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| # coding=utf-8 | |
| # Copyright 2020 The HuggingFace Team Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a clone of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import time | |
| import unittest | |
| from transformers import is_torch_available | |
| from transformers.testing_utils import require_torch, torch_device | |
| from ..test_modeling_common import ids_tensor | |
| if is_torch_available(): | |
| import torch | |
| from transformers.generation import ( | |
| MaxLengthCriteria, | |
| MaxNewTokensCriteria, | |
| MaxTimeCriteria, | |
| StoppingCriteriaList, | |
| validate_stopping_criteria, | |
| ) | |
| class StoppingCriteriaTestCase(unittest.TestCase): | |
| def _get_tensors(self, length): | |
| batch_size = 3 | |
| vocab_size = 250 | |
| input_ids = ids_tensor((batch_size, length), vocab_size) | |
| scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length | |
| return input_ids, scores | |
| def test_list_criteria(self): | |
| input_ids, scores = self._get_tensors(5) | |
| criteria = StoppingCriteriaList( | |
| [ | |
| MaxLengthCriteria(max_length=10), | |
| MaxTimeCriteria(max_time=0.1), | |
| ] | |
| ) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| input_ids, scores = self._get_tensors(9) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| input_ids, scores = self._get_tensors(10) | |
| self.assertTrue(criteria(input_ids, scores)) | |
| def test_max_length_criteria(self): | |
| criteria = MaxLengthCriteria(max_length=10) | |
| input_ids, scores = self._get_tensors(5) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| input_ids, scores = self._get_tensors(9) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| input_ids, scores = self._get_tensors(10) | |
| self.assertTrue(criteria(input_ids, scores)) | |
| def test_max_new_tokens_criteria(self): | |
| criteria = MaxNewTokensCriteria(start_length=5, max_new_tokens=5) | |
| input_ids, scores = self._get_tensors(5) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| input_ids, scores = self._get_tensors(9) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| input_ids, scores = self._get_tensors(10) | |
| self.assertTrue(criteria(input_ids, scores)) | |
| criteria_list = StoppingCriteriaList([criteria]) | |
| self.assertEqual(criteria_list.max_length, 10) | |
| def test_max_time_criteria(self): | |
| input_ids, scores = self._get_tensors(5) | |
| criteria = MaxTimeCriteria(max_time=0.1) | |
| self.assertFalse(criteria(input_ids, scores)) | |
| criteria = MaxTimeCriteria(max_time=0.1, initial_timestamp=time.time() - 0.2) | |
| self.assertTrue(criteria(input_ids, scores)) | |
| def test_validate_stopping_criteria(self): | |
| validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 10) | |
| with self.assertWarns(UserWarning): | |
| validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 11) | |
| stopping_criteria = validate_stopping_criteria(StoppingCriteriaList(), 11) | |
| self.assertEqual(len(stopping_criteria), 1) | |