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aclnnHardswish&aclnnInplaceHardswish

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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: Activation function. The output tensor has the same shape as the input tensor. If the input value is less than –3, the output is 0. If the input value is greater than 3, the output is the value. Otherwise, the output is (value + 3) × value/6.
  • Formula:Hardswish(x)={x,x>30,x≤−3x⋅(x+3)6,otherwiseHardswish(x)=\begin{cases} x, & x\gt3 \\ 0, & x\le -3 \\ \frac{x · (x + 3)}{6}, & otherwise \end{cases}

Prototype

  • aclnnHardswish and aclnnInplaceHardswish implement the same function in different ways. Select a proper operator based on your requirements.

    • aclnnHardswish: An output tensor object needs to be created to store the computation result.
    • aclnnInplaceHardswish: 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, aclnnHardswishGetWorkspaceSize or aclnnInplaceHardswishGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnHardswish or aclnnInplaceHardswish is called to perform computation.

aclnnStatus aclnnHardswishGetWorkspaceSize(
  const aclTensor* self,
  const aclTensor* out,
  uint64_t*        workspaceSize,
  aclOpExecutor**  executor)
aclnnStatus aclnnHardswish(
  void*             workspace,
  uint64_t          workspaceSize,
  aclOpExecutor*    executor,
  const aclrtStream stream)
aclnnStatus aclnnInplaceHardswishGetWorkspaceSize(
  const aclTensor* self,
  uint64_t*        workspaceSize,
  aclOpExecutor**  executor)
aclnnStatus aclnnInplaceHardswish(
  void*             workspace,
  uint64_t          workspaceSize,
  aclOpExecutor*    executor,
  const aclrtStream stream)

aclnnHardswishGetWorkspaceSize

  • Parameters:

    Name Input/Output Description Precaution Data Type Data Format Dimension (Shape) Non-contiguous Tensor
    self Input Input tensor of the activation function, corresponding to x in the formula. BFLOAT16, FLOAT16, FLOAT32 ND 0–8 √
    out Output Output tensor of the activation function, corresponding to Hardswish in the formula. The data type of out must be convertible to that of self (see Conversion Relationship). BFLOAT16, FLOAT16, FLOAT32 ND 0–8 √
    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 FLOAT16 or FLOAT32.

  • 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 or format of self or out is not supported.
    The shapes of self and out are inconsistent.
    The data types of self and out do not meet the conversion rule.

aclnnHardswish

  • 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 aclnnHardswishGetWorkspaceSize.
    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.

aclnnInplaceHardswishGetWorkspaceSize

  • Parameters:

    Name Input/Output Description Precaution Data Type Data Format Dimension (Shape) Non-contiguous Tensor
    self Input Input/Output tensor of the activation function, corresponding to x and the output result in the formula. Empty tensors are supported. BFLOAT16, FLOAT16, FLOAT32 ND 0–8 √
    workspaceSize Output Size of the workspace required to be allocated on the device. - - - - -
    executor Output Operator executor, containing the operator computation process. - - - - -

  • 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 is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data type or format of self is not supported.

aclnnInplaceHardswish

  • 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 aclnnInplaceHardswishGetWorkspaceSize.
    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 computation:
    • aclnnHardswish and aclnnInplaceHardswish 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_hardswish.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 == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
  // 2. Construct inputs and outputs based on the API definition.
  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 = {-4, -3, -2, 0, 1, 2, 4, 5};
  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);

  // aclnnHardswish API call example
  // 3. Call the CANN operator library API.
  // Call the first-phase API of aclnnHardswish.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  ret = aclnnHardswishGetWorkspaceSize(self, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnHardswishGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on workspaceSize computed 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 aclnnHardswish.
  ret = aclnnHardswish(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnHardswish 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 code 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(float),
                    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]);
  }
 
  // aclnnInplaceHardswish API call example
  // 3. Call the CANN operator library API.
  LOG_PRINT("\ntest aclnnInplaceHardswish\n");
  // Call the first-phase API of aclnnInplaceHardswish.
  uint64_t inplaceWorkspaceSize = 0;
  aclOpExecutor* inplaceExecutor;
  ret = aclnnInplaceHardswishGetWorkspaceSize(self, &inplaceWorkspaceSize, &inplaceExecutor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceHardswishGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on workspaceSize computed by the first-phase API.
  void* inplaceWorkspaceAddr = nullptr;
  if (inplaceWorkspaceSize > 0) {
    ret = aclrtMalloc(&inplaceWorkspaceAddr, inplaceWorkspaceSize, 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 aclnnInplaceHardswish.
  ret = aclnnInplaceHardswish(inplaceWorkspaceAddr, inplaceWorkspaceSize, inplaceExecutor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceHardswish 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 code based on the API definition.
  size = GetShapeSize(outShape);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), selfDeviceAddr, size * sizeof(float),
                    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 the code based on the API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(out);
 
  // 7. Release device resources. Modify the code based on the API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  if (inplaceWorkspaceSize > 0) {
    aclrtFree(inplaceWorkspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}
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