当前位置：首页 > article >正文

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

article 2025/3/20 9:19:09

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_token
    model = 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 inputs

    tokenized_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Trainer
    trainer = 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_step
    total_time = end_time - start_time
    throughput = total_steps / total_time

    print(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
    }
}