import os import platform import argparse import time import math import warnings import torch import torch.distributed as dist from torch import optim from torch.nn.parallel import DistributedDataParallel from torch.optim.lr_scheduler import CosineAnnealingLR from torch.utils.data import DataLoader, DistributedSampler from contextlib import nullcontext from model.model import Transformer from model.LMConfig import LMConfig from model.dataset import PretrainDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(it, all): warmup_iters = args.warmup_iters lr_decay_iters = all min_lr = args.learning_rate / 10 if it < warmup_iters: return args.learning_rate * it / warmup_iters if it > lr_decay_iters: return min_lr decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) assert 0 <= decay_ratio <= 1 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) return min_lr + coeff * (args.learning_rate - min_lr) def train_epoch(epoch, wandb): start_time = time.time() for step, (X, Y) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: out = model(X, Y) loss = out.last_loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( epoch, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if wandb is not None: wandb.log({"loss": loss.item() * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/pretrain_{lm_config.dim}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() torch.save(state_dict, ckp) model.train() def init_model(): def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) model = Transformer(lm_config).to(args.device) moe_path = '_moe' if lm_config.use_moe else '' Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万') return model def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) # torchrun --nproc_per_node 2 1-pretrain.py if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Pretraining") parser.add_argument("--out_dir", type=str, default="out", help="Output directory") parser.add_argument("--epochs", type=int, default=20, help="Number of epochs") parser.add_argument("--batch_size", type=int, default=64, help="Batch size") parser.add_argument("--learning_rate", type=float, default=2e-4, help="Learning rate") parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use") parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type") parser.add_argument("--use_wandb", action="store_true", help="Use Weights & Biases") parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain", help="Weights & Biases project name") parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading") parser.add_argument("--data_path", type=str, default="./dataset/pretrain_data.bin", help="Path to training data") parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel") parser.add_argument("--accumulation_steps", type=int, default=8, help="Gradient accumulation steps") parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold") parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations") parser.add_argument("--log_interval", type=int, default=100, help="Logging interval") parser.add_argument("--save_interval", type=int, default=1000, help="Model saving interval") args = parser.parse_args() lm_config = LMConfig() max_seq_len = lm_config.max_seq_len args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * max_seq_len torch.manual_seed(1337) device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" if ddp: init_distributed_mode() args.device = torch.device(DEVICE) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None data_path_list = [args.data_path] train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) model = init_model() scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == args.dtype)) optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2: Logger("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb)