wehub-resource-sync
192 lines
8.0 KiB
Python
192 lines
8.0 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# 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 tempfile
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import unittest
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from paddlenlp.transformers import SPIECE_UNDERLINE, MBart50Tokenizer
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from paddlenlp.transformers.mbart.modeling import shift_tokens_right
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from ...testing_utils import get_tests_dir, nested_simplify
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from ..test_tokenizer_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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EN_CODE = 250004
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RO_CODE = 250020
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class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = MBart50Tokenizer
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test_sentencepiece = True
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test_offsets = False
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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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[
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SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",",
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SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é",
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"."
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],
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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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[
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SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",",
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SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s",
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"<unk>", "."
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],
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# fmt: on
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)
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class MBart50OneToManyIntegrationTest(unittest.TestCase):
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checkpoint_name = "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"
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' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
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" 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_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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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="pd")
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targets = self.tokenizer(self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pd")
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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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def test_tokenizer_translation(self):
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inputs = self.tokenizer._build_translation_inputs(
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"A test", return_tensors="pd", 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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