/**

 * Copyright (c) 2025 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.

 */





#include <vector>

#include <iostream>

#include "utils.h"

#include "model_utils.h"

#include "acl/acl.h"

#include "aclnnop/aclnn_add.h"

#include "aclnnop/aclnn_mul.h"

using namespace std;



// aclnnadd计算：out = self + other * alpha

// aclnnmul计算: out = self * other

// 做一个model来计算： out = (self + other * alpha) * other

int main()

{

    int deviceId = 0;

    void *selfDevice = nullptr;

    void *otherDevice = nullptr;

    void *outDevice = nullptr;

    void *outTmpDevice = nullptr;

    void *selfHost = nullptr;

    void *otherHost = nullptr;

    aclTensor *self = nullptr;

    aclTensor *other = nullptr;

    aclScalar *alpha = nullptr;

    aclTensor *out = nullptr;

    aclTensor *outTmp = nullptr;

    vector<float> selfHostData = {1, 2, 3, 4, 5, 6, 7, 8};

    vector<float> otherHostData = {2, 2, 2, 2, 2, 2, 2, 2};

    vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};

    vector<float> outTempHostData = {0, 0, 0, 0, 0, 0, 0, 0};

    vector<int64_t> shape = {4, 2};

    float alphaValue = 1.1f;

    uint64_t addWorkspaceSize = 0;

    uint64_t mulWorkspaceSize = 0;

    aclOpExecutor *addExecutor;

    aclOpExecutor *mulExecutor;

    aclrtContext context;

    const int loopCount = 4;

    int64_t size =  ModelUtils::GetShapeSize(shape) * sizeof(float);



    CHECK_ERROR(aclInit(NULL));

    CHECK_ERROR(aclrtSetDevice(deviceId));

    CHECK_ERROR(aclrtCreateContext(&context, deviceId));

    // 创建tensor来执行aclnn算子，tensor与数据的device地址是绑定的

    ModelUtils::CreateAclTensor(shape, &selfDevice, aclDataType::ACL_FLOAT, &self);

    ModelUtils::CreateAclTensor(shape, &otherDevice, aclDataType::ACL_FLOAT, &other);

    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);

    ModelUtils::CreateAclTensor(shape, &outDevice, aclDataType::ACL_FLOAT, &out);

    ModelUtils::CreateAclTensor(shape, &outTmpDevice, aclDataType::ACL_FLOAT, &outTmp);

    

    // 创建workspace来执行算子，不同算子的workspace可能不同，该处计算outTmp = self + other * alpha

    aclnnAddGetWorkspaceSize(self, other, alpha, outTmp, &addWorkspaceSize, &addExecutor);

    void *addWorkspaceAddr = nullptr;

    if (addWorkspaceSize > 0) {

        CHECK_ERROR(aclrtMalloc(&addWorkspaceAddr, addWorkspaceSize, ACL_MEM_MALLOC_HUGE_FIRST));

    }

    // 该算子计算out = outTmp * other，outTmp是上一个add算子的输出。

    aclnnMulGetWorkspaceSize(outTmp, other, out, &mulWorkspaceSize, &mulExecutor);

    void *mulWorkspaceAddr = nullptr;

    if (mulWorkspaceSize > 0) {

        CHECK_ERROR(aclrtMalloc(&mulWorkspaceAddr, mulWorkspaceSize, ACL_MEM_MALLOC_HUGE_FIRST));

    }



    aclmdlRI modelRI;

    aclrtStream stream;

    CHECK_ERROR(aclrtCreateStream(&stream));

    CHECK_ERROR(aclrtMallocHost(&selfHost, size));

    CHECK_ERROR(aclrtMemcpy(selfHost, size, selfHostData.data(), size, ACL_MEMCPY_HOST_TO_HOST));

    

    // 标志着model的开始直到end。此处设置的mode为ACL_MODEL_RI_CAPTURE_MODE_GLOBAL，禁止执行非安全的函数

    // 该model执行了将内存从host复制到device侧，然后调用了一个add算子和一个mul算子

    CHECK_ERROR(aclmdlRICaptureBegin(stream, ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));

    // 异步复制为安全函数，可以在GLOBAL的mode下调用，该函数会入图。

    CHECK_ERROR(aclrtMemcpyAsync(selfDevice, size, selfHost, size, ACL_MEMCPY_HOST_TO_DEVICE, stream));

    aclmdlRICaptureMode mode = ACL_MODEL_RI_CAPTURE_MODE_RELAXED;

    // 可以通过该函数设置mode，此处修改为ACL_MODEL_RI_CAPTURE_MODE_RELAXED，可以执行非安全的函数

    CHECK_ERROR(aclmdlRICaptureThreadExchangeMode(&mode));

    // 同步复制为非安全函数，要设置为RELAXED，该调用不会入图，会直接执行

    CHECK_ERROR(aclrtMemcpy(otherDevice, size, otherHostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE));

    // 再次调用会回到上次设置的Mode

    CHECK_ERROR(aclmdlRICaptureThreadExchangeMode(&mode));

    // 调用aclnn算子，会入图

    aclnnAdd(addWorkspaceAddr, addWorkspaceSize, addExecutor, stream);

    aclnnMul(mulWorkspaceAddr, mulWorkspaceSize, mulExecutor, stream);

    // 标志model创建结束，且保存为modelRI,后续调用可以通过modelRI进行

    CHECK_ERROR(aclmdlRICaptureEnd(stream, &modelRI));

    // 打印信息可以在日志中查找

    const char *jsonPath = "./modelRI.json";

    CHECK_ERROR(aclmdlRIDebugJsonPrint(modelRI, jsonPath, 0));

    

    // 循环执行刚才创建的modelRI

    for (int i = 0; i < loopCount; i++) {

        INFO_LOG("execute model, loop count: %d.", i + 1);

        CHECK_ERROR(aclmdlRIExecuteAsync(modelRI, stream));

        CHECK_ERROR(aclrtSynchronizeStream(stream));

        CHECK_ERROR(aclrtMemcpy(outHostData.data(), size, outDevice, size, ACL_MEMCPY_DEVICE_TO_HOST));

        ModelUtils::PrintArray(outHostData);

    }



    // 释放资源

    CHECK_ERROR(aclmdlRIDestroy(modelRI));

    CHECK_ERROR(aclrtDestroyStreamForce(stream));

    CHECK_ERROR(aclDestroyTensor(self));

    CHECK_ERROR(aclDestroyTensor(other));

    CHECK_ERROR(aclDestroyTensor(out));

    CHECK_ERROR(aclDestroyTensor(outTmp));

    CHECK_ERROR(aclDestroyScalar(alpha));

    CHECK_ERROR(aclrtFree(selfDevice));

    CHECK_ERROR(aclrtFree(otherDevice));

    CHECK_ERROR(aclrtFree(outDevice));

    CHECK_ERROR(aclrtFree(outTmpDevice));

    if (addWorkspaceAddr != nullptr) {

        CHECK_ERROR(aclrtFree(addWorkspaceAddr));

    }

    if (mulWorkspaceAddr != nullptr) {

        CHECK_ERROR(aclrtFree(mulWorkspaceAddr));

    }

    CHECK_ERROR(aclrtDestroyContext(context));

    CHECK_ERROR(aclrtResetDeviceForce(deviceId));

    CHECK_ERROR(aclFinalize());

}