# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """ Testing BertTokenizer op in DE """ import numpy as np import pytest import mindspore.dataset as ds from mindspore import log as logger import mindspore.dataset.text as text BERT_TOKENIZER_FILE = "../data/dataset/testTokenizerData/bert_tokenizer.txt" vocab_bert = [ "床", "前", "明", "月", "光", "疑", "是", "地", "上", "霜", "举", "头", "望", "低", "思", "故", "乡", "繁", "體", "字", "嘿", "哈", "大", "笑", "嘻", "i", "am", "mak", "make", "small", "mistake", "##s", "during", "work", "##ing", "hour", "😀", "😃", "😄", "😁", "+", "/", "-", "=", "12", "28", "40", "16", " ", "I", "[CLS]", "[SEP]", "[UNK]", "[PAD]", "[MASK]", "[unused1]", "[unused10]" ] pad = '' test_paras = [ # test chinese text dict( first=1, last=4, expect_str=[['床', '前', '明', '月', '光'], ['疑', '是', '地', '上', '霜'], ['举', '头', '望', '明', '月'], ['低', '头', '思', '故', '乡']], expected_offsets_start=[[0, 3, 6, 9, 12], [0, 3, 6, 9, 12], [0, 3, 6, 9, 12], [0, 3, 6, 9, 12]], expected_offsets_limit=[[3, 6, 9, 12, 15], [3, 6, 9, 12, 15], [3, 6, 9, 12, 15], [3, 6, 9, 12, 15]], vocab_list=vocab_bert ), # test english text dict( first=5, last=5, expect_str=[['i', 'am', 'mak', '##ing', 'small', 'mistake', '##s', 'during', 'work', '##ing', 'hour', '##s']], expected_offsets_start=[[0, 2, 5, 8, 12, 18, 25, 27, 34, 38, 42, 46]], expected_offsets_limit=[[1, 4, 8, 11, 17, 25, 26, 33, 38, 41, 46, 47]], lower_case=True, vocab_list=vocab_bert ), dict( first=5, last=5, expect_str=[['I', "am", 'mak', '##ing', 'small', 'mistake', '##s', 'during', 'work', '##ing', 'hour', '##s']], expected_offsets_start=[[0, 2, 5, 8, 12, 18, 25, 27, 34, 38, 42, 46]], expected_offsets_limit=[[1, 4, 8, 11, 17, 25, 26, 33, 38, 41, 46, 47]], lower_case=False, vocab_list=vocab_bert ), # test emoji tokens dict( first=6, last=7, expect_str=[ ['😀', '嘿', '嘿', '😃', '哈', '哈', '😄', '大', '笑', '😁', '嘻', '嘻'], ['繁', '體', '字']], expected_offsets_start=[[0, 4, 7, 10, 14, 17, 20, 24, 27, 30, 34, 37], [0, 3, 6]], expected_offsets_limit=[[4, 7, 10, 14, 17, 20, 24, 27, 30, 34, 37, 40], [3, 6, 9]], normalization_form=text.utils.NormalizeForm.NFKC, vocab_list=vocab_bert ), # test preserved tokens dict( first=8, last=14, expect_str=[ ['[UNK]', '[CLS]'], ['[UNK]', '[SEP]'], ['[UNK]', '[UNK]'], ['[UNK]', '[PAD]'], ['[UNK]', '[MASK]'], ['[unused1]'], ['[unused10]'] ], expected_offsets_start=[[0, 7], [0, 7], [0, 7], [0, 7], [0, 7], [0], [0]], expected_offsets_limit=[[6, 12], [6, 12], [6, 12], [6, 12], [6, 13], [9], [10]], lower_case=False, vocab_list=vocab_bert, preserve_unused_token=True, ), dict( first=8, last=14, expect_str=[ ['[UNK]', '[CLS]'], ['[UNK]', '[SEP]'], ['[UNK]', '[UNK]'], ['[UNK]', '[PAD]'], ['[UNK]', '[MASK]'], ['[unused1]'], ['[unused10]'] ], expected_offsets_start=[[0, 7], [0, 7], [0, 7], [0, 7], [0, 7], [0], [0]], expected_offsets_limit=[[6, 12], [6, 12], [6, 12], [6, 12], [6, 13], [9], [10]], lower_case=True, vocab_list=vocab_bert, preserve_unused_token=True, ), # test special symbol dict( first=15, last=15, expect_str=[['12', '+', '/', '-', '28', '=', '40', '/', '-', '16']], expected_offsets_start=[[0, 2, 3, 4, 5, 7, 8, 10, 11, 12]], expected_offsets_limit=[[2, 3, 4, 5, 7, 8, 10, 11, 12, 14]], preserve_unused_token=True, vocab_list=vocab_bert ), # test non-default params dict( first=8, last=8, expect_str=[['[UNK]', ' ', '[CLS]']], expected_offsets_start=[[0, 6, 7]], expected_offsets_limit=[[6, 7, 12]], lower_case=False, vocab_list=vocab_bert, preserve_unused_token=True, keep_whitespace=True ), dict( first=8, last=8, expect_str=[['unused', ' ', '[CLS]']], expected_offsets_start=[[0, 6, 7]], expected_offsets_limit=[[6, 7, 12]], lower_case=False, vocab_list=vocab_bert, preserve_unused_token=True, keep_whitespace=True, unknown_token='' ), dict( first=8, last=8, expect_str=[['unused', ' ', '[', 'CLS', ']']], expected_offsets_start=[[0, 6, 7, 8, 11]], expected_offsets_limit=[[6, 7, 8, 11, 12]], lower_case=False, vocab_list=vocab_bert, preserve_unused_token=False, keep_whitespace=True, unknown_token='' ), ] def check_bert_tokenizer_default(first, last, expect_str, expected_offsets_start, expected_offsets_limit, vocab_list, suffix_indicator='##', max_bytes_per_token=100, unknown_token='[UNK]', lower_case=False, keep_whitespace=False, normalization_form=text.utils.NormalizeForm.NONE, preserve_unused_token=False): dataset = ds.TextFileDataset(BERT_TOKENIZER_FILE, shuffle=False) if first > 1: dataset = dataset.skip(first - 1) if last >= first: dataset = dataset.take(last - first + 1) vocab = text.Vocab.from_list(vocab_list) tokenizer_op = text.BertTokenizer( vocab=vocab, suffix_indicator=suffix_indicator, max_bytes_per_token=max_bytes_per_token, unknown_token=unknown_token, lower_case=lower_case, keep_whitespace=keep_whitespace, normalization_form=normalization_form, preserve_unused_token=preserve_unused_token) dataset = dataset.map(operations=tokenizer_op) count = 0 for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']) logger.info("Out:", token) logger.info("Exp:", expect_str[count]) np.testing.assert_array_equal(token, expect_str[count]) count = count + 1 def check_bert_tokenizer_with_offsets(first, last, expect_str, expected_offsets_start, expected_offsets_limit, vocab_list, suffix_indicator='##', max_bytes_per_token=100, unknown_token='[UNK]', lower_case=False, keep_whitespace=False, normalization_form=text.utils.NormalizeForm.NONE, preserve_unused_token=False): dataset = ds.TextFileDataset(BERT_TOKENIZER_FILE, shuffle=False) if first > 1: dataset = dataset.skip(first - 1) if last >= first: dataset = dataset.take(last - first + 1) vocab = text.Vocab.from_list(vocab_list) tokenizer_op = text.BertTokenizer( vocab=vocab, suffix_indicator=suffix_indicator, max_bytes_per_token=max_bytes_per_token, unknown_token=unknown_token, lower_case=lower_case, keep_whitespace=keep_whitespace, normalization_form=normalization_form, preserve_unused_token=preserve_unused_token, with_offsets=True) dataset = dataset.map(operations=tokenizer_op, input_columns=['text'], output_columns=['token', 'offsets_start', 'offsets_limit'], column_order=['token', 'offsets_start', 'offsets_limit']) count = 0 for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['token']) logger.info("Out:", token) logger.info("Exp:", expect_str[count]) np.testing.assert_array_equal(token, expect_str[count]) np.testing.assert_array_equal(i['offsets_start'], expected_offsets_start[count]) np.testing.assert_array_equal(i['offsets_limit'], expected_offsets_limit[count]) count = count + 1 def test_bert_tokenizer_default(): """ Test WordpieceTokenizer when with_offsets=False """ for paras in test_paras: check_bert_tokenizer_default(**paras) def test_bert_tokenizer_with_offsets(): """ Test WordpieceTokenizer when with_offsets=True """ for paras in test_paras: check_bert_tokenizer_with_offsets(**paras) def test_bert_tokenizer_callable_invalid_input(): """ Test WordpieceTokenizer in eager mode with invalid input """ data = {'张三': 18, '王五': 20} vocab = text.Vocab.from_list(vocab_bert) tokenizer_op = text.BertTokenizer(vocab=vocab) with pytest.raises(TypeError) as info: _ = tokenizer_op(data) assert "Invalid user input. Got : {'张三': 18, '王五': 20}, cannot be converted into tensor." in str(info) if __name__ == '__main__': test_bert_tokenizer_callable_invalid_input() test_bert_tokenizer_default() test_bert_tokenizer_with_offsets()