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aclnnForeachSubScalarList

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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 element-wise subtraction between each tensor in the input tensor list and the corresponding scalar in the scalars list.

  • Formula:

    x=[x0,x1,...xn−1]scalars=[scalars0,scalars1,...scalarsn−1]y=[y0,y1,...yn−1]x = [{x_0}, {x_1}, ... {x_{n-1}}]\\ scalars = [{scalars_0}, {scalars_1}, ... {scalars_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\ yi=xi−scalarsi(i=0,1,...n−1)y_i = x_i - scalars_i (i=0,1,...n-1)

Function Prototype

Each operator uses a two-phase API. You must first call the "aclnnForeachSubScalarListGetWorkspaceSize" API to obtain the input parameters and calculate the required workspace size based on the computation process, and then call the "aclnnForeachSubScalarList" API to perform the computation.

aclnnStatus aclnnForeachSubScalarListGetWorkspaceSize(
  const aclTensorList        *x,
  const aclScalarList        *scalars,
  const aclTensorList        *out,
  uint64_t                   *workspaceSize,
  aclOpExecutor             **executor)
aclnnStatus aclnnForeachSubScalarList(
  void                  *workspace,
  uint64_t               workspaceSize,
  aclOpExecutor         *executor,
  aclrtStream            stream)

aclnnForeachSubScalarListGetWorkspaceSize

  • Parameters

    Parameter Input/Output Description Instruction Data Type Data Format Shape Non-contiguous Tensor
    x (aclTensorList*) Input Represents the first input tensor list for the subtraction operation, corresponding to `x` in the formula.
    • Empty tensors are supported.
    • All tensors in this parameter must have the same data type.
    		 FLOAT32, FLOAT16, BFLOAT16, INT32 ND 0-8 √
    scalars (aclScalarList*) Input Represents Subtraction the second input scalar list, corresponding to `scalars` in the formula.
    • The number of elements equals the number of tensors in `x`.
    • The data type has a certain correspondence with the data type of the input parameter `x`:
      • When the data type of the input parameter `x` is FLOAT32, FLOAT16, or BFLOAT16, the data type of `scalars` only supports FLOAT32.
      • When the data type of the input parameter `x` is INT32, the data type of `scalars` only supports INT64.
    		 FLOAT32, INT64 - - -
    out (aclTensorList*) Output Represents the output tensor list of the subtraction operation, corresponding to `y` in the formula.
    • Supports empty tensors.
    • All tensors in this parameter must have the same data type.
    • The data type and data format are consistent with those of the input `x`, and the shape size is greater than or equal to the shape size of the input `x`.
    		 FLOAT32, FLOAT16, BFLOAT16, INT32 ND 0-8 √
    workspaceSize (uint64_t*) Output Returns the workspace size to be allocated on the Device side. - - - - -
    executor (aclOpExecutor**) Output Returns the op executor, which contains the operator computation flow. - - - - -

  • Return Value

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

    Return Code Error Code Description
    ACLNN_ERR_PARAM_NULLPTR 161001 The passed x, scalar, or out is a null pointer.
    ACLNN_ERR_PARAM_INVALID 161002 The data type of x, scalar, or out is not within the supported range.
    The data type of x and out is inconsistent.
    ACLNN_ERR_PARAM_INVALID 561002 The shapes of x and out do not satisfy the constraints.
    The data types of tensors in x or out are inconsistent.
    The dimension of a tensor in x or out exceeds 8.

aclnnForeachSubScalarList

  • Parameters

    Parameter Input/Output Description
    workspace Input The workspace memory address applied for on the Device side.
    workspaceSize Input The workspace size applied for on the Device side, obtained from the first-phase API aclnnForeachSubScalarListGetWorkspaceSize.
    executor Input Operator executor, which contains the operator computation flow.
    stream Input Specifies the stream that executes the task.

  • Return Value

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

Constraints

  • Deterministic computation:

    • aclnnForeachSubScalarList 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_sub_scalar_list.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 the host-side data to the 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);

  // 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];
  }

  // Create an aclTensor by calling the aclCreateTensor API.
  *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 the inputs and outputs. Customize the construction based on the API interface.
  std::vector<int64_t> selfShape1 = {2, 3};
  std::vector<int64_t> selfShape2 = {1, 3};
  std::vector<int64_t> outShape1 = {2, 3};
  std::vector<int64_t> outShape2 = {1, 3};
  void* input1DeviceAddr = nullptr;
  void* input2DeviceAddr = nullptr;
  void* out1DeviceAddr = nullptr;
  void* out2DeviceAddr = nullptr;
  aclTensor* input1 = nullptr;
  aclTensor* input2 = nullptr;
  aclScalar* alpha1 = nullptr;
  aclScalar* alpha2 = 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> out1HostData(6, 0);
  std::vector<float> out2HostData(3, 0);
  float alpha1Value = 1.2f;
  float alpha2Value = 2.2f;
  // Create the input1 aclTensor.
  ret = CreateAclTensor(input1HostData, selfShape1, &input1DeviceAddr, aclDataType::ACL_FLOAT, &input1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the input2 aclTensor.
  ret = CreateAclTensor(input2HostData, selfShape2, &input2DeviceAddr, aclDataType::ACL_FLOAT, &input2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the alpha1 aclScalar.
  alpha1 = aclCreateScalar(&alpha1Value, aclDataType::ACL_FLOAT);
  CHECK_RET(alpha1 != nullptr, return ret);
  // Create the alpha2 aclScalar.
  alpha2 = aclCreateScalar(&alpha2Value, aclDataType::ACL_FLOAT);
  CHECK_RET(alpha2 != 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*> tempInput{input1, input2};
  aclTensorList* tensorListInput = aclCreateTensorList(tempInput.data(), tempInput.size());
  std::vector<aclTensor*> tempOutput{out1, out2};
  aclTensorList* tensorListOutput = aclCreateTensorList(tempOutput.data(), tempOutput.size());
  std::vector<aclScalar*> tempscalar{alpha1, alpha2};
  aclScalarList* scalarlist = aclCreateScalarList(tempscalar.data(), tempscalar.size());

  // 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 aclnnForeachSubScalarList.
  ret = aclnnForeachSubScalarListGetWorkspaceSize(tensorListInput, scalarlist, tensorListOutput, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachSubScalarListGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // Allocate device memory based on the 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 aclnnForeachSubScalarList.
  ret = aclnnForeachSubScalarList(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachSubScalarList failed. ERROR: %d\n", ret); return ret);

  // 4. (Boilerplate) Synchronously 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 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(tensorListInput);
  aclDestroyTensorList(tensorListOutput);
  aclDestroyScalarList(scalarlist);

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