_type=self.config.lr_scheduler_type,
  101. optim=self.config.optim,
  102. logging_steps=self.config.logging_steps,
  103. bf16=is_bfloat16_supported(),
  104. fp16=not is_bfloat16_supported(),
  105. per_device_train_batch_size=self.config.per_device_train_batch_size,
  106. gradient_accumulation_steps=self.config.gradient_accumulation_steps,
  107. num_generations=self.config.num_generations,
  108. max_prompt_length=self.config.max_prompt_length,
  109. max_completion_length=self.config.max_completion_length,
  110. num_train_epochs=self.config.num_train_epochs,
  111. max_steps=self.config.max_steps,
  112. save_steps=self.config.save_steps,
  113. max_grad_norm=self.config.max_grad_norm,
  114. report_to=self.config.report_to,
  115. output_dir=self.config.output_dir,
  116. )
  117. trainer = GRPOTrainer(
  118. model=model,
  119. processing_class=tokenizer, # 用于处理输入文本的分词器(tokenizer)
  120. reward_funcs=[
  121. self.xmlcount_reward_func, # XML 标签完整性奖励函数
  122. self.soft_format_reward_func, # 软格式奖励函数
  123. self.strict_format_reward_func, # 严格格式奖励函数
  124. self.int_reward_func, # 整数奖励函数
  125. self.correctness_reward_func, # 正确性奖励函数
  126. self.semantic_correctness_reward_func, # 语义正确性奖励函数
  127. self.reasoning_quality_reward_func, # 推理质量奖励函数
  128. self.combined_reward_func, # 综合奖励函数
  129. ],
  130. args=training_args, # 定义的训练超参数
  131. train_dataset=train_dataset, # 训练数据集
  132. )
  133. trainer.train()
  134. return model
  135. def save_model(self, model, tokenizer, save_path):
  136. """
  137. 保存训练后的模型和分词器。
  138. :param model: 训练后的模型
  139. :param tokenizer: 分词器
  140. :param save_path: 保存路径
  141. """
  142. model.save_pretrained(save_path)
  143. tokenizer.save_pretrained(save_path)
  144. print(f"Model saved to {save_path}")
  145. @staticmethod
  146. def cosine_similarity(vec1, vec2):
  147. """
  148. 计算两个向量的余弦相似度。
  149. :param vec1: 第一个向量,形状为 (1, 768)
  150. :param vec2: 第二个向量,形状为 (1, 768)
  151. :return: 余弦相似度
  152. """
  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. return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
  156. def semantic_correctness_reward_func(self, prompts, completions, answer, **kwargs):
  157. """
  158. 使用 Longformer 计算生成答案与标准答案的语义相似度。
  159. """
  160. responses = [completion[0]['content'] for completion in completions]
  161. extracted_responses = [self.extract_xml_answer(r) for r in responses]
  162. scores = []
  163. for resp, ans in zip(extracted_responses, answer):
  164. # 截断文本,确保长度不超过 4096
  165. resp = self.tokenizer.decode(self.tokenizer.encode(resp, truncation=True, max_length=4096))
  166. ans = self.tokenizer.decode(self.tokenizer.encode(ans, truncation=True, max_length=4096))
  167. # 编码生成答案和标准答案
  168. inputs_resp = self.tokenizer(resp, return_tensors='pt', padding=True, truncation=True, max_length=4096)
  169. inputs_ans = self.tokenizer(ans, return_tensors='pt', padding=True, truncation=True, max_length=4096)
  170. with torch.no_grad():
  171. outputs_resp = self.longformer_model(**inputs_resp).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
  172. outputs_ans = self.longformer_model(**inputs_ans).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
  173. # 计算余弦相似度
  174. similarity = self.cosine_similarity(outputs_resp.numpy(), outputs_ans.numpy())
  175. scores.append(similarity)
  176. return scores
  177. def combined_reward_func(self, prompts, completions, answer, **kwargs):
  178. """
  179. 综合多个奖励函数,动态调整权重。
  180. :param prompts: 输入提示
  181. :param completions: 模型生成的补全内容
  182. :param answer: 标准答案
  183. :return: 综合得分列表
  184. """
  185. # 计算各奖励函数的得分
  186. format_score = self.strict_format_reward_func(completions)
  187. semantic_score = self.semantic_correctness_reward_func(prompts, completions, answer)
  188. correctness_score = self.correctness_reward_func(prompts, completions, answer)
  189. # 动态调整权重
  190. combined_scores = []
  191. for fs, ss, cs in zip(format_score, semantic_score, correctness_score):
  192. if cs == 2.0: # 答案完全正确
  193. combined_scores.append(fs * 0.2 + ss * 0.3 + cs * 0.5)
  194. else: # 答案不完全正确
  195. combined_scores.append(fs * 0.4 + ss * 0.4 + cs * 0.2)
  196. return combined_scores
  197. @staticmethod
  198. def reasoning_quality_reward_func(completions, **kwargs):
  199. """
  200. 检查推理过程的质量。
  201. :param completions: 模型生成的补全内容
  202. :return: 推理过程质量得分列表
  203. """
  204. responses = [completion[0]["content"] for completion in completions]
  205. scores = []
  206. for response in responses:
  207. reasoning_match = re.search(r"<reasoning>\n(.+?)\n</reasoning>", response, re.DOTALL)
  208. if reasoning_match:
  209. reasoning_content = reasoning_match.group(1).strip()
  210. # 简单检查推理内容是否包含关键词
  211. if "诊断" in reasoning_content and "原因" in reasoning_content:
  212. scores.append(1.0)
  213. else:
  214. scores.append(0.5)
  215. else:
  216. scores.append(0.0)
  217. return scores
  218. @staticmethod
  219. def extract_xml_answer(text: str) -> str:
  220. """
  221. 从文本中提取 XML 格式的答案。
  222. :param text: 包含 XML 格式的文本
  223. :return: 提取的答案
  224. """
  225. try:
  226. print("text -> \n", text)
  227. if "<answer>" in text and "</answer>" in text:
  228. answer = text.split("<answer>")[-1]
  229. answer = answer.split("</answer>")[0]
  230. return answer.strip()
  231. else:
  232. print("Warning: <answer> tag not found in response.")
  233. # 尝试提取其他有意义的部分
  234. if "诊断" in text:
  235. return text.split("诊断")[-1].strip()
  236. elif "排查建议" in text:
  237. return text.split("排查建议")[-1].strip()
  238. else:
  239. return text.strip() # 返回原始文本作为备用
  240. except Exception as e:
  241. print(f"Error extracting XML answer: {e}")
  242. return "" # 返回空字符串或其他默认值
  243. @staticmethod
  244. def count_xml(text) -> float:
  245. """
  246. 计算 XML 标签的数量和完整性。
  247. :param text: 包含 XML 格式的文本
  248. :return: XML 标签的完整性得分
  249. """
  250. count = 0.0
  251. if text.count("<reasoning>\n") == 1:
  252. count += 0.125
  253. if text.count("\n</reasoning>\n") == 1:
  254. count += 0.125
  255. if text.count("\n<answer>\n") == 1:
  256. count += 0.125
  257. count -= len(text.split("\n</answer>\n")[-1]) * 0.001
  258. if text.count("\n</answer>") == 1:
  259. count += 0.125
  260. count -= (len(text.split("\n</answer>")[-1]) - 1) * 0.001
  261. return count
  262. @staticmethod
  263. def xmlcount_reward_func(completions, **kwargs):
  264. """
  265. 计算 XML 标签的完整性得分。
  266. :param completions: 模型生成的补全内容
  267. :return: XML 标签的完整性得分列表
  268. """
  269. contents = [completion[0]["content"] for completion in completions]
  270. return [ModelTrainer.count_xml(c) for c in contents]
  271. @staticmethod
  272. def soft_format_reward_func(completions, **kwargs):
  273. """
  274. 检查补全内容是否符合软格式要求。
  275. :param completions: 模型生成的补全内容
  276. :return: 符合软格式要求的得分列表
  277. """
  278. pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
  279. responses = [completion[0]["content"] for completion in completions]
  280. matches = [re.match(pattern, r) for r in responses]
  281. return [0.5 if match else 0.0 for match in matches]
  282. @staticmethod
  283. def strict_format_reward_func(completions, **kwargs):
  284. """
  285. 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。
  286. :param completions: 模型生成的补全内容
  287. :return: 符合严格格式要求的得分列表
  288. """
  289. pattern = r"^<reasoning>\n(.+?)\n</reasoning>\n<answer>\n(.+?)\n</answer>\n$"
  290. responses = [completion[0]["content"] for completion in completions]
  291. scores = []
  292. for response in responses:
  293. match = re.match(pattern, response, re.DOTALL)
  294. if match:
  295. reasoning_content = match.group(1).strip()
  296. answer_content = match.group(2).strip()
  297. # 检查内容是否非空
  298. if reasoning_content and answer_content:
  299. scores.append(1.0) # 格式和内容均符合要求
  300. else:
  301. scores.append(0.5) # 格式符合但内容为空
  302. else:
  303. scores.append(0.0) # 格式不符合
  304. return scores
  305. @staticmethod
  306. def int_reward_func(completions, **kwargs):
  307. """
  308. 检查补全内容是否包含整数。
  309. :param completions: 模型生成的补全内容
  310. :return: 包含整数的得分列表
  311. """
  312. responses = [completion[0]['content'] for completion in completions]
  313. extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
  314. return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]
  315. @staticmethod
  316. def correctness_reward_func(prompts, completions, answer, **kwargs):
  317. """
  318. 检查补全内容是否正确。
  319. :param prompts: 输入提示
  320. :param completions: 模型生成的补全内容
  321. :param answer: 正确答案
  322. :return: 补全内容正确的得分列表
  323. """
  324. print("completions : \n ", completions)
  325. responses = [completion[0]['content'] for completion in completions]
  326. q = prompts[0][-1]['content']
  327. extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
  328. print('-' * 20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
  329. return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
  330. if __name__ == "__main__":
  331. try:
  332. # 加载配置文件
  333. config = load_config(f"../conf/conf_train.yaml")
  334. print("train config:\n", config)
  335. # 初始化 ModelTrainer
  336. trainer = ModelTrainer(config)
  337. # 加载模型和分词器
  338. model, tokenizer = trainer.load_model()
  339. # 加载数据集
  340. train_dataset = trainer.load_data(config.train_data_path)
  341. # 训练模型
  342. model = trainer.train(model, tokenizer, train_dataset)
  343. # 保存模型
  344. trainer.save_model(model, tokenizer, config.save_path)
  345. print("Training completed.")
  346. except Exception as e:
  347. print("exception \n ", e)
  348. finally:
  349. print("end")