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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>();
weight_t eta_sparse = conf["learning_rate_sparse"].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>();
vector<string> dense_features;
boost::split(dense_features, conf["dense_features"].as<string>(),
boost::is_any_of(" "));
const bool output_derivation = conf["output_derivation"].as<bool>();
// 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, original_lambdas;
if (conf.count("input_weights")) {
Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
Weights::InitSparseVector(decoder_weights, &lambdas);
Weights::InitSparseVector(decoder_weights, &original_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) << "eta " << eta << endl;
cerr << setw(25) << "eta (sparse) " << eta_sparse << 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;
cerr << setw(25) << "debug " << "'" << debug_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;
string done = "done";
size_t i = 0;
while(true)
{
// debug --
debug_output.str(string());
debug_output.clear();
debug_output << "{" << endl; // hack us a nice JSON output
// -- 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 (boost::starts_with(in, "set_learning_rate")) { // set learning rate
stringstream ss(in);
string x; weight_t w;
ss >> x; ss >> w;
cerr << "[dtrain] setting (dense) learning rate to " << w << " (was: " << eta << ")" << endl;
eta = w;
cerr << "[dtrain] done, looping again" << endl;
sock.send(done.c_str(), done.size()+1, 0);
continue;
} else if (boost::starts_with(in, "set_sparse_learning_rate")) { // set sparse learning rate
stringstream ss(in);
string x; weight_t w;
ss >> x; ss >> w;
cerr << "[dtrain] setting sparse learning rate to " << w << " (was: " << eta_sparse << ")" << endl;
eta_sparse = w;
cerr << "[dtrain] done, looping again" << endl;
sock.send(done.c_str(), done.size()+1, 0);
continue;
} else if (boost::starts_with(in, "reset_weights")) { // reset weights
cerr << "[dtrain] resetting weights" << endl;
lambdas = original_lambdas;
cerr << "[dtrain] done, looping again" << endl;
sock.send(done.c_str(), done.size()+1, 0);
continue;
} else 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;
if (output_derivation) {
os << observer->GetViterbiTreeStr() << endl;
} else {
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 << " ||| ";
for (auto o: s.f)
debug_output << FD::Convert(o.first) << "=" << o.second << " ";
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;
weightsToJson(lambdas, debug_output);
debug_output << "}," << endl;
// -- debug
// get pairs and update
SparseVector<weight_t> updates;
size_t num_up = CollectUpdates(samples, updates, margin);
updates *= eta_sparse; // apply learning rate for sparse features
for (auto feat: dense_features) { // apply learning rate for dense features
updates[FD::Convert(feat)] /= eta_sparse;
updates[FD::Convert(feat)] *= eta;
}
// debug --
debug_output << "\"num_up\":" << num_up << "," << endl;
debug_output << "\"updated_features\":" << updates.size() << "," << endl;
debug_output << "\"learning_rate\":" << eta << "," << endl;
debug_output << "\"learning_rate_sparse\":" << eta_sparse << "," << 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, 1.0);
i++;
// debug --
debug_output << "\"weights_after\":{" << endl;
weightsToJson(lambdas, debug_output);
debug_output << "}" << endl;
debug_output << "}" << endl;
// -- debug
cerr << "[dtrain] done learning, looping again" << endl;
sock.send(done.c_str(), done.size()+1, 0);
// debug --
WriteFile f(debug_fn);
f.get() << debug_output.str();
f.get() << std::flush;
// -- debug
// write current weights
lambdas.init_vector(decoder_weights);
ostringstream fn;
fn << output_fn << "." << i << ".gz";
Weights::WriteToFile(fn.str(), decoder_weights, true);
} // input loop
string shutdown = "off";
sock.send(shutdown.c_str(), shutdown.size()+1, 0);
cerr << "[dtrain] shutting down, goodbye" << endl;
return 0;
}
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