华为昇腾9100+Atlas 950超节点深度解析：300PFlops国产训练芯片，灵衢2.0光互联8192卡规模

import numpy as np
from typing import Tuple

class MixedPrecisionEngine:
    """
    昇腾9100混合精度计算引擎
    
    支持FP16/FP8/FP4混合精度，硬件级动态切换
    """
    
    def __init__(self):
        self.supported_precisions = ["FP16", "FP8", "FP4"]
        self.current_precision = "FP8"
        
    def auto_select_precision(self, layer_type: str, 
                              input_shape: Tuple[int, ...]) -> str:
        """
        根据层类型和输入形状自动选择最优精度
        
        规则：
        - Attention/QKV投影 → FP16（精度敏感）
        - FFN层 → FP8（计算密集型）
        - MoE路由 → FP16
        - 权重矩阵 → FP8/FP4（可量化）
        """
        if layer_type in ["attention_qkv", "attention_out", "rms_norm"]:
            return "FP16"
        elif layer_type in ["ffn_gate", "ffn_up", "ffn_down"]:
            return "FP8"
        elif layer_type == "moe_router":
            return "FP16"
        elif layer_type in ["embedding", "lm_head"]:
            return "FP16"
        else:
            return "FP8"
    
    def cast_and_compute(self, a: np.ndarray, b: np.ndarray, 
                         precision: str) -> np.ndarray:
        """按指定精度执行矩阵乘法"""
        if precision == "FP16":
            a_fp16 = a.astype(np.float16)
            b_fp16 = b.astype(np.float16)
            return np.matmul(a_fp16, b_fp16).astype(np.float32)
            
        elif precision == "FP8":
            # 昇腾9100 FP8：E4M3格式
            a_fp8 = self._to_fp8(a, "e4m3")
            b_fp8 = self._to_fp8(b, "e4m3")
            result = np.matmul(a_fp8.astype(np.float16), 
                              b_fp8.astype(np.float16))
            return result.astype(np.float32)
            
        elif precision == "FP4":
            # 昇腾9100 FP4：NF4格式（归一化浮点4）
            a_fp4 = self._to_nf4(a)
            b_fp4 = self._to_nf4(b)
            result = np.matmul(
                self._from_nf4(a_fp4, a.shape),
                self._from_nf4(b_fp4, b.shape)
            )
            return result
            
        return np.matmul(a, b)
    
    def _to_fp8(self, x: np.ndarray, fmt: str = "e4m3") -> np.ndarray:
        """转换为FP8格式"""
        # E4M3: 1 sign + 4 exponent + 3 mantissa
        max_val = 448.0 if fmt == "e4m3" else 57344.0  # E4M3 vs E5M2
        return np.clip(x, -max_val, max_val).astype(np.float16)
    
    def _to_nf4(self, x: np.ndarray) -> np.ndarray:
        """转换为NF4（归一化FP4）格式"""
        # NF4: 4-bit NormalFloat
        abs_max = np.max(np.abs(x))
        if abs_max == 0:
            return np.zeros(x.shape, dtype=np.uint8)
        
        normalized = x / abs_max
        
        # NF4量化等级（16级）
        nf4_levels = np.array([
            -1.0, -0.6961928, -0.5250731, -0.3949175,
            -0.2844414, -0.1847734, -0.0910500, 0.0,
            0.0795803, 0.1609116, 0.2462118, 0.3379152,
            0.4407098, 0.5626170, 0.7229563, 1.0
        ])
        
        quantized = np.argmin(
            np.abs(normalized[..., np.newaxis] - nf4_levels),
            axis=-1
        ).astype(np.uint8)
        
        return quantized
    
    def _from_nf4(self, x: np.ndarray, shape: Tuple) -> np.ndarray:
        """从NF4还原为float32"""
        nf4_levels = np.array([
            -1.0, -0.6961928, -0.5250731, -0.3949175,
            -0.2844414, -0.1847734, -0.0910500, 0.0,
            0.0795803, 0.1609116, 0.2462118, 0.3379152,
            0.4407098, 0.5626170, 0.7229563, 1.0
        ])
        return nf4_levels[x.astype(int)].reshape(shape)

package memory

import (
	"sync"
	"sync/atomic"
	"unsafe"
)

// UnifiedAddressSpace 内存统一编址
// Atlas 950超节点的核心创新：所有节点的内存资源
// 整合为统一的共享资源池，通过load/store指令直接访存
type UnifiedAddressSpace struct {
	mu               sync.RWMutex
	nodeMemoryMap    map[uint64]*NodeMemory
	globalAddressMap map[uint64]uint64  // global addr → (node_id << 48 | local_addr)
	totalCapacity    uint64             // 总容量: 1152TB
	pageSize         uint64             // 页大小: 2MB
}

// NodeMemory 单个节点的内存信息
type NodeMemory struct {
	NodeID      uint32
	LocalBase   uint64
	Capacity    uint64
	HBMType     string     // HBM4
	Bandwidth   float64    // TB/s
	IsHealthy   atomic.Bool
}

// GlobalMemoryRequest 全局内存分配请求
type GlobalMemoryRequest struct {
	Size      uint64
	Affinity  []uint32   // 倾向的节点ID列表
	Alignment uint64     // 对齐要求
}

// GlobalMemoryAllocation 全局内存分配结果
type GlobalMemoryAllocation struct {
	GlobalAddr  uint64        // 全局地址
	Size        uint64        // 分配大小
	PhysicalMap []PhysicalPage // 物理页映射
}

// PhysicalPage 物理页描述
type PhysicalPage struct {
	NodeID    uint32
	LocalAddr uint64
	Size      uint64
}

// AllocateGlobalMemory 分配全局统一内存
func (uas *UnifiedAddressSpace) AllocateGlobalMemory(req *GlobalMemoryRequest) (*GlobalMemoryAllocation, error) {
	uas.mu.Lock()
	defer uas.mu.Unlock()

	alloc := &GlobalMemoryAllocation{
		Size:        req.Size,
		PhysicalMap: make([]PhysicalPage, 0),
	}

	remaining := req.Size
	pageCount := (remaining + uas.pageSize - 1) / uas.pageSize

	for i := uint64(0); i < pageCount && remaining > 0; i++ {
		// 选择最优节点（亲和性优先）
		nodeID := uas.selectOptimalNode(req.Affinity)

		if nodeID == 0 {
			return nil, &ErrOutOfMemory{Requested: req.Size}
		}

		nodeMem := uas.nodeMemoryMap[uint64(nodeID)]
		if nodeMem == nil {
			continue
		}

		// 在本节点上分配
		localAddr := atomic.AddUint64(&nodeMem.LocalBase, 0) // CAS-based alloc
		pageSize := min64(remaining, uas.pageSize)

		// 生成全局地址
		// 高32位: NodeID, 低32位: LocalAddr
		globalAddr := (uint64(nodeID) << 48) | localAddr

		page := PhysicalPage{
			NodeID:    nodeID,
			LocalAddr: localAddr,
			Size:      pageSize,
		}
		alloc.PhysicalMap = append(alloc.PhysicalMap, page)
		uas.globalAddressMap[globalAddr] = (uint64(nodeID) << 48) | localAddr

		remaining -= pageSize
	}

	if remaining > 0 {
		return nil, &ErrOutOfMemory{Requested: req.Size}
	}

	// 更新首地址
	alloc.GlobalAddr = alloc.PhysicalMap[0].GlobalAddr()

	return alloc, nil
}

// RemoteLoad 远程加载（load/store指令级访存）
// 直接读写远端NPU内存资源，无需序列化-网络传输-反序列化流程
func (uas *UnifiedAddressSpace) RemoteLoad(globalAddr uint64, size uint64) ([]byte, error) {
	nodeID := uint32(globalAddr >> 48)
	localAddr := globalAddr & 0xFFFFFFFFFFFF

	nodeMem, ok := uas.nodeMemoryMap[uint64(nodeID)]
	if !ok {
		return nil, &ErrInvalidNode{NodeID: nodeID}
	}

	if !nodeMem.IsHealthy.Load() {
		return nil, &ErrNodeUnhealthy{NodeID: nodeID}
	}

	// 通过灵衢2.0光互联，使用load语义直接读取远端内存
	// 延迟：200ns（单跳），传统网络方式为2μs
	data := nodeMem.RemoteRead(localAddr, size)

	// 统计
	atomic.AddUint64(&remoteLoadCount, 1)
	atomic.AddUint64(&remoteLoadBytes, size)

	return data, nil
}

// selectOptimalNode 选择最优节点
func (uas *UnifiedAddressSpace) selectOptimalNode(affinity []uint32) uint32 {
	// 1. 优先选择亲和性列表中的节点
	for _, nodeID := range affinity {
		nodeMem := uas.nodeMemoryMap[uint64(nodeID)]
		if nodeMem != nil && nodeMem.IsHealthy.Load() && nodeMem.HasAvailableCapacity(uas.pageSize) {
			return nodeID
		}
	}

	// 2. 选择负载最低的健康节点
	var bestNode uint32
	var minLoad float64 = 1.0

	for nodeID, nodeMem := range uas.nodeMemoryMap {
		if !nodeMem.IsHealthy.Load() {
			continue
		}
		load := nodeMem.GetUtilization()
		if load < minLoad {
			minLoad = load
			bestNode = uint32(nodeID)
		}
	}

	return bestNode
}