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#include "lm/builder/interpolate.hh"
#include "lm/builder/hash_gamma.hh"
#include "lm/builder/joint_order.hh"
#include "lm/builder/ngram_stream.hh"
#include "lm/builder/sort.hh"
#include "lm/lm_exception.hh"
#include "util/fixed_array.hh"
#include "util/murmur_hash.hh"
#include <assert.h>
#include <math.h>
namespace lm { namespace builder {
namespace {
/* Calculate q, the collapsed probability and backoff, as defined in
* @inproceedings{Heafield-rest,
* author = {Kenneth Heafield and Philipp Koehn and Alon Lavie},
* title = {Language Model Rest Costs and Space-Efficient Storage},
* year = {2012},
* month = {July},
* booktitle = {Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
* address = {Jeju Island, Korea},
* pages = {1169--1178},
* url = {http://kheafield.com/professional/edinburgh/rest\_paper.pdf},
* }
* This is particularly convenient to calculate during interpolation because
* the needed backoff terms are already accessed at the same time.
*/
class OutputQ {
public:
explicit OutputQ(std::size_t order) : q_delta_(order) {}
void Gram(unsigned order_minus_1, float full_backoff, ProbBackoff &out) {
float &q_del = q_delta_[order_minus_1];
if (order_minus_1) {
// Divide by context's backoff (which comes in as out.backoff)
q_del = q_delta_[order_minus_1 - 1] / out.backoff * full_backoff;
} else {
q_del = full_backoff;
}
out.prob = log10f(out.prob * q_del);
// TODO: stop wastefully outputting this!
out.backoff = 0.0;
}
private:
// Product of backoffs in the numerator divided by backoffs in the
// denominator. Does not include
std::vector<float> q_delta_;
};
/* Default: output probability and backoff */
class OutputProbBackoff {
public:
explicit OutputProbBackoff(std::size_t /*order*/) {}
void Gram(unsigned /*order_minus_1*/, float full_backoff, ProbBackoff &out) const {
// Correcting for numerical precision issues. Take that IRST.
out.prob = std::min(0.0f, log10f(out.prob));
out.backoff = log10f(full_backoff);
}
};
template <class Output> class Callback {
public:
Callback(float uniform_prob, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t> &prune_thresholds)
: backoffs_(backoffs.size()), probs_(backoffs.size() + 2),
prune_thresholds_(prune_thresholds),
output_(backoffs.size() + 1 /* order */) {
probs_[0] = uniform_prob;
for (std::size_t i = 0; i < backoffs.size(); ++i) {
backoffs_.push_back(backoffs[i]);
}
}
~Callback() {
for (std::size_t i = 0; i < backoffs_.size(); ++i) {
if(prune_thresholds_[i + 1] > 0)
while(backoffs_[i])
++backoffs_[i];
if (backoffs_[i]) {
std::cerr << "Backoffs do not match for order " << (i + 1) << std::endl;
abort();
}
}
}
void Enter(unsigned order_minus_1, NGram &gram) {
Payload &pay = gram.Value();
pay.complete.prob = pay.uninterp.prob + pay.uninterp.gamma * probs_[order_minus_1];
probs_[order_minus_1 + 1] = pay.complete.prob;
float out_backoff;
if (order_minus_1 < backoffs_.size() && *(gram.end() - 1) != kUNK && *(gram.end() - 1) != kEOS) {
if(prune_thresholds_[order_minus_1 + 1] > 0) {
//Compute hash value for current context
uint64_t current_hash = util::MurmurHashNative(gram.begin(), gram.Order() * sizeof(WordIndex));
const HashGamma *hashed_backoff = static_cast<const HashGamma*>(backoffs_[order_minus_1].Get());
while(current_hash != hashed_backoff->hash_value && ++backoffs_[order_minus_1])
hashed_backoff = static_cast<const HashGamma*>(backoffs_[order_minus_1].Get());
if(current_hash == hashed_backoff->hash_value) {
out_backoff = hashed_backoff->gamma;
++backoffs_[order_minus_1];
} else {
// Has been pruned away so it is not a context anymore
out_backoff = 1.0;
}
} else {
out_backoff = *static_cast<const float*>(backoffs_[order_minus_1].Get());
++backoffs_[order_minus_1];
}
} else {
// Not a context.
out_backoff = 1.0;
}
output_.Gram(order_minus_1, out_backoff, pay.complete);
}
void Exit(unsigned, const NGram &) const {}
private:
util::FixedArray<util::stream::Stream> backoffs_;
std::vector<float> probs_;
const std::vector<uint64_t>& prune_thresholds_;
Output output_;
};
} // namespace
Interpolate::Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t>& prune_thresholds, bool output_q)
: uniform_prob_(1.0 / static_cast<float>(vocab_size)), // Includes <unk> but excludes <s>.
backoffs_(backoffs),
prune_thresholds_(prune_thresholds),
output_q_(output_q) {}
// perform order-wise interpolation
void Interpolate::Run(const util::stream::ChainPositions &positions) {
assert(positions.size() == backoffs_.size() + 1);
if (output_q_) {
typedef Callback<OutputQ> C;
C callback(uniform_prob_, backoffs_, prune_thresholds_);
JointOrder<C, SuffixOrder>(positions, callback);
} else {
typedef Callback<OutputProbBackoff> C;
C callback(uniform_prob_, backoffs_, prune_thresholds_);
JointOrder<C, SuffixOrder>(positions, callback);
}
}
}} // namespaces
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