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

@@ -0,0 +1,102 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug Train Pretrain Accelerate",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/train_pretrain_accelerate.py",
+            "console": "integratedTerminal",
+            "python": "/opt/conda/envs/mini/bin/python",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}",
+                "CUDA_VISIBLE_DEVICES": "0"
+            },
+            "justMyCode": false,
+            "stopOnEntry": false,
+            "redirectOutput": true
+        },
+        {
+            "name": "Debug Train Pretrain Accelerate (Multi-GPU)",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/train_pretrain_accelerate.py",
+            "console": "integratedTerminal",
+            "python": "/opt/conda/envs/mini/bin/python",
+            "args": [
+                "--hidden_size", "512",
+                "--max_seq_len", "512", 
+                "--n_layers", "8",
+                "--batch_size", "8",
+                "--epochs", "1"
+            ],
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}",
+                "CUDA_VISIBLE_DEVICES": "0,1"
+            },
+            "justMyCode": false,
+            "stopOnEntry": false,
+            "redirectOutput": true
+        },
+        {
+            "name": "Debug Train Pretrain Accelerate (Small Test)",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/train_pretrain_accelerate.py",
+            "console": "integratedTerminal",
+            "python": "/opt/conda/envs/mini/bin/python",
+            "args": [
+                "--hidden_size", "512",
+                "--max_seq_len", "512", 
+                "--n_layers", "8",
+                "--batch_size", "2",
+                "--epochs", "1",
+                "--log_interval", "10",
+                "--save_interval", "100",
+                "--accumulation_steps", "4"
+            ],
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}",
+                "CUDA_VISIBLE_DEVICES": "0",
+                "WANDB_MODE": "offline"
+            },
+            "justMyCode": false,
+            "stopOnEntry": false,
+            "redirectOutput": true
+        },
+        {
+            "name": "Debug ExtractDB Comparison",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/train_pretrain_accelerate.py",
+            "console": "integratedTerminal",
+            "python": "/opt/conda/envs/mini/bin/python",
+            "args": [
+                "--hidden_size", "512",
+                "--max_seq_len", "256", 
+                "--n_layers", "4",
+                "--batch_size", "2",
+                "--epochs", "1",
+                "--log_interval", "10",
+                "--save_interval", "200",
+                "--accumulation_steps", "2",
+                "--comparison_mode",
+                "--knowledge_num", "256",
+                "--knowledge_length", "64",
+                "--comparison_mode"
+            ],
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}",
+                "CUDA_VISIBLE_DEVICES": "0",
+                "WANDB_MODE": "offline"
+            },
+            "justMyCode": false,
+            "stopOnEntry": false,
+            "redirectOutput": true
+        }
+    ]
+} 

.vscode/settings.json

@@ -0,0 +1,18 @@
+{
+    "python.pythonPath": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
+    "python.defaultInterpreterPath": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
+    "python.terminal.activateEnvironment": true,
+    "python.terminal.activateEnvInCurrentTerminal": true,
+    "python.linting.enabled": true,
+    "python.linting.pylintEnabled": false,
+    "python.linting.flake8Enabled": true,
+    "python.formatting.provider": "black",
+    "python.analysis.autoImportCompletions": true,
+    "python.analysis.typeCheckingMode": "off",
+    "files.exclude": {
+        "**/__pycache__": true,
+        "**/*.pyc": true,
+        "**/.git": false,
+        "**/wandb": false
+    }
+} 

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
             ####################################################
@@ -39,6 +40,7 @@ class LMConfig(PretrainedConfig):
             ####################################################
             knowledge_num: int = 64*64,
             knowledge_length: int = 8,
+            knowledge_dim: int = 128,
             **kwargs,
     ):
         self.dim = dim
@@ -53,6 +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
         ####################################################
@@ -72,4 +75,5 @@ class LMConfig(PretrainedConfig):
         ####################################################
         self.knowledge_num = knowledge_num
         self.knowledge_length = knowledge_length
+        self.knowledge_dim = knowledge_dim
         super().__init__(**kwargs)

model/model.py

@@ -11,14 +11,9 @@ import torch.nn.functional as F
 from torch import nn
 from transformers import PreTrainedModel
 from transformers.modeling_outputs import CausalLMOutputWithPast
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-def exists(val):
-    return val is not None
 
 
-# RMSNorm 类定义了一个用于归一化输入张量的模块。
+
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, eps: float = 1e-6):
         super().__init__()
@@ -31,7 +26,7 @@ class RMSNorm(torch.nn.Module):
     def forward(self, x):
         return self.weight * self._norm(x.float()).type_as(x)
 
-# precompute_pos_cis 函数用于预计算位置编码（复数版本）。
+
 def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
     t = torch.arange(end, device=freqs.device)  # type: ignore
@@ -39,7 +34,7 @@ def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
     pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
     return pos_cis
 
-# apply_rotary_emb 函数用于应用旋转位置编码（复数版本）。
+
 def apply_rotary_emb(xq, xk, pos_cis):
     def unite_shape(pos_cis, x):
         ndim = x.ndim
@@ -55,200 +50,194 @@ def apply_rotary_emb(xq, xk, pos_cis):
     xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
     return xq_out.type_as(xq), xk_out.type_as(xk)
 
-# precompute_pos_cis_real 函数用于预计算位置编码（实数版本）。
+class KnowledgeDataset(nn.Module):
-def precompute_pos_cis_real(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
+    def __init__(self, params, tok_embeddings, is_train=True):
-    """使用实数张量实现位置编码，避免使用复数张量
-
-    这个函数与precompute_pos_cis完全等价，但使用实数张量而非复数张量。
-    原始函数生成形状为[seq_len, dim//2]的复数张量，其中实部全为1，虚部为旋转角度。
-    这个函数生成形状为[seq_len, dim]的实数张量，其中偶数索引是cos(角度)，奇数索引是sin(角度)。
-    """
-    # 确保dim是偶数
-    if dim % 2 != 0:
-        raise ValueError(f"维度必须是偶数，但得到了 {dim}")
-
-    # 复制原始函数的频率计算逻辑
-    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
-    t = torch.arange(end, device=freqs.device)
-    freqs = torch.outer(t, freqs).float()
-
-    # 计算cos和sin值
-    # 在复数版本中，pos_cis = torch.polar(torch.ones_like(freqs), freqs)
-    # 等价于 cos(freqs) + i*sin(freqs)
-    cos = torch.cos(freqs)
-    sin = torch.sin(freqs)
-
-    # 创建实数张量，交错排列cos和sin
-    pos_emb = torch.zeros((end, dim), device=freqs.device)
-    pos_emb[:, 0::2] = cos  # 偶数索引放cos
-    pos_emb[:, 1::2] = sin  # 奇数索引放sin
-
-    return pos_emb
-
-# apply_rotary_emb_real 函数用于应用旋转位置编码（实数版本）。
-def apply_rotary_emb_real(xq, xk, pos_emb):
-    """使用实数张量实现旋转位置编码，避免使用复数张量
-
-    这个函数与apply_rotary_emb完全等价，但使用实数张量而非复数张量。
-    原始函数将输入张量转换为复数形式，与位置编码相乘，然后再转回实数形式。
-    这个函数直接使用实数运算实现相同的旋转操作。
-    """
-    # 获取形状信息
-    bsz, seq_len, n_heads, head_dim = xq.shape
-
-    # 确保pos_emb形状正确
-    assert pos_emb.shape[0] >= seq_len, f"位置编码长度 {pos_emb.shape[0]} 小于序列长度 {seq_len}"
-    assert pos_emb.shape[1] == head_dim, f"位置编码维度 {pos_emb.shape[1]} 与头维度 {head_dim} 不匹配"
-
-    # 截取需要的位置编码长度
-    pos_emb = pos_emb[:seq_len]
-
-    # 将pos_emb调整为广播形状 [1, seq_len, 1, head_dim]
-    pos_emb = pos_emb.unsqueeze(0).unsqueeze(2)
-
-    # 将head_dim分成两半
-    half_head_dim = head_dim // 2
-
-    # 提取cos和sin值（偶数索引是cos，奇数索引是sin）
-    cos = pos_emb[..., 0::2]
-    sin = pos_emb[..., 1::2]
-
-    # 将xq和xk重新排列，以便进行旋转操作
-    # 原始复数版本中，xq和xk被重塑为复数张量，其中实部和虚部交错排列
-    # 在实数版本中，我们需要将偶数索引和奇数索引分开处理
-
-    # 分离偶数和奇数索引
-    xq_even = xq[..., 0::2]  # 偶数索引，对应复数的实部
-    xq_odd = xq[..., 1::2]   # 奇数索引，对应复数的虚部
-    xk_even = xk[..., 0::2]
-    xk_odd = xk[..., 1::2]
-
-    # 应用旋转（等价于复数乘法）
-    # (a + bi)(cos + sin*i) = (a*cos - b*sin) + (a*sin + b*cos)i
-    # 其中a是偶数索引，b是奇数索引
-    xq_out_even = xq_even * cos - xq_odd * sin  # 新的偶数索引（实部）
-    xq_out_odd = xq_even * sin + xq_odd * cos   # 新的奇数索引（虚部）
-    xk_out_even = xk_even * cos - xk_odd * sin
-    xk_out_odd = xk_even * sin + xk_odd * cos
-
-    # 重新组合偶数和奇数索引
-    xq_out = torch.zeros_like(xq)
-    xk_out = torch.zeros_like(xk)
-    xq_out[..., 0::2] = xq_out_even
-    xq_out[..., 1::2] = xq_out_odd
-    xk_out[..., 0::2] = xk_out_even
-    xk_out[..., 1::2] = xk_out_odd
-
-    return xq_out.type_as(xq), xk_out.type_as(xk)
-
-# repeat_kv 函数用于重复键值对。
-def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
-    bs, slen, n_kv_heads, head_dim = x.shape
-    if n_rep == 1:
-        return x
-    return (
-        x[:, :, :, None, :]
-        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
-        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
-    )
-
-
-class Attention(nn.Module):
-    def __init__(self, args: LMConfig):
         super().__init__()
-        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        self.is_train = is_train
-        assert args.n_heads % self.n_kv_heads == 0
+        self.params = params
-        self.n_local_heads = args.n_heads
+        self.tok_embeddings = tok_embeddings
-        self.n_local_kv_heads = self.n_kv_heads
-        self.n_rep = self.n_local_heads // self.n_local_kv_heads
-        self.head_dim = args.dim // args.n_heads
-        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
-        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
-        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
-        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
-        self.attn_dropout = nn.Dropout(args.dropout)
-        self.resid_dropout = nn.Dropout(args.dropout)
-        self.dropout = args.dropout
-        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
-        # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
-        mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
-        mask = torch.triu(mask, diagonal=1)
-        self.register_buffer("mask", mask, persistent=False)
 
-    def forward(self,
+        # 嵌入参数
-                x: torch.Tensor,
+        self.knowledge_dim = params.knowledge_dim
-                pos_cis: torch.Tensor,
+        self.key_dim = self.knowledge_dim // 2
-                db_value=None):
+        self.to_queries = nn.Sequential(
-        bsz, seq_len, _ = x.shape #bsz: 批量大小, seq_len: 序列长度, _: 隐藏维度
+                nn.Linear(params.dim, self.knowledge_dim, bias=False),
-        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) #将输入张量x分别通过线性层wq, wk, wv进行变换，得到查询、键和值。
-        xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) #将变换后的张量xq重塑为形状为(bsz, seq_len, n_local_heads, head_dim)的形状。
-        xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) #将变换后的张量xk重塑为形状为(bsz, seq_len, n_local_kv_heads, head_dim)的形状。
-        xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) #将变换后的张量xv重塑为形状为(bsz, seq_len, n_local_kv_heads, head_dim)的形状。
-
-        # 应用旋转位置编码（使用实数版本）
-        xq, xk = apply_rotary_emb_real(xq, xk, pos_cis)
-        # kv_cache实现 REMOVED
-        # if past_key_value is not None:
-        #     xk = torch.cat([past_key_value[0], xk], dim=1)
-        #     xv = torch.cat([past_key_value[1], xv], dim=1)
-        # past_kv = (xk, xv) if use_cache else None
-
-        # 重复键值对
-        xq, xk, xv = (
-            xq.transpose(1, 2),
-            repeat_kv(xk, self.n_rep).transpose(1, 2),
-            repeat_kv(xv, self.n_rep).transpose(1, 2)
         )
 
-        # 如果提供了db_value，根据头的数量调整它的形状并与xv合并
+        ## 数据库参数
-        if db_value is not None:
+        self.knowledge_num = params.knowledge_num
-            # 确保db_value的形状与xv兼容，假设db_value形状为[B, N, H, D]
+        self.knowledge_length = params.knowledge_length
-            if db_value.ndim == 4:  # [B, N, H, D]
+        self.keys = nn.Parameter(torch.randn(self.knowledge_num, self.knowledge_dim) * 0.02, requires_grad=True)
-                db_value = db_value.transpose(1, 2)  # -> [B, H, N, D]
+        self.product_key_topk = min(16, self.knowledge_num)
         
-                # 检查是否需要调整D维度
+        # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
-                if db_value.shape[-1] != xv.shape[-1]:
+        self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num))
-                    # 如果db_value的维度与xv不同，可以添加一个投影层
-                    # 或者在这里使用简单的调整方法
-                    # 这里我们简单地通过均值池化或重复来调整维度
-                    if db_value.shape[-1] > xv.shape[-1]:
-                        # 降维
-                        factor = db_value.shape[-1] // xv.shape[-1]
-                        db_value = db_value.view(bsz, self.n_local_heads, seq_len, factor, xv.shape[-1])
-                        db_value = db_value.mean(dim=3)
-                    else:
-                        # 升维
-                        factor = xv.shape[-1] // db_value.shape[-1]
-                        db_value = db_value.unsqueeze(-1).repeat(1, 1, 1, 1, factor)
-                        db_value = db_value.view(bsz, self.n_local_heads, seq_len, xv.shape[-1])
 
-                # 将db_value与xv相加或融合
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
-                # 这里我们简单地将它们相加，但你也可以使用其他融合方法
+        self.register_buffer('knowledge_dataset', 
-                xv = xv + db_value
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)
+        )
 
-        # 使用Flash Attention
+        # 计算step数目，用于动态调整权重
-        if self.flash and seq_len != 1:
+        self.step_counter = 0
-            dropout_p = self.dropout if self.training else 0.0
-            output = F.scaled_dot_product_attention(
-                xq, xk, xv,
-                attn_mask=None,
-                dropout_p=dropout_p,
-                is_causal=True
-            )
-        else:
-            scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
-            scores += self.mask[:, :, :seq_len, :seq_len]
-            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
-            scores = self.attn_dropout(scores)
-            output = scores @ xv
 
-        output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        self.freeze_embedding = False
-        output = self.resid_dropout(self.wo(output))
-        return output
 
         
 
+    def intelligent_selection(self, query, all_scores, all_indices):
+        """智能分层选择策略"""
+        if self.is_train == False:
+            return all_scores, all_indices
+        
+        batch_size = all_scores.size(0)
+        device = all_scores.device
+        dtype = all_scores.dtype
+
+        # 对每个batch进行分层选择
+        enhanced_scores = all_scores.clone()
+        query_features = query.mean(dim=1)  # [batch_size, dim]
+
+        # 预先计算所有候选条目的嵌入（批量优化）
+        all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0)
+        unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True)
+
+        # 批量计算唯一候选条目的嵌入
+        candidate_tokens = self.knowledge_dataset[unique_indices]
+        flat_tokens = candidate_tokens.view(-1)
+        flat_embeddings = self.tok_embeddings(flat_tokens)
+        #获取flat_tokens对应的index
+        pre_update_indices = unique_indices.view(-1)
+        pre_update_embeddings = flat_embeddings.view(
+            len(unique_indices), self.knowledge_length, -1
+        )
+
+        unique_candidate_features = flat_embeddings.view(
+            len(unique_indices), self.knowledge_length, -1
+        ).mean(dim=1)  # [num_unique_candidates, dim]
+            
+        # 归一化候选特征（优化相似度计算）
+        normalized_candidates = F.normalize(unique_candidate_features, dim=-1)
+        normalized_queries = F.normalize(query_features, dim=-1)
+
+        # 收集所有batch的best_tokens
+        batch_best_tokens = []
+        batch_best_tokens_embeddings = []
+
+        for batch_idx in range(batch_size):
+            indices = all_indices[batch_idx]
+                
+            # 获取当前batch候选条目对应的特征索引
+            start_idx = batch_idx * len(indices)
+            end_idx = start_idx + len(indices)
+            batch_inverse_indices = inverse_indices[start_idx:end_idx]
+                
+            # 使用预计算的归一化特征进行优化相似度计算
+            batch_candidate_features = normalized_candidates[batch_inverse_indices]
+            query_feature = normalized_queries[batch_idx]
+                
+            # 使用矩阵乘法计算余弦相似度
+            similarity_scores = torch.mv(batch_candidate_features, query_feature)
+            
+            # 找到最大相似度分数的索引
+            max_similarity_idx = torch.argmax(similarity_scores)
+            
+            # 获取最大相似度对应的候选条目索引
+            best_candidate_idx = indices[max_similarity_idx]
+            
+            # 获取对应的tokens
+            best_tokens = self.knowledge_dataset[best_candidate_idx]
+            best_tokens_embeddings = self.tok_embeddings(best_tokens)
+            
+            # 将当前batch的best_tokens添加到列表中
+            batch_best_tokens.append(best_tokens)
+            batch_best_tokens_embeddings.append(best_tokens_embeddings)
+
+        # 将所有batch的best_tokens堆叠成一个张量
+        # [batch_size, knowledge_length]
+        all_best_tokens = torch.stack(batch_best_tokens, dim=0)
+        all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0)
+
+        # 获取
+        
+        # 使用重新计算的embeddings更新self.keys
+        if self.is_train:
+            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 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]
+            pre_update_embeddings = self.to_queries(pre_update_embeddings)
+            self.keys[pre_update_indices] = pre_update_embeddings
+    
+    def search_index(self,x):
+        batch_size, seq_len, dim = x.shape
+
+        # collapse sequence dimension by averaging
+        x_flat = x.mean(dim=1)  # [batch_size, dim]
+
+        queries = self.to_queries(x_flat) # [batch_size, 2*dim_key]
+        # queries = queries.reshape(batch_size, 2, self.key_dim)
+        # queries = queries.permute(1, 0, 2)
+
+        # 2. 计算queries与keys的相似度
+        sim = torch.einsum('b d, k d -> b k', queries, self.keys)
+
+        # 3. 在两个子空间分别做top-k
+        scores_and_indices = sim.topk(self.product_key_topk, dim=-1)
+        scores, indices = scores_and_indices[0], scores_and_indices[1]
+
+        # 5. 应用智能分层选择策略
+        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
+
+        # 6. 更新1%的keys
+        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]
+                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(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
 
 class CrossAttention(nn.Module):
     def __init__(
@@ -295,6 +284,58 @@ class CrossAttention(nn.Module):
 
         return context
 
+class Attention(nn.Module):
+    def __init__(self, args: LMConfig):
+        super().__init__()
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        assert args.n_heads % self.n_kv_heads == 0
+        self.n_local_heads = args.n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = args.dim // args.n_heads
+        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+        self.attn_dropout = nn.Dropout(args.dropout)
+        self.resid_dropout = nn.Dropout(args.dropout)
+        self.dropout = args.dropout
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
+        # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+        mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+        mask = torch.triu(mask, diagonal=1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self,
+                x: torch.Tensor,
+                pos_cis: torch.Tensor):
+        bsz, seq_len, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+        xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+        if self.flash and seq_len != 1:
+            dropout_p = self.dropout if self.training else 0.0
+            output = F.scaled_dot_product_attention(
+                xq, xk, xv,
+                attn_mask=None,
+                dropout_p=dropout_p,
+                is_causal=True
+            )
+        else:
+            scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
+            scores += self.mask[:, :, :seq_len, :seq_len]
+            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+            scores = self.attn_dropout(scores)
+            output = scores @ xv
+
+        output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        output = self.resid_dropout(self.wo(output))
+        return output
+
+
 class FeedForward(nn.Module):
     def __init__(self, config: LMConfig):
         super().__init__()
@@ -427,170 +468,31 @@ class MOEFeedForward(nn.Module):
 
 
 class MiniMindBlock(nn.Module):
-    def __init__(self, layer_id: int, config: LMConfig):
+    def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset):
         super().__init__()
         self.n_heads = config.n_heads
         self.dim = config.dim
         self.head_dim = config.dim // config.n_heads
-        self.attention = Attention(config)
+        self.self_attention = Attention(config)
-        self.cross_att = CrossAttention(config)
+        self.cross_attention = CrossAttention(config)
+        self.knowledge_dataset = knowledge_dataset
 
         self.layer_id = layer_id
         self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
         self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
         self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
 
-        # 假设num_experts是已定义的总专家数量的平方根
+    def forward(self, x, pos_cis):
-
+        h_attn = self.self_attention(
-
-        # 查询生成的参数
-
-
-        # 创建查询生成模块
-        # if weight_down_embed is not None:
-        #     self.to_queries = nn.Sequential(
-        #         nn.Linear(config.dim, self.dim_key * 2, bias=False),
-        #         # nn.Unflatten(2, (2, self.n_heads, self.dim_key))  # 替代Rearrange
-        #     )
-
-        #     # 超参数
-        #     self.product_key_topk = min(16, self.num_keys)  # 确保不超过num_keys
-        #     self.num_experts_per_head_topk = 1  # 最终每个头选取的专家数
-
-    def forward(self, x, db_value, pos_cis):
-        # import pdb;pdb.set_trace()
-        # db_value = None
-
-        # # 如果有weight_down_embed，使用Product Key机制
-        # if self.weight_down_embed is not None:
-        #     # 1. 生成queries
-        #     batch_size, seq_len, dim = x.shape
-
-        #     # collapse sequence dimension by averaging
-        #     x_flat = x.mean(dim=1)  # [batch_size, dim]
-        #     queries = self.to_queries(x_flat)  # [batch_size, 2*dim_key]
-        #     queries = queries.reshape(batch_size, 2, self.dim_key)  # [batch_size, 2, dim_key]
-        #     queries = queries.permute(1, 0, 2)  # [2, batch_size, dim_key]
-
-        #     # 2. 计算queries与keys的相似度
-        #     sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
-
-        #     # 3. 在两个子空间分别做top-k
-        #     scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)]
-        #     scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0]
-        #     indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1]
-
-        #     # 4. 组合两个子空间的分数和索引
-        #     all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
-        #     all_scores = all_scores.view(*all_scores.shape[:-2], -1)
-
-        #     all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
-        #     all_indices = all_indices.view(*all_indices.shape[:-2], -1)
-
-        #     # 5. 最终top-k选择
-        #     scores, pk_indices = all_scores.topk(self.num_experts_per_head_topk, dim=-1)
-        #     indices = all_indices.gather(-1, pk_indices)
-
-        #     # 6. 从embedding中获取专家值
-
-        #     # 从embedding中获取值
-        #     flat_indices = indices.view(-1)  # 将索引展平为一维张量
-        #     db_values = self.weight_down_embed(flat_indices)
-
-        #     # 重塑回原始形状
-        #     db_value = db_values.view(batch_size, -1, dim)
-
-
-        # 注意力计算
-        h_attn = self.attention(
             self.attention_norm(x),
-            pos_cis,
+            pos_cis
-            db_value=db_value
         )
-
+        db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
-        h_attn = self.cross_att(h_attn, db_value)
+        h_attn = self.cross_attention(h_attn, db_embeddings)
-
-        # 残差连接
         h = x + h_attn
-
-        # 前馈神经网络
         out = h + self.feed_forward(self.ffn_norm(h))
         return out
 
-class ExtractDB(nn.Module):
-    def __init__(self,params):
-         # 修改专家数量和知识维度，确保能开方
-        super().__init__()
-        self.batch_size = None
-        self.dim = params.dim
-        self.dim_key = self.dim // 2
-        self.knowledge_num = params.knowledge_num  # 100专家，确保是完全平方数
-        # 将knowledge_dim设置为与head_dim相同，以便在attention中直接使用
-        self.head_dim = params.dim // params.n_heads
-        self.knowledge_length = params.knowledge_length
-
-        # 使用register_buffer代替nn.Parameter，避免梯度问题
-        # self.register_buffer('weight_down_embed', torch.randn(self.knowledge_num, self.knowledge_length) * 0.02)
-        self.register_buffer('weight_down_embed',torch.randint(low=0,high=6400, size=(self.knowledge_num, self.knowledge_length),dtype=torch.long))
-    
-    
-
-
-        self.num_keys = int(math.sqrt(self.knowledge_num)) if self.knowledge_num > 0 else 0
-        self.product_key_topk = min(16, self.num_keys)
-        self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.dim_key) * 0.02)
-        self.num_experts_per_head_topk = 1
-        self.to_queries = nn.Sequential(
-                nn.Linear(params.dim, self.dim_key * 2, bias=False),
-        )
- 
-    def q_to_k(self,x):
-        # 1. 生成queries
-            self.batch_size, seq_len, dim = x.shape
-
-            # collapse sequence dimension by averaging
-            x_flat = x.mean(dim=1)  # [batch_size, dim]
-
-            queries = self.to_queries(x_flat)  # [batch_size, 2*dim_key]
-            queries = queries.reshape(self.batch_size, 2, self.dim_key)  # [batch_size, 2, dim_key]
-            queries = queries.permute(1, 0, 2)  # [2, batch_size, dim_key]
-
-            # 2. 计算queries与keys的相似度
-            sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
-
-            # 3. 在两个子空间分别做top-k
-            scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)]
-            scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0]
-            indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1]
-
-            # 4. 组合两个子空间的分数和索引
-            all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
-            all_scores = all_scores.view(*all_scores.shape[:-2], -1)
-
-            all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
-            all_indices = all_indices.view(*all_indices.shape[:-2], -1)
-
-            # 5. 最终top-k选择
-            scores, pk_indices = all_scores.topk(self.num_experts_per_head_topk, dim=-1)
-            indices = all_indices.gather(-1, pk_indices)
-            flat_indices = indices.view(-1)
-            return flat_indices
-
-    def get_data(self, index):
-        # 直接从GPU获取embedding
-        db_values = self.weight_down_embed[index]#变成token了所以是1,后续再过emb
-        # db_value = db_values.view(self.batch_size,-1) 
-        return db_values
-
-    @torch.no_grad()
-    def updata_value(self, k, v):#要加一个从向量返回index的过程
-        # 直接更新buffer上的值 (不需要梯度)
-        v_reshaped = v.view(v.size(0), -1)
-        # 确保数据类型匹配
-        v_reshaped = v_reshaped.to(dtype=self.weight_down_embed.dtype)
-        self.weight_down_embed[k] = v_reshaped
-
-
 
 class MiniMindLM(PreTrainedModel):
     config_class = LMConfig
@@ -601,110 +503,36 @@ class MiniMindLM(PreTrainedModel):
         self.vocab_size, self.n_layers = params.vocab_size, params.n_layers
         self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
         self.dropout = nn.Dropout(params.dropout)
-        # 移除旧的weight_down_embed声明
+        self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
-        self.extract_db = ExtractDB(self.params)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) for l in range(self.n_layers)])
-
-        # 将self.weight_down_embed传递给每个MiniMindBlock
-        self.layers = nn.ModuleList([MiniMindBlock(l, params) for l in range(self.n_layers)])
         self.norm = RMSNorm(params.dim, eps=params.norm_eps)
         self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
-        self.database_output = nn.Linear(params.dim, params.knowledge_length, bias=False)
         self.tok_embeddings.weight = self.output.weight
-        self.database_output.weight = self.output.weight
+        self.register_buffer("pos_cis",
-
+                             precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
-        # Calculate input dimension
-        input_dim = (self.params.max_seq_len-1)*self.params.n_layers
-        # Use a bottleneck architecture to reduce parameters
-        bottleneck_dim = 256  # Significantly smaller bottleneck dimension
-
-        # Factorized shared downsampling using two smaller convolutions
-        self.shared_downsample = nn.Sequential(
-            # First reduce input dimension to bottleneck
-            nn.Conv1d(input_dim, bottleneck_dim, kernel_size=1, padding='same'),
-            nn.ReLU(),  # Non-linearity to improve representation capacity
-            # Then expand to target dimension
-            nn.Conv1d(bottleneck_dim, 128*8, kernel_size=1, padding='same')
-        )
-
-        # Specific layers for v path
-        self.downsample_v_specific = nn.Sequential(
-            nn.Conv1d(128*8, 128, kernel_size=1, padding='same'),
-            nn.Conv1d(128, self.params.knowledge_length, kernel_size=1, padding='same')
-        )
-        
-        # Specific layers for q path
-        self.downsample_q_specific = nn.Sequential(
-            nn.Conv1d(128*8, 512, kernel_size=1, padding='same')
-        )
-        # 使用实数版本的位置编码，避免复数张量可能导致的段错误
-        self.register_buffer("pos_cis_real",
-                             precompute_pos_cis_real(dim=params.dim // params.n_heads, theta=params.rope_theta),
                              persistent=False)
-        self.params = params
+        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_real = self.pos_cis_real[start_pos:start_pos + input_ids.size(1)]
+        pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
-        h_list = []
-
         for l, layer in enumerate(self.layers):
-            # 禁用数据库模式，使用固定值替代数据库查询
-            if self.params.disable_db:
-                # 创建一个形状为[batch_size, n_layers, dim]的tensor，所有元素值为1e-4
-                batch_size = h.size(0)
-                db_value = torch.full((batch_size, self.n_layers, self.params.dim), 1e-4,
-                                      dtype=h.dtype, device=h.device)
-            else:
-                # 正常模式，使用数据库查询
-                # import pdb;pdb.set_trace()
-                index = self.extract_db.q_to_k(h)
-            
-                token_idx = self.extract_db.get_data(index) #这里是index
-
-                db_value =self.tok_embeddings(token_idx)
-                
             h = layer(
-                h, db_value, pos_cis_real
+                h, pos_cis
             )
 
-            h_list.append(h.unsqueeze(0))
-
-        h_tensor = torch.cat(h_list, dim=0).permute(1, 0, 2, 3)
-
-        # 只在非禁用数据库模式下执行数据库更新逻辑
-        if not self.params.disable_db:
-            # 使用detach()分离计算图，避免多次反向传播
-            h_tensor_detached = h_tensor.detach()
-            h_tensor_detached = h_tensor_detached.reshape(h_tensor_detached.shape[0], -1, self.params.dim)
-
-            # 数据库更新逻辑与主计算图分离
-            with torch.no_grad():
-                
-                # Compute shared downsampling layer once
-                shared_features = self.shared_downsample(h_tensor_detached)
-                
-                # Get features from v path - now we output embedding-dimension vectors
-                z_v_features = self.downsample_v_specific(shared_features)
-                batch_z, seq_len, dim_z = z_v_features.shape
-                
-                # Reshape to batch_size * knowledge_length, dim
-                z_v_flat = z_v_features.reshape(-1, dim_z)
-                
-                # Direct token prediction - like the main language model head
-                token_logits = self.database_output(z_v_flat)  # [batch_z * seq_len, vocab_size]
-                # Get token indices directly from logits
-                token_indices_flat = torch.argmax(token_logits, dim=-1)
-                token_indices = token_indices_flat.reshape(batch_z, -1)
-                
-                # Process query path as before
-                z_q = self.downsample_q_specific(shared_features)
-                z_k = self.extract_db.q_to_k(z_q)
-                # self.extract_db.updata_value(z_k, token_indices)
-
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
         logits = self.output(self.norm(h)[:, slice_indices, :])
         aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))
@@ -717,12 +545,6 @@ class MiniMindLM(PreTrainedModel):
 
         output.aux_loss = aux_loss
 
-        # 尝试添加其他属性（如果支持的话）
-        # try:
-        #     output.hidden_states = h
-        # except:
-        #     pass
-
         return output
 
     @torch.inference_mode()
@@ -755,13 +577,14 @@ class MiniMindLM(PreTrainedModel):
         return res
 
     def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
-        start, first_seq = input_ids.shape[1], True
+        start, first_seq, past_kvs = input_ids.shape[1], True, None
         while input_ids.shape[1] < max_new_tokens - 1:
             if first_seq:
                 out, first_seq = self(input_ids, **args), False
             else:
-                out = self(input_ids[:, -1:], start_pos=input_ids.shape[1] - 1, **args)
+                out = self(input_ids[:, -1:],
-            logits = out.logits[:, -1, :]
+                           start_pos=input_ids.shape[1] - 1, **args)
+            logits, past_kvs = out.logits[:, -1, :], out.past_key_values
             logits[:, list(set(input_ids.tolist()[0]))] /= rp
             logits /= (temperature + 1e-9)
             if top_p is not None and top_p < 1.0:

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

@@ -74,8 +74,8 @@ def init_model(lm_config, pretrained_embedding_path=None, database_init_path=Non
                 nn.init.ones_(module.weight)
     
     # 初始化位置编码相关参数
-    if hasattr(model.extract_db, 'keys'):
+    if hasattr(model.knowledge_dataset, 'keys'):
-        nn.init.normal_(model.extract_db.keys, mean=0.0, std=0.02)
+        nn.init.normal_(model.knowledge_dataset.keys, mean=0.0, std=0.02)
     
     Logger("Default model initialization completed")
 
@@ -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:
+            pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
-                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
+            # 处理所需数量的句子
-            Logger("Processing sentences for embeddings and token lengths...")
+            num_to_process = min(knowledge_num, len(sorted_sentences))
             
-            # 提取所有句子
+            for i in range(num_to_process):
-            sentences = [sentence_data.get('corrected_sentence', '') for sentence_data in sorted_sentences]
+                sentence_data = sorted_sentences[i]
+                sentence = sentence_data.get('corrected_sentence', '')
                 
-            # 批量计算token长度
+                # 将句子转换为tokens
-            Logger("Computing token lengths...")
+                sentence_tokens = tokenizer.encode(sentence, add_special_tokens=False)
-            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
+                # 截断或填充到knowledge_length
-            Logger("Computing embeddings in batches...")
+                if len(sentence_tokens) > knowledge_length:
-            embeddings_list = []
+                    # 如果超过长度，截断
-            batch_size = 256  # 可以根据GPU内存调整
+                    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:
+                    # 如果不足长度，用空token填充
+                    original_length = len(sentence_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")
                 
-            if embedding_model is not None:
+                processed_rows.append(sentence_tokens)
-                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:
+                if (i + 1) % 1000 == 0:
-                            Logger(f"Processed {min(i + batch_size, len(sentences))}/{len(sentences)} sentences")
+                    Logger(f"Processed {i + 1}/{num_to_process} sentences")
             
-                    Logger("Batch embedding computation completed")
+            # 如果句子数量不足，用空token填充剩余位置
-                except Exception as e:
+            while len(processed_rows) < knowledge_num:
-                    Logger(f"Error in batch encoding: {e}")
+                empty_tokens = [pad_token_id] * knowledge_length
-                    Logger("Falling back to random embeddings")
+                processed_rows.append(empty_tokens)
-                    embeddings_list = [np.random.randn(384).astype(np.float32) for _ in sentences]
+                if len(processed_rows) % 1000 == 0:
-            else:
+                    Logger(f"Added empty entry {len(processed_rows)}/{knowledge_num}")
-                # 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
+            Logger(f"Finished adding empty entries. Total: {len(processed_rows)}/{knowledge_num}")
-            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)
-                    
-                    if (cluster_idx + 1) % 1000 == 0:
-                        total_remaining = len(high_importance) + len(medium_importance) + len(low_importance)
-                        Logger(f"Fast clustering: {cluster_idx + 1}/{knowledge_num} clusters, {total_remaining} sentences remaining")
-            
-            else:
-                # 原始优化算法（适用于中等规模数据集）
-                # 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:
-                        cluster_tokens = cluster_tokens[:knowledge_length]
-                    else:
-                        # 用pad_token_id填充
-                        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)
-                    
-                    # 优化4: 减少日志频率
-                    if (cluster_idx + 1) % 500 == 0:
-                        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
-            while len(clustered_rows) < knowledge_num:
-                pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
-                random_tokens = [pad_token_id] * knowledge_length
-                clustered_rows.append(random_tokens)
             
             # 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, 'extract_db') and hasattr(model.extract_db, 'weight_down_embed'):
+        if hasattr(model, 'knowledge_dataset') and hasattr(model.knowledge_dataset, 'knowledge_dataset'):
-            model.extract_db.weight_down_embed.data.copy_(clustered_tensor)
+            model.knowledge_dataset.knowledge_dataset.data.copy_(processed_tensor)
-            Logger("Successfully initialized model.extract_db.weight_down_embed with clustered data")
+            Logger("Successfully initialized model.knowledge_dataset.knowledge_dataset with processed data")
         else:
-            Logger("Warning: Could not find model.extract_db.weight_down_embed to initialize")
+            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'
 
@@ -659,21 +438,22 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
 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=3)
+    parser.add_argument("--epochs", type=int, default=4)
-    parser.add_argument("--batch_size", type=int, default=24)
+    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")
     parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain")
-    parser.add_argument("--num_workers", type=int, default=48)
+    parser.add_argument("--num_workers", type=int, default=8)
     parser.add_argument("--accumulation_steps", type=int, default=32)
     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=10000)
-    parser.add_argument('--dim', default=1024, type=int)
+    parser.add_argument('--dim', default=512, type=int)
-    parser.add_argument('--n_layers', default=32, type=int)
+    parser.add_argument('--n_layers', default=8, type=int)
-    parser.add_argument('--max_seq_len', default=1024, type=int)
+    parser.add_argument('--max_seq_len', default=512, type=int)
     parser.add_argument('--use_moe', default=False, type=bool)
     parser.add_argument('--disable_db', action='store_true', help="禁用数据库功能，使用固定值1e-4替代")
     parser.add_argument("--data_path", type=str, default="./dataset/pretrain_hq.jsonl")
@@ -681,11 +461,11 @@ 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=65536,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=64,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.pt", help="聚类结果缓存文件路径")
+    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")
     parser.add_argument("--recompute_clusters", action="store_true", default=False, help="强制重新计算聚类，忽略缓存文件")
     args = parser.parse_args()
 
@@ -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
     )
 
     #########################################################