inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_optimize_bias.cpp

   1 /*
   2 // Copyright (c) 2018 Intel Corporation
   3 //
   4 // Licensed under the Apache License, Version 2.0 (the "License");
   5 // you may not use this file except in compliance with the License.
   6 // You may obtain a copy of the License at
   7 //
   8 //      http://www.apache.org/licenses/LICENSE-2.0
   9 //
  10 // Unless required by applicable law or agreed to in writing, software
  11 // distributed under the License is distributed on an "AS IS" BASIS,
  12 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 // See the License for the specific language governing permissions and
  14 // limitations under the License.
  15 */
  16
  17 ///////////////////////////////////////////////////////////////////////////////////////////////////
  18
  19 #include "pass_manager.h"
  20 #include "program_node.h"
  21 #include "layout_optimizer.h"
  22 #include "program_impl.h"
  23 #include "program_helpers.h"
  24 #include "fully_connected_inst.h"
  25
  26 using namespace cldnn;
  27
  28 pre_optimize_bias::pre_optimize_bias(layout_optimizer& lo_ref) : base_pass("pre_optimize_bias"), _lo(lo_ref) {}
  29
  30 void pre_optimize_bias::run(program_impl& p) {
  31     run(p, _lo);
  32 }
  33
  34 //function which prepares given primitive for weights optimization
  35 template <typename T>
  36 void pre_optimize_bias::optimize_bias(T& node, layout_optimizer& lo, program_impl& p)
  37 {
  38     layout output_layout = node.get_output_layout();
  39
  40     size_t weights_offset = node.get_primitive()->input.size();
  41     size_t bias_offset = weights_offset + program_helpers::wrap_if_single(node.get_primitive()->weights).size();
  42     for (size_t i = bias_offset; i < node.get_dependencies().size(); ++i)
  43     {
  44         //find weights primitive with given pimitive_id and add it to weights_optimizer
  45         const program_node& bias = node.get_dependency(i);
  46         const auto bias_type = layout_optimizer::data_type::bias;
  47         auto reorder = lo.get_reorder(
  48             bias.get_output_layout(),
  49             bias.id(),
  50             bias_type,
  51             node,
  52             output_layout);
  53
  54         if (reorder.first)
  55             p.add_intermediate(reorder.first, node, i, !reorder.second);
  56     }
  57 }
  58 template void pre_optimize_bias::optimize_bias<convolution_node>(convolution_node& node, layout_optimizer& lo, program_impl& p);
  59 template void pre_optimize_bias::optimize_bias<deconvolution_node>(deconvolution_node& node, layout_optimizer& lo, program_impl& p);
  60 template void pre_optimize_bias::optimize_bias<fully_connected_node>(fully_connected_node& node, layout_optimizer& lo, program_impl& p);
  61 template void pre_optimize_bias::optimize_bias<embed_node>(embed_node& node, layout_optimizer& lo, program_impl& p);
  62
  63
  64 void pre_optimize_bias::run(program_impl& p, layout_optimizer& lo)
  65 {
  66     for (auto& prim : p.get_processing_order())
  67     {
  68         if (prim->type() == convolution::type_id())
  69         {
  70             if (!prim->as<convolution>().weights_quantization_term())
  71                 optimize_bias(prim->as<convolution>(), lo, p);
  72         }
  73         else if (prim->type() == deconvolution::type_id())
  74         {
  75             optimize_bias(prim->as<deconvolution>(), lo, p);
  76         }
  77         else if (prim->type() == fully_connected::type_id())
  78         {
  79             if (!prim->as<fully_connected>().weights_quantization_term())
  80                 optimize_bias(prim->as<fully_connected>(), lo, p);
  81         }
  82         else if (prim->type() == embed::type_id())
  83         {
  84             optimize_bias(prim->as<embed>(), lo, p);
  85         }
  86     }
  87 }