update ch05

docs/chapter5/code/ddp_sft_full.py

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+import os
+import platform
+import argparse
+import time
+import warnings
+import math
+import pandas as pd
+import torch
+from torch import optim
+from torch.utils.data import DataLoader
+from contextlib import nullcontext
+
+from transformers import AutoTokenizer
+
+from k_model import ModelConfig, Transformer
+from dataset import SFTDataset
+
+import swanlab
+
+warnings.filterwarnings('ignore')
+
+
+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 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):
+    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)
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+
+        with ctx:
+            out = model(X, Y)
+            loss = out.last_loss / args.accumulation_steps
+            loss_mask = loss_mask.view(-1)
+            loss = torch.sum(loss * loss_mask) / loss_mask.sum()
+
+        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 + 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 args.use_swanlab:
+                swanlab.log({
+                    "loss": loss.item() * args.accumulation_steps,
+                    "lr": optimizer.param_groups[-1]['lr']
+                })
+
+        if (step + 1) % args.save_interval == 0:
+            model.eval()
+            ckp = f'{args.save_dir}/sft_dim{lm_config.dim}_layers{lm_config.n_layers}_vocab_size{lm_config.vocab_size}.pth'
+
+            # 处理多卡保存
+            state_dict = model.module.state_dict() if isinstance(model, torch.nn.DataParallel) else model.state_dict()
+            torch.save(state_dict, ckp)
+            model.train()
+        
+        if (step + 1) % 20000 == 0:
+            model.eval()
+            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'
+
+            state_dict = model.module.state_dict() if isinstance(model, torch.nn.DataParallel) else 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)
+
+    tokenizer = AutoTokenizer.from_pretrained('./tokenizer_k/')
+
+    model = Transformer(lm_config)
+
+    ckp = './base_monkey_215M/pretrain_1024_18_6144.pth'
+    state_dict = torch.load(ckp, map_location=args.device)
+    unwanted_prefix = '_orig_mod.'
+    for k, v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+    model.load_state_dict(state_dict, strict=False)
+    
+    # 多卡初始化
+    num_gpus = torch.cuda.device_count()
+    if num_gpus > 1:
+        Logger(f"Using {num_gpus} GPUs with DataParallel!")
+        model = torch.nn.DataParallel(model)
+    
+    model = model.to(args.device)
+    Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
+    return model, tokenizer
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Tiny-LLM Pretraining")
+    parser.add_argument("--out_dir", type=str, default="BeelGroup_sft_model_215M", help="Output directory")
+    parser.add_argument("--epochs", type=int, default=1, 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_swanlab", type=bool, default=True, help="Use Weights & Biases")
+    parser.add_argument("--num_workers", type=int, default=4, help="Number of workers for data loading")
+    parser.add_argument("--data_path", type=str, default="/home/user/szx/dataset/BelleGroup/sft.jsonl", help="Path to training data")
+    parser.add_argument("--accumulation_steps", type=int, default=4, 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")
+    # 添加多卡参数
+    parser.add_argument("--gpus", type=str, default='0,1', help="Comma-separated GPU IDs (e.g. '0,1,2')")
+
+    args = parser.parse_args()
+
+    # 设置可见GPU
+    if args.gpus is not None:
+        os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
+        # 自动设置主设备为第一个GPU
+        if torch.cuda.is_available():
+            args.device = "cuda:0"
+        else:
+            args.device = "cpu"
+
+    if args.use_swanlab:
+        swanlab.login(api_key='BIYVGq2rfWmD9sFMCehUG')
+        run = swanlab.init(
+            project="Tiny-LLM",
+            experiment_name="BelleGropu-sft-215M",
+            config=args,
+        )
+
+    lm_config = ModelConfig(
+        dim=1024,
+        n_layers=18,
+    )
+    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)
+    torch.manual_seed(42)
+    device_type = "cuda" if "cuda" in args.device else "cpu"
+
+    ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
+
+    model, tokenizer = init_model()
+    
+    train_ds = SFTDataset(args.data_path, tokenizer, max_length=max_seq_len)
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=args.batch_size,
+        pin_memory=True,
+        drop_last=False,
+        shuffle=True,
+        num_workers=args.num_workers
+    )
+
+    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
+    optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
+
+    iter_per_epoch = len(train_loader)
+    for epoch in range(args.epochs):
+        train_epoch(epoch)