/**
 * 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_nsa_compress_attention.cpp
 */

#include <iostream>
#include <vector>
#include <cstring>
#include "acl/acl.h"
#include "aclnn/opdev/fp16_t.h"
#include "aclnnop/aclnn_nsa_compress_attention.h"

using namespace std;

#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;
}

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("mean result[%ld] is: %f\n", i, resultData[i]);
    }
}

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
    int64_t T1 = 1024;
    int64_t T2 = 64;
    int64_t N1 = 16;
    int64_t N2 = 4;
    int64_t D1 = 192;
    int64_t D2 = 128;
    int64_t selectBlockSize = 64;
    int64_t selectBlockCount = 16;
    int64_t compressBlockSize = 32;
    int64_t compressStride = 16;
    std::vector<int64_t> qShape = {T1, N1, D1};
    std::vector<int64_t> kShape = {T2, N2, D1};
    std::vector<int64_t> vShape = {T2, N2, D2};
    std::vector<int64_t> attenmaskShape = {T1, T2};                        //[maxS1, maxS2]
    std::vector<int64_t> topkmaskShape = {T1, T1 / selectBlockSize};       //[maxS1, maxSelS2]
    std::vector<int64_t> softmaxMaxShape = {T1, N1, 8};
    std::vector<int64_t> softmaxSumShape = {T1, N1, 8};
    std::vector<int64_t> attenOutShape = {T1, N1, D2};                     //[T1, N1, D2]
    std::vector<int64_t> topkIndicesOutShape = {T1, N2, selectBlockCount}; //[T1, N2, selectBlockCount]

    void* qDeviceAddr = nullptr;
    void* kDeviceAddr = nullptr;
    void* vDeviceAddr = nullptr;
    void* attenmaskDeviceAddr = nullptr;
    void* topkmaskDeviceAddr = nullptr;
    void* softmaxMaxDeviceAddr = nullptr;
    void* softmaxSumDeviceAddr = nullptr;
    void* attentionOutDeviceAddr = nullptr;
    void* topkIndicesOutDeviceAddr = nullptr;

    aclTensor* q = nullptr;
    aclTensor* k = nullptr;
    aclTensor* v = nullptr;
    aclTensor* attenmask = nullptr;
    aclTensor* topkmask = nullptr;
    aclTensor* softmaxMax = nullptr;
    aclTensor* softmaxSum = nullptr;
    aclTensor* attentionOut = nullptr;
    aclTensor* topkIndicesOut = nullptr;

    std::vector<op::fp16_t> qHostData(T1 * N1 * D1, 1.0);
    std::vector<op::fp16_t> kHostData(T2 * N2 * D1, 1.0);
    std::vector<op::fp16_t> vHostData(T2 * N2 * D2, 1.0);
    std::vector<uint8_t> attenmaskHostData(T1 * T2, 0);
    std::vector<uint8_t> topkmaskHostData(T1 * (T1 / selectBlockSize), 0);
    std::vector<float> softmaxMaxHostData(N1 * T1 * 8, 1.0);
    std::vector<float> softmaxSumHostData(N1 * T1 * 8, 1.0);
    std::vector<op::fp16_t> attenOutHostData(T1 * N1 * D2, 1.0);
    std::vector<int32_t> topkIndicesHostData(T1 * N2 * selectBlockCount, 1);

    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(vHostData, vShape, &vDeviceAddr, aclDataType::ACL_FLOAT16, &v);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    ret = CreateAclTensor(attenmaskHostData, attenmaskShape, &attenmaskDeviceAddr, aclDataType::ACL_UINT8, &attenmask);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    ret = CreateAclTensor(topkmaskHostData, topkmaskShape, &topkmaskDeviceAddr, aclDataType::ACL_UINT8, &topkmask);
    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(attenOutHostData, attenOutShape, &attentionOutDeviceAddr, aclDataType::ACL_FLOAT16, &attentionOut);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    ret = CreateAclTensor(topkIndicesHostData, topkIndicesOutShape, &topkIndicesOutDeviceAddr, aclDataType::ACL_INT32, &topkIndicesOut);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    std::vector<int64_t> actualSeqQLenVec(1, T1);
    auto actualSeqQLen = aclCreateIntArray(actualSeqQLenVec.data(), actualSeqQLenVec.size());
    std::vector<int64_t> actualCmpKvSeqVec(1, T2);
    auto actualCmpKvSeqLen = aclCreateIntArray(actualCmpKvSeqVec.data(), actualCmpKvSeqVec.size());
    std::vector<int64_t> actualSelKvSeqVec(1, T1 / selectBlockSize);
    auto actualSelKvSeqLen = aclCreateIntArray(actualSelKvSeqVec.data(), actualSelKvSeqVec.size());

    double scale = 1.0;
    int64_t headNum = N1;
    char inputLayout[5] = {'T', 'N', 'D', 0};
    int64_t sparseMode = 1;

    // 3. 调用CANN算子库API
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;

    // 调用第一段接口
    ret = aclnnNsaCompressAttentionGetWorkspaceSize(q, k, v, attenmask, topkmask, actualSeqQLen, actualCmpKvSeqLen,
        actualSelKvSeqLen, scale, headNum, inputLayout, sparseMode, compressBlockSize, compressStride, selectBlockSize, selectBlockCount, 
        softmaxMax, softmaxSum, attentionOut, topkIndicesOut, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNsaCompressAttentionGetWorkspaceSize 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);
    }

    // 调用第二段接口
    ret = aclnnNsaCompressAttention(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnNsaCompressAttention 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(attenOutShape, &attentionOutDeviceAddr);
    PrintOutResult(softmaxMaxShape, &softmaxMaxDeviceAddr);
    PrintOutResult(softmaxSumShape, &softmaxSumDeviceAddr);
    PrintOutResult(topkIndicesOutShape, &topkIndicesOutDeviceAddr);

    // 6. 释放资源
    aclDestroyTensor(q);
    aclDestroyTensor(k);
    aclDestroyTensor(v);
    aclDestroyTensor(attenmask);
    aclDestroyTensor(topkmask);
    aclDestroyTensor(softmaxMax);
    aclDestroyTensor(softmaxSum);
    aclDestroyTensor(attentionOut);
    aclDestroyTensor(topkIndicesOut);
    aclrtFree(qDeviceAddr);
    aclrtFree(kDeviceAddr);
    aclrtFree(vDeviceAddr);
    aclrtFree(attenmaskDeviceAddr);
    aclrtFree(topkmaskDeviceAddr);
    aclrtFree(softmaxMaxDeviceAddr);
    aclrtFree(softmaxSumDeviceAddr);
    aclrtFree(attentionOutDeviceAddr);
    aclrtFree(topkIndicesOutDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}