本文分享自华为云社区《Ascend C 自定义PRelu算子》，作者： jackwangcumt。
1 PRelu算子概述
PReLU是 Parametric Rectified Linear Unit的缩写，首次由何凯明团队提出，和LeakyReLU非常类似，是Relu的改进版本，在几乎没有增加额外参数的前提下既可以提升模型的拟合能力，又能减小过拟合风险。PReLU的数学表达式我们可以参考pytorch中PReLU的描述（https://pytorch.org/docs/2.1/generated/torch.nn.PReLU.html#prelu）：
![QQ截图20240407151901.png]()
2 Ascend C自定义算子
基于Ascend C进行自定义算子开发之前，需要成功基于昇腾设备安装相关的驱动、固件以及开发者套件。我之前安装的开发者套件版本过低，编译运行官方的Sample部分示例会报错，因此，需要重新安装一个8.0新版本，依次用root执行如下命令：
# 卸载 cann-toolkit_7.0.RC1
root@atlas500ai:/home/kzroot/mysoft# ./Ascend-cann-toolkit_7.0.RC1_linux-aarch64.run --uninstall
# 清空遗留文件
rm -rf /usr/local/Ascend/ascend-toolkit/*
# 安装 cann-toolkit_8.0.RC1.alpha002
./Ascend-cann-toolkit_8.0.RC1.alpha002_linux-aarch64.run --install --install-for-all --quiet
#安装依赖protobuf
pip3 install protobuf==3.20.0
在一个目录下新建单算子工程描述文件 PReluCustom.json ，内容参考如下：
[
{
"op": "PReluCustom",
"language": "cpp",
"input_desc": [
{
"name": "x",
"param_type": "required",
"format": [
"ND"
],
"type": [
"float"
]
}
],
"output_desc": [
{
"name": "y",
"param_type": "required",
"format": [
"ND"
],
"type": [
"float"
]
}
],
"attr": [
{
"name": "alpha",
"param_type": "optional",
"type": "float",
"default_value": "0.002"
}
]
}
]
用开发者套件中内置的算子工程生成工具msopgen ，通过描述文件自动生成单算子工程代码目录：
/usr/local/Ascend/ascend-toolkit/8.0.RC1.alpha002/python/site-packages/bin/msopgen gen -i ./PReluCustom.json
-c ai_core-Ascend310P3 -lan cpp -out ./PReluCustom
执行成功后，会基于C++语言生成单算子工程代码目录PReluCustom，其中包含的CMakePresets.json文件，有几个重要的配置项，特别是开发者套件安装的路径ASCEND_CANN_PACKAGE_PATH，需要根据本地情况进行修改，我这里是 /usr/local/Ascend/ascend-toolkit/latest 否则会出现编译错误，我这里修改的部分代码如下：
{
"version": 1,
"cmakeMinimumRequired": {
"major": 3,
"minor": 19,
"patch": 0
},
"configurePresets": [
{
"name": "default",
"displayName": "Default Config",
"description": "Default build using Unix Makefiles generator",
"generator": "Unix Makefiles",
"binaryDir": "${sourceDir}/build_out",
"cacheVariables": {
"CMAKE_BUILD_TYPE": {
"type": "STRING",
"value": "Release"
},
"ENABLE_SOURCE_PACKAGE": {
"type": "BOOL",
"value": "True"
},
"ENABLE_BINARY_PACKAGE": {
"type": "BOOL",
"value": "True"
},
"ASCEND_COMPUTE_UNIT": {
"type": "STRING",
"value": "ascend310p"
},
"ENABLE_TEST": {
"type": "BOOL",
"value": "True"
},
"vendor_name": {
"type": "STRING",
"value": "customize"
},
"ASCEND_CANN_PACKAGE_PATH": {
"type": "PATH",
"value": "/usr/local/Ascend/ascend-toolkit/latest"
},
"ASCEND_PYTHON_EXECUTABLE": {
"type": "STRING",
"value": "python3"
},
"CMAKE_INSTALL_PREFIX": {
"type": "PATH",
"value": "${sourceDir}/build_out"
},
"ENABLE_CROSS_COMPILE": {
"type": "BOOL",
"value": "False"
},
"CMAKE_CROSS_PLATFORM_COMPILER": {
"type": "PATH",
"value": "/usr/bin/aarch64-linux-gnu-g++"
}
}
}
]
}
其中的vendor_name 可以根据自己的情况进行修改，默认的算子部署后会放于customize 目录下，这里可以修改，比如改成jackwangcumt。而且单算子工程每次部署会进行覆盖，因此，这里需要注意一下。生成的p_relu_custom.cpp文件，重点的算子计算为:
__aicore__ inline void Compute(int32_t progress)
{
// deque input tensors from VECIN queue
LocalTensor<float> xLocal = inQueueX.DeQue<float>();
LocalTensor<float> yLocal = outQueueY.AllocTensor<float>();
LocalTensor<float> tmpTensor1 = tmpBuffer1.Get<float>();
float inputVal = 0.0;
Maxs(tmpTensor1, xLocal, inputVal, this->tileLength); // x >= 0 --> x
// x < 0
Mins(xLocal, xLocal, inputVal, this->tileLength);
Muls(xLocal, xLocal, this->alpha, this->tileLength);
Add(yLocal, xLocal, tmpTensor1, this->tileLength);
outQueueY.EnQue<float>(yLocal);
// free input tensors for reuse
inQueueX.FreeTensor(xLocal);
}
这里通过内置的原生算子来分别处理输入x<0和x>=0两个部分的数据处理，再通过Add将两个部分合并，得到最终的数据。在op_host目录下的p_relu_custom_tiling.h代码如下所示：
#include "register/tilingdata_base.h"
namespace optiling {
BEGIN_TILING_DATA_DEF(TilingData)
TILING_DATA_FIELD_DEF(uint32_t, totalLength);
TILING_DATA_FIELD_DEF(uint32_t, tileNum);
TILING_DATA_FIELD_DEF(float, alpha);
END_TILING_DATA_DEF;
REGISTER_TILING_DATA_CLASS(PReluCustom, TilingData)
}
p_relu_custom.cpp 核心代码如下所示：
#include "p_relu_custom_tiling.h"
#include "register/op_def_registry.h"
namespace optiling {
const uint32_t BLOCK_DIM = 8;
const uint32_t TILE_NUM = 16 ; // 这个数可能影响测试是否通过
static ge::graphStatus TilingFunc(gert::TilingContext* context)
{
TilingData tiling;
uint32_t totalLength = context->GetInputTensor(0)->GetShapeSize();
const gert::RuntimeAttrs *attrs = context->GetAttrs();
const float *alpha = attrs->GetAttrPointer<float>(0);
context->SetBlockDim(BLOCK_DIM);
tiling.set_totalLength(totalLength);
tiling.set_tileNum(TILE_NUM);
tiling.set_alpha(*alpha);
tiling.SaveToBuffer(context->GetRawTilingData()->GetData(), context->GetRawTilingData()->GetCapacity());
context->GetRawTilingData()->SetDataSize(tiling.GetDataSize());
size_t *currentWorkspace = context->GetWorkspaceSizes(1);
currentWorkspace[0] = 0;
return ge::GRAPH_SUCCESS;
}
}
namespace ge {
static ge::graphStatus InferShape(gert::InferShapeContext* context)
{
const gert::Shape* x1_shape = context->GetInputShape(0);
gert::Shape* y_shape = context->GetOutputShape(0);
*y_shape = *x1_shape;
return GRAPH_SUCCESS;
}
}
namespace ops {
class PReluCustom : public OpDef {
public:
explicit PReluCustom(const char* name) : OpDef(name)
{
this->Input("x")
.ParamType(REQUIRED)
.DataType({ge::DT_FLOAT})
.Format({ge::FORMAT_ND})
.UnknownShapeFormat({ge::FORMAT_ND});
this->Output("y")
.ParamType(REQUIRED)
.DataType({ge::DT_FLOAT})
.Format({ge::FORMAT_ND})
.UnknownShapeFormat({ge::FORMAT_ND});
this->Attr("alpha").AttrType(OPTIONAL).Float(0.002);
this->SetInferShape(ge::InferShape);
this->AICore()
.SetTiling(optiling::TilingFunc);
this->AICore().AddConfig("ascend310p");
}
};
OP_ADD(PReluCustom);
}
执行如下命令，编译算子工程：
root@atlas500ai:/home/kzroot/mysoft/myAscendC/PReluSample/PReluCustom# bash build.sh
![QQ截图20240407154618.png]()
Self-extractable archive "custom_opp_ubuntu_aarch64.run" successfully created. 则表明编译成功。执行如下命令进行算子部署：
PReluCustom# ./build_out/custom_opp_ubuntu_aarch64.run
![QQ截图20240407155028.png]()
3 Ascend C自定义算子验证
基于Ascend C 自定义算子需要进行正确性验证，这里新建一个AclNNInvocation目录（可以参考官方示例中的相关内容），目录结构如下所示：
![QQ截图20240407155301.png]()
其中的gen_data.py用于生成测试的输入和输出数据，verity_result.py用于验证精度。gen_data.py内容如下所示：
import numpy as np
import os
def gen_golden_data_simple():
alpha = np.array(0.002, dtype=np.float32)
input_x = np.random.uniform(-100, 100, [8, 200, 1024]).astype(np.float32)
golden = np.where(input_x >= 0, input_x, input_x * alpha).astype(np.float32)
os.system("mkdir -p input")
os.system("mkdir -p output")
input_x.tofile("./input/input_x.bin")
golden.tofile("./output/golden.bin")
if __name__ == "__main__":
gen_golden_data_simple()
src目录下的CMakeLists.txt有一个环境变量可能需要修改，即 set(CUST_PKG_PATH "${INC_PATH}/opp/vendors/customize/op_api") ，默认是不需要修改的，他需要和vendor_name一致。执行如下命令进行测试：
PReluSample/AclNNInvocation# bash run.sh
![QQ截图20240407155800.png]()
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