// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/fluid/inference/anakin/engine.h" #include #include #include #include #include "paddle/fluid/framework/ddim.h" using anakin::Precision; using anakin::OpRunType; using paddle::framework::LoDTensor; template using AnakinNetT = anakin::Net; template using AnakinGraphT = anakin::graph::Graph; namespace paddle { namespace inference { namespace anakin { template AnakinEngine::AnakinEngine(bool need_summary) : graph_(new AnakinGraphT()), net_(new AnakinNetT(need_summary)) {} template AnakinEngine::~AnakinEngine() {} template void AnakinEngine::SetInputShape( const std::string &name, std::vector shape) { graph_->AddOpAttr<::anakin::PTuple>(name, "input_shape", std::move(shape)); } template void AnakinEngine::InitGraph() { net_->init(*graph_); } template void AnakinEngine::AddOp( const std::string &name, const std::string &type, const std::vector &inputs, const std::vector &outputs) { PADDLE_ENFORCE(graph_->AddOp(name, type, inputs, outputs), "Add operation."); } template void AnakinEngine::Execute( const std::map &inputs, const std::map &outputs) { for (const auto &input : inputs) { auto *tensor = input.second; auto *data = tensor->data(); auto shape = framework::vectorize2int(tensor->dims()); ::anakin::saber::Shape anakin_shape(shape); auto *anakin_input = net_->get_in(input.first); ::anakin::saber::Tensor tmp_anakin_tensor(data, TargetT(), 0, anakin_shape); anakin_input->share_from(tmp_anakin_tensor); } for (const auto &output : outputs) { auto *tensor = output.second; auto *data = tensor->data(); auto shape = framework::vectorize2int(tensor->dims()); ::anakin::saber::Shape anakin_shape(shape); auto *anakin_output = net_->get_out(output.first); ::anakin::saber::Tensor tmp_anakin_tensor(data, TargetT(), 0, anakin_shape); anakin_output->share_from(tmp_anakin_tensor); } net_->prediction(); } template void AnakinEngine::Freeze() { PADDLE_ENFORCE(graph_->Freeze(), "Freeze anakin subgraph."); } template void AnakinEngine::Optimize() { PADDLE_ENFORCE(graph_->Optimize(), "Graph optimization."); } template std::unique_ptr> AnakinEngine::Clone() { auto *engine = new AnakinEngine(); engine->net_ = std::move(net_->Clone()); return std::unique_ptr(engine); } template class AnakinEngine<::anakin::saber::NV, ::anakin::Precision::FP32>; } // namespace anakin } // namespace inference } // namespace paddle