aclnnXLogYTensor&aclnnInplaceXLogYTensor

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Supported Products

Function

• Description: Computes the result of self * log(other).
• Formula:$$out_i = self_i * log(other_i)$$

Prototype

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

  • aclnnXLogYTensor: An output tensor object needs to be created to store the computation result.
  • aclnnInplaceXLogYTensor: 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, aclnnXLogYTensorGetWorkspaceSize or aclnnInplaceXLogYTensorGetWorkspaceSize is called to obtain input parameters and compute the required workspace size based on the computation process. Then, aclnnXLogYTensor or aclnnInplaceXLogYTensor is called to perform computation.

  • aclnnStatus aclnnXLogYTensorGetWorkspaceSize(const aclTensor *self, const aclTensor *other, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
  • aclnnStatus aclnnXLogYTensor(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
  • aclnnStatus aclnnInplaceXLogYTensorGetWorkspaceSize(aclTensor *selfRef, const aclTensor *other, uint64_t *workspaceSize, aclOpExecutor **executor)
  • aclnnStatus aclnnInplaceXLogYTensor(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

aclnnXLogYTensorGetWorkspaceSize

• Parameters

  • self (aclTensor*, compute input): input self in the formula, aclTensor on the device. The data type must meet the type deduction rules with other. The shape must meet the broadcast relationship with other. Non-contiguous tensors are supported. The data format can be ND.
    • Atlas inference products, Atlas training products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, or BOOL.
    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, BOOL, or BFLOAT16.
  • other (aclTensor*, compute input): input other in the formula, aclTensor on the device. The data type must meet the type deduction rules with self. The shape must meet the broadcast relationship with self. Non-contiguous tensors are supported. The data format can be ND.
    • Atlas inference products, Atlas training products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, or BOOL.
    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, BOOL, or BFLOAT16.
  • out (aclTensor *, compute output): out in the formula, aclTensor on the device. The shape must be that after broadcasting between self and other. Non-contiguous tensors are supported. The data format can be ND.
    • Atlas inference products, Atlas training products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, or BOOL.
    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, BOOL, or BFLOAT16.
  • 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 data type of `self` or `other` is not supported.
                                       2. The data type deduced from `self` and `other` cannot be converted to the data type of `out`.
                                       3. Broadcasting cannot be performed for the shapes of `self` and `other`, or their shapes after broadcasting are different from that of `out`.

aclnnXLogYTensor

• 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 aclnnXLogYTensorGetWorkspaceSize.
  • 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.

aclnnInplaceXLogYTensorGetWorkspaceSize

• Parameters

  • selfRef (aclTensor *, compute input/output): input self/out in the formula, aclTensor on the device. The data type must be convertible from that after deduction between selfRef and other, and must meet the type deduction rules with other. The shape must meet the broadcast relationship with other. Non-contiguous tensors are supported. The data format can be ND.
    • Atlas inference products, Atlas training products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, or BOOL.
    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, BOOL, or BFLOAT16.
  • other (aclTensor*, compute input): other input in the formula, aclTensor on the device. The data type must meet the type deduction rules with selfRef. The shape must meet the broadcast relationship with selfRef. Non-contiguous tensors are supported. The data format can be ND.
    • Atlas inference products, Atlas training products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, or BOOL.
    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT, FLOAT16, INT32, INT64, INT16, INT8, UINT8, BOOL, or BFLOAT16.
  • 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 of `selfRef` or `other` is not supported.
                                       2. The deduced data types of `selfRef` and `other` cannot be converted to the output data type of `selfRef`.
                                       3. Broadcasting cannot be performed for the shapes of `selfRef` and `other`.

aclnnInplaceXLogYTensor

• 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 aclnnInplaceXLogYTensorGetWorkspaceSize.
  • 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 computing:
  • aclnnXLogYTensor and aclnnInplaceXLogYTensor default to a deterministic implementation.

Calling Examples

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_x_log_y_tensor.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 the device ID in use.
  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 = {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, 2, 3, 4, 5, 6, 7};
  std::vector<float> otherHostData = {1, 1, 1, 2, 2, 2, 3, 3};
  std::vector<float> outHostData(8, 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);

  // 3. Call the CANN operator library API, which needs to be replaced with the actual API.
  // aclnnXLogYTensor API call example
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of `aclnnXLogYTensor`.
  ret = aclnnXLogYTensorGetWorkspaceSize(self, other, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnXLogYTensorGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on the computed `workspaceSize`.
  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 `aclnnXLogYTensor`.
  ret = aclnnXLogYTensor(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnXLogYTensor failed. ERROR: %d\n", ret); return ret);

  // aclnnInplaceXLogYTensor API call example
  uint64_t inplaceWorkspaceSize = 0;
  aclOpExecutor* inplaceExecutor;
  // Call the first-phase API of `aclnnInplaceXLogYTensor`.
  ret = aclnnInplaceXLogYTensorGetWorkspaceSize(self, other, &inplaceWorkspaceSize, &inplaceExecutor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceXLogYTensorGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on the computed `workspaceSize`.
  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 `aclnnInplaceXLogYTensor`.
  ret = aclnnInplaceXLogYTensor(inplaceWorkspaceAddr, inplaceWorkspaceSize, inplaceExecutor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceXLogYTensor 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<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("aclnnXLogYTensor result[%ld] is: %f\n", i, resultData[i]);
  }

  auto inplaceSize = GetShapeSize(selfShape);
  std::vector<float> inplaceResultData(inplaceSize, 0);
  ret = aclrtMemcpy(inplaceResultData.data(), inplaceResultData.size() * sizeof(inplaceResultData[0]), selfDeviceAddr, inplaceSize * sizeof(inplaceResultData[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 < inplaceSize; i++) {
    LOG_PRINT("aclnnInplaceXLogYTensor result[%ld] is: %f\n", i, inplaceResultData[i]);
  }

  // 6. Release `aclTensor` and `aclScalar`. Modify the configuration based on the API definition.
  aclDestroyTensor(self);
  aclDestroyTensor(other);
  aclDestroyTensor(out);

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