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aclnnBinaryCrossEntropyWithLogits

接口原型

每个算子有两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。两段式接口如下：

  • 第一段接口：aclnnStatus aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize(const aclTensor* self, const aclTensor* target, const aclTensor* weightOptional, const aclTensor* posWeightOptional, int64_t reduction, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)
  • 第二段接口：aclnnstatus aclnnBinaryCrossEntropyWithLogits(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

功能描述

  • 算子功能：计算logits输出self与标签target之间的BCE损失函数（Binary CrossEntropy Loss）。
  • 计算公式：
    • 单标签场景

    • 多标签场景

aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize

  • 接口定义：

    aclnnStatus aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize(const aclTensor* self, const aclTensor* target, const aclTensor* weightOptional, const aclTensor* posWeightOptional, int64_t reduction, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)

  • 参数说明：
    • self：连接层输出，Device侧的aclTensor，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND。
    • target：label标签值，Device侧的aclTensor，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND。
    • weightOptional：可选入参，二分交叉熵权重，Device侧的aclTensor，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND。
    • posWeightOptional：可选入参，各类的正类权重，Device侧的aclTensor，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND。
    • reduction：可选入参，输出结果计算方式，Host侧的int类型值。 取值支持0、1、2，0代表None不操作，1代表Mean求损失函数均值，2代表Sum求损失函数的和。
    • out：输出误差，Device侧的aclTensor，数据类型支持FLOAT，支持非连续的Tensor，数据格式支持ND。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。
  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    第一段接口完成入参校验，出现以下场景时报错：

    • 返回161001（ACLNN_ERR_PARAM_NULLPTR）：传入的tensor或out是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • self、target、weightOptional和posWeightOptional的数据类型和数据格式不在支持的范围之内。
      • self和target维度不一致。
      • weightOptional、posWeightOptional不能扩展成self、target形状。
      • reduction取值非0、1、2三值之一。

aclnnBinaryCrossEntropyWithLogits

  • 接口定义：

    aclnnstatus aclnnBinaryCrossEntropyWithLogits(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

  • 参数说明：
    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。
  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例

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#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_binary_cross_entropy_with_logits.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;
}

void PrintOutResult(std::vector<int64_t> &shape, void** deviceAddr) {
  auto size = GetShapeSize(shape);
  std::vector<float> resultData(size, 0);
  auto ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]),
                         *deviceAddr, size * sizeof(float), ACL_MEMCPY_DEVICE_TO_HOST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  }
}

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) {
  // 固定写法，AscendCL初始化
  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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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);

  // 调用aclrtMalloc申请device侧内存
  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);

  // 调用aclrtMemcpy将Host侧数据拷贝到device侧内存上
  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);

  // 计算连续tensor的strides
  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];
  }

  // 调用aclCreateTensor接口创建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. （固定写法）device/context/stream初始化, 参考AscendCL对外接口列表
  // 根据自己的实际device填写deviceId
  int32_t deviceId = 0;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);  // check根据自己的需要处理

  // 2. 构造输入与输出，需要根据API的接口自定义构造
  std::vector<int64_t> inputShape = {4, 2};
  std::vector<int64_t> targetShape = {4, 2};
  std::vector<int64_t> weightShape = {4, 2};
  std::vector<int64_t> posWeightShape = {4, 2};
  std::vector<int64_t> outShape = {4, 2};
  void* inputDeviceAddr = nullptr;
  void* targetDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* posWeightDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* input = nullptr;
  aclTensor* target = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* posWeight = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> inputHostData = {0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4};
  std::vector<float> targetHostData = {0.2, 0.2, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1};
  std::vector<float> weightHostData = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
  std::vector<float> posWeightHostData = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
  std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0};

  ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(targetHostData, targetShape, &targetDeviceAddr, aclDataType::ACL_FLOAT, &target);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(posWeightHostData, posWeightShape, &posWeightDeviceAddr, aclDataType::ACL_FLOAT, &posWeight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  int64_t reduction = 0;

  // 3.调用CANN算子库API，需要修改为具体的算子接口
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize第一段接口
  ret = aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize(input, target, weight, posWeight, reduction, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBinaryCrossEntropyWithLogitsGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存
  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;);
  }
  // 调用aclnnBinaryCrossEntropyWithLogits第二段接口
  ret = aclnnBinaryCrossEntropyWithLogits(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBinaryCrossEntropyWithLogits failed. ERROR: %d\n", ret); return ret);

  // 4. （固定写法）同步等待任务执行结束
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // 5. 获取输出的值，将device侧内存上的结果拷贝至Host侧，需要根据具体API的接口定义修改
  PrintOutResult(outShape, &outDeviceAddr);

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(input);
  aclDestroyTensor(target);
  aclDestroyTensor(weight);
  aclDestroyTensor(posWeight);
  aclDestroyTensor(out);
  return 0;
}
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