aclnnConvDepthwise2d

Atlas 训练系列产品支持该算子。

Atlas A2训练系列产品支持该算子。

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

aclnnConvDepthwise2dGetWorkspaceSize

aclnnConvDepthwise2d

调用示例

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/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，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_NCHW,
                            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根据自己的需要处理
  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
  std::vector<int64_t> shapeSelf = {2, 2, 2, 2};
  std::vector<int64_t> shapeWeight = {2, 1, 1, 1};
  std::vector<int64_t> shapeBias = {2};
  std::vector<int64_t> shapeResult = {2, 2, 4, 4};
  void* deviceDataSelf = nullptr;
  void* deviceDataWeight = nullptr;
  void* deviceDataBias = nullptr;
  void* deviceDataResult = nullptr;
  aclTensor* self = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* bias = nullptr;
  aclTensor* result = nullptr;
  aclIntArray* kernelSize = nullptr;
  aclIntArray* stride = nullptr;
  aclIntArray* padding = nullptr;
  aclIntArray* dilation = nullptr;
  std::vector<float> selfData(GetShapeSize(shapeSelf) * 2, 1);
  std::vector<float> weightData(GetShapeSize(shapeWeight) * 2, 1);
  std::vector<float> biasData(GetShapeSize(shapeBias) * 2, 1);
  std::vector<float> outData(GetShapeSize(shapeResult) * 2, 1);
  std::vector<int64_t> kernelSizeData = {1, 1};
  std::vector<int64_t> strideData = {1, 1};
  std::vector<int64_t> paddingData = {1, 1};
  std::vector<int64_t> dilationData = {1, 1};

  // 创建self aclTensor
  ret = CreateAclTensor(selfData, shapeSelf, &deviceDataSelf, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建weight aclTensor
  ret = CreateAclTensor(weightData, shapeWeight, &deviceDataWeight, aclDataType::ACL_FLOAT, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建bias aclTensor
  ret = CreateAclTensor(biasData, shapeBias, &deviceDataBias, aclDataType::ACL_FLOAT, &bias);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outData, shapeResult, &deviceDataResult, aclDataType::ACL_FLOAT, &result);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  kernelSize = aclCreateIntArray(kernelSizeData.data(), 2);
  stride = aclCreateIntArray(strideData.data(), 2);
  padding = aclCreateIntArray(paddingData.data(), 2);
  dilation = aclCreateIntArray(dilationData.data(), 2);

  // 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aclnnConvDepthwise2d第一段接口
  ret = aclnnConvDepthwise2dGetWorkspaceSize(self, weight, kernelSize, bias, stride, padding, dilation, result, 1, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvDepthwise2dGetWorkspaceSize 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aclnnConvDepthwise2d第二段接口
  ret = aclnnConvDepthwise2d(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnConvDepthwise2d 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，需要根据具体API的接口定义修改
  aclDestroyTensor(self);
  aclDestroyTensor(weight);
  aclDestroyTensor(bias);
  aclDestroyTensor(result);
  aclDestroyIntArray(kernelSize);
  aclDestroyIntArray(stride);
  aclDestroyIntArray(padding);
  aclDestroyIntArray(dilation);
  return 0;
}
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