Documentation
Download

aclnnClampMinTensor&aclnnInplaceClampMinTensor

📄 View source code

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: Limits all input elements within the range of [min, inf].

  • Formula:

    yi=max(xi,min_valuei){y}_{i} = max({{x}_{i}},{min\_value}_{i})

Prototype

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

    • aclnnClampMinTensor: An output tensor object needs to be created to store the computation result.
    • aclnnInplaceClampMinTensor: 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, aclnnClampMinTensorGetWorkspaceSize or aclnnInplaceClampMinTensorGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnClampMinTensor or aclnnInplaceClampMinTensor is called to perform computation.

    • aclnnStatus aclnnClampMinTensorGetWorkspaceSize(const aclTensor* self, const aclTensor* clipValueMin, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)
    • aclnnStatus aclnnClampMinTensor(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, const aclrtStream stream)
    • aclnnStatus aclnnInplaceClampMinTensorGetWorkspaceSize(aclTensor* selfRef, const aclTensor* clipValueMin, uint64_t *workspaceSize, aclOpExecutor **executor)
    • aclnnStatus aclnnInplaceClampMinTensor(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, const aclrtStream stream)

aclnnClampMinTensorGetWorkspaceSize

  • Parameters

    • self (aclTensor*, computation input): input tensor, aclTensor on the device. The shape must be 1D to 8D. The data type must meet the data type deduction rules (see Deduction Relationship) with clipValueMin. Non-contiguous tensors are supported. The data format can be ND.

      • Atlas training products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, or INT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, or BFLOAT16.

    • clipValueMin (aclTensor*, computation input): input lower limit tensor. The data type must meet the data type deduction rules (see Deduction Relationship) with self. The shape must be broadcastable with that of self (see Broadcast Relationship). Non-contiguous tensors are supported. The data format can be ND.

      • Atlas training products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, or BOOL.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, BFLOAT16, or BOOL.

    • out (aclTensor*, computation output): output tensor. The shape must be broadcastable with that of self (see Broadcast Relationship). The data format can be ND.

      • Atlas training products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, or INT64. The data type must be the same as that of self and must be convertible from the deduced data type of self and clipValueMin.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, or BFLOAT16. The data type must be the same as that of self and must be convertible from the deduced data type of self and clipValueMin.

    • 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 validation. The following error codes may be returned:
 161001 (ACLNN_ERR_PARAM_NULLPTR): 1. The passed self or out is a null pointer, or min is a null pointer.
 161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type or format of self or out is not supported.
                                   2. The data type deduction of self and clipValueMin failed, or the deduced type cannot be converted to the type of out.

aclnnClampMinTensor

  • 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 aclnnClampMinTensorGetWorkspaceSize.
    • 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.

aclnnInplaceClampMinTensorGetWorkspaceSize

  • Parameters

    • selfRef (aclTensor*, computation input | computation output): The data type must meet the data type deduction rules (see Deduction Relationship) with clipValueMin. The deduced data type must be convertible to that of selfRef. Non-contiguous tensors are supported. The data format can be ND.

      • Atlas training products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, or INT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, or BFLOAT16.

    • clipValueMin (aclTensor*, computation input): input lower limit tensor. The data type must meet the data type deduction rules (see Deduction Relationship) with selfRef. The shape must be broadcastable with that of selfRef (see Broadcast Relationship). Non-contiguous tensors are supported. The data format can be ND, and the number of dimensions must not exceed 8.

      • Atlas training products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, or INT64.
      • Atlas A2 training products/Atlas A2 inference products and Atlas A3 training products/Atlas A3 inference products: The data type can be FLOAT16, FLOAT, FLOAT64, INT8, UINT8, INT16, INT32, INT64, 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 validation. The following error codes may be returned:
 161001 (ACLNN_ERR_PARAM_NULLPTR): 1. The passed selfRef or min is a null pointer.
 161002 (ACLNN_ERR_PARAM_INVALID): 1. The data type or format of selfRef is not supported.
                                   2. The shapes of selfRef and min are not broadcastable.
                                   3. The data type deduction of selfRef and min failed, or the deduced data type cannot be converted to that of selfRef.
                                   4. The number of dimensions of selfRef or min exceeds 8.

aclnnInplaceClampMinTensor

  • 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 aclnnInplaceClampMinTensorGetWorkspaceSize.
    • 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 computation:
    • aclnnClampMinTensor and aclnnInplaceClampMinTensor default to a deterministic implementation.

Examples

The following examples are for reference only. For details, see Compilation and Running Sample.

aclnnClampMinTensor Sample Code

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_clamp.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 device memory.
  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 host data 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 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 PrepareInputAndOutput(
    std::vector<int64_t>& selfShape, std::vector<int64_t>& outShape, std::vector<int64_t>& minShape,
    void** selfDeviceAddr, aclTensor** self, void** minDeviceAddr, aclTensor** min, void** outDeviceAddr,
    aclTensor** out)
{
  std::vector<double> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<double> minHostData = {2, 1, 1, 2, 2, 6, 6, 9};
  std::vector<double> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};
  // Create a self aclTensor.
  auto ret = CreateAclTensor(selfHostData, selfShape, selfDeviceAddr, aclDataType::ACL_DOUBLE, self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create a min aclTensor.
  ret = CreateAclTensor(minHostData, minShape, minDeviceAddr, aclDataType::ACL_DOUBLE, min);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create an out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, outDeviceAddr, aclDataType::ACL_DOUBLE, out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  return ACL_SUCCESS;
}

void ReleaseTensorAndScalar(aclTensor* self, aclTensor* min, aclTensor* out)
{
  aclDestroyTensor(self);
  aclDestroyTensor(min);
  aclDestroyTensor(out);
}

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

int main() {
  // 1. (Boilerplate) Initialize the device and stream. For details, see the AscendCL API manual.
  // Set the device ID based on the actual device.
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  // Handle the check as required.
  CHECK_RET(ret == 0, 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> minShape = {4, 2};
  std::vector<int64_t> outShape = {4, 2};
  void* selfDeviceAddr = nullptr;
  void* minDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* min = nullptr;
  aclTensor* out = nullptr;

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

  // 3. Call the CANN operator library API. Replace it with the actual API name.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnClampMinTensor.
  ret = aclnnClampMinTensorGetWorkspaceSize(self, min, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnClampMinTensorGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on workspaceSize computed 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 aclnnClampMinTensor.
  ret = aclnnClampMinTensor(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnClampMinTensor failed. ERROR: %d\n", ret); return ret);
  // 4. (Boilerplate) Synchronize the stream and wait 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. Modify the code based on the API definition.
  auto size = GetShapeSize(outShape);
  std::vector<double> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(double),
                    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 aclTensors and aclScalars. Modify the code based on the API definition.
  ReleaseTensorAndScalar(self, min, out);

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

  return 0;
}

aclnnInplaceClampMinTensor Sample Code

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_clamp.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 device memory.
  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 host data 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 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 PrepareInputAndOutput(
    std::vector<int64_t>& selfShape, std::vector<int64_t>& minShape,
    void** selfDeviceAddr, aclTensor** self, void** minDeviceAddr, aclTensor** min)
{
  std::vector<double> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<double> minHostData = {2, 1, 1, 2, 2, 6, 6, 9};

  // Create a self aclTensor.
  auto ret = CreateAclTensor(selfHostData, selfShape, selfDeviceAddr, aclDataType::ACL_DOUBLE, self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create a min aclTensor.
  ret = CreateAclTensor(minHostData, minShape, minDeviceAddr, aclDataType::ACL_DOUBLE, min);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  return ACL_SUCCESS;
}

void ReleaseTensorAndScalar(aclTensor* self, aclTensor* min)
{
  aclDestroyTensor(self);
  aclDestroyTensor(min);
}

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

int main() {
  // 1. (Boilerplate) Initialize the device and stream. For details, see the AscendCL API manual.
  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> minShape = {4, 2};
  void* selfDeviceAddr = nullptr;
  void* minDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* min = nullptr;

  ret = PrepareInputAndOutput(selfShape, minShape, &selfDeviceAddr, &self, &minDeviceAddr, &min);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. Call the CANN operator library API.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnInplaceClampMinTensor.
  ret = aclnnInplaceClampMinTensorGetWorkspaceSize(self, min, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceClampMinTensorGetWorkspaceSize failed. ERROR: %d\n", ret);
            return ret);
  // Allocate device memory based on workspaceSize computed 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 aclnnInplaceClampMinTensor.
  ret = aclnnInplaceClampMinTensor(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceClampMinTensor failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) Synchronize the stream and wait 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.
  auto size = GetShapeSize(selfShape);
  std::vector<double> 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 aclTensors and aclScalars. Modify the code based on the API definition.
  ReleaseTensorAndScalar(self, min);

  // 7. Release device resources.
  ReleaseDevice(selfDeviceAddr, minDeviceAddr, workspaceSize, workspaceAddr, stream, deviceId);

  return 0;
}
This site uses cookies to improve your browsing experience. By continuing to browse this site, you agree to our use of cookies.View Details