aclnnAminmax

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Function

Returns the minimum and maximum values of each row of the input tensor along the specified dimension.

Function Prototype

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

aclnnStatus aclnnAminmaxGetWorkspaceSize(
const aclTensor   *self,
const aclIntArray *dim,
bool               keepDim,
aclTensor         *minOut,
aclTensor         *maxOut,
uint64_t          *workspaceSize,
aclOpExecutor    **executor)

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

aclnnAminmaxGetWorkspaceSize

• Parameters:

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (shape)	Non-contiguous Tensor
  self	Input	Input tensor.	-	FLOAT, BFLOAT16, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, BOOL	ND	-	√
  dim	Input	Specifies the dimension to reduce.	Range: [-self.dim(), self.dim() - 1].	INT64	-	-	-
  keepdim	Input	Whether to retain the reduced dimension.	-	BOOL	-	-	-
  minOut	Input.	Output minimum tensor.	The data type is the same as that of self.	FLOAT, BFLOAT16, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, BOOL	ND	-	√
  maxOut	Output	The output maximum value tensor.	Data type consistent with self.	FLOAT, BFLOAT16, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, BOOL	ND	-	√
  workspaceSize	Output	Returns the workspace size to be applied on the Device side.	-	-	-	-	-
  executor	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 Code	Error Code	Description
  ACLNN_ERR_PARAM_NULLPTR	161001	The input self, dim, minOut, or maxOut is a null pointer.
  ACLNN_ERR_PARAM_INVALID	161002	The data type of self is not within the supported range.
  		The data type of minOut or maxOut is inconsistent with that of self.
  		The shape of self, minOut, or maxOut exceeds eight dimensions.
  		dim is out of range.
  		The number of dim equals 1 and the shape of the dim axis in self is 0.
  		The number of dim is not 1 and self is an empty tensor.

aclnnAminmax

• 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 by the first-phase API aclnnAminmaxGetWorkspaceSize.
  executor	Input	Operator executor, which contains the operator computation process.
  stream	Input	Stream that executes the task.

• Return value

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

Constraints

• Deterministic computation:

  • aclnnAminmax 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_aminmax.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);

  // 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 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);
  // Handle the check based on your requirements.
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

  // 2. Construct the input and output. Customize the construction based on the API interface.
  std::vector<int64_t> selfShape = {2, 3, 2};
  std::vector<int64_t> outShape = {1, 3, 2};
  void* selfDeviceAddr = nullptr;
  void* minOutDeviceAddr = nullptr;
  void* maxOutDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* minOut = nullptr;
  aclTensor* maxOut = nullptr;
  std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
  std::vector<float> minOutHostData = {0, 0, 0, 0, 0, 0};
  std::vector<float> maxOutHostData = {0, 0, 0, 0, 0, 0};
  std::vector<int64_t> dimData = {0};
  bool keepDim = true;
  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the dim aclIntArray.
  aclIntArray *dim = aclCreateIntArray(dimData.data(), dimData.size());
  // Create the out aclTensor.
  ret = CreateAclTensor(minOutHostData, outShape, &minOutDeviceAddr, aclDataType::ACL_FLOAT, &minOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(maxOutHostData, outShape, &maxOutDeviceAddr, aclDataType::ACL_FLOAT, &maxOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 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 aclnnAminmax.
  ret = aclnnAminmaxGetWorkspaceSize(self, dim, keepDim, minOut, maxOut, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAminmaxGetWorkspaceSize 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 aclnnAminmax.
  ret = aclnnAminmax(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAminmax failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) Synchronously wait for the 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. Modify based on the specific API definition.
  auto size = GetShapeSize(outShape);
  std::vector<float> minResultData(size, 0);
  ret = aclrtMemcpy(minResultData.data(), minResultData.size() * sizeof(minResultData[0]), minOutDeviceAddr,
                    size * sizeof(minResultData[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, minResultData[i]);
  }
  std::vector<float> maxResultData(size, 0);
  ret = aclrtMemcpy(maxResultData.data(), maxResultData.size() * sizeof(maxResultData[0]), maxOutDeviceAddr,
                    size * sizeof(maxResultData[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, maxResultData[i]);
  }

  // 6. Release aclTensor. Modify based on the specific API definition.
  aclDestroyTensor(self);
  aclDestroyIntArray(dim);
  aclDestroyTensor(minOut);
  aclDestroyTensor(maxOut);

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