/**

 * 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_apply_adam_w.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，AscendCL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AscendCL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根据自己的需要处理

  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> varShape = {2, 2};

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

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

  std::vector<int64_t> beta1PowerShape = {1};

  std::vector<int64_t> beta2PowerShape = {1};

  std::vector<int64_t> lrShape = {1};

  std::vector<int64_t> weightDecayShape = {1};

  std::vector<int64_t> beta1Shape = {1};

  std::vector<int64_t> beta2Shape = {1};

  std::vector<int64_t> epsShape = {1};

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

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

  void* varDeviceAddr = nullptr;

  void* mDeviceAddr = nullptr;

  void* vDeviceAddr = nullptr;

  void* beta1PowerDeviceAddr = nullptr;

  void* beta2PowerDeviceAddr = nullptr;

  void* lrDeviceAddr = nullptr;

  void* weightDecayDeviceAddr = nullptr;

  void* beta1DeviceAddr = nullptr;

  void* beta2DeviceAddr = nullptr;

  void* epsDeviceAddr = nullptr;

  void* gradDeviceAddr = nullptr;

  void* maxgradDeviceAddr = nullptr;

  aclTensor* var = nullptr;

  aclTensor* m = nullptr;

  aclTensor* v = nullptr;

  aclTensor* beta1Power = nullptr;

  aclTensor* beta2Power = nullptr;

  aclTensor* lr = nullptr;

  aclTensor* weightDecay = nullptr;

  aclTensor* beta1 = nullptr;

  aclTensor* beta2 = nullptr;

  aclTensor* eps = nullptr;

  aclTensor* grad = nullptr;

  aclTensor* maxgrad = nullptr;

  std::vector<float> varHostData = {0, 1, 2, 3};

  std::vector<float> mHostData = {0, 1, 2, 3};

  std::vector<float> vHostData = {0, 1, 2, 3};

  std::vector<float> beta1PowerHostData = {0.431};

  std::vector<float> beta2PowerHostData = {0.992};

  std::vector<float> lrHostData = {0.001};

  std::vector<float> weightDecayHostData = {0.01};

  std::vector<float> beta1HostData = {0.9};

  std::vector<float> beta2HostData = {0.999};

  std::vector<float> epsHostData = {1e-8};

  std::vector<float> gradHostData = {0, 1, 2, 3};

  std::vector<float> maxgradHostData = {0, 1, 2, 3};

  bool amsgrad = true;

  bool maximize = true;

  // 创建var aclTensor

  ret = CreateAclTensor(varHostData, varShape, &varDeviceAddr, aclDataType::ACL_FLOAT, &var);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建m aclTensor

  ret = CreateAclTensor(mHostData, mShape, &mDeviceAddr, aclDataType::ACL_FLOAT, &m);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建v aclTensor

  ret = CreateAclTensor(vHostData, vShape, &vDeviceAddr, aclDataType::ACL_FLOAT, &v);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建beta1Power aclTensor

  ret = CreateAclTensor(beta1PowerHostData, beta1PowerShape, &beta1PowerDeviceAddr, aclDataType::ACL_FLOAT, &beta1Power);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建beta2Power aclTensor

  ret = CreateAclTensor(beta2PowerHostData, beta2PowerShape, &beta2PowerDeviceAddr, aclDataType::ACL_FLOAT, &beta2Power);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建lr aclTensor

  ret = CreateAclTensor(lrHostData, lrShape, &lrDeviceAddr, aclDataType::ACL_FLOAT, &lr);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建weightDecay aclTensor

  ret = CreateAclTensor(weightDecayHostData, weightDecayShape, &weightDecayDeviceAddr, aclDataType::ACL_FLOAT, &weightDecay);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建beta1 aclTensor

  ret = CreateAclTensor(beta1HostData, beta1Shape, &beta1DeviceAddr, aclDataType::ACL_FLOAT, &beta1);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建beta2 aclTensor

  ret = CreateAclTensor(beta2HostData, beta2Shape, &beta2DeviceAddr, aclDataType::ACL_FLOAT, &beta2);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建eps aclTensor

  ret = CreateAclTensor(epsHostData, epsShape, &epsDeviceAddr, aclDataType::ACL_FLOAT, &eps);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建grad aclTensor

  ret = CreateAclTensor(gradHostData, gradShape, &gradDeviceAddr, aclDataType::ACL_FLOAT, &grad);

  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 创建maxgrad aclTensor

  ret = CreateAclTensor(maxgradHostData, maxgradShape, &maxgradDeviceAddr, aclDataType::ACL_FLOAT, &maxgrad);

  CHECK_RET(ret == ACL_SUCCESS, 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aclnnApplyAdamW第一段接口

  ret = aclnnApplyAdamWGetWorkspaceSize(var, m, v, beta1Power, beta2Power, lr, weightDecay, beta1, beta2, eps, grad, maxgrad, amsgrad, maximize, &workspaceSize, &executor);

  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnApplyAdamWGetWorkspaceSize 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aclnnApplyAdamW第二段接口

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

  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnApplyAdamW 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(varShape);

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

  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), varDeviceAddr, 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，需要根据具体API的接口定义修改

  aclDestroyTensor(var);

  aclDestroyTensor(m);

  aclDestroyTensor(v);

  aclDestroyTensor(beta1Power);

  aclDestroyTensor(beta2Power);

  aclDestroyTensor(lr);

  aclDestroyTensor(weightDecay);

  aclDestroyTensor(beta1);

  aclDestroyTensor(beta2);

  aclDestroyTensor(eps);

  aclDestroyTensor(grad);

  aclDestroyTensor(maxgrad);



  // 7. 释放device 资源

  aclrtFree(varDeviceAddr);

  aclrtFree(mDeviceAddr);

  aclrtFree(vDeviceAddr);

  aclrtFree(beta1PowerDeviceAddr);

  aclrtFree(beta2PowerDeviceAddr);

  aclrtFree(lrDeviceAddr);

  aclrtFree(weightDecayDeviceAddr);

  aclrtFree(beta1DeviceAddr);

  aclrtFree(beta2DeviceAddr);

  aclrtFree(epsDeviceAddr);

  aclrtFree(gradDeviceAddr);

  aclrtFree(maxgradDeviceAddr);

  if (workspaceSize > 0) {

    aclrtFree(workspaceAddr);

  }

  aclrtDestroyStream(stream);

  aclrtResetDevice(deviceId);

  aclFinalize();



  return 0;

}