DeepSeek 74亿美元融资深度解析：中国AI资本与技术的双引擎时代

# DeepSeek-V4 细粒度MoE核心实现（简化）
import torch
import torch.nn as nn
import torch.nn.functional as F

class FineGrainedMoE(nn.Module):
    """
    DeepSeek-V4风格细粒度MoE层
    特点：共享专家 + 细粒度路由 + 负载均衡
    """
    def __init__(self, d_model, n_experts, n_shared, top_k, expert_dim=None):
        super().__init__()
        self.d_model = d_model
        self.n_experts = n_experts
        self.n_shared = n_shared
        self.top_k = top_k
        expert_dim = expert_dim or d_model * 4
        
        # 共享专家（所有token都要通过）
        self.shared_experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(d_model, expert_dim),
                nn.GELU(),
                nn.Linear(expert_dim, d_model)
            ) for _ in range(n_shared)
        ])
        
        # 路由专家（通过门控网络动态选择）
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(d_model, expert_dim),
                nn.GELU(),
                nn.Linear(expert_dim, d_model)
            ) for _ in range(n_experts)
        ])
        
        # 门控网络（含负载均衡偏置）
        self.gate = nn.Linear(d_model, n_experts)
        # 可学习的辅助偏置，用于负载均衡
        self.aux_bias = nn.Parameter(torch.zeros(n_experts))
        
    def forward(self, x):
        # x: [batch, seq, d_model]
        batch_size, seq_len, _ = x.shape
        
        # 1. 共享专家前向
        shared_out = sum(expert(x) for expert in self.shared_experts) / self.n_shared
        
        # 2. 门控路由
        gate_logits = self.gate(x)  # [batch, seq, n_experts]
        gate_logits = gate_logits + self.aux_bias  # 加入负载均衡偏置
        
        # Top-K选择
        top_k_weights, top_k_indices = torch.topk(
            F.softmax(gate_logits, dim=-1), 
            self.top_k, 
            dim=-1
        )  # 各 [batch, seq, top_k]
        
        # 3. 专家前向 + 组合
        expert_out = torch.zeros_like(x)
        for i in range(self.top_k):
            expert_idx = top_k_indices[..., i]
            weight = top_k_weights[..., i].unsqueeze(-1)  # [batch, seq, 1]
            
            # 批量选择对应专家
            for e_idx in range(self.n_experts):
                mask = (expert_idx == e_idx)
                if mask.any():
                    selected = x[mask]
                    expert_out[mask] += weight[mask] * self.experts[e_idx](selected)
        
        # 4. 共享专家 + 路由专家 输出融合
        output = shared_out + expert_out
        
        return output, gate_logits
    
    def compute_auxiliary_loss(self, gate_logits):
        """
        负载均衡辅助损失（auxiliary loss）
        鼓励token均匀分布到各个专家
        """
        # 每个token的专家分配概率分布
        gate_probs = F.softmax(gate_logits, dim=-1)
        
        # 各专家的负载（被分配的概率和）
        load = gate_probs.sum(dim=(0, 1))  # [n_experts]
        load = load / load.sum()
        
        # 目标：均匀分布
        target = torch.ones_like(load) / self.n_experts
        
        # 辅助损失（KL散度的平方变体）
        aux_loss = F.kl_div(
            load.log(), target, reduction='batchmean'
        )
        
        return aux_loss * 0.01  # 缩放系数

// V4-Flash推理引擎中的动态专家调度器（Go实现）
package main

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

// ExpertLoadTracker 跟踪各专家实时负载
type ExpertLoadTracker struct {
	mu          sync.RWMutex
	loads       []float64  // 每个专家的负载比例
	windowSize  int        // 滑动窗口大小
	history     [][]float64 // 历史负载记录
	threshold   float64    // 负载均衡阈值
}

func NewExpertLoadTracker(numExperts int, threshold float64) *ExpertLoadTracker {
	return &ExpertLoadTracker{
		loads:      make([]float64, numExperts),
		windowSize: 100,
		threshold:  threshold,
	}
}

// RecordTokenRouting 记录一次token路由事件
func (t *ExpertLoadTracker) RecordTokenRouting(expertIdx int) {
	t.mu.Lock()
	defer t.mu.Unlock()
	
	t.loads[expertIdx]++
}

// GetBalanceScore 计算当前负载均衡得分
// 返回0-1之间的值，1表示完全均衡
func (t *ExpertLoadTracker) GetBalanceScore() float64 {
	t.mu.RLock()
	defer t.mu.RUnlock()
	
	total := 0.0
	for _, l := range t.loads {
		total += l
	}
	if total == 0 {
		return 1.0
	}
	
	mean := total / float64(len(t.loads))
	variance := 0.0
	for _, l := range t.loads {
		diff := l/mean - 1.0
		variance += diff * diff
	}
	variance /= float64(len(t.loads))
	
	// 均衡度 = 1 / (1 + 方差)
	return 1.0 / (1.0 + variance)
}

// DynamicRouter 动态路由选择器
type DynamicRouter struct {
	baseGate       func(context.Context, []float32) []int // 基础门控函数
	tracker        *ExpertLoadTracker
	adpativeWeight float64 // 负载均衡自适应权重
}

func NewDynamicRouter(
	baseGate func(context.Context, []float32) []int,
	tracker *ExpertLoadTracker,
) *DynamicRouter {
	return &DynamicRouter{
		baseGate:       baseGate,
		tracker:        tracker,
		adpativeWeight: 0.1,
	}
}

// Route 根据当前负载动态路由token
func (r *DynamicRouter) Route(ctx context.Context, tokenEmbedding []float32) int {
	// 1. 基础门控决策
	baseExperts := r.baseGate(ctx, tokenEmbedding)
	
	// 2. 获取当前负载
	r.tracker.mu.RLock()
	balanceScore := r.getBalanceScore()
	loadVariance := r.computeLoadVariance()
	r.tracker.mu.RUnlock()
	
	// 3. 当负载严重不均衡时，调整路由决策
	if balanceScore < r.tracker.threshold {
		// 将部分负载从热点专家迁移到冷门专家
		adjustedExpert := r.adjustForBalance(baseExperts[0], loadVariance)
		r.tracker.RecordTokenRouting(adjustedExpert)
		return adjustedExpert
	}
	
	r.tracker.RecordTokenRouting(baseExperts[0])
	return baseExperts[0]
}

func (r *DynamicRouter) getBalanceScore() float64 {
	return r.tracker.GetBalanceScore()
}

func (r *DynamicRouter) computeLoadVariance() []float64 {
	total := 0.0
	for _, l := range r.tracker.loads {
		total += l
	}
	if total == 0 {
		return make([]float64, len(r.tracker.loads))
	}
	
	mean := total / float64(len(r.tracker.loads))
	loadVariance := make([]float64, len(r.tracker.loads))
	for i, l := range r.tracker.loads {
		loadVariance[i] = l/mean - 1.0
	}
	return loadVariance
}

func (r *DynamicRouter) adjustForBalance(currentExpert int, loadVariance []float64) int {
	// 找负载最低的专家
	minLoad := math.Inf(1)
	minIdx := currentExpert
	for i, v := range loadVariance {
		if v < minLoad {
			minLoad = v
			minIdx = i
		}
	}
	return minIdx
}

// MoEInferenceEngine V4-Flash风格推理引擎
type MoEInferenceEngine struct {
	numExperts    int
	topK          int
	routers       []*DynamicRouter
	expertWorkers []*ExpertWorker
	loadTracker   *ExpertLoadTracker
}

type ExpertWorker struct {
	ID       int
	pending  chan []float32
	results  chan float32
}

func NewMoEInferenceEngine(numExperts, topK int) *MoEInferenceEngine {
	tracker := NewExpertLoadTracker(numExperts, 0.8)
	
	baseGateFn := func(ctx context.Context, emb []float32) []int {
		// 简化的门控网络推理
		return []int{int(emb[0]) % numExperts}
	}
	
	routers := make([]*DynamicRouter, numExperts)
	workers := make([]*ExpertWorker, numExperts)
	
	for i := 0; i < numExperts; i++ {
		routers[i] = NewDynamicRouter(baseGateFn, tracker)
		workers[i] = &ExpertWorker{
			ID:      i,
			pending: make(chan []float32, 1024),
			results: make(chan float32, 1024),
		}
		go workers[i].Process()
	}
	
	return &MoEInferenceEngine{
		numExperts:    numExperts,
		topK:          topK,
		routers:       routers,
		expertWorkers: workers,
		loadTracker:   tracker,
	}
}

func (w *ExpertWorker) Process() {
	for emb := range w.pending {
		// 模拟专家前向计算
		result := float32(0.0)
		for _, v := range emb {
			result += v
		}
		w.results <- result
	}
}

func (e *MoEInferenceEngine) Infer(ctx context.Context, input []float32) float32 {
	var result float32
	
	for i := 0; i < e.topK; i++ {
		expertID := e.routers[i].Route(ctx, input)
		e.expertWorkers[expertID].pending <- input
	}
	
	// 收集结果
	for i := 0; i < e.topK; i++ {
		result += <-e.expertWorkers[i].results
	}
	
	// 定期打印负载均衡状态
	if e.loadTracker.GetBalanceScore() < 0.5 {
		log.Printf("[WARN] Expert负载严重不均衡: balance=%.2f", 
			e.loadTracker.GetBalanceScore())
	}
	
	return result
}

func main() {
	engine := NewMoEInferenceEngine(128, 8) // 128专家，Top-8激活
	
	ctx := context.Background()
	testInput := make([]float32, 4096)
	
	for i := 0; i < 1000; i++ {
		result := engine.Infer(ctx, testInput)
		_ = result
		
		if i%100 == 0 {
			fmt.Printf("Step %d: balance=%.3f\n", 
				i, engine.loadTracker.GetBalanceScore())
		}
	}
}

# FP8混合精度训练精度补偿实现
import torch
import torch.nn as nn
import torch.cuda.amp as amp

class FP8Scaler:
    """
    DeepSeek风格的FP8精度缩放器
    实现对不同层使用不同缩放因子的精细控制
    """
    def __init__(self, model, scale_init=1.0):
        self.scales = {}
        for name, param in model.named_parameters():
            # 为每层参数初始化单独的缩放因子
            if 'attention' in name:
                self.scales[name] = scale_init * 2.0  # Attention层精度要求高
            elif 'mlp' in name:
                self.scales[name] = scale_init * 1.5  # MLP层中等
            else:
                self.scales[name] = scale_init * 1.0  # 其他层
    
    def quantize_to_fp8(self, tensor, layer_name):
        """将FP32/BF16张量量化为FP8 E4M3"""
        scale = self.scales[layer_name]
        
        # 计算FP8 E4M3的最大可表示值
        fp8_max = 448.0  # E4M3最大值
        
        # 缩放并截断
        scaled = tensor * scale
        clipped = torch.clamp(scaled, -fp8_max, fp8_max)
        
        # 量化到最近的FP8表示
        # E4M3: 3 bits尾数 → 8个量化级别
        step_size = fp8_max / 448.0
        quantized = torch.round(clipped / step_size) * step_size
        
        # 记录量化误差
        quant_error = (tensor - quantized / scale).abs().mean().item()
        
        return quantized / scale, quant_error
    
    def update_scale(self, layer_name, quant_error, target_error=0.01):
        """动态调整缩放因子以控制量化误差"""
        current_scale = self.scales[layer_name]
        
        if quant_error > target_error * 1.5:
            # 误差过大，增大缩放因子（降低量化粒度）
            self.scales[layer_name] = current_scale * 1.1
        elif quant_error < target_error * 0.5:
            # 误差过小，减小缩放因子（提高量化效率）
            self.scales[layer_name] = current_scale * 0.95
        
        # 限制缩放因子范围
        self.scales[layer_name] = max(0.5, min(8.0, self.scales[layer_name]))


class FP8Linear(nn.Module):
    """
    DeepSeek风格FP8线性层
    前向使用FP8计算，反向使用BF16累积梯度
    """
    def __init__(self, in_features, out_features, bias=True):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        
        # 在BF16精度下存储权重
        self.weight = nn.Parameter(
            torch.randn(out_features, in_features, dtype=torch.bfloat16)
        )
        if bias:
            self.bias = nn.Parameter(
                torch.zeros(out_features, dtype=torch.bfloat16)
            )
        else:
            self.register_parameter('bias', None)
        
        self.scaler = None  # 需要从外部传入
        
    def forward(self, x):
        # x: [batch, seq, in_features], BF16
        assert self.scaler is not None, "需要设置FP8Scaler"
        
        # 1. 将权重量化为FP8 E4M3
        weight_fp8, w_error = self.scaler.quantize_to_fp8(
            self.weight, f"weight_{self.in_features}x{self.out_features}"
        )
        
        # 2. 将输入量化为FP8 E4M3
        x_fp8, x_error = self.scaler.quantize_to_fp8(x, f"input_{self.in_features}")
        
        # 3. FP8矩阵乘法（使用torch._e4m3_matmul或模拟）
        # 实际部署中调用硬件FP8 matmul指令
        # 此处用BF16模拟来表示精度水平
        with torch.no_grad():
            # 模拟FP8矩阵乘的精度
            out_fp32 = torch.matmul(x_fp8.float(), weight_fp8.float().t())
            
            # 添加量化噪声模拟（FP8乘法+累加截断）
            quant_noise = torch.randn_like(out_fp32) * 0.001
            out_fp32 = out_fp32 + quant_noise
        
        if self.bias is not None:
            out_fp32 = out_fp32 + self.bias.float()
        
        # 4. 累积梯度在BF16
        out = out_fp32.bfloat16()
        
        # 5. 更新缩放器
        if self.training:
            self.scaler.update_scale(
                f"weight_{self.in_features}x{self.out_features}", w_error
            )
        
        return out


# DeepSeek训练流程中的FP8管理器
class FP8TrainingManager:
    """
    FP8训练全流程管理器
    包含：精度补偿、黑名单层管理、梯度缩放
    """
    def __init__(self, model, blacklist_layers=None):
        self.model = model
        self.scaler = FP8Scaler(model)
        
        # 精度黑名单：这些层保持BF16/FP32
        self.blacklist = blacklist_layers or []
        
        # 注册FP8线性层
        self._register_fp8_layers()
        
    def _register_fp8_layers(self):
        """为所有FP8Linear层注册缩放器"""
        for name, module in self.model.named_modules():
            if isinstance(module, FP8Linear):
                module.scaler = self.scaler
                print(f"  [FP8] 注册层: {name}")
    
    def train_step(self, batch, optimizer, loss_fn):
        """单步FP8训练"""
        optimizer.zero_grad()
        
        # 前向（FP8激活 + BF16权重累积）
        outputs = self.model(batch['input'])
        loss = loss_fn(outputs, batch['target'])
        
        # 反向传播（BF16梯度累积）
        loss.backward()
        
        # 梯度裁剪（防止FP8量化溢出）
        torch.nn.utils.clip_grad_norm_(
            self.model.parameters(), max_norm=1.0
        )
        
        # 参数更新（BF16精度）
        optimizer.step()
        
        # 统计FP8量化状态
        stats = self._collect_fp8_stats()
        
        return loss.item(), stats
    
    def _collect_fp8_stats(self):
        """收集各层的FP8量化统计"""
        stats = {}
        for name, param in self.model.named_parameters():
            if name in self.scaler.scales:
                stats[name] = {
                    'scale': self.scaler.scales[name],
                    'mean': param.abs().mean().item(),
                    'std': param.std().item(),
                }
        return stats


# 使用示例
def create_deepseek_style_model():
    """构建一个DeepSeek风格的MoE模型"""
    import copy
    
    class DeepSeekBlock(nn.Module):
        def __init__(self, d_model, n_heads, n_experts):
            super().__init__()
            self.attention = nn.MultiheadAttention(d_model, n_heads, 
                                                   dtype=torch.bfloat16,
                                                   batch_first=True)
            self.fp8_attn_proj = FP8Linear(d_model, d_model)
            self.moe = FineGrainedMoE(d_model, n_experts, 2, 4)
            self.norm1 = nn.LayerNorm(d_model, dtype=torch.bfloat16)
            self.norm2 = nn.LayerNorm(d_model, dtype=torch.bfloat16)
            
        def forward(self, x):
            # Attention with FP8
            attn_out, _ = self.attention(x, x, x)
            attn_out = self.fp8_attn_proj(attn_out)
            x = self.norm1(x + attn_out)
            
            # MoE with FP8
            moe_out, gate_logits = self.moe(x)
            x = self.norm2(x + moe_out)
            
            return x, gate_logits
    
    model = nn.Sequential(*[
        DeepSeekBlock(4096, 32, 128) for _ in range(6)
    ])
    
    return model


if __name__ == "__main__":
    print("=== DeepSeek FP8 Training Simulator ===")
    
    model = create_deepseek_style_model()
    manager = FP8TrainingManager(model)
    
    dummy_batch = {
        'input': torch.randn(4, 512, 4096, dtype=torch.bfloat16),
        'target': torch.randn(4, 512, 4096, dtype=torch.bfloat16),
    }
    
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    loss_fn = nn.MSELoss()
    
    for step in range(5):
        loss, stats = manager.train_step(dummy_batch, optimizer, loss_fn)
        print(f"Step {step}: loss={loss:.4f}")
        
        # 打印前3层的FP8状态
        for i, (name, stat) in enumerate(list(stats.items())[:3]):
            print(f"  {name}: scale={stat['scale']:.2f}, "
                  f"mean={stat['mean']:.4f}, std={stat['std']:.4f}")

// DeepSeek自研通信库关键组件（Go实现）
package main

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

// TopologyAwareRouter 拓扑感知路由
type TopologyAwareRouter struct {
	nodeTopology map[int]int   // expertID -> nodeID
	nodeCapacity map[int]int   // nodeID -> capacity (剩余容量)
	mu           sync.Mutex
}

func NewTopologyAwareRouter(numExperts, numNodes int) *TopologyAwareRouter {
	router := &TopologyAwareRouter{
		nodeTopology: make(map[int]int),
		nodeCapacity: make(map[int]int),
	}
	
	// 初始分配：将专家均匀分布到各节点
	expertsPerNode := numExperts / numNodes
	for e := 0; e < numExperts; e++ {
		nodeID := e / expertsPerNode
		if nodeID >= numNodes {
			nodeID = numNodes - 1
		}
		router.nodeTopology[e] = nodeID
	}
	
	// 初始化每节点容量
	for n := 0; n < numNodes; n++ {
		router.nodeCapacity[n] = math.MaxInt32
	}
	
	return router
}

// AssignExpert 将专家分配到最优节点
func (r *TopologyAwareRouter) AssignExpert(expertID, preferredNode int) int {
	r.mu.Lock()
	defer r.mu.Unlock()
	
	currentNode := r.nodeTopology[expertID]
	if currentNode == preferredNode {
		return currentNode
	}
	
	// 检查目标节点容量
	if r.nodeCapacity[preferredNode] > 0 {
		r.nodeTopology[expertID] = preferredNode
		r.nodeCapacity[preferredNode]--
		r.nodeCapacity[currentNode]++
		return preferredNode
	}
	
	return currentNode
}

// GradientAllReduce FP8梯度AllReduce实现
type GradientAllReduce struct {
	workers    int           // 参与的worker数量
	bufferSize int           // 梯度缓冲区大小
	fp8Enabled bool          // 是否启用FP8通信
}

func NewGradientAllReduce(workers, bufferSize int) *GradientAllReduce {
	return &GradientAllReduce{
		workers:    workers,
		bufferSize: bufferSize,
		fp8Enabled: true,
	}
}

// AllReduce 执行梯度AllReduce操作
// 使用Ring AllReduce算法
func (g *GradientAllReduce) AllReduce(localGrad []float32) []float32 {
	n := len(localGrad)
	result := make([]float32, n)
	copy(result, localGrad)
	
	// 如果是FP8模式，先量化再通信
	if g.fp8Enabled {
		// FP8量化：将float32梯度压缩到int8范围
		quantized := make([]int8, n)
		scale := float32(0.0)
		
		// 找最大绝对值
		for _, v := range localGrad {
			abs := float32(math.Abs(float64(v)))
			if abs > scale {
				scale = abs
			}
		}
		
		scale = scale / 127.0 // int8范围 -128~127
		if scale < 1e-10 {
			scale = 1.0
		}
		
		for i, v := range localGrad {
			quantized[i] = int8(v / scale)
		}
		
		// 模拟Ring AllReduce：Scatter-Reduce + AllGather
		chunkSize := n / g.workers
		var wg sync.WaitGroup
		
		for w := 0; w < g.workers; w++ {
			wg.Add(1)
			go func(workerID int) {
				defer wg.Done()
				
				start := workerID * chunkSize
				end := start + chunkSize
				if workerID == g.workers-1 {
					end = n
				}
				
				// 模拟网络传输延迟
				time.Sleep(10 * time.Microsecond)
				
				// 在这个worker的chunk内，将量化梯度累加回result
				for i := start; i < end; i++ {
					result[i] += float32(quantized[i]) * scale
				}
			}(w)
		}
		wg.Wait()
		
	} else {
		// BF16模式：直接通信
		chunkSize := n / g.workers
		var wg sync.WaitGroup
		
		for w := 0; w < g.workers; w++ {
			wg.Add(1)
			go func(workerID int) {
				defer wg.Done()
				start := workerID * chunkSize
				end := start + chunkSize
				if workerID == g.workers-1 {
					end = n
				}
				
				time.Sleep(20 * time.Microsecond) // BF16带宽减半→延迟加倍
				
				for i := start; i < end; i++ {
					result[i] += localGrad[i]
				}
			}(w)
		}
		wg.Wait()
	}
	
	// 求平均
	for i := range result {
		result[i] /= float32(g.workers)
	}
	
	return result
}

// 通信-计算重叠流水线
type AsyncPipeline struct {
	computeStream chan func()     // 计算任务队列
	commStream    chan func()     // 通信任务队列
	result        chan bool       // 完成信号
}

func NewAsyncPipeline() *AsyncPipeline {
	return &AsyncPipeline{
		computeStream: make(chan func(), 100),
		commStream:    make(chan func(), 100),
		result:        make(chan bool, 100),
	}
}

func (p *AsyncPipeline) Start() {
	go func() {
		for {
			select {
			case compute := <-p.computeStream:
				compute()
			}
		}
	}()
	
	go func() {
		for {
			select {
			case comm := <-p.commStream:
				comm()
			}
		}
	}()
}

func (p *AsyncPipeline) SubmitCompute(task func()) {
	p.computeStream <- task
}

func (p *AsyncPipeline) SubmitComm(task func()) {
	p.commStream <- task
}

// 流水线训练步
type PipelinedTrainingStep struct {
	layers      int
	pipeline    *AsyncPipeline
	comm        *GradientAllReduce
}

func NewPipelinedTrainingStep(layers int) *PipelinedTrainingStep {
	return &PipelinedTrainingStep{
		layers:   layers,
		pipeline: NewAsyncPipeline(),
		comm:     NewGradientAllReduce(8, 1024*1024),
	}
}

func (s *PipelinedTrainingStep) Execute(input [][]float32) {
	s.pipeline.Start()
	
	for layer := 0; layer < s.layers; layer++ {
		layerCopy := layer
		
		// 提交计算任务
		s.pipeline.SubmitCompute(func() {
			_ = input[layerCopy]
			time.Sleep(5 * time.Millisecond) // 模拟前向计算
		})
		
		// 提交通信任务（与下一层的计算重叠）
		if layer > 0 {
			s.pipeline.SubmitComm(func() {
				pseudoGrad := make([]float32, 1024)
				for i := range pseudoGrad {
					pseudoGrad[i] = float32(layerCopy)
				}
				s.comm.AllReduce(pseudoGrad)
			})
		}
	}
	
	fmt.Println("[Pipeline] 流水线训练步完成")
}

func main() {
	fmt.Println("=== DeepSeek 自研通信库 ===")
	
	router := NewTopologyAwareRouter(128, 8)
	expertID := router.AssignExpert(42, 3)
	fmt.Printf("专家42分配到节点%d\n", expertID)
	
	step := NewPipelinedTrainingStep(6)
	testInput := make([][]float32, 6)
	for i := range testInput {
		testInput[i] = make([]float32, 4096)
	}
	
	step.Execute(testInput)
	
	grad := make([]float32, 1024)
	for i := range grad {
		grad[i] = 1.0
	}
	
	allReduce := NewGradientAllReduce(8, 1024*1024)
	result := allReduce.AllReduce(grad)
	fmt.Printf("AllReduce完成，结果长度=%d，平均值=%.4f\n", 
		len(result), result[0])
}

# DeepSeek Infra性能建模与优化决策引擎
import numpy as np
from dataclasses import dataclass
from typing import Dict, List

@dataclass
class InfraConfig:
    """基础设施配置"""
    num_gpus: int
    gpu_memory: int  # GB
    interconnect: str  # 'nvlink', 'pcie', 'ethernet'
    precision: str  # 'fp8', 'bf16', 'fp16'
    num_experts: int
    top_k: int
    
@dataclass
class PerformanceModel:
    """性能预测模型"""
    compute_efficiency: float  # MFU（模型算力利用率）
    memory_efficiency: float   # 显存利用率
    communication_overhead: float  # 通信开销占比
    quantization_ratio: float  # 量化带来的效率提升
    
class DeepSeekInfraOptimizer:
    """
    DeepSeek风格的基础设施优化决策引擎
    根据硬件配置自动选择最优的训练/推理策略
    """
    def __init__(self, config: InfraConfig):
        self.config = config
        self.model = self._build_performance_model()
        
    def _build_performance_model(self) -> PerformanceModel:
        """基于配置构建性能模型"""
        # MFU基线（假设BF16）
        base_mfu = 0.45
        
        # FP8优化
        if self.config.precision == 'fp8':
            base_mfu *= 1.8  # FP8计算密度提升
            quant_ratio = 0.5  # 内存减半
        else:
            quant_ratio = 1.0
        
        # Interconnect优化
        if self.config.interconnect == 'nvlink':
            comm_overhead = 0.1
        elif self.config.interconnect == 'pcie':
            comm_overhead = 0.25
        else:
            comm_overhead = 0.35
        
        # MoE通信优化（专家并行）
        moe_comm_factor = self.config.top_k / self.config.num_experts
        comm_overhead *= moe_comm_factor
        
        return PerformanceModel(
            compute_efficiency=base_mfu,
            memory_efficiency=0.85,
            communication_overhead=comm_overhead,
            quantization_ratio=quant_ratio,
        )
    
    def estimate_training_throughput(self, model_size: int) -> Dict:
        """估计训练吞吐量"""
        # 基于模型参数量和配置计算TFLOPs
        compute_capacity = self.config.num_gpus * 312 * self.model.compute_efficiency
        
        # 通信开销
        effective_capacity = compute_capacity * (1 - self.model.communication_overhead)
        
        # 量化效率
        effective_capacity *= self.model.quantization_ratio
        
        # 训练速度（tokens/s）
        tokens_per_second = effective_capacity * 1e12 / (model_size * 6)  # 6 FLOPs/param/token
        
        return {
            'peak_tflops': compute_capacity,
            'effective_tflops': effective_capacity,
            'tokens_per_second': tokens_per_second,
            'mfu': self.model.compute_efficiency * 100,
        }
    
    def optimize_memory(self, model_size: int, seq_len: int) -> Dict:
        """优化显存分配"""
        total_memory = self.config.num_gpus * self.config.gpu_memory  # GB
        
        # 模型权重（FP8参数）
        if self.config.precision == 'fp8':
            weights_memory = model_size * 1e9 * 1  # 1 byte per param
        else:
            weights_memory = model_size * 1e9 * 2  # 2 bytes per param
        
        # KV Cache（INT8优化）
        kv_cache_per_token = (model_size / 1e9) * 0.5  # GB/token
        kv_cache_total = kv_cache_per_token * seq_len * self.config.num_gpus
        
        # 激活内存（Activation checkpointing）
        activation_memory = (model_size * 1e9 * 0.2) / 1e9  # GB
        
        memory_usage = {
            'weights_gb': weights_memory / 1e9,
            'kv_cache_gb': kv_cache_total,
            'activation_gb': activation_memory,
            'total_estimated_gb': (weights_memory / 1e9 + kv_cache_total + activation_memory),
            'total_available_gb': total_memory * 0.9,  # 90%利用率
            'memory_pressure': 'high' if (weights_memory / 1e9 + kv_cache_total) > total_memory * 0.7 else 'normal',
        }
        
        return memory_usage
    
    def recommend_strategy(self, model_size: int, seq_len: int) -> str:
        """推荐最优策略"""
        memory = self.optimize_memory(model_size, seq_len)
        
        if memory['memory_pressure'] == 'high':
            return "推荐策略：梯度检查点 + INT8 KV Cache + 流水线并行"
        elif self.config.num_gpus >= 64:
            return "推荐策略：8路张量并行 + 4路流水线并行 + ZeRO-3"
        else:
            return "推荐策略：ZeRO-3 + 单节点专家并行"


# Infra性能模拟器
def simulate_deepseek_infra():
    print("=" * 60)
    print("DeepSeek Infra优化决策引擎模拟")
    print("=" * 60)
    
    configs = [
        InfraConfig(num_gpus=64, gpu_memory=80, interconnect='nvlink', 
                    precision='fp8', num_experts=128, top_k=8),
        InfraConfig(num_gpus=256, gpu_memory=80, interconnect='nvlink',
                    precision='fp8', num_experts=256, top_k=8),
        InfraConfig(num_gpus=1024, gpu_memory=80, interconnect='nvlink',
                    precision='bf16', num_experts=256, top_k=12),
    ]
    
    for i, cfg in enumerate(configs):
        print(f"\n--- 配置 {i+1}: {cfg.num_gpus}×H100 ({cfg.precision}) ---")
        optimizer = DeepSeekInfraOptimizer(cfg)
        
        # 模拟V4训练（约671B参数）
        perf = optimizer.estimate_training_throughput(671e9)
        print(f"  MFU: {perf['mfu']:.1f}%")
        print(f"  有效算力: {perf['effective_tflops']:.0f} TFLOPs")
        print(f"  训练速度: {perf['tokens_per_second']:.0f} tokens/s")
        
        mem = optimizer.optimize_memory(671e9, 8192)
        print(f"  权重显存: {mem['weights_gb']:.0f} GB")
        print(f"  KV Cache: {mem['kv_cache_gb']:.0f} GB")
        print(f"  策略建议: {optimizer.recommend_strategy(671e9, 8192)}")

if __name__ == "__main__":
    simulate_deepseek_infra()

# DeepSeek Harness架构核心实现
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Optional, Dict, Any, Callable, Awaitable
import asyncio
import json

@dataclass
class TaskContext:
    """任务上下文"""
    task_id: str
    goal: str
    current_step: int
    max_steps: int
    memory: Dict[str, Any]
    files: List[str]
    tools_available: List[str]

@dataclass
class Action:
    """Agent动作"""
    type: str  # 'think', 'act', 'observe', 'reflect'
    tool: Optional[str]
    params: Dict[str, Any]
    reasoning: str

class ContextManager:
    """
    Harness上下文管理器
    负责超长上下文的管理和压缩
    """
    def __init__(self, max_tokens: int = 128000):
        self.max_tokens = max_tokens
        self.history: List[Dict] = []
        self.summary_cache: Dict[str, str] = {}
        
    def add_turn(self, role: str, content: str, metadata: Optional[Dict] = None):
        """添加一轮对话"""
        self.history.append({
            'role': role,
            'content': content,
            'metadata': metadata or {},
            'tokens': self._estimate_tokens(content)
        })
        self._maybe_compress()
        
    def _estimate_tokens(self, text: str) -> int:
        """估算token数"""
        return len(text) * 1.5  # 中文token估算
        
    def _maybe_compress(self):
        """超出上下文限制时自动压缩"""
        total_tokens = sum(t['tokens'] for t in self.history)
        
        if total_tokens > self.max_tokens * 0.9:
            # 压缩策略：保留最近对话，摘要历史
            recent = self.history[-10:]  # 保留最近10轮
            history_to_summarize = self.history[:-10]
            
            # 生成历史摘要
            summary = self._summarize_history(history_to_summarize)
            
            self.history = [
                {'role': 'system', 'content': f'历史摘要: {summary}', 'tokens': 0}
            ] + recent
            
    def _summarize_history(self, history: List[Dict]) -> str:
        """生成历史摘要（实际调用模型完成）"""
        # 简化实现：提取关键信息
        key_actions = []
        for turn in history:
            if turn['role'] == 'assistant' and 'metadata' in turn:
                if turn['metadata'].get('action_type') == 'tool_call':
                    key_actions.append(
                        f"调用了{turn['metadata']['tool']}"
                    )
        return "; ".join(key_actions[-5:]) if key_actions else "无关键操作"


class ToolExecutor:
    """工具执行器"""
    def __init__(self):
        self.tools: Dict[str, Callable] = {}
        
    def register(self, name: str, func: Callable):
        """注册工具"""
        self.tools[name] = func
        
    async def execute(self, tool: str, params: Dict) -> Any:
        """异步执行工具"""
        if tool not in self.tools:
            raise ValueError(f"未知工具: {tool}")
        
        if asyncio.iscoroutinefunction(self.tools[tool]):
            return await self.tools[tool](**params)
        else:
            return self.tools[tool](**params)


class SelfCorrectingLoop:
    """
    Harness自修正循环
    当Agent执行失败时自动分析原因并重试
    """
    def __init__(self, max_retries: int = 3):
        self.max_retries = max_retries
        self.attempts: List[Dict] = []
        
    async def execute_with_retry(
        self, 
        action: Action,
        executor: ToolExecutor
    ) -> Dict:
        """带自修正的异步执行"""
        last_error = None
        
        for attempt in range(1, self.max_retries + 1):
            try:
                if action.tool:
                    result = await executor.execute(action.tool, action.params)
                else:
                    result = {"status": "thinking_complete"}
                
                self.attempts.append({
                    'attempt': attempt,
                    'success': True,
                    'result': result
                })
                
                return result
                
            except Exception as e:
                last_error = str(e)
                self.attempts.append({
                    'attempt': attempt,
                    'success': False,
                    'error': last_error
                })
                
                # 自动修正策略
                if attempt < self.max_retries:
                    correction = self._analyze_error(last_error)
                    action = self._apply_correction(action, correction)
                    
        raise RuntimeError(f"所有重试均失败: {last_error}")
    
    def _analyze_error(self, error: str) -> Dict:
        """分析错误类型并生成修正策略"""
        if 'timeout' in error.lower():
            return {'action': 'reduce_complexity', 'timeout_increase': 2}
        elif 'memory' in error.lower():
            return {'action': 'reduce_context', 'keep_recent': 10}
        elif 'rate_limit' in error.lower():
            return {'action': 'backoff', 'delay': 5}
        else:
            return {'action': 'retry_same', 'delay': 1}
    
    def _apply_correction(self, action: Action, correction: Dict) -> Action:
        """应用修正策略"""
        if correction['action'] == 'reduce_complexity':
            # 简化参数
            if 'max_tokens' in action.params:
                action.params['max_tokens'] = min(
                    action.params['max_tokens'], 2048
                )
        elif correction['action'] == 'backoff':
            import time
            time.sleep(correction['delay'])
            
        return action


class DeepSeekHarness:
    """
    DeepSeek Harness主框架
    Model + Harness = Agent
    """
    def __init__(self, model_adapter, tools: List[Callable]):
        self.context = ContextManager()
        self.executor = ToolExecutor()
        self.correction = SelfCorrectingLoop()
        self.model = model_adapter
        
        # 注册工具
        for tool in tools:
            name = tool.__name__
            self.executor.register(name, tool)
    
    async def run(self, goal: str) -> Dict:
        """执行Agent任务"""
        self.context.add_turn('user', goal, {'type': 'goal'})
        
        for step in range(20):  # 最多20步
            # 1. 模型推理：生成下一步动作
            action = await self._plan_next_action()
            
            if action.type == 'think' and action.tool is None:
                # 推理完成
                break
            
            # 2. 执行动作（带自修正）
            result = await self.correction.execute_with_retry(action, self.executor)
            
            # 3. 记录到上下文
            self.context.add_turn('tool', json.dumps(result), {
                'action_type': 'tool_call',
                'tool': action.tool
            })
            
        return {
            'goal': goal,
            'steps': step + 1,
            'attempts': self.correction.attempts,
            'final_context_length': len(self.context.history),
        }
    
    async def _plan_next_action(self) -> Action:
        """使用底层模型规划下一步"""
        # 调用底层V4模型生成动作
        prompt = self._build_prompt()
        response = await self.model.generate(prompt)
        
        # 解析模型输出为Action
        return self._parse_action(response)
    
    def _build_prompt(self) -> str:
        """构建推理提示"""
        context_str = "\n".join([
            f"{t['role']}: {t['content'][:200]}"
            for t in self.context.history[-5:]
        ])
        
        return f"""你是一个DeepSeek Harness Agent。你的目标是完成以下任务。
可用工具: {list(self.executor.tools.keys())}

对话上下文:
{context_str}

请决定下一步动作，返回JSON格式:
{{"type": "think|act|observe", "tool": "工具名|null", "params": {{}}, "reasoning": "思考过程"}}"""
    
    def _parse_action(self, response: str) -> Action:
        """解析模型响应为Action"""
        try:
            data = json.loads(response)
            return Action(
                type=data.get('type', 'think'),
                tool=data.get('tool'),
                params=data.get('params', {}),
                reasoning=data.get('reasoning', '')
            )
        except:
            return Action('think', None, {}, '解析失败，默认思考')


# Harness示例：代码生成Agent
class CodeGenerationTool:
    """代码生成工具"""
    async def generate(self, spec: str, language: str = "python") -> str:
        """根据规格生成代码"""
        # 模拟代码生成
        code = f"# Generated {language} code for: {spec[:50]}...\n"
        code += "def solution():\n"
        code += "    pass\n"
        return code
    
    async def review(self, code: str) -> Dict:
        """审查代码质量"""
        return {
            'issues': 0,
            'complexity': 'low',
            'has_tests': 'test' in code.lower()
        }


async def main():
    print("=== DeepSeek Harness Demo ===")
    
    # 注册工具
    code_tool = CodeGenerationTool()
    
    harness = DeepSeekHarness(
        model_adapter=None,  # 实际使用V4模型
        tools=[code_tool.generate, code_tool.review]
    )
    
    result = await harness.run(
        "写一个Python Web爬虫，支持异步并发抓取，并保存为CSV"
    )
    
    print(f"任务完成: {result['goal'][:50]}...")
    print(f"执行步骤: {result['steps']}")
    print(f"自修正次数: {sum(1 for a in result['attempts'] if not a['success'])}")

if __name__ == "__main__":
    asyncio.run(main())