aclnnQuantMatmulWeightNz

支持的产品型号

Atlas A2 训练系列产品/Atlas 800I A2 推理产品
Atlas 推理系列产品

接口原型

每个算子分为两段式接口，必须先调用“aclnnQuantMatmulWeightNzGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器，再调用“aclnnQuantMatmulWeightNz”接口执行计算。

aclnnStatus aclnnQuantMatmulWeightNzGetWorkspaceSize(const aclTensor *x1, const aclTensor *x2, const aclTensor *x1Scale, const aclTensor *x2Scale, const aclTensor *yScale, const aclTensor *x1Offset, const aclTensor *x2Offset, const aclTensor *yOffset, const aclTensor *bias, bool transposeX1, bool transposeX2, int64_t groupSize, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
aclnnStatus aclnnQuantMatmulWeightNz(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

功能描述

算子功能：完成量化的矩阵乘计算。相似接口有aclnnMm（仅支持2维Tensor作为输入的矩阵乘）和aclnnBatchMatMul（仅支持三维的矩阵乘，其中第一维是Batch维度）。
计算公式：
- 无x1Scale无bias：
$out = x1@x2 * x2Scale + x2Offset$
- bias int32：
$out = (x1@x2 + bias) * x2Scale + x2Offset$
- bias bfloat16/float32（此场景无x2Offset）：
$out = x1@x2 * x2Scale + bias$
- x1Scale无bias：
$out = x1@x2 * x2Scale * x1Scale$
- x1Scale， bias int32(此场景无x2Offset)：
$out = (x1@x2 + bias) * x2Scale * x1Scale$
- x1Scale， bias bfloat16/float16/float32（此场景无x2Offset）：
$out = x1@x2 * x2Scale * x1Scale + bias$

aclnnQuantMatmulWeightNzGetWorkspaceSize

参数说明：
- x1（aclTensor*，计算输入）：公式中的输入x1，device侧的aclTensor。数据类型支持INT8，支持最后两根轴转置情况下的非连续tensor，其他场景的非连续的Tensor不支持，数据格式支持ND，shape支持2~6维。
  - 在transposeX1为false情况下各个维度表示：（batch，m，k），batch可不存在。
  - 在transposeX1为true情况下各个维度表示：（batch，k，m），batch可不存在。
- x2（aclTensor*，计算输入）：公式中的输入x2，device侧的aclTensor。数据类型支持INT8，支持最后两根轴转置情况下的非连续tensor，其他场景的非连续的Tensor不支持，数据格式支持昇腾亲和数据排布格式。shape支持4~8维。
  - 在transposeX2为true情况下各个维度表示：（batch，k1，n1，n0，k0），batch可不存在，其中k0 = 32， n0 = 16， x1 shape中的k和x2 shape中的k1需要满足以下关系：ceilDiv（k，32） = k1。
  - 在transposeX2为false情况下各个维度表示：（batch，n1，k1，k0，n0），batch可不存在，其中k0 = 16， n0 = 32， x1 shape中的k和x2 shape中的k1需要满足以下关系：ceilDiv（k，16） = k1。
  - 可使用aclnnCalculateMatmulWeightSizeV2接口以及aclnnTransMatmulWeight接口完成输入Format从ND到昇腾亲和数据排布格式的转换。
- x1Scale（aclTensor*，计算输入）：公式中的输入x1Scale，device侧的aclTensor。可选的量化参数。
  - Atlas A2 训练系列产品/Atlas 800I A2 推理产品：数据类型支持FLOAT32，数据格式支持ND，shape是1维（t，），t = m，其中m与x1的m一致。
  - Atlas 推理系列产品：不支持x1Scale。
- x2Scale（aclTensor*，计算输入）：公式中的输入x2Scale，device侧的aclTensor。量化参数，数据格式支持ND，shape是1维（t，），t = 1或n，其中n与x2的n一致。
  - Atlas A2 训练系列产品/Atlas 800I A2 推理产品：数据类型支持UINT64、INT64、FLOAT32、BFLOAT16。
  - Atlas 推理系列产品：数据类型支持UINT64、INT64。
  - 当原始输入类型不满足约束与限制中的数据类型组合时，需要提前调用TransQuantParamV2算子的aclnn接口来将scale转成INT64、UINT64数据类型。
- yScale（aclTensor*，计算输入）：预留参数，当前版本不支持，需要传入nullptr或者空tensor。
- x1Offset（aclTensor*，计算输入）：预留参数，当前版本不支持，需要传入nullptr或者空tensor。
- x2Offset（aclTensor*，计算输入）：公式中的输入x2Offset，device侧的aclTensor。可选量化参数，数据类型支持FLOAT32，数据格式支持ND，shape是1维（t，），t = 1或n，其中n与x2的n一致。
- yOffset（aclTensor*，计算输入）：预留参数，当前版本不支持，需要传入nullptr或者空tensor。
- bias（aclTensor*，计算输入）：公式中的输入bias，device侧的aclTensor。可选参数。数据格式支持ND，shape支持1维（n，）或3维（batch，1，n），n与x2的n一致。当out的shape为2、4、5、6维时，bias的shape只支持1维（n，）。
  - Atlas A2 训练系列产品/Atlas 800I A2 推理产品：数据类型支持INT32、BFLOAT16、FLOAT16、FLOAT32。
  - Atlas 推理系列产品：数据类型支持INT32。
- transposeX1（bool，计算输入）：表示x1的输入shape是否包含transpose，在transposeX1为false情况下各个维度表示：（batch，m，k），在transposeX1为true情况下各个维度表示：（batch，k，m），batch可不存在。
- transposeX2（bool，计算输入）：表示x2的输入shape是否包含transpose，在transposeX2为true情况下各个维度表示：（batch，k1，n1，n0，k0），batch可不存在，其中k0 = 32，n0 = 16，x1 shape中的k和x2 shape中的k1需要满足以下关系：ceilDiv（k，32） = k1。在transposeX2为false情况下各个维度表示：（batch，n1，k1，k0，n0），batch可不存在，其中k0 = 16，n0 = 32，x1 shape中的k和x2 shape中的k1需要满足以下关系：ceilDiv（k，16） = k1。
- groupSize（int64_t，计算输入）：预留参数，当前版本不支持，需要传入0。
- out（aclTensor*，计算输出）：公式中的输出out，device侧的aclTensor。支持非连续的Tensor，数据格式支持ND，shape支持2~6维，（batch，m，n），batch可不存在，支持x1与x2的batch维度broadcast，输出batch与broadcast之后的batch一致，m与x1的m一致，n与x2的n一致。
  - Atlas A2 训练系列产品/Atlas 800I A2 推理产品：数据类型支持FLOAT16、INT8、BFLOAT16、INT32。
  - Atlas 推理系列产品：数据类型支持FLOAT16、INT8。
- workspaceSize（uint64_t*，出参）：返回需要在Device侧申请的workspace大小。
- executor（aclOpExecutor**，出参）：返回op执行器，包含了算子计算流程。

返回值：

aclnnStatus：返回状态码，具体参见aclnn返回码。

第一段接口完成入参校验，出现以下场景时报错：
- 161001(ACLNN_ERR_PARAM_NULLPTR)：
1. 传入的x1、x2、x2Scale或out是空指针。
- 161002(ACLNN_ERR_PARAM_INVALID)：
1. x1、x2、bias、x2Scale、x2Offset或out的数据类型和数据格式不在支持的范围之内。
2. x1、x2、bias、x2Scale、x2Offset或out的shape不满足校验条件。
3. x1、x2、bias、x2Scale、x2Offset或out是空tensor。
4. x1与x2的最后一维大小超过65535，x1的最后一维指transposeX1为true时的m或transposeX1为false时的k，x2的最后一维指transposeX2为true时的k或transposeX2为false时的n。
5. 输入的yScale、x1Offset和yOffset不是nullptr并且不是空tensor。
6. groupSize不为0。

aclnnQuantMatmulWeightNz

参数说明：
- workspace(void*, 入参)：在Device侧申请的workspace内存地址。
- workspaceSize(uint64_t, 入参)：在Device侧申请的workspace大小，由第一段接口aclnnQuantMatmulWeightNzGetWorkspaceSize获取。
- executor(aclOpExecutor*, 入参)：op执行器，包含了算子计算流程。
- stream(aclrtStream, 入参)：指定执行任务的AscendCL Stream流。
返回值：

aclnnStatus：返回状态码，具体参见aclnn返回码。

约束与限制

输入和输出支持以下数据类型组合：

Atlas A2 训练系列产品/Atlas 800I A2 推理产品：

x1	x2	x1Scale	x2Scale	x2Offset	bias	out
int8	int8	null	uint64/int64	null	int32	float16
int8	int8	null	uint64/int64	float32	int32	int8
int8	int8	null/float32	float32/bfloat16	null	int32/bfloat16/float32	bfloat16
int8	int8	float32	float32	null	int32/float16/float32	float16
int8	int8	null	float32/bfloat16	null	int32	int32

Atlas 推理系列产品：

x1 x2 x1Scale x2Scale x2Offset bias out

int8 int8 null uint64/int64 null int32 float16

int8 int8 null uint64/int64 float32 int32 int8

调用示例

Atlas A2 训练系列产品/Atlas 800I A2 推理产品：x2为昇腾亲和数据排布格式场景下的示例代码如下(transposeX2=false)，仅供参考，具体编译和执行过程请参考编译与运行样例。

#include <iostream>
#include <memory>
#include <vector>

#include "acl/acl.h"
#include "aclnnop/aclnn_permute.h"
#include "aclnnop/aclnn_quant_matmul_weight_nz.h"
#include "aclnnop/aclnn_trans_matmul_weight.h"
#include "aclnnop/aclnn_trans_quant_param_v2.h"

#define CHECK_RET(cond, return_expr) \
    do {                             \
        if (!(cond)) {               \
            return_expr;             \
        }                            \
    } while (0)

#define CHECK_FREE_RET(cond, return_expr) \
    do {                                  \
        if (!(cond)) {                    \
            Finalize(deviceId, stream);   \
            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，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 = 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;
}

void Finalize(int32_t deviceId, aclrtStream stream)
{
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
}

template <typename T>
int CreateAclTensorX2(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
                      aclDataType dataType, aclTensor **tensor)
{
    auto size = static_cast<uint64_t>(GetShapeSize(shape));

    const aclIntArray *mat2Size = aclCreateIntArray(shape.data(), shape.size());
    auto ret = aclnnCalculateMatmulWeightSizeV2(mat2Size, dataType, &size);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCalculateMatmulWeightSizeV2 failed. ERROR: %d\n", ret);
              return ret);
    size *= sizeof(T);

    // 调用aclrtMalloc申请device侧内存
    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];
    }

    std::vector<int64_t> storageShape;
    storageShape.push_back(GetShapeSize(shape));

    // 调用aclCreateTensor接口创建aclTensor
    *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
                              storageShape.data(), storageShape.size(), *deviceAddr);
    return 0;
}

int aclnnQuantMatmulWeightNzTest(int32_t deviceId, 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> x1Shape = {5, 32};
    std::vector<int64_t> x2Shape = {32, 32};
    std::vector<int64_t> biasShape = {32};
    std::vector<int64_t> offsetShape = {32};
    std::vector<int64_t> scaleShape = {32};
    std::vector<int64_t> outShape = {5, 32};
    void *x1DeviceAddr = nullptr;
    void *x2DeviceAddr = nullptr;
    void *scaleDeviceAddr = nullptr;
    void *quantParamDeviceAddr = nullptr;
    void *offsetDeviceAddr = nullptr;
    void *biasDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *x1 = nullptr;
    aclTensor *x2 = nullptr;
    aclTensor *bias = nullptr;
    aclTensor *scale = nullptr;
    aclTensor *quantParam = nullptr;
    aclTensor *offset = nullptr;
    aclTensor *out = nullptr;
    std::vector<int8_t> x1HostData(5 * 32, 1);
    std::vector<int8_t> x2HostData(32 * 32, 1);
    std::vector<int32_t> biasHostData(32, 1);
    std::vector<float> scaleHostData(32, 1);
    std::vector<float> offsetHostData(32, 1);
    std::vector<uint16_t> outHostData(5 * 32, 1);  // 实际上是float16半精度方式
    // 创建x1 aclTensor
    ret = CreateAclTensor(x1HostData, x1Shape, &x1DeviceAddr, aclDataType::ACL_INT8, &x1);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x1TensorPtr(x1, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x1DeviceAddrPtr(x1DeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建昇腾亲和数据排布格式的x2 aclTensor
    ret = CreateAclTensorX2(x2HostData, x2Shape, &x2DeviceAddr, aclDataType::ACL_INT8, &x2);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x2HPTensorPtr(x2, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x2HPDeviceAddrPtr(x2DeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建scale aclTensor
    ret = CreateAclTensor(scaleHostData, scaleShape, &scaleDeviceAddr, aclDataType::ACL_FLOAT, &scale);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> scaleTensorPtr(scale, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> scaleDeviceAddrPtr(scaleDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建quantParam aclTensor
    ret = CreateAclTensor(scaleHostData, scaleShape, &quantParamDeviceAddr, aclDataType::ACL_UINT64, &quantParam);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> quantParamTensorPtr(quantParam,
                                                                                      aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> quantParamDeviceAddrPtr(quantParamDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建offset aclTensor
    ret = CreateAclTensor(offsetHostData, offsetShape, &offsetDeviceAddr, aclDataType::ACL_FLOAT, &offset);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> offsetTensorPtr(offset, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> offsetDeviceAddrPtr(offsetDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建bias aclTensor
    ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_INT32, &bias);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> biasTensorPtr(bias, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> biasDeviceAddrPtr(biasDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建out aclTensor
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT16, &out);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> outTensorPtr(out, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> outDeviceAddrPtr(outDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    bool transposeX1 = false;
    bool transposeX2 = false;

    // 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;
    void *workspaceAddr = nullptr;

    // 调用aclnnTransMatmulWeight第一段接口
    ret = aclnnTransMatmulWeightGetWorkspaceSize(x2, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransMatmulWeightGetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrTrans(nullptr, aclrtFree);
    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);
        workspaceAddrPtrTrans.reset(workspaceAddr);
    }
    // 调用aclnnTransMatmulWeight第二段接口
    ret = aclnnTransMatmulWeight(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransMatmulWeight failed. ERROR: %d\n", ret); return ret);

    // FLOAT数据类型的scale需要提前调用TransQuantParamV2算子的aclnn接口
    // 调用aclnnaclnnTransQuantParamV2第一段接口
    ret = aclnnTransQuantParamV2GetWorkspaceSize(scale, offset, quantParam, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransQuantParamV2GetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    workspaceAddr = nullptr;
    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrV2(nullptr, aclrtFree);
    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);
        workspaceAddrPtrV2.reset(workspaceAddr);
    }
    // 调用aclnnTransQuantParamV2第二段接口
    ret = aclnnTransQuantParamV2(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransQuantParamV2 failed. ERROR: %d\n", ret); return ret);

    // 调用aclnnQuantMatmulWeightNz第一段接口
    workspaceSize = 0;
    ret = aclnnQuantMatmulWeightNzGetWorkspaceSize(x1, x2, nullptr, quantParam, nullptr, nullptr, nullptr, nullptr,
                                                  bias, transposeX1, transposeX2, 0, out, &workspaceSize,
                                                  &executor);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulWeightNzGetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存

    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrNZ(nullptr, aclrtFree);
    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);
        workspaceAddrPtrNZ.reset(workspaceAddr);
    }
    // 调用aclnnQuantMatmulWeightNz第二段接口
    ret = aclnnQuantMatmulWeightNz(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulWeightNz 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);  // C语言中无法直接打印fp16的数据，需要用uint16读出来，自行通过二进制转成fp16
    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: %u\n", i, resultData[i]);
    }
    return ACL_SUCCESS;
}

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 = aclnnQuantMatmulWeightNzTest(deviceId, stream);
    CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulWeightNzTest failed. ERROR: %d\n", ret);
                  return ret);

    Finalize(deviceId, stream);
    return 0;
}

Atlas 推理系列产品：x2为昇腾亲和数据排布格式场景下的示例代码如下(transposeX2=true)，仅供参考，具体编译和执行过程请参考编译与运行样例。

#include <iostream>
#include <memory>
#include <vector>

#include "acl/acl.h"
#include "aclnnop/aclnn_permute.h"
#include "aclnnop/aclnn_quant_matmul_weight_nz.h"
#include "aclnnop/aclnn_trans_matmul_weight.h"
#include "aclnnop/aclnn_trans_quant_param_v2.h"

#define CHECK_RET(cond, return_expr) \
    do {                             \
        if (!(cond)) {               \
            return_expr;             \
        }                            \
    } while (0)

#define CHECK_FREE_RET(cond, return_expr) \
    do {                                  \
        if (!(cond)) {                    \
            Finalize(deviceId, stream);   \
            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，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 = 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;
}

void Finalize(int32_t deviceId, aclrtStream stream)
{
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
}

template <typename T>
int CreateAclTensorX2(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
                      aclDataType dataType, aclTensor **tensor)
{
    auto size = static_cast<uint64_t>(GetShapeSize(shape));

    const aclIntArray *mat2Size = aclCreateIntArray(shape.data(), shape.size());
    auto ret = aclnnCalculateMatmulWeightSizeV2(mat2Size, dataType, &size);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCalculateMatmulWeightSizeV2 failed. ERROR: %d\n", ret);
              return ret);
    size *= sizeof(T);

    // 调用aclrtMalloc申请device侧内存
    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];
    }

    std::vector<int64_t> storageShape;
    storageShape.push_back(GetShapeSize(shape));

    // 调用aclCreateTensor接口创建aclTensor
    *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
                              storageShape.data(), storageShape.size(), *deviceAddr);
    return 0;
}

int aclnnQuantMatmulWeightNzTest(int32_t deviceId, 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> x1Shape = {5, 32};
    std::vector<int64_t> x2Shape = {32, 32};
    std::vector<int64_t> x2TransposedShape = {32, 32};
    std::vector<int64_t> biasShape = {32};
    std::vector<int64_t> offsetShape = {32};
    std::vector<int64_t> scaleShape = {32};
    std::vector<int64_t> outShape = {5, 32};
    void *x1DeviceAddr = nullptr;
    void *x2DeviceAddr = nullptr;
    void *x2TransposedDeviceAddr = nullptr;
    void *scaleDeviceAddr = nullptr;
    void *quantParamDeviceAddr = nullptr;
    void *offsetDeviceAddr = nullptr;
    void *biasDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *x1 = nullptr;
    aclTensor *x2 = nullptr;
    aclTensor *x2Transposed = nullptr;
    aclTensor *bias = nullptr;
    aclTensor *scale = nullptr;
    aclTensor *quantParam = nullptr;
    aclTensor *offset = nullptr;
    aclTensor *out = nullptr;
    std::vector<int8_t> x1HostData(5 * 32, 1);
    std::vector<int8_t> x2HostData(32 * 32, 1);
    std::vector<int8_t> x2TransposedHostData(32 * 32, 1);
    std::vector<int32_t> biasHostData(32, 1);
    std::vector<float> scaleHostData(32, 1);
    std::vector<float> offsetHostData(32, 1);
    std::vector<uint16_t> outHostData(5 * 32, 1);  // 实际上是float16半精度方式
    // 创建x1 aclTensor
    ret = CreateAclTensor(x1HostData, x1Shape, &x1DeviceAddr, aclDataType::ACL_INT8, &x1);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x1TensorPtr(x1, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x1DeviceAddrPtr(x1DeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建昇腾亲和数据排布格式的x2 aclTensor
    ret = CreateAclTensorX2(x2HostData, x2Shape, &x2DeviceAddr, aclDataType::ACL_INT8, &x2);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x2HPTensorPtr(x2, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x2HPDeviceAddrPtr(x2DeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建昇腾亲和数据排布格式的x2Transposed aclTensor
    ret = CreateAclTensorX2(x2TransposedHostData, x2TransposedShape, &x2TransposedDeviceAddr,
                            aclDataType::ACL_INT8, &x2Transposed);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x2TransposedHPTensorPtr(x2Transposed,
                                                                                          aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x2TransposedHPDeviceAddrPtr(x2TransposedDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建scale aclTensor
    ret = CreateAclTensor(scaleHostData, scaleShape, &scaleDeviceAddr, aclDataType::ACL_FLOAT, &scale);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> scaleTensorPtr(scale, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> scaleDeviceAddrPtr(scaleDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建quantParam aclTensor
    ret = CreateAclTensor(scaleHostData, scaleShape, &quantParamDeviceAddr, aclDataType::ACL_UINT64, &quantParam);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> quantParamTensorPtr(quantParam,
                                                                                      aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> quantParamDeviceAddrPtr(quantParamDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建offset aclTensor
    ret = CreateAclTensor(offsetHostData, offsetShape, &offsetDeviceAddr, aclDataType::ACL_FLOAT, &offset);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> offsetTensorPtr(offset, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> offsetDeviceAddrPtr(offsetDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建bias aclTensor
    ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_INT32, &bias);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> biasTensorPtr(bias, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> biasDeviceAddrPtr(biasDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建out aclTensor
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT16, &out);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> outTensorPtr(out, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> outDeviceAddrPtr(outDeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    bool transposeX1 = false;
    bool transposeX2 = true;

    // 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;
    void *workspaceAddr = nullptr;

    // x2的shape需要transpose成nk格式，再进行transdata
    std::vector<int64_t> dimsData = {1, 0};
    // 创建dims aclIntArray
    aclIntArray *dims = aclCreateIntArray(dimsData.data(), dimsData.size());
    // 调用aclnnPermute第一段接口
    ret = aclnnPermuteGetWorkspaceSize(x2, dims, x2Transposed, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnPermuteGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrPermute(nullptr, aclrtFree);
    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);
        workspaceAddrPtrPermute.reset(workspaceAddr);
    }
    // 调用aclnnPermute第二段接口
    ret = aclnnPermute(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnPermuteGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);

    workspaceSize = 0;
    // 调用aclnnTransMatmulWeight第一段接口
    ret = aclnnTransMatmulWeightGetWorkspaceSize(x2Transposed, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransMatmulWeightGetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrTrans(nullptr, aclrtFree);
    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);
        workspaceAddrPtrTrans.reset(workspaceAddr);
    }
    // 调用aclnnTransMatmulWeight第二段接口
    ret = aclnnTransMatmulWeight(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransMatmulWeight failed. ERROR: %d\n", ret); return ret);

    // FLOAT数据类型的scale需要提前调用TransQuantParamV2算子的aclnn接口
    // 调用aclnnaclnnTransQuantParamV2第一段接口
    ret = aclnnTransQuantParamV2GetWorkspaceSize(scale, offset, quantParam, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransQuantParamV2GetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存
    workspaceAddr = nullptr;
    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrV2(nullptr, aclrtFree);
    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);
        workspaceAddrPtrV2.reset(workspaceAddr);
    }
    // 调用aclnnTransQuantParamV2第二段接口
    ret = aclnnTransQuantParamV2(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnTransQuantParamV2 failed. ERROR: %d\n", ret); return ret);

    // 调用aclnnQuantMatmulWeightNz第一段接口, <term>Atlas 推理系列产品</term>暂时不支持x1Scale
    workspaceSize = 0;
    ret = aclnnQuantMatmulWeightNzGetWorkspaceSize(x1, x2Transposed, nullptr, quantParam, nullptr, nullptr,
                                                  nullptr, nullptr, bias, transposeX1, transposeX2, 0, out,
                                                  &workspaceSize, &executor);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulWeightNzGetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // 根据第一段接口计算出的workspaceSize申请device内存

    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrNZ(nullptr, aclrtFree);
    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);
        workspaceAddrPtrNZ.reset(workspaceAddr);
    }
    // 调用aclnnQuantMatmulWeightNz第二段接口
    ret = aclnnQuantMatmulWeightNz(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulWeightNz 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);  // C语言中无法直接打印fp16的数据，需要用uint16读出来，自行通过二进制转成fp16
    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: %u\n", i, resultData[i]);
    }
    return ACL_SUCCESS;
}

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 = aclnnQuantMatmulWeightNzTest(deviceId, stream);
    CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulWeightNzTest failed. ERROR: %d\n", ret);
                  return ret);

    Finalize(deviceId, stream);
    return 0;
}