┌─────────────────────────────────────────────────────┐
│                  应用交互层                          │
│  流式输出 / 意图识别 / 多模态对齐 / 业务SLA策略      │
├─────────────────────────────────────────────────────┤
│                  协同调度层                          │
│  动态路由决策 / 负载均衡 / 熔断降级 / 资源感知       │
├─────────────────────────────────────────────────────┤
│                  推测执行层                          │
│  草稿生成 / 验证回滚 / 接受率统计 / 并行解码控制     │
├─────────────────────────────────────────────────────┤
│                  通信传输层                          │
│  gRPC/QUIC / 消息分帧 / 流控拥塞 / 零拷贝序列化      │
└─────────────────────────────────────────────────────┘

class RoutingFeatures:
    def __init__(self, ctx: RequestContext):
        # 请求侧特征
        self.prompt_len = ctx.prompt_tokens
        self.task_type = ctx.task_classification  # code/chat/rag/agent
        self.quality_sla = ctx.quality_level      # high/balanced/fast
        
        # 系统侧特征
        self.edge_load = ctx.edge_utilization     # 端侧NPU/GPU负载
        self.cloud_queue = ctx.cloud_queue_depth  # 云侧排队深度
        self.rtt_p99 = ctx.network_rtt_p99        # 网络尾延迟
        self.accept_rate_ewma = ctx.accept_rate_ewma  # 指数加权移动平均
        
        # 模型侧特征
        self.draft_model_version = ctx.draft_version
        self.target_model_version = ctx.target_version
        self.kv_cache_hit_rate = ctx.kv_hit_rate

class ContextualBanditRouter:
    def __init__(self, arms: List[RoutingArm], alpha: float = 0.1):
        self.arms = arms
        self.alpha = alpha  # 学习率
        self.theta = {arm: np.zeros(feature_dim) for arm in arms}
        self.A = {arm: np.eye(feature_dim) for arm in arms}
        self.b = {arm: np.zeros(feature_dim) for arm in arms}
    
    def select_arm(self, features: np.ndarray) -> RoutingArm:
        ucb_scores = {}
        for arm in self.arms:
            theta_hat = np.linalg.solve(self.A[arm], self.b[arm])
            score = theta_hat @ features + self.alpha * np.sqrt(
                features @ np.linalg.inv(self.A[arm]) @ features
            )
            ucb_scores[arm] = score
        return max(ucb_scores, key=ucb_scores.get)
    
    def update(self, arm: RoutingArm, features: np.ndarray, reward: float):
        self.A[arm] += np.outer(features, features)
        self.b[arm] += reward * features

class DraftQualityPredictor:
    """基于轻量探针网络预测草稿接受率，提前规避低质量推测"""
    def __init__(self, probe_model: nn.Module, threshold: float = 0.6):
        self.probe = probe_model
        self.threshold = threshold
    
    def should_speculate(self, hidden_states: torch.Tensor) -> bool:
        # 探针网络：2层MLP，输入hidden_dim，输出accept_rate预测
        pred_accept = self.probe(hidden_states.mean(dim=1)).sigmoid()
        return pred_accept > self.threshold

class AdaptiveVerifier:
    def __init__(self, target_model, max_spec_len: int = 8):
        self.target = target_model
        self.max_spec_len = max_spec_len
        self.accept_history = deque(maxlen=1000)
    
    def verify_with_rollback(self, draft_tokens: List[int], 
                             draft_probs: List[torch.Tensor],
                             context: KVCache) -> VerificationResult:
        accepted = []
        rollback_depth = 0
        
        for t, (token, prob) in enumerate(zip(draft_tokens, draft_probs)):
            target_logits = self.target.forward_one(token, context)
            target_prob = F.softmax(target_logits, dim=-1)[token]
            
            accept_prob = min(1.0, target_prob.item() / prob[token].item())
            
            if random.random() < accept_prob:
                accepted.append(token)
                context.append(token)
                rollback_depth = 0
            else:
                # 触发回滚：重新采样并同步KV Cache
                resampled = self._resample(target_logits)
                accepted.append(resampled)
                context.rewind(rollback_depth + 1)
                context.append(resampled)
                rollback_depth += 1
                
                if rollback_depth > self.max_rollback_depth:
                    # 容错边界：深度回滚触发降级
                    return self._fallback_to_autoregressive(context)
        
        self.accept_history.append(len(accepted) / len(draft_tokens))
        return VerificationResult(accepted, rollback_depth)

message KVCacheDelta {
  int64 version = 1;           // 单调递增版本号
  repeated LayerDelta layers = 2;
  bool is_full_sync = 3;       // 全量同步标记
}

message LayerDelta {
  int32 layer_idx = 1;
  bytes key_delta = 2;         // 压缩后的Key增量
  bytes value_delta = 3;       // 压缩后的Value增量
  CompressionType compression = 4;  // ZSTD/QUANT_INT8
}

class StreamingOutputManager:
    def __init__(self, min_chunk: int = 1, max_latency_ms: int = 50):
        self.buffer = []
        self.min_chunk = min_chunk
        self.max_latency = max_latency_ms / 1000.0
        self.last_flush = time.time()
    
    def push(self, tokens: List[int]) -> Optional[List[int]]:
        self.buffer.extend(tokens)
        now = time.time()
        
        # 双触发条件：积累足够token 或 超过最大延迟
        if len(self.buffer) >= self.min_chunk or 
           (now - self.last_flush) > self.max_latency:
            chunk = self.buffer[:self.min_chunk]
            self.buffer = self.buffer[self.min_chunk:]
            self.last_flush = now
            return chunk
        return None

# Prometheus规则示例
groups:
- name: speculative_decoding
  rules:
  - alert: AcceptRateDegraded
    expr: |
      (rate(spec_accept_total[5m]) / rate(spec_verify_total[5m])) < 0.6
    for: 3m
    labels:
      severity: warning
    annotations:
      summary: "推测接受率降至60%以下"
      
  - alert: RollbackDepthHigh
    expr: |
      histogram_quantile(0.99, rate(spec_rollback_depth_bucket[5m])) > 3
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "P99回滚深度超过3，疑似草稿模型失配"

用户交互日志 → 困难样本挖掘 (低接受率/高回滚) → 
蒸馏训练集构建 → 草稿模型微调 (KD + RLHF) → 
影子验证 → 全量发布 → 持续监控

class SpeculativeBatchScheduler:
    def __init__(self, max_batch_tokens: int = 8192, max_spec_depth: int = 8):
        self.max_batch_tokens = max_batch_tokens
        self.max_spec_depth = max_spec_depth
        self.waiting_queue = asyncio.PriorityQueue()  # (priority, request_id, request)
        self.running_spec: Dict[str, SpecContext] = {}
        self.running_auto: List[AutoContext] = []
    
    async def schedule_step(self) -> Batch:
        # 1. 收集已完成验证的推测请求，释放KV Cache槽位
        self._reclaim_finished_spec()
        
        # 2. 优先填充推测验证批次（高优先级、低延迟敏感）
        spec_batch = self._build_spec_verify_batch()
        
        # 3. 剩余算力填充自回归预填/解码
        auto_batch = self._build_autoregressive_batch(
            remaining_tokens=self.max_batch_tokens - spec_batch.estimated_tokens
        )
        
        # 4. 融合批次：统一KV Cache管理、统一CUDA Graph捕获
        return self._fuse_batches(spec_batch, auto_batch)
    
    def _build_spec_verify_batch(self) -> SpecBatch:
        """构建验证批次：按推测长度分桶，减少padding浪费"""
        buckets = defaultdict(list)
        while not self.waiting_queue.empty() and len(buckets) < self.max_spec_depth:
            _, rid, req = await self.waiting_queue.get()
            if req.mode == "speculative" and req.draft_ready:
                buckets[len(req.draft_tokens)].append(req)
        
        # 选取最大桶或合并相邻桶（动态padding掩码）
        best_len = max(buckets, key=lambda k: len(buckets[k]) * k)
        return SpecBatch(requests=buckets[best_len], spec_len=best_len)

// 伪代码：关键帧冗余传输策略
struct ResilientTransport {
    quic_conn: Connection,
    fec_encoder: RaptorQEncoder,    // 系统码率 1.2x
    rtt_estimator: RttEstimator,
    spec_frame_id: AtomicU64,
}

impl ResilientTransport {
    async fn send_speculative_frame(&mut self, frame: SpecFrame) -> Result<()> {
        let frame_id = self.spec_frame_id.fetch_add(1, Ordering::Relaxed);
        let packets = self.fec_encoder.encode(&frame.serialize(), frame_id);
        
        // 关键帧（首token验证结果、回滚指令）发送2份冗余
        let redundancy = if frame.is_critical { 2 } else { 1 };
        
        for _ in 0..redundancy {
            for pkt in &packets {
                self.quic_conn.send(pkt).await?;
            }
        }
        
        // 自适应FEC码率：丢包率>10% 时提升至1.5x
        self.adjust_fec_rate().await;
        Ok(())
    }
}

class CompliantSpeculativeClient:
    def __init__(self, pii_detector: PIIDetector, dp_mechanism: GaussianMechanism):
        self.pii = pii_detector
        self.dp = dp_mechanism
    
    async def prepare_draft_request(self, prompt: str) -> DraftRequest:
        # 1. 本地脱敏
        clean_prompt, pii_spans = self.pii.redact(prompt)
        
        # 2. 本地生成草稿
        draft_tokens = self.draft_model.generate(clean_prompt)
        
        # 3. 差分隐私保护草稿分布特征（防模型反演攻击）
        noisy_entropy = self.dp.add_noise(self._calc_entropy(draft_tokens))
        
        return DraftRequest(
            prompt_hash=sha256(clean_prompt),  # 不传明文
            draft_tokens=draft_tokens,
            draft_entropy=noisy_entropy,       # 用于云侧路由决策
            pii_spans=pii_spans,               # 位置标记，云侧不解密
            user_consent_token=self.consent_mgr.get_token()
        )

┌────────────────────────────────────────────────────────────┐
│  多模态推测执行流水线                                        │
├────────────────────────────────────────────────────────────┤
│  1. 端侧：轻量ViT (MobileViT/EdgeViT) 编码图像 → Visual Tokens │
│  2. 端云协同：Visual Tokens + Prompt → 云侧Target VLM Prefill  │
│  3. 端侧：Draft LLM (纯文本小模型) 推测生成文本 Token          │
│  4. 云侧：Target VLM 验证（冻结Visual Embedding，仅解码头验证） │
│  5. 回滚策略：文本Token拒绝 → 重采样；视觉Token从不回滚        │
└────────────────────────────────────────────────────────────┘

class AgenticSpeculator:
    def __init__(self, draft_agent: Agent, target_agent: Agent):
        self.draft = draft_agent
        self.target = target_agent
        self.state_checkpoint = {}
    
    async def speculative_step(self, state: AgentState) -> SpecResult:
        # 1. 端侧Draft Agent快速推演：思考+工具调用+观测预测
        draft_trace = await self.draft.predict_trajectory(state, max_steps=3)
        
        # 2. 云侧Target Agent并行验证：仅验证关键决策节点
        verification_points = self._identify_critical_nodes(draft_trace)
        # 例如：工具选择、参数合法性、终止条件
        
        results = await asyncio.gather(*[
            self.target.verify_node(state, node) 
            for node in verification_points
        ])
        
        # 3. 语义级接受/回滚
        if all(r.accepted for r in results):
            # 全链路接受：直接应用Draft轨迹，提交状态检查点
            self.state_checkpoint[state.session_id] = draft_trace.final_state
            return SpecResult(accepted=True, trace=draft_trace)
        else:
            # 部分拒绝：回滚至最后一个接受节点，Target接管
            rollback_state = self._find_last_accepted(state, results)
            return SpecResult(accepted=False, fallback_state=rollback_state)