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#ifndef LM_BINARY_FORMAT__
#define LM_BINARY_FORMAT__
#include "lm/config.hh"
#include "lm/read_arpa.hh"
#include "util/file_piece.hh"
#include "util/mmap.hh"
#include "util/scoped.hh"
#include <cstddef>
#include <vector>
#include <inttypes.h>
namespace lm {
namespace ngram {
/* Not the best numbering system, but it grew this way for historical reasons
* and I want to preserve existing binary files. */
typedef enum {HASH_PROBING=0, HASH_SORTED=1, TRIE_SORTED=2, QUANT_TRIE_SORTED=3, ARRAY_TRIE_SORTED=4, QUANT_ARRAY_TRIE_SORTED=5} ModelType;
const static ModelType kQuantAdd = static_cast<ModelType>(QUANT_TRIE_SORTED - TRIE_SORTED);
const static ModelType kArrayAdd = static_cast<ModelType>(ARRAY_TRIE_SORTED - TRIE_SORTED);
/*Inspect a file to determine if it is a binary lm. If not, return false.
* If so, return true and set recognized to the type. This is the only API in
* this header designed for use by decoder authors.
*/
bool RecognizeBinary(const char *file, ModelType &recognized);
struct FixedWidthParameters {
unsigned char order;
float probing_multiplier;
// What type of model is this?
ModelType model_type;
// Does the end of the file have the actual strings in the vocabulary?
bool has_vocabulary;
};
// Parameters stored in the header of a binary file.
struct Parameters {
FixedWidthParameters fixed;
std::vector<uint64_t> counts;
};
struct Backing {
// File behind memory, if any.
util::scoped_fd file;
// Vocabulary lookup table. Not to be confused with the vocab words themselves.
util::scoped_memory vocab;
// Raw block of memory backing the language model data structures
util::scoped_memory search;
};
void SeekOrThrow(int fd, off_t off);
// Seek forward
void AdvanceOrThrow(int fd, off_t off);
// Create just enough of a binary file to write vocabulary to it.
uint8_t *SetupJustVocab(const Config &config, uint8_t order, std::size_t memory_size, Backing &backing);
// Grow the binary file for the search data structure and set backing.search, returning the memory address where the search data structure should begin.
uint8_t *GrowForSearch(const Config &config, std::size_t vocab_pad, std::size_t memory_size, Backing &backing);
// Write header to binary file. This is done last to prevent incomplete files
// from loading.
void FinishFile(const Config &config, ModelType model_type, const std::vector<uint64_t> &counts, Backing &backing);
namespace detail {
bool IsBinaryFormat(int fd);
void ReadHeader(int fd, Parameters ¶ms);
void MatchCheck(ModelType model_type, const Parameters ¶ms);
void SeekPastHeader(int fd, const Parameters ¶ms);
uint8_t *SetupBinary(const Config &config, const Parameters ¶ms, std::size_t memory_size, Backing &backing);
void ComplainAboutARPA(const Config &config, ModelType model_type);
} // namespace detail
template <class To> void LoadLM(const char *file, const Config &config, To &to) {
Backing &backing = to.MutableBacking();
backing.file.reset(util::OpenReadOrThrow(file));
try {
if (detail::IsBinaryFormat(backing.file.get())) {
Parameters params;
detail::ReadHeader(backing.file.get(), params);
detail::MatchCheck(To::kModelType, params);
// Replace the run-time configured probing_multiplier with the one in the file.
Config new_config(config);
new_config.probing_multiplier = params.fixed.probing_multiplier;
detail::SeekPastHeader(backing.file.get(), params);
To::UpdateConfigFromBinary(backing.file.get(), params.counts, new_config);
std::size_t memory_size = To::Size(params.counts, new_config);
uint8_t *start = detail::SetupBinary(new_config, params, memory_size, backing);
to.InitializeFromBinary(start, params, new_config, backing.file.get());
} else {
detail::ComplainAboutARPA(config, To::kModelType);
to.InitializeFromARPA(file, config);
}
} catch (util::Exception &e) {
e << " File: " << file;
throw;
}
}
} // namespace ngram
} // namespace lm
#endif // LM_BINARY_FORMAT__
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