aclnnPreluBackward
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 aclnnPreluBackwardGetWorkspaceSize(const aclTensor *gradOutput, const aclTensor *self, const aclTensor *weight, aclTensor *gradIntput, aclTensor *gradWeight, uint64_t *workspaceSize, aclOpExecutor **executor)
- 第二段接口：aclnnStatus aclnnPreluBackward(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
功能描述
- 算子功能：激活函数PReLU（aclnnPrelu）的反向计算。
- 计算公式：
gradWeight的计算公式如下：
aclnnPreluBackwardGetWorkspaceSize
- 接口定义：
aclnnStatus aclnnPreluBackwardGetWorkspaceSize(const aclTensor *gradOutput, const aclTensor *self, const aclTensor *weight, aclTensor *gradIntput, aclTensor *gradWeight, uint64_t *workspaceSize, aclOpExecutor **executor)
- 参数说明：
- gradOutput：Device侧的aclTensor，输入张量，反向传播的梯度值，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND。
- self：Device侧的aclTensor，输入张量，prelu正向的输入，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND。
- weight：Device侧的aclTensor，输入张量，prelu的权重，数据类型支持FLOAT、FLOAT16，支持非连续的Tensor，数据格式支持ND，且元素个数必须等于self的通道数或者1。
- gradIntput：Device侧的aclTensor，输出张量，是self的反向梯度，与self的维度和数据类型相同。
- gradWeight：Device侧的aclTensor，输出张量，是weight的反向梯度，数据类型支持FLOAT16、FLOAT32。支持非连续的Tensor，数据格式支持ND，需要与weight的数据类型相同，weight的元素个数为1时，shape需要与weight相同；weight元素个数不为1时，需要为1维Tensor，且元素个数与weight的元素个数相同。
- 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、weight、gradInput、gradWeight是空指针。
- 返回161002（ACLNN_ERR_PARAM_INVALID）：
- gradOutput、self、weight、gradInput、gradWeight的数据类型不在支持的范围之内。
- gradOutput、self、weight、gradInput、gradWeight数据类型不同。
- gradOutput、self、weight、gradInput、gradWeight维度超过8。
- weight的元素个数不等于self的通道数或者1。
- weight的元素个数为1时，gradWeight的shape与weight不相同。
aclnnPreluBackward
- 接口定义：
aclnnStatus aclnnPreluBackward(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aclnnPreluBackwardGetWorkspaceSize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_prelu_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 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 == 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> selfShape = {4, 2};
std::vector<int64_t> weightShape = {2};
std::vector<int64_t> gradOutputShape = {4, 2};
std::vector<int64_t> gradInputShape = {4, 2};
std::vector<int64_t> gradWeightShape = {2};
void* selfDeviceAddr = nullptr;
void* gradOutputDeviceAddr = nullptr;
void* weightDeviceAddr = nullptr;
void* gradInputDeviceAddr = nullptr;
void* gradWeightDeviceAddr = nullptr;
aclTensor* self = nullptr;
aclTensor* weight = nullptr;
aclTensor* gradOutput = nullptr;
aclTensor* gradInput = nullptr;
aclTensor* gradWeight = nullptr;
std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
std::vector<float> weightHostData = {0.5, 0.5};
std::vector<float> gradOutputHostData = {1, 1, 1, 1, 1, 1, 1, 1};
std::vector<float> gradInputHostData = {0, 0, 0, 0, 0, 0, 0, 0};
std::vector<float> gradWeightHostData = {0, 0};
// 创建weight aclTensor
ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT, &weight);
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gradOutput aclTensor
ret = CreateAclTensor(gradOutputHostData, gradOutputShape, &gradOutputDeviceAddr, aclDataType::ACL_FLOAT,
&gradOutput);
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gradWeight aclTensor
ret = CreateAclTensor(gradWeightHostData, gradWeightShape, &gradWeightDeviceAddr, aclDataType::ACL_FLOAT, &gradWeight);
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aclnnPreluBackward第一段接口
ret = aclnnPreluBackwardGetWorkspaceSize(gradOutput, self, weight, gradInput, gradWeight, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnPreluBackwardGetWorkspaceSize 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aclnnPreluBackward第二段接口
ret = aclnnPreluBackward(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnPreluBackward 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 gradInputSize = GetShapeSize(gradInputShape);
std::vector<float> gradInputResultData(gradInputSize, 0);
ret = aclrtMemcpy(gradInputResultData.data(), gradInputResultData.size() * sizeof(gradInputResultData[0]), gradInputDeviceAddr, gradInputSize * 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 < gradInputSize; i++) {
LOG_PRINT("gradInput[%ld] is: %f\n", i, gradInputResultData[i]);
}
auto gradWeightSize = GetShapeSize(gradWeightShape);
std::vector<float> gradWeightResultData(gradWeightSize, 0);
ret = aclrtMemcpy(gradWeightResultData.data(), gradWeightResultData.size() * sizeof(gradWeightResultData[0]), gradWeightDeviceAddr, gradWeightSize * 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 < gradWeightSize; i++) {
LOG_PRINT("gradWeight[%ld] is: %f\n", i, gradWeightResultData[i]);
}
// 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
aclDestroyTensor(gradOutput);
aclDestroyTensor(self);
aclDestroyTensor(weight);
aclDestroyTensor(gradInput);
aclDestroyTensor(gradWeight);
// 7. 释放device资源， 需要根据具体API的接口定义修改
aclrtFree(selfDeviceAddr);
aclrtFree(gradOutputDeviceAddr);
aclrtFree(weightDeviceAddr);
aclrtFree(gradInputDeviceAddr);
aclrtFree(gradWeightDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtDestroyContext(context);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}
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