DynamicKV-LLM Pretrain v1.2.1

.gitignore

@@ -7,3 +7,5 @@ models/sentence_transformers/
 models/sentence_transformers_cache/
 **/*.pyc
 qwen2-1.7B/
+images/
+cache/

.vscode/launch.json

@@ -7,7 +7,7 @@
             "request": "launch",
             "program": "${workspaceFolder}/train_pretrain_accelerate.py",
             "console": "integratedTerminal",
-            "python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
+            "python": "/opt/conda/envs/mini/bin/python",
             "cwd": "${workspaceFolder}",
             "env": {
                 "PYTHONPATH": "${workspaceFolder}",
@@ -23,7 +23,7 @@
             "request": "launch",
             "program": "${workspaceFolder}/train_pretrain_accelerate.py",
             "console": "integratedTerminal",
-            "python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
+            "python": "/opt/conda/envs/mini/bin/python",
             "args": [
                 "--hidden_size", "512",
                 "--max_seq_len", "512", 
@@ -46,7 +46,7 @@
             "request": "launch",
             "program": "${workspaceFolder}/train_pretrain_accelerate.py",
             "console": "integratedTerminal",
-            "python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
+            "python": "/opt/conda/envs/mini/bin/python",
             "args": [
                 "--hidden_size", "512",
                 "--max_seq_len", "512", 
@@ -73,7 +73,7 @@
             "request": "launch",
             "program": "${workspaceFolder}/train_pretrain_accelerate.py",
             "console": "integratedTerminal",
-            "python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
+            "python": "/opt/conda/envs/mini/bin/python",
             "args": [
                 "--hidden_size", "512",
                 "--max_seq_len", "256", 

model/LMConfig.py

@@ -19,6 +19,7 @@ class LMConfig(PretrainedConfig):
             rope_theta: int = 1e6,
             dropout: float = 0.0,
             flash_attn: bool = True,
+            embeddings_epoch: int = 2,
             ####################################################
             # DB related configurations
             ####################################################
@@ -54,6 +55,7 @@ class LMConfig(PretrainedConfig):
         self.rope_theta = rope_theta
         self.dropout = dropout
         self.flash_attn = flash_attn
+        self.embeddings_epoch = embeddings_epoch
         ####################################################
         # DB related configurations
         ####################################################

model/model.py

@@ -81,6 +81,8 @@ class KnowledgeDataset(nn.Module):
         # 计算step数目，用于动态调整权重
         self.step_counter = 0
 
+        self.freeze_embedding = False
+
         
 
     def intelligent_selection(self, query, all_scores, all_indices):
@@ -169,6 +171,8 @@ class KnowledgeDataset(nn.Module):
         return all_best_tokens, all_best_tokens_embeddings
     
     def _update_keys_with_embeddings(self, pre_update_indices, pre_update_embeddings):
+        if self.freeze_embedding:
+            return
         # 使用pre_update_embeddings更新self.keys
         with torch.no_grad():
             pre_update_embeddings = pre_update_embeddings.mean(dim=1)  # [337, 512]
@@ -199,8 +203,26 @@ class KnowledgeDataset(nn.Module):
         if self.is_train:
             # 获取未更新过的keys的索引
             not_updated_indices = torch.where(self.has_update_keys == 0)[0]
+                
             # 如果有未更新的keys，随机选择num_update_keys个进行更新
             if len(not_updated_indices) > 0:
+                num_update_keys = int(self.knowledge_num * 0.01)
+                perm = torch.randperm(len(not_updated_indices))[:num_update_keys]
+                perm_num = perm.shape[0]
+                pre_update_indices = not_updated_indices[perm]
+                pre_update_tokens = self.knowledge_dataset[pre_update_indices]
+                pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1))
+                pre_update_embeddings = pre_update_embeddings.view(perm_num, self.knowledge_length, -1)
+                self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
+                # 更新被修改过的key
+                with torch.no_grad():
+                    self.has_update_keys[pre_update_indices] = 1
+            else:
+                print("all keys are updated")
+                # 重置所有keys的更新状态
+                self.has_update_keys.zero_()
+                # 重新获取所有可更新的索引
+                not_updated_indices = torch.arange(len(self.has_update_keys), device=self.has_update_keys.device)
                 num_update_keys = int(self.knowledge_num * 0.01)
                 perm = torch.randperm(len(not_updated_indices))[:num_update_keys]
                 pre_update_indices = not_updated_indices[perm]
@@ -208,6 +230,12 @@ class KnowledgeDataset(nn.Module):
                 pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1))
                 pre_update_embeddings = pre_update_embeddings.view(num_update_keys, self.knowledge_length, -1)
                 self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
+                # 更新被修改过的key
+                with torch.no_grad():
+                    self.has_update_keys[pre_update_indices] = 1
+            
+
+
         
         return best_tokens, best_tokens_embeddings
 
@@ -484,12 +512,20 @@ class MiniMindLM(PreTrainedModel):
                              precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
                              persistent=False)
         self.OUT = CausalLMOutputWithPast()
+        self.freeze_embedding = False
 
     def forward(self,
                 input_ids: Optional[torch.Tensor] = None,
                 logits_to_keep: Union[int, torch.Tensor] = 0,
+                step: int = 0,
                 **args):
         start_pos = args.get('start_pos', 0)
+        if self.freeze_embedding and step == 0:
+            self.tok_embeddings.weight.requires_grad = False
+            # 同时冻结KnowledgeDataset的嵌入更新
+            self.knowledge_dataset.freeze_embedding = True
+            print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
+            print("knowledge_dataset.freeze_embedding: ", self.knowledge_dataset.freeze_embedding)
         h = self.dropout(self.tok_embeddings(input_ids))
         pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
         for l, layer in enumerate(self.layers):

run_file/DynamicKV-LLM_Mini_Minimind.sh

@@ -1,8 +1,8 @@
 #!/bin/bash
 
 # 激活conda环境
-# source $(conda info --base)/etc/profile.d/conda.sh
+source $(conda info --base)/etc/profile.d/conda.sh
-# conda activate ycz_accelerate
+conda activate mini
 
 # 设置环境变量以帮助调试
 export NCCL_DEBUG=INFO
@@ -26,24 +26,9 @@ export PYTHONFAULTHANDLER=1
 #     --profile_interval 10
 
 # 方法2: 使用命令行参数直接配置accelerate
-CUDA_VISIBLE_DEVICES=0 accelerate launch \
+CUDA_VISIBLE_DEVICES=0 /opt/conda/envs/mini/bin/python -m accelerate.commands.launch \
     --num_processes=1 \
     --mixed_precision=bf16 \
     --main_process_port=29500 \
     train_pretrain_accelerate.py \
-    --epochs 3 \
+
-    --batch_size 24 \
-    --learning_rate 2e-4 \
-    --dtype bfloat16 \
-    --accumulation_steps 32 \
-    --grad_clip 1.0 \
-    --log_interval 100 \
-    --save_interval 10000 \
-    --dim 512 \
-    --n_layers 12 \
-    --max_seq_len 512 \
-    --use_flash_attn \
-    --profile \
-    --profile_interval 10\
-    --knowledge_num 4096 \
-    --knowledge_length 8

train_pretrain_accelerate.py

@@ -88,54 +88,52 @@ def init_model(lm_config, pretrained_embedding_path=None, database_init_path=Non
     
     if database_init_path:
         import json
-        import numpy as np
-        from sentence_transformers import SentenceTransformer
         import os
         
-        # 聚类参数（需要提前定义用于缓存检查）
+        # 数据库参数
         knowledge_num = args.knowledge_num
         knowledge_length = args.knowledge_length
         
-        # 检查是否使用缓存（提前检查，避免不必要的数据处理）
+        # 检查是否使用缓存
         cache_dir = os.path.dirname(args.cluster_cache_path)
         if cache_dir:
             os.makedirs(cache_dir, exist_ok=True)
         
-        clustered_tensor = None
+        processed_tensor = None
         
-        # 尝试加载缓存的聚类结果
+        # 尝试加载缓存的处理结果
         if not args.recompute_clusters and os.path.exists(args.cluster_cache_path):
             try:
-                Logger(f"Loading cached cluster results from {args.cluster_cache_path}")
+                Logger(f"Loading cached processed results from {args.cluster_cache_path}")
-                clustered_tensor = torch.load(args.cluster_cache_path)
+                processed_tensor = torch.load(args.cluster_cache_path)
                 
                 # 验证缓存文件的形状是否可用
-                cached_knowledge_num, cached_knowledge_length = clustered_tensor.shape
+                cached_knowledge_num, cached_knowledge_length = processed_tensor.shape
                 
                 if cached_knowledge_length == knowledge_length:
                     if cached_knowledge_num >= knowledge_num:
                         # 缓存足够大，可以截取使用
-                        clustered_tensor = clustered_tensor[:knowledge_num, :]
+                        processed_tensor = processed_tensor[:knowledge_num, :]
-                        Logger(f"Successfully loaded cached clusters with shape {clustered_tensor.shape}")
+                        Logger(f"Successfully loaded cached data with shape {processed_tensor.shape}")
                         Logger(f"Truncated from cached shape ({cached_knowledge_num}, {cached_knowledge_length}) to required shape ({knowledge_num}, {knowledge_length})")
-                        Logger("Skipping database initialization and clustering - using cached results")
+                        Logger("Skipping database initialization - using cached results")
                     else:
                         # 缓存太小，需要重新计算
                         Logger(f"Cached knowledge_num ({cached_knowledge_num}) < required knowledge_num ({knowledge_num}), recomputing...")
-                        clustered_tensor = None
+                        processed_tensor = None
                 else:
                     # knowledge_length不匹配，需要重新计算
                     Logger(f"Cached knowledge_length ({cached_knowledge_length}) != required knowledge_length ({knowledge_length}), recomputing...")
-                    clustered_tensor = None
+                    processed_tensor = None
             except Exception as e:
-                Logger(f"Failed to load cached clusters: {e}, recomputing...")
+                Logger(f"Failed to load cached data: {e}, recomputing...")
-                clustered_tensor = None
+                processed_tensor = None
         
-        # 只有在没有有效缓存时才进行数据库初始化和聚类计算
+        # 只有在没有有效缓存时才进行数据库初始化和处理
-        if clustered_tensor is None:
+        if processed_tensor is None:
             Logger(f"Loading database initialization data from {database_init_path}")
             
-            # 1. 加载JSON文件并转换为字典
+            # 1. 加载JSON文件
             with open(database_init_path, 'r', encoding='utf-8') as f:
                 database_data = json.load(f)
             
@@ -147,300 +145,73 @@ def init_model(lm_config, pretrained_embedding_path=None, database_init_path=Non
             sorted_sentences = sorted(sentences_data, key=lambda x: x.get('importance_score', 0.0), reverse=True)
             Logger(f"Sorted sentences by importance score (highest: {sorted_sentences[0].get('importance_score', 0.0)}, lowest: {sorted_sentences[-1].get('importance_score', 0.0)})")
             
-            # 3. 下载并初始化本地嵌入模型
+            # 3. 处理每条数据，不进行聚类
-            embedding_model_name = "sentence-transformers/all-mpnet-base-v2"  # 轻量级但效果好的模型
+            Logger("Processing individual sentences...")
-            embedding_model_dir = "./models/sentence_transformers/models--sentence-transformers--all-mpnet-base-v2"
+            processed_rows = []
-            embedding_cache_dir = "./models/sentence_transformers/cache"
-            os.makedirs(embedding_cache_dir, exist_ok=True)
             
-            Logger(f"Loading embedding model: {embedding_model_name}")
+            # 获取空token的id（用于填充）
-            try:
-                embedding_model = SentenceTransformer(embedding_model_dir, cache_folder=embedding_cache_dir)
-                Logger("Embedding model loaded successfully")
-            except Exception as e:
-                Logger(f"Failed to load embedding model: {e}")
-                Logger("Falling back to random embeddings")
-                embedding_model = None
-            
-            # 4. 对每个corrected_sentence进行嵌入和token长度计算
-            Logger("Processing sentences for embeddings and token lengths...")
-            
-            # 提取所有句子
-            sentences = [sentence_data.get('corrected_sentence', '') for sentence_data in sorted_sentences]
-            
-            # 批量计算token长度
-            Logger("Computing token lengths...")
-            token_lengths = []
-            for sentence in sentences:
-                tokens = tokenizer.encode(sentence, add_special_tokens=False)
-                token_lengths.append(len(tokens))
-            
-            # 批量计算嵌入 - 大幅提升速度
-            Logger("Computing embeddings in batches...")
-            embeddings_list = []
-            batch_size = 256  # 可以根据GPU内存调整
-            
-            if embedding_model is not None:
-                try:
-                    for i in range(0, len(sentences), batch_size):
-                        batch_sentences = sentences[i:i+batch_size]
-                        batch_embeddings = embedding_model.encode(
-                            batch_sentences, 
-                            convert_to_tensor=False,
-                            show_progress_bar=True if i == 0 else False,
-                            batch_size=batch_size
-                        )
-                        embeddings_list.extend(batch_embeddings)
-                        
-                        if (i + batch_size) % (batch_size * 10) == 0:
-                            Logger(f"Processed {min(i + batch_size, len(sentences))}/{len(sentences)} sentences")
-                            
-                    Logger("Batch embedding computation completed")
-                except Exception as e:
-                    Logger(f"Error in batch encoding: {e}")
-                    Logger("Falling back to random embeddings")
-                    embeddings_list = [np.random.randn(384).astype(np.float32) for _ in sentences]
-            else:
-                # 使用随机嵌入
-                embeddings_list = [np.random.randn(384).astype(np.float32) for _ in sentences]
-            
-            # 创建处理后的句子列表
-            processed_sentences = []
-            for i, (sentence_data, embedding, token_length) in enumerate(zip(sorted_sentences, embeddings_list, token_lengths)):
-                processed_sentences.append({
-                    'sentence': sentence_data.get('corrected_sentence', ''),
-                    'importance_score': sentence_data.get('importance_score', 0.0),
-                    'token_length': token_length,
-                    'embedding': embedding,  # Convert numpy array to list
-                    'original_index': i
-                })
-            
-            # 转换为numpy数组以便后续处理
-            embeddings_array = np.array(embeddings_list)
-            token_lengths_array = np.array(token_lengths)
-            
-            Logger(f"Embedding processing completed:")
-            Logger(f"  - Total sentences: {len(processed_sentences)}")
-            Logger(f"  - Embedding shape: {embeddings_array.shape}")
-            Logger(f"  - Average token length: {np.mean(token_lengths_array):.2f}")
-            Logger(f"  - Token length range: {np.min(token_lengths_array)} - {np.max(token_lengths_array)}")
-            
-            # 聚类参数定义
-            min_tokens = int(0.85 * knowledge_length)
-            max_tokens = int(0.95 * knowledge_length)
-            
-            # 优化1: 预计算所有嵌入的相似度矩阵（如果数据量不太大）
-            if len(processed_sentences) <= 10000:  # 只有在数据量不太大时才预计算
-                Logger("Pre-computing similarity matrix for faster clustering...")
-                embeddings_matrix = np.array([s['embedding'] for s in processed_sentences])
-                similarity_matrix = cosine_similarity(embeddings_matrix)
-                Logger(f"Similarity matrix computed: {similarity_matrix.shape}")
-            else:
-                similarity_matrix = None
-                embeddings_matrix = np.array([s['embedding'] for s in processed_sentences])
-            
-            clustered_rows = []
-            remaining_indices = list(range(len(processed_sentences)))  # 使用索引而不是对象
-            
-            Logger(f"Target: {knowledge_num} clusters, each with {min_tokens}-{max_tokens} tokens")
-            
-            # 选择聚类算法
-            if args.fast_clustering and len(processed_sentences) > 5000:
-                Logger("Using ultra-fast approximate clustering algorithm...")
-                
-                # 超快速聚类：随机采样 + 批量处理
-                import random
-                random.seed(42)  # 确保可重现性
-                
-                # 按重要性分层采样
-                high_importance = [i for i, s in enumerate(processed_sentences) if s['importance_score'] > 0.7]
-                medium_importance = [i for i, s in enumerate(processed_sentences) if 0.3 <= s['importance_score'] <= 0.7]
-                low_importance = [i for i, s in enumerate(processed_sentences) if s['importance_score'] < 0.3]
-                
-                Logger(f"Importance distribution: High={len(high_importance)}, Medium={len(medium_importance)}, Low={len(low_importance)}")
-                
-                for cluster_idx in tqdm(range(knowledge_num)):
-                    # 分层选择种子：优先选择高重要性句子
-                    if high_importance:
-                        seed_pool = high_importance
-                    elif medium_importance:
-                        seed_pool = medium_importance
-                    else:
-                        seed_pool = low_importance if low_importance else list(range(len(processed_sentences)))
-                    
-                    if not seed_pool:
-                        break
-                    
-                    # 随机选择种子（在同一重要性层级内）
-                    seed_global_idx = random.choice(seed_pool)
-                    seed_sentence = processed_sentences[seed_global_idx]
-                    
-                    # 从所有池中移除种子
-                    for pool in [high_importance, medium_importance, low_importance]:
-                        if seed_global_idx in pool:
-                            pool.remove(seed_global_idx)
-                    
-                    current_cluster_indices = [seed_global_idx]
-                    current_tokens = seed_sentence['token_length']
-                    
-                    if current_tokens < max_tokens:
-                        # 快速选择：只从附近的句子中随机选择
-                        all_remaining = high_importance + medium_importance + low_importance
-                        if all_remaining:
-                            # 随机采样候选句子（而不是计算所有相似度）
-                            sample_size = min(2000, len(all_remaining))
-                            candidates = random.sample(all_remaining, sample_size)
-                            
-                            # 简单按token长度和重要性选择
-                            for candidate_idx in candidates:
-                                candidate = processed_sentences[candidate_idx]
-                                candidate_tokens = candidate['token_length']
-                                
-                                if current_tokens + candidate_tokens + 1 <= max_tokens:
-                                    current_cluster_indices.append(candidate_idx)
-                                    current_tokens += candidate_tokens + 1
-                                    
-                                    # 从池中移除
-                                    for pool in [high_importance, medium_importance, low_importance]:
-                                        if candidate_idx in pool:
-                                            pool.remove(candidate_idx)
-                                            break
-                                    
-                                if current_tokens >= min_tokens:
-                                    break
-                    
-                    # 生成聚类文本
-                    cluster_sentences = [processed_sentences[idx]['sentence'] for idx in current_cluster_indices]
-                    cluster_text = '\n '.join(cluster_sentences)
-                    
-                    # 转换为tokens
-                    cluster_tokens = tokenizer.encode(cluster_text, add_special_tokens=False)
-                    if len(cluster_tokens) > knowledge_length:
-                        cluster_tokens = cluster_tokens[:knowledge_length]
-                    else:
             pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
-                        cluster_tokens.extend([pad_token_id] * (knowledge_length - len(cluster_tokens)))
             
-                    clustered_rows.append(cluster_tokens)
+            # 处理所需数量的句子
+            num_to_process = min(knowledge_num, len(sorted_sentences))
             
-                    if (cluster_idx + 1) % 1000 == 0:
+            for i in range(num_to_process):
-                        total_remaining = len(high_importance) + len(medium_importance) + len(low_importance)
+                sentence_data = sorted_sentences[i]
-                        Logger(f"Fast clustering: {cluster_idx + 1}/{knowledge_num} clusters, {total_remaining} sentences remaining")
+                sentence = sentence_data.get('corrected_sentence', '')
                 
-            else:
+                # 将句子转换为tokens
-                # 原始优化算法（适用于中等规模数据集）
+                sentence_tokens = tokenizer.encode(sentence, add_special_tokens=False)
-                # 优化2: 批量处理和更高效的数据结构
-                for cluster_idx in tqdm(range(knowledge_num)):
-                    if not remaining_indices:
-                        Logger(f"No more sentences available. Created {cluster_idx} clusters.")
-                        break
-                    
-                    # 2.1 选择importance_score最高的句子作为种子
-                    remaining_sentences_subset = [processed_sentences[i] for i in remaining_indices]
-                    seed_idx_in_subset = max(range(len(remaining_sentences_subset)), 
-                                           key=lambda i: remaining_sentences_subset[i]['importance_score'])
-                    seed_global_idx = remaining_indices[seed_idx_in_subset]
-                    seed_sentence = processed_sentences[seed_global_idx]
-                    
-                    # 从剩余索引中移除种子
-                    remaining_indices.remove(seed_global_idx)
-                    
-                    # 当前聚类
-                    current_cluster_indices = [seed_global_idx]
-                    current_tokens = seed_sentence['token_length']
-                    
-                    if current_tokens >= max_tokens:
-                        # 如果种子句子已经超过最大token数，直接作为一个聚类
-                        cluster_text = seed_sentence['sentence']
-                    else:
-                        # 2.2 优化的相似度计算和选择
-                        if remaining_indices:
-                            if similarity_matrix is not None:
-                                # 使用预计算的相似度矩阵
-                                similarities = similarity_matrix[seed_global_idx][remaining_indices]
-                            else:
-                                # 动态计算相似度（批量）
-                                seed_embedding = embeddings_matrix[seed_global_idx:seed_global_idx+1]
-                                remaining_embeddings = embeddings_matrix[remaining_indices]
-                                similarities = cosine_similarity(seed_embedding, remaining_embeddings)[0]
-                            
-                            # 创建(相似度, 原始索引, 在remaining_indices中的位置)的元组列表
-                            similarity_tuples = [(similarities[i], remaining_indices[i], i) 
-                                               for i in range(len(remaining_indices))]
-                            
-                            # 按相似度排序（降序）
-                            similarity_tuples.sort(key=lambda x: x[0], reverse=True)
-                            
-                            # 优化3: 贪心选择，但限制搜索范围以提高速度
-                            max_candidates = min(len(similarity_tuples), 500)  # 只考虑前500个最相似的句子
-                            
-                            selected_indices_in_remaining = []
-                            for sim_score, global_idx, pos_in_remaining in similarity_tuples[:max_candidates]:
-                                candidate = processed_sentences[global_idx]
-                                candidate_tokens = candidate['token_length']
-                                
-                                if current_tokens + candidate_tokens + 1 <= max_tokens:  # +1 for newline
-                                    current_cluster_indices.append(global_idx)
-                                    selected_indices_in_remaining.append(pos_in_remaining)
-                                    current_tokens += candidate_tokens + 1
-                                    
-                                if current_tokens >= min_tokens:
-                                    break
-                            
-                            # 批量移除选中的句子（从后往前移除以避免索引问题）
-                            for pos in sorted(selected_indices_in_remaining, reverse=True):
-                                remaining_indices.pop(pos)
-                        
-                        # 拼接句子
-                        cluster_sentences = [processed_sentences[idx]['sentence'] for idx in current_cluster_indices]
-                        cluster_text = '\n'.join(cluster_sentences)
-                    
-                    # 将聚类文本转换为token
-                    cluster_tokens = tokenizer.encode(cluster_text, add_special_tokens=False)
                 
                 # 截断或填充到knowledge_length
-                    if len(cluster_tokens) > knowledge_length:
+                if len(sentence_tokens) > knowledge_length:
-                        cluster_tokens = cluster_tokens[:knowledge_length]
+                    # 如果超过长度，截断
+                    sentence_tokens = sentence_tokens[:knowledge_length]
+                    Logger(f"Sentence {i+1} truncated from {len(tokenizer.encode(sentence, add_special_tokens=False))} to {knowledge_length} tokens")
                 else:
-                        # 用pad_token_id填充
+                    # 如果不足长度，用空token填充
-                        pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+                    original_length = len(sentence_tokens)
-                        cluster_tokens.extend([pad_token_id] * (knowledge_length - len(cluster_tokens)))
+                    sentence_tokens.extend([pad_token_id] * (knowledge_length - len(sentence_tokens)))
+                    if original_length < knowledge_length:
+                        Logger(f"Sentence {i+1} padded from {original_length} to {knowledge_length} tokens")
                 
-                    clustered_rows.append(cluster_tokens)
+                processed_rows.append(sentence_tokens)
                 
-                    # 优化4: 减少日志频率
+                if (i + 1) % 1000 == 0:
-                    if (cluster_idx + 1) % 500 == 0:
+                    Logger(f"Processed {i + 1}/{num_to_process} sentences")
-                        Logger(f"Created {cluster_idx + 1}/{knowledge_num} clusters, {len(remaining_indices)} sentences remaining")
             
-            # 如果聚类数量不足，用随机token填充
+            # 如果句子数量不足，用空token填充剩余位置
-            while len(clustered_rows) < knowledge_num:
+            while len(processed_rows) < knowledge_num:
-                pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+                empty_tokens = [pad_token_id] * knowledge_length
-                random_tokens = [pad_token_id] * knowledge_length
+                processed_rows.append(empty_tokens)
-                clustered_rows.append(random_tokens)
+                if len(processed_rows) % 1000 == 0:
+                    Logger(f"Added empty entry {len(processed_rows)}/{knowledge_num}")
+            
+            Logger(f"Finished adding empty entries. Total: {len(processed_rows)}/{knowledge_num}")
             
             # 转换为tensor
-            clustered_tensor = torch.tensor(clustered_rows, dtype=torch.long)
+            processed_tensor = torch.tensor(processed_rows, dtype=torch.long)
             
-            Logger(f"Clustering completed:")
+            Logger(f"Data processing completed:")
-            Logger(f"  - Created {len(clustered_rows)} clusters")
+            Logger(f"  - Processed {num_to_process} sentences")
-            Logger(f"  - Cluster shape: {clustered_tensor.shape}")
+            Logger(f"  - Added {knowledge_num - num_to_process} empty entries")
+            Logger(f"  - Final shape: {processed_tensor.shape}")
             Logger(f"  - Expected shape: ({knowledge_num}, {knowledge_length})")
             
-            # 保存聚类结果到缓存文件
+            # 保存处理结果到缓存文件
             try:
-                torch.save(clustered_tensor, args.cluster_cache_path)
+                torch.save(processed_tensor, args.cluster_cache_path)
-                Logger(f"Cluster results saved to {args.cluster_cache_path}")
+                Logger(f"Processed results saved to {args.cluster_cache_path}")
             except Exception as e:
-                Logger(f"Failed to save cluster results: {e}")
+                Logger(f"Failed to save processed results: {e}")
         
-        # 3. 初始化模型的weight_down_embed
+        # 4. 初始化模型的knowledge_dataset
         if hasattr(model, 'knowledge_dataset') and hasattr(model.knowledge_dataset, 'knowledge_dataset'):
-            model.knowledge_dataset.knowledge_dataset.data.copy_(clustered_tensor)
+            model.knowledge_dataset.knowledge_dataset.data.copy_(processed_tensor)
-            Logger("Successfully initialized model.knowledge_dataset.knowledge_dataset with clustered data")
+            Logger("Successfully initialized model.knowledge_dataset.knowledge_dataset with processed data")
         else:
             Logger("Warning: Could not find model.knowledge_dataset.knowledge_dataset to initialize")
             # 存储为全局变量作为备选
-            globals()['clustered_database'] = clustered_tensor
+            globals()['processed_database'] = processed_tensor
 
     Logger(f"Database embeddings and sentences stored in model")
     
@@ -453,6 +224,7 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
     total_steps_in_epoch = len(train_loader)
     total_training_steps = args.epochs * total_steps_in_epoch
     moe_path = '_moe' if args.use_moe else ''
+    best_loss = float('10000')
 
     # 添加CUDA事件来分析性能 (只在主进程进行)
     if args.profile and accelerator.is_main_process:
@@ -516,7 +288,12 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
 
             # 前向传播
             with ctx:
-                res = model(X)
+                if step == 0 and args.embedding_epoch == epoch:
+                    # 需要设置原始模型的freeze_embedding属性，而不是包装后的模型
+                    unwrapped_model = accelerator.unwrap_model(model)
+                    unwrapped_model.freeze_embedding = True
+                    Logger(f"Set freeze_embedding=True for epoch {epoch}, step {step}", accelerator)
+                res = model(X, step=step)
                 loss = loss_fct(
                     res.logits.view(-1, res.logits.size(-1)),
                     Y.view(-1)
@@ -640,7 +417,9 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
                     wandb.log(log_dict)
 
             # 保存模型 (只在主进程进行)
-            if (step + 1) % args.save_interval == 0 and accelerator.is_main_process:
+            loss_total = loss.item() * args.accumulation_steps
+            if best_loss > loss_total and accelerator.is_main_process:
+                best_loss = loss_total
                 # 使用函数开始处定义的moe_path变量
                 ckp = f'{args.save_dir}/pretrain_{args.dim}{moe_path}.pth'
 
@@ -660,7 +439,8 @@ def main():
     parser = argparse.ArgumentParser(description="MiniMind Pretraining with Accelerate")
     parser.add_argument("--out_dir", type=str, default="out")
     parser.add_argument("--epochs", type=int, default=4)
-    parser.add_argument("--batch_size", type=int, default=48)
+    parser.add_argument("--embedding_epoch", type=int, default=2, help="embedding训练的epoch数")
+    parser.add_argument("--batch_size", type=int, default=64)
     parser.add_argument("--learning_rate", type=float, default=2e-4)
     parser.add_argument("--dtype", type=str, default="bfloat16")
     parser.add_argument("--use_wandb", default=True, action="store_true")
@@ -681,8 +461,8 @@ def main():
     parser.add_argument("--profile", action="store_true", default=True, help="启用性能分析")
     parser.add_argument("--profile_interval", type=int, default=10, help="性能分析打印间隔（步数）")
     parser.add_argument("--use_flash_attn", action="store_true", default=True, help="启用FlashAttention")
-    parser.add_argument("--knowledge_num", type=int, default=4096,help="知识库的数据数目")
+    parser.add_argument("--knowledge_num", type=int, default=8192,help="知识库的数据数目")
-    parser.add_argument("--knowledge_length", type=int, default=16,help="知识库的句子长度")
+    parser.add_argument("--knowledge_length", type=int, default=32,help="知识库的句子长度")
     parser.add_argument("--database_init_path", type=str, default="./dataset/database_init.json", help="数据库初始化路径")
     parser.add_argument("--fast_clustering", action="store_true", default=True, help="使用快速近似聚类算法（适用于大数据集）")
     parser.add_argument("--cluster_cache_path", type=str, default="./cache/cluster_tokens_single.pt", help="聚类结果缓存文件路径")
@@ -724,7 +504,8 @@ def main():
         disable_db=args.disable_db,
         flash_attn=args.use_flash_attn,
         knowledge_num=args.knowledge_num,
-        knowledge_length=args.knowledge_length
+        knowledge_length=args.knowledge_length,
+        embeddings_epoch=args.embedding_epoch
     )
 
     #########################################################