/**
 * 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 <cmath>
#include "acl/acl.h"
#include "aclnnop/aclnn_transpose_batch_mat_mul.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)

// 将FP16的uint16_t表示转换为float表示
float Fp16ToFloat(uint16_t h) {
  int s = (h >> 15) & 0x1;              // sign
  int e = (h >> 10) & 0x1F;             // exponent
  int f =  h        & 0x3FF;            // fraction
  if (e == 0) {
    // Zero or Denormal
    if (f == 0) {
      return s ? -0.0f : 0.0f;
    }
    // Denormals
    float sig = f / 1024.0f;
    float result = sig * pow(2, -24);
    return s ? -result : result;
  } else if (e == 31) {
      // Infinity or NaN
      return f == 0 ? (s ? -INFINITY : INFINITY) : NAN;
  }
  // Normalized
  float result = (1.0f + f / 1024.0f) * pow(2, e - 15);
  return s ? -result : result;
}

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
  int32_t M = 32;
  int32_t K = 512;
  int32_t N = 128;
  int32_t Batch = 16;
  std::vector<int64_t> x1Shape = {M, Batch, K};
  std::vector<int64_t> x2Shape = {Batch, K, N};
  std::vector<int64_t> outShape = {M, Batch, N};
  std::vector<int64_t> permX1Series = {1, 0, 2};
  std::vector<int64_t> permX2Series = {0, 1, 2};
  std::vector<int64_t> permYSeries = {1, 0, 2};
  void* x1DeviceAddr = nullptr;
  void* x2DeviceAddr = nullptr;
  void* scaleDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* x1 = nullptr;
  aclTensor* x2 = nullptr;
  aclTensor* scale = nullptr;
  aclTensor* out = nullptr;
  std::vector<uint16_t> x1HostData(GetShapeSize(x1Shape),0x3C00);
  std::vector<uint16_t> x2HostData(GetShapeSize(x2Shape),0x3C00);
  std::vector<uint16_t> outHostData(GetShapeSize(outShape),0);
  int8_t cubeMathType = 1;
  int8_t batchSplitFactor = 1;

  // 创建x1 aclTensor
  ret = CreateAclTensor(x1HostData, x1Shape, &x1DeviceAddr, aclDataType::ACL_FLOAT16, &x1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建x2 aclTensor
  ret = CreateAclTensor(x2HostData, x2Shape, &x2DeviceAddr, aclDataType::ACL_FLOAT16, &x2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT16, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  aclIntArray *permX1 = aclCreateIntArray(permX1Series.data(), permX1Series.size());
  aclIntArray *permX2 = aclCreateIntArray(permX2Series.data(), permX2Series.size());
  aclIntArray *permY = aclCreateIntArray(permYSeries.data(), permYSeries.size());
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor = nullptr;

  // aclnnTransposeBatchMatMul接口调用示例
  // 3. 调用CANN算子库API，需要修改为具体的API名称
  // 调用aclnnTransposeBatchMatMul第一段接口
  ret = aclnnTransposeBatchMatMulGetWorkspaceSize(x1, x2, (const aclTensor*)nullptr, (const aclTensor*)nullptr,
                                                  permX1, permX2, permY, cubeMathType, batchSplitFactor, out,
                                                  &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransposeBatchMatMulGetWorkspaceSize 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aclnnTransposeBatchMatMul第二段接口
  ret = aclnnTransposeBatchMatMul(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransposeBatchMatMul 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<uint16_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++) {
    float fp16Float = Fp16ToFloat(resultData[i]);
    LOG_PRINT("result[%ld] is: %f\n", i, fp16Float);
  }

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(x1);
  aclDestroyTensor(x2);
  aclDestroyTensor(out);

  // 7. 释放device资源，需要根据具体API的接口定义修改
  aclrtFree(x1DeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}