Source code for onmt.translate.penalties

from __future__ import division
import torch


[docs]class PenaltyBuilder(object): """Returns the Length and Coverage Penalty function for Beam Search. Args: length_pen (str): option name of length pen cov_pen (str): option name of cov pen Attributes: has_cov_pen (bool): Whether coverage penalty is None (applying it is a no-op). Note that the converse isn't true. Setting beta to 0 should force coverage length to be a no-op. has_len_pen (bool): Whether length penalty is None (applying it is a no-op). Note that the converse isn't true. Setting alpha to 1 should force length penalty to be a no-op. coverage_penalty (callable[[FloatTensor, float], FloatTensor]): Calculates the coverage penalty. length_penalty (callable[[int, float], float]): Calculates the length penalty. """ def __init__(self, cov_pen, length_pen): self.has_cov_pen = not self._pen_is_none(cov_pen) self.coverage_penalty = self._coverage_penalty(cov_pen) self.has_len_pen = not self._pen_is_none(length_pen) self.length_penalty = self._length_penalty(length_pen) @staticmethod def _pen_is_none(pen): return pen == "none" or pen is None def _coverage_penalty(self, cov_pen): if cov_pen == "wu": return self.coverage_wu elif cov_pen == "summary": return self.coverage_summary elif self._pen_is_none(cov_pen): return self.coverage_none else: raise NotImplementedError("No '{:s}' coverage penalty.".format( cov_pen)) def _length_penalty(self, length_pen): if length_pen == "wu": return self.length_wu elif length_pen == "avg": return self.length_average elif self._pen_is_none(length_pen): return self.length_none else: raise NotImplementedError("No '{:s}' length penalty.".format( length_pen)) # Below are all the different penalty terms implemented so far. # Subtract coverage penalty from topk log probs. # Divide topk log probs by length penalty.
[docs] def coverage_wu(self, cov, beta=0.): """GNMT coverage re-ranking score. See "Google's Neural Machine Translation System" :cite:`wu2016google`. ``cov`` is expected to be sized ``(*, seq_len)``, where ``*`` is probably ``batch_size x beam_size`` but could be several dimensions like ``(batch_size, beam_size)``. If ``cov`` is attention, then the ``seq_len`` axis probably sums to (almost) 1. """ penalty = -torch.min(cov, cov.clone().fill_(1.0)).log().sum(-1) return beta * penalty
[docs] def coverage_summary(self, cov, beta=0.): """Our summary penalty.""" penalty = torch.max(cov, cov.clone().fill_(1.0)).sum(-1) penalty -= cov.size(-1) return beta * penalty
[docs] def coverage_none(self, cov, beta=0.): """Returns zero as penalty""" none = torch.zeros((1,), device=cov.device, dtype=torch.float) if cov.dim() == 3: none = none.unsqueeze(0) return none
[docs] def length_wu(self, cur_len, alpha=0.): """GNMT length re-ranking score. See "Google's Neural Machine Translation System" :cite:`wu2016google`. """ return ((5 + cur_len) / 6.0) ** alpha
[docs] def length_average(self, cur_len, alpha=0.): """Returns the current sequence length.""" return cur_len
[docs] def length_none(self, cur_len, alpha=0.): """Returns unmodified scores.""" return 1.0