/* // Copyright (c) 2018 Intel Corporation // // 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 "pass_manager.h" #include "program_node.h" #include "layout_optimizer.h" #include "program_impl.h" #include "program_helpers.h" #include "fully_connected_inst.h" using namespace cldnn; pre_optimize_bias::pre_optimize_bias(reorder_factory& rf_ref) : base_pass("pre_optimize_bias"), _rf(rf_ref) {} void pre_optimize_bias::run(program_impl& p) { run(p, _rf); } // function which prepares given primitive for weights optimization template <typename T> void pre_optimize_bias::optimize_bias(T& node, reorder_factory& rf, program_impl& p) { layout output_layout = node.get_output_layout(); size_t weights_offset = node.get_primitive()->input.size(); size_t bias_offset = weights_offset + program_helpers::wrap_if_single(node.get_primitive()->weights).size(); for (size_t i = bias_offset; i < node.get_dependencies().size() - node.get_fused_inputs_count(); ++i) { // find weights primitive with given pimitive_id and add it to weights_optimizer const program_node& bias = node.get_dependency(i); auto new_layout = layout(output_layout.data_type, format::bfyx, { 1, static_cast<tensor::value_type>(bias.get_output_layout().count()), 1, 1 }); if (new_layout.data_type == data_types::bin) { new_layout.data_type = bias.get_output_layout().data_type; } auto reorder = rf.get_reorder(bias.id(), bias.get_output_layout(), new_layout); if (reorder.first) p.add_intermediate(reorder.first, node, i, !reorder.second); } } template void pre_optimize_bias::optimize_bias<convolution_node>(convolution_node& node, reorder_factory& rf, program_impl& p); template void pre_optimize_bias::optimize_bias<deconvolution_node>(deconvolution_node& node, reorder_factory& rf, program_impl& p); template void pre_optimize_bias::optimize_bias<fully_connected_node>(fully_connected_node& node, reorder_factory& rf, program_impl& p); template void pre_optimize_bias::optimize_bias<embed_node>(embed_node& node, reorder_factory& rf, program_impl& p); void pre_optimize_bias::run(program_impl& p, reorder_factory& rf) { for (auto& prim : p.get_processing_order()) { if (prim->type() == convolution::type_id()) { if (!prim->as<convolution>().weights_quantization_term()) optimize_bias(prim->as<convolution>(), rf, p); } else if (prim->type() == deconvolution::type_id()) { optimize_bias(prim->as<deconvolution>(), rf, p); } else if (prim->type() == fully_connected::type_id()) { if (!prim->as<fully_connected>().weights_quantization_term()) optimize_bias(prim->as<fully_connected>(), rf, p); } else if (prim->type() == embed::type_id()) { optimize_bias(prim->as<embed>(), rf, p); } } }