Source code for onmt.translate.beam_search

import torch
from onmt.translate import penalties
from onmt.translate.decode_strategy import DecodeStrategy

import warnings

class BeamSearchBase(DecodeStrategy):
    """Generation beam search.

    Note that the attributes list is not exhaustive. Rather, it highlights
    tensors to document their shape. (Since the state variables' "batch"
    size decreases as beams finish, we denote this axis with a B rather than

        beam_size (int): Number of beams to use (see base ``parallel_paths``).
        batch_size (int): See base.
        pad (int): See base.
        bos (int): See base.
        eos (int): See base.
        unk (int): See base.
        start (int): See base.
        n_best (int): Don't stop until at least this many beams have
            reached EOS.
        global_scorer (onmt.translate.GNMTGlobalScorer): Scorer instance.
        min_length (int): See base.
        max_length (int): See base.
        return_attention (bool): See base.
        block_ngram_repeat (int): See base.
        exclusion_tokens (set[int]): See base.

        top_beam_finished (ByteTensor): Shape ``(B,)``.
        _batch_offset (LongTensor): Shape ``(B,)``.
        _beam_offset (LongTensor): Shape ``(batch_size x beam_size,)``.
        alive_seq (LongTensor): See base.
        topk_log_probs (FloatTensor): Shape ``(B, beam_size,)``. These
            are the scores used for the topk operation.
        src_len (LongTensor): Lengths of encodings. Used for
            masking attentions.
        select_indices (LongTensor or NoneType): Shape
            ``(B x beam_size,)``. This is just a flat view of the
        topk_scores (FloatTensor): Shape
            ``(B, beam_size)``. These are the
            scores a sequence will receive if it finishes.
        topk_ids (LongTensor): Shape ``(B, beam_size)``. These are the
            word indices of the topk predictions.
        _batch_index (LongTensor): Shape ``(B, beam_size)``.
        _prev_penalty (FloatTensor or NoneType): Shape
            ``(B, beam_size)``. Initialized to ``None``.
        _coverage (FloatTensor or NoneType): Shape
            ``(1, B x beam_size, inp_seq_len)``.
        hypotheses (list[list[Tuple[Tensor]]]): Contains a tuple
            of score (float), sequence (long), and attention (float or None).

    def __init__(
        super(BeamSearchBase, self).__init__(
        # beam parameters
        self.beam_size = beam_size
        self.n_best = n_best
        self.ratio = ratio

        # beam state
        self.top_beam_finished = torch.zeros([batch_size], dtype=torch.uint8)
        # BoolTensor was introduced in pytorch 1.2
            self.top_beam_finished = self.top_beam_finished.bool()
        except AttributeError:
        self._batch_offset = torch.arange(batch_size, dtype=torch.long)

        self.select_indices = None
        self.done = False
        # "global state" of the old beam
        self._prev_penalty = None
        self._coverage = None

        self._stepwise_cov_pen = stepwise_penalty and self.global_scorer.has_cov_pen
        self._vanilla_cov_pen = not stepwise_penalty and self.global_scorer.has_cov_pen
        self._cov_pen = self.global_scorer.has_cov_pen

        self.src_len = None

    def initialize(self, *args, **kwargs):
        raise NotImplementedError

    def initialize_(self, enc_out, src_len, src_map, device, target_prefix):
        super(BeamSearchBase, self).initialize(
            enc_out, src_len, src_map, device, target_prefix

        self.best_scores = torch.full(
            [self.batch_size], -1e10, dtype=torch.float, device=device
        self._beam_offset = torch.arange(
            self.batch_size * self.beam_size,
        self.topk_log_probs = (
            torch.tensor([0.0] + [float("-inf")] * (self.beam_size - 1), device=device)
            .reshape(self.batch_size, self.beam_size)
        # buffers for the topk scores and 'backpointer'
        self.topk_scores = torch.empty(
            (self.batch_size, self.beam_size), dtype=torch.float, device=device
        self.topk_ids = torch.empty(
            (self.batch_size, self.beam_size), dtype=torch.long, device=device
        self._batch_index = torch.empty(
            [self.batch_size, self.beam_size], dtype=torch.long, device=device

    def current_predictions(self):
        return self.alive_seq[:, -1]

    def current_backptr(self):
        # for testing
        return self.select_indices.view(self.batch_size, self.beam_size).fmod(

    def batch_offset(self):
        return self._batch_offset

    def _pick(self, log_probs, out=None):
        """Take a token pick decision for a step.

            log_probs (FloatTensor): (B * beam_size, vocab_size)
            out (Tensor, LongTensor): output buffers to reuse, optional.

            topk_scores (FloatTensor): (B, beam_size)
            topk_ids (LongTensor): (B, beam_size)
        vocab_size = log_probs.size(-1)
        # maybe fix some prediction at this step by modifying log_probs
        log_probs = self.target_prefixing(log_probs)

        # Flatten probs into a list of possibilities.
        curr_scores = log_probs.reshape(-1, self.beam_size * vocab_size)
        if out is not None:
            torch.topk(curr_scores, self.beam_size, dim=-1, out=out)
        topk_scores, topk_ids = torch.topk(curr_scores, self.beam_size, dim=-1)
        return topk_scores, topk_ids

    def update_finished(self):
        # Penalize beams that finished.
        _B_old = self.topk_log_probs.shape[0]
        step = self.alive_seq.shape[-1]  # 1 greater than the step in advance
        self.topk_log_probs.masked_fill_(self.is_finished, -1e10)
        # on real data (newstest2017) with the pretrained transformer,
        # it's faster to not move this back to the original device
        self.is_finished ="cpu")
        self.top_beam_finished |= self.is_finished[:, 0].eq(1)
        predictions = self.alive_seq.view(_B_old, self.beam_size, step)
        attention = (
                _B_old, self.beam_size, step - 1, self.alive_attn.size(-1)
            if self.alive_attn is not None
            else None
        non_finished_batch = []
        for i in range(self.is_finished.size(0)):  # Batch level
            b = self._batch_offset[i]
            finished_hyp = self.is_finished[i].nonzero(as_tuple=False).view(-1)
            # Store finished hypotheses for this batch.
            for j in finished_hyp:  # Beam level: finished beam j in batch i
                if self.ratio > 0:
                    s = self.topk_scores[i, j] / (step + 1)
                    if self.best_scores[b] < s:
                        self.best_scores[b] = s
                        self.topk_scores[i, j],
                        predictions[i, j, 1:],  # Ignore start_token.
                        attention[i, j, :, : self.src_len[i]]
                        if attention is not None
                        else None,
            # End condition is the top beam finished and we can return
            # n_best hypotheses.
            if self.ratio > 0:
                pred_len = self.src_len[i] * self.ratio
                finish_flag = (
                    (self.topk_scores[i, 0] / pred_len) <= self.best_scores[b]
                ) or self.is_finished[i].all()
                finish_flag = self.top_beam_finished[i] != 0
            if finish_flag and len(self.hypotheses[b]) >= self.beam_size:
                best_hyp = sorted(self.hypotheses[b], key=lambda x: x[0], reverse=True)[
                    : self.n_best
                for n, (score, pred, attn) in enumerate(best_hyp):
                    self.predictions[b].append(pred)  # ``(batch, n_best,)``
                    self.attention[b].append(attn if attn is not None else [])

        non_finished = torch.tensor(non_finished_batch)
        # If all sentences are translated, no need to go further.
        if len(non_finished) == 0:
            self.done = True

        _B_new = non_finished.shape[0]
            _B_new, _B_old, non_finished, predictions, attention, step

    def remove_finished_batches(
        self, _B_new, _B_old, non_finished, predictions, attention, step
        # Remove finished batches for the next step.
        self.top_beam_finished = self.top_beam_finished.index_select(0, non_finished)
        self._batch_offset = self._batch_offset.index_select(0, non_finished)
        non_finished =
        self.topk_log_probs = self.topk_log_probs.index_select(0, non_finished)
        self._batch_index = self._batch_index.index_select(0, non_finished)
        self.select_indices = self._batch_index.view(_B_new * self.beam_size)
        self.alive_seq = predictions.index_select(0, non_finished).view(
            -1, self.alive_seq.size(-1)
        self.topk_scores = self.topk_scores.index_select(0, non_finished)
        self.topk_ids = self.topk_ids.index_select(0, non_finished)
        if self.alive_attn is not None:
            inp_seq_len = self.alive_attn.size(-1)
            self.alive_attn = attention.index_select(0, non_finished).view(
                _B_new * self.beam_size, step - 1, inp_seq_len
            if self._cov_pen:
                self._coverage = (
                    self._coverage.view(_B_old, self.beam_size, 1, inp_seq_len)
                    .index_select(0, non_finished)
                    .view(_B_new * self.beam_size, 1, inp_seq_len)
                if self._stepwise_cov_pen:
                    self._prev_penalty = self._prev_penalty.index_select(
                        0, non_finished

    def advance(self, log_probs, attn):
        vocab_size = log_probs.size(-1)

        # using integer division to get an integer _B without casting
        _B = log_probs.shape[0] // self.beam_size

        if self._stepwise_cov_pen and self._prev_penalty is not None:
            self.topk_log_probs += self._prev_penalty
            self.topk_log_probs -= self.global_scorer.cov_penalty(
                self._coverage + attn, self.global_scorer.beta
            ).view(_B, self.beam_size)

        # force the output to be longer than self.min_length
        step = len(self)
        # Multiply probs by the beam probability.
        log_probs += self.topk_log_probs.view(_B * self.beam_size, 1)

        # if the sequence ends now, then the penalty is the current
        # length + 1, to include the EOS token
        length_penalty = self.global_scorer.length_penalty(
            step + 1, alpha=self.global_scorer.alpha

        curr_scores = log_probs / length_penalty

        # Avoid any direction that would repeat unwanted ngrams

        # Pick up candidate token by curr_scores
        self._pick(curr_scores, out=(self.topk_scores, self.topk_ids))

        # Recover log probs.
        # Length penalty is just a scalar. It doesn't matter if it's applied
        # before or after the topk.
        torch.mul(self.topk_scores, length_penalty, out=self.topk_log_probs)

        # Resolve beam origin and map to batch index flat representation.
        self._batch_index = torch.div(self.topk_ids, vocab_size, rounding_mode="trunc")
        self._batch_index += self._beam_offset[:_B].unsqueeze(1)
        self.select_indices = self._batch_index.view(_B * self.beam_size)
        self.topk_ids.fmod_(vocab_size)  # resolve true word ids

        # Append last prediction.
        self.alive_seq =
                self.alive_seq.index_select(0, self.select_indices),
                self.topk_ids.view(_B * self.beam_size, 1),


        if self.return_attention or self._cov_pen:
            current_attn = attn.index_select(0, self.select_indices)
            if step == 1:
                self.alive_attn = current_attn
                # update global state (step == 1)
                if self._cov_pen:  # coverage penalty
                    self._prev_penalty = torch.zeros_like(self.topk_log_probs)
                    self._coverage = current_attn
                self.alive_attn = self.alive_attn.index_select(0, self.select_indices)
                self.alive_attn =[self.alive_attn, current_attn], 1)
                # update global state (step > 1)
                if self._cov_pen:
                    self._coverage = self._coverage.index_select(0, self.select_indices)
                    self._coverage += current_attn
                    self._prev_penalty = self.global_scorer.cov_penalty(
                        self._coverage, beta=self.global_scorer.beta
                    ).view(_B, self.beam_size)

        if self._vanilla_cov_pen:
            # shape: (batch_size x beam_size, 1)
            cov_penalty = self.global_scorer.cov_penalty(
                self._coverage, beta=self.global_scorer.beta
            self.topk_scores -= cov_penalty.view(_B, self.beam_size).float()

        self.is_finished = self.topk_ids.eq(self.eos)

[docs]class BeamSearch(BeamSearchBase): """ Beam search for seq2seq/encoder-decoder models """
[docs] def initialize( self, enc_out, src_len, src_map=None, device=None, target_prefix=None ): """Initialize for decoding. Repeat src objects `beam_size` times. """ (fn_map_state, enc_out, src_map, target_prefix) = self.initialize_tile( enc_out, src_len, src_map, target_prefix ) if device is None: device = self.get_device_from_enc_out(enc_out) super(BeamSearch, self).initialize_( enc_out, self.src_len, src_map, device, target_prefix ) return fn_map_state, enc_out, self.src_len, src_map
class BeamSearchLM(BeamSearchBase): """ Beam search for language/decoder only models """ def initialize(self, src, src_len, src_map=None, device=None, target_prefix=None): """Initialize for decoding. Repeat src objects `beam_size` times. """ (fn_map_state, _, src_map, target_prefix) = self.initialize_tile( None, src_len, src_map, target_prefix ) if device is None: device = src.device super(BeamSearchLM, self).initialize_( None, self.src_len, src_map=src_map, device=device, target_prefix=target_prefix, ) return fn_map_state, src, self.src_len, src_map def advance(self, log_probs, attn): super(BeamSearchLM, self).advance(log_probs, attn) # in LM task src_len is associated with currently generated src # and therefore needs to follow the generation self.src_len += 1 def remove_finished_batches( self, _B_new, _B_old, non_finished, predictions, attention, step ): super(BeamSearchLM, self).remove_finished_batches( _B_new, _B_old, non_finished, predictions, attention, step ) # in LM task src_len is associated with currently generated src # and therefore needs to follow the generation non_finished = self.src_len = ( self.src_len.view(_B_old, self.beam_size) .index_select(0, non_finished) .view(_B_new * self.beam_size) )
[docs]class GNMTGlobalScorer(object): """NMT re-ranking. Args: alpha (float): Length parameter. beta (float): Coverage parameter. length_penalty (str): Length penalty strategy. coverage_penalty (str): Coverage penalty strategy. Attributes: alpha (float): See above. beta (float): See above. length_penalty (callable): See :class:`penalties.PenaltyBuilder`. coverage_penalty (callable): See :class:`penalties.PenaltyBuilder`. has_cov_pen (bool): See :class:`penalties.PenaltyBuilder`. has_len_pen (bool): See :class:`penalties.PenaltyBuilder`. """ @classmethod def from_opt(cls, opt): return cls(opt.alpha, opt.beta, opt.length_penalty, opt.coverage_penalty) def __init__(self, alpha, beta, length_penalty, coverage_penalty): self._validate(alpha, beta, length_penalty, coverage_penalty) self.alpha = alpha self.beta = beta penalty_builder = penalties.PenaltyBuilder(coverage_penalty, length_penalty) self.has_cov_pen = penalty_builder.has_cov_pen # Term will be subtracted from probability self.cov_penalty = penalty_builder.coverage_penalty self.has_len_pen = penalty_builder.has_len_pen # Probability will be divided by this self.length_penalty = penalty_builder.length_penalty @classmethod def _validate(cls, alpha, beta, length_penalty, coverage_penalty): # these warnings indicate that either the alpha/beta # forces a penalty to be a no-op, or a penalty is a no-op but # the alpha/beta would suggest otherwise. if length_penalty is not None and alpha == 0.0: warnings.warn( "Using length penalty with alpha==0 " "is equivalent to using length penalty none." ) if coverage_penalty is None or coverage_penalty == "none": if beta != 0: warnings.warn( "Non-default `beta` with no coverage penalty. " "`beta` has no effect." ) else: # using some coverage penalty if beta == 0.0: warnings.warn( "Non-default coverage penalty with beta==0 " "is equivalent to using coverage penalty none." )