aclnnRemainderTensorScalar&aclnnInplaceRemainderTensorScalar

Supported Products

Product	Supported
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: Returns the remainder of each element in tensor self divided by scalar other. The sign of the result is the same as that of the divisor other, and the absolute value of the result is less than that of other.
The actual computation of remainder(self, other) is equivalent to the following formula:
$out_i = self_i - floor(self_i / other) * other$

Example:

self = tensor([[-1, -2],
               [-3, -4]]).type(int32)
other = 3.5   # float
result = remainder(self, other)

# Value of result
# tensor([[2.5000, 1.5000],
#         [0.5000, 3.0000]])   float

# For -1 in self, the computation result is -1 - floor(-1 / 3.5) * 3.5 = 2.5.
# The absolute value of the final result 2.5 is less than that of other, which is 3.5.

Prototype

aclnnRemainderTensorScalar and aclnnInplaceRemainderTensorScalar implement the same function in different ways. Select a proper operator based on your requirements.
- aclnnRemainderTensorScalar: An output tensor object needs to be created to store the computation result.
- aclnnInplaceRemainderTensorScalar: 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, aclnnRemainderTensorScalarGetWorkspaceSize or aclnnInplaceRemainderTensorScalarGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnRemainderTensorScalar or aclnnInplaceRemainderTensorScalar is called to perform computation.
- aclnnStatus aclnnRemainderTensorScalarGetWorkspaceSize(const aclTensor *self, const aclScalar *other, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
- aclnnStatus aclnnRemainderTensorScalar(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
- aclnnStatus aclnnInplaceRemainderTensorScalarGetWorkspaceSize(aclTensor *selfRef, const aclScalar *other, uint64_t *workspaceSize, aclOpExecutor **executor)
- aclnnStatus aclnnInplaceRemainderTensorScalar(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

aclnnRemainderTensorScalarGetWorkspaceSize

Parameters:

self (aclTensor*, computation input): input self in the formula. It supports non-contiguous tensors. Its data format can be ND, and the data dimensions cannot exceed 8.
- Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16. Its data type and the data type of other must meet the data type deduction rules (see Deduction Relationship).
- Atlas training products: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE. Its data type and the data type of other must meet the data type deduction rules (see Deduction Relationship).
- other (aclScalar*, computation input): input other in the formula.
  - Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16. Its data type and the data type of self must meet the data type deduction rules (see Deduction Relationship).
  - Atlas training products: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE. Its data type and the data type of self must meet the data type deduction rules (see Deduction Relationship).
- out (aclTensor*, computation output): output out in the formula. The data type must be convertible to that after deduction between self and other. The shape must be the same as that of self. Non-contiguous tensors are supported. The data format can be ND. The data dimensions cannot be greater than 8.
  - Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be UINT8, INT8, INT16, INT32, INT64, FLOAT16, FLOAT, DOUBLE, COMPLEX64, COMPLEX128, or BFLOAT16.
  - Atlas training products: The data type can be UINT8, INT8, INT16, INT32, INT64, FLOAT16, FLOAT, DOUBLE, COMPLEX64, or COMPLEX128.
- 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 passed self, other, or out is a null pointer.
 161002 (ACLNN_ERR_PARAM_INVALID): 1. The shapes of self and out are inconsistent.
                                   2. Data type deduction cannot be performed for self and other.
                                   3. The data type deduced from self and other is not supported.
                                   4. The data type deduced from self and other cannot be converted to the data type of out.
                                   5. The dimensions of self or out are greater than 8.
                                   6. The data formats of self and out are inconsistent.

aclnnRemainderTensorScalar

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 aclnnRemainderTensorScalarGetWorkspaceSize.
- 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.

aclnnInplaceRemainderTensorScalarGetWorkspaceSize

Parameters:
- selfRef (aclTensor*, computation input|computation output): input and output tensors. Non-contiguous tensors are supported. The data format can be ND. The data dimensions cannot be greater than 8.
  - Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16. Its data type and the data type of other must meet the data type deduction rules (see Deduction Relationship) and must be convertible after deduction.
  - Atlas training products: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE. Its data type and the data type of other must meet the data type deduction rules (see Deduction Relationship) and must be convertible after deduction.
- other (aclScalar*, computation input): input other in the formula.
  - Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16. Its data type and the data type of selfRef must meet the data type deduction rules (see Deduction Relationship).
  - Atlas training products: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE. Its data type and the data type of selfRef must meet the data type deduction rules (see Deduction Relationship).
- 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 passed selfRef or other is a null pointer.
 161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type cannot be deduced from selfRef and other.
                                   2. The data type deduced from selfRef and other is not supported.
                                   3. The data type deduced from selfRef and other cannot be converted to the data type of selfRef.
                                   4. The dimensions of selfRef are greater than 8.

aclnnInplaceRemainderTensorScalar

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 aclnnInplaceRemainderTensorScalarGetWorkspaceSize.
- 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.

Restrictions

Deterministic computation:
- aclnnRemainderTensorScalar&aclnnInplaceRemainderTensorScalar defaults to deterministic implementation.
- When the data type of self is INT32, the functionality and precision within the range of [–2^24, 2^24] are preferentially ensured.
- When other is 0 and the data type of self is an integer, the result of out is self.

Calling Example

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

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_remainder.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 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. (Boilerplate) Initialize the device and stream. For details, see the ACL API manual.
  // Set device ID based on the actual device.
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

  // 2. Construct the inputs and outputs based on the API definition.
  std::vector<int64_t> selfShape = {3, 3};
  std::vector<int64_t> outShape = {3, 3};
  void* selfDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclScalar* other = nullptr;
  aclTensor* out = nullptr;
  std::vector<int64_t> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7, 8};
  std::vector<int64_t> outHostData = {0, 0, 0, 0, 0, 0, 0, 0, 0};
  int64_t Other = 3;

  // Create a self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_INT64, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an other aclScalar.
  other = aclCreateScalar(&Other, aclDataType::ACL_INT64);
  CHECK_RET(other != nullptr, return ret);
  // Create an out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_INT64, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. Call the CANN operator library API. Change the API name to the actual one.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnRemainderTensorScalar.
  ret = aclnnRemainderTensorScalarGetWorkspaceSize(self, other, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnRemainderTensorScalarGetWorkspaceSize 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 aclnnRemainderTensorScalar.
  ret = aclnnRemainderTensorScalar(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnRemainderTensorScalar 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<int64_t> 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: %ld\n", i, resultData[i]);
  }

  // 6. Release aclTensors and aclScalars. Modify the code based on the API definition.
  aclDestroyTensor(self);
  aclDestroyScalar(other);
  aclDestroyTensor(out);

  // 7. Release device resources.
  aclrtFree(selfDeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}

aclnnInplaceRemainderTensorScalar sample code:

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_remainder.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 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. (Boilerplate) Initialize the device and stream. For details, see the ACL API manual.
  // Set device ID based on the actual device.
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

  // 2. Construct the inputs and outputs based on the API definition.
  std::vector<int64_t> selfRefShape = {3, 3};
  void* selfRefDeviceAddr = nullptr;
  aclTensor* selfRef = nullptr;
  aclScalar* other = nullptr;
  std::vector<int64_t> selfRefHostData = {0, 1, 2, 3, 4, 5, 6, 7, 8};
  int64_t Other = 3;

  // Create a self aclTensor.
  ret = CreateAclTensor(selfRefHostData, selfRefShape, &selfRefDeviceAddr, aclDataType::ACL_INT64, &selfRef);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an other aclScalar.
  other = aclCreateScalar(&Other, aclDataType::ACL_INT64);
  CHECK_RET(other != nullptr, return ret);

  // 3. Call the CANN operator library API. Change the API name to the actual one.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnInplaceRemainderTensorScalar.
  ret = aclnnInplaceRemainderTensorScalarGetWorkspaceSize(selfRef, other, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceRemainderTensorScalarGetWorkspaceSize 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 aclnnInplaceRemainderTensorScalar.
  ret = aclnnInplaceRemainderTensorScalar(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceRemainderTensorScalar 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(selfRefShape);
  std::vector<int64_t> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), selfRefDeviceAddr,
                    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: %ld\n", i, resultData[i]);
  }

  // 6. Release aclTensors and aclScalars. Modify the code based on the API definition.
  aclDestroyTensor(selfRef);
  aclDestroyScalar(other);

  // 7. Release device resources.
  aclrtFree(selfRefDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}