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aclnnSelu&aclnnInplaceSelu

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Supported Products

ProductSupported
Atlas A3 training series products/Atlas A3 inference series products√
Atlas A2 training products/Atlas A2 inference products√
Atlas 200I/500 A2 inference products×
Atlas inference series products×
Atlas training series products√

Function

  • Description: Computes the Selu sign function element-wise on the input tensor and outputs the result tensor.
  • Formula:outputi=scale⋅[max⁡(0,inputi)+min⁡(0,α⋅exp⁡(inputi−1))]α=1.6732632423543772848170429916717scale=1.0507009873554804934193349852946output_i = scale \cdot [\max(0, input_i) + \min(0, \alpha \cdot\exp(input_i - 1))] \\ \alpha = 1.6732632423543772848170429916717 \\ scale = 1.0507009873554804934193349852946

Prototype

  • aclnnSelu and aclnnInplaceSelu implement the same function in different ways. Select a proper operator based on your requirements.
    • aclnnSelu: An output tensor object needs to be created to store the computation result.
    • aclnnInplaceSelu: 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, aclnnSeluGetWorkspaceSize or aclnnInplaceSeluGetWorkspaceSize is called to obtain input parameters and compute the required workspace size based on the computation process. Then, aclnnSelu or aclnnInplaceSelu is called to perform computation.
aclnnStatus aclnnSeluGetWorkspaceSize(
  const aclTensor    *self,
  aclTensor          *out,
  uint64_t           *workspaceSize,
  aclOpExecutor     **executor)
aclnnStatus aclnnSelu(
  void              *workspace,
  uint64_t           workspaceSize,
  aclOpExecutor     *executor,
  aclrtStream        stream)
aclnnStatus aclnnInplaceSeluGetWorkspaceSize(
  aclTensor         *selfRef,
  uint64_t          *workspaceSize,
  aclOpExecutor    **executor)
aclnnStatus aclnnInplaceSelu(
  void             *workspace,
  uint64_t          workspaceSize,
  aclOpExecutor    *executor,
  aclrtStream       stream)

aclnnSeluGetWorkspaceSize

  • Parameters:

    Name Input/Output Description Usage Notes Data Type Data Format Dimension (Shape) Non-contiguous Tensor
    self Input Input for the Selu sign function computation, corresponding to input_i in the formula.
    • Empty tensors are supported.
    • The shape must be the same as that of out.
    		 FLOAT, FLOAT16, INT32, INT8, BFLOAT16 ND 1–8 √
    out Output Output of the Selu sign function computation, corresponding to output_i in the formula. The shape must be the same as that of self. FLOAT, FLOAT16, INT32, INT8, BFLOAT16 ND 1–8 √
    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, FLOAT16, INT32, or INT8.

  • 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 or out is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data type of self or out is not supported.
    The shapes of self and out do not match.
    The types of self and out do not match.

aclnnSelu

  • 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 aclnnSeluGetWorkspaceSize.
    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.

aclnnInplaceSeluGetWorkspaceSize

  • Parameters:

    Name Input/Output Description Usage Notes Data Type Data Format Dimension (Shape) Non-contiguous Tensor
    selfRef Input Input for the Selu sign function computation, corresponding to input_i in the formula. Empty tensors are supported. FLOAT, FLOAT16, INT32, INT8, BFLOAT16 ND 1–8 √
    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, FLOAT16, INT32, or INT8.

  • 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 is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data type of selfRef is not supported.

aclnnInplaceSelu

  • 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 aclnnInplaceSeluGetWorkspaceSize.
    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:
    • aclnnSelu and aclnnInplaceSelu default to a deterministic implementation.

Example

The following example is for reference only. For details, see Compilation and Running Sample.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_selu.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) {
  // (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** tensor) {
  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 on 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 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. (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);
  // Handle the check as required.
  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 = {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 = {0, 2, 3, 4, 5, 6, 7, 7};
  std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  // Create a self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
    
  // aclnnSelu API call example
  // 3. Call the first-phase API of aclnnSelu.
  ret = aclnnSeluGetWorkspaceSize(self, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSeluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on the computed workspaceSize.
  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 aclnnSelu.
  ret = aclnnSelu(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSelu 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]);
  }

  // aclnnInplaceSelu API call example
  // 3. Call the first-phase API of aclnnInplaceSelu.
  ret = aclnnInplaceSeluGetWorkspaceSize(self, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceSeluGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on the computed workspaceSize.
  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 aclnnInplaceSelu.
  ret = aclnnInplaceSelu(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceSelu 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.
  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. Modify the configuration based on the API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(out);

  // 7. Release device resources. Set the parameters based on the API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}
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