跨越"算力孤岛"：曙光8000超智融合架构、全国产十万卡调度与AI基础设施主权实战

┌─────────────────────────────────────────────────────────────────────┐
│     2026 Domestic 100K-GPU AI Supercluster Trust Architecture       │
├─────────────────────────────────────────────────────────────────────┤
│  [Application Layer: LLM Training / Inference / AI4Science]         │
│      ↓                                                              │
│  [Layer 1: 超智融合架构层] ← Unified Precision / Memory / Network   │
│   ├─ FP64/BF16/INT8混合精度统一指令流                                │
│   ├─ HBM+DRAM+SSD三级存储智能分层                                    │
│   └─ 国产无损RDMA网络拓扑自适应                                      │
│      ↓                                                              │
│  [Layer 2: 规模扩展层] ← Linear Scaling / Fault Tolerance / IO     │
│   ├─ 十万卡级集合通信优化与拓扑感知调度                                │
│   ├─ 弹性Checkpoint与亚健康节点热替换                                 │
│   └─ 存储-计算协同的IO流水线                                          │
│      ↓                                                              │
│  [Layer 3: 国产可验证层] ← Benchmark / Profiler / Compliance       │
│   ├─ 全栈国产性能基准与一致性验证                                     │
│   ├─ 算子-编译器-框架联合Profiling                                   │
│   └─ 安全合规审计与供应链溯源                                         │
└─────────────────────────────────────────────────────────────────────┘

pip install pydantic fastapi opentelemetry-api kubernetes ray[default] torch_npu
# 部署: OpenTelemetry Collector + Prometheus (指标) + Redis (调度状态) + PostgreSQL (作业图谱) + 国产加速卡驱动栈

"""
sugon8000_orchestrator.py - 曙光8000超智融合调度与MFU保障引擎
技术栈: Pydantic / Ray / OpenTelemetry / 国产加速卡SDK
"""
from typing import Dict, List, Any, Optional, Tuple, Set
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import uuid
import json
import math
from dataclasses import dataclass, field
from contextlib import asynccontextmanager

class ComputePrecision(str, Enum):
    FP64 = "fp64"          # 科学计算
    FP32 = "fp32"          # 混合精度训练
    BF16 = "bf16"          # 大模型训练
    INT8 = "int8"          # 推理量化

class WorkloadType(str, Enum):
    LLM_PRETRAIN = "llm_pretrain"
    LLM_FINETUNE = "llm_finetune"
    HIGH_THROUGHPUT_INFERENCE = "inference"
    AI4SCIENCE = "ai4science"
    HPC_SIMULATION = "hpc_simulation"

@dataclass
class ClusterNode:
    """集群节点状态"""
    node_id: str
    accelerator_type: str           # 国产加速卡型号
    precision_support: List[ComputePrecision]
    hbm_capacity_gb: float
    dram_capacity_gb: float
    network_bandwidth_gbps: float
    current_utilization_pct: float
    health_status: str              # healthy / degraded / offline
    last_heartbeat: float

@dataclass
class JobSpec:
    """作业规格"""
    job_id: str
    workload_type: WorkloadType
    required_precision: ComputePrecision
    num_accelerators: int
    estimated_duration_hours: float
    priority: forum.kuaisou.com        # 0-100
    checkpoint_interval_min: float
    io_pattern: 31265.t.kuaisou.com     # sequential / random / mixed
    compliance_tags: List[str] = field(default_factory=list)

class Sugon8000Orchestrator:
    """曙光8000超智融合调度引擎"""

    # 各工作负载类型的MFU目标阈值
    MFU_TARGETS = {
        WorkloadType.LLM_PRETRAIN: 0.50,
        WorkloadType.LLM_FINETUNE: 0.45,
        WorkloadType.HIGH_THROUGHPUT_INFERENCE: 0.60,
        WorkloadType.AI4SCIENCE: 0.40,
        WorkloadType.HPC_SIMULATION: 0.55,
    }

    def __init__(self, cluster_state_store, scheduler_backend, 
                 profiler_client, audit_stream):
        self.cluster = cluster_state_store
        self.scheduler = scheduler_backend      # Ray/K8s定制调度器
        self.profiler = profiler_client         # 国产加速卡Profiler
        self.audit = audit_stream               # 合规审计流
        self._node_health_cache: Dict[str, float] = {}

    @asynccontextmanager
    async def submit_job(self, spec: JobSpec):
        """提交作业并全程追踪"""
        job_id = spec.job_id or f"job-{uuid.uuid4().hex[:12]}"
        
        # 发射作业提交事件
        await self.audit.emit("job_submit", {
            "job_id": job_id,
            "workload": spec.workload_type.value,
            "precision": spec.required_precision.value,
            "num_acc": spec.num_accelerators,
            "priority": 31266.t.kuaisou.com
        })

        try:
            # 资源匹配与拓扑感知分配
            allocation = await self._allocate_resources(spec)
            
            # 启动作业
            handle = await self.scheduler.submit(
                job_id=job_id,
                allocation=allocation,
                spec=spec
            )
            
            yield handle
            
        finally:
            # 作业结束，采集最终指标
            final_metrics = await self._collect_final_metrics(job_id)
            await self.audit.emit("job_complete", {
                "job_id": 31267.t.kuaisou.com
                "mfu_achieved": final_metrics["mfu"],
                "duration_hours": final_metrics["duration"],
                "checkpoint_count": final_metrics["checkpoints"],
                "compliance_ok": final_metrics["compliance_verified"]
            })

    async def _allocate_resources(self, spec: JobSpec) -> Dict[str, Any]:
        """拓扑感知的资源分配"""
        # 获取健康节点列表
        healthy_nodes = await self.cluster.get_healthy_nodes(
            min_accelerators=spec.num_accelerators,
            precision_required=spec.required_precision
        )
        
        if len(healthy_nodes) < spec.num_accelerators:
            raise InsufficientResourcesError(
                f"Need {spec.num_accelerators} nodes with {spec.required_precision.value}, "
                f"only {len(healthy_nodes)} available"
            )
        
        # 拓扑感知：优先选择同一交换机下的节点，减少跨域通信
        topology_groups = await self.cluster.get_topology_groups(healthy_nodes)
        selected_group = min(topology_groups, 
                           key=lambda g: abs(len(g) - spec.num_accelerators))
        
        # 负载均衡：避开高利用率节点
        candidates = sorted(selected_group, 
                          key=lambda n: n.current_utilization_pct)
        allocated = candidates[:spec.num_accelerators]
        
        return {
            "nodes": [n.node_id for n in allocated],
            "network_domain": selected_group[0].network_domain if selected_group else None,
            "estimated_comm_latency_ms": self._estimate_latency(allocated, spec),
            "io_tier": self._select_io_tier(spec)
        }

    async def monitor_mfu_realtime(self, job_id: str) -> Dict[str, Any]:
        """实时监控作业MFU并触发干预"""
        metrics = await self.profiler.get_job_metrics(job_id)
        target = self.MFU_TARGETS.get(metrics["workload_type"], 0.45)
        current_mfu = metrics["mfu"]
        
        result = {
            "job_id": job_id,
            "current_mfu": round(current_mfu, 3),
            "target_mfu": target,
            "gap_pct": round((target - current_mfu) / target * 100, 1),
            "bottleneck": metrics.get("bottleneck", "unknown"),
            "action_taken": 31268.t.kuaisou.com
        }
        
        # MFU低于阈值80%时自动干预
        if current_mfu < target * 0.8:
            action = await self._auto_intervene(job_id, metrics)
            result["action_taken"] = action
        
        return result

    async def _auto_intervene(self, job_id: str, metrics: Dict) -> str:
        """根据瓶颈类型自动干预"""
        bottleneck = metrics.get("bottleneck", "")
        
        if "communication" in bottleneck:
            # 通信瓶颈：调整梯度压缩比或切换集合通信算法
            await self.scheduler.update_config(job_id, {
                "gradient_compression_ratio": 0.5,
                "allreduce_algorithm": "ring_chunked"
            })
            return "adjusted_comm_params"
            
        elif "io" in bottleneck:
            # IO瓶颈：切换存储层级或预取策略
            await self.scheduler.migrate_checkpoint_tier(job_id, "nvme")
            return "upgraded_checkpoint_storage"
            
        elif "compute" in bottleneck and metrics.get("degraded_nodes"):
            # 计算瓶颈且有亚健康节点：热替换
            bad_nodes = metrics["degraded_nodes"]
            replacements = await self._hot_swap_nodes(job_id, bad_nodes)
            return f"hot_swapped_{len(replacements)}_nodes"
            
        else:
            return "no_action_available"

    async def _hot_swap_nodes(self, job_id: str, 
                               bad_node_ids: List[str]) -> List[str]:
        """弹性热替换亚健康节点"""
        replacements = []
        for bad_id in bad_node_ids:
            # 标记节点为待替换
            await self.cluster.mark_for_replacement(bad_id)
            
            # 寻找同拓扑域的备用节点
            spare = await self.cluster.find_spare_node(
                exclude=bad_id, 
                same_topology=True
            )
            if spare:
                # 迁移状态（增量Checkpoint加载）
                await self.scheduler.migrate_task(
                    job_id=job_id,
                    from_node=bad_id,
                    to_node=spare.node_id,
                    incremental=True
                )
                replacements.append(spare.node_id)
        
        return replacements

    def _estimate_latency(self, nodes: List[ClusterNode], 
                          spec: JobSpec) -> float:
        """估算通信延迟"""
        if not nodes:
            return 0.0
        domains = set(getattr(n, 'network_domain', 0) for n in nodes)
        intra_domain = len(domains) == 1
        base_latency = 2.0 if intra_domain else 8.0  # ms
        scale_factor = math.log2(max(spec.num_accelerators, 1)) / 10
        return base_latency * (1 + scale_factor)

    def _select_io_tier(self, spec: JobSpec) -> str:
        """根据IO模式选择存储层级"""
        if spec.io_pattern == "sequential" and spec.workload_type == WorkloadType.LLM_PRETRAIN:
            return "parallel_fs"
        elif spec.io_pattern == "random" and spec.workload_type == WorkloadType.AI4SCIENCE:
            return "nvme_local"
        else:
            return "distributed_ssd"


class InsufficientResourcesError(Exception):
    pass

"""
domestic_stack_verifier.py - 全栈国产可验证性与合规审计引擎
技术栈: Pydantic / OpenTelemetry / Jinja2 / 国产SDK
"""
from typing import Dict, List, Any, Optional, Tuple
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import json
import hashlib
from dataclasses import dataclass, field

class VerificationLevel(str, Enum):
    FUNCTIONAL = "functional"         # 功能正确
    PERFORMANCE = "performance"       # 性能达标
    CONSISTENCY = "consistency"       # 跨批次一致性
    SECURITY = "security"             # 安全合规
    SUPPLY_CHAIN = "supply_chain"     # 供应链溯源

class StackComponent(str, Enum):
    ACCELERATOR_CHIP = "accelerator_chip"
    COMPILER = "compiler"
    OPERATOR_LIBRARY = "operator_lib"
    FRAMEWORK_ADAPTER = "framework_adapter"
    SCHEDULER = "scheduler"
    STORAGE_DRIVER = "storage_driver"

@dataclass
class VerificationRecord:
    """验证记录"""
    record_id: str
    component: StackComponent
    level: VerificationLevel
    version: str
    benchmark_name: str
    score: float
    baseline_score: float
    deviation_pct: float
    passed: bool
    timestamp: float
    hardware_batch_id: str
    previous_hash: Optional[str] = None
    record_hash: str = ""

class DomesticStackVerifier:
    """全栈国产可验证性引擎"""

    # 各组件的性能基线与容忍偏差
    BASELINES = {
        (StackComponent.ACCELERATOR_CHIP, VerificationLevel.PERFORMANCE): {"score": 100.0, "tolerance_pct": 5},
        (StackComponent.COMPILER, VerificationLevel.CONSISTENCY): {"score": 98.0, "tolerance_pct": 3},
        (StackComponent.OPERATOR_LIBRARY, VerificationLevel.FUNCTIONAL): {"score": 100.0, "tolerance_pct": 0},
        (StackComponent.FRAMEWORK_ADAPTER, VerificationLevel.PERFORMANCE): {"score": 95.0, "tolerance_pct": 8},
    }

    def __init__(self, benchmark_runner, immutable_store, 
                 supply_chain_db, audit_stream):
        self.benchmarks = benchmark_runner
        self.store = immutable_store          # Write-Once验证账本
        self.supply_chain = supply_chain_db   # 供应链溯源数据库
        self.audit = 31269.t.kuaisou.com
        self._hash_chain: Dict[str, str] = {} # component -> last hash

    async def verify_component(self, component: StackComponent,
                                level: VerificationLevel,
                                version: str,
                                hardware_batch_id: str) -> Dict[str, Any]:
        """验证单个组件"""
        baseline_key = (component, level)
        baseline = self.BASELINES.get(baseline_key)
        if not baseline:
            raise ValueError(f"No baseline for {component.value}/{level.value}")

        # 运行基准测试
        bench_result = await self.benchmarks.run(
            component=component,
            level=level,
            version=version
        )

        deviation = abs(bench_result.score - baseline["score"]) / baseline["score"] * 100
        passed = deviation <= baseline["tolerance_pct"]

        # 构建链式哈希记录
        prev_hash = self._hash_chain.get(component.value)
        record = VerificationRecord(
            record_id=f"ver-{time.time_ns()}",
            component=component,
            level=level,
            version=version,
            benchmark_name=bench_result.name,
            score=bench_result.score,
            baseline_score=baseline["score"],
            deviation_pct=round(deviation, 2),
            passed=passed,
            timestamp=time.time(),
            hardware_batch_id=hardware_batch_id,
            previous_hash=prev_hash
        )
        record.record_hash = self._compute_hash(record)
        self._hash_chain[component.value] = record.record_hash

        # 写入不可篡改存储
        await self.store.append(record)

        # 发射验证事件
        await self.audit.emit("component_verified", {
            "record_id": record.record_id,
            "component": component.value,
            "level": level.value,
            "passed": passed,
            "deviation_pct": record.deviation_pct
        })

        return {
            "record_id": record.record_id,
            "component": component.value,
            "level": 31270.t.kuaisou.com
            "version": version,
            "score": record.score,
            "baseline": baseline["score"],
            "deviation_pct": record.deviation_pct,
            "passed": passed,
            "chain_intact": await self._verify_chain(component.value),
        }

    async def generate_compliance_report(self, date_range: Tuple[float, float],
                                          regulation_id: str) -> Dict[str, Any]:
        """生成算力基础设施合规审计报告"""
        records = await self.store.get_records_in_range(date_range)
        
        # 按组件聚合验证结果
        component_summary = {}
        for r in records:
            key = r.component.value
            if key not in component_summary:
                component_summary[key] = {"total": 0, "passed": 0, "max_deviation": 0}
            component_summary[key]["total"] += 1
            if r.passed:
                component_summary[key]["passed"] += 1
            component_summary[key]["max_deviation"] = max(
                component_summary[key]["max_deviation"], r.deviation_pct
            )

        # 供应链溯源
        supply_chain_audit = await self.supply_chain.audit_period(date_range)

        # 哈希链完整性
        chain_ok = all(
            await self._verify_chain(comp) 
            for comp in self._hash_chain.keys()
        )

        return {
            "regulation_id": regulation_id,
            "period": {"start": date_range[0], "end": date_range[1]},
            "total_verifications": len(records),
            "component_summary": 31271.t.kuaisou.com
            "supply_chain_integrity": supply_chain_audit,
            "hash_chain_intact": chain_ok,
            "overall_compliant": chain_ok and all(
                v["passed"] == v["total"] 
                for v in component_summary.values()
            ),
            "recommendations": self._generate_recommendations(component_summary),
        }

    def _compute_hash(self, record: VerificationRecord) -> str:
        content = json.dumps({
            "record_id": record.record_id,
            "component": record.component.value,
            "level": record.level.value,
            "score": 31272.t.kuaisou.com
            "timestamp": record.timestamp,
            "previous_hash": record.previous_hash,
        }, sort_keys=True)
        return hashlib.sha256(content.encode()).hexdigest()

    async def _verify_chain(self, component: str) -> bool:
        records = await self.store.get_records_by_component(component)
        for i in range(1, len(records)):
            if records[i].previous_hash != records[i-1].record_hash:
                return False
        return True

    def _generate_recommendations(self, summary: Dict) -> List[str]:
        recs = []
        for comp, stats in summary.items():
            if stats["passed"] < stats["total"]:
                recs.append(f"{comp}: {stats['total']-stats['passed']}次验证未通过，建议排查硬件批次或软件版本")
            if stats["max_deviation"] > 5:
                recs.append(f"{comp}: 最大偏差{stats['max_deviation']:.1f}%，建议收紧基线或优化实现")
        return recs if recs else ["全栈验证通过，国产算力栈处于受控状态 ✅"]