Ascend C 自定义PRelu算子
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）：
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
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
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：
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
