272 lines
10 KiB
Python
272 lines
10 KiB
Python
# coding=utf-8
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# Copyright (c) 2019 Yang Liu
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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"""
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A general wrapper around models with LM heads to generate sequences
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using beam search.
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"""
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import torch
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from torch import nn
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class TransformerBeamSearch(nn.Module):
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def __init__(
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self,
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model,
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tokenizer,
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batch_size,
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beam_size,
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min_length,
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max_length,
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alpha=0,
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block_repeating_trigram=True,
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):
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"""
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Attributes:
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mask_word_id: token id that corresponds to the mask
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"""
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super(TransformerBeamSearch, self).__init__()
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self.model = model
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self.tokenizer = tokenizer
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self.start_token_id = tokenizer.start_token_id
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self.end_token_id = tokenizer.end_token_id
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self.pad_token_id = tokenizer.pad_token_id
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self.beam_size = beam_size
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self.min_length = min_length
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self.max_length = max_length
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self.block_repeating_trigram = block_repeating_trigram
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self.apply_length_penalty = False if alpha == 0 else True
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self.alpha = alpha
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# State of the beam
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self.hypotheses = [[] for _ in range(batch_size)]
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self.batch_offset = torch.arange(batch_size, dtype=torch.long)
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self.beam_offset = torch.arange(
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0, batch_size * self.beam_size, step=self.beam_size, dtype=torch.long
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)
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self.growing_beam = torch.full(
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(batch_size * self.beam_size, 1), self.start_token_id, dtype=torch.long
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)
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self.topk_log_probabilities = torch.tensor(
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[0.0] + [float("-inf")] * (self.beam_size - 1), dtype=torch.float
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).repeat(batch_size)
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self.results = {
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"prediction": [[] for _ in batch_size],
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"scores": [[] for _ in batch_size],
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}
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self._step = 0
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self.is_done = False
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def step(self, log_probabilities):
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""" Grows the beam by one step. """
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self._step += 1
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# The batch size changes as some beams finish so we define _B
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vocab_size = log_probabilities.size(-1)
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_B = log_probabilities.size(0) // self.beam_size
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# Multiply each beam probability with the probability of the
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# next token (conditioned on the words in the beam).
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log_probabilities += self.topk_log_probabilities.view(-1, 1)
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self.enforce_min_length(log_probabilities)
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if self.block_repeating_trigram:
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self.remove_repeating_trigrams(log_probabilities, _B)
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# Find the `beam_size` (previous_beam + token) combinations with
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# the highest score
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topk_log_probabilities, topk_ids = log_probabilities.topk(
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log_probabilities.view(_B, self.beam_size * vocab_size),
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self.beam_size,
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dim=1,
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)
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# Apply the length penalty. The +1 accounts for the [EOS] token
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# that will be added if the beam ends.
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topk_scores = topk_log_probabilities / self.length_penalty()
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# Retrieve the corresponding respective beam and token id
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# topk_token_ids[i] will be added to topk_beam_ids[i]
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topk_beam_ids = topk_ids.div(vocab_size)
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topk_token_ids = topk_ids.fmod(vocab_size)
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# Retrieve the row index of the surviving beams in the original
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# view of the log_probabilities tensor
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surviving_beams_rows = (topk_beam_ids + self.beam_offset[:_B].view(-1, 1)).view(
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-1
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)
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# Append the last predictions
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self.growing_beam = torch.cat(
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[
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self.growing_beam.index_select(0, surviving_beams_rows),
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topk_token_ids.view(-1, 1),
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],
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1,
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)
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# Check if any of the beam searches has ended during this
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# growth step. Also if top beam (most probable) has ended
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# for one element of the batch.
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is_finished = topk_token_ids.eq(self.end_token_id)
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self.enforce_max_length()
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is_top_beam_finished = is_finished[:, 0].eq(1)
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# Save the finished searches
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if is_finished.any():
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predictions = self.growing_beam.view(
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-1, self.beam_size, self.growing_beam.size(1)
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)
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for i in range(is_finished.size(0)):
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if is_top_beam_finished[i]:
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is_finished[i].fill_(1)
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finished_hyp = is_finished[i].nonzero().view(-1)
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# Store finished hypotheses for this batch.
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b = self.batch_offset[i]
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for j in finished_hyp:
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self.hypotheses[b].append((topk_scores[i, j], predictions[i, j, :]))
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# If the batch reached the end, save the best hypotheses
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# in terms of length-penalized score.
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if is_top_beam_finished[i]:
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best_hyp = sorted(
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self.hypotheses[b], key=lambda x: x[0], reverse=True
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)
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best_score, best_prediction = best_hyp[0]
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self.results["scores"][b].append(best_score)
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self.results["predictions"][b].append(best_prediction)
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non_finished = is_top_beam_finished.eq(0).nonzero().view(-1)
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if len(non_finished) == 0:
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self.is_done = True
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# Remove finished batches for the next step.
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topk_log_probabilities = topk_log_probabilities.index_select(
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0, non_finished
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)
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self.batch_offset = self.batch_offset.index_select(0, non_finished)
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self.growing_beam = predictions.index_select(0, non_finished).view(
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-1, self.growing_beam.size(-1)
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)
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surviving_beams_rows = surviving_beams_rows.index_select(0, non_finished)
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return surviving_beams_rows
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def forward(self, encoder_input_ids, **kwargs):
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# keyword arguments come in 3 flavors: encoder-specific (prefixed by
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# `encoder_`), decoder-specific (prefixed by `decoder_`) and those
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# that apply to the model as whole.
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# We let the specific kwargs override the common ones in case of conflict.
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kwargs_encoder = {
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argument[len("encoder_"):]: value
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for argument, value in kwargs.items()
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if argument.startswith("encoder_")
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}
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kwargs_decoder = {
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argument[len("decoder_"):]: value
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for argument, value in kwargs.items()
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if argument.startswith("decoder_")
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}
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kwargs_common = {
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argument: value
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for argument, value in kwargs.items()
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if not (argument.startswith("encoder_") or argument.startswith("decoder_"))
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}
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kwargs_decoder = dict(kwargs_common, **kwargs_decoder)
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kwargs_encoder = dict(kwargs_common, **kwargs_encoder)
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# forward pass on the encoder
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encoder_outputs = self.model.encoder.forward(encoder_input_ids, kwargs_encoder)
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kwargs_decoder["encoder_hidden_states"] = tile(
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encoder_outputs, self.beam_size, dim=0
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)
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# grow the beam by generating sequences in an autoregressive way
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self.growing_beam = torch.full(
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(self.batch_size * self.beam_size, 1), self.start_token_id, dtype=torch.long
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)
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for step in range(self.max_length):
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decoder_input = self.growing_beam[:, -1]
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outputs = self.model.decoder(decoder_input, kwargs_decoder)
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log_probabilities = torch.nn.functional.log_softmax(outputs[1])
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surviving_beams_rows = self.step(log_probabilities)
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if self.is_done:
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break
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kwargs_decoder["encoder_hidden_states"] = kwargs_decoder[
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"encoder_hidden_states"
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].index_select(0, surviving_beams_rows)
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return self.results
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def remove_repeating_trigrams(self, log_probabilities, _B):
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if(self._step + 1 > 3):
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for i in range(_B * self.beam_size):
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tokens = [t for t in self.growing_beam[i]]
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trigrams = [(tokens[i-1], tokens[i], tokens[i+1]) for i in range(1, len(words) - 1)]
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last_trigram = tuple(trigrams[-1])
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if last_trigram in trigrams[:-1]:
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log_probabilities[i] = -1e20
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def enforce_min_length(self):
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if self._step < self.min_length:
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self.log_probabilities[self.end_token_id] = -1e20
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def enforce_max_length(self):
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if self._step + 1 == self.max_length:
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self.is_finished.fill_(1)
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def length_penalty(self):
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return ((5.0 + (self._step + 1)) / 6.0) ** self.alpha
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def tile(x, count, dim=0):
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"""
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Tiles `x` along dimension `dim` `count` times.
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Example:
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>> ex = torch.tensor([1,2],[3,4])
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>> tile(ex, 2, 0)
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torch.Tensor([[1,2],[1,2],[3,4],[3,4]])
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"""
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perm = list(range(len(x.size())))
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if dim != 0:
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perm[0], perm[dim] = perm[dim], perm[0]
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x = x.permute(perm).contiguous()
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out_size = list(x.size())
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out_size[0] *= count
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batch = x.size(0)
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x = (
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x.view(batch, -1)
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.transpose(0, 1)
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.repeat(count, 1)
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.transpose(0, 1)
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.contiguous()
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.view(*out_size)
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)
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if dim != 0:
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x = x.permute(perm).contiguous()
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return x
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