/**

 * Copyright (c) 2026 Huawei Technologies Co., Ltd.

 * This program is free software, you can redistribute it and/or modify it under the terms and conditions of

 * CANN Open Software License Agreement Version 2.0 (the "License").

 * Please refer to the License for details. You may not use this file except in compliance with the License.

 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,

 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.

 * See LICENSE in the root of the software repository for the full text of the License.

 */



/*!

 * \file test_aclnn_inplace_addcmul.cpp

 * \brief

 */

#include <iostream>

#include <vector>

#include "acl/acl.h"

#include "aclnnop/aclnn_addcmul.h"



#define CHECK_RET(cond, return_expr) \

    do {                             \

        if (!(cond)) {               \

            return_expr;             \

        }                            \

    } while (0)



#define LOG_PRINT(message, ...)         \

    do {                                \

        printf(message, ##__VA_ARGS__); \

    } while (0)



int64_t GetShapeSize(const std::vector<int64_t>& shape)

{

    int64_t shapeSize = 1;

    for (auto i : shape) {

        shapeSize *= i;

    }

    return shapeSize;

}



int Init(int32_t deviceId, aclrtStream* stream)

{

    // 固定写法，资源初始化

    auto ret = aclInit(nullptr);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);

    ret = aclrtSetDevice(deviceId);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);

    ret = aclrtCreateStream(stream);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);

    return 0;

}



template <typename T>

int CreateAclTensor(

    const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr, aclDataType dataType,

    aclTensor** tensor)

{

    auto size = GetShapeSize(shape) * sizeof(T);

    // 调用aclrtMalloc申请device侧内存

    auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);

    // 调用aclrtMemcpy将host侧数据拷贝到device侧内存上

    ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);



    // 计算连续tensor的strides

    std::vector<int64_t> strides(shape.size(), 1);

    for (int64_t i = shape.size() - 2; i >= 0; i--) {

        strides[i] = shape[i + 1] * strides[i + 1];

    }



    // 调用aclCreateTensor接口创建aclTensor

    *tensor = aclCreateTensor(

        shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(),

        *deviceAddr);

    return 0;

}



int main()

{

    // 1. （固定写法）device/stream初始化，参考acl API手册

    // 根据自己的实际device填写deviceId

    int32_t deviceId = 0;

    aclrtStream stream;

    auto ret = Init(deviceId, &stream);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);



    // 2. 构造输入与输出，需要根据API的接口自定义构造

    std::vector<int64_t> selfShape = {4, 2};

    std::vector<int64_t> tensor1Shape = {4, 2};

    std::vector<int64_t> tensor2Shape = {4, 2};

    void* selfDeviceAddr = nullptr;

    void* tensor1DeviceAddr = nullptr;

    void* tensor2DeviceAddr = nullptr;

    aclTensor* self = nullptr;

    aclTensor* tensor1 = nullptr;

    aclTensor* tensor2 = nullptr;

    aclScalar* value = nullptr;



    std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};

    std::vector<float> tensor1HostData = {2, 2, 2, 2, 2, 2, 2, 2};

    std::vector<float> tensor2HostData = {2, 2, 2, 2, 2, 2, 2, 2};

    float scalarValue = 1.2f;



    // 创建self aclTensor

    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);

    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 创建tensor1 aclTensor

    ret = CreateAclTensor(tensor1HostData, tensor1Shape, &tensor1DeviceAddr, aclDataType::ACL_FLOAT, &tensor1);

    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 创建tensor2 aclTensor

    ret = CreateAclTensor(tensor2HostData, tensor2Shape, &tensor2DeviceAddr, aclDataType::ACL_FLOAT, &tensor2);

    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 创建value aclScalar

    value = aclCreateScalar(&scalarValue, aclDataType::ACL_FLOAT);

    CHECK_RET(value != nullptr, return ret);



    // 3. 调用CANN算子库API，需要修改为具体的Api名称

    uint64_t workspaceSize = 0;

    aclOpExecutor* executor;

    // 调用aclnnInplaceAddcmul第一段接口

    ret = aclnnInplaceAddcmulGetWorkspaceSize(self, tensor1, tensor2, value, &workspaceSize, &executor);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAddcmulGetWorkspaceSize failed. ERROR: %d\n", ret);

              return ret);

    // 根据第一段接口计算出的workspaceSize申请device内存

    void* workspaceAddr = nullptr;

    if (workspaceSize > 0) {

        ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);

        CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);

    }

    // 调用aclnnInplaceAddcmul第二段接口

    ret = aclnnInplaceAddcmul(workspaceAddr, workspaceSize, executor, stream);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAddcmul failed. ERROR: %d\n", ret); return ret);



    // 4. （固定写法）同步等待任务执行结束

    ret = aclrtSynchronizeStream(stream);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);



    // 5. 获取输出的值，将device侧内存上的结果拷贝至host侧，需要根据具体API的接口定义修改

    auto size = GetShapeSize(selfShape);

    std::vector<float> resultData(size, 0);

    ret = aclrtMemcpy(

        resultData.data(), resultData.size() * sizeof(resultData[0]), selfDeviceAddr, size * sizeof(resultData[0]),

        ACL_MEMCPY_DEVICE_TO_HOST);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);

    for (int64_t i = 0; i < size; i++) {

        LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);

    }



    // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改

    aclDestroyTensor(self);

    aclDestroyTensor(tensor1);

    aclDestroyTensor(tensor2);

    aclDestroyScalar(value);



    // 7.释放device资源，需要根据具体API的接口定义修改

    aclrtFree(selfDeviceAddr);

    aclrtFree(tensor1DeviceAddr);

    aclrtFree(tensor2DeviceAddr);

    if (workspaceSize > 0) {

        aclrtFree(workspaceAddr);

    }

    aclrtDestroyStream(stream);

    aclrtResetDevice(deviceId);

    aclFinalize();



    return 0;

}