aclnnForeachAbs
Applicable Products
| Product | Supported |
|---|---|
| × | |
| √ | |
| √ | |
| × | |
| × | |
| × |
Function
Description: Performs element-wise absolute value operation on each tensor in the input tensor list.
Formula:
Function Prototype
Each operator is divided into a two-phase API. You must first call the "aclnnForeachAbsGetWorkspaceSize" API to obtain the input parameters and calculate the required workspace size based on the computation process, and then call the "aclnnForeachAbs" API to perform the computation.
aclnnStatus aclnnForeachAbsGetWorkspaceSize(
const aclTensorList *x,
const aclTensorList *out,
uint64_t *workspaceSize,
aclOpExecutor **executor)aclnnStatus aclnnForeachAbs(
void *workspace,
uint64_t workspaceSize,
aclOpExecutor *executor,
aclrtStream stream)aclnnForeachAbsGetWorkspaceSize
Parameters
Parameter Input/Output Description Instruction Data Type Data Format Shape Non-contiguous Tensor x (aclTensorList*) Input List of input tensors for absolute value computation, corresponding to `x` in the formula. - Empty tensors are supported.
- All tensors in this parameter must have the same data type.
BFLOAT16, FLOAT16, FLOAT32 ND 0-8 √ out (aclTensorList*) Output Represents the output tensor list for absolute value computation, corresponding to `y` in the formula. - Supports empty tensors.
- All tensors in this parameter must have the same data type.
- The data type and data format are consistent with those of the input `x`, and the shape size is greater than or equal to that of the input `x`.
BFLOAT16, FLOAT16, FLOAT32 ND 0-8 √ workspaceSize (uint64_t*) Output Returns the workspace size to be allocated on the Device side. - - - - - executor (aclOpExecutor**) output Returns the operator executor, which contains the operator computation process. - - - - - 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 x or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of x or out is not within the supported range. The data types of x and out are inconsistent. ACLNN_ERR_INNER_TILING_ERROR 561002 The shapes of x and out do not satisfy the constraints. The data types of tensors in x or out are inconsistent. The number of dimensions of a tensor in x or out exceeds 8.
aclnnForeachAbs
Parameters
Parameter Input/Output Description workspace Input The workspace memory address applied for on the Device side. workspaceSize Input The workspace size applied for on the Device side, obtained through the first-phase API aclnnForeachAbsGetWorkspaceSize. executor Input Operator executor that 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:
- aclnnForeachAbs defaults to a deterministic implementation.
Example
The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_foreach_abs.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 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);
// Copy the host-side data to the device-side memory by calling 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. Custom construction is required based on the API interface.
std::vector<int64_t> selfShape1 = {2, 3};
std::vector<int64_t> selfShape2 = {1, 3};
std::vector<int64_t> outShape1 = {2, 3};
std::vector<int64_t> outShape2 = {1, 3};
void* input1DeviceAddr = nullptr;
void* input2DeviceAddr = nullptr;
void* out1DeviceAddr = nullptr;
void* out2DeviceAddr = nullptr;
aclTensor* input1 = nullptr;
aclTensor* input2 = nullptr;
aclTensor* out1 = nullptr;
aclTensor* out2 = nullptr;
std::vector<float> input1HostData = {1, 2, 3, 4, 5, 6};
std::vector<float> input2HostData = {7, 8, 9};
std::vector<float> out1HostData(6, 0);
std::vector<float> out2HostData(3, 0);
// Create input1 aclTensor.
ret = CreateAclTensor(input1HostData, selfShape1, &input1DeviceAddr, aclDataType::ACL_FLOAT, &input1);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create input2 aclTensor.
ret = CreateAclTensor(input2HostData, selfShape2, &input2DeviceAddr, aclDataType::ACL_FLOAT, &input2);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create out1 aclTensor.
ret = CreateAclTensor(out1HostData, outShape1, &out1DeviceAddr, aclDataType::ACL_FLOAT, &out1);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create out2 aclTensor.
ret = CreateAclTensor(out2HostData, outShape2, &out2DeviceAddr, aclDataType::ACL_FLOAT, &out2);
CHECK_RET(ret == ACL_SUCCESS, return ret);
std::vector<aclTensor*> tempInput{input1, input2};
aclTensorList* tensorListInput = aclCreateTensorList(tempInput.data(), tempInput.size());
std::vector<aclTensor*> tempOutput{out1, out2};
aclTensorList* tensorListOutput = aclCreateTensorList(tempOutput.data(), tempOutput.size());
// 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 aclnnForeachAbs.
ret = aclnnForeachAbsGetWorkspaceSize(tensorListInput, tensorListOutput, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachAbsGetWorkspaceSize 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 aclnnForeachAbs.
ret = aclnnForeachAbs(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachAbs 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(outShape1);
std::vector<float> out1Data(size, 0);
ret = aclrtMemcpy(out1Data.data(), out1Data.size() * sizeof(out1Data[0]), out1DeviceAddr,
size * sizeof(out1Data[0]), ACL_MEMCPY_DEVICE_TO_HOST);
for (int64_t i = 0; i < size; i++) {
LOG_PRINT("out1 result[%ld] is: %f\n", i, out1Data[i]);
}
size = GetShapeSize(outShape2);
std::vector<float> out2Data(size, 0);
ret = aclrtMemcpy(out2Data.data(), out2Data.size() * sizeof(out2Data[0]), out2DeviceAddr,
size * sizeof(out2Data[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("out2 result[%ld] is: %f\n", i, out2Data[i]);
}
// 6. Release aclTensor. Modify based on the specific API definition.
aclDestroyTensorList(tensorListInput);
aclDestroyTensorList(tensorListOutput);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(input1DeviceAddr);
aclrtFree(input2DeviceAddr);
aclrtFree(out1DeviceAddr);
aclrtFree(out2DeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}