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Add tests for Reformer tokenizer (#6485)
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Denisa Roberts
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tests/test_tokenization_reformer.py
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253
tests/test_tokenization_reformer.py
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 os
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import unittest
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from transformers.file_utils import cached_property
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from transformers.testing_utils import require_torch, slow
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from transformers.tokenization_reformer import SPIECE_UNDERLINE, ReformerTokenizer
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from .test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
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class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = ReformerTokenizer
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def setUp(self):
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super().setUp()
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tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(
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ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
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)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@cached_property
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def big_tokenizer(self):
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return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
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@slow
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def test_tokenization_base_easy_symbols(self):
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symbols = "Hello World!"
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original_tokenizer_encodings = [126, 32, 262, 152, 38, 72, 287]
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self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
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@slow
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def test_tokenization_base_hard_symbols(self):
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symbols = 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
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original_tokenizer_encodings = [
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108,
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265,
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24,
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111,
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4,
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258,
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156,
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35,
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28,
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275,
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3,
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259,
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297,
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260,
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84,
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4,
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35,
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110,
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44,
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8,
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259,
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91,
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268,
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21,
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11,
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209,
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274,
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109,
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266,
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277,
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117,
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86,
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93,
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315,
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258,
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278,
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258,
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277,
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258,
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0,
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258,
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288,
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258,
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319,
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258,
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0,
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258,
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0,
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258,
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0,
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258,
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0,
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258,
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287,
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258,
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315,
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258,
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289,
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258,
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278,
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99,
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269,
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266,
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262,
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8,
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259,
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241,
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4,
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217,
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230,
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268,
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266,
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55,
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168,
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106,
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75,
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193,
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266,
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223,
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27,
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49,
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26,
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282,
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25,
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264,
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299,
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19,
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26,
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0,
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258,
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277,
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117,
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86,
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93,
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176,
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183,
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270,
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11,
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262,
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42,
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61,
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265,
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]
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self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
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@slow
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@require_torch
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def test_torch_encode_plus_sent_to_model(self):
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import torch
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from transformers import ReformerModel, ReformerConfig
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# Build sequence
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first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
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sequence = " ".join(first_ten_tokens)
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encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt")
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batch_encoded_sequence = self.big_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
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config = ReformerConfig()
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# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
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config.axial_pos_shape = encoded_sequence["input_ids"].shape
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model = ReformerModel(config)
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# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
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assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
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with torch.no_grad():
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model(**encoded_sequence)
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model(**batch_encoded_sequence)
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