From 7ff60b69cc21c90695ca20829375e6bf9b5f452d Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 10 Jul 2011 23:00:21 -0400 Subject: starting implementation of Hopkins&May (2011) optimizer --- pro-train/mr_pro_reduce.cc | 81 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 pro-train/mr_pro_reduce.cc (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc new file mode 100644 index 00000000..3df52020 --- /dev/null +++ b/pro-train/mr_pro_reduce.cc @@ -0,0 +1,81 @@ +#include +#include +#include +#include + +#include +#include + +#include "sparse_vector.h" +#include "error_surface.h" +#include "line_optimizer.h" +#include "b64tools.h" + +using namespace std; +namespace po = boost::program_options; + +void InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("loss_function,l",po::value(), "Loss function being optimized") + ("help,h", "Help"); + po::options_description dcmdline_options; + dcmdline_options.add(opts); + po::store(parse_command_line(argc, argv, dcmdline_options), *conf); + bool flag = conf->count("loss_function") == 0; + if (flag || conf->count("help")) { + cerr << dcmdline_options << endl; + exit(1); + } +} + +int main(int argc, char** argv) { + po::variables_map conf; + InitCommandLine(argc, argv, &conf); + const string loss_function = conf["loss_function"].as(); + ScoreType type = ScoreTypeFromString(loss_function); + LineOptimizer::ScoreType opt_type = LineOptimizer::MAXIMIZE_SCORE; + if (type == TER || type == AER) { + opt_type = LineOptimizer::MINIMIZE_SCORE; + } + string last_key; + vector esv; + while(cin) { + string line; + getline(cin, line); + if (line.empty()) continue; + size_t ks = line.find("\t"); + assert(string::npos != ks); + assert(ks > 2); + string key = line.substr(2, ks - 2); + string val = line.substr(ks + 1); + if (key != last_key) { + if (!last_key.empty()) { + float score; + double x = LineOptimizer::LineOptimize(esv, opt_type, &score); + cout << last_key << "|" << x << "|" << score << endl; + } + last_key = key; + esv.clear(); + } + if (val.size() % 4 != 0) { + cerr << "B64 encoding error 1! Skipping.\n"; + continue; + } + string encoded(val.size() / 4 * 3, '\0'); + if (!B64::b64decode(reinterpret_cast(&val[0]), val.size(), &encoded[0], encoded.size())) { + cerr << "B64 encoding error 2! Skipping.\n"; + continue; + } + esv.push_back(ErrorSurface()); + esv.back().Deserialize(type, encoded); + } + if (!esv.empty()) { + // cerr << "ESV=" << esv.size() << endl; + // for (int i = 0; i < esv.size(); ++i) { cerr << esv[i].size() << endl; } + float score; + double x = LineOptimizer::LineOptimize(esv, opt_type, &score); + cout << last_key << "|" << x << "|" << score << endl; + } + return 0; +} -- cgit v1.2.3 From a8a8aeba08d5c0f6841394087bb4ec0b6ade0694 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Mon, 11 Jul 2011 20:39:45 -0400 Subject: sort of working hopkins&may optimizer --- pro-train/Makefile.am | 4 +- pro-train/dist-pro.pl | 308 ++++++++++-------------------- pro-train/mr_pro_generate_mapper_input.pl | 18 ++ pro-train/mr_pro_map.cc | 118 ++++++++++-- pro-train/mr_pro_reduce.cc | 167 ++++++++++++---- 5 files changed, 349 insertions(+), 266 deletions(-) create mode 100755 pro-train/mr_pro_generate_mapper_input.pl (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/Makefile.am b/pro-train/Makefile.am index 945ed5c3..fdaf43e2 100644 --- a/pro-train/Makefile.am +++ b/pro-train/Makefile.am @@ -8,6 +8,6 @@ mr_pro_map_SOURCES = mr_pro_map.cc mr_pro_map_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz mr_pro_reduce_SOURCES = mr_pro_reduce.cc -mr_pro_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz +mr_pro_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/training/optimize.o $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz -AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval +AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I$(top_srcdir)/training diff --git a/pro-train/dist-pro.pl b/pro-train/dist-pro.pl index 35bccea4..55d7f1fa 100755 --- a/pro-train/dist-pro.pl +++ b/pro-train/dist-pro.pl @@ -21,7 +21,7 @@ my $bin_dir = $SCRIPT_DIR; die "Bin directory $bin_dir missing/inaccessible" unless -d $bin_dir; my $FAST_SCORE="$bin_dir/../mteval/fast_score"; die "Can't execute $FAST_SCORE" unless -x $FAST_SCORE; -my $MAPINPUT = "$bin_dir/mr_pro_generate_mapper_input"; +my $MAPINPUT = "$bin_dir/mr_pro_generate_mapper_input.pl"; my $MAPPER = "$bin_dir/mr_pro_map"; my $REDUCER = "$bin_dir/mr_pro_reduce"; my $parallelize = "$VEST_DIR/parallelize.pl"; @@ -37,8 +37,7 @@ die "Can't find decoder in $cdec" unless -x $cdec; die "Can't find $parallelize" unless -x $parallelize; die "Can't find $libcall" unless -e $libcall; my $decoder = $cdec; -my $lines_per_mapper = 400; -my $rand_directions = 15; +my $lines_per_mapper = 100; my $iteration = 1; my $run_local = 0; my $best_weights; @@ -58,7 +57,6 @@ my $metric = "ibm_bleu"; my $dir; my $iniFile; my $weights; -my $initialWeights; my $decoderOpt; my $noprimary; my $maxsim=0; @@ -67,7 +65,6 @@ my $oracleb=20; my $bleu_weight=1; my $use_make; # use make to parallelize line search my $dirargs=''; -my $density_prune; my $usefork; my $pass_suffix = ''; my $cpbin=1; @@ -76,7 +73,6 @@ Getopt::Long::Configure("no_auto_abbrev"); if (GetOptions( "decoder=s" => \$decoderOpt, "decode-nodes=i" => \$decode_nodes, - "density-prune=f" => \$density_prune, "dont-clean" => \$disable_clean, "pass-suffix=s" => \$pass_suffix, "use-fork" => \$usefork, @@ -91,8 +87,6 @@ if (GetOptions( "normalize=s" => \$normalize, "pmem=s" => \$pmem, "cpbin!" => \$cpbin, - "rand-directions=i" => \$rand_directions, - "random_directions=i" => \$rand_directions, "bleu_weight=s" => \$bleu_weight, "no-primary!" => \$noprimary, "max-similarity=s" => \$maxsim, @@ -103,18 +97,12 @@ if (GetOptions( "ref-files=s" => \$refFiles, "metric=s" => \$metric, "source-file=s" => \$srcFile, - "weights=s" => \$initialWeights, "workdir=s" => \$dir, - "opt-iterations=i" => \$optimization_iters, ) == 0 || @ARGV!=1 || $help) { print_help(); exit; } -if (defined $density_prune) { - die "--density_prune n: n must be greater than 1.0\n" unless $density_prune > 1.0; -} - if ($usefork) { $usefork = "--use-fork"; } else { $usefork = ''; } if ($metric =~ /^(combi|ter)$/i) { @@ -146,7 +134,7 @@ if ($metric =~ /^ter$|^aer$/i) { my $refs_comma_sep = get_comma_sep_refs('r',$refFiles); unless ($dir){ - $dir = "vest"; + $dir = "protrain"; } unless ($dir =~ /^\//){ # convert relative path to absolute path my $basedir = check_output("pwd"); @@ -203,18 +191,19 @@ sub dirsize { opendir ISEMPTY,$_[0]; return scalar(readdir(ISEMPTY))-1; } +my @allweights; if ($dryrun){ write_config(*STDERR); exit 0; } else { - if (-e $dir && dirsize($dir)>1 && -e "$dir/hgs" ){ # allow preexisting logfile, binaries, but not dist-vest.pl outputs + if (-e $dir && dirsize($dir)>1 && -e "$dir/hgs" ){ # allow preexisting logfile, binaries, but not dist-pro.pl outputs die "ERROR: working dir $dir already exists\n\n"; } else { -e $dir || mkdir $dir; mkdir "$dir/hgs"; modbin("$dir/bin",\$LocalConfig,\$cdec,\$SCORER,\$MAPINPUT,\$MAPPER,\$REDUCER,\$parallelize,\$sentserver,\$sentclient,\$libcall) if $cpbin; mkdir "$dir/scripts"; - my $cmdfile="$dir/rerun-vest.sh"; + my $cmdfile="$dir/rerun-pro.sh"; open CMD,'>',$cmdfile; print CMD "cd ",&getcwd,"\n"; # print CMD &escaped_cmdline,"\n"; #buggy - last arg is quoted. @@ -223,13 +212,8 @@ if ($dryrun){ close CMD; print STDERR $cline; chmod(0755,$cmdfile); - unless (-e $initialWeights) { - print STDERR "Please specify an initial weights file with --initial-weights\n"; - print_help(); - exit; - } - check_call("cp $initialWeights $dir/weights.0"); - die "Can't find weights.0" unless (-e "$dir/weights.0"); + check_call("touch $dir/weights.0"); + die "Can't find weights.0" unless (-e "$dir/weights.0"); } write_config(*STDERR); } @@ -255,6 +239,7 @@ my $random_seed = int(time / 1000); my $lastWeightsFile; my $lastPScore = 0; # main optimization loop +my @mapoutputs = (); # aggregate map outputs over all iters while (1){ print STDERR "\n\nITERATION $iteration\n==========\n"; @@ -276,10 +261,8 @@ while (1){ print STDERR unchecked_output("date"); my $im1 = $iteration - 1; my $weightsFile="$dir/weights.$im1"; + push @allweights, "-w $dir/weights.$im1"; my $decoder_cmd = "$decoder -c $iniFile --weights$pass_suffix $weightsFile -O $dir/hgs"; - if ($density_prune) { - $decoder_cmd .= " --density_prune $density_prune"; - } my $pcmd; if ($run_local) { $pcmd = "cat $srcFile |"; @@ -320,163 +303,111 @@ while (1){ # run optimizer print STDERR "RUNNING OPTIMIZER AT "; print STDERR unchecked_output("date"); + print STDERR " - GENERATE TRAINING EXEMPLARS\n"; my $mergeLog="$logdir/prune-merge.log.$iteration"; my $score = 0; my $icc = 0; my $inweights="$dir/weights.$im1"; - for (my $opt_iter=1; $opt_iter<$optimization_iters; $opt_iter++) { - print STDERR "\nGENERATE OPTIMIZATION STRATEGY (OPT-ITERATION $opt_iter/$optimization_iters)\n"; - print STDERR unchecked_output("date"); - $icc++; - my $nop=$noprimary?"--no_primary":""; - my $targs=$oraclen ? "--decoder_translations='$runFile.gz' ".get_comma_sep_refs('-references',$refFiles):""; - my $bwargs=$bleu_weight!=1 ? "--bleu_weight=$bleu_weight":""; - $cmd="$MAPINPUT -w $inweights -r $dir/hgs $bwargs -s $devSize -d $rand_directions --max_similarity=$maxsim --oracle_directions=$oraclen --oracle_batch=$oracleb $targs $dirargs > $dir/agenda.$im1-$opt_iter"; - print STDERR "COMMAND:\n$cmd\n"; - check_call($cmd); - check_call("mkdir -p $dir/splag.$im1"); - $cmd="split -a 3 -l $lines_per_mapper $dir/agenda.$im1-$opt_iter $dir/splag.$im1/mapinput."; - print STDERR "COMMAND:\n$cmd\n"; - check_call($cmd); - opendir(DIR, "$dir/splag.$im1") or die "Can't open directory: $!"; - my @shards = grep { /^mapinput\./ } readdir(DIR); - closedir DIR; - die "No shards!" unless scalar @shards > 0; - my $joblist = ""; - my $nmappers = 0; - my @mapoutputs = (); - @cleanupcmds = (); - my %o2i = (); - my $first_shard = 1; - my $mkfile; # only used with makefiles - my $mkfilename; - if ($use_make) { - $mkfilename = "$dir/splag.$im1/domap.mk"; - open $mkfile, ">$mkfilename" or die "Couldn't write $mkfilename: $!"; - print $mkfile "all: $dir/splag.$im1/map.done\n\n"; - } - my @mkouts = (); # only used with makefiles - for my $shard (@shards) { - my $mapoutput = $shard; - my $client_name = $shard; - $client_name =~ s/mapinput.//; - $client_name = "vest.$client_name"; - $mapoutput =~ s/mapinput/mapoutput/; - push @mapoutputs, "$dir/splag.$im1/$mapoutput"; - $o2i{"$dir/splag.$im1/$mapoutput"} = "$dir/splag.$im1/$shard"; - my $script = "$MAPPER -s $srcFile -l $metric $refs_comma_sep < $dir/splag.$im1/$shard | sort -t \$'\\t' -k 1 > $dir/splag.$im1/$mapoutput"; - if ($run_local) { - print STDERR "COMMAND:\n$script\n"; - check_bash_call($script); - } elsif ($use_make) { - my $script_file = "$dir/scripts/map.$shard"; - open F, ">$script_file" or die "Can't write $script_file: $!"; - print F "#!/bin/bash\n"; - print F "$script\n"; - close F; - my $output = "$dir/splag.$im1/$mapoutput"; - push @mkouts, $output; - chmod(0755, $script_file) or die "Can't chmod $script_file: $!"; - if ($first_shard) { print STDERR "$script\n"; $first_shard=0; } - print $mkfile "$output: $dir/splag.$im1/$shard\n\t$script_file\n\n"; - } else { - my $script_file = "$dir/scripts/map.$shard"; - open F, ">$script_file" or die "Can't write $script_file: $!"; - print F "$script\n"; - close F; - if ($first_shard) { print STDERR "$script\n"; $first_shard=0; } - - $nmappers++; - my $qcmd = "$QSUB_CMD -N $client_name -o /dev/null -e $logdir/$client_name.ER $script_file"; - my $jobid = check_output("$qcmd"); - chomp $jobid; - $jobid =~ s/^(\d+)(.*?)$/\1/g; - $jobid =~ s/^Your job (\d+) .*$/\1/; - push(@cleanupcmds, "qdel $jobid 2> /dev/null"); - print STDERR " $jobid"; - if ($joblist == "") { $joblist = $jobid; } - else {$joblist = $joblist . "\|" . $jobid; } - } - } + $cmd="$MAPINPUT $dir/hgs > $dir/agenda.$im1"; + print STDERR "COMMAND:\n$cmd\n"; + check_call($cmd); + check_call("mkdir -p $dir/splag.$im1"); + $cmd="split -a 3 -l $lines_per_mapper $dir/agenda.$im1 $dir/splag.$im1/mapinput."; + print STDERR "COMMAND:\n$cmd\n"; + check_call($cmd); + opendir(DIR, "$dir/splag.$im1") or die "Can't open directory: $!"; + my @shards = grep { /^mapinput\./ } readdir(DIR); + closedir DIR; + die "No shards!" unless scalar @shards > 0; + my $joblist = ""; + my $nmappers = 0; + @cleanupcmds = (); + my %o2i = (); + my $first_shard = 1; + my $mkfile; # only used with makefiles + my $mkfilename; + if ($use_make) { + $mkfilename = "$dir/splag.$im1/domap.mk"; + open $mkfile, ">$mkfilename" or die "Couldn't write $mkfilename: $!"; + print $mkfile "all: $dir/splag.$im1/map.done\n\n"; + } + my @mkouts = (); # only used with makefiles + for my $shard (@shards) { + my $mapoutput = $shard; + my $client_name = $shard; + $client_name =~ s/mapinput.//; + $client_name = "pro.$client_name"; + $mapoutput =~ s/mapinput/mapoutput/; + push @mapoutputs, "$dir/splag.$im1/$mapoutput"; + $o2i{"$dir/splag.$im1/$mapoutput"} = "$dir/splag.$im1/$shard"; + my $script = "$MAPPER -s $srcFile -l $metric $refs_comma_sep @allweights < $dir/splag.$im1/$shard > $dir/splag.$im1/$mapoutput"; if ($run_local) { - print STDERR "\nProcessing line search complete.\n"; + print STDERR "COMMAND:\n$script\n"; + check_bash_call($script); } elsif ($use_make) { - print $mkfile "$dir/splag.$im1/map.done: @mkouts\n\ttouch $dir/splag.$im1/map.done\n\n"; - close $mkfile; - my $mcmd = "make -j $use_make -f $mkfilename"; - print STDERR "\nExecuting: $mcmd\n"; - check_call($mcmd); + my $script_file = "$dir/scripts/map.$shard"; + open F, ">$script_file" or die "Can't write $script_file: $!"; + print F "#!/bin/bash\n"; + print F "$script\n"; + close F; + my $output = "$dir/splag.$im1/$mapoutput"; + push @mkouts, $output; + chmod(0755, $script_file) or die "Can't chmod $script_file: $!"; + if ($first_shard) { print STDERR "$script\n"; $first_shard=0; } + print $mkfile "$output: $dir/splag.$im1/$shard\n\t$script_file\n\n"; } else { - print STDERR "\nLaunched $nmappers mappers.\n"; - sleep 8; - print STDERR "Waiting for mappers to complete...\n"; - while ($nmappers > 0) { - sleep 5; - my @livejobs = grep(/$joblist/, split(/\n/, unchecked_output("qstat | grep -v ' C '"))); - $nmappers = scalar @livejobs; - } - print STDERR "All mappers complete.\n"; + my $script_file = "$dir/scripts/map.$shard"; + open F, ">$script_file" or die "Can't write $script_file: $!"; + print F "$script\n"; + close F; + if ($first_shard) { print STDERR "$script\n"; $first_shard=0; } + + $nmappers++; + my $qcmd = "$QSUB_CMD -N $client_name -o /dev/null -e $logdir/$client_name.ER $script_file"; + my $jobid = check_output("$qcmd"); + chomp $jobid; + $jobid =~ s/^(\d+)(.*?)$/\1/g; + $jobid =~ s/^Your job (\d+) .*$/\1/; + push(@cleanupcmds, "qdel $jobid 2> /dev/null"); + print STDERR " $jobid"; + if ($joblist == "") { $joblist = $jobid; } + else {$joblist = $joblist . "\|" . $jobid; } } - my $tol = 0; - my $til = 0; - for my $mo (@mapoutputs) { - my $olines = get_lines($mo); - my $ilines = get_lines($o2i{$mo}); - $tol += $olines; - $til += $ilines; - die "$mo: output lines ($olines) doesn't match input lines ($ilines)" unless $olines==$ilines; - } - print STDERR "Results for $tol/$til lines\n"; - print STDERR "\nSORTING AND RUNNING VEST REDUCER\n"; - print STDERR unchecked_output("date"); - $cmd="sort -t \$'\\t' -k 1 @mapoutputs | $REDUCER -l $metric > $dir/redoutput.$im1"; - print STDERR "COMMAND:\n$cmd\n"; - check_bash_call($cmd); - $cmd="sort -nk3 $DIR_FLAG '-t|' $dir/redoutput.$im1 | head -1"; - # sort returns failure even when it doesn't fail for some reason - my $best=unchecked_output("$cmd"); chomp $best; - print STDERR "$best\n"; - my ($oa, $x, $xscore) = split /\|/, $best; - $score = $xscore; - print STDERR "PROJECTED SCORE: $score\n"; - if (abs($x) < $epsilon) { - print STDERR "\nOPTIMIZER: no score improvement: abs($x) < $epsilon\n"; - last; - } - my $psd = $score - $last_score; - $last_score = $score; - if (abs($psd) < $epsilon) { - print STDERR "\nOPTIMIZER: no score improvement: abs($psd) < $epsilon\n"; - last; - } - my ($origin, $axis) = split /\s+/, $oa; - - my %ori = convert($origin); - my %axi = convert($axis); - - my $finalFile="$dir/weights.$im1-$opt_iter"; - open W, ">$finalFile" or die "Can't write: $finalFile: $!"; - my $norm = 0; - for my $k (sort keys %ori) { - my $dd = $ori{$k} + $axi{$k} * $x; - $norm += $dd * $dd; - } - $norm = sqrt($norm); - $norm = 1; - for my $k (sort keys %ori) { - my $v = ($ori{$k} + $axi{$k} * $x) / $norm; - print W "$k $v\n"; + } + if ($run_local) { + print STDERR "\nCompleted extraction of training exemplars.\n"; + } elsif ($use_make) { + print $mkfile "$dir/splag.$im1/map.done: @mkouts\n\ttouch $dir/splag.$im1/map.done\n\n"; + close $mkfile; + my $mcmd = "make -j $use_make -f $mkfilename"; + print STDERR "\nExecuting: $mcmd\n"; + check_call($mcmd); + } else { + print STDERR "\nLaunched $nmappers mappers.\n"; + sleep 8; + print STDERR "Waiting for mappers to complete...\n"; + while ($nmappers > 0) { + sleep 5; + my @livejobs = grep(/$joblist/, split(/\n/, unchecked_output("qstat | grep -v ' C '"))); + $nmappers = scalar @livejobs; } - check_call("rm $dir/splag.$im1/*"); - $inweights = $finalFile; + print STDERR "All mappers complete.\n"; } - $lastWeightsFile = "$dir/weights.$iteration"; - check_call("cp $inweights $lastWeightsFile"); - if ($icc < 2) { - print STDERR "\nREACHED STOPPING CRITERION: score change too little\n"; - last; + my $tol = 0; + my $til = 0; + print STDERR "MO: @mapoutputs\n"; + for my $mo (@mapoutputs) { + #my $olines = get_lines($mo); + #my $ilines = get_lines($o2i{$mo}); + #die "$mo: no training instances generated!" if $olines == 0; } + print STDERR "\nRUNNING CLASSIFIER (REDUCER)\n"; + print STDERR unchecked_output("date"); + $cmd="cat @mapoutputs | $REDUCER -w $dir/weights.$im1 > $dir/weights.$iteration"; + print STDERR "COMMAND:\n$cmd\n"; + check_bash_call($cmd); + $lastWeightsFile = "$dir/weights.$iteration"; $lastPScore = $score; $iteration++; print STDERR "\n==========\n"; @@ -488,24 +419,6 @@ print STDOUT "$lastWeightsFile\n"; exit 0; -sub normalize_weights { - my ($rfn, $rpts, $feat) = @_; - my @feat_names = @$rfn; - my @pts = @$rpts; - my $z = 1.0; - for (my $i=0; $i < scalar @feat_names; $i++) { - if ($feat_names[$i] eq $feat) { - $z = $pts[$i]; - last; - } - } - for (my $i=0; $i < scalar @feat_names; $i++) { - $pts[$i] /= $z; - } - print STDERR " NORM WEIGHTS: @pts\n"; - return @pts; -} - sub get_lines { my $fn = shift @_; open FL, "<$fn" or die "Couldn't read $fn: $!"; @@ -563,7 +476,6 @@ sub write_config { print $fh "HEAD NODE: $host\n"; print $fh "PMEM (DECODING): $pmem\n"; print $fh "CLEANUP: $cleanup\n"; - print $fh "INITIAL WEIGHTS: $initialWeights\n"; } sub update_weights_file { @@ -603,6 +515,7 @@ sub enseg { } close SRC; close NEWSRC; + die "Empty dev set!" if ($i == 0); } sub print_help { @@ -634,10 +547,6 @@ Options: --decoder Decoder binary to use. - --density-prune - Limit the density of the hypergraph on each iteration to N times - the number of edges on the Viterbi path. - --help Print this message and exit. @@ -668,18 +577,9 @@ Options: After each iteration, rescale all feature weights such that feature- name has a weight of 1.0. - --rand-directions - MERT will attempt to optimize along all of the principle directions, - set this parameter to explore other directions. Defaults to 5. - --source-file Dev set source file. - --weights - A file specifying initial feature weights. The format is - FeatureName_1 value1 - FeatureName_2 value2 - --workdir Directory for intermediate and output files. If not specified, the name is derived from the ini filename. Assuming that the ini diff --git a/pro-train/mr_pro_generate_mapper_input.pl b/pro-train/mr_pro_generate_mapper_input.pl new file mode 100755 index 00000000..b30fc4fd --- /dev/null +++ b/pro-train/mr_pro_generate_mapper_input.pl @@ -0,0 +1,18 @@ +#!/usr/bin/perl -w +use strict; + +die "Usage: $0 HG_DIR\n" unless scalar @ARGV == 1; +my $d = shift @ARGV; +die "Can't find directory $d" unless -d $d; + +opendir(DIR, $d) or die "Can't read $d: $!"; +my @hgs = grep { /\.gz$/ } readdir(DIR); +closedir DIR; + +for my $hg (@hgs) { + my $file = $hg; + my $id = $hg; + $id =~ s/(\.json)?\.gz//; + print "$d/$file $id\n"; +} + diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc index b046cdea..128d93ce 100644 --- a/pro-train/mr_pro_map.cc +++ b/pro-train/mr_pro_map.cc @@ -10,6 +10,7 @@ #include "sampler.h" #include "filelib.h" #include "stringlib.h" +#include "weights.h" #include "scorer.h" #include "inside_outside.h" #include "hg_io.h" @@ -27,10 +28,10 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { po::options_description opts("Configuration options"); opts.add_options() ("reference,r",po::value >(), "[REQD] Reference translation (tokenized text)") - ("source,s",po::value(), "Source file (ignored, except for AER)") + ("source,s",po::value()->default_value(""), "Source file (ignored, except for AER)") ("loss_function,l",po::value()->default_value("ibm_bleu"), "Loss function being optimized") ("input,i",po::value()->default_value("-"), "Input file to map (- is STDIN)") - ("weights,w",po::value(), "[REQD] Current weights file") + ("weights,w",po::value >(), "[REQD] Weights files from previous and current iterations") ("kbest_size,k",po::value()->default_value(1500u), "Top k-hypotheses to extract") ("candidate_pairs,G", po::value()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)") ("best_pairs,X", po::value()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)") @@ -44,6 +45,10 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { cerr << "Please specify one or more references using -r \n"; flag = true; } + if (!conf->count("weights")) { + cerr << "Please specify one or more weights using -w \n"; + flag = true; + } if (flag || conf->count("help")) { cerr << dcmdline_options << endl; exit(1); @@ -51,18 +56,78 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { } struct HypInfo { - HypInfo(const vector& h, const SparseVector& feats) : hyp(h), g_(-1), x(feats) {} - double g() { + HypInfo(const vector& h, const SparseVector& feats) : hyp(h), g_(-100.0), x(feats) {} + + // lazy evaluation + double g(const SentenceScorer& scorer) const { + if (g_ == -100.0) + g_ = scorer.ScoreCandidate(hyp)->ComputeScore(); return g_; } - private: - int sent_id; vector hyp; - double g_; + mutable double g_; public: SparseVector x; }; +struct ThresholdAlpha { + explicit ThresholdAlpha(double t = 0.05) : threshold(t) {} + double operator()(double mag) const { + if (mag < threshold) return 0.0; else return 1.0; + } + const double threshold; +}; + +struct TrainingInstance { + TrainingInstance(const SparseVector& feats, bool positive, double diff) : x(feats), y(positive), gdiff(diff) {} + SparseVector x; +#ifdef DEBUGGING_PRO + vector a; + vector b; +#endif + bool y; + double gdiff; +}; + +struct DiffOrder { + bool operator()(const TrainingInstance& a, const TrainingInstance& b) const { + return a.gdiff > b.gdiff; + } +}; + +template +void Sample(const unsigned gamma, const unsigned xi, const vector& J_i, const SentenceScorer& scorer, const Alpha& alpha_i, bool invert_score, vector* pv) { + vector v; + for (unsigned i = 0; i < gamma; ++i) { + size_t a = rng->inclusive(0, J_i.size() - 1)(); + size_t b = rng->inclusive(0, J_i.size() - 1)(); + if (a == b) continue; + double ga = J_i[a].g(scorer); + double gb = J_i[b].g(scorer); + bool positive = ga < gb; + if (invert_score) positive = !positive; + double gdiff = fabs(ga - gb); + if (!gdiff) continue; + if (rng->next() < alpha_i(gdiff)) { + v.push_back(TrainingInstance((J_i[a].x - J_i[b].x).erase_zeros(), positive, gdiff)); +#ifdef DEBUGGING_PRO + v.back().a = J_i[a].hyp; + v.back().b = J_i[b].hyp; +#endif + } + } + vector::iterator mid = v.begin() + xi; + if (xi > v.size()) mid = v.end(); + partial_sort(v.begin(), mid, v.end(), DiffOrder()); + copy(v.begin(), mid, back_inserter(*pv)); +#ifdef DEBUGGING_PRO + if (v.size() >= 5) + for (int i =0; i < 5; ++i) { + cerr << v[i].gdiff << " y=" << v[i].y << "\tA:" << TD::GetString(v[i].a) << "\n\tB: " << TD::GetString(v[i].b) << endl; + } +#endif +} + int main(int argc, char** argv) { po::variables_map conf; InitCommandLine(argc, argv, &conf); @@ -81,7 +146,15 @@ int main(int argc, char** argv) { const unsigned kbest_size = conf["kbest_size"].as(); const unsigned gamma = conf["candidate_pairs"].as(); const unsigned xi = conf["best_pairs"].as(); + vector weights_files = conf["weights"].as >(); + vector > weights(weights_files.size()); + for (int i = 0; i < weights.size(); ++i) { + Weights w; + w.InitFromFile(weights_files[i]); + w.InitVector(&weights[i]); + } while(in) { + vector v; string line; getline(in, line); if (line.empty()) continue; @@ -92,18 +165,27 @@ int main(int argc, char** argv) { is >> file >> sent_id; ReadFile rf(file); HypergraphIO::ReadFromJSON(rf.stream(), &hg); - KBest::KBestDerivations, ESentenceTraversal> kbest(hg, kbest_size); - vector J_i; - for (int i = 0; i < kbest_size; ++i) { - const KBest::KBestDerivations, ESentenceTraversal>::Derivation* d = - kbest.LazyKthBest(hg.nodes_.size() - 1, i); - if (!d) break; - float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore(); - // if (invert_score) sentscore *= -1.0; - // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl; - d->feature_values; - sentscore; + int start = weights.size(); + start -= 4; + if (start < 0) start = 0; + for (int i = start; i < weights.size(); ++i) { + hg.Reweight(weights[i]); + KBest::KBestDerivations, ESentenceTraversal> kbest(hg, kbest_size); + + for (int i = 0; i < kbest_size; ++i) { + const KBest::KBestDerivations, ESentenceTraversal>::Derivation* d = + kbest.LazyKthBest(hg.nodes_.size() - 1, i); + if (!d) break; + J_i.push_back(HypInfo(d->yield, d->feature_values)); + } + } + + Sample(gamma, xi, J_i, *ds[sent_id], ThresholdAlpha(0.05), (type == TER), &v); + for (unsigned i = 0; i < v.size(); ++i) { + const TrainingInstance& vi = v[i]; + cout << vi.y << "\t" << vi.x << endl; + cout << (!vi.y) << "\t" << (vi.x * -1.0) << endl; } } return 0; diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 3df52020..2b9c5ce7 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -1,3 +1,4 @@ +#include #include #include #include @@ -6,24 +7,29 @@ #include #include +#include "weights.h" #include "sparse_vector.h" -#include "error_surface.h" -#include "line_optimizer.h" -#include "b64tools.h" +#include "optimize.h" using namespace std; namespace po = boost::program_options; +// since this is a ranking model, there should be equal numbers of +// positive and negative examples so the bias should be 0 +static const double MAX_BIAS = 1e-10; + void InitCommandLine(int argc, char** argv, po::variables_map* conf) { po::options_description opts("Configuration options"); opts.add_options() - ("loss_function,l",po::value(), "Loss function being optimized") + ("weights,w", po::value(), "Weights from previous iteration (used as initialization and interpolation") + ("interpolation,p",po::value()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev") + ("memory_buffers,m",po::value()->default_value(200), "Number of memory buffers (LBFGS)") + ("sigma_squared,s",po::value()->default_value(0.5), "Sigma squared for Gaussian prior") ("help,h", "Help"); po::options_description dcmdline_options; dcmdline_options.add(opts); po::store(parse_command_line(argc, argv, dcmdline_options), *conf); - bool flag = conf->count("loss_function") == 0; - if (flag || conf->count("help")) { + if (conf->count("help")) { cerr << dcmdline_options << endl; exit(1); } @@ -32,50 +38,127 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { int main(int argc, char** argv) { po::variables_map conf; InitCommandLine(argc, argv, &conf); - const string loss_function = conf["loss_function"].as(); - ScoreType type = ScoreTypeFromString(loss_function); - LineOptimizer::ScoreType opt_type = LineOptimizer::MAXIMIZE_SCORE; - if (type == TER || type == AER) { - opt_type = LineOptimizer::MINIMIZE_SCORE; + string line; + vector > > training; + int lc = 0; + bool flag = false; + SparseVector old_weights; + const double psi = conf["interpolation"].as(); + if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } + if (conf.count("weights")) { + Weights w; + w.InitFromFile(conf["weights"].as()); + w.InitSparseVector(&old_weights); } - string last_key; - vector esv; - while(cin) { - string line; - getline(cin, line); + while(getline(cin, line)) { + ++lc; + if (lc % 1000 == 0) { cerr << '.'; flag = true; } + if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; } if (line.empty()) continue; - size_t ks = line.find("\t"); + const size_t ks = line.find("\t"); assert(string::npos != ks); - assert(ks > 2); - string key = line.substr(2, ks - 2); - string val = line.substr(ks + 1); - if (key != last_key) { - if (!last_key.empty()) { - float score; - double x = LineOptimizer::LineOptimize(esv, opt_type, &score); - cout << last_key << "|" << x << "|" << score << endl; + assert(ks == 1); + const bool y = line[0] == '1'; + SparseVector x; + size_t last_start = ks + 1; + size_t last_comma = string::npos; + size_t cur = last_start; + while(cur <= line.size()) { + if (line[cur] == ' ' || cur == line.size()) { + if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) { + cerr << "[ERROR] " << line << endl << " position = " << cur << endl; + exit(1); + } + const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); + if (cur < line.size()) line[cur] = 0; + const double val = strtod(&line[last_comma + 1], NULL); + x.set_value(fid, val); + + last_comma = string::npos; + last_start = cur+1; + } else { + if (line[cur] == '=') + last_comma = cur; + } + ++cur; + } + training.push_back(make_pair(y, x)); + } + if (flag) cerr << endl; + + cerr << "Number of features: " << FD::NumFeats() << endl; + vector x(FD::NumFeats(), 0.0); // x[0] is bias + for (SparseVector::const_iterator it = old_weights.begin(); + it != old_weights.end(); ++it) + x[it->first] = it->second; + vector vg(FD::NumFeats(), 0.0); + SparseVector g; + bool converged = false; + LBFGSOptimizer opt(FD::NumFeats(), conf["memory_buffers"].as()); + while(!converged) { + double cll = 0; + double dbias = 0; + g.clear(); + for (int i = 0; i < training.size(); ++i) { + const double dotprod = training[i].second.dot(x) + x[0]; // x[0] is bias + double lp_false = dotprod; + double lp_true = -dotprod; + if (0 < lp_true) { + lp_true += log1p(exp(-lp_true)); + lp_false = log1p(exp(lp_false)); + } else { + lp_true = log1p(exp(lp_true)); + lp_false += log1p(exp(-lp_false)); + } + lp_true*=-1; + lp_false*=-1; + if (training[i].first) { // true label + cll -= lp_true; + g -= training[i].second * exp(lp_false); + dbias -= exp(lp_false); + } else { // false label + cll -= lp_false; + g += training[i].second * exp(lp_true); + dbias += exp(lp_true); } - last_key = key; - esv.clear(); } - if (val.size() % 4 != 0) { - cerr << "B64 encoding error 1! Skipping.\n"; - continue; + vg.clear(); + g.init_vector(&vg); + vg[0] = dbias; +#if 1 + const double sigsq = conf["sigma_squared"].as(); + double norm = 0; + for (int i = 1; i < x.size(); ++i) { + const double mean_i = 0.0; + const double param = (x[i] - mean_i); + norm += param * param; + vg[i] += param / sigsq; + } + const double reg = norm / (2.0 * sigsq); +#else + double reg = 0; +#endif + cll += reg; + cerr << cll << " (REG=" << reg << ")\t"; + bool failed = false; + try { + opt.Optimize(cll, vg, &x); + } catch (...) { + cerr << "Exception caught, assuming convergence is close enough...\n"; + failed = true; } - string encoded(val.size() / 4 * 3, '\0'); - if (!B64::b64decode(reinterpret_cast(&val[0]), val.size(), &encoded[0], encoded.size())) { - cerr << "B64 encoding error 2! Skipping.\n"; - continue; + if (fabs(x[0]) > MAX_BIAS) { + cerr << "Biased model learned. Are your training instances wrong?\n"; + cerr << " BIAS: " << x[0] << endl; } - esv.push_back(ErrorSurface()); - esv.back().Deserialize(type, encoded); + converged = failed || opt.HasConverged(); } - if (!esv.empty()) { - // cerr << "ESV=" << esv.size() << endl; - // for (int i = 0; i < esv.size(); ++i) { cerr << esv[i].size() << endl; } - float score; - double x = LineOptimizer::LineOptimize(esv, opt_type, &score); - cout << last_key << "|" << x << "|" << score << endl; + Weights w; + if (conf.count("weights")) { + for (int i = 1; i < x.size(); ++i) + x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi); } + w.InitFromVector(x); + w.WriteToFile("-"); return 0; } -- cgit v1.2.3 From 5e3c68b62dd72255db95c5822835a3931770f285 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Tue, 12 Jul 2011 22:34:34 -0400 Subject: debugged pro trainer --- pro-train/dist-pro.pl | 9 +- pro-train/mr_pro_map.cc | 244 +++++++++++++++++++++++++++++++++++++-------- pro-train/mr_pro_reduce.cc | 57 ++++++----- utils/filelib.cc | 12 +++ utils/filelib.h | 1 + 5 files changed, 253 insertions(+), 70 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/dist-pro.pl b/pro-train/dist-pro.pl index 55d7f1fa..c42e3876 100755 --- a/pro-train/dist-pro.pl +++ b/pro-train/dist-pro.pl @@ -66,6 +66,7 @@ my $bleu_weight=1; my $use_make; # use make to parallelize line search my $dirargs=''; my $usefork; +my $initial_weights; my $pass_suffix = ''; my $cpbin=1; # Process command-line options @@ -79,6 +80,7 @@ if (GetOptions( "dry-run" => \$dryrun, "epsilon=s" => \$epsilon, "help" => \$help, + "weights=s" => \$initial_weights, "interval" => \$interval, "iteration=i" => \$iteration, "local" => \$run_local, @@ -212,7 +214,7 @@ if ($dryrun){ close CMD; print STDERR $cline; chmod(0755,$cmdfile); - check_call("touch $dir/weights.0"); + check_call("cp $initial_weights $dir/weights.0"); die "Can't find weights.0" unless (-e "$dir/weights.0"); } write_config(*STDERR); @@ -239,7 +241,6 @@ my $random_seed = int(time / 1000); my $lastWeightsFile; my $lastPScore = 0; # main optimization loop -my @mapoutputs = (); # aggregate map outputs over all iters while (1){ print STDERR "\n\nITERATION $iteration\n==========\n"; @@ -262,6 +263,7 @@ while (1){ my $im1 = $iteration - 1; my $weightsFile="$dir/weights.$im1"; push @allweights, "-w $dir/weights.$im1"; + `rm -f $dir/hgs/*.gz`; my $decoder_cmd = "$decoder -c $iniFile --weights$pass_suffix $weightsFile -O $dir/hgs"; my $pcmd; if ($run_local) { @@ -333,6 +335,7 @@ while (1){ print $mkfile "all: $dir/splag.$im1/map.done\n\n"; } my @mkouts = (); # only used with makefiles + my @mapoutputs = (); for my $shard (@shards) { my $mapoutput = $shard; my $client_name = $shard; @@ -341,7 +344,7 @@ while (1){ $mapoutput =~ s/mapinput/mapoutput/; push @mapoutputs, "$dir/splag.$im1/$mapoutput"; $o2i{"$dir/splag.$im1/$mapoutput"} = "$dir/splag.$im1/$shard"; - my $script = "$MAPPER -s $srcFile -l $metric $refs_comma_sep @allweights < $dir/splag.$im1/$shard > $dir/splag.$im1/$mapoutput"; + my $script = "$MAPPER -s $srcFile -l $metric $refs_comma_sep -w $inweights -K $dir/kbest < $dir/splag.$im1/$shard > $dir/splag.$im1/$mapoutput"; if ($run_local) { print STDERR "COMMAND:\n$script\n"; check_bash_call($script); diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc index 128d93ce..4324e8de 100644 --- a/pro-train/mr_pro_map.cc +++ b/pro-train/mr_pro_map.cc @@ -2,7 +2,9 @@ #include #include #include +#include +#include #include #include #include @@ -22,16 +24,63 @@ using namespace std; namespace po = boost::program_options; +struct ApproxVectorHasher { + static const size_t MASK = 0xFFFFFFFFull; + union UType { + double f; + size_t i; + }; + static inline double round(const double x) { + UType t; + t.f = x; + size_t r = t.i & MASK; + if ((r << 1) > MASK) + t.i += MASK - r + 1; + else + t.i &= (1ull - MASK); + return t.f; + } + size_t operator()(const SparseVector& x) const { + size_t h = 0x573915839; + for (SparseVector::const_iterator it = x.begin(); it != x.end(); ++it) { + UType t; + t.f = it->second; + if (t.f) { + size_t z = (t.i >> 32); + boost::hash_combine(h, it->first); + boost::hash_combine(h, z); + } + } + return h; + } +}; + +struct ApproxVectorEquals { + bool operator()(const SparseVector& a, const SparseVector& b) const { + SparseVector::const_iterator bit = b.begin(); + for (SparseVector::const_iterator ait = a.begin(); ait != a.end(); ++ait) { + if (bit == b.end() || + ait->first != bit->first || + ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second)) + return false; + ++bit; + } + if (bit != b.end()) return false; + return true; + } +}; + boost::shared_ptr rng; void InitCommandLine(int argc, char** argv, po::variables_map* conf) { po::options_description opts("Configuration options"); opts.add_options() ("reference,r",po::value >(), "[REQD] Reference translation (tokenized text)") + ("weights,w",po::value(), "[REQD] Weights files from current iterations") + ("kbest_repository,K",po::value()->default_value("./kbest"),"K-best list repository (directory)") + ("input,i",po::value()->default_value("-"), "Input file to map (- is STDIN)") ("source,s",po::value()->default_value(""), "Source file (ignored, except for AER)") ("loss_function,l",po::value()->default_value("ibm_bleu"), "Loss function being optimized") - ("input,i",po::value()->default_value("-"), "Input file to map (- is STDIN)") - ("weights,w",po::value >(), "[REQD] Weights files from previous and current iterations") ("kbest_size,k",po::value()->default_value(1500u), "Top k-hypotheses to extract") ("candidate_pairs,G", po::value()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)") ("best_pairs,X", po::value()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)") @@ -46,7 +95,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { flag = true; } if (!conf->count("weights")) { - cerr << "Please specify one or more weights using -w \n"; + cerr << "Please specify weights using -w \n"; flag = true; } if (flag || conf->count("help")) { @@ -56,6 +105,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { } struct HypInfo { + HypInfo() : g_(-100.0) {} HypInfo(const vector& h, const SparseVector& feats) : hyp(h), g_(-100.0), x(feats) {} // lazy evaluation @@ -66,10 +116,92 @@ struct HypInfo { } vector hyp; mutable double g_; - public: SparseVector x; }; +struct HypInfoCompare { + bool operator()(const HypInfo& a, const HypInfo& b) const { + ApproxVectorEquals comp; + return (a.hyp == b.hyp && comp(a.x,b.x)); + } +}; + +struct HypInfoHasher { + size_t operator()(const HypInfo& x) const { + boost::hash > hhasher; + ApproxVectorHasher vhasher; + size_t ha = hhasher(x.hyp); + boost::hash_combine(ha, vhasher(x.x)); + return ha; + } +}; + +void WriteKBest(const string& file, const vector& kbest) { + WriteFile wf(file); + ostream& out = *wf.stream(); + out.precision(10); + for (int i = 0; i < kbest.size(); ++i) { + out << TD::GetString(kbest[i].hyp) << endl; + out << kbest[i].x << endl; + } +} + +void ParseSparseVector(string& line, size_t cur, SparseVector* out) { + SparseVector& x = *out; + size_t last_start = cur; + size_t last_comma = string::npos; + while(cur <= line.size()) { + if (line[cur] == ' ' || cur == line.size()) { + if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) { + cerr << "[ERROR] " << line << endl << " position = " << cur << endl; + exit(1); + } + const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); + if (cur < line.size()) line[cur] = 0; + const double val = strtod(&line[last_comma + 1], NULL); + x.set_value(fid, val); + + last_comma = string::npos; + last_start = cur+1; + } else { + if (line[cur] == '=') + last_comma = cur; + } + ++cur; + } +} + +void ReadKBest(const string& file, vector* kbest) { + cerr << "Reading from " << file << endl; + ReadFile rf(file); + istream& in = *rf.stream(); + string cand; + string feats; + while(getline(in, cand)) { + getline(in, feats); + assert(in); + kbest->push_back(HypInfo()); + TD::ConvertSentence(cand, &kbest->back().hyp); + ParseSparseVector(feats, 0, &kbest->back().x); + } + cerr << " read " << kbest->size() << " hypotheses\n"; +} + +void Dedup(vector* h) { + cerr << "Dedup in=" << h->size(); + tr1::unordered_set u; + while(h->size() > 0) { + u.insert(h->back()); + h->pop_back(); + } + tr1::unordered_set::iterator it = u.begin(); + while (it != u.end()) { + h->push_back(*it); + it = u.erase(it); + } + cerr << " out=" << h->size() << endl; +} + struct ThresholdAlpha { explicit ThresholdAlpha(double t = 0.05) : threshold(t) {} double operator()(double mag) const { @@ -81,6 +213,7 @@ struct ThresholdAlpha { struct TrainingInstance { TrainingInstance(const SparseVector& feats, bool positive, double diff) : x(feats), y(positive), gdiff(diff) {} SparseVector x; +#undef DEBUGGING_PRO #ifdef DEBUGGING_PRO vector a; vector b; @@ -88,6 +221,11 @@ struct TrainingInstance { bool y; double gdiff; }; +#ifdef DEBUGGING_PRO +ostream& operator<<(ostream& os, const TrainingInstance& d) { + return os << d.gdiff << " y=" << d.y << "\tA:" << TD::GetString(d.a) << "\n\tB: " << TD::GetString(d.b) << "\n\tX: " << d.x; +} +#endif struct DiffOrder { bool operator()(const TrainingInstance& a, const TrainingInstance& b) const { @@ -95,36 +233,51 @@ struct DiffOrder { } }; -template -void Sample(const unsigned gamma, const unsigned xi, const vector& J_i, const SentenceScorer& scorer, const Alpha& alpha_i, bool invert_score, vector* pv) { - vector v; +void Sample(const unsigned gamma, const unsigned xi, const vector& J_i, const SentenceScorer& scorer, const bool invert_score, vector* pv) { + vector v1, v2; + double avg_diff = 0; for (unsigned i = 0; i < gamma; ++i) { - size_t a = rng->inclusive(0, J_i.size() - 1)(); - size_t b = rng->inclusive(0, J_i.size() - 1)(); + const size_t a = rng->inclusive(0, J_i.size() - 1)(); + const size_t b = rng->inclusive(0, J_i.size() - 1)(); if (a == b) continue; double ga = J_i[a].g(scorer); double gb = J_i[b].g(scorer); - bool positive = ga < gb; + bool positive = gb < ga; if (invert_score) positive = !positive; - double gdiff = fabs(ga - gb); + const double gdiff = fabs(ga - gb); if (!gdiff) continue; - if (rng->next() < alpha_i(gdiff)) { - v.push_back(TrainingInstance((J_i[a].x - J_i[b].x).erase_zeros(), positive, gdiff)); + avg_diff += gdiff; + SparseVector xdiff = (J_i[a].x - J_i[b].x).erase_zeros(); + if (xdiff.empty()) { + cerr << "Empty diff:\n " << TD::GetString(J_i[a].hyp) << endl << "x=" << J_i[a].x << endl; + cerr << " " << TD::GetString(J_i[b].hyp) << endl << "x=" << J_i[b].x << endl; + continue; + } + v1.push_back(TrainingInstance(xdiff, positive, gdiff)); #ifdef DEBUGGING_PRO - v.back().a = J_i[a].hyp; - v.back().b = J_i[b].hyp; + v1.back().a = J_i[a].hyp; + v1.back().b = J_i[b].hyp; + cerr << "N: " << v1.back() << endl; #endif - } } - vector::iterator mid = v.begin() + xi; - if (xi > v.size()) mid = v.end(); - partial_sort(v.begin(), mid, v.end(), DiffOrder()); - copy(v.begin(), mid, back_inserter(*pv)); + avg_diff /= v1.size(); + + for (unsigned i = 0; i < v1.size(); ++i) { + double p = 1.0 / (1.0 + exp(-avg_diff - v1[i].gdiff)); + // cerr << "avg_diff=" << avg_diff << " gdiff=" << v1[i].gdiff << " p=" << p << endl; + if (rng->next() < p) v2.push_back(v1[i]); + } + vector::iterator mid = v2.begin() + xi; + if (xi > v2.size()) mid = v2.end(); + partial_sort(v2.begin(), mid, v2.end(), DiffOrder()); + copy(v2.begin(), mid, back_inserter(*pv)); #ifdef DEBUGGING_PRO - if (v.size() >= 5) - for (int i =0; i < 5; ++i) { - cerr << v[i].gdiff << " y=" << v[i].y << "\tA:" << TD::GetString(v[i].a) << "\n\tB: " << TD::GetString(v[i].b) << endl; + if (v2.size() >= 5) { + for (int i =0; i < (mid - v2.begin()); ++i) { + cerr << v2[i] << endl; } + cerr << pv->back() << endl; + } #endif } @@ -136,6 +289,7 @@ int main(int argc, char** argv) { else rng.reset(new MT19937); const string loss_function = conf["loss_function"].as(); + ScoreType type = ScoreTypeFromString(loss_function); DocScorer ds(type, conf["reference"].as >(), conf["source"].as()); cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function << endl; @@ -146,13 +300,15 @@ int main(int argc, char** argv) { const unsigned kbest_size = conf["kbest_size"].as(); const unsigned gamma = conf["candidate_pairs"].as(); const unsigned xi = conf["best_pairs"].as(); - vector weights_files = conf["weights"].as >(); - vector > weights(weights_files.size()); - for (int i = 0; i < weights.size(); ++i) { + string weightsf = conf["weights"].as(); + vector weights; + { Weights w; - w.InitFromFile(weights_files[i]); - w.InitVector(&weights[i]); + w.InitFromFile(weightsf); + w.InitVector(&weights); } + string kbest_repo = conf["kbest_repository"].as(); + MkDirP(kbest_repo); while(in) { vector v; string line; @@ -164,24 +320,26 @@ int main(int argc, char** argv) { // path-to-file (JSON) sent_id is >> file >> sent_id; ReadFile rf(file); - HypergraphIO::ReadFromJSON(rf.stream(), &hg); + ostringstream os; vector J_i; - int start = weights.size(); - start -= 4; - if (start < 0) start = 0; - for (int i = start; i < weights.size(); ++i) { - hg.Reweight(weights[i]); - KBest::KBestDerivations, ESentenceTraversal> kbest(hg, kbest_size); - - for (int i = 0; i < kbest_size; ++i) { - const KBest::KBestDerivations, ESentenceTraversal>::Derivation* d = - kbest.LazyKthBest(hg.nodes_.size() - 1, i); - if (!d) break; - J_i.push_back(HypInfo(d->yield, d->feature_values)); - } + os << kbest_repo << "/kbest." << sent_id << ".txt.gz"; + const string kbest_file = os.str(); + if (FileExists(kbest_file)) + ReadKBest(kbest_file, &J_i); + HypergraphIO::ReadFromJSON(rf.stream(), &hg); + hg.Reweight(weights); + KBest::KBestDerivations, ESentenceTraversal> kbest(hg, kbest_size); + + for (int i = 0; i < kbest_size; ++i) { + const KBest::KBestDerivations, ESentenceTraversal>::Derivation* d = + kbest.LazyKthBest(hg.nodes_.size() - 1, i); + if (!d) break; + J_i.push_back(HypInfo(d->yield, d->feature_values)); } + Dedup(&J_i); + WriteKBest(kbest_file, J_i); - Sample(gamma, xi, J_i, *ds[sent_id], ThresholdAlpha(0.05), (type == TER), &v); + Sample(gamma, xi, J_i, *ds[sent_id], (type == TER), &v); for (unsigned i = 0; i < v.size(); ++i) { const TrainingInstance& vi = v[i]; cout << vi.y << "\t" << vi.x << endl; diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 2b9c5ce7..e1a7db8a 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -24,7 +24,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { ("weights,w", po::value(), "Weights from previous iteration (used as initialization and interpolation") ("interpolation,p",po::value()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev") ("memory_buffers,m",po::value()->default_value(200), "Number of memory buffers (LBFGS)") - ("sigma_squared,s",po::value()->default_value(0.5), "Sigma squared for Gaussian prior") + ("sigma_squared,s",po::value()->default_value(1.0), "Sigma squared for Gaussian prior") ("help,h", "Help"); po::options_description dcmdline_options; dcmdline_options.add(opts); @@ -35,6 +35,31 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { } } +void ParseSparseVector(string& line, size_t cur, SparseVector* out) { + SparseVector& x = *out; + size_t last_start = cur; + size_t last_comma = string::npos; + while(cur <= line.size()) { + if (line[cur] == ' ' || cur == line.size()) { + if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) { + cerr << "[ERROR] " << line << endl << " position = " << cur << endl; + exit(1); + } + const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); + if (cur < line.size()) line[cur] = 0; + const double val = strtod(&line[last_comma + 1], NULL); + x.set_value(fid, val); + + last_comma = string::npos; + last_start = cur+1; + } else { + if (line[cur] == '=') + last_comma = cur; + } + ++cur; + } +} + int main(int argc, char** argv) { po::variables_map conf; InitCommandLine(argc, argv, &conf); @@ -60,28 +85,7 @@ int main(int argc, char** argv) { assert(ks == 1); const bool y = line[0] == '1'; SparseVector x; - size_t last_start = ks + 1; - size_t last_comma = string::npos; - size_t cur = last_start; - while(cur <= line.size()) { - if (line[cur] == ' ' || cur == line.size()) { - if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) { - cerr << "[ERROR] " << line << endl << " position = " << cur << endl; - exit(1); - } - const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); - if (cur < line.size()) line[cur] = 0; - const double val = strtod(&line[last_comma + 1], NULL); - x.set_value(fid, val); - - last_comma = string::npos; - last_start = cur+1; - } else { - if (line[cur] == '=') - last_comma = cur; - } - ++cur; - } + ParseSparseVector(line, ks + 1, &x); training.push_back(make_pair(y, x)); } if (flag) cerr << endl; @@ -95,6 +99,7 @@ int main(int argc, char** argv) { SparseVector g; bool converged = false; LBFGSOptimizer opt(FD::NumFeats(), conf["memory_buffers"].as()); + double ppl = 0; while(!converged) { double cll = 0; double dbias = 0; @@ -114,14 +119,18 @@ int main(int argc, char** argv) { lp_false*=-1; if (training[i].first) { // true label cll -= lp_true; + ppl += lp_true / log(2); g -= training[i].second * exp(lp_false); dbias -= exp(lp_false); } else { // false label cll -= lp_false; + ppl += lp_false / log(2); g += training[i].second * exp(lp_true); dbias += exp(lp_true); } } + ppl /= training.size(); + ppl = pow(2.0, - ppl); vg.clear(); g.init_vector(&vg); vg[0] = dbias; @@ -139,7 +148,7 @@ int main(int argc, char** argv) { double reg = 0; #endif cll += reg; - cerr << cll << " (REG=" << reg << ")\t"; + cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t"; bool failed = false; try { opt.Optimize(cll, vg, &x); diff --git a/utils/filelib.cc b/utils/filelib.cc index 79ad2847..a0969b1a 100644 --- a/utils/filelib.cc +++ b/utils/filelib.cc @@ -20,3 +20,15 @@ bool DirectoryExists(const string& dir) { return false; } +void MkDirP(const string& dir) { + if (DirectoryExists(dir)) return; + if (mkdir(dir.c_str(), 0777)) { + perror(dir.c_str()); + abort(); + } + if (chmod(dir.c_str(), 07777)) { + perror(dir.c_str()); + abort(); + } +} + diff --git a/utils/filelib.h b/utils/filelib.h index dda98671..a8622246 100644 --- a/utils/filelib.h +++ b/utils/filelib.h @@ -12,6 +12,7 @@ bool FileExists(const std::string& file_name); bool DirectoryExists(const std::string& dir_name); +void MkDirP(const std::string& dir_name); // reads from standard in if filename is - // uncompresses if file ends with .gz -- cgit v1.2.3 From b8f7fc10e14eb07b17f1ef46f8ecd3c13f128814 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Tue, 12 Jul 2011 23:32:11 -0400 Subject: minor optimization --- pro-train/mr_pro_reduce.cc | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index e1a7db8a..5382e1a5 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -149,18 +149,20 @@ int main(int argc, char** argv) { #endif cll += reg; cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t"; - bool failed = false; try { - opt.Optimize(cll, vg, &x); + vector old_x = x; + do { + opt.Optimize(cll, vg, &x); + converged = opt.HasConverged(); + } while (!converged && x == old_x); } catch (...) { cerr << "Exception caught, assuming convergence is close enough...\n"; - failed = true; + converged = true; } if (fabs(x[0]) > MAX_BIAS) { cerr << "Biased model learned. Are your training instances wrong?\n"; cerr << " BIAS: " << x[0] << endl; } - converged = failed || opt.HasConverged(); } Weights w; if (conf.count("weights")) { -- cgit v1.2.3 From 9b469ea153e5ae63f4524a71caf3c4518e5f775d Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Wed, 13 Jul 2011 16:25:05 -0400 Subject: faster code, optional held-out test set --- pro-train/mr_pro_reduce.cc | 140 ++++++++++++++++++++++++++++----------------- 1 file changed, 89 insertions(+), 51 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 5382e1a5..491ceb3a 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -7,6 +7,7 @@ #include #include +#include "filelib.h" #include "weights.h" #include "sparse_vector.h" #include "optimize.h" @@ -25,6 +26,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { ("interpolation,p",po::value()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev") ("memory_buffers,m",po::value()->default_value(200), "Number of memory buffers (LBFGS)") ("sigma_squared,s",po::value()->default_value(1.0), "Sigma squared for Gaussian prior") + ("testset,t",po::value(), "Optional held-out test set to tune regularizer") ("help,h", "Help"); po::options_description dcmdline_options; dcmdline_options.add(opts); @@ -60,13 +62,79 @@ void ParseSparseVector(string& line, size_t cur, SparseVector* out) { } } +void ReadCorpus(istream* pin, vector > >* corpus) { + istream& in = *pin; + corpus->clear(); + bool flag = false; + int lc = 0; + string line; + SparseVector x; + while(getline(in, line)) { + ++lc; + if (lc % 1000 == 0) { cerr << '.'; flag = true; } + if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; } + if (line.empty()) continue; + const size_t ks = line.find("\t"); + assert(string::npos != ks); + assert(ks == 1); + const bool y = line[0] == '1'; + x.clear(); + ParseSparseVector(line, ks + 1, &x); + corpus->push_back(make_pair(y, x)); + } + if (flag) cerr << endl; +} + +void GradAdd(const SparseVector& v, const double scale, vector* acc) { + for (SparseVector::const_iterator it = v.begin(); + it != v.end(); ++it) { + (*acc)[it->first] += it->second * scale; + } +} + +double TrainingInference(const vector& x, + const vector > >& corpus, + vector* g = NULL) { + if (g) fill(g->begin(), g->end(), 0.0); + + double cll = 0; + for (int i = 0; i < corpus.size(); ++i) { + const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias + double lp_false = dotprod; + double lp_true = -dotprod; + if (0 < lp_true) { + lp_true += log1p(exp(-lp_true)); + lp_false = log1p(exp(lp_false)); + } else { + lp_true = log1p(exp(lp_true)); + lp_false += log1p(exp(-lp_false)); + } + lp_true*=-1; + lp_false*=-1; + if (corpus[i].first) { // true label + cll -= lp_true; + if (g) { + // g -= corpus[i].second * exp(lp_false); + GradAdd(corpus[i].second, -exp(lp_false), g); + (*g)[0] -= exp(lp_false); // bias + } + } else { // false label + cll -= lp_false; + if (g) { + // g += corpus[i].second * exp(lp_true); + GradAdd(corpus[i].second, exp(lp_true), g); + (*g)[0] += exp(lp_true); // bias + } + } + } + return cll; +} + int main(int argc, char** argv) { po::variables_map conf; InitCommandLine(argc, argv, &conf); string line; - vector > > training; - int lc = 0; - bool flag = false; + vector > > training, testing; SparseVector old_weights; const double psi = conf["interpolation"].as(); if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } @@ -75,20 +143,11 @@ int main(int argc, char** argv) { w.InitFromFile(conf["weights"].as()); w.InitSparseVector(&old_weights); } - while(getline(cin, line)) { - ++lc; - if (lc % 1000 == 0) { cerr << '.'; flag = true; } - if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; } - if (line.empty()) continue; - const size_t ks = line.find("\t"); - assert(string::npos != ks); - assert(ks == 1); - const bool y = line[0] == '1'; - SparseVector x; - ParseSparseVector(line, ks + 1, &x); - training.push_back(make_pair(y, x)); + ReadCorpus(&cin, &training); + if (conf.count("testset")) { + ReadFile rf(conf["testset"].as()); + ReadCorpus(rf.stream(), &testing); } - if (flag) cerr << endl; cerr << "Number of features: " << FD::NumFeats() << endl; vector x(FD::NumFeats(), 0.0); // x[0] is bias @@ -96,44 +155,23 @@ int main(int argc, char** argv) { it != old_weights.end(); ++it) x[it->first] = it->second; vector vg(FD::NumFeats(), 0.0); - SparseVector g; bool converged = false; LBFGSOptimizer opt(FD::NumFeats(), conf["memory_buffers"].as()); - double ppl = 0; while(!converged) { - double cll = 0; - double dbias = 0; - g.clear(); - for (int i = 0; i < training.size(); ++i) { - const double dotprod = training[i].second.dot(x) + x[0]; // x[0] is bias - double lp_false = dotprod; - double lp_true = -dotprod; - if (0 < lp_true) { - lp_true += log1p(exp(-lp_true)); - lp_false = log1p(exp(lp_false)); - } else { - lp_true = log1p(exp(lp_true)); - lp_false += log1p(exp(-lp_false)); - } - lp_true*=-1; - lp_false*=-1; - if (training[i].first) { // true label - cll -= lp_true; - ppl += lp_true / log(2); - g -= training[i].second * exp(lp_false); - dbias -= exp(lp_false); - } else { // false label - cll -= lp_false; - ppl += lp_false / log(2); - g += training[i].second * exp(lp_true); - dbias += exp(lp_true); - } - } + double cll = TrainingInference(x, training, &vg); + double ppl = cll / log(2); ppl /= training.size(); - ppl = pow(2.0, - ppl); - vg.clear(); - g.init_vector(&vg); - vg[0] = dbias; + ppl = pow(2.0, ppl); + double tppl = 0.0; + + // evaluate optional held-out test set + if (testing.size()) { + tppl = TrainingInference(x, testing) / log(2); + tppl /= testing.size(); + tppl = pow(2.0, tppl); + } + + // handle regularizer #if 1 const double sigsq = conf["sigma_squared"].as(); double norm = 0; @@ -148,7 +186,7 @@ int main(int argc, char** argv) { double reg = 0; #endif cll += reg; - cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t"; + cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t"; try { vector old_x = x; do { -- cgit v1.2.3 From b89c1f03c89c6c30b88099e4f3e0c1753d338ea7 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sat, 16 Jul 2011 19:13:21 -0400 Subject: tune regularizer --- mteval/scorer.cc | 12 +++- pro-train/dist-pro.pl | 139 ++++++++++++++++++++++++++------------------- pro-train/mr_pro_reduce.cc | 128 ++++++++++++++++++++++++++++++----------- 3 files changed, 185 insertions(+), 94 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/mteval/scorer.cc b/mteval/scorer.cc index 2daa0daa..a83b9e2f 100644 --- a/mteval/scorer.cc +++ b/mteval/scorer.cc @@ -430,6 +430,7 @@ float BLEUScore::ComputeScore(vector* precs, float* bp) const { float log_bleu = 0; if (precs) precs->clear(); int count = 0; + vector total_precs(N()); for (int i = 0; i < N(); ++i) { if (hyp_ngram_counts[i] > 0) { float cor_count = correct_ngram_hit_counts[i]; @@ -440,14 +441,21 @@ float BLEUScore::ComputeScore(vector* precs, float* bp) const { log_bleu += lprec; ++count; } + total_precs[i] = log_bleu; } - log_bleu /= static_cast(count); + vector bleus(N()); float lbp = 0.0; if (hyp_len < ref_len) lbp = (hyp_len - ref_len) / hyp_len; log_bleu += lbp; if (bp) *bp = exp(lbp); - return exp(log_bleu); + float wb = 0; + for (int i = 0; i < N(); ++i) { + bleus[i] = exp(total_precs[i] / (i+1) + lbp); + wb += bleus[i] / pow(2.0, 4.0 - i); + } + //return wb; + return bleus.back(); } diff --git a/pro-train/dist-pro.pl b/pro-train/dist-pro.pl index c42e3876..dbfa329a 100755 --- a/pro-train/dist-pro.pl +++ b/pro-train/dist-pro.pl @@ -37,42 +37,36 @@ die "Can't find decoder in $cdec" unless -x $cdec; die "Can't find $parallelize" unless -x $parallelize; die "Can't find $libcall" unless -e $libcall; my $decoder = $cdec; -my $lines_per_mapper = 100; +my $lines_per_mapper = 30; my $iteration = 1; my $run_local = 0; my $best_weights; -my $max_iterations = 15; -my $optimization_iters = 6; +my $max_iterations = 30; my $decode_nodes = 15; # number of decode nodes -my $pmem = "9g"; +my $pmem = "4g"; my $disable_clean = 0; my %seen_weights; -my $normalize; my $help = 0; my $epsilon = 0.0001; -my $interval = 5; my $dryrun = 0; my $last_score = -10000000; my $metric = "ibm_bleu"; my $dir; my $iniFile; my $weights; -my $decoderOpt; -my $noprimary; -my $maxsim=0; -my $oraclen=0; -my $oracleb=20; -my $bleu_weight=1; -my $use_make; # use make to parallelize line search -my $dirargs=''; +my $use_make; # use make to parallelize my $usefork; my $initial_weights; my $pass_suffix = ''; my $cpbin=1; + +# regularization strength +my $tune_regularizer = 0; +my $reg = 1e-2; + # Process command-line options Getopt::Long::Configure("no_auto_abbrev"); if (GetOptions( - "decoder=s" => \$decoderOpt, "decode-nodes=i" => \$decode_nodes, "dont-clean" => \$disable_clean, "pass-suffix=s" => \$pass_suffix, @@ -81,21 +75,13 @@ if (GetOptions( "epsilon=s" => \$epsilon, "help" => \$help, "weights=s" => \$initial_weights, - "interval" => \$interval, - "iteration=i" => \$iteration, + "tune-regularizer" => \$tune_regularizer, + "reg=f" => \$reg, "local" => \$run_local, "use-make=i" => \$use_make, "max-iterations=i" => \$max_iterations, - "normalize=s" => \$normalize, "pmem=s" => \$pmem, "cpbin!" => \$cpbin, - "bleu_weight=s" => \$bleu_weight, - "no-primary!" => \$noprimary, - "max-similarity=s" => \$maxsim, - "oracle-directions=i" => \$oraclen, - "n-oracle=i" => \$oraclen, - "oracle-batch=i" => \$oracleb, - "directions-args=s" => \$dirargs, "ref-files=s" => \$refFiles, "metric=s" => \$metric, "source-file=s" => \$srcFile, @@ -108,9 +94,7 @@ if (GetOptions( if ($usefork) { $usefork = "--use-fork"; } else { $usefork = ''; } if ($metric =~ /^(combi|ter)$/i) { - $lines_per_mapper = 40; -} elsif ($metric =~ /^meteor$/i) { - $lines_per_mapper = 2000; # start up time is really high + $lines_per_mapper = 5; } ($iniFile) = @ARGV; @@ -144,8 +128,6 @@ unless ($dir =~ /^\//){ # convert relative path to absolute path $dir = "$basedir/$dir"; } -if ($decoderOpt){ $decoder = $decoderOpt; } - # Initializations and helper functions srand; @@ -378,6 +360,22 @@ while (1){ else {$joblist = $joblist . "\|" . $jobid; } } } + my @dev_outs = (); + my @devtest_outs = (); + if ($tune_regularizer) { + for (my $i = 0; $i < scalar @mapoutputs; $i++) { + if ($i % 3 == 1) { + push @devtest_outs, $mapoutputs[$i]; + } else { + push @dev_outs, $mapoutputs[$i]; + } + } + if (scalar @devtest_outs == 0) { + die "Not enough training instances for regularization tuning! Rerun without --tune-regularizer\n"; + } + } else { + @dev_outs = @mapoutputs; + } if ($run_local) { print STDERR "\nCompleted extraction of training exemplars.\n"; } elsif ($use_make) { @@ -399,7 +397,13 @@ while (1){ } my $tol = 0; my $til = 0; - print STDERR "MO: @mapoutputs\n"; + my $dev_test_file = "$dir/splag.$im1/devtest.gz"; + if ($tune_regularizer) { + my $cmd = "cat @devtest_outs | gzip > $dev_test_file"; + check_bash_call($cmd); + die "Can't find file $dev_test_file" unless -f $dev_test_file; + } + #print STDERR "MO: @mapoutputs\n"; for my $mo (@mapoutputs) { #my $olines = get_lines($mo); #my $ilines = get_lines($o2i{$mo}); @@ -407,10 +411,24 @@ while (1){ } print STDERR "\nRUNNING CLASSIFIER (REDUCER)\n"; print STDERR unchecked_output("date"); - $cmd="cat @mapoutputs | $REDUCER -w $dir/weights.$im1 > $dir/weights.$iteration"; + $cmd="cat @dev_outs | $REDUCER -w $dir/weights.$im1 -s $reg"; + if ($tune_regularizer) { + $cmd .= " -T -t $dev_test_file"; + } + $cmd .= " > $dir/weights.$iteration"; print STDERR "COMMAND:\n$cmd\n"; check_bash_call($cmd); $lastWeightsFile = "$dir/weights.$iteration"; + if ($tune_regularizer) { + open W, "<$lastWeightsFile" or die "Can't read $lastWeightsFile: $!"; + my $line = ; + close W; + my ($sharp, $label, $nreg) = split /\s|=/, $line; + print STDERR "REGULARIZATION STRENGTH ($label) IS $nreg\n"; + $reg = $nreg; + # only tune regularizer on first iteration? + $tune_regularizer = 0; + } $lastPScore = $score; $iteration++; print STDERR "\n==========\n"; @@ -473,7 +491,6 @@ sub write_config { print $fh "SOURCE (DEV): $srcFile\n"; print $fh "REFS (DEV): $refFiles\n"; print $fh "EVAL METRIC: $metric\n"; - print $fh "START ITERATION: $iteration\n"; print $fh "MAX ITERATIONS: $max_iterations\n"; print $fh "DECODE NODES: $decode_nodes\n"; print $fh "HEAD NODE: $host\n"; @@ -535,31 +552,38 @@ Usage: $executable [options] based on certain conventions. For details, refer to descriptions of the options --decoder, --weights, and --workdir. -Options: +Required: + + --ref-files + Dev set ref files. This option takes only a single string argument. + To use multiple files (including file globbing), this argument should + be quoted. + + --source-file + Dev set source file. + + --weights + Initial weights file (use empty file to start from 0) + +General options: --local Run the decoder and optimizer locally with a single thread. - --use-make - Use make -j to run the optimizer commands (useful on large - shared-memory machines where qsub is unavailable). - --decode-nodes Number of decoder processes to run in parallel. [default=15] - --decoder - Decoder binary to use. - --help Print this message and exit. - --iteration - Starting iteration number. If not specified, defaults to 1. - --max-iterations Maximum number of iterations to run. If not specified, defaults to 10. + --metric + Metric to optimize. + Example values: IBM_BLEU, NIST_BLEU, Koehn_BLEU, TER, Combi + --pass-suffix If the decoder is doing multi-pass decoding, the pass suffix "2", "3", etc., is used to control what iteration of weights is set. @@ -567,21 +591,9 @@ Options: --pmem Amount of physical memory requested for parallel decoding jobs. - --ref-files - Dev set ref files. This option takes only a single string argument. - To use multiple files (including file globbing), this argument should - be quoted. - - --metric - Metric to optimize. - Example values: IBM_BLEU, NIST_BLEU, Koehn_BLEU, TER, Combi - - --normalize - After each iteration, rescale all feature weights such that feature- - name has a weight of 1.0. - - --source-file - Dev set source file. + --use-make + Use make -j to run the optimizer commands (useful on large + shared-memory machines where qsub is unavailable). --workdir Directory for intermediate and output files. If not specified, the @@ -591,6 +603,14 @@ Options: the filename. E.g. an ini file named decoder.foo.ini would have a default working directory name foo. +Regularization options: + + --tune-regularizer + Hold out one third of the tuning data and used this to tune the + regularization parameter. + + --reg + Help } @@ -606,7 +626,6 @@ sub convert { } - sub cmdline { return join ' ',($0,@ORIG_ARGV); } diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 491ceb3a..9b422f33 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -16,7 +16,7 @@ using namespace std; namespace po = boost::program_options; // since this is a ranking model, there should be equal numbers of -// positive and negative examples so the bias should be 0 +// positive and negative examples, so the bias should be 0 static const double MAX_BIAS = 1e-10; void InitCommandLine(int argc, char** argv, po::variables_map* conf) { @@ -25,8 +25,11 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { ("weights,w", po::value(), "Weights from previous iteration (used as initialization and interpolation") ("interpolation,p",po::value()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev") ("memory_buffers,m",po::value()->default_value(200), "Number of memory buffers (LBFGS)") - ("sigma_squared,s",po::value()->default_value(1.0), "Sigma squared for Gaussian prior") - ("testset,t",po::value(), "Optional held-out test set to tune regularizer") + ("sigma_squared,s",po::value()->default_value(0.1), "Sigma squared for Gaussian prior") + ("min_reg,r",po::value()->default_value(1e-8), "When tuning (-T) regularization strength, minimum regularization strenght") + ("max_reg,R",po::value()->default_value(10.0), "When tuning (-T) regularization strength, maximum regularization strenght") + ("testset,t",po::value(), "Optional held-out test set") + ("tune_regularizer,T", "Use the held out test set (-t) to tune the regularization strength") ("help,h", "Help"); po::options_description dcmdline_options; dcmdline_options.add(opts); @@ -95,8 +98,6 @@ void GradAdd(const SparseVector& v, const double scale, vector* double TrainingInference(const vector& x, const vector > >& corpus, vector* g = NULL) { - if (g) fill(g->begin(), g->end(), 0.0); - double cll = 0; for (int i = 0; i < corpus.size(); ++i) { const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias @@ -130,39 +131,23 @@ double TrainingInference(const vector& x, return cll; } -int main(int argc, char** argv) { - po::variables_map conf; - InitCommandLine(argc, argv, &conf); - string line; - vector > > training, testing; - SparseVector old_weights; - const double psi = conf["interpolation"].as(); - if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } - if (conf.count("weights")) { - Weights w; - w.InitFromFile(conf["weights"].as()); - w.InitSparseVector(&old_weights); - } - ReadCorpus(&cin, &training); - if (conf.count("testset")) { - ReadFile rf(conf["testset"].as()); - ReadCorpus(rf.stream(), &testing); - } - - cerr << "Number of features: " << FD::NumFeats() << endl; - vector x(FD::NumFeats(), 0.0); // x[0] is bias - for (SparseVector::const_iterator it = old_weights.begin(); - it != old_weights.end(); ++it) - x[it->first] = it->second; +// return held-out log likelihood +double LearnParameters(const vector > >& training, + const vector > >& testing, + const double sigsq, + const unsigned memory_buffers, + vector* px) { + vector& x = *px; vector vg(FD::NumFeats(), 0.0); bool converged = false; - LBFGSOptimizer opt(FD::NumFeats(), conf["memory_buffers"].as()); + LBFGSOptimizer opt(FD::NumFeats(), memory_buffers); + double tppl = 0.0; while(!converged) { + fill(vg.begin(), vg.end(), 0.0); double cll = TrainingInference(x, training, &vg); double ppl = cll / log(2); ppl /= training.size(); ppl = pow(2.0, ppl); - double tppl = 0.0; // evaluate optional held-out test set if (testing.size()) { @@ -173,7 +158,6 @@ int main(int argc, char** argv) { // handle regularizer #if 1 - const double sigsq = conf["sigma_squared"].as(); double norm = 0; for (int i = 1; i < x.size(); ++i) { const double mean_i = 0.0; @@ -202,11 +186,91 @@ int main(int argc, char** argv) { cerr << " BIAS: " << x[0] << endl; } } + return tppl; +} + +int main(int argc, char** argv) { + po::variables_map conf; + InitCommandLine(argc, argv, &conf); + string line; + vector > > training, testing; + SparseVector old_weights; + const bool tune_regularizer = conf.count("tune_regularizer"); + if (tune_regularizer && !conf.count("testset")) { + cerr << "--tune_regularizer requires --testset to be set\n"; + return 1; + } + const double min_reg = conf["min_reg"].as(); + const double max_reg = conf["max_reg"].as(); + double sigsq = conf["sigma_squared"].as(); + assert(sigsq > 0.0); + assert(min_reg > 0.0); + assert(max_reg > 0.0); + assert(max_reg > min_reg); + const double psi = conf["interpolation"].as(); + if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } + if (conf.count("weights")) { + Weights w; + w.InitFromFile(conf["weights"].as()); + w.InitSparseVector(&old_weights); + } + ReadCorpus(&cin, &training); + if (conf.count("testset")) { + ReadFile rf(conf["testset"].as()); + ReadCorpus(rf.stream(), &testing); + } + cerr << "Number of features: " << FD::NumFeats() << endl; + vector x(FD::NumFeats(), 0.0); // x[0] is bias + for (SparseVector::const_iterator it = old_weights.begin(); + it != old_weights.end(); ++it) + x[it->first] = it->second; + double tppl = 0.0; + vector > sp; + vector smoothed; + if (tune_regularizer) { + sigsq = min_reg; + const double steps = 18; + double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps); + cerr << "SWEEP FACTOR: " << sweep_factor << endl; + while(sigsq < max_reg) { + tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as(), &x); + sp.push_back(make_pair(sigsq, tppl)); + sigsq *= sweep_factor; + } + smoothed.resize(sp.size(), 0); + smoothed[0] = sp[0].second; + smoothed.back() = sp.back().second; + for (int i = 1; i < sp.size()-1; ++i) { + double prev = sp[i-1].second; + double next = sp[i+1].second; + double cur = sp[i].second; + smoothed[i] = (prev*0.2) + cur * 0.6 + (0.2*next); + } + double best_ppl = 9999999; + unsigned best_i = 0; + for (unsigned i = 0; i < sp.size(); ++i) { + if (smoothed[i] < best_ppl) { + best_ppl = smoothed[i]; + best_i = i; + } + } + sigsq = sp[best_i].first; + tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as(), &x); + } Weights w; if (conf.count("weights")) { for (int i = 1; i < x.size(); ++i) x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi); } + cout.precision(15); + cout << "# sigma^2=" << sigsq << "\theld out perplexity="; + if (tppl) { cout << tppl << endl; } else { cout << "N/A\n"; } + if (sp.size()) { + cout << "# Parameter sweep:\n"; + for (int i = 0; i < sp.size(); ++i) { + cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl; + } + } w.InitFromVector(x); w.WriteToFile("-"); return 0; -- cgit v1.2.3 From bb86637332d49f71c485df34576e464eaf053656 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Tue, 13 Sep 2011 17:36:23 +0100 Subject: get rid of bad Weights class so it no longer keeps a copy of a vector inside it --- decoder/decoder.cc | 64 ++++++++--------- decoder/decoder.h | 9 ++- mira/kbest_mira.cc | 62 ++++------------- pro-train/mr_pro_map.cc | 8 +-- pro-train/mr_pro_reduce.cc | 16 ++--- training/Makefile.am | 8 --- training/augment_grammar.cc | 4 +- training/collapse_weights.cc | 6 +- training/compute_cllh.cc | 23 +++--- training/grammar_convert.cc | 8 +-- training/mpi_batch_optimize.cc | 127 ++++++++-------------------------- training/mpi_online_optimize.cc | 69 +++++++----------- training/mr_optimize_reduce.cc | 19 ++--- utils/fdict.h | 2 + utils/phmt.cc | 8 +-- utils/weights.cc | 75 ++++++++++++-------- utils/weights.h | 22 +++--- vest/mr_vest_generate_mapper_input.cc | 6 +- 18 files changed, 201 insertions(+), 335 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/decoder/decoder.cc b/decoder/decoder.cc index 25eb2de4..4d4b6245 100644 --- a/decoder/decoder.cc +++ b/decoder/decoder.cc @@ -159,8 +159,7 @@ struct RescoringPass { shared_ptr models; shared_ptr inter_conf; vector ffs; - shared_ptr w; // null == use previous weights - vector weight_vector; + shared_ptr > weight_vector; int fid_summary; // 0 == no summary feature double density_prune; // 0 == don't density prune double beam_prune; // 0 == don't beam prune @@ -169,7 +168,7 @@ struct RescoringPass { ostream& operator<<(ostream& os, const RescoringPass& rp) { os << "[num_fn=" << rp.ffs.size(); if (rp.inter_conf) { os << " int_alg=" << *rp.inter_conf; } - if (rp.w) os << " new_weights"; + //if (rp.weight_vector.size() > 0) os << " new_weights"; if (rp.fid_summary) os << " summary_feature=" << FD::Convert(rp.fid_summary); if (rp.density_prune) os << " density_prune=" << rp.density_prune; if (rp.beam_prune) os << " beam_prune=" << rp.beam_prune; @@ -181,13 +180,8 @@ struct DecoderImpl { DecoderImpl(po::variables_map& conf, int argc, char** argv, istream* cfg); ~DecoderImpl(); bool Decode(const string& input, DecoderObserver*); - void SetWeights(const vector& weights) { - init_weights = weights; - for (int i = 0; i < rescoring_passes.size(); ++i) { - if (rescoring_passes[i].models) - rescoring_passes[i].models->SetWeights(weights); - rescoring_passes[i].weight_vector = weights; - } + vector& CurrentWeightVector() { + return *rescoring_passes.back().weight_vector; } void SetId(int next_sent_id) { sent_id = next_sent_id - 1; } @@ -300,8 +294,7 @@ struct DecoderImpl { OracleBleu oracle; string formalism; shared_ptr translator; - Weights w_init_weights; // used with initial parse - vector init_weights; // weights used with initial parse + shared_ptr > init_weights; // weights used with initial parse vector > pffs; #ifdef FSA_RESCORING CFGOptions cfg_options; @@ -557,13 +550,18 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream exit(1); } - // load initial feature weights (and possibly freeze feature set) - if (conf.count("weights")) { - w_init_weights.InitFromFile(str("weights",conf)); - w_init_weights.InitVector(&init_weights); - init_weights.resize(FD::NumFeats()); + // load perfect hash function for features + if (conf.count("cmph_perfect_feature_hash")) { + cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as() << " ...\n"; + FD::EnableHash(conf["cmph_perfect_feature_hash"].as()); + cerr << " " << FD::NumFeats() << " features in map\n"; } + // load initial feature weights (and possibly freeze feature set) + init_weights.reset(new vector); + if (conf.count("weights")) + Weights::InitFromFile(str("weights",conf), init_weights.get()); + // cube pruning pop-limit: we may want to configure this on a per-pass basis pop_limit = conf["cubepruning_pop_limit"].as(); @@ -582,9 +580,8 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream RescoringPass& rp = rescoring_passes.back(); // only configure new weights if pass > 0, otherwise we reuse the initial chart weights if (nth_pass_condition && conf.count(ws)) { - rp.w.reset(new Weights); - rp.w->InitFromFile(str(ws.c_str(), conf)); - rp.w->InitVector(&rp.weight_vector); + rp.weight_vector.reset(new vector()); + Weights::InitFromFile(str(ws.c_str(), conf), rp.weight_vector.get()); } bool has_stateful = false; if (conf.count(ff)) { @@ -624,11 +621,15 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream } // set up weight vectors since later phases may reuse weights from earlier phases - const vector* prev = &init_weights; + shared_ptr > prev_weights = init_weights; for (int pass = 0; pass < rescoring_passes.size(); ++pass) { RescoringPass& rp = rescoring_passes[pass]; - if (!rp.w) { rp.weight_vector = *prev; } else { prev = &rp.weight_vector; } - rp.models.reset(new ModelSet(rp.weight_vector, rp.ffs)); + if (!rp.weight_vector) { + rp.weight_vector = prev_weights; + } else { + prev_weights = rp.weight_vector; + } + rp.models.reset(new ModelSet(*rp.weight_vector, rp.ffs)); string ps = "Pass1 "; ps[4] += pass; if (!SILENT) show_models(conf,*rp.models,ps.c_str()); } @@ -650,12 +651,6 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream FD::Freeze(); // this means we can't see the feature names of not-weighted features } - if (conf.count("cmph_perfect_feature_hash")) { - cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as() << " ...\n"; - FD::EnableHash(conf["cmph_perfect_feature_hash"].as()); - cerr << " " << FD::NumFeats() << " features in map\n"; - } - // set up translation back end if (formalism == "scfg") translator.reset(new SCFGTranslator(conf)); @@ -685,7 +680,7 @@ DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream } if (!fsa_ffs.empty()) { cerr<<"FSA: "; - show_all_features(fsa_ffs,init_weights,cerr,cerr,true,true); + show_all_features(fsa_ffs,*init_weights,cerr,cerr,true,true); } #endif @@ -733,7 +728,8 @@ bool Decoder::Decode(const string& input, DecoderObserver* o) { if (del) delete o; return res; } -void Decoder::SetWeights(const vector& weights) { pimpl_->SetWeights(weights); } +vector& Decoder::CurrentWeightVector() { return pimpl_->CurrentWeightVector(); } +const vector& Decoder::CurrentWeightVector() const { return pimpl_->CurrentWeightVector(); } void Decoder::SetSupplementalGrammar(const std::string& grammar_string) { assert(pimpl_->translator->GetDecoderType() == "SCFG"); static_cast(*pimpl_->translator).SetSupplementalGrammar(grammar_string); @@ -774,7 +770,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) { translator->ProcessMarkupHints(smeta.sgml_); Timer t("Translation"); const bool translation_successful = - translator->Translate(to_translate, &smeta, init_weights, &forest); + translator->Translate(to_translate, &smeta, *init_weights, &forest); translator->SentenceComplete(); if (!translation_successful) { @@ -812,7 +808,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) { for (int pass = 0; pass < rescoring_passes.size(); ++pass) { const RescoringPass& rp = rescoring_passes[pass]; - const vector& cur_weights = rp.weight_vector; + const vector& cur_weights = *rp.weight_vector; if (!SILENT) cerr << endl << " RESCORING PASS #" << (pass+1) << " " << rp << endl; #ifdef FSA_RESCORING cfg_options.maybe_output_source(forest); @@ -933,7 +929,7 @@ bool DecoderImpl::Decode(const string& input, DecoderObserver* o) { #endif } - const vector& last_weights = (rescoring_passes.empty() ? init_weights : rescoring_passes.back().weight_vector); + const vector& last_weights = (rescoring_passes.empty() ? *init_weights : *rescoring_passes.back().weight_vector); // Oracle Rescoring if(get_oracle_forest) { diff --git a/decoder/decoder.h b/decoder/decoder.h index 5491369f..9d009ffa 100644 --- a/decoder/decoder.h +++ b/decoder/decoder.h @@ -7,6 +7,8 @@ #include #include +#include "weights.h" // weight_t + #undef CP_TIME //#define CP_TIME #ifdef CP_TIME @@ -39,7 +41,12 @@ struct Decoder { Decoder(int argc, char** argv); Decoder(std::istream* config_file); bool Decode(const std::string& input, DecoderObserver* observer = NULL); - void SetWeights(const std::vector& weights); + + // access this to either *read* or *write* to the decoder's last + // weight vector (i.e., the weights of the finest past) + std::vector& CurrentWeightVector(); + const std::vector& CurrentWeightVector() const; + void SetId(int id); ~Decoder(); const boost::program_options::variables_map& GetConf() const { return conf; } diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc index 6918a9a1..459a5e6f 100644 --- a/mira/kbest_mira.cc +++ b/mira/kbest_mira.cc @@ -32,21 +32,6 @@ namespace po = boost::program_options; bool invert_score; boost::shared_ptr rng; -void SanityCheck(const vector& w) { - for (int i = 0; i < w.size(); ++i) { - assert(!isnan(w[i])); - assert(!isinf(w[i])); - } -} - -struct FComp { - const vector& w_; - FComp(const vector& w) : w_(w) {} - bool operator()(int a, int b) const { - return fabs(w_[a]) > fabs(w_[b]); - } -}; - void RandomPermutation(int len, vector* p_ids) { vector& ids = *p_ids; ids.resize(len); @@ -58,21 +43,6 @@ void RandomPermutation(int len, vector* p_ids) { } } -void ShowLargestFeatures(const vector& w) { - vector fnums(w.size()); - for (int i = 0; i < w.size(); ++i) - fnums[i] = i; - vector::iterator mid = fnums.begin(); - mid += (w.size() > 10 ? 10 : w.size()); - partial_sort(fnums.begin(), mid, fnums.end(), FComp(w)); - cerr << "TOP FEATURES:"; - --mid; - for (vector::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() @@ -209,14 +179,16 @@ int main(int argc, char** argv) { cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n"; return 1; } - // load initial weights - Weights weights; - weights.InitFromFile(conf["input_weights"].as()); - SparseVector lambdas; - weights.InitSparseVector(&lambdas); ReadFile ini_rf(conf["decoder_config"].as()); Decoder decoder(ini_rf.stream()); + + // load initial weights + vector& dense_weights = decoder.CurrentWeightVector(); + SparseVector lambdas; + Weights::InitFromFile(conf["input_weights"].as(), &dense_weights); + Weights::InitSparseVector(dense_weights, &lambdas); + const double max_step_size = conf["max_step_size"].as(); const double mt_metric_scale = conf["mt_metric_scale"].as(); @@ -230,7 +202,6 @@ int main(int argc, char** argv) { double tot_loss = 0; int dots = 0; int cur_pass = 0; - vector dense_weights; SparseVector tot; tot += lambdas; // initial weights normalizer++; // count for initial weights @@ -240,27 +211,22 @@ int main(int argc, char** argv) { vector order; RandomPermutation(corpus.size(), &order); while (lcount <= max_iteration) { - dense_weights.clear(); - weights.InitFromVector(lambdas); - weights.InitVector(&dense_weights); - decoder.SetWeights(dense_weights); + lambdas.init_vector(&dense_weights); if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; } if (corpus.size() == cur_sent) { cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n"; - ShowLargestFeatures(dense_weights); + Weights::ShowLargestFeatures(dense_weights); cur_sent = 0; tot_loss = 0; dots = 0; ostringstream os; os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz"; - weights.WriteToFile(os.str(), true, &msg); SparseVector x = tot; x /= normalizer; ostringstream sa; sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz"; - Weights ww; - ww.InitFromVector(x); - ww.WriteToFile(sa.str(), true, &msga); + x.init_vector(&dense_weights); + Weights::WriteToFile(os.str(), dense_weights, true, &msg); ++cur_pass; RandomPermutation(corpus.size(), &order); } @@ -294,11 +260,11 @@ int main(int argc, char** argv) { ++cur_sent; } cerr << endl; - weights.WriteToFile("weights.mira-final.gz", true, &msg); + Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg); tot /= normalizer; - weights.InitFromVector(tot); + tot.init_vector(dense_weights); msg = "# MIRA tuned weights (averaged vector)"; - weights.WriteToFile("weights.mira-final-avg.gz", true, &msg); + Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg); cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n"; return 0; } diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc index 4324e8de..bc59285b 100644 --- a/pro-train/mr_pro_map.cc +++ b/pro-train/mr_pro_map.cc @@ -301,12 +301,8 @@ int main(int argc, char** argv) { const unsigned gamma = conf["candidate_pairs"].as(); const unsigned xi = conf["best_pairs"].as(); string weightsf = conf["weights"].as(); - vector weights; - { - Weights w; - w.InitFromFile(weightsf); - w.InitVector(&weights); - } + vector weights; + Weights::InitFromFile(weightsf, &weights); string kbest_repo = conf["kbest_repository"].as(); MkDirP(kbest_repo); while(in) { diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 9b422f33..9caaa1d1 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -194,7 +194,7 @@ int main(int argc, char** argv) { InitCommandLine(argc, argv, &conf); string line; vector > > training, testing; - SparseVector old_weights; + SparseVector old_weights; const bool tune_regularizer = conf.count("tune_regularizer"); if (tune_regularizer && !conf.count("testset")) { cerr << "--tune_regularizer requires --testset to be set\n"; @@ -210,9 +210,9 @@ int main(int argc, char** argv) { const double psi = conf["interpolation"].as(); if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } if (conf.count("weights")) { - Weights w; - w.InitFromFile(conf["weights"].as()); - w.InitSparseVector(&old_weights); + vector dt; + Weights::InitFromFile(conf["weights"].as(), &dt); + Weights::InitSparseVector(dt, &old_weights); } ReadCorpus(&cin, &training); if (conf.count("testset")) { @@ -220,8 +220,8 @@ int main(int argc, char** argv) { ReadCorpus(rf.stream(), &testing); } cerr << "Number of features: " << FD::NumFeats() << endl; - vector x(FD::NumFeats(), 0.0); // x[0] is bias - for (SparseVector::const_iterator it = old_weights.begin(); + vector x(FD::NumFeats(), 0.0); // x[0] is bias + for (SparseVector::const_iterator it = old_weights.begin(); it != old_weights.end(); ++it) x[it->first] = it->second; double tppl = 0.0; @@ -257,7 +257,6 @@ int main(int argc, char** argv) { sigsq = sp[best_i].first; tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as(), &x); } - Weights w; if (conf.count("weights")) { for (int i = 1; i < x.size(); ++i) x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi); @@ -271,7 +270,6 @@ int main(int argc, char** argv) { cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl; } } - w.InitFromVector(x); - w.WriteToFile("-"); + Weights::WriteToFile("-", x); return 0; } diff --git a/training/Makefile.am b/training/Makefile.am index e075e417..6e2c06f5 100644 --- a/training/Makefile.am +++ b/training/Makefile.am @@ -12,9 +12,7 @@ bin_PROGRAMS = \ cllh_filter_grammar \ mpi_online_optimize \ mpi_batch_optimize \ - mpi_em_optimize \ compute_cllh \ - feature_expectations \ augment_grammar noinst_PROGRAMS = \ @@ -29,12 +27,6 @@ mpi_online_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc optimize.cc mpi_batch_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz -feature_expectations_SOURCES = feature_expectations.cc -feature_expectations_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz - -mpi_em_optimize_SOURCES = mpi_em_optimize.cc optimize.cc -mpi_em_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz - compute_cllh_SOURCES = compute_cllh.cc compute_cllh_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz diff --git a/training/augment_grammar.cc b/training/augment_grammar.cc index df8d4ee8..e89a92d5 100644 --- a/training/augment_grammar.cc +++ b/training/augment_grammar.cc @@ -134,9 +134,7 @@ int main(int argc, char** argv) { } else { ngram = NULL; } extra_feature = conf.count("extra_lex_feature") > 0; if (conf.count("collapse_weights")) { - Weights w; - w.InitFromFile(conf["collapse_weights"].as()); - w.InitVector(&col_weights); + Weights::InitFromFile(conf["collapse_weights"].as(), &col_weights); } clear_features = conf.count("clear_features_after_collapse") > 0; gather_rules = false; diff --git a/training/collapse_weights.cc b/training/collapse_weights.cc index 4fb742fb..dc480f6c 100644 --- a/training/collapse_weights.cc +++ b/training/collapse_weights.cc @@ -59,10 +59,8 @@ int main(int argc, char** argv) { InitCommandLine(argc, argv, &conf); const string wfile = conf["weights"].as(); const string gfile = conf["grammar"].as(); - Weights wm; - wm.InitFromFile(wfile); - vector w; - wm.InitVector(&w); + vector w; + Weights::InitFromFile(wfile, &w); MarginalMap e_tots; MarginalMap f_tots; prob_t tot; diff --git a/training/compute_cllh.cc b/training/compute_cllh.cc index 332f6d0c..b496d196 100644 --- a/training/compute_cllh.cc +++ b/training/compute_cllh.cc @@ -148,15 +148,6 @@ int main(int argc, char** argv) { if (!InitCommandLine(argc, argv, &conf)) return false; - // load initial weights - Weights weights; - if (conf.count("weights")) - weights.InitFromFile(conf["weights"].as()); - - // freeze feature set - //const bool freeze_feature_set = conf.count("freeze_feature_set"); - //if (freeze_feature_set) FD::Freeze(); - // load cdec.ini and set up decoder ReadFile ini_rf(conf["decoder_config"].as()); Decoder decoder(ini_rf.stream()); @@ -165,17 +156,22 @@ int main(int argc, char** argv) { abort(); } + // load weights + vector& weights = decoder.CurrentWeightVector(); + if (conf.count("weights")) + Weights::InitFromFile(conf["weights"].as(), &weights); + + // freeze feature set + //const bool freeze_feature_set = conf.count("freeze_feature_set"); + //if (freeze_feature_set) FD::Freeze(); + vector corpus; vector ids; ReadTrainingCorpus(conf["training_data"].as(), rank, size, &corpus, &ids); assert(corpus.size() > 0); assert(corpus.size() == ids.size()); - vector wv; - weights.InitVector(&wv); - decoder.SetWeights(wv); TrainingObserver observer; double objective = 0; - bool converged = false; observer.Reset(); if (rank == 0) @@ -197,3 +193,4 @@ int main(int argc, char** argv) { return 0; } + diff --git a/training/grammar_convert.cc b/training/grammar_convert.cc index 8d292f8a..bf8abb26 100644 --- a/training/grammar_convert.cc +++ b/training/grammar_convert.cc @@ -251,12 +251,10 @@ int main(int argc, char **argv) { const bool is_split_input = (conf["format"].as() == "split"); const bool is_json_input = is_split_input || (conf["format"].as() == "json"); const bool collapse_weights = conf.count("collapse_weights"); - Weights wts; vector w; - if (conf.count("weights")) { - wts.InitFromFile(conf["weights"].as()); - wts.InitVector(&w); - } + if (conf.count("weights")) + Weights::InitFromFile(conf["weights"].as(), &w); + if (collapse_weights && !w.size()) { cerr << "--collapse_weights requires a weights file to be specified!\n"; exit(1); diff --git a/training/mpi_batch_optimize.cc b/training/mpi_batch_optimize.cc index 39a8af7d..cc5953f6 100644 --- a/training/mpi_batch_optimize.cc +++ b/training/mpi_batch_optimize.cc @@ -31,42 +31,12 @@ using namespace std; using boost::shared_ptr; namespace po = boost::program_options; -void SanityCheck(const vector& w) { - for (int i = 0; i < w.size(); ++i) { - assert(!isnan(w[i])); - assert(!isinf(w[i])); - } -} - -struct FComp { - const vector& w_; - FComp(const vector& w) : w_(w) {} - bool operator()(int a, int b) const { - return fabs(w_[a]) > fabs(w_[b]); - } -}; - -void ShowLargestFeatures(const vector& w) { - vector fnums(w.size()); - for (int i = 0; i < w.size(); ++i) - fnums[i] = i; - vector::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::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(),"Input feature weights file") ("training_data,t",po::value(),"Training data") ("decoder_config,d",po::value(),"Decoder configuration file") - ("sharded_input,s",po::value(), "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()->default_value("-"),"Output feature weights file") ("optimization_method,m", po::value()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)") ("correction_buffers,M", po::value()->default_value(10), "Number of gradients for LBFGS to maintain in memory") @@ -88,14 +58,10 @@ 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; } @@ -236,42 +202,9 @@ 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(); - 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()); - 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 cdec_ini; ReadConfig(conf["decoder_config"].as(), &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() << " 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 +215,28 @@ 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& lambdas = decoder->CurrentWeightVector(); + Weights::InitFromFile(conf["input_weights"].as(), &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 means(num_feats, 0); + vector 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()); - if (num_feats != FD::NumFeats()) { - cerr << "[ERROR] Means file had unexpected features!\n"; - exit(1); - } - wm.InitVector(&means); + Weights::InitFromFile(conf["means"].as(), &means); } shared_ptr o; if (rank == 0) { @@ -309,26 +248,13 @@ int main(int argc, char** argv) { cerr << "Optimizer: " << o->Name() << endl; } double objective = 0; - vector 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 gradient(num_feats, 0.0); - vector rcv_grad(num_feats, 0.0); + vector rcv_grad; + rcv_grad.clear(); bool converged = false; vector 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(), rank, size, &corpus); - } + ReadTrainingCorpus(conf["training_data"].as(), rank, size, &corpus); assert(corpus.size() > 0); TrainingObserver observer; @@ -341,19 +267,20 @@ 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(), 0); - mpi::reduce(world, objective, to, plus(), 0); swap(gradient, rcv_grad); + rcv_grad.clear(); + + mpi::reduce(world, objective, to, plus(), 0); objective = to; #endif @@ -378,7 +305,7 @@ 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 old = lambdas; + vector old = lambdas; int c = 0; while (old == lambdas) { ++c; @@ -387,9 +314,8 @@ 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 +325,7 @@ 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 +337,4 @@ int main(int argc, char** argv) { } return 0; } + diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc index 32033c19..2ef4a2e7 100644 --- a/training/mpi_online_optimize.cc +++ b/training/mpi_online_optimize.cc @@ -31,35 +31,6 @@ namespace mpi = boost::mpi; using namespace std; namespace po = boost::program_options; -void SanityCheck(const vector& w) { - for (int i = 0; i < w.size(); ++i) { - assert(!isnan(w[i])); - assert(!isinf(w[i])); - } -} - -struct FComp { - const vector& w_; - FComp(const vector& w) : w_(w) {} - bool operator()(int a, int b) const { - return fabs(w_[a]) > fabs(w_[b]); - } -}; - -void ShowLargestFeatures(const vector& w) { - vector fnums(w.size()); - for (int i = 0; i < w.size(); ++i) - fnums[i] = i; - vector::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::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() @@ -250,10 +221,25 @@ int main(int argc, char** argv) { if (!InitCommandLine(argc, argv, &conf)) return 1; + vector > agenda; + if (!LoadAgenda(conf["training_agenda"].as(), &agenda)) + return 1; + if (rank == 0) + cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n"; + + assert(agenda.size() > 0); + + if (1) { // hack to load the feature hash functions -- TODO this should not be in cdec.ini + const string& cur_config = agenda[0].first; + const unsigned max_iteration = agenda[0].second; + ReadFile ini_rf(cur_config); + Decoder decoder(ini_rf.stream()); + } + // load initial weights - Weights weights; + vector init_weights; if (conf.count("input_weights")) - weights.InitFromFile(conf["input_weights"].as()); + Weights::InitFromFile(conf["input_weights"].as(), &init_weights); vector frozen_fids; if (conf.count("frozen_features")) { @@ -310,19 +296,12 @@ int main(int argc, char** argv) { rng.reset(new MT19937); SparseVector x; - weights.InitSparseVector(&x); + Weights::InitSparseVector(init_weights, &x); TrainingObserver observer; int write_weights_every_ith = 100; // TODO configure int titer = -1; - vector > agenda; - if (!LoadAgenda(conf["training_agenda"].as(), &agenda)) - return 1; - if (rank == 0) - cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n"; - - vector lambdas; for (int ai = 0; ai < agenda.size(); ++ai) { const string& cur_config = agenda[ai].first; const unsigned max_iteration = agenda[ai].second; @@ -331,6 +310,8 @@ int main(int argc, char** argv) { // load cdec.ini and set up decoder ReadFile ini_rf(cur_config); Decoder decoder(ini_rf.stream()); + vector& lambdas = decoder.CurrentWeightVector(); + if (ai == 0) { lambdas.swap(init_weights); init_weights.clear(); } if (rank == 0) o->ResetEpoch(); // resets the learning rate-- TODO is this good? @@ -341,15 +322,13 @@ int main(int argc, char** argv) { #ifdef HAVE_MPI mpi::timer timer; #endif - weights.InitFromVector(x); - weights.InitVector(&lambdas); + x.init_vector(&lambdas); ++iter; ++titer; observer.Reset(); - decoder.SetWeights(lambdas); if (rank == 0) { converged = (iter == max_iteration); - SanityCheck(lambdas); - ShowLargestFeatures(lambdas); + Weights::SanityCheck(lambdas); + Weights::ShowLargestFeatures(lambdas); string fname = "weights.cur.gz"; if (iter % write_weights_every_ith == 0) { ostringstream o; o << "weights.epoch_" << (ai+1) << '.' << iter << ".gz"; @@ -360,7 +339,7 @@ int main(int argc, char** argv) { vv << "total iter=" << titer << " (of current config iter=" << iter << ") minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << x.size() << '/' << FD::NumFeats() << " passes_thru_data=" << (titer * size_per_proc / static_cast(corpus.size())) << " eta=" << lr->eta(titer); const string svv = vv.str(); cerr << svv << endl; - weights.WriteToFile(fname, true, &svv); + Weights::WriteToFile(fname, lambdas, true, &svv); } for (int i = 0; i < size_per_proc; ++i) { diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc index b931991d..15e28fa1 100644 --- a/training/mr_optimize_reduce.cc +++ b/training/mr_optimize_reduce.cc @@ -88,25 +88,19 @@ int main(int argc, char** argv) { const bool use_b64 = conf["input_format"].as() == "b64"; - Weights weights; - weights.InitFromFile(conf["input_weights"].as()); + vector lambdas; + Weights::InitFromFile(conf["input_weights"].as(), &lambdas); const string s_obj = "**OBJ**"; int num_feats = FD::NumFeats(); cerr << "Number of features: " << num_feats << endl; const bool gaussian_prior = conf.count("gaussian_prior"); - vector means(num_feats, 0); + vector 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()); - if (num_feats != FD::NumFeats()) { - cerr << "[ERROR] Means file had unexpected features!\n"; - exit(1); - } - wm.InitVector(&means); + Weights::InitFromFile(conf["means"].as(), &means); } shared_ptr o; const string omethod = conf["optimization_method"].as(); @@ -124,8 +118,6 @@ int main(int argc, char** argv) { cerr << "No state file found, assuming ITERATION 1\n"; } - vector lambdas(num_feats, 0); - weights.InitVector(&lambdas); double objective = 0; vector gradient(num_feats, 0); // 0**OBJ**=12.2;Feat1=2.3;Feat2=-0.2; @@ -223,8 +215,7 @@ int main(int argc, char** argv) { old.clear(); SanityCheck(lambdas); ShowLargestFeatures(lambdas); - weights.InitFromVector(lambdas); - weights.WriteToFile(conf["output_weights"].as(), false); + Weights::WriteToFile(conf["output_weights"].as(), lambdas, false); const bool conv = o->HasConverged(); if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; } diff --git a/utils/fdict.h b/utils/fdict.h index 771e8b91..f0871b9a 100644 --- a/utils/fdict.h +++ b/utils/fdict.h @@ -28,6 +28,8 @@ struct FD { } static void EnableHash(const std::string& cmph_file) { #ifdef HAVE_CMPH + assert(dict_.max() == 0); // dictionary must not have + // been added to hash_ = new PerfectHashFunction(cmph_file); #endif } diff --git a/utils/phmt.cc b/utils/phmt.cc index 1f59afaf..48d9f093 100644 --- a/utils/phmt.cc +++ b/utils/phmt.cc @@ -19,22 +19,18 @@ int main(int argc, char** argv) { cerr << "LexFE = " << FD::Convert("LexFE") << endl; cerr << "LexEF = " << FD::Convert("LexEF") << endl; { - Weights w; vector v(FD::NumFeats()); v[FD::Convert("LexFE")] = 1.0; v[FD::Convert("LexEF")] = 0.5; - w.InitFromVector(v); cerr << "Writing...\n"; - w.WriteToFile("weights.bin"); + Weights::WriteToFile("weights.bin", v); cerr << "Done.\n"; } { - Weights w; vector v(FD::NumFeats()); cerr << "Reading...\n"; - w.InitFromFile("weights.bin"); + Weights::InitFromFile("weights.bin", &v); cerr << "Done.\n"; - w.InitVector(&v); assert(v[FD::Convert("LexFE")] == 1.0); assert(v[FD::Convert("LexEF")] == 0.5); } diff --git a/utils/weights.cc b/utils/weights.cc index 0916b72a..c49000be 100644 --- a/utils/weights.cc +++ b/utils/weights.cc @@ -8,7 +8,10 @@ using namespace std; -void Weights::InitFromFile(const std::string& filename, vector* feature_list) { +void Weights::InitFromFile(const string& filename, + vector* pweights, + vector* feature_list) { + vector& weights = *pweights; if (!SILENT) cerr << "Reading weights from " << filename << endl; ReadFile in_file(filename); istream& in = *in_file.stream(); @@ -47,16 +50,16 @@ void Weights::InitFromFile(const std::string& filename, vector* feature_ int end = 0; while(end < buf.size() && buf[end] != ' ') ++end; const int fid = FD::Convert(buf.substr(start, end - start)); + if (feature_list) { feature_list->push_back(buf.substr(start, end - start)); } while(end < buf.size() && buf[end] == ' ') ++end; val = strtod(&buf.c_str()[end], NULL); if (isnan(val)) { cerr << FD::Convert(fid) << " has weight NaN!\n"; abort(); } - if (wv_.size() <= fid) - wv_.resize(fid + 1); - wv_[fid] = val; - if (feature_list) { feature_list->push_back(FD::Convert(fid)); } + if (weights.size() <= fid) + weights.resize(fid + 1); + weights[fid] = val; ++weight_count; if (!SILENT) { if (weight_count % 50000 == 0) { cerr << '.' << flush; fl = true; } @@ -76,8 +79,8 @@ void Weights::InitFromFile(const std::string& filename, vector* feature_ cerr << "Hash function reports " << FD::NumFeats() << " keys but weights file contains " << num_keys[0] << endl; abort(); } - wv_.resize(num_keys[0]); - in.get(reinterpret_cast(&wv_[0]), num_keys[0] * sizeof(weight_t)); + weights.resize(num_keys[0]); + in.get(reinterpret_cast(&weights[0]), num_keys[0] * sizeof(weight_t)); if (!in.good()) { cerr << "Error loading weights!\n"; abort(); @@ -85,7 +88,10 @@ void Weights::InitFromFile(const std::string& filename, vector* feature_ } } -void Weights::WriteToFile(const std::string& fname, bool hide_zero_value_features, const string* extra) const { +void Weights::WriteToFile(const string& fname, + const vector& weights, + bool hide_zero_value_features, + const string* extra) { WriteFile out(fname); ostream& o = *out.stream(); assert(o); @@ -96,41 +102,54 @@ void Weights::WriteToFile(const std::string& fname, bool hide_zero_value_feature o.precision(17); const int num_feats = FD::NumFeats(); for (int i = 1; i < num_feats; ++i) { - const weight_t val = (i < wv_.size() ? wv_[i] : 0.0); + const weight_t val = (i < weights.size() ? weights[i] : 0.0); if (hide_zero_value_features && val == 0.0) continue; o << FD::Convert(i) << ' ' << val << endl; } } else { o.write("_PHWf", 5); const size_t keys = FD::NumFeats(); - assert(keys <= wv_.size()); + assert(keys <= weights.size()); o.write(reinterpret_cast(&keys), sizeof(keys)); - o.write(reinterpret_cast(&wv_[0]), keys * sizeof(weight_t)); + o.write(reinterpret_cast(&weights[0]), keys * sizeof(weight_t)); } } -void Weights::InitVector(std::vector* w) const { - *w = wv_; +void Weights::InitSparseVector(const vector& dv, + SparseVector* sv) { + sv->clear(); + for (unsigned i = 1; i < dv.size(); ++i) { + if (dv[i]) sv->set_value(i, dv[i]); + } } -void Weights::InitSparseVector(SparseVector* w) const { - for (int i = 1; i < wv_.size(); ++i) { - const weight_t& weight = wv_[i]; - if (weight) w->set_value(i, weight); +void Weights::SanityCheck(const vector& w) { + for (int i = 0; i < w.size(); ++i) { + assert(!isnan(w[i])); + assert(!isinf(w[i])); } } -void Weights::InitFromVector(const std::vector& w) { - wv_ = w; - if (wv_.size() > FD::NumFeats()) - cerr << "WARNING: initializing weight vector has more features than the global feature dictionary!\n"; - wv_.resize(FD::NumFeats(), 0); -} +struct FComp { + const vector& w_; + FComp(const vector& w) : w_(w) {} + bool operator()(int a, int b) const { + return fabs(w_[a]) > fabs(w_[b]); + } +}; -void Weights::InitFromVector(const SparseVector& w) { - wv_.clear(); - wv_.resize(FD::NumFeats(), 0.0); - for (int i = 1; i < FD::NumFeats(); ++i) - wv_[i] = w.value(i); +void Weights::ShowLargestFeatures(const vector& w) { + vector fnums(w.size()); + for (int i = 0; i < w.size(); ++i) + fnums[i] = i; + vector::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::iterator i = fnums.begin(); i != mid; ++i) { + cerr << ' ' << FD::Convert(*i) << '=' << w[*i]; + } + cerr << endl; } + diff --git a/utils/weights.h b/utils/weights.h index 7664810b..30f71db0 100644 --- a/utils/weights.h +++ b/utils/weights.h @@ -10,15 +10,21 @@ typedef double weight_t; class Weights { public: - Weights() {} - void InitFromFile(const std::string& fname, std::vector* feature_list = NULL); - void WriteToFile(const std::string& fname, bool hide_zero_value_features = true, const std::string* extra = NULL) const; - void InitVector(std::vector* w) const; - void InitSparseVector(SparseVector* w) const; - void InitFromVector(const std::vector& w); - void InitFromVector(const SparseVector& w); + static void InitFromFile(const std::string& fname, + std::vector* weights, + std::vector* feature_list = NULL); + static void WriteToFile(const std::string& fname, + const std::vector& weights, + bool hide_zero_value_features = true, + const std::string* extra = NULL); + static void InitSparseVector(const std::vector& dv, + SparseVector* sv); + // check for infinities, NaNs, etc + static void SanityCheck(const std::vector& w); + // write weights with largest magnitude to cerr + static void ShowLargestFeatures(const std::vector& w); private: - std::vector wv_; + Weights(); }; #endif diff --git a/vest/mr_vest_generate_mapper_input.cc b/vest/mr_vest_generate_mapper_input.cc index b84c44bc..0c094fd5 100644 --- a/vest/mr_vest_generate_mapper_input.cc +++ b/vest/mr_vest_generate_mapper_input.cc @@ -223,16 +223,16 @@ struct oracle_directions { cerr << "Forest repo: " << forest_repository << endl; assert(DirectoryExists(forest_repository)); vector features; - weights.InitFromFile(weights_file, &features); + vector dorigin; + Weights::InitFromFile(weights_file, &dorigin, &features); if (optimize_features.size()) features=optimize_features; - weights.InitSparseVector(&origin); + Weights::InitSparseVector(dorigin, &origin); fids.clear(); AddFeatureIds(features); oracles.resize(dev_set_size); } - Weights weights; void AddFeatureIds(vector const& features) { int i = fids.size(); fids.resize(fids.size()+features.size()); -- cgit v1.2.3 From 6c8309c58dc4a6015dfb2f478a2cef5f65f92961 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Wed, 14 Sep 2011 12:17:04 +0100 Subject: weight_t refactoring --- pro-train/mr_pro_map.cc | 42 +++++++++++++++++++++--------------------- pro-train/mr_pro_reduce.cc | 34 +++++++++++++++++----------------- 2 files changed, 38 insertions(+), 38 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc index bc59285b..0a9b75d7 100644 --- a/pro-train/mr_pro_map.cc +++ b/pro-train/mr_pro_map.cc @@ -27,7 +27,7 @@ namespace po = boost::program_options; struct ApproxVectorHasher { static const size_t MASK = 0xFFFFFFFFull; union UType { - double f; + double f; // leave as double size_t i; }; static inline double round(const double x) { @@ -40,9 +40,9 @@ struct ApproxVectorHasher { t.i &= (1ull - MASK); return t.f; } - size_t operator()(const SparseVector& x) const { + size_t operator()(const SparseVector& x) const { size_t h = 0x573915839; - for (SparseVector::const_iterator it = x.begin(); it != x.end(); ++it) { + for (SparseVector::const_iterator it = x.begin(); it != x.end(); ++it) { UType t; t.f = it->second; if (t.f) { @@ -56,9 +56,9 @@ struct ApproxVectorHasher { }; struct ApproxVectorEquals { - bool operator()(const SparseVector& a, const SparseVector& b) const { - SparseVector::const_iterator bit = b.begin(); - for (SparseVector::const_iterator ait = a.begin(); ait != a.end(); ++ait) { + bool operator()(const SparseVector& a, const SparseVector& b) const { + SparseVector::const_iterator bit = b.begin(); + for (SparseVector::const_iterator ait = a.begin(); ait != a.end(); ++ait) { if (bit == b.end() || ait->first != bit->first || ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second)) @@ -105,18 +105,18 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { } struct HypInfo { - HypInfo() : g_(-100.0) {} - HypInfo(const vector& h, const SparseVector& feats) : hyp(h), g_(-100.0), x(feats) {} + HypInfo() : g_(-100.0f) {} + HypInfo(const vector& h, const SparseVector& feats) : hyp(h), g_(-100.0f), x(feats) {} // lazy evaluation double g(const SentenceScorer& scorer) const { - if (g_ == -100.0) + if (g_ == -100.0f) g_ = scorer.ScoreCandidate(hyp)->ComputeScore(); return g_; } vector hyp; - mutable double g_; - SparseVector x; + mutable float g_; + SparseVector x; }; struct HypInfoCompare { @@ -146,8 +146,8 @@ void WriteKBest(const string& file, const vector& kbest) { } } -void ParseSparseVector(string& line, size_t cur, SparseVector* out) { - SparseVector& x = *out; +void ParseSparseVector(string& line, size_t cur, SparseVector* out) { + SparseVector& x = *out; size_t last_start = cur; size_t last_comma = string::npos; while(cur <= line.size()) { @@ -211,15 +211,15 @@ struct ThresholdAlpha { }; struct TrainingInstance { - TrainingInstance(const SparseVector& feats, bool positive, double diff) : x(feats), y(positive), gdiff(diff) {} - SparseVector x; + TrainingInstance(const SparseVector& feats, bool positive, float diff) : x(feats), y(positive), gdiff(diff) {} + SparseVector x; #undef DEBUGGING_PRO #ifdef DEBUGGING_PRO vector a; vector b; #endif bool y; - double gdiff; + float gdiff; }; #ifdef DEBUGGING_PRO ostream& operator<<(ostream& os, const TrainingInstance& d) { @@ -235,19 +235,19 @@ struct DiffOrder { void Sample(const unsigned gamma, const unsigned xi, const vector& J_i, const SentenceScorer& scorer, const bool invert_score, vector* pv) { vector v1, v2; - double avg_diff = 0; + float avg_diff = 0; for (unsigned i = 0; i < gamma; ++i) { const size_t a = rng->inclusive(0, J_i.size() - 1)(); const size_t b = rng->inclusive(0, J_i.size() - 1)(); if (a == b) continue; - double ga = J_i[a].g(scorer); - double gb = J_i[b].g(scorer); + float ga = J_i[a].g(scorer); + float gb = J_i[b].g(scorer); bool positive = gb < ga; if (invert_score) positive = !positive; - const double gdiff = fabs(ga - gb); + const float gdiff = fabs(ga - gb); if (!gdiff) continue; avg_diff += gdiff; - SparseVector xdiff = (J_i[a].x - J_i[b].x).erase_zeros(); + SparseVector xdiff = (J_i[a].x - J_i[b].x).erase_zeros(); if (xdiff.empty()) { cerr << "Empty diff:\n " << TD::GetString(J_i[a].hyp) << endl << "x=" << J_i[a].x << endl; cerr << " " << TD::GetString(J_i[b].hyp) << endl << "x=" << J_i[b].x << endl; diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 9caaa1d1..239649c1 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -40,8 +40,8 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { } } -void ParseSparseVector(string& line, size_t cur, SparseVector* out) { - SparseVector& x = *out; +void ParseSparseVector(string& line, size_t cur, SparseVector* out) { + SparseVector& x = *out; size_t last_start = cur; size_t last_comma = string::npos; while(cur <= line.size()) { @@ -52,7 +52,7 @@ void ParseSparseVector(string& line, size_t cur, SparseVector* out) { } const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); if (cur < line.size()) line[cur] = 0; - const double val = strtod(&line[last_comma + 1], NULL); + const weight_t val = strtod(&line[last_comma + 1], NULL); x.set_value(fid, val); last_comma = string::npos; @@ -65,13 +65,13 @@ void ParseSparseVector(string& line, size_t cur, SparseVector* out) { } } -void ReadCorpus(istream* pin, vector > >* corpus) { +void ReadCorpus(istream* pin, vector > >* corpus) { istream& in = *pin; corpus->clear(); bool flag = false; int lc = 0; string line; - SparseVector x; + SparseVector x; while(getline(in, line)) { ++lc; if (lc % 1000 == 0) { cerr << '.'; flag = true; } @@ -88,16 +88,16 @@ void ReadCorpus(istream* pin, vector > >* corpus if (flag) cerr << endl; } -void GradAdd(const SparseVector& v, const double scale, vector* acc) { - for (SparseVector::const_iterator it = v.begin(); +void GradAdd(const SparseVector& v, const double scale, vector* acc) { + for (SparseVector::const_iterator it = v.begin(); it != v.end(); ++it) { (*acc)[it->first] += it->second * scale; } } -double TrainingInference(const vector& x, - const vector > >& corpus, - vector* g = NULL) { +double TrainingInference(const vector& x, + const vector > >& corpus, + vector* g = NULL) { double cll = 0; for (int i = 0; i < corpus.size(); ++i) { const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias @@ -132,13 +132,13 @@ double TrainingInference(const vector& x, } // return held-out log likelihood -double LearnParameters(const vector > >& training, - const vector > >& testing, +double LearnParameters(const vector > >& training, + const vector > >& testing, const double sigsq, const unsigned memory_buffers, - vector* px) { - vector& x = *px; - vector vg(FD::NumFeats(), 0.0); + vector* px) { + vector& x = *px; + vector vg(FD::NumFeats(), 0.0); bool converged = false; LBFGSOptimizer opt(FD::NumFeats(), memory_buffers); double tppl = 0.0; @@ -172,7 +172,7 @@ double LearnParameters(const vector > >& trainin cll += reg; cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t"; try { - vector old_x = x; + vector old_x = x; do { opt.Optimize(cll, vg, &x); converged = opt.HasConverged(); @@ -193,7 +193,7 @@ int main(int argc, char** argv) { po::variables_map conf; InitCommandLine(argc, argv, &conf); string line; - vector > > training, testing; + vector > > training, testing; SparseVector old_weights; const bool tune_regularizer = conf.count("tune_regularizer"); if (tune_regularizer && !conf.count("testset")) { -- cgit v1.2.3 From 9ba06c6f1a7e751da245219da291e329efa2b7e5 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Wed, 14 Sep 2011 13:12:01 +0100 Subject: fix pro train bug causing it not to optimize when there is no held-out test set --- pro-train/mr_pro_reduce.cc | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 239649c1..e71347ba 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -194,7 +194,6 @@ int main(int argc, char** argv) { InitCommandLine(argc, argv, &conf); string line; vector > > training, testing; - SparseVector old_weights; const bool tune_regularizer = conf.count("tune_regularizer"); if (tune_regularizer && !conf.count("testset")) { cerr << "--tune_regularizer requires --testset to be set\n"; @@ -202,28 +201,28 @@ int main(int argc, char** argv) { } const double min_reg = conf["min_reg"].as(); const double max_reg = conf["max_reg"].as(); - double sigsq = conf["sigma_squared"].as(); + double sigsq = conf["sigma_squared"].as(); // will be overridden if parameter is tuned assert(sigsq > 0.0); assert(min_reg > 0.0); assert(max_reg > 0.0); assert(max_reg > min_reg); const double psi = conf["interpolation"].as(); if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } - if (conf.count("weights")) { - vector dt; - Weights::InitFromFile(conf["weights"].as(), &dt); - Weights::InitSparseVector(dt, &old_weights); - } ReadCorpus(&cin, &training); if (conf.count("testset")) { ReadFile rf(conf["testset"].as()); ReadCorpus(rf.stream(), &testing); } cerr << "Number of features: " << FD::NumFeats() << endl; - vector x(FD::NumFeats(), 0.0); // x[0] is bias - for (SparseVector::const_iterator it = old_weights.begin(); - it != old_weights.end(); ++it) - x[it->first] = it->second; + + vector x, prev_x; // x[0] is bias + if (conf.count("weights")) { + Weights::InitFromFile(conf["weights"].as(), &x); + prev_x = x; + } + cerr << " Number of features: " << x.size() << endl; + cerr << "Number of training examples: " << training.size() << endl; + cerr << "Number of testing examples: " << testing.size() << endl; double tppl = 0.0; vector > sp; vector smoothed; @@ -255,11 +254,12 @@ int main(int argc, char** argv) { } } sigsq = sp[best_i].first; - tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as(), &x); - } + } // tune regularizer + tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as(), &x); if (conf.count("weights")) { - for (int i = 1; i < x.size(); ++i) - x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi); + for (int i = 1; i < x.size(); ++i) { + x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi); + } } cout.precision(15); cout << "# sigma^2=" << sigsq << "\theld out perplexity="; -- cgit v1.2.3 From fc5c72f9c5ce60c5d9a3dcd363eb51ccdd543bc9 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Wed, 14 Sep 2011 14:43:03 +0100 Subject: fix for potential segv with no weights --- pro-train/mr_pro_reduce.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index e71347ba..6b491918 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -100,7 +100,7 @@ double TrainingInference(const vector& x, vector* g = NULL) { double cll = 0; for (int i = 0; i < corpus.size(); ++i) { - const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias + const double dotprod = corpus[i].second.dot(x) + (x.size() ? x[0] : weight_t()); // x[0] is bias double lp_false = dotprod; double lp_true = -dotprod; if (0 < lp_true) { -- cgit v1.2.3 From 9acb1f98b698f9fd0c09f6b7c122011651dcc435 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Wed, 14 Sep 2011 14:47:06 +0100 Subject: fix for more problems caused by hash refactoring --- pro-train/mr_pro_reduce.cc | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'pro-train/mr_pro_reduce.cc') diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index 6b491918..aff410a0 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -218,6 +218,10 @@ int main(int argc, char** argv) { vector x, prev_x; // x[0] is bias if (conf.count("weights")) { Weights::InitFromFile(conf["weights"].as(), &x); + x.resize(FD::NumFeats()); + prev_x = x; + } else { + x.resize(FD::NumFeats()); prev_x = x; } cerr << " Number of features: " << x.size() << endl; -- cgit v1.2.3