aclnnUnique

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Supported Products

Function

Description: Returns the unique elements of the input tensor.

Prototype

Each operator has two-phase API calls. First, aclnnUniqueGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUnique is called to perform computation.

• aclnnStatus aclnnUniqueGetWorkspaceSize(const aclTensor* self, bool sorted, bool returnInverse, aclTensor* valueOut, aclTensor* inverseOut, uint64_t* workspaceSize, aclOpExecutor** executor)
• aclnnStatus aclnnUnique(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream)

aclnnUniqueGetWorkspaceSize

• Parameters:

  • self (aclTensor*, compute input): aclTensor on the device. The data format can be ND, and the shape cannot be greater than eight-dimensional.

    • Atlas training series products: The data type can be BOOL, FLOAT, FLOAT16, DOUBLE, UINT8, INT8, UINT16, INT16, INT32, UINT32, UINT64, or INT64.
    • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training series products/Atlas A3 inference series products: The data type can be BOOL, FLOAT, FLOAT16, DOUBLE, UINT8, INT8, UINT16, INT16, INT32, UINT32, UINT64, INT64, or BFLOAT16.

  • sorted (bool, compute input): whether to sort valueOut in ascending order. The value true indicates sorting in ascending order, and the value false indicates no sorting.

  • returnInverse (bool, compute input): whether to return the indexes of each input element in valueOut.

  • valueOut (aclTensor*, compute output): aclTensor on the device, the first output tensor, unique elements in the input tensor. The data format can be ND.

    • Atlas training series products: The data type can be BOOL, FLOAT, FLOAT16, DOUBLE, UINT8, INT8, UINT16, INT16, INT32, UINT32, UINT64, or INT64.
    • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training series products/Atlas A3 inference series products: The data type can be BOOL, FLOAT, FLOAT16, DOUBLE, UINT8, INT8, UINT16, INT16, INT32, UINT32, UINT64, INT64, or BFLOAT16.

  • inverseOut (aclTensor*, compute output): the second output tensor. This parameter is valid when returnInverse is True. It returns the indexes of self elements in valueOut. The data type can be INT64, and the shape must be the same as that of self.

  • workspaceSize (uint64_t*, output): size of the workspace required to be allocated on the device.

  • executor (aclOpExecutor**, 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:
  161001(ACLNN_ERR_PARAM_NULLPTR): 1. The passed self, valueOut, or inverseOut is a null pointer.
  161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type of self or valueOut is not supported.
                                      2. self is a non-contiguous tensor.
                                      3. The value of returnInverse is True, and the shapes of inverseOut and self are inconsistent.
                                      4. The value of returnInverse is True, and the data type of inverseOut is not INT64.

aclnnUnique

• Parameters:

  • workspace (void*, input): address of the workspace to be allocated on the device.
  • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by the first-phase API aclnnUniqueGetWorkspaceSize.
  • executor (aclOpExecutor*, input): operator executor, containing the operator computation process.
  • stream (aclrtStream, input): stream for executing the task.

• Returns:

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

Constraints

• Deterministic compute:

  • aclnnUnique defaults to a deterministic implementation.

  *Atlas training series products, Atlas A2 training products/Atlas A2 inference products, and Atlas A3 training series products/Atlas A3 inference series products: If the input self contains 0, the output of the operator contains positive 0s and negative 0s, instead of only one 0.

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_unique.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 the input and output based on the API definition.
  std::vector<int64_t> selfShape = {8};
  std::vector<int64_t> valueShape = {8};
  std::vector<int64_t> inverseShape = {8};
  void* selfDeviceAddr = nullptr;
  void* valueDeviceAddr = nullptr;
  void* inverseDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* valueOut = nullptr;
  aclTensor* inverseOut = nullptr;
  std::vector<float> selfHostData = {1, 2, 3, 4, 5, 6, 7, 8};
  std::vector<float> valueHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  std::vector<int64_t> inverseHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  bool sorted = true;
  bool returnInverse = true;

  // Create a self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create a valueOut aclTensor.
  ret = CreateAclTensor(valueHostData, valueShape, &valueDeviceAddr, aclDataType::ACL_FLOAT, &valueOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an inverseOut aclTensor.
  ret = CreateAclTensor(inverseHostData, inverseShape, &inverseDeviceAddr, aclDataType::ACL_INT64, &inverseOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. Call the CANN operator library API.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnUnique.
  ret = aclnnUniqueGetWorkspaceSize(self, sorted, returnInverse, valueOut, inverseOut, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUniqueGetWorkspaceSize 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 aclnnUnique.
  ret = aclnnUnique(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUnique 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(valueShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), valueDeviceAddr, 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]);
  }
  std::vector<int64_t> resultData1(size, 0);
  ret = aclrtMemcpy(resultData1.data(), resultData1.size() * sizeof(resultData1[0]), inverseDeviceAddr, size * sizeof(int64_t),
                    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("result1[%ld] is: %f\n", i, resultData1[i]);
  }

  // 6. Release aclTensor and aclScalar. Modify the configuration based on the API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(valueOut);
  aclDestroyTensor(inverseOut);

  // 7. Release device resources. Modify the configuration based on the API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(valueDeviceAddr);
  aclrtFree(inverseDeviceAddr);
  if (workspaceSize > 0) {
      aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}