aclnnConvolution

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

aclnnConvolutionGetWorkspaceSize

aclnnConvolution

约束与限制

对于Atlas 训练系列产品，Cube单元不支持FLOAT32计算。当输入为FLOAT32，可通过设置cubeMathType=1（ALLOW_FP32_DOWN_PRECISION）来允许接口内部cast到FLOAT16进行计算。

调用示例

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/level2/aclnn_convolution.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;
}

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_NCHW,
                            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根据自己的需要处理
  CHECK_RET(ret == 0, 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
  std::vector<int64_t> shapeInput = {2, 2, 2, 2};
  std::vector<int64_t> shapeWeight = {1, 2, 1, 1};
  std::vector<int64_t> shapeResult = {2, 1, 4, 4};
  std::vector<int64_t> convStrides;
  std::vector<int64_t> convPads;
  std::vector<int64_t> convOutPads;
  std::vector<int64_t> convDilations;

  void *deviceDataA = nullptr;
  void *deviceDataB = nullptr;
  void *deviceDataResult = nullptr;

  aclTensor* input = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* result = nullptr;
  std::vector<float> inputData(GetShapeSize(shapeInput) * 2, 1);
  std::vector<float> weightData(GetShapeSize(shapeWeight) * 2, 1);
  std::vector<float> outputData(GetShapeSize(shapeResult) * 2, 1);

  convStrides = {1, 1, 1, 1};
  convPads = {1, 1, 1, 1};
  convOutPads = {1, 1, 1, 1};
  convDilations = {1, 1, 1, 1};
  // 创建self aclTensor
  ret = CreateAclTensor(inputData, shapeInput, &deviceDataA, aclDataType::ACL_FLOAT16, &input);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建other aclTensor
  ret = CreateAclTensor(weightData, shapeWeight, &deviceDataB, aclDataType::ACL_FLOAT16, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outputData, shapeResult, &deviceDataResult, aclDataType::ACL_FLOAT16, &result);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  aclIntArray *strides = aclCreateIntArray(convStrides.data(), 2);
  aclIntArray *pads = aclCreateIntArray(convPads.data(), 2);
  aclIntArray *outPads = aclCreateIntArray(convOutPads.data(), 2);
  aclIntArray *dilations = aclCreateIntArray(convDilations.data(), 2);

  // 3. 调用CANN算子库API，需要修改为具体的HostApi
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnConvolution第一段接口
  ret = aclnnConvolutionGetWorkspaceSize( input, weight,  nullptr, strides, pads, dilations, false, outPads,  1, result, 1, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvolutionGetWorkspaceSize 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aclnnConvolution第二段接口
  ret = aclnnConvolution(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvolution 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 size = GetShapeSize(shapeResult);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), deviceDataResult, 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 ret);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  }

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(input);
  aclDestroyTensor(weight);
  aclDestroyTensor(result);
  aclDestroyIntArray(strides);
  aclDestroyIntArray(pads);
  aclDestroyIntArray(outPads);
  aclDestroyIntArray(dilations);
  return 0;
}
父主题： NN类算子接口（aclnn）