# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # Copyright 2020 The HuggingFace Team. All rights reserved. # # 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. import unittest import numpy as np from parameterized import parameterized from paddlenlp.transformers import ChatGLMTokenizer from paddlenlp.transformers.tokenizer_utils import PretrainedTokenizer from ...transformers.test_tokenizer_common import TokenizerTesterMixin VOCAB_FILES_NAMES = { "vocab_file": "ice_text.model", } class ChatGLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = ChatGLMTokenizer from_pretrained_vocab_key = "model_file" test_decode_token = True def get_tokenizer(self, **kwargs) -> PretrainedTokenizer: tokenizer = ChatGLMTokenizer.from_pretrained("THUDM/chatglm-6b", **kwargs) return tokenizer def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.get_tokenizer() text = "lower newer" bpe_tokens = ["▁lower", "▁newer"] tokens = tokenizer.tokenize(text, add_prefix_space=True) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [680, 10243, 0] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_pretokenized_inputs(self, *args, **kwargs): pass def test_tokenizers_common_ids_setters(self, *args, **kwargs): pass def test_mask_output(self): pass def test_offsets_mapping(self): pass def test_offsets_mapping_with_unk(self): pass def test_special_tokens_mask(self): pass def test_special_tokens_mask_input_pairs(self): pass def test_right_and_left_padding(self): pass def test_encode_decode_with_spaces(self): # TODO Fix decode in tokenizer. pass def test_add_special_tokens(self): pass def test_padding_to_multiple_of(self): pass def test_padding_side_in_kwargs(self): tokenizer = self.get_tokenizer(padding_side="left") self.assertEqual(tokenizer.padding_side, "left") tokenizer = self.get_tokenizer() self.assertEqual(tokenizer.padding_side, "left") def test_truncation_side_in_kwargs(self): tokenizer = self.get_tokenizer(truncation_side="left") self.assertEqual(tokenizer.truncation_side, "left") tokenizer = self.get_tokenizer(truncation_side="right") self.assertEqual(tokenizer.truncation_side, "right") def test_add_tokens(self): tokenizer = self.get_tokenizer() vocab_size = len(tokenizer) self.assertEqual(tokenizer.add_tokens(""), 0) self.assertEqual(tokenizer.add_tokens("testoken"), 1) self.assertEqual(tokenizer.add_tokens(["testoken1", "testtoken2"]), 2) self.assertEqual(len(tokenizer), vocab_size + 3) self.assertEqual(tokenizer.add_special_tokens({}), 0) self.assertRaises(AssertionError, tokenizer.add_special_tokens, {"additional_special_tokens": ""}) self.assertEqual(tokenizer.add_special_tokens({"additional_special_tokens": [""]}), 1) self.assertEqual( tokenizer.add_special_tokens({"additional_special_tokens": ["", ""]}), 2 ) self.assertIn("", tokenizer.special_tokens_map["additional_special_tokens"]) self.assertIsInstance(tokenizer.special_tokens_map["additional_special_tokens"], list) self.assertGreaterEqual(len(tokenizer.special_tokens_map["additional_special_tokens"]), 2) self.assertEqual(len(tokenizer), vocab_size + 6) def test_add_tokens_tokenizer(self): tokenizer = self.get_tokenizer() vocab_size = tokenizer.vocab_size all_size = len(tokenizer) self.assertNotEqual(vocab_size, 0) new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"] added_toks = tokenizer.add_tokens(new_toks) vocab_size_2 = tokenizer.vocab_size all_size_2 = len(tokenizer) self.assertNotEqual(vocab_size_2, 0) self.assertEqual(vocab_size, vocab_size_2) self.assertEqual(added_toks, len(new_toks)) self.assertEqual(all_size_2, all_size + len(new_toks)) tokens = tokenizer.encode( "aaaaa bbbbbb low cccccccccdddddddd l", return_token_type_ids=None, add_special_tokens=False )["input_ids"] self.assertGreaterEqual(len(tokens), 4) self.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) def test_consecutive_unk_string(self): tokenizer = self.get_tokenizer(add_bos_token=False) tokens = [tokenizer.unk_token for _ in range(2)] string = tokenizer.convert_tokens_to_string(tokens) encoding = tokenizer( text=string, add_special_tokens=False, runcation=True, return_offsets_mapping=True, ) # TODO (wanghuijuan): Aligned with transformers, but 2 expected. self.assertEqual(len(encoding["input_ids"]), 3) self.assertEqual(len(encoding["offset_mapping"]), 3) def test_padding_if_pad_token_set_slow(self): tokenizer = self.get_tokenizer() # Simple input s = "This is a simple input" s2 = ["This is a simple input looooooooong", "This is a simple input"] p = ("This is a simple input", "This is a pair") pad_token_id = tokenizer.pad_token_id out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np", return_attention_mask=True) out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np", return_attention_mask=True) out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np", return_attention_mask=True) # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1], 30) self.assertTrue(pad_token_id in out_s["input_ids"]) self.assertTrue(0 in out_s["attention_mask"][..., 0]) # s2 # test automatic padding self.assertEqual(out_s2["input_ids"].shape[-1], 11) # long slice doesn't have padding self.assertFalse(pad_token_id in out_s2["input_ids"][0]) self.assertFalse(0 in out_s2["attention_mask"][0][..., 0]) # short slice does have padding self.assertTrue(pad_token_id in out_s2["input_ids"][1]) self.assertTrue(0 in out_s2["attention_mask"][1][..., 0]) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1], 60) self.assertTrue(pad_token_id in out_p["input_ids"]) self.assertTrue(0 in out_p["attention_mask"][..., 0]) def test_add_bos_token_slow(self): tokenizer = self.get_tokenizer() s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] bos_token_id = tokenizer.bos_token_id out_s = tokenizer(s) out_s2 = tokenizer(s2) self.assertEqual(out_s.input_ids[-1], bos_token_id) self.assertTrue(all(o[-1] == bos_token_id for o in out_s2["input_ids"])) def test_pretrained_model_lists(self): # No max_model_input_sizes self.assertGreaterEqual(len(self.tokenizer_class.pretrained_resource_files_map), 1) self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_resource_files_map.values())[0]), 1) @parameterized.expand([(True,), (False,)]) def test_encode_plus_with_padding(self, use_padding_as_call_kwarg: bool): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequence = "Sequence" self._check_no_pad_token_padding(tokenizer, sequence) padding_size = 10 padding_idx = tokenizer.pad_token_id token_type_padding_idx = tokenizer.pad_token_type_id encoded_sequence = tokenizer.encode(sequence, return_special_tokens_mask=True) input_ids = encoded_sequence["input_ids"] special_tokens_mask = encoded_sequence["special_tokens_mask"] sequence_length = len(input_ids) # Test right padding tokenizer_kwargs_right = { "max_length": sequence_length + padding_size, "padding": "max_length", "return_special_tokens_mask": True, } if not use_padding_as_call_kwarg: tokenizer.padding_side = "right" else: tokenizer_kwargs_right["padding_side"] = "right" self.assertRaises(AssertionError, lambda: tokenizer.encode_plus(sequence, **tokenizer_kwargs_right)) # Test left padding tokenizer_kwargs_left = { "max_length": sequence_length + padding_size, "padding": "max_length", "return_special_tokens_mask": True, } if not use_padding_as_call_kwarg: tokenizer.padding_side = "left" else: tokenizer_kwargs_left["padding_side"] = "left" left_padded_sequence = tokenizer.encode_plus(sequence, **tokenizer_kwargs_left) left_padded_input_ids = left_padded_sequence["input_ids"] left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"] left_padded_sequence_length = len(left_padded_input_ids) self.assertEqual(sequence_length + padding_size, left_padded_sequence_length) self.assertEqual([padding_idx] * padding_size + input_ids, left_padded_input_ids) self.assertEqual([1] * padding_size + special_tokens_mask, left_padded_special_tokens_mask) if "token_type_ids" in tokenizer.model_input_names: token_type_ids = encoded_sequence["token_type_ids"] left_padded_token_type_ids = left_padded_sequence["token_type_ids"] self.assertEqual( [token_type_padding_idx] * padding_size + token_type_ids, left_padded_token_type_ids ) if "attention_mask" in tokenizer.model_input_names and "attention_mask" in encoded_sequence: attention_mask = encoded_sequence["attention_mask"] left_padded_attention_mask = left_padded_sequence["attention_mask"] self.assertEqual([0] * padding_size + attention_mask, left_padded_attention_mask) def test_padding_to_max_length(self): """We keep this test for backward compatibility but it should be remove when `pad_to_max_seq_len` is deprecated.""" tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequence = "Sequence" padding_size = 10 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, sequence) padding_idx = tokenizer.pad_token_id # Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "left" encoded_sequence = tokenizer.encode(sequence)["input_ids"] sequence_length = len(encoded_sequence) # FIXME: the next line should be padding(max_length) to avoid warning padded_sequence = tokenizer.encode( sequence, max_length=sequence_length + padding_size, pad_to_max_seq_len=True )["input_ids"] padded_sequence_length = len(padded_sequence) self.assertEqual(sequence_length + padding_size, padded_sequence_length) self.assertEqual([padding_idx] * padding_size + encoded_sequence, padded_sequence) # Check that nothing is done when a maximum length is not specified encoded_sequence = tokenizer.encode(sequence)["input_ids"] sequence_length = len(encoded_sequence) tokenizer.padding_side = "left" padded_sequence_left = tokenizer.encode(sequence, pad_to_max_seq_len=True)["input_ids"] padded_sequence_left_length = len(padded_sequence_left) self.assertEqual(sequence_length, padded_sequence_left_length) self.assertEqual(encoded_sequence, padded_sequence_left) def test_padding_with_attention_mask(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): if tokenizer.pad_token is None: self.skipTest("No padding token.") if "attention_mask" not in tokenizer.model_input_names: self.skipTest("This model does not use attention mask.") features = [ {"input_ids": [1, 2, 3], "attention_mask": np.array([[[0, 0, 0], [0, 0, 0], [0, 0, 1]]])}, { "input_ids": [ 1, 2, ], "attention_mask": np.array([[[0, 0], [0, 1]]]), }, ] padded_features = tokenizer.pad(features) print(padded_features["attention_mask"]) self.assertListEqual( [x.tolist() for x in padded_features["attention_mask"]], [ [[[0, 0, 0], [0, 0, 0], [0, 0, 1]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 1]]], ], ) def test_batch_encode_plus_padding(self): # Test that padded sequences are equivalent between batch_encode_plus and encode_plus # Left padding tests tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): tokenizer.padding_side = "left" sequences = [ "Testing batch encode plus", "Testing batch encode plus with different sequence lengths", "Testing batch encode plus with different sequence lengths correctly pads", ] max_length = 100 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, sequences) encoded_sequences = [ tokenizer.encode(sequence, max_length=max_length, padding="max_length") for sequence in sequences ] encoded_sequences_batch = tokenizer.batch_encode( sequences, max_length=max_length, padding="max_length" ) self.assertListEqual( [x["input_ids"] for x in encoded_sequences], [ x["input_ids"] for x in self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch) ], ) def test_batch_encode_plus_batch_sequence_length(self): # Tests that all encoded values have the correct size tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequences = [ "Testing batch encode plus", "Testing batch encode plus with different sequence lengths", "Testing batch encode plus with different sequence lengths correctly pads", ] def tolist(input_dict_list): if isinstance(input_dict_list, np.ndarray): return input_dict_list.tolist() unwrap = False if isinstance(input_dict_list, dict): input_dict_list = [input_dict_list] unwrap = True for i, input_dict in enumerate(input_dict_list): for k in input_dict: if isinstance(input_dict[k], np.ndarray): input_dict_list[i][k] = input_dict[k].tolist() return input_dict_list[0] if unwrap else input_dict_list encoded_sequences = [tokenizer.encode(sequence) for sequence in sequences] encoded_sequences_batch = tokenizer.batch_encode(sequences, padding=False) self.assertListEqual( tolist(encoded_sequences), tolist(self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)), ) maximum_length = len( max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len) ) # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, sequences) encoded_sequences_padded = [ tokenizer.encode(sequence, max_length=maximum_length, padding="max_length") for sequence in sequences ] encoded_sequences_batch_padded = tokenizer.batch_encode(sequences, padding=True) self.assertListEqual( tolist(encoded_sequences_padded), tolist(self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded)), ) # check 'longest' is unsensitive to a max length encoded_sequences_batch_padded_1 = tokenizer.batch_encode(sequences, padding=True) encoded_sequences_batch_padded_2 = tokenizer.batch_encode( sequences, max_length=maximum_length + 10, padding="longest" ) for key in encoded_sequences_batch_padded_1.keys(): self.assertListEqual( [x.tolist() for x in encoded_sequences_batch_padded_1[key]] if key != "input_ids" else encoded_sequences_batch_padded_1[key], [x.tolist() for x in encoded_sequences_batch_padded_2[key]] if key != "input_ids" else encoded_sequences_batch_padded_2[key], ) # check 'no_padding' is unsensitive to a max length encoded_sequences_batch_padded_1 = tokenizer.batch_encode(sequences, padding=False) encoded_sequences_batch_padded_2 = tokenizer.batch_encode( sequences, max_length=maximum_length + 10, padding=False ) for key in encoded_sequences_batch_padded_1.keys(): self.assertListEqual( [x.tolist() for x in encoded_sequences_batch_padded_1[key]] if key != "input_ids" else encoded_sequences_batch_padded_1[key], [x.tolist() for x in encoded_sequences_batch_padded_2[key]] if key != "input_ids" else encoded_sequences_batch_padded_2[key], )