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#include "dtrain_net_interface.h"
#include "sample_net_interface.h"
#include "score_net_interface.h"
#include "update.h"
#include <nanomsg/nn.h>
#include <nanomsg/pair.h>
#include "nn.hpp"
using namespace dtrain;
int
main(int argc, char** argv)
{
// get configuration
po::variables_map conf;
if (!dtrain_net_init(argc, argv, &conf))
exit(1); // something is wrong
const size_t k = conf["k"].as<size_t>();
const size_t N = conf["N"].as<size_t>();
weight_t eta = conf["learning_rate"].as<weight_t>();
const weight_t margin = conf["margin"].as<weight_t>();
const string master_addr = conf["addr"].as<string>();
const string output_fn = conf["output"].as<string>();
const string debug_fn = conf["debug_output"].as<string>();
// setup decoder
register_feature_functions();
SetSilent(true);
ReadFile f(conf["decoder_conf"].as<string>());
Decoder decoder(f.stream());
ScoredKbest* observer = new ScoredKbest(k, new PerSentenceBleuScorer(N));
// weights
vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
SparseVector<weight_t> lambdas, w_average;
if (conf.count("input_weights")) {
Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
Weights::InitSparseVector(decoder_weights, &lambdas);
}
cerr << _p4;
// output configuration
cerr << "dtrain_net_interface" << endl << "Parameters:" << endl;
cerr << setw(25) << "k " << k << endl;
cerr << setw(25) << "N " << N << endl;
cerr << setw(25) << "margin " << margin << endl;
cerr << setw(25) << "decoder conf " << "'"
<< conf["decoder_conf"].as<string>() << "'" << endl;
cerr << setw(25) << "output " << output_fn << endl;
// setup socket
nn::socket sock(AF_SP, NN_PAIR);
sock.bind(master_addr.c_str());
string hello = "hello";
sock.send(hello.c_str(), hello.size()+1, 0);
// debug
ostringstream debug_output;
size_t i = 0;
while(true)
{
// debug --
debug_output.str(string());
debug_output.clear();
debug_output << "{" << endl;
// -- debug
char *buf = NULL;
string source;
vector<Ngrams> refs;
vector<size_t> rsz;
bool next = true;
size_t sz = sock.recv(&buf, NN_MSG, 0);
if (buf) {
const string in(buf, buf+sz);
nn::freemsg(buf);
cerr << "[dtrain] got input '" << in << "'" << endl;
if (in == "shutdown") { // shut down
cerr << "[dtrain] got shutdown signal" << endl;
next = false;
} else { // translate
vector<string> parts;
boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
if (parts[0] == "act:translate") {
cerr << "[dtrain] translating ..." << endl;
lambdas.init_vector(&decoder_weights);
observer->dont_score = true;
decoder.Decode(parts[1], observer);
observer->dont_score = false;
vector<ScoredHyp>* samples = observer->GetSamples();
ostringstream os;
cerr << "[dtrain] 1best features " << (*samples)[0].f << endl;
PrintWordIDVec((*samples)[0].w, os);
sock.send(os.str().c_str(), os.str().size()+1, 0);
cerr << "[dtrain] done translating, looping again" << endl;
continue;
} else { // learn
cerr << "[dtrain] learning ..." << endl;
source = parts[0];
// debug --
debug_output << "\"source\":\"" << source.substr(source.find_first_of(">")+1, source.find_last_of("<")-3) << "\"," << endl;
debug_output << "\"target\":\"" << parts[1] << "\"," << endl;
// -- debug
parts.erase(parts.begin());
for (auto s: parts) {
vector<WordID> r;
vector<string> toks;
boost::split(toks, s, boost::is_any_of(" "));
for (auto tok: toks)
r.push_back(TD::Convert(tok));
refs.emplace_back(MakeNgrams(r, N));
rsz.push_back(r.size());
}
}
}
}
if (!next)
break;
// decode
lambdas.init_vector(&decoder_weights);
observer->SetReference(refs, rsz);
decoder.Decode(source, observer);
vector<ScoredHyp>* samples = observer->GetSamples();
// debug --
debug_output << "\"1best\":\"";
PrintWordIDVec((*samples)[0].w, debug_output);
debug_output << "\"," << endl;
debug_output << "\"kbest\":[" << endl;
size_t h = 0;
for (auto s: *samples) {
debug_output << "\"" << s.gold << " ||| " << s.model << " ||| " << s.rank << " ||| ";
debug_output << "EgivenFCoherent=" << s.f[FD::Convert("EgivenFCoherent")] << " ";
debug_output << "SampleCountF=" << s.f[FD::Convert("CountEF")] << " ";
debug_output << "MaxLexFgivenE=" << s.f[FD::Convert("MaxLexFgivenE")] << " ";
debug_output << "MaxLexEgivenF=" << s.f[FD::Convert("MaxLexEgivenF")] << " ";
debug_output << "IsSingletonF=" << s.f[FD::Convert("IsSingletonF")] << " ";
debug_output << "IsSingletonFE=" << s.f[FD::Convert("IsSingletonFE")] << " ";
debug_output << "Glue=:" << s.f[FD::Convert("Glue")] << " ";
debug_output << "WordPenalty=" << s.f[FD::Convert("WordPenalty")] << " ";
debug_output << "PassThrough=" << s.f[FD::Convert("PassThrough")] << " ";
debug_output << "LanguageModel=" << s.f[FD::Convert("LanguageModel_OOV")];
debug_output << " ||| ";
PrintWordIDVec(s.w, debug_output);
h += 1;
debug_output << "\"";
if (h < samples->size()) {
debug_output << ",";
}
debug_output << endl;
}
debug_output << "]," << endl;
debug_output << "\"samples_size\":" << samples->size() << "," << endl;
debug_output << "\"weights_before\":{" << endl;
debug_output << "\"EgivenFCoherent\":" << lambdas[FD::Convert("EgivenFCoherent")] << "," << endl;
debug_output << "\"SampleCountF\":" << lambdas[FD::Convert("CountEF")] << "," << endl;
debug_output << "\"MaxLexFgivenE\":" << lambdas[FD::Convert("MaxLexFgivenE")] << "," << endl;
debug_output << "\"MaxLexEgivenF\":" << lambdas[FD::Convert("MaxLexEgivenF")] << "," << endl;
debug_output << "\"IsSingletonF\":" << lambdas[FD::Convert("IsSingletonF")] << "," << endl;
debug_output << "\"IsSingletonFE\":" << lambdas[FD::Convert("IsSingletonFE")] << "," << endl;
debug_output << "\"Glue\":" << lambdas[FD::Convert("Glue")] << "," << endl;
debug_output << "\"WordPenalty\":" << lambdas[FD::Convert("WordPenalty")] << "," << endl;
debug_output << "\"PassThrough\":" << lambdas[FD::Convert("PassThrough")] << "," << endl;
debug_output << "\"LanguageModel\":" << lambdas[FD::Convert("LanguageModel_OOV")] << endl;
debug_output << "}," << endl;
// -- debug
// get pairs and update
SparseVector<weight_t> updates;
size_t num_up = CollectUpdates(samples, updates, margin);
// debug --
debug_output << "\"num_up\":" << num_up << "," << endl;
debug_output << "\"updated_features\":" << updates.size() << "," << endl;
debug_output << "\"learning_rate\":" << eta << "," << endl;
debug_output << "\"best_match\":\"";
PrintWordIDVec((*samples)[0].w, debug_output);
debug_output << "\"," << endl;
debug_output << "\"best_match_score\":" << (*samples)[0].gold << "," << endl ;
// -- debug
lambdas.plus_eq_v_times_s(updates, eta);
i++;
// debug --
debug_output << "\"weights_after\":{" << endl;
debug_output << "\"EgivenFCoherent\":" << lambdas[FD::Convert("EgivenFCoherent")] << "," << endl;
debug_output << "\"SampleCountF\":" << lambdas[FD::Convert("CountEF")] << "," << endl;
debug_output << "\"MaxLexFgivenE\":" << lambdas[FD::Convert("MaxLexFgivenE")] << "," << endl;
debug_output << "\"MaxLexEgivenF\":" << lambdas[FD::Convert("MaxLexEgivenF")] << "," << endl;
debug_output << "\"IsSingletonF\":" << lambdas[FD::Convert("IsSingletonF")] << "," << endl;
debug_output << "\"IsSingletonFE\":" << lambdas[FD::Convert("IsSingletonFE")] << "," << endl;
debug_output << "\"Glue\":" << lambdas[FD::Convert("Glue")] << "," << endl;
debug_output << "\"WordPenalty\":" << lambdas[FD::Convert("WordPenalty")] << "," << endl;
debug_output << "\"PassThrough\":" << lambdas[FD::Convert("PassThrough")] << "," << endl;
debug_output << "\"LanguageModel\":" << lambdas[FD::Convert("LanguageModel_OOV")] << endl;
debug_output << "}" << endl;
debug_output << "}" << endl;
// -- debug
cerr << "[dtrain] done learning, looping again" << endl;
string done = "done";
sock.send(done.c_str(), done.size()+1, 0);
// debug --
WriteFile f(debug_fn);
*f << debug_output.str();
// -- debug
} // input loop
if (output_fn != "") {
cerr << "[dtrain] writing final weights to '" << output_fn << "'" << endl;
lambdas.init_vector(decoder_weights);
Weights::WriteToFile(output_fn, decoder_weights, true);
}
string shutdown = "off";
sock.send(shutdown.c_str(), shutdown.size()+1, 0);
cerr << "[dtrain] shutting down, goodbye" << endl;
return 0;
}
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