ops-nn算子开发实战：从设计到部署的完整流程

cann组织链接：https://atomgit.com/cann
ops-nn仓库链接：https://atomgit.com/cann/
ops
-nn

本文导读

本文提供ops-nn算子开发的完整实战指南，从算子设计、实现、测试到
部署
的全流程讲解。通过实际案例，帮助开发者掌握在CANN平台上开发高性能算子的完整技能链。本文将覆盖算子规范、AscendC编程、性能调优、以及与深度学习框架的集成等关键环节。

CANN算子开发平台

CANN（Compute Architecture for Neural Networks）为开发者提供了完善的算子开发工具链，包括AscendC编程语言、编译工具、
仿真
器、性能分析器等。通过CANN提供的开发套件，开发者可以充分利用昇腾AI处理器的硬件特性，实现高效的算子实现。

ops-nn算子库架构

ops-nn作为CANN的核心算子库，采用了清晰的分层架构：从底层的
kernel
实现（op_kernel）、主机端调度（op_host）、API封装（op_api）到图层优化（op_graph），每一层都有明确的职责和接口规范。理解这种架构是进行算子开发的基础。

算子开发全流程

第一步：需求分析与设计

确定算子功能：

以自定义的FusedAttention算子为例：

# 功能：融合的多头注意力
# 输入：
#   - Q, K, V: [batch, seq_len, num_heads, head_dim]
#   - mask: [batch, seq_len, seq_len]
#   - scale: float
# 输出：
#   - attention_output: [batch, seq_len, num_heads, head_dim]
#   - attention_weights: [batch, num_heads, seq_len, seq_len]

def fused_attention(Q, K, V, mask, scale):
    # QK^T / sqrt(d_k)
    scores = torch.matmul(Q, K.transpose(-2, -1)) * scale
    
    # 加mask
    scores = scores + mask
    
    # Softmax
    attn_weights = torch.softmax(scores, dim=-1)
    
    # 加权求和
    output = torch.matmul(attn_weights, V)
    
    return output, attn_weights





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

算子规范设计：

// fused_attention.h
namespace custom_ops {

// 算子接口
aclnnStatus aclnnFusedAttention(
    const aclTensor* query,        // 输入Q
    const aclTensor* key,          // 输入K
    const aclTensor* value,        // 输入V
    const aclTensor* mask,         // 注意力mask
    float scale,                    // 缩放因子
    aclTensor* output,             // 输出
    aclTensor* attn_weights,       // 注意力权重
    aclrtStream stream             // CANN流
);

} // namespace custom_ops





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

性能目标：

目标1：融合后的性能优于分开调用5个算子
目标2：大batch（batch>=32）下，吞吐量提升50%
目标3：内存占用减少30%（避免中间结果存储）

第二步：Kernel实现

创建算子目录结构：

ops-nn/
└── fused_attention/
    ├── op_kernel/
    │   ├── fused_attention.h          # Kernel头文件
    │   └── fused_attention.cpp        # Kernel实现
    ├── op_host/
    │   └── fused_attention_host.cpp   # 主机端实现
    ├── op_api/
    │   └── fused_attention_api.cpp    # API封装
    └── test/
        └── test_fused_attention.cpp   # 单元测试





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

Kernel头文件：

// op_kernel/fused_attention.h
#pragma once
#include "kernel_operator.h"

namespace AscendC {

template<typename T>
class FusedAttention {
public:
    __aicore__ inline FusedAttention() {}
    
    __aicore__ inline void Init(
        GM_ADDR query,
        GM_ADDR key,
        GM_ADDR value,
        GM_ADDR mask,
        GM_ADDR output,
        GM_ADDR attn_weights,
        const FusedAttentionTilingData* tiling
    );
    
    __aicore__ inline void Process();

private:
    __aicore__ inline void ComputeScores();
    __aicore__ inline void ApplyMaskAndSoftmax();
    __aicore__ inline void ComputeOutput();
    
    // 全局内存地址
    GlobalTensor<T> query_gm;
    GlobalTensor<T> key_gm;
    GlobalTensor<T> value_gm;
    GlobalTensor<T> mask_gm;
    GlobalTensor<T> output_gm;
    GlobalTensor<T> attn_weights_gm;
    
    // Tiling参数
    FusedAttentionTilingData tiling_data;
    
    // 本地内存缓冲区
    TPipe pipe;
    LocalTensor<T> query_local;
    LocalTensor<T> key_local;
    LocalTensor<T> value_local;
    LocalTensor<T> scores_local;
    LocalTensor<T> softmax_local;
    LocalTensor<T> output_local;
};

} // namespace AscendC





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

Kernel实现：

// op_kernel/fused_attention.cpp
#include "fused_attention.h"

namespace AscendC {

template<typename T>
__aicore__ inline void FusedAttention<T>::Init(
    GM_ADDR query,
    GM_ADDR key,
    GM_ADDR value,
    GM_ADDR mask,
    GM_ADDR output,
    GM_ADDR attn_weights,
    const FusedAttentionTilingData* tiling
) {
    // 初始化全局内存
    query_gm.SetGlobalBuffer((__gm__ T*)query);
    key_gm.SetGlobalBuffer((__gm__ T*)key);
    value_gm.SetGlobalBuffer((__gm__ T*)value);
    mask_gm.SetGlobalBuffer((__gm__ T*)mask);
    output_gm.SetGlobalBuffer((__gm__ T*)output);
    attn_weights_gm.SetGlobalBuffer((__gm__ T*)attn_weights);
    
    // 保存tiling参数
    tiling_data = *tiling;
    
    // 初始化pipeline
    pipe.InitBuffer(inQueueQ, 1, tiling_data.tile_size_q);
    pipe.InitBuffer(inQueueK, 1, tiling_data.tile_size_k);
    pipe.InitBuffer(inQueueV, 1, tiling_data.tile_size_v);
    pipe.InitBuffer(outQueue, 1, tiling_data.tile_size_out);
}

template<typename T>
__aicore__ inline void FusedAttention<T>::ComputeScores() {
    // 计算 Q @ K^T
    uint32_t batch = tiling_data.batch;
    uint32_t seq_len = tiling_data.seq_len;
    uint32_t head_dim = tiling_data.head_dim;
    
    for (uint32_t b = 0; b < batch; b++) {
        for (uint32_t i = 0; i < seq_len; i += TILE_SEQ) {
            // 加载Q的一个tile
            DataCopy(query_local, query_gm[b * seq_len * head_dim + i * head_dim], 
                    TILE_SEQ * head_dim);
            
            for (uint32_t j = 0; j < seq_len; j += TILE_SEQ) {
                // 加载K的一个tile
                DataCopy(key_local, key_gm[b * seq_len * head_dim + j * head_dim], 
                        TILE_SEQ * head_dim);
                
                // 矩阵乘法：Q @ K^T
                MatMul(scores_local, query_local, key_local, 
                      TILE_SEQ, head_dim, TILE_SEQ, true);  // transpose K
                
                // 缩放
                Muls(scores_local, scores_local, tiling_data.scale, TILE_SEQ * TILE_SEQ);
                
                // 保存scores
                DataCopy(attn_weights_gm[b * seq_len * seq_len + i * seq_len + j],
                        scores_local, TILE_SEQ * TILE_SEQ);
            }
        }
    }
}

template<typename T>
__aicore__ inline void FusedAttention<T>::ApplyMaskAndSoftmax() {
    uint32_t batch = tiling_data.batch;
    uint32_t seq_len = tiling_data.seq_len;
    
    for (uint32_t b = 0; b < batch; b++) {
        for (uint32_t i = 0; i < seq_len; i++) {
            // 加载scores的一行
            DataCopy(scores_local, 
                    attn_weights_gm[b * seq_len * seq_len + i * seq_len], 
                    seq_len);
            
            // 加载mask
            DataCopy(mask_local, 
                    mask_gm[b * seq_len * seq_len + i * seq_len], 
                    seq_len);
            
            // 加mask
            Add(scores_local, scores_local, mask_local, seq_len);
            
            // Softmax
            // 1. 找最大值
            T max_val = ReduceMax(scores_local, seq_len);
            
            // 2. exp(x - max)
            Subs(scores_local, scores_local, max_val, seq_len);
            Exp(scores_local, scores_local, seq_len);
            
            // 3. 求和
            T sum = ReduceSum(scores_local, seq_len);
            
            // 4. 归一化
            Divs(softmax_local, scores_local, sum, seq_len);
            
            // 写回
            DataCopy(attn_weights_gm[b * seq_len * seq_len + i * seq_len],
                    softmax_local, seq_len);
        }
    }
}

template<typename T>
__aicore__ inline void FusedAttention<T>::ComputeOutput() {
    // 计算 attn_weights @ V
    uint32_t batch = tiling_data.batch;
    uint32_t seq_len = tiling_data.seq_len;
    uint32_t head_dim = tiling_data.head_dim;
    
    for (uint32_t b = 0; b < batch; b++) {
        for (uint32_t i = 0; i < seq_len; i++) {
            // 加载attention weights的一行
            DataCopy(softmax_local,
                    attn_weights_gm[b * seq_len * seq_len + i * seq_len],
                    seq_len);
            
            // 初始化输出
            Duplicate(output_local, (T)0, head_dim);
            
            // 加权求和
            for (uint32_t j = 0; j < seq_len; j++) {
                // 加载V的一行
                DataCopy(value_local,
                        value_gm[b * seq_len * head_dim + j * head_dim],
                        head_dim);
                
                // output += weight * V
                T weight = softmax_local[j];
                Axpy(output_local, value_local, weight, head_dim);
            }
            
            // 写回输出
            DataCopy(output_gm[b * seq_len * head_dim + i * head_dim],
                    output_local, head_dim);
        }
    }
}

template<typename T>
__aicore__ inline void FusedAttention<T>::Process() {
    // 三阶段计算
    ComputeScores();         // Q @ K^T
    ApplyMaskAndSoftmax();   // Mask + Softmax
    ComputeOutput();         // Attn @ V
}

} // namespace AscendC

// 实例化模板
extern "C" __global__ __aicore__ void fused_attention_kernel(
    GM_ADDR query,
    GM_ADDR key,
    GM_ADDR value,
    GM_ADDR mask,
    GM_ADDR output,
    GM_ADDR attn_weights,
    GM_ADDR tiling_gm
) {
    AscendC::FusedAttention<half> op;
    op.Init(query, key, value, mask, output, attn_weights, 
           (FusedAttentionTilingData*)tiling_gm);
    op.Process();
}





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

第三步：主机端实现

// op_host/fused_attention_host.cpp
#include "aclnn_base.h"
#include "fused_attention_tiling.h"

namespace op_host {

// Tiling计算
void CalculateTiling(const aclTensor* query, 
                     const aclTensor* key,
                     FusedAttentionTilingData* tiling) {
    // 获取输入shape
    auto shape = aclGetTensorShape(query);
    tiling->batch = aclGetDimSize(shape, 0);
    tiling->seq_len = aclGetDimSize(shape, 1);
    tiling->num_heads = aclGetDimSize(shape, 2);
    tiling->head_dim = aclGetDimSize(shape, 3);
    
    // 计算tile大小（根据L1 Buffer大小）
    const uint32_t L1_BUFFER_SIZE = 256 * 1024;  // 256KB
    uint32_t elem_size = sizeof(half);
    
    // 每个tile最多占用L1的1/4
    uint32_t max_tile_elements = L1_BUFFER_SIZE / 4 / elem_size;
    
    // 计算seq_len的tile大小
    tiling->tile_seq = std::min(tiling->seq_len, 
                                static_cast<uint32_t>(sqrt(max_tile_elements)));
    
    // 计算需要的block数量
    tiling->num_blocks = (tiling->batch * tiling->num_heads + 
                         GetCoreNum() - 1) / GetCoreNum();
    
    // 计算scale
    tiling->scale = 1.0f / sqrt(static_cast<float>(tiling->head_dim));
}

// 主机端算子实现
aclnnStatus aclnnFusedAttentionGetWorkspaceSize(
    const aclTensor* query,
    const aclTensor* key,
    const aclTensor* value,
    const aclTensor* mask,
    float scale,
    const aclTensor* output,
    const aclTensor* attn_weights,
    uint64_t* workspaceSize,
    aclOpExecutor** executor
) {
    // 计算tiling
    FusedAttentionTilingData tiling;
    CalculateTiling(query, key, &tiling);
    tiling.scale = scale;
    
    // workspace大小 = tiling数据大小
    *workspaceSize = sizeof(FusedAttentionTilingData);
    
    // 创建executor
    *executor = CreateOpExecutor("FusedAttention", &tiling, sizeof(tiling));
    
    return ACL_SUCCESS;
}

aclnnStatus aclnnFusedAttention(
    void* workspace,
    uint64_t workspaceSize,
    aclOpExecutor* executor,
    aclrtStream stream
) {
    // 获取tiling数据
    auto* tiling = reinterpret_cast<FusedAttentionTilingData*>(workspace);
    
    // 获取输入输出地址
    auto query_addr = GetTensorDeviceAddr(executor, "query");
    auto key_addr = GetTensorDeviceAddr(executor, "key");
    auto value_addr = GetTensorDeviceAddr(executor, "value");
    auto mask_addr = GetTensorDeviceAddr(executor, "mask");
    auto output_addr = GetTensorDeviceAddr(executor, "output");
    auto attn_weights_addr = GetTensorDeviceAddr(executor, "attn_weights");
    
    // 拷贝tiling到设备
    GM_ADDR tiling_gm = AllocDeviceMemory(workspaceSize, stream);
    aclrtMemcpy(tiling_gm, workspaceSize, tiling, workspaceSize,
               ACL_MEMCPY_HOST_TO_DEVICE);
    
    // 启动kernel
    uint32_t block_dim = tiling->num_blocks;
    LaunchKernel("fused_attention_kernel",
                block_dim,
                query_addr, key_addr, value_addr, mask_addr,
                output_addr, attn_weights_addr, tiling_gm,
                stream);
    
    // 释放临时内存
    FreeDeviceMemory(tiling_gm, stream);
    
    return ACL_SUCCESS;
}

} // namespace op_host





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

第四步：API封装

// op_api/fused_attention_api.cpp
#include "aclnn/aclnn_fused_attention.h"

extern "C" {

aclnnStatus aclnnFusedAttention(
    const aclTensor* query,
    const aclTensor* key,
    const aclTensor* value,
    const aclTensor* mask,
    float scale,
    aclTensor* output,
    aclTensor* attn_weights,
    aclrtStream stream
) {
    // 1. 参数校验
    CHECK_TENSOR_NOT_NULL(query);
    CHECK_TENSOR_NOT_NULL(key);
    CHECK_TENSOR_NOT_NULL(value);
    CHECK_TENSOR_NOT_NULL(output);
    
    // 2. Shape校验
    auto q_shape = aclGetTensorShape(query);
    auto k_shape = aclGetTensorShape(key);
    auto v_shape = aclGetTensorShape(value);
    
    CHECK_SHAPE_MATCH(q_shape, k_shape);
    CHECK_SHAPE_MATCH(q_shape, v_shape);
    
    // 3. 数据类型校验
    auto dtype = aclGetTensorDataType(query);
    CHECK_DTYPE_SUPPORTED(dtype, {ACL_FLOAT16, ACL_FLOAT});
    
    // 4. 获取workspace大小
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor = nullptr;
    
    auto ret = op_host::aclnnFusedAttentionGetWorkspaceSize(
        query, key, value, mask, scale, output, attn_weights,
        &workspaceSize, &executor);
    if (ret != ACL_SUCCESS) {
        return ret;
    }
    
    // 5. 分配workspace
    void* workspace = nullptr;
    if (workspaceSize > 0) {
        workspace = AllocWorkspace(workspaceSize);
    }
    
    // 6. 执行算子
    ret = op_host::aclnnFusedAttention(
        workspace, workspaceSize, executor, stream);
    
    // 7. 清理
    if (workspace) {
        FreeWorkspace(workspace);
    }
    DestroyExecutor(executor);
    
    return ret;
}

} // extern "C"





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

第五步：编译部署

编译脚本：

# build_fused_attention.sh
#!/bin/bash

SOC_VERSION="ascend910b"
OPS_NAME="fused_attention"

# 编译kernel
ascendc-compile \
    --soc=${SOC_VERSION} \
    --kernel_name=fused_attention_kernel \
    --input=op_kernel/fused_attention.cpp \
    --output=build/${OPS_NAME}_kernel.o

# 编译主机端
g++ -c op_host/fused_attention_host.cpp \
    -I${ASCEND_HOME}/include \
    -o build/${OPS_NAME}_host.o

# 编译API
g++ -c op_api/fused_attention_api.cpp \
    -I${ASCEND_HOME}/include \
    -o build/${OPS_NAME}_api.o

# 链接
g++ -shared \
    build/${OPS_NAME}_kernel.o \
    build/${OPS_NAME}_host.o \
    build/${OPS_NAME}_api.o \
    -L${ASCEND_HOME}/lib64 \
    -lascendcl \
    -o build/lib${OPS_NAME}.so

echo "Build completed: build/lib${OPS_NAME}.so"





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

安装部署：

# 安装算子库
sudo cp build/libfused_attention.so ${ASCEND_HOME}/opp/vendors/custom_nn/op_impl/lib/

# 注册算子
cat > fused_attention.json << EOF
{
  "op": "FusedAttention",
  "input_desc": [
    {"name": "query", "param_type": "required"},
    {"name": "key", "param_type": "required"},
    {"name": "value", "param_type": "required"},
    {"name": "mask", "param_type": "optional"}
  ],
  "attr": [
    {"name": "scale", "type": "float", "default_value": "1.0"}
  ],
  "output_desc": [
    {"name": "output", "param_type": "required"},
    {"name": "attn_weights", "param_type": "optional"}
  ]
}
EOF

sudo cp fused_attention.json ${ASCEND_HOME}/opp/vendors/custom_nn/op_impl/





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

第六步：测试验证

单元测试：

# test/test_fused_attention.py
import torch
import torch_npu

def test_fused_attention_correctness():
    """测试算子正确性"""
    batch, seq_len, num_heads, head_dim = 2, 128, 8, 64
    
    # 准备输入
    Q = torch.randn(batch, seq_len, num_heads, head_dim, device='npu', dtype=torch.float16)
    K = torch.randn(batch, seq_len, num_heads, head_dim, device='npu', dtype=torch.float16)
    V = torch.randn(batch, seq_len, num_heads, head_dim, device='npu', dtype=torch.float16)
    mask = torch.zeros(batch, seq_len, seq_len, device='npu', dtype=torch.float16)
    
    # 参考实现
    scale = 1.0 / (head_dim ** 0.5)
    scores_ref = torch.matmul(Q, K.transpose(-2, -1)) * scale
    scores_ref = scores_ref + mask
    attn_weights_ref = torch.softmax(scores_ref, dim=-1)
    output_ref = torch.matmul(attn_weights_ref, V)
    
    # 自定义算子
    output_custom, attn_weights_custom = torch_npu.npu_fused_attention(
        Q, K, V, mask, scale)
    
    # 对比结果
    assert torch.allclose(output_ref, output_custom, rtol=1e-3, atol=1e-3)
    assert torch.allclose(attn_weights_ref, attn_weights_custom, rtol=1e-3, atol=1e-3)
    
    print("✓ Correctness test passed")

def test_fused_attention_performance():
    """测试算子性能"""
    import time
    
    batch, seq_len, num_heads, head_dim = 32, 512, 12, 64
    num_iters = 100
    
    Q = torch.randn(batch, seq_len, num_heads, head_dim, device='npu', dtype=torch.float16)
    K = torch.randn(batch, seq_len, num_heads, head_dim, device='npu', dtype=torch.float16)
    V = torch.randn(batch, seq_len, num_heads, head_dim, device='npu', dtype=torch.float16)
    mask = torch.zeros(batch, seq_len, seq_len, device='npu', dtype=torch.float16)
    scale = 1.0 / (head_dim ** 0.5)
    
    # 预热
    for _ in range(10):
        _ = torch_npu.npu_fused_attention(Q, K, V, mask, scale)
    torch_npu.synchronize()
    
    # 测试融合算子
    start = time.time()
    for _ in range(num_iters):
        output, _ = torch_npu.npu_fused_attention(Q, K, V, mask, scale)
    torch_npu.synchronize()
    fused_time = (time.time() - start) / num_iters
    
    # 测试分开调用
    start = time.time()
    for _ in range(num_iters):
        scores = torch.matmul(Q, K.transpose(-2, -1)) * scale
        scores = scores + mask
        attn_weights = torch.softmax(scores, dim=-1)
        output = torch.matmul(attn_weights, V)
    torch_npu.synchronize()
    unfused_time = (time.time() - start) / num_iters
    
    speedup = unfused_time / fused_time
    print(f"Fused: {fused_time*1000:.2f} ms")
    print(f"Unfused: {unfused_time*1000:.2f} ms")
    print(f"Speedup: {speedup:.2f}x")
    
    assert speedup > 1.3, f"Speedup {speedup:.2f}x is less than expected 1.3x"
    print("✓ Performance test passed")

if __name__ == "__main__":
    test_fused_attention_correctness()
    test_fused_attention_performance()





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

性能优化技巧

1. Tiling优化

根据L1 Buffer大小合理切分：

// 计算最优tile大小
uint32_t CalculateOptimalTileSize(uint32_t seq_len, uint32_t head_dim) {
    const uint32_t L1_SIZE = 256 * 1024;  // 256KB
    const uint32_t ELEM_SIZE = sizeof(half);
    
    // Q tile + K tile + Score tile 都要放入L1
    // tile_seq^2 * head_dim * 3 < L1_SIZE
    uint32_t max_tile = sqrt(L1_SIZE / (3 * head_dim * ELEM_SIZE));
    
    // 对齐到16的倍数（硬件要求）
    max_tile = (max_tile / 16) * 16;
    
    return std::min(max_tile, seq_len);
}





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

2. 双缓冲

// 双缓冲pipeline
__aicore__ void ProcessWithDoubleBuffer() {
    // 加载第一个tile
    LoadTile(buffer[0], tile_0);
    
    for (int i = 0; i < num_tiles; i++) {
        int curr = i % 2;
        int next = (i + 1) % 2;
        
        // 异步加载下一个tile
        if (i + 1 < num_tiles) {
            LoadTileAsync(buffer[next], tile_{i+1});
        }
        
        // 计算当前tile
        ComputeTile(buffer[curr]);
        
        // 等待加载完成
        WaitLoad();
    }
}





![](/img/posts/ops-nn算子开发实战：从设计到部署的完整流程/0e277fd1e3527b618d9c5ae63382b0e5.webp)

3. 向量化

// 使用向量指令
const int VEC_SIZE = 8;  // FP16向量大小

for (int i = 0; i < size; i += VEC_SIZE) {
    // 向量化的乘加
    VecMuls(output + i, input + i, scale, VEC_SIZE);
}

最佳实践建议

模块化设计：将复杂算子分解为多个子函数
充分测试：单元测试、集成测试、压力测试
性能分析：使用msprof分析瓶颈
文档完善：API文档、使用示例、性能指标
版本管理：算子版本控制，保持向后兼容

总结

本文提供了ops-nn算子开发的完整实战流程，从需求分析、设计、实现到测试部署的全链条讲解。通过掌握这套流程，开发者可以：

理解CANN算子开发的完整链路
掌握AscendC编程和性能优化技巧
实现高性能的自定义算子
将算子集成到深度学习框架

建议开发者：

从简单算子入手，逐步提升难度
充分利用ops-nn现有算子的代码作为参考
重视测试和性能分析
遵循算子开发规范和最佳实践

算子开发是AI系统性能优化的核心技能，掌握这项技能将为构建高效
AI应用
打下坚实基础。