Add "Leveraging Pretrained Checkpoints for Generation" Seq2Seq models. (#6594)
* add conversion script * improve conversion script * make style * add tryout files * fix * update * add causal bert * better names * add tokenizer file as well * finish causal_bert * fix small bugs * improve generate * change naming * renaming * renaming * renaming * remove leftover files * clean files * add fix tokenizer * finalize * correct slow test * update docs * small fixes * fix link * adapt check repo * apply sams and sylvains recommendations * fix import * implement Lysandres recommendations * fix logger warn
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Patrick von Platen
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tests/test_tokenization_bert_generation.py
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tests/test_tokenization_bert_generation.py
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# coding=utf-8
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# Copyright 2020 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_bert_generation import BertGenerationTokenizer
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from .test_tokenization_common import TokenizerTesterMixin
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SPIECE_UNDERLINE = "▁"
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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 BertForSeqGenerationTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = BertGenerationTokenizer
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def setUp(self):
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super().setUp()
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tokenizer = BertGenerationTokenizer(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 = BertGenerationTokenizer(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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[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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[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 BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
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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 = [18536, 2260, 101]
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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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871,
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419,
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358,
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946,
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991,
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2521,
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452,
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358,
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1357,
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387,
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7751,
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3536,
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112,
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985,
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456,
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126,
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865,
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938,
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5400,
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5734,
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458,
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1368,
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467,
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786,
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2462,
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5246,
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1159,
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633,
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865,
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4519,
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457,
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582,
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852,
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2557,
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427,
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916,
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508,
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405,
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34324,
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497,
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391,
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408,
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11342,
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1244,
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385,
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100,
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938,
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985,
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456,
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574,
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362,
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12597,
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3200,
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3129,
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1172,
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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 BertGenerationConfig, BertGenerationEncoder
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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", return_token_type_ids=False)
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batch_encoded_sequence = self.big_tokenizer.batch_encode_plus(
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[sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False
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)
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config = BertGenerationConfig()
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model = BertGenerationEncoder(config)
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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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