lmplz_main.cc
#include "lm/builder/output.hh"
#include "lm/builder/pipeline.hh"
#include "lm/common/size_option.hh"
#include "lm/lm_exception.hh"
#include "util/file.hh"
#include "util/file_piece.hh"
#include "util/usage.hh"
#include <iostream>
#include <boost/program_options.hpp>
#include <boost/version.hpp>
#include <vector>
namespace {
// Parse and validate pruning thresholds then return vector of threshold counts
// for each n-grams order.
std::vector<uint64_t> ParsePruning(const std::vector<std::string> ¶m, std::size_t order) {
// convert to vector of integers
std::vector<uint64_t> prune_thresholds;
prune_thresholds.reserve(order);
for (std::vector<std::string>::const_iterator it(param.begin()); it != param.end(); ++it) {
try {
prune_thresholds.push_back(boost::lexical_cast<uint64_t>(*it));
} catch(const boost::bad_lexical_cast &) {
UTIL_THROW(util::Exception, "Bad pruning threshold " << *it);
}
}
// Fill with zeros by default.
if (prune_thresholds.empty()) {
prune_thresholds.resize(order, 0);
return prune_thresholds;
}
// validate pruning threshold if specified
// throw if each n-gram order has not threshold specified
UTIL_THROW_IF(prune_thresholds.size() > order, util::Exception, "You specified pruning thresholds for orders 1 through " << prune_thresholds.size() << " but the model only has order " << order);
// threshold for unigram can only be 0 (no pruning)
// check if threshold are not in decreasing order
uint64_t lower_threshold = 0;
for (std::vector<uint64_t>::iterator it = prune_thresholds.begin(); it != prune_thresholds.end(); ++it) {
UTIL_THROW_IF(lower_threshold > *it, util::Exception, "Pruning thresholds should be in non-decreasing order. Otherwise substrings would be removed, which is bad for query-time data structures.");
lower_threshold = *it;
}
// Pad to all orders using the last value.
prune_thresholds.resize(order, prune_thresholds.back());
return prune_thresholds;
}
lm::builder::Discount ParseDiscountFallback(const std::vector<std::string> ¶m) {
lm::builder::Discount ret;
UTIL_THROW_IF(param.size() > 3, util::Exception, "Specify at most three fallback discounts: 1, 2, and 3+");
UTIL_THROW_IF(param.empty(), util::Exception, "Fallback discounting enabled, but no discount specified");
ret.amount[0] = 0.0;
for (unsigned i = 0; i < 3; ++i) {
float discount = boost::lexical_cast<float>(param[i < param.size() ? i : (param.size() - 1)]);
UTIL_THROW_IF(discount < 0.0 || discount > static_cast<float>(i+1), util::Exception, "The discount for count " << (i+1) << " was parsed as " << discount << " which is not in the range [0, " << (i+1) << "].");
ret.amount[i + 1] = discount;
}
return ret;
}
} // namespace
int main(int argc, char *argv[]) {
try {
namespace po = boost::program_options;
po::options_description options("Language model building options");
lm::builder::PipelineConfig pipeline;
std::string text, intermediate, arpa;
std::vector<std::string> pruning;
std::vector<std::string> discount_fallback;
std::vector<std::string> discount_fallback_default;
discount_fallback_default.push_back("0.5");
discount_fallback_default.push_back("1");
discount_fallback_default.push_back("1.5");
bool verbose_header;
options.add_options()
("help,h", po::bool_switch(), "Show this help message")
("order,o", po::value<std::size_t>(&pipeline.order)
#if BOOST_VERSION >= 104200
->required()
#endif
, "Order of the model")
("interpolate_unigrams", po::value<bool>(&pipeline.initial_probs.interpolate_unigrams)->default_value(true)->implicit_value(true), "Interpolate the unigrams (default) as opposed to giving lots of mass to <unk> like SRI. If you want SRI's behavior with a large <unk> and the old lmplz default, use --interpolate_unigrams 0.")
("skip_symbols", po::bool_switch(), "Treat <s>, </s>, and <unk> as whitespace instead of throwing an exception")
("temp_prefix,T", po::value<std::string>(&pipeline.sort.temp_prefix)->default_value("/tmp/lm"), "Temporary file prefix")
("memory,S", lm:: SizeOption(pipeline.sort.total_memory, util::GuessPhysicalMemory() ? "80%" : "1G"), "Sorting memory")
("minimum_block", lm::SizeOption(pipeline.minimum_block, "8K"), "Minimum block size to allow")
("sort_block", lm::SizeOption(pipeline.sort.buffer_size, "64M"), "Size of IO operations for sort (determines arity)")
("block_count", po::value<std::size_t>(&pipeline.block_count)->default_value(2), "Block count (per order)")
("vocab_estimate", po::value<lm::WordIndex>(&pipeline.vocab_estimate)->default_value(1000000), "Assume this vocabulary size for purposes of calculating memory in step 1 (corpus count) and pre-sizing the hash table")
("vocab_pad", po::value<uint64_t>(&pipeline.vocab_size_for_unk)->default_value(0), "If the vocabulary is smaller than this value, pad with <unk> to reach this size. Requires --interpolate_unigrams")
("verbose_header", po::bool_switch(&verbose_header), "Add a verbose header to the ARPA file that includes information such as token count, smoothing type, etc.")
("text", po::value<std::string>(&text), "Read text from a file instead of stdin")
("arpa", po::value<std::string>(&arpa), "Write ARPA to a file instead of stdout")
("intermediate", po::value<std::string>(&intermediate), "Write ngrams to intermediate files. Turns off ARPA output (which can be reactivated by --arpa file). Forces --renumber on.")
("renumber", po::bool_switch(&pipeline.renumber_vocabulary), "Rrenumber the vocabulary identifiers so that they are monotone with the hash of each string. This is consistent with the ordering used by the trie data structure.")
("collapse_values", po::bool_switch(&pipeline.output_q), "Collapse probability and backoff into a single value, q that yields the same sentence-level probabilities. See http://kheafield.com/professional/edinburgh/rest_paper.pdf for more details, including a proof.")
("prune", po::value<std::vector<std::string> >(&pruning)->multitoken(), "Prune n-grams with count less than or equal to the given threshold. Specify one value for each order i.e. 0 0 1 to prune singleton trigrams and above. The sequence of values must be non-decreasing and the last value applies to any remaining orders. Default is to not prune, which is equivalent to --prune 0.")
("limit_vocab_file", po::value<std::string>(&pipeline.prune_vocab_file)->default_value(""), "Read allowed vocabulary separated by whitespace. N-grams that contain vocabulary items not in this list will be pruned. Can be combined with --prune arg")
("discount_fallback", po::value<std::vector<std::string> >(&discount_fallback)->multitoken()->implicit_value(discount_fallback_default, "0.5 1 1.5"), "The closed-form estimate for Kneser-Ney discounts does not work without singletons or doubletons. It can also fail if these values are out of range. This option falls back to user-specified discounts when the closed-form estimate fails. Note that this option is generally a bad idea: you should deduplicate your corpus instead. However, class-based models need custom discounts because they lack singleton unigrams. Provide up to three discounts (for adjusted counts 1, 2, and 3+), which will be applied to all orders where the closed-form estimates fail.");
po::variables_map vm;
po::store(po::parse_command_line(argc, argv, options), vm);
if (argc == 1 || vm["help"].as<bool>()) {
std::cerr <<
"Builds unpruned language models with modified Kneser-Ney smoothing.\n\n"
"Please cite:\n"
"@inproceedings{Heafield-estimate,\n"
" author = {Kenneth Heafield and Ivan Pouzyrevsky and Jonathan H. Clark and Philipp Koehn},\n"
" title = {Scalable Modified {Kneser-Ney} Language Model Estimation},\n"
" year = {2013},\n"
" month = {8},\n"
" booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics},\n"
" address = {Sofia, Bulgaria},\n"
" url = {http://kheafield.com/professional/edinburgh/estimate\\_paper.pdf},\n"
"}\n\n"
"Provide the corpus on stdin. The ARPA file will be written to stdout. Order of\n"
"the model (-o) is the only mandatory option. As this is an on-disk program,\n"
"setting the temporary file location (-T) and sorting memory (-S) is recommended.\n\n"
"Memory sizes are specified like GNU sort: a number followed by a unit character.\n"
"Valid units are \% for percentage of memory (supported platforms only) and (in\n"
"increasing powers of 1024): b, K, M, G, T, P, E, Z, Y. Default is K (*1024).\n";
uint64_t mem = util::GuessPhysicalMemory();
if (mem) {
std::cerr << "This machine has " << mem << " bytes of memory.\n\n";
} else {
std::cerr << "Unable to determine the amount of memory on this machine.\n\n";
}
std::cerr << options << std::endl;
return 1;
}
po::notify(vm);
// required() appeared in Boost 1.42.0.
#if BOOST_VERSION < 104200
if (!vm.count("order")) {
std::cerr << "the option '--order' is required but missing" << std::endl;
return 1;
}
#endif
if (pipeline.vocab_size_for_unk && !pipeline.initial_probs.interpolate_unigrams) {
std::cerr << "--vocab_pad requires --interpolate_unigrams be on" << std::endl;
return 1;
}
if (vm["skip_symbols"].as<bool>()) {
pipeline.disallowed_symbol_action = lm::COMPLAIN;
} else {
pipeline.disallowed_symbol_action = lm::THROW_UP;
}
if (vm.count("discount_fallback")) {
pipeline.discount.fallback = ParseDiscountFallback(discount_fallback);
pipeline.discount.bad_action = lm::COMPLAIN;
} else {
// Unused, just here to prevent the compiler from complaining about uninitialized.
pipeline.discount.fallback = lm::builder::Discount();
pipeline.discount.bad_action = lm::THROW_UP;
}
// parse pruning thresholds. These depend on order, so it is not done as a notifier.
pipeline.prune_thresholds = ParsePruning(pruning, pipeline.order);
if (!vm["limit_vocab_file"].as<std::string>().empty()) {
pipeline.prune_vocab = true;
}
else {
pipeline.prune_vocab = false;
}
util::NormalizeTempPrefix(pipeline.sort.temp_prefix);
lm::builder::InitialProbabilitiesConfig &initial = pipeline.initial_probs;
// TODO: evaluate options for these.
initial.adder_in.total_memory = 32768;
initial.adder_in.block_count = 2;
initial.adder_out.total_memory = 32768;
initial.adder_out.block_count = 2;
pipeline.read_backoffs = initial.adder_out;
// Read from stdin, write to stdout by default
util::scoped_fd in(0), out(1);
if (vm.count("text")) {
in.reset(util::OpenReadOrThrow(text.c_str()));
}
if (vm.count("arpa")) {
out.reset(util::CreateOrThrow(arpa.c_str()));
}
try {
bool writing_intermediate = vm.count("intermediate");
if (writing_intermediate) {
pipeline.renumber_vocabulary = true;
}
lm::builder::Output output(writing_intermediate ? intermediate : pipeline.sort.temp_prefix, writing_intermediate, pipeline.output_q);
if (!writing_intermediate || vm.count("arpa")) {
output.Add(new lm::builder::PrintHook(out.release(), verbose_header));
}
lm::builder::Pipeline(pipeline, in.release(), output);
} catch (const util::MallocException &e) {
std::cerr << e.what() << std::endl;
std::cerr << "Try rerunning with a more conservative -S setting than " << vm["memory"].as<std::string>() << std::endl;
return 1;
}
util::PrintUsage(std::cerr);
} catch (const std::exception &e) {
std::cerr << e.what() << std::endl;
return 1;
}
}