添加了train_embedding用于预训练嵌入模型

model/model.py

@@ -173,31 +173,42 @@ class CrossAttention(nn.Module):
     ):
         super().__init__()
         self.config = config
+        self.num_heads = 8  
+        self.head_dim = 768 // self.num_heads  
         self.to_q = nn.Linear(768, 768, bias=False)
         self.to_k = nn.Linear(768, 768, bias=False)
         self.to_v = nn.Linear(768, 768, bias=False)
-
+        
+        self.to_out = nn.Linear(768, 768, bias=False)
     
     def forward(self, x, db, context_mask=None, pos_emb=None):
-        # db = db.permute(0, 2, 1)
+        batch_size = x.size(0)
-
+        
-        q = self.to_q(x)
+        # 分离多头
-        k = self.to_k(db)
+        q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
-        v = self.to_v(db)
+        k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
         
         if pos_emb is not None:
+            pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
             q = q + pos_emb
             k = k + pos_emb
             v = v + pos_emb
         
-        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(k.size(-1))
+        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
         
         if context_mask is not None:
-            attn_scores = attn_scores.masked_fill(context_mask == 0, -1e10)
+            expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1)
+            attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10)
         
         attn_weights = F.softmax(attn_scores, dim=-1)
+
         context = torch.matmul(attn_weights, v)
         
+        context = context.transpose(1, 2).contiguous().view(batch_size, -1, 768)
+
+        context = self.to_out(context)
+        
         return context
 
 class FeedForward(nn.Module):

train_embedding.py

@@ -0,0 +1,418 @@
+import os
+# 设置环境变量
+os.environ["WANDB_MODE"] = "offline"  # 或者使用 "dryrun"
+import platform
+import argparse
+import time
+import math
+import warnings
+import pandas as pd
+import torch
+import torch.distributed as dist
+from torch import optim, nn
+from torch.nn.parallel import DistributedDataParallel
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from torch.utils.data import DataLoader, DistributedSampler, Dataset
+from contextlib import nullcontext
+import random
+import numpy as np
+import json
+
+from transformers import AutoTokenizer
+
+# Removed: from model.model import MiniMindLM
+from model.LMConfig import LMConfig
+# from model.dataset import PretrainDataset
+
+warnings.filterwarnings('ignore')
+
+
+# Define a Word2Vec-style CBOW model
+class CBOWModel(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.vocab_size = config.vocab_size
+        self.embedding_dim = config.dim
+        
+        # Input embeddings (context words)
+        self.embeddings = nn.Embedding(config.vocab_size, config.dim)
+        
+        # Output weights for target prediction
+        self.output_weights = nn.Linear(config.dim, config.vocab_size, bias=False)
+        
+        # Initialize weights
+        self.init_weights()
+        
+    def init_weights(self):
+        # Xavier initialization for better convergence
+        nn.init.xavier_uniform_(self.embeddings.weight)
+        nn.init.xavier_uniform_(self.output_weights.weight)
+    
+    def forward(self, context_words):
+        # context_words shape: [batch_size, context_size]，context_size可变
+        
+        # Get embeddings for all context words
+        embeds = self.embeddings(context_words)  # [batch_size, context_size, embedding_dim]
+        
+        # Average the context word embeddings along context dimension
+        embeds = torch.mean(embeds, dim=1)  # [batch_size, embedding_dim]
+        
+        # Predict the target word
+        output = self.output_weights(embeds)  # [batch_size, vocab_size]
+        
+        return output
+
+
+# Word2Vec CBOW dataset
+class CBOWDataset(Dataset):
+    def __init__(self, data_path, tokenizer, max_length=512, window_size=5):
+        super().__init__()
+        self.tokenizer = tokenizer
+        self.window_size = window_size
+        self.max_length = max_length
+        self.samples = self.load_data(data_path)
+        
+    def load_data(self, path):
+        samples = []
+        with open(path, 'r', encoding='utf-8') as f:
+            for line_num, line in enumerate(f, 1):
+                data = json.loads(line.strip())
+                samples.append(data)
+        return samples
+    
+    def __len__(self):
+        return len(self.samples)
+    
+    def __getitem__(self, index):
+        sample = self.samples[index]
+        
+        # 构建输入文本
+        text = f"{self.tokenizer.bos_token}{str(sample['text'])}{self.tokenizer.eos_token}"
+        encoding = self.tokenizer(
+            text,
+            max_length=self.max_length,
+            padding='max_length',
+            truncation=True,
+            return_tensors='pt'
+        )
+        
+        # 获取token ids
+        input_ids = encoding.input_ids.squeeze()
+        # 过滤掉padding
+        attention_mask = encoding.attention_mask.squeeze()
+        valid_indices = torch.where(attention_mask == 1)[0]
+        valid_input_ids = input_ids[valid_indices]
+        
+        # 确保有足够的token进行CBOW训练
+        if len(valid_input_ids) <= 2 * self.window_size + 1:
+            # 如果token不足，随机选择一个不同的样本
+            return self.__getitem__(random.randint(0, len(self.samples) - 1))
+        
+        # 随机选择一个中心位置（不包括首尾的特殊token）
+        # 确保中心位置两边都有至少window_size个token
+        min_center_pos = self.window_size + 1  # 避开起始token
+        max_center_pos = len(valid_input_ids) - self.window_size - 1  # 避开结束token
+        
+        if max_center_pos <= min_center_pos:
+            return self.__getitem__(random.randint(0, len(self.samples) - 1))
+            
+        center_pos = random.randint(min_center_pos, max_center_pos)
+        
+        # 目标词（中心词）
+        target = valid_input_ids[center_pos].unsqueeze(0)
+        
+        # 上下文词（中心词前后的词）
+        context = torch.cat([
+            valid_input_ids[center_pos - self.window_size:center_pos],
+            valid_input_ids[center_pos + 1:center_pos + self.window_size + 1]
+        ])
+        
+        return context, target
+
+
+def Logger(content):
+    # 如果没有使用ddp或者ddp的主设备，那么就打印
+    if not ddp or dist.get_rank() == 0:
+        print(content)
+
+
+def get_lr(current_step, total_steps, lr):
+    # 更新学习率
+    # \text{get\_lr}(c, t, l) = \frac{l}{10} + 0.5 \cdot l \cdot \left(1 + \cos\left(\frac{\pi \cdot c}{t}\right)\right)
+    return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
+
+
+def train_epoch(epoch, wandb):
+    loss_fct = nn.CrossEntropyLoss()
+    start_time = time.time()
+    total_loss = 0
+    total_samples = 0
+    
+    for step, (context, target) in enumerate(train_loader):
+        try:
+            # 将数据加载到设备上
+            context = context.to(args.device)
+            target = target.to(args.device)
+
+            # 更新学习率
+            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:
+                # Forward pass
+                logits = model(context)  # [batch_size, vocab_size]
+                # target是[batch_size, 1]，需要squeeze成[batch_size]来匹配CrossEntropyLoss的预期
+                loss = loss_fct(logits, target.squeeze())
+                loss = loss / args.accumulation_steps
+            
+            # Print data types for debugging
+            if step == 0 and (not ddp or dist.get_rank() == 0):
+                Logger("---- Data Type Check ----")
+                Logger(f"context.dtype: {context.dtype}")
+                Logger(f"context.shape: {context.shape}")
+                Logger(f"target.dtype: {target.dtype}")
+                Logger(f"target.shape: {target.shape}")
+                if hasattr(model, 'module'):  # DDP case
+                    Logger(f"Model parameter dtype: {next(model.module.parameters()).dtype}")
+                else:  # Non-DDP case
+                    Logger(f"Model parameter dtype: {next(model.parameters()).dtype}")
+                Logger(f"logits.dtype: {logits.dtype}")
+                Logger(f"logits.shape: {logits.shape}")
+                Logger(f"loss.dtype: {loss.dtype}")
+                Logger("-------------------------")
+
+            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)
+            
+            total_loss += loss.item() * args.accumulation_steps
+            total_samples += 1
+
+            # 打印日志
+            if step % args.log_interval == 0:
+                spend_time = time.time() - start_time
+                avg_loss = total_loss / total_samples if total_samples > 0 else 0
+                Logger(
+                    'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format(
+                        epoch + 1,
+                        args.epochs,
+                        step,
+                        iter_per_epoch,
+                        avg_loss,
+                        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": avg_loss,
+                               "lr": optimizer.param_groups[-1]['lr'],
+                               "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
+
+        except Exception as e:
+            print(f"Error occurred: {str(e)}")
+            import traceback
+            traceback.print_exc()
+            # Modified checkpoint path for error
+            save_path = f'{args.save_dir}/word2vec_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_ERROR.pth'
+            if os.path.exists(save_path):
+                os.remove(save_path)
+            
+            if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+                state_dict = model.module.embeddings.state_dict()
+            else:
+                state_dict = model.embeddings.state_dict()
+            torch.save(state_dict, save_path)
+
+            for name, param in model.named_parameters():
+                if param.grad is not None and torch.isnan(param.grad).any():
+                    print(f"NaN gradient in parameter: {name}")
+            
+            for name, param in model.named_parameters():
+                if param.grad is not None and torch.isnan(param.grad).any():
+                    print(f"Parameter {name} values: {param.data}")
+                    print(f"Parameter {name} gradients: {param.grad}")
+            
+            raise ValueError("NaN gradient detected")
+    
+    # Save model once at the end of each epoch
+    if not ddp or dist.get_rank() == 0:
+        model.eval()
+        ckp = f'{args.save_dir}/word2vec_embedding_dim{lm_config.dim}_vocab{lm_config.vocab_size}_epoch{epoch+1}.pth'
+        
+        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            embedding_state_dict = model.module.embeddings.state_dict()
+        else:
+            embedding_state_dict = model.embeddings.state_dict()
+
+        torch.save(embedding_state_dict, ckp)
+        Logger(f"Saved word2vec embedding for epoch {epoch+1} to {ckp}")
+        model.train()
+
+
+def init_model(lm_config_params: LMConfig):
+    # 加载tokenizer
+    tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
+    # Update vocab_size in lm_config if tokenizer has a different one
+    if tokenizer.vocab_size != lm_config_params.vocab_size:
+        Logger(f"Updating lm_config.vocab_size from {lm_config_params.vocab_size} to {tokenizer.vocab_size} based on tokenizer.")
+        lm_config_params.vocab_size = tokenizer.vocab_size
+
+    # 加载word2vec CBOW模型
+    model = CBOWModel(lm_config_params).to(args.device)
+    # 打印模型参数
+    Logger(f'CBOW Model total parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} Million')
+    return model, tokenizer
+
+
+def init_distributed_mode():
+    if not ddp: return #如果没有启用分布式数据并行(DDP)，直接返回，不执行任何操作。
+    global ddp_local_rank, DEVICE #声明这两个变量为全局变量，以便在函数外部也能访问它们。
+
+    dist.init_process_group(backend="nccl") #初始化分布式进程组，使用NCCL后端（NVIDIA Collective Communications Library），这是NVIDIA GPU之间通信的优化库。
+    ddp_rank = int(os.environ["RANK"]) #从环境变量获取当前进程的全局编号。
+    ddp_local_rank = int(os.environ["LOCAL_RANK"]) #从环境变量获取当前进程的本地编号。
+    ddp_world_size = int(os.environ["WORLD_SIZE"]) #从环境变量获取当前进程组中的进程总数。
+    DEVICE = f"cuda:{ddp_local_rank}" #根据本地编号选择GPU设备。
+    torch.cuda.set_device(DEVICE) #设置当前进程的GPU设备。
+
+
+# torchrun --nproc_per_node 2 train_embedding.py
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="MiniMind Word2Vec Embedding Training")
+    parser.add_argument("--out_dir", type=str, default="out_word2vec")
+    parser.add_argument("--epochs", type=int, default=3)
+    parser.add_argument("--batch_size", type=int, default=256)
+    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", default=False, action="store_true")
+    parser.add_argument("--wandb_project", type=str, default="MiniMind-Word2Vec-Training")
+    parser.add_argument("--num_workers", type=int, default=32)
+    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("--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('--dim', default=768, type=int)
+    parser.add_argument('--max_seq_len', default=512, type=int)
+    parser.add_argument("--data_path", type=str, default="./dataset/pretrain_hq.jsonl")
+    parser.add_argument('--vocab_size', default=6400, type=int)
+    parser.add_argument('--window_size', default=5, type=int)
+
+
+    args = parser.parse_args()
+
+    # Create LMConfig with relevant parameters for embedding
+    lm_config = LMConfig(
+        dim=args.dim, 
+        vocab_size=args.vocab_size, # Will be updated by tokenizer
+        max_seq_len=args.max_seq_len,
+        n_layers=1, # Minimal
+        n_heads=1, # Minimal
+        n_kv_heads=1 #Minimal
+    ) 
+    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 * lm_config.max_seq_len
+    print(f"tokens_per_iter: {tokens_per_iter}")
+    device_type = "cuda" if "cuda" in args.device else "cpu"
+
+    # Determine the torch dtype
+    pt_dtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[args.dtype]
+
+    args.wandb_run_name = f"MiniMind-Word2Vec-Dim-{args.dim}-Vocab-{lm_config.vocab_size}-Window-{args.window_size}"
+
+    ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=pt_dtype)
+
+    ddp = int(os.environ.get("RANK", -1)) != -1  # is this a ddp run?
+    ddp_local_rank, DEVICE = 0, "cuda:0" # Default values, will be overwritten in DDP
+
+    base_seed = 1337
+    torch.manual_seed(base_seed)
+    torch.cuda.manual_seed(base_seed)
+
+    if ddp:
+        init_distributed_mode() # This sets DEVICE and ddp_local_rank
+        args.device = torch.device(DEVICE) # Ensure args.device is updated
+        rank = dist.get_rank()
+        torch.manual_seed(base_seed + rank)
+        # 同时设置 CUDA 的随机种子
+        torch.cuda.manual_seed_all(base_seed + rank) # Use seed_all for DDP
+
+    if args.use_wandb and (not ddp or dist.get_rank() == 0): # Check rank for DDP wandb init
+        import wandb
+
+        wandb.init(project=args.wandb_project, name=args.wandb_run_name, config=args)
+    else:
+        wandb = None
+
+    model, tokenizer = init_model(lm_config) # Pass the lm_config instance
+    
+    # Update lm_config vocab_size again after tokenizer to ensure consistency for save path name
+    if lm_config.vocab_size != tokenizer.vocab_size:
+        lm_config.vocab_size = tokenizer.vocab_size
+        args.wandb_run_name = f"MiniMind-Word2Vec-Dim-{args.dim}-Vocab-{lm_config.vocab_size}-Window-{args.window_size}"
+        if wandb is not None and (not ddp or dist.get_rank() == 0):
+            wandb.config.update({'vocab_size': lm_config.vocab_size, 'wandb_run_name': args.wandb_run_name}, allow_val_change=True)
+
+    # 添加collate函数处理不同长度的序列
+    def collate_cbow_batch(batch):
+        # 提取context和target
+        contexts, targets = zip(*batch)
+        
+        # 获取当前批次中最长的context长度
+        max_len = max([ctx.size(0) for ctx in contexts])
+        
+        # 创建填充后的tensor
+        padded_contexts = torch.zeros(len(contexts), max_len, dtype=torch.long)
+        
+        # 填充每个context
+        for i, ctx in enumerate(contexts):
+            ctx_len = ctx.size(0)
+            padded_contexts[i, :ctx_len] = ctx
+        
+        # 将targets stack成一个tensor
+        stacked_targets = torch.stack(targets)
+        
+        return padded_contexts, stacked_targets
+
+    # Create Word2Vec CBOW dataset
+    train_ds = CBOWDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len, window_size=args.window_size)
+    train_sampler = DistributedSampler(train_ds, shuffle=True, seed=base_seed) if ddp else None
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=args.batch_size,
+        pin_memory=True,
+        drop_last=True,
+        shuffle=(train_sampler is None),
+        num_workers=args.num_workers,
+        sampler=train_sampler,
+        collate_fn=collate_cbow_batch
+    )
+
+    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
+    optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
+
+    if ddp:
+        model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
+        
+    iter_per_epoch = len(train_loader)
+    Logger(f"Starting Word2Vec CBOW training for {args.epochs} epochs with {iter_per_epoch} iterations per epoch.")
+    for epoch in range(args.epochs):
+        if ddp:
+            train_sampler.set_epoch(epoch)
+        train_epoch(epoch, wandb)
+
+    if wandb is not None and (not ddp or dist.get_rank() == 0):
+        wandb.finish()
+    
+    Logger("Word2Vec embedding training finished.") 

train_pretrain.py

@@ -14,6 +14,7 @@ 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 typing import Optional
 
 from transformers import AutoTokenizer
 
@@ -37,80 +38,124 @@ def get_lr(current_step, total_steps, lr):
 
 
 def train_epoch(epoch, wandb):
-    loss_fct = nn.CrossEntropyLoss(reduction='none') #交叉熵损失（Cross-Entropy Loss）；当 reduction='none' 时，nn.CrossEntropyLoss 不会对损失进行任何汇总操作，而是返回每个样本的单独损失值。
+    loss_fct = nn.CrossEntropyLoss(reduction='none')
     start_time = time.time()
     for step, (X, Y, loss_mask) in enumerate(train_loader):
-        # 将数据加载到设备上
+        try:
-        X = X.to(args.device)
+            # 将数据加载到设备上
-        Y = Y.to(args.device)
+            X = X.to(args.device)
-        loss_mask = loss_mask.to(args.device)
+            Y = Y.to(args.device)
+            loss_mask = loss_mask.to(args.device)
 
-        # 更新学习率
+            # 更新学习率
-        lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)
+            lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)
-        for param_group in optimizer.param_groups:
+            for param_group in optimizer.param_groups:
-            param_group['lr'] = lr
+                param_group['lr'] = lr
 
-        with ctx:
+            with ctx:
-            res = model(X) #获取输出
+                res = model(X)
-            loss = loss_fct(
+                loss = loss_fct(
-                res.logits.view(-1, res.logits.size(-1)),
+                    res.logits.view(-1, res.logits.size(-1)),
-                Y.view(-1)
+                    Y.view(-1)
-            ).view(Y.size())#计算损失
+                ).view(Y.size())
-            loss = (loss * loss_mask).sum() / loss_mask.sum() #计算总的loss
+                loss = (loss * loss_mask).sum() / loss_mask.sum()
-            # 为了批次堆叠进行的处理，真正的batch size为num gpu*batch size per gpu*accumulation steps
+                loss += res.aux_loss 
-            loss += res.aux_loss 
+                loss = loss / args.accumulation_steps
-            loss = loss / args.accumulation_steps
 
-        scaler.scale(loss).backward() #用于处理混合精度训练。它的作用是自动缩放损失值，以防止在使用低精度（如 FP16）计算时出现数值不稳定的问题。
+            # Print data types for debugging
+            if step == 0 and (not ddp or dist.get_rank() == 0): # Print only for the first step of the first epoch on the main process
+                Logger("---- Data Type Check ----")
+                Logger(f"X.dtype: {X.dtype}")
+                if hasattr(model, 'module'): # DDP case
+                    Logger(f"Model parameter dtype: {next(model.module.parameters()).dtype}")
+                else: # Non-DDP case
+                    Logger(f"Model parameter dtype: {next(model.parameters()).dtype}")
+                Logger(f"res.logits.dtype: {res.logits.dtype}")
+                Logger(f"loss.dtype: {loss.dtype}")
+                Logger("-------------------------")
 
-        # 如果达到堆叠数目就进行处理
+            scaler.scale(loss).backward()
-        if (step + 1) % args.accumulation_steps == 0:
-            scaler.unscale_(optimizer) #PyTorch 自动混合精度(AMP)训练的一部分。它"反缩放"之前为防止在混合精度训练中出现下溢而缩放的梯度。
-            torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) #应用梯度裁剪以防止梯度爆炸。它会缩放梯度，使其范数不超过args.grad_clip。
 
-            scaler.step(optimizer) #使用优化器更新模型权重，但由缩放器控制以适应混合精度训练。
+            if (step + 1) % args.accumulation_steps == 0:
-            scaler.update() #根据本次迭代是否有梯度溢出来更新下一次迭代的缩放因子。
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
 
-            optimizer.zero_grad(set_to_none=True) #为下一次迭代清零所有梯度。set_to_none=True参数通过将梯度设置为None而不是零来提高内存效率。
+                scaler.step(optimizer)
+                scaler.update()
 
-        # 打印日志
+                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):
+            # 打印日志
-                wandb.log({"loss": loss.item() * args.accumulation_steps,
+            if step % args.log_interval == 0:
-                           "lr": optimizer.param_groups[-1]['lr'],
+                spend_time = time.time() - start_time
-                           "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
+                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))
 
-        # 保存模型
+                if (wandb is not None) and (not ddp or dist.get_rank() == 0):
-        if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
+                    wandb.log({"loss": loss.item() * args.accumulation_steps,
-            model.eval()
+                               "lr": optimizer.param_groups[-1]['lr'],
-            moe_path = '_moe' if lm_config.use_moe else ''
+                               "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
-            ckp = f'{args.save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
 
+            # 保存模型
+            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() #获取模型参数
+                else:
+                    state_dict = model.state_dict() #获取模型参数
+
+                torch.save(state_dict, ckp) #只保存参数
+                model.train()
+
+        except Exception as e:
+            print(f"Error occurred: {str(e)}")
+            save_path = f'{args.save_dir}/pretrain_{lm_config.dim}{moe_path}_nanERROR.pth'
+            if  os.path.exists(save_path):
+                os.remove(save_path)
+            
             if isinstance(model, torch.nn.parallel.DistributedDataParallel):
-                state_dict = model.module.state_dict() #获取模型参数
+                state_dict = model.module.state_dict() 
             else:
-                state_dict = model.state_dict() #获取模型参数
+                state_dict = model.state_dict()
+            torch.save(state_dict, save_path)
 
-            torch.save(state_dict, ckp) #只保存参数
+            for name, param in model.named_parameters():
-            model.train()
+                if param.grad is not None and torch.isnan(param.grad).any():
+                    print(f"NaN gradient in parameter: {name}")
+            
+            for name, param in model.named_parameters():
+                if param.grad is not None and torch.isnan(param.grad).any():
+                    print(f"Parameter {name} values: {param.data}")
+                    print(f"Parameter {name} gradients: {param.grad}")
+            
+            raise ValueError("NaN gradient detected")
 
 
-def init_model(lm_config):
+def init_model(lm_config, pretrained_embedding_path: Optional[str] = None):
     # 加载tokenizer
     tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
     # 加载模型
     model = MiniMindLM(lm_config).to(args.device)
+
+    # Load pretrained token embeddings if path is provided
+    if pretrained_embedding_path and os.path.exists(pretrained_embedding_path):
+        Logger(f"Loading pretrained token embeddings from {pretrained_embedding_path}")
+        embedding_weights = torch.load(pretrained_embedding_path, map_location=args.device)
+        model.tok_embeddings.load_state_dict(embedding_weights)
+        Logger("Successfully loaded pretrained token embeddings.")
+    elif pretrained_embedding_path:
+        Logger(f"Warning: Pretrained embedding path {pretrained_embedding_path} provided but file does not exist. Initializing embeddings from scratch.")
+
     # 打印模型参数
     Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
     return model, tokenizer
@@ -153,6 +198,7 @@ if __name__ == "__main__":
     parser.add_argument('--max_seq_len', default=512, type=int) #最大序列长度，用于控制输入序列的最大长度。
     parser.add_argument('--use_moe', default=False, type=bool) #是否使用MOE，用于控制是否使用MOE。
     parser.add_argument("--data_path", type=str, default="./dataset/pretrain_hq.jsonl") #数据路径，用于控制数据集的路径。
+    parser.add_argument("--pretrained_embedding_path", type=str, default=None, help="Path to pretrained token embedding weights (.pth file)")
     args = parser.parse_args()
 
     lm_config = LMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len, use_moe=args.use_moe) #创建LMConfig对象，用于控制模型配置。
@@ -163,9 +209,12 @@ if __name__ == "__main__":
     print(f"tokens_per_iter: {tokens_per_iter}")
     device_type = "cuda" if "cuda" in args.device else "cpu" #确定设备类型。
 
+    # Determine the torch dtype
+    pt_dtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[args.dtype]
+
     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()
+    ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=pt_dtype)
 
     ddp = int(os.environ.get("RANK", -1)) != -1  # is this a ddp run?
     ddp_local_rank, DEVICE = 0, "cuda:0"
@@ -189,7 +238,7 @@ if __name__ == "__main__":
     else:
         wandb = None
 
-    model, tokenizer = init_model(lm_config)
+    model, tokenizer = init_model(lm_config, args.pretrained_embedding_path)
     train_ds = PretrainDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len)
     train_sampler = DistributedSampler(train_ds) if ddp else None
     train_loader = DataLoader(
@@ -202,13 +251,14 @@ if __name__ == "__main__":
         sampler=train_sampler
     )
 
-    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
+    scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16']))
     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])
-
+        
+    torch.autograd.set_detect_anomaly(True)
     iter_per_epoch = len(train_loader)
     for epoch in range(args.epochs):
         train_epoch(epoch, wandb)