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https://github.com/huggingface/transformers.git
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aad95c7cde
* Removed `max_length` from being mandatory within `generate`. - Moving on to fully using `StoppingCriteria` for `greedy` and `sample` modes. - `max_length` still used for `beam_search` and `group_beam_search` (Follow up PR) - Fixes a bug with MaxLengthStoppingCriteria (we should stop as soon a we hit the max_length, the comparison needs to be or equal, that affects the tests). - Added options to use `logits_processor` and `stopping_criteria` directly within `generate` function (so some users can define their own `logits_processor` and `stopping_criteria`). - Modified the backward compat tests to make sure we issue a warning. * Fix `max_length` argument in `generate`. * Moving validate to being functional. - Renamed `smax_length` to `stoppping_max_length`. * Removing `logits_processor` and `stopping_criteria` from `generate` arguments. * Deepcopy. * Fix global variable name.
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import time
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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 ids_tensor
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if is_torch_available():
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import torch
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from transformers.generation_stopping_criteria import (
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MaxLengthCriteria,
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MaxTimeCriteria,
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StoppingCriteriaList,
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validate_stopping_criteria,
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)
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@require_torch
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class StoppingCriteriaTestCase(unittest.TestCase):
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def _get_tensors(self, length):
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batch_size = 3
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vocab_size = 250
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input_ids = ids_tensor((batch_size, length), vocab_size)
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scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length
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return input_ids, scores
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def test_list_criteria(self):
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input_ids, scores = self._get_tensors(5)
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criteria = StoppingCriteriaList(
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[
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MaxLengthCriteria(max_length=10),
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MaxTimeCriteria(max_time=0.1),
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]
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)
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self.assertFalse(criteria(input_ids, scores))
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input_ids, scores = self._get_tensors(9)
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self.assertFalse(criteria(input_ids, scores))
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input_ids, scores = self._get_tensors(10)
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self.assertTrue(criteria(input_ids, scores))
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def test_max_length_criteria(self):
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criteria = MaxLengthCriteria(max_length=10)
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input_ids, scores = self._get_tensors(5)
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self.assertFalse(criteria(input_ids, scores))
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input_ids, scores = self._get_tensors(9)
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self.assertFalse(criteria(input_ids, scores))
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input_ids, scores = self._get_tensors(10)
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self.assertTrue(criteria(input_ids, scores))
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def test_max_time_criteria(self):
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input_ids, scores = self._get_tensors(5)
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criteria = MaxTimeCriteria(max_time=0.1)
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self.assertFalse(criteria(input_ids, scores))
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criteria = MaxTimeCriteria(max_time=0.1, initial_timestamp=time.time() - 0.2)
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self.assertTrue(criteria(input_ids, scores))
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def test_validate_stopping_criteria(self):
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validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 10)
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with self.assertWarns(UserWarning):
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validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 11)
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stopping_criteria = validate_stopping_criteria(StoppingCriteriaList(), 11)
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self.assertEqual(len(stopping_criteria), 1)
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