aclnnElu&aclnnInplaceElu

Product Support

Product	Supported
Atlas A3 training series products/Atlas A3 inference series products	√
Atlas A2 training products/Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	×
Atlas inference series products	√
Atlas training series products	√

Function

Description: Applies the exponential linear unit (ELU) activation function to each element x in the input tensor self and stores the result in the output tensor out.
Formula: $ELU(x) = \begin{cases} scale \ast x, \quad x > 0\\ \alpha \ast scale \ast (exp(x \ast inputScale)-1), \quad x \leq 0 \end{cases}$

Prototype

aclnnElu and aclnnInplaceElu implement the same function. Select the appropriate operator based on actual service scenarios.
- aclnnElu: An output tensor object must be created to store the computation result.
- aclnnInplaceElu: No output tensor object needs to be created, and the computation result is stored in the memory of the input tensor.
Each operator has two-phase API calls. First, aclnnEluGetWorkspaceSize or aclnnInplaceEluGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation flow. Then, aclnnElu or aclnnInplaceElu is called to perform computation.

aclnnStatus aclnnEluGetWorkspaceSize(
  const aclTensor *self,
  const aclScalar *alpha,
  const aclScalar *scale,
  const aclScalar *inputScale,
  aclTensor       *out,
  uint64_t        *workspaceSize,
  aclOpExecutor  **executor)

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

aclnnStatus aclnnInplaceEluGetWorkspaceSize(
  aclTensor       *selfRef,
  const aclScalar *alpha,
  const aclScalar *scale,
  const aclScalar *inputScale,
  uint64_t        *workspaceSize,
  aclOpExecutor  **executor)

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

aclnnEluGetWorkspaceSize

Parameters

Name	Input/Output	Description	Usage Notes	Data Type	Data Format	Shape	Non-contiguous Tensor
self	Input	Input of the ELU activation function, corresponding to x in the formula.	Empty tensors are supported.	FLOAT, FLOAT16, DOUBLE, BFLOAT16	ND	0-8	√
alpha	Input	Activation coefficient of the ELU activation function, corresponding to α in the formula.	The data type must be convertible to FLOAT.	-	-	-	-
scale	Input	Scaling factor of the ELU activation function, corresponding to scale in the formula.	The data type must be convertible to FLOAT.	-	-	-	-
inputScale	Input	Input scaling factor of the ELU activation function, corresponding to inputScale in the formula.	The data type must be convertible to FLOAT.	-	-	-	-
out	Output	Output of the ELU activation function.	The shape must be the same as that of self.	FLOAT, FLOAT16, DOUBLE, BFLOAT16	ND	0-8	√
workspaceSize	Output	Size of the workspace to be allocated on the device.	-	-	-	-	-
executor	Output	Operator executor, containing the operator computation flow.	-	-	-	-	-

Atlas training series products: The data type can be FLOAT, FLOAT16, or DOUBLE.

Returns

aclnnStatus: status code. For details, see aclnn Return Codes.

The first-phase API implements input parameter verification. The following errors may be thrown.

Return	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The self, alpha, scale, inputScale, or out parameter is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of self is not supported.
		The data type of alpha, scale, or inputScale cannot be converted to FLOAT.
		The data type of out is not convertible from that of self.
		The shapes of self and out do not match.
		self or out has more than eight dimensions.

aclnnElu

Parameters

Name	Input/Output	Description
workspace	Input	Address of the workspace to be allocated on the device.
workspaceSize	Input	Size of the workspace to be allocated on the device, which is obtained by calling the first-phase API aclnnEluGetWorkspaceSize.
executor	Input	Operator executor, containing the operator computation flow.
stream	Input	Stream for executing the task.

Returns

aclnnStatus: status code. For details, see aclnn Return Codes.

aclnnInplaceEluGetWorkspaceSize

Parameters

Name	Input/Output	Description	Usage Notes	Data Type	Data Format	Shape	Non-contiguous Tensor
selfRef	Input	Input of the ELU activation function, corresponding to x in the formula.	Empty tensors are supported.	FLOAT, FLOAT16, DOUBLE, BFLOAT16	ND	0-8	√
alpha	Input	Activation coefficient of the ELU activation function, corresponding to α in the formula.	The data type must be convertible to FLOAT.	-	-	-	-
scale	Input	Activation coefficient of the ELU activation function, corresponding to scale in the formula.	The data type must be convertible to FLOAT.	-	-	-	-
inputScale	Input	Input scaling factor of the ELU activation function.	The data type must be convertible to FLOAT.	-	-	-	-
workspaceSize	Output	Size of the workspace to be allocated on the device.	-	-	-	-	-
executor	Output	Operator executor, containing the operator computation flow.	-	-	-	-	-

Atlas training series products: The data type can be FLOAT, FLOAT16, or DOUBLE.

Returns

aclnnStatus: status code. For details, see aclnn Return Codes.

The first-phase API implements input parameter verification. The following errors may be thrown.

Return	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The selfRef, alpha, scale, or inputScale parameter is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of selfRef is not supported.
		The data type of alpha, scale, or inputScale cannot be converted to FLOAT.
		selfRef has more than eight dimensions.

aclnnInplaceElu

Parameters

Name	Input/Output	Description
workspace	Input	Address of the workspace to be allocated on the device.
workspaceSize	Input	Size of the workspace to be allocated on the device, which is obtained by calling the first-phase API aclnnInplaceEluGetWorkspaceSize.
executor	Input	Operator executor, containing the operator computation flow.
stream	Input	Stream for executing the task.

Returns

aclnnStatus: status code. For details, see aclnn Return Codes.

Constraints

Deterministic computation:
- aclnnElu and aclnnInplaceElu default to a deterministic implementation.

Example

The following example is for reference only. For details, see Compilation and Running Sample.

aclnnElu API call example:

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_elu.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)
{
    // (Boilerplate) Initialize resources.
    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** selfOrResult)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // Call aclrtMalloc to allocate memory on the device.
    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);
    // Call aclrtMemcpy to copy the data on the host to the memory on the device.
    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);

    // Compute the strides of the contiguous selfOrResult.
    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];
    }

    // Call aclCreateTensor to create an aclTensor.
    *selfOrResult = aclCreateTensor(
        shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(),
        *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Boilerplate) Initialize the device and stream. For details, see the ACL API manual.
    // Set the device ID (deviceId) based on the actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

    // 2. Construct inputs and outputs based on API definitions.
    std::vector<int64_t> selfShape = {2, 2};
    std::vector<int64_t> outShape = {2, 2};
    void* selfDeviceAddr = nullptr;
    void* outDeviceAddr = nullptr;
    aclTensor* self = nullptr;
    aclScalar* alpha = nullptr;
    aclScalar* scale = nullptr;
    aclScalar* inputScale = nullptr;
    aclTensor* out = nullptr;
    std::vector<float> selfHostData = {-2, -1, 0, 1};
    std::vector<float> outHostData = {0, 0, 0, 0};
    float alphaValue = 1.0f;
    float scaleValue = 1.0f;
    float inputScaleValue = 1.0f;
    // Create a self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create an alpha aclScalar.
    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
    CHECK_RET(alpha != nullptr, return ret);
    // Create a scale aclScalar.
    scale = aclCreateScalar(&scaleValue, aclDataType::ACL_FLOAT);
    CHECK_RET(scale != nullptr, return ret);
    // Create an inputScale aclScalar.
    inputScale = aclCreateScalar(&inputScaleValue, aclDataType::ACL_FLOAT);
    CHECK_RET(inputScale != nullptr, return ret);
    // Create an out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    // 3. Call the CANN operator library API. Modify the API as required.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnElu.
    ret = aclnnEluGetWorkspaceSize(self, alpha, scale, inputScale, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnEluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate device memory based on workspaceSize computed by the first-phase API.
    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);
    }
    // Call the second-phase API of aclnnElu.
    ret = aclnnElu(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnElu failed. ERROR: %d\n", ret); return ret);

    // 4. (Boilerplate) Synchronize the stream and wait for task completion.
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

    // 5. Obtain the output value and copy the result from the device to the host. Modify the code based on the API definition.
    auto size = GetShapeSize(outShape);
    std::vector<float> resultData(size, 0);
    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: %f\n", i, resultData[i]);
    }

    // 6. Destroy aclTensor and aclScalar. Modify the code based on the API definition.
    aclDestroyTensor(self);
    aclDestroyScalar(alpha);
    aclDestroyScalar(scale);
    aclDestroyScalar(inputScale);
    aclDestroyTensor(out);

    // 7. Release device resources. Modify the code based on the API definition.
    aclrtFree(selfDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}

aclnnInplaceElu API call example:

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_elu.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)
{
    // (Boilerplate) Initialize resources.
    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** selfOrResult)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // Call aclrtMalloc to allocate memory on the device.
    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);
    // Call aclrtMemcpy to copy the data on the host to the memory on the device.
    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);

    // Compute the strides of the contiguous selfOrResult.
    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];
    }

    // Call aclCreateTensor to create an aclTensor.
    *selfOrResult = aclCreateTensor(
        shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(),
        *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Boilerplate) Initialize the device and stream. For details, see the ACL API manual.
    // Set the device ID (deviceId) based on the actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

    // 2. Construct inputs and outputs based on API definitions.
    std::vector<int64_t> selfRefShape = {2, 2};
    void* selfRefDeviceAddr = nullptr;
    aclTensor* selfRef = nullptr;
    aclScalar* alpha = nullptr;
    aclScalar* scale = nullptr;
    aclScalar* inputScale = nullptr;
    std::vector<float> selfRefHostData = {-2, -1, 0, 1};
    float alphaValue = 1.0f;
    float scaleValue = 1.0f;
    float inputScaleValue = 1.0f;
    // Create a selfRef aclTensor.
    ret = CreateAclTensor(selfRefHostData, selfRefShape, &selfRefDeviceAddr, aclDataType::ACL_FLOAT, &selfRef);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create an alpha aclScalar.
    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
    CHECK_RET(alpha != nullptr, return ret);
    // Create a scale aclScalar.
    scale = aclCreateScalar(&scaleValue, aclDataType::ACL_FLOAT);
    CHECK_RET(scale != nullptr, return ret);
    // Create an inputScale aclScalar.
    inputScale = aclCreateScalar(&inputScaleValue, aclDataType::ACL_FLOAT);
    CHECK_RET(inputScale != nullptr, return ret);

    // 3. Call the CANN operator library API. Modify the API as required.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnInplaceElu.
    ret = aclnnInplaceEluGetWorkspaceSize(selfRef, alpha, scale, inputScale, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceEluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate device memory based on workspaceSize computed by the first-phase API.
    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);
    }
    // Call the second-phase API of aclnnInplaceElu.
    ret = aclnnInplaceElu(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceElu failed. ERROR: %d\n", ret); return ret);

    // 4. (Boilerplate) Synchronize the stream and wait for task completion.
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

    // 5. Obtain the output value and copy the result from the device to the host. Modify the code based on the API definition.
    auto size = GetShapeSize(selfRefShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(
        resultData.data(), resultData.size() * sizeof(resultData[0]), selfRefDeviceAddr, 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: %f\n", i, resultData[i]);
    }

    // 6. Destroy aclTensor and aclScalar. Modify the code based on the API definition.
    aclDestroyTensor(selfRef);
    aclDestroyScalar(alpha);
    aclDestroyScalar(scale);
    aclDestroyScalar(inputScale);

    // 7. Release device resources. Modify the code based on the API definition.
    aclrtFree(selfRefDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}