Refactoring the generate() function (#6949)
* first draft * show design proposition for new generate method * up * make better readable * make first version * gpt2 tests pass * make beam search for gpt2 work * add first encoder-decoder code * delete typo * make t5 work * save indermediate * make bart work with beam search * finish beam search bart / t5 * add default kwargs * make more tests pass * fix no bad words sampler * some fixes and tests for all distribution processors * fix test * fix rag slow tests * merge to master * add nograd to generate * make all slow tests pass * speed up generate * fix edge case bug * small fix * correct typo * add type hints and docstrings * fix typos in tests * add beam search tests * add tests for beam scorer * fix test rag * finish beam search tests * move generation tests in seperate file * fix generation tests * more tests * add aggressive generation tests * fix tests * add gpt2 sample test * add more docstring * add more docs * finish doc strings * apply some more of sylvains and sams comments * fix some typos * make fix copies * apply lysandres and sylvains comments * final corrections on examples * small fix for reformer
This commit is contained in:
Patrick von Platen
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GitHub
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b63beb743c
commit
a1bbcf3f6c
239
tests/test_generation_beam_search.py
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239
tests/test_generation_beam_search.py
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# 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_beam_search import BeamHypotheses, BeamSearchScorer
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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 randomely 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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max_length=kwargs.get("max_length", self.max_length),
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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_scorer.process(input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id)
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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
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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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expected_output_indices = (
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cut_expected_tensor(next_indices)
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+ (torch.arange(self.num_beams * self.batch_size, device=torch_device) // self.num_beams) * self.num_beams
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)
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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(), beam_scorer._beam_hyps[batch_idx].beams[0][-1].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(
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num_beam_hyps_to_keep=1, max_length=max_length, length_penalty=1.0, do_early_stopping=False
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)
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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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decoded = 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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)
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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(decoded.shape), [self.batch_size, max_length])
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# first batch has to finish with eos_token
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self.parent.assertEqual(decoded[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(decoded[1, -1].item(), self.eos_token_id)
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self.parent.assertNotEqual(decoded[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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decoded = 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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)
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self.parent.assertListEqual(list(decoded.shape), [self.num_beams * self.batch_size, max_length])
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@require_torch
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class BeamSearchTest(unittest.TestCase):
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def setUp(self):
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self.beam_search_tester = BeamSearchTester(self)
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def test_beam_hypotheses(self):
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inputs = self.beam_search_tester.prepare_inputs()
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self.beam_search_tester.check_beam_hypotheses(*inputs)
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def test_beam_scorer_update(self):
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inputs = self.beam_search_tester.prepare_inputs()
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self.beam_search_tester.check_beam_scorer_update(*inputs)
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def test_beam_scorer_finalize(self):
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inputs = self.beam_search_tester.prepare_inputs()
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self.beam_search_tester.check_beam_scores_finalize(*inputs)
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