summaryrefslogtreecommitdiff
path: root/training/mpi_batch_optimize.cc
diff options
context:
space:
mode:
Diffstat (limited to 'training/mpi_batch_optimize.cc')
-rw-r--r--training/mpi_batch_optimize.cc164
1 files changed, 63 insertions, 101 deletions
diff --git a/training/mpi_batch_optimize.cc b/training/mpi_batch_optimize.cc
index 39a8af7d..046e921c 100644
--- a/training/mpi_batch_optimize.cc
+++ b/training/mpi_batch_optimize.cc
@@ -22,6 +22,7 @@ namespace mpi = boost::mpi;
#include "ff_register.h"
#include "decoder.h"
#include "filelib.h"
+#include "stringlib.h"
#include "optimize.h"
#include "fdict.h"
#include "weights.h"
@@ -31,47 +32,18 @@ using namespace std;
using boost::shared_ptr;
namespace po = boost::program_options;
-void SanityCheck(const vector<double>& w) {
- for (int i = 0; i < w.size(); ++i) {
- assert(!isnan(w[i]));
- assert(!isinf(w[i]));
- }
-}
-
-struct FComp {
- const vector<double>& w_;
- FComp(const vector<double>& w) : w_(w) {}
- bool operator()(int a, int b) const {
- return fabs(w_[a]) > fabs(w_[b]);
- }
-};
-
-void ShowLargestFeatures(const vector<double>& w) {
- vector<int> fnums(w.size());
- for (int i = 0; i < w.size(); ++i)
- fnums[i] = i;
- vector<int>::iterator mid = fnums.begin();
- mid += (w.size() > 10 ? 10 : w.size());
- partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
- cerr << "TOP FEATURES:";
- for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
- cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
- }
- cerr << endl;
-}
-
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("input_weights,w",po::value<string>(),"Input feature weights file")
("training_data,t",po::value<string>(),"Training data")
("decoder_config,d",po::value<string>(),"Decoder configuration file")
- ("sharded_input,s",po::value<string>(), "Corpus and grammar files are 'sharded' so each processor loads its own input and grammar file. Argument is the directory containing the shards.")
("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
("gaussian_prior,p","Use a Gaussian prior on the weights")
("means,u", po::value<string>(), "File containing the means for Gaussian prior")
+ ("per_sentence_grammar_scratch,P", po::value<string>(), "(Optional) location of scratch space to copy per-sentence grammars for fast access, useful if a RAM disk is available")
("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior");
po::options_description clo("Command line options");
clo.add_options()
@@ -88,14 +60,10 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
po::notify(*conf);
- if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data") | conf->count("sharded_input")) || !conf->count("decoder_config")) {
+ if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data")) || !conf->count("decoder_config")) {
cerr << dcmdline_options << endl;
return false;
}
- if (conf->count("training_data") && conf->count("sharded_input")) {
- cerr << "Cannot specify both --training_data and --sharded_input\n";
- return false;
- }
return true;
}
@@ -124,7 +92,7 @@ struct TrainingObserver : public DecoderObserver {
void SetLocalGradientAndObjective(vector<double>* g, double* o) const {
*o = acc_obj;
for (SparseVector<prob_t>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it)
- (*g)[it->first] = it->second;
+ (*g)[it->first] = it->second.as_float();
}
virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
@@ -220,6 +188,36 @@ struct VectorPlus : public binary_function<vector<T>, vector<T>, vector<T> > {
}
};
+void MovePerSentenceGrammars(const string& root, int size, int rank, vector<string>* c) {
+ if (!DirectoryExists(root)) {
+ cerr << "Can't find scratch space at " << root << endl;
+ abort();
+ }
+ ostringstream os;
+ os << root << "/psg." << size << "_of_" << rank;
+ const string path = os.str();
+ MkDirP(path);
+ string sent;
+ map<string, string> attr;
+ for (unsigned i = 0; i < c->size(); ++i) {
+ sent = (*c)[i];
+ attr.clear();
+ ProcessAndStripSGML(&sent, &attr);
+ map<string, string>::iterator it = attr.find("grammar");
+ if (it != attr.end()) {
+ string src_file = it->second;
+ bool is_gzipped = (src_file.size() > 3) && (src_file.rfind(".gz") == (src_file.size() - 3));
+ string new_name = path + "/" + md5(sent);
+ if (is_gzipped) new_name += ".gz";
+ CopyFile(src_file, new_name);
+ it->second = new_name;
+ }
+ ostringstream ns;
+ ns << SGMLOpenSegTag(attr) << ' ' << sent << " </seg>";
+ (*c)[i] = ns.str();
+ }
+}
+
int main(int argc, char** argv) {
#ifdef HAVE_MPI
mpi::environment env(argc, argv);
@@ -236,42 +234,9 @@ int main(int argc, char** argv) {
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
- string shard_dir;
- if (conf.count("sharded_input")) {
- shard_dir = conf["sharded_input"].as<string>();
- if (!DirectoryExists(shard_dir)) {
- if (rank == 0) cerr << "Can't find shard directory: " << shard_dir << endl;
- return 1;
- }
- if (rank == 0)
- cerr << "Shard directory: " << shard_dir << endl;
- }
-
- // load initial weights
- Weights weights;
- if (rank == 0) { cerr << "Loading weights...\n"; }
- weights.InitFromFile(conf["input_weights"].as<string>());
- if (rank == 0) { cerr << "Done loading weights.\n"; }
-
- // freeze feature set (should be optional?)
- const bool freeze_feature_set = true;
- if (freeze_feature_set) FD::Freeze();
-
// load cdec.ini and set up decoder
vector<string> cdec_ini;
ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
- if (shard_dir.size()) {
- if (rank == 0) {
- for (int i = 0; i < cdec_ini.size(); ++i) {
- if (cdec_ini[i].find("grammar=") == 0) {
- cerr << "!!! using sharded input and " << conf["decoder_config"].as<string>() << " contains a grammar specification:\n" << cdec_ini[i] << "\n VERIFY THAT THIS IS CORRECT!\n";
- }
- }
- }
- ostringstream g;
- g << "grammar=" << shard_dir << "/grammar." << rank << "_of_" << size << ".gz";
- cdec_ini.push_back(g.str());
- }
istringstream ini;
StoreConfig(cdec_ini, &ini);
if (rank == 0) cerr << "Loading grammar...\n";
@@ -282,22 +247,28 @@ int main(int argc, char** argv) {
}
if (rank == 0) cerr << "Done loading grammar!\n";
+ // load initial weights
+ if (rank == 0) { cerr << "Loading weights...\n"; }
+ vector<weight_t>& lambdas = decoder->CurrentWeightVector();
+ Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
+ if (rank == 0) { cerr << "Done loading weights.\n"; }
+
+ // freeze feature set (should be optional?)
+ const bool freeze_feature_set = true;
+ if (freeze_feature_set) FD::Freeze();
+
const int num_feats = FD::NumFeats();
if (rank == 0) cerr << "Number of features: " << num_feats << endl;
+ lambdas.resize(num_feats);
+
const bool gaussian_prior = conf.count("gaussian_prior");
- vector<double> means(num_feats, 0);
+ vector<weight_t> means(num_feats, 0);
if (conf.count("means")) {
if (!gaussian_prior) {
cerr << "Don't use --means without --gaussian_prior!\n";
exit(1);
}
- Weights wm;
- wm.InitFromFile(conf["means"].as<string>());
- if (num_feats != FD::NumFeats()) {
- cerr << "[ERROR] Means file had unexpected features!\n";
- exit(1);
- }
- wm.InitVector(&means);
+ Weights::InitFromFile(conf["means"].as<string>(), &means);
}
shared_ptr<BatchOptimizer> o;
if (rank == 0) {
@@ -309,28 +280,18 @@ int main(int argc, char** argv) {
cerr << "Optimizer: " << o->Name() << endl;
}
double objective = 0;
- vector<double> lambdas(num_feats, 0.0);
- weights.InitVector(&lambdas);
- if (lambdas.size() != num_feats) {
- cerr << "Initial weights file did not have all features specified!\n feats="
- << num_feats << "\n weights file=" << lambdas.size() << endl;
- lambdas.resize(num_feats, 0.0);
- }
vector<double> gradient(num_feats, 0.0);
- vector<double> rcv_grad(num_feats, 0.0);
+ vector<double> rcv_grad;
+ rcv_grad.clear();
bool converged = false;
vector<string> corpus;
- if (shard_dir.size()) {
- ostringstream os; os << shard_dir << "/corpus." << rank << "_of_" << size;
- ReadTrainingCorpus(os.str(), 0, 1, &corpus);
- cerr << os.str() << " has " << corpus.size() << " training examples. " << endl;
- if (corpus.size() > 500) { corpus.resize(500); cerr << " TRUNCATING\n"; }
- } else {
- ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
- }
+ ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
assert(corpus.size() > 0);
+ if (conf.count("per_sentence_grammar_scratch"))
+ MovePerSentenceGrammars(conf["per_sentence_grammar_scratch"].as<string>(), rank, size, &corpus);
+
TrainingObserver observer;
while (!converged) {
observer.Reset();
@@ -341,19 +302,20 @@ int main(int argc, char** argv) {
if (rank == 0) {
cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
}
- decoder->SetWeights(lambdas);
for (int i = 0; i < corpus.size(); ++i)
decoder->Decode(corpus[i], &observer);
cerr << " process " << rank << '/' << size << " done\n";
fill(gradient.begin(), gradient.end(), 0);
- fill(rcv_grad.begin(), rcv_grad.end(), 0);
observer.SetLocalGradientAndObjective(&gradient, &objective);
double to = 0;
#ifdef HAVE_MPI
+ rcv_grad.resize(num_feats, 0.0);
mpi::reduce(world, &gradient[0], gradient.size(), &rcv_grad[0], plus<double>(), 0);
- mpi::reduce(world, objective, to, plus<double>(), 0);
swap(gradient, rcv_grad);
+ rcv_grad.clear();
+
+ mpi::reduce(world, objective, to, plus<double>(), 0);
objective = to;
#endif
@@ -378,7 +340,7 @@ int main(int argc, char** argv) {
for (int i = 0; i < gradient.size(); ++i)
gnorm += gradient[i] * gradient[i];
cerr << " GNORM=" << sqrt(gnorm) << endl;
- vector<double> old = lambdas;
+ vector<weight_t> old = lambdas;
int c = 0;
while (old == lambdas) {
++c;
@@ -387,9 +349,8 @@ int main(int argc, char** argv) {
assert(c < 5);
}
old.clear();
- SanityCheck(lambdas);
- ShowLargestFeatures(lambdas);
- weights.InitFromVector(lambdas);
+ Weights::SanityCheck(lambdas);
+ Weights::ShowLargestFeatures(lambdas);
converged = o->HasConverged();
if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
@@ -399,7 +360,7 @@ int main(int argc, char** argv) {
ostringstream vv;
vv << "Objective = " << objective << " (eval count=" << o->EvaluationCount() << ")";
const string svv = vv.str();
- weights.WriteToFile(fname, true, &svv);
+ Weights::WriteToFile(fname, lambdas, true, &svv);
} // rank == 0
int cint = converged;
#ifdef HAVE_MPI
@@ -411,3 +372,4 @@ int main(int argc, char** argv) {
}
return 0;
}
+
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 16:39:49 +0000