aclnnEqScalar&aclnnInplaceEqScalar

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: Compares the values of elements in self with those in other, and writes the comparison result to out.
Formula: $out_i = (self_i == \mathit{other} ) ? [True] : [False]$

Prototype

aclnnEqScalar and aclnnInplaceEqScalar implement the same function in different ways. Select a proper operator based on your requirements.
- aclnnEqScalar: An output tensor object needs to be created to store the computation result.
- aclnnInplaceEqScalar: 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, aclnnEqScalarGetWorkspaceSize or aclnnInplaceEqScalarGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnEqScalar or aclnnInplaceEqScalar is called to perform computation.

aclnnStatus aclnnEqScalarGetWorkspaceSize(
  const aclTensor *self, 
  const aclScalar *other, 
  aclTensor       *out, 
  uint64_t        *workspaceSize, 
  aclOpExecutor  **executor)

aclnnStatus aclnnEqScalar(
  void*             workspace, 
  uint64_t          workspaceSize, 
  aclOpExecutor*    executor, 
  const aclrtStream stream)

aclnnStatus aclnnInplaceEqScalarGetWorkspaceSize(
  const aclTensor *selfRef, 
  const aclScalar *other, 
  uint64_t        *workspaceSize, 
  aclOpExecutor  **executor)

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

aclnnEqScalarGetWorkspaceSize

Parameters

Name	Input/Output	Description	Usage Notes	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
self	Input	`self` in the formula.	-	DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT32, UINT64	ND	Must not exceed 8D.	√
other	Input	`other` in the formula.	-	DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT32, UINT64	-	-	-
out	Output	`out` in the formula.	The data type must be convertible from BOOL (see Conversion Relationship).	DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT64, UINT32, UINT16	ND	The shape must be identical to that of `self`.	√
workspaceSize	Output	Size of the workspace required to be allocated on the device.	-	-	-	-	-
executor	Output	Operator executor, containing the operator computation process.	-	-	-	-	-

Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products:
- The data types of self and other must meet the data type deduction rules (see Deduction Relationship).
- The data types of self and other cannot be UINT32 or UINT64.
- The data type of out cannot be UINT64, UINT32, or UINT16.
Atlas training products:
- The data types of self and other must meet the data type deduction rules (see Deduction Relationship).
- The data type cannot be BFLOAT16.
- The data type of out cannot be UINT64, UINT32, UINT16, or BFLOAT16.

Returns

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

The first-phase API implements input parameter validation. The following error codes may be returned.

Return	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The passed `self`, `other`, or `out` is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of `self`, `other`, or `out` is not supported.
		The data types of `self` and `other` do not meet the type deduction rules.
		The shape of `self` is different from that of `out`.
		The number of dimensions of `self` or `out` exceeds 8.

aclnnEqScalar

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 `aclnnEqScalarGetWorkspaceSize`.
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.

aclnnInplaceEqScalarGetWorkspaceSize

Parameters

Name	Input/Output	Description	Usage Notes	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
selfRef	Input/Output	`selfRef` in the formula.	-	DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT32, UINT64	ND	Must not exceed 8D.	√
other	Input	`other` in the formula.	-	DOUBLE, FLOAT16, FLOAT, BFLOAT16, INT64, INT32, INT8, UINT8, BOOL, INT16, COMPLEX64, COMPLEX128, UINT32, UINT64	-	-	-
workspaceSize	Output	Size of the workspace required to be allocated on the device.	-	-	-	-	-
executor	Output	Operator executor, containing the operator computation process.	-	-	-	-	-

Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products:
- The data types of selfRef and other must meet the data type deduction rules (see Deduction Relationship).
- The data types of selfRef and other cannot be UINT32 or UINT64.
Atlas training products:
- The data types of selfRef and other must meet the data type deduction rules (see Deduction Relationship).
- The data type cannot be BFLOAT16.

Returns

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

The first-phase API implements input parameter validation. The following error codes may be returned.

Return	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The passed `selfRef` or `other` is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of `selfRef` or `other` is not supported.
		The data types of `selfRef` and `other` do not meet the type deduction rules.
		The number of dimensions of `selfRef` exceeds 8D.

aclnnInplaceEqScalar

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 `aclnnInplaceEqScalarGetWorkspaceSize`.
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:
- aclnnEqScalar and aclnnInplaceEqScalar default to a deterministic implementation.

Examples

The following examples are for reference only. For details, see Compilation and Running Sample.

aclnnEqScalar Sample Code

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_eq_scalar.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)
{
  // (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 device memory.
  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 host data to the device memory.
  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 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;
}

aclError InitAcl(int32_t deviceId, aclrtStream* stream)
{
  auto ret = Init(deviceId, stream);
  CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
  return ACL_SUCCESS;
}

aclError CreateInputs(
    std::vector<int64_t>& selfShape, std::vector<int64_t>& outShape, void** selfDeviceAddr, void** outDeviceAddr,
    aclTensor** self, aclScalar** other, aclTensor** out)
{
  std::vector<double> selfHostData = {0, 1, 1.2, 0.3, 4.1, 5, 1.6, 7};
  std::vector<char> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  double otherValue = 1.2;

  // Create a self tensor.
  auto ret = CreateAclTensor(selfHostData, selfShape, selfDeviceAddr, aclDataType::ACL_DOUBLE, self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // Create an other scalar.
  *other = aclCreateScalar(&otherValue, aclDataType::ACL_DOUBLE);
  CHECK_RET(*other != nullptr, return ret);

  // Create an out tensor.
  ret = CreateAclTensor(outHostData, outShape, &(*outDeviceAddr), aclDataType::ACL_BOOL, out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  return ACL_SUCCESS;
}

aclError ExecOpApi(
    aclTensor* self, aclScalar* other, aclTensor* out, void** workspaceAddrOut, uint64_t& workspaceSize,
    void* outDeviceAddr, std::vector<int64_t>& outShape, aclrtStream stream)
{
  aclOpExecutor* executor;

  // Obtain the workspace size.
  auto ret = aclnnEqScalarGetWorkspaceSize(self, other, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnEqScalarGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);

  // Allocate the workspace (released in main).
  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);
  }
  *workspaceAddrOut = workspaceAddr;

  // Call the operator.
  ret = aclnnEqScalar(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnEqScalar failed. ERROR: %d\n", ret); return ret);

  // Synchronize.
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // Copy the output.
  auto size = GetShapeSize(outShape);
  std::vector<char> resultData(size, 0);

  ret = aclrtMemcpy(
      resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(char),
      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: %d\n", i, resultData[i]);
  }

  return ACL_SUCCESS;
}

int main()
{
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = InitAcl(deviceId, &stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("InitAcl failed. ERROR: %d\n", ret); return ret);

  std::vector<int64_t> selfShape = {4, 2};
  std::vector<int64_t> outShape = {4, 2};

  void* selfDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclScalar* other = nullptr;
  aclTensor* out = nullptr;

  ret = CreateInputs(selfShape, outShape, &selfDeviceAddr, &outDeviceAddr, &self, &other, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  uint64_t workspaceSize = 0;
  void* workspaceAddr = nullptr;

  ret = ExecOpApi(self, other, out, &workspaceAddr, workspaceSize, outDeviceAddr, outShape, stream);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // Release tensors and scalars.
  aclDestroyTensor(self);
  aclDestroyScalar(other);
  aclDestroyTensor(out);

  // Release device memory.
  aclrtFree(selfDeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }

  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}

aclnnInplaceEqScalar Sample Code

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_eq_scalar.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 device memory.
  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 host data to the device memory.
  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 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;
}

aclError InitAcl(int32_t deviceId, aclrtStream* stream)
{
  auto ret = Init(deviceId, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
  return ACL_SUCCESS;
}

aclError CreateInputs(std::vector<int64_t>& selfShape, void** selfDeviceAddr, aclTensor** self, aclScalar** other)
{
  std::vector<double> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  double otherValue = 2.0;

  // Create a self aclTensor.
  auto ret = CreateAclTensor(selfHostData, selfShape, selfDeviceAddr, aclDataType::ACL_DOUBLE, self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // Create an other aclScalar.
  *other = aclCreateScalar(&otherValue, aclDataType::ACL_DOUBLE);
  CHECK_RET(*other != nullptr, return ACL_ERROR_INVALID_PARAM);

  return ACL_SUCCESS;
}

aclError ExecOpApi(
    aclTensor* self, aclScalar* other, void* selfDeviceAddr, std::vector<int64_t>& selfShape, aclrtStream stream,
    void** workspaceAddrOut)
{
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;

  auto ret = aclnnInplaceEqScalarGetWorkspaceSize(self, other, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceEqScalarGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);

  // Allocate workspace.
  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);
  }

  *workspaceAddrOut = workspaceAddr;

  // Execute.
  ret = aclnnInplaceEqScalar(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceEqScalar failed. ERROR: %d\n", ret); return ret);

  // Synchronize.
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // Copy the output.
  auto size = GetShapeSize(selfShape);
  std::vector<double> resultData(size);

  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: %lf\n", i, resultData[i]);
  }

  return ACL_SUCCESS;
}

int main()
{
  int32_t deviceId = 0;
  aclrtStream stream;

  auto ret = InitAcl(deviceId, &stream);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  std::vector<int64_t> selfShape = {4, 2};
  void* selfDeviceAddr = nullptr;

  aclTensor* self = nullptr;
  aclScalar* other = nullptr;

  ret = CreateInputs(selfShape, &selfDeviceAddr, &self, &other);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  void* workspaceAddr = nullptr;
  ret = ExecOpApi(self, other, selfDeviceAddr, selfShape, stream, &workspaceAddr);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // Destroy.
  aclDestroyTensor(self);
  aclDestroyScalar(other);

  aclrtFree(selfDeviceAddr);
  if (workspaceAddr != nullptr) {
    aclrtFree(workspaceAddr);
  }
  // Release.
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}