Minimind/model/model.py

import math
import struct
import inspect
import time

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
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__()
        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)

# 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
    freqs = torch.outer(t, freqs).float()  # type: ignore
    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
        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)

# precompute_pos_cis_real 函数用于预计算位置编码（实数版本）。
def precompute_pos_cis_real(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
    """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
        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,
                db_value=None):
        bsz, seq_len, _ = x.shape #bsz: 批量大小, seq_len: 序列长度, _: 隐藏维度
        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)

        # 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
        if db_value is not None:
            # 确保db_value的形状与xv兼容，假设db_value形状为[B, N, H, D]
            if db_value.ndim == 4:  # [B, N, H, D]
                db_value = db_value.transpose(1, 2)  # -> [B, H, N, D]

                # 检查是否需要调整D维度
                if db_value.shape[-1] != xv.shape[-1]:
                    # 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
                # 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
        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 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 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):
        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.cross_att = CrossAttention(config)

        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, db_value, pos_cis):

        # 注意力计算
        h_attn = self.attention(
            self.attention_norm(x),
            pos_cis,
            db_value=db_value
        )

        h_attn = self.cross_att(h_attn, db_value)

        # 残差连接
        h = x + h_attn

        # 前馈神经网络
        out = h + self.feed_forward(self.ffn_norm(h))
        return out 

class ExtractDB(nn.Module):
    def __init__(self, params, tok_embeddings=None):
         # 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

        # 智能负载均衡相关参数
        self.enable_intelligent_balance = getattr(params, 'db_intelligent_balance', True)
        self.relevance_threshold = getattr(params, 'db_relevance_threshold', 0.7)
        self.base_balance_strength = getattr(params, 'db_balance_strength', 0.3)
        self.momentum = getattr(params, 'db_momentum', 0.9)
        self.adaptive_weights = getattr(params, 'db_adaptive_weights', True)
        
        # 动态权重调整参数
        self.current_relevance_weight = 0.8  # 开始时更重视相关性
        self.current_balance_weight = 0.2
        self.weight_update_frequency = 100  # 每100步调整一次权重
        self.step_counter = 0
        
        # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
        self.register_buffer('usage_counts', torch.zeros(self.knowledge_num))
        self.register_buffer('total_queries', torch.tensor(0.0))

        # 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('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),
        )
        
        # 存储token embeddings的引用，用于计算真实的语义相关性
        self.tok_embeddings = tok_embeddings

    def update_usage_statistics(self, selected_indices):
        """更新数据库条目的使用统计"""
        if not self.training or not self.enable_intelligent_balance:
            return
            
        with torch.no_grad():
            # 统计当前batch中每个条目的使用次数
            batch_usage = torch.zeros(self.knowledge_num, device=selected_indices.device)
            unique_indices, counts = torch.unique(selected_indices, return_counts=True)
            batch_usage[unique_indices] = counts.float()
            
            # 使用简单的tensor操作来更新统计
            current_usage = self.usage_counts.clone()
            current_total = self.total_queries.clone()
            
            new_usage = self.momentum * current_usage + (1 - self.momentum) * batch_usage
            new_total = current_total + selected_indices.numel()
            
            # 直接替换buffer内容
            self.usage_counts.copy_(new_usage)
            self.total_queries.copy_(new_total)

    def update_dynamic_weights(self):
        """动态调整相关性和平衡权重"""
        if not self.adaptive_weights or not self.training:
            return
            
        self.step_counter += 1
        
        # 每隔一定步数调整权重
        if self.step_counter % self.weight_update_frequency == 0:
            with torch.no_grad():
                if self.total_queries > 0:
                    # 计算使用分布的方差（不平衡程度）
                    usage_rates = self.usage_counts / self.total_queries
                    usage_variance = usage_rates.var().item()
                    
                    # 根据不平衡程度调整权重
                    if usage_variance > 0.01:  # 高度不平衡
                        self.current_relevance_weight = max(0.5, self.current_relevance_weight - 0.1)
                        self.current_balance_weight = min(0.5, self.current_balance_weight + 0.1)
                    elif usage_variance < 0.001:  # 已经很平衡
                        self.current_relevance_weight = min(0.9, self.current_relevance_weight + 0.1)
                        self.current_balance_weight = max(0.1, self.current_balance_weight - 0.1)
                    
                    # 确保权重和为1
                    total_weight = self.current_relevance_weight + self.current_balance_weight
                    self.current_relevance_weight /= total_weight
                    self.current_balance_weight /= total_weight

    def intelligent_selection(self, query, all_scores, all_indices):
        """智能分层选择策略"""
        if not self.enable_intelligent_balance or not self.training:
            return all_scores
            
        with torch.no_grad():
            batch_size = all_scores.size(0)
            device = all_scores.device
            dtype = all_scores.dtype
            
            # 更新动态权重
            self.update_dynamic_weights()
            
            # 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.weight_down_embed[unique_indices]
            flat_tokens = candidate_tokens.view(-1)
            flat_embeddings = self.tok_embeddings(flat_tokens)
            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)
            
            for batch_idx in range(batch_size):
                indices = all_indices[batch_idx]
                scores = all_scores[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)
                
                # 应用相关性阈值过滤
                relevance_probs = F.softmax(similarity_scores.float(), dim=-1).to(dtype)
                mean_prob = relevance_probs.mean()
                adaptive_threshold = max(self.relevance_threshold * mean_prob, mean_prob * 0.5)
                relevant_mask = relevance_probs > adaptive_threshold
                
                if relevant_mask.sum() == 0:
                    # 如果没有相关候选，选择相似度最高的
                    top_k = min(5, len(indices))
                    _, top_indices = similarity_scores.topk(top_k)
                    relevant_mask = torch.zeros_like(relevant_mask, dtype=torch.bool)
                    relevant_mask[top_indices] = True
                
                # 在相关候选中应用负载均衡
                if relevant_mask.sum() > 1:
                    relevant_indices = indices[relevant_mask]
                    relevant_usage = self.usage_counts[relevant_indices]
                    
                    # 计算平衡分数
                    balance_scores = 1.0 / (relevant_usage + 1.0)
                    balance_scores = balance_scores / (balance_scores.sum() + 1e-8)
                    
                    # 相关性分数
                    relevant_rel_scores = relevance_probs[relevant_mask]
                    relevant_rel_scores = relevant_rel_scores / (relevant_rel_scores.sum() + 1e-8)
                    
                    # 综合分数
                    combined_scores = (self.current_relevance_weight * relevant_rel_scores + 
                                     self.current_balance_weight * balance_scores.to(dtype))
                    
                    # 应用调整
                    adjustment = self.base_balance_strength * combined_scores.to(dtype)
                    enhanced_scores[batch_idx, relevant_mask] = scores[relevant_mask] + adjustment
            
            return enhanced_scores.to(device)

    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. 应用智能分层选择策略
        enhanced_scores = self.intelligent_selection(x, all_scores, all_indices)

        # 6. 基于增强后的分数进行最终top-k选择
        scores, pk_indices = enhanced_scores.topk(self.num_experts_per_head_topk, dim=-1)
        indices = all_indices.gather(-1, pk_indices)
        
        flat_indices = indices.view(-1)
        
        # 7. 更新使用统计
        self.update_usage_statistics(flat_indices)
        
        return flat_indices

    def get_data(self, index):
        # 直接从GPU获取embedding
        db_values = self.weight_down_embed[index]#变成token了所以是1,后续再过emb
        # db_value = db_values.view(self.batch_size,-1) 
        return db_values

    @torch.no_grad()
    def updata_value(self, k, v):#要加一个从向量返回index的过程
        # 直接更新buffer上的值 (不需要梯度)
        v_reshaped = v.view(v.size(0), -1)
        # 确保数据类型匹配
        v_reshaped = v_reshaped.to(dtype=self.weight_down_embed.dtype)
        self.weight_down_embed[k] = v_reshaped



class MiniMindLM(PreTrainedModel):
    config_class = LMConfig

    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
        
        # 先创建token embeddings
        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
        self.dropout = nn.Dropout(params.dropout)
        
        # 根据配置选择ExtractDB版本
        # use_direct_semantic = getattr(params, 'use_direct_semantic', False)
        # if use_direct_semantic:
        #     self.extract_db = ExtractDB_DirectSemantic(self.params, self.tok_embeddings)
        # else:
        #     self.extract_db = ExtractDB(self.params, self.tok_embeddings)
        self.extract_db = ExtractDB_DirectSemantic(self.params, self.tok_embeddings)

        # 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

        # Calculate input dimension
        input_dim = (self.params.max_seq_len-1)*self.params.n_layers
        # Use a bottleneck architecture to reduce parameters
        bottleneck_dim = 256  # Significantly smaller bottleneck dimension

        # Factorized shared downsampling using two smaller convolutions
        self.shared_downsample = nn.Sequential(
            # First reduce input dimension to bottleneck
            nn.Conv1d(input_dim, bottleneck_dim, kernel_size=1, padding='same'),
            nn.ReLU(),  # Non-linearity to improve representation capacity
            # Then expand to target dimension
            nn.Conv1d(bottleneck_dim, 128*8, kernel_size=1, padding='same')
        )

        # Specific layers for v path
        self.downsample_v_specific = nn.Sequential(
            nn.Conv1d(128*8, 128, kernel_size=1, padding='same'),
            nn.Conv1d(128, self.params.knowledge_length, kernel_size=1, padding='same')
        )
        
        # Specific layers for q path
        self.downsample_q_specific = nn.Sequential(
            nn.Conv1d(128*8, 512, kernel_size=1, padding='same')
        )
        # 使用实数版本的位置编码，避免复数张量可能导致的段错误
        self.register_buffer("pos_cis_real",
                             precompute_pos_cis_real(dim=params.dim // params.n_heads, theta=params.rope_theta),
                             persistent=False)
        self.params = params

    def forward(self,
                input_ids: Optional[torch.Tensor] = None,
                logits_to_keep: Union[int, torch.Tensor] = 0,
                **args):
        start_pos = args.get('start_pos', 0)
        h = self.dropout(self.tok_embeddings(input_ids))
        pos_cis_real = self.pos_cis_real[start_pos:start_pos + input_ids.size(1)]
        h_list = []

        for l, layer in enumerate(self.layers):
            # 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_list.append(h.unsqueeze(0))

        h_tensor = torch.cat(h_list, dim=0).permute(1, 0, 2, 3)

        # 只在非禁用数据库模式下执行数据库更新逻辑
        if not self.params.disable_db:
            # 使用detach()分离计算图，避免多次反向传播
            h_tensor_detached = h_tensor.detach()
            h_tensor_detached = h_tensor_detached.reshape(h_tensor_detached.shape[0], -1, self.params.dim)

            # 数据库更新逻辑与主计算图分离
            with torch.no_grad():
                
                # Compute shared downsampling layer once
                shared_features = self.shared_downsample(h_tensor_detached)
                
                # Get features from v path - now we output embedding-dimension vectors
                z_v_features = self.downsample_v_specific(shared_features)
                batch_z, seq_len, dim_z = z_v_features.shape
                
                # Reshape to batch_size * knowledge_length, dim
                z_v_flat = z_v_features.reshape(-1, dim_z)
                
                # Direct token prediction - like the main language model head
                token_logits = self.database_output(z_v_flat)  # [batch_z * seq_len, vocab_size]
                # Get token indices directly from logits
                token_indices_flat = torch.argmax(token_logits, dim=-1)
                token_indices = token_indices_flat.reshape(batch_z, -1)
                
                # Process query path as before
                z_q = self.downsample_q_specific(shared_features)
                z_k = self.extract_db.q_to_k(z_q)
                # self.extract_db.updata_value(z_k, token_indices)

        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.output(self.norm(h)[:, slice_indices, :])
        aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))

        # 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

        # 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()
    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 = input_ids.shape[1], True
        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 = out.logits[:, -1, :]
            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

class ExtractDB_DirectSemantic(nn.Module):
    """直接语义匹配的数据库检索模块，完全移除Product Key"""
    def __init__(self, params, tok_embeddings=None):
        super().__init__()
        self.batch_size = None
        self.dim = params.dim
        self.knowledge_num = params.knowledge_num
        self.knowledge_length = params.knowledge_length
        self.tok_embeddings = tok_embeddings
        self.num_experts_per_head_topk = 1
        
        # 训练步数管理
        self.current_step = 0
        self.realtime_threshold = getattr(params, 'realtime_steps', 800)  # 前800步实时计算
        
        # 渐进式缓存策略参数
        self.knowledge_update_rate = 0.01  # 每步更新1%的知识
        self.knowledge_per_step = max(1, int(self.knowledge_num * self.knowledge_update_rate))
        self.update_cycle = 100  # 100步循环
        
        # 知识库存储
        self.register_buffer('weight_down_embed', 
            torch.randint(low=0, high=6400, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)
        )
        
        # 嵌入缓存
        self.knowledge_embeddings_cache = None
        self.cache_update_mask = torch.zeros(self.knowledge_num, dtype=torch.bool)  # 跟踪哪些已更新
        
        # 归一化缓存（用于优化相似度计算）
        self.normalized_knowledge_cache = None
        self.normalization_valid = False
        
        # 负载均衡组件
        self.register_buffer('usage_counts', torch.zeros(self.knowledge_num))
        self.register_buffer('total_queries', torch.tensor(0.0))
        self.momentum = getattr(params, 'db_momentum', 0.9)
        self.balance_strength = getattr(params, 'db_balance_strength', 0.1)
        
    def should_use_realtime_computation(self):
        """判断是否应该使用实时计算"""
        return self.current_step < self.realtime_threshold
        
    def get_knowledge_indices_to_update(self):
        """获取本步需要更新的知识条目索引"""
        if self.should_use_realtime_computation():
            # 前800步：全部实时计算
            return torch.arange(self.knowledge_num)
        
        # 后续步数：循环更新策略
        cycle_position = self.current_step % self.update_cycle
        start_idx = (cycle_position * self.knowledge_per_step) % self.knowledge_num
        end_idx = min(start_idx + self.knowledge_per_step, self.knowledge_num)
        
        return torch.arange(start_idx, end_idx)
    
    def update_knowledge_embeddings(self, force_all=False):
        """智能更新知识嵌入缓存"""
        if force_all or self.should_use_realtime_computation():
            # 全量更新
            indices_to_update = torch.arange(self.knowledge_num)
        else:
            # 渐进式更新
            indices_to_update = self.get_knowledge_indices_to_update()
        
        if len(indices_to_update) == 0:
            return
            
        # 初始化缓存
        if self.knowledge_embeddings_cache is None:
            # 获取tok_embeddings的dtype，确保类型一致
            dummy_input = torch.zeros(1, dtype=torch.long, device=self.weight_down_embed.device)
            dummy_embedding = self.tok_embeddings(dummy_input)
            embedding_dtype = dummy_embedding.dtype
            
            self.knowledge_embeddings_cache = torch.zeros(
                self.knowledge_num, self.dim, 
                device=self.weight_down_embed.device,
                dtype=embedding_dtype  # 使用与tok_embeddings相同的dtype
            )
        
        with torch.no_grad():
            # 只更新指定的知识条目
            tokens_to_update = self.weight_down_embed[indices_to_update]  # [num_update, knowledge_length]
            flat_tokens = tokens_to_update.view(-1)  # [num_update * knowledge_length]
            
            # 批量计算嵌入
            flat_embeddings = self.tok_embeddings(flat_tokens)  # [num_update * knowledge_length, dim]
            
            # 重塑并平均池化
            updated_embeddings = flat_embeddings.view(
                len(indices_to_update), self.knowledge_length, -1
            ).mean(dim=1)  # [num_update, dim]
            
            # 更新缓存 - 现在类型应该匹配了
            self.knowledge_embeddings_cache[indices_to_update] = updated_embeddings
            self.cache_update_mask[indices_to_update] = True
            
        # 使归一化缓存失效
        self.normalization_valid = False
        
    def get_normalized_knowledge_embeddings(self):
        """获取归一化的知识嵌入（用于优化相似度计算）"""
        if not self.normalization_valid or self.normalized_knowledge_cache is None:
            if self.knowledge_embeddings_cache is None:
                self.update_knowledge_embeddings(force_all=True)
            
            self.normalized_knowledge_cache = F.normalize(
                self.knowledge_embeddings_cache, dim=-1
            )
            self.normalization_valid = True
            
        return self.normalized_knowledge_cache
    
    def optimized_similarity_computation(self, query_features):
        """优化的相似度计算"""
        # 归一化查询特征
        normalized_query = F.normalize(query_features, dim=-1)  # [batch_size, dim]
        
        # 获取归一化的知识嵌入
        normalized_knowledge = self.get_normalized_knowledge_embeddings()  # [knowledge_num, dim]
        
        # 使用矩阵乘法计算余弦相似度
        similarities = torch.mm(normalized_query, normalized_knowledge.t())  # [batch_size, knowledge_num]
        
        return similarities
    
    def apply_load_balancing(self, similarities):
        """应用负载均衡策略"""
        if not self.training or self.total_queries == 0:
            return similarities
            
        # 计算使用频率
        usage_rates = self.usage_counts / (self.total_queries + 1e-8)
        
        # 创建平衡偏置（低频率条目获得正偏置）
        max_usage = usage_rates.max()
        balance_bias = self.balance_strength * (max_usage - usage_rates + 1e-8).log()
        
        # 应用偏置
        balanced_similarities = similarities + balance_bias.unsqueeze(0)
        
        return balanced_similarities
    
    def update_usage_statistics(self, selected_indices):
        """更新使用统计"""
        if not self.training:
            return
            
        with torch.no_grad():
            # 统计当前batch中每个条目的使用次数
            batch_usage = torch.zeros(self.knowledge_num, device=selected_indices.device)
            unique_indices, counts = torch.unique(selected_indices, return_counts=True)
            batch_usage[unique_indices] = counts.float()
            
            # 更新统计
            self.usage_counts.copy_(
                self.momentum * self.usage_counts + (1 - self.momentum) * batch_usage
            )
            self.total_queries.copy_(self.total_queries + selected_indices.numel())
    
    def q_to_k(self, x):
        """直接语义检索的主方法"""
        self.current_step += 1
        batch_size, seq_len, dim = x.shape
        
        # 智能更新知识嵌入缓存
        self.update_knowledge_embeddings()
        
        # 计算查询特征（序列平均）
        query_features = x.mean(dim=1)  # [batch_size, dim]
        
        # 优化的相似度计算
        similarities = self.optimized_similarity_computation(query_features)
        
        # 应用负载均衡
        balanced_similarities = self.apply_load_balancing(similarities)
        
        # 选择top-k
        _, indices = balanced_similarities.topk(self.num_experts_per_head_topk, dim=-1)
        flat_indices = indices.view(-1)
        
        # 更新使用统计
        self.update_usage_statistics(flat_indices)
        
        return flat_indices
    
    def get_data(self, index):
        """获取数据，与原版本兼容"""
        return self.weight_down_embed[index]
    
    @torch.no_grad()
    def updata_value(self, k, v):
        """更新数据，与原版本兼容"""
        v_reshaped = v.view(v.size(0), -1)
        v_reshaped = v_reshaped.to(dtype=self.weight_down_embed.dtype)
        self.weight_down_embed[k] = v_reshaped
        
        # 标记相关缓存需要更新
        self.cache_update_mask[k] = False
        self.normalization_valid = False