aclnnSoftMarginLossBackward

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Function

Computes the backward propagation of the aclnnSoftMarginLoss binary logistic loss function. reduction specifies the computation method of the loss function, supporting 'none', 'mean', and 'sum'. 'none' indicates that reduction is not applied, 'mean' indicates that the sum of the output is divided by the number of elements in the output, and 'sum' indicates that the output is summed.

Function Prototype

Each operator is divided into a two-phase API. First call the "aclnnSoftMarginLossBackwardGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that includes the operator computation process, and then call the "aclnnSoftMarginLossBackward" API to perform the computation.

aclnnStatus aclnnSoftMarginLossBackwardGetWorkspaceSize(
    const aclTensor* gradOutput,
    const aclTensor* self,
    const aclTensor* target,
    int64_t          reduction,
    aclTensor*       out,
    uint64_t*        workspaceSize,
    aclOpExecutor**  executor)

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

aclnnSoftMarginLossBackwardGetWorkspaceSize

• Parameters:

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
  gradOutput (aclTensor*)	Input	Gradient backpropagation input.	The shape needs to satisfy the broadcast relationship with self and target. The data type needs to satisfy the data type derivation rule with the data types of self and target (see Conversion Relationship).	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
  self（aclTensor*）	Input.	Input tensor.	The shape needs to satisfy the broadcast relationship with gradOutput and target. The data type must satisfy the data type derivation rule with the data types of gradOutput and target (see conversion relationship).	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
  target (aclTensor*)	Input	The ground-truth label, i.e., input y in the formula.	Its shape needs to satisfy the broadcast relationship with gradOutput and self. The data types of gradOutput and target must satisfy the data type derivation rule (see conversion relationship).	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
  reduction (int64_t)	Input	Specifies the reduction to apply to the output, i.e., the reduction parameter in the formula.	Supports 0 (none) | 1 (mean) | 2 (sum). 'none' indicates no reduction is applied. 'mean' indicates the sum of the output is divided by the number of elements in the output. 'sum' indicates the output is summed.	INT64	-	-	√
  out (aclTensor*)	Output	Computes the output.	The shape is the broadcast result of gradOutput, self, and target.	FLOAT, FLOAT16, BFLOAT16	ND	1-8	√
  workspaceSize (uint64_t*)	Output	Returns the workspace size to be applied on the Device side.	-	-	-	-	-
  executor (aclOpExecutor**)	Output	Returns the op executor, which includes the computation process.	-	-	-	-	-

• 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 Value	Return Code	Description
  ACLNN_ERR_PARAM_NULLPTR	161001	The input gradOutput, self, target, or out is a null pointer.
  ACLNN_ERR_PARAM_INVALID	161002	The data type of gradOutput, self, target, or out is not within the supported range.
  		The shapes of gradOutput, self, target, and out do not satisfy the broadcast rule.
  		The shape after broadcasting gradOutput, self, and target is inconsistent with out.

aclnnSoftMarginLossBackward

• Parameters:

  Parameter	Input/Output	Description
  workspace	Input	Device-side-applied workspace memory address.
  workspaceSize	Input	Device-side-applied workspace size, obtained from the first-phase API aclnnSoftMarginLossBackwardGetWorkspaceSize.
  Executor	Input	Operator executor, which includes the computation flow of the operator.
  Stream	Input	Specifies the stream for task execution.

• Return value:

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

Constraints

• Deterministic computation:

• aclnnSoftMarginLossBackward 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_soft_margin_loss_backward.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 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);
  // Call aclrtMemcpy to copy host-side data to device-side 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);

  // 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];
  }

  // 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);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init failed. ERROR: %d\n", ret); return ret);

  // 2. Construct the input and output. Customize the construction of gradOutput based on the API interface.
  std::vector<int64_t> gradOutputShape = {2, 2};
  std::vector<int64_t> selfShape = {2, 2};
  std::vector<int64_t> targetShape = {2, 2};
  std::vector<int64_t> outShape = {2, 2};
  void* gradOutputDeviceAddr = nullptr;
  void* selfDeviceAddr = nullptr;
  void* targetDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* gradOutput = nullptr;
  aclTensor* self = nullptr;
  aclTensor* target = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> gradOutputHostData = {0, 1, 2, 3};
  std::vector<float> selfHostData = {0, 1, 2, 3};
  std::vector<float> targetHostData = {1, 1, 1, 1};
  std::vector<float> outHostData(4, 0);
  // Create the gradOutput aclTensor.
  ret = CreateAclTensor(gradOutputHostData, gradOutputShape, &gradOutputDeviceAddr,
                        aclDataType::ACL_FLOAT, &gradOutput);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the self aclTensor.
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the target aclTensor.
  ret = CreateAclTensor(targetHostData, targetShape, &targetDeviceAddr, aclDataType::ACL_FLOAT, &target);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the out aclTensor.
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // Create the reduction.
  int64_t reduction = 1;

  // 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 aclnnSoftMarginLossBackward.
  ret = aclnnSoftMarginLossBackwardGetWorkspaceSize(gradOutput, self, target, reduction, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSoftMarginLossBackwardGetWorkspaceSize failed. ERROR: %d\n", ret);
            return ret);
  // Apply for 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 aclnnSoftMarginLossBackward.
  ret = aclnnSoftMarginLossBackward(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnSoftMarginLossBackward 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 the device memory to the host memory. Modify based on the specific API definition.
  auto size = GetShapeSize(outShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr,
                    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 aclTensor and aclScalar. Modify based on the specific API definition.
  aclDestroyTensor(gradOutput);
  aclDestroyTensor(self);
  aclDestroyTensor(target);
  aclDestroyTensor(out);

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