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#include "lm/quantize.hh"
#include "lm/binary_format.hh"
#include "lm/lm_exception.hh"
#include <algorithm>
#include <numeric>
#include <unistd.h>
namespace lm {
namespace ngram {
/* Quantize into bins of equal size as described in
* M. Federico and N. Bertoldi. 2006. How many bits are needed
* to store probabilities for phrase-based translation? In Proc.
* of the Workshop on Statistical Machine Translation, pages
* 94–101, New York City, June. Association for Computa-
* tional Linguistics.
*/
namespace {
void MakeBins(float *values, float *values_end, float *centers, uint32_t bins) {
std::sort(values, values_end);
const float *start = values, *finish;
for (uint32_t i = 0; i < bins; ++i, ++centers, start = finish) {
finish = values + (((values_end - values) * static_cast<uint64_t>(i + 1)) / bins);
if (finish == start) {
// zero length bucket.
*centers = i ? *(centers - 1) : -std::numeric_limits<float>::infinity();
} else {
*centers = std::accumulate(start, finish, 0.0) / static_cast<float>(finish - start);
}
}
}
const char kSeparatelyQuantizeVersion = 2;
} // namespace
void SeparatelyQuantize::UpdateConfigFromBinary(int fd, const std::vector<uint64_t> &/*counts*/, Config &config) {
char version;
if (read(fd, &version, 1) != 1 || read(fd, &config.prob_bits, 1) != 1 || read(fd, &config.backoff_bits, 1) != 1)
UTIL_THROW(util::ErrnoException, "Failed to read header for quantization.");
if (version != kSeparatelyQuantizeVersion) UTIL_THROW(FormatLoadException, "This file has quantization version " << (unsigned)version << " but the code expects version " << (unsigned)kSeparatelyQuantizeVersion);
AdvanceOrThrow(fd, -3);
}
void SeparatelyQuantize::SetupMemory(void *start, const Config &config) {
// Reserve 8 byte header for bit counts.
start_ = reinterpret_cast<float*>(static_cast<uint8_t*>(start) + 8);
prob_bits_ = config.prob_bits;
backoff_bits_ = config.backoff_bits;
// We need the reserved values.
if (config.prob_bits == 0) UTIL_THROW(ConfigException, "You can't quantize probability to zero");
if (config.backoff_bits == 0) UTIL_THROW(ConfigException, "You can't quantize backoff to zero");
if (config.prob_bits > 25) UTIL_THROW(ConfigException, "For efficiency reasons, quantizing probability supports at most 25 bits. Currently you have requested " << static_cast<unsigned>(config.prob_bits) << " bits.");
if (config.backoff_bits > 25) UTIL_THROW(ConfigException, "For efficiency reasons, quantizing backoff supports at most 25 bits. Currently you have requested " << static_cast<unsigned>(config.backoff_bits) << " bits.");
}
void SeparatelyQuantize::Train(uint8_t order, std::vector<float> &prob, std::vector<float> &backoff) {
TrainProb(order, prob);
// Backoff
float *centers = start_ + TableStart(order) + ProbTableLength();
*(centers++) = kNoExtensionBackoff;
*(centers++) = kExtensionBackoff;
MakeBins(&*backoff.begin(), &*backoff.end(), centers, (1ULL << backoff_bits_) - 2);
}
void SeparatelyQuantize::TrainProb(uint8_t order, std::vector<float> &prob) {
float *centers = start_ + TableStart(order);
MakeBins(&*prob.begin(), &*prob.end(), centers, (1ULL << prob_bits_));
}
void SeparatelyQuantize::FinishedLoading(const Config &config) {
uint8_t *actual_base = reinterpret_cast<uint8_t*>(start_) - 8;
*(actual_base++) = kSeparatelyQuantizeVersion; // version
*(actual_base++) = config.prob_bits;
*(actual_base++) = config.backoff_bits;
}
} // namespace ngram
} // namespace lm
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