Merge branch 'cy' of http://110.42.53.85:3000/iomgaa/Minimind into cy

.gitignore

@@ -4,4 +4,8 @@
 wandb/
 **/*.log
 models/sentence_transformers/
-models/sentence_transformers_cache/
+models/sentence_transformers_cache/
+**/*.pyc
+qwen2-1.7B/
+images/
+cache/

analyze_database.py

@@ -0,0 +1,97 @@
+import json
+import os
+import torch
+from transformers import AutoTokenizer
+
+def analyze_database(json_path, tokenizer_path='./model/minimind_tokenizer'):
+    """分析database_init.json文件中的数据条目数量和质量"""
+    
+    print(f"开始分析数据库文件: {json_path}")
+    
+    # 1. 加载tokenizer
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        print(f"成功加载tokenizer: {tokenizer_path}")
+    except Exception as e:
+        print(f"加载tokenizer失败: {e}")
+        return
+    
+    # 2. 加载JSON文件
+    try:
+        with open(json_path, 'r', encoding='utf-8') as f:
+            database_data = json.load(f)
+        
+        # 提取sentences列表
+        sentences_data = database_data.get('sentences', [])
+        print(f"加载了 {len(sentences_data)} 条sentences数据")
+    except Exception as e:
+        print(f"加载JSON文件失败: {e}")
+        return
+    
+    # 3. 分析句子长度分布
+    if len(sentences_data) == 0:
+        print("没有找到有效的句子数据")
+        return
+    
+    # 按照importance_score排序
+    sorted_sentences = sorted(sentences_data, key=lambda x: x.get('importance_score', 0.0), reverse=True)
+    print(f"按importance_score排序完成，最高分: {sorted_sentences[0].get('importance_score', 0.0)}, 最低分: {sorted_sentences[-1].get('importance_score', 0.0)}")
+    
+    # 统计句子长度分布
+    token_lengths = []
+    pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+    
+    # 4. 分析token长度分布
+    for i, sentence_data in enumerate(sorted_sentences):
+        sentence = sentence_data.get('corrected_sentence', '')
+        if not sentence:
+            print(f"警告: 第 {i+1} 条数据没有corrected_sentence字段")
+            continue
+        
+        # 将句子转换为tokens
+        sentence_tokens = tokenizer.encode(sentence, add_special_tokens=False)
+        token_lengths.append(len(sentence_tokens))
+        
+        if i < 5:  # 显示前5条数据样例
+            print(f"样例 {i+1}: {sentence[:50]}... (tokens: {len(sentence_tokens)})")
+    
+    # 5. 统计分析结果
+    token_lengths = torch.tensor(token_lengths)
+    stats = {
+        "总条目数": len(sorted_sentences),
+        "有效条目数": len(token_lengths),
+        "token长度-平均值": token_lengths.float().mean().item(),
+        "token长度-最小值": token_lengths.min().item(),
+        "token长度-最大值": token_lengths.max().item(),
+        "token长度-中位数": token_lengths.median().item(),
+        "token长度-标准差": token_lengths.float().std().item(),
+    }
+    
+    # 统计长度分布
+    length_bins = {
+        "小于16": (token_lengths < 16).sum().item(),
+        "16-32": ((token_lengths >= 16) & (token_lengths < 32)).sum().item(),
+        "32-64": ((token_lengths >= 32) & (token_lengths < 64)).sum().item(),
+        "64-128": ((token_lengths >= 64) & (token_lengths < 128)).sum().item(),
+        "128-256": ((token_lengths >= 128) & (token_lengths < 256)).sum().item(),
+        "256及以上": (token_lengths >= 256).sum().item(),
+    }
+    
+    # 打印统计信息
+    print("\n===== 数据库分析结果 =====")
+    for key, value in stats.items():
+        print(f"{key}: {value}")
+    
+    print("\n===== Token长度分布 =====")
+    for bin_name, count in length_bins.items():
+        percentage = (count / len(token_lengths)) * 100
+        print(f"{bin_name}: {count} ({percentage:.1f}%)")
+    
+    print(f"\n结论: 该数据库文件包含 {stats['有效条目数']} 条有效数据，可以全部填充到知识库中。")
+    
+    return stats, length_bins
+
+if __name__ == "__main__":
+    # 指定数据库文件路径
+    database_path = "./dataset/database_init.json"
+    analyze_database(database_path)

loss.py

@@ -1,33 +1,112 @@
 import re
 import matplotlib.pyplot as plt
+import numpy as np
 
-log_file = 'out/train.log'
+def parse_log_file(file_path):
-steps_per_epoch = 58880  # 你需要根据实际日志设置
+    """
+    Parse the training log file to extract epoch, step, and loss information.
+    """
+    # Regular expression to match log entries with loss information
+    pattern = r'\[.*?\] Epoch (\d+)/\d+, Step (\d+)/\d+, Loss: ([\d\.]+)'
+    
+    epochs = []
+    steps = []
+    losses = []
+    
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            log_content = f.read()
+            
+            # Find all matches
+            matches = re.findall(pattern, log_content)
+            
+            for match in matches:
+                epoch = int(match[0])
+                step = int(match[1])
+                loss = float(match[2])
+                
+                epochs.append(epoch)
+                steps.append(step)
+                losses.append(loss)
+                
+        return epochs, steps, losses
+    
+    except Exception as e:
+        print(f"Error parsing log file: {e}")
+        return [], [], []
 
-with open(log_file, 'r', encoding='utf-8') as f:
+def plot_loss_curve(epochs, steps, losses, output_file='loss_curve.png'):
-    log_text = f.read()
+    """
+    Plot the loss curve and save it to a file.
+    """
+    plt.figure(figsize=(12, 6))
+    
+    # Create continuous steps for better visualization
+    continuous_steps = []
+    current_max_step = 0
+    prev_epoch = 0
+    
+    for i, (e, s) in enumerate(zip(epochs, steps)):
+        if e > prev_epoch:
+            # New epoch starts
+            if i > 0:
+                current_max_step = continuous_steps[-1]
+            prev_epoch = e
+        continuous_steps.append(s + current_max_step)
+    
+    # 修改：减小线条宽度和点的大小
+    plt.plot(continuous_steps, losses, marker='.', linestyle='-', 
+             color='#1f77b4', markersize=3, linewidth=0.8)
+    
+    plt.title('Training Loss Over Steps', fontsize=16)
+    plt.xlabel('Steps (Continuous)', fontsize=14)
+    plt.ylabel('Loss', fontsize=14)
+    plt.grid(True, linestyle='--', alpha=0.5, linewidth=0.5)
+    
+    # 修改：减小红线宽度
+    for i in range(1, len(epochs)):
+        if epochs[i] > epochs[i-1]:
+            plt.axvline(x=continuous_steps[i], color='r', 
+                       linestyle='--', alpha=0.5, linewidth=0.8)
+    
+    unique_epochs = sorted(set(epochs))
+    # Add epoch labels
+    for e in unique_epochs:
+        indices = [i for i, epoch in enumerate(epochs) if epoch == e]
+        if indices:
+            mid_idx = indices[len(indices) // 2]
+            plt.text(continuous_steps[mid_idx], max(losses) * 0.95, f'Epoch {e}', 
+                     horizontalalignment='center', verticalalignment='center',
+                     fontsize=10,
+                     bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 3})
+    
+    # 移除悬停注释，简化图表
+    # for i, (e, s, l) in enumerate(zip(epochs, steps, losses)):
+    #     plt.annotate(...)
+    
+    plt.tight_layout()
+    plt.savefig(output_file, dpi=300)
+    print(f"Loss curve saved as {output_file}")
+    
+    # Also display the data in a table format
+    print("\nExtracted training data:")
+    print("-" * 50)
+    print(f"{'Epoch':<10}{'Step':<10}{'Loss':<15}")
+    print("-" * 50)
+    for e, s, l in zip(epochs, steps, losses):
+        print(f"{e:<10}{s:<10}{l:<15.6f}")
 
-# 提取 epoch, step, loss
+def main():
-pattern = re.compile(r'Epoch\s+(\d+)/\d+,\s+Step\s+(\d+)/\d+,\s+Loss:\s*([0-9.]+)', re.MULTILINE)
+    # Specify the path to your log file
-matches = pattern.findall(log_text)
+    log_file_path = 'out/train.log'
+    
+    # Parse the log file
+    epochs, steps, losses = parse_log_file(log_file_path)
+    
+    if epochs and steps and losses:
+        plot_loss_curve(epochs, steps, losses)
+    else:
+        print("No data extracted from log file. Please check if the file format is correct.")
 
-global_steps = []
+if __name__ == "__main__":
-losses = []
+    main()
-
-for epoch, step, loss in matches:
-    epoch = int(epoch)
-    step = int(step)
-    global_step = (epoch - 1) * steps_per_epoch + step
-    global_steps.append(global_step)
-    losses.append(float(loss))
-
-plt.figure(figsize=(12, 6))
-plt.plot(global_steps, losses, label='Loss')
-plt.xlabel('Global Step')
-plt.ylabel('Loss')
-plt.title('Training Loss Curve')
-plt.legend()
-plt.grid(True)
-plt.tight_layout()
-plt.savefig('out/loss_curve.png')
-plt.show()

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

@@ -2,7 +2,7 @@ import math
 import struct
 import inspect
 import time
-
+#子空间二维分解+梯度更新
 from .LMConfig import LMConfig
 from typing import Any, Optional, Tuple, List, Union
 import numpy as np
@@ -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 +50,195 @@ 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]
+        
-                db_value = db_value.transpose(1, 2)  # -> [B, H, N, D]
+        # 修改键存储为二维分解空间，设置为可训练参数
+        self.num_keys = int(math.sqrt(self.knowledge_num))
+        # 确保keys是可训练参数
+        self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True)
+        self.product_key_topk = min(16, self.num_keys)
+        
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
+        self.register_buffer('knowledge_dataset', 
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long))
 
-                # 检查是否需要调整D维度
+        # 计算step数目，用于动态调整权重
-                if db_value.shape[-1] != xv.shape[-1]:
+        self.step_counter = 0
-                    # 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
+        # 移除批次计数器和更新频率相关代码
-                # 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
-                xv = xv + db_value
 
-        # 使用Flash Attention
+    def intelligent_selection(self, query, all_scores, all_indices):
-        if self.flash and seq_len != 1:
+        """智能分层选择策略"""
-            dropout_p = self.dropout if self.training else 0.0
+        if self.is_train == False:
-            output = F.scaled_dot_product_attention(
+            return all_scores, all_indices
-                xq, xk, xv,
+        
-                attn_mask=None,
+        batch_size = all_scores.size(0)
-                dropout_p=dropout_p,
+        device = all_scores.device
-                is_causal=True
+        dtype = all_scores.dtype
-            )
-        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
 
+        # 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（保留这些变量以便其他地方使用）
+        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)
+
+        return all_best_tokens, all_best_tokens_embeddings
+    
+        
+        with torch.no_grad():
+            # 1. 计算token序列的平均嵌入
+            pre_update_embeddings = pre_update_embeddings.mean(dim=1)  # [num_indices, dim]
+            # 2. 转换维度
+            pre_update_embeddings = self.to_queries(pre_update_embeddings)  # [num_indices, knowledge_dim]
+            
+            # 3. 将one-hot索引转换为子空间索引
+            indices_x = pre_update_indices // self.num_keys
+            indices_y = pre_update_indices % self.num_keys
+            
+            # 4. 收集需要更新的唯一子键
+            unique_x = torch.unique(indices_x)
+            unique_y = torch.unique(indices_y)
+            
+            # 5. 更新第一个子空间的键
+            for k1 in unique_x:
+                # 找出所有使用该子键的索引
+                mask_k1 = (indices_x == k1)
+                if mask_k1.sum() == 0:
+                    continue
+                    
+                # 获取所有相关嵌入并计算平均值
+                k1_embeddings = pre_update_embeddings[mask_k1]
+                k1_avg_embedding = k1_embeddings.mean(dim=0)
+                
+                # 拆分为两个子空间并更新第一个子空间
+                self.keys[k1, 0] = k1_avg_embedding[:self.key_dim]
+            
+            # 6. 更新第二个子空间的键
+            for k2 in unique_y:
+                # 找出所有使用该子键的索引
+                mask_k2 = (indices_y == k2)
+                if mask_k2.sum() == 0:
+                    continue
+                    
+                # 获取所有相关嵌入并计算平均值
+                k2_embeddings = pre_update_embeddings[mask_k2]
+                k2_avg_embedding = k2_embeddings.mean(dim=0)
+                
+                # 更新第二个子空间
+                self.keys[k2, 1] = k2_avg_embedding[self.key_dim:]
+    
+    def search_index(self, x):
+        batch_size, seq_len, dim = x.shape
+
+        # 1. 序列维度平均
+        x_flat = x.mean(dim=1)  # [batch_size, dim]
+
+        # 2. 生成查询向量并重塑为两个子查询
+        queries = self.to_queries(x_flat)  # [batch_size, knowledge_dim]
+        queries = queries.reshape(batch_size, 2, self.key_dim)  # [batch_size, 2, key_dim]
+        # 调整维度顺序，使子空间维度位于首位
+        queries = queries.permute(1, 0, 2)  # [2, batch_size, key_dim]
+
+        # 3. 计算每个子空间的相似度
+        sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
+
+        # 4. 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]
+
+        # 5. 组合两个子空间的结果
+        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk]
+        all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 6. 将结果重塑为二维
+        all_scores = all_scores.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        all_indices = all_indices.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        # 7. 选择最终的top-k结果
+        scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1)
+        indices = torch.gather(all_indices, 1, indices_of_indices)
+
+        # 8. 应用智能分层选择策略
+        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
 
 
+        return best_tokens, best_tokens_embeddings
 
 class CrossAttention(nn.Module):
     def __init__(
@@ -295,6 +285,58 @@ 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__()
@@ -427,183 +469,30 @@ 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 
+        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
-
-    @torch.no_grad()
-    def update_keys_with_zq(self, flat_indices, z_q):
-        """
-        flat_indices: [batch]，q_to_k输出的检索到的key的全局索引（0~knowledge_num-1）
-        z_q: [batch, 2, dim_key]，每个样本的两个子空间query
-        """
-        num_keys = self.num_keys
-        idx_x = flat_indices // num_keys   # [batch]
-        idx_y = flat_indices % num_keys    # [batch]
-
-        # 对于每个样本，把keys的两个子空间分别替换为z_q的对应部分
-        for i in range(flat_indices.size(0)):
-            self.keys.data[idx_x[i], 0, :] = z_q[i, 0, :].to(self.keys.dtype)
-            self.keys.data[idx_y[i], 1, :] = z_q[i, 1, :].to(self.keys.dtype)
 
 
 class MiniMindLM(PreTrainedModel):
@@ -615,115 +504,35 @@ 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, self.params.dim, 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.value_update_schedule = 0.9  # 前%冻结
+        self.freeze_embedding = False
-        self.global_step = 0              # 当前步数
-        self.total_steps = None           # 总步数，训练脚本里赋值
 
     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
+            # 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制
+            # self.knowledge_dataset.freeze_embedding = True
+            print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
         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
-                z_v_features = self.downsample_v_specific(shared_features)
-                batch_z, seq_len, dim_z = z_v_features.shape
-                z_v_flat = z_v_features.reshape(-1, dim_z)
-                token_logits = self.database_output(z_v_flat)
-                token_indices_flat = torch.argmax(token_logits, dim=-1)
-                token_indices = token_indices_flat.reshape(batch_z, -1)
-                
-                # Process query path
-                z_q_input = self.downsample_q_specific(shared_features)  # [batch, dim, seq_len]
-                z_q_input = z_q_input.permute(0, 2, 1)                  # [batch, seq_len, dim]
-                z_k = self.extract_db.q_to_k(z_q_input)                 # [batch]
-                z_q_pooled = z_q_input.mean(dim=1)                      # [batch, dim]
-                z_q_vec = self.extract_db.to_queries(z_q_pooled)        # [batch, 2*dim_key]
-                z_q_vec = z_q_vec.view(z_q_vec.size(0), 2, self.extract_db.dim_key)  # [batch, 2, dim_key]
-
-                progress = self.global_step / self.total_steps if self.total_steps else 0
-                if progress >= self.value_update_schedule:
-                    self.extract_db.updata_value(z_k, token_indices)
-                self.extract_db.update_keys_with_zq(z_k, z_q_vec)
-
         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))
@@ -736,12 +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()
@@ -774,13 +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:
@@ -798,4 +602,3 @@ class MiniMindLM(PreTrainedModel):
             if input_ids_next.item() == eos_token_id:
                 break
 
-

model/model0.py

@@ -0,0 +1,603 @@
+import math
+import struct
+import inspect
+import time
+#子空间不分解+嵌入更新
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+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
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+class KnowledgeDataset(nn.Module):
+    def __init__(self, params, tok_embeddings, is_train=True):
+        super().__init__()
+        self.is_train = is_train
+        self.params = params
+        self.tok_embeddings = tok_embeddings
+
+        # 嵌入参数
+        self.knowledge_dim = params.knowledge_dim
+        self.key_dim = self.knowledge_dim // 2
+        self.to_queries = nn.Sequential(
+                nn.Linear(params.dim, self.knowledge_dim, bias=False),
+        )
+
+        ## 数据库参数
+        self.knowledge_num = params.knowledge_num
+        self.knowledge_length = params.knowledge_length
+        self.keys = nn.Parameter(torch.randn(self.knowledge_num, self.knowledge_dim) * 0.02, requires_grad=True)
+        self.product_key_topk = min(16, self.knowledge_num)
+        
+        # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
+        self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num))
+
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
+        self.register_buffer('knowledge_dataset', 
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)
+        )
+
+        # 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):
+        """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__(
+        self,
+        config
+    ):
+        super().__init__()
+        self.config = config
+        self.num_heads = 8
+        self.head_dim = self.config.dim // self.num_heads
+        self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+        self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+    def forward(self, x, db, context_mask=None, pos_emb=None):
+        batch_size = x.size(0)
+
+        # 分离多头
+        q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if pos_emb is not None:
+            pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+            q = q + pos_emb
+            k = k + pos_emb
+            v = v + pos_emb
+
+        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+
+        if context_mask is not None:
+            expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1)
+            attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10)
+
+        attn_weights = F.softmax(attn_scores, dim=-1)
+
+        context = torch.matmul(attn_weights, v)
+
+        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim)
+
+        context = self.to_out(context)
+
+        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__()
+        if config.hidden_dim is None:
+            hidden_dim = 4 * config.dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
+        self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = 0
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(config)
+            for _ in range(config.n_routed_experts)
+        ])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(config)
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        self.aux_loss = aux_loss
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
+        # 且token_idxs = [3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...] 时
+        # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok）
+        # 接下来9个位置token_idxs[6:15] -> [4,  5,  6, 10, 11, 12...]属于专家1处理的token...依此类推
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens).to(expert_cache.dtype)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class MiniMindBlock(nn.Module):
+    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.self_attention = Attention(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)
+
+    def forward(self, x, pos_cis):
+        h_attn = self.self_attention(
+            self.attention_norm(x),
+            pos_cis
+        )
+        db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
+        h_attn = self.cross_attention(h_attn, db_embeddings)
+        h = x + h_attn
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class MiniMindLM(PreTrainedModel):
+    config_class = LMConfig
+
+    def __init__(self, params: LMConfig = None):
+        self.params = params or LMConfig()
+        super().__init__(self.params)
+        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)
+        self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) 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.tok_embeddings.weight = self.output.weight
+        self.register_buffer("pos_cis",
+                             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):
+            h = layer(
+                h, pos_cis
+            )
+
+        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))
+
+        # 进一步简化，只保留必要的参数
+        output = CausalLMOutputWithPast(
+            logits=logits,
+        )
+        output.hidden_states = h
+
+        output.aux_loss = aux_loss
+
+        return output
+
+    @torch.inference_mode()
+    def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
+                 stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args):
+        # 流式生成
+        if stream:
+            return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+
+        # 直接生成
+        generated = []
+        for i in range(input_ids.size(0)):
+            non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
+            for _ in range(num_return_sequences):
+                out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+                tokens_list = [tokens[:, -1:] for tokens in out]
+                gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
+                full_sequence = torch.cat([non_pad, gen], dim=-1)
+                generated.append(full_sequence)
+
+        max_length = max(seq.size(1) for seq in generated)
+        generated = [
+            torch.cat(
+                [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
+                dim=-1)
+            for seq in generated
+        ]
+        output = torch.cat(generated, dim=0)
+        res = output.view(input_ids.size(0) * num_return_sequences, -1)
+        return res
+
+    def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
+        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)
+            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:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+                sorted_probs = F.softmax(sorted_logits, dim=-1)
+                cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = -float('Inf')
+            input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+            input_ids = torch.cat((input_ids, input_ids_next), dim=1)
+            yield input_ids[:, start:]
+            if input_ids_next.item() == eos_token_id:
+                break

model/model1.py

@@ -0,0 +1,675 @@
+import math
+import struct
+import inspect
+import time
+#子空间二维分解+全局嵌入更新
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+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
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+class KnowledgeDataset(nn.Module):
+    def __init__(self, params, tok_embeddings, is_train=True):
+        super().__init__()
+        self.is_train = is_train
+        self.params = params
+        self.tok_embeddings = tok_embeddings
+
+        # 嵌入参数
+        self.knowledge_dim = params.knowledge_dim
+        self.key_dim = self.knowledge_dim // 2
+        self.to_queries = nn.Sequential(
+                nn.Linear(params.dim, self.knowledge_dim, bias=False),
+        )
+
+        ## 数据库参数
+        self.knowledge_num = params.knowledge_num
+        self.knowledge_length = params.knowledge_length
+        
+        # 修改键存储为二维分解空间
+        self.num_keys = int(math.sqrt(self.knowledge_num))
+        self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True)
+        self.product_key_topk = min(16, self.num_keys)
+        
+        # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
+        self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num))
+
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
+        self.register_buffer('knowledge_dataset', 
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long))
+
+        # 计算step数目，用于动态调整权重
+        self.step_counter = 0
+
+        self.freeze_embedding = False
+
+        # 添加批次计数器和更新频率
+        self.batch_counter = 0
+        self.update_frequency = 100  # 每100个批次更新一次
+
+    def _global_keys_update(self):
+        """全局更新所有子键"""
+        # 移除对self.freeze_embedding的检查，确保在调用时总是执行更新
+        with torch.no_grad():
+            # 创建用于存储每个子键的嵌入和计数的张量
+            k1_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device)
+            k2_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device)
+            k1_counts = torch.zeros(self.num_keys, device=self.keys.device)
+            k2_counts = torch.zeros(self.num_keys, device=self.keys.device)
+            
+            # 分批处理所有知识条目，避免内存溢出
+            batch_size = 1000  # 可根据可用内存调整
+            for i in range(0, self.knowledge_num, batch_size):
+                end_idx = min(i + batch_size, self.knowledge_num)
+                batch_indices = torch.arange(i, end_idx, device=self.keys.device)
+                
+                # 获取批次的嵌入
+                batch_tokens = self.knowledge_dataset[batch_indices]
+                batch_embeddings = self.tok_embeddings(batch_tokens.view(-1))
+                batch_embeddings = batch_embeddings.view(len(batch_indices), self.knowledge_length, -1).mean(dim=1)
+                batch_embeddings = self.to_queries(batch_embeddings)
+                
+                # 计算批次中每个条目对应的子键索引
+                indices_x = batch_indices // self.num_keys
+                indices_y = batch_indices % self.num_keys
+                
+                # 累加每个子键对应的嵌入
+                for j in range(len(batch_indices)):
+                    k1, k2 = indices_x[j].item(), indices_y[j].item()
+                    embedding = batch_embeddings[j]
+                    
+                    # 更新第一个子空间累加值
+                    k1_embeddings_sum[k1] += embedding[:self.key_dim]
+                    k1_counts[k1] += 1
+                    
+                    # 更新第二个子空间累加值
+                    k2_embeddings_sum[k2] += embedding[self.key_dim:]
+                    k2_counts[k2] += 1
+            
+            # 计算平均值并更新键
+            # 避免除零错误
+            k1_counts = torch.clamp(k1_counts, min=1)
+            k2_counts = torch.clamp(k2_counts, min=1)
+            
+            # 计算每个子键的平均嵌入
+            self.keys[:, 0] = k1_embeddings_sum / k1_counts.unsqueeze(1)
+            self.keys[:, 1] = k2_embeddings_sum / k2_counts.unsqueeze(1)
+            
+            print(f"执行了全局键更新，批次: {self.batch_counter}")
+        
+
+    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（保留这些变量以便其他地方使用）
+        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)
+
+        with torch.no_grad():
+            self.has_update_keys[pre_update_indices] = 1
+        
+        return all_best_tokens, all_best_tokens_embeddings
+    
+        
+        with torch.no_grad():
+            # 1. 计算token序列的平均嵌入
+            pre_update_embeddings = pre_update_embeddings.mean(dim=1)  # [num_indices, dim]
+            # 2. 转换维度
+            pre_update_embeddings = self.to_queries(pre_update_embeddings)  # [num_indices, knowledge_dim]
+            
+            # 3. 将one-hot索引转换为子空间索引
+            indices_x = pre_update_indices // self.num_keys
+            indices_y = pre_update_indices % self.num_keys
+            
+            # 4. 收集需要更新的唯一子键
+            unique_x = torch.unique(indices_x)
+            unique_y = torch.unique(indices_y)
+            
+            # 5. 更新第一个子空间的键
+            for k1 in unique_x:
+                # 找出所有使用该子键的索引
+                mask_k1 = (indices_x == k1)
+                if mask_k1.sum() == 0:
+                    continue
+                    
+                # 获取所有相关嵌入并计算平均值
+                k1_embeddings = pre_update_embeddings[mask_k1]
+                k1_avg_embedding = k1_embeddings.mean(dim=0)
+                
+                # 拆分为两个子空间并更新第一个子空间
+                self.keys[k1, 0] = k1_avg_embedding[:self.key_dim]
+            
+            # 6. 更新第二个子空间的键
+            for k2 in unique_y:
+                # 找出所有使用该子键的索引
+                mask_k2 = (indices_y == k2)
+                if mask_k2.sum() == 0:
+                    continue
+                    
+                # 获取所有相关嵌入并计算平均值
+                k2_embeddings = pre_update_embeddings[mask_k2]
+                k2_avg_embedding = k2_embeddings.mean(dim=0)
+                
+                # 更新第二个子空间
+                self.keys[k2, 1] = k2_avg_embedding[self.key_dim:]
+    
+    def search_index(self, x):
+        batch_size, seq_len, dim = x.shape
+
+        # 1. 序列维度平均
+        x_flat = x.mean(dim=1)  # [batch_size, dim]
+
+        # 2. 生成查询向量并重塑为两个子查询
+        queries = self.to_queries(x_flat)  # [batch_size, knowledge_dim]
+        queries = queries.reshape(batch_size, 2, self.key_dim)  # [batch_size, 2, key_dim]
+        # 调整维度顺序，使子空间维度位于首位
+        queries = queries.permute(1, 0, 2)  # [2, batch_size, key_dim]
+
+        # 3. 计算每个子空间的相似度
+        sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
+
+        # 4. 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]
+
+        # 5. 组合两个子空间的结果
+        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk]
+        all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 6. 将结果重塑为二维
+        all_scores = all_scores.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        all_indices = all_indices.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        # 7. 选择最终的top-k结果
+        scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1)
+        indices = torch.gather(all_indices, 1, indices_of_indices)
+
+        # 8. 应用智能分层选择策略
+        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
+
+        # 9. 更新批次计数并在特定批次执行全局更新
+        if self.is_train:
+            self.batch_counter += 1
+            
+            # 每update_frequency个批次执行一次全局更新，其余时间保持冻结
+            if self.batch_counter % self.update_frequency == 0:
+                # 只在特定批次更新键，无论freeze_embedding状态如何
+                self._global_keys_update()
+                # 标记所有键为已更新状态
+                with torch.no_grad():
+                    self.has_update_keys.fill_(1)
+
+        return best_tokens, best_tokens_embeddings
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        config
+    ):
+        super().__init__()
+        self.config = config
+        self.num_heads = 8
+        self.head_dim = self.config.dim // self.num_heads
+        self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+        self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+    def forward(self, x, db, context_mask=None, pos_emb=None):
+        batch_size = x.size(0)
+
+        # 分离多头
+        q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if pos_emb is not None:
+            pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+            q = q + pos_emb
+            k = k + pos_emb
+            v = v + pos_emb
+
+        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+
+        if context_mask is not None:
+            expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1)
+            attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10)
+
+        attn_weights = F.softmax(attn_scores, dim=-1)
+
+        context = torch.matmul(attn_weights, v)
+
+        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim)
+
+        context = self.to_out(context)
+
+        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__()
+        if config.hidden_dim is None:
+            hidden_dim = 4 * config.dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
+        self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = 0
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(config)
+            for _ in range(config.n_routed_experts)
+        ])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(config)
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        self.aux_loss = aux_loss
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
+        # 且token_idxs = [3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...] 时
+        # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok）
+        # 接下来9个位置token_idxs[6:15] -> [4,  5,  6, 10, 11, 12...]属于专家1处理的token...依此类推
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens).to(expert_cache.dtype)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class MiniMindBlock(nn.Module):
+    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.self_attention = Attention(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)
+
+    def forward(self, x, pos_cis):
+        h_attn = self.self_attention(
+            self.attention_norm(x),
+            pos_cis
+        )
+        db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
+        h_attn = self.cross_attention(h_attn, db_embeddings)
+        h = x + h_attn
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class MiniMindLM(PreTrainedModel):
+    config_class = LMConfig
+
+    def __init__(self, params: LMConfig = None):
+        self.params = params or LMConfig()
+        super().__init__(self.params)
+        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)
+        self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) 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.tok_embeddings.weight = self.output.weight
+        self.register_buffer("pos_cis",
+                             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
+            # 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制
+            # self.knowledge_dataset.freeze_embedding = True
+            print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
+        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):
+            h = layer(
+                h, pos_cis
+            )
+
+        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))
+
+        # 进一步简化，只保留必要的参数
+        output = CausalLMOutputWithPast(
+            logits=logits,
+        )
+        output.hidden_states = h
+
+        output.aux_loss = aux_loss
+
+        return output
+
+    @torch.inference_mode()
+    def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
+                 stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args):
+        # 流式生成
+        if stream:
+            return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+
+        # 直接生成
+        generated = []
+        for i in range(input_ids.size(0)):
+            non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
+            for _ in range(num_return_sequences):
+                out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+                tokens_list = [tokens[:, -1:] for tokens in out]
+                gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
+                full_sequence = torch.cat([non_pad, gen], dim=-1)
+                generated.append(full_sequence)
+
+        max_length = max(seq.size(1) for seq in generated)
+        generated = [
+            torch.cat(
+                [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
+                dim=-1)
+            for seq in generated
+        ]
+        output = torch.cat(generated, dim=0)
+        res = output.view(input_ids.size(0) * num_return_sequences, -1)
+        return res
+
+    def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
+        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)
+            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:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+                sorted_probs = F.softmax(sorted_logits, dim=-1)
+                cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = -float('Inf')
+            input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+            input_ids = torch.cat((input_ids, input_ids_next), dim=1)
+            yield input_ids[:, start:]
+            if input_ids_next.item() == eos_token_id:
+                break
+

model/model2.py

@@ -0,0 +1,679 @@
+import math
+import struct
+import inspect
+import time
+#子空间四维分解+全局嵌入更新
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+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
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+class KnowledgeDataset(nn.Module):
+    def __init__(self, params, tok_embeddings, is_train=True):
+        super().__init__()
+        self.is_train = is_train
+        self.params = params
+        self.tok_embeddings = tok_embeddings
+
+        # 嵌入参数
+        self.knowledge_dim = params.knowledge_dim
+        # 修改：子空间维度从原来的一半变为四分之一
+        self.key_dim = self.knowledge_dim // 4
+        self.to_queries = nn.Sequential(
+                nn.Linear(params.dim, self.knowledge_dim, bias=False),
+        )
+
+        ## 数据库参数
+        self.knowledge_num = params.knowledge_num
+        self.knowledge_length = params.knowledge_length
+        
+        # 修改：将键存储从二维分解空间改为四维分解空间
+        # 计算每个子空间的键数量(使用四次根号N)
+        self.num_keys = int(self.knowledge_num ** 0.25)
+        # 修改子空间个数从2变为4
+        self.keys = nn.Parameter(torch.randn(self.num_keys, 4, self.key_dim) * 0.02, requires_grad=True)
+        self.product_key_topk = min(16, self.num_keys)
+        
+        # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
+        self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num))
+
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
+        self.register_buffer('knowledge_dataset', 
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long))
+
+        # 计算step数目，用于动态调整权重
+        self.step_counter = 0
+
+        self.freeze_embedding = False
+
+        # 添加批次计数器和更新频率
+        self.batch_counter = 0
+        self.update_frequency = 100  # 每100个批次更新一次
+
+    def _global_keys_update(self):
+        """全局更新所有子键"""
+        # 移除对self.freeze_embedding的检查，确保在调用时总是执行更新
+        with torch.no_grad():
+            # 创建用于存储每个子键的嵌入和计数的张量（修改为4个子空间）
+            k1_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device)
+            k2_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device)
+            k3_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device)
+            k4_embeddings_sum = torch.zeros(self.num_keys, self.key_dim, device=self.keys.device)
+            
+            k1_counts = torch.zeros(self.num_keys, device=self.keys.device)
+            k2_counts = torch.zeros(self.num_keys, device=self.keys.device)
+            k3_counts = torch.zeros(self.num_keys, device=self.keys.device)
+            k4_counts = torch.zeros(self.num_keys, device=self.keys.device)
+            
+            # 分批处理所有知识条目，避免内存溢出
+            batch_size = 1000  # 可根据可用内存调整
+            for i in range(0, self.knowledge_num, batch_size):
+                end_idx = min(i + batch_size, self.knowledge_num)
+                batch_indices = torch.arange(i, end_idx, device=self.keys.device)
+                
+                # 获取批次的嵌入
+                batch_tokens = self.knowledge_dataset[batch_indices]
+                batch_embeddings = self.tok_embeddings(batch_tokens.view(-1))
+                batch_embeddings = batch_embeddings.view(len(batch_indices), self.knowledge_length, -1).mean(dim=1)
+                batch_embeddings = self.to_queries(batch_embeddings)
+                
+                # 计算批次中每个条目对应的子键索引（修改为4个子空间的索引计算）
+                # 使用整数除法和取模运算来提取四维索引
+                temp = batch_indices
+                indices_4 = temp % self.num_keys
+                temp = temp // self.num_keys
+                indices_3 = temp % self.num_keys
+                temp = temp // self.num_keys
+                indices_2 = temp % self.num_keys
+                indices_1 = temp // self.num_keys
+                
+                # 累加每个子键对应的嵌入
+                for j in range(len(batch_indices)):
+                    k1, k2, k3, k4 = indices_1[j].item(), indices_2[j].item(), indices_3[j].item(), indices_4[j].item()
+                    embedding = batch_embeddings[j]
+                    
+                    # 将嵌入分为四份并分别累加到对应的子空间
+                    quarter = self.key_dim
+                    k1_embeddings_sum[k1] += embedding[:quarter]
+                    k1_counts[k1] += 1
+                    
+                    k2_embeddings_sum[k2] += embedding[quarter:2*quarter]
+                    k2_counts[k2] += 1
+                    
+                    k3_embeddings_sum[k3] += embedding[2*quarter:3*quarter]
+                    k3_counts[k3] += 1
+                    
+                    k4_embeddings_sum[k4] += embedding[3*quarter:]
+                    k4_counts[k4] += 1
+            
+            # 计算平均值并更新键
+            # 避免除零错误
+            k1_counts = torch.clamp(k1_counts, min=1)
+            k2_counts = torch.clamp(k2_counts, min=1)
+            k3_counts = torch.clamp(k3_counts, min=1)
+            k4_counts = torch.clamp(k4_counts, min=1)
+            
+            # 计算每个子键的平均嵌入
+            self.keys[:, 0] = k1_embeddings_sum / k1_counts.unsqueeze(1)
+            self.keys[:, 1] = k2_embeddings_sum / k2_counts.unsqueeze(1)
+            self.keys[:, 2] = k3_embeddings_sum / k3_counts.unsqueeze(1)
+            self.keys[:, 3] = k4_embeddings_sum / k4_counts.unsqueeze(1)
+            
+            print(f"执行了全局键更新，批次: {self.batch_counter}")
+        
+
+    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（保留这些变量以便其他地方使用）
+        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)
+
+        with torch.no_grad():
+            self.has_update_keys[pre_update_indices] = 1
+        
+        return all_best_tokens, all_best_tokens_embeddings
+    
+    def search_index(self, x):
+        batch_size, seq_len, dim = x.shape
+
+        # 1. 序列维度平均
+        x_flat = x.mean(dim=1)  # [batch_size, dim]
+
+        # 2. 生成查询向量并重塑为四个子查询
+        queries = self.to_queries(x_flat)  # [batch_size, knowledge_dim]
+        # 修改：重塑为四个子查询而非两个
+        queries = queries.reshape(batch_size, 4, self.key_dim)  # [batch_size, 4, key_dim]
+        # 调整维度顺序，使子空间维度位于首位
+        queries = queries.permute(1, 0, 2)  # [4, batch_size, key_dim]
+
+        # 3. 计算每个子空间的相似度
+        sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
+
+        # 4. 在四个子空间分别做top-k
+        scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(4)]
+        scores_1, scores_2, scores_3, scores_4 = [scores_and_indices[p][0] for p in range(4)]
+        indices_1, indices_2, indices_3, indices_4 = [scores_and_indices[p][1] for p in range(4)]
+
+        # 5. 组合四个子空间的结果
+        # 首先组合第一、第二子空间
+        scores_12 = scores_1.unsqueeze(-1) + scores_2.unsqueeze(-2)  # [batch_size, topk, topk]
+        indices_12_base = (indices_1.unsqueeze(-1) * self.num_keys) + indices_2.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 然后组合第三、第四子空间
+        scores_34 = scores_3.unsqueeze(-1) + scores_4.unsqueeze(-2)  # [batch_size, topk, topk]
+        indices_34_base = (indices_3.unsqueeze(-1) * self.num_keys) + indices_4.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 最后组合所有子空间
+        scores_flat_12 = scores_12.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        indices_flat_12 = indices_12_base.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        scores_flat_34 = scores_34.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        indices_flat_34 = indices_34_base.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        # 对12和34组合的结果进行top-k选择
+        topk_scores_12, topk_indices_12 = scores_flat_12.topk(min(self.product_key_topk, scores_flat_12.size(1)), dim=-1)
+        topk_indices_12 = torch.gather(indices_flat_12, 1, topk_indices_12)
+        
+        topk_scores_34, topk_indices_34 = scores_flat_34.topk(min(self.product_key_topk, scores_flat_34.size(1)), dim=-1)
+        topk_indices_34 = torch.gather(indices_flat_34, 1, topk_indices_34)
+        
+        # 将12和34的结果组合
+        all_scores = topk_scores_12.unsqueeze(-1) + topk_scores_34.unsqueeze(-2)  # [batch_size, topk, topk]
+        all_indices = (topk_indices_12.unsqueeze(-1) * (self.num_keys**2)) + topk_indices_34.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 6. 将结果重塑为二维
+        all_scores = all_scores.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        all_indices = all_indices.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        # 7. 选择最终的top-k结果
+        scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1)
+        indices = torch.gather(all_indices, 1, indices_of_indices)
+
+        # 8. 应用智能分层选择策略
+        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
+
+        # 9. 更新批次计数并在特定批次执行全局更新
+        if self.is_train:
+            self.batch_counter += 1
+            
+            # 每update_frequency个批次执行一次全局更新，其余时间保持冻结
+            if self.batch_counter % self.update_frequency == 0:
+                # 只在特定批次更新键，无论freeze_embedding状态如何
+                self._global_keys_update()
+                # 标记所有键为已更新状态
+                with torch.no_grad():
+                    self.has_update_keys.fill_(1)
+
+        return best_tokens, best_tokens_embeddings
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        config
+    ):
+        super().__init__()
+        self.config = config
+        self.num_heads = 8
+        self.head_dim = self.config.dim // self.num_heads
+        self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+        self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+    def forward(self, x, db, context_mask=None, pos_emb=None):
+        batch_size = x.size(0)
+
+        # 分离多头
+        q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if pos_emb is not None:
+            pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+            q = q + pos_emb
+            k = k + pos_emb
+            v = v + pos_emb
+
+        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+
+        if context_mask is not None:
+            expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1)
+            attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10)
+
+        attn_weights = F.softmax(attn_scores, dim=-1)
+
+        context = torch.matmul(attn_weights, v)
+
+        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim)
+
+        context = self.to_out(context)
+
+        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__()
+        if config.hidden_dim is None:
+            hidden_dim = 4 * config.dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
+        self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = 0
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(config)
+            for _ in range(config.n_routed_experts)
+        ])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(config)
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        self.aux_loss = aux_loss
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
+        # 且token_idxs = [3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...] 时
+        # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok）
+        # 接下来9个位置token_idxs[6:15] -> [4,  5,  6, 10, 11, 12...]属于专家1处理的token...依此类推
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens).to(expert_cache.dtype)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class MiniMindBlock(nn.Module):
+    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.self_attention = Attention(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)
+
+    def forward(self, x, pos_cis):
+        h_attn = self.self_attention(
+            self.attention_norm(x),
+            pos_cis
+        )
+        db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
+        h_attn = self.cross_attention(h_attn, db_embeddings)
+        h = x + h_attn
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class MiniMindLM(PreTrainedModel):
+    config_class = LMConfig
+
+    def __init__(self, params: LMConfig = None):
+        self.params = params or LMConfig()
+        super().__init__(self.params)
+        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)
+        self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) 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.tok_embeddings.weight = self.output.weight
+        self.register_buffer("pos_cis",
+                             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
+  
+            print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
+        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):
+            h = layer(
+                h, pos_cis
+            )
+
+        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))
+
+        # 进一步简化，只保留必要的参数
+        output = CausalLMOutputWithPast(
+            logits=logits,
+        )
+        output.hidden_states = h
+
+        output.aux_loss = aux_loss
+
+        return output
+
+    @torch.inference_mode()
+    def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
+                 stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args):
+        # 流式生成
+        if stream:
+            return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+
+        # 直接生成
+        generated = []
+        for i in range(input_ids.size(0)):
+            non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
+            for _ in range(num_return_sequences):
+                out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+                tokens_list = [tokens[:, -1:] for tokens in out]
+                gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
+                full_sequence = torch.cat([non_pad, gen], dim=-1)
+                generated.append(full_sequence)
+
+        max_length = max(seq.size(1) for seq in generated)
+        generated = [
+            torch.cat(
+                [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
+                dim=-1)
+            for seq in generated
+        ]
+        output = torch.cat(generated, dim=0)
+        res = output.view(input_ids.size(0) * num_return_sequences, -1)
+        return res
+
+    def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
+        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)
+            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:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+                sorted_probs = F.softmax(sorted_logits, dim=-1)
+                cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = -float('Inf')
+            input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+            input_ids = torch.cat((input_ids, input_ids_next), dim=1)
+            yield input_ids[:, start:]
+            if input_ids_next.item() == eos_token_id:
+                break
+

model/model_ADMIN_Jun-17-112121-2025_Conflict.py

@@ -0,0 +1,604 @@
+import math
+import struct
+import inspect
+import time
+#子空间二维分解+梯度更新
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+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
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+class KnowledgeDataset(nn.Module):
+    def __init__(self, params, tok_embeddings, is_train=True):
+        super().__init__()
+        self.is_train = is_train
+        self.params = params
+        self.tok_embeddings = tok_embeddings
+
+        # 嵌入参数
+        self.knowledge_dim = params.knowledge_dim
+        self.key_dim = self.knowledge_dim // 2
+        self.to_queries = nn.Sequential(
+                nn.Linear(params.dim, self.knowledge_dim, bias=False),
+        )
+
+        ## 数据库参数
+        self.knowledge_num = params.knowledge_num
+        self.knowledge_length = params.knowledge_length
+        
+        # 修改键存储为二维分解空间，设置为可训练参数
+        self.num_keys = int(math.sqrt(self.knowledge_num))
+        # 确保keys是可训练参数
+        self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True)
+        self.product_key_topk = min(16, self.num_keys)
+        
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
+        self.register_buffer('knowledge_dataset', 
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long))
+
+        # 计算step数目，用于动态调整权重
+        self.step_counter = 0
+
+        # 移除批次计数器和更新频率相关代码
+
+    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（保留这些变量以便其他地方使用）
+        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)
+
+        return all_best_tokens, all_best_tokens_embeddings
+    
+        
+        with torch.no_grad():
+            # 1. 计算token序列的平均嵌入
+            pre_update_embeddings = pre_update_embeddings.mean(dim=1)  # [num_indices, dim]
+            # 2. 转换维度
+            pre_update_embeddings = self.to_queries(pre_update_embeddings)  # [num_indices, knowledge_dim]
+            
+            # 3. 将one-hot索引转换为子空间索引
+            indices_x = pre_update_indices // self.num_keys
+            indices_y = pre_update_indices % self.num_keys
+            
+            # 4. 收集需要更新的唯一子键
+            unique_x = torch.unique(indices_x)
+            unique_y = torch.unique(indices_y)
+            
+            # 5. 更新第一个子空间的键
+            for k1 in unique_x:
+                # 找出所有使用该子键的索引
+                mask_k1 = (indices_x == k1)
+                if mask_k1.sum() == 0:
+                    continue
+                    
+                # 获取所有相关嵌入并计算平均值
+                k1_embeddings = pre_update_embeddings[mask_k1]
+                k1_avg_embedding = k1_embeddings.mean(dim=0)
+                
+                # 拆分为两个子空间并更新第一个子空间
+                self.keys[k1, 0] = k1_avg_embedding[:self.key_dim]
+            
+            # 6. 更新第二个子空间的键
+            for k2 in unique_y:
+                # 找出所有使用该子键的索引
+                mask_k2 = (indices_y == k2)
+                if mask_k2.sum() == 0:
+                    continue
+                    
+                # 获取所有相关嵌入并计算平均值
+                k2_embeddings = pre_update_embeddings[mask_k2]
+                k2_avg_embedding = k2_embeddings.mean(dim=0)
+                
+                # 更新第二个子空间
+                self.keys[k2, 1] = k2_avg_embedding[self.key_dim:]
+    
+    def search_index(self, x):
+        batch_size, seq_len, dim = x.shape
+
+        # 1. 序列维度平均
+        x_flat = x.mean(dim=1)  # [batch_size, dim]
+
+        # 2. 生成查询向量并重塑为两个子查询
+        queries = self.to_queries(x_flat)  # [batch_size, knowledge_dim]
+        queries = queries.reshape(batch_size, 2, self.key_dim)  # [batch_size, 2, key_dim]
+        # 调整维度顺序，使子空间维度位于首位
+        queries = queries.permute(1, 0, 2)  # [2, batch_size, key_dim]
+
+        # 3. 计算每个子空间的相似度
+        sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
+
+        # 4. 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]
+
+        # 5. 组合两个子空间的结果
+        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk]
+        all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 6. 将结果重塑为二维
+        all_scores = all_scores.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        all_indices = all_indices.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        # 7. 选择最终的top-k结果
+        scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1)
+        indices = torch.gather(all_indices, 1, indices_of_indices)
+
+        # 8. 应用智能分层选择策略
+        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
+
+
+        return best_tokens, best_tokens_embeddings
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        config
+    ):
+        super().__init__()
+        self.config = config
+        self.num_heads = 8
+        self.head_dim = self.config.dim // self.num_heads
+        self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+        self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+    def forward(self, x, db, context_mask=None, pos_emb=None):
+        batch_size = x.size(0)
+
+        # 分离多头
+        q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if pos_emb is not None:
+            pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+            q = q + pos_emb
+            k = k + pos_emb
+            v = v + pos_emb
+
+        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+
+        if context_mask is not None:
+            expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1)
+            attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10)
+
+        attn_weights = F.softmax(attn_scores, dim=-1)
+
+        context = torch.matmul(attn_weights, v)
+
+        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim)
+
+        context = self.to_out(context)
+
+        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__()
+        if config.hidden_dim is None:
+            hidden_dim = 4 * config.dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
+        self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = 0
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(config)
+            for _ in range(config.n_routed_experts)
+        ])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(config)
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        self.aux_loss = aux_loss
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
+        # 且token_idxs = [3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...] 时
+        # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok）
+        # 接下来9个位置token_idxs[6:15] -> [4,  5,  6, 10, 11, 12...]属于专家1处理的token...依此类推
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens).to(expert_cache.dtype)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class MiniMindBlock(nn.Module):
+    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.self_attention = Attention(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)
+
+    def forward(self, x, pos_cis):
+        h_attn = self.self_attention(
+            self.attention_norm(x),
+            pos_cis
+        )
+        db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
+        h_attn = self.cross_attention(h_attn, db_embeddings)
+        h = x + h_attn
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class MiniMindLM(PreTrainedModel):
+    config_class = LMConfig
+
+    def __init__(self, params: LMConfig = None):
+        self.params = params or LMConfig()
+        super().__init__(self.params)
+        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)
+        self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) 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.tok_embeddings.weight = self.output.weight
+        self.register_buffer("pos_cis",
+                             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
+            # 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制
+            # self.knowledge_dataset.freeze_embedding = True
+            print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
+        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):
+            h = layer(
+                h, pos_cis
+            )
+
+        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))
+
+        # 进一步简化，只保留必要的参数
+        output = CausalLMOutputWithPast(
+            logits=logits,
+        )
+        output.hidden_states = h
+
+        output.aux_loss = aux_loss
+
+        return output
+
+    @torch.inference_mode()
+    def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
+                 stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args):
+        # 流式生成
+        if stream:
+            return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+
+        # 直接生成
+        generated = []
+        for i in range(input_ids.size(0)):
+            non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
+            for _ in range(num_return_sequences):
+                out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+                tokens_list = [tokens[:, -1:] for tokens in out]
+                gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
+                full_sequence = torch.cat([non_pad, gen], dim=-1)
+                generated.append(full_sequence)
+
+        max_length = max(seq.size(1) for seq in generated)
+        generated = [
+            torch.cat(
+                [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
+                dim=-1)
+            for seq in generated
+        ]
+        output = torch.cat(generated, dim=0)
+        res = output.view(input_ids.size(0) * num_return_sequences, -1)
+        return res
+
+    def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
+        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)
+            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:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+                sorted_probs = F.softmax(sorted_logits, dim=-1)
+                cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = -float('Inf')
+            input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+            input_ids = torch.cat((input_ids, input_ids_next), dim=1)
+            yield input_ids[:, start:]
+            if input_ids_next.item() == eos_token_id:
+                break
+

preprocessing/README_trex_processor.md

@@ -4,10 +4,35 @@
 1. **句子提取**：从 TREx 数据集提取三元组并转换为自然语言句子
 2. **LLM 处理**：使用 ollama qwen3:4b 模型进行句子修正和重要性评分
 
+## 🆕 防卡死机制
+
+为了解决LLM处理时可能出现的卡死问题，新增了以下功能：
+
+### 超时和重试机制
+- **超时时间**：每个LLM请求60秒超时
+- **重试机制**：失败后最多重试2次，采用指数退避策略
+- **并发控制**：降低并发数至4个，减少服务器压力
+
+### 心跳监控系统
+- **实时监控**：每30秒检查一次LLM响应状态
+- **异常警告**：超过30秒无成功响应时发出警告
+- **服务检测**：自动检查ollama服务状态
+- **详细统计**：实时显示成功率、超时率等统计信息
+
+### 日志系统
+- **详细日志**：所有操作都记录在 `logs/` 目录下
+- **双重输出**：同时输出到日志文件和控制台
+- **时间戳标记**：日志文件包含启动时间戳
+
+### 改进的错误处理
+- **异常恢复**：LLM处理失败时使用原句子和默认评分
+- **状态监控**：处理前检查ollama服务状态
+- **批次间休息**：批次之间休息5秒，避免过度压力
+
 ## 安装依赖
 
 ```bash
-pip install agno asyncio pydantic
+pip install agno asyncio pydantic requests
 ```
 
 确保已安装并启动 ollama，并下载 qwen3:4b 模型：
@@ -50,24 +75,52 @@ python trex_to_sentences_simple.py --step llm --sentences_json my_sentences.json
 
 ## 输出文件
 
-**注意：所有输出文件都会自动保存在 `./output/` 目录中**
+**注意：所有输出文件都会自动保存在相应目录中**
 
-### 步骤1输出
+### 句子提取输出
 - `output/extracted_sentences.json`: 提取的原始句子，包含元数据
 
-### 步骤2输出  
+### LLM处理输出  
 - `output/{output_file}.txt`: 修正后的句子文本文件
 - `output/{output_file}.json`: 完整的处理结果（包含原句、修正句、评分）
 - `output/{output_file}_sorted_by_importance.txt`: 按重要性评分排序的句子
 
 ### 检查点文件
-- `output/{output_file}_checkpoint_{数量}.json`: 每2000条句子自动保存的检查点
+- `output/{output_file}_checkpoint_{数量}.json`: 每1000条句子自动保存的检查点
+
+### 日志文件
+- `logs/trex_processor_{时间戳}.log`: 详细的处理日志
+
+## 🆕 故障诊断
+
+### 如果遇到卡死问题：
+
+1. **检查日志文件**：查看 `logs/` 目录下的最新日志
+2. **观察心跳监控**：注意控制台的心跳警告信息
+3. **检查ollama服务**：
+   ```bash
+   ps aux | grep ollama
+   curl http://localhost:11434/api/tags
+   ```
+4. **重启ollama服务**（如果需要）：
+   ```bash
+   pkill ollama
+   ollama serve &
+   ```
+
+### 常见警告信息：
+
+- `⚠️ 心跳检测`: 30秒无成功响应（正常情况下会自动恢复）
+- `❌ 严重警告`: 90秒无成功响应（可能需要检查服务）
+- `💀 Ollama服务异常`: ollama服务可能已停止
+- `💀 致命错误`: 连续多次警告（建议重启程序）
 
 ## 检查点恢复机制
 
 - 步骤2会自动检测已有的检查点文件（在 `output/` 目录中）
 - 只处理尚未处理的句子，避免重复工作
 - 如果所有句子都已处理，会直接生成最终输出文件
+- 中断后重新运行会自动从最新检查点继续
 
 ## 示例工作流
 
@@ -84,14 +137,18 @@ python trex_to_sentences_simple.py --step llm
 
 ## 性能特点
 
-- **并发处理**: 最大54个并发LLM请求
+- **保守的并发**: 最大4个并发LLM请求（降低卡死风险）
-- **检查点保存**: 每2000条句子自动保存，支持断点续传
+- **检查点保存**: 每1000条句子自动保存，支持断点续传
-- **进度显示**: 详细的处理进度和时间预估
+- **智能监控**: 详细的处理进度和时间预估
-- **错误处理**: LLM请求失败时使用原句子和默认评分
+- **健壮的错误处理**: LLM请求失败时使用原句子和默认评分
+- **服务监控**: 自动检测ollama服务状态
 
 ## 注意事项
 
 1. 首次运行步骤2前，必须先完成步骤1
-2. 检查点文件会占用额外磁盘空间（每个都包含所有已处理数据）
+2. 检查点文件会占用额外磁盘空间（每个都包含所有已处理数据）  
 3. LLM处理速度取决于模型性能和网络状况
-4. 建议先用`--max_files`参数测试小批量数据 
+4. 建议先用`--max_files`参数测试小批量数据
+5. **新增**：如果遇到卡死，查看日志文件和心跳监控信息
+6. **新增**：程序会自动检测并报告ollama服务状态
+7. **新增**：所有处理过程都有详细日志记录，便于问题诊断 

preprocessing/merge_output_json.py

@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+"""
+JSON文件合并脚本
+读取多个JSON文件并合并为一个JSON文件
+"""
+
+import json
+import os
+from typing import Dict, List, Any, Union
+
+# 需要合并的JSON文件列表
+JSON_FILES_TO_MERGE = [
+    "output/trex_sentences_enhanced_checkpoint_360000.json"
+]
+for i in range(1, 1010):
+    JSON_FILES_TO_MERGE.append(f"output/trex_sentences_enhanced_batch_{i}.json")
+
+def load_json_file(file_path: str) -> Union[Dict, List, None]:
+    """加载JSON文件"""
+    if not os.path.exists(file_path):
+        print(f"警告: 文件 {file_path} 不存在")
+        return None
+    
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        print(f"成功加载: {file_path}")
+        return data
+    except json.JSONDecodeError as e:
+        print(f"错误: 无法解析JSON文件 {file_path} - {e}")
+        return None
+    except Exception as e:
+        print(f"错误: 读取文件 {file_path} 失败 - {e}")
+        return None
+
+def merge_json_data(data1: Union[Dict, List], data2: Union[Dict, List]) -> Union[Dict, List]:
+    """合并两个JSON数据结构"""
+    
+    # 如果两个都是列表，直接合并
+    if isinstance(data1, list) and isinstance(data2, list):
+        print(f"合并两个列表: {len(data1)} + {len(data2)} = {len(data1) + len(data2)} 项")
+        return data1 + data2
+    
+    # 如果两个都是字典
+    elif isinstance(data1, dict) and isinstance(data2, dict):
+        print("合并两个字典结构")
+        merged = data1.copy()
+        
+        # 特殊处理：如果都有'sentences'字段且为列表，合并sentences
+        if 'sentences' in data1 and 'sentences' in data2:
+            if isinstance(data1['sentences'], list) and isinstance(data2['sentences'], list):
+                print(f"合并sentences字段: {len(data1['sentences'])} + {len(data2['sentences'])} = {len(data1['sentences']) + len(data2['sentences'])} 项")
+                merged['sentences'] = data1['sentences'] + data2['sentences']
+                
+                # 更新metadata if exists
+                if 'metadata' in merged:
+                    if isinstance(merged['metadata'], dict):
+                        merged['metadata']['total_sentences'] = len(merged['sentences'])
+                        merged['metadata']['merged_from'] = [os.path.basename(f) for f in JSON_FILES_TO_MERGE if os.path.exists(f)]
+                
+                # 合并其他字段
+                for key, value in data2.items():
+                    if key != 'sentences' and key not in merged:
+                        merged[key] = value
+                        
+                return merged
+        
+        # 普通字典合并
+        for key, value in data2.items():
+            if key in merged:
+                # 如果key重复且都是列表，合并列表
+                if isinstance(merged[key], list) and isinstance(value, list):
+                    merged[key] = merged[key] + value
+                # 如果key重复且都是字典，递归合并
+                elif isinstance(merged[key], dict) and isinstance(value, dict):
+                    merged[key] = merge_json_data(merged[key], value)
+                else:
+                    # 其他情况保留第二个文件的值
+                    merged[key] = value
+                    print(f"字段 '{key}' 被覆盖")
+            else:
+                merged[key] = value
+        
+        return merged
+    
+    # 类型不匹配的情况，创建一个包含两者的新结构
+    else:
+        print("数据类型不匹配，创建包含两者的新结构")
+        return {
+            "data_from_save.json": data1,
+            "data_from_save2.json": data2,
+            "merged_at": "test.py"
+        }
+
+def save_merged_json(data: Union[Dict, List], output_path: str):
+    """保存合并后的JSON数据"""
+    try:
+        # 确保输出目录存在
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+        
+        print(f"合并结果已保存到: {output_path}")
+        
+        # 显示统计信息
+        if isinstance(data, dict):
+            if 'sentences' in data and isinstance(data['sentences'], list):
+                print(f"总计句子数: {len(data['sentences'])}")
+            print(f"总计字段数: {len(data)}")
+        elif isinstance(data, list):
+            print(f"总计列表项数: {len(data)}")
+            
+    except Exception as e:
+        print(f"错误: 保存文件失败 - {e}")
+
+def remove_duplicates_from_sentences(data: Union[Dict, List]) -> Union[Dict, List]:
+    """从合并结果中移除重复的句子（基于句子内容）"""
+    if isinstance(data, dict) and 'sentences' in data:
+        if isinstance(data['sentences'], list):
+            original_count = len(data['sentences'])
+            seen_sentences = set()
+            unique_sentences = []
+            
+            for item in data['sentences']:
+                if isinstance(item, dict):
+                    # 如果是字典，使用sentence字段或corrected_sentence字段作为唯一标识
+                    sentence_key = item.get('sentence') or item.get('corrected_sentence') or item.get('original_sentence')
+                elif isinstance(item, str):
+                    sentence_key = item
+                else:
+                    sentence_key = str(item)
+                
+                if sentence_key and sentence_key not in seen_sentences:
+                    seen_sentences.add(sentence_key)
+                    unique_sentences.append(item)
+            
+            data['sentences'] = unique_sentences
+            
+            # 更新metadata
+            if 'metadata' in data and isinstance(data['metadata'], dict):
+                data['metadata']['total_sentences'] = len(unique_sentences)
+                data['metadata']['duplicates_removed'] = original_count - len(unique_sentences)
+            
+            print(f"去重完成: {original_count} -> {len(unique_sentences)} (移除了 {original_count - len(unique_sentences)} 个重复项)")
+    
+    return data
+
+def merge_multiple_json_data(data_list: List[Union[Dict, List]]) -> Union[Dict, List]:
+    """合并多个JSON数据结构"""
+    if not data_list:
+        return {}
+    
+    if len(data_list) == 1:
+        return data_list[0]
+    
+    print(f"准备合并 {len(data_list)} 个JSON数据结构")
+    
+    # 从第一个数据开始，逐步合并其他数据
+    merged_data = data_list[0]
+    
+    for i, data in enumerate(data_list[1:], 1):
+        print(f"正在合并第 {i+1} 个数据结构...")
+        merged_data = merge_json_data(merged_data, data)
+    
+    return merged_data
+
+def main():
+    """主函数"""
+    print("=== JSON文件合并脚本 ===")
+    
+    # 输出路径
+    output_path = "output/merged.json"
+    
+    print(f"准备合并以下文件:")
+    for i, file_path in enumerate(JSON_FILES_TO_MERGE, 1):
+        print(f"  {i}. {file_path}")
+    print(f"输出文件: {output_path}")
+    print()
+    
+    # 加载所有文件
+    loaded_data = []
+    successfully_loaded = []
+    
+    for file_path in JSON_FILES_TO_MERGE:
+        data = load_json_file(file_path)
+        if data is not None:
+            loaded_data.append(data)
+            successfully_loaded.append(file_path)
+    
+    # 检查是否至少有一个文件加载成功
+    if not loaded_data:
+        print("错误: 没有文件能够成功加载，退出")
+        return
+    
+    print(f"成功加载了 {len(loaded_data)} 个文件:")
+    for file_path in successfully_loaded:
+        print(f"  ✓ {file_path}")
+    
+    if len(loaded_data) < len(JSON_FILES_TO_MERGE):
+        failed_count = len(JSON_FILES_TO_MERGE) - len(loaded_data)
+        print(f"警告: {failed_count} 个文件加载失败")
+    print()
+    
+    # 合并所有数据
+    if len(loaded_data) == 1:
+        print("只有一个文件可用，直接使用...")
+        merged_data = loaded_data[0]
+    else:
+        print("开始合并所有文件...")
+        merged_data = merge_multiple_json_data(loaded_data)
+    
+    # 去重处理
+    print("\n检查并去除重复项...")
+    merged_data = remove_duplicates_from_sentences(merged_data)
+    
+    # 保存合并结果
+    print("\n保存合并结果...")
+    save_merged_json(merged_data, output_path)
+    
+    print("\n=== 合并完成 ===")
+    print(f"合并了 {len(successfully_loaded)} 个文件的数据")
+
+if __name__ == "__main__":
+    main()

run_file/DynamicKV-LLM_Mini_Minimind.sh

@@ -1,8 +1,10 @@
 #!/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
+source /mnt/wcy/miniconda/bin/activate
+conda activate accelerate
 
 # 设置环境变量以帮助调试
 export NCCL_DEBUG=INFO
@@ -26,24 +28,9 @@ 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 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")
 
@@ -88,329 +88,130 @@ 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
         
-        Logger(f"Loading database initialization data from {database_init_path}")
+        # 数据库参数
-        
-        # 1. 加载JSON文件并转换为字典
-        with open(database_init_path, 'r', encoding='utf-8') as f:
-            database_data = json.load(f)
-        
-        # 提取sentences列表
-        sentences_data = database_data.get('sentences', [])
-        Logger(f"Loaded {len(sentences_data)} sentences from database")
-        
-        # 2. 按照importance_score进行排序（从高到低）
-        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
-        embedding_model_name = "sentence-transformers/all-mpnet-base-v2"  # 轻量级但效果好的模型
-        embedding_model_dir = "./models/sentence_transformers/models--sentence-transformers--all-mpnet-base-v2"
-        embedding_cache_dir = "./models/sentence_transformers/cache"
-        os.makedirs(embedding_cache_dir, exist_ok=True)
-        
-        Logger(f"Loading embedding model: {embedding_model_name}")
-        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
-            })
-        
-        # # Create a JSON-serializable version for saving
-        # json_serializable_sentences = []
-        # for sentence in processed_sentences:
-        #     json_sentence = sentence.copy()
-        #     # Convert embedding to list if it's a numpy array
-        #     if hasattr(json_sentence['embedding'], 'tolist'):
-        #         json_sentence['embedding'] = json_sentence['embedding'].tolist()
-        #     json_serializable_sentences.append(json_sentence)
-        
-        # json.dump(json_serializable_sentences, open('processed_sentences.json', 'w', encoding='utf-8'))
-        
-        # processed_sentences = json.load(open('processed_sentences.json', 'r', encoding='utf-8'))
-        
-        # 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)}")
-        
-        # 2. 聚类处理 - 优化版本
-        Logger("Starting optimized clustering process...")
-        
-        # 聚类参数
         knowledge_num = args.knowledge_num
         knowledge_length = args.knowledge_length
-        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）
+        # 检查是否使用缓存
-        if len(processed_sentences) <= 10000:  # 只有在数据量不太大时才预计算
+        cache_dir = os.path.dirname(args.cluster_cache_path)
-            Logger("Pre-computing similarity matrix for faster clustering...")
+        if cache_dir:
-            embeddings_matrix = np.array([s['embedding'] for s in processed_sentences])
+            os.makedirs(cache_dir, exist_ok=True)
-            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 = []
+        processed_tensor = None
-        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")
+        # 尝试加载缓存的处理结果
-        
+        if not args.recompute_clusters and os.path.exists(args.cluster_cache_path):
-        # 选择聚类算法
+            try:
-        if args.fast_clustering and len(processed_sentences) > 5000:
+                Logger(f"Loading cached processed results from {args.cluster_cache_path}")
-            Logger("Using ultra-fast approximate clustering algorithm...")
+                processed_tensor = torch.load(args.cluster_cache_path)
-            
+                
-            # 超快速聚类：随机采样 + 批量处理
+                # 验证缓存文件的形状是否可用
-            import random
+                cached_knowledge_num, cached_knowledge_length = processed_tensor.shape
-            random.seed(42)  # 确保可重现性
+                
-            
+                if cached_knowledge_length == knowledge_length:
-            # 按重要性分层采样
+                    if cached_knowledge_num >= knowledge_num:
-            high_importance = [i for i, s in enumerate(processed_sentences) if s['importance_score'] > 0.7]
+                        # 缓存足够大，可以截取使用
-            medium_importance = [i for i, s in enumerate(processed_sentences) if 0.3 <= s['importance_score'] <= 0.7]
+                        processed_tensor = processed_tensor[:knowledge_num, :]
-            low_importance = [i for i, s in enumerate(processed_sentences) if s['importance_score'] < 0.3]
+                        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(f"Importance distribution: High={len(high_importance)}, Medium={len(medium_importance)}, Low={len(low_importance)}")
+                        Logger("Skipping database initialization - using cached results")
-            
+                    else:
-            for cluster_idx in tqdm(range(knowledge_num)):
+                        # 缓存太小，需要重新计算
-                # 分层选择种子：优先选择高重要性句子
+                        Logger(f"Cached knowledge_num ({cached_knowledge_num}) < required knowledge_num ({knowledge_num}), recomputing...")
-                if high_importance:
+                        processed_tensor = None
-                    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)))
+                    # 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...")
-                if not seed_pool:
+                    processed_tensor = None
-                    break
+            except Exception as e:
-                
+                Logger(f"Failed to load cached data: {e}, recomputing...")
-                # 随机选择种子（在同一重要性层级内）
+                processed_tensor = None
-                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(100, 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:
+        # 只有在没有有效缓存时才进行数据库初始化和处理
-            # 原始优化算法（适用于中等规模数据集）
+        if processed_tensor is None:
-            # 优化2: 批量处理和更高效的数据结构
+            Logger(f"Loading database initialization data from {database_init_path}")
-            for cluster_idx in tqdm(range(knowledge_num)):
+            
-                if not remaining_indices:
+            # 1. 加载JSON文件
-                    Logger(f"No more sentences available. Created {cluster_idx} clusters.")
+            with open(database_init_path, 'r', encoding='utf-8') as f:
-                    break
+                database_data = json.load(f)
+            
+            # 提取sentences列表
+            sentences_data = database_data.get('sentences', [])
+            Logger(f"Loaded {len(sentences_data)} sentences from database")
+            
+            # 2. 按照importance_score进行排序（从高到低）
+            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. 处理每条数据，不进行聚类
+            Logger("Processing individual sentences...")
+            processed_rows = []
+            
+            # 获取空token的id（用于填充）
+            pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+            
+            # 处理所需数量的句子
+            num_to_process = min(knowledge_num, len(sorted_sentences))
+            
+            for i in range(num_to_process):
+                sentence_data = sorted_sentences[i]
+                sentence = sentence_data.get('corrected_sentence', '')
                 
-                # 2.1 选择importance_score最高的句子作为种子
+                # 将句子转换为tokens
-                remaining_sentences_subset = [processed_sentences[i] for i in remaining_indices]
+                sentence_tokens = tokenizer.encode(sentence, add_special_tokens=False)
-                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")
+            
+            # 如果句子数量不足，用空token填充剩余位置
+            while len(processed_rows) < knowledge_num:
+                empty_tokens = [pad_token_id] * knowledge_length
+                processed_rows.append(empty_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
+            processed_tensor = torch.tensor(processed_rows, dtype=torch.long)
+            
+            Logger(f"Data processing completed:")
+            Logger(f"  - Processed {num_to_process} sentences")
+            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})")
+            
+            # 保存处理结果到缓存文件
+            try:
+                torch.save(processed_tensor, args.cluster_cache_path)
+                Logger(f"Processed results saved to {args.cluster_cache_path}")
+            except Exception as e:
+                Logger(f"Failed to save processed results: {e}")
         
-        # 如果聚类数量不足，用随机token填充
+        # 4. 初始化模型的knowledge_dataset
-        while len(clustered_rows) < knowledge_num:
+        if hasattr(model, 'knowledge_dataset') and hasattr(model.knowledge_dataset, 'knowledge_dataset'):
-            pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+            model.knowledge_dataset.knowledge_dataset.data.copy_(processed_tensor)
-            random_tokens = [pad_token_id] * knowledge_length
+            Logger("Successfully initialized model.knowledge_dataset.knowledge_dataset with processed data")
-            clustered_rows.append(random_tokens)
-        
-        # 转换为tensor
-        clustered_tensor = torch.tensor(clustered_rows, dtype=torch.long)
-        
-        Logger(f"Clustering completed:")
-        Logger(f"  - Created {len(clustered_rows)} clusters")
-        Logger(f"  - Cluster shape: {clustered_tensor.shape}")
-        Logger(f"  - Expected shape: ({knowledge_num}, {knowledge_length})")
-        
-        # 3. 初始化模型的weight_down_embed
-        if hasattr(model, 'extract_db') and hasattr(model.extract_db, 'weight_down_embed'):
-            model.extract_db.weight_down_embed.data.copy_(clustered_tensor)
-            Logger("Successfully initialized model.extract_db.weight_down_embed with clustered 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")
     
@@ -423,6 +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:
@@ -486,7 +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)
@@ -610,7 +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'
 
@@ -629,21 +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")
@@ -651,12 +461,14 @@ 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=64*64,help="知识库的数据数目")
+    parser.add_argument("--knowledge_num", type=int, default=960400,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_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()
-
+ 
     #########################################################
     # 初始化accelerator和deepspeed
     #########################################################
@@ -692,7 +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
     )
 
     #########################################################