// // TRTLayerNorm.cpp // MNN // // Created by MNN on 2021/02/08. // Copyright © 2018, Alibaba Group Holding Limited // #include "TRTLayerNorm.hpp" #include #include "TRTBackend.hpp" #include "schema/current/MNNPlugin_generated.h" using namespace std; namespace MNN { TRTLayerNorm::TRTLayerNorm(Backend *b, const Op *op, const std::vector &inputs, const std::vector &outputs) : MNN::TRTCommonExecution(b, op) { } std::vector TRTLayerNorm::onEncode(const std::vector &xOp) { #ifdef TRT_LOG printf("TRTLayerNorm in\n"); #endif auto plu = createPluginWithOutput(mOutputs); const auto* layer_norm_param = mOp->main_as_LayerNorm(); int axis_size = layer_norm_param->axis()->size(); std::vector axis_; axis_.resize(axis_size); for (int i = 0; i < axis_size; ++i) { axis_[i] = layer_norm_param->axis()->Get(i); } int outter_size_ = 1; int inner_size_ = 1; int rank = mInputs[0]->dimensions(); std::vector axis(axis_.size()); for (int i = 0; i < axis_.size(); ++i) { if (axis_[i] < 0) { axis[i] += rank; } } std::sort(axis.begin(), axis.end()); for (int i = 0; i < rank - axis.size(); ++i) { outter_size_ *= mInputs[0]->length(i); } for (int i = rank - axis.size(); i < rank; ++i) { inner_size_ *= mInputs[0]->length(i); } plu->main.type = MNNTRTPlugin::Parameter_OneHotInfo; plu->main.value = new MNNTRTPlugin::OneHotInfoT; auto onehotp = plu->main.AsOneHotInfo(); onehotp->outerSize = outter_size_; onehotp->innerSize = inner_size_; auto interpPlugin = (nvinfer1::IPluginExt *)MNNTRTCreatePlugion(mOp, plu.get()); nvinfer1::IPluginLayer *plugin = mTrtBackend->getNetwork()->addPluginExt(&xOp[0], mInputs.size(), *((nvinfer1::IPluginExt *)interpPlugin)); if (plugin == nullptr) { printf("Interp plugin == nullptr !!!\n"); } mTrtBackend->pushReleaseLayer(interpPlugin); return {plugin->getOutput(0)}; } TRTCreatorRegister> __layer_norm_op(OpType_LayerNorm); } // namespace MNN