228 lines
8.1 KiB
Python
228 lines
8.1 KiB
Python
import os
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import platform
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import argparse
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import time
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import warnings
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import math
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import pandas as pd
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import torch
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from torch import optim
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from torch.utils.data import DataLoader
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from contextlib import nullcontext
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from transformers import AutoTokenizer
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from k_model import ModelConfig, Transformer
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from dataset import SFTDataset
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import swanlab
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# 忽略警告
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warnings.filterwarnings('ignore')
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def Logger(content):
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"""日志记录器"""
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print(content)
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def get_lr(it, all):
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"""获取学习率"""
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# 1) linear warmup for warmup_iters steps
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# 1) 预热迭代的线性预热
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warmup_iters = args.warmup_iters
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lr_decay_iters = all
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min_lr = args.learning_rate / 10
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if it < warmup_iters:
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return args.learning_rate * it / warmup_iters
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# 2) if it > lr_decay_iters, return min learning rate
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# 2) 如果迭代次数超过学习率衰减迭代次数，则返回最小学习率
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if it > lr_decay_iters:
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return min_lr
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# 3) in between, use cosine decay down to min learning rate
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# 3) 在两者之间，使用余弦衰减至最小学习率
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decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
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assert 0 <= decay_ratio <= 1
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coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
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return min_lr + coeff * (args.learning_rate - min_lr)
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def train_epoch(epoch):
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"""训练一个epoch"""
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start_time = time.time()
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for step, (X, Y, loss_mask) in enumerate(train_loader):
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X = X.to(args.device)
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Y = Y.to(args.device)
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loss_mask = loss_mask.to(args.device)
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# 获取学习率并更新优化器
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lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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# 前向传播
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with ctx:
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out = model(X, Y)
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loss = out.last_loss / args.accumulation_steps
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loss_mask = loss_mask.view(-1)
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loss = torch.sum(loss * loss_mask) / loss_mask.sum()
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# 反向传播
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scaler.scale(loss).backward()
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# 更新权重
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if (step + 1) % args.accumulation_steps == 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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# 打印日志
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if step % args.log_interval == 0:
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spend_time = time.time() - start_time
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
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epoch + 1,
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args.epochs,
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step,
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iter_per_epoch,
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loss.item() * args.accumulation_steps,
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optimizer.param_groups[-1]['lr'],
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spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
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if args.use_swanlab:
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swanlab.log({
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"loss": loss.item() * args.accumulation_steps,
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"lr": optimizer.param_groups[-1]['lr']
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})
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# 保存模型
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if (step + 1) % args.save_interval == 0:
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model.eval()
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ckp = f'{args.save_dir}/sft_dim{lm_config.dim}_layers{lm_config.n_layers}_vocab_size{lm_config.vocab_size}.pth'
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# 处理多卡保存
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state_dict = model.module.state_dict() if isinstance(model, torch.nn.DataParallel) else model.state_dict()
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torch.save(state_dict, ckp)
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model.train()
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# 定期保存模型
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if (step + 1) % 20000 == 0:
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model.eval()
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ckp = f'{args.save_dir}/sft_dim{lm_config.dim}_layers{lm_config.n_layers}_vocab_size{lm_config.vocab_size}_step{step+1}.pth'
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state_dict = model.module.state_dict() if isinstance(model, torch.nn.DataParallel) else model.state_dict()
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torch.save(state_dict, ckp)
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model.train()
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def init_model():
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"""初始化模型"""
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def count_parameters(model):
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"""计算模型参数量"""
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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# 加载分词器
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tokenizer = AutoTokenizer.from_pretrained('./tokenizer_k/')
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# 初始化模型
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model = Transformer(lm_config)
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# 加载预训练权重
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ckp = './base_model_215M/pretrain_1024_18_6144.pth'
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state_dict = torch.load(ckp, map_location=args.device)
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unwanted_prefix = '_orig_mod.'
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for k, v in list(state_dict.items()):
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if k.startswith(unwanted_prefix):
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state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
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model.load_state_dict(state_dict, strict=False)
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# 多卡初始化
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num_gpus = torch.cuda.device_count()
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if num_gpus > 1:
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Logger(f"Using {num_gpus} GPUs with DataParallel!")
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model = torch.nn.DataParallel(model)
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model = model.to(args.device)
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Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
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return model, tokenizer
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Tiny-LLM Pretraining")
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parser.add_argument("--out_dir", type=str, default="sft_model_215M", help="输出目录")
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parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=64, help="批处理大小")
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parser.add_argument("--learning_rate", type=float, default=2e-4, help="学习率")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="使用的设备")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="数据类型")
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parser.add_argument("--use_swanlab", action="store_true", help="是否使用SwanLab进行实验跟踪")
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parser.add_argument("--num_workers", type=int, default=8, help="数据加载的工作进程数")
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parser.add_argument("--data_path", type=str, default="./BelleGroup_sft.jsonl", help="训练数据路径")
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parser.add_argument("--accumulation_steps", type=int, default=8, help="梯度累积步数")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
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parser.add_argument("--warmup_iters", type=int, default=0, help="预热迭代次数")
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parser.add_argument("--log_interval", type=int, default=100, help="日志记录间隔")
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parser.add_argument("--save_interval", type=int, default=1000, help="模型保存间隔")
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# 添加多卡参数
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parser.add_argument("--gpus", type=str, default='0,1,2,3,4,5,6,7', help="逗号分隔的GPU ID (例如 '0,1,2')")
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args = parser.parse_args()
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# 设置可见GPU
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if args.gpus is not None:
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
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# 自动设置主设备为第一个GPU
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if torch.cuda.is_available():
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args.device = "cuda:0"
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else:
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args.device = "cpu"
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# 初始化swanlab
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if args.use_swanlab:
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run = swanlab.init(
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project="Happy-LLM",
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experiment_name="SFT-215M",
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config=args,
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)
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# 模型配置
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lm_config = ModelConfig(
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dim=1024,
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n_layers=18,
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)
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max_seq_len = lm_config.max_seq_len
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args.save_dir = os.path.join(args.out_dir)
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os.makedirs(args.save_dir, exist_ok=True)
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os.makedirs(args.out_dir, exist_ok=True)
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torch.manual_seed(42)
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device_type = "cuda" if "cuda" in args.device else "cpu"
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# 上下文管理器
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ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
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# 初始化模型和分词器
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model, tokenizer = init_model()
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# 创建数据集和数据加载器
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train_ds = SFTDataset(args.data_path, tokenizer, max_length=max_seq_len)
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train_loader = DataLoader(
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train_ds,
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batch_size=args.batch_size,
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pin_memory=True,
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drop_last=False,
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shuffle=True,
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num_workers=args.num_workers
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)
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# 缩放器和优化器
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
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# 开始训练
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iter_per_epoch = len(train_loader)
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for epoch in range(args.epochs):
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train_epoch(epoch) |