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aclnnRoundDecimals&aclnnInplaceRoundDecimals

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Supported Products

ProductSupported
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

Description: Rounds the elements of the input tensor to the specified number of decimal places.

Prototype

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

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

    • aclnnStatus aclnnRoundDecimalsGetWorkspaceSize(const aclTensor* self, int64_t decimals, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)

    • aclnnStatus aclnnRoundDecimals(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

    • aclnnStatus aclnnInplaceRoundDecimalsGetWorkspaceSize(aclTensor* selfRef, int64_t decimals, uint64_t* workspaceSize, aclOpExecutor** executor)

    • aclnnStatus aclnnInplaceRoundDecimals(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

aclnnRoundDecimalsGetWorkspaceSize

  • Parameters:

    • self (aclTensor*, compute input): input tensor, aclTensor on the device. Non-contiguous tensors are supported. The data format supports ND. The number of dimensions cannot exceed 8, and the shape must be the same as that of out.

      • Atlas training products: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, or INT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, BFLOAT16, FLOAT16, DOUBLE, INT32, or INT64.

    • decimals (int64_t, compute input): number of decimal places to round.

    • out (aclTensor *, compute output): output tensor, aclTensor on the device. Non-contiguous tensors are supported. The data format supports ND. The number of dimensions cannot exceed 8, and the shape must be the same as that of self.

      • Atlas training products: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, or INT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, BFLOAT16, FLOAT16, DOUBLE, INT32, or INT64.

    • workspaceSize (uint64_t *, output): size of the workspace 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 input self or out is a null pointer.
161002 ACLNN_ERR_PARAM_INVALID: 1. The data type of self or out is not supported.
                         2. The data types of self and out are inconsistent.
                         3. The shapes of self and out are inconsistent.
                         4. The dimensions of self or out are greater than 8.

aclnnRoundDecimals

  • Parameters:

    • workspace (void *, input): address of the workspace memory allocated on the device.

    • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling aclnnRoundDecimalsGetWorkspaceSize.

    • 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:

    • aclnnRoundDecimals&aclnnInplaceRoundDecimals defaults to a deterministic implementation.

  • When decimals is not 0: If the input data exceeds the range of (–347000, 347000), the precision may be affected.

aclnnInplaceRoundDecimalsGetWorkspaceSize

  • Parameters:

    • selfRef (aclTensor*, compute input): input tensor, aclTensor on the device. Non-contiguous tensors are supported. The data format supports ND. The number of dimensions cannot exceed 8.

      • Atlas training products: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, or INT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, BFLOAT16, FLOAT16, DOUBLE, INT32, or INT64.

    • decimals (int64_t, compute input): number of decimal places to round.

    • workspaceSize (uint64_t *, output): size of the workspace 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 input selfRef is a null pointer.
161002 ACLNN_ERR_PARAM_INVALID: 1. The data type of selfRef is not supported.
                         2. The dimensions of selfRef are greater than 8.

aclnnInplaceRoundDecimals

  • Parameters:

    • workspace (void *, input): address of the workspace memory allocated on the device.

    • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling aclnnInplaceRoundDecimalsGetWorkspaceSize.

    • 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

  • When decimals is not 0: If the input data exceeds the range of (–347000, 347000), the precision may be affected.

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_round.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) 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 from the host to 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. (Boilerplate) Initialize the device and stream. For details, see the ACL API manual.
  // Set deviceId based on the actual device.
  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 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<double> selfHostData = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8};
  std::vector<double> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  int decimals = 0;

  // Create a self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_DOUBLE, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_DOUBLE, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  LOG_PRINT("Example of aclnnRoundDecimals API call \n");
  // 3. Call the CANN operator library API, which needs to be replaced with the actual API.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnRoundDecimals.
  ret = aclnnRoundDecimalsGetWorkspaceSize(self, decimals, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnRoundDecimalsGetWorkspaceSize 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 aclnnRoundDecimals.
  ret = aclnnRoundDecimals(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnRoundDecimals failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) 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 to the host. Modify the code based on the API definition.
  auto size = GetShapeSize(outShape);
  std::vector<double> 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]);
  }

  LOG_PRINT("Example of aclnnInplaceRoundDecimals API call \n");
  uint64_t inplaceWorkspaceSize = 0;
  aclOpExecutor* inplaceExecutor;
  // Call the first-phase API of aclnnInplaceRoundDecimals.
  ret = aclnnInplaceRoundDecimalsGetWorkspaceSize(self, decimals, &inplaceWorkspaceSize, &inplaceExecutor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceRoundDecimalsGetWorkspaceSize 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 aclnnInplaceRoundDecimals.
  ret = aclnnInplaceRoundDecimals(inplaceWorkspaceAddr, inplaceWorkspaceSize, inplaceExecutor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceRoundDecimals failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) 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 to the host. Modify the code 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 the aclTensor. Modify the code based on the API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(out);

  // 7. Release device resources.
  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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