要在16卡服务器上使用最新版的CUDA和驱动训练llama - 2 - 7b和llama - 2 - 70b模型，并生成训练指标数据，你可以按照以下步骤进行：
1. 环境准备
确保你的服务器已经安装了最新版的CUDA和驱动，并且安装了必要的Python库，如torch、transformers、datasets等。可以使用以下命令安装：
pip install torch transformers datasets accelerate deepspeed
2. 代码实现
import torch
from torch.utils.data import DataLoader
from transformers import (AutoModelForCausalLM,AutoTokenizer,TrainingArguments,Trainer,default_data_collator
)
from datasets import load_dataset
import time# 定义模型名称
model_names = ["meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-70b-hf"]# 加载数据集
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")for model_name in model_names:print(f"Training {model_name}...")# 加载模型和分词器tokenizer = AutoTokenizer.from_pretrained(model_name)tokenizer.pad_token = tokenizer.eos_tokenmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)# 预处理数据集def preprocess_function(examples):inputs = tokenizer(examples["text"], truncation=True, max_length=512, padding="max_length")return inputstokenized_dataset = dataset.map(preprocess_function, batched=True)# 定义训练参数training_args = TrainingArguments(output_dir=f"./results/{model_name}",num_train_epochs=1,per_device_train_batch_size=4,gradient_accumulation_steps=1,fp16=True,logging_steps=10,save_steps=1000,evaluation_strategy="steps",eval_steps=500,warmup_steps=500,weight_decay=0.01,logging_dir=f"./logs/{model_name}",deepspeed="ds_config.json" # 使用DeepSpeed进行分布式训练)# 定义Trainertrainer = Trainer(model=model,args=training_args,train_dataset=tokenized_dataset["train"],eval_dataset=tokenized_dataset["validation"],data_collator=default_data_collator,)# 开始训练并记录时间start_time = time.time()trainer.train()end_time = time.time()# 计算训练指标total_steps = trainer.state.global_steptotal_time = end_time - start_timethroughput = total_steps / total_timeprint(f"Model: {model_name}")print(f"Total steps: {total_steps}")print(f"Total time (s): {total_time}")print(f"Throughput (steps/s): {throughput}")3. DeepSpeed配置文件（ds_config.json）
{"train_batch_size": 64,"optimizer": {"type": "Adam","params": {"lr": 0.0001,"betas": [0.9,0.999],"eps": 1e-8,"weight_decay": 0.01}},"fp16": {"enabled": true,"loss_scale": 0,"initial_scale_power": 16},"zero_optimization": {"stage": 2,"allgather_partitions": true,"allgather_bucket_size": 2e8,"overlap_comm": true,"reduce_scatter": true,"reduce_bucket_size": 2e8,"contiguous_gradients": true}
}
4. 运行代码
将上述代码保存为train_llama.py，并在终端中运行：
deepspeed --num_gpus 16 train_llama.py
注意事项
- 模型权限：
Llama - 2系列模型需要在Hugging Face上申请访问权限，确保你已经获得了相应的权限。 - 硬件资源：
llama - 2 - 70b模型非常大，需要足够的显存和内存资源。确保你的服务器能够支持该模型的训练。 - 数据处理：这里使用的是
wikitext - 2 - raw - v1数据集，你可以根据需要替换为自己的数据集。