aclnnQuantMatmulV4

须知：该接口后续版本会废弃，请使用最新aclnnQuantMatmulV5接口。

产品支持情况

产品	是否支持
Ascend 950PR/Ascend 950DT	✓
Atlas A3 训练系列产品/Atlas A3 推理系列产品	✓
Atlas A2 训练系列产品/Atlas A2 推理系列产品	✓
Atlas 200I/500 A2 推理产品	×
Atlas 推理系列产品	✓
Atlas 训练系列产品	×

功能说明

接口功能：兼容aclnnQuantMatmulV3接口功能，并在其基础上支持K-C && K-T 量化模式。完成量化的矩阵乘计算，最小支持输入维度为2维，最大支持输入维度为6维。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：
- Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品、Ascend 950PR/Ascend 950DT：
  - 无pertoken、无bias：
    $o u t = x 1 @ x 2 * s c a l e + o f f s e t$
  - bias INT32：
    $o u t = (x 1 @ x 2 + b i a s) * s c a l e + o f f s e t$
  - bias BFLOAT16/FLOAT32（此场景无offset）：
    $o u t = x 1 @ x 2 * s c a l e + b i a s$
  - pertoken无bias：
    $o u t = x 1 @ x 2 * s c a l e * p e r t o k e n S c a l e O p t i o n a l$
  - pertoken、bias INT32（此场景无offset）：
    $o u t = (x 1 @ x 2 + b i a s) * s c a l e * p e r t o k e n S c a l e O p t i o n a l$
  - pertoken、bias BFLOAT16/FLOAT16/FLOAT32（此场景无offset）：
    $o u t = x 1 @ x 2 * s c a l e * p e r t o k e n S c a l e O p t i o n a l + b i a s$
- Atlas 推理系列产品：
  - 无pertokenScaleOptional、无bias：
    $o u t = x 1 @ x 2 * s c a l e + o f f s e t$
  - 无pertokenScaleOptional、bias INT32：
    $o u t = (x 1 @ x 2 + b i a s) * s c a l e + o f f s e t$
  - 有pertokenScaleOptional、无bias
    $o u t = x 1 @ x 2 * s c a l e * p e r t o k e n S c a l e O p t i o n a l$
  - 有pertokenScaleOptional、bias INT32
    $o u t = (x 1 @ x 2 + b i a s) * s c a l e * p e r t o k e n S c a l e O p t i o n a l$

函数原型

每个算子分为两段式接口，必须先调用“aclnnQuantMatmulV4GetWorkspaceSize”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“aclnnQuantMatmulV4”接口执行计算。

aclnnStatus aclnnQuantMatmulV4GetWorkspaceSize(
    const aclTensor *x1,
    const aclTensor *x2,
    const aclTensor *scale,
    const aclTensor *offset,
    const aclTensor *pertokenScaleOptional,
    const aclTensor *bias,
    bool             transposeX1,
    bool             transposeX2,
    const aclTensor *out,
    uint64_t        *workspaceSize,
    aclOpExecutor   **executor)

aclnnStatus aclnnQuantMatmulV4(
    void          *workspace,
    uint64_t       workspaceSize,
    aclOpExecutor *executor,
    aclrtStream    stream)

aclnnQuantMatmulV4GetWorkspaceSize

参数说明：

参数名	输入/输出	描述	使用说明	数据类型	数据格式	维度(shape)	非连续Tensor
x1	输入	公式中的输入x1。	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。为false时shape为：（batch，m，k）。为true时shape为：（batch，k，m），batch可不存在。	INT8、INT32、INT4	ND	2-6	×
x2	输入	公式中的输入x2。	NZ格式下，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：ceil（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：ceil（k / 16） = k1。可使用aclnnCalculateMatmulWeightSizeV2接口以及aclnnTransMatmulWeight接口完成输入Format从ND到NZ格式的转换。 ND格式下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。 transposeX2为false时shape为：（batch，k，n）。 transposeX2为true时shape为：（batch，n，k），batch可不存在，其中k与x1的shape中的k一致。	INT8、INT32、INT4	ND、NZ	2-6（ND） 4-8（NZ）	×
scale	输入	表示量化参数，公式中的输入scale。	shape是1维（t，），t = 1或n，其中n与x2的n一致。当原始输入类型不满足约束说明中类型组合时，需提前调用TransQuantParamV2算子的aclnn接口来将scale转成INT64、UINT64数据类型。	UINT64、INT64、FLOAT32、BFLOAT16	ND	1	-
offset	可选输入	公式中的输入offset。	shape是1维（t，），t = 1或n，其中n与x2的n一致。当out数据类型为INT8时，offset可以存在，其他输入类型需要传入nullptr。	FLOAT32	ND	1	-
pertokenScaleOptional	可选输入	公式中的输入pertokenScaleOptional。 shape是1维（t，），t = m，其中m与x1的m一致。	-	FLOAT32	ND	1	-
bias	可选输入	公式中的输入bias。	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，）。	INT32，BFLOAT16，FLOAT16，FLOAT32	ND	1、3	-
transposeX1	输入	表示x1的输入shape是否包含transpose。	为false时shape为：（batch，m，k）。为true时shape为：（batch，k，m），batch可不存在。	BOOL	-	-	-
transposeX2	输入	表示x2的输入shape是否包含transpose。	为false时shape为：（batch，k，n）。为true时shape为：（batch，n，k），batch可不存在，其中k与x1的shape中的k一致。 NZ格式下： transposeX2为true时shape为：（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：ceil（k / 32） = k1。 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：ceil（k / 16） = k1。	BOOL	-	-	-
out	输出	公式中的输出out。	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。	FLOAT16、INT8、BFLOAT16、INT32	ND	（batch，m，n）	✓
workspaceSize	输出	返回需要在Device侧申请的workspace大小。	-	-	-	-	-
executor	输出	返回op执行器，包含了算子计算流程。	-	-	-	-	-

Atlas 推理系列产品：
- x1的最后一维大小不能超过65535，x1的最后一维指transposeX1为true时的m或transposeX1为false时的k。
- x2的最后一维大小不能超过65535，x2的最后一维指transposeX2为true时的k或transposeX2为false时的n。
- x1数据类型支持INT8。
- x2数据类型支持INT8，为NZ格式时，不支持transposeX2为false的场景。当pertokenScaleOptional不为空tensor时，必须调用aclnnTransMatmulWeight对format为ND的x2处理得到AI处理器亲和数据排布格式。
- bias数据类型支持INT32。
- 当pertokenScaleOptional不为空tensor时，scale的数据类型支持FLOAT32；当pertokenScaleOptional为空tensor时，scale数据类型支持UINT64、INT64。
- out数据类型支持FLOAT16、INT8，当pertokenScaleOptional不为空tensor时，out数据类型只支持FLOAT16。
Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品：
- x1的最后一维大小不能超过65535，x1的最后一维指transposeX1为true时的m或transposeX1为false时的k。
- x2的最后一维大小不能超过65535，x2的最后一维指transposeX2为true时的k或transposeX2为false时的n。
- x1数据类型支持INT8、INT32、INT4。当数据类型为INT32、INT4时，为INT4量化场景，当前仅支持2-6维ND格式，transposeX1为false情况。其中当x1数据类型为INT4时，维度表示：（batch，m，k），要求k为偶数，当x1数据类型为INT32时，每个INT32数据存放8个INT4数据，对应维度表示：（batch，m，k // 8），要求k为8的倍数。
- x2数据类型支持INT8、INT32、INT4。当数据类型为INT32、INT4时，为INT4量化场景，当前仅支持2维ND格式。
- 数据类型为INT4时，在transposeX2为true情况下各个维度表示：（n，k），要求k为偶数；在transposeX2为false情况下各个维度表示：（k，n），要求n为偶数。
- 数据类型为INT32时，每个INT32数据存放8个INT4数据，在transposeX2为true情况下各个维度表示：（n，k // 8），要求k为8的倍数；在transposeX2为false情况下各个维度表示：（k，n // 8），要求n为8的倍数。
- 可使用aclnnConvertWeightToINT4Pack接口完成x2从INT32（1个int32在0~3bit位存储1个int4）到INT32（1个int32存储8个int4）或INT4（1个int4表示1个int4）的数据格式转换，具体参见aclnnConvertWeightToINT4Pack接口。
- bias数据类型支持INT32，BFLOAT16，FLOAT16，FLOAT32。当x1和x2为INT32、INT4时，bias的shape只支持1维（n，）。
- x1和x2为INT32、INT4时，transposeX1仅支持false。
- out数据类型支持FLOAT16、INT8、BFLOAT16、INT32。
Ascend 950PR/Ascend 950DT：
- x1数据类型支持INT8、INT4。
- x2数据类型支持INT8、INT4。
- bias数据类型支持INT32，BFLOAT16，FLOAT16，FLOAT32。
- out数据类型支持FLOAT16、INT8、BFLOAT16、INT32。
- x2仅支持ND格式，当输入x1为m=0的空tensor或x2为n=0的空tensor时，输出为空tensor。

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：

返回值	错误码	描述
ACLNN_ERR_PARAM_NULLPTR	161001	传入的x1、x2、x2Scale或out是空指针。
ACLNN_ERR_PARAM_INVALID	161002	x1、x2、bias、x1Scale、x2Scale、x2Offset或out的数据类型和数据格式不在支持的范围之内。
		x1、x2、bias、x1Scale、x2Scale、x2Offset或out的shape不满足校验条件。
		x1、x2、bias、x1Scale、x2Scale、x2Offset或out是空tensor。

aclnnQuantMatmulV4

参数说明：

参数名	输入/输出	描述
workspace	输入	在Device侧申请的workspace内存地址。
workspaceSize	输入	在Device侧申请的workspace大小，由第一段接口aclnnQuantMatmulV4GetWorkspaceSize获取。
executor	输入	op执行器，包含了算子计算流程。
stream	输入	指定执行任务的Stream。

返回值：

aclnnStatus：返回状态码，具体参见aclnn返回码。

约束说明

确定性说明：
- Atlas 训练系列产品、Atlas 推理系列产品：aclnnQuantMatmulV4默认确定性实现。
- Ascend 950PR/Ascend 950DT：aclnnQuantMatmulV4默认确定性实现。
Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品：支持调用本接口前，通过aclnnTransMatmulWeight对format为ND的x2处理得到AI处理器亲和数据排布格式。
Ascend 950PR/Ascend 950DT： x2仅支持ND格式。

输入和输出支持以下数据类型组合：

Atlas 推理系列产品：

x1 x2 scale offset bias pertokenScaleOptional out

INT8 INT8 UINT64/INT64 null null/INT32 null FLOAT16

INT8 INT8 UINT64/INT64 null/FLOAT32 null/INT32 null INT8

Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品：

x1	x2	scale	offset	bias	pertokenScaleOptional	out
INT8	INT8	UINT64/INT64	null	null/INT32	null	FLOAT16
INT8	INT8	UINT64/INT64	null/FLOAT32	null/INT32	null	INT8
INT8	INT8	FLOAT32/BFLOAT16	null	null/INT32/BFLOAT16/FLOAT32	null/FLOAT32	BFLOAT16
INT8	INT8	FLOAT32	null	null/INT32/FLOAT16/FLOAT32	FLOAT32	FLOAT16
INT4/INT32	INT4/INT32	UINT64/INT64	null	null/INT32	null	FLOAT16
INT8	INT8	FLOAT32/BFLOAT16	null	null/INT32	null	INT32
INT4/INT32	INT4/INT32	FLOAT32/BFLOAT16	null	null/INT32/BFLOAT16/FLOAT32	FLOAT32	BFLOAT16
INT4/INT32	INT4/INT32	FLOAT32	null	null/INT32/FLOAT16/FLOAT32	FLOAT32	FLOAT16

Ascend 950PR/Ascend 950DT：

以下数据类型组合在pertokenScaleOptional为null时，支持T-C && T-T量化模式，在pertokenScaleOptional不为null时支持K-C量化 && K-T量化模式。

x1	x2	scale	offset	bias	pertokenScaleOptional	out
INT8	INT8	UINT64/INT64	null	null/INT32	null	FLOAT16/BFLOAT16
INT8	INT8	UINT64/INT64	null/FLOAT32	null/INT32	null	INT8
INT8	INT8	FLOAT32/BFLOAT16	null	null/INT32/FLOAT32/BFLOAT16	null/FLOAT32	BFLOAT16
INT8	INT8	FLOAT32	null	null/INT32/FLOAT32/FLOAT16	FLOAT32	FLOAT16
INT8	INT8	FLOAT32/BFLOAT16	null	null/INT32	null	INT32
INT4	INT4	UINT64/INT64	null	null/INT32	null	FLOAT16
INT4	INT4	FLOAT32/BFLOAT16	null	null/INT32/FLOAT32/BFLOAT16	FLOAT32	BFLOAT16
INT4	INT4	FLOAT32	null	null/INT32/FLOAT32/FLOAT16	FLOAT32	FLOAT16

调用示例

示例代码如下，仅供参考，具体编译和执行过程请参考编译与运行样例。

Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品、Ascend 950PR/Ascend 950DT：

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

#include "acl/acl.h"
#include "aclnnop/aclnn_quant_matmul_v4.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，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;
}

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

int aclnnQuantMatmulV4Test(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, 2};
    std::vector<int64_t> x2Shape = {2, 3};
    std::vector<int64_t> biasShape = {3};
    std::vector<int64_t> offsetShape = {3};
    std::vector<int64_t> pertokenScaleShape = {5};
    std::vector<int64_t> scaleShape = {3};
    std::vector<int64_t> outShape = {5, 3};
    void *x1DeviceAddr = nullptr;
    void *x2DeviceAddr = nullptr;
    void *scaleDeviceAddr = nullptr;
    void *offsetDeviceAddr = nullptr;
    void *pertokenScaleDeviceAddr = nullptr;
    void *biasDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *x1 = nullptr;
    aclTensor *x2 = nullptr;
    aclTensor *bias = nullptr;
    aclTensor *scale = nullptr;
    aclTensor *offset = nullptr;
    aclTensor *pertokenScale = nullptr;
    aclTensor *out = nullptr;
    std::vector<int8_t> x1HostData = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
    std::vector<int8_t> x2HostData = {1, 1, 1, 1, 1, 1};
    std::vector<int32_t> biasHostData = {1, 1, 1};
    std::vector<float> scaleHostData = {1, 1, 1};
    std::vector<float> offsetHostData = {1, 1, 1};
    std::vector<float> pertokenScaleHostData = {1, 1, 1, 1, 1};
    std::vector<uint16_t> outHostData = {1, 1, 1, 1, 1, 1, 1, 1,
                                        1, 1, 1, 1, 1, 1, 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x2 aclTensor
    ret = CreateAclTensor(x2HostData, x2Shape, &x2DeviceAddr, aclDataType::ACL_INT8, &x2);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x2TensorPtr(x2, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x2DeviceAddrPtr(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pertokenScale aclTensor
    ret = CreateAclTensor(pertokenScaleHostData, pertokenScaleShape, &pertokenScaleDeviceAddr,
                          aclDataType::ACL_FLOAT, &pertokenScale);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> pertokenScaleTensorPtr(pertokenScale,
                                                                                          aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> pertokenScaleDeviceAddrPtr(pertokenScaleDeviceAddr, 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 = nullptr;
    // 调用aclnnQuantMatmulV4第一段接口
    ret = aclnnQuantMatmulV4GetWorkspaceSize(x1, x2, scale, nullptr, pertokenScale, bias, transposeX1, transposeX2,
                                            out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4GetWorkspaceSize 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;
    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtr(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);
        workspaceAddrPtr.reset(workspaceAddr);
    }
    // 调用aclnnQuantMatmulV4第二段接口
    ret = aclnnQuantMatmulV4(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4 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: %hu\n", i, resultData[i]);
    }
    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 = aclnnQuantMatmulV4Test(deviceId, stream);
    CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4Test failed. ERROR: %d\n", ret); return ret);

    Finalize(deviceId, stream);
    return 0;
}

Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品： x2为NZ格式场景(transposeX2=false)。

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

#include "acl/acl.h"
#include "aclnnop/aclnn_permute.h"
#include "aclnnop/aclnn_quant_matmul_v4.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，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;
}

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 aclnnQuantMatmulV4Test(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, 2};
    std::vector<int64_t> x2Shape = {2, 3};
    std::vector<int64_t> biasShape = {3};
    std::vector<int64_t> offsetShape = {3};
    std::vector<int64_t> pertokenScaleShape = {5};
    std::vector<int64_t> scaleShape = {3};
    std::vector<int64_t> outShape = {5, 3};
    void *x1DeviceAddr = nullptr;
    void *x2DeviceAddr = nullptr;
    void *scaleDeviceAddr = nullptr;
    void *quantParamDeviceAddr = nullptr;
    void *offsetDeviceAddr = nullptr;
    void *pertokenScaleDeviceAddr = 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 *pertokenScale = nullptr;
    aclTensor *out = nullptr;
    std::vector<int8_t> x1HostData = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
    std::vector<int8_t> x2HostData = {1, 1, 1, 1, 1, 1};
    std::vector<int32_t> biasHostData = {1, 1, 1};
    std::vector<float> scaleHostData = {1, 1, 1};
    std::vector<float> offsetHostData = {1, 1, 1};
    std::vector<float> pertokenScaleHostData = {1, 1, 1, 1, 1};
    std::vector<uint16_t> outHostData = {1, 1, 1, 1, 1, 1, 1, 1,
                                        1, 1, 1, 1, 1, 1, 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NZ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pertokenScale aclTensor
    ret = CreateAclTensor(pertokenScaleHostData, pertokenScaleShape, &pertokenScaleDeviceAddr,
                          aclDataType::ACL_FLOAT, &pertokenScale);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> pertokenScaleTensorPtr(pertokenScale,
                                                                                          aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> pertokenScaleDeviceAddrPtr(pertokenScaleDeviceAddr, 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 = nullptr;
    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aclnnTransQuantParamV2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aclnnQuantMatmulV4第一段接口
    workspaceSize = 0;
    ret = aclnnQuantMatmulV4GetWorkspaceSize(x1, x2, quantParam, nullptr, nullptr, bias, transposeX1, transposeX2,
                                            out, &workspaceSize, &executor);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4GetWorkspaceSize 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 *)> workspaceAddrPtrV4(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);
        workspaceAddrPtrV4.reset(workspaceAddr);
    }
    // 调用aclnnQuantMatmulV4第二段接口
    ret = aclnnQuantMatmulV4(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4 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: %hu\n", i, resultData[i]);
    }
    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 = aclnnQuantMatmulV4Test(deviceId, stream);
    CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4Test failed. ERROR: %d\n", ret); return ret);

    Finalize(deviceId, stream);
    return 0;
}

x2为NZ格式场景(transposeX2=true)。

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

#include "acl/acl.h"
#include "aclnnop/aclnn_permute.h"
#include "aclnnop/aclnn_quant_matmul_v4.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，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;
}

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 aclnnQuantMatmulV4Test(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, 2};
    std::vector<int64_t> x2Shape = {2, 3};
    std::vector<int64_t> x2TransposedShape = {3, 2};
    std::vector<int64_t> biasShape = {3};
    std::vector<int64_t> offsetShape = {3};
    std::vector<int64_t> pertokenScaleShape = {5};
    std::vector<int64_t> scaleShape = {3};
    std::vector<int64_t> outShape = {5, 3};
    void *x1DeviceAddr = nullptr;
    void *x2DeviceAddr = nullptr;
    void *x2TransposedDeviceAddr = nullptr;
    void *scaleDeviceAddr = nullptr;
    void *quantParamDeviceAddr = nullptr;
    void *offsetDeviceAddr = nullptr;
    void *pertokenScaleDeviceAddr = 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 *pertokenScale = nullptr;
    aclTensor *out = nullptr;
    std::vector<int8_t> x1HostData = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
    std::vector<int8_t> x2HostData = {1, 1, 1, 1, 1, 1};
    std::vector<int8_t> x2TransposedHostData = {1, 1, 1, 1, 1, 1};
    std::vector<int32_t> biasHostData = {1, 1, 1};
    std::vector<float> scaleHostData = {1, 1, 1};
    std::vector<float> offsetHostData = {1, 1, 1};
    std::vector<float> pertokenScaleHostData = {1, 1, 1, 1, 1};
    std::vector<uint16_t> outHostData = {1, 1, 1, 1, 1, 1, 1, 1,
                                        1, 1, 1, 1, 1, 1, 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NZ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NZ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pertokenScale aclTensor
    ret = CreateAclTensor(pertokenScaleHostData, pertokenScaleShape, &pertokenScaleDeviceAddr,
                          aclDataType::ACL_FLOAT, &pertokenScale);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> pertokenScaleTensorPtr(pertokenScale,
                                                                                          aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> pertokenScaleDeviceAddrPtr(pertokenScaleDeviceAddr, 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 = nullptr;
    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aclnnTransQuantParamV2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aclnnQuantMatmulV4第一段接口
    workspaceSize = 0;
    ret = aclnnQuantMatmulV4GetWorkspaceSize(x1, x2Transposed, quantParam, nullptr, nullptr, bias, transposeX1,
                                            transposeX2, out, &workspaceSize, &executor);

    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4GetWorkspaceSize 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 *)> workspaceAddrPtrV4(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);
        workspaceAddrPtrV4.reset(workspaceAddr);
    }
    // 调用aclnnQuantMatmulV4第二段接口
    ret = aclnnQuantMatmulV4(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4 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: %hu\n", i, resultData[i]);
    }
    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 = aclnnQuantMatmulV4Test(deviceId, stream);
    CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4Test failed. ERROR: %d\n", ret); return ret);

    Finalize(deviceId, stream);
    return 0;
}

Atlas A2 训练系列产品/Atlas A2 推理系列产品、Atlas A3 训练系列产品/Atlas A3 推理系列产品： INT4量化场景(x1和x2数据类型为INT4，transposeX2=false)。

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

#include "acl/acl.h"
#include "aclnnop/aclnn_convert_weight_to_int4_pack.h"
#include "aclnnop/aclnn_quant_matmul_v4.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，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)
{
    // 通过hostData获取申请和拷贝的内存byte数
    auto size = hostData.size() * 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();
}

int aclnnQuantMatmulV4Test(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
    int64_t m = 16;
    int64_t k = 8;
    int64_t n = 32;
    aclDataType x1Dtype = aclDataType::ACL_INT4;
    aclDataType x2Int4PackDtype = aclDataType::ACL_INT4;
    std::vector<int64_t> x1Shape = {m, k};
    std::vector<int64_t> x2Shape = {k, n};
    std::vector<int64_t> x2Int4PackShape = {k, n};
    std::vector<int64_t> biasShape = {n};
    std::vector<int64_t> scaleShape = {n};
    std::vector<int64_t> outShape = {m, n};
    void *x1DeviceAddr = nullptr;
    void *x2DeviceAddr = nullptr;
    void *x2Int4PackDeviceAddr = nullptr;
    void *scaleDeviceAddr = nullptr;
    void *quantParamDeviceAddr = nullptr;
    void *offsetDeviceAddr = nullptr;
    void *biasDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *x1 = nullptr;
    aclTensor *x2 = nullptr;
    aclTensor *x2Int4Pack = nullptr;
    aclTensor *bias = nullptr;
    aclTensor *scale = nullptr;
    aclTensor *quantParam = nullptr;
    aclTensor *offset = nullptr;
    aclTensor *pertokenScale = nullptr;
    aclTensor *out = nullptr;
    std::vector<int8_t> x1HostData(m * k / 2, 17);  // int8: 0001 0001
    std::vector<int8_t> x2HostData(k * n, 1);
    std::vector<int8_t> x2Int4PackHostData(n * k / 2, 1);
    std::vector<int32_t> biasHostData(n, 1);
    std::vector<float> scaleHostData(n, 1);
    std::vector<uint16_t> outHostData(m * n, 1);

    // 创建x1 aclTensor
    ret = CreateAclTensor(x1HostData, x1Shape, &x1DeviceAddr, x1Dtype, &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x2 aclTensor
    ret = CreateAclTensor(x2HostData, x2Shape, &x2DeviceAddr, aclDataType::ACL_INT32, &x2);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x2TensorPtr(x2, aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x2DeviceAddrPtr(x2DeviceAddr, aclrtFree);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 创建x2Int4Pack aclTensor
    ret =
        CreateAclTensor(x2Int4PackHostData, x2Int4PackShape, &x2Int4PackDeviceAddr, x2Int4PackDtype, &x2Int4Pack);
    std::unique_ptr<aclTensor, aclnnStatus (*)(const aclTensor *)> x2Int4PackTensorPtr(x2Int4Pack,
                                                                                      aclDestroyTensor);
    std::unique_ptr<void, aclError (*)(void *)> x2Int4PackDeviceAddrPtr(x2Int4PackDeviceAddr, 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 = nullptr;

    // 可以先调用aclnnConvertWeightToINT4Pack接口来构建x2输入数据
    // 调用aclnnConvertWeightToINT4Pack第一段接口
    ret = aclnnConvertWeightToINT4PackGetWorkspaceSize(x2, x2Int4Pack, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS,
              LOG_PRINT("aclnnConvertWeightToINT4PackGetWorkspaceSize 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;
    std::unique_ptr<void, aclError (*)(void *)> workspaceINT4PackAddrPtr(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);
        workspaceINT4PackAddrPtr.reset(workspaceAddr);
    }
    // 调用aclnnConvertWeightToINT4Pack第二段接口
    ret = aclnnConvertWeightToINT4Pack(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvertWeightToINT4Pack 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aclnnTransQuantParamV2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aclnnQuantMatmulV4第一段接口
    ret = aclnnQuantMatmulV4GetWorkspaceSize(x1, x2Int4Pack, quantParam, nullptr, pertokenScale, bias, transposeX1,
                                            transposeX2, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4GetWorkspaceSize 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 *)> workspaceAddrPtrV3(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);
        workspaceAddrPtrV3.reset(workspaceAddr);
    }

    // 调用aclnnQuantMatmulV4第二段接口
    ret = aclnnQuantMatmulV4(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4 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: %hu\n", i, resultData[i]);
    }
    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 = aclnnQuantMatmulV4Test(deviceId, stream);
    CHECK_FREE_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnQuantMatmulV4Test failed. ERROR: %d\n", ret); return ret);

    Finalize(deviceId, stream);
    return 0;
}