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aclnnAddcdiv&aclnnInplaceAddcdiv

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Applicable Products

ProductSupported
Ascend 950PR/Ascend 950DT√
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: Performs element-wise division of tensor1 by tensor2, multiplies the result by the scalar value, and adds it to self.

  • Formula:

    outi=selfi+value×tensor1itensor2iout_i = self_i + value \times {tensor1_i \over tensor2_i}

Function Prototype

  • aclnnAddcdiv and aclnnInplaceAddcdiv implement the same functionality. The differences are as follows. Select the appropriate operator based on your actual scenario.

    • aclnnAddcdiv: Requires creating a new output tensor object to store the computation result.

    • aclnnInplaceAddcdiv: Does not require creating a new output tensor object. The computation result is stored directly in the memory of the input tensor.

  • Each operator is divided into a two-phase API. You must first call the "aclnnAddcdivGetWorkspaceSize" or "aclnnInplaceAddcdivGetWorkspaceSize" API to obtain the input parameters and calculate the required workspace size based on the computation process, and then call the "aclnnAddcdiv" or "aclnnInplaceAddcdiv" API to perform the computation.

    aclnnStatus aclnnAddcdivGetWorkspaceSize(
  const aclTensor* self,
  const aclTensor* tensor1,
  const aclTensor* tensor2,
  const aclScalar* value,
  const aclTensor* out,
  uint64_t*        workspaceSize,
  aclOpExecutor**  executor)
    aclnnStatus aclnnAddcdiv(
  void*             workspace,
  uint64_t          workspaceSize,
  aclOpExecutor*    executor,
  const aclrtStream stream)
    aclnnStatus aclnnInplaceAddcdivGetWorkspaceSize(
  const aclTensor* selfRef,
  const aclTensor* tensor1,
  const aclTensor* tensor2,
  const aclScalar* value,
  uint64_t*        workspaceSize,
  aclOpExecutor**  executor)
    aclnnStatus aclnnInplaceAddcdiv(
  void*             workspace,
  uint64_t          workspaceSize,
  aclOpExecutor*    executor,
  const aclrtStream stream)

aclnnAddcdivGetWorkspaceSize

  • Parameters:

    Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor
    self Input self in the formula.
    • The data types of self, tensor1, and tensor2 must satisfy the data type deduction rules (see deduction relationship), and the deduced type must be among the supported input types.
    • The shapes of self, tensor1, and tensor2 must satisfy the broadcast relationship.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    tensor1 Input Input tensor1 in the formula.
    • The data types of self, tensor1, and tensor2 must satisfy the data type deduction rule (see deduction relationship), and the deduced type must be within the supported input types.
    • The shapes of self, tensor1, and tensor2 must satisfy the broadcast relationship.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    tensor2 Input Input tensor2 in the formula.
    • The data types of self, tensor1, and tensor2 must satisfy the data type deduction rules (see deduction relationship), and the deduced type must be within the supported input types.
    • The shapes of self, tensor1, and tensor2 must satisfy the broadcast relationship.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    value Input Input value in the formula. The data type must be convertible to the data type derived from self, tensor1, and tensor2 (see Conversion Relationship). BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 - - -
    out Output Output out in the formula.
    • The data type is the data type to which self, tensor1, and tensor2 can be converted after deduction (see Conversion Relationship).
    • The shape of out is the same as the shape of the tensor after self, tensor1, and tensor2 are broadcast.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    workspaceSize Output Returns the workspace size to be applied for on the Device side. - - - - -
    executor Output Returns the op executor, which contains the operator computation flow. - - - - -
    • Atlas training products: does not support the BFLOAT16 data type.

  • Return value

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

    The first-phase API performs input parameter validation and returns an error in the following scenarios:

    Return Value Error Code Description
    ACLNN_ERR_PARAM_NULLPTR 161001 Indicates that self, tensor1, tensor2, value, or out is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data types and data formats of self, tensor1, and tensor2 are not within the supported range.
    The data types of self, tensor1, and tensor2 cannot be deduced.
    The deduced data type cannot be converted to the type of the specified output out.
    The shape of self, tensor1, or tensor2 exceeds 8 dimensions.
    The shapes of self, tensor1, and tensor2 do not satisfy the broadcast deduction relationship.
    The shape of out is inconsistent with the shape after broadcasting self, tensor1, and tensor2.

aclnnAddcdiv

  • Parameters:

    Parameter Input/Output Description
    workspace Input Specifies the workspace memory address applied for on the Device side.
    workspaceSize Input Specifies the workspace size applied for on the Device side, obtained from the first-phase API aclnnAddcdivGetWorkspaceSize.
    Executor Input Operator executor, which contains the operator computation flow.
    stream Input Specifies the stream for task execution.

  • Return value

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

aclnnInplaceAddcdivGetWorkspaceSize

  • Parameters:

    Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor
    selfRef Input/Output self and out in the formula.
    • The data types of selfRef, tensor1, and tensor2 satisfy the data type deduction rule (see deduction relationship), the deduced data type can be converted to the data type of selfRef (see conversion relationship), and the deduced type must be within the supported input types.
    • The shapes of selfRef and the tensor after broadcasting tensor1 and tensor2 satisfy the broadcast relationship.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    tensor1 Input Represents the input tensor1 in the formula.
    • The data types of selfRef, tensor1, and tensor2 must satisfy the data type deduction rule (see deduction relationship), and the deduced type must be among the supported input types.
    • The shapes of tensor1 and tensor2 must satisfy the broadcast relationship.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    tensor2 Input Input tensor2 in the formula.
    • The data types of selfRef, tensor1, and tensor2 must satisfy the data type deduction rules (see deduction relationship), and the deduced type must be within the supported input types.
    • The shapes of tensor1 and tensor2 must satisfy the broadcast relationship.
    		 BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 ND - √
    value Input Input value in the formula. The data type must be convertible to the data type derived from selfRef, tensor1, and tensor2 (see Conversion Relationship). BFLOAT16, FLOAT16, FLOAT, DOUBLE, INT64 - - -
    workspaceSize Output Returns the workspace size to be applied on the Device side. - - - - -
    executor Output Returns the operator executor, which contains the operator computation flow. - - - - -
    • Atlas training products: The BFLOAT16 data type is not supported.

  • Return value

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

    The first-phase API performs input parameter validation and returns an error in the following scenarios:

    Return Value Error Code Description
    ACLNN_ERR_PARAM_NULLPTR 161001 The input selfRef, tensor1, tensor2, or value is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data types and data formats of selfRef, tensor1, and tensor2 are not within the supported range.
    The data types of selfRef, tensor1, and tensor2 cannot be deduced.
    The deduced data type cannot be converted to the type of the specified output selfRef.
    The shapes of selfRef, tensor1, and tensor2 exceed 8 dimensions.
    The shapes of selfRef, tensor1, and tensor2 do not satisfy the broadcast deduction relationship.
    The shape of selfRef is inconsistent with the shape obtained after broadcasting selfRef, tensor1, and tensor2.

aclnnInplaceAddcdiv

  • Parameters:

    Parameter Input/Output Description
    workspace Input Specifies the workspace memory address applied for on the Device side.
    workspaceSize Input Specifies the workspace size applied for on the Device side, which is obtained from the first-phase API aclnnInplaceAddcdivGetWorkspaceSize.
    executor Input Specifies the operator executor, which contains the operator computation flow.
    stream Input Specifies the stream for task execution.

  • Return value

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

Constraints

  • Deterministic computation:

    • aclnnAddcdiv&aclnnInplaceAddcdiv defaults to a deterministic implementation.

Example

The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_addcdiv.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 for resource initialization.
  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);
  // Calls aclrtMalloc to allocate memory on the device side.
  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);
  // Calls aclrtMemcpy to copy data from the host side 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 the strides of a 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/stream. See the ACL API manual.
  // Enter the deviceId 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. Custom construction is required based on the API.
  std::vector<int64_t> selfShape = {4, 2};
  std::vector<int64_t> tensor1Shape = {4, 2};
  std::vector<int64_t> tensor2Shape = {4, 2};
  std::vector<int64_t> outShape = {4, 2};
  void* selfDeviceAddr = nullptr;
  void* tensor1DeviceAddr = nullptr;
  void* tensor2DeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* tensor1 = nullptr;
  aclTensor* tensor2 = nullptr;
  aclScalar* value = nullptr;
  aclTensor* out = nullptr;

  std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<float> tensor1HostData = {2, 2, 2, 2, 2, 2, 2, 2};
  std::vector<float> tensor2HostData = {2, 2, 2, 2, 2, 2, 2, 2};
  std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  float scalarValue = 1.2f;

  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the tensor1 aclTensor.
  ret = CreateAclTensor(tensor1HostData, tensor1Shape, &tensor1DeviceAddr, aclDataType::ACL_FLOAT, &tensor1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the tensor2 aclTensor.
  ret = CreateAclTensor(tensor2HostData, tensor2Shape, &tensor2DeviceAddr, aclDataType::ACL_FLOAT, &tensor2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the value aclScalar.
  value = aclCreateScalar(&scalarValue, aclDataType::ACL_FLOAT);
  // Create the out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. Call the CANN operator library API. Replace with the actual API name.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnAddcdiv.
  ret = aclnnAddcdivGetWorkspaceSize(self, tensor1, tensor2, value, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddcdivGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Apply for device memory based on the workspaceSize calculated 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 aclnnAddcdiv.
  ret = aclnnAddcdiv(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddcdiv failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) Wait synchronously for the task to 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 memory. Modify based on the specific API definition.
  auto size = GetShapeSize(outShape);
  std::vector<float> 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 resultData from device to host failed. ERROR: %d\n", ret);
            return ret);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("resultData[%ld] is: %f\n", i, resultData[i]);
  }

  // 6. Release aclTensor and aclScalar. Modify based on the specific API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(tensor1);
  aclDestroyTensor(tensor2);
  aclDestroyTensor(out);
  aclDestroyScalar(value);

  // 7. Release device resources. Modify based on the specific API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(tensor1DeviceAddr);
  aclrtFree(tensor2DeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}

aclnnInplaceAddcdiv

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_addcdiv.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 for resource initialization.
  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 apply for memory on the device side.
  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 data from the host side 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);

  // Calculate 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 the aclCreateTensor API 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/stream. See the ACL API manual.
  // Fill in the deviceId based on your 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. Customize the construction based on the API interface.
  std::vector<int64_t> selfShape = {4, 2};
  std::vector<int64_t> tensor1Shape = {4, 2};
  std::vector<int64_t> tensor2Shape = {4, 2};
  void* selfDeviceAddr = nullptr;
  void* tensor1DeviceAddr = nullptr;
  void* tensor2DeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* tensor1 = nullptr;
  aclTensor* tensor2 = nullptr;
  aclScalar* value = nullptr;

  std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<float> tensor1HostData = {2, 2, 2, 2, 2, 2, 2, 2};
  std::vector<float> tensor2HostData = {2, 2, 2, 2, 2, 2, 2, 2};
  float scalarValue = 1.2f;

  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the tensor1 aclTensor.
  ret = CreateAclTensor(tensor1HostData, tensor1Shape, &tensor1DeviceAddr, aclDataType::ACL_FLOAT, &tensor1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the tensor2 aclTensor.
  ret = CreateAclTensor(tensor2HostData, tensor2Shape, &tensor2DeviceAddr, aclDataType::ACL_FLOAT, &tensor2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the value aclScalar.
  value = aclCreateScalar(&scalarValue, aclDataType::ACL_FLOAT);

  // 3. Call the CANN operator library API. Replace with the specific API name.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnInplaceAddcdiv.
  ret = aclnnInplaceAddcdivGetWorkspaceSize(self, tensor1, tensor2, value, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAddcdivGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Apply for device memory based on the workspaceSize calculated 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 aclnnInplaceAddcdiv.
  ret = aclnnInplaceAddcdiv(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAddcdiv failed. ERROR: %d\n", ret); return ret);

  // step4 (boilerplate): Synchronously wait for task execution to complete.
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // step5: Obtain the output value and copy the result from the device-side memory to the host side.
  auto size = GetShapeSize(selfShape);
  std::vector<float> resultData(size, 0);
  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 aclTensor and aclScalar. Modify based on the specific API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(tensor1);
  aclDestroyTensor(tensor2);
  aclDestroyScalar(value);

  // 7. Release device resources. Modify based on the specific API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(tensor1DeviceAddr);
  aclrtFree(tensor2DeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();

  return 0;
}
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