aclnnGridSampler2DBackward

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

算子功能：算子GridSampler2D（aclnnGridSampler2D）的反向计算。

aclnnGridSampler2DBackwardGetWorkspaceSize

aclnnGridSampler2DBackward

约束与限制

无

调用示例

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/level2/aclnn_grid_sampler2d_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, 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 == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

  // 2. 构造输入与输出，需要根据API的接口自定义构造
  int64_t interpolationMode = 0;
  int64_t paddingMode = 0;
  bool alignCorners = false;
  aclBoolArray* outputMask = nullptr;
  std::vector<int64_t> gradOutputShape = {1, 1, 3, 3};
  std::vector<int64_t> inputShape = {1, 1, 5, 8};
  std::vector<int64_t> gridShape = {1, 3, 3, 2};
  std::vector<int64_t> inputGradShape = {1, 1, 5, 8};
  std::vector<int64_t> gridGradShape = {1, 3, 3, 2};
  void* gradOutputDeviceAddr = nullptr;
  void* inputDeviceAddr = nullptr;
  void* gridDeviceAddr = nullptr;
  void* inputGradDeviceAddr = nullptr;
  void* gridGradDeviceAddr = nullptr;
  aclTensor* gradOutput = nullptr;
  aclTensor* input = nullptr;
  aclTensor* grid = nullptr;
  aclTensor* inputGrad = nullptr;
  aclTensor* gridGrad = nullptr;
  std::vector<float> gradOutputHostData = {1, 1, 1, 1, 1, 1, 1, 1, 1};
  std::vector<float> inputHostData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
                                      24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40};
  std::vector<float> gridHostData = {-1, -1, 0, -1, 1, -1, -1, 0, 0, 0, 1, 0, -1, 1, 0, 1, 1, 1};
  std::vector<float> inputGradHostData = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                                          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
  std::vector<float> gridGradHostData = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
  bool maskValue[2] = {true, true};
  outputMask = aclCreateBoolArray(&(maskValue[0]), 2);

  // 创建gradOutput aclTensor
  ret = CreateAclTensor(gradOutputHostData, gradOutputShape, &gradOutputDeviceAddr, aclDataType::ACL_FLOAT, &gradOutput);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建input aclTensor
  ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建grid aclTensor
  ret = CreateAclTensor(gridHostData, gridShape, &gridDeviceAddr, aclDataType::ACL_FLOAT, &grid);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建inputGrad aclTensor
  ret = CreateAclTensor(inputGradHostData, inputGradShape, &inputGradDeviceAddr, aclDataType::ACL_FLOAT, &inputGrad);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建gridGrad aclTensor
  ret = CreateAclTensor(gridGradHostData, gridGradShape, &gridGradDeviceAddr, aclDataType::ACL_FLOAT, &gridGrad);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. 调用CANN算子库API，需要修改为具体的Api名称
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnGridSampler2DBackward第一段接口
  ret = aclnnGridSampler2DBackwardGetWorkspaceSize(gradOutput, input, grid, interpolationMode, paddingMode,
                                                   alignCorners, outputMask, inputGrad, gridGrad,
                                                   &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGridSampler2DBackwardGetWorkspaceSize 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aclnnGridSampler2DBackward第二段接口
  ret = aclnnGridSampler2DBackward(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnGridSampler2DBackward 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
  auto inputGradSize = GetShapeSize(inputGradShape);
  std::vector<float> inputGradResultData(inputGradSize, 0);
  ret = aclrtMemcpy(inputGradResultData.data(), inputGradResultData.size() * sizeof(inputGradResultData[0]),
                    inputGradDeviceAddr, inputGradSize * sizeof(inputGradResultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy inputGradResultData from device to host failed. ERROR: %d\n", ret);
            return ret);
  for (int64_t i = 0; i < inputGradSize; i++) {
    LOG_PRINT("inputGradResultData[%ld] is: %f\n", i, inputGradResultData[i]);
  }

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

  // 6. 释放aclTensor和aclBoolArray，需要根据具体API的接口定义修改
  aclDestroyTensor(gradOutput);
  aclDestroyTensor(input);
  aclDestroyTensor(grid);
  aclDestroyTensor(inputGrad);
  aclDestroyTensor(gridGrad);
  aclDestroyBoolArray(outputMask);
  return 0;
}
父主题： NN类算子接口（aclnn）