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// Copyright 2016 Google Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.!
#include <map>
#include "filesystem.h"
#include "init.h"
#include "sentencepiece_model.pb.h"
#include "sentencepiece_trainer.h"
#include "third_party/absl/flags/flag.h"
#include "third_party/absl/strings/ascii.h"
#include "third_party/absl/strings/str_join.h"
#include "third_party/absl/strings/str_split.h"
#include "util.h"
using sentencepiece::NormalizerSpec;
using sentencepiece::TrainerSpec;
namespace {
static sentencepiece::TrainerSpec kDefaultTrainerSpec;
static sentencepiece::NormalizerSpec kDefaultNormalizerSpec;
} // namespace
ABSL_FLAG(std::string, input, "", "comma separated list of input sentences");
ABSL_FLAG(std::string, input_format, kDefaultTrainerSpec.input_format(),
"Input format. Supported format is `text` or `tsv`.");
ABSL_FLAG(std::string, model_prefix, "", "output model prefix");
ABSL_FLAG(std::string, model_type, "unigram",
"model algorithm: unigram, bpe, word or char");
ABSL_FLAG(int32, vocab_size, kDefaultTrainerSpec.vocab_size(),
"vocabulary size");
ABSL_FLAG(std::string, accept_language, "",
"comma-separated list of languages this model can accept");
ABSL_FLAG(int32, self_test_sample_size,
kDefaultTrainerSpec.self_test_sample_size(),
"the size of self test samples");
ABSL_FLAG(double, character_coverage, kDefaultTrainerSpec.character_coverage(),
"character coverage to determine the minimum symbols");
ABSL_FLAG(int32, input_sentence_size, kDefaultTrainerSpec.input_sentence_size(),
"maximum size of sentences the trainer loads");
ABSL_FLAG(bool, shuffle_input_sentence,
kDefaultTrainerSpec.shuffle_input_sentence(),
"Randomly sample input sentences in advance. Valid when "
"--input_sentence_size > 0");
ABSL_FLAG(int32, seed_sentencepiece_size,
kDefaultTrainerSpec.seed_sentencepiece_size(),
"the size of seed sentencepieces");
ABSL_FLAG(double, shrinking_factor, kDefaultTrainerSpec.shrinking_factor(),
"Keeps top shrinking_factor pieces with respect to the loss");
ABSL_FLAG(int32, num_threads, kDefaultTrainerSpec.num_threads(),
"number of threads for training");
ABSL_FLAG(int32, num_sub_iterations, kDefaultTrainerSpec.num_sub_iterations(),
"number of EM sub-iterations");
ABSL_FLAG(int32, max_sentencepiece_length,
kDefaultTrainerSpec.max_sentencepiece_length(),
"maximum length of sentence piece");
ABSL_FLAG(int32, max_sentence_length, kDefaultTrainerSpec.max_sentence_length(),
"maximum length of sentence in byte");
ABSL_FLAG(bool, split_by_unicode_script,
kDefaultTrainerSpec.split_by_unicode_script(),
"use Unicode script to split sentence pieces");
ABSL_FLAG(bool, split_by_number, kDefaultTrainerSpec.split_by_number(),
"split tokens by numbers (0-9)");
ABSL_FLAG(bool, split_by_whitespace, kDefaultTrainerSpec.split_by_whitespace(),
"use a white space to split sentence pieces");
ABSL_FLAG(bool, split_digits, kDefaultTrainerSpec.split_digits(),
"split all digits (0-9) into separate pieces");
ABSL_FLAG(bool, treat_whitespace_as_suffix,
kDefaultTrainerSpec.treat_whitespace_as_suffix(),
"treat whitespace marker as suffix instead of prefix.");
ABSL_FLAG(std::string, control_symbols, "",
"comma separated list of control symbols");
ABSL_FLAG(std::string, control_symbols_file, "",
"load control_symbols from file.");
ABSL_FLAG(std::string, user_defined_symbols, "",
"comma separated list of user defined symbols");
ABSL_FLAG(std::string, user_defined_symbols_file, "",
"load user_defined_symbols from file.");
ABSL_FLAG(std::string, required_chars, "",
"UTF8 characters in this flag are always used in the character "
"set regardless of --character_coverage");
ABSL_FLAG(std::string, required_chars_file, "",
"load required_chars from file.");
ABSL_FLAG(bool, byte_fallback, kDefaultTrainerSpec.byte_fallback(),
"decompose unknown pieces into UTF-8 byte pieces");
ABSL_FLAG(bool, vocabulary_output_piece_score,
kDefaultTrainerSpec.vocabulary_output_piece_score(),
"Define score in vocab file");
ABSL_FLAG(std::string, normalization_rule_name, "nmt_nfkc",
"Normalization rule name. "
"Choose from nfkc or identity");
ABSL_FLAG(std::string, normalization_rule_tsv, "",
"Normalization rule TSV file. ");
ABSL_FLAG(std::string, denormalization_rule_tsv, "",
"Denormalization rule TSV file.");
ABSL_FLAG(bool, add_dummy_prefix, kDefaultNormalizerSpec.add_dummy_prefix(),
"Add dummy whitespace at the beginning of text");
ABSL_FLAG(bool, remove_extra_whitespaces,
kDefaultNormalizerSpec.remove_extra_whitespaces(),
"Removes leading, trailing, and "
"duplicate internal whitespace");
ABSL_FLAG(bool, hard_vocab_limit, kDefaultTrainerSpec.hard_vocab_limit(),
"If set to false, --vocab_size is considered as a soft limit.");
ABSL_FLAG(bool, use_all_vocab, kDefaultTrainerSpec.use_all_vocab(),
"If set to true, use all tokens as vocab. "
"Valid for word/char models.");
ABSL_FLAG(int32, unk_id, kDefaultTrainerSpec.unk_id(),
"Override UNK (<unk>) id.");
ABSL_FLAG(int32, bos_id, kDefaultTrainerSpec.bos_id(),
"Override BOS (<s>) id. Set -1 to disable BOS.");
ABSL_FLAG(int32, eos_id, kDefaultTrainerSpec.eos_id(),
"Override EOS (</s>) id. Set -1 to disable EOS.");
ABSL_FLAG(int32, pad_id, kDefaultTrainerSpec.pad_id(),
"Override PAD (<pad>) id. Set -1 to disable PAD.");
ABSL_FLAG(std::string, unk_piece, kDefaultTrainerSpec.unk_piece(),
"Override UNK (<unk>) piece.");
ABSL_FLAG(std::string, bos_piece, kDefaultTrainerSpec.bos_piece(),
"Override BOS (<s>) piece.");
ABSL_FLAG(std::string, eos_piece, kDefaultTrainerSpec.eos_piece(),
"Override EOS (</s>) piece.");
ABSL_FLAG(std::string, pad_piece, kDefaultTrainerSpec.pad_piece(),
"Override PAD (<pad>) piece.");
ABSL_FLAG(std::string, unk_surface, kDefaultTrainerSpec.unk_surface(),
"Dummy surface string for <unk>. In decoding <unk> is decoded to "
"`unk_surface`.");
ABSL_FLAG(bool, train_extremely_large_corpus,
kDefaultTrainerSpec.train_extremely_large_corpus(),
"Increase bit depth for unigram tokenization.");
int main(int argc, char *argv[]) {
sentencepiece::ParseCommandLineFlags(argv[0], &argc, &argv, true);
sentencepiece::TrainerSpec trainer_spec;
sentencepiece::NormalizerSpec normalizer_spec;
NormalizerSpec denormalizer_spec;
CHECK(!absl::GetFlag(FLAGS_input).empty());
CHECK(!absl::GetFlag(FLAGS_model_prefix).empty());
auto load_lines = [](absl::string_view filename) {
std::vector<std::string> lines;
auto input = sentencepiece::filesystem::NewReadableFile(filename);
CHECK_OK(input->status());
std::string line;
while (input->ReadLine(&line)) lines.emplace_back(line);
return lines;
};
// Populates the value from flags to spec.
#define SetTrainerSpecFromFlag(name) \
trainer_spec.set_##name(absl::GetFlag(FLAGS_##name));
#define SetNormalizerSpecFromFlag(name) \
normalizer_spec.set_##name(absl::GetFlag(FLAGS_##name));
#define SetTrainerSpecFromFile(name) \
if (!absl::GetFlag(FLAGS_##name##_file).empty()) { \
const auto lines = load_lines(absl::GetFlag(FLAGS_##name##_file)); \
trainer_spec.set_##name(absl::StrJoin(lines, "")); \
}
#define SetRepeatedTrainerSpecFromFlag(name) \
if (!absl::GetFlag(FLAGS_##name).empty()) { \
for (const auto &v : \
sentencepiece::util::StrSplitAsCSV(absl::GetFlag(FLAGS_##name))) { \
trainer_spec.add_##name(v); \
} \
}
#define SetRepeatedTrainerSpecFromFile(name) \
if (!absl::GetFlag(FLAGS_##name##_file).empty()) { \
for (const auto &v : load_lines(absl::GetFlag(FLAGS_##name##_file))) { \
trainer_spec.add_##name(v); \
} \
}
SetRepeatedTrainerSpecFromFlag(input);
SetTrainerSpecFromFlag(input_format);
SetTrainerSpecFromFlag(model_prefix);
SetTrainerSpecFromFlag(vocab_size);
SetTrainerSpecFromFlag(self_test_sample_size);
SetTrainerSpecFromFlag(character_coverage);
SetTrainerSpecFromFlag(input_sentence_size);
SetTrainerSpecFromFlag(shuffle_input_sentence);
SetTrainerSpecFromFlag(seed_sentencepiece_size);
SetTrainerSpecFromFlag(shrinking_factor);
SetTrainerSpecFromFlag(num_threads);
SetTrainerSpecFromFlag(num_sub_iterations);
SetTrainerSpecFromFlag(max_sentencepiece_length);
SetTrainerSpecFromFlag(max_sentence_length);
SetTrainerSpecFromFlag(split_by_unicode_script);
SetTrainerSpecFromFlag(split_by_whitespace);
SetTrainerSpecFromFlag(split_by_number);
SetTrainerSpecFromFlag(split_digits);
SetTrainerSpecFromFlag(byte_fallback);
SetTrainerSpecFromFlag(treat_whitespace_as_suffix);
SetTrainerSpecFromFlag(hard_vocab_limit);
SetTrainerSpecFromFlag(use_all_vocab);
SetTrainerSpecFromFlag(unk_id);
SetTrainerSpecFromFlag(bos_id);
SetTrainerSpecFromFlag(eos_id);
SetTrainerSpecFromFlag(pad_id);
SetTrainerSpecFromFlag(unk_piece);
SetTrainerSpecFromFlag(bos_piece);
SetTrainerSpecFromFlag(eos_piece);
SetTrainerSpecFromFlag(pad_piece);
SetTrainerSpecFromFlag(unk_surface);
SetTrainerSpecFromFlag(required_chars);
SetTrainerSpecFromFile(required_chars);
SetTrainerSpecFromFlag(vocabulary_output_piece_score);
SetRepeatedTrainerSpecFromFlag(accept_language);
SetRepeatedTrainerSpecFromFlag(control_symbols);
SetRepeatedTrainerSpecFromFlag(user_defined_symbols);
SetTrainerSpecFromFlag(train_extremely_large_corpus);
SetRepeatedTrainerSpecFromFile(control_symbols);
SetRepeatedTrainerSpecFromFile(user_defined_symbols);
normalizer_spec.set_name(absl::GetFlag(FLAGS_normalization_rule_name));
SetNormalizerSpecFromFlag(normalization_rule_tsv);
SetNormalizerSpecFromFlag(add_dummy_prefix);
SetNormalizerSpecFromFlag(remove_extra_whitespaces);
if (!absl::GetFlag(FLAGS_denormalization_rule_tsv).empty()) {
denormalizer_spec.set_normalization_rule_tsv(
absl::GetFlag(FLAGS_denormalization_rule_tsv));
denormalizer_spec.set_add_dummy_prefix(false);
denormalizer_spec.set_remove_extra_whitespaces(false);
denormalizer_spec.set_escape_whitespaces(false);
}
CHECK_OK(sentencepiece::SentencePieceTrainer::PopulateModelTypeFromString(
absl::GetFlag(FLAGS_model_type), &trainer_spec));
CHECK_OK(sentencepiece::SentencePieceTrainer::Train(
trainer_spec, normalizer_spec, denormalizer_spec));
return 0;
}
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