---
title: aclnnHardshrinkBackward
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_0313.html
sourcePath: /source/zh/Atlas200IDKA2DeveloperKit/23.0.RC2/Application Development Guide/aadgc/aclcppdevg_03_0313.html
indexId: 5fafb6d3382b5a70934c9218c7b21d9bee81ed58c9ee822e81f06afd8f2a3c16105
---
# aclnnHardshrinkBackward

#### 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 aclnnHardshrinkBackwardGetWorkspaceSize(const aclTensor *gradOutput, const aclTensor *self, const aclScalar *lambd, aclTensor *gradInput, uint64_t *workspaceSize, aclOpExecutor **executor)
- 第二段接口：aclnnStatus aclnnHardshrinkBackward(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)


#### 功能描述

- 算子功能：Hardshrink函数(  aclnnHardshrink
)的反向接口，计算反向传播的梯度gradInput。
- 计算公式：


#### aclnnHardshrinkBackwardGetWorkspaceSize

- 接口定义：
  aclnnStatus aclnnHardshrinkBackwardGetWorkspaceSize(const aclTensor *gradOutput, const aclTensor *self, const aclScalar *lambd, aclTensor *gradInput, uint64_t *workspaceSize, aclOpExecutor **executor)

- 参数说明：

  - gradOutput：Device侧的aclTensor，公式中的输入gradOutput，数据类型支持FLOAT16、FLOAT。shape与self满足broadcast条件，支持非连续的Tensor，支持空Tensor传入，数据格式支持ND。
  - self：Device侧的aclTensor，公式中的输入x，数据类型支持FLOAT16、FLOAT。shape与gradOutput满足broadcast条件，支持非连续的Tensor，支持空Tensor传入，数据格式支持ND。
  - lambd：Host侧的aclScalar，公式中的输入λ，数据类型支持FLOAT。当λ小于0时，按0计算。
  - gradInput：Device侧的aclTensor，公式中的gradInput，数据类型支持FLOAT16、FLOAT，且shape需要与self和gradOutput的broadcast结果一致。支持非连续的Tensor，支持空Tensor传入，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gradOutput、self、lambd或gradInput是空指针。 返回161002（ACLNN_ERR_PARAM_INVALID）： self、lambd和gradInput的数据类型不在支持的范围之内。 self和gradIOutput的shape不满足broadcast条件。 gradInput shape与self和gradOutput的broadcast结果不一致。


#### aclnnHardshrinkBackward

- 接口定义：
  aclnnStatus aclnnHardshrinkBackward(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aclnnHardshrinkBackwardGetWorkspaceSize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_hardshrink_backward.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 shapeSize = 1;
for (auto i : shape) {
shapeSize *= i;
}
return shapeSize;
}
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_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> gradOutputShape = {2, 2};
std::vector<int64_t> selfShape = {2, 2};
std::vector<int64_t> gradInputShape = {2, 2};
void* gradOutputDeviceAddr = nullptr;
void* selfDeviceAddr = nullptr;
void* gradInputDeviceAddr = nullptr;
aclTensor* gradOutput = nullptr;
aclTensor* self = nullptr;
aclTensor* gradInput = nullptr;
aclScalar* lambd = nullptr;
std::vector<float> gradOutputHostData = {0, 0, 1, 1};
std::vector<float> selfHostData = {1, 2, 3, 4};
std::vector<float> gradInputHostData(4, 0);
float lambdValue = 2.0f;
// 创建gradOutput aclTensor
ret = CreateAclTensor(gradOutputHostData, gradOutputShape, &gradOutputDeviceAddr, aclDataType::ACL_FLOAT, &gradOutput);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建self aclTensor
ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建gradInput aclTensor
ret = CreateAclTensor(gradInputHostData, gradInputShape, &gradInputDeviceAddr, aclDataType::ACL_FLOAT, &gradInput);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 创建lambd aclScalar
lambd = aclCreateScalar(&lambdValue, aclDataType::ACL_FLOAT);
CHECK_RET(lambd != nullptr, return ret);
// 3. 调用CANN算子库API，需要修改为具体的Api名称
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// 调用aclnnHardshrinkBackward第一段接口
ret =
aclnnHardshrinkBackwardGetWorkspaceSize(gradOutput, self, lambd, gradInput, &workspaceSize, &executor)
;
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnHardshrinkBackward 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aclnnHardshrinkBackward第二段接口
ret =
aclnnHardshrinkBackward(workspaceAddr, workspaceSize, executor, stream)
;
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnHardshrinkBackward 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(gradInputShape);
std::vector<float> resultData(size, 0);
ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), gradInputDeviceAddr,
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，需要根据具体API的接口定义修改
aclDestroyTensor(gradOutput);
aclDestroyTensor(self);
aclDestroyTensor(gradInput);
aclDestroyScalar(lambd);
// 7. 释放device资源，需要根据具体API的接口定义参数
aclrtFree(gradOutputDeviceAddr);
aclrtFree(selfDeviceAddr);
aclrtFree(gradInputDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtDestroyContext(context);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}
```