aclnnCelu&aclnnInplaceCelu
Supported Products
| Product | Supported |
|---|---|
| √ | |
| √ | |
| × | |
| × | |
| √ |
Function
Description: aclnnCelu applies the Continuously Differentiable Exponential Linear Unit (CELU) function for each element x in the input tensor self, and stores the obtained result in out. aclnnInplaceCelu applies the CELU function for each element x in the input tensor selfRef, and stores the obtained result in selfRef.
Formula:
Prototype
aclnnCelu and aclnnInplaceCelu implement the same function in different ways. Select a proper operator based on your requirements.
- aclnnCelu: An output tensor object needs to be created to store the computation result.
- aclnnInplaceCelu: 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, aclnnCeluGetWorkspaceSize or aclnnInplaceCeluGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnCelu or aclnnInplaceCelu is called to perform 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:
Name Input/Output Description Precaution Data Type Data Format Dimension (Shape) Non-contiguous Tensor self Input Input of the CELU activation function, corresponding to x in the formula. Empty tensors are supported. FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ alpha Input Activation coefficient of the CELU activation function, corresponding to \alpha in the formula. - - - - out Output Output of the CELU activation function. FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ workspaceSize Output Size of the workspace required to be allocated on the device. - - - - - executor Output Operator executor, containing the operator computation process. - - - - - Atlas training series products : The data type can be FLOAT or FLOAT16.
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 passed self, alpha, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of self is not supported. The data type of alpha cannot be converted to FLOAT. The data type of out cannot be converted from that of self. The shapes of self and out are inconsistent. The shape of self or out is greater than 8D. The value of alpha is 0.
aclnnCelu
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 **aclnnCeluGetWorkspaceSize**. executor Input Operator executor, containing the operator computation process. stream Input Stream for executing the task. Returns:
aclnnStatus: status code. For details, see aclnn Return Codes.
aclnnInplaceCeluGetWorkspaceSize
Parameters:
Name Input/Output Description Precaution Data Type Data Format Dimension (Shape) Non-contiguous Tensor selfRef Input Input of the CELU activation function, corresponding to x in the formula. Empty tensors are supported. FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ alpha Input Activation coefficient of the CELU activation function, corresponding to \alpha in the formula. - - - - workspaceSize Output Size of the workspace required to be allocated on the device. - - - - - executor Output Operator executor, containing the operator computation process. - - - - - Atlas training series products : The data type can be FLOAT or FLOAT16.
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 passed selfRef or alpha is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of selfRef is not supported. The data type of alpha cannot be converted to FLOAT. The shape of selfRef is greater than 8D. The value of alpha is 0.
aclnnInplaceCelu
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 **aclnnInplaceCeluGetWorkspaceSize**. executor Input Operator executor, containing the operator computation process. stream Input Stream for executing the task. Returns:
aclnnStatus: status code. For details, see aclnn Return Codes.
Constraints
- Deterministic compute:
- aclnnCelu&aclnnInplaceCelu defaults to a deterministic implementation.
Example
The following example is for reference only. For details, see Compilation and Running Sample.
aclnnCelu 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) {
// (Fixed writing) 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 from 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. (Fixed writing) Initialize the device and stream. For details, see the ACL API manual.
// Set the device ID in use.
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 based on the API.
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 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 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, which needs to be replaced with the actual API.
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);
// 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 aclnnCelu.
ret = aclnnCelu(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCelu failed. ERROR: %d\n", ret); return ret);
// 4. (Fixed writing) Wait until the task execution is 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 memory to the host. Modify the configuration 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. Release aclTensor and aclScalar. Modify the configuration based on the API definition.
aclDestroyTensor(self);
aclDestroyScalar(alpha);
aclDestroyTensor(out);
// 7. Release device resources. Modify the configuration based on the API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}aclnnInplaceCelu 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) {
// (Fixed writing) 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 from 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. (Fixed writing) Initialize the device and stream. For details, see the ACL API manual.
// Set the device ID in use.
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 based on the API.
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 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);
// 3. Call the CANN operator library API, which needs to be replaced with the actual API.
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);
// 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 aclnnInplaceCelu.
ret = aclnnInplaceCelu(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceCelu failed. ERROR: %d\n", ret); return ret);
// 4. (Fixed writing) Wait until the task execution is 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 memory to the host. Modify the configuration 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. Release aclTensor and aclScalar. Modify the configuration based on the API definition.
aclDestroyTensor(selfRef);
aclDestroyScalar(alpha);
// 7. Release device resources. Modify the configuration based on the API definition.
aclrtFree(selfRefDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}