209 lines
11 KiB
Python
209 lines
11 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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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 copy 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 PegasusTokenizer, PegasusTokenizerFast
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from transformers.file_utils import cached_property
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
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from .test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
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@require_sentencepiece
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@require_tokenizers
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class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = PegasusTokenizer
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rust_tokenizer_class = PegasusTokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = PegasusTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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@cached_property
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def _large_tokenizer(self):
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return PegasusTokenizer.from_pretrained("google/pegasus-large")
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def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
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return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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return ("This is a test", "This is a test")
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "</s>"
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token_id = 1
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "<pad>")
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self.assertEqual(vocab_keys[1], "</s>")
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self.assertEqual(vocab_keys[-1], "v")
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self.assertEqual(len(vocab_keys), 1_103)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 1_103)
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def test_mask_tokens_rust_pegasus(self):
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rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
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py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname)
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raw_input_str = "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important </s> <pad> <pad> <pad>"
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rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
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py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
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self.assertListEqual(py_ids, rust_ids)
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def test_large_mask_tokens(self):
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tokenizer = self._large_tokenizer
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# <mask_1> masks whole sentence while <mask_2> masks single word
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raw_input_str = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
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desired_result = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
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ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
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self.assertListEqual(desired_result, ids)
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def test_large_tokenizer_settings(self):
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tokenizer = self._large_tokenizer
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# The tracebacks for the following asserts are **better** without messages or self.assertEqual
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assert tokenizer.vocab_size == 96103
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assert tokenizer.pad_token_id == 0
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assert tokenizer.eos_token_id == 1
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assert tokenizer.offset == 103
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assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
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assert tokenizer.unk_token == "<unk>"
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assert tokenizer.model_max_length == 1024
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raw_input_str = "To ensure a smooth flow of bank resolutions."
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desired_result = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
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ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
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self.assertListEqual(desired_result, ids)
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assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
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@require_torch
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def test_large_seq2seq_truncation(self):
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src_texts = ["This is going to be way too long." * 150, "short example"]
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tgt_texts = ["not super long but more than 5 tokens", "tiny"]
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batch = self._large_tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt")
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with self._large_tokenizer.as_target_tokenizer():
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targets = self._large_tokenizer(
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tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pt"
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)
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assert batch.input_ids.shape == (2, 1024)
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assert batch.attention_mask.shape == (2, 1024)
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assert targets["input_ids"].shape == (2, 5)
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assert len(batch) == 2 # input_ids, attention_mask.
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@slow
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def test_tokenizer_integration(self):
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# fmt: off
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expected_encoding = {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
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# fmt: on
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="google/bigbird-pegasus-large-arxiv",
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revision="ba85d0851d708441f91440d509690f1ab6353415",
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)
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@require_sentencepiece
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@require_tokenizers
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class BigBirdPegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = PegasusTokenizer
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rust_tokenizer_class = PegasusTokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = PegasusTokenizer(SAMPLE_VOCAB, offset=0, mask_token_sent=None, mask_token="[MASK]")
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tokenizer.save_pretrained(self.tmpdirname)
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@cached_property
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def _large_tokenizer(self):
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return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
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def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
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return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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return ("This is a test", "This is a test")
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def test_mask_tokens_rust_pegasus(self):
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rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
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py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname)
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raw_input_str = "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s> <pad> <pad> <pad>"
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rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
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py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
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self.assertListEqual(py_ids, rust_ids)
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@require_torch
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def test_large_seq2seq_truncation(self):
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src_texts = ["This is going to be way too long." * 1000, "short example"]
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tgt_texts = ["not super long but more than 5 tokens", "tiny"]
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batch = self._large_tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt")
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with self._large_tokenizer.as_target_tokenizer():
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targets = self._large_tokenizer(
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tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pt"
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)
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assert batch.input_ids.shape == (2, 4096)
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assert batch.attention_mask.shape == (2, 4096)
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assert targets["input_ids"].shape == (2, 5)
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assert len(batch) == 2 # input_ids, attention_mask.
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def test_equivalence_to_orig_tokenizer(self):
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"""
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To run with original TF tokenizer:
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!wget https://github.com/google-research/bigbird/raw/master/bigbird/vocab/pegasus.model
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!pip install tensorflow-text
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import tensorflow.compat.v2 as tf
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import tensorflow_text as tft
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VOCAB_FILE = "./pegasus.model"
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tf.enable_v2_behavior()
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test_str = "This is an example string that is used to test the original TF implementation against the HF implementation"
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tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(VOCAB_FILE, "rb").read())
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tokenizer.tokenize(test_str)
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"""
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test_str = "This is an example string that is used to test the original TF implementation against the HF implementation"
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token_ids = self._large_tokenizer(test_str).input_ids
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self.assertListEqual(
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token_ids,
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[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1],
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)
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