.. _program_listing_file_src_models_nematus.h:

Program Listing for File nematus.h
==================================

|exhale_lsh| :ref:`Return to documentation for file <file_src_models_nematus.h>` (``src/models/nematus.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #pragma once
   
   #include "marian.h"
   
   #include "models/s2s.h"
   
   namespace marian {
   
   class Nematus : public EncoderDecoder {
   public:
     Nematus(Ptr<ExpressionGraph> graph, Ptr<Options> options) : EncoderDecoder(graph, options), nameMap_(createNameMap()) {
       ABORT_IF(options_->get<std::string>("enc-type") != "bidirectional",
                "--type nematus does not support other encoder "
                "type than bidirectional, use --type s2s");
   
       ABORT_IF(options_->get<std::string>("enc-cell") != "gru-nematus",
                "--type nematus does not support other rnn cells "
                "than gru-nematus, use --type s2s");
       ABORT_IF(options_->get<std::string>("dec-cell") != "gru-nematus",
                "--type nematus does not support other rnn cells "
                "than gru-nematus, use --type s2s");
   
       ABORT_IF(options_->get<int>("dec-cell-high-depth") > 1,
                "--type nematus does not currently support "
                "--dec-cell-high-depth > 1, use --type s2s");
     }
   
     void load(Ptr<ExpressionGraph> graph,
               const std::vector<io::Item>& items,
               bool /*markReloaded*/ = true) override {
       auto ioItems = items;
       // map names and remove a dummy matrix 'decoder_c_tt' from items to avoid creating isolated node
       for(auto it = ioItems.begin(); it != ioItems.end();) {
         // for backwards compatibility, turn one-dimensional vector into two dimensional matrix with first dimension being 1 and second dimension of the original size
         // @TODO: consider dropping support for Nematus models
         if(it->shape.size() == 1) {
           int dim = it->shape[-1];
           it->shape.resize(2);
           it->shape.set(0, 1);
           it->shape.set(1, dim);
         }
   
         if(it->name == "decoder_c_tt") {
           it = ioItems.erase(it);
         } else if(it->name == "uidx") {
           it = ioItems.erase(it);
         } else if(it->name == "history_errs") {
           it = ioItems.erase(it);
         } else {
           auto pair = nameMap_.find(it->name);
           if(pair != nameMap_.end())
             it->name = pair->second;
           it++;
         }
       }
       // load items into the graph
       graph->load(ioItems);
     }
   
     void load(Ptr<ExpressionGraph> graph,
               const std::string& name,
               bool /*markReloaded*/ = true) override {
       LOG(info, "Loading model from {}", name);
       auto ioItems = io::loadItems(name);
       load(graph, ioItems);
     }
   
     void save(Ptr<ExpressionGraph> graph,
               const std::string& name,
               bool saveTranslatorConfig = false) override {
       LOG(info, "Saving model to {}", name);
   
       // prepare reversed map
       if(nameMapRev_.empty())
         for(const auto& kv : nameMap_)
           nameMapRev_.insert({kv.second, kv.first});
   
       // get parameters from the graph to items
       std::vector<io::Item> ioItems;
       graph->save(ioItems);
       // replace names to be compatible with Nematus
       for(auto& item : ioItems) {
         auto newItemName = nameMapRev_.find(item.name);
         if(newItemName != nameMapRev_.end())
           item.name = newItemName->second;
       }
       // add a dummy matrix 'decoder_c_tt' required for Amun and Nematus
       ioItems.emplace_back();
       ioItems.back().name = "decoder_c_tt";
       ioItems.back().shape = Shape({1, 0});
       ioItems.back().bytes.emplace_back((char)0);
   
       io::addMetaToItems(getModelParametersAsString(), "special:model.yml", ioItems);
       io::saveItems(name, ioItems);
   
       if(saveTranslatorConfig) {
         createAmunConfig(name);
         createDecoderConfig(name);
       }
     }
   
   private:
     std::map<std::string, std::string> nameMap_;
     std::map<std::string, std::string> nameMapRev_;
   
     std::map<std::string, std::string> createNameMap() {
       std::map<std::string, std::string> nameMap
           = {{"decoder_U", "decoder_cell1_U"},
              {"decoder_Ux", "decoder_cell1_Ux"},
              {"decoder_W", "decoder_cell1_W"},
              {"decoder_Wx", "decoder_cell1_Wx"},
              {"decoder_b", "decoder_cell1_b"},
              {"decoder_bx", "decoder_cell1_bx"},
              {"decoder_U_nl", "decoder_cell2_U"},
              {"decoder_Ux_nl", "decoder_cell2_Ux"},
              {"decoder_Wc", "decoder_cell2_W"},
              {"decoder_Wcx", "decoder_cell2_Wx"},
              {"decoder_b_nl", "decoder_cell2_b"},
              {"decoder_bx_nl", "decoder_cell2_bx"},
              {"ff_logit_prev_W", "decoder_ff_logit_l1_W0"},
              {"ff_logit_lstm_W", "decoder_ff_logit_l1_W1"},
              {"ff_logit_ctx_W", "decoder_ff_logit_l1_W2"},
              {"ff_logit_prev_b", "decoder_ff_logit_l1_b0"},
              {"ff_logit_lstm_b", "decoder_ff_logit_l1_b1"},
              {"ff_logit_ctx_b", "decoder_ff_logit_l1_b2"},
              {"ff_logit_W", "decoder_ff_logit_l2_W"},
              {"ff_logit_b", "decoder_ff_logit_l2_b"},
              {"ff_state_W", "decoder_ff_state_W"},
              {"ff_state_b", "decoder_ff_state_b"},
              {"Wemb_dec", "decoder_Wemb"},
              {"Wemb", "encoder_Wemb"},
              {"encoder_U", "encoder_bi_U"},
              {"encoder_Ux", "encoder_bi_Ux"},
              {"encoder_W", "encoder_bi_W"},
              {"encoder_Wx", "encoder_bi_Wx"},
              {"encoder_b", "encoder_bi_b"},
              {"encoder_bx", "encoder_bi_bx"},
              {"encoder_r_U", "encoder_bi_r_U"},
              {"encoder_r_Ux", "encoder_bi_r_Ux"},
              {"encoder_r_W", "encoder_bi_r_W"},
              {"encoder_r_Wx", "encoder_bi_r_Wx"},
              {"encoder_r_b", "encoder_bi_r_b"},
              {"encoder_r_bx", "encoder_bi_r_bx"},
              {"ff_state_ln_s", "decoder_ff_state_ln_s"},
              {"ff_state_ln_b", "decoder_ff_state_ln_b"},
              {"ff_logit_prev_ln_s", "decoder_ff_logit_l1_ln_s0"},
              {"ff_logit_lstm_ln_s", "decoder_ff_logit_l1_ln_s1"},
              {"ff_logit_ctx_ln_s", "decoder_ff_logit_l1_ln_s2"},
              {"ff_logit_prev_ln_b", "decoder_ff_logit_l1_ln_b0"},
              {"ff_logit_lstm_ln_b", "decoder_ff_logit_l1_ln_b1"},
              {"ff_logit_ctx_ln_b", "decoder_ff_logit_l1_ln_b2"}};
   
       // add mapping for deep encoder cells
       std::vector<std::string> suffixes = {"_U", "_Ux", "_b", "_bx"};
       for(int i = 1; i < options_->get<int>("enc-cell-depth"); ++i) {
         std::string num1 = std::to_string(i);
         std::string num2 = std::to_string(i + 1);
         for(auto suf : suffixes) {
           nameMap.insert({"encoder" + suf + "_drt_" + num1, "encoder_bi_cell" + num2 + suf});
           nameMap.insert({"encoder_r" + suf + "_drt_" + num1, "encoder_bi_r_cell" + num2 + suf});
         }
       }
       // add mapping for deep decoder cells
       for(int i = 3; i <= options_->get<int>("dec-cell-base-depth"); ++i) {
         std::string num1 = std::to_string(i - 2);
         std::string num2 = std::to_string(i);
         for(auto suf : suffixes)
           nameMap.insert({"decoder" + suf + "_nl_drt_" + num1, "decoder_cell" + num2 + suf});
       }
       // add mapping for normalization layers
       std::map<std::string, std::string> nameMapCopy(nameMap);
       for(auto& kv : nameMapCopy) {
         std::string prefix = kv.first.substr(0, 7);
   
         if(prefix == "encoder" || prefix == "decoder") {
           nameMap.insert({kv.first + "_lns", kv.second + "_lns"});
           nameMap.insert({kv.first + "_lnb", kv.second + "_lnb"});
         }
       }
   
       return nameMap;
     }
   
     void createAmunConfig(const std::string& name) {
       Config::YamlNode amun;
       // Amun has only CPU decoder for deep Nematus models
       amun["cpu-threads"] = 16;
       amun["gpu-threads"] = 0;
       amun["maxi-batch"] = 1;
       amun["mini-batch"] = 1;
   
       auto vocabs = options_->get<std::vector<std::string>>("vocabs");
       amun["source-vocab"] = vocabs[0];
       amun["target-vocab"] = vocabs[1];
       amun["devices"] = options_->get<std::vector<size_t>>("devices");
       amun["normalize"] = true;
       amun["beam-size"] = 5;
       amun["relative-paths"] = false;
   
       amun["scorers"]["F0"]["path"] = name;
       amun["scorers"]["F0"]["type"] = "nematus2";
       amun["weights"]["F0"] = 1.0f;
   
       io::OutputFileStream out(name + ".amun.yml");
       out << amun;
     }
   };
   }  // namespace marian