美团LongCat-2.0万亿大模型开源深度解析：国产算力撑起1.6T参数的工程奇迹

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
import math

class LongCatSparseAttention(nn.Module):
    """
    LongCat稀疏注意力机制（LSA）
    将传统 O(n²) 注意力优化为 O(n) 线性复杂度
    """
    def __init__(self, 
                 hidden_dim: int = 7168,
                 num_heads: int = 64,
                 top_k_ratio: float = 0.1,
                 streaming_window: int = 4096,
                 use_cross_layer: bool = True):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        self.head_dim = hidden_dim // num_heads
        self.top_k_ratio = top_k_ratio
        self.streaming_window = streaming_window
        self.use_cross_layer = use_cross_layer
        
        # QKV投影
        self.q_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
        self.k_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
        self.v_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
        self.o_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
        
        # 流感知索引器
        self.stream_selector = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 4),
            nn.GELU(),
            nn.Linear(hidden_dim // 4, 1),
        )
        
        # 跨层索引缓存
        self.cross_layer_cache: Optional[torch.Tensor] = None
        
    def forward(self, 
                x: torch.Tensor,
                past_key_value: Optional[Tuple[torch.Tensor]] = None,
                cross_layer_indices: Optional[torch.Tensor] = None) -> torch.Tensor:
        """
        x: [batch, seq_len, hidden_dim]
        """
        batch, seq_len, _ = x.shape
        
        # QKV投影
        q = self.q_proj(x)
        k = self.k_proj(x)
        v = self.v_proj(x)
        
        # 重塑为多头
        q = q.view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        
        # 流感知索引：选择Top-K最重要位置
        with torch.no_grad():
            importance_scores = self.stream_selector(x).squeeze(-1)  # [batch, seq]
            
            # 近端窗口：最近的token总是保留
            if seq_len > self.streaming_window:
                importance_scores[:, :seq_len - self.streaming_window] -= 10.0
            
            top_k = max(1, int(seq_len * self.top_k_ratio))
            _, top_indices = torch.topk(importance_scores, top_k, dim=-1)
            
            # 合并跨层索引
            if cross_layer_indices is not None and self.use_cross_layer:
                top_indices = torch.cat([top_indices, cross_layer_indices], dim=-1)
                top_indices = torch.unique(top_indices, dim=-1)
        
        # 稀疏注意力计算
        # 只计算选中的key/value位置
        k_selected = torch.gather(
            k, 2,
            top_indices.unsqueeze(1).unsqueeze(-1).expand(-1, self.num_heads, -1, self.head_dim)
        )
        v_selected = torch.gather(
            v, 2,
            top_indices.unsqueeze(1).unsqueeze(-1).expand(-1, self.num_heads, -1, self.head_dim)
        )
        
        # 注意力分数
        attn_weights = torch.matmul(q, k_selected.transpose(-2, -1)) / math.sqrt(self.head_dim)
        attn_weights = F.softmax(attn_weights, dim=-1)
        
        # 加权求和
        attn_output = torch.matmul(attn_weights, v_selected)
        
        # 合并头
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch, seq_len, self.hidden_dim)
        output = self.o_proj(attn_output)
        
        # 缓存跨层索引
        if self.use_cross_layer:
            self.cross_layer_cache = top_indices.detach()
        
        return output
    
    def compute_flops(self, seq_len: int) -> dict:
        """计算FLOPs对比"""
        full_attn_flops = 2 * self.num_heads * self.head_dim * seq_len * seq_len
        sparse_k = max(1, int(seq_len * self.top_k_ratio))
        sparse_attn_flops = 2 * self.num_heads * self.head_dim * seq_len * sparse_k
        
        return {
            "full_attention_flops": full_attn_flops,
            "sparse_attention_flops": sparse_attn_flops,
            "reduction_ratio": full_attn_flops / max(sparse_attn_flops, 1),
        }


# 测试LSA计算效率
def benchmark_lsa():
    model = LongCatSparseAttention(hidden_dim=7168, num_heads=64)
    
    for seq_len in [10000, 50000, 100000, 500000]:
        flops = model.compute_flops(seq_len)
        print(f"Seq={seq_len:>8}: Full={flops['full_attention_flops']:.2e} | "
              f"LSA={flops['sparse_attention_flops']:.2e} | "
              f"加速={flops['reduction_ratio']:.1f}x")

class NGramEmbedding(nn.Module):
    """
    N-gram Embedding模块
    将135B参数投入词元序列的N-gram级表示学习
    参数效率远超堆叠MoE专家
    """
    def __init__(self, 
                 vocab_size: int = 200000,
                 embedding_dim: int = 7168,
                 max_ngram: int = 5,
                 compression_factor: int = 4):
        super().__init__()
        self.max_ngram = max_ngram
        self.embedding_dim = embedding_dim
        self.compression_factor = compression_factor
        
        # 各阶N-gram嵌入表
        # 1-gram: token级别
        self.ngram_embeddings = nn.ModuleList([
            nn.Embedding(vocab_size ** i, embedding_dim // compression_factor)
            if i > 0 else nn.Embedding(vocab_size, embedding_dim)
            for i in range(1, max_ngram + 1)
        ])
        
        # N-gram融合网络
        self.fusion = nn.Sequential(
            nn.Linear(embedding_dim // compression_factor * max_ngram, embedding_dim),
            nn.GELU(),
            nn.Linear(embedding_dim, embedding_dim),
        )
        
        # 门控网络：为每个位置选择最优N-gram组合
        self.gate = nn.Sequential(
            nn.Linear(embedding_dim, max_ngram),
            nn.Softmax(dim=-1),
        )
        
    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
        """
        input_ids: [batch, seq_len]
        returns: [batch, seq_len, embedding_dim]
        """
        batch, seq_len = input_ids.shape
        device = input_ids.device
        
        # 计算各阶N-gram嵌入
        ngram_features = []
        for n in range(1, self.max_ngram + 1):
            if n == 1:
                emb = self.ngram_embeddings[n-1](input_ids)  # [batch, seq, dim/4]
            else:
                # 构建N-gram索引
                ngram_ids = torch.stack([
                    input_ids[:, i:i+seq_len-n+1] 
                    for i in range(n)
                ], dim=-1)  # [batch, seq-n+1, n]
                
                # Hash到嵌入表索引（简化版）
                hash_factor = torch.tensor([vocab_size ** (n-1-i) for i in range(n)], 
                                           device=device)
                ngram_hash = (ngram_ids * hash_factor).sum(dim=-1).long()
                
                emb_padded = torch.zeros(batch, seq_len, self.embedding_dim // self.compression_factor, 
                                         device=device)
                if ngram_hash.shape[1] > 0:
                    emb = self.ngram_embeddings[n-1](ngram_hash)
                    emb_padded[:, :emb.shape[1]] = emb
                ngram_features.append(emb_padded)
        
        # 拼接所有N-gram特征
        concat = torch.cat(ngram_features, dim=-1)  # [batch, seq, dim/4*5]
        
        # 门控融合
        gate_weights = self.gate(concat.mean(dim=-1, keepdim=True).expand(-1, -1, self.max_ngram))
        fused = (concat.view(batch, seq_len, self.max_ngram, -1) * 
                 gate_weights.unsqueeze(-1)).sum(dim=2)
        
        return self.fusion(fused)

from enum import Enum
from typing import Dict, List

class ExpertType(Enum):
    AGENT = "agent"        # 自主执行
    REASONING = "reasoning" # 自适应推理
    INTERACTION = "interaction"  # 安全对齐与交互

@dataclass
class TeacherModel:
    name: str
    expert_type: ExpertType
    weight: float
    logit_scale: float

class MultiTeacherDistillation:
    """
    多教师在线蒸馏框架
    三教师分别专注于Agent执行、推理能力、安全对齐
    """
    def __init__(self):
        self.teachers = [
            TeacherModel("agent_teacher", ExpertType.AGENT, 0.4, 2.0),
            TeacherModel("reasoning_teacher", ExpertType.REASONING, 0.35, 1.5),
            TeacherModel("interaction_teacher", ExpertType.INTERACTION, 0.25, 1.0),
        ]
        self.temperature = 2.0
        self.alpha_ce = 0.5  # 蒸馏损失权重
        self.alpha_kl = 0.5  # KL散度权重
        
    def compute_distillation_loss(self, 
                                   student_logits: torch.Tensor,
                                   teacher_logits_list: List[torch.Tensor],
                                   labels: torch.Tensor) -> torch.Tensor:
        """
        计算多教师蒸馏损失
        """
        # 加权融合教师logits
        fused_teacher_logits = sum(
            t.weight * teacher_logits * t.logit_scale
            for t, teacher_logits in zip(self.teachers, teacher_logits_list)
        )
        
        # 知识蒸馏损失 (KL散度)
        student_soft = F.log_softmax(student_logits / self.temperature, dim=-1)
        teacher_soft = F.softmax(fused_teacher_logits / self.temperature, dim=-1)
        kl_loss = F.kl_div(student_soft, teacher_soft, reduction='batchmean')
        kl_loss *= (self.temperature ** 2)
        
        # 交叉熵损失
        ce_loss = F.cross_entropy(student_logits, labels)
        
        return self.alpha_ce * ce_loss + self.alpha_kl * kl_loss

package main

import (
	"fmt"
	"math"
	"sync"
	"time"
)

// PDSeparatedDeployment PD分离部署架构
// Prefill端：处理长序列预填充
// Decode端：低延迟逐Token生成
type PDSeparatedDeployment struct {
	prefillNodes int
	decodeNodes  int
	kvCacheSize  int64 // tokens per node
	
	mu            sync.RWMutex
	kvCacheHits   int64
	kvCacheMisses int64
}

func NewPDDeployment(prefill, decode int, kvSize int64) *PDSeparatedDeployment {
	return &PDSeparatedDeployment{
		prefillNodes: prefill,
		decodeNodes:  decode,
		kvCacheSize:  kvSize,
	}
}

// SplitStrategy 根据序列长度动态决定分裂策略
func (pd *PDSeparatedDeployment) SplitStrategy(seqLen int) string {
	const (
		shortSeq  = 4096
		mediumSeq = 32768
	)
	
	switch {
	case seqLen <= shortSeq:
		return "single_node"  // 短序列单节点
	case seqLen <= mediumSeq:
		return "pd_split"     // 中等长度PD分离
	default:
		return "pd_split_ep"  // 超长序列PD分离+专家并行
	}
}

// KVPCache KV-cache切分管理器
type KVPCache struct {
	numPartitions int           // 切分份数
	partitionSize  int64        // 每份容量
	partitions     []*Partition
}

type Partition struct {
	ID      int
	Data    map[int64][]float32 // token_id -> 向量
	HitRate float64
	mu      sync.RWMutex
}

func NewKVPCache(numParts int, totalSize int64) *KVPCache {
	parts := make([]*Partition, numParts)
	for i := 0; i < numParts; i++ {
		parts[i] = &Partition{
			ID:   i,
			Data: make(map[int64][]float32),
		}
	}
	return &KVPCache{
		numPartitions: numParts,
		partitionSize:  totalSize / int64(numParts),
		partitions:     parts,
	}
}

func (k *KVPCache) hash(tokenID int64) int {
	return int(tokenID) % k.numPartitions
}

func (k *KVPCache) Store(tokenID int64, vector []float32) {
	pid := k.hash(tokenID)
	k.partitions[pid].mu.Lock()
	defer k.partitions[pid].mu.Unlock()
	
	if len(k.partitions[pid].Data) < int(k.partitionSize/128) {
		k.partitions[pid].Data[tokenID] = vector
	}
}

func (k *KVPCache) Lookup(tokenID int64) ([]float32, bool) {
	pid := k.hash(tokenID)
	k.partitions[pid].mu.RLock()
	defer k.partitions[pid].mu.RUnlock()
	
	v, ok := k.partitions[pid].Data[tokenID]
	return v, ok
}

// SuperKernel 超级算子：合并多个小算子减少启动开销
type SuperKernel struct {
	name    string
	ops     []string
	batchSize int
}

func NewSuperKernel(name string, ops []string) *SuperKernel {
	return &SuperKernel{
		name: name,
		ops:  ops,
	}
}

func (sk *SuperKernel) Launch(input []float32) []float32 {
	// 将多个算子合并为单个启动
	// 减少kernel launch overhead
	result := make([]float32, len(input))
	
	// Weight Prefetch: 将I/O延迟隐藏在前序计算中
	var wg sync.WaitGroup
	wg.Add(2)
	
	go func() {
		defer wg.Done()
		// 计算任务
		for i := range result {
			result[i] = input[i] * float32(math.Sin(float64(i)))
		}
	}()
	
	go func() {
		defer wg.Done()
		// 同时预取下一个权重（模拟）
		_ = make([]float32, 1024*1024)
	}()
	
	wg.Wait()
	return result
}

// AsyncExpertLoadBalancer 异步专家并行负载均衡
type AsyncExpertLoadBalancer struct {
	expertLoads  map[int]int64
	totalLoad    int64
	balanceInterval time.Duration
	stopCh       chan struct{}
}

func NewLoadBalancer(interval time.Duration) *AsyncExpertLoadBalancer {
	lb := &AsyncExpertLoadBalancer{
		expertLoads:     make(map[int]int64),
		balanceInterval: interval,
		stopCh:          make(chan struct{}),
	}
	go lb.run()
	return lb
}

func (lb *AsyncExpertLoadBalancer) run() {
	ticker := time.NewTicker(lb.balanceInterval)
	defer ticker.Stop()
	
	for {
		select {
		case <-ticker.C:
			lb.rebalance()
		case <-lb.stopCh:
			return
		}
	}
}

func (lb *AsyncExpertLoadBalancer) rebalance() {
	lb.mu.Lock()
	defer lb.mu.Unlock()
	
	if lb.totalLoad == 0 {
		return
	}
	
	avgLoad := lb.totalLoad / int64(len(lb.expertLoads))
	
	// 将超载专家的部分负载迁移到低载专家
	for id, load := range lb.expertLoads {
		if load > avgLoad*12/10 { // 超过平均负载20%
			excess := load - avgLoad
			fmt.Printf("Rebalancing expert %d: moving %d tokens\n", id, excess)
			lb.expertLoads[id] = avgLoad
		}
	}
}

func main() {
	fmt.Println("===== LongCat-2.0 国产算力部署引擎 =====")
	
	// PD分离部署
	pd := NewPDDeployment(32, 64, 1_000_000)
	
	scenarios := []struct {
		seqLen int
		name   string
	}{
		{2048, "短文本问答"},
		{16384, "中等文档分析"},
		{65536, "长代码库理解"},
		{1000000, "百万Token超长上下文"},
	}
	
	fmt.Printf("%-25s %-15s\n", "场景", "部署策略")
	fmt.Println("----------------------------------------")
	for _, s := range scenarios {
		strategy := pd.SplitStrategy(s.seqLen)
		fmt.Printf("%-25s %-15s\n", s.name, strategy)
	}
	
	// KV-cache切分
	kvc := NewKVPCache(8, 5_000_000_000) // 5B tokens
	kvc.Store(12345, make([]float32, 7168))
	if _, ok := kvc.Lookup(12345); ok {
		fmt.Println("\nKV-cache切分: 查找命中 ✓")
	}
	
	// 负载均衡
	fmt.Println("\n异步专家并行负载均衡已启动...")
	lb := NewLoadBalancer(5 * time.Second)
	defer close(lb.stopCh)
	
	_ = lb
	fmt.Println("异步负载均衡守护进程运行中")
}