/**
 * 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.
 */

/*!
 * \file test_aclnn_sparse_lightning_indexer_grad_kl_loss.cpp
 * \brief
 */

#include <iostream>
#include <vector>
#include <cstdint>
#include <cmath>
#include "acl/acl.h"
#include "aclnnop/aclnn_sparse_lightning_indexer_grad_kl_loss.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;
}

void PrintOutResult(std::vector<int64_t> &shape, void** deviceAddr) {
  auto size = GetShapeSize(shape);
  std::vector<aclFloat16> 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("mean result[%ld] is: %f\n", i, aclFloat16ToFloat(resultData[i]));
  }
}

int Init(int32_t deviceId, aclrtContext* context, 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &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
  int64_t s1 = 1;
  int64_t s2 = 1;
  int64_t n1 = 128;
  int64_t n2 = 1;
  int64_t nQueryIndex = 64;
  int64_t dQuery = 512;
  int64_t dRope = 64;
  int64_t dQueryIndex = 128;
  int64_t topK = 2048;
  int64_t t1 = s1;
  int64_t t2 = s2;
  int64_t G = n1 / n2;

  std::vector<int64_t> qShape = {t1, n1, dQuery};
  std::vector<int64_t> kShape = {t2, n2, dQuery};
  std::vector<int64_t> qRopeShape = {t1, n1, dRope};
  std::vector<int64_t> kRopeShape = {t2, n2, dRope};
  std::vector<int64_t> qIndexShape = {t1, nQueryIndex, dQueryIndex};
  std::vector<int64_t> kIndexShape = {t2, n2, dQueryIndex};
  std::vector<int64_t> weightShape = {t1, nQueryIndex};
  std::vector<int64_t> sparseIndicesShape = {t1, n2, topK};
  std::vector<int64_t> softmaxMaxShape = {n2, t1, G};
  std::vector<int64_t> softmaxSumShape = {n2, t1, G};

  std::vector<int64_t> dQIndexShape = {t1, nQueryIndex, dQueryIndex};
  std::vector<int64_t> dKIndexShape = {t2, n2, dQueryIndex};
  std::vector<int64_t> dWeightShape = {t1, nQueryIndex};
  std::vector<int64_t> lossShape = {1};

  void* qDeviceAddr = nullptr;
  void* kDeviceAddr = nullptr;
  void* qRopeDeviceAddr = nullptr;
  void* kRopeDeviceAddr = nullptr;
  void* qIndexDeviceAddr = nullptr;
  void* kIndexDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* sparseIndicesDeviceAddr = nullptr;
  void* softmaxMaxDeviceAddr = nullptr;
  void* softmaxSumDeviceAddr = nullptr;
  
  void* dQIndexDeviceAddr = nullptr;
  void* dKIndexDeviceAddr = nullptr;
  void* dWeightDeviceAddr = nullptr;
  void* lossDeviceAddr = nullptr;

  aclTensor* q = nullptr;
  aclTensor* k = nullptr;
  aclTensor* qRope = nullptr;
  aclTensor* kRope = nullptr;
  aclTensor* qIndex = nullptr;
  aclTensor* kIndex = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* sparseIndices = nullptr;
  aclTensor* softmaxMax = nullptr;
  aclTensor* softmaxSum = nullptr;

  aclTensor* dQIndex = nullptr;
  aclTensor* dKIndex = nullptr;
  aclTensor* dWeight = nullptr;
  aclTensor* loss = nullptr;

  std::vector<aclFloat16> qHostData(t1 * n1 * dQuery, aclFloatToFloat16(0.1));
  std::vector<aclFloat16> kHostData(t2 * dQuery, aclFloatToFloat16(0.2));
  std::vector<aclFloat16> qRopeHostData(t1 * n1 * dRope, aclFloatToFloat16(0.1));
  std::vector<aclFloat16> kRopeHostData(t2 * dRope, aclFloatToFloat16(0.2));
  std::vector<aclFloat16> qIndexHostData(t1 * nQueryIndex * dQueryIndex, aclFloatToFloat16(0.2));
  std::vector<aclFloat16> kIndexHostData(t2 * dQueryIndex, aclFloatToFloat16(0.1));
  std::vector<aclFloat16> weightHostData(t1 * nQueryIndex, aclFloatToFloat16(0.005));
  std::vector<int32_t> sparseIndicesHostData(t1*topK, -1);

  for (int i = 0; i < t2; i++) {
    sparseIndicesHostData[i] = i;
  }

  std::vector<float> softmaxMaxHostData(t1 * n1, 25.4483f);
  std::vector<float> softmaxSumHostData(t1 * n1, 1.0f);

  std::vector<aclFloat16> dQIndexHostData(t1 * nQueryIndex * dQueryIndex);
  std::vector<aclFloat16> dKIndexHostData(t2 * dQueryIndex);
  std::vector<aclFloat16> dWeightHostData(t1 * nQueryIndex);
  std::vector<float> lossHostData(1, 1.0f);

  ret = CreateAclTensor(qHostData, qShape, &qDeviceAddr, aclDataType::ACL_FLOAT16, &q);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(kHostData, kShape, &kDeviceAddr, aclDataType::ACL_FLOAT16, &k);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(qRopeHostData, qRopeShape, &qRopeDeviceAddr, aclDataType::ACL_FLOAT16, &qRope);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(kRopeHostData, kRopeShape, &kRopeDeviceAddr, aclDataType::ACL_FLOAT16, &kRope);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(qIndexHostData, qIndexShape, &qIndexDeviceAddr, aclDataType::ACL_FLOAT16, &qIndex);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(kIndexHostData, kIndexShape, &kIndexDeviceAddr, aclDataType::ACL_FLOAT16, &kIndex);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT16, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(sparseIndicesHostData, sparseIndicesShape, &sparseIndicesDeviceAddr, aclDataType::ACL_INT32, &sparseIndices);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(softmaxMaxHostData, softmaxMaxShape, &softmaxMaxDeviceAddr, aclDataType::ACL_FLOAT, &softmaxMax);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(softmaxSumHostData, softmaxSumShape, &softmaxSumDeviceAddr, aclDataType::ACL_FLOAT, &softmaxSum);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dQIndexHostData, dQIndexShape, &dQIndexDeviceAddr, aclDataType::ACL_FLOAT16, &dQIndex);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dKIndexHostData, dKIndexShape, &dKIndexDeviceAddr, aclDataType::ACL_FLOAT16, &dKIndex);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(dWeightHostData, dWeightShape, &dWeightDeviceAddr, aclDataType::ACL_FLOAT16, &dWeight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(lossHostData, lossShape, &lossDeviceAddr, aclDataType::ACL_FLOAT, &loss);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  std::vector<int64_t>  acSeqQLenOp = {t1};
  std::vector<int64_t>  acSeqKvLenOp = {t2};
  aclIntArray* acSeqQLen = aclCreateIntArray(acSeqQLenOp.data(), acSeqQLenOp.size());
  aclIntArray* acSeqKvLen = aclCreateIntArray(acSeqKvLenOp.data(), acSeqKvLenOp.size());
  float scaleValue = 1.0 / sqrt(dQuery);
  int64_t preTokens = 2147483647;
  int64_t nextTokens = 2147483647;
  int64_t sparseMode = 3;
  bool deterministic = false;

  char layOut[5] = {'T', 'N', 'D', 0};

  // 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aclnnSparseLightningIndexerGradKLLossGetWorkspaceSize第一段接口
  ret = aclnnSparseLightningIndexerGradKLLossGetWorkspaceSize(
            q, k, qIndex, kIndex, weight, sparseIndices, softmaxMax, softmaxSum, qRope, kRope, acSeqQLen, acSeqKvLen,
            scaleValue, layOut, sparseMode, preTokens, nextTokens, deterministic, dQIndex, dKIndex, dWeight, loss,
            &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSparseLightningIndexerGradKLLossGetWorkspaceSize 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aclnnSparseLightningIndexerGradKLLoss第二段接口
  ret = aclnnSparseLightningIndexerGradKLLoss(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSparseLightningIndexerGradKLLoss 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
  PrintOutResult(dQIndexShape, &dQIndexDeviceAddr);
  PrintOutResult(dKIndexShape, &dKIndexDeviceAddr);
  PrintOutResult(dWeightShape, &dWeightDeviceAddr);
  PrintOutResult(lossShape, &lossDeviceAddr);
  
  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(q);
  aclDestroyTensor(k);
  aclDestroyTensor(qIndex);
  aclDestroyTensor(kIndex);
  aclDestroyTensor(qRope);
  aclDestroyTensor(kRope);
  aclDestroyTensor(weight);
  aclDestroyTensor(sparseIndices);
  aclDestroyTensor(softmaxMax);
  aclDestroyTensor(softmaxSum);

  aclDestroyTensor(dQIndex);
  aclDestroyTensor(dKIndex);
  aclDestroyTensor(dWeight);
  aclDestroyTensor(loss);
  
  // 7. 释放device资源
  aclrtFree(qDeviceAddr);
  aclrtFree(kDeviceAddr);
  aclrtFree(qIndexDeviceAddr);
  aclrtFree(kIndexDeviceAddr);
  aclrtFree(qRopeDeviceAddr);
  aclrtFree(kRopeDeviceAddr);
  aclrtFree(weightDeviceAddr);
  aclrtFree(sparseIndicesDeviceAddr);
  aclrtFree(softmaxMaxDeviceAddr);
  aclrtFree(softmaxSumDeviceAddr);

  aclrtFree(dQIndexDeviceAddr);
  aclrtFree(dKIndexDeviceAddr);
  aclrtFree(dWeightDeviceAddr);
  aclrtFree(lossDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtDestroyContext(context);
  aclrtResetDevice(deviceId);
  aclFinalize();
  
  return 0;
}