// Copyright (c) 2019 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/lite/kernels/host/fc_compute.h" #include #include "paddle/fluid/lite/core/op_registry.h" namespace paddle { namespace lite { namespace kernels { namespace host { // NOTE should use pure std C++ implementation. void FcCompute::Run() { using matrix_t = Eigen::Matrix; using matrix_map_t = Eigen::Map; auto& param = this->param(); CHECK_GE(param.input->dims().size(), 2UL); CHECK_EQ(param.output->dims().size(), 2UL); Eigen::Map input( param.input->data(), product(param.input->dims().begin(), param.input->dims().begin() + param.in_num_col_dims), product(param.input->dims().begin() + param.in_num_col_dims, param.input->dims().end())); Eigen::Map weight(param.w->data(), param.w->dims()[0], param.w->dims()[1]); matrix_map_t output(param.output->mutable_data(), param.output->dims()[0], param.output->dims()[1]); output = weight.transpose() * input; if (param.bias) { Eigen::Map bias(param.bias->data(), param.bias->dims()[0], param.bias->dims()[1]); output += bias; } } TargetType FcCompute::target() const { return TARGET(kHost); } PrecisionType FcCompute::precision() const { return PRECISION(kFloat); } } // namespace host } // namespace kernels } // namespace lite } // namespace paddle REGISTER_LITE_KERNEL(fc, kHost, kFloat, paddle::lite::kernels::host::FcCompute);