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aclnnForeachLerpScalar

aclnnForeachLerpScalar

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Applicable Products

ProductSupported
Ascend 950PR/Ascend 950DT√
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: Performs interpolation on corresponding elements of two tensor lists, where the scalar weight serves as the interpolation coefficient.

  • Formula:

    x1=[x10,x11,...x1n−1],x2=[x20,x21,...x2n−1]y=[y0,y1,...yn−1]x1 = [{x1_0}, {x1_1}, ... {x1_{n-1}}], x2 = [{x2_0}, {x2_1}, ... {x2_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\ yi=x1i+weight×(x2i−x1i)(i=0,1,...n−1){\rm y}_i = x1_i + {\rm weight} × (x2_i - x1_i) (i=0,1,...n-1)

Function Prototype

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

aclnnStatus aclnnForeachLerpScalarGetWorkspaceSize(
  const aclTensorList *x1,
  const aclTensorList *x2,
  const aclScalar     *weight,
  const aclTensorList *out,
  uint64_t            *workspaceSize,
  aclOpExecutor      **executor)
aclnnStatus aclnnForeachLerpScalar(
  void          *workspace,
  uint64_t       workspaceSize,
  aclOpExecutor *executor,
  aclrtStream    stream)

aclnnForeachLerpScalarGetWorkspaceSize

  • Parameters

    Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor
    x1 (aclTensorList*) Input Indicates the first input tensor list for linear interpolation computation. Corresponds to `x1` in the formula.
    • Supports null tensor.
    • All tensors in this parameter have the same data type.
    • The data type and data format remain consistent with those of the input parameter `x2`.
    		 FLOAT32, FLOAT16, BFLOAT16 ND 0-8 √
    x2 (aclTensorList*) Input Indicates Performing linear interpolation computation on the second input tensor list. Corresponds to `x2` in the formula.
    • Supports null Tensor.
    • All tensors in this parameter have the same data type.
    • The data type and data format remain consistent with those of the input parameter x1.
    		 FLOAT32, FLOAT16, BFLOAT16 ND 0-8 √
    weight (aclScalar*) Input Indicates the interpolation coefficient for performing linear interpolation computation. Corresponds to `weight` in the formula. - FLOAT32, DOUBLE - - -
    out (aclTensorList*) Output Indicates the list of output tensors for linear interpolation computation. Corresponds to `y` in the formula.
    • Supports null Tensor.
    • All tensors in this parameter have the same data type.
    • The data type and data format are consistent with those of the input parameter `x1`.
    		 FLOAT32, FLOAT16, BFLOAT16 ND 0-8 √
    workspaceSize (uint64_t*) Output Returns the workspace size to be applied on the Device side. - - - - -
    executor (aclOpExecutor**) Output Returns the operator executor, which contains the operator computation flow. - - - - -

    • Atlas A2 training products/Atlas A2 inference products, Atlas A3 training products/Atlas A3 inference products:

      • The shapes of parameters x1 and x2 remain consistent.

      • The shape size of parameter out is greater than or equal to the shape size of input parameter x1.

    • Ascend 950PR/Ascend 950DT:

      The shape sizes of parameters x1, x2, and out remain consistent. A maximum of 50 tensors is supported.

  • Return Value

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

    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 passed x1, x2, and out are null pointers.
    ACLNN_ERR_PARAM_INVALID 161002 The data types of x1, x2, weight, and out are not within the supported range.
    The data types of x1, x2, weight, and out are inconsistent.
    ACLNN_ERR_INNER_TILING_ERROR 561002 The shapes of x1, x2, and out do not satisfy the corresponding relationship.
    The data types of tensors in x1, x2, or out are inconsistent.
    The tensor dimension in x1, x2, or out exceeds 8 dimensions.

aclnnForeachLerpScalar

  • Parameters

    Parameter Input/Output Description
    workspace Input Specifies the workspace memory address applied on the Device side.
    workspaceSize Input Specifies the workspace size applied on the Device side, obtained through the first-phase API aclnnForeachLerpScalarGetWorkspaceSize.
    executor Input Operator executor, which contains the operator computation process.
    stream Input Specifies the stream for executing the task.

  • Return Value

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

Constraints

  • Deterministic computation:

    • aclnnForeachLerpScalar 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_foreach_lerp_scalar.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: 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 device-side 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);
  // Copy host-side data to device-side memory by calling aclrtMemcpy.
  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);

  // Calculate 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];
  }

  // Create an aclTensor by calling aclCreateTensor.
  *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. Refer to the ACL API manual.
  // Set 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 inputs and outputs. Custom construction is required based on the API.
  std::vector<int64_t> selfShape1 = {2, 3};
  std::vector<int64_t> selfShape2 = {1, 3};
  std::vector<int64_t> otherShape1 = {2, 3};
  std::vector<int64_t> otherShape2 = {1, 3};
  std::vector<int64_t> outShape1 = {2, 3};
  std::vector<int64_t> outShape2 = {1, 3};
  void* input1DeviceAddr = nullptr;
  void* input2DeviceAddr = nullptr;
  void* other1DeviceAddr = nullptr;
  void* other2DeviceAddr = nullptr;
  void* out1DeviceAddr = nullptr;
  void* out2DeviceAddr = nullptr;
  aclTensor* input1 = nullptr;
  aclTensor* input2 = nullptr;
  aclTensor* other1 = nullptr;
  aclTensor* other2 = nullptr;
  aclScalar* weight = nullptr;
  aclTensor* out1 = nullptr;
  aclTensor* out2 = nullptr;
  std::vector<float> input1HostData = {1, 2, 3, 4, 5, 6};
  std::vector<float> input2HostData = {7, 8, 9};
  std::vector<float> other1HostData = {4, 6, 7, 8, 4, 3};
  std::vector<float> other2HostData = {5, 2, 1};
  std::vector<float> out1HostData(6, 0);
  std::vector<float> out2HostData(3, 0);
  float weightValue = 1.2f;
  // Create input1 aclTensor.
  ret = CreateAclTensor(input1HostData, selfShape1, &input1DeviceAddr, aclDataType::ACL_FLOAT, &input1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create input2 aclTensor.
  ret = CreateAclTensor(input2HostData, selfShape2, &input2DeviceAddr, aclDataType::ACL_FLOAT, &input2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create other1 aclTensor.
  ret = CreateAclTensor(other1HostData, otherShape1, &other1DeviceAddr, aclDataType::ACL_FLOAT, &other1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create other2 aclTensor.
  ret = CreateAclTensor(other2HostData, otherShape2, &other2DeviceAddr, aclDataType::ACL_FLOAT, &other2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the weight aclScalar.
  weight = aclCreateScalar(&weightValue, aclDataType::ACL_FLOAT);
  CHECK_RET(weight != nullptr, return ret);
  // Create the out1 aclTensor.
  ret = CreateAclTensor(out1HostData, outShape1, &out1DeviceAddr, aclDataType::ACL_FLOAT, &out1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the out2 aclTensor.
  ret = CreateAclTensor(out2HostData, outShape2, &out2DeviceAddr, aclDataType::ACL_FLOAT, &out2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  std::vector<aclTensor*> tempInput1{input1, input2};
  aclTensorList* tensorListInput1 = aclCreateTensorList(tempInput1.data(), tempInput1.size());
  std::vector<aclTensor*> tempInput2{other1, other2};
  aclTensorList* tensorListInput2 = aclCreateTensorList(tempInput2.data(), tempInput2.size());
  std::vector<aclTensor*> tempOutput{out1, out2};
  aclTensorList* tensorListOutput = aclCreateTensorList(tempOutput.data(), tempOutput.size());

  // 3. Call the CANN operator library API. Replace with the actual API name.
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // Call the first-phase API of aclnnForeachLerpScalar.
  ret = aclnnForeachLerpScalarGetWorkspaceSize(tensorListInput1, tensorListInput2, weight, tensorListOutput, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachLerpScalarGetWorkspaceSize 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 aclnnForeachLerpScalar.
  ret = aclnnForeachLerpScalar(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachLerpScalar 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 device memory to host memory. Modify based on the specific API definition.
  auto size = GetShapeSize(outShape1);
  std::vector<float> out1Data(size, 0);
  ret = aclrtMemcpy(out1Data.data(), out1Data.size() * sizeof(out1Data[0]), out1DeviceAddr,
                    size * sizeof(out1Data[0]), ACL_MEMCPY_DEVICE_TO_HOST);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("out1 result[%ld] is: %f\n", i, out1Data[i]);
  }

  size = GetShapeSize(outShape2);
  std::vector<float> out2Data(size, 0);
  ret = aclrtMemcpy(out2Data.data(), out2Data.size() * sizeof(out2Data[0]), out2DeviceAddr,
                    size * sizeof(out2Data[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("out2 result[%ld] is: %f\n", i, out2Data[i]);
  }

  // 6. Release aclTensor and aclScalar. Modify based on the specific API definition.
  aclDestroyTensorList(tensorListInput1);
  aclDestroyTensorList(tensorListInput2);
  aclDestroyTensorList(tensorListOutput);
  aclDestroyScalar(weight);

  // 7. Release device resources. Modify based on the specific API definition.
  aclrtFree(input1DeviceAddr);
  aclrtFree(input2DeviceAddr);
  aclrtFree(other1DeviceAddr);
  aclrtFree(other2DeviceAddr);
  aclrtFree(out1DeviceAddr);
  aclrtFree(out2DeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}
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