aclnnLeakyRelu&aclnnInplaceLeakyRelu
Applicable Products
| Product | Supported |
|---|---|
| √ | |
| √ | |
| √ | |
| × | |
| × | |
| √ |
Function
Description: An activation function used to solve the problem that the ReLU function outputs 0 when the input is less than 0, preventing neurons from being unable to update parameters.
Formula:
Function Prototype
aclnnLeakyRelu and aclnnInplaceLeakyRelu implement the same function. The differences are as follows. Select the appropriate operator based on your actual scenario.
aclnnLeakyRelu: A new output tensor object needs to be created to store the computation result.
aclnnInplaceLeakyRelu: No need to create a new output tensor object. The computation result is stored directly in the memory of the input tensor.
Each operator consists of a two-phase API. You must first call the "aclnnLeakyReluGetWorkspaceSize" or "aclnnInplaceLeakyReluGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnLeakyRelu" or "aclnnInplaceLeakyRelu" API to perform the computation.
aclnnStatus aclnnLeakyReluGetWorkspaceSize(
const aclTensor *self,
const aclScalar *negativeSlope,
aclTensor *out,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnLeakyRelu(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnStatus aclnnInplaceLeakyReluGetWorkspaceSize(
aclTensor *selfRef,
const aclScalar *negativeSlope,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnInplaceLeakyRelu(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnLeakyReluGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor self (aclTensor*) Input Input parameter for the LeakyReLU activation function, corresponding to self in the formula. - shape supports 0 to 8 dimensions, and must be consistent with out.
- Supports empty tensor.
FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ negativeSlope (aclScalar*) Input Indicates the slope when self < 0, i.e., negativeSlope in the formula. - FLOAT - - - out (aclTensor*) Output Output parameter for the LeakyReLU activation function. The data type of out must be a data type to which self can be converted (see Conversion Relationship). FLOAT, FLOAT16, BFLOAT16 ND 0-8 √ workspaceSize (uint64_t*) Output Returns the workspace size to be applied for on the Device side. - - - - - executor (aclOpExecutor**) Output Returns the op executor, which contains the operator computation flow. - - - - - Atlas training products : FLOAT, 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 self, negativeSlope, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of self is not within the supported range. The shape of self exceeds 8 dimensions. The data type of out is not convertible from self. The shape of out is inconsistent with self.
aclnnLeakyRelu
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 by the first-phase API aclnnLeakyReluGetWorkspaceSize. executor Input Operator executor, which contains the operator computation flow. stream Input Specifies the stream for executing the task. Return value
aclnnStatus: return code. For details, see aclnn Return Code.
aclnnInplaceLeakyReluGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (Shape) Non-contiguous Tensor selfRef (aclTensor*) Input/Output Refers to self and out in the formula. Supports empty tensors. FLOAT, FLOAT16, BFLOAT16, DOUBLE ND 0-8 √ negativeSlope (aclScalar*) Input Indicates the slope when self < 0, i.e., negativeSlope in the formula. - FLOAT - - - workspaceSize (uint64_t*) Output Returns the workspace size to be requested on the Device side. - - - - - executor (aclOpExecutor**) Output Returns the operator executor, which contains the operator computation flow. - - - - - Atlas training products : The data type supports FLOAT, FLOAT16, and DOUBLE.
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 negativeSlope is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of selfRef is not within the supported range.
aclnnInplaceLeakyRelu
Parameters:
Parameter Input/Output Description workspace Input Address of the workspace memory applied on the Device side. workspaceSize Input Size of the workspace applied on the Device side, obtained through the first-phase API aclnnInplaceLeakyReluGetWorkspaceSize. executor Input Operator executor, which contains the operator computation flow. stream Input Specifies the stream for task execution. Return value
aclnnStatus: return code. For details, see aclnn Return Code.
Constraints
Deterministic computation:
- aclnnLeakyRelu&aclnnInplaceLeakyRelu default to a deterministic implementation.
If negativeSlope uses an integer type as the attribute input while the input self is of the FLOAT type, precision loss may occur when negativeSlope is greater than 2^24 or less than -2^24. Similarly, if the input self is of the FLOAT16 type, precision loss may occur when negativeSlope is greater than 2^11 or less than -2^11.
Example
The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.
aclnnLeakyRelu sample code:
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_leaky_relu.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** tensor) {
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 the strides of a contiguous tensor.
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.
*tensor = 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. See the ACL API.
// Set 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 the input and output. Customize the construction based on the API.
std::vector<int64_t> selfShape = {4};
std::vector<int64_t> outShape = {4};
void* selfDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* self = nullptr;
aclScalar* negativeSlope = nullptr;
aclTensor* out = nullptr;
std::vector<float> selfHostData = {1, 2, 3, 4};
std::vector<float> outHostData = {0, 0, 0, 0};
float negativeSlopeValue = 0.01f;
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the negativeSlope aclScalar.
negativeSlope = aclCreateScalar(&negativeSlopeValue, aclDataType::ACL_FLOAT);
CHECK_RET(negativeSlope != nullptr, LOG_PRINT("negativeSlope is null!\n"); return false);
// 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 actual API name.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnLeakyRelu.
ret = aclnnLeakyReluGetWorkspaceSize(self, negativeSlope, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnLeakyReluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// Allocate 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 aclnnLeakyRelu.
ret = aclnnLeakyRelu(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnLeakyRelu failed. ERROR: %d\n", ret); return ret);
// 4. (Boilerplate) Synchronously 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 memory to the host memory. 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(negativeSlope);
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;
}aclnnInplaceLeakyRelu sample code:
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_leaky_relu.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** tensor) {
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 the strides of the contiguous tensor.
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.
*tensor = 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. See the acl API.
// Set 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.
std::vector<int64_t> selfRefShape = {4};
void* selfRefDeviceAddr = nullptr;
aclTensor* selfRef = nullptr;
aclScalar* negativeSlope = nullptr;
std::vector<float> selfRefHostData = {1, 2, 3, 4};
float negativeSlopeValue = 0.01f;
// Create the selfRef aclTensor.
ret = CreateAclTensor(selfRefHostData, selfRefShape, &selfRefDeviceAddr, aclDataType::ACL_FLOAT, &selfRef);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the negativeSlope aclScalar.
negativeSlope = aclCreateScalar(&negativeSlopeValue, aclDataType::ACL_FLOAT);
CHECK_RET(negativeSlope != nullptr, LOG_PRINT("negativeSlope is null!\n"); return false);
// 3. Call the CANN operator library API. Replace with the actual API name.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnInplaceLeakyRelu.
ret = aclnnInplaceLeakyReluGetWorkspaceSize(selfRef, negativeSlope, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceLeakyReluGetWorkspaceSize 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 aclnnInplaceLeakyRelu.
ret = aclnnInplaceLeakyRelu(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceLeakyRelu failed. ERROR: %d\n", ret); return ret);
// 4. (Boilerplate) Synchronously wait for the 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 memory to the host memory. 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(negativeSlope);
// 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;
}