aclnnMean

📄 View Source Code

Applicable Products

Function

• Description: Computes the mean of a tensor along specified dimensions.

• Formula:

Function Prototype

Each operator is divided into a two-phase API. The "aclnnMeanGetWorkspaceSize" API must be called first to obtain the workspace size required for computation and the executor that encapsulates the operator computation flow. Then, the "aclnnMean" API is called to perform the computation.

aclnnStatus aclnnMeanGetWorkspaceSize(
  const aclTensor*     self,
  const aclIntArray*   dim,
  bool                 keepDim,
  aclDataType          dtype,
  aclTensor*           out,
  uint64_t*            workspaceSize,
  aclOpExecutor**      executor)

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

aclnnMeanGetWorkspaceSize

• Parameters:

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (shape)	Non-contiguous Tensor
  self (aclTensor*)	Input	X in the formula.	When the input is an empty tensor, the output type cannot be the complex types COMPLEX64 and COMPLEX128. The data type must be convertible to the dtype data type (see Conversion Relationship).	FLOAT, FLOAT16, BFLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, COMPLEX64, COMPLEX128	ND	0-8	√
  dim (aclIntArray*)	Input	Input array.	-	INT64	-	-	-
  keepDim (bool)	Input	Whether to retain the reduced dimension.	-	BOOL	-	-	-
  dtype (aclDataType)	Input	Specifies the data type of the output tensor.	The data type must be convertible to the data type of out (see Conversion Relationship).	FLOAT, FLOAT16, BFLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, COMPLEX64, COMPLEX128	-	-	-
  out (aclTensor*)	Output	Output tensor.	-	FLOAT, FLOAT16, BFLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, COMPLEX64, COMPLEX128	ND	0-8	√
  workspaceSize (uint64_t*)	Output	Returns the workspace size to be applied for on the Device side.	-	-	-	-	-
  executor (aclOpExecutor**)	Output	Returns the op executor, which contains the operator computation flow.	-	-	-	-	-

  • Atlas inference products and 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 passed self, out, and dim are null pointers.
  ACLNN_ERR_PARAM_INVALID	161002	The data type of self, out, or dim is not within the supported range, or dtype specifies an unsupported data type.
  		The data format of self or out is not within the supported range.
  		A dimension in the dim array exceeds the dimension range of the input tensor.
  		Duplicate elements exist in the dim array.
  		The out shape does not match the actual shape.
  		The data type of self cannot be converted to the data type of dtype.
  		The data type of dtype cannot be converted to the data type of out.

aclnnMean

• Parameters:

  Parameter	Input/Output	Description
  workspace	Input	Address of the workspace memory applied on the Device side.
  workspaceSize	Input	Size of the workspace applied on the Device side, obtained through the first-phase API aclnnMeanGetWorkspaceSize.
  executor	Input	Op executor that 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:

  • aclnnMean is implemented as deterministic computation by default.

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_mean.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 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 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 for 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 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;
}

template <typename T>
int CreateAclIntArray(const std::vector<T>& hostData, aclIntArray** intArray) {
  auto size = GetShapeSize(hostData) * sizeof(T);

  // Call the aclCreateIntArray API to create an aclIntArray.
  *intArray = aclCreateIntArray(hostData.data(), hostData.size());
  return 0;
}

int PrepareInputAndOutput(
    std::vector<int64_t>& selfShape, std::vector<int64_t>& outShape, void** selfDeviceAddr, aclTensor** self, aclIntArray** dim,
    void** outDeviceAddr, aclTensor** out)
{
    std::vector<int64_t> selfHostData = {2, 3, 5, 8, 4, 12, 6, 7};
    std::vector<int64_t> outHostData = {2, 3, 5, 8};
    std::vector<int64_t> dimData = {1, 2};

    // Create the self aclTensor.
    auto ret = CreateAclTensor(selfHostData, selfShape, selfDeviceAddr, aclDataType::ACL_INT64, self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the dim aclIntArray.
    ret = CreateAclIntArray(dimData, dim);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, outDeviceAddr, aclDataType::ACL_INT64, out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);

    return ACL_SUCCESS;
}

void ReleaseTensorAndIntArray(aclTensor* self, aclIntArray* dim, aclTensor* out)
{
    aclDestroyTensor(self);
    aclDestroyIntArray(dim);
    aclDestroyTensor(out);
}

void ReleaseDevice(
    void* selfDeviceAddr, void* outDeviceAddr, uint64_t workspaceSize, void* workspaceAddr, aclrtStream stream,
    int32_t deviceId)
{
    aclrtFree(selfDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
}

int main() {
  // 1. (Boilerplate) Initialize the device/stream. See the ACL API manual.
  // Fill in 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 input and output. Custom construction is required based on the API.
  std::vector<int64_t> selfShape = {2, 2, 2};
  std::vector<int64_t> outShape = {2};

  void* selfDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclIntArray* dim = nullptr;
  aclTensor* out = nullptr;

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

  // 3. Call the CANN operator library API.
  uint64_t workspaceSize = 0;
  bool keepdim = false;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnMean.
  ret = aclnnMeanGetWorkspaceSize(self, dim, keepdim, aclDataType::ACL_INT64, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMeanGetWorkspaceSize 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 aclnnMean.
  ret = aclnnMean(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMean failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) Synchronize and wait for task execution 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 this 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("result[%ld] is: %f\n", i, resultData[i]);
  }

  // 6. Release aclTensor. Modify based on the specific API definition.
  ReleaseTensorAndIntArray(self, dim, out);

  // 7. Release device resources. Modify based on the specific API definition.
  ReleaseDevice(selfDeviceAddr, outDeviceAddr, workspaceSize, workspaceAddr, stream, deviceId);

  return 0;
}