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aclnnForeachAddcmulScalar

aclnnForeachAddcmulScalar

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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 multiplication on tensor list x2 and tensor list x3, then multiplies by the tensor scalar, and finally performs element-wise addition of the previously computed result with tensor list x1.

  • Formula:

    x1=[x10,x11,...x1n−1],x2=[x20,x21,...x2n−1],x3=[x30,x31,...x3n−1]y=[y0,y1,...yn−1]x1 = [{x1_0}, {x1_1}, ... {x1_{n-1}}], x2 = [{x2_0}, {x2_1}, ... {x2_{n-1}}], x3 = [{x3_0}, {x3_1}, ... {x3_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\ yi=x1i+scalar×x2i×x3i(i=0,1,...n−1){\rm y}_i = x1_{i} + {\rm scalar} \times x2_{i} \times x3_{i} (i=0,1,...n-1)

Function Prototype

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

aclnnStatus aclnnForeachAddcmulScalarGetWorkspaceSize(
  const aclTensorList *x1,
  const aclTensorList *x2,
  const aclTensorList *x3,
  const aclTensor     *scalar,
  const aclTensorList *out,
  uint64_t            *workspaceSize,
  aclOpExecutor      **executor)
aclnnStatus aclnnForeachAddcmulScalar(
  void          *workspace,
  uint64_t       workspaceSize,
  aclOpExecutor *executor,
  aclrtStream    stream)

aclnnForeachAddcmulScalarGetWorkspaceSize

  • Parameters

    Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-Contiguous Tensor
    x1 (aclTensorList*) Input Represents the first input tensor list for addition in the mixed operation. Corresponds to `x1` in the formula.
    • Supports empty tensors.
    • All tensors in this parameter must have the same data type.
    • The shape must be consistent with the shapes of x2 and x3.
    		 FLOAT32, FLOAT16, BFLOAT16, INT32 ND 0-8 √
    x2 (aclTensorList*) Input Represents the second input tensor list for the multiplication in the mixed operation. Corresponds to `x2` in the formula.
    • Empty tensors are supported.
    • All tensors in this parameter must have the same data type.
    • The data type, data format, and shape are consistent with the input parameter x1.
    		 FLOAT32, FLOAT16, BFLOAT16, INT32 ND 0-8 √
    x3 (aclTensorList*) Input Represents the third input tensor list for multiplication in the mixed operation. Corresponds to `x3` in the formula.
    • Supports empty tensors.
    • All tensors in this parameter must have the same data type.
    • The data type, data format, and shape must be consistent with the input `x1`.
    		 FLOAT32, FLOAT16, BFLOAT16, INT32 ND 0-8 √
    scalar (aclTensor*) Input Indicates the first input tensor of the multiplication in the mixed operation. Corresponds to `scalar` in the formula.
    • Empty tensors are not supported.
    • The number of elements is 1.
    • The data format is consistent with the input `x1`.
    		 FLOAT32, FLOAT16, INT32 ND 0-8 √
    out (aclTensorList*) Output Represents the output tensor list of the mixed operation. Corresponds 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 the same as those of the input parameter `x1`, and the shape size is greater than or equal to the shape size of the input parameter `x1`.
    		 FLOAT32, FLOAT16, BFLOAT16, INT32 ND 0-8 √
    workspaceSize (uint64_t*) Output Returns the workspace size to be requested on the Device side. - - - - -
    executor (aclOpExecutor**) Output Returns the op executor, which contains the operator computation flow. - - - - -

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

      The data type of the scalar parameter has a certain correspondence with the data type of the input x1:

      • When the data type of x1 is FLOAT32, FLOAT16, or INT32, the data type of scalar must be the same as that of x1.

      • When the data type of x1 is BFLOAT16, the data type of scalar supports FLOAT32.

    • Ascend 950PR/Ascend 950DT:

      • The maximum number of tensors supported by the x1, x2, x3, and out parameters is 50.

      • The data type of the scalar parameter has a specific correspondence with the data type of the input x1:

        • When the data type of x1 is FLOAT32 or INT32, the data type of scalar must be consistent with the data type of x1.

        • When the data type of x1 is BFLOAT16, the data type of scalar supports FLOAT32.

        • When the data type of x1 is FLOAT16, the data type of scalar supports FLOAT16 and FLOAT32.

  • 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 x1, x2, x3, scalar, and out are null pointers.
    ACLNN_ERR_PARAM_INVALID 161002 The data types of x1, x2, x3, scalar, and out are not within the supported range.
    The data types of x1, x2, x3, and out are inconsistent.
    ACLNN_ERR_INNER_TILING_ERROR 561002 The shapes of x1, x2, x3, and out do not meet the constraints.
    The data types of the tensors in x1, x2, x3, or out are inconsistent.
    The tensors in x1, x2, x3, or out exceed 8 dimensions.
    The number of elements in scalar is not 1.

aclnnForeachAddcmulScalar

  • Parameters

    Parameter Input/Output Description
    workspace Input Workspace memory address applied for on the Device side.
    workspaceSize Input Workspace size applied for on the Device side, obtained from the first-phase API aclnnForeachAddcmulScalarGetWorkspaceSize.
    executor Input Operator 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 Code.

Constraints

  • Deterministic computation:

    • aclnnForeachAddcmulScalar 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_addcmul_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: 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 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);

  // 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. 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);
  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.
  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> anotherShape1 = {2, 3};
  std::vector<int64_t> anotherShape2 = {1, 3};
  std::vector<int64_t> outShape1 = {2, 3};
  std::vector<int64_t> outShape2 = {1, 3};
  std::vector<int64_t> alphaShape = {1};
  void* input1DeviceAddr = nullptr;
  void* input2DeviceAddr = nullptr;
  void* other1DeviceAddr = nullptr;
  void* other2DeviceAddr = nullptr;
  void* another1DeviceAddr = nullptr;
  void* another2DeviceAddr = nullptr;
  void* out1DeviceAddr = nullptr;
  void* out2DeviceAddr = nullptr;
  void* alphaDeviceAddr = nullptr;
  aclTensor* input1 = nullptr;
  aclTensor* input2 = nullptr;
  aclTensor* other1 = nullptr;
  aclTensor* other2 = nullptr;
  aclTensor* another1 = nullptr;
  aclTensor* another2 = nullptr;
  aclTensor* alpha = 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 = {1, 2, 3, 4, 5, 6};
  std::vector<float> other2HostData = {7, 8, 9};
  std::vector<float> another1HostData = {1, 2, 3, 4, 5, 6};
  std::vector<float> another2HostData = {7, 8, 9};
  std::vector<float> out1HostData(6, 0);
  std::vector<float> out2HostData(3, 0);
  std::vector<float> alphaValueHostData = {1.2f};
  float alphaValue = 1.2f;
  // 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 other1 aclTensor.
  ret = CreateAclTensor(other1HostData, otherShape1, &other1DeviceAddr, aclDataType::ACL_FLOAT, &other1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the other2 aclTensor.
  ret = CreateAclTensor(other2HostData, otherShape2, &other2DeviceAddr, aclDataType::ACL_FLOAT, &other2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create another1 aclTensor.
  ret = CreateAclTensor(another1HostData, anotherShape1, &another1DeviceAddr, aclDataType::ACL_FLOAT, &another1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create another2 aclTensor.
  ret = CreateAclTensor(another2HostData, anotherShape2, &another2DeviceAddr, aclDataType::ACL_FLOAT, &another2);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create alpha aclTensor.
  ret = CreateAclTensor(alphaValueHostData, alphaShape, &alphaDeviceAddr, aclDataType::ACL_FLOAT, &alpha);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create out1 aclTensor.
  ret = CreateAclTensor(out1HostData, outShape1, &out1DeviceAddr, aclDataType::ACL_FLOAT, &out1);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create 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*> tempAnother{another1, another2};
  aclTensorList* tensorListAnother = aclCreateTensorList(tempAnother.data(), tempAnother.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 aclnnForeachAddcmulScalar.
  ret = aclnnForeachAddcmulScalarGetWorkspaceSize(tensorListInput1, tensorListInput2, tensorListAnother, alpha, tensorListOutput, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachAddcmulScalarGetWorkspaceSize 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 aclnnForeachAddcmulScalar.
  ret = aclnnForeachAddcmulScalar(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachAddcmulScalar 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. Copy the result from the device memory to the host. 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. Modify based on the specific API definition.
  aclDestroyTensorList(tensorListInput1);
  aclDestroyTensorList(tensorListInput2);
  aclDestroyTensorList(tensorListAnother);
  aclDestroyTensorList(tensorListOutput);
  aclDestroyTensor(alpha);

  // 7. Release device resources. Modify based on the specific API definition.
  aclrtFree(input1DeviceAddr);
  aclrtFree(input2DeviceAddr);
  aclrtFree(other1DeviceAddr);
  aclrtFree(other2DeviceAddr);
  aclrtFree(another1DeviceAddr);
  aclrtFree(another2DeviceAddr);
  aclrtFree(out1DeviceAddr);
  aclrtFree(out2DeviceAddr);
  aclrtFree(alphaDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}
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