578 lines
24 KiB
Python
578 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a clone of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, torch_device
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from ..test_modeling_common import floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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from transformers.generation import (
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BeamHypotheses,
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BeamSearchScorer,
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ConstrainedBeamSearchScorer,
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DisjunctiveConstraint,
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PhrasalConstraint,
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)
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class BeamSearchTester:
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def __init__(
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self,
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parent,
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batch_size=3,
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sequence_length=10,
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vocab_size=99,
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pad_token_id=0,
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max_length=20,
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num_beams=4,
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length_penalty=2.0,
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do_early_stopping=True,
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num_beam_hyps_to_keep=2,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.sequence_length = sequence_length
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self.vocab_size = vocab_size
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self.pad_token_id = pad_token_id
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self.max_length = max_length
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self.num_beams = num_beams
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self.length_penalty = length_penalty
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self.do_early_stopping = do_early_stopping
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self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
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# cannot be randomly generated
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self.eos_token_id = vocab_size + 1
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def prepare_beam_scorer(self, **kwargs):
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return BeamSearchScorer(
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batch_size=kwargs.get("batch_size", self.batch_size),
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num_beams=kwargs.get("num_beams", self.num_beams),
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device=torch_device,
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length_penalty=kwargs.get("length_penalty", self.length_penalty),
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do_early_stopping=kwargs.get("do_early_stopping", self.do_early_stopping),
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num_beam_hyps_to_keep=kwargs.get("num_beam_hyps_to_keep", self.num_beam_hyps_to_keep),
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)
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def prepare_inputs(self):
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input_ids = ids_tensor((self.batch_size * self.num_beams, self.sequence_length), self.vocab_size)
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next_tokens = ids_tensor((self.batch_size, 2 * self.num_beams), self.vocab_size).to(torch_device)
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next_indices = ids_tensor((self.batch_size, 2 * self.num_beams), self.num_beams).to(torch_device)
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next_scores, _ = (-floats_tensor((self.batch_size, 2 * self.num_beams)).to(torch_device)).sort(descending=True)
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return (input_ids, next_tokens, next_indices, next_scores)
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def check_beam_hypotheses(self, input_ids, *args):
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# check that correct number of beam hypotheses is set in beam scorer
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beam_scorer = self.prepare_beam_scorer(do_early_stopping=True)
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beam_hyp = beam_scorer._beam_hyps[0]
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self.parent.assertEqual(len(beam_scorer._beam_hyps), self.batch_size)
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# check correct type
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self.parent.assertTrue(isinstance(beam_hyp, BeamHypotheses))
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# check that num_beams is correctly set
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self.parent.assertEqual(beam_hyp.num_beams, self.num_beams)
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# check for early stopping deactivated
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for beam_idx in range(self.num_beams):
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beam_hyp.add(input_ids[beam_idx], -10.0)
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# if early stopping True -> score does not matter
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self.parent.assertTrue(beam_hyp.is_done(-10.0, 5))
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# re-init
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beam_scorer = self.prepare_beam_scorer(do_early_stopping=False)
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beam_hyp = beam_scorer._beam_hyps[0]
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# add `num_beams + 1` beams to change `worst_score`
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for beam_idx in range(self.num_beams + 1):
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beam_hyp.add(input_ids[beam_idx], -10.0 + float(beam_idx))
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# -10.0 is removed => -9.0 is worst score
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self.parent.assertAlmostEqual(beam_hyp.worst_score, -9.0 / (self.sequence_length**beam_hyp.length_penalty))
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# -5.0 is better than worst score => should not be finished
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self.parent.assertFalse(beam_hyp.is_done(-5.0, self.sequence_length))
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# -20.0 is worse than worst score => should be finished
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self.parent.assertTrue(beam_hyp.is_done(-20.0, self.sequence_length))
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def check_beam_scorer_update(self, input_ids, next_tokens, next_indices, next_scores):
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# check too many eos tokens
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beam_scorer = self.prepare_beam_scorer()
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tokens = next_tokens.clone()
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tokens[0, :] = self.eos_token_id
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with self.parent.assertRaises(ValueError):
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beam_scorer.process(input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id)
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# check all batches are done
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beam_scorer = self.prepare_beam_scorer()
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tokens = next_tokens.clone()
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tokens[:, : self.num_beams] = self.eos_token_id
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beam_indices = torch.zeros_like(input_ids) + torch.arange(input_ids.shape[-1], device=input_ids.device)
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beam_indices = tuple(tuple(b) for b in beam_indices)
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beam_scorer.process(
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input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id, beam_indices=beam_indices
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)
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# beam scorer should be done
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self.parent.assertTrue(beam_scorer.is_done)
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# check
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beam_scorer = self.prepare_beam_scorer()
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tokens = next_tokens.clone()
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tokens[:, 1] = self.eos_token_id
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beam_outputs = beam_scorer.process(
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input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id, beam_indices=beam_indices
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)
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output_scores = beam_outputs["next_beam_scores"]
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output_tokens = beam_outputs["next_beam_tokens"]
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output_indices = beam_outputs["next_beam_indices"]
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def cut_expected_tensor(tensor):
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return torch.cat([tensor[:, :1], tensor[:, 2 : self.num_beams + 1]], dim=1).flatten()
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# check all outptus
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# cut out id of eos token and take best `num_beams` outputs
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expected_output_tokens = cut_expected_tensor(tokens)
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expected_output_scores = cut_expected_tensor(next_scores)
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# add num_beams * batch_idx
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offset = torch.div(
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torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams, rounding_mode="floor"
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)
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expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
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self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
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self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())
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self.parent.assertTrue(torch.allclose(expected_output_scores, output_scores, atol=1e-3))
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# make sure ids of eos token are correctly saved in beam_hyps of beam scorer
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expected_beam_indices = list(range(10))
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for batch_idx in range(self.batch_size):
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correct_idx = batch_idx * self.num_beams + next_indices[batch_idx, 1]
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self.parent.assertListEqual(
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input_ids[correct_idx].tolist(), beam_scorer._beam_hyps[batch_idx].beams[0][1].tolist()
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)
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self.parent.assertListEqual(
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expected_beam_indices + [correct_idx],
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torch.tensor(beam_scorer._beam_hyps[batch_idx].beams[0][2]).tolist(),
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)
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def check_beam_scores_finalize(self, input_ids, next_tokens, next_indices, next_scores):
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# max_length should be only one more than current input_ids to check that eos is correctly appended
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max_length = self.sequence_length + 1
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beam_scorer = self.prepare_beam_scorer(num_beam_hyps_to_keep=1, length_penalty=1.0, do_early_stopping=False)
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# update beams and append to input_ids
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tokens = next_tokens.clone()
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# first batch, first output has to finish with eos token id since scores are correctly sorted
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tokens[0, 0] = self.eos_token_id
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# make sure corresponding score is as good as possible to surely be picked first
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next_scores[0, 0] = 0.0
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beam_outputs = beam_scorer.process(
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input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id
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)
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output_scores = beam_outputs["next_beam_scores"]
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output_tokens = beam_outputs["next_beam_tokens"]
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output_indices = beam_outputs["next_beam_indices"]
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input_ids = torch.cat([input_ids[output_indices, :], output_tokens.unsqueeze(-1)], dim=-1)
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# finalize
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beam_indices = torch.zeros_like(input_ids) + torch.arange(input_ids.shape[-1], device=input_ids.device)
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beam_indices = tuple(tuple(b) for b in beam_indices)
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sequence_output = beam_scorer.finalize(
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input_ids,
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output_scores,
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output_tokens,
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output_indices,
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pad_token_id=self.pad_token_id,
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eos_token_id=self.eos_token_id,
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max_length=max_length,
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beam_indices=beam_indices,
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)
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sequences = sequence_output["sequences"]
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sequence_scores = sequence_output["sequence_scores"]
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# since `num_beam_hyps_to_keep` = 1 => only return `batch_size` x `max_length`
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self.parent.assertListEqual(list(sequences.shape), [self.batch_size, max_length])
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self.parent.assertListEqual(list(sequence_scores.shape), [self.batch_size])
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# check sequence_scores
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self.parent.assertFalse((sequence_scores > 0).any().item())
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# first batch has to finish with eos_token
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self.parent.assertEqual(sequences[0, -1].item(), self.eos_token_id)
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# other batches cannot finish with eos token
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self.parent.assertNotEqual(sequences[1, -1].item(), self.eos_token_id)
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self.parent.assertNotEqual(sequences[2, -1].item(), self.eos_token_id)
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# now test that if `num_beam_hyps_to_keep` is 3 => all beams are returned
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beam_scorer.num_beam_hyps_to_keep = self.num_beams
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sequence_output = beam_scorer.finalize(
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input_ids,
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output_scores,
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output_tokens,
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output_indices,
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pad_token_id=self.pad_token_id,
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eos_token_id=self.eos_token_id,
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max_length=max_length,
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beam_indices=beam_indices,
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)
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sequences = sequence_output["sequences"]
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sequence_scores = sequence_output["sequence_scores"]
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self.parent.assertListEqual(list(sequences.shape), [self.num_beams * self.batch_size, max_length])
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self.parent.assertListEqual(list(sequence_scores.shape), [self.num_beams * self.batch_size])
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class ConstrainedBeamSearchTester:
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def __init__(
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self,
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parent,
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constraints=None,
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batch_size=3,
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sequence_length=10,
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vocab_size=99,
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pad_token_id=0,
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max_length=20,
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num_beams=4,
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length_penalty=2.0,
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do_early_stopping=True,
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num_beam_hyps_to_keep=2,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.sequence_length = sequence_length
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self.vocab_size = vocab_size
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self.pad_token_id = pad_token_id
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self.max_length = max_length
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self.num_beams = num_beams
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self.length_penalty = length_penalty
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self.do_early_stopping = do_early_stopping
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self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
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if constraints is None:
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force_tokens = torch.randint(10, 50, (1, 2))[0].tolist()
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disjunctive_tokens = torch.randint(10, 50, (2, 2)).tolist()
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constraints = [PhrasalConstraint(force_tokens), DisjunctiveConstraint(disjunctive_tokens)]
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self.constraints = constraints
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# cannot be randomly generated
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self.eos_token_id = vocab_size + 1
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def prepare_constrained_beam_scorer(self, **kwargs):
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return ConstrainedBeamSearchScorer(
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constraints=kwargs.get("constraints", self.constraints),
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batch_size=kwargs.get("batch_size", self.batch_size),
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num_beams=kwargs.get("num_beams", self.num_beams),
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device=torch_device,
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length_penalty=kwargs.get("length_penalty", self.length_penalty),
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do_early_stopping=kwargs.get("do_early_stopping", self.do_early_stopping),
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num_beam_hyps_to_keep=kwargs.get("num_beam_hyps_to_keep", self.num_beam_hyps_to_keep),
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)
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def prepare_inputs(self):
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input_ids = ids_tensor((self.batch_size * self.num_beams, self.sequence_length), self.vocab_size)
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next_tokens = ids_tensor((self.batch_size, 2 * self.num_beams), self.vocab_size).to(torch_device)
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next_indices = ids_tensor((self.batch_size, 2 * self.num_beams), self.num_beams).to(torch_device)
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next_scores, _ = (-floats_tensor((self.batch_size, 2 * self.num_beams)).to(torch_device)).sort(descending=True)
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scores_for_all_vocab, _ = (
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-floats_tensor((self.batch_size * self.num_beams, self.vocab_size)).to(torch_device)
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).sort(descending=True)
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return (input_ids, next_tokens, next_indices, next_scores, scores_for_all_vocab)
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def check_beam_hypotheses(self, input_ids, *args):
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# check that correct number of beam hypotheses is set in beam scorer
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constrained_beam_scorer = self.prepare_constrained_beam_scorer(do_early_stopping=True)
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beam_hyp = constrained_beam_scorer._beam_hyps[0]
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self.parent.assertEqual(len(constrained_beam_scorer._beam_hyps), self.batch_size)
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# check correct type
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self.parent.assertTrue(isinstance(beam_hyp, BeamHypotheses))
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# check that num_beams is correctly set
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self.parent.assertEqual(beam_hyp.num_beams, self.num_beams)
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# check for early stopping deactivated
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for beam_idx in range(self.num_beams):
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beam_hyp.add(input_ids[beam_idx], -10.0)
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# if early stopping True -> score does not matter
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self.parent.assertTrue(beam_hyp.is_done(-10.0, 5))
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# re-init
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constrained_beam_scorer = self.prepare_constrained_beam_scorer(do_early_stopping=False)
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beam_hyp = constrained_beam_scorer._beam_hyps[0]
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# add `num_beams + 1` beams to change `worst_score`
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for beam_idx in range(self.num_beams + 1):
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beam_hyp.add(input_ids[beam_idx], -10.0 + float(beam_idx))
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# -10.0 is removed => -9.0 is worst score
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self.parent.assertAlmostEqual(beam_hyp.worst_score, -9.0 / (self.sequence_length**beam_hyp.length_penalty))
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# -5.0 is better than worst score => should not be finished
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self.parent.assertFalse(beam_hyp.is_done(-5.0, self.sequence_length))
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# -20.0 is worse than worst score => should be finished
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self.parent.assertTrue(beam_hyp.is_done(-20.0, self.sequence_length))
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def check_constrained_beam_scorer_update(
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self, input_ids, next_tokens, next_indices, next_scores, scores_for_all_vocab
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):
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# check too many eos tokens
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constrained_beam_scorer = self.prepare_constrained_beam_scorer()
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stacked_token_ids = []
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for constraint in self.constraints:
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token_ids = constraint.token_ids
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token_ids = token_ids[0] if isinstance(token_ids[0], list) else token_ids
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stacked_token_ids = stacked_token_ids + token_ids
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fulfilling_sequence = torch.LongTensor(stacked_token_ids)
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fulfill_len = fulfilling_sequence.size(0)
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input_ids[:, :fulfill_len] = fulfilling_sequence
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tokens = next_tokens.clone()
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tokens[0, :] = self.eos_token_id
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with self.parent.assertRaises(ValueError):
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constrained_beam_scorer.process(
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input_ids, next_scores, tokens, next_indices, scores_for_all_vocab, eos_token_id=self.eos_token_id
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)
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# check all batches are done
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constrained_beam_scorer = self.prepare_constrained_beam_scorer()
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tokens = next_tokens.clone()
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tokens[:, : self.num_beams] = self.eos_token_id
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constrained_beam_scorer.process(
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input_ids, next_scores, tokens, next_indices, scores_for_all_vocab, eos_token_id=self.eos_token_id
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)
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# beam scorer should be done
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self.parent.assertTrue(constrained_beam_scorer.is_done)
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# check
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constrained_beam_scorer = self.prepare_constrained_beam_scorer()
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tokens = next_tokens.clone()
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tokens[:, 1] = self.eos_token_id
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beam_outputs = constrained_beam_scorer.process(
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input_ids, next_scores, tokens, next_indices, scores_for_all_vocab, eos_token_id=self.eos_token_id
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)
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output_scores = beam_outputs["next_beam_scores"]
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output_tokens = beam_outputs["next_beam_tokens"]
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output_indices = beam_outputs["next_beam_indices"]
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def cut_expected_tensor(tensor):
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return torch.cat([tensor[:, :1], tensor[:, 2 : self.num_beams + 1]], dim=1).flatten()
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# check all outptus
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# cut out id of eos token and take best `num_beams` outputs
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expected_output_tokens = cut_expected_tensor(tokens)
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expected_output_scores = cut_expected_tensor(next_scores)
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# add num_beams * batch_idx
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offset = torch.div(
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torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams, rounding_mode="floor"
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)
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expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
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self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
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self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())
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self.parent.assertTrue(torch.allclose(expected_output_scores, output_scores, atol=1e-3))
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# make sure ids of eos token are correctly saved in beam_hyps of beam scorer
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for batch_idx in range(self.batch_size):
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correct_idx = batch_idx * self.num_beams + next_indices[batch_idx, 1]
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self.parent.assertListEqual(
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input_ids[correct_idx].tolist(), constrained_beam_scorer._beam_hyps[batch_idx].beams[0][1].tolist()
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|
)
|
|
|
|
def check_constrained_beam_scorer_finalize(
|
|
self, input_ids, next_tokens, next_indices, next_scores, scores_for_all_vocab
|
|
):
|
|
# max_length should be only one more than current input_ids to check that eos is correctly appended
|
|
max_length = self.sequence_length + 1
|
|
|
|
# for testing finalize, we do want to have fulfilled constraints
|
|
stacked_token_ids = []
|
|
for constraint in self.constraints:
|
|
token_ids = constraint.token_ids
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|
token_ids = token_ids[0] if isinstance(token_ids[0], list) else token_ids
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|
stacked_token_ids = stacked_token_ids + token_ids
|
|
|
|
fulfilling_sequence = torch.LongTensor(stacked_token_ids)
|
|
|
|
fulfill_len = fulfilling_sequence.size(0)
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|
input_ids[:, :fulfill_len] = fulfilling_sequence
|
|
|
|
constrained_beam_scorer = self.prepare_constrained_beam_scorer(
|
|
num_beam_hyps_to_keep=1, length_penalty=1.0, do_early_stopping=False
|
|
)
|
|
|
|
constraints = constrained_beam_scorer.constraints
|
|
# update beams and append to input_ids
|
|
tokens = next_tokens.clone()
|
|
# first batch, first output has to finish with eos token id since scores are correctly sorted
|
|
tokens[0, 0] = self.eos_token_id
|
|
# make sure corresponding score is as good as possible to surely be picked first
|
|
next_scores[0, 0] = 0.0
|
|
|
|
beam_outputs = constrained_beam_scorer.process(
|
|
input_ids, next_scores, tokens, next_indices, scores_for_all_vocab, eos_token_id=self.eos_token_id
|
|
)
|
|
output_scores = beam_outputs["next_beam_scores"]
|
|
output_tokens = beam_outputs["next_beam_tokens"]
|
|
output_indices = beam_outputs["next_beam_indices"]
|
|
input_ids = torch.cat([input_ids[output_indices, :], output_tokens.unsqueeze(-1)], dim=-1)
|
|
|
|
# finalize
|
|
sequence_output = constrained_beam_scorer.finalize(
|
|
input_ids,
|
|
output_scores,
|
|
output_tokens,
|
|
output_indices,
|
|
pad_token_id=self.pad_token_id,
|
|
eos_token_id=self.eos_token_id,
|
|
max_length=max_length,
|
|
)
|
|
|
|
sequences = sequence_output["sequences"]
|
|
sequence_scores = sequence_output["sequence_scores"]
|
|
|
|
# since `num_beam_hyps_to_keep` = 1 => only return `batch_size` x `max_length`
|
|
self.parent.assertListEqual(list(sequences.shape), [self.batch_size, max_length])
|
|
self.parent.assertListEqual(list(sequence_scores.shape), [self.batch_size])
|
|
|
|
# check sequence_scores
|
|
self.parent.assertFalse((sequence_scores > 0).any().item())
|
|
|
|
# first batch has to finish with eos_token
|
|
self.parent.assertEqual(sequences[0, -1].item(), self.eos_token_id)
|
|
|
|
# other batches cannot finish with eos token
|
|
self.parent.assertNotEqual(sequences[1, -1].item(), self.eos_token_id)
|
|
self.parent.assertNotEqual(sequences[2, -1].item(), self.eos_token_id)
|
|
|
|
# test that the constraint is indeed fulfilled
|
|
for output, constraint in [(s, c) for s in sequences for c in constraints]:
|
|
forced_token_ids = constraint.token_ids
|
|
if isinstance(forced_token_ids[0], list):
|
|
# disjunctive case
|
|
flag = False
|
|
for token_ids in forced_token_ids:
|
|
if self._check_sequence_inside_sequence(output, token_ids):
|
|
flag = True
|
|
break
|
|
self.parent.assertEqual(flag, True)
|
|
else:
|
|
self.parent.assertEqual(self._check_sequence_inside_sequence(output, forced_token_ids), True)
|
|
|
|
# now test that if `num_beam_hyps_to_keep` is 3 => all beams are returned
|
|
|
|
# constrained_beam_scorer.num_beam_hyps_to_keep = self.num_beams
|
|
constrained_beam_scorer = self.prepare_constrained_beam_scorer(
|
|
num_beam_hyps_to_keep=self.num_beams, length_penalty=1.0, do_early_stopping=False
|
|
)
|
|
|
|
sequence_output = constrained_beam_scorer.finalize(
|
|
input_ids,
|
|
output_scores,
|
|
output_tokens,
|
|
output_indices,
|
|
pad_token_id=self.pad_token_id,
|
|
eos_token_id=self.eos_token_id,
|
|
max_length=max_length,
|
|
)
|
|
sequences = sequence_output["sequences"]
|
|
sequence_scores = sequence_output["sequence_scores"]
|
|
|
|
self.parent.assertListEqual(list(sequences.shape), [self.num_beams * self.batch_size, max_length])
|
|
self.parent.assertListEqual(list(sequence_scores.shape), [self.num_beams * self.batch_size])
|
|
|
|
def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
|
|
# check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
|
|
# set to same device. we don't care what device.
|
|
|
|
if not isinstance(tensor_1, list):
|
|
tensor_1 = tensor_1.cpu().tolist()
|
|
if not isinstance(tensor_2, list):
|
|
tensor_2 = tensor_2.cpu().tolist()
|
|
|
|
in_order = len(tensor_1) <= len(tensor_2)
|
|
longer = tensor_2 if in_order else tensor_1
|
|
shorter = tensor_1 if in_order else tensor_2
|
|
|
|
flag = False
|
|
chunk_size = len(shorter)
|
|
for chunk_idx in range(len(longer) - chunk_size + 1):
|
|
subseq = longer[chunk_idx : chunk_idx + chunk_size]
|
|
if subseq == shorter:
|
|
flag = True
|
|
break
|
|
|
|
return flag
|
|
|
|
|
|
@require_torch
|
|
class BeamSearchTest(unittest.TestCase):
|
|
def setUp(self):
|
|
self.beam_search_tester = BeamSearchTester(self)
|
|
|
|
def test_beam_hypotheses(self):
|
|
inputs = self.beam_search_tester.prepare_inputs()
|
|
self.beam_search_tester.check_beam_hypotheses(*inputs)
|
|
|
|
def test_beam_scorer_update(self):
|
|
inputs = self.beam_search_tester.prepare_inputs()
|
|
self.beam_search_tester.check_beam_scorer_update(*inputs)
|
|
|
|
def test_beam_scorer_finalize(self):
|
|
inputs = self.beam_search_tester.prepare_inputs()
|
|
self.beam_search_tester.check_beam_scores_finalize(*inputs)
|
|
|
|
|
|
@require_torch
|
|
class ConstrainedBeamSearchTest(unittest.TestCase):
|
|
def setUp(self):
|
|
self.constrained_beam_search_tester = ConstrainedBeamSearchTester(self)
|
|
|
|
def test_constrained_beam_hypotheses(self):
|
|
inputs = self.constrained_beam_search_tester.prepare_inputs()
|
|
self.constrained_beam_search_tester.check_beam_hypotheses(*inputs)
|
|
|
|
def test_constrained_beam_scorer_update(self):
|
|
inputs = self.constrained_beam_search_tester.prepare_inputs()
|
|
self.constrained_beam_search_tester.check_constrained_beam_scorer_update(*inputs)
|
|
|
|
def test_constrained_beam_scorer_finalize(self):
|
|
inputs = self.constrained_beam_search_tester.prepare_inputs()
|
|
self.constrained_beam_search_tester.check_constrained_beam_scorer_finalize(*inputs)
|