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-rw-r--r--training/crf/mpi_adagrad_optimize.cc60
-rw-r--r--training/crf/mpi_compute_cllh.cc1
2 files changed, 47 insertions, 14 deletions
diff --git a/training/crf/mpi_adagrad_optimize.cc b/training/crf/mpi_adagrad_optimize.cc
index af963e3a..39bd763e 100644
--- a/training/crf/mpi_adagrad_optimize.cc
+++ b/training/crf/mpi_adagrad_optimize.cc
@@ -188,7 +188,7 @@ class AdaGradOptimizer {
explicit AdaGradOptimizer(double e) :
eta(e),
G() {}
- void update(const SparseVector<double>& g, vector<double>* x) {
+ void update(const SparseVector<double>& g, vector<double>* x, SparseVector<double>* sx) {
if (x->size() > G.size()) G.resize(x->size(), 0.0);
#if HAVE_CXX11
for (auto& gi : g) {
@@ -199,6 +199,7 @@ class AdaGradOptimizer {
if (gi.second) {
G[gi.first] += gi.second * gi.second;
(*x)[gi.first] -= eta / sqrt(G[gi.first]) * gi.second;
+ sx->add_value(gi.first, -eta / sqrt(G[gi.first]) * gi.second);
}
}
}
@@ -213,7 +214,7 @@ class AdaGradL1Optimizer {
eta(e),
lambda(l),
G() {}
- void update(const SparseVector<double>& g, vector<double>* x) {
+ void update(const SparseVector<double>& g, vector<double>* x, SparseVector<double>* sx) {
t += 1.0;
if (x->size() > G.size()) {
G.resize(x->size(), 0.0);
@@ -228,13 +229,37 @@ class AdaGradL1Optimizer {
if (gi.second) {
u[gi.first] += gi.second;
G[gi.first] += gi.second * gi.second;
- double z = fabs(u[gi.first] / t) - lambda;
- double s = 1;
- if (u[gi.first] > 0) s = -1;
- if (z > 0 && G[gi.first])
- (*x)[gi.first] = eta * s * z * t / sqrt(G[gi.first]);
- else
- (*x)[gi.first] = 0.0;
+ sx->set_value(gi.first, 1.0); // this is a dummy value to trigger recomputation
+ }
+ }
+
+ // compute updates (avoid invalidating iterators by putting them all
+ // in the vector vupdate and applying them after this)
+ vector<pair<unsigned, double>> vupdate;
+#if HAVE_CXX11
+ for (auto& xi : *sx) {
+#else
+ for (SparseVector<double>::const_iterator it = sx->begin(); it != sx->end(); ++it) {
+ const pair<unsigned,double>& gi = *it;
+#endif
+ double z = fabs(u[xi.first] / t) - lambda;
+ double s = 1;
+ if (u[xi.first] > 0) s = -1;
+ if (z > 0 && G[xi.first]) {
+ vupdate.push_back(make_pair(xi.first, eta * s * z * t / sqrt(G[xi.first])));
+ } else {
+ vupdate.push_back(make_pair(xi.first, 0.0));
+ }
+ }
+
+ // apply updates
+ for (unsigned i = 0; i < vupdate.size(); ++i) {
+ if (vupdate[i].second) {
+ sx->set_value(vupdate[i].first, vupdate[i].second);
+ (*x)[vupdate[i].first] = vupdate[i].second;
+ } else {
+ (*x)[vupdate[i].first] = 0.0;
+ sx->erase(vupdate[i].first);
}
}
}
@@ -323,6 +348,8 @@ int main(int argc, char** argv) {
lambdas.swap(init_weights);
init_weights.clear();
}
+ SparseVector<double> lambdas_sparse;
+ Weights::InitSparseVector(lambdas, &lambdas_sparse);
//AdaGradOptimizer adagrad(conf["eta"].as<double>());
AdaGradL1Optimizer adagrad(conf["eta"].as<double>(), conf["regularization_strength"].as<double>());
@@ -338,6 +365,13 @@ int main(int argc, char** argv) {
mpi::timer timer;
#endif
++iter;
+ if (iter > 1) {
+ lambdas_sparse.init_vector(&lambdas);
+ if (rank == 0) {
+ Weights::SanityCheck(lambdas);
+ Weights::ShowLargestFeatures(lambdas);
+ }
+ }
observer.Reset();
if (rank == 0) {
converged = (iter == max_iteration);
@@ -353,7 +387,7 @@ int main(int argc, char** argv) {
}
ostringstream vv;
double minutes = (cur_time - start_time) / 60.0;
- vv << "total walltime=" << minutes << "min iter=" << iter << " minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << non_zeros(lambdas) << '/' << FD::NumFeats() << " passes_thru_data=" << (iter * size_per_proc / static_cast<double>(corpus.size()));
+ vv << "total walltime=" << minutes << " min iter=" << iter << " minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << non_zeros(lambdas) << '/' << FD::NumFeats() << " passes_thru_data=" << (iter * size_per_proc / static_cast<double>(corpus.size()));
const string svv = vv.str();
cerr << svv << endl;
Weights::WriteToFile(fname, lambdas, true, &svv);
@@ -376,12 +410,10 @@ int main(int argc, char** argv) {
if (rank == 0) {
g /= minibatch_size;
lambdas.resize(FD::NumFeats(), 0.0); // might have seen new features
- adagrad.update(g, &lambdas);
- Weights::SanityCheck(lambdas);
- Weights::ShowLargestFeatures(lambdas);
+ adagrad.update(g, &lambdas, &lambdas_sparse);
}
#ifdef HAVE_MPI
- broadcast(world, lambdas, 0);
+ broadcast(world, lambdas_sparse, 0);
broadcast(world, converged, 0);
world.barrier();
if (rank == 0) { cerr << " ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
diff --git a/training/crf/mpi_compute_cllh.cc b/training/crf/mpi_compute_cllh.cc
index 066389d0..7e38da3a 100644
--- a/training/crf/mpi_compute_cllh.cc
+++ b/training/crf/mpi_compute_cllh.cc
@@ -120,6 +120,7 @@ int main(int argc, char** argv) {
reduce(world, observer.trg_words, total_words, std::plus<unsigned>(), 0);
#else
objective = observer.acc_obj;
+ total_words = observer.trg_words;
#endif
if (rank == 0) {