aclnnUnique2

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：返回输入张量中的唯一元素。

aclnnUnique2GetWorkspaceSize

aclnnUnique2

约束与限制

无

调用示例

#include <iostream>
 #include <vector>
 #include "acl/acl.h"
 #include "aclnnop/level2/aclnn_unique2.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_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> selfShape = {4, 2};
   std::vector<int64_t> valueShape = {8};
   std::vector<int64_t> inverseShape = {4, 2};
   std::vector<int64_t> countsShape = {8};
   void* selfDeviceAddr = nullptr;
   void* valueDeviceAddr = nullptr;
   void* inverseDeviceAddr = nullptr;
   void* countsDeviceAddr = nullptr;
   aclTensor* self = nullptr;
   aclTensor* valueOut = nullptr;
   aclTensor* inverseOut = nullptr;
   aclTensor* countsOut = nullptr;
   std::vector<float> selfHostData = {0, 1, 2, 3, 4, 1, 2, 3};
   std::vector<float> valueHostData = {0, 0, 0, 0, 0, 0, 0, 0};
   std::vector<int64_t> inverseHostData = {0, 0, 0, 0, 0, 0, 0, 0};
   std::vector<int64_t> countsHostData = {0, 0, 0, 0, 0, 0, 0, 0};
   bool sorted = false;
   bool returnInverse = false;
   bool returnCounts = false;

   // 创建self aclTensor
   ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
   CHECK_RET(ret == ACL_SUCCESS, return ret);
   // 创建valueOut aclTensor
   ret = CreateAclTensor(valueHostData, valueShape, &valueDeviceAddr, aclDataType::ACL_FLOAT, &valueOut);
   CHECK_RET(ret == ACL_SUCCESS, return ret);
   // 创建inverseOut aclTensor
   ret = CreateAclTensor(inverseHostData, inverseShape, &inverseDeviceAddr, aclDataType::ACL_INT64, &inverseOut);
   CHECK_RET(ret == ACL_SUCCESS, return ret);
   // 创建countsOut aclTensor
   ret = CreateAclTensor(countsHostData, countsShape, &countsDeviceAddr, aclDataType::ACL_INT64, &countsOut);
   CHECK_RET(ret == ACL_SUCCESS, return ret);

   // 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aclnnUnique2第一段接口
   ret = aclnnUnique2GetWorkspaceSize(self, sorted, returnInverse, returnCounts, valueOut, inverseOut, countsOut, &workspaceSize, &executor);
   CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUnique2GetWorkspaceSize 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aclnnUnique2第二段接口
   ret = aclnnUnique2(workspaceAddr, workspaceSize, executor, stream);
   CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUnique2 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(valueShape);
   std::vector<float> resultData(size, 0);
   ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), valueDeviceAddr, 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(self);
   aclDestroyTensor(valueOut);
   aclDestroyTensor(inverseOut);
   aclDestroyTensor(countsOut);
   return 0;
 }
父主题： NN类算子接口（aclnn）