/**

 * 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.

 */



/*!

 * \file test_aclnn_conv2d_v2.cpp

 * \brief

 */



#include <iostream>

#include <memory>

#include <vector>

#include "acl/acl.h"

#include "aclnnop/aclnn_convolution.h"



#define CHECK_RET(cond, return_expr) \

  do {                               \

    if (!(cond)) {                   \

      return_expr;                   \

    }                                \

  } while (0)



#define CHECK_FREE_RET(cond, return_expr) \

  do {                                    \

    if (!(cond)) {                        \

      Finalize(deviceId, stream);         \

      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 shape_size = 1;

  for (auto i: shape) {

    shape_size *= i;

  }

  return shape_size;

}



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_NCHW,

                            shape.data(), shape.size(), *deviceAddr);

  return 0;

}



void Finalize(int32_t deviceId, aclrtStream& stream)

{

  aclrtDestroyStream(stream);

  aclrtResetDevice(deviceId);

  aclFinalize();

}



int aclnnConvolutionTest(int32_t deviceId, aclrtStream& stream)

{

  auto ret = Init(deviceId, &stream);

  // check根据自己的需要处理

  CHECK_FREE_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> shapeInput = {2, 2, 2, 2};

  std::vector<int64_t> shapeWeight = {1, 2, 1, 1};

  std::vector<int64_t> shapeResult = {2, 1, 2, 2};

  std::vector<int64_t> convStrides;

  std::vector<int64_t> convPads;

  std::vector<int64_t> convOutPads;

  std::vector<int64_t> convDilations;



  void* deviceDataA = nullptr;

  void* deviceDataB = nullptr;

  void* deviceDataResult = nullptr;



  aclTensor* input = nullptr;

  aclTensor* weight = nullptr;

  aclTensor* result = nullptr;

  std::vector<float> inputData(GetShapeSize(shapeInput), 1);

  std::vector<float> weightData(GetShapeSize(shapeWeight), 1);

  std::vector<float> outputData(GetShapeSize(shapeResult), 1);

  convStrides = {1, 1, 1, 1};

  convPads = {0, 0, 0, 0};

  convOutPads = {0, 0, 0, 0};

  convDilations = {1, 1, 1, 1};



  // 创建input aclTensor

  ret = CreateAclTensor(inputData, shapeInput, &deviceDataA, aclDataType::ACL_FLOAT, &input);

  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> inputTensorPtr(input, aclDestroyTensor);

  std::unique_ptr<void, aclError (*)(void *)> deviceDataAPtr(deviceDataA, aclrtFree);

  CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);



  // 创建weight aclTensor

  ret = CreateAclTensor(weightData, shapeWeight, &deviceDataB, aclDataType::ACL_FLOAT, &weight);

  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> weightTensorPtr(weight, aclDestroyTensor);

  std::unique_ptr<void, aclError (*)(void *)> deviceDataBPtr(deviceDataB, aclrtFree);

  CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);



  // 创建out aclTensor

  ret = CreateAclTensor(outputData, shapeResult, &deviceDataResult, aclDataType::ACL_FLOAT, &result);

  std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> outputTensorPtr(result, aclDestroyTensor);

  std::unique_ptr<void, aclError (*)(void *)> deviceDataResultPtr(deviceDataResult, aclrtFree);

  CHECK_FREE_RET(ret == ACL_SUCCESS, return ret);



  aclIntArray *strides = aclCreateIntArray(convStrides.data(), 2);

  std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> stridesPtr(strides, aclDestroyIntArray);

  CHECK_FREE_RET(strides != nullptr, return ACL_ERROR_INTERNAL_ERROR);

  aclIntArray *pads = aclCreateIntArray(convPads.data(), 2);

  std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> padsPtr(pads, aclDestroyIntArray);

  CHECK_FREE_RET(pads != nullptr, return ACL_ERROR_INTERNAL_ERROR);

  aclIntArray *outPads = aclCreateIntArray(convOutPads.data(), 2);

  std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> outPadsPtr(outPads, aclDestroyIntArray);

  CHECK_FREE_RET(outPads != nullptr, return ACL_ERROR_INTERNAL_ERROR);

  aclIntArray *dilations = aclCreateIntArray(convDilations.data(), 2);

  std::unique_ptr<aclIntArray, aclnnStatus (*)(const aclIntArray *)> dilationsPtr(dilations, aclDestroyIntArray);

  CHECK_FREE_RET(dilations != nullptr, return ACL_ERROR_INTERNAL_ERROR);



  // 3. 调用CANN算子库API，需要修改为具体的API

  uint64_t workspaceSize = 0;

  aclOpExecutor* executor;

  // 调用aclnnConvolution第一段接口

  ret = aclnnConvolutionGetWorkspaceSize(input, weight, nullptr, strides, pads, dilations, false, outPads, 1, result, 1,

                                         &workspaceSize, &executor);

  CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvolutionGetWorkspaceSize 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;

  std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtr(nullptr, aclrtFree);

  if (workspaceSize > static_cast<uint64_t>(0)) {

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

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

    workspaceAddrPtr.reset(workspaceAddr);

  }

  // 调用aclnnConvolution第二段接口

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

  CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvolution 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_FREE_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(shapeResult);

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

  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), deviceDataResult,

                    size * sizeof(float), ACL_MEMCPY_DEVICE_TO_HOST);

  CHECK_FREE_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]);

  }



  return ACL_SUCCESS;

}



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 = aclnnConvolutionTest(deviceId, stream);

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



  Finalize(deviceId, stream);

  return 0;

}