---
title: aclnnOneHot
description: "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："
url: https://www.hiascend.com/document/detail/zh/Atlas200IDKA2DeveloperKit/latest/Application%20Development%20Guide/aadgc/aclcppdevg_03_0479.html
sourcePath: /source/zh/Atlas200IDKA2DeveloperKit/23.0.RC2/Application Development Guide/aadgc/aclcppdevg_03_0479.html
indexId: 7ec3db5f190f43d316401ae76a80641852851c4e93ee772f4b50cf5433be2e22105
---
# aclnnOneHot

#### 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：

- 第一段接口：aclnnStatus aclnnOneHotGetWorkspaceSize(const aclTensor *self, int64_t numClasses, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
- 第二段接口：aclnnStatus aclnnOneHot(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)


#### 功能描述

- 算子功能：于长度为n的输入self，经过OneHot计算后得到一个大小为[n∗k]的输出out，其中k的值等于numClasses。
- 计算公式：
- 示例：

```
self = tensor([0, 1, 2, 0, 1])
out = aclnnOneHot(self, 5)
# numClasses=5，输出结果如下
tensor([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0]])
```


#### aclnnOneHotGetWorkspaceSize

- 接口定义：
  aclnnStatus aclnnOneHotGetWorkspaceSize(const aclTensor *self, int64_t numClasses, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)

- 参数说明：

  - self：Device侧的aclTensor，数据类型支持INT32、INT64，支持非连续的Tensor，数据格式支持ND。
  - numClasses：Host侧整型，类别数，数据类型支持INT64，最小值为-1。如果self存在元素大于numClasses，这些元素会被编码成全0的Tensor。
  - out：Device侧的aclTensor，数据类型支持INT32、INT64。支持非连续的Tensor，数据格式支持ND。
  - workspaceSize：返回用户需要在Device侧申请的workspace大小。
  - executor：返回op执行器，包含了算子计算流程。
- 返回值：
  返回aclnnStatus状态码，具体参见aclnn返回码。

  第一段接口完成入参校验，出现以下场景时报错： 返回161001（ACLNN_ERR_PARAM_NULLPTR）：传入的self或out是空指针。 返回161002（ACLNN_ERR_PARAM_INVALID）： self和out的数据类型或数据格式不在支持的范围之内。 self为空且numClasses≤0。 numClasses≤-1。


#### aclnnOneHot

- 接口定义：
  aclnnStatus aclnnOneHot(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

- 参数说明：

  - workspace：在Device侧申请的workspace内存起址。
  - workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnOneHotGetWorkspaceSize获取。
  - executor：op执行器，包含了算子计算流程。
  - stream：指定执行任务的AscendCL stream流。
- 返回值：
  返回aclnnStatus状态码，具体参见aclnn返回码。


#### 调用示例

```
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_one_hot.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_ND,
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 == 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};
int64_t numClasses = 4;
std::vector<int64_t> outShape = {4, 2, 4};
void *selfDeviceAddr = nullptr;
void *outDeviceAddr = nullptr;
aclTensor *self = nullptr;
aclTensor *out = nullptr;
std::vector<long int> selfHostData = {0, 1, 2, 3, 3, 2, 1, 0};
std::vector<long int> outHostData = {
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};
// 创建self aclTensor
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_INT64, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建out aclTensor
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_INT64, &out);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 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aclnnOneHot第一段接口
ret =
aclnnOneHotGetWorkspaceSize(self, numClasses, out, &workspaceSize, &executor)
;
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnOneHotGetWorkspaceSize 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aclnnOneHot第二段接口
ret =
aclnnOneHot(workspaceAddr, workspaceSize, executor, stream)
;
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnOneHot 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(outShape);
std::vector<long int> resultData(size, 0);
ret = aclrtMemcpy(resultData.data(),
resultData.size() * sizeof(resultData[0]),
outDeviceAddr,
size * sizeof(long int),
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: %ld\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(out);
// 7. 释放device资源，需要根据具体API的接口定义修改
aclrtFree(selfDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtDestroyContext(context);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}
```