DeepSeek-V3/R1 架构深度全景解析：MLA 注意力压缩与 Multi-Token 预测内核实战

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadLatentAttention(nn.Module):
    def __init__(self, dim=7168, num_heads=128, head_dim=128, kv_lora_rank=512, qk_rope_dim=64):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.kv_lora_rank = kv_lora_rank
        self.qk_rope_dim = qk_rope_dim
        
        # KV Compression Matrix (Down-projection & Up-projection)
        self.W_DKV = nn.Linear(dim, kv_lora_rank, bias=False)
        self.W_UK = nn.Linear(kv_lora_rank, num_heads * (head_dim - qk_rope_dim), bias=False)
        self.W_UV = nn.Linear(kv_lora_rank, num_heads * head_dim, bias=False)
        
        # RoPE Decoupled Projection
        self.W_KR = nn.Linear(dim, qk_rope_dim, bias=False)
        self.W_QR = nn.Linear(dim, num_heads * qk_rope_dim, bias=False)
        self.W_DQ = nn.Linear(dim, 1536, bias=False)
        self.W_UQ = nn.Linear(1536, num_heads * (head_dim - qk_rope_dim), bias=False)

    def forward_inference(self, x, kv_cache_latent, kv_cache_rope):
        # 1. 压缩当前 token 的 KV 状态并存入超紧凑 Cache
        c_kv = self.W_DKV(x) # Shape: [Batch, 1, 512]
        k_rope = self.apply_rope(self.W_KR(x)) # Shape: [Batch, 1, 64]
        
        # 2. 矩阵结合律优化：在 Q 侧吸收 W_UK，避免还原巨大的 KV 缓存矩阵
        # Q_absorbed = Q @ W_UK^T (在计算 attention score 前预乘)
        # Score = (Q_absorbed @ c_kv^T) + (Q_rope @ k_rope^T)
        return c_kv, k_rope

// 高性能零拷贝锁无关 KV 潜在向量 Ring Buffer 内存池
pub struct LatentKVCachePool {
    latent_buffer: Vec<f16>,
    rope_buffer: Vec<f16>,
    slot_stride: usize,
    capacity: usize,
}

impl LatentKVCachePool {
    pub fn allocate_slot(&mut self, token_pos: usize) -> (*mut f16, *mut f16) {
        let latent_offset = token_pos * 512;
        let rope_offset = token_pos * 64;
        unsafe {
            (
                self.latent_buffer.as_mut_ptr().add(latent_offset),
                self.rope_buffer.as_mut_ptr().add(rope_offset)
            )
        }
    }
}