rnnlm-example.cc
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// rnnlm/rnnlm-example.cc
// Copyright 2017 Daniel Povey
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include <numeric>
#include "rnnlm/rnnlm-example.h"
namespace kaldi {
namespace rnnlm {
void RnnlmExample::Write(std::ostream &os, bool binary) const {
WriteToken(os, binary, "<RnnlmExample>");
WriteToken(os, binary, "<VocabSize>");
WriteBasicType(os, binary, vocab_size);
WriteToken(os, binary, "<NumChunks>");
WriteBasicType(os, binary, num_chunks);
WriteToken(os, binary, "<ChunkLength>");
WriteBasicType(os, binary, chunk_length);
WriteToken(os, binary, "<SampleGroupSize>");
WriteBasicType(os, binary, sample_group_size);
WriteToken(os, binary, "<NumSamples>");
WriteBasicType(os, binary, num_samples);
WriteToken(os, binary, "<InputWords>");
WriteIntegerVector(os, binary, input_words);
WriteToken(os, binary, "<OutputWords>");
WriteIntegerVector(os, binary, output_words);
WriteToken(os, binary, "<OutputWeights>");
output_weights.Write(os, binary);
WriteToken(os, binary, "<SampledWords>");
WriteIntegerVector(os, binary, sampled_words);
WriteToken(os, binary, "<SampleInvProbs>");
sample_inv_probs.Write(os, binary);
WriteToken(os, binary, "</RnnlmExample>");
}
void RnnlmExample::Read(std::istream &is, bool binary) {
ExpectToken(is, binary, "<RnnlmExample>");
ExpectToken(is, binary, "<VocabSize>");
ReadBasicType(is, binary, &vocab_size);
ExpectToken(is, binary, "<NumChunks>");
ReadBasicType(is, binary, &num_chunks);
ExpectToken(is, binary, "<ChunkLength>");
ReadBasicType(is, binary, &chunk_length);
ExpectToken(is, binary, "<SampleGroupSize>");
ReadBasicType(is, binary, &sample_group_size);
ExpectToken(is, binary, "<NumSamples>");
ReadBasicType(is, binary, &num_samples);
ExpectToken(is, binary, "<InputWords>");
ReadIntegerVector(is, binary, &input_words);
ExpectToken(is, binary, "<OutputWords>");
ReadIntegerVector(is, binary, &output_words);
ExpectToken(is, binary, "<OutputWeights>");
output_weights.Read(is, binary);
ExpectToken(is, binary, "<SampledWords>");
ReadIntegerVector(is, binary, &sampled_words);
ExpectToken(is, binary, "<SampleInvProbs>");
sample_inv_probs.Read(is, binary);
ExpectToken(is, binary, "</RnnlmExample>");
}
RnnlmExampleSampler::RnnlmExampleSampler(
const RnnlmEgsConfig &config, const SamplingLm &arpa_sampling):
config_(config), arpa_sampling_(arpa_sampling) {
config_.Check();
// The unigram distribution from the LM, modified according to
// config_.special_symbol_prob and config_.uniform_prob_mass...
std::vector<BaseFloat> unigram_distribution =
arpa_sampling.GetUnigramDistribution();
double sum = std::accumulate(unigram_distribution.begin(),
unigram_distribution.end(),
0.0);
KALDI_ASSERT(std::fabs(sum - 1.0) < 0.01 &&
"Unigram distribution from ARPA does not sum "
"to (close to) 1");
int32 num_words = unigram_distribution.size();
if (config_.uniform_prob_mass > 0.0) {
BaseFloat x = config_.uniform_prob_mass / (num_words - 1);
for (int32 i = 1; i < num_words; i++)
if (i != config_.bos_symbol && i != config_.brk_symbol)
unigram_distribution[i] += x;
}
// If these are not zero, either something is wrong with your language model
// or you supplied the wrong --bos-symbol or --brk-symbol options. We allow
// tiny values because the ARPA files sometimes give -99 as the unigram prob
// for <s>.
KALDI_ASSERT(unigram_distribution[config_.bos_symbol] < 1.0e-10);
// we don't check that the <brk> symbol has very tiny prob because
// it could have accumulated some probability mass via smoothing;
// this is harmless.
unigram_distribution[config_.bos_symbol] = config_.special_symbol_prob;
unigram_distribution[config_.brk_symbol] = config_.special_symbol_prob;
double new_sum = std::accumulate(unigram_distribution.begin(),
unigram_distribution.end(),
0.0),
scale = 1.0 / new_sum;
// rescale so it sums to almost 1; this is a requirement of the constructor
// of class Sampler.
int32 num_words_nonzero_prob = 0;
for (std::vector<BaseFloat>::iterator iter = unigram_distribution.begin(),
end = unigram_distribution.end(); iter != end; ++iter) {
if (*iter != 0.0) num_words_nonzero_prob++;
*iter *= scale;
}
if (config_.num_samples > num_words_nonzero_prob) {
KALDI_WARN << "The number of samples (--num-samples=" << config_.num_samples
<< ") exceeds the number of words with nonzero probability "
<< num_words_nonzero_prob << " -> not doing sampling. You could "
<< "skip creating the ARPA file, and not provide it, which "
<< "might save some bother.";
config_.num_samples = 0;
}
if (config_.num_samples == 0) {
sampler_ = NULL;
} else {
sampler_ = new Sampler(unigram_distribution);
}
}
void RnnlmExampleSampler::SampleForMinibatch(RnnlmExample *minibatch) const {
if (sampler_ == NULL) return; // we're not actually sampling.
KALDI_ASSERT(minibatch->chunk_length == config_.chunk_length &&
minibatch->num_chunks == config_.num_chunks_per_minibatch &&
config_.chunk_length % config_.sample_group_size == 0 &&
static_cast<int32>(minibatch->input_words.size()) ==
config_.chunk_length * config_.num_chunks_per_minibatch);
int32 num_samples = config_.num_samples,
sample_group_size = config_.sample_group_size,
chunk_length = config_.chunk_length,
num_groups = chunk_length / sample_group_size;
minibatch->num_samples = num_samples;
minibatch->sample_group_size = sample_group_size;
minibatch->sampled_words.resize(num_groups * num_samples);
minibatch->sample_inv_probs.Resize(num_groups * num_samples);
for (int32 g = 0; g < num_groups; g++) {
SampleForGroup(g, minibatch);
}
}
void RnnlmExampleSampler::SampleForGroup(int32 g,
RnnlmExample *minibatch) const {
// All words that appear on the output are required to appear in the sample. we
// need to figure what this set of words is.
int32 num_chunks_per_minibatch = config_.num_chunks_per_minibatch;
std::vector<int32> words_we_must_sample;
for (int32 t = g * config_.sample_group_size;
t < (g + 1) * config_.sample_group_size; t++) {
for (int32 n = 0; n < num_chunks_per_minibatch; n++) {
int32 i = t * num_chunks_per_minibatch + n;
int32 output_word = minibatch->output_words[i];
words_we_must_sample.push_back(output_word);
}
}
SortAndUniq(&words_we_must_sample);
// 'hist_weights' is a representation of a weighted set of histories.
std::vector<std::pair<std::vector<int32>, BaseFloat> > hist_weights;
GetHistoriesForGroup(g, *minibatch, &hist_weights);
KALDI_ASSERT(!hist_weights.empty()); // we made sure of this.
// 'higher_order_probs' and 'unigram_weight' are a compact representation of
// an (unnormalized) distribution that is the suitably weighted sum of the
// distributions that the language model predicts given the history states
// present in 'hist_weights'.
// We represent the distribution in this way, instead of just as a vector,
// so that it is efficient even when the vocabulary size is very large.
std::vector<std::pair<int32, BaseFloat> > higher_order_probs;
BaseFloat unigram_weight = arpa_sampling_.GetDistribution(hist_weights,
&higher_order_probs);
// 'sample' will be a list of pairs (integer word-id, inclusion probability).
std::vector<std::pair<int32, BaseFloat> > sample;
// the 'sampler_' object knows how to sample from an unnormalized distribution
// represented as unigram-weight and a list of higher-than-unigram (word-id,
// additional-weight) pairs.
int32 num_samples = config_.num_samples;
sampler_->SampleWords(num_samples, unigram_weight,
higher_order_probs, words_we_must_sample,
&sample);
KALDI_ASSERT(sample.size() == static_cast<size_t>(num_samples));
std::sort(sample.begin(), sample.end());
// write to the 'sampled_words' and 'sample_inv_probs' arrays.
for (int32 s = 0; s < num_samples; s++) {
int32 i = (g * num_samples) + s;
minibatch->sampled_words[i] = sample[s].first;
KALDI_ASSERT(sample[s].second > 0.0);
minibatch->sample_inv_probs(i) = 1.0 / sample[s].second;
}
RenumberOutputWordsForGroup(g, minibatch);
}
void RnnlmExampleSampler::RenumberOutputWordsForGroup(
int32 g, RnnlmExample *minibatch) const {
int32 sample_group_size = config_.sample_group_size,
num_samples = config_.num_samples,
num_chunks_per_minibatch = config_.num_chunks_per_minibatch,
num_outputs_per_group = sample_group_size * num_chunks_per_minibatch,
vocab_size = minibatch->vocab_size;
// get the range of 'sampled_words' that covers this group.
const int32 *sampled_words_ptr = &(minibatch->sampled_words[0]),
*sampled_words_begin = sampled_words_ptr + (g * num_samples),
*sampled_words_end = sampled_words_begin + num_samples;
int32 *output_words_ptr = &(minibatch->output_words[0]),
*output_words_iter = output_words_ptr + (g * num_outputs_per_group),
*output_words_end = output_words_iter + num_outputs_per_group;
for (; output_words_iter != output_words_end; ++output_words_iter) {
int32 output_word = *output_words_iter;
// note: output_word is > 0 because epsilon won't ever occur there,
// although in a sense 0 is a valid output-word id.
KALDI_ASSERT(output_word > 0 && output_word < vocab_size);
const int32 *sampled_words_ptr = std::lower_bound(sampled_words_begin,
sampled_words_end,
output_word);
if (*sampled_words_ptr != output_word) {
KALDI_ERR << "Output word not found in samples (indicates code error)";
}
int32 renumbered_output_word = sampled_words_ptr - sampled_words_begin;
*output_words_iter = renumbered_output_word;
}
}
void RnnlmExampleSampler::GetHistoriesForGroup(
int32 g, const RnnlmExample &minibatch,
std::vector<std::pair<std::vector<int32>, BaseFloat> > *hist_weights) const {
// initially store as an unordered_map so we can remove duplicates.
// hist_to_weight maps from the history to the (unnormalized) weight for that
// history. It represents a weighted combination of history-states that we
// will get a distribution for (from the ARPA LM) and sample from.
std::unordered_map<std::vector<int32>, BaseFloat, VectorHasher<int32> > hist_to_weight;
hist_weights->clear();
KALDI_ASSERT(arpa_sampling_.Order() > 0);
int32 max_history_length = arpa_sampling_.Order() - 1,
num_chunks_per_minibatch = config_.num_chunks_per_minibatch;
// This block sets up the 'hist_to_weight' map. Note: sample_group_size
// will normally be small, like 1, 2 or 4.
for (int32 t = g * config_.sample_group_size;
t < (g + 1) * config_.sample_group_size; t++) {
for (int32 n = 0; n < num_chunks_per_minibatch; n++) {
int32 i = t * num_chunks_per_minibatch + n;
BaseFloat this_weight = minibatch.output_weights(i);
KALDI_ASSERT(this_weight >= 0);
if (this_weight == 0.0)
continue;
std::vector<int32> history;
GetHistory(t, n, minibatch, max_history_length, &history);
// note: if the key did not exist in the map, it is as
// if the value were zero, see here:
// https://stackoverflow.com/questions/8943261/stdunordered-map-initialization
// .. this is at least since C++11, maybe since C++03.
hist_to_weight[history] += this_weight;
}
}
if (hist_to_weight.empty()) {
KALDI_WARN << "No histories seen (we don't expect to see this very often)";
std::vector<int32> empty_history;
hist_to_weight[empty_history] = 1.0;
}
std::unordered_map<std::vector<int32>, BaseFloat, VectorHasher<int32> >::const_iterator
iter = hist_to_weight.begin(), end = hist_to_weight.end();
hist_weights->reserve(hist_to_weight.size());
for (; iter != end; ++iter)
hist_weights->push_back(std::pair<std::vector<int32>, BaseFloat>(
iter->first, iter->second));
}
void RnnlmExampleSampler::GetHistory(
int32 t, int32 n,
const RnnlmExample &minibatch,
int32 max_history_length,
std::vector<int32> *history) const {
history->reserve(max_history_length);
history->clear();
int32 num_chunks_per_minibatch = config_.num_chunks_per_minibatch;
// e.g. if 'max_history_length' is 2, we iterate over t_step = [0, -1].
// you'll notice that the first history-position we look for when
// predicting position 't' is 'hist_t = t + 0 = t'. This may be
// surprising-- you might be expecting that t-1 would be the first
// position we'd look at-- but notice that we're looking at the
// input word, not the output word.
for (int32 t_step = 0; t_step > -max_history_length; t_step--) {
int32 hist_t = t + t_step;
KALDI_ASSERT(hist_t >= 0); // .. or we should have done 'break' below
// before reaching this value of t_step. If
// this assert fails it means that a minibatch
// doesn't start with input_word equal to
// bos_symbol or brk_symbol, which is a bug.
int32 i = hist_t * num_chunks_per_minibatch + n,
history_word = minibatch.input_words[i];
history->push_back(history_word);
if (history_word == config_.bos_symbol ||
history_word == config_.brk_symbol)
break;
}
// we want the most recent word to be the last word in 'history', so the order
// needs to be reversed.
std::reverse(history->begin(), history->end());
}
void RnnlmExampleCreator::AcceptSequence(
BaseFloat weight, const std::vector<int32> &words) {
CheckSequence(weight, words);
SplitSequenceIntoChunks(weight, words);
num_sequences_processed_++;
while (chunks_.size() > static_cast<size_t>(config_.chunk_buffer_size)) {
if (!ProcessOneMinibatch())
break;
}
}
RnnlmExampleCreator::~RnnlmExampleCreator() {
Flush();
BaseFloat words_per_chunk = num_words_processed_ * 1.0 /
num_chunks_processed_,
chunks_per_minibatch = num_chunks_processed_ * 1.0 /
num_minibatches_written_;
KALDI_LOG << "Combined " << num_sequences_processed_ << "/"
<< num_chunks_processed_
<< " sequences/chunks into " << num_minibatches_written_
<< " minibatches (" << chunks_.size()
<< " chunks left over)";
KALDI_LOG << "Overall there were "
<< words_per_chunk << " words per chunk; "
<< chunks_per_minibatch << " chunks per minibatch.";
for (size_t i = 0; i < chunks_.size(); i++)
delete chunks_[i];
}
RnnlmExampleCreator::SingleMinibatchCreator::SingleMinibatchCreator(
const RnnlmEgsConfig &config):
config_(config),
eg_chunks_(config_.num_chunks_per_minibatch) {
for (int32 i = 0; i < config_.num_chunks_per_minibatch; i++)
empty_eg_chunks_.push_back(i);
}
bool RnnlmExampleCreator::SingleMinibatchCreator::AcceptChunk(
RnnlmExampleCreator::SequenceChunk *chunk) {
int32 chunk_len = chunk->Length();
if (chunk_len == config_.chunk_length) { // maximum-sized chunk.
if (empty_eg_chunks_.empty()) {
return false;
} else {
int32 i = empty_eg_chunks_.back();
KALDI_ASSERT(size_t(i) < eg_chunks_.size() && eg_chunks_[i].empty());
eg_chunks_[i].push_back(chunk);
empty_eg_chunks_.pop_back();
return true;
}
} else { // smaller-sized chunk than maximum chunk size.
KALDI_ASSERT(chunk_len < config_.chunk_length);
// Find the index best_i into partial_eg_chunks_, such
// that partial_eg_chunks_[best_i] is a pair (best_j,
// best_space_left) such that space_left >= chunk_len, with
// best_space_left as small as possible.
int32 best_i = -1, best_j = -1,
best_space_left = std::numeric_limits<int32>::max(),
size = partial_eg_chunks_.size();
for (int32 i = 0; i < size; i++) {
int32 this_space_left = partial_eg_chunks_[i].second;
if (this_space_left >= chunk_len && this_space_left < best_space_left) {
best_i = i;
best_j = partial_eg_chunks_[i].first;
best_space_left = this_space_left;
}
}
if (best_i != -1) {
partial_eg_chunks_[best_i] = partial_eg_chunks_.back();
partial_eg_chunks_.pop_back();
} else {
// consume a currently-unused chunk, if available.
if (empty_eg_chunks_.empty()) {
return false;
} else {
best_j = empty_eg_chunks_.back();
empty_eg_chunks_.pop_back();
best_space_left = config_.chunk_length;
}
}
int32 new_space_left = best_space_left - chunk_len;
KALDI_ASSERT(new_space_left >= 0);
if (new_space_left > 0) {
partial_eg_chunks_.push_back(std::pair<int32, int32>(best_j,
new_space_left));
}
eg_chunks_[best_j].push_back(chunk);
return true;
}
}
RnnlmExampleCreator::SingleMinibatchCreator::~SingleMinibatchCreator() {
for (size_t i = 0; i < eg_chunks_.size(); i++)
for (size_t j = 0; j < eg_chunks_[i].size(); j++)
delete eg_chunks_[i][j];
}
void RnnlmExampleCreator::SingleMinibatchCreator::CreateMinibatchOneSequence(
int32 n, RnnlmExample *minibatch) {
// Much of the code here is about figuring out what to do if we haven't
// completely used up the potential length of the sequence. We first try
// giving extra left-context to any split-up pieces of sequence that could potentially
// use extra left-context; when that avenue is exhausted, we
// pad at the end with </s> symbols with zero weight.
KALDI_ASSERT(static_cast<size_t>(n) < eg_chunks_.size());
const std::vector<SequenceChunk*> &this_chunks = eg_chunks_[n];
int32 num_chunks = this_chunks.size();
// note: often num_chunks will be 1, occasionally 0 (if we've run out of
// data), and sometimes more than 1 (if we're appending multiple chunks
// together because they were shorter than config_.chunk_length).
// total_current_chunk_length is the total Length() of all the chunks.
int32 total_current_chunk_length = 0;
for (int32 c = 0; c < num_chunks; c++) {
total_current_chunk_length += this_chunks[c]->Length();
}
KALDI_ASSERT(total_current_chunk_length <= config_.chunk_length);
int32 extra_length_available = config_.chunk_length - total_current_chunk_length;
while (true) {
bool changed = false;
for (int32 c = 0; c < num_chunks; c++) {
if (this_chunks[c]->context_begin > 0 && extra_length_available > 0) {
changed = true;
this_chunks[c]->context_begin--;
extra_length_available--;
}
}
if (!changed)
break;
}
int32 pos = 0; // position in the sequence (we increase this every time a word
// gets added).
for (int32 c = 0; c < num_chunks; c++) {
SequenceChunk &chunk = *(this_chunks[c]);
// note: begin and end are the indexes of the first and the last-plus-one
// words in the sequence that we *predict*.
// you can think of real_begin as the index of the first real word in the
// sequence that we use as left context (however it will be preceded by
// either a <s> or a <brk>, depending whether 'real_begin' is 0 or >0).
// For these positions that are only used as left context, and not predicted
// the weight of the output (predicted) word is zero. 'begin' is the index
// of the first predicted word.
int32 context_begin = chunk.context_begin,
begin = chunk.begin,
end = chunk.end;
for (int32 i = context_begin; i < end; i++) {
int32 output_word = (*chunk.sequence)[i],
input_word;
if (i == context_begin) {
if (context_begin == 0) input_word = config_.bos_symbol;
else input_word = config_.brk_symbol;
} else {
input_word = (*chunk.sequence)[i - 1];
}
BaseFloat weight = (i < begin ? 0.0 : chunk.weight);
Set(n, pos, input_word, output_word, weight, minibatch);
pos++;
}
}
for (; pos < config_.chunk_length; pos++) {
// fill the rest with <s> as input and </s> as output
// and weight of 0.0. The symbol-id doesn't really matter
// so we pick ones that we know are valid inputs and outputs.
int32 input_word = config_.bos_symbol,
output_word = config_.eos_symbol;
BaseFloat weight = 0.0;
Set(n, pos, input_word, output_word, weight, minibatch);
}
}
void RnnlmExampleCreator::SingleMinibatchCreator::Set(
int32 n, int32 t, int32 input_word, int32 output_word,
BaseFloat weight, RnnlmExample *minibatch) const {
KALDI_ASSERT(n >= 0 && n < config_.num_chunks_per_minibatch &&
t >= 0 && t < config_.chunk_length &&
weight >= 0.0);
int32 i = t * config_.num_chunks_per_minibatch + n;
minibatch->input_words[i] = input_word;
minibatch->output_words[i] = output_word;
minibatch->output_weights(i) = weight;
}
void RnnlmExampleCreator::SingleMinibatchCreator::CreateMinibatch(
RnnlmExample *minibatch) {
minibatch->vocab_size = config_.vocab_size;
minibatch->num_chunks = config_.num_chunks_per_minibatch;
minibatch->chunk_length = config_.chunk_length;
minibatch->num_samples = config_.num_samples;
int32 num_words = config_.chunk_length * config_.num_chunks_per_minibatch;
minibatch->input_words.resize(num_words);
minibatch->output_words.resize(num_words);
minibatch->output_weights.Resize(num_words);
minibatch->sampled_words.clear();
for (int32 n = 0; n < config_.num_chunks_per_minibatch; n++) {
CreateMinibatchOneSequence(n, minibatch);
}
}
RnnlmExampleCreator::SequenceChunk* RnnlmExampleCreator::GetRandomChunk() {
KALDI_ASSERT(!chunks_.empty());
int32 pos = RandInt(0, chunks_.size() - 1);
SequenceChunk *ans = chunks_[pos];
chunks_[pos] = chunks_.back();
chunks_.pop_back();
return ans;
}
bool RnnlmExampleCreator::ProcessOneMinibatch() {
// A couple of configuration values that are not important enough
// to go in the config...
// 'chunks_proportion' controls when we discard a small number of
// chunks rather than form a new minibatch, after we've finished
// reading the data and have a small bit left over.
const BaseFloat chunks_proportion = 0.0; // TODO: revert to 0.5.
// 'max_rejections' is the maximum number of successive chunks that
// can be rejected for being 'too big', before we give up an accept
// the minibatch as-is.
const int32 max_rejections = 5;
if (chunks_.size() <
std::max<size_t>(1,
config_.num_chunks_per_minibatch * chunks_proportion)) {
// there's not enough data to form one minibatch.
return false;
}
SingleMinibatchCreator s(config_);
int32 cur_rejections = 0;
while (!chunks_.empty() && cur_rejections < max_rejections) {
int32 i = RandInt(0, chunks_.size() - 1);
if (s.AcceptChunk(chunks_[i])) {
num_chunks_processed_++;
num_words_processed_ += chunks_[i]->Length();
chunks_[i] = chunks_.back();
chunks_.pop_back();
cur_rejections = 0;
} else {
cur_rejections++;
}
}
RnnlmExample *minibatch = new RnnlmExample();
s.CreateMinibatch(minibatch);
std::ostringstream os;
os << "minibatch-" << num_minibatches_written_;
std::string key = os.str();
num_minibatches_written_++;
if (minibatch_sampler_ == NULL) {
// write it directly from this function.
writer_->Write(key, *minibatch);
delete minibatch;
} else {
// the sampling, since it can be slow, will be done in parallel by as many
// background threads as the user specified via the --num-threads option.
// SamplerTask will also write it out.
sampling_sequencer_.Run(new SamplerTask(*minibatch_sampler_,
key, writer_, minibatch));
}
return true;
}
void RnnlmExampleCreator::SplitSequenceIntoChunks(
BaseFloat weight, const std::vector<int32> &words) {
std::shared_ptr<std::vector<int32> > ptr (new std::vector<int32>());
ptr->reserve(words.size() + 1);
ptr->insert(ptr->end(), words.begin(), words.end());
ptr->push_back(config_.eos_symbol); // add the terminating </s>.
int32 sequence_length = ptr->size(); // == words.size() + 1
if (sequence_length <= config_.chunk_length) {
chunks_.push_back(new SequenceChunk(config_, ptr, weight,
0, sequence_length));
} else {
std::vector<int32> chunk_lengths;
ChooseChunkLengths(sequence_length, &chunk_lengths);
int32 cur_start = 0;
for (size_t i = 0; i < chunk_lengths.size(); i++) {
int32 this_end = cur_start + chunk_lengths[i];
chunks_.push_back(new SequenceChunk(config_, ptr, weight,
cur_start, this_end));
cur_start = this_end;
}
}
}
// see comment in rnnlm-example.h, by its declaration.
void RnnlmExampleCreator::ChooseChunkLengths(
int32 sequence_length,
std::vector<int32> *chunk_lengths) {
KALDI_ASSERT(sequence_length > config_.chunk_length);
chunk_lengths->clear();
int32 tot = sequence_length - config_.min_split_context,
chunk_length_no_context = config_.chunk_length - config_.min_split_context;
KALDI_ASSERT(chunk_length_no_context > 0);
// divide 'tot' into pieces of size <= config_.chunk_length - config_.min_split_context.
// note:
for (int32 i = 0; i < tot / chunk_length_no_context; i++)
chunk_lengths->push_back(chunk_length_no_context);
KALDI_ASSERT(!chunk_lengths->empty());
int32 remaining_size = tot % chunk_length_no_context;
if (remaining_size != 0) {
// put the smaller piece in a random location.
(*chunk_lengths)[RandInt(0, chunk_lengths->size() - 1)] = remaining_size;
chunk_lengths->push_back(chunk_length_no_context);
}
(*chunk_lengths)[0] += config_.min_split_context;
KALDI_ASSERT(std::accumulate(chunk_lengths->begin(), chunk_lengths->end(), 0)
== sequence_length);
}
void RnnlmExampleCreator::CheckSequence(
BaseFloat weight,
const std::vector<int32> &words) {
KALDI_ASSERT(weight > 0.0);
int32 bos_symbol = config_.bos_symbol,
brk_symbol = config_.brk_symbol,
eos_symbol = config_.eos_symbol,
vocab_size = config_.vocab_size;
for (size_t i = 0; i < words.size(); i++) {
// note: eos_symbol within a sequence isn't disallowed; this
// is allowed as a way to encode multiple turns of a conversation,
// and similar scenarios.
KALDI_ASSERT(words[i] != bos_symbol && words[i] != brk_symbol &&
words[i] > 0 && words[i] < vocab_size);
}
if (!words.empty() && words.back() == eos_symbol) {
// we may rate-limit this warning eventually if people legitimately need to
// do this.
KALDI_WARN << "Raw word sequence contains </s> at the end. "
"Is this a bug in your data preparation? We'll add another one.";
}
}
void RnnlmExampleCreator::Check() const {
config_.Check();
if (minibatch_sampler_ != NULL) {
if (minibatch_sampler_->VocabSize() > config_.vocab_size) {
KALDI_ERR << "Option --vocab-size=" << config_.vocab_size
<< " is inconsistent with the language model.";
}
}
}
void RnnlmExampleCreator::Process(std::istream &is) {
int32 num_lines = 0;
std::vector<int32> words;
std::string line;
while (getline(is, line)) {
num_lines++;
std::istringstream line_is(line);
BaseFloat weight;
line_is >> weight;
words.clear();
int32 word;
while (line_is >> word) {
words.push_back(word);
}
if (!line_is.eof()) {
KALDI_ERR << "Could not interpret input: " << line;
}
this->AcceptSequence(weight, words);
}
KALDI_LOG << "Processed " << num_lines << " lines of input.";
}
void RnnlmExample::Swap(RnnlmExample *other) {
std::swap(vocab_size, other->vocab_size);
std::swap(num_chunks, other->num_chunks);
std::swap(chunk_length, other->chunk_length);
std::swap(sample_group_size, other->sample_group_size);
std::swap(num_samples, other->num_samples);
input_words.swap(other->input_words);
output_words.swap(other->output_words);
output_weights.Swap(&(other->output_weights));
sampled_words.swap(other->sampled_words);
sample_inv_probs.Swap(&(other->sample_inv_probs));
}
} // namespace rnnlm
} // namespace kaldi