aclnnSign

Applicable Products

Product	Supported
Ascend 950PR/Ascend 950DT	√
Atlas A3 training products/Atlas A3 inference products	√
Atlas A2 training products/Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	×
Atlas inference products	×
Atlas training products	×

Function

Operator function: Performs the Sign function element-wise on the input tensor and outputs the result tensor.
Formula:
$resultput_i = \left\{ \begin{aligned} 1,\quad input_i > 0\\ 0,\quad input_i = 0\\ -1,\quad input_i < 0 \end{aligned} \right.$
Formula (BOOL type case):
$resultput_i = \left\{ \begin{aligned} \text{True},\quad input_i = \text{True}\\ \text{False},\quad input_i = \text{False}\\ \end{aligned} \right.$
Formula (complex number case, where real and imag denote the real part and imaginary part respectively):
$resultput_i = \alpha \cdot cos(\theta_i) + \beta \cdot sin(\theta_i) \\ \alpha = \left\{ \begin{aligned} 1,\quad real(input_i) > 0 \\ 0,\quad real(input_i) = 0 \\ -1,\quad real(input_i) < 0 \\ \end{aligned} \right. \\ \beta = \left\{ \begin{aligned} 1,\quad imag(input_i) > 0 \\ 0,\quad imag(input_i) = 0 \\ -1,\quad imag(input_i) < 0 \\ \end{aligned} \right. \\ \theta_i = arctan(\frac{imag(input_i)}{real(input_i)})$

Function Prototype

Each operator is divided into a two-phase API. The "aclnnSignGetWorkspaceSize" API must be called first to obtain the workspace size required for computation and the executor that encapsulates the operator computation flow. Then, the "aclnnSign" API is called to perform the computation.

aclnnStatus aclnnSignGetWorkspaceSize(const aclTensor *self, aclTensor *result, uint64_t *workspaceSize, aclOpExecutor **executor)
aclnnStatus aclnnSign(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

aclnnSignGetWorkspaceSize

Parameters:
- self(const aclTensor, computation input): A device-side tensor, supporting 1 to 8 dimensions. Non-contiguous tensors are supported, and the Data Format supports ND.
  - Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The supported data types include DOUBLE, FLOAT, FLOAT16, INT32, INT64, COMPLEX64, COMPLEX128, BOOL, and BFLOAT16.
- result(aclTensor, computation output): A device-side tensor, supporting 1 to 8 dimensions. Non-contiguous tensors are supported, and the Data Format supports ND. The type and shape must be consistent with self, and the dtype must be consistent with self.
  - Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The supported data types include DOUBLE, FLOAT, FLOAT16, INT32, INT64, COMPLEX64, COMPLEX128, BOOL, and BFLOAT16.
  - Ascend 950PR/Ascend 950DT: Supported data types include DOUBLE, FLOAT, FLOAT16, INT32, INT64, COMPLEX64, COMPLEX128, BOOL, and BFLOAT16.
- workspaceSize (uint64_t*, output parameter): Returns the workspace size to be allocated on the Device side.
- executor (aclOpExecutor**, output parameter): Returns the op executor, which includes computation.

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 or result is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type and data format of self and result are not within the supported range.
		The shapes of self and result do not match.
		The types of self and result do not match.

aclnnSign

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 by the first-phase API aclnnSignGetWorkspaceSize.
executor	Input	Operator executor that contains the operator computation flow.
stream	Input	Specifies the stream that executes the task.

Return value

aclnnStatus: return code. For details, see aclnn Return Codes.

Constraints

Deterministic computation:
- aclnnSign 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_sign.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 shape_size = 1;
    for (auto i: shape) {
        shape_size *= i;
    }
    return shape_size;
}

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 allocate 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 strides for 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 device/stream. See the ACL API manual.
  // Enter the actual device ID.
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  // Handle the check as needed.
  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> selfShape = {4, 2};
  std::vector<int64_t> outShape = {4, 2};
  void* selfDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> selfHostData = {1, 2, 3, 4, 5, 6, 7,8};
  std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, 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. Modify the API name as needed.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnSign.
  ret = aclnnSignGetWorkspaceSize(self, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSignGetWorkspaceSize 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 aclnnSign.
  ret = aclnnSign(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSign 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. 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. Modify based on the specific API definition.
  aclDestroyTensor(self);
  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;
}