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aclnnAtan2&aclnnInplaceAtan2

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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

  • Description: Computes the element-wise inverse tangent (arctan) of the input tensors self and other. (self indicates the y coordinate, and other indicates the x coordinate.)

  • Formula:

outi=tan−1(selfi/otheri)out_{i}=tan^{-1}(self_{i}/other_{i})

Prototype

  • aclnnAtan2 and aclnnInplaceAtan2 implement the same function in different ways. Select a proper operator based on your requirements.

    • aclnnAtan2: An output tensor object needs to be created to store the computation result.
    • aclnnInplaceAtan2: No output tensor object needs to be created, and the computation result is written in place to the input tensor's memory.

  • Each operator has two-phase API calls. First, aclnnAtan2GetWorkspaceSize or aclnnInplaceAtan2GetWorkspaceSize is called to obtain input parameters and compute the required workspace size based on the computation process. Then, aclnnAtan2 or aclnnInplaceAtan2 is called to perform computation.

    • aclnnStatus aclnnAtan2GetWorkspaceSize(const aclTensor *self, const aclTensor *other, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
    • aclnnStatus aclnnAtan2(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
    • aclnnStatus aclnnInplaceAtan2GetWorkspaceSize(aclTensor* selfRef, aclTensor* other, uint64_t* workspace_size, aclOpExecutor** executor)
    • aclnnStatus aclnnInplaceAtan2(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

aclnnAtan2GetWorkspaceSize

  • Parameters:

    • self (aclTensor*, computation input): aclTensor on the device. It supports non-contiguous tensors. Its data format can be ND. Its dimensions cannot exceed 8. The shapes of self and other must meet the broadcast relationship.

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

    • other (aclTensor*, computation input): aclTensor on the device. It supports non-contiguous tensors. Its data format can be ND. Its dimensions cannot exceed 8. The shapes of other and self must meet the broadcast relationship.

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

    • out (aclTensor*, computation output): output tensor, aclTensor on the device. It supports non-contiguous tensors. Its data format can be ND. Its dimensions cannot exceed 8. The shape of out must be the shape obtained by broadcasting self and other.

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

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

    • executor (aclOpExecutor **, output): operator executor, covering 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. self, other, or out is a null pointer.
161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type of self, other, or out is not supported.
                                  2. The shapes of self and other are not broadcastable.
                                  3. self, other, or out has more than 8 dimensions.
                                  4. The shape of out does not match the shape obtained by broadcasting self and other.

aclnnAtan2

  • 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 calling the first-phase API aclnnAtan2GetWorkspaceSize.
    • executor (aclOpExecutor*, input): operator executor, covering the operator computation process.
    • stream (aclrtStream, input parameter): stream for executing the task.

  • Returns:

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

aclnnInplaceAtan2GetWorkspaceSize

  • Parameters:

    • selfRef (aclTensor * , computation input/output): aclTensor on the device. It supports non-contiguous tensors. Its data format can be ND. Its dimensions cannot exceed 8. The shapes of selfRef and other must meet the broadcast relationship.

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

    • other (aclTensor*, computation input): aclTensor on the device. It supports non-contiguous tensors. Its data format can be ND. Its dimensions cannot exceed 8. The shapes of other and selfRef must meet the broadcast relationship.

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

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

    • executor (aclOpExecutor **, output): operator executor, covering 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. selfRef or other is a null pointer.
161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type of selfRef or other is not supported.
                                  2. The shapes of selfRef and other are not broadcastable.
                                  3. selfRef or other has more than 8 dimensions.

aclnnInplaceAtan2

  • 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 calling the first-phase API aclnnInplaceAtan2GetWorkspaceSize.
    • executor (aclOpExecutor*, input): operator executor, covering the operator computation process.
    • stream (aclrtStream, input): stream for executing the task.

  • Returns:

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

Constraints

  • Deterministic computation:
    • aclnnAtan2 and aclnnInplaceAtan2 each default to a deterministic implementation.

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_atan2.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 code 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 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 code for device/stream initialization. For details, see the ACL API manual.
  // Set the device ID in use.
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  // Customize the error handling as needed.
  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 = {4, 2};
  std::vector<int64_t> otherShape = {4, 2};
  std::vector<int64_t> outShape = {4, 2};
  void* selfDeviceAddr = nullptr;
  void* otherDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* other = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> selfHostData = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8};
  std::vector<float> otherHostData = {0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,0.1};
  std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  // Create a self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an other aclTensor.
  ret = CreateAclTensor(otherHostData, otherShape, &otherDeviceAddr, aclDataType::ACL_FLOAT, &other);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // aclnnAtan2 calling example
  // 3. Call the CANN operator library API.
  // Call the first-phase API of aclnnAtan2.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  ret = aclnnAtan2GetWorkspaceSize(self, other, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAtan2GetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate 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 aclnnAtan2.
  ret = aclnnAtan2(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAtan2 failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate code) 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<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 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]);
  }

  // aclnnInplaceAtan2 calling example
  // 3. Call the CANN operator library API.
  LOG_PRINT("\ntest aclnnInplaceAtan2\n");
  // Call the first-phase API of aclnnInplaceAtan2.
  uint64_t inplaceWorkspaceSize = 0;
  aclOpExecutor* inplaceExecutor;
  ret = aclnnInplaceAtan2GetWorkspaceSize(self, other, &inplaceWorkspaceSize, &inplaceExecutor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAtan2GetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on the workspaceSize calculated 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 aclnnInplaceAtan2.
  ret = aclnnInplaceAtan2(inplaceWorkspaceAddr, inplaceWorkspaceSize, inplaceExecutor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceAtan2 failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate code) 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.
  size = GetShapeSize(outShape);
  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]);
  }

  // 6. Destroy aclTensor. Modify the code based on the API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(other);
  aclDestroyTensor(out);

  // 7. Free device resources. Modify the code based on the API definition.
  aclrtFree(selfDeviceAddr);
  aclrtFree(otherDeviceAddr);
  aclrtFree(outDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}
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