aclnnCelu&aclnnInplaceCelu
Applicable Products
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Function
Description: aclnnCelu calls the continuously differentiable exponential linear unit (CELU) activation function on each element x in the input tensor self, and stores the result in the output tensor out. aclnnInplaceCelu calls the continuously differentiable exponential linear unit (CELU) activation function on each element x in the input tensor selfRef, and stores the result in the input tensor selfRef.
Formula:
Function Prototype
aclnnCelu and aclnnInplaceCelu implement the same function. The differences in usage are as follows. Select the appropriate operator based on your actual scenario.
aclnnCelu: Create a new output tensor object to store the computation result.
aclnnInplaceCelu: No need to create a new output tensor object. The computation result is stored directly in the memory of the input tensor.
Each operator is divided into a two-phase API. You must first call the "aclnnCeluGetWorkspaceSize" or "aclnnInplaceCeluGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that includes the operator computation process, and then call the "aclnnCelu" or "aclnnInplaceCelu" API to perform the computation.
aclnnStatus aclnnCeluGetWorkspaceSize(
const aclTensor* self,
const aclScalar* alpha,
aclTensor* out,
uint64_t* workspaceSize,
aclOpExecutor** executor)aclnnStatus aclnnCelu(
void* workspace,
uint64_t workspaceSize,
aclOpExecutor* executor,
aclrtStream stream)aclnnStatus aclnnInplaceCeluGetWorkspaceSize(
aclTensor* selfRef,
const aclScalar* alpha,
uint64_t* workspaceSize,
aclOpExecutor** executor)aclnnStatus aclnnInplaceCelu(
void* workspace,
uint64_t workspaceSize,
aclOpExecutor* executor,
aclrtStream stream)aclnnCeluGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (Shape) Non-Contiguous Tensor self (aclTensor*) Input Indicates the input of the CELU activation function, x in the formula. Supports empty tensors. FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ alpha (aclScalar*) Input Indicates the activation coefficient of the CELU activation function, \alpha in the formula. - - - - out (aclTensor*) Output Indicates the output of the CELU activation function. FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ workspaceSize (uint64_t*) Output Returns the workspace size to be applied on the Device side. - - - - - executor (aclOpExecutor**) Output Returns the op executor, which includes the operator computation flow. - - - - - Atlas training products : The data type supports FLOAT and FLOAT16.
Return value
aclnnStatus: return status. For details, see aclnn Return Code. The first-phase API performs input parameter validation and reports an error in the following scenarios:
Return Code Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The passed self, alpha, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of self is not within the supported range. The data type of alpha cannot be converted to FLOAT. The data type of out is not convertible from self. The shapes of self and out are inconsistent. The dimensions of self and out are greater than 8. alpha is equal to 0.
aclnnCelu
Parameters:
Parameter Input/Output Description workspace Input Workspace memory address applied on the Device side. workspaceSize Input Workspace size applied on the Device side, obtained from the first-phase API aclnnCeluGetWorkspaceSize. executor Input Operator executor, which includes the computation process of the operator. stream Input Specifies the stream for executing the task. Return value
aclnnStatus: return code. For details, see aclnn Return Code.
aclnnInplaceCeluGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor selfRef (aclTensor*) Input Indicates the input of the CELU activation function, which is x in the formula. Supports empty tensors. FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ alpha (aclScalar*) Input Indicates the activation coefficient of the CELU activation function, i.e., \alpha in the formula. - - - - workspaceSize (uint64_t*) Output Returns the workspace size to be applied on the Device side. - - - - - executor (aclOpExecutor**) Output Returns the op executor, which includes the operator computation process. - - - - - Atlas training products : The data type supports FLOAT and FLOAT16.
Return value
aclnnStatus: return code. For details, see aclnn Return Code.
The first-phase API performs input parameter validation and returns an error in the following scenarios:
Return Code Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The passed selfRef or alpha is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of selfRef is not within the supported range. The data type of alpha cannot be converted to FLOAT. The dimension of selfRef is greater than 8. alpha is equal to 0.
aclnnInplaceCelu
Parameters:
Parameter Input/Output Description workspace Input Address of the workspace memory applied for on the Device side. workspaceSize Input Size of the workspace applied for on the Device side, obtained from the first-phase API aclnnInplaceCeluGetWorkspaceSize. executor Input Operator executor, which includes the computation process. stream Input Specifies the stream for executing the task. Return value
aclnnStatus: return code. For details, see aclnn Return Code.
Constraints
Deterministic computation:
- aclnnCelu & aclnnInplaceCelu default to a deterministic implementation.
Example
The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.
Example code for the aclnnCelu API:
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_celu.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: resource initialization.
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 apply for device-side memory.
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 host-side data to device-side memory.
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);
// Calculate strides of 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 the aclCreateTensor API 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/stream. For details, see the ACL API Reference.
// Set deviceId based on your 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 the input and output. Customize the construction based on the API interface.
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;
aclTensor* out = nullptr;
std::vector<float> selfHostData = {-2, -1, 0, 1};
std::vector<float> outHostData = {0, 0, 0, 0};
float alphaValue = 1.0f;
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the alpha aclScalar.
alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
CHECK_RET(alpha != nullptr, return ret);
// Create the 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. Replace with the specific API name.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnCelu.
ret = aclnnCeluGetWorkspaceSize(self, alpha, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCeluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// Apply for device memory based on the workspaceSize calculated 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 aclnnCelu.
ret = aclnnCelu(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCelu failed. ERROR: %d\n", ret); return ret);
// 4. (Boilerplate) Synchronously wait for task execution to finish.
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 side memory to the host side. Modify based on the specific 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. Release aclTensor and aclScalar. Modify based on the specific API definition.
aclDestroyTensor(self);
aclDestroyScalar(alpha);
aclDestroyTensor(out);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}aclnnInplaceCelu API call sample code:
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_celu.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: resource initialization.
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 apply for memory on the device side.
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 data from the host side to the device memory.
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);
// Calculate 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 the aclCreateTensor API 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/stream. For details, refer to the acl API manual.
// Fill in the deviceId based on your 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 the input and output. Customize the construction based on the API interface.
std::vector<int64_t> selfRefShape = {2, 2};
void* selfRefDeviceAddr = nullptr;
aclTensor* selfRef = nullptr;
aclScalar* alpha = nullptr;
std::vector<float> selfRefHostData = {-2, -1, 0, 1};
float alphaValue = 1.0f;
// Create the selfRef aclTensor.
ret = CreateAclTensor(selfRefHostData, selfRefShape, &selfRefDeviceAddr, aclDataType::ACL_FLOAT, &selfRef);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the alpha aclScalar.
alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
CHECK_RET(alpha != nullptr, return ret);
// 3. Call the CANN operator library API. Replace with the specific API name.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnInplaceCelu.
ret = aclnnInplaceCeluGetWorkspaceSize(selfRef, alpha, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceCeluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// Apply for device memory based on the workspaceSize calculated 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 aclnnInplaceCelu.
ret = aclnnInplaceCelu(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceCelu failed. ERROR: %d\n", ret); return ret);
// 4. (Boilerplate) Synchronize and wait for task execution to complete.
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 side memory to the host side. Modify based on the specific 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. Release aclTensor and aclScalar. Modify based on the specific API definition.
aclDestroyTensor(selfRef);
aclDestroyScalar(alpha);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(selfRefDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}