/**
 * 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_moe_finalize_routing_v2_grad.h"
#include <iostream>

#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;
}

void PrintOutResult(std::vector<int64_t> &shape, void **deviceAddr) {
  auto size = GetShapeSize(shape);
  std::vector<float> resultData(size, 0);
  auto ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), *deviceAddr,
                         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);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  }
}

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_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> gradYShape = {2, 2};
  std::vector<int64_t> expandedRowIdxShape = {4};
  std::vector<int64_t> expandedXShape = {4, 2};
  std::vector<int64_t> scalesShape = {2, 2};
  std::vector<int64_t> expertIdxShape = {2, 2};
  std::vector<int64_t> biasShape = {2, 2};
  std::vector<int64_t> gradExpandedXShape = {4, 2};
  std::vector<int64_t> gradScalesShape = {2, 2};
  void* gradYDeviceAddr = nullptr;
  void* expandedRowIdxDeviceAddr = nullptr;
  void* expandedXDeviceAddr = nullptr;
  void* scalesDeviceAddr = nullptr;
  void* expertIdxDeviceAddr = nullptr;
  void* biasDeviceAddr = nullptr;
  void* gradExpandedXDeviceAddr = nullptr;
  void* gradScalesDeviceAddr = nullptr;

  aclTensor* gradY = nullptr;
  aclTensor* expandedRowIdx = nullptr;
  aclTensor* expandedX = nullptr;
  aclTensor* scales = nullptr;
  aclTensor* expertIdx = nullptr;
  aclTensor* bias = nullptr;
  int64_t dropPadMode = 0;
  int64_t activeNum = 0;
  int64_t expertNum = 0;
  int64_t expertCapacity = 0;
  aclTensor* gradExpandedX = nullptr;
  aclTensor* gradScales = nullptr;

  std::vector<float> gradYHostData = {0.3816, 0.3939, 0.8474, 0.1652};
  std::vector<int> expandedRowIdxHostData = {1, 3, 0, 2};
  std::vector<float> expandedXHostData = {0.6049, 0.3315, 0.4954, 0.3284, 0.7060, 0.4359, 0.6514, 0.9476};
  std::vector<float> scalesHostData = {0.4708, 0.0656, 0.9652, 0.9512};
  std::vector<int> expertIdxHostData = {0, 1, 0, 1};
  std::vector<float> biasHostData = {0.6452, 0.1981, 0.4159, 0.9575};
  std::vector<float> gradExpandedXHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  std::vector<float> gradScalesHostData = {0, 0, 0, 0};

  ret = CreateAclTensor(gradYHostData, gradYShape, &gradYDeviceAddr, aclDataType::ACL_FLOAT, &gradY);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(expandedRowIdxHostData, expandedRowIdxShape, &expandedRowIdxDeviceAddr, aclDataType::ACL_INT32,
                        &expandedRowIdx);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(expandedXHostData, expandedXShape, &expandedXDeviceAddr, aclDataType::ACL_FLOAT, &expandedX);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(scalesHostData, scalesShape, &scalesDeviceAddr, aclDataType::ACL_FLOAT, &scales);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(expertIdxHostData, expertIdxShape, &expertIdxDeviceAddr, aclDataType::ACL_INT32, &expertIdx);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_FLOAT, &bias);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(gradExpandedXHostData, gradExpandedXShape, &gradExpandedXDeviceAddr, aclDataType::ACL_FLOAT,
                        &gradExpandedX);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(gradScalesHostData, gradScalesShape, &gradScalesDeviceAddr, aclDataType::ACL_FLOAT, &gradScales);
  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aclnnMoeFinalizeRoutingV2Grad第一段接口
  ret = aclnnMoeFinalizeRoutingV2GradGetWorkspaceSize(gradY, expandedRowIdx, expandedX, scales, expertIdx, bias,
                                                      dropPadMode, activeNum, expertNum, expertCapacity, gradExpandedX,gradScales, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMoeFinalizeRoutingV2GradGetWorkspaceSize 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aclnnMoeFinalizeRoutingV2Grad第二段接口
  ret = aclnnMoeFinalizeRoutingV2Grad(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMoeFinalizeRoutingV2Grad 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
  LOG_PRINT("gradExpandedX result is: \n");
  PrintOutResult(gradExpandedXShape, &gradExpandedXDeviceAddr);
  LOG_PRINT("gradScales result is: \n");
  PrintOutResult(gradScalesShape, &gradScalesDeviceAddr);

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(gradY);
  aclDestroyTensor(expandedRowIdx);
  aclDestroyTensor(expandedX);
  aclDestroyTensor(scales);
  aclDestroyTensor(expertIdx);
  aclDestroyTensor(bias);
  aclDestroyTensor(gradExpandedX);
  aclDestroyTensor(gradScales);

  // 7. 释放device资源
  aclrtFree(gradYDeviceAddr);
  aclrtFree(expandedRowIdxDeviceAddr);
  aclrtFree(expandedXDeviceAddr);
  aclrtFree(scalesDeviceAddr);
  aclrtFree(expertIdxDeviceAddr);
  aclrtFree(biasDeviceAddr);
  aclrtFree(gradExpandedXDeviceAddr);
  aclrtFree(gradScalesDeviceAddr);

  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}