CANN 算子优化引擎：模型训练效率提升的全链路解决方案

// 升腾C语言示例
void FusedConvBNReLU(const Tensor& input, const Tensor& weight, 
                     const Tensor& bias, const Tensor& gamma, 
                     const Tensor& beta, const Tensor& mean, 
                     const Tensor& var, Tensor& output) {
    // 1. 执行卷积
    Conv2D(input, weight, bias, output_temp);
    
    // 2. BN计算（融合到卷积输出）
    BatchNorm(output_temp, gamma, beta, mean, var, output_bn);
    
    // 3. ReLU激活（直接在BN输出上操作）
    ReLU(output_bn, output);
}

# MindSpore伪代码
class DarkNet53(nn.Cell):
    def construct(self, x):
        # 原始计算图
        x = Conv2d(x) + BatchNorm(x) + ReLU(x)
        x = Conv2d(x) + BatchNorm(x) + ReLU(x)
        x = Concat(x1, x2) + MaxPool(x)
        
        # 融合后计算图
        x = FusedConvBNReLU.ConvBNReLU(x)  # 自动融合
        x = FusedConvBNReLU.ConvBNReLU(x)  # 自动融合
        x = FusedConcatMaxPool.ConcatMaxPool(x1, x2)  # 自动融合

# 使用msprof进行性能分析
from mindspore import Profiler

profiler = Profiler(profile_path="./output")
model.train(1, dataset, callbacks=[profiler])
profiler.analyze()

# Tile Size优化建议
Recommended Tile M=256, N=128, K=64 for MatMul
Memory Bandwidth Utilization: 92.7%

// 升腾C语言Tile配置
__aicpu__ void MatMul(TileConfig config) {
    config.SetTileSize("M", 256);   // 根据硬件特性自动生成
    config.SetTileSize("N", 128);
    config.SetTileSize("K", 64);
    // 自动选择最优内存布局
    config.SetMemoryLayout("HWCN");
}

# PyTorch示例
import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.const = torch.tensor(3.14)

    def forward(self, x):
        # 会被常量折叠优化
        return x * self.const + x * self.const

def forward(self, x):
    return x * (2 * self.const)  # 常量合并

// Ascend C语言动态Shape支持
void DynamicMatMul(const Tensor& A, const Tensor& B, Tensor& C) {
    Shape shape_A = A.GetShape();
    Shape shape_B = B.GetShape();
    
    // 自动推导输出Shape
    Shape shape_C = InferShape(shape_A, shape_B);
    
    // 动态调整Tile策略
    TileConfig config = GetOptimalTile(shape_C);
    
    // 执行矩阵乘法
    MatMul(A, B, C, config);
}

# MindSpore分布式训练
from mindspore.communication import init, get_rank, get_group_size
from mindspore.nn.wrap import WithLossCell, _DistributedDataParallel

init()  # 初始化通信
strategy = (_DistributedDataParallel, (get_group_size(), 1, 1))
model = _DistributedDataParallel(model, strategy=strategy)

# 使用NB 2.0通信算法
from mindspore import context
context.set_auto_parallel_context(allreduce_fusion=3)

// 伪量化实现
void MSDQuantize(const Tensor& input, Tensor& output) {
    // 将16位浮点转换为8位整数
    Quantize(input, output, scale=0.01, zero_point=0);
    
    // 多尺度反量化
    Tensor dequantized = Dequantize(output, scale=0.01, zero_point=0);
    
    // 多线性组合
    Tensor result = LinearCombination(dequantized);
}

# PyTorch兼容CANN优化
import torch

model = torch.compile(model)  # 自动启用CANN优化
model.train()