/**
 * 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_grouped_matmul_swiglu_quant_v2.cpp
 * \brief
 */

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_grouped_matmul_swiglu_quant_weight_nz_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, aclFormat formatType, 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侧数据复制到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, formatType,
                            shape.data(), shape.size(), *deviceAddr);
    return 0;
}

template <typename T>
int CreateAclTensorFromPtr(const T* data, const std::vector<int64_t>& shape,
                           void** deviceAddr, aclDataType dataType, aclFormat formatType, aclTensor** tensor) {
    auto size = GetShapeSize(shape) * sizeof(T);
    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);
    ret = aclrtMemcpy(*deviceAddr, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

    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];
    }
    *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, formatType,
                              shape.data(), shape.size(), *deviceAddr);
    return 0;
}

template <typename T>
int CreateAclTensorList(const std::vector<T> &hostData, const std::vector<std::vector<int64_t>> &shapes,
                        void **deviceAddr, aclDataType dataType, aclFormat formatType, aclTensorList **tensor) {
    int size = shapes.size();
    std::vector<aclTensor*> tensors(size);
    int64_t offset = 0;
    for (int i = 0; i < size; i++) {
        int64_t numElements = GetShapeSize(shapes[i]);
        int ret = CreateAclTensorFromPtr<T>(hostData.data() + offset, shapes[i], deviceAddr + i, dataType, formatType, &tensors[i]);
        CHECK_RET(ret == ACL_SUCCESS, return ret);
        offset += numElements;
    }
    *tensor = aclCreateTensorList(tensors.data(), size);
    return ACL_SUCCESS;
}

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
    int64_t E = 4;
    int64_t M = 8192;
    int64_t N = 4096;
    int64_t K = 7168;
    std::vector<int64_t> xShape = {M, K};
    // weightShape: 长度为 E，每个元素 shape 为 {N/32, K/16, 16, 32}
    std::vector<std::vector<int64_t>> weightShape(E, {N / 32, K / 16, 16, 32});
    // weightScaleShape: 长度为 E，每个元素 shape 为 {N}
    std::vector<std::vector<int64_t>> weightScaleShape(E, {N});
    std::vector<int64_t> xScaleShape = {M};
    std::vector<int64_t> groupListShape = {E};
    std::vector<int64_t> outputShape = {M, N / 2};
    std::vector<int64_t> outputScaleShape = {M};

    void* xDeviceAddr = nullptr;
    std::vector<void*> weightDeviceAddr(E, nullptr);
    std::vector<void*> weightScaleDeviceAddr(E, nullptr);
    void* xScaleDeviceAddr = nullptr;
    void* groupListDeviceAddr = nullptr;
    void* outputDeviceAddr = nullptr;
    void* outputScaleDeviceAddr = nullptr;

    aclTensor* x = nullptr;
    aclTensorList* weight = nullptr;
    aclTensorList* weightScale = nullptr;
    aclTensor* xScale = nullptr;
    aclTensor* groupList = nullptr;
    aclTensor* output = nullptr;
    aclTensor* outputScale = nullptr;

    std::vector<int8_t> xHostData(M * K, 1);
    std::vector<int8_t> weightHostData(E * N * K, 1);
    std::vector<float> weightScaleHostData(E * N, 0.5f);
    std::vector<float> xScaleHostData(M, 0.0314f);
    std::vector<int64_t> groupListHostData = {1, 2, 2, 3};
    std::vector<int8_t> outputHostData(M * N / 2, 0);
    std::vector<float> outputScaleHostData(M, 0);

    // 创建x aclTensor
    ret = CreateAclTensor(xHostData, xShape, &xDeviceAddr, aclDataType::ACL_INT8, aclFormat::ACL_FORMAT_ND, &x);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建weight aclTensorList
    ret = CreateAclTensorList(weightHostData, weightShape, weightDeviceAddr.data(), aclDataType::ACL_INT8, aclFormat::ACL_FORMAT_FRACTAL_NZ, &weight);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建weightScale aclTensorList
    ret = CreateAclTensorList(weightScaleHostData, weightScaleShape, weightScaleDeviceAddr.data(), aclDataType::ACL_FLOAT,  aclFormat::ACL_FORMAT_ND, &weightScale);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建xScale aclTensor
    ret = CreateAclTensor(xScaleHostData, xScaleShape, &xScaleDeviceAddr, aclDataType::ACL_FLOAT, aclFormat::ACL_FORMAT_ND, &xScale);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建groupList aclTensor
    ret = CreateAclTensor(groupListHostData, groupListShape, &groupListDeviceAddr, aclDataType::ACL_INT64, aclFormat::ACL_FORMAT_ND, &groupList);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建output aclTensor
    ret = CreateAclTensor(outputHostData, outputShape, &outputDeviceAddr, aclDataType::ACL_INT8, aclFormat::ACL_FORMAT_ND, &output);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建outputScale aclTensor
    ret = CreateAclTensor(outputScaleHostData, outputScaleShape, &outputScaleDeviceAddr, aclDataType::ACL_FLOAT, aclFormat::ACL_FORMAT_ND, &outputScale);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 新增V2参数
    aclTensorList* weightAssistMatrix = nullptr;
    aclTensor* bias = nullptr;
    aclTensor* smoothScale = nullptr;
    int64_t dequantMode = 0;
    int64_t dequantDtype = 28;
    int64_t quantMode = 0;
    int64_t quantDtype = 28;
    int64_t groupListType = 0;

    std::vector<int64_t> tuningConfigData = {};
    aclIntArray* tuningConfig = aclCreateIntArray(tuningConfigData.data(), 1);

    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;

    // 3. 调用CANN算子库API
    // 调用aclnnGroupedMatmulSwigluQuantWeightNzV2第一段接口
    ret = aclnnGroupedMatmulSwigluQuantWeightNzV2GetWorkspaceSize(
        x, weight, weightScale, weightAssistMatrix, bias, xScale, smoothScale, groupList, dequantMode, dequantDtype,
        quantMode, groupListType, tuningConfig, output, outputScale, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, 
    LOG_PRINT("aclnnGroupedMatmulSwigluQuantWeightNzV2GetWorkspaceSize 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aclnnGroupedMatmulSwigluQuantWeightNzV2第二段接口
    ret = aclnnGroupedMatmulSwigluQuantWeightNzV2(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, 
    LOG_PRINT("aclnnGroupedMatmulSwigluQuantWeightNzV2 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 = 10;
    std::vector<int8_t> out1Data(size, 0);
    ret = aclrtMemcpy(out1Data.data(), out1Data.size() * sizeof(out1Data[0]), outputDeviceAddr,
                        size * sizeof(out1Data[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 j = 0; j < size; j++) {
        LOG_PRINT("result[%ld] is: %d\n", j, out1Data[j]);
    }
    std::vector<float> out2Data(size, 0);
    ret = aclrtMemcpy(out2Data.data(), out2Data.size() * sizeof(out2Data[0]), outputScaleDeviceAddr,
                        size * sizeof(out2Data[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 j = 0; j < size; j++) {
        LOG_PRINT("result[%ld] is: %f\n", j, out2Data[j]);
    }
    // 6. 释放aclTensor、aclTensorList和aclScalar，需要根据具体API的接口定义修改
    aclDestroyTensor(x);
    aclDestroyTensorList(weight);
    aclDestroyTensorList(weightScale);
    aclDestroyTensor(xScale);
    aclDestroyTensor(groupList);
    aclDestroyTensor(output);
    aclDestroyTensor(outputScale);

    aclDestroyIntArray(tuningConfig);

    // 7. 释放device资源，需要根据具体API的接口定义修改
    aclrtFree(xDeviceAddr);
    for (int64_t i = 0; i < E; i++) {
        aclrtFree(weightDeviceAddr[i]);
        aclrtFree(weightScaleDeviceAddr[i]);
    }
    aclrtFree(xScaleDeviceAddr);
    aclrtFree(groupListDeviceAddr);
    aclrtFree(outputDeviceAddr);
    aclrtFree(outputScaleDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}