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https://github.com/huggingface/transformers.git
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9aeacb58ba
* [WIP] SP tokenizers * fixing tests for T5 * WIP tokenizers * serialization * update T5 * WIP T5 tokenization * slow to fast conversion script * Refactoring to move tokenzier implementations inside transformers * Adding gpt - refactoring - quality * WIP adding several tokenizers to the fast world * WIP Roberta - moving implementations * update to dev4 switch file loading to in-memory loading * Updating and fixing * advancing on the tokenizers - updating do_lower_case * style and quality * moving forward with tokenizers conversion and tests * MBart, T5 * dumping the fast version of transformer XL * Adding to autotokenizers + style/quality * update init and space_between_special_tokens * style and quality * bump up tokenizers version * add protobuf * fix pickle Bert JP with Mecab * fix newly added tokenizers * style and quality * fix bert japanese * fix funnel * limite tokenizer warning to one occurence * clean up file * fix new tokenizers * fast tokenizers deep tests * WIP adding all the special fast tests on the new fast tokenizers * quick fix * adding more fast tokenizers in the fast tests * all tokenizers in fast version tested * Adding BertGenerationFast * bump up setup.py for CI * remove BertGenerationFast (too early) * bump up tokenizers version * Clean old docstrings * Typo * Update following Lysandre comments Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
215 lines
8.7 KiB
Python
215 lines
8.7 KiB
Python
import tempfile
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import unittest
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from transformers import AutoTokenizer, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
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from transformers.testing_utils import require_torch
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from .test_tokenization_common import TokenizerTesterMixin
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from .test_tokenization_xlm_roberta import SAMPLE_VOCAB, SPIECE_UNDERLINE
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if is_torch_available():
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from transformers.modeling_bart import shift_tokens_right
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EN_CODE = 250004
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RO_CODE = 250020
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class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = MBartTokenizer
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rust_tokenizer_class = MBartTokenizerFast
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test_rust_tokenizer = 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 = MBartTokenizer(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 = MBartTokenizer(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),
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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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[
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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,
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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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# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
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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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[
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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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@require_torch
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class MBartEnroIntegrationTest(unittest.TestCase):
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checkpoint_name = "facebook/mbart-large-en-ro"
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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 = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
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@classmethod
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def setUpClass(cls):
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cls.tokenizer: MBartTokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name)
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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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def test_enro_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_enro_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_enro_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.prepare_seq2seq_batch(
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src_text,
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return_tensors=None,
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max_length=desired_max_length,
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).input_ids[0]
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self.assertEqual(ids[-2], 2)
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self.assertEqual(ids[-1], EN_CODE)
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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"]), [250026, 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 = MBartTokenizer.from_pretrained(tmpdirname)
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self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
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# prepare_seq2seq_batch tests below
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@require_torch
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def test_batch_fairseq_parity(self):
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batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(
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self.src_text, tgt_texts=self.tgt_text, return_tensors="pt"
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)
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batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id)
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for k in batch:
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batch[k] = batch[k].tolist()
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# batch = {k: v.tolist() for k,v in batch.items()}
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# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
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# batch.decoder_inputs_ids[0][0] ==
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assert batch.input_ids[1][-2:] == [2, EN_CODE]
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assert batch.decoder_input_ids[1][0] == RO_CODE
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assert batch.decoder_input_ids[1][-1] == 2
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assert batch.labels[1][-2:] == [2, RO_CODE]
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@require_torch
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def test_enro_tokenizer_prepare_seq2seq_batch(self):
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batch = self.tokenizer.prepare_seq2seq_batch(
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self.src_text,
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tgt_texts=self.tgt_text,
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max_length=len(self.expected_src_tokens),
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)
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batch["decoder_input_ids"] = shift_tokens_right(batch.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, -1]) # EOS
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# Test that special tokens are reset
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self.assertEqual(self.tokenizer.prefix_tokens, [])
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self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE])
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def test_seq2seq_max_target_length(self):
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batch = self.tokenizer.prepare_seq2seq_batch(
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self.src_text, tgt_texts=self.tgt_text, max_length=3, max_target_length=10
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)
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batch["decoder_input_ids"] = shift_tokens_right(batch.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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# max_target_length will default to max_length if not specified
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batch = self.tokenizer.prepare_seq2seq_batch(self.src_text, tgt_texts=self.tgt_text, max_length=3)
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batch["decoder_input_ids"] = shift_tokens_right(batch.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], 3)
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