/**

 * 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 <iostream>

#include <vector>

#include "acl/acl.h"

#include "aclnnop/aclnn_avgpool3d_backward.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, aclFormat Format = aclFormat::ACL_FORMAT_ND) {

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

  return 0;

}



int main() {

  // 1. （固定写法）device/stream初始化，参考acl对外接口列表

  // 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> gradOutputShape = {1, 16, 1, 1, 1};

  std::vector<int64_t> selfShape = {1, 16, 4, 4, 4};

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

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

  std::vector<int64_t> paddingDims = {0, 0, 0};

  std::vector<int64_t> outputShape = {1, 16, 4, 4, 4};

  bool ceilMode = false;

  int64_t divisorOverride = 0;

  bool countIncludePad = false;



  void* gradOutputDeviceAddr = nullptr;

  void* selfAddr = nullptr;

  void* outputAddr = nullptr;



  aclTensor* gradOutput = nullptr;

  aclTensor* selfInput = nullptr;

  aclTensor* output = nullptr;



  std::vector<float> gradOutputHostData(GetShapeSize(gradOutputShape), 1);

  std::vector<float> selfHostData(GetShapeSize(selfShape), 1);

  std::vector<float> outputHostData(GetShapeSize(outputShape), 1);

  // 创建gradOutput aclTensor

  ret = CreateAclTensor(gradOutputHostData, gradOutputShape, &gradOutputDeviceAddr, aclDataType::ACL_FLOAT, &gradOutput);

  CHECK_RET(ret == ACL_SUCCESS, return ret);



  // 创建inputshape aclTensor

  ret = CreateAclTensor(selfHostData, selfShape, &selfAddr, aclDataType::ACL_FLOAT,

  &selfInput);

  CHECK_RET(ret == ACL_SUCCESS, return ret);



  // 创建output

  ret = CreateAclTensor(outputHostData, outputShape, &outputAddr, aclDataType::ACL_FLOAT, &output);

  CHECK_RET(ret == ACL_SUCCESS, return ret);



  // 创建kernel aclIntArray

  aclIntArray *kernelSize = aclCreateIntArray(kernelDims.data(), 3);



  // 创建stride aclIntArray

  aclIntArray *stride = aclCreateIntArray(strideDims.data(), 3);



  // 创建paddings aclIntArray

  aclIntArray *padding = aclCreateIntArray(paddingDims.data(), 3);



  // 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aclnnAvgPool3dBackward第一段接口

  ret = aclnnAvgPool3dBackwardGetWorkspaceSize(gradOutput,

                                        selfInput,

                                        kernelSize,

                                        stride,

                                        padding,

                                        ceilMode,

                                        countIncludePad,

                                        divisorOverride,

                                        output,

                                        &workspaceSize,

                                        &executor);

  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAvgPool3dBackwardGetWorkspaceSize 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aclnnAvgPool3dBackward第二段接口

  ret = aclnnAvgPool3dBackward(workspaceAddr, workspaceSize, executor, stream);

  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAvgPool3dBackward 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(outputShape);

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

  ret = aclrtMemcpy(outData.data(), outData.size() * sizeof(outData[0]), outputAddr,

                    size * sizeof(outData[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("out result[%ld] is: %f\n", i, outData[i]);

  }



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

  aclDestroyTensor(gradOutput);

  aclDestroyTensor(selfInput);

  aclDestroyTensor(output);

  aclDestroyIntArray(kernelSize);

  aclDestroyIntArray(stride);

  aclDestroyIntArray(padding);



  // 7. 释放device资源，需要根据具体API的接口定义修改

  aclrtFree(gradOutputDeviceAddr);

  aclrtFree(selfAddr);

  aclrtFree(outputAddr);

  if (workspaceSize > 0) {

    aclrtFree(workspaceAddr);

  }

  aclrtDestroyStream(stream);

  aclrtResetDevice(deviceId);

  aclFinalize();



  return 0;

}