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@@ -9,8 +9,11 @@ from trl import GRPOConfig, GRPOTrainer
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from datasets import load_dataset
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from conf_train import Config, load_config # 导入配置文件
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import re
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+# from transformers import BertTokenizer, BertModel # 分词模型最大支持 512 个token
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+from transformers import LongformerTokenizer, LongformerModel # 分词模型最大支持 4096 个token
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import numpy as np
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-
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+from datasets import load_dataset, Dataset
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+import json
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class ModelTrainer:
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def __init__(self, config: Config):
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@@ -26,6 +29,9 @@ class ModelTrainer:
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self.fast_inference = config.fast_inference
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self.lora_rank = config.lora_rank
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self.gpu_memory_utilization = config.gpu_memory_utilization
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+ # 初始化 BERT 模型和分词器
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+ self.tokenizer = LongformerTokenizer.from_pretrained(f'../models/allenai/longformer-base-4096')
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+ self.longformer_model = LongformerModel.from_pretrained(f'../models/allenai/longformer-base-4096')
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def load_model(self):
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"""
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@@ -35,7 +41,7 @@ class ModelTrainer:
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name=self.model_name,
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max_seq_length=self.max_seq_length,
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- load_in_4bit=self.load_in_4bit, # False for LoRA 16bit
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+ load_in_4bit=self.load_in_4bit, # False for LoRA 16bit
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dtype=self.dtype,
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fast_inference=self.fast_inference,
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max_lora_rank=self.lora_rank,
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@@ -53,12 +59,13 @@ class ModelTrainer:
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model = FastLanguageModel.get_peft_model(
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model,
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max_seq_length=self.max_seq_length,
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- r=self.lora_rank, # Choose any number > 0! Suggested 8, 16, 32, 64, 128
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+ r=self.lora_rank, # Choose any number>0!suggested 8,16,32,64,128
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
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- "gate_proj", "up_proj", "down_proj"], # Remove QKVO if out of memory
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+ "gate_proj", "up_proj", "down_proj"], # Remove QKVO if out of memory
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lora_alpha=16,
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- lora_dropout=0, # Supports any, but = 0 is optimized
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- bias="none", # Supports any, but = "none" is optimized
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+ lora_dropout=0, #Supports any, but = 0 is optimized
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+ bias="none", # Supports any, but = "none" is optimized
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+ #[NEW]"unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
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use_gradient_checkpointing="unsloth", # True or "unsloth" for very long context
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random_state=3407,
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use_rslora=False, # We support rank stabilized LoRA
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@@ -73,11 +80,15 @@ class ModelTrainer:
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:param train_data_path: 训练数据路径
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:return: 返回加载的训练数据集
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"""
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+
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+ data = []
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with open(train_data_path, 'r') as f:
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- train_dataset = load_dataset("json", data_files={"train": train_data_path}, split="train")
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- print("train_dataset -->\n", train_dataset)
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- # 打印第一条数据,检查格式是否正确
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- print("First example in train_dataset:", train_dataset[0])
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+ for line in f:
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+ data.append(json.loads(line))
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+
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+ # 将列表转换为 HuggingFace Dataset 对象
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+ data = Dataset.from_list(data)
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+
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return train_dataset
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def train(self, model, tokenizer, train_dataset):
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@@ -123,18 +134,27 @@ class ModelTrainer:
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output_dir=self.config.output_dir,
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)
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+ """
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+ PyTorch 的分布式进程组已初始化,但并行模式不等于 “分布式并行模式(ParallelMode.DISTRIBUTED)”。
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+ 为了使用 PyTorch 的分布式数据并行(DDP),请使用 python -m torch.distributed.launch 来启动你的脚本。
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+ """
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+
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trainer = GRPOTrainer(
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model=model,
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- processing_class=tokenizer, # 用于处理输入文本的分词器(tokenizer)
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+ processing_class=tokenizer, # 用于处理输入文本的分词器(tokenizer)。它将文本转换为模型可以理解的数字格式。
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reward_funcs=[
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- self.xmlcount_reward_func,
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- self.soft_format_reward_func,
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- self.strict_format_reward_func,
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- self.int_reward_func,
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- self.correctness_reward_func,
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- ],
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- args=training_args, # 定义的训练超参数
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- train_dataset=train_dataset, # 训练数据集
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+ self.xmlcount_reward_func, # 某种特定的基于XML计数的奖励函数
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+ self.soft_format_reward_func, # 基于软格式的奖励函数。
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+ self.strict_format_reward_func, # 基于严格格式的奖励函数。
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+ self.int_reward_func, # 整数奖励函数。
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+ self.correctness_reward_func, # 基于输出正确性的奖励函数
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+ ###
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+ # self.semantic_correctness_reward_func, # 语义正确性奖励函数
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+ # self.reasoning_quality_reward_func, # 推理质量奖励函数
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+ # self.combined_reward_func, # combined_reward_func
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+ ], # 这是一个奖励函数的列表,决定了模型输出的好坏。在GRPO训练中,奖励函数通常用来评估模型输出的质量。
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+ args=training_args, # 定义的训练超参数。
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+ train_dataset=train_dataset, # 训练数据集,
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)
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trainer.train()
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@@ -147,18 +167,130 @@ class ModelTrainer:
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:param tokenizer: 分词器
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:param save_path: 保存路径
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"""
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+ """
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+ # Save to 8bit Q8_0
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+ if False: model.save_pretrained_gguf("model", tokenizer,)
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+ # Remember to go to https://huggingface.co/settings/tokens for a token!
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+ # And change hf to your username!
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+ if False: model.push_to_hub_gguf("hf/model", tokenizer, token = "")
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+
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+ # Save to 16bit GGUF
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+ if False: model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16")
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+ if False: model.push_to_hub_gguf("hf/model", tokenizer, quantization_method = "f16", token = "")
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+
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+ # Save to q4_k_m GGUF
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+ if False: model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
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+ if False: model.push_to_hub_gguf("hf/model", tokenizer, quantization_method = "q4_k_m", token = "")
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+
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+ ###
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+
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+ model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",) # save_method = "merged_4bit" Merge to 4bit ; save_method = "lora" Just LoRA adapters ;
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+ """
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+
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model.save_pretrained(save_path)
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tokenizer.save_pretrained(save_path)
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+
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print(f"Model saved to {save_path}")
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+
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+ @staticmethod
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+ def cosine_similarity(vec1, vec2):
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+ """
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+ 计算两个向量的余弦相似度。
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+ :param vec1: 第一个向量,形状为 (1, 768)
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+ :param vec2: 第二个向量,形状为 (1, 768)
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+ :return: 余弦相似度
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+ """
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+ # 将 (1, 768) 的矩阵转换为 (768,) 的一维向量
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+ vec1 = vec1.squeeze() # 形状从 (1, 768) 变为 (768,)
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+ vec2 = vec2.squeeze() # 形状从 (1, 768) 变为 (768,)
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+ # print(f"vec1 shape: {vec1.shape}, vec2 shape: {vec2.shape}")
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+ # 计算余弦相似度
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+ return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
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+
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+ def semantic_correctness_reward_func(self, prompts, completions, answer, **kwargs):
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+ """
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+ 使用 Longformer 计算生成答案与标准答案的语义相似度。
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+ """
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+ responses = [completion[0]['content'] for completion in completions]
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+ extracted_responses = [self.extract_xml_answer(r) for r in responses]
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+ scores = []
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+ for resp, ans in zip(extracted_responses, answer):
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+ # 截断文本,确保长度不超过 4096
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+ resp = self.tokenizer.decode(self.tokenizer.encode(resp, truncation=True, max_length=4096))
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+ ans = self.tokenizer.decode(self.tokenizer.encode(ans, truncation=True, max_length=4096))
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+ # 编码生成答案和标准答案
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+ inputs_resp = self.tokenizer(resp, return_tensors='pt', padding=True, truncation=True, max_length=4096)
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+ inputs_ans = self.tokenizer(ans, return_tensors='pt', padding=True, truncation=True, max_length=4096)
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+ with torch.no_grad():
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+ outputs_resp = self.longformer_model(**inputs_resp).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
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+ outputs_ans = self.longformer_model(**inputs_ans).last_hidden_state.mean(dim=1) # 形状为 (1, 768)
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+ # 计算余弦相似度
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+ similarity = self.cosine_similarity(outputs_resp.numpy(), outputs_ans.numpy())
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+ scores.append(similarity)
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+ return scores
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+
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+ def combined_reward_func(self, prompts, completions, answer, **kwargs):
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+ """
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+ 综合多个奖励函数,动态调整权重。
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+ :param prompts: 输入提示
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+ :param completions: 模型生成的补全内容
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+ :param answer: 标准答案
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+ :return: 综合得分列表
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+ """
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+ # 计算各奖励函数的得分
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+ format_score = self.strict_format_reward_func(completions)
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+ semantic_score = self.semantic_correctness_reward_func(prompts, completions, answer)
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+ correctness_score = self.correctness_reward_func(prompts, completions, answer)
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+
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+ # 动态调整权重
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+ combined_scores = []
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+ for fs, ss, cs in zip(format_score, semantic_score, correctness_score):
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+ if cs == 2.0: # 答案完全正确
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+ combined_scores.append(fs * 0.2 + ss * 0.3 + cs * 0.5)
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+ else: # 答案不完全正确
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+ combined_scores.append(fs * 0.4 + ss * 0.4 + cs * 0.2)
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+ return combined_scores
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+
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+ @staticmethod
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+ def reasoning_quality_reward_func(completions, **kwargs):
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+ """
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+ 检查推理过程的质量。
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+ :param completions: 模型生成的补全内容
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+ :return: 推理过程质量得分列表
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+ """
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+ responses = [completion[0]["content"] for completion in completions]
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+ scores = []
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+ for response in responses:
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+ reasoning_match = re.search(r"<reasoning>\n(.+?)\n</reasoning>", response, re.DOTALL)
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+ if reasoning_match:
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+ reasoning_content = reasoning_match.group(1).strip()
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+ # 简单检查推理内容是否包含关键词
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+ if "诊断" in reasoning_content and "原因" in reasoning_content:
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+ scores.append(1.0)
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+ else:
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+ scores.append(0.5)
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+ else:
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+ scores.append(0.0)
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+ return scores
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@staticmethod
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def extract_xml_answer(text: str) -> str:
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+ """
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+ 从文本中提取 XML 格式的答案。
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+ :param text: 包含 XML 格式的文本
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+ :return: 提取的答案
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+ """
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answer = text.split("<answer>")[-1]
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answer = answer.split("</answer>")[0]
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return answer.strip()
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-
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+
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@staticmethod
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def count_xml(text) -> float:
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+ """
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+ 计算 XML 标签的数量和完整性。
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+ :param text: 包含 XML 格式的文本
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+ :return: XML 标签的完整性得分
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+ """
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count = 0.0
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if text.count("<reasoning>\n") == 1:
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count += 0.125
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@@ -166,17 +298,18 @@ class ModelTrainer:
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count += 0.125
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if text.count("\n<answer>\n") == 1:
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count += 0.125
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- count -= len(text.split("\n</answer>\n")[-1])*0.001
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+ count -= len(text.split("\n</answer>\n")[-1]) * 0.001
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if text.count("\n</answer>") == 1:
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count += 0.125
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- count -= (len(text.split("\n</answer>")[-1]) - 1)*0.001
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+ count -= (len(text.split("\n</answer>")[-1]) - 1) * 0.001
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return count
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-
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@staticmethod
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def xmlcount_reward_func(completions, **kwargs):
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"""
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- Reward function that counts XML tags in the completion.
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+ 计算 XML 标签的完整性得分。
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+ :param completions: 模型生成的补全内容
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+ :return: XML 标签的完整性得分列表
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"""
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contents = [completion[0]["content"] for completion in completions]
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return [ModelTrainer.count_xml(c) for c in contents]
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@@ -184,7 +317,9 @@ class ModelTrainer:
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@staticmethod
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def soft_format_reward_func(completions, **kwargs):
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"""
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- Reward function that checks if the completion has a specific format.
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+ 检查补全内容是否符合软格式要求。
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+ :param completions: 模型生成的补全内容
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+ :return: 符合软格式要求的得分列表
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"""
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pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
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responses = [completion[0]["content"] for completion in completions]
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@@ -194,17 +329,33 @@ class ModelTrainer:
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@staticmethod
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def strict_format_reward_func(completions, **kwargs):
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"""
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- Reward function that checks if the completion has a specific format.
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+ 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。
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+ :param completions: 模型生成的补全内容
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+ :return: 符合严格格式要求的得分列表
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"""
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- pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
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+ pattern = r"^<reasoning>\n(.+?)\n</reasoning>\n<answer>\n(.+?)\n</answer>\n$"
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responses = [completion[0]["content"] for completion in completions]
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- matches = [re.match(pattern, r) for r in responses]
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- return [0.5 if match else 0.0 for match in matches]
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+ scores = []
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+ for response in responses:
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+ match = re.match(pattern, response, re.DOTALL)
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+ if match:
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+ reasoning_content = match.group(1).strip()
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+ answer_content = match.group(2).strip()
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+ # 检查内容是否非空
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+ if reasoning_content and answer_content:
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+ scores.append(1.0) # 格式和内容均符合要求
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+ else:
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+ scores.append(0.5) # 格式符合但内容为空
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+ else:
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+ scores.append(0.0) # 格式不符合
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+ return scores
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@staticmethod
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def int_reward_func(completions, **kwargs):
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"""
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- Reward function that checks if the completion contains an integer.
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+ 检查补全内容是否包含整数。
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+ :param completions: 模型生成的补全内容
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+ :return: 包含整数的得分列表
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"""
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responses = [completion[0]['content'] for completion in completions]
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extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
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@@ -213,19 +364,47 @@ class ModelTrainer:
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@staticmethod
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def correctness_reward_func(prompts, completions, answer, **kwargs):
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"""
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- Reward function that checks if the completion matches the correct answer.
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+ 检查补全内容是否正确。
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+ :param prompts: 输入提示
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+ :param completions: 模型生成的补全内容
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+ :param answer: 正确答案
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+ :return: 补全内容正确的得分列表
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"""
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+ print("completions : \n ",completions)
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responses = [completion[0]['content'] for completion in completions]
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q = prompts[0][-1]['content']
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extracted_responses = [ModelTrainer.extract_xml_answer(r) for r in responses]
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- print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
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+ # print(f"\n Response:\n {responses}",f"\n Extracted:\n {responses}")
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+ print('-' * 20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
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return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
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if __name__ == "__main__":
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try:
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# 加载配置文件
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config = load_config(f"../conf/conf_train.yaml")
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- print("train config:\n", config)
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+ print("train config:\n",config)
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+
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+ # 设置环境变量
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+ """
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+ # 多机多卡
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+ # export RANK=0 # 第一台机器的 rank
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+ # export WORLD_SIZE=4 # 总共有 4 台机器
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+ # export MASTER_ADDR=<主节点 IP>
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+ # export MASTER_PORT=12345
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+
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+ # 单机多卡
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+ os.environ['RANK'] = '0'
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+ os.environ['WORLD_SIZE'] = '1'
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+ os.environ['MASTER_ADDR'] = 'localhost'
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+ os.environ['MASTER_PORT'] = '12345'
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+
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+ # 根据操作系统选择后端
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|
|
+ backend = 'gloo' if os.name == 'nt' else 'nccl'
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|
|
+ # 使用文件初始化方法 2025-3-11 成功验证支持windows
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+ init_method = f'env://' # env:// # 文件路径需要所有进程都能访问
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|
+ dist.init_process_group(backend=backend, init_method=init_method)
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|
|
+ print(f"Initialized distributed training with backend: {backend}")
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|
|
+ """
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|
|
|
|
|
# 初始化 ModelTrainer
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|
|
trainer = ModelTrainer(config)
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|
@@ -241,9 +420,12 @@ if __name__ == "__main__":
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|
|
|
|
# 保存模型
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|
|
trainer.save_model(model, tokenizer, config.save_path)
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|
|
-
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+
|
|
|
print("Training completed.")
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|
|
except Exception as e:
|
|
|
- print("exception \n ", e)
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|
|
+ print("exception \n ",e)
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|
|
finally:
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|
|
- print("end")
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|
|
+ # # 确保进程组被销毁
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|
|
+ # if dist.is_initialized():
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|
|
+ # dist.destroy_process_group()
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|
|
+ print("end")
|