/**
 * 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_dequant_swiglu_quant_v2.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) {
  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acl API手册
  // 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> xShape = {2, 64};
  std::vector<int64_t> weightScaleShape = {1, 64};
  std::vector<int64_t> activationScaleShape = {2};
  std::vector<int64_t> biasShape = {1, 64};
  std::vector<int64_t> scaleShape = {1, 32}; // quantscale
  std::vector<int64_t> offsetShape = {1};
  std::vector<int64_t> groupIndexShape = {1};
  std::vector<int64_t> outShape = {2, 32};
  std::vector<int64_t> scaleOutShape = {2};

  void* xDeviceAddr = nullptr;
  void* weightScaleDeviceAddr = nullptr;
  void* activationScaleDeviceAddr = nullptr;
  void* biasDeviceAddr = nullptr;

  void* scaleDeviceAddr = nullptr;
  void* offsetDeviceAddr = nullptr;
  void* groupIndexDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  void* scaleOutDeviceAddr = nullptr;

  aclTensor* x = nullptr;
  aclTensor* weightScale = nullptr;
  aclTensor* activationScale= nullptr;
  aclTensor* bias = nullptr;

  aclTensor* scale = nullptr;
  aclTensor* offset = nullptr;
  aclTensor* groupIndex = nullptr;
  aclTensor* out = nullptr;
  aclTensor* scaleOut = nullptr;

  std::vector<int32_t> xHostData = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
                                    23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
                                    43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
                                    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
                                    23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
                                    43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
  std::vector<float> weightScaleData = {1.0};
  std::vector<float> activationScaleData = {1.0};
  std::vector<float> biasData = {1.0};
  std::vector<int64_t> groupIndexData = {1};
  std::vector<float> scaleHostData = {1};
  std::vector<float> offsetHostData = {1};
  std::vector<int8_t> outHostData = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
  std::vector<float> scaleOutHostData = {0, 0};

  bool activateLeft = true;
  int64_t dstType = 2;
  int64_t activateDim = -1;
  int64_t swigluMode = 1;
  float clampLimit = 7.0;
  float gluAlpha = 1.0;
  float gluBias = 1.702;

  // 创建x aclTensor
  ret = CreateAclTensor(xHostData, xShape, &xDeviceAddr, aclDataType::ACL_INT32, &x);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建weightScale
  ret = CreateAclTensor(weightScaleData, weightScaleShape, &weightScaleDeviceAddr, aclDataType::ACL_FLOAT, &weightScale);
  // 创建activationScale
  ret = CreateAclTensor(activationScaleData, activationScaleShape, &activationScaleDeviceAddr, aclDataType::ACL_FLOAT, &activationScale);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建bias
  ret = CreateAclTensor(biasData, biasShape, &biasDeviceAddr, aclDataType::ACL_FLOAT, &bias);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建scale aclTensor
  ret = CreateAclTensor(scaleHostData, scaleShape, &scaleDeviceAddr, aclDataType::ACL_FLOAT, &scale);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建offset aclTensor
  ret = CreateAclTensor(offsetHostData, offsetShape, &offsetDeviceAddr, aclDataType::ACL_FLOAT, &offset);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建groupIndex aclTensor
  ret = CreateAclTensor(groupIndexData, groupIndexShape, &groupIndexDeviceAddr, aclDataType::ACL_INT64, &groupIndex);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_INT8, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建scaleOut aclTensor
  ret = CreateAclTensor(scaleOutHostData, scaleOutShape, &scaleOutDeviceAddr, aclDataType::ACL_FLOAT, &scaleOut);
  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aclnnDequantSwigluQuantV2第一段接口
  ret = aclnnDequantSwigluQuantV2GetWorkspaceSize(x, weightScale, activationScale, bias, scale, nullptr, groupIndex, activateLeft, "dynamic", dstType, "rint", activateDim, swigluMode, clampLimit, gluAlpha, gluBias, out, scaleOut, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnDequantSwigluQuantV2GetWorkspaceSize 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aclnnDequantSwigluQuantV2第二段接口
  ret = aclnnDequantSwigluQuantV2(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnDequantSwigluQuantV2 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(outShape);
  std::vector<int8_t> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr,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: %d\n", i, resultData[i]);
  }
  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(x);
  aclDestroyTensor(scale);
  aclDestroyTensor(offset);
  aclDestroyTensor(out);
  aclDestroyTensor(scaleOut);
  // 7. 释放device资源，需要根据具体API的接口定义修改
  aclrtFree(xDeviceAddr);
  aclrtFree(scaleDeviceAddr);
  aclrtFree(offsetDeviceAddr);
  aclrtFree(outDeviceAddr);
  aclrtFree(scaleOutDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}