aclnnAddLayerNorm
支持的产品型号
- Atlas 推理系列产品
- Atlas A2训练系列产品/Atlas 800I A2推理产品
接口原型
每个算子分为两段式接口，必须先调用aclnnAddLayerNormGetWorkspaceSize接口获取入参并根据计算流程所需workspace大小，再调用aclnnAddLayerNorm接口执行计算。
aclnnStatus aclnnAddLayerNormGetWorkspaceSize(const aclTensor *x1, const aclTensor *x2, const aclTensor *gamma, const aclTensor *beta, const aclTensor *bias, double epsilon, bool additionalOut, const aclTensor *yOut, const aclTensor *meanOut, const aclTensor *rstdOut, const aclTensor *xOut, uint64_t *workspaceSize, aclOpExecutor **executor)aclnnStatus aclnnAddLayerNorm(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
功能描述
- 算子功能：实现AddLayerNorm功能。
- 计算公式：
aclnnAddLayerNormGetWorkspaceSize
参数说明：
- x1（aclTensor *，计算输入）：表示AddLayerNorm中加法计算的输入，将会在算子内做 x1 + x2 + bias 的计算并对计算结果做层归一化；是Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），shape支持2-8维度，数据格式支持ND。
- x2（aclTensor *，计算输入）：表示AddLayerNorm中加法计算的输入，将会在算子内做 x1 + x2 + bias 的计算并对计算结果做层归一化；是Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），shape支持2-8维度，数据格式支持ND。
- beta（aclTensor *，计算输入）：对应LayerNorm计算公式中的 beta ，表示层归一化中的 beta 参数；是Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），shape支持2-8维度，数据格式支持ND，数据维度和x1/x2的尾轴相同。
- gamma（aclTensor *，计算输入）：对应LayerNorm计算公式中的 gamma，表示层归一化中的 gamma 参数；是Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），shape支持2-8维度，数据格式支持ND，数据维度和x1/x2的尾轴相同。
- bias（aclTensor *，计算输入）：可选输入参数，表示AddLayerNorm中加法计算的输入，将会在算子内做 x1 + x2 + bias 的计算并对计算结果做层归一化；shape可以和gamma/beta或是和x1/x2一致，是Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），shape支持2-8维度，数据格式支持ND。
- epsilon（double *，计算输入）：表对应LayerNorm中的epsilon，添加到分母中的值，以确保数值稳定；host侧的aclScalar，数据类型为double，默认值为1e-5。
- additionalOut（bool *，计算输入）：表示是否开启x=x1+x2的输出，host侧的aclScalar，数据类型为bool。
- meanOut（aclTensor *，计算输出）：输出 LayerNorm 计算过程中 (x1 + x2) 的结果的均值，Device 侧的aclTensor，数据类型为FLOAT，shape需要与输入x1/x2一致，最后一轴的size为1，数据格式支持ND，该输出在Atlas 推理系列产品上无效。计算逻辑：
- rstdOut（aclTensor *，计算输出）：输出 LayerNorm 计算过程中 rstd 的结果，Device 侧的aclTensor，数据类型为FLOAT，shape需要与输入x1/x2一致，最后一轴的size为1，数据格式支持ND，该输出在Atlas 推理系列产品上无效。计算逻辑：
- yOut（aclTensor *，计算输出）：表示LayerNorm的结果输出y，Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），需要与输入x1/x2一致，shape需要与输入x1/x2一致，数据格式支持ND。
- xOut（aclTensor *，计算输出）：表示LayerNorm的结果输出x，Device 侧的aclTensor，数据类型支持FLOAT、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持），需要与输入x1/x2一致，shape需要与输入x1/x2一致，数据格式支持ND。
- workspaceSize（uint64_t *，出参）：返回用户需要在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。（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)：如果传入参数是必选输入，输出或者必选属性，且是空指针，则返回161001。
aclnnAddLayerNorm
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aclnnAddLayerNormGetWorkspaceSize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
- 功能维度
- 数据类型支持
- x1、x2、beta、gamma、bias支持：FLOAT32、FLOAT16、BFLOAT16（Atlas 推理系列产品不支持）。
- rstd、mean支持：FLOAT32。
- 数据格式支持：ND。
- 数据类型支持
- 未支持类型说明
- DOUBLE：指令不支持DOUBLE。
- 是否支持空tensor：不支持空进空出。
- 是否非连续tensor：不支持输入非连续，不支持数据非连续。
- 边界值场景说明
- 当输入是inf时，输出为inf。
- 当输入是nan时，输出为nan。
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。
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_add_layer_norm.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，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;
}
int main() {
// 1. （固定写法）device/stream初始化，参考AscendCL对外接口列表
// 根据自己的实际device填写deviceId
int32_t deviceId = 0;
aclrtStream stream;
auto ret = Init(deviceId, &stream);
// check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，本示例中将各调用一次不带bias可选输入的和带bias输入的用例
float eps = 1e-6;
bool additionalOut = true;
std::vector<int64_t> x1Shape = {1, 2, 8};
std::vector<int64_t> x2Shape = {1, 2, 8};
std::vector<int64_t> gammaShape = {8};
std::vector<int64_t> betaShape = {8};
std::vector<int64_t> biasShape = {8};
std::vector<int64_t> outputYShape = {1, 2, 8};
std::vector<int64_t> outputMeanShape = {1, 2, 1};
std::vector<int64_t> outputRstdShape = {1, 2, 1};
std::vector<int64_t> outputXShape = {1, 2, 8};
void *x1DeviceAddr = nullptr;
void *x2DeviceAddr = nullptr;
void *betaDeviceAddr = nullptr;
void *gammaDeviceAddr = nullptr;
void *biasDeviceAddr = nullptr;
// 用于不带bias的输出 Device 地址
void *outputYDeviceAddr = nullptr;
void *outputMeanDeviceAddr = nullptr;
void *outputRstdDeviceAddr = nullptr;
void *outputXDeviceAddr = nullptr;
// 用于带bias的输出 Device 地址
void *outputYDeviceAddrBias = nullptr;
void *outputMeanDeviceAddrBias = nullptr;
void *outputRstdDeviceAddrBias = nullptr;
void *outputXDeviceAddrBias = nullptr;
aclTensor *x1 = nullptr;
aclTensor *x2 = nullptr;
aclTensor *beta = nullptr;
aclTensor *gamma = nullptr;
aclTensor *bias = nullptr;
// 用于不带bias的aclTensor
aclTensor *outputY = nullptr;
aclTensor *outputMean = nullptr;
aclTensor *outputRstd = nullptr;
aclTensor *outputX = nullptr;
// 用于带bias的aclTensor
aclTensor *outputYBias = nullptr;
aclTensor *outputMeanBias = nullptr;
aclTensor *outputRstdBias = nullptr;
aclTensor *outputXBias = nullptr;
std::vector<float> x1HostData = {1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2};
std::vector<float> x2HostData = {4, 4, 4, 4, 4, 4, 4, 4, -3, -3, -3, -3, -3, -3, -3, -3};
std::vector<float> gammaHostData = {2, 2, 2, 2, 2, 2, 2, 2};
std::vector<float> betaHostData = {0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1};
std::vector<float> biasHostData = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
// 用于不带bias的HostData
std::vector<float> outputYHostData(1 * 2 * 8);
std::vector<float> outputMeanHostData(2);
std::vector<float> outputRstdHostData(2);
std::vector<float> outputXHostData(1 * 2 * 8);
// 用于带bias的HostData
std::vector<float> outputYHostDataBias(1 * 2 * 8);
std::vector<float> outputMeanHostDataBias(2);
std::vector<float> outputRstdHostDataBias(2);
std::vector<float> outputXHostDataBias(1 * 2 * 8);
// 创建self aclTensor
ret = CreateAclTensor(x1HostData, x1Shape, &x1DeviceAddr, aclDataType::ACL_FLOAT, &x1);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(x2HostData, x2Shape, &x2DeviceAddr, aclDataType::ACL_FLOAT, &x2);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(betaHostData, betaShape, & betaDeviceAddr, aclDataType::ACL_FLOAT, &beta);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(gammaHostData, gammaShape, &gammaDeviceAddr, aclDataType::ACL_FLOAT, &gamma);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(biasHostData, biasShape, &biasDeviceAddr, aclDataType::ACL_FLOAT, &bias);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建不带 bias 的 aclTensor
ret = CreateAclTensor(outputYHostData, outputYShape, &outputYDeviceAddr, aclDataType::ACL_FLOAT, &outputY);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outputMeanHostData, outputMeanShape, &outputMeanDeviceAddr, aclDataType::ACL_FLOAT, &outputMean);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outputRstdHostData, outputRstdShape, &outputRstdDeviceAddr, aclDataType::ACL_FLOAT, &outputRstd);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outputXHostData, outputXShape, &outputXDeviceAddr, aclDataType::ACL_FLOAT, &outputX);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建带 bias 的 aclTensor
ret = CreateAclTensor(outputYHostDataBias, outputYShape, &outputYDeviceAddrBias, aclDataType::ACL_FLOAT, &outputYBias);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outputMeanHostDataBias, outputMeanShape, &outputMeanDeviceAddrBias, aclDataType::ACL_FLOAT, &outputMeanBias);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outputRstdHostDataBias, outputRstdShape, &outputRstdDeviceAddrBias, aclDataType::ACL_FLOAT, &outputRstdBias);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outputXHostDataBias, outputXShape, &outputXDeviceAddrBias, aclDataType::ACL_FLOAT, &outputXBias);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// aclnnAddLayerNorm接口调用示例，包含带bias和不带bias的各一次
// 3. 调用CANN算子库API，需要修改为具体的Api名称
// 3.1 不带bias可选输入的示例
// 调用aclnnAddLayerNorm第一段接口
uint64_t workspaceSize = 0;
aclOpExecutor *executor;
LOG_PRINT("\nUse aclnnAddLayerNorm Non-Bias Port.");
// bias参数直接传入nullptr即可
ret = aclnnAddLayerNormGetWorkspaceSize(x1, x2, gamma, beta, nullptr, eps, additionalOut,
outputY, outputMean, outputRstd, outputX,
&workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddLayerNormGetWorkspaceSize 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aclnnAddLayerNorm第二段接口
ret = aclnnAddLayerNorm(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddLayerNorm failed. ERROR: %d\n", ret); return ret);
// 3.2 带bias可选输入的示例
// 调用aclnnAddLayerNorm第一段接口
uint64_t workspaceSizeBias = 0;
aclOpExecutor *executorBias;
LOG_PRINT("\nUse aclnnAddLayerNorm Bias Port.");
// 正常传入bias即可
ret = aclnnAddLayerNormGetWorkspaceSize(x1, x2, gamma, beta, bias, eps, additionalOut,
outputYBias, outputMeanBias, outputRstdBias, outputXBias,
&workspaceSizeBias, &executorBias);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddLayerNormGetWorkspaceSize 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 *workspaceAddrBias = nullptr;
if (workspaceSizeBias > 0) {
ret = aclrtMalloc(&workspaceAddrBias, workspaceSizeBias, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret;);
}
// 调用aclnnAddLayerNorm第二段接口
ret = aclnnAddLayerNorm(workspaceAddrBias, workspaceSizeBias, executorBias, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddLayerNorm 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
// 5.1 考出不带bias的输出
auto outputYSize = GetShapeSize(outputYShape);
std::vector<float> resultDataY(outputYSize, 0);
ret = aclrtMemcpy(resultDataY.data(), resultDataY.size() * sizeof(resultDataY[0]), outputYDeviceAddr,
outputYSize * sizeof(resultDataY[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);
LOG_PRINT("==== AddLayerNorm non-bias: y output");
for (int64_t i = 0; i < outputYSize; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataY[i]);
}
auto outputMeanSize = GetShapeSize(outputMeanShape);
std::vector<float> resultDataMean(outputMeanSize, 0);
ret = aclrtMemcpy(resultDataMean.data(), resultDataMean.size() * sizeof(resultDataMean[0]), outputMeanDeviceAddr,
outputMeanSize * sizeof(resultDataMean[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);
LOG_PRINT("==== AddLayerNorm non-bias: mean output");
for (int64_t i = 0; i < outputMeanSize; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataMean[i]);
}
auto outputRstdSize = GetShapeSize(outputRstdShape);
std::vector<float> resultDataRstd(outputRstdSize, 0);
ret = aclrtMemcpy(resultDataRstd.data(), resultDataRstd.size() * sizeof(resultDataRstd[0]), outputRstdDeviceAddr,
outputRstdSize * sizeof(resultDataRstd[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);
LOG_PRINT("==== AddLayerNorm non-bias: rstd output");
for (int64_t i = 0; i < outputRstdSize; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataRstd[i]);
}
auto outputXSize = GetShapeSize(outputXShape);
std::vector<float> resultDataX(outputXSize, 0);
ret = aclrtMemcpy(resultDataX.data(), resultDataX.size() * sizeof(resultDataX[0]), outputXDeviceAddr,
outputXSize * sizeof(resultDataX[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);
LOG_PRINT("==== AddLayerNorm non-bias: x output");
for (int64_t i = 0; i < outputXSize; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataX[i]);
}
// 5.2 考出带bias的输出
auto outputYSizeBias = GetShapeSize(outputYShape);
std::vector<float> resultDataYBias(outputYSizeBias, 0);
ret = aclrtMemcpy(resultDataYBias.data(), resultDataYBias.size() * sizeof(resultDataYBias[0]), outputYDeviceAddrBias,
outputYSizeBias * sizeof(resultDataYBias[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);
LOG_PRINT("==== AddLayerNorm bias: y output");
for (int64_t i = 0; i < outputYSizeBias; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataYBias[i]);
}
auto outputMeanSizeBias = GetShapeSize(outputMeanShape);
std::vector<float> resultDataMeanBias(outputMeanSizeBias, 0);
ret = aclrtMemcpy(resultDataMeanBias.data(), resultDataMeanBias.size() * sizeof(resultDataMeanBias[0]), outputMeanDeviceAddrBias,
outputMeanSizeBias * sizeof(resultDataMeanBias[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);
LOG_PRINT("==== AddLayerNorm bias: mean output");
for (int64_t i = 0; i < outputMeanSizeBias; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataMeanBias[i]);
}
auto outputRstdSizeBias = GetShapeSize(outputRstdShape);
std::vector<float> resultDataRstdBias(outputRstdSizeBias, 0);
ret = aclrtMemcpy(resultDataRstdBias.data(), resultDataRstdBias.size() * sizeof(resultDataRstdBias[0]), outputRstdDeviceAddrBias,
outputRstdSizeBias * sizeof(resultDataRstdBias[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);
LOG_PRINT("==== AddLayerNorm bias: rstd output");
for (int64_t i = 0; i < outputRstdSizeBias; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataRstdBias[i]);
}
auto outputXSizeBias = GetShapeSize(outputXShape);
std::vector<float> resultDataXBias(outputXSizeBias, 0);
ret = aclrtMemcpy(resultDataXBias.data(), resultDataXBias.size() * sizeof(resultDataXBias[0]), outputXDeviceAddrBias,
outputXSizeBias * sizeof(resultDataXBias[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);
LOG_PRINT("==== AddLayerNorm bias: x output");
for (int64_t i = 0; i < outputXSizeBias; i++) {
LOG_PRINT("result[%ld] is: %f\n", i, resultDataXBias[i]);
}
// 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
aclDestroyTensor(x1);
aclDestroyTensor(x2);
aclDestroyTensor(beta);
aclDestroyTensor(gamma);
aclDestroyTensor(bias);
aclDestroyTensor(outputY);
aclDestroyTensor(outputMean);
aclDestroyTensor(outputRstd);
aclDestroyTensor(outputX);
aclDestroyTensor(outputYBias);
aclDestroyTensor(outputMeanBias);
aclDestroyTensor(outputRstdBias);
aclDestroyTensor(outputXBias);
// 7. 释放device资源，需要根据具体API的接口定义修改
aclrtFree(x1DeviceAddr);
aclrtFree(x2DeviceAddr);
aclrtFree(gammaDeviceAddr);
aclrtFree(betaDeviceAddr);
aclrtFree(biasDeviceAddr);
aclrtFree(outputYDeviceAddr);
aclrtFree(outputMeanDeviceAddr);
aclrtFree(outputRstdDeviceAddr);
aclrtFree(outputXDeviceAddr);
aclrtFree(outputYDeviceAddrBias);
aclrtFree(outputMeanDeviceAddrBias);
aclrtFree(outputRstdDeviceAddrBias);
aclrtFree(outputXDeviceAddrBias);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
if (workspaceSizeBias > 0) {
aclrtFree(workspaceAddrBias);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}