aclnnAddcmul&aclnnInplaceAddcmul
Applicable Products
| Product | Supported |
|---|---|
| √ | |
| √ | |
| √ | |
| × | |
| × | |
| √ |
Function
Description: Performs element-wise multiplication of tensor1 and tensor2, multiplies the result by the scalar value, and then performs element-wise addition with the input self/selfRef.
Formula:
Specifically, when using aclnnAddcmul, self in the formula corresponds to self in the first-phase API, and out in the formula corresponds to out in the first-phase API. When using aclnnInplaceAddcmul, self in the formula corresponds to selfRef in the first-phase API, and out in the formula corresponds to selfRef in the first-phase API.
Function Prototype
aclnnAddcmul and aclnnInplaceAddcmul implement the same functionality. The differences are as follows. Select the appropriate operator based on your actual scenario.
aclnnAddcmul: Requires creating a new output tensor object to store the computation result.
aclnnInplaceAddcmul: Does not require creating a new output tensor object. The computation result is stored directly in the memory of the input tensor.
Each operator uses a two-phase API. You must first call the "aclnnAddcmulGetWorkspaceSize" or "aclnnInplaceAddcmulGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that encapsulates the operator computation process, and then call the "aclnnAddcmul" or "aclnnInplaceAddcmul" API to perform the computation.
aclnnStatus aclnnAddcmulGetWorkspaceSize( const aclTensor* self, const aclTensor* tensor1, const aclTensor* tensor2, const aclScalar* value, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)aclnnStatus aclnnAddcmul( void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream)aclnnStatus aclnnInplaceAddcmulGetWorkspaceSize( const aclTensor* selfRef, const aclTensor* tensor1, const aclTensor* tensor2, const aclScalar* value, uint64_t* workspaceSize, aclOpExecutor** executor)aclnnStatus aclnnInplaceAddcmul( void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream)
aclnnAddcmulGetWorkspaceSize
Parameters:
Atlas training products : The BFLOAT16 data type is not supported.
Return value
aclnnStatus: return code. For details, see aclnn Return Codes.
The first-phase API performs input parameter validation and returns an error in the following scenarios:
Return Value Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 The passed self, tensor1, tensor2, value, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data types and data formats of self, tensor1, and tensor2 are not within the supported range. self, tensor1, and tensor2 do not satisfy the data type derivation rule. The data types derived from self, tensor1, and tensor2 are not within the supported range. The data type derived from self, tensor1, and tensor2 cannot be converted to the specified output type of out. The shape of self, tensor1, or tensor2 exceeds 8 dimensions. The shapes of self, tensor1, and tensor2 do not satisfy the broadcast relationship. The shape of out is inconsistent with the broadcast shape of self, tensor1, and tensor2.
aclnnAddcmul
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 aclnnAddcmulGetWorkspaceSize. executor Input Operator executor that contains the operator computation process. stream Input Specifies the stream for task execution. Return value
aclnnStatus: return code. For details, see aclnn Return Codes.
aclnnInplaceAddcmulGetWorkspaceSize
Parameters:
Atlas training products : does not support the BFLOAT16 data type.
Return value
aclnnStatus: return code. For details, see aclnn Return Codes.
The first-phase API performs input parameter validation and returns an error in the following scenarios:
Return Value Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 Indicates that selfRef, tensor1, tensor2, or value is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data types or data formats of selfRef, tensor1, and tensor2 are not within the supported range. selfRef, tensor1, and tensor2 do not satisfy the data type deduction rule. The deduced data type of selfRef, tensor1, and tensor2 is not within the supported range. The data type of selfRef after deduction with tensor1 and tensor2 cannot be converted to the specified output type of selfRef. The shape of selfRef, tensor1, or tensor2 exceeds 8 dimensions. The shapes of selfRef, tensor1, and tensor2 do not satisfy the broadcast relationship. The shape of selfRef is inconsistent with the shape after broadcasting selfRef, tensor1, and tensor2.
aclnnInplaceAddcmul
Parameters:
Parameter Input/Output Description workspace Input Workspace memory address applied for on the Device side. workspaceSize Input Workspace size applied for on the Device side, obtained through the first-phase API aclnnInplaceAddcmulGetWorkspaceSize. Executor Input Operator executor, which contains the operator computation process. stream Input Specifies the stream for task execution. Return value
aclnnStatus: return code. For details, see aclnn Return Codes.
Constraints
- Deterministic computation:
- aclnnAddcmul and aclnnInplaceAddcmul 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.
aclnnAddcmul
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_addcmul.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 for 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 allocate device 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);
// Copy data from the host to the device using aclrtMemcpy.
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];
}
// Create an aclTensor by calling aclCreateTensor.
*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 manual.
// 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 interface.
std::vector<int64_t> selfShape = {4, 2};
std::vector<int64_t> tensor1Shape = {4, 2};
std::vector<int64_t> tensor2Shape = {4, 2};
std::vector<int64_t> outShape = {4, 2};
void* selfDeviceAddr = nullptr;
void* tensor1DeviceAddr = nullptr;
void* tensor2DeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* self = nullptr;
aclTensor* tensor1 = nullptr;
aclTensor* tensor2 = nullptr;
aclScalar* value = nullptr;
aclTensor* out = nullptr;
std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
std::vector<float> tensor1HostData = {2, 2, 2, 2, 2, 2, 2, 2};
std::vector<float> tensor2HostData = {2, 2, 2, 2, 2, 2, 2, 2};
std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
float scalarValue = 1.2f;
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the tensor1 aclTensor.
ret = CreateAclTensor(tensor1HostData, tensor1Shape, &tensor1DeviceAddr, aclDataType::ACL_FLOAT, &tensor1);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the tensor2 aclTensor.
ret = CreateAclTensor(tensor2HostData, tensor2Shape, &tensor2DeviceAddr, aclDataType::ACL_FLOAT, &tensor2);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the value aclScalar.
value = aclCreateScalar(&scalarValue, aclDataType::ACL_FLOAT);
CHECK_RET(value != 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 actual API name.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnAddcmul.
ret = aclnnAddcmulGetWorkspaceSize(self, tensor1, tensor2, value, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddcmulGetWorkspaceSize 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 aclnnAddcmul.
ret = aclnnAddcmul(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddcmul 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 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 resultData from device to host failed. ERROR: %d\n", ret);
return ret);
for (int64_t i = 0; i < size; i++) {
LOG_PRINT("resultData[%ld] is: %f\n", i, resultData[i]);
}
// 6. Release aclTensor and aclScalar. Modify based on the specific API definition.
aclDestroyTensor(self);
aclDestroyTensor(tensor1);
aclDestroyTensor(tensor2);
aclDestroyTensor(out);
aclDestroyScalar(value);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(tensor1DeviceAddr);
aclrtFree(tensor2DeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}aclnnInplaceAddcmul
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_addcmul.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 the aclCreateTensor API 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 and stream. See the ACL API documentation.
// 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 interface.
std::vector<int64_t> selfShape = {4, 2};
std::vector<int64_t> tensor1Shape = {4, 2};
std::vector<int64_t> tensor2Shape = {4, 2};
void* selfDeviceAddr = nullptr;
void* tensor1DeviceAddr = nullptr;
void* tensor2DeviceAddr = nullptr;
aclTensor* self = nullptr;
aclTensor* tensor1 = nullptr;
aclTensor* tensor2 = nullptr;
aclScalar* value = nullptr;
std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
std::vector<float> tensor1HostData = {2, 2, 2, 2, 2, 2, 2, 2};
std::vector<float> tensor2HostData = {2, 2, 2, 2, 2, 2, 2, 2};
float scalarValue = 1.2f;
// Create the self aclTensor.
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the tensor1 aclTensor.
ret = CreateAclTensor(tensor1HostData, tensor1Shape, &tensor1DeviceAddr, aclDataType::ACL_FLOAT, &tensor1);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create tensor2 aclTensor.
ret = CreateAclTensor(tensor2HostData, tensor2Shape, &tensor2DeviceAddr, aclDataType::ACL_FLOAT, &tensor2);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create value aclScalar.
value = aclCreateScalar(&scalarValue, aclDataType::ACL_FLOAT);
CHECK_RET(value != 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 aclnnInplaceAddcmul.
ret = aclnnInplaceAddcmulGetWorkspaceSize(self, tensor1, tensor2, value, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAddcmulGetWorkspaceSize 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 aclnnInplaceAddcmul.
ret = aclnnInplaceAddcmul(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAddcmul failed. ERROR: %d\n", ret); return ret);
// 4. (Boilerplate) Synchronize and 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 memory to the host. Modify based on the specific API definition.
auto size = GetShapeSize(selfShape);
std::vector<float> resultData(size, 0);
ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), selfDeviceAddr,
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);
aclDestroyTensor(tensor1);
aclDestroyTensor(tensor2);
aclDestroyScalar(value);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(selfDeviceAddr);
aclrtFree(tensor1DeviceAddr);
aclrtFree(tensor2DeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}