aclnnBatchNormReduceBackward

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：

功能描述

aclnnBatchNormReduceBackwardGetWorkspaceSize

aclnnBatchNormReduceBackward

约束与限制

无

调用示例

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/level2/aclnn_batch_norm_backward_reduce.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，acl初始化
  auto 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);

  ret = aclInit(nullptr);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit 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acl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> gradOutShape = {4, 2};
  std::vector<int64_t> inputShape = {4, 2};
  std::vector<int64_t> meanShape = {2};
  std::vector<int64_t> invstdShape = {2};
  std::vector<int64_t> weightShape = {2};
  std::vector<int64_t> sumDyShape = {2};
  std::vector<int64_t> sumDyXmuShape = {2};
  std::vector<int64_t> gradWeightShape = {2};
  std::vector<int64_t> gradBiasShape = {2};

  void* inputDeviceAddr = nullptr;
  void* gradOutDeviceAddr = nullptr;
  void* meanDeviceAddr = nullptr;
  void* invstdDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* sumDyDeviceAddr = nullptr;
  void* sumDyXmuDeviceAddr = nullptr;
  void* gradWeightDeviceAddr = nullptr;
  void* gradBiasDeviceAddr = nullptr;
  aclTensor* input = nullptr;
  aclTensor* gradOut = nullptr;
  aclTensor* mean = nullptr;
  aclTensor* invstd = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* sumDy = nullptr;
  aclTensor* sumDyXmu = nullptr;
  aclTensor* gradWeight = nullptr;
  aclTensor* gradBias = nullptr;

  std::vector<float> gradOutHostData = {1, 1, 1, 2, 2, 2, 3, 3};
  std::vector<float> inputHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<float> meanHostData = {1, 1};
  std::vector<float> invstdHostData = {1, 1};
  std::vector<float> weightHostData = {1, 1};
  std::vector<float> sumDyHostData = {1, 1};
  std::vector<float> sumDyXmuHostData = {1, 1};
  std::vector<float> gradWeightHostData = {1, 1};
  std::vector<float> gradBiasHostData = {1, 1};

  ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(meanHostData, meanShape, &meanDeviceAddr, aclDataType::ACL_FLOAT, &mean);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(invstdHostData, invstdShape, &invstdDeviceAddr, aclDataType::ACL_FLOAT, &invstd);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  bool inputG = true;
  bool weightG = true;
  bool biasG = true;

  ret = CreateAclTensor(sumDyHostData, sumDyShape, &sumDyDeviceAddr, aclDataType::ACL_FLOAT, &sumDy);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(sumDyXmuHostData, sumDyXmuShape, &sumDyXmuDeviceAddr, aclDataType::ACL_FLOAT, &sumDyXmu);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(gradWeightHostData, gradWeightShape, &gradWeightDeviceAddr, aclDataType::ACL_FLOAT, &gradWeight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  ret = CreateAclTensor(gradBiasHostData, gradBiasShape, &gradBiasDeviceAddr, aclDataType::ACL_FLOAT, &gradBias);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. 调用CANN算子库API
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;

  // 调用aclnnBatchNormReduceBackward第一段接口
  ret = aclnnBatchNormReduceBackwardGetWorkspaceSize(gradOut, input, mean, invstd, weight,
                                                     inputG, weightG, biasG,
                                                     sumDy, sumDyXmu, gradWeight, gradBias,
                                                     &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBatchNormReduceBackwardGetWorkspaceSize 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aclnnBatchNormReduceBackwardNpuImpl第二段接口
  ret = aclnnBatchNormReduceBackward(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBatchNormReduceBackward 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(sumDyShape, &sumDyDeviceAddr);
  PrintOutResult(sumDyXmuShape, &sumDyXmuDeviceAddr);
  PrintOutResult(gradWeightShape, &gradWeightDeviceAddr);
  PrintOutResult(gradBiasShape, &gradBiasDeviceAddr);

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(input);
  aclDestroyTensor(gradOut);
  aclDestroyTensor(mean);
  aclDestroyTensor(invstd);
  aclDestroyTensor(weight);
  aclDestroyTensor(sumDy);
  aclDestroyTensor(sumDyXmu);
  aclDestroyTensor(gradWeight);
  aclDestroyTensor(gradBias);

  return 0;
}
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