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Configuration error
Configuration error
| import os | |
| import sys | |
| __package__ = "trainer" | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) | |
| import argparse | |
| import time | |
| import math | |
| import warnings | |
| import torch | |
| import torch.distributed as dist | |
| from torch import optim, nn | |
| from torch.nn.parallel import DistributedDataParallel | |
| from torch.utils.data import DataLoader, DistributedSampler | |
| from contextlib import nullcontext | |
| from transformers import AutoTokenizer | |
| from model.model_minimind import MiniMindConfig, MiniMindForCausalLM | |
| from dataset.lm_dataset import PretrainDataset | |
| warnings.filterwarnings('ignore') | |
| def Logger(content): | |
| if not ddp or dist.get_rank() == 0: | |
| print(content) | |
| def get_lr(current_step, total_steps, lr): | |
| return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) | |
| def train_epoch(epoch, wandb): | |
| loss_fct = nn.CrossEntropyLoss(reduction='none') | |
| start_time = time.time() | |
| for step, (X, Y, loss_mask) in enumerate(train_loader): | |
| X = X.to(args.device) | |
| Y = Y.to(args.device) | |
| loss_mask = loss_mask.to(args.device) | |
| lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) | |
| for param_group in optimizer.param_groups: | |
| param_group['lr'] = lr | |
| with ctx: | |
| res = model(X) | |
| loss = loss_fct( | |
| res.logits.view(-1, res.logits.size(-1)), | |
| Y.view(-1) | |
| ).view(Y.size()) | |
| loss = (loss * loss_mask).sum() / loss_mask.sum() | |
| loss += res.aux_loss | |
| loss = 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:{:.12f} epoch_Time:{}min:'.format( | |
| epoch + 1, | |
| 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) and (not ddp or dist.get_rank() == 0): | |
| 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.hidden_size}{moe_path}.pth' | |
| if isinstance(model, torch.nn.parallel.DistributedDataParallel): | |
| state_dict = model.module.state_dict() | |
| else: | |
| state_dict = model.state_dict() | |
| state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 | |
| torch.save(state_dict, ckp) | |
| model.train() | |
| def init_model(lm_config): | |
| tokenizer = AutoTokenizer.from_pretrained('../model/') | |
| model = MiniMindForCausalLM(lm_config).to(args.device) | |
| Logger(f'LLM可训练总参数量:{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') | |
| return model, tokenizer | |
| 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") | |
| # 若要以最快速度实现zero则epochs设置为1轮;否则应当利用有限的数据训练2~6个epochs。 | |
| parser.add_argument("--epochs", type=int, default=1) | |
| parser.add_argument("--batch_size", type=int, default=32) | |
| parser.add_argument("--learning_rate", type=float, default=5e-4) | |
| parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") | |
| parser.add_argument("--dtype", type=str, default="bfloat16") | |
| parser.add_argument("--use_wandb", action="store_true") | |
| parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain") | |
| parser.add_argument("--num_workers", type=int, default=1) | |
| parser.add_argument("--ddp", action="store_true") | |
| parser.add_argument("--accumulation_steps", type=int, default=8) | |
| parser.add_argument("--grad_clip", type=float, default=1.0) | |
| parser.add_argument("--warmup_iters", type=int, default=0) | |
| parser.add_argument("--log_interval", type=int, default=100) | |
| parser.add_argument("--save_interval", type=int, default=100) | |
| parser.add_argument('--local_rank', type=int, default=-1) | |
| parser.add_argument('--hidden_size', default=512, type=int) | |
| parser.add_argument('--num_hidden_layers', default=8, type=int) | |
| parser.add_argument('--max_seq_len', default=512, type=int) | |
| parser.add_argument('--use_moe', default=False, type=bool) | |
| parser.add_argument("--data_path", type=str, default="../dataset/pretrain_hq.jsonl") | |
| args = parser.parse_args() | |
| lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) | |
| 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 * args.max_seq_len | |
| 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" | |
| base_seed = 1337 | |
| torch.manual_seed(base_seed) | |
| torch.cuda.manual_seed(base_seed) | |
| if ddp: | |
| init_distributed_mode() | |
| args.device = torch.device(DEVICE) | |
| rank = dist.get_rank() | |
| torch.manual_seed(base_seed + rank) | |
| # 同时设置 CUDA 的随机种子 | |
| torch.cuda.manual_seed(base_seed + rank) | |
| 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 | |
| model, tokenizer = init_model(lm_config) | |
| train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len) | |
| 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 | |
| ) | |
| scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) | |
| optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) | |
| 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) | |