更新 Mamba/mamba-main/train.py
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import os
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import pandas as pd
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, MambaConfig
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from trl import SFTTrainer
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from peft import LoraConfig
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from datasets import Dataset
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# 设置环境变量来避免内存碎片化
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
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# 数据文件夹路径
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data_folder = r'/mnt/Mamba/mamba-main/data/dataset'
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# 检查路径是否存在
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if not os.path.exists(data_folder):
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raise ValueError(f"路径不存在: {data_folder}")
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# 加载分词器和模型
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path = "/mnt/Mamba/mamba-130m-hf" # 模型路径
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tokenizer = AutoTokenizer.from_pretrained(path, local_files_only=True)
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model = AutoModelForCausalLM.from_pretrained(path, local_files_only=True, num_labels=8, use_mambapy=True)
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print("加载成功")
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# 配置训练参数
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=3,
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per_device_train_batch_size=12, # 减少批处理大小
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logging_dir='./logs',
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logging_steps=10,
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learning_rate=2e-3,
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gradient_accumulation_steps=2, # 使用梯度累积减少显存占用
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fp16=True, # 启用混合精度训练
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)
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# LoRA配置
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lora_config = LoraConfig(
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r=8, # 低秩分解的秩
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target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
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task_type="SEQ_CLS", # 序列分类任务类型
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bias="none"
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)
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# 初始化Trainer
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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args=training_args,
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peft_config=lora_config,
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max_seq_length=512, # 设置max_seq_length参数
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)
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# 分块加载和处理数据
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chunksize = 40000 # 设置合适的分块大小,每次读取数据的行数
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def preprocess_data(chunk):
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chunk = chunk.dropna() # 处理缺失值
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texts = chunk[["acc_x", "acc_y", "acc_z", "gyr_x", "gyr_y", "gyr_z", "mag_x", "mag_y", "mag_z"]].astype(str).apply(
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' '.join, axis=1).tolist()
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labels = chunk["Person_id"].astype(int).tolist() # 确保标签是整数类型
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encodings = tokenizer(texts, truncation=True, padding=True, max_length=1024)
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return {"input_ids": encodings["input_ids"], "attention_mask": encodings["attention_mask"], "labels": labels}
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# 读取训练数据并进行训练
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train_file_path = os.path.join(data_folder, 'train_data.csv')
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chunk_iter = pd.read_csv(train_file_path, chunksize=chunksize, header=0)
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for chunk in chunk_iter:
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# 数据预处理
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processed_data = preprocess_data(chunk)
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dataset = Dataset.from_dict(processed_data)
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# 训练模型
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trainer.train_dataset = dataset
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trainer.train()
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# 清理CUDA缓存
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torch.cuda.empty_cache()
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# 保存训练后的模型
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model.save_pretrained("./trained_model")
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tokenizer.save_pretrained("./trained_model")
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print("模型保存成功")
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# 读取测试数据并进行预测
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test_file_path = os.path.join(data_folder, 'test_data.csv')
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test_data = pd.read_csv(test_file_path, header=0)
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processed_test_data = preprocess_data(test_data)
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test_dataset = Dataset.from_dict(processed_test_data)
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# 预测Person_id
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predictions = trainer.predict(test_dataset)
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# 输出预测结果
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print(predictions)
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import os
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import pandas as pd
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, MambaConfig
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from trl import SFTTrainer
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from peft import LoraConfig
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from datasets import Dataset
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import torch
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# 设置环境变量来避免内存碎片化
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
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# 数据文件夹路径
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data_folder = r'/mnt/Mamba/mamba-main/data/dataset'
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# 检查路径是否存在
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if not os.path.exists(data_folder):
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raise ValueError(f"路径不存在: {data_folder}")
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# 加载分词器和模型
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path = "/mnt/Mamba/mamba-130m-hf" # 模型路径
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tokenizer = AutoTokenizer.from_pretrained(path, local_files_only=True)
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model = AutoModelForCausalLM.from_pretrained(path, local_files_only=True, num_labels=8, use_mambapy=True)
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print("加载成功")
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# 配置训练参数
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=3,
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per_device_train_batch_size=12, # 减少批处理大小
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logging_dir='./logs',
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logging_steps=10,
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learning_rate=2e-3,
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gradient_accumulation_steps=2, # 使用梯度累积减少显存占用
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fp16=True, # 启用混合精度训练
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)
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# LoRA配置
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lora_config = LoraConfig(
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r=8, # 低秩分解的秩
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target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
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task_type="SEQ_CLS", # 序列分类任务类型
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bias="none"
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)
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# 初始化Trainer
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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args=training_args,
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peft_config=lora_config,
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max_seq_length=512, # 设置max_seq_length参数
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)
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# 分块加载和处理数据
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chunksize = 40000 # 设置合适的分块大小,每次读取数据的行数
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def preprocess_data(chunk):
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chunk = chunk.dropna() # 处理缺失值
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texts = chunk[["acc_x", "acc_y", "acc_z", "gyr_x", "gyr_y", "gyr_z", "mag_x", "mag_y", "mag_z"]].astype(str).apply(
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' '.join, axis=1).tolist()
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labels = chunk["Person_id"].astype(int).tolist() # 确保标签是整数类型
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encodings = tokenizer(texts, truncation=True, padding=True, max_length=1024)
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return {"input_ids": encodings["input_ids"], "attention_mask": encodings["attention_mask"], "labels": labels}
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# 读取训练数据并进行训练
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train_file_path = os.path.join(data_folder, 'train_data.csv')
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chunk_iter = pd.read_csv(train_file_path, chunksize=chunksize, header=0)
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for chunk in chunk_iter:
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# 数据预处理
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processed_data = preprocess_data(chunk)
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dataset = Dataset.from_dict(processed_data)
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# 训练模型
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trainer.train_dataset = dataset
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trainer.train()
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# 清理CUDA缓存
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torch.cuda.empty_cache()
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# 保存训练后的模型
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model.save_pretrained("./trained_model")
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tokenizer.save_pretrained("./trained_model")
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print("模型保存成功")
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# 读取测试数据并进行预测
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test_file_path = os.path.join(data_folder, 'test_data.csv')
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test_data = pd.read_csv(test_file_path, header=0)
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processed_test_data = preprocess_data(test_data)
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test_dataset = Dataset.from_dict(processed_test_data)
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# 预测Person_id
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predictions = trainer.predict(test_dataset)
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# 输出预测结果
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print(predictions)
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