---
title: aclnnBincount
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_0237.html
sourcePath: /source/zh/Atlas200IDKA2DeveloperKit/23.0.RC2/Application Development Guide/aadgc/aclcppdevg_03_0237.html
indexId: ffa2be62e08cf4bf01ec11d56a655a9f4004d1a86607d22558faf9c29d76db79105
---
# aclnnBincount

#### 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 aclnnBincountGetWorkspaceSize(const aclTensor *self, const aclTensor * weights, int64_t minlength, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
- 第二段接口：aclnnStatus aclnnBincount(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)


#### 功能描述

- 算子功能：计算非负整数数组中每个值的频率。
  当weights为空时，self中self[i]对应的数每出现1次，则其频率加1；当weights不为空时，self中self[i]对应的数每出现1次，则其频率加weights[i]，最后存放到out的第self[i]+1 位置上；因此out大小为self 中最大值+1。

- 计算公式：
  如果selfi是self位置i上的值，如果指定了weights，公式如下：

  否则公式如下：


#### aclnnBincountGetWorkspaceSize

- 接口定义：
  aclnnStatus aclnnBincountGetWorkspaceSize(const aclTensor *self, const aclTensor * weights, int64_t minlength, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)

- 参数说明：

  - self：Device侧aclTensor，输入张量，数据类型支持INT8、INT16、INT32、INT64、UINT8，且必须是非负整数。支持非连续的Tensor，数据格式支持1维ND。
  - weights：Device侧aclTensor，self每个值的权重，可为空指针。数据类型支持FLOAT16、FLOAT、DOUBLE、INT8、INT16、INT64、INT32、UINT8、BOOL。支持非连续的Tensor，数据格式支持1维ND，且shape需要与self一致。
  - minlength：Host侧int类型，指定输出最小长度。如果计算出来的self最大值小于minlength，则输出长度为minlength。数据类型支持INT64。
  - out：Device侧aclTensor，输出张量，数据类型支持FLOAT、INT32、INT64、DOUBLE，数据格式为1维ND，支持非连续的Tensor。

  - 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是空指针。 返回161002（ACLNN_ERR_PARAM_INVALID）： self、weights的数据类型和数据格式不在支持的范围内。 当weights不为空时，self、weights的shape不一致。


#### aclnnBincount

- 接口定义：
  aclnnStatus aclnnBincount(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const 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aclnnBincountGetWorkspaceSize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 <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_max.h"
#include "aclnnop/aclnn_bincount.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 contextMax;
aclrtStream streamMax;
auto ret = Init(deviceId, &contextMax, &streamMax);
// 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. 先调max获取self中的最大值，然后使用该最大值与minlength比较最大值去获得输出tensor的size
// 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 = {8};
std::vector<int64_t> maxOutShape={1};
std::vector<int32_t> selfHostData = {8,1,2,3,4,5,6,7};
std::vector<int32_t> maxOutHostData(1, 0);
void* selfDeviceAddr = nullptr;
void* maxOutDeviceAddr = nullptr;
aclTensor* self = nullptr;
aclTensor* maxOut = nullptr;
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_INT32, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(maxOutHostData, maxOutShape, &maxOutDeviceAddr, aclDataType::ACL_INT32, &maxOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
//调用CANN算子库API，需要修改为具体的算子接口
uint64_t workspaceSizeMax = 0;
aclOpExecutor* executorMax;
// 调用aclnnMax第一段接口
ret = aclnnMaxGetWorkspaceSize(self, maxOut, &workspaceSizeMax, &executorMax);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMaxGetWorkspaceSize 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* workspaceAddrMax = nullptr;
if (workspaceSizeMax > 0) {
ret = aclrtMalloc(&workspaceAddrMax, workspaceSizeMax, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret;);
}
// 调用aclnnMax第二段接口
ret = aclnnMax(workspaceAddrMax, workspaceSizeMax, executorMax, streamMax);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnMax failed. ERROR: %d\n", ret); return ret);
// （固定写法）同步等待任务执行结束
ret = aclrtSynchronizeStream(streamMax);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
// 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
std::vector<int32_t> resultDataMax(1, 0);
ret = aclrtMemcpy(resultDataMax.data(), sizeof(resultDataMax[0]), maxOutDeviceAddr, sizeof(int32_t), 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);
// 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
aclDestroyTensor(maxOut);
// 3.
构造输入与输出，需要根据API的接口自定义构造
int64_t minlength = 0;
int64_t outSize = (resultDataMax[0] < minlength) ? minlength : resultDataMax[0] + 1;
std::vector<int64_t> weightsShape = {8};
std::vector<int64_t> outShape = {outSize};
void* weightsDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* weights = nullptr;
aclTensor* out = nullptr;
std::vector<float> weightsHostData = {1, 1, 1.1, 2, 2, 2, 3, 3};
std::vector<float> outHostData(outSize, 0);
ret = CreateAclTensor(weightsHostData, weightsShape, &weightsDeviceAddr, aclDataType::ACL_FLOAT, &weights);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
CHECK_RET(ret == ACL_SUCCESS, return ret);
//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aclnnBincount第一段接口
ret =
aclnnBincountGetWorkspaceSize(self, weights, minlength, out, &workspaceSize, &executor)
;
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBincountGetWorkspaceSize 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aclnnBincount第二段接口
ret =
aclnnBincount(workspaceAddr, workspaceSize, executor, streamMax)
;
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBincount failed. ERROR: %d\n", ret); return ret);
// 4. （固定写法）同步等待任务执行结束
ret = aclrtSynchronizeStream(streamMax);
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<float> resultData(size, 55);
ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(resultData[0]),
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(weights);
aclDestroyTensor(out);
return 0;
}
```