246 lines
13 KiB
Python
246 lines
13 KiB
Python
# Copyright 2021 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 os
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import tempfile
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import unittest
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from transformers import SPIECE_UNDERLINE, BatchEncoding, MBart50Tokenizer, MBart50TokenizerFast, is_torch_available
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from transformers.testing_utils import nested_simplify, require_sentencepiece, require_tokenizers, require_torch, slow
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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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if is_torch_available():
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from transformers.models.mbart.modeling_mbart import shift_tokens_right
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EN_CODE = 250004
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RO_CODE = 250020
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@require_sentencepiece
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@require_tokenizers
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class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = MBart50Tokenizer
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rust_tokenizer_class = MBart50TokenizerFast
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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 = MBart50Tokenizer(SAMPLE_VOCAB, src_lang="en_XX", tgt_lang="ro_RO", keep_accents=True)
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tokenizer.save_pretrained(self.tmpdirname)
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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 = 0
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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], "<s>")
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self.assertEqual(vocab_keys[1], "<pad>")
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self.assertEqual(vocab_keys[-1], "<mask>")
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self.assertEqual(len(vocab_keys), 1_054)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 1_054)
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def test_full_tokenizer(self):
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tokenizer = MBart50Tokenizer(SAMPLE_VOCAB, src_lang="en_XX", tgt_lang="ro_RO", 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),
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[value + tokenizer.fairseq_offset for value in [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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# fmt: off
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[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."],
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# fmt: on
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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,
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[
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value + tokenizer.fairseq_offset
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for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
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],
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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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# fmt: off
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[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."],
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# fmt: on
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)
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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': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 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]], '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, 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, 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, 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, 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="facebook/mbart-large-50",
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revision="d3913889c59cd5c9e456b269c376325eabad57e2",
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)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class MBartOneToManyIntegrationTest(unittest.TestCase):
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checkpoint_name = "facebook/mbart-large-50-one-to-many-mmt"
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src_text = [
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" UN Chief Says There Is No Military Solution in Syria",
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""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
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]
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tgt_text = [
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"Şeful ONU declară că nu există o soluţie militară în Siria",
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'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
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]
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expected_src_tokens = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]
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@classmethod
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def setUpClass(cls):
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cls.tokenizer: MBart50Tokenizer = MBart50Tokenizer.from_pretrained(
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cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO"
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)
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cls.pad_token_id = 1
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return cls
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def check_language_codes(self):
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self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001)
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self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004)
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self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020)
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self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"], 250038)
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def test_tokenizer_batch_encode_plus(self):
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ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
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self.assertListEqual(self.expected_src_tokens, ids)
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def test_tokenizer_decode_ignores_language_codes(self):
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self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
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generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
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result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
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expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
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self.assertEqual(result, expected_romanian)
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self.assertNotIn(self.tokenizer.eos_token, result)
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def test_tokenizer_truncation(self):
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src_text = ["this is gunna be a long sentence " * 20]
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assert isinstance(src_text[0], str)
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desired_max_length = 10
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ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
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self.assertEqual(ids[0], EN_CODE)
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self.assertEqual(ids[-1], 2)
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self.assertEqual(len(ids), desired_max_length)
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def test_mask_token(self):
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self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250053, 250001])
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def test_special_tokens_unaffacted_by_save_load(self):
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tmpdirname = tempfile.mkdtemp()
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original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
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self.tokenizer.save_pretrained(tmpdirname)
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new_tok = MBart50Tokenizer.from_pretrained(tmpdirname)
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self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
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@require_torch
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def test_batch_fairseq_parity(self):
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batch = self.tokenizer(self.src_text, padding=True)
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with self.tokenizer.as_target_tokenizer():
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targets = self.tokenizer(self.tgt_text, padding=True, return_tensors="pt")
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labels = targets["input_ids"]
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batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id).tolist()
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labels = labels.tolist()
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# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
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assert batch.input_ids[1][0] == EN_CODE
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assert batch.input_ids[1][-1] == 2
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assert labels[1][0] == RO_CODE
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assert labels[1][-1] == 2
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assert batch.decoder_input_ids[1][:2] == [2, RO_CODE]
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@require_torch
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def test_tokenizer_prepare_batch(self):
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batch = self.tokenizer(
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self.src_text, padding=True, truncation=True, max_length=len(self.expected_src_tokens), return_tensors="pt"
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)
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with self.tokenizer.as_target_tokenizer():
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targets = self.tokenizer(
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self.tgt_text,
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padding=True,
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truncation=True,
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max_length=len(self.expected_src_tokens),
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return_tensors="pt",
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)
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labels = targets["input_ids"]
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batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual((2, 14), batch.input_ids.shape)
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self.assertEqual((2, 14), batch.attention_mask.shape)
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result = batch.input_ids.tolist()[0]
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self.assertListEqual(self.expected_src_tokens, result)
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self.assertEqual(2, batch.decoder_input_ids[0, 0]) # decoder_start_token_id
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# Test that special tokens are reset
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self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE])
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self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
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def test_seq2seq_max_target_length(self):
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batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
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with self.tokenizer.as_target_tokenizer():
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targets = self.tokenizer(self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt")
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labels = targets["input_ids"]
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batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
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self.assertEqual(batch.input_ids.shape[1], 3)
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self.assertEqual(batch.decoder_input_ids.shape[1], 10)
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@require_torch
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def test_tokenizer_translation(self):
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inputs = self.tokenizer._build_translation_inputs(
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"A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR"
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)
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self.assertEqual(
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nested_simplify(inputs),
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{
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# en_XX, A, test, EOS
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"input_ids": [[250004, 62, 3034, 2]],
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"attention_mask": [[1, 1, 1, 1]],
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# ar_AR
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"forced_bos_token_id": 250001,
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},
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)
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