4771c9275c
* Add lm decode for the Python API. * fix style. * Fix LogAdd, Shouldn't double lm_log_prob when merge same prefix path * sort the import alphabetically
82 lines
2.4 KiB
C++
82 lines
2.4 KiB
C++
/**
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* Copyright (c) 2023 Xiaomi Corporation
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* Copyright (c) 2023 Pingfeng Luo
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*/
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#include "sherpa-onnx/csrc/hypothesis.h"
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#include <algorithm>
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#include <utility>
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namespace sherpa_onnx {
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void Hypotheses::Add(Hypothesis hyp) {
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auto key = hyp.Key();
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auto it = hyps_dict_.find(key);
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if (it == hyps_dict_.end()) {
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hyps_dict_[key] = std::move(hyp);
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} else {
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it->second.log_prob = LogAdd<double>()(it->second.log_prob, hyp.log_prob);
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}
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}
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Hypothesis Hypotheses::GetMostProbable(bool length_norm) const {
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if (length_norm == false) {
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return std::max_element(hyps_dict_.begin(), hyps_dict_.end(),
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[](const auto &left, auto &right) -> bool {
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return left.second.TotalLogProb() <
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right.second.TotalLogProb();
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})
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->second;
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} else {
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// for length_norm is true
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return std::max_element(
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hyps_dict_.begin(), hyps_dict_.end(),
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[](const auto &left, const auto &right) -> bool {
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return left.second.TotalLogProb() / left.second.ys.size() <
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right.second.TotalLogProb() / right.second.ys.size();
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})
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->second;
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}
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}
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std::vector<Hypothesis> Hypotheses::GetTopK(int32_t k, bool length_norm) const {
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k = std::max(k, 1);
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k = std::min(k, Size());
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std::vector<Hypothesis> all_hyps = Vec();
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if (length_norm == false) {
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std::partial_sort(all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
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[](const auto &a, const auto &b) {
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return a.TotalLogProb() > b.TotalLogProb();
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});
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} else {
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// for length_norm is true
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std::partial_sort(all_hyps.begin(), all_hyps.begin() + k, all_hyps.end(),
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[](const auto &a, const auto &b) {
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return a.TotalLogProb() / a.ys.size() >
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b.TotalLogProb() / b.ys.size();
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});
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}
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return {all_hyps.begin(), all_hyps.begin() + k};
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}
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const std::vector<int32_t> GetHypsRowSplits(
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const std::vector<Hypotheses> &hyps) {
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std::vector<int32_t> row_splits;
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row_splits.reserve(hyps.size() + 1);
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row_splits.push_back(0);
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int32_t s = 0;
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for (const auto &h : hyps) {
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s += h.Size();
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row_splits.push_back(s);
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}
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return row_splits;
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}
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} // namespace sherpa_onnx
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