From 89011ebb3a5b42dcc33702803253ef335ea0dd39 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=B4=AB=E8=8B=8F?=
 <148778141+zisu09@users.noreply.github.com>
Date: Wed, 28 May 2025 17:42:44 +0800
Subject: [PATCH] Add files via upload
MIME-Version: 1.0
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接续训练
---
 train_full_sft.py | 229 +++++++++++++++++++++++++++++++++++++++++++++
 train_pretrain.py | 234 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 463 insertions(+)
 create mode 100644 train_full_sft.py
 create mode 100644 train_pretrain.py

diff --git a/train_full_sft.py b/train_full_sft.py
new file mode 100644
index 0000000..cc6e47d
--- /dev/null
+++ b/train_full_sft.py
@@ -0,0 +1,229 @@
+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 contextlib import nullcontext
+from torch import optim, nn
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils.data import DataLoader, DistributedSampler
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
+from dataset.lm_dataset import SFTDataset
+
+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, start_step=0):
+    loss_fct = nn.CrossEntropyLoss(reduction='none')
+    start_time = time.time()
+    for step, (X, Y, loss_mask) in enumerate(train_loader):
+        if step < start_step:
+            continue
+        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 * 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}/full_sft_{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)
+
+            # 保存优化器状态、当前epoch和step
+            checkpoint = {
+                'epoch': epoch,
+                'step': step + 1,
+                'optimizer_state_dict': optimizer.state_dict(),
+                'scaler_state_dict': scaler.state_dict()
+            }
+            torch.save(checkpoint, f'{args.save_dir}/checkpoint.pth')
+            model.train()
+
+
+def init_model(lm_config):
+    tokenizer = AutoTokenizer.from_pretrained('../model')
+    model = MiniMindForCausalLM(lm_config)
+    moe_path = '_moe' if lm_config.use_moe else ''
+    ckp = f'{args.save_dir}/pretrain_{lm_config.hidden_size}{moe_path}.pth'
+    state_dict = torch.load(ckp, map_location=args.device)
+    model.load_state_dict(state_dict, strict=False)
+
+    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')
+    model = model.to(args.device)
+    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)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="MiniMind Full SFT")
+    parser.add_argument("--out_dir", type=str, default="../out")
+    parser.add_argument("--epochs", type=int, default=2)
+    parser.add_argument("--batch_size", type=int, default=16)
+    parser.add_argument("--learning_rate", type=float, default=5e-7)
+    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-Full-SFT")
+    parser.add_argument("--num_workers", type=int, default=1)
+    parser.add_argument("--ddp", action="store_true")
+    parser.add_argument("--accumulation_steps", type=int, default=1)
+    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/sft_mini_512.jsonl")
+    parser.add_argument("--resume", action="store_true", help="Resume training from checkpoint")
+
+    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-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 = 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 = SFTDataset(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)
+
+    start_epoch = 0
+    start_step = 0
+    if args.resume:
+        checkpoint_path = f'{args.save_dir}/checkpoint.pth'
+        if os.path.exists(checkpoint_path):
+            checkpoint = torch.load(checkpoint_path)
+            start_epoch = checkpoint['epoch']
+            start_step = checkpoint['step']
+            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+            scaler.load_state_dict(checkpoint['scaler_state_dict'])
+            Logger(f"Resuming training from epoch {start_epoch + 1}, step {start_step}")
+        else:
+            Logger("Checkpoint not found, starting training from scratch.")
+
+    for epoch in range(start_epoch, args.epochs):
+        train_epoch(epoch, wandb, start_step if epoch == start_epoch else 0)
\ No newline at end of file
diff --git a/train_pretrain.py b/train_pretrain.py
new file mode 100644
index 0000000..a614933
--- /dev/null
+++ b/train_pretrain.py
@@ -0,0 +1,234 @@
+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))
+
+loss_fct = nn.CrossEntropyLoss(reduction='none')
+
+def train_epoch(epoch, start_step, wandb):
+    start_time = time.time()
+    if epoch == start_epoch:
+        train_loader_iter = iter(train_loader)
+        for _ in range(start_step + 1):
+            next(train_loader_iter)
+        for step, (X, Y, loss_mask) in enumerate(train_loader_iter, start=start_step + 1):
+            train_step(epoch, step, X, Y, loss_mask, wandb, start_time)
+    else:
+        for step, (X, Y, loss_mask) in enumerate(train_loader):
+            train_step(epoch, step, X, Y, loss_mask, wandb, start_time)
+
+
+def train_step(epoch, step, X, Y, loss_mask, wandb, start_time):
+    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)
+
+        # 保存训练状态
+        training_state = {
+            'epoch': epoch,
+            'step': step,
+            'optimizer_state_dict': optimizer.state_dict(),
+            'scaler_state_dict': scaler.state_dict() if scaler is not None else None
+        }
+        training_state_path = f'{args.save_dir}/training_state.pth'
+        torch.save(training_state, training_state_path)
+
+        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__":
+    # 命令行参数解析
+    parser = argparse.ArgumentParser(description="MiniMind Pretraining")
+    parser.add_argument("--out_dir", type=str, default="../out")  # 输出目录
+    parser.add_argument("--epochs", type=int, default=8)          # 训练轮次
+    parser.add_argument("--batch_size", type=int, default=8)     # 物理批次大小
+    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")  # 混合精度类型
+    parser.add_argument("--use_wandb", action="store_true")       # 是否使用WandB
+    parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain")
+    parser.add_argument("--num_workers", type=int, default=1)     # 数据加载线程数
+    parser.add_argument("--ddp", action="store_true")             # 是否启用DDP
+    parser.add_argument("--accumulation_steps", type=int, default=4)  # 梯度累积步数
+    parser.add_argument("--grad_clip", type=float, default=1.0)   # 梯度裁剪阈值
+    parser.add_argument("--log_interval", type=int, default=100)  # 日志间隔（步）
+    parser.add_argument("--save_interval", type=int, default=100) # 保存间隔（步）
+    parser.add_argument('--local_rank', type=int, default=-1)     # DDP自动传入参数
+    # 模型架构参数
+    parser.add_argument('--hidden_size', default=512, type=int)   # 隐藏层维度
+    parser.add_argument('--num_hidden_layers', default=8, type=int) # 层数
+    parser.add_argument('--max_seq_len', default=4096, type=int)   # 序列长度
+    parser.add_argument('--use_moe', default=False, type=bool)    # 是否使用MoE
+    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)
+
+    # 检查是否存在保存的训练状态文件
+    training_state_path = f'{args.save_dir}/training_state.pth'
+    if os.path.exists(training_state_path):
+        training_state = torch.load(training_state_path)
+        start_epoch = training_state['epoch']
+        start_step = training_state['step']
+        optimizer.load_state_dict(training_state['optimizer_state_dict'])
+        if scaler is not None and training_state['scaler_state_dict'] is not None:
+            scaler.load_state_dict(training_state['scaler_state_dict'])
+    else:
+        start_epoch = 0
+        start_step = 0
+
+    for epoch in range(start_epoch, args.epochs):
+        train_epoch(epoch, start_step if epoch == start_epoch else 0, wandb)
\ No newline at end of file