添加了argparse，方便命令行输入参数
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parent
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126
1-pretrain.py
126
1-pretrain.py
@ -1,5 +1,6 @@
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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 math
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import warnings
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@ -23,66 +24,65 @@ def Logger(content):
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def get_lr(it, all):
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warmup_iters = 0
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warmup_iters = args.warmup_iters
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lr_decay_iters = all
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min_lr = learning_rate / 10
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min_lr = args.learning_rate / 10
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if it < warmup_iters:
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return learning_rate * it / warmup_iters
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return args.learning_rate * it / warmup_iters
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if it > lr_decay_iters:
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return min_lr
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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 * (learning_rate - min_lr)
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return min_lr + coeff * (args.learning_rate - min_lr)
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def train_epoch(epoch, wandb, accumulation_steps=8):
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def train_epoch(epoch, wandb):
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start_time = time.time()
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for step, (X, Y) in enumerate(train_loader):
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X = X.to(device)
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Y = Y.to(device)
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X = X.to(args.device)
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Y = Y.to(args.device)
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lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch)
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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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with ctx:
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out = model(X, Y)
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loss = out.last_loss / accumulation_steps
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loss = out.last_loss / args.accumulation_steps
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scaler.scale(loss).backward()
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if (step + 1) % accumulation_steps == 0:
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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(), 1.0)
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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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if step % 100 == 0:
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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,
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epochs,
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args.epochs,
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step,
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iter_per_epoch,
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loss.item() * accumulation_steps,
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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 wandb != None:
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wandb.log({"loss": loss.item() * accumulation_steps,
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if wandb is not None:
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wandb.log({"loss": loss.item() * args.accumulation_steps,
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"lr": optimizer.param_groups[-1]['lr'],
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"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0):
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if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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# torch.save(model.state_dict(), '{}/iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch)))
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
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ckp = f'{args.save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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@ -97,17 +97,8 @@ def init_model():
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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# model init
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model = Transformer(lm_config).to(device)
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model = Transformer(lm_config).to(args.device)
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moe_path = '_moe' if lm_config.use_moe else ''
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# ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
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#
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# state_dict = torch.load(ckp, map_location=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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Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
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return model
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@ -125,81 +116,78 @@ def init_distributed_mode():
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torch.cuda.set_device(DEVICE)
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# torchrun --nproc_per_node 2 1-pretrain.py
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# I/O
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if __name__ == "__main__":
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# -----------------------------------------------------------------------------
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parser = argparse.ArgumentParser(description="MiniMind Pretraining")
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parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
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parser.add_argument("--epochs", type=int, default=20, help="Number of epochs")
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parser.add_argument("--batch_size", type=int, default=64, help="Batch size")
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parser.add_argument("--learning_rate", type=float, default=2e-4, help="Learning rate")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
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parser.add_argument("--use_wandb", action="store_true", help="Use Weights & Biases")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain", help="Weights & Biases project name")
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parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading")
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parser.add_argument("--data_path", type=str, default="./dataset/pretrain_data.bin", help="Path to training data")
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parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel")
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parser.add_argument("--accumulation_steps", type=int, default=8, help="Gradient accumulation steps")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold")
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parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations")
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parser.add_argument("--log_interval", type=int, default=100, help="Logging interval")
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parser.add_argument("--save_interval", type=int, default=1000, help="Model saving interval")
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args = parser.parse_args()
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lm_config = LMConfig()
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max_seq_len = lm_config.max_seq_len
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out_dir = 'out'
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epochs = 20
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batch_size = 64
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learning_rate = 2e-4
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device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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dtype = 'bfloat16'
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save_dir = os.path.join(out_dir)
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os.makedirs(save_dir, exist_ok=True)
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os.makedirs(out_dir, exist_ok=True)
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tokens_per_iter = batch_size * 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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tokens_per_iter = args.batch_size * max_seq_len
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torch.manual_seed(1337)
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device_type = device if "cuda" in device else "cpu"
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device_type = "cuda" if "cuda" in args.device else "cpu"
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use_wandb = True #是否使用wandb
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wandb_project = "MiniMind-Pretrain"
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wandb_run_name = f"MiniMind-Pretrain-Epoch-{epochs}-BatchSize-{batch_size}-LearningRate-{learning_rate}"
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args.wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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ctx = (
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nullcontext()
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if device_type == "cpu"
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else torch.cuda.amp.autocast()
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)
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ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
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ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
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ddp_local_rank, DEVICE = 0, "cuda:0"
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if ddp:
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init_distributed_mode()
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device = torch.device(DEVICE)
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args.device = torch.device(DEVICE)
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if use_wandb and (not ddp or ddp_local_rank == 0):
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if args.use_wandb and (not ddp or ddp_local_rank == 0):
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import wandb
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wandb.init(project=wandb_project, name=wandb_run_name)
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wandb.init(project=args.wandb_project, name=args.wandb_run_name)
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else:
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wandb = None
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# -----------------------------------------------------------------------------
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# -----init dataloader------
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data_path_list = ['./dataset/pretrain_data.bin']
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data_path_list = [args.data_path]
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train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True)
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train_sampler = DistributedSampler(train_ds) if ddp else None
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num_workers = 8 # 可以根据系统的 CPU 核心数来调整
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train_loader = DataLoader(
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train_ds,
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batch_size=batch_size,
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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=False,
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num_workers=num_workers,
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num_workers=args.num_workers,
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sampler=train_sampler
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)
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# init model
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model = init_model()
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scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype))
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# optimizer
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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# compile the model
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == args.dtype))
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optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
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if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2:
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Logger("compiling the model... (takes a ~minute)")
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unoptimized_model = model
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model = torch.compile(model)
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if ddp:
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# Ignore the freqs_cis buffer so that DDP does not broadcast it at
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# construction time since NCCL does not support ComplexFloat
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model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
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# training loop
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iter_per_epoch = len(train_loader)
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for epoch in range(epochs):
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for epoch in range(args.epochs):
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train_epoch(epoch, wandb)
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157
3-full_sft.py
157
3-full_sft.py
@ -1,5 +1,6 @@
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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 math
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import warnings
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@ -12,7 +13,6 @@ from contextlib import nullcontext
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from torch import optim
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from torch.nn.parallel import DistributedDataParallel
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from torch.utils.data import DataLoader, DistributedSampler
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from transformers import AutoTokenizer, AutoModel
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from model.model import Transformer
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@ -28,28 +28,27 @@ def Logger(content):
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def get_lr(it, all):
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warmup_iters = 0
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warmup_iters = args.warmup_iters
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lr_decay_iters = all
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min_lr = learning_rate / epochs
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min_lr = args.learning_rate / 10
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if it < warmup_iters:
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return learning_rate * it / warmup_iters
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return args.learning_rate * it / warmup_iters
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if it > lr_decay_iters:
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return min_lr
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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 * (learning_rate - min_lr)
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return min_lr + coeff * (args.learning_rate - min_lr)
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# ------------------------------------------------------------------------------
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def train_epoch(epoch, wandb):
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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(device)
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Y = Y.to(device)
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loss_mask = loss_mask.to(device)
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lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch)
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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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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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@ -59,41 +58,38 @@ def train_epoch(epoch, wandb):
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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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# Backward pass
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scaler.scale(loss).backward()
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# Unscale gradients and clip them
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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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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# Update parameters
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scaler.step(optimizer)
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scaler.update()
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scaler.step(optimizer)
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scaler.update()
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# Zero the gradients
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optimizer.zero_grad(set_to_none=True)
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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 % 100 == 0:
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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:{:.8f} epoch_Time:{}min:'.format(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
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epoch,
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epochs,
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args.epochs,
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step,
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iter_per_epoch,
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loss,
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loss.item(),
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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 use_wandb != None:
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wandb.log({"loss": loss, "lr": optimizer.param_groups[-1]['lr'],
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"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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if wandb is not None:
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wandb.log({"loss": loss.item(),
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"lr": optimizer.param_groups[-1]['lr'],
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"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0):
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if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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# torch.save(model.state_dict(), '{}/sft_iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch)))
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{save_dir}/full_sft_{lm_config.dim}{moe_path}.pth'
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ckp = f'{args.save_dir}/full_sft_{lm_config.dim}{moe_path}.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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else:
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@ -103,7 +99,7 @@ def train_epoch(epoch, wandb):
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model.train()
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def init_model(lm_config):
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def init_model():
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
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model_from = 1 # 1从权重，2用transformers
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@ -114,7 +110,7 @@ def init_model(lm_config):
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model = Transformer(lm_config)
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'./out/pretrain_{lm_config.dim}{moe_path}.pth'
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state_dict = torch.load(ckp, map_location=device)
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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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@ -124,7 +120,7 @@ def init_model(lm_config):
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model = AutoModel.from_pretrained('./minimind', trust_remote_code=True)
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Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
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model = model.to(device)
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model = model.to(args.device)
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return model, tokenizer
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@ -141,83 +137,78 @@ def init_distributed_mode():
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torch.cuda.set_device(DEVICE)
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# I/O
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if __name__ == "__main__":
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# -----------------------------------------------------------------------------
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parser = argparse.ArgumentParser(description="MiniMind Full SFT")
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parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
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parser.add_argument("--epochs", type=int, default=19, help="Number of epochs")
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parser.add_argument("--batch_size", type=int, default=40, help="Batch size")
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parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
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parser.add_argument("--use_wandb", action="store_true", help="Use Weights & Biases")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT", help="Weights & Biases project name")
|
||||
parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading")
|
||||
parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel")
|
||||
parser.add_argument("--accumulation_steps", type=int, default=1, 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
|
||||
out_dir = 'out'
|
||||
epochs = 19
|
||||
gradient_accumulation_steps = 1
|
||||
batch_size = 40
|
||||
learning_rate = 1e-4
|
||||
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
|
||||
dtype = 'bfloat16'
|
||||
# dtype = 'float16'
|
||||
save_dir = os.path.join(out_dir)
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
tokens_per_iter = gradient_accumulation_steps * batch_size * max_seq_len
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
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 = device if "cuda" in device else "cpu"
|
||||
device_type = "cuda" if "cuda" in args.device else "cpu"
|
||||
|
||||
use_wandb = True #是否使用wandb
|
||||
wandb_project = "MiniMind-Full-SFT"
|
||||
wandb_run_name = f"MiniMind-Full-SFT-Epoch-{epochs}-BatchSize-{batch_size}-LearningRate-{learning_rate}"
|
||||
if use_wandb:
|
||||
import wandb
|
||||
wandb.init(project=wandb_project, name=wandb_run_name)
|
||||
else:
|
||||
wandb = None
|
||||
args.wandb_run_name = f"MiniMind-Full-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
|
||||
|
||||
ctx = (
|
||||
nullcontext()
|
||||
if device_type == "cpu"
|
||||
else torch.cuda.amp.autocast()
|
||||
)
|
||||
|
||||
### ddp config
|
||||
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()
|
||||
device = torch.device(DEVICE)
|
||||
# -----------------------------------------------------------------------------
|
||||
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
|
||||
|
||||
model, tokenizer = init_model()
|
||||
|
||||
model, tokenizer = init_model(lm_config)
|
||||
# -----init dataloader------
|
||||
df = pd.read_csv('./dataset/sft_data_single.csv')
|
||||
df = df.sample(frac=1.0)
|
||||
train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len)
|
||||
train_sampler = DistributedSampler(train_ds) if ddp else None
|
||||
train_loader = DataLoader(
|
||||
train_ds,
|
||||
batch_size=batch_size,
|
||||
pin_memory=False,
|
||||
batch_size=args.batch_size,
|
||||
pin_memory=True,
|
||||
drop_last=False,
|
||||
shuffle=False,
|
||||
num_workers=8,
|
||||
num_workers=args.num_workers,
|
||||
sampler=train_sampler
|
||||
)
|
||||
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype))
|
||||
# optimizer
|
||||
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == args.dtype))
|
||||
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
|
||||
|
||||
iter_per_epoch = len(train_loader)
|
||||
# compile the model
|
||||
if False and not lm_config.use_moe and platform.system() != 'Windows' and float(
|
||||
torch.__version__.split('.')[0]) >= 2:
|
||||
if False and not lm_config.use_moe 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) # requires PyTorch 2.0
|
||||
model = torch.compile(model)
|
||||
|
||||
if ddp:
|
||||
# Ignore the pos_cis buffer so that DDP does not broadcast it at
|
||||
# construction time since NCCL does not support ComplexFloat
|
||||
model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
|
||||
model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
|
||||
|
||||
# training loop
|
||||
for epoch in range(epochs,wandb):
|
||||
train_epoch(epoch)
|
||||
iter_per_epoch = len(train_loader)
|
||||
for epoch in range(args.epochs):
|
||||
train_epoch(epoch, wandb)
|
||||
|
||||
142
4-lora_sft.py
142
4-lora_sft.py
@ -1,5 +1,6 @@
|
||||
import os
|
||||
import platform
|
||||
import argparse
|
||||
import time
|
||||
import math
|
||||
import warnings
|
||||
@ -16,32 +17,36 @@ from torch.utils.data import DataLoader
|
||||
from model.LMConfig import LMConfig
|
||||
from model.dataset import SFTDataset
|
||||
|
||||
warnings.filterwarnings('ignore', category=UserWarning)
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
|
||||
def get_lr(it):
|
||||
warmup_iters = 1000
|
||||
lr_decay_iters = 80000
|
||||
min_lr = 1e-5
|
||||
def Logger(content):
|
||||
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 learning_rate * 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 * (learning_rate - min_lr)
|
||||
return min_lr + coeff * (args.learning_rate - min_lr)
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------
|
||||
def train_epoch(epoch, wandb):
|
||||
start_time = time.time()
|
||||
for step, (X, Y, loss_mask) in enumerate(train_loader):
|
||||
X = X.to(device)
|
||||
Y = Y.to(device)
|
||||
loss_mask = loss_mask.to(device)
|
||||
lr = get_lr(epoch * iter_per_epoch + step)
|
||||
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)
|
||||
for param_group in optimizer.param_groups:
|
||||
param_group['lr'] = lr
|
||||
|
||||
@ -50,31 +55,37 @@ def train_epoch(epoch, wandb):
|
||||
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none')
|
||||
loss_mask = loss_mask.view(-1)
|
||||
loss = torch.sum(loss * loss_mask) / loss_mask.sum()
|
||||
loss = loss / args.accumulation_steps
|
||||
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
scaler.unscale_(optimizer)
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||||
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()
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
if step % 100 == 0:
|
||||
if step % args.log_interval == 0:
|
||||
spend_time = time.time() - start_time
|
||||
print(
|
||||
Logger(
|
||||
'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
|
||||
epoch,
|
||||
epochs,
|
||||
args.epochs,
|
||||
step,
|
||||
iter_per_epoch,
|
||||
loss.item(),
|
||||
loss.item() * args.accumulation_steps,
|
||||
optimizer.param_groups[-1]['lr'],
|
||||
spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
|
||||
if use_wandb != None:
|
||||
wandb.log({"loss": loss.item(), "lr": optimizer.param_groups[-1]['lr'],
|
||||
"epoch_Time": 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:
|
||||
model.save_pretrained(args.save_dir)
|
||||
|
||||
|
||||
def find_all_linear_names(model):
|
||||
@ -94,7 +105,7 @@ def init_model():
|
||||
model_name_or_path = "./minimind"
|
||||
tokenizer_name_or_path = "./minimind"
|
||||
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, trust_remote_code=True, use_fast=False)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True).to(device)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True).to(args.device)
|
||||
|
||||
target_modules = find_all_linear_names(model)
|
||||
peft_config = LoraConfig(
|
||||
@ -107,73 +118,70 @@ def init_model():
|
||||
)
|
||||
model = get_peft_model(model, peft_config)
|
||||
model.print_trainable_parameters()
|
||||
model = model.to(device)
|
||||
model = model.to(args.device)
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
# I/O
|
||||
if __name__ == "__main__":
|
||||
# -----------------------------------------------------------------------------
|
||||
parser = argparse.ArgumentParser(description="MiniMind LoRA Fine-tuning")
|
||||
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=16, help="Batch size")
|
||||
parser.add_argument("--learning_rate", type=float, default=1e-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-LoRA", help="Weights & Biases project name")
|
||||
parser.add_argument("--num_workers", type=int, default=0, help="Number of workers for data loading")
|
||||
parser.add_argument("--accumulation_steps", type=int, default=1, 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=1000, 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
|
||||
out_dir = 'out'
|
||||
epochs = 20
|
||||
gradient_accumulation_steps = 1
|
||||
batch_size = 16
|
||||
learning_rate = 1e-4
|
||||
weight_decay = 1e-1
|
||||
device = 'cuda:0'
|
||||
dtype = 'bfloat16'
|
||||
save_dir = os.path.join(out_dir)
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
tokens_per_iter = gradient_accumulation_steps * batch_size * max_seq_len
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
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 = device if "cuda" in device else "cpu"
|
||||
device_type = "cuda" if "cuda" in args.device else "cpu"
|
||||
|
||||
use_wandb = True #是否使用wandb
|
||||
wandb_project = "MiniMind-LoRA"
|
||||
wandb_run_name = f"MiniMind-LoRA-Epoch-{epochs}-BatchSize-{batch_size}-LearningRate-{learning_rate}"
|
||||
if use_wandb:
|
||||
args.wandb_run_name = f"MiniMind-LoRA-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
|
||||
|
||||
ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
|
||||
|
||||
if args.use_wandb:
|
||||
import wandb
|
||||
wandb.init(project=wandb_project, name=wandb_run_name)
|
||||
wandb.init(project=args.wandb_project, name=args.wandb_run_name)
|
||||
else:
|
||||
wandb = None
|
||||
|
||||
ctx = (
|
||||
nullcontext()
|
||||
if device_type == "cpu"
|
||||
else torch.cuda.amp.autocast()
|
||||
)
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
model, tokenizer = init_model()
|
||||
|
||||
# -----init dataloader------
|
||||
df = pd.read_csv('./dataset/sft_data.csv')
|
||||
df = df.sample(frac=1.0)
|
||||
train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len)
|
||||
train_loader = DataLoader(
|
||||
train_ds,
|
||||
batch_size=batch_size,
|
||||
pin_memory=False,
|
||||
batch_size=args.batch_size,
|
||||
pin_memory=True,
|
||||
drop_last=False,
|
||||
shuffle=False,
|
||||
num_workers=0,
|
||||
num_workers=args.num_workers,
|
||||
)
|
||||
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))
|
||||
# optimizer
|
||||
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
|
||||
iter_per_epoch = len(train_loader)
|
||||
# compile the model
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
|
||||
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
|
||||
|
||||
if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2:
|
||||
print("compiling the model... (takes a ~minute)")
|
||||
Logger("compiling the model... (takes a ~minute)")
|
||||
unoptimized_model = model
|
||||
model = torch.compile(model)
|
||||
|
||||
raw_model = model
|
||||
# training loop
|
||||
for epoch in range(epochs):
|
||||
iter_per_epoch = len(train_loader)
|
||||
for epoch in range(args.epochs):
|
||||
train_epoch(epoch, wandb)
|
||||
model.save_pretrained('minimind')
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user